Verteilung Von Rest Korrelationen In Autoregressive Integrierte Moving Durchschnittliche Zeit Serie Modelle

Technische Hinweise für die aktuelle Erhebung über die Beschäftigungsstatistiken (PDF) Einleitung Das Bureau of Labor Statistics (BLS) sammelt monatlich Daten zu Beschäftigung, Stunden und Einnahmen aus einer Stichprobe von Nonfarm-Einrichtungen durch das CES-Programm (Current Employment Statistics). Die CES-Umfrage umfasst rund 146.000 Unternehmen und Regierungsbehörden, die etwa 623.000 einzelne Baustellen aus einem Stichprobenrahmen der Steuerrechnungen der Arbeitslosenversicherung (UI) abdecken, die etwa 9,3 Millionen Betriebe umfassen. Die aktive CES-Stichprobe umfasst etwa ein Drittel aller Personalvermittler in den 50 Bundesstaaten und dem District of Columbia. Aus diesen Daten werden eine große Anzahl von Beschäftigungs-, Stunden - und Ergebnisserien in beträchtlichen Branchen - und geographischen Details erstellt und monatlich veröffentlicht. Historische Statistiken für die Nation sind auf der CES National Website unter bls. govcesdata. htm. Historische Statistiken für Staaten und Metropolregionen sind auf der CES State und Metro Area Website unter bls. govsaedata. htm. Inhaltsverzeichnis Verwenden Sie die folgenden Links, um zu bestimmten Themen über die CES-Stichprobe, Datenerhebung, Branchenklassifikation, verfügbare Statistiken, Schätzungen und Revisionen zu überspringen. Ein Link ist enthalten, um zu einer Liste von Gleichungen, Tabellen und Abbildungen zu gelangen, die in den CES-Technischen Anmerkungen enthalten sind. Das Muster Die Stichprobe der aktuellen Beschäftigungsstatistik (CES) ist eine geschichtete, einfache Zufallsstichprobe von Arbeitsplätzen, geclustert durch die Kontonummer der Arbeitslosenversicherung (UI). Die UI-Kontonummer ist eine wesentliche Kennung für die LSB (LSB) der Arbeitgeberrekorde, die sowohl als Stichprobenrahmen als auch als Benchmarkquelle für die CES-Beschäftigungsschätzungen dient. Die Probenschichten oder Subpopulationen werden durch den Zustand, die Industrie und die Arbeitsgröße definiert, was ein staatliches Design ergibt. Die Abtastraten für jede Schicht werden durch eine als optimale Zuweisung bekannte Methode bestimmt, die eine feste Anzahl von Abtasteinheiten über einen Satz von Schichten verteilt, um die Gesamtabweichung oder den Abtastfehler auf der primären Schätzung von Interesse zu minimieren. Das gesamte Nonfarm-Beschäftigungsniveau ist die primäre Schätzung des Interesses, und das CES-Musterdesign hat oberste Priorität, um es so genau wie möglich zu messen oder den statistischen Fehler um die landesweiten Gesamtarbeitslosenschätzungen zu minimieren. Rahmen - und Musterauswahl Die LDB ist das Universum, aus dem die CES die Erhebungsstichprobe zieht. Die LDB enthält Daten über die etwa 9,3 Millionen US-amerikanischen Unternehmen, die von der UI abgedeckt werden und fast alle Elemente der US-Wirtschaft repräsentieren. Im Rahmen des Quarterly Census of Employment and Wages (QCEW) werden diese Daten von den Arbeitgebern vierteljährlich in Zusammenarbeit mit Arbeitsmarktinformationsagenturen (LMIs) gesammelt. Die LDB enthält Beschäftigungs - und Lohninformationen von Arbeitgebern sowie Name, Adresse und Standortinformationen. Es enthält auch Identifikationsinformationen wie UI-Kontonummer und Berichtseinheit oder Baustellennummer. Der LDB enthält Aufzeichnungen aller Arbeitgeber, die unter das UI-Steuersystem fallen. Dieses System deckt 97 Prozent aller Beschäftigung im Rahmen der CES in den 50 Staaten, dem District of Columbia, Puerto Rico und den Vereinigten Staaten von Amerika ab. Es gibt ein paar Abschnitte der Wirtschaft, die nicht durch die QCEW abgedeckt sind, darunter die Selbständigen, unbezahlte Familienangehörige, Eisenbahnen, religiöse Organisationen, kleine landwirtschaftliche Arbeitgeber und gewählte Beamte. Daten für Arbeitgeber werden in der Regel auf der Arbeitsebene gemeldet. Arbeitgeber, die mehrere Einrichtungen in einem Staat haben in der Regel berichten Daten für jede einzelne Einrichtung. Die LDB verfolgt die Betriebe im Laufe der Zeit und verbindet sie von Quartal zu Quartal. Die privaten und privaten Teile der CES-Stichprobe werden nach zwei verschiedenen Methoden ausgewählt. Private Betriebe im CES-Musterrahmen sind nach Staat, Industrie und Größe geschichtet. Schichtung Gruppen Population Mitglieder zusammen für die Zwecke der Probenallokation und Auswahl. Die Schichten oder Gruppen bestehen aus homogenen Einheiten. Mit 13 Industrien (Behandlung der Produktion als eine Branche und nicht einschließlich der Regierung) und 8 Größenklassen. Es gibt 104 Gesamtzuweisungszellen pro Zustand. Die Abtastrate für jede Schicht wird durch ein Verfahren bestimmt, das als optimale Zuteilung bekannt ist. Eine optimale Allokation minimiert die Abweichung zu festen Kosten oder minimiert die Kosten für eine feste Abweichung. Unter dem CES-Wahrscheinlichkeitsdesign wird eine feste Anzahl von Abtasteinheiten für jeden Zustand über die Zuteilungsschichten verteilt, so dass die Gesamtabweichung oder der Abtastfehler des gesamten staatlichen Beschäftigungsniveaus minimiert wird. Die Anzahl der Stichprobeneinheiten in der CES-Wahrscheinlichkeitsstichprobe wurde nach den verfügbaren Programmressourcen festgelegt. Die optimale Zuteilungsformel setzt mehr Proben in Zellen, für die Daten weniger kosten, um Zellen zu sammeln, Zellen mit mehr Einheiten und Zellen, die eine größere Varianz aufweisen. Die Stichprobe der CES-Regierung ist nicht Teil der Programme Wahrscheinlichkeit-basiertes Design. CES ist in der Lage, ein sehr hohes Niveau der Universum Beschäftigung Abdeckung in staatlichen Industrien zu erreichen, indem sie volle Lohn-und Gehaltsabrechnung Beschäftigung zählt für viele Regierungsstellen, wodurch die Notwendigkeit für eine Wahrscheinlichkeit-basierte Muster-Design. Die Schätzungen der Regierung werden mit der Summe der privaten Schätzungen kombiniert, um Werte für die gesamte Nichtfarm zu erhalten. Die jährliche Stichprobenauswahl hilft, die CES-Umfrage im Hinblick auf die Beschäftigung von Wirtschaftsgeburten und geschäftlichen Todesfällen aktuell zu halten. Darüber hinaus bieten die aktualisierten Universum-Dateien die neuesten Informationen über Industrie, Größe und Metropolregion. Jedes Jahr wird die CES-Stichprobe aus den Daten des ersten Quartals der Longitudinal Database (LDB) im Herbst dieses Jahres gezogen. Eine Geburtenaktualisierung wird im Frühsommer ab dem dritten Quartal des Vorjahres hinzugefügt. Nachdem alle Out-of-Scope-Einträge entfernt wurden, wird der Abtastrahmen in Zuordnungszellen unterteilt. Innerhalb jeder Zuteilungszelle werden die Einheiten nach dem Metropolitan Statistical Area (MSA) gruppiert, und diese MSAs sind nach der Größe des MSA sortiert, definiert als die Anzahl der UI-Konten in diesem MSA. Da die Abtastrate über die gesamte Zuteilungszelle gleichförmig ist, stellt eine implizite Schichtung durch MSA sicher, dass eine proportionale Anzahl von Einheiten von jedem MSA abgetastet wird. Einige MSAs haben möglicherweise zu wenige UI-Konten in der Zuweisungszelle diese MSAs sind zusammengebrochen und als eine einzelne MSA behandelt. Permanent Random Numbers (PRNs) werden allen UI-Konten auf dem Sampling-Frame zugewiesen. Da neue Einheiten auf dem Rahmen erscheinen, werden diesen Einheiten auch Zufallszahlen zugewiesen. Da Datensätze über die Zeit verknüpft werden, wird das PRN in der Verknüpfung vorgetragen. Innerhalb jeder Selektionszelle werden die Einheiten nach PRN sortiert, und die Einheiten werden gemäß der festgelegten Probenauswahlrate ausgewählt. Die Anzahl von Einheiten, die zufällig aus jeder Selektionszelle ausgewählt werden, ist gleich dem Produkt aus der Probenauswahlrate und der Anzahl der in Frage kommenden Einheiten in der Zelle plus irgendeinem Übertrag von der vorherigen Selektionszelle. Das Ergebnis wird auf die nächste ganze Zahl gerundet. Carryover ist definiert als der Betrag, der auf - oder abgerundet wird auf die nächste ganze Zahl. Wegen der Kosten und der Arbeitsbelastung, die mit der Einschreibung neuer Probeneinheiten verbunden sind, verbleiben alle Einheiten in der Probe mindestens 2 Jahre. Um sicherzustellen, dass alle Einheiten diese Mindestvoraussetzung erfüllen, hat CES ein Swapping-Verfahren eingeführt. Das Verfahren erlaubt es, Einheiten in die Probe zu tauschen, die im vorigen Probenjahr neu ausgewählt wurden und nicht als Teil der aktuellen Wahrscheinlichkeitsstichprobe wiedergewählt wurden. Das Verfahren entfernt eine Einheit innerhalb derselben Auswahlzelle und legt die neu ausgewählte Einheit aus dem Vorjahr zurück in die Probe. Um die Befragtenbelastung zu reduzieren, tauscht ein ähnliches Verfahren Einheiten aus der Stichprobe aus, die seit vier oder mehr aufeinanderfolgenden Jahren Stichprobenmitglieder waren. Das Auslagerungsverfahren entfernt eine alte Einheit innerhalb derselben Auswahlzelle und ersetzt sie durch eine neue Einheit. Etwa 60 Prozent der CES-Stichprobe für private Branchen überschneiden sich von der vorherigen Stichprobe zur aktuellen Stichprobe. Auswahlgewichte Sobald die Stichprobe entnommen wird, werden die Stichprobengewichte anhand der Anzahl der tatsächlich in jeder Zuteilungszelle ausgewählten UI-Konten berechnet. Das Probenauswahlgewicht ist annähernd gleich dem Kehrwert der Wahrscheinlichkeit der Auswahl oder dem Kehrwert der Abtastrate. Sie wird berechnet als: Gleichung 1. Auswahl der Stichprobengewichte Auswahl der Stichprobenauswahl N hnh N h Anzahl der Noncertainty-Benutzerkonten innerhalb der Zuteilungszelle, die für die Stichprobenauswahl geeignet sind nh die Anzahl der in der Zuweisungszelle Rahmenwartung und Stichproben ausgewählten Unwägbarkeits-UI-Konten Updates Aufgrund der dynamischen Wirtschaft gibt es einen ständigen Kreislauf von Geschäftsöffnungen (Geburten) und Schließungen (Todesfälle). Im Sommer wird jedes Jahr eine Musteraktualisierung durchgeführt, die die LDB-Daten der letzten Jahre des dritten Quartals darstellt. Dieses Update wählt Einheiten aus der Population von Öffnungen und anderen Einheiten aus, die zuvor nicht für die Auswahl geeignet sind, und schließt sie als Teil der Stichprobe ein. Die Standort-, Kontakt - und Verwaltungsinformationen werden für alle Einrichtungen aktualisiert, die im Rahmen der jährlichen Stichprobe ausgewählt wurden. Tabelle 1 zeigt das Beschäftigungsniveau von 2015 und den ungefähren Anteil der gesamten Universitätsbeschäftigung auf der Gesamtniederlassung und in den wichtigsten Industriesektoren. Die Abdeckung für einzelne Branchen innerhalb der Supersektoren kann von den gezeigten Proportionen abweichen. Die in Tabelle 1 gezeigten UI-Zählungen und Niederlassungsnummern stammen aus dem Benchmark-Jahr und nicht aus dem aktuellen Stichprobenjahr und unterscheiden sich daher von UI - und Betriebsdaten für das laufende Stichprobenjahr. Tabelle 1: Beschäftigungs-Benchmarks und etwaige Deckung der BLS-Beschäftigungs - und Lohn - und Gehaltsliste, März 2015 CES-Industrie-Code CES-Industrie-Titel Beschäftigungs-Benchmarks (in Tausend) Fußnoten: (1) Weil nicht alle Betriebe Lohn - und Stundeninformationen melden, stunden - und ergebnisschätzungen basieren auf einer kleineren Stichprobe als die Beschäftigungsschätzungen. (2) Beschäftigung der gemeldeten Werte für März 2015. (3) Der Surface Transportation Board bietet eine vollständige Anzahl der Beschäftigung für die Klasse I Eisenbahnen plus Amtrak. CES-Stichprobe nach Branchen Die Stichprobenverteilung nach Branchen spiegelt das Ziel der Minimierung des Stichprobenfehlers in der gesamten Schätzung der nichtfarmbedingten Beschäftigung wider. Sample Coverage Preise variieren je nach Branche als Ergebnis der Konstruktion eines Designs, um diese Ziele zu erreichen (siehe Tabelle 1). Zum Beispiel haben die Herstellung und Freizeit-und Hospitality-Industrie von ähnlicher Größe. Die Fertigung hat 12,3 Millionen Beschäftigte, während Freizeit und Gastfreundschaft etwa 14,6 Millionen Beschäftigte hat. Allerdings sind ihre relativen Probengrößen unterschiedlich. Im verarbeitenden Gewerbe gibt es rund 19.100 Musterbetriebe mit insgesamt 3,1 Millionen Beschäftigten, während Freizeit und Gastfreundschaft viele weitere Musterbetriebe, etwa 65.300 Musterbetriebe, aber nur etwa 2,9 Millionen Beschäftigte umfasst. Die Fertigungsprobe umfasst somit rund 26 Prozent aller Beschäftigung in der Fertigung, während die Freizeit-und Hospitality-Stichprobe etwa 20 Prozent aller Beschäftigung in dieser Branche umfasst. Die Unterschiede sind zum Teil mit der Tatsache verbunden, dass die Herstellung durch eine viel größere durchschnittliche Unternehmensgröße als Freizeit und Gastfreundschaft gekennzeichnet ist. Diese Arten von Unterschieden führen nicht zu einer Vorspannung in der CES Beschäftigung Schätzungen aufgrund der Verwendung von Industrie Probenahme Schichten und Stichprobengewichte, die jede Firma ist richtig dargestellt in den Schätzungen sicherzustellen. Regierungsbeispiel Die Stichprobe der CES-Regierung ist nicht Teil des Programms, das auf die Wahrscheinlichkeit beruht, um die Beschäftigung für alle privaten Wirtschaftszweige abzuschätzen. Ein sehr hohes Maß an Universum Beschäftigung Abdeckung (75 Prozent) wird durch die Erreichung voller Lohn-und Gehaltsabrechnung Beschäftigung zählt für viele Regierungsstellen erreicht. Folglich ist für diese Branche kein probability-basiertes Beispieldesign erforderlich. Die hohe Deckungsrate gewährleistet praktisch ein hohes Maß an Zuverlässigkeit für die staatlichen Beschäftigungsschätzungen. Weil es verwendet wird, um nur den Regierungsanteil der gesamten nonfarm Beschäftigung zu schätzen, die Hauptregierungsprobe voreingenommen nicht die gesamten nonfarm Beschäftigungschätzungen. Die privaten und staatlichen Schätzungen werden summiert, um die Gesamtarbeitslosenschätzungen abzuleiten. Beispielimplementierung CES-Einschreibungsbemühungen beginnen sofort, nachdem eine Stichprobe ausgewählt wurde, und die Sammlung beginnt in der Regel im ersten Monat nach der Immatrikulation. Vor der ersten Erstveröffentlichung vom Juli 2014 hat CES die neuen Sample-Einheiten für alle Branchen einmal im Jahr eingeführt, beginnend mit der dritten Veröffentlichung von November-Schätzungen. Im Januar wurde die neue Stichprobe erstmals zur Schätzung der dritten vorläufigen Schätzungen des Vorjahres, der zweiten Vorabschätzungen des Vorjahres und der ersten ersten vorläufigen Schätzungen für das laufende Jahr verwendet. Warten auf die Einführung neuer Stichproben für alle Branchen gleichzeitig bedeutete neu eingeschriebenen Befragten, die Auswertung der Abrechnungsdaten begann sofort nach der Probenahme hatte nützliche Daten für fast ein Jahr vor der Daten verwendet wurden, um CES Schätzungen zu produzieren. Der jährliche Durchführungsplan trug auch zum Teil zu Revisionen in nationalen CES-Schätzungen zwischen der November-zweiten vorläufigen und endgültigen Veröffentlichungen sowie zwischen der ersten und der zweiten vorläufigen Schätzung im Dezember bei. In der Vergangenheit nahm die Implementierung von neuen Stichprobeneinheiten in die CES-Umfrage eine große Menge an Ressourcen und Zeit in Anspruch. CES aktualisierte Prozesse für mehrere Jahre zur Verbesserung der Effizienz der Beispiel-Updates und erforschte die Auswirkungen dieser Änderung auf die Schätzungen. Beginnend mit der ersten vorläufigen Veröffentlichung Juli 2014 begann CES eine vierteljährliche Beispielimplementierung. Nach dem vierteljährlichen Beispielumsetzungsplan wurden alle Branchen in vier Gruppen eingeteilt, die mit der Registrierung und der Datenerhebung in einem bestimmten Quartal beginnen, nachdem die Stichprobe für das Jahr gezogen wurde. Jede Gruppe von Branchen beginnt mit der Einschreibung und Datenerhebung Verfahren das Quartal vor der Verwendung in der Schätzung und werden in Schätzungen auf den ersten Berichtsmonat des folgenden Quartals (siehe Tabelle 2) verwendet. Alle Geburtseinheiten, die als Teil der halbjährlichen Aktualisierung ausgewählt werden, werden in der letzten Gruppe, unabhängig von der Industrie, implementiert. Jeder Referenzmonat wird anhand der gleichen Stichprobe aus der Schätzung der ersten vorläufigen Schätzung anhand der dritten vorläufigen Schätzung geschätzt. Da die vierteljährliche Stichprobenimplementierung mit den ersten vorläufigen Schätzungen im Juli 2014 begann, umfaßte die erste Implementierung die in den Gruppen 1 und 2 identifizierten Industrien. Tabelle 2. Industriegruppierungen für die vierteljährliche Stichprobenimplementierung der CES CES-Industriekodex Wichtige Industriezweige Geburtseinheiten für alle privaten Wirtschaftszweige, Das dritte Quartal des LDB, das im ersten Quartal der LDB-Fußnoten nicht bestand (1) Da die vierteljährliche Stichprobenerhebung mit den ersten ersten Schätzungen des ersten Halbjahres 2014 begonnen hatte, umfaßte die erste Stichprobe im Jahr 2013 die in den Gruppen 1 und 2 ausgewiesenen Branchen 2. Nachfolgende Quartale implementiert neue Sample-Einheiten eine Gruppe zu einem Zeitpunkt. Unter dem vierteljährlichen Beispielumsetzungsplan kann jede vierteljährliche Beispielimplementierungsgruppe Auswirkungen auf Branchen außerhalb der Gruppe haben. Alle mit einem UI-Konto verknüpften Arbeitsschritte, die in einer Gruppe implementiert werden, werden gleichzeitig in die Stichprobe eingeführt, auch wenn sie einer anderen Branche zugeordnet werden. Der Wechsel zur vierteljährlichen Probenimplementierung ermöglicht es, dass Einheiten, die nicht in der neuen Probe liegen, gleichzeitig mit der Einführung der neuen Probe fallengelassen werden. Der vierteljährliche Sample-Implementierungsprozess wird voraussichtlich die Befragtenbelastung reduzieren. CES-Stichprobe nach Beschäftigungsgrößenklassen Das Beschäftigungsuniversum, das die CES-Stichprobe schätzt, ist nach Tabelle 3 stark verzerrt. Die größten UI-Konten (mit mehr als 1.000 Mitarbeitern) umfassen nur 0,2 Prozent aller UI-Konten, beinhalten jedoch rund 28,0 Prozent Beschäftigung insgesamt. Die kleinste Größenklasse (0-9 Mitarbeiter) enthält fast 70,8 Prozent aller UIs, aber nur etwa 10,1 Prozent der gesamten privaten Beschäftigung. CES-Proben größere Unternehmen mit einer höheren Rate als kleinere Unternehmen, die eine Standard-Technik häufig in Unternehmensgründungen Umfragen verwendet wird. Tabelle 3. Gesamte Beschäftigung des privaten Universums nach Größe der UI, März 2014 Prozent aller UIs Prozent der Beschäftigung Tabelle 4 zeigt die Verteilung der aktiven CES-Stichprobeneinheiten. Ein viel größerer Anteil von großen als kleinen UIs wird jedoch ausgewählt, die keine Vorspannung entweder in der Probe oder den Schätzungen aus der Probe erzeugt. Jeder ausgewählten Probeneinheit wird ein Gewicht zugewiesen, basierend auf ihrer Wahrscheinlichkeit der Selektion, die sicherstellt, dass alle Firmen ihrer Größe in den Schätzungen richtig dargestellt werden. Zum Beispiel werden UIs in der kleinsten festen Schicht, in der 1 in alle 100 Firmen selektiert wird, ein Gewicht von 100 zugewiesen, weil sie sich selbst und 99 andere Firmen darstellen, die nicht abgetastet wurden. Die Verwendung von Probengewichten in dem Schätzverfahren verhindert eine große (oder kleine) feste Vorspannung in den Schätzungen. Tabelle 4. Gesamtzahl der privaten CES-Stichprobenbeschäftigung nach Größe der UI, März 2014 Prozent aller Stichprobenuntersuchungen Prozent der Stichprobenbeschäftigung 2 (10-19 Mitarbeiter) 3 (20-49 Mitarbeiter) 4 (50-99 Mitarbeiter) 5 (100-249 Mitarbeiter) 6 (250-499 Mitarbeiter) 7 (500-999 Mitarbeiter 8 (1000 Mitarbeiter) Zuverlässigkeit Messungen des Fehlers Die Erhebungsstudie unterliegt - wie bei anderen Stichprobenerhebungen - zwei Arten von Fehlern, Stichproben und Stichprobenfehlern Fehler oder Varianz, ist direkt mit der Größe der Stichprobe und dem Prozentsatz der von der Stichprobe erzielten Universumabdeckung verknüpft Die Stichprobe der Erhebungsbefragung umfasst mehr als ein Drittel der gesamten Universumsbeschäftigung, was eine sehr geringe Abweichung der gesamten nichtarmen Schätzungen ergibt. Tabelle 5: Fehler bei den vorläufigen Beschäftigungsschätzungen (1) CES-Branchenkodex (CES-Branchenkodex). In der Tabelle 5 sind die Fehlerquellen, die mit den Probenschätzungen assoziiert sind, in Tabelle 5 und den Standardtabellen für alle Mitarbeiter (AE), Produktionsmitarbeiter (PE) und Frauenarbeiter (WE) angegeben (1) Fehler beruhen auf Differenzen für die Monate Januar bis Oktober der Jahre 2011 bis 2015. (2) Der Fehler im Mittelwert-Quadrat ist Die Quadratwurzel des mittleren quadratischen Fehlers. Der mittlere quadratische Fehler ist das Quadrat der Differenz zwischen den endgültigen und vorläufigen Schätzungen, gemittelt über eine Reihe monatlicher Beobachtungen. Benchmark-Revision als Maß für den Vermessungsfehler Die Summe aus Stichproben - und Nicht-Abtastfehler kann als Gesamtfehler der Umfrage betrachtet werden. Anders als bei den meisten Stichprobenerhebungen, die Stichprobenfehler als ihre einzige Fehlerquelle veröffentlichen, kann die CES aufgrund der Verfügbarkeit der unabhängig abgeleiteten Universumsdaten eine jährliche Annäherung des Gesamtfehlers auf verzögerter Basis ableiten. Während der Benchmarkfehler häufig als Proxy - Maß für den Gesamtfehler für die CES - Erhebungsschätzung verwendet wird, stellt er tatsächlich den Unterschied zwischen zwei Beschäftigungsschätzungen dar, die aus separaten statistischen Prozessen (dh dem CES - Musterprozess und dem UI - Verwaltungsprozess) abgeleitet werden Abzüglich der in jedem Programm vorhandenen Fehler. Historisch gesehen war die Benchmark-Revision für die Gesamtarbeitslosigkeit gering. In den vorangegangenen 10 Jahren hat der absolute Prozentsatz des Benchmark-Fehlers durchschnittlich 0,3 Prozent, mit einem absoluten Bereich von weniger als 0,05 Prozent bis 0,7 Prozent. Weitere Diskussionen über die jährliche Benchmark der CES finden Sie im Abschnitt Revisionen dieses Dokuments unter Benchmarks. Revisionen zwischen vorläufigen und endgültigen Daten Erste vorläufige Schätzungen der Beschäftigung, der Stunden und des Ergebnisses, basierend auf weniger als der Gesamtstichprobe, werden unmittelbar nach dem Referenzmonat veröffentlicht. Die endgültigen, überarbeiteten, probenbasierten Schätzungen werden zwei Monate später veröffentlicht, wenn nahezu alle Berichte in der Stichprobe vorliegen. In Tabelle 5 sind der mittlere quadratische Fehler, der mittlere Prozentsatz und die mittlere absolute Prozentrevision in den letzten fünf Jahren zwischen den vorläufigen und endgültigen Beschäftigungsschätzungen dargestellt. Die Revisionen der vorläufigen Stunden und der Ertragsschätzungen betragen in der Regel nicht mehr als 0,1 Stunden pro Woche für wöchentliche Stunden und 1 Cent für Stundenlohn auf der gesamten privaten Ebene und können für die detaillierteren Industriegruppierungen etwas größer sein. Weitere Diskussionen über die monatlichen Revisionen der CES-Muster auf Schätzungen finden Sie im Abschnitt zu Revisionen dieses Dokuments unter Musterbasierte Revisionen. Varianzschätzung Die Schätzung der Stichprobenvarianz für AE. PE. Und WIR für die CES-Umfrage wird durch Anwendung der Methode der Balanced Half Samples (BHS) erreicht. Diese Replikationstechnik verwendet halbe Abtastwerte der ursprünglichen Abtastprobe und berechnet Schätzwerte unter Verwendung dieser Teilabtastungen. Die Probenabweichung wird berechnet, indem die Variabilität der Teilprobenschätzungen gemessen wird. Der Schätzer für gewichtete Verbindungen wird verwendet, um sowohl Schätzwerte als auch Abweichungen zu berechnen. Die Abtasteinheiten in jeder Zelle mdash, in der eine Zelle auf Zustand, Industrie und Grßenklassifizierung mdash basiert, werden in zwei zufällige Gruppen unterteilt. Die grundlegende BHS-Methode wird für beide Gruppen angewendet. Die Unterteilung der Zellen erfolgt systematisch in der gleichen Reihenfolge wie die anfängliche Probenselektion. Die Gewichte für Einheiten in der Halbprobe werden mit einem Faktor von 1 Gamma multipliziert, wobei die Gewichte für Einheiten, die nicht in der Halbprobe sind, mit dem Faktor 1 minus gamma multipliziert werden. Schätzungen aus diesen Untergruppen werden unter Verwendung der Schätzformel berechnet, die in Gleichung 2 beschrieben ist. Die Formel, die verwendet wird, um CES-Varianzen zu berechnen, ist wie folgt: Gleichung 2. CES-Varianz ist die Halbprobenschätzeinrichtung gamma frac12 k ist die Anzahl der halben Abtastwerte die ursprüngliche Vollzahl - Beispielschätzung. Angemessene Verwendung von Stichprobenabweichungen Variationsstatistiken sind für Vergleichszwecke nützlich, haben aber einige Einschränkungen. Abweichungen spiegeln die Fehlerkomponente der Schätzungen wider, die auf einer Vermessung nur einer Teilmenge der Population beruht, anstatt eine vollständige Zählung der gesamten Population durchzuführen. Sie reflektieren jedoch nicht den Nichtabtastfehler, wie etwa Antwortfehler und Bias aufgrund von Nicht-Ansprechen. Die Abweichungen der monatlichen Änderungsschätzungen sind sehr nützlich bei der Bestimmung, wann Änderungen auf einem gewissen Vertrauensniveau signifikant sind. Für AE stehen Varianzstatistiken für erste und zweite Schließungen zur Verfügung. PE. und wir. Darüber hinaus sind auf Anfrage auch dritte Abschlussvarianten verfügbar. Stichprobenfehler Die Stichprobenfehler, die für alle privaten Wirtschaftszweige und für die gesamte Nichtfarm ausgewiesen wurden, wurden für Schätzungen berechnet, die der Benchmark-Beschäftigungsrevision um einen Zeitraum von 16 bis 20 Monaten folgen. Die Fehler werden als Medianwerte der beobachteten Fehlerabschätzungen dargestellt. Diese Schätzungen wurden unter Verwendung des Verfahrens von BHS mit den Wahrscheinlichkeitsprobendaten und den Probengewichten, die zum Zeitpunkt der Probenauswahl zugewiesen wurden, abgeschätzt. Darstellung der Verwendung von relativen Standardfehlertabellen AE. PE. Und WE-Standardfehlertabellen liefern eine Referenz für relative Standardfehler aller Hauptreihen, die von der CES entwickelt wurden. Die Standardfehler der Differenzen zwischen Schätzungen in zwei nichtüberlappenden Industrien werden als Gleichung 3 berechnet. CES-relativer Standardfehler, da die beiden Schätzungen unabhängig sind. Die Fehler werden als relative Standardfehler dargestellt (Standardfehler geteilt durch die Schätzung und ausgedrückt als Prozent). Das Multiplizieren des relativen Standardfehlers durch seinen Schätzwert ergibt die Schätzung des Standardfehlers. Angenommen, das Niveau aller Mitarbeiter für Finanztätigkeiten in einem bestimmten Monat bei der ersten Schließung wird auf 9.923.000 geschätzt. Der ungefähre relative Standardfehler dieser Schätzung (0,4 Prozent) ist in den AE-Standardfehlertabellen vorgesehen. Ein 90-prozentiges Konfidenzintervall wäre dann das Intervall: 9,923,000 plusmn (1,645 mal 0,004 mal 9,923,000) 9,923,000 plusmn 65,293 9,857,707 bis 9,988,293 Darstellung der Verwendung von Standardfehlertabellen AE. PE. Und WE-Standard-Fehlertabellen eine Referenz für die Standardfehler von 1-, 3- und 12-Monatsänderungen in der Beschäftigungs-, Stunden - und Ergebnisserie. Die Fehler werden als Standardfehler der Änderungen dargestellt. Die Standard - und relativen Standardfehler für AE. PE. Und WE sind sowohl für saisonbereinigte als auch für saisonbereinigte CES-Daten geeignet. Nehmen wir an, dass die Veränderung des gesamten durchschnittlichen Stundenlohns (AHE) von einem bestimmten Monat zum nächsten im zweiten Jahr bei der Kohleförderung bei 0,23 liegt. Der Standardfehler für eine 1-Monatsänderung für den Kohleabbau aus der Tabelle ist 0,28. Die Intervallschätzung der monatlichen Veränderung in der AHE, die die wahre über dem Monat liegende Änderung mit einem 90-prozentigen Vertrauen enthalten wird, wird berechnet: 0,23 plusmn (1,645 mal 0,28) 0,23 plusmn 0,46-0,23, 0,69 Der wahre Wert von Die Veränderung über dem Monat liegt im Intervall -0,23 bis 0,69. Da dieses Intervall 0,00 (keine Änderung) enthält, ist die gezeigte Änderung von 0,23 bei dem Konfidenzniveau von 90% nicht signifikant. Alternativ übersteigt die geschätzte Änderung von 0,23 nicht 0,46 (1,645 mal 0,28), daher könnte man aus diesen Daten schließen, dass die Änderung bei dem Konfidenzniveau von 90% nicht signifikant ist. Datenerhebungsmethoden Jedes Monat sammelt das Bureau of Labor Statistics (BLS) Daten über Beschäftigung, Gehaltsabrechnung und bezahlte Stunden von einer Stichprobe von Einrichtungen. Vor 1991 wurden die meisten der aktuellen Beschäftigungsstatistiken (CES) per Post in einem dezentralisierten Umfeld von jeder Arbeitsmarktinformationsagentur (LMI) gesammelt. Die CES hat die Sammlung nach und nach zentralisiert und automatisierte Probensammelmethoden eingeführt, mit dem Ergebnis, dass die Erhebungsraten im Laufe der Zeit allmählich angestiegen sind. Nun hat die CES ein umfassendes Programm von neuen Sample Unit Solicitation in vier CES Regional Data Collection Centers (DCCs). Die DCCs führen die Erstregistrierung jeder Firma per Telefon durch, sammeln die Daten für mehrere Monate über computergestützte telefonische Befragung (CATI) und übertragen die Befragten nach Möglichkeit zu einem Selbstberichterstattungsmodus wie Touchtone Data Entry (TDE), Fax oder Web . Darüber hinaus führen die DCCs ein laufendes Programm der Ablehnung Konvertierung. Sehr große Firmen werden häufig über persönlichen Besuch angemeldet und die laufende Berichterstattung erfolgt über Electronic Data Interchange (EDI). Die Befragten bieten eine Auswahl von Berichtsmethoden, um die Rücklaufquoten bei dieser freiwilligen Umfrage zu unterstützen. Der größte Teil der CES-Stichprobe wird über EDI gesammelt (44 Prozent), während Internet-Sammlung und CATI verwendet werden, um etwa 17 Prozent bzw. 28 Prozent aller Berichte zu sammeln. Unter EDI stellt das Unternehmen eine elektronische Datei an CES jeden Monat in einem vorgeschriebenen Dateiformat zur Verfügung. Diese Datei enthält Daten für alle Betriebsobjekte. Die Datei wird vom CES-EDI-Center empfangen, verarbeitet und bearbeitet. Web ist eine der am schnellsten wachsenden Sammlungsmethoden. Unter Web-Sammlung, der Befragte Links zu einer sicheren Website, die ein Bild des Fragebogens und trägt ihre Daten in das Online-Formular. Die Daten werden vor der Übertragung an die CES einer Reihe von Bearbeitungsprüfungen unterzogen. TDE, ein weiterer Selbst-Reporting-Modus, wird verwendet, um etwa 3 Prozent der monatlichen Berichte zu sammeln. Unter dem TDE-System nutzt der Befragte ein telefonisches Telefon, um eine gebührenfreie Nummer anzurufen und eine Interviewsitzung zu aktivieren. Der Fragebogen befindet sich auf dem Computer in Form von vorbestellten Fragen, die dem Befragten vorgelesen werden. Der Befragte gibt die numerischen Antworten ein, indem er die Tasten des Telefons drückt. Jede Antwort wird für die Überprüfung der Befragten zurückgelesen. Die Mehrwertsteuererfassung durch die kombinierten regionalen CES-DCCs macht den größten Teil der Berichte aus (4 Prozent). Für die wenigen Einrichtungen, die nicht die oben genannten Methoden verwenden, werden die Daten per Post, Transkript, Magnetband oder Computerdiskette (4 Prozent) gesammelt. Abbildung 1 zeigt den Prozentsatz der Betriebe mit unterschiedlichen Datenerhebungsmethoden. Abbildung 1. Datenerhebungsmethoden der derzeitigen Erhebung über Beschäftigungsstatistiken in Prozent Verfügbare Daten Nationale Datenverfügbarkeit Das Programm "Beschäftigungsstatistiken" (CES) stellt für alle Beschäftigten (AE), Produktions - und Nichtaufnahmemitarbeiter (PE) und Frauen (WE) . Für AE und PE produziert CES außerdem durchschnittliche Stundenverdienste (AHE), durchschnittliche wöchentliche Stunden (AWH) und in den verarbeitenden Industrien nur durchschnittliche wöchentliche Überstunden (AWOH). Die meisten detaillierten Beschäftigungsreihen beginnen im Jahr 1990, obwohl die Beschäftigung im gesamten Branchensektor und in den meisten großen Industriezweigen bereits 1939 veröffentlicht wird. Eine Liste der aktuell veröffentlichten CES-Serien gibt es unter bls. govwebempsitcesseriespub. htm. Über 2.200 nicht saisonbereinigte Beschäftigungsreihen für AE, PE und WE werden monatlich veröffentlicht. Die Serie für AE umfasst über 900 Industrien auf verschiedenen Ebenen der Aggregation. Etwa 2.600 AE - und PE-Serien für AHE, AWH und im verarbeitenden Gewerbe werden AWOH monatlich nicht saisonbereinigt veröffentlicht und decken rund 600 Industriezweige ab. Rund 5.900 saisonbereinigte Beschäftigungs-, Stunden - und Ergebnisserien für AE, PE und WE werden veröffentlicht. Über 8.700 nicht saisonbereinigte Sonderderivatserien wie das durchschnittliche wöchentliche Ergebnis (AWE), Indizes und konstante Dollarserien für AE und PE werden ebenfalls für ca. 600 Branchen veröffentlicht. Staatliche und regionale Datenverfügbarkeit Für Staaten und Ballungszentren produziert das CES-Programm Beschäftigungs-, Stunden - und Ergebnisserien für AE und PE. Die meisten Beschäftigungsserien beginnen im Jahr 1990. Metropolitan Bereiche sind durch die US-Office of Management und Budget (OMB) definiert. Weitere Informationen zu den Daten des Staates und der Metropolregion finden Sie im Abschnitt "Statistiken für Staaten und Gebiete" dieses Dokuments. Beschäftigung Beschäftigungsdaten beziehen sich auf Personen auf Einrichtung Lohnsummen, arbeitete oder erhielt Lohn für jeden Teil der Lohnzeit, die den 12. Tag des Monats enthält. Die Daten schließen nicht Eigentümer, die uneingetragene Selbständige, unbezahlte Freiwilliger oder Familienangehörige, landwirtschaftliche Angestellte und inländische Angestellte aus. Angestellte Angestellte von Kapitalgesellschaften sind inbegriffen. Regierung Beschäftigung umfasst nur Zivilangestellte militärische Personal sind ausgeschlossen. Die Mitarbeiter der Central Intelligence Agency, der National Security Agency, der National Imagery and Mapping Agency und der Defense Intelligence Agency sind ebenfalls ausgeschlossen. Personen, die eine bezahlte Erwerbsunfähigkeit (für Fälle, in denen das Entgelt direkt vom Unternehmen eingeht), bezahlten Urlaub oder bezahlten Urlaub bezahlen oder während eines Teils der Entlohnungszeit arbeiten, obwohl sie arbeitslos sind oder an denen sie arbeiten Streik während des Restes des Zeitraums werden als Arbeitnehmer gezählt. Nicht als Arbeitnehmer gezählt werden Personen, die in der Entlassung, auf Urlaub ohne Bezahlung oder Streik für den gesamten Zeitraum, oder die wurden gemietet, aber noch nicht gemeldet während der Periode. Produktions - und nicht aufsichtsrechtliche Mitarbeiter (PE) sind für bestimmte wichtige Branchen unterschiedlich definiert. In manufacturing and in mining and logging, PE includes only production and related employees. In construction, PE includes only construction employees. In private service-providing industries, PE includes all nonsupervisory employees. These distinctions are clarified below. Production and related employees This category includes working supervisors and all nonsupervisory employees (including group leaders and trainees) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shipping, trucking, hauling, maintenance, repair, janitorial, guard services, product development, auxiliary production for plants own use (for example, power plant), recordkeeping, and other services closely associated with the above production operations. Construction employees This group includes the following employees in the construction sector: working supervisors, qualified craft employees, mechanics, apprentices, helpers, laborers, and so forth, engaged in new work, alterations, demolition, repair, maintenance, and the like, whether working at the site of construction or in shops or yards at jobs (such as precutting and preassembling) ordinarily performed by members of the construction trades. Nonsupervisory employees These are employees (not above the working-supervisor level) such as office and clerical employees, repairers, salespersons, operators, drivers, physicians, lawyers, accountants, nurses, social employees, research aides, teachers, drafters, photographers, beauticians, musicians, restaurant employees, custodial employees, attendants, line installers and repairers, laborers, janitors, guards, and other employees at similar occupational levels whose services are closely associated with those of the employees listed. Hours and Earnings Concurrent with the release of January 2010 data, the CES program began publishing all employee hours and earnings as official BLS series. These series were developed to measure the AHE and AWH of all nonfarm private sector employees and the AWOH of all manufacturing employees. AE hours and earnings were first released as experimental series in April 2007, and included national level estimates at a total private sector level and limited industry detail. Historically, the CES program has published average hours and earnings series for production employees in the goods-producing industries and for non-supervisory employees in the service-providing industries. These employees account for about 82 percent of total private nonfarm employment. The AE hours and earnings series are more comprehensive in coverage, covering 100 percent of all paid employees in the private sector, thereby providing improved information for analyzing economic trends and for constructing other major economic indicators, including nonfarm productivity and personal income. AE average hours and earnings data are derived from reports of hours and payrolls for all employees. PE average hours and earnings data are derived from reports of production and related employees in manufacturing and mining and logging, construction employees in construction, and nonsupervisory employees in private service-providing industries. These are the hours worked or for which pay was received during the pay period that includes the 12th of the month for all employees, production, construction, and nonsupervisory employees. Included are hours paid for holidays, for vacations, and for sick leave when pay is received directly from the firm. Payroll refers to dollars paid for full - and part-time all employees, production, construction, and nonsupervisory employees who received pay for any part of the pay period that includes the 12th day of the month. The payroll is reported before deductions of any kind, such as those for old-age and unemployment insurance, group insurance, withholding tax, bonds, or union dues also included is pay for overtime, tips, holidays, and vacation and for sick leave paid directly by the firm. Excluded from the payroll are bonuses (unless earned and paid regularly each pay period) other pay not earned in the pay period reported (such as retroactive pay) and the value of free rent, fuel, meals, or other payment in kind. Commissions are also included if paid at least monthly. Overtime hours These are hours worked by all employees, production and related employees, and nonsupervisory employees in manufacturing for which overtime premiums were paid because the hours were in excess of the number of hours of either the straight-time workday or the workweek during the pay period that included the 12th of the month. Weekend and holiday hours are included only if overtime premiums were paid. Hours for which only shift differential, hazard, incentive, or other similar types of premiums were paid are excluded. Average weekly hours The workweek information relates to the average hours for which pay was received and is different from standard or scheduled hours. Such factors as unpaid absenteeism, labor turnover, part-time work, and stoppages cause average weekly hours to be lower than scheduled hours of work for an establishment. Industry supersector averages further reflect changes in the workweek of component industries. Average hourly earnings Average hourly earnings are on a quotgrossquot basis. They reflect not only changes in basic hourly and incentive wage rates, but also such variable factors as premium pay for overtime and late-shift work and changes in output of employees paid on an incentive plan. They also reflect shifts in the number of employees between relatively high-paid and low-paid work and changes in employees earnings in individual establishments. Averages for groups and divisions further reflect changes in AHE for individual industries. The earnings series do not measure the level of total labor costs on the part of the employer because the following are excluded: benefits, irregular bonuses, retroactive items, and payroll taxes paid by employers. Average overtime hours Overtime hours represent that portion of weekly hours that exceeded regular hours and for which overtime premiums were paid in the manufacturing sector. If an employee were to work on a paid holiday at regular rates, receiving as total compensation his holiday pay plus straight-time pay for hours worked that day, no overtime hours would be reported. This applies to both AE and PE average overtime hours. Because overtime hours are premium hours by definition, weekly hours and overtime hours do not necessarily move in the same direction from month to month. Such factors as work stoppages, absenteeism, and labor turnover may not have the same influence on overtime hours as on average hours. Diverse trends at the industry group level also may be caused by a marked change in hours for a component industry in which little or no overtime was worked in both the previous and current months. Derivative Series Three-month moving average These estimates are an average of the over-the-month change for the last 3 months calculated only at the total nonfarm and total private levels. The current months employment change as well as the previous 2 months employment change are averaged to create the 3-month moving average. Each month, the average is moved forward 1 month. Average weekly earnings These estimates are derived by multiplying AWH estimates by AHE estimates. Therefore, AWE are affected not only by changes in AHE but also by changes in the length of the workweek. Monthly variations in such factors as the proportion of part-time employees, stoppages for varying reasons, labor turnover during the survey period, and absenteeism for which employees are not paid may cause the average workweek to fluctuate. Long-term trends of AWE can be affected by structural changes in the makeup of the workforce. For example, persistent long-term increases in the proportion of part-time employees in retail trade and many of the services industries have reduced average workweeks in these industries and have affected the average weekly earnings series. Real earnings These earnings are in constant dollars and are calculated from the earnings averages for the current month using a deflator. The Consumer Price Index (CPI) for All Urban Consumers (CPI-U) is used to deflate the earnings series for AE, while the CPI for Urban Wage Earners and Clerical employees (CPI-W) is used to deflate the earnings series for PE. The scope for the CPI-W is similar to that of PE earnings, both in the type of employee who is covered and the amount of the population that is covered by these series. The CPI-U used to deflate AE earnings is more inclusive than the CPI-W. Since AE earnings include all private sector employees, the more inclusive deflator is used in the calculation. The reference base for the CPI series is the 36-month period covering the years 1982, 1983, and 1984. Average hourly earnings, excluding overtime Average hourly earnings, excluding overtime-premium pay, are produced for manufacturing only and are computed by dividing the total AE or PE payroll for the industry group by the corresponding sum of total AE or PE hours and one-half of total AE or PE overtime hours. No adjustments are made for other premium payment provisions, such as holiday pay, late-shift premiums, and overtime rates other than time and one-half. Indexes of aggregate weekly hours and payrolls For basic estimating industries, aggregate hours are the product of AWH for AE times the employment for AE or AWH for PE times the employment for PE. At all higher levels of industry aggregation, aggregate hours are the sum of the component aggregates. The indexes for AE aggregate weekly hours are calculated by dividing the current months aggregate by the average of the 12 monthly figures for 2007. The indexes of aggregate weekly hours for PE are calculated by dividing the current months aggregate by the average of the 12 monthly figures for 2002. For basic industries, the aggregate payroll is the product of AHE for AE and aggregate weekly hours for AE or AHE for PE and aggregate weekly hours for PE. At all higher levels of industry aggregation, aggregate payroll is the sum of the component aggregates. The indexes of aggregate weekly payrolls are calculated by dividing the current months aggregate by the average of the 12 monthly figures for 2007 for AE and 2002 for PE. Indexes of diffusion of employment change Diffusion indexes measure the dispersion of employment change across industries over a specified time span (1-, 3-, 6-, or 12-month). The overall indexes are calculated from 262 seasonally adjusted employment series (primarily 4-digit NAICS industries) covering nonfarm payroll employment in the private sector. The manufacturing diffusion indexes are based on 79 4-digit NAICS industries. To derive the indexes, each component industry is assigned a value of 0, 50, or 100 percent, depending on whether its employment showed a decrease, no change, or an increase, respectively, over the time span. The average value (mean) is then calculated, and this percent is the diffusion index number. The reference point for diffusion analysis is 50 percent, the value indicating that the same number of component industries had increased as had decreased. Index numbers above 50 show that more industries had increasing employment and values below 50 indicate that more had decreasing employment. The margin between the percent that increased and the percent that decreased is equal to the difference between the index and its complement - that is, 100 minus the index. For example, an index of 65 percent means that 30 percent more industries had increasing employment than had decreasing employment (65-(100-65) 30). However, for dispersion analysis, the distance of the index number from the 50-percent reference point is the most significant observation. Although diffusion indexes commonly are interpreted as showing the percent of components that increased over the time span, the index reflects half of the unchanged components as well. (This is the effect of assigning a value of 50 percent to the unchanged components when computing the index.) Forms of Publication The Employment Situation Each month, usually 3 weeks after the reference period including the 12 th of the month, CES releases The Employment Situation, which contains CES national first preliminary (first closing) estimates of employment, hours, and earnings for all 3-digit NAICS series. The remaining series published by CES are released with the following months Employment Situation. For a list of CES published series, see bls. govwebempsitcesseriespub. htm. Real Earnings Each month, coincident with the CPI release, CES releases Real Earnings, which contains earnings data indexed to the CPI. For more information about real earnings, see Real Earnings in this document or visit bls. govnews. releaserealer. tn. htm . Other forms of publication CES data are also available in the following forms of publication: Statistics for States and Areas CES independently develops national and state and area employment, hours, and earnings series. Both sets of estimates are based on the same establishment reports however, CES uses the full establishment survey sample to produce monthly national employment estimates, while CES uses only the state-specific portion of the sample to develop state employment estimates. CES area statistics relate to metropolitan areas. CES uses the most recent OMB bulletin regarding statistical area definitions (OMB Bulletin No. 10-02 whitehouse. govsitesdefaultfilesombassetsbulletinsb10-02.pdf ) to define metropolitan statistical areas and metropolitan divisions. CES also produces area statistics for non-standard areas (areas which are not defined in the OMB Bulletin), noted at bls. govsaesaenonstd. htm. Changes in definitions are noted as they occur. Estimates for states and areas are produced using two methods. The majority of state and area estimates are produced using direct sample-based estimation. However, published area and industry combinations (domains) that do not have a large enough sample to support estimation using only sample responses have been estimated using modeling techniques. For more state and area employment (SAE) information please see the CES SAE home page at bls. govsaehome. htm. State and area estimates use smaller amounts of sample by industry than the national industry estimates. This increases the error component associated with state and metropolitan level estimates. For this reason, aggregating state data to the national level will also sum this error component, resulting in different estimates of U. S. employment, hours, and earnings. Summed state level CES estimates should not be compared to national CES estimates. Estimation Methods Monthly Estimation The Current Employment Statistics (CES) program uses a matched sample concept and weighted link relative estimator to produce employment, hours, and earnings estimates. These methods are described in Table 8. A matched sample is defined to be all sample members that have reported data for the reference month and the month prior. Excluded from the matched sample is any sample unit that reports that it is out-of-business and has zero employees. This aspect of the estimation methodology is more fully described below in the section on BirthDeath Model estimation. Table 8. Summary of methods for computing industry statistics on employment, hours, and earnings estimates Employment, hours, and earnings Basic estimating cell (industry, 6-digit published level) Aggregate industry level (super sector and, where stratified, industry) Annual average data BirthDeath Model The CES sample alone is not sufficient for estimating the total employment level because each month new firms generate employment that cannot be captured through the sample. There is an unavoidable lag between a firm opening for business and its appearance on the CES sample frame. The sample frame is built from Unemployment Insurance (UI) quarterly tax records. These records cover virtually all U. S. employers and include business births, but they only become available for updating the CES sampling frame 7 to 9 months after the reference month. After the births appear on the frame, there is also time required for sampling, contacting, and soliciting cooperation from the firm, and verifying the initial data provided. In practice, CES cannot sample and begin to collect data from new firms until they are at least a year old. There is a parallel though somewhat different issue in capturing employment loss from business deaths through monthly sample collection. Businesses that have closed are unlikely to respond to the survey, and data collectors may not be able to ascertain until after the monthly collection period that firms have in fact gone out of business. As with business births, hard information on business deaths eventually becomes available from the lagged UI tax records. Difficulty in capturing information from business birth and death units is not unique to the CES virtually all current business surveys face these limitations. Unlike many surveys, CES adjusts for these limitations explicitly, using a statistical modeling technique. Other surveys that do not explicitly adjust for business births and deaths are implicitly using the continuing sample units to represent birth and death units. This approach is viable when the primary characteristic of interest is an average measure of some type. However, because the goal of the CES program is to estimate an employment total each month and business births and deaths are important components contributing to these totals, CES uses a model-based adjustment in conjunction with the sample. Without the net birthdeath model-based adjustment, the CES nonfarm payroll employment estimates would be considerably less accurate. CES birthdeath modeling technique Prior to the Current Employment Statistics (CES) program adopting the current birthdeath modeling technique, research using historical information indicated that the business birth and death portions of total employment were substantial, but the net contribution of, or the difference between, the two components was relatively small and stable. The research was done using the nearly complete counts of employment developed from the UI tax records that are tabulated under the BLS Quarterly Census of Employment and Wages (QCEW) (bls. govorepdfst020090.pdf ). These QCEW tabulations also form the basis for both the sample frame and annual benchmark for the CES program. Beyond the research cited above, the Business Employment Dynamics (BED) series published quarterly by BLS, also illustrate how business birth and death employment substantially offset each other. The BED series are also derived from the QCEW. The BED series demonstrate that most of the net employment change each quarter is generated by the expansions and contractions in employment of the continuing businesses and a relatively smaller piece from business openings and closings (which CES refers to as net business births and deaths). As shown in Figure 2 below, continuing businesses which are adding employees (expansions) or subtracting employees (contractions) over the quarter comprise the vast majority of total change these movements are measured by the CES sample. Employment change contributions from openings (or births) and closings (or deaths) are much smaller and more stable, and the two series offset each other to a large degree. It is these underlying relationships among the components of net employment change that allow the CES to produce accurate estimates using a current monthly sample of continuing businesses and a model-based approach for the residual of net business births and deaths. Figure 2. Total private not seasonally adjusted BED series (in thousands) Birthdeath modeling methodology The CES birthdeath methodology has two steps. Step One mdash Employment losses from business deaths are excluded from the sample in order to offset the missing employment gains from new business births. Because employment increases from births nearly offset employment decreases from deaths in most months (as illustrated above by the BED data), this step accounts for most of the net of business birth and death employment. Operationally this is accomplished in the following manner each month. Business deaths that are non-respondents to the survey are automatically excluded because they have no current month data. Death establishments that report zero employment to the survey for the current month are treated the same as non-respondents and also excluded. As a result, the over-the-month change calculation from the sample is based solely on continuing businesses. For the months subsequent to a business death, the deaths are kept alive in the CES estimation process the growth rate of the continuing units in the sample is applied to them each month. This estimates for the growth of the new business births in the months after their birth but before they can be brought into the sample. This step accounts for most of the birthdeath employment but not all of it. The residual net employment that is not captured by this step is estimated through an econometric model, described below as step two. Step Two mdash Modeling for the residual of birthdeath employment change. In this step, the CES adjusts its sample-based estimates for the net birthdeath employment that step one misses. This adjustment is derived from an econometric technique known as ARIMA modeling. ARIMA is a standard econometric modeling technique that is often used to estimate relatively stable series. Outliers, level shifts, and temporary ramps are automatically identified. CES refits the ARIMA models each year for each basic estimation cell as part of its annual benchmarking process. Table 10 shows the net birthdeath model figures for the post-benchmark period of the benchmark from April to October of 2015. For more recent months of birthdeath information, see bls. govwebempsitcesbd. htm . Table 10. Net birthdeath estimates, post-benchmark 2015 (in thousands) CES Industry Code The inputs to the ARIMA model are historical observations of the residual net birthdeath employment that is not captured by either the sample or the step one imputation described above. These historical observations are derived empirically from the most recent five years of QCEW historical data. From the QCEW universe employment series, CES classifies each establishment each month as a continuing unit, a birth, or a death. Then sample-based estimates are simulated using the month-to-month change of the continuing units and using the deaths-to-impute-for-births technique described above in step one. The difference between these simulated estimates and the actual total employment measured by the QCEW each month is the net birthdeath employment. The net birthdeath series assumed the following form: Equation 9. Net birthdeath Net birthdeath Population minus Sample-based estimate Error During the net birthdeath modeling process, simulated monthly probability estimates over a 5-year period are created and compared with population employment levels. Moving from a simulated benchmark, the differences between the series across time represent a cumulative birthdeath component. Those residuals are converted to month-to-month differences and used as input series to the modeling process. Because the net birthdeath employment component is relatively stable, the ratio of it to total employment change can vary substantially from year to year. In slower growth years (for example, March 03-March 04), the ratio is much different than in stronger growth years (for example March 04-March 05). Put another way, the net birth death amount itself is relatively stable but its relationship to overall net employment change varies, depending on the magnitude of the overall change, almost by definition. Year one and year two models The birthdeath model is forecast using 24-month long spans of input data, representing historical net births and deaths. These spans are separated into two models referred to as year 1 (Y1) and year 2 (Y2) models. The age of the firms that contribute to the imputation step (step 1) of the birthdeath process impact the trend calculation. Y2 models are forecast using a sample that is a year older (relative to the reference month) than the Y1 models. While the results of the two models are similar, there are differences. Birthdeath model under quarterly sample rotation Using quarterly sample rotation, different industries have differently aged sample. Therefore, the mix of Y1 and Y2 models used varies by quarter. Y1 birthdeath values are appropriate for the newest sample, and Y2 values are phased in as the sample ages. Table 11 shows the forecast value used with each rotation group for each quarter. Table 11. Net birthdeath forecast year of industry groupings for CES quarterly sample rotation CES Industry Code Quarterly updates to the CES birthdeath model Prior to the release of preliminary January 2011 employment estimates in February 2011, birthdeath residuals were calculated on an annual basis and then applied each month during development of monthly estimates. With the release of the January 2011 preliminary estimates, CES began updating the net birthdeath model component of the estimation process on a quarterly basis instead of annually. This change allows for the incorporation of QCEW data into the birthdeath model as soon as it becomes available and reduces the post-benchmark revision in the CES series. This change does not impact the timing or frequency of CES monthly and annual releases or when benchmarking is done. For more information about the CES switch to quarterly net birthdeath forecasting, see bls. govcescesquarterlybirthdeath. htm . Quarterly and annual net birthdeath forecasts Table 12 shows a comparison of the CES birthdeath model adjustment using either a quarterly or annual forecasting frequency. The March 2003 benchmark is the first in which all industries were estimated using annually updated net birthdeath forecasts, and quarterly updated net birthdeath forecasts have been used in estimates from January 2011 forward. The differences between annual and quarterly forecasting of birthdeath are small in most cases. However, the CES estimates reflect more current business openings and closings more rapidly by increasing the frequency of updates to inputs to the net birthdeath model. For more information about the CES switch to quarterly net birthdeath forecasting, see bls. govcescesquarterlybirthdeath. htm. Historical comparisons, including simulated quarterly net birthdeath forecasts for years before 2011 and simulated annual net birthdeath forecasts for years after 2011, are available at bls. govcescesqbdcomp. htm . Table 12. Comparison of annual birthdeath to quarterly birthdeath for 2014 Limitations The primary limitation stems from the fact that the model is, of necessity, based on historical data. If there is a substantial departure from historical patterns of employment changes in net business births and deaths, as occurred from 2008 into 2009 during the 2009 benchmark. the models contribution to error reduction can erode. As with any model that is based on historical data, turning points that do not resemble historical patterns are difficult to incorporate in real time. Because there is no current monthly information available on business births, and because only incomplete sample data is available on business deaths, estimation of this component will always be potentially more problematic than estimation of change from continuing businesses. The net birthdeath model and seasonal adjustment The birthdeath model component is added to the sample-based component to form the not seasonally adjusted employment estimate for each month, as described above. These employment estimates are subsequently seasonally adjusted. Seasonal adjustment smooths the employment series by removing normal seasonal variations due to factors such as weather and holidays therefore the seasonally adjusted over the month employment changes are generally much smaller than the unadjusted changes. Users who wish to compare the models contribution to overall employment change reported for a month should compare against the unadjusted estimates, not the seasonally adjusted series. Comparing the model amounts to seasonally adjusted estimates generally results in an overstatement of the model-based components contribution to over-the-month employment change. The birthdeath model component generally shows the same overall seasonal patterns as the sample-based component. For example, total nonfarm employment shows a large seasonal increase in employment each April the model also shows a relatively large net addition to employment each April. Similarly total nonfarm employment records a large drop in employment each January and the model estimates a substantial drop in net birthdeath employment each January. An example of the net birthdeath model components versus overall net employment change from April 2014 to March 2015 (subsequent to the March 2015 benchmark implementation) is shown below in Table 13. The April 2014 model amount of 263,000 should be viewed as a component of the 1,163,000 not seasonally adjusted employment change, rather than as a component of the 330,000 seasonally adjusted change. Table 13. Net birthdeath and over the month change in total nonfarm employment (in thousands) Aggregation Procedures CES estimates at the basic estimating level and then aggregates these estimates to higher industry levels. Aggregation procedures are specific to the data type and published level of precision (i. e. the degree of rounding). Publication precision For employment data types, CES publishes estimates for major industry and aggregate industry sectors in thousands rounded to the nearest whole number, except for major industry sectors 41-420000 wholesale trade, 42-000000 retail trade, 43-000000 transportation and warehousing, and 44-220000 utilities, which are published in thousands rounded to the tenths place. More detailed employment estimates are published in thousands rounded to the tenths place. For hours and earnings data types, estimates are published using the same procedures for all levels of detail. Hours data types are published in hours rounded to the tenths place. Earnings data types are published in dollars rounded to the cent. Employment (AE, PE, and WE) AE, PE, and WE data types use the same method for aggregation. Basic level estimates rounded to the hundreds are aggregated to summary level estimates up to and including major industry sectors and are then rounded to the published precision. Aggregate industry sector estimates are then calculated by summing the rounded major industry and aggregate industry sector estimates that make up the aggregate industry sector and then rounded according to the published precision. Average weekly hours (AE and PE) The aggregation method for average weekly hours (AWH) of AE and PE is identical with the appropriate substitution of AE values or PE values in the following formulas. AWH are estimated at the basic level and combined with employment estimates for the same basic level to calculate aggregate employee hours. Aggregate employee hours (AH) are rounded to the tenths at the basic estimating level and calculated as shown: Equation 10. Aggregate hours AH AWH times Emp AH current month aggregate employee hours calculation for the basic level rounded to the tenths AWH current month AWH estimate for the basic level rounded as published Emp current month employment estimate for the basic level rounded as published Next, aggregate employee hours are added up to the summary levels. Average weekly hours rounded to the tenths are calculated for the summary level by: Equation 11. Summary level average weekly hours AWH AH divide Emp AWH current month average weekly hours estimate for the summary level rounded to the tenths AH current month aggregate employee hours calculation for the summary level rounded to the tenths Emp current month employment estimate for the summary level rounded according to published precision Average hourly earnings (AE and PE) The aggregation method for average hourly earnings (AHE) of AE and PE is identical, with the appropriate substitution of AE values or PE values in the following formulas. AHE are estimated at the basic level and combined with employment estimates for the same basic level to calculate aggregate employee hours (AH). Calculation of AH is identical to that described for AWH. Aggregate payroll (PR) is calculated using basic level AWH, AHE, and employment. Basic level PR calculations are rounded to the cent and are defined as: Equation 12. Aggregate payroll PR AHE times AWH times Emp PR current month aggregate payroll calculation for the basic level rounded to the cent AHE current month average hourly earnings estimate for the basic level rounded to the cent AWH current month average weekly hours estimate for the basic level rounded to the tenths place Emp current month employment estimate for the basic level rounded according to published precision To calculate the summary level estimates, summarize the aggregate employee hours and aggregate payroll to the summary level. Average hourly earnings rounded to the cent are calculated for the summary level by: Equation 13. Summary level average hourly earnings AHE PR divide AH AHE current month average hourly earnings estimate for the summary level rounded to the cent AH current month aggregate employee hours calculation for the summary level rounded to the tenths PR current month aggregate payroll calculation for the summary level rounded to the cent Average weekly overtime hours (AE and PE) Aggregation of average weekly overtime hours is identical to that described for AWH with the appropriate substitution of overtime hours values for the weekly hours values in the previous formula. Caution in aggregating state data The national estimation procedures used by CES are designed to produce accurate national data by detailed industry correspondingly, the state estimation procedures are designed to produce accurate data for each individual state. State estimates are not forced to sum to national totals nor vice versa. Because each state series is subject to larger sampling and nonsampling errors than the national series, summing them cumulates individual state level errors and can cause distortion at an aggregate level. For more information about state and metropolitan area level CES data, see the state and area employment website at bls. govsaehome. htm . Seasonal Adjustment The CES program employs a concurrent seasonal adjustment methodology to seasonally adjust its national estimates of employment, hours, and earnings. Under concurrent methodology, new seasonal factors are calculated each month using all relevant data up to and including the current month period. Many CES data users are interested in the seasonally adjusted over-the-month changes as a primary measure of overall national economic trends. Therefore, accurate seasonal adjustment is an important component in the usefulness of these monthly data. This following section discusses in detail the seasonal adjustment methodology and software employed by CES. It is important to note that this describes seasonal adjustment only as it relates to the CES programs implementation. There are other aspects of seasonal adjustment that are not discussed here. Seasonal adjustment and X8209138209ARIMA8209SEATS The CES program uses X8209138209ARIMA8209SEATS software developed by the U. S. Census Bureau to seasonally adjust the monthly estimates. The X8209138209ARIMA8209SEATS software is available on the U. S. Census Bureau web site at census. govsrdwwwx13as. The site contains the following information: Effective with the February 6, 2015 release of January 2015 data, the Current Employment Statistics (CES) survey will transition from using X8209128209ARIMA to X8209138209ARIMA8209SEATS to produce seasonally adjusted series and forecasts of birthdeath residuals. For more information about X8209138209ARIMA8209SEATS please visit the U. S. Census Bureau website at census. govsrdwwwx13as. Historical data will not be revised to be seasonally adjusted using X8209138209ARIMA8209SEATS. The CES program has been running parallel seasonal adjustment using X8209138209ARIMA8209SEATS, and no differences were observed. Examples of the specification files used by X8209138209ARIMA8209SEATS can be found at bls. govcescesspec. examples. zip. Program files for the latest PC version of X8209138209ARIMA8209SEATS Program files for the latest UNIX workstation version of X8209138209ARIMA8209SEATS Program files for X8209138209Graph, a companion graphics package Installation instructions Reference manual The remainder of this documentation describes how the CES program employs X8209138209ARIMA8209SEATS for seasonal adjustment purposes. Specifically, it describes the input files used in the CES programs implementation and commands used to invoke the software. This is not a substitute for formal X8209138209ARIMA8209SEATS training. There are other uses and features of X8209138209ARIMA8209SEATS that are not discussed in this section. The U. S. Census Bureau offers more intensive training for X8209138209ARIMA8209SEATS and seasonal adjustment. Contact the Census Bureau or visit their website at census. gov for more details. Seasonally adjusting CES data For published AE series, the CES program seasonally adjusts many series at the 3-, 4-, 5-, and 6-digit NAICS level. However, only the seasonally adjusted 3-digit NAICS level estimates are used to aggregate to the higher levels. The seasonally adjusted series that are published at more detailed levels than the 3-digit NAICS are considered to be independent series and are not included in aggregation of seasonally adjusted series. For example, seasonally adjusted data at the 5-digit NAICS are not aggregated to form seasonally adjusted 4-digit NAICS series. Instead the 4-digit NAICS and the 5-digit NAICS level series are independently seasonally adjusted. Most series are seasonally adjusted by directly applying the seasonal adjustment factors to the series with the exception of the component series used in indirect seasonal adjustment. In some cases, 3-digit NAICS series are indirectly seasonally adjusted by aggregating the seasonally adjusted employment level of their component series. For indirectly seasonally adjusted 3-digit NAICS series, the seasonal adjustment factors are applied to the component series rather than to the 3-digit NAICS series. The component series are then aggregated to create the 3-digit NAICS series. Indirectly seasonally adjusted series are noted in Table 15 . For published PE series and for published hours and earnings series for both PE and AE, the CES program seasonally adjusts at the major industry sector level for all industries except manufacturing which is seasonally adjusted at the 3-digit NAICS level. The seasonally adjusted PE, seasonally adjusted hours and earnings for PE, and seasonally adjusted hours and earnings for AE are aggregated from the 3-digit level in manufacturing industries and are aggregated from the major industry sector level for all other industries to get seasonally adjusted aggregate sectors. For published PE and AE overtime series, the CES program seasonally adjusts manufacturing series at the 2-digit NAICS level, or the durable goods and nondurable goods levels. These seasonally adjusted overtime series are aggregated to the manufacturing level. For published WE series, the CES program seasonally adjusts at the major industry sector level for all industries. The seasonally adjusted WE are aggregated from the major industry sector level for all industries. Special model adjustments The CES programs current implementation of seasonal adjustment controls for several calendar effects, explained below. Variable survey intervals. Beginning with the release of the 1995 benchmark, BLS refined the seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. Although the CES survey is referenced to a consistent concept mdash the pay period including the 12th of each month mdash inconsistencies arise because there are sometimes 4 and sometimes 5 weeks between the week including the 12th in a given pair of months. In highly seasonal industries, these variations can be an important determinant of the magnitude of seasonal hires or layoffs that have occurred at the time the survey is taken, thereby complicating seasonal adjustment. Standard seasonal adjustment methodology relies heavily on the experience of the most recent 3 years to determine the expected seasonal change in employment for each month of the current year. Prior to the implementation of the adjustment, the procedure did not distinguish between 4- and 5-week survey intervals, and the accuracy of the seasonal expectation depended in large measure on how well the current years survey interval corresponded with those of the previous 3 years. All else the same, the greatest potential for distortion occurred when the current month being estimated had a 5-week interval but the 3 years preceding it were all 4-week intervals, or conversely when the current month had a 4-week interval but the 3 years preceding it were all 5-week intervals. BLS adopted REGARIMA (regression with auto-correlated errors) modeling to identify the estimated size and significance of the calendar effect for each published series. REGARIMA combines standard regression analysis, which measures correlation among two or more variables, with ARIMA modeling, which describes and predicts the behavior of data series based on its own past history. For many economic time series, including nonfarm payroll employment, observations are auto-correlated over time each months value is significantly dependent on the observations that precede it. These series, therefore, usually can be successfully fit using ARIMA models. If auto-correlated time series are modeled through regression analysis alone, the measured relationships among other variables of interest may be distorted due to the influence of the auto-correlation. Thus, the REGARIMA technique is appropriate for measuring relationships among variables of interest in series that exhibit auto-correlation, such as nonfarm payroll employment. In this application, the correlations of interest are those between employment levels in individual calendar months and the lengths of the survey intervals for those months. The REGARIMA models evaluate the variation in employment levels attributable to eleven separate survey interval variables, one specified for each month, except March. March is excluded because there are almost always 4 weeks between the February and March surveys. Models for individual basic series are fit with the most recent 10 years of data available, the standard time span used for CES seasonal adjustment. The REGARIMA procedure yields regression coefficients for each of the 11 months specified in the model. These coefficients provide estimates of the strength of the relationship between employment levels and the number of weeks between surveys for the 11 modeled months. The X8209138209ARIMA8209SEATS software also produces diagnostic statistics that permit the assessment of the statistical significance of the regression coefficients, and all series are reviewed for model adequacy. Because the eleven coefficients derived from the REGARIMA models provide an estimate of the magnitude of variation in employment levels associated with the length of the survey interval, these coefficients are used to adjust the CES data to remove the calendar effect. These filtered series then are seasonally adjusted using the standard X8209138209ARIMA8209SEATS software. Weather-related outliers in construction series. Beginning with the 1996 benchmark revision, BLS utilized special treatment to adjust construction industry series. In the application of the interval effect modeling process to the construction series, there initially was difficulty in accurately identifying and measuring the effect because of the strong influence of variable weather patterns on employment movements in the industry. Further research allowed BLS to incorporate interval effect modeling for the construction industry by disaggregating the construction series into its finer industry and geographic estimating cells and tightening outlier designation parameters. This allowed a more precise identification of weather-related outliers that had masked the interval effect and clouded the seasonal adjustment patterns in general. With these outliers removed, interval effect modeling became feasible. The result is a seasonally adjusted series for construction that is improved because it is controlled for two potential distortions: unusual weather events and the 4- versus 5-week effect. Length of pay adjustment. With the release of the 1997 benchmark, BLS implemented refinements to the seasonal adjustment process for the hours and earnings series to correct for distortions related to the method of accounting for the varying length of payroll periods across months. There is a significant correlation between over-the-month changes in both the average weekly hours (AWH) and the average hourly earnings (AHE) series and the number of weekdays in a month, resulting in noneconomic fluctuations in these two series. Both AWH and AHE show more growth in short months (20 or 21 weekdays) than in long months (22 or 23 weekdays). The effect is stronger for the AWH than for the AHE series. The calendar effect is traceable to response and processing errors associated with converting payroll and hours information from sample respondents with semi-monthly or monthly pay periods to a weekly equivalent. The response error comes from sample respondents reporting a fixed number of total hours for workers regardless of the length of the reference month, while the CES conversion process assumes that the hours reporting will be variable. A constant level of hours reporting most likely occurs when employees are salaried rather than paid by the hour, as employers are less likely to keep actual detailed hours records for such employees. This causes artificial peaks in the AWH series in shorter months that are reversed in longer months. The processing error occurs when respondents with salaried workers report hours correctly (vary them according to the length of the month), which dictates that different conversion factors be applied to payroll and hours. The CES processing system uses the hours conversion factor for both fields, resulting in peaks in the AHE series in short months and reversals in long months. REGARIMA modeling is used to identify, measure, and remove the length-of-pay-period effect for seasonally adjusted average weekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaining AWH movements in all the service-providing industries except utilities. For AHE, the length-of-pay-period variable is significant for wholesale trade, retail trade, information, financial activities, professional and business services, and other services. All AWH series in the service-providing industries except utilities have been adjusted from January 1990 forward. The AHE series for wholesale trade, retail trade, information, financial activities, professional and business services, and other services have been adjusted from January 1990 forward as well. For this reason, calculations of over-the-year change in the establishment hours and earnings series should use seasonally adjusted data. The series to which the length-of-pay-period adjustment is applied are not subject to the 4- versus 5-week adjustment, as the modeling cannot support the number of variables that would be required in the regression equation to make both adjustments. Poll workers in local government series. A special adjustment is made in November each year to account for variations in employment due to the presence or absence of poll workers in local government, excluding educational services. This procedure was first introduced in November 1988 to prevent fluctuations in seasonally adjusted local government, excluding education series, resulting from the short-term employment of poll workers during presidential election years. Initially this effect was estimated using an X-11 ARIMA extension analogous to the early method used to adjust for the floating holiday effect described below. This is not a true seasonal effect because it occurs only once every 4 years in November. In addition, according to CES definition, poll workers who receive even just one days pay are correctly counted as employed. However, a decision was made by BLS to remove this effect due to its confounding the analysis of economic trends in total nonfarm employment. The adjustment procedure is now accomplished through X8209138209ARIMA8209SEATS it removes an estimate of the number of poll workers in the series prior to seasonal adjustment in order to prevent November spikes in total nonfarm employment that result from the 1-day employment of many thousands of poll workers. The current procedure was introduced with the first preliminary release of May 1998 data and is used for the national local government, excluding education employment series only. Floating holiday adjustment. This adjustment to average weekly hours and average weekly overtime series accounts for significant effects due to the timing of the survey reference period (the pay period including the 12th of the month) overlapping with the Good Friday (Easter) and Labor Day holidays. These holidays do not occur at exactly the same time every year mdash sometimes they occur during the survey reference period and sometimes not mdash which complicates the seasonal adjustment process. The presence or absence of these holidays in the survey reference period causes a significant variation in hours reported by respondents in some industries (i. e. more hours are reported when the holiday does not fall in the week of the 12th). The special adjustment procedure identifies the magnitude of the effect and adjusts for it prior to seasonally adjusting the series, thereby neutralizing the effect. The floating holiday adjustment is accomplished through the REGARIMA option within the X820912 procedure. Essentially a regression model estimate of the significance of the presence or absence of the holiday during the week of the 12th is made, using a dummy variable to indicate in which years the holiday is present or absent. For industry series where the dummy variable test is significant, an adjustment is made to the original series before it is input into the seasonal adjustment procedure, using the estimated regression parameters. The floating holiday procedure was first introduced in 1990, pre-dating X820912 REGARIMA availability. The adjustment was accomplished using an extension of the X-11 ARIMA procedure. This process was based on the same concepts described above and yielded similar results to the procedure currently in use. X8209128209ARIMA was introduced with the release of first preliminary May 1997 estimates in June 1997. With the 2015 benchmark release, CES transitioned from using X8209128209ARIMA to X8209138209ARIMA8209SEATS to produce seasonally adjusted series and forecasts of birthdeath residuals. For more information about X8209138209ARIMA8209SEATS please visit the U. S. Census Bureau website at census. govsrdwwwx13as . More information about the calendar-related fluctuations in CES data is available on the BLS website at bls. govcescesfltxt. htm. Residential and nonresidential specialty trade contractors raking procedure. Concurrent with the release of the 2004 benchmark, the CES Program began producing and publishing employment series for residential specialty trade contractors (20-238001) and nonresidential specialty trade contractors (20-238002). The two employment series are derived independently from the traditionally published 3-digit NAICS series specialty trade contractors (20-238000). A raking procedure is used to ensure that the sum of the seasonally adjusted residential specialty trade contractors and seasonally adjusted nonresidential specialty trade contractors series is consistent with the published seasonally adjusted total for specialty trade contractors at the 3-digit NAICS level. The raking procedure begins by seasonally adjusting the two series independently for the residential and nonresidential groups at the 3-digit NAICS level. The seasonally adjusted residential and nonresidential series are summed at the 3-digit NAICS level to get a 3-digit total. Ratios of seasonally adjusted residential-to-total employment and seasonally adjusted nonresidential-to-total employment are calculated. The sum of the seasonally adjusted residentialnonresidential series is subtracted from the official 3-digit seasonally adjusted estimate for specialty trade contractors to determine the amount that must be raked. The total amount that must be raked is multiplied by the ratios to determine what percentage of the raked amount should be applied to the residential group and what percentage should be applied to the nonresidential group. Once the seasonally adjusted residential and nonresidential groups receive their proportional amount of raked employment, the two groups are aggregated again to get a 3-digit total. At this point their sum should be equal to the official 3-digit seasonally adjusted estimate for specialty trade contractors. Additive and multiplicative models. Prior to the March 2002 benchmark release in June 2003, all CES series were adjusted using multiplicative seasonal adjustment models. Although the X8209138209ARIMA8209SEATS seasonal adjustment program provides for either an additive or a multiplicative adjustment depending on which model best fits the individual series, the previous CES processing system was unable to use additive seasonal adjustments. A new processing system, introduced simultaneously with the conversion to NAICS in June 2003, is able to use both additive and multiplicative adjustments. The seasonal adjustment website (bls. govwebempsitcesseasadj. htm ) contains a list of which series are adjusted with additive or multiplicative seasonal adjustment models. Special notice regarding seasonal adjustment for AE hours and earnings Concurrent with the release of January 2010 data, the CES program began publishing AE hours and earnings as official BLS series. The AE hours and earnings series are published at the same level of industry detail as PE hours and earnings series and are published on both a not seasonally adjusted and a seasonally adjusted basis. CES has at least 5 full years of history for the AE hours and earnings series, which allows for incorporating the special model adjustments for variation due to the calendar effects (4- vs. 5-week, 10- vs. 11-day). Also, generally CES uses 10 years of not seasonally adjusted data as an input to seasonal adjustment. Until CES has a full 10 years of input data for the AE hours and earnings series, CES will use the entire history of the not seasonally adjusted series as inputs and replace the entire history of the seasonally adjusted data. Continuing these updates until all years have been adjusted using a full 10 years of input data ensures that all data are adjusted using the same methodology. CES seasonal adjustment input files All controllable variables remain fixed during the year. For example, the ARIMA model, outliers, transformation specification, and historical data are held constant, and the same calendar treatments are used throughout the year. Once a year, as part of the annual CES benchmark procedure, all seasonal adjustment specifications are reviewed for each series. Any changes are implemented and kept constant until the next annual benchmark. Also during the annual benchmark, estimates for the 5 most recent years are re-seasonally adjusted using the new specifications. After 5 years of revisions, seasonally adjusted data are frozen. The CES program uses the following input files when seasonally adjusting estimates: Specification file Input data file Prior-adjustment file User-defined regression variables (dummy variables) file Metafile Recent outliers More details follow on each input. Specification file An input specification file, or a quotspecquot file, is a text file used to specify program operations. The spec file is composed of functional units called specifications (or quotspecsquot). Each spec unit comprising the spec file controls the options for a specific function. There are 15 different specs that can be used in a spec file however, the CES programs implementation typically employs only 8 specs. These specs are: SERIES spec mdash this specifies the location and format of the data TRANSFORM spec mdash this specifies a data transformation REGRESSION spec mdash this specifies any regression components ARIMA spec mdash this specifies the ARIMA model to be used ESTIMATE spec mdash this estimates the regARIMA model FORECAST spec mdash this generates forecasts of seasonal factors OUTLIER spec mdash this specifies automatic outlier detection X11 spec mdash this generates and controls the seasonal adjustment process COMPOSITE spec mdash this is a special spec used only during indirect seasonal adjustment Each spec used by the CES program is covered in greater detail at the end of this section in Anatomy of a Spec File . In the CES programs implementation, each seasonally adjusted employment series has its own spec file ending in a quot. spcquot file extension. The quot. spcquot extension is not recognizable by all operating systems and usually needs to be opened with a text editor such as TextPad, Wordpad, or Notepad. Also, it is important to remember that when running X8209138209ARIMA8209SEATS in DOS, the name of the spec file must be 8 characters or less. This is a limitation of DOS, not X8209138209ARIMA8209SEATS. All of the spec files currently used in production can be downloaded from bls. govwebempsitcesseasadj. htm. Input data file The input data file consists of not seasonally adjusted CES estimates for all series that have a corresponding seasonally adjusted series and is referred to in the SERIES spec of the spec file. The CES implementation reads input data from a text file in quotfree formatquot style. In the free-format style, data are delimited with either tabs or spaces, and only the input data are included mdash dates and other descriptive information are excluded. Instead, information describing the data is specified in the SERIES spec using the START and PERIOD arguments. The full path and name of the input data file is specified using the FILE argument (see Figure 3 ). Figure 3. Input data file specifications CES data can be extracted from the BLS website at bls. govcesdata. htm. However, in some cases, not seasonally adjusted data extracted from the BLS website will differ from what the CES program actually uses in seasonal adjustment. In particular, data extracted from the BLS website will reflect any strikes or other prior adjustments that have taken place. Before running seasonal adjustment, the CES program will reverse these effects so that they will not be considered when calculating the seasonal factors. Also, the CES program uses unrounded data when running seasonal adjustment mdash data on the BLS website are rounded. Prior adjustment file As mentioned in the previous section, in some cases the CES program will modify the not seasonally adjusted estimates (input data) before running X8209138209ARIMA8209SEATS. This is done to ensure that non-seasonal events such as strikes are not included in the calculation of the seasonal factors. Once the seasonal factors are calculated, they are applied to the not seasonally adjusted data used as inputs. Then the prior adjustment removed before running X8209138209ARIMA8209SEATS are incorporated to create the seasonally adjusted estimates. To read more about the impact of strikes on CES data, visit the BLS website at bls. govcescesstrk. htm . The latest prior adjustment file used in the seasonal adjustment of CES data can be downloaded from bls. govwebempsitcesseasadj. htm. The prior adjustment file is updated annually to reflect the series structure adopted with the benchmark, and it is updated monthly with each release of CES national estimates to include strike data. In the example shown below in Figure 4. the first column contains the 14-digit CES NAICS tabcode. This tabcode identifies the series by an 8-digit industry code. followed by three zeros used as placeholders, a 2-digit data type code. and a single digit indicating seasonal adjustment (3 for not seasonally adjusted, 5 for seasonally adjusted). The tabcode structure is similar to the CES series ID structure, described on the CES NAICS webpage (bls. govcescesnaics. htm2.3 ). The second column contains the year, and the next 12 columns represent the months of the year in sequential order (January through December). The file contains both positive and negative numbers. The positive numbers reflect a strike and are added to the not seasonally adjusted data before running X8209138209ARIMA8209SEATS. The negative numbers reflect the buildup of employment associated with the decennial census and are added to the not seasonally adjusted data before calculating the seasonal factors. Figure 4. Prior adjustment file format (1) The dummy values are usually 1 and 0, with weights assigned so that the effect over a 10 year period sums to zero. The latest user-defined regression files used in the seasonal adjustment of CES data can be downloaded from bls. govwebempsitcesseasadj. htm. The metafile is a text file ending in a quot. mtaquot file extension and is used when running X8209138209ARIMA8209SEATS on more than one series. It is essentially a list of the complete path and filename mdash without the extension mdash of all of the input spec files. Only one spec file is listed per row. As with the individual spec files, it is important to remember that when running X8209138209ARIMA8209SEATS in DOS, the name of the metafile must be 8 characters or less. Recent outliers An excel table called outliers. xlsx lists the month, year, and industry code of recent outliers manually identified during analyst review. The file contains outliers from the November following the most recent benchmark to the present month. Running X820913 on a single series Use the following command at the DOS prompt when running X8209138209ARIMA8209SEATS on a single series: x13as spec file name - options Example: At the DOS prompt, type: c:x13asx13as - m c:x13seasadjpubAE - w (where pubAE. mta is the metafile you are using) Output from X8209138209ARIMA8209SEATS When X8209138209ARIMA8209SEATS is run, several output files are generated by default. The output files are saved in the same location as the input specification files. Main output file (.out) Error output file (.err) Log output file (.log) More details follow on each of the output files. Main output file (.out) The X8209138209ARIMA8209SEATS output is written to a text file ending in a quot. outquot extension. Output from the CES implementation contains many different tables and statistics, including: Table displaying the original, not seasonally adjusted series Table displaying the final seasonally adjusted series Table displaying the final seasonal factors Statistics related to model selection Statistics related to outlier detection A summary of seasonal adjustment diagnostics Quality control statistics Individual specs in the spec file control their contribution to this output using optional PRINT arguments. For example, within the X11 spec, BRIEF specifies that only certain tables or plots are printed, while the minus sign in front of a name (such as - SPECSA or - SPECIRR) means that particular table or plot should be suppressed from the output. In this example, without the options - SPECSA and - SPECIRR, both of the plots would be printed by default under the BRIEF option. Figure 5. The PRINT argument in the X11 spec It is important to remember that every time X8209138209ARIMA8209SEATS is run on a particular series, the. out file is overwritten, unless an alternate name or directory is specified. Error output file (.err) Input errors are written to a text file ending in an quot. errquot extension. If the error is fatal, ERROR: will be displayed before the error message. If the error is not fatal, WARNING: will be printed before the message. Non-fatal errors (or warnings) will not stop the program, but should be an alert to use caution and to check input and output carefully. It is important to remember that, as is the case with all output files, every time X8209138209ARIMA8209SEATS is run on a particular series, the. err file is overwritten, unless an alternate name or directory is specified. Log output file (.log) A summary of modeling and seasonal adjustment diagnostics are written to a text file ending in a quot. logquot extension. Individual specs in the specification file control their contribution to this output using optional SAVELOG arguments. When X8209138209ARIMA8209SEATS is run on an individual spec file, the log file is stored with the same name and directory as the spec file. However, when X820913 is run using a metafile, the log file is stored with the same name and directory as the metafile. As is with all output files, every time X8209138209ARIMA8209SEATS is run, the. log file is overwritten unless an alternate name or directory is specified. Other output files Other output files are generated as specified in the spec file using the SAVE argument. In the CES programs implementation, the following additional output files are generated:.a1 ndash This file contains the not seasonally adjusted data with associated dates and is specified in the SERIES spec. ao ndash This file contains outlier factors with associated dates and is specified in the REGRESSION spec. d10 ndash This file contains final seasonal factors with associated dates and is specified in the X11 spec. d11 ndash This file contains final seasonally adjusted data with associated dates and is specified in the X11 spec. d16 ndash This file contains combined seasonal and trading day factors with associated dates and is specified in the X11 spec. td ndash This file contains final trading day factors with associated dates and is specified in the REGRESSION spec Indirect seasonal adjustment The CES program generally seasonally adjusts published series directly at the 3-digit NAICS level and aggregates to the higher levels. However, there are some exceptions to this rule. In a few of the AE series, the CES program will seasonally adjust at a level lower than the 3-digit NAICS level. In these instances, the CES program seasonally adjusts the 3-digit series indirectly i. e. all of the component (lower level) series are seasonally adjusted directly and aggregated up to the composite (3-digit) level. Indirect seasonal adjustment is performed on these series because some of the individual component series that aggregate to the composite series exhibit different seasonal patterns that may be masked if seasonally adjusted directly at the aggregate level. The spec file for the composite series differs somewhat from normal CES implementation. The most significant difference is at the beginning of the spec file, where the SERIES spec is replaced with the COMPOSITE spec. Running X820913 employing the COMPOSITE spec produces an indirect seasonal adjustment of the composite series as well as a direct adjustment. Output from the indirect adjustment is saved under non-standard file extensions. Aggregated not seasonally adjusted data with associated dates are saved in a text file with the extension. cms (instead of. a1 under direct seasonal adjustment) Final indirect (aggregated) seasonally adjusted data with associated dates are saved in a text file with the extension. isa (instead of. d11 under direct seasonal adjustment) Final seasonal factors for aggregated series with associated dates are saved in a text file with the extension. isf (instead of. d16 under direct seasonal adjustment) The COMPOSITE spec is covered in greater detail at the end of this section in Anatomy of a Spec File. Seasonal adjustment of the component series that go into a composite series is run using X8209138209ARIMA8209SEATS in the same way as a standard seasonally adjusted series, but is then summed to the composite level. A metafile listing the file locations and names (without the. spc extension) of the composite series followed by all of its component series is used to seasonally adjust a composite series. A current list of industries that are indirectly seasonally adjusted follows in Table 15. along with their component series. For any given series, not all of the component series are published at first closing. Some series are published during a later release. In the table below, component series published with the first preliminary data release are denoted with a footnote (1). Table 15. Indirectly seasonally adjusted CES series Component Series (1) Footnotes (1) For CES industry titles of the component series, see bls. govwebempsitcesseriespub. htm. (2) The component series for this industry are published with the second preliminary release. Anatomy of a spec file For published series, the CES program generally seasonally adjusts at the 3-digit NAICS level and aggregates to the higher levels. A small number of series are independently seasonally adjusted at a higher level of detail, but these are not included in the aggregation of seasonally adjusted data. One of the main inputs to the seasonal adjustment process is a unique file called a spec file. The spec file contains a set of specs that give X8209138209ARIMA8209SEATS various information about the data and the desired seasonal adjustment options and output. Each specification inside the spec file controls options for a specific function. For example, the SERIES spec contains specifications on the location and format of the data, while the X11 spec sets seasonal adjustment options such as seasonal adjustment transformation mode, output files to save, and diagnostic statistics to print. Figure 6. CES seasonal adjustment spec file The spec file is free format, and blank spaces, tabs, and blank lines may be used as desired to make the spec file more readable. The order of the specification statements in the spec file (with one exception), and the order of the arguments within the braces of any spec do not matter. The only requirement is that the SERIES spec or COMPOSITE spec must be the first spec. More detail on each spec used by CES follows. NAME 10113310 ndash AE The main function of the SERIES spec is to specify details about the input data series such as the name, format, and location of the data. The CES implementation employs seven options or arguments with the SERIES spec. TITLE mdash A descriptive title for the series. In this example, the title is quotLoggingquot . START mdash The start date of the time series being adjusted. In this example, the start date is January, 1993 . PERIOD mdash Seasonal period of the series. In this example, the period is 12 (which means monthly). SAVE mdash Specifies output to be saved. In this example, the time series data with associated dates will be saved in an output file called AE10113310. A1 . PRINT mdash Specifies output to be printed. In this example, BRIEF specifies that only certain tables are printed. NAME mdash The name of the time series. In this example, the name is quot10113310 ndash AEquot . FILE mdash The complete path and name of the file containing the time series data. In this example, the complete path and filename is c:AE10113310.dat . The main function of the TRANSFORM spec is to transform or adjust the time series prior to estimating a regARIMA model. The CES implementation employs one argument with the TRANSFORM spec. FUNCTION mdash Specifies the method to transform the time series. In this example, the transformation method is log transformation, which means X820913 will compute a multiplicative seasonal decomposition. VARIABLES (AO1995.02 AO1996.01 AO1999.01) USER (dum1 dum2 dum3 dum4 dum5 dum6 dum7 dum8 dum9 dum10 dum11) The main function of the REGRESSION spec is to specify the regression components of a regARIMA model. The CES implementation employs up to six options with the REGRESSION spec. VARIABLES mdash Specifies any predefined regression variables to be included in the model. In the CES implementation, predetermined outliers are listed after the VARIABLES argument. In this example, predetermined outliers include AO1995.02 (February 1995), AO1996.01 (January 1996), and AO1999.01 (January 1999). USER mdash Specifies the names for any user-defined regression variables. CES defines regression variables to adjust for significant effects associated with calendar related events such as (1) the relative timing of the reference period of the survey and the Good Friday (Easter) and Labor Day holidays (2) variations of 4 or 5 weeks between reference periods in any given pair of months, and (3) differences in the number of working days in a pay period from month-to-month. In this example, the regression variables are named dum1, dum2, dum3, dum4, dum5, dum6, dum7, dum8, dum9, dum10, and dum11 . START mdash Specifies the start date for the data values for the user-defined regression variables. In this example, the start date is January, 1986 . FILE mdash The complete name of the file containing the data values for the user-defined regression variables, including the path. In this example, the filename, including the path, is quotc:FDUM8606.datquot . USERTYPE mdash Specifies a type of model-estimated regression effect to each user-defined regression variable. In this example, the type of model-estimated regression effect is defined as TD . or trading day. SAVE mdash Specifies output to be saved. In this example, trading day factors with associated dates will be saved in an output file called AE10113310.TD, and outlier factors with associated dates will be saved in an output file called AE10113310.AO . Note: Not every option is used in every spec file. For example, if no predetermined outliers exist, then the VARIABLES argument will not be used. Likewise, if we are not treating a particular series for calendar effects, then the USER, START, FILE, and USERTYPE arguments will not be used. The main function of the ARIMA spec is to specify the ARIMA part of a regARIMA model. The CES implementation employs 1 option with the ARIMA spec. MODEL mdash Specifies the actual ARIMA model to be used. In this example, the model is (2 1 0) (0 1 1) . The main function of the ESTIMATE spec is to estimate the regARIMA model specified by the REGRESSION and ARIMA specs. The CES implementation employs 1 argument with the ESTIMATE spec. MAXITER mdash Specifies the maximum number allowed of autoregressive moving average (ARMA) nonlinear iterations. ARMA is a time-series model that includes both autoregressive (AR) and moving average (MA) nonlinear components. In this example, the maximum number allowed of ARMA iterations is 1000 . The main function of the FORECAST spec is to generate forecasts (andor backcasts) for the time series model given in the SERIES spec using the estimated regARIMA model. The CES implementation employs 1 argument with the FORECAST spec. MAXLEAD mdash Specifies the number of forecasts produced. In this example, the number of forecasts specified is 24 months . The main function of the OUTLIER spec is to perform automatic detection of point outliers, temporary change outliers, level shifts, or any combination of the three. The CES implementation uses this spec to automatically detect point outliers only. CES employs 2 arguments with the OUTLIER spec. CRITICAL mdash Specifies the value to which the absolute values of the outlier t-statistics are compared to detect outliers. In this example, the critical value is 3.5 . TYPES mdash Specifies the types of outliers to detect. The CES implementation uses the OUTLIER spec to automatically detect point outliers only. In this example, the outlier type is AO (which signifies point outliers). PRINT (BRIEF - SPECSA - SPECIRR) SAVE (D10 D11 D16) SAVELOG (Q Q2 M7 FB1 FD8 MSF) The function of the X11 spec is to control certain aspects of the seasonal adjustment process. For example, the CES implementation uses the X11 spec to control the type of seasonal adjustment decomposition calculated (mode). CES employs 6 arguments with the X11 spec. MODE mdash Specifies the mode of the seasonal adjustment decomposition to be performed. There are four choices: multiplicative, additive, pseudo-additive, and log-additive. In the CES implementation, only the multiplicative or additive modes are employed. In this example, the mode specified is multiplicative ( MULT ). PRINT mdash Specifies output to be printed. In this example, BRIEF specifies that only certain tables or plots are printed. The minus sign in front of a name means that particular table or plot should be suppressed. In this example, - SPECSA specifies that a spectral plot of differenced, seasonally adjusted series be suppressed, while - SPECIRR specifies that a spectral plot of outlier-modified irregular series be suppressed. Without these options, both plots would be printed under the BRIEF option by default. SAVE mdash Specifies output to be saved. In this example, final seasonal factors with associated dates will be saved in an output file called AE10113310.D10 the final seasonally adjusted series with associated dates will be saved in an output file called AE10113310.D11 and combined seasonal and trading day factors with associated dates will be saved in an output file called AE10113310.D16 . APPENDFCST mdash Determines if forecasts of seasonal factors will be included in the X820913 output files and tables that were selected in the SAVE option. If APPENDFCST yes, then forecasted seasonal factors will be stored. In this example, the APPENDFCST value is YES . FINAL mdash Specifies the types of prior adjustment factors (obtained from the REGRESSION and OUTLIER specs) that are to be applied to the final seasonally adjusted series. In this example, FINAL USER . which means that factors derived from user-defined regressors (or in this example, the dummy variables) are to be applied to the final seasonally adjusted series, removing significant effects associated with calendar related events. SAVELOG mdash Specifies the diagnostic statistics to be printed to the log file. In this example, the following diagnostics will be printed: Q . which is the overall index of the acceptability of the seasonal adjustment. The adjustment may be poor if Q 1. Q2 . which is the Q statistic computed without the M2 Quality Control Statistic. The M2 values can sometimes be misleading if the trend shows several changes of direction. M7 . which measures the moving seasonality relative to the stable seasonality found in the series. Any M 1 indicates a source of potential problems for the adjustment procedure. FB1 . which is an F-test for stable seasonality, performed on the original series. FB8 . which is an F-test for stable seasonality, performed on the final ratio of the seasonal-to-irregular components. MSF . which is an F-test for moving seasonality. As previously mentioned, the CES program generally seasonally adjusts published series at the 3-digit NAICS level and aggregates to the higher levels. However, there are a few cases in which CES seasonally adjusts published series at a level lower than the 3-digit NAICS level. In these instances, CES seasonally adjusts the 3-digit NAICS level indirectly i. e. all of the component or lower level series are seasonally adjusted directly and then aggregated up to the 3-digit level. When this happens, the SERIES spec is replaced by the COMPOSITE spec in the specification file of the 3-digit series. TITLE quotConstruction of buildingsquot SAVE (ISF ISA CMS) NAME 20236000 - AE SAVELOG (INDTEST INDQ) The COMPOSITE spec is used as part of the procedure for obtaining both indirect and direct adjustments of a composite series data series. This spec is required for obtaining composite adjustments and is used in place of the SERIES spec. The COMPOSITE spec can also specify details about the input data series such as the name of the series and which tables are to be printed or stored. The CES implementation employs five options or arguments with the COMPOSITE spec. TITLE mdash A descriptive title for the series. In this example, the title is quotConstruction of buildingsquot . SAVE mdash Specifies output to be saved. In this example, the aggregated time series data with associated dates will be saved in an output file called AE20236000.CMS . the final seasonal factors for the indirect adjustment with associated dates will be saved in an output file called AE20236000.ISF . and the final indirect seasonally adjusted series with associated dates will be saved in an output file called AE20236000.ISA . PRINT mdash Specifies output to be printed. In this example, BRIEF specifies that only certain tables are printed. NAME mdash The name of the time series. In this example, the name is quot20236000 ndash AEquot . SAVELOG mdash Specifies the diagnostic statistics to be printed to the log file. In this example, the following diagnostics will be printed: IND TEST . which is a test for adequacy of composite adjustment. IND Q . which is an overall index of the acceptability of the indirect seasonal adjustment. Sample-based Revisions Effect of sample receipts CES data users typically are most concerned with revisions to over-the-month changes. This section profiles these monthly revisions of CES seasonally adjusted over-the-month changes and the sample collection rates that underlie the revisions. CES begins collecting sample reports for a reference month as soon as the reference period, the establishments pay period that includes the 12th of the month, is complete. Collection time available for first preliminary estimates ranges from 9 to 15 days, depending on the scheduled date for the Employment Situation news release. The Employment Situation is scheduled for the third Friday following the week including the 12th of the prior month, with an exception for January. (For January, the news release is delayed a week if the third Friday following the week of the 12th occurs on January 1, 2, or 3.) Given this short collection cycle for the first preliminary estimates, many establishments are not able to provide their payroll information in time to be included in these estimates. Therefore, CES sample responses for the reference month continue to be collected for 2 more months and are incorporated into the second preliminary and final sample-based estimates published in subsequent months. (Second preliminary estimates for a reference month are published the month following the initial release, and final sample-based estimates are published 2 months after the initial release.) Additional sample receipts are the primary source of the monthly CES employment revisions. Sample-based estimates remain final until employment levels are reset to universe employment counts, or benchmarks, for March of each year the benchmarks are primarily derived from Unemployment Insurance (UI) tax records. The annual benchmarking process results in revised data back to the last annual benchmark for not seasonally adjusted series and back 5 years for seasonally adjusted series. Monthly revisions Revisions to CES over-the-month changes are calculated by comparing each months second preliminary over-the-month change to the first preliminary over-the-month change, the final sample-based over-the month change with the second preliminary over-the-month change, and the final sample-based over-the-month change to the first preliminary over-the-month change. See bls. govwebempsitcesnaicsrev. htm for a table of revisions to seasonally adjusted total nonfarm over-the-month changes from January 1979 forward. The monthly employment change figures shown in the table do not reflect subsequent changes due to the introduction of benchmark revisions, seasonal adjustment, or other updates. Mean revisions and mean absolute revisions for each calendar year are included in the table. Mean absolute revisions indicate the overall magnitude of change to the estimates, while the mean revisions are a measure of whether there is a bias in direction of the revisions. The closer the mean revision is to zero, the less indication that revisions are predominantly either upward or downward. For example, if in a given year there were 6 upward revisions of 50,000 and 6 downward revisions of 50,000, the mean revision would be 0 however, the mean absolute revision would be 50,000. Collection rates Collection rates are defined as the percent of reports received for a monthly estimate compared to the total number of actively-reporting sample units on the sample registry. CES collection rates back to 1981 can be found on bls. govwebempsitcesregrec. htm . Much of the month-to-month variation in the first preliminary collection rates is a function of the number of collection days in the individual months. The overall upward trend over time is attributable to replacing decentralized mail collection with automated techniques. For more information about the methods used to calculate CES estimates of employment, hours, and earnings at all closings, see the section on Monthly Estimation in this documentation. Benchmarks For the establishment, or CES, survey, annual benchmarks are constructed in order to realign the sample-based employment totals for March of each year with the Unemployment Insurance (UI) based population counts for March. These population counts are much less timely than sample-based estimates and are used to provide an annual point-in-time census for employment. For national series, only the March sample-based estimates are replaced with UI counts. For state and metropolitan area series, all available months of UI data are used to replace sample-based estimates. State and area series are based on smaller samples and are therefore more vulnerable to both sampling and non-sampling errors than national estimates. Population counts are derived from the administrative file of employees covered by UI. All employers covered by UI laws are required to report employment and wage information to the appropriate Labor Market Information Agency (LMI) four times a year. Approximately 97 percent of private and total nonfarm employment within the scope of the establishment survey is covered by UI. A benchmark for the remaining 3 percent is constructed from alternate sources, primarily records from the Railroad Retirement Board (RRB) and County Business Patterns (CBP). This 3 percent is collectively referred to as noncovered employment and is explained further in the calculating noncovered employment section of this document. The full benchmark developed for March replaces the March sample-based estimate for each basic cell. The monthly sample-based estimates for the year preceding and the year following the benchmark are also then subject to revision. Each annual benchmark revision affects 21 months of data for not seasonally adjusted series and 5 years of data for seasonally adjusted series. Monthly estimates for the year preceding the March benchmark are readjusted using a quotwedge backquot procedure. The difference between the final benchmark level and the previously published March sample estimate is calculated and spread back across the previous 11 months. The wedge is linear eleven-twelfths of the March difference is added to the February estimate, ten-twelfths to the January estimate, and so on, back to the previous April estimate, which receives one-twelfth of the March difference. This assumes that the total estimation error since the last benchmark accumulated at a steady rate throughout the current benchmark year. Estimates for the 7 months following the March benchmark (April through October) also are recalculated each year. These post-benchmark estimates reflect the application of sample-based monthly changes to new benchmark levels for March and the re-computation of business birthdeath factors for each month. Following the revision of basic employment estimates, all other derivative series also are recalculated. New seasonal adjustment factors are calculated and all data series for the previous 5 years are re-seasonally adjusted before full publication of all revised data in February of each year. Estimates for the November and December following the March benchmark revise due to both impacts of benchmarking and additional sample. Additionally, new sample units are rotated into the survey starting with November. As an example of benchmark effects, the March 2014 benchmark revisions (published in February 2015) resulted in revised series from April 2013 through December 2014 on a not seasonally-adjusted-basis and revised series from January 2010 through December 2014 on a seasonally-adjusted-basis for all series except seasonally adjusted AE hours and earnings, which were revised back to January 2006. Annual CES benchmark revisions are published along with January first preliminary estimates in February of each year. For example, the annual CES benchmark revisions for March 2014 were published along with the January 2015 first preliminary estimates on February 6, 2015. The benchmark revision is the difference between the universe count of employment for March and its corresponding sample-based estimate after removing the effect of any changes in employment scope. A table of benchmark revisions from 1979 forward is included in Table 16 below. See bls. govwebempsitcesbmart. htm for more details about the benchmarking process. Table 16. CES total nonfarm benchmark revisions (1) Footnotes (1) The table reflects the benchmark revisions after removing the effect of any changes in employment scope. (2) With the conversion from SIC to NAICS, support activities for animal production (NAICS 1152) was removed from CES scope. Also, the federal government employment level derivations were changed from end-of-month counts provided by the Office of Personnel Management that excluded some workers, mostly employees of U. S. Department of Defense-owned establishments such as military base commissaries, to QCEW derived benchmark employment levels. (3) A review of industries for the possible presence of noncovered employment yielded 13 additional industries. As a result of including these industries, employment in the amount of 95,000 was added to the nonfarm level. The final difference between the benchmarked and published March estimate levels was 162,000. (4) A large non economic code change related to state-run programs brought 466,000 employment into the CES scope from outside of the CES scope. The final difference between the benchmarked and published March estimate levels was 347,000. (5) With the 2015 benchmark, CES reconstructed the national employment series for CES series 65-624120 services for the elderly and persons with disabilities back to January 2000. CES previously reconstructed this series with the 2013 benchmark however, between the 2013 and 2015 benchmark, a better source of information for the employment within NAICS 62412 for the state of California was found. The inclusion of the reconstructed series resulted in total nonfarm and total private employment that was 27,000 less than the originally published March 2015 estimate level. This table displays March 2015 data after accounting for the decrease of 27,000 from the reconstructed series. Similarly, for the education and health services supersector, this table displays March 2015 data after incorporating the reconstructed series. Calculating noncovered employment Noncovered employment results from a difference in scope between the CES program and the Quarterly Census of Employment and Wages (QCEW) program. The QCEW employment counts are derived from UI tax reports that individual firms file with their State Employment Security Agency (SESA). Most firms are required to pay UI tax for their employees however, there are some types of employees that are exempt from UI tax law, but are still within scope for the CES estimates. Examples of the types of employees that are exempt are students paid by their school as part of a work study program interns of hospitals paid by the hospital for which they work employees paid by State and local government and elected officials independent or contract insurance agents employees of non-profits and religious organizations (this is the largest group of employees not covered) and railroad employees covered under a different system of UI administered by the Railroad Retirement Board (RRB). This employment needs to be accounted for in order to set the benchmark level for CES employment. No single source of noncovered data exists therefore, CES uses a number of sources to generate the employment counts, including County Business Patterns (CBP) and the Annual Survey of Public Employment and Payroll (ASPEP) both from the US Census Bureau, the RRB, and the Labor Market Information Agencies (LMIs). The majority of noncovered employment is calculated using CBP data. Industries for which noncovered employment is derived from the CBP are provided in Table 17. The CBP mdash which draws from Social Security filings and other records which do include those employees not covered by UI tax laws mdash is lagged in its publication by approximately 2 years (e. g. in 2014 the 2012 CBP data was published). To adjust for this lag, CES assumes that the noncovered portion of employment grows or declines at the same rate as the covered portion and trends the CPB data forward using the QCEW trend. The current QCEW employment level is subtracted from the trended CBP figure, and the residual is the noncovered employment level. Noncovered employment for all CBP based industries, with the exception of religious organizations, is calculated as follows: Equation 14. Noncovered employment for CBP-based industries, except religious organizations N Noncovered employment estimate C CBP employment data for North American Industry Classification System (NAICS) code E QCEW employment for NAICS code t Benchmark year Noncovered employment for religious organizations is calculated by: Equation 15. Noncovered employment for Religious organizations N Noncovered employment estimate C CBP employment data for NAICS 813110 E QCEW employment for NAICS 813110 t Benchmark year Table 17. Noncovered industries calculated using CBP data All other transit and ground passenger transportation Over time some sources from which CES draws input data have become unreliable. Where possible CES has tried to find new sources of input data, but for series that no longer have reliable input data, CES trends forward the previous years noncovered employment levels using a ratio derived from QCEW employment data. These industries are contained in Table 20 and are calculated using the following method. Equation 17. Noncovered employment for QCEW-trend-based industries N noncovered employment estimate E QCEW employment t Benchmark year Table 20. Noncovered industries calculated using QCEW trend Footnotes (1) Indicates that noncovered employment is calculated only for firms owned by state and local government. Corporate officers are one of the largest exemptions outside of the industries listed. In several states, corporate officers are exempt from UI coverage and as a result noncovered employment exists in most NAICS industries in those states. Corporate officers and other state specific employment exemptions outside of those listed above are collected from state offices annually by CES. Noncovered employment industries are reviewed and refined periodically. This review is done to identify any changes in state UI coverage, as well as to ensure that CES captures all exempted employment within the scope of the CES survey and that our methodology and external data sources are as accurate as possible. When additions and changes are identified during review, they are incorporated with the following March benchmark. Changing data ratios for education and religious organizations Due to the small sample in religious organizations (NAICS 8131) and definitional exclusions in the collection of data for educational services (NAICS 611), certain ratios for these series are recalculated with each benchmark to allow for the creation of aggregate totals. Production or nonsupervisory employee (PE) and women employee (WE) ratios, all employee (AE) average hourly earnings (AHE) and average weekly hours (AWH), and PE AHE and AWH for these series are calculated based on the weighted average of the previous years professional and technical services, education and health services, leisure and hospitality, and other services supersectors annual averages. This year the March 2014 values were set based on the 2013 annual averages. The education services series uses the PE ratio, AHE, and AWH calculated from the weighted average. The religious organizations series uses the PE ratio, WE ratio, AHE, and AWH calculated from the weighted average. In both cases, the ratios, AHE, and AWH for AE and PE are held constant through the next benchmark. Historical Reconstructions Beyond the monthly revisions and the benchmark revisions, CES employment, hours, and earnings estimates have been reconstructed several times in order to avoid series breaks and to provide users with continuous, comparable employment time series suitable for economic analysis when incorporating methodological changes. The major reconstruction efforts are briefly described below. Improvement to seasonal adjustment methodology With the release of the 1995 benchmark revision (in June 1996), CES refined its seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. This improvement mitigated the effects that a variable number of weeks between surveys had on the measurement of employment change, thus improving the measurement of true economic trends. At that time, data for 1988 forward were revised to incorporate this new methodology. CES sample redesign Over a 4-year period, CES introduced a new probability-based sample design it replaced an outmoded and less scientific quota sample-based design. The new design was phased in by major industry division with the June 2000 through June 2003 benchmark releases (see Table 21 ). As each industry was phased in, the post-benchmark estimates for that year were affected by the new sample composition. Table 21.CES sample redesign phase-in schedule Industries converted to new sample design Industry reclassification CES periodically updates the national nonfarm payroll series to revised NAICS structures. This update usually occurs every 4 to 5 years. For all NAICS updates, affected series are reconstructed back to at least 1990, and in some cases, where longer histories are available, they are reconstructed back further. With the release of the 2011 benchmark in February 2012, CES converted from NAICS 2007 to NAICS 2012. The conversion to NAICS 2012 resulted in minor content changes within the manufacturing and the retail trade sectors, as well as minor coding changes within the utilities and the leisure and hospitality sectors. Several industry titles and descriptions were also updated. Prior to the NAICS 2012 structure, CES estimates were classified under NAICS 2007 system, preceded by the NAICS 2002 system. The NAICS system was updated from NAICS 2002 to NAICS 2007 in early 2008. Before switching to NAICS 2002, the CES estimates were classified under the Standard Industrial Classification (SIC) system. CES estimates were converted from SIC to NAICS 2002 in mid-2003. For more information about NAICS in the CES program, see bls. govcescesnaics. htm . Other Factors Contributing to Revisions Over the time period covered by the revision and collection rate tables, CES has introduced many program improvements some of these affect the revision patterns observed over time. Monthly revisions As noted above, the overall magnitude of these revisions has trended down over time mainly due to automated and improved data collection techniques which raised the collection rates for the first and second preliminary estimates. Other factors of note include: Timing of benchmark revisions Between 1980 and 2003, annual benchmark revision updates were introduced in June of each year, concurrent with the March final sample-based estimates and the April second preliminary estimates. The monthly revisions for March and April for these years were often larger than for other months, because the March final and April second preliminary estimates were incorporating not only additional sample but also other benchmark-related changes. Beginning with the 2003 benchmark revision (published in 2004), CES reduced the time required to produce the annual revisions by 4 months and thus began publishing benchmark revisions in February rather than June. Therefore from 2004 forward, the November final and December second preliminary estimates are affected by benchmark revision updates, rather than the March final and April second preliminary estimates. Timing of seasonal adjustment updates Between 1980 and June 1996 seasonal factors were updated on an annual basis along with the benchmark revisions. Thus March final and April second preliminary were affected by the recomputation of seasonal factors as well as other benchmarking procedures and additional sample receipts. Between November 1996 and November 2002, CES updated seasonal factors on a semi-annual basis, meaning that September final and October second preliminary estimates as well as March final and April second preliminary revisions were affected by seasonal factor updates. Since June 2003 the CES program has used a concurrent seasonal adjustment procedure, meaning that seasonal adjustment is rerun every month using all available months of estimates including the month currently being estimated for first preliminary. This technique yields the best possible seasonal adjustment for the current month and reduces benchmark revisions to over-the-month changes. In the application of the concurrent procedure, the previous 2 months are revised to incorporate not only additional sample receipts but also new seasonal factors. Thus there are no longer individual months that are more affected than others by seasonal factor updates. However, this practice does mean that revisions from second preliminary to final sample-based estimates for each month are affected by the CES replacement policy. Because CES revises only 2 months of estimates each month, the fourth month back from the current first preliminary estimate is adjusted using a different set of seasonal factors than the third month back. For example, with the release of October first preliminary data, factors are revised for September and August, but not July. Table of Figures Use the links below to skip to specific equations, tables, and figures describing the CES sample, data collection, available statistics, estimation, and revisions. Technical Notes for the Current Employment Statistics Survey (PDF ) Introduction The Bureau of Labor Statistics (BLS) collects data each month on employment, hours, and earnings from a sample of nonfarm establishments through the Current Employment Statistics (CES) program. The CES survey includes about 146,000 businesses and government agencies, which cover approximately 623,000 individual worksites drawn from a sampling frame of Unemployment Insurance (UI) tax accounts covering roughly 9.3 million establishments. The active CES sample includes approximately one-third of all nonfarm payroll employees in the 50 states and the District of Columbia. From these data, a large number of employment, hours, and earnings series in considerable industry and geographic detail are prepared and published each month. Historical statistics for the nation are available on the CES National website at bls. govcesdata. htm. Historical statistics for states and metropolitan areas are available on the CES State and Metro Area website at bls. govsaedata. htm . Table of Contents Use the links below to skip to specific topics about the CES sample, data collection, industry classification, available statistics, estimation, and revisions. A link is included to skip to a list of equations, tables, and figures included in the CES Technical Notes. The Sample The Current Employment Statistics (CES) sample is a stratified, simple random sample of worksites, clustered by Unemployment Insurance (UI) account number. The UI account number is a major identifier on the Bureau of Labor Statistics (BLS) Longitudinal Database (LDB) of employer records, which serves as both the sampling frame and the benchmark source for the CES employment estimates. The sample strata, or subpopulations, are defined by state, industry, and employment size, yielding a state-based design. The sampling rates for each stratum are determined through a method known as optimum allocation, which distributes a fixed number of sample units across a set of strata to minimize the overall variance, or sampling error, on the primary estimate of interest. The total nonfarm employment level is the primary estimate of interest, and the CES sample design gives top priority to measuring it as precisely as possible, or minimizing the statistical error around the statewide total nonfarm employment estimates. Frame and sample selection The LDB is the universe from which CES draws the establishment survey sample. The LDB contains data on the roughly 9.3 million U. S. business establishments covered by UI, representing nearly all elements of the U. S. economy. The Quarterly Census of Employment and Wages (QCEW) program collects these data from employers on a quarterly basis in cooperation with Labor Market Information Agencies (LMIs). The LDB contains employment and wage information from employers, as well as name, address, and location information. It also contains identification information such as UI account number and reporting unit or worksite number. The LDB contains records of all employers covered under the UI tax system. That system covers 97 percent of all employment within the scope of CES in the 50 states, the District of Columbia, Puerto Rico, and the U. S. Virgin Islands. There are a few sections of the economy that are not covered by the QCEW, including the self-employed, unpaid family workers, railroads, religious organizations, small agricultural employers, and elected officials. Data for employers generally are reported at the worksite level. Employers who have multiple establishments within a state usually report data for each individual establishment. The LDB tracks establishments over time and links them from quarter to quarter. The total private and government portions of the CES sample are selected using two different methods. Private establishments in the CES sample frame are stratified by state, industry, and size. Stratification groups population members together for the purpose of sample allocation and selection. The strata, or groups, are composed of homogeneous units. With 13 industries (treating manufacturing as one industry and not including government) and 8 size classes. there are 104 total allocation cells per state. The sampling rate for each stratum is determined through a method known as optimum allocation. Optimum allocation minimizes variance at a fixed cost or minimizes cost for a fixed variance. Under the CES probability design, a fixed number of sample units for each state is distributed across the allocation strata in such a way as to minimize the overall variance, or sampling error, of the total state employment level. The number of sample units in the CES probability sample was fixed according to available program resources. The optimum allocation formula places more sample in cells for which data cost less to collect, cells that have more units, and cells that have a larger variance. The CES government sample is not part of the programs probability-based design. CES is able to achieve a very high level of universe employment coverage in government industries by obtaining full payroll employment counts for many government agencies, eliminating the need for a probability-based sample design. Government estimates are combined with the total private estimates to obtain values for total nonfarm. Annual sample selection helps keep the CES survey current with respect to employment from business births and business deaths. In addition, the updated universe files provide the most recent information about industry, size, and metropolitan area designation. Each year the CES sample is drawn from the first quarter Longitudinal Database (LDB) data in the fall of that year. A birth update is added in the early summer from the third quarter of the previous year. After all out-of-scope records are removed, the sampling frame is separated into allocation cells. Within each allocation cell, units are grouped by metropolitan statistical area (MSA), and these MSAs are sorted by the size of the MSA, defined as the number of UI accounts in that MSA. As the sampling rate is uniform across the entire allocation cell, implicit stratification by MSA ensures that a proportional number of units are sampled from each MSA. Some MSAs may have too few UI accounts in the allocation cell these MSAs are collapsed and treated as a single MSA. Permanent Random Numbers (PRNs) are assigned to all UI accounts on the sampling frame. As new units appear on the frame, random numbers are assigned to those units as well. As records are linked across time, the PRN is carried forward in the linkage. Within each selection cell, the units are sorted by PRN, and units are selected according to the specified sample selection rate. The number of units selected randomly from each selection cell is equal to the product of the sample selection rate and the number of eligible units in the cell plus any carryover from the prior selection cell. The result is rounded to the nearest whole number. Carryover is defined as the amount that is rounded up or down to the nearest whole number. Because of the cost and workload associated with enrolling new sample units, all units remain in the sample a minimum of 2 years. To ensure all units meet this minimum requirement, CES has established a swapping procedure. The procedure allows units to be swapped into the sample that were newly selected during the previous sample year and not reselected as part of the current probability sample. The procedure removes a unit within the same selection cell and places the newly selected unit from the previous year back into the sample. To reduce respondent burden, a similar procedure swaps units out of the sample that have been sample members for 4 or more consecutive years. The swap out procedure removes an old unit within the same selection cell and replaces it with a new unit. Approximately 60 percent of the CES sample for private industries overlaps from the previous sample to the current sample. Selection weights Once the sample is drawn, sample selection weights are calculated based on the number of UI accounts actually selected within each allocation cell. The sample selection weight is approximately equal to the inverse of the probability of selection, or the inverse of the sampling rate. It is computed as: Equation 1. Sample selection weights Sample selection weight N h n h N h the number of noncertainty UI accounts within the allocation cell that are eligible for sample selection n h the number of noncertainty UI accounts selected within the allocation cell Frame maintenance and sample updates Due to the dynamic economy, there is a constant cycle of business openings (births) and closings (deaths). A sample update is performed during the summer each year drawing from the previous years third quarter LDB data. This update selects units from the population of openings and other units not previously eligible for selection and includes them as part of the sample. Location, contact, and administrative information are updated for all establishments that were selected as part of the annual sample. Table 1 shows the 2015 benchmark employment levels and the approximate proportion of total universe employment coverage at the total nonfarm and major industry sector levels. The coverage for individual industries within the supersectors may vary from the proportions shown. The UI counts and establishment numbers shown in Table 1 are from the benchmark year, not the current sample year, and therefore differ from UI and establishment totals for the current sample year. Table 1. Employment benchmarks and approximate coverage of BLS employment and payrolls sample, March 2015 CES Industry Code CES Industry Title Employment Benchmarks (in thousands) Footnotes: (1) Counts reflect active sample reports. Because not all establishments report payroll and hours information, hours and earnings estimates are based on a smaller sample than are the employment estimates. (2) Employment of reported values for March 2015. (3) The Surface Transportation Board provides a complete count of employment for Class I railroads plus Amtrak. CES sample by industry The sample distribution by industry reflects the goal of minimizing the sampling error in the total nonfarm employment estimate, while also providing reliable employment estimates by industry. Sample coverage rates vary by industry as a result of building a design to meet these goals (see Table 1 ). For example, manufacturing and leisure and hospitality industries are of similar size. Manufacturing has 12.3 million employees while leisure and hospitality has about 14.6 million employees. However their relative sample sizes are different. Manufacturing has about 19,100 sample establishments with a total of 3.1 million employees while leisure and hospitality has many more sample establishments, about 65,300 sample establishments, but covers only about 2.9 million employees. The manufacturing sample therefore covers about 26 percent of all employment in manufacturing while the leisure and hospitality sample covers about 20 percent of all employment in that industry. The differences are linked in part to the fact that manufacturing is characterized by a much larger average firm size than leisure and hospitality. These types of differences do not cause a bias in the CES employment estimates because of the use of industry sampling strata and sampling weights which ensure each firm is properly represented in the estimates. Government sample The CES government sample is not part of the programs probability-based design, which is used to estimate employment for all private industries. A very high level of universe employment coverage (75 percent) is achieved by obtaining full payroll employment counts for many government agencies. Consequently, a probability-based sample design is not necessary for this industry. The high coverage rate virtually assures a high degree of reliability for the government employment estimates. Because it is used to estimate only the government portion of total nonfarm employment, the large government sample does not bias the total nonfarm employment estimates. The private and government estimates are summed to derive total nonfarm employment estimates. Sample implementation CES enrollment efforts begin immediately after a sample is selected, and collection generally begins in the first month after enrollment. Prior to the July 2014 first preliminary release, CES incorporated the new sample units for all industries once a year, starting with the third release of November estimates. In January, the new sample was used for the first time to estimate November third preliminary estimates of the previous year, December second preliminary estimates of the previous year, and January first preliminary estimates for the current year. Waiting to introduce new sample for all industries simultaneously meant newly enrolled respondents that started reporting payroll data immediately after the sample draw had provided useful data for almost a year before the data were used to produce CES estimates. The annual implementation schedule also contributed in part to revisions in national CES estimates between the November second preliminary and final releases and between the December first and second preliminary estimates. In the past, implementation of new sample units into the CES survey took a large amount of resources and time. CES updated processes for several years to improve the efficiency of sample updates and researched the effects of this change on the estimates. Beginning with the July 2014 first preliminary release, CES began a quarterly sample implementation schedule. Under the quarterly sample implementation schedule all industries have been classified into four groups that begin enrollment and data collection at a specific quarter after the sample is drawn for the year. Each group of industries begins enrollment and data collection procedures the quarter prior to being used in estimation and are used in estimation on the first reference month of the following quarter (see Table 2 ). All birth units selected as part of the semi-annual update are implemented in the last group, regardless of industry. Each reference month is estimated using the same sample from the estimation of the first preliminary estimate through the third preliminary estimate. Because quarterly sample implementation began with the July 2014 first preliminary estimates, the first implementation included the industries identified in groups 1 and 2. Table 2. Industry groupings for CES quarterly sample implementation CES Industry Code Major Industry Sector Birth units for all private industries sampled from the third quarter of the LDB that did not exist on the first quarter of the LDB Footnotes (1) Because quarterly sample implementation began with the July 2014 first preliminary estimates, the first implementation of sample drawn in 2013 included the industries identified in groups 1 and 2. Subsequent quarters implemented new sample units one group at a time. Under the quarterly sample implementation schedule, any quarterly sample implementation group can have an effect on industries outside the group. All the worksites associated with a UI account that are being implemented in a group are introduced into the sample at the same time, even if they are classified under a different industry. The switch to quarterly sample implementation allows units not in the new sample to be dropped at the same time as the new sample is introduced. The quarterly sample implementation process is expected to reduce respondent burden. CES sample by employment size class The employment universe that the CES sample is estimating is highly skewed as shown by Table 3. The largest UI accounts (those with 1,000 employees or more) comprise only 0.2 percent of all UI accounts but contain approximately 28.0 percent of total private employment. The smallest size class (0-9 employees) contains nearly 70.8 percent of all UIs but only about 10.1 percent of total private employment. CES samples larger firms at a higher rate than smaller firms, which is a standard technique commonly used in business establishment surveys. Table 3. Total private universe employment by size of UI, March 2014 Percent of All UIs Percent of Employment Table 4 shows the distribution of the active CES sample units. A much greater proportion of large than small UIs are selected however, that does not create a bias in either the sample or the estimates made from the sample. Each sample unit selected is assigned a weight based on its probability of selection, which ensures that all firms of its size are properly represented in the estimates. For example, UIs in the smallest firm stratum where 1 in every 100 firms are selected are assigned a weight of 100 because they represent themselves and 99 other firms that were not sampled. The use of sample weights in the estimation process prevents a large (or small) firm bias in the estimates. Table 4. Total private CES sample employment by size of UI, March 2014 Percent of All Sample UIs Percent of Sample Employment 2 (10-19 employees) 3 (20-49 employees) 4 (50-99 employees) 5 (100-249 employees) 6 (250-499 employees) 7 (500-999 employees 8 (1000 employees) Reliability Measurements of error The establishment survey, like other sample surveys, is subject to two types of error, sampling and nonsampling error. The magnitude of sampling error, or variance, is directly related to the size of the sample and the percentage of universe coverage achieved by the sample. The establishment survey sample covers over one-third of total universe employment this yields a very small variance on the total nonfarm estimates. Measurements of error associated with sample estimates are provided in Table 5 and the all employee (AE), production employee (PE), and women employee (WE) standard error tables. Table 5. Errors of preliminary employment estimates (1) CES Industry Code CES Industry Title Root-Mean-Square Error of Monthly Level (2) Mean Percent Revision Footnotes (1) Errors are based on differences for the months January through October of years 2011 to 2015. (2) The root-mean-square error is the square root of the mean squared error. The mean squared error is the square of the difference between the final and preliminary estimates averaged across a series of monthly observations. Benchmark revision as a measure of survey error The sum of sampling and nonsampling error can be considered total survey error. Unlike most sample surveys which publish sampling error as their only measure of error, the CES can derive an annual approximation of total error, on a lagged basis, because of the availability of the independently derived universe data. While the benchmark error is often used as a proxy measure of total error for the CES survey estimate, it actually represents the difference between two employment estimates derived from separate statistical processes (i. e. the CES sample process and the UI administrative process) and thus reflects the net of the errors present in each program. Historically, the benchmark revision has been small for total nonfarm employment. Over the prior 10 years, absolute percentage benchmark error has averaged 0.3 percent, with an absolute range from less than 0.05 percent to 0.7 percent. Further discussion about the CES annual benchmark can be found in the Revisions section of this document under Benchmarks. Revisions between preliminary and final data First preliminary estimates of employment, hours, and earnings, based on less than the total sample, are published immediately following the reference month. Final revised sample-based estimates are published 2 months later when nearly all the reports in the sample have been received. Table 5 presents the root-mean-square error, the mean percent, and the mean absolute percent revision over the past 5 years between the preliminary and final employment estimates. Revisions of preliminary hours and earnings estimates are normally not greater than 0.1 of an hour for weekly hours and 1 cent for hourly earnings at the total private level and may be slightly larger for the more detailed industry groupings. Further discussion about the CES sample-based monthly revisions to estimates can be found in the Revisions section of this document under Sample-based Revisions. Variance estimation The estimation of sample variance for AE. PE. and WE for the CES survey is accomplished through use of the method of Balanced Half Samples (BHS). This replication technique uses half samples of the original sample and calculates estimates using those subsamples. The sample variance is calculated by measuring the variability of the subsample estimates. The weighted link estimator is used to calculate both estimates and variances. The sample units in each cell mdash where a cell is based on state, industry, and size classification mdash are divided into two random groups. The basic BHS method is applied to both groups. The subdivision of the cells is done systematically in the same order as the initial sample selection. Weights for units in the half sample are multiplied by a factor of 1 gamma where weights for units not in the half sample are multiplied by a factor of 1 minus gamma. Estimates from these subgroups are calculated using the estimation formula described in Equation 2. The formula used to calculate CES variances is as follows: Equation 2. CES variance is the half-sample estimator gamma frac12 k is the number of half samples is the original full-sample estimate. Appropriate uses of sampling variances Variance statistics are useful for comparison purposes, but they do have some limitations. Variances reflect the error component of the estimates that is due to surveying only a subset of the population, rather than conducting a complete count of the entire population. However, they do not reflect nonsampling error, such as response errors, and bias due to nonresponse. The variances of the over-the-month change estimates are very useful in determining when changes are significant at some level of confidence. Variance statistics for first and second closings are available for AE. PE. and WE. In addition, third closing variances are available upon request. Sampling errors The sampling errors shown for all private industries and total nonfarm have been calculated for estimates that follow the benchmark employment revision by a period of 16 to 20 months. The errors are presented as median values of the observed error estimates. These estimates have been estimated using the method of BHS with the probability sample data and sample weights assigned at the time of sample selection. Illustration of the use of relative standard error tables AE. PE. and WE standard error tables provide a reference for relative standard errors of all major series developed from the CES. The standard errors of differences between estimates in two non-overlapping industries are calculated as Equation 3. CES relative standard error because the two estimates are independent. The errors are presented as relative standard errors (standard error divided by the estimate and expressed as a percent). Multiplying the relative standard error by its estimated value gives the estimate of the standard error. Suppose that the level of all employees for financial activities in a given month at first closing is estimated at 9,923,000. The approximate relative standard error of this estimate (0.4 percent) is provided in the AE standard error tables. A 90-percent confidence interval would then be the interval: 9,923,000 plusmn (1.645 times .004 times 9,923,000) 9,923,000 plusmn 65,293 9,857,707 to 9,988,293 Illustration of the use of standard error tables AE. PE. and WE standard error tables provide a reference for the standard errors of 1-, 3-, and 12-month changes in the employment, hours, and earnings series. The errors are presented as standard errors of the changes. The standard and relative standard errors for AE. PE. and WE are appropriate for use with both seasonally adjusted and not seasonally adjusted CES data. Suppose that the over-the-month change in all employee average hourly earnings (AHE) from a given month to the next in coal mining at second closing is 0.23. The standard error for a 1-month change for coal mining from the table is 0.28. The interval estimate of the over-the-month change in AHE that will include the true over-the-month change with 90-percent confidence is calculated: 0.23 plusmn (1.645 times 0.28) 0.23 plusmn 0.46 -0.23, 0.69 The true value of the over-the-month change is in the interval -0.23 to 0.69. Because this interval includes 0.00 (no change), the change of 0.23 shown is not significant at the 90-percent confidence level. Alternatively, the estimated change of 0.23 does not exceed 0.46 (1.645 times 0.28) therefore, one could conclude from these data that the change is not significant at the 90-percent confidence level. Data Collection Collection Methods Each month, the Bureau of Labor Statistics (BLS) collects data on employment, payroll, and paid hours from a sample of establishments. Prior to 1991, most of the Current Employment Statistics (CES) sample was collected by mail in a decentralized environment by each Labor Market Information Agency (LMI). CES has gradually centralized collection and adopted automated sample collection methods with the result that collection rates have gradually risen over time. Now, CES has a comprehensive program of new sample unit solicitation in four CES Regional Data Collection Centers (DCCs). The DCCs perform initial enrollment of each firm via telephone, collect the data for several months via Computer Assisted Telephone Interviewing (CATI), and where possible transfer respondents to a self-reporting mode such as Touchtone Data Entry (TDE), fax, or web. In addition, the DCCs conduct an ongoing program of refusal conversion. Very large firms are often enrolled via personal visit and ongoing reporting is established via Electronic Data Interchange (EDI). Offering survey respondents a choice of reporting methods helps sustain response rates to this voluntary survey. The largest portion of the CES sample is collected via EDI (44 percent), while Internet collection and CATI are used to collect approximately 17 percent and 28 percent of all reports, respectively. Under EDI, the firm provides an electronic file to CES each month in a prescribed file format. This file includes data for all of the firms worksites. The file is received, processed, and edited by the CES operated EDI Center. Web is one of the fastest growing collection methods. Under web collection, the respondent links to a secure website that contains an image of the questionnaire and enters their data into the on-line form. The data are subject to a series of edit checks before being transmitted to CES. TDE, another self-reporting mode, is used to collect about 3 percent of the monthly reports. Under the TDE system, the respondent uses a touchtone telephone to call a toll-free number and activate an interview session. The questionnaire resides on the computer in the form of prerecorded questions that are read to the respondent. The respondent enters numeric responses by pressing the touchtone phone buttons. Each answer is read back for respondent verification. Fax collection through the combined Regional CES DCCs account for most of the remainder of the reports (4 percent). For the few establishments that do not use the above methods, data are collected using mail, transcript, magnetic tape, or computer diskette (4 percent). Figure 1 shows the percentage of the establishments using different data collection methods. Figure 1. Current Employment Statistics survey data collection methods by percent Available Data National data availability The Current Employment Statistics (CES) program produces nonfarm employment series for all employees (AE), production and nonsupervisory employees (PE), and women employees (WE). For AE and PE, CES also produces average hourly earnings (AHE), average weekly hours (AWH), and, in manufacturing industries only, average weekly overtime hours (AWOH). Most detailed employment series begin in 1990, although employment by aggregate industry sector and most major industry sectors is published as far back as 1939. A list of currently published CES series is available at bls. govwebempsitcesseriespub. htm. Over 2,200 not seasonally adjusted employment series for AE, PE, and WE are published monthly. The series for AE include over 900 industries at various levels of aggregation. Approximately 2,600 AE and PE series for AHE, AWH, and, in manufacturing, AWOH are published monthly on a not seasonally adjusted basis and cover about 600 industries. About 5,900 seasonally adjusted employment, hours, and earnings series for AE, PE, and WE are published. Over 8,700 not seasonally adjusted special derivative series such as average weekly earnings (AWE), indexes, and constant dollar series for AE and PE are also published for approximately 600 industries. State and area data availability For states and metropolitan areas, the CES program produces nonfarm industry employment, hours, and earnings series for AE and PE. Most employment series begin in 1990. Metropolitan areas are defined by the U. S. Office of Management and Budget (OMB). Further information about state and metropolitan area data is available in the Statistics for States and Areas section of this document. Employment Employment data refer to persons on establishment payrolls who worked or received pay for any part of the pay period that includes the 12th day of the month. The data exclude proprietors, the unincorporated self-employed, unpaid volunteer or family employees, farm employees, and domestic employees. Salaried officers of corporations are included. Government employment covers only civilian employees military personnel are excluded. Employees of the Central Intelligence Agency, the National Security Agency, the National Imagery and Mapping Agency, and the Defense Intelligence Agency also are excluded. Persons on establishment payrolls who are on paid sick leave (for cases in which pay is received directly from the firm), on paid holiday, or on paid vacation, or who work during a part of the pay period even though they are unemployed or on strike during the rest of the period are counted as employed. Not counted as employed are persons who are on layoff, on leave without pay, or on strike for the entire period, or who were hired but have not yet reported during the period. Production and nonsupervisory employees (PE) are defined differently for certain major industry sectors. In manufacturing and in mining and logging, PE includes only production and related employees. In construction, PE includes only construction employees. In private service-providing industries, PE includes all nonsupervisory employees. These distinctions are clarified below. Production and related employees This category includes working supervisors and all nonsupervisory employees (including group leaders and trainees) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shipping, trucking, hauling, maintenance, repair, janitorial, guard services, product development, auxiliary production for plants own use (for example, power plant), recordkeeping, and other services closely associated with the above production operations. Construction employees This group includes the following employees in the construction sector: working supervisors, qualified craft employees, mechanics, apprentices, helpers, laborers, and so forth, engaged in new work, alterations, demolition, repair, maintenance, and the like, whether working at the site of construction or in shops or yards at jobs (such as precutting and preassembling) ordinarily performed by members of the construction trades. Nonsupervisory employees These are employees (not above the working-supervisor level) such as office and clerical employees, repairers, salespersons, operators, drivers, physicians, lawyers, accountants, nurses, social employees, research aides, teachers, drafters, photographers, beauticians, musicians, restaurant employees, custodial employees, attendants, line installers and repairers, laborers, janitors, guards, and other employees at similar occupational levels whose services are closely associated with those of the employees listed. Hours and Earnings Concurrent with the release of January 2010 data, the CES program began publishing all employee hours and earnings as official BLS series. These series were developed to measure the AHE and AWH of all nonfarm private sector employees and the AWOH of all manufacturing employees. AE hours and earnings were first released as experimental series in April 2007, and included national level estimates at a total private sector level and limited industry detail. Historically, the CES program has published average hours and earnings series for production employees in the goods-producing industries and for non-supervisory employees in the service-providing industries. These employees account for about 82 percent of total private nonfarm employment. The AE hours and earnings series are more comprehensive in coverage, covering 100 percent of all paid employees in the private sector, thereby providing improved information for analyzing economic trends and for constructing other major economic indicators, including nonfarm productivity and personal income. AE average hours and earnings data are derived from reports of hours and payrolls for all employees. PE average hours and earnings data are derived from reports of production and related employees in manufacturing and mining and logging, construction employees in construction, and nonsupervisory employees in private service-providing industries. These are the hours worked or for which pay was received during the pay period that includes the 12th of the month for all employees, production, construction, and nonsupervisory employees. Included are hours paid for holidays, for vacations, and for sick leave when pay is received directly from the firm. Payroll refers to dollars paid for full - and part-time all employees, production, construction, and nonsupervisory employees who received pay for any part of the pay period that includes the 12th day of the month. The payroll is reported before deductions of any kind, such as those for old-age and unemployment insurance, group insurance, withholding tax, bonds, or union dues also included is pay for overtime, tips, holidays, and vacation and for sick leave paid directly by the firm. Excluded from the payroll are bonuses (unless earned and paid regularly each pay period) other pay not earned in the pay period reported (such as retroactive pay) and the value of free rent, fuel, meals, or other payment in kind. Commissions are also included if paid at least monthly. Overtime hours These are hours worked by all employees, production and related employees, and nonsupervisory employees in manufacturing for which overtime premiums were paid because the hours were in excess of the number of hours of either the straight-time workday or the workweek during the pay period that included the 12th of the month. Weekend and holiday hours are included only if overtime premiums were paid. Hours for which only shift differential, hazard, incentive, or other similar types of premiums were paid are excluded. Average weekly hours The workweek information relates to the average hours for which pay was received and is different from standard or scheduled hours. Such factors as unpaid absenteeism, labor turnover, part-time work, and stoppages cause average weekly hours to be lower than scheduled hours of work for an establishment. Industry supersector averages further reflect changes in the workweek of component industries. Average hourly earnings Average hourly earnings are on a quotgrossquot basis. They reflect not only changes in basic hourly and incentive wage rates, but also such variable factors as premium pay for overtime and late-shift work and changes in output of employees paid on an incentive plan. They also reflect shifts in the number of employees between relatively high-paid and low-paid work and changes in employees earnings in individual establishments. Averages for groups and divisions further reflect changes in AHE for individual industries. The earnings series do not measure the level of total labor costs on the part of the employer because the following are excluded: benefits, irregular bonuses, retroactive items, and payroll taxes paid by employers. Average overtime hours Overtime hours represent that portion of weekly hours that exceeded regular hours and for which overtime premiums were paid in the manufacturing sector. If an employee were to work on a paid holiday at regular rates, receiving as total compensation his holiday pay plus straight-time pay for hours worked that day, no overtime hours would be reported. This applies to both AE and PE average overtime hours. Because overtime hours are premium hours by definition, weekly hours and overtime hours do not necessarily move in the same direction from month to month. Such factors as work stoppages, absenteeism, and labor turnover may not have the same influence on overtime hours as on average hours. Diverse trends at the industry group level also may be caused by a marked change in hours for a component industry in which little or no overtime was worked in both the previous and current months. Derivative Series Three-month moving average These estimates are an average of the over-the-month change for the last 3 months calculated only at the total nonfarm and total private levels. The current months employment change as well as the previous 2 months employment change are averaged to create the 3-month moving average. Each month, the average is moved forward 1 month. Average weekly earnings These estimates are derived by multiplying AWH estimates by AHE estimates. Therefore, AWE are affected not only by changes in AHE but also by changes in the length of the workweek. Monthly variations in such factors as the proportion of part-time employees, stoppages for varying reasons, labor turnover during the survey period, and absenteeism for which employees are not paid may cause the average workweek to fluctuate. Long-term trends of AWE can be affected by structural changes in the makeup of the workforce. For example, persistent long-term increases in the proportion of part-time employees in retail trade and many of the services industries have reduced average workweeks in these industries and have affected the average weekly earnings series. Real earnings These earnings are in constant dollars and are calculated from the earnings averages for the current month using a deflator. The Consumer Price Index (CPI) for All Urban Consumers (CPI-U) is used to deflate the earnings series for AE, while the CPI for Urban Wage Earners and Clerical employees (CPI-W) is used to deflate the earnings series for PE. The scope for the CPI-W is similar to that of PE earnings, both in the type of employee who is covered and the amount of the population that is covered by these series. The CPI-U used to deflate AE earnings is more inclusive than the CPI-W. Since AE earnings include all private sector employees, the more inclusive deflator is used in the calculation. The reference base for the CPI series is the 36-month period covering the years 1982, 1983, and 1984. Average hourly earnings, excluding overtime Average hourly earnings, excluding overtime-premium pay, are produced for manufacturing only and are computed by dividing the total AE or PE payroll for the industry group by the corresponding sum of total AE or PE hours and one-half of total AE or PE overtime hours. No adjustments are made for other premium payment provisions, such as holiday pay, late-shift premiums, and overtime rates other than time and one-half. Indexes of aggregate weekly hours and payrolls For basic estimating industries, aggregate hours are the product of AWH for AE times the employment for AE or AWH for PE times the employment for PE. At all higher levels of industry aggregation, aggregate hours are the sum of the component aggregates. The indexes for AE aggregate weekly hours are calculated by dividing the current months aggregate by the average of the 12 monthly figures for 2007. The indexes of aggregate weekly hours for PE are calculated by dividing the current months aggregate by the average of the 12 monthly figures for 2002. For basic industries, the aggregate payroll is the product of AHE for AE and aggregate weekly hours for AE or AHE for PE and aggregate weekly hours for PE. At all higher levels of industry aggregation, aggregate payroll is the sum of the component aggregates. The indexes of aggregate weekly payrolls are calculated by dividing the current months aggregate by the average of the 12 monthly figures for 2007 for AE and 2002 for PE. Indexes of diffusion of employment change Diffusion indexes measure the dispersion of employment change across industries over a specified time span (1-, 3-, 6-, or 12-month). The overall indexes are calculated from 262 seasonally adjusted employment series (primarily 4-digit NAICS industries) covering nonfarm payroll employment in the private sector. The manufacturing diffusion indexes are based on 79 4-digit NAICS industries. To derive the indexes, each component industry is assigned a value of 0, 50, or 100 percent, depending on whether its employment showed a decrease, no change, or an increase, respectively, over the time span. The average value (mean) is then calculated, and this percent is the diffusion index number. The reference point for diffusion analysis is 50 percent, the value indicating that the same number of component industries had increased as had decreased. Index numbers above 50 show that more industries had increasing employment and values below 50 indicate that more had decreasing employment. The margin between the percent that increased and the percent that decreased is equal to the difference between the index and its complement - that is, 100 minus the index. For example, an index of 65 percent means that 30 percent more industries had increasing employment than had decreasing employment (65-(100-65) 30). However, for dispersion analysis, the distance of the index number from the 50-percent reference point is the most significant observation. Although diffusion indexes commonly are interpreted as showing the percent of components that increased over the time span, the index reflects half of the unchanged components as well. (This is the effect of assigning a value of 50 percent to the unchanged components when computing the index.) Forms of Publication The Employment Situation Each month, usually 3 weeks after the reference period including the 12 th of the month, CES releases The Employment Situation, which contains CES national first preliminary (first closing) estimates of employment, hours, and earnings for all 3-digit NAICS series. The remaining series published by CES are released with the following months Employment Situation. For a list of CES published series, see bls. govwebempsitcesseriespub. htm. Real Earnings Each month, coincident with the CPI release, CES releases Real Earnings, which contains earnings data indexed to the CPI. For more information about real earnings, see Real Earnings in this document or visit bls. govnews. releaserealer. tn. htm . Other forms of publication CES data are also available in the following forms of publication: Statistics for States and Areas CES independently develops national and state and area employment, hours, and earnings series. Both sets of estimates are based on the same establishment reports however, CES uses the full establishment survey sample to produce monthly national employment estimates, while CES uses only the state-specific portion of the sample to develop state employment estimates. CES area statistics relate to metropolitan areas. CES uses the most recent OMB bulletin regarding statistical area definitions (OMB Bulletin No. 10-02 whitehouse. govsitesdefaultfilesombassetsbulletinsb10-02.pdf ) to define metropolitan statistical areas and metropolitan divisions. CES also produces area statistics for non-standard areas (areas which are not defined in the OMB Bulletin), noted at bls. govsaesaenonstd. htm. Changes in definitions are noted as they occur. Estimates for states and areas are produced using two methods. The majority of state and area estimates are produced using direct sample-based estimation. However, published area and industry combinations (domains) that do not have a large enough sample to support estimation using only sample responses have been estimated using modeling techniques. For more state and area employment (SAE) information please see the CES SAE home page at bls. govsaehome. htm. State and area estimates use smaller amounts of sample by industry than the national industry estimates. This increases the error component associated with state and metropolitan level estimates. For this reason, aggregating state data to the national level will also sum this error component, resulting in different estimates of U. S. employment, hours, and earnings. Summed state level CES estimates should not be compared to national CES estimates. Estimation Methods Monthly Estimation The Current Employment Statistics (CES) program uses a matched sample concept and weighted link relative estimator to produce employment, hours, and earnings estimates. These methods are described in Table 8. A matched sample is defined to be all sample members that have reported data for the reference month and the month prior. Excluded from the matched sample is any sample unit that reports that it is out-of-business and has zero employees. This aspect of the estimation methodology is more fully described below in the section on BirthDeath Model estimation. Table 8. Summary of methods for computing industry statistics on employment, hours, and earnings estimates Employment, hours, and earnings Basic estimating cell (industry, 6-digit published level) Aggregate industry level (super sector and, where stratified, industry) Annual average data BirthDeath Model The CES sample alone is not sufficient for estimating the total employment level because each month new firms generate employment that cannot be captured through the sample. There is an unavoidable lag between a firm opening for business and its appearance on the CES sample frame. The sample frame is built from Unemployment Insurance (UI) quarterly tax records. These records cover virtually all U. S. employers and include business births, but they only become available for updating the CES sampling frame 7 to 9 months after the reference month. After the births appear on the frame, there is also time required for sampling, contacting, and soliciting cooperation from the firm, and verifying the initial data provided. In practice, CES cannot sample and begin to collect data from new firms until they are at least a year old. There is a parallel though somewhat different issue in capturing employment loss from business deaths through monthly sample collection. Businesses that have closed are unlikely to respond to the survey, and data collectors may not be able to ascertain until after the monthly collection period that firms have in fact gone out of business. As with business births, hard information on business deaths eventually becomes available from the lagged UI tax records. Difficulty in capturing information from business birth and death units is not unique to the CES virtually all current business surveys face these limitations. Unlike many surveys, CES adjusts for these limitations explicitly, using a statistical modeling technique. Other surveys that do not explicitly adjust for business births and deaths are implicitly using the continuing sample units to represent birth and death units. This approach is viable when the primary characteristic of interest is an average measure of some type. However, because the goal of the CES program is to estimate an employment total each month and business births and deaths are important components contributing to these totals, CES uses a model-based adjustment in conjunction with the sample. Without the net birthdeath model-based adjustment, the CES nonfarm payroll employment estimates would be considerably less accurate. CES birthdeath modeling technique Prior to the Current Employment Statistics (CES) program adopting the current birthdeath modeling technique, research using historical information indicated that the business birth and death portions of total employment were substantial, but the net contribution of, or the difference between, the two components was relatively small and stable. The research was done using the nearly complete counts of employment developed from the UI tax records that are tabulated under the BLS Quarterly Census of Employment and Wages (QCEW) (bls. govorepdfst020090.pdf ). These QCEW tabulations also form the basis for both the sample frame and annual benchmark for the CES program. Beyond the research cited above, the Business Employment Dynamics (BED) series published quarterly by BLS, also illustrate how business birth and death employment substantially offset each other. The BED series are also derived from the QCEW. The BED series demonstrate that most of the net employment change each quarter is generated by the expansions and contractions in employment of the continuing businesses and a relatively smaller piece from business openings and closings (which CES refers to as net business births and deaths). As shown in Figure 2 below, continuing businesses which are adding employees (expansions) or subtracting employees (contractions) over the quarter comprise the vast majority of total change these movements are measured by the CES sample. Employment change contributions from openings (or births) and closings (or deaths) are much smaller and more stable, and the two series offset each other to a large degree. It is these underlying relationships among the components of net employment change that allow the CES to produce accurate estimates using a current monthly sample of continuing businesses and a model-based approach for the residual of net business births and deaths. Figure 2. Total private not seasonally adjusted BED series (in thousands) Birthdeath modeling methodology The CES birthdeath methodology has two steps. Step One mdash Employment losses from business deaths are excluded from the sample in order to offset the missing employment gains from new business births. Because employment increases from births nearly offset employment decreases from deaths in most months (as illustrated above by the BED data), this step accounts for most of the net of business birth and death employment. Operationally this is accomplished in the following manner each month. Business deaths that are non-respondents to the survey are automatically excluded because they have no current month data. Death establishments that report zero employment to the survey for the current month are treated the same as non-respondents and also excluded. As a result, the over-the-month change calculation from the sample is based solely on continuing businesses. For the months subsequent to a business death, the deaths are kept alive in the CES estimation process the growth rate of the continuing units in the sample is applied to them each month. This estimates for the growth of the new business births in the months after their birth but before they can be brought into the sample. This step accounts for most of the birthdeath employment but not all of it. The residual net employment that is not captured by this step is estimated through an econometric model, described below as step two. Step Two mdash Modeling for the residual of birthdeath employment change. In this step, the CES adjusts its sample-based estimates for the net birthdeath employment that step one misses. This adjustment is derived from an econometric technique known as ARIMA modeling. ARIMA is a standard econometric modeling technique that is often used to estimate relatively stable series. Outliers, level shifts, and temporary ramps are automatically identified. CES refits the ARIMA models each year for each basic estimation cell as part of its annual benchmarking process. Table 10 shows the net birthdeath model figures for the post-benchmark period of the benchmark from April to October of 2015. For more recent months of birthdeath information, see bls. govwebempsitcesbd. htm . Table 10. Net birthdeath estimates, post-benchmark 2015 (in thousands) CES Industry Code The inputs to the ARIMA model are historical observations of the residual net birthdeath employment that is not captured by either the sample or the step one imputation described above. These historical observations are derived empirically from the most recent five years of QCEW historical data. From the QCEW universe employment series, CES classifies each establishment each month as a continuing unit, a birth, or a death. Then sample-based estimates are simulated using the month-to-month change of the continuing units and using the deaths-to-impute-for-births technique described above in step one. The difference between these simulated estimates and the actual total employment measured by the QCEW each month is the net birthdeath employment. The net birthdeath series assumed the following form: Equation 9. Net birthdeath Net birthdeath Population minus Sample-based estimate Error During the net birthdeath modeling process, simulated monthly probability estimates over a 5-year period are created and compared with population employment levels. Moving from a simulated benchmark, the differences between the series across time represent a cumulative birthdeath component. Those residuals are converted to month-to-month differences and used as input series to the modeling process. Because the net birthdeath employment component is relatively stable, the ratio of it to total employment change can vary substantially from year to year. In slower growth years (for example, March 03-March 04), the ratio is much different than in stronger growth years (for example March 04-March 05). Put another way, the net birth death amount itself is relatively stable but its relationship to overall net employment change varies, depending on the magnitude of the overall change, almost by definition. Year one and year two models The birthdeath model is forecast using 24-month long spans of input data, representing historical net births and deaths. These spans are separated into two models referred to as year 1 (Y1) and year 2 (Y2) models. The age of the firms that contribute to the imputation step (step 1) of the birthdeath process impact the trend calculation. Y2 models are forecast using a sample that is a year older (relative to the reference month) than the Y1 models. While the results of the two models are similar, there are differences. Birthdeath model under quarterly sample rotation Using quarterly sample rotation, different industries have differently aged sample. Therefore, the mix of Y1 and Y2 models used varies by quarter. Y1 birthdeath values are appropriate for the newest sample, and Y2 values are phased in as the sample ages. Table 11 shows the forecast value used with each rotation group for each quarter. Table 11. Net birthdeath forecast year of industry groupings for CES quarterly sample rotation CES Industry Code Quarterly updates to the CES birthdeath model Prior to the release of preliminary January 2011 employment estimates in February 2011, birthdeath residuals were calculated on an annual basis and then applied each month during development of monthly estimates. With the release of the January 2011 preliminary estimates, CES began updating the net birthdeath model component of the estimation process on a quarterly basis instead of annually. This change allows for the incorporation of QCEW data into the birthdeath model as soon as it becomes available and reduces the post-benchmark revision in the CES series. This change does not impact the timing or frequency of CES monthly and annual releases or when benchmarking is done. For more information about the CES switch to quarterly net birthdeath forecasting, see bls. govcescesquarterlybirthdeath. htm . Quarterly and annual net birthdeath forecasts Table 12 shows a comparison of the CES birthdeath model adjustment using either a quarterly or annual forecasting frequency. The March 2003 benchmark is the first in which all industries were estimated using annually updated net birthdeath forecasts, and quarterly updated net birthdeath forecasts have been used in estimates from January 2011 forward. The differences between annual and quarterly forecasting of birthdeath are small in most cases. However, the CES estimates reflect more current business openings and closings more rapidly by increasing the frequency of updates to inputs to the net birthdeath model. For more information about the CES switch to quarterly net birthdeath forecasting, see bls. govcescesquarterlybirthdeath. htm. Historical comparisons, including simulated quarterly net birthdeath forecasts for years before 2011 and simulated annual net birthdeath forecasts for years after 2011, are available at bls. govcescesqbdcomp. htm . Table 12. Comparison of annual birthdeath to quarterly birthdeath for 2014 Limitations The primary limitation stems from the fact that the model is, of necessity, based on historical data. If there is a substantial departure from historical patterns of employment changes in net business births and deaths, as occurred from 2008 into 2009 during the 2009 benchmark. the models contribution to error reduction can erode. As with any model that is based on historical data, turning points that do not resemble historical patterns are difficult to incorporate in real time. Because there is no current monthly information available on business births, and because only incomplete sample data is available on business deaths, estimation of this component will always be potentially more problematic than estimation of change from continuing businesses. The net birthdeath model and seasonal adjustment The birthdeath model component is added to the sample-based component to form the not seasonally adjusted employment estimate for each month, as described above. These employment estimates are subsequently seasonally adjusted. Seasonal adjustment smooths the employment series by removing normal seasonal variations due to factors such as weather and holidays therefore the seasonally adjusted over the month employment changes are generally much smaller than the unadjusted changes. Users who wish to compare the models contribution to overall employment change reported for a month should compare against the unadjusted estimates, not the seasonally adjusted series. Comparing the model amounts to seasonally adjusted estimates generally results in an overstatement of the model-based components contribution to over-the-month employment change. The birthdeath model component generally shows the same overall seasonal patterns as the sample-based component. For example, total nonfarm employment shows a large seasonal increase in employment each April the model also shows a relatively large net addition to employment each April. Similarly total nonfarm employment records a large drop in employment each January and the model estimates a substantial drop in net birthdeath employment each January. An example of the net birthdeath model components versus overall net employment change from April 2014 to March 2015 (subsequent to the March 2015 benchmark implementation) is shown below in Table 13. The April 2014 model amount of 263,000 should be viewed as a component of the 1,163,000 not seasonally adjusted employment change, rather than as a component of the 330,000 seasonally adjusted change. Table 13. Net birthdeath and over the month change in total nonfarm employment (in thousands) Aggregation Procedures CES estimates at the basic estimating level and then aggregates these estimates to higher industry levels. Aggregation procedures are specific to the data type and published level of precision (i. e. the degree of rounding). Publication precision For employment data types, CES publishes estimates for major industry and aggregate industry sectors in thousands rounded to the nearest whole number, except for major industry sectors 41-420000 wholesale trade, 42-000000 retail trade, 43-000000 transportation and warehousing, and 44-220000 utilities, which are published in thousands rounded to the tenths place. More detailed employment estimates are published in thousands rounded to the tenths place. For hours and earnings data types, estimates are published using the same procedures for all levels of detail. Hours data types are published in hours rounded to the tenths place. Earnings data types are published in dollars rounded to the cent. Employment (AE, PE, and WE) AE, PE, and WE data types use the same method for aggregation. Basic level estimates rounded to the hundreds are aggregated to summary level estimates up to and including major industry sectors and are then rounded to the published precision. Aggregate industry sector estimates are then calculated by summing the rounded major industry and aggregate industry sector estimates that make up the aggregate industry sector and then rounded according to the published precision. Average weekly hours (AE and PE) The aggregation method for average weekly hours (AWH) of AE and PE is identical with the appropriate substitution of AE values or PE values in the following formulas. AWH are estimated at the basic level and combined with employment estimates for the same basic level to calculate aggregate employee hours. Aggregate employee hours (AH) are rounded to the tenths at the basic estimating level and calculated as shown: Equation 10. Aggregate hours AH AWH times Emp AH current month aggregate employee hours calculation for the basic level rounded to the tenths AWH current month AWH estimate for the basic level rounded as published Emp current month employment estimate for the basic level rounded as published Next, aggregate employee hours are added up to the summary levels. Average weekly hours rounded to the tenths are calculated for the summary level by: Equation 11. Summary level average weekly hours AWH AH divide Emp AWH current month average weekly hours estimate for the summary level rounded to the tenths AH current month aggregate employee hours calculation for the summary level rounded to the tenths Emp current month employment estimate for the summary level rounded according to published precision Average hourly earnings (AE and PE) The aggregation method for average hourly earnings (AHE) of AE and PE is identical, with the appropriate substitution of AE values or PE values in the following formulas. AHE are estimated at the basic level and combined with employment estimates for the same basic level to calculate aggregate employee hours (AH). Calculation of AH is identical to that described for AWH. Aggregate payroll (PR) is calculated using basic level AWH, AHE, and employment. Basic level PR calculations are rounded to the cent and are defined as: Equation 12. Aggregate payroll PR AHE times AWH times Emp PR current month aggregate payroll calculation for the basic level rounded to the cent AHE current month average hourly earnings estimate for the basic level rounded to the cent AWH current month average weekly hours estimate for the basic level rounded to the tenths place Emp current month employment estimate for the basic level rounded according to published precision To calculate the summary level estimates, summarize the aggregate employee hours and aggregate payroll to the summary level. Average hourly earnings rounded to the cent are calculated for the summary level by: Equation 13. Summary level average hourly earnings AHE PR divide AH AHE current month average hourly earnings estimate for the summary level rounded to the cent AH current month aggregate employee hours calculation for the summary level rounded to the tenths PR current month aggregate payroll calculation for the summary level rounded to the cent Average weekly overtime hours (AE and PE) Aggregation of average weekly overtime hours is identical to that described for AWH with the appropriate substitution of overtime hours values for the weekly hours values in the previous formula. Caution in aggregating state data The national estimation procedures used by CES are designed to produce accurate national data by detailed industry correspondingly, the state estimation procedures are designed to produce accurate data for each individual state. State estimates are not forced to sum to national totals nor vice versa. Because each state series is subject to larger sampling and nonsampling errors than the national series, summing them cumulates individual state level errors and can cause distortion at an aggregate level. For more information about state and metropolitan area level CES data, see the state and area employment website at bls. govsaehome. htm . Seasonal Adjustment The CES program employs a concurrent seasonal adjustment methodology to seasonally adjust its national estimates of employment, hours, and earnings. Under concurrent methodology, new seasonal factors are calculated each month using all relevant data up to and including the current month period. Many CES data users are interested in the seasonally adjusted over-the-month changes as a primary measure of overall national economic trends. Therefore, accurate seasonal adjustment is an important component in the usefulness of these monthly data. This following section discusses in detail the seasonal adjustment methodology and software employed by CES. It is important to note that this describes seasonal adjustment only as it relates to the CES programs implementation. There are other aspects of seasonal adjustment that are not discussed here. Seasonal adjustment and X8209138209ARIMA8209SEATS The CES program uses X8209138209ARIMA8209SEATS software developed by the U. S. Census Bureau to seasonally adjust the monthly estimates. The X8209138209ARIMA8209SEATS software is available on the U. S. Census Bureau web site at census. govsrdwwwx13as. The site contains the following information: Effective with the February 6, 2015 release of January 2015 data, the Current Employment Statistics (CES) survey will transition from using X8209128209ARIMA to X8209138209ARIMA8209SEATS to produce seasonally adjusted series and forecasts of birthdeath residuals. For more information about X8209138209ARIMA8209SEATS please visit the U. S. Census Bureau website at census. govsrdwwwx13as. Historical data will not be revised to be seasonally adjusted using X8209138209ARIMA8209SEATS. The CES program has been running parallel seasonal adjustment using X8209138209ARIMA8209SEATS, and no differences were observed. Examples of the specification files used by X8209138209ARIMA8209SEATS can be found at bls. govcescesspec. examples. zip. Program files for the latest PC version of X8209138209ARIMA8209SEATS Program files for the latest UNIX workstation version of X8209138209ARIMA8209SEATS Program files for X8209138209Graph, a companion graphics package Installation instructions Reference manual The remainder of this documentation describes how the CES program employs X8209138209ARIMA8209SEATS for seasonal adjustment purposes. Specifically, it describes the input files used in the CES programs implementation and commands used to invoke the software. This is not a substitute for formal X8209138209ARIMA8209SEATS training. There are other uses and features of X8209138209ARIMA8209SEATS that are not discussed in this section. The U. S. Census Bureau offers more intensive training for X8209138209ARIMA8209SEATS and seasonal adjustment. Contact the Census Bureau or visit their website at census. gov for more details. Seasonally adjusting CES data For published AE series, the CES program seasonally adjusts many series at the 3-, 4-, 5-, and 6-digit NAICS level. However, only the seasonally adjusted 3-digit NAICS level estimates are used to aggregate to the higher levels. The seasonally adjusted series that are published at more detailed levels than the 3-digit NAICS are considered to be independent series and are not included in aggregation of seasonally adjusted series. For example, seasonally adjusted data at the 5-digit NAICS are not aggregated to form seasonally adjusted 4-digit NAICS series. Instead the 4-digit NAICS and the 5-digit NAICS level series are independently seasonally adjusted. Most series are seasonally adjusted by directly applying the seasonal adjustment factors to the series with the exception of the component series used in indirect seasonal adjustment. In some cases, 3-digit NAICS series are indirectly seasonally adjusted by aggregating the seasonally adjusted employment level of their component series. For indirectly seasonally adjusted 3-digit NAICS series, the seasonal adjustment factors are applied to the component series rather than to the 3-digit NAICS series. The component series are then aggregated to create the 3-digit NAICS series. Indirectly seasonally adjusted series are noted in Table 15 . For published PE series and for published hours and earnings series for both PE and AE, the CES program seasonally adjusts at the major industry sector level for all industries except manufacturing which is seasonally adjusted at the 3-digit NAICS level. The seasonally adjusted PE, seasonally adjusted hours and earnings for PE, and seasonally adjusted hours and earnings for AE are aggregated from the 3-digit level in manufacturing industries and are aggregated from the major industry sector level for all other industries to get seasonally adjusted aggregate sectors. For published PE and AE overtime series, the CES program seasonally adjusts manufacturing series at the 2-digit NAICS level, or the durable goods and nondurable goods levels. These seasonally adjusted overtime series are aggregated to the manufacturing level. For published WE series, the CES program seasonally adjusts at the major industry sector level for all industries. The seasonally adjusted WE are aggregated from the major industry sector level for all industries. Special model adjustments The CES programs current implementation of seasonal adjustment controls for several calendar effects, explained below. Variable survey intervals. Beginning with the release of the 1995 benchmark, BLS refined the seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. Although the CES survey is referenced to a consistent concept mdash the pay period including the 12th of each month mdash inconsistencies arise because there are sometimes 4 and sometimes 5 weeks between the week including the 12th in a given pair of months. In highly seasonal industries, these variations can be an important determinant of the magnitude of seasonal hires or layoffs that have occurred at the time the survey is taken, thereby complicating seasonal adjustment. Standard seasonal adjustment methodology relies heavily on the experience of the most recent 3 years to determine the expected seasonal change in employment for each month of the current year. Prior to the implementation of the adjustment, the procedure did not distinguish between 4- and 5-week survey intervals, and the accuracy of the seasonal expectation depended in large measure on how well the current years survey interval corresponded with those of the previous 3 years. All else the same, the greatest potential for distortion occurred when the current month being estimated had a 5-week interval but the 3 years preceding it were all 4-week intervals, or conversely when the current month had a 4-week interval but the 3 years preceding it were all 5-week intervals. BLS adopted REGARIMA (regression with auto-correlated errors) modeling to identify the estimated size and significance of the calendar effect for each published series. REGARIMA combines standard regression analysis, which measures correlation among two or more variables, with ARIMA modeling, which describes and predicts the behavior of data series based on its own past history. For many economic time series, including nonfarm payroll employment, observations are auto-correlated over time each months value is significantly dependent on the observations that precede it. These series, therefore, usually can be successfully fit using ARIMA models. If auto-correlated time series are modeled through regression analysis alone, the measured relationships among other variables of interest may be distorted due to the influence of the auto-correlation. Thus, the REGARIMA technique is appropriate for measuring relationships among variables of interest in series that exhibit auto-correlation, such as nonfarm payroll employment. In this application, the correlations of interest are those between employment levels in individual calendar months and the lengths of the survey intervals for those months. The REGARIMA models evaluate the variation in employment levels attributable to eleven separate survey interval variables, one specified for each month, except March. March is excluded because there are almost always 4 weeks between the February and March surveys. Models for individual basic series are fit with the most recent 10 years of data available, the standard time span used for CES seasonal adjustment. The REGARIMA procedure yields regression coefficients for each of the 11 months specified in the model. These coefficients provide estimates of the strength of the relationship between employment levels and the number of weeks between surveys for the 11 modeled months. The X8209138209ARIMA8209SEATS software also produces diagnostic statistics that permit the assessment of the statistical significance of the regression coefficients, and all series are reviewed for model adequacy. Because the eleven coefficients derived from the REGARIMA models provide an estimate of the magnitude of variation in employment levels associated with the length of the survey interval, these coefficients are used to adjust the CES data to remove the calendar effect. These filtered series then are seasonally adjusted using the standard X8209138209ARIMA8209SEATS software. Weather-related outliers in construction series. Beginning with the 1996 benchmark revision, BLS utilized special treatment to adjust construction industry series. In the application of the interval effect modeling process to the construction series, there initially was difficulty in accurately identifying and measuring the effect because of the strong influence of variable weather patterns on employment movements in the industry. Further research allowed BLS to incorporate interval effect modeling for the construction industry by disaggregating the construction series into its finer industry and geographic estimating cells and tightening outlier designation parameters. This allowed a more precise identification of weather-related outliers that had masked the interval effect and clouded the seasonal adjustment patterns in general. With these outliers removed, interval effect modeling became feasible. The result is a seasonally adjusted series for construction that is improved because it is controlled for two potential distortions: unusual weather events and the 4- versus 5-week effect. Length of pay adjustment. With the release of the 1997 benchmark, BLS implemented refinements to the seasonal adjustment process for the hours and earnings series to correct for distortions related to the method of accounting for the varying length of payroll periods across months. There is a significant correlation between over-the-month changes in both the average weekly hours (AWH) and the average hourly earnings (AHE) series and the number of weekdays in a month, resulting in noneconomic fluctuations in these two series. Both AWH and AHE show more growth in short months (20 or 21 weekdays) than in long months (22 or 23 weekdays). The effect is stronger for the AWH than for the AHE series. The calendar effect is traceable to response and processing errors associated with converting payroll and hours information from sample respondents with semi-monthly or monthly pay periods to a weekly equivalent. The response error comes from sample respondents reporting a fixed number of total hours for workers regardless of the length of the reference month, while the CES conversion process assumes that the hours reporting will be variable. A constant level of hours reporting most likely occurs when employees are salaried rather than paid by the hour, as employers are less likely to keep actual detailed hours records for such employees. This causes artificial peaks in the AWH series in shorter months that are reversed in longer months. The processing error occurs when respondents with salaried workers report hours correctly (vary them according to the length of the month), which dictates that different conversion factors be applied to payroll and hours. The CES processing system uses the hours conversion factor for both fields, resulting in peaks in the AHE series in short months and reversals in long months. REGARIMA modeling is used to identify, measure, and remove the length-of-pay-period effect for seasonally adjusted average weekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaining AWH movements in all the service-providing industries except utilities. For AHE, the length-of-pay-period variable is significant for wholesale trade, retail trade, information, financial activities, professional and business services, and other services. All AWH series in the service-providing industries except utilities have been adjusted from January 1990 forward. The AHE series for wholesale trade, retail trade, information, financial activities, professional and business services, and other services have been adjusted from January 1990 forward as well. For this reason, calculations of over-the-year change in the establishment hours and earnings series should use seasonally adjusted data. The series to which the length-of-pay-period adjustment is applied are not subject to the 4- versus 5-week adjustment, as the modeling cannot support the number of variables that would be required in the regression equation to make both adjustments. Poll workers in local government series. A special adjustment is made in November each year to account for variations in employment due to the presence or absence of poll workers in local government, excluding educational services. This procedure was first introduced in November 1988 to prevent fluctuations in seasonally adjusted local government, excluding education series, resulting from the short-term employment of poll workers during presidential election years. Initially this effect was estimated using an X-11 ARIMA extension analogous to the early method used to adjust for the floating holiday effect described below. This is not a true seasonal effect because it occurs only once every 4 years in November. In addition, according to CES definition, poll workers who receive even just one days pay are correctly counted as employed. However, a decision was made by BLS to remove this effect due to its confounding the analysis of economic trends in total nonfarm employment. The adjustment procedure is now accomplished through X8209138209ARIMA8209SEATS it removes an estimate of the number of poll workers in the series prior to seasonal adjustment in order to prevent November spikes in total nonfarm employment that result from the 1-day employment of many thousands of poll workers. The current procedure was introduced with the first preliminary release of May 1998 data and is used for the national local government, excluding education employment series only. Floating holiday adjustment. This adjustment to average weekly hours and average weekly overtime series accounts for significant effects due to the timing of the survey reference period (the pay period including the 12th of the month) overlapping with the Good Friday (Easter) and Labor Day holidays. These holidays do not occur at exactly the same time every year mdash sometimes they occur during the survey reference period and sometimes not mdash which complicates the seasonal adjustment process. The presence or absence of these holidays in the survey reference period causes a significant variation in hours reported by respondents in some industries (i. e. more hours are reported when the holiday does not fall in the week of the 12th). The special adjustment procedure identifies the magnitude of the effect and adjusts for it prior to seasonally adjusting the series, thereby neutralizing the effect. The floating holiday adjustment is accomplished through the REGARIMA option within the X820912 procedure. Essentially a regression model estimate of the significance of the presence or absence of the holiday during the week of the 12th is made, using a dummy variable to indicate in which years the holiday is present or absent. For industry series where the dummy variable test is significant, an adjustment is made to the original series before it is input into the seasonal adjustment procedure, using the estimated regression parameters. The floating holiday procedure was first introduced in 1990, pre-dating X820912 REGARIMA availability. The adjustment was accomplished using an extension of the X-11 ARIMA procedure. This process was based on the same concepts described above and yielded similar results to the procedure currently in use. X8209128209ARIMA was introduced with the release of first preliminary May 1997 estimates in June 1997. With the 2015 benchmark release, CES transitioned from using X8209128209ARIMA to X8209138209ARIMA8209SEATS to produce seasonally adjusted series and forecasts of birthdeath residuals. For more information about X8209138209ARIMA8209SEATS please visit the U. S. Census Bureau website at census. govsrdwwwx13as . More information about the calendar-related fluctuations in CES data is available on the BLS website at bls. govcescesfltxt. htm. Residential and nonresidential specialty trade contractors raking procedure. Concurrent with the release of the 2004 benchmark, the CES Program began producing and publishing employment series for residential specialty trade contractors (20-238001) and nonresidential specialty trade contractors (20-238002). The two employment series are derived independently from the traditionally published 3-digit NAICS series specialty trade contractors (20-238000). A raking procedure is used to ensure that the sum of the seasonally adjusted residential specialty trade contractors and seasonally adjusted nonresidential specialty trade contractors series is consistent with the published seasonally adjusted total for specialty trade contractors at the 3-digit NAICS level. The raking procedure begins by seasonally adjusting the two series independently for the residential and nonresidential groups at the 3-digit NAICS level. The seasonally adjusted residential and nonresidential series are summed at the 3-digit NAICS level to get a 3-digit total. Ratios of seasonally adjusted residential-to-total employment and seasonally adjusted nonresidential-to-total employment are calculated. The sum of the seasonally adjusted residentialnonresidential series is subtracted from the official 3-digit seasonally adjusted estimate for specialty trade contractors to determine the amount that must be raked. The total amount that must be raked is multiplied by the ratios to determine what percentage of the raked amount should be applied to the residential group and what percentage should be applied to the nonresidential group. Once the seasonally adjusted residential and nonresidential groups receive their proportional amount of raked employment, the two groups are aggregated again to get a 3-digit total. At this point their sum should be equal to the official 3-digit seasonally adjusted estimate for specialty trade contractors. Additive and multiplicative models. Prior to the March 2002 benchmark release in June 2003, all CES series were adjusted using multiplicative seasonal adjustment models. Although the X8209138209ARIMA8209SEATS seasonal adjustment program provides for either an additive or a multiplicative adjustment depending on which model best fits the individual series, the previous CES processing system was unable to use additive seasonal adjustments. A new processing system, introduced simultaneously with the conversion to NAICS in June 2003, is able to use both additive and multiplicative adjustments. The seasonal adjustment website (bls. govwebempsitcesseasadj. htm ) contains a list of which series are adjusted with additive or multiplicative seasonal adjustment models. Special notice regarding seasonal adjustment for AE hours and earnings Concurrent with the release of January 2010 data, the CES program began publishing AE hours and earnings as official BLS series. The AE hours and earnings series are published at the same level of industry detail as PE hours and earnings series and are published on both a not seasonally adjusted and a seasonally adjusted basis. CES has at least 5 full years of history for the AE hours and earnings series, which allows for incorporating the special model adjustments for variation due to the calendar effects (4- vs. 5-week, 10- vs. 11-day). Also, generally CES uses 10 years of not seasonally adjusted data as an input to seasonal adjustment. Until CES has a full 10 years of input data for the AE hours and earnings series, CES will use the entire history of the not seasonally adjusted series as inputs and replace the entire history of the seasonally adjusted data. Continuing these updates until all years have been adjusted using a full 10 years of input data ensures that all data are adjusted using the same methodology. CES seasonal adjustment input files All controllable variables remain fixed during the year. For example, the ARIMA model, outliers, transformation specification, and historical data are held constant, and the same calendar treatments are used throughout the year. Once a year, as part of the annual CES benchmark procedure, all seasonal adjustment specifications are reviewed for each series. Any changes are implemented and kept constant until the next annual benchmark. Also during the annual benchmark, estimates for the 5 most recent years are re-seasonally adjusted using the new specifications. After 5 years of revisions, seasonally adjusted data are frozen. The CES program uses the following input files when seasonally adjusting estimates: Specification file Input data file Prior-adjustment file User-defined regression variables (dummy variables) file Metafile Recent outliers More details follow on each input. Specification file An input specification file, or a quotspecquot file, is a text file used to specify program operations. The spec file is composed of functional units called specifications (or quotspecsquot). Each spec unit comprising the spec file controls the options for a specific function. There are 15 different specs that can be used in a spec file however, the CES programs implementation typically employs only 8 specs. These specs are: SERIES spec mdash this specifies the location and format of the data TRANSFORM spec mdash this specifies a data transformation REGRESSION spec mdash this specifies any regression components ARIMA spec mdash this specifies the ARIMA model to be used ESTIMATE spec mdash this estimates the regARIMA model FORECAST spec mdash this generates forecasts of seasonal factors OUTLIER spec mdash this specifies automatic outlier detection X11 spec mdash this generates and controls the seasonal adjustment process COMPOSITE spec mdash this is a special spec used only during indirect seasonal adjustment Each spec used by the CES program is covered in greater detail at the end of this section in Anatomy of a Spec File . In the CES programs implementation, each seasonally adjusted employment series has its own spec file ending in a quot. spcquot file extension. The quot. spcquot extension is not recognizable by all operating systems and usually needs to be opened with a text editor such as TextPad, Wordpad, or Notepad. Also, it is important to remember that when running X8209138209ARIMA8209SEATS in DOS, the name of the spec file must be 8 characters or less. This is a limitation of DOS, not X8209138209ARIMA8209SEATS. All of the spec files currently used in production can be downloaded from bls. govwebempsitcesseasadj. htm. Input data file The input data file consists of not seasonally adjusted CES estimates for all series that have a corresponding seasonally adjusted series and is referred to in the SERIES spec of the spec file. The CES implementation reads input data from a text file in quotfree formatquot style. In the free-format style, data are delimited with either tabs or spaces, and only the input data are included mdash dates and other descriptive information are excluded. Instead, information describing the data is specified in the SERIES spec using the START and PERIOD arguments. The full path and name of the input data file is specified using the FILE argument (see Figure 3 ). Figure 3. Input data file specifications CES data can be extracted from the BLS website at bls. govcesdata. htm. However, in some cases, not seasonally adjusted data extracted from the BLS website will differ from what the CES program actually uses in seasonal adjustment. In particular, data extracted from the BLS website will reflect any strikes or other prior adjustments that have taken place. Before running seasonal adjustment, the CES program will reverse these effects so that they will not be considered when calculating the seasonal factors. Also, the CES program uses unrounded data when running seasonal adjustment mdash data on the BLS website are rounded. Prior adjustment file As mentioned in the previous section, in some cases the CES program will modify the not seasonally adjusted estimates (input data) before running X8209138209ARIMA8209SEATS. This is done to ensure that non-seasonal events such as strikes are not included in the calculation of the seasonal factors. Once the seasonal factors are calculated, they are applied to the not seasonally adjusted data used as inputs. Then the prior adjustment removed before running X8209138209ARIMA8209SEATS are incorporated to create the seasonally adjusted estimates. To read more about the impact of strikes on CES data, visit the BLS website at bls. govcescesstrk. htm . The latest prior adjustment file used in the seasonal adjustment of CES data can be downloaded from bls. govwebempsitcesseasadj. htm. The prior adjustment file is updated annually to reflect the series structure adopted with the benchmark, and it is updated monthly with each release of CES national estimates to include strike data. In the example shown below in Figure 4. the first column contains the 14-digit CES NAICS tabcode. This tabcode identifies the series by an 8-digit industry code. followed by three zeros used as placeholders, a 2-digit data type code. and a single digit indicating seasonal adjustment (3 for not seasonally adjusted, 5 for seasonally adjusted). The tabcode structure is similar to the CES series ID structure, described on the CES NAICS webpage (bls. govcescesnaics. htm2.3 ). The second column contains the year, and the next 12 columns represent the months of the year in sequential order (January through December). The file contains both positive and negative numbers. The positive numbers reflect a strike and are added to the not seasonally adjusted data before running X8209138209ARIMA8209SEATS. The negative numbers reflect the buildup of employment associated with the decennial census and are added to the not seasonally adjusted data before calculating the seasonal factors. Figure 4. Prior adjustment file format (1) The dummy values are usually 1 and 0, with weights assigned so that the effect over a 10 year period sums to zero. The latest user-defined regression files used in the seasonal adjustment of CES data can be downloaded from bls. govwebempsitcesseasadj. htm. The metafile is a text file ending in a quot. mtaquot file extension and is used when running X8209138209ARIMA8209SEATS on more than one series. It is essentially a list of the complete path and filename mdash without the extension mdash of all of the input spec files. Only one spec file is listed per row. As with the individual spec files, it is important to remember that when running X8209138209ARIMA8209SEATS in DOS, the name of the metafile must be 8 characters or less. Recent outliers An excel table called outliers. xlsx lists the month, year, and industry code of recent outliers manually identified during analyst review. The file contains outliers from the November following the most recent benchmark to the present month. Running X820913 on a single series Use the following command at the DOS prompt when running X8209138209ARIMA8209SEATS on a single series: x13as spec file name - options Example: At the DOS prompt, type: c:x13asx13as - m c:x13seasadjpubAE - w (where pubAE. mta is the metafile you are using) Output from X8209138209ARIMA8209SEATS When X8209138209ARIMA8209SEATS is run, several output files are generated by default. The output files are saved in the same location as the input specification files. Main output file (.out) Error output file (.err) Log output file (.log) More details follow on each of the output files. Main output file (.out) The X8209138209ARIMA8209SEATS output is written to a text file ending in a quot. outquot extension. Output from the CES implementation contains many different tables and statistics, including: Table displaying the original, not seasonally adjusted series Table displaying the final seasonally adjusted series Table displaying the final seasonal factors Statistics related to model selection Statistics related to outlier detection A summary of seasonal adjustment diagnostics Quality control statistics Individual specs in the spec file control their contribution to this output using optional PRINT arguments. For example, within the X11 spec, BRIEF specifies that only certain tables or plots are printed, while the minus sign in front of a name (such as - SPECSA or - SPECIRR) means that particular table or plot should be suppressed from the output. In this example, without the options - SPECSA and - SPECIRR, both of the plots would be printed by default under the BRIEF option. Figure 5. The PRINT argument in the X11 spec It is important to remember that every time X8209138209ARIMA8209SEATS is run on a particular series, the. out file is overwritten, unless an alternate name or directory is specified. Error output file (.err) Input errors are written to a text file ending in an quot. errquot extension. If the error is fatal, ERROR: will be displayed before the error message. If the error is not fatal, WARNING: will be printed before the message. Non-fatal errors (or warnings) will not stop the program, but should be an alert to use caution and to check input and output carefully. It is important to remember that, as is the case with all output files, every time X8209138209ARIMA8209SEATS is run on a particular series, the. err file is overwritten, unless an alternate name or directory is specified. Log output file (.log) A summary of modeling and seasonal adjustment diagnostics are written to a text file ending in a quot. logquot extension. Individual specs in the specification file control their contribution to this output using optional SAVELOG arguments. When X8209138209ARIMA8209SEATS is run on an individual spec file, the log file is stored with the same name and directory as the spec file. However, when X820913 is run using a metafile, the log file is stored with the same name and directory as the metafile. As is with all output files, every time X8209138209ARIMA8209SEATS is run, the. log file is overwritten unless an alternate name or directory is specified. Other output files Other output files are generated as specified in the spec file using the SAVE argument. In the CES programs implementation, the following additional output files are generated:.a1 ndash This file contains the not seasonally adjusted data with associated dates and is specified in the SERIES spec. ao ndash This file contains outlier factors with associated dates and is specified in the REGRESSION spec. d10 ndash This file contains final seasonal factors with associated dates and is specified in the X11 spec. d11 ndash This file contains final seasonally adjusted data with associated dates and is specified in the X11 spec. d16 ndash This file contains combined seasonal and trading day factors with associated dates and is specified in the X11 spec. td ndash This file contains final trading day factors with associated dates and is specified in the REGRESSION spec Indirect seasonal adjustment The CES program generally seasonally adjusts published series directly at the 3-digit NAICS level and aggregates to the higher levels. However, there are some exceptions to this rule. In a few of the AE series, the CES program will seasonally adjust at a level lower than the 3-digit NAICS level. In these instances, the CES program seasonally adjusts the 3-digit series indirectly i. e. all of the component (lower level) series are seasonally adjusted directly and aggregated up to the composite (3-digit) level. Indirect seasonal adjustment is performed on these series because some of the individual component series that aggregate to the composite series exhibit different seasonal patterns that may be masked if seasonally adjusted directly at the aggregate level. The spec file for the composite series differs somewhat from normal CES implementation. The most significant difference is at the beginning of the spec file, where the SERIES spec is replaced with the COMPOSITE spec. Running X820913 employing the COMPOSITE spec produces an indirect seasonal adjustment of the composite series as well as a direct adjustment. Output from the indirect adjustment is saved under non-standard file extensions. Aggregated not seasonally adjusted data with associated dates are saved in a text file with the extension. cms (instead of. a1 under direct seasonal adjustment) Final indirect (aggregated) seasonally adjusted data with associated dates are saved in a text file with the extension. isa (instead of. d11 under direct seasonal adjustment) Final seasonal factors for aggregated series with associated dates are saved in a text file with the extension. isf (instead of. d16 under direct seasonal adjustment) The COMPOSITE spec is covered in greater detail at the end of this section in Anatomy of a Spec File. Seasonal adjustment of the component series that go into a composite series is run using X8209138209ARIMA8209SEATS in the same way as a standard seasonally adjusted series, but is then summed to the composite level. A metafile listing the file locations and names (without the. spc extension) of the composite series followed by all of its component series is used to seasonally adjust a composite series. A current list of industries that are indirectly seasonally adjusted follows in Table 15. along with their component series. For any given series, not all of the component series are published at first closing. Some series are published during a later release. In the table below, component series published with the first preliminary data release are denoted with a footnote (1). Table 15. Indirectly seasonally adjusted CES series Component Series (1) Footnotes (1) For CES industry titles of the component series, see bls. govwebempsitcesseriespub. htm. (2) The component series for this industry are published with the second preliminary release. Anatomy of a spec file For published series, the CES program generally seasonally adjusts at the 3-digit NAICS level and aggregates to the higher levels. A small number of series are independently seasonally adjusted at a higher level of detail, but these are not included in the aggregation of seasonally adjusted data. One of the main inputs to the seasonal adjustment process is a unique file called a spec file. The spec file contains a set of specs that give X8209138209ARIMA8209SEATS various information about the data and the desired seasonal adjustment options and output. Each specification inside the spec file controls options for a specific function. For example, the SERIES spec contains specifications on the location and format of the data, while the X11 spec sets seasonal adjustment options such as seasonal adjustment transformation mode, output files to save, and diagnostic statistics to print. Figure 6. CES seasonal adjustment spec file The spec file is free format, and blank spaces, tabs, and blank lines may be used as desired to make the spec file more readable. The order of the specification statements in the spec file (with one exception), and the order of the arguments within the braces of any spec do not matter. The only requirement is that the SERIES spec or COMPOSITE spec must be the first spec. More detail on each spec used by CES follows. NAME 10113310 ndash AE The main function of the SERIES spec is to specify details about the input data series such as the name, format, and location of the data. The CES implementation employs seven options or arguments with the SERIES spec. TITLE mdash A descriptive title for the series. In this example, the title is quotLoggingquot . START mdash The start date of the time series being adjusted. In this example, the start date is January, 1993 . PERIOD mdash Seasonal period of the series. In this example, the period is 12 (which means monthly). SAVE mdash Specifies output to be saved. In this example, the time series data with associated dates will be saved in an output file called AE10113310. A1 . PRINT mdash Specifies output to be printed. In this example, BRIEF specifies that only certain tables are printed. NAME mdash The name of the time series. In this example, the name is quot10113310 ndash AEquot . FILE mdash The complete path and name of the file containing the time series data. In this example, the complete path and filename is c:AE10113310.dat . The main function of the TRANSFORM spec is to transform or adjust the time series prior to estimating a regARIMA model. The CES implementation employs one argument with the TRANSFORM spec. FUNCTION mdash Specifies the method to transform the time series. In this example, the transformation method is log transformation, which means X820913 will compute a multiplicative seasonal decomposition. VARIABLES (AO1995.02 AO1996.01 AO1999.01) USER (dum1 dum2 dum3 dum4 dum5 dum6 dum7 dum8 dum9 dum10 dum11) The main function of the REGRESSION spec is to specify the regression components of a regARIMA model. The CES implementation employs up to six options with the REGRESSION spec. VARIABLES mdash Specifies any predefined regression variables to be included in the model. In the CES implementation, predetermined outliers are listed after the VARIABLES argument. In this example, predetermined outliers include AO1995.02 (February 1995), AO1996.01 (January 1996), and AO1999.01 (January 1999). USER mdash Specifies the names for any user-defined regression variables. CES defines regression variables to adjust for significant effects associated with calendar related events such as (1) the relative timing of the reference period of the survey and the Good Friday (Easter) and Labor Day holidays (2) variations of 4 or 5 weeks between reference periods in any given pair of months, and (3) differences in the number of working days in a pay period from month-to-month. In this example, the regression variables are named dum1, dum2, dum3, dum4, dum5, dum6, dum7, dum8, dum9, dum10, and dum11 . START mdash Specifies the start date for the data values for the user-defined regression variables. In this example, the start date is January, 1986 . FILE mdash The complete name of the file containing the data values for the user-defined regression variables, including the path. In this example, the filename, including the path, is quotc:FDUM8606.datquot . USERTYPE mdash Specifies a type of model-estimated regression effect to each user-defined regression variable. In this example, the type of model-estimated regression effect is defined as TD . or trading day. SAVE mdash Specifies output to be saved. In this example, trading day factors with associated dates will be saved in an output file called AE10113310.TD, and outlier factors with associated dates will be saved in an output file called AE10113310.AO . Note: Not every option is used in every spec file. For example, if no predetermined outliers exist, then the VARIABLES argument will not be used. Likewise, if we are not treating a particular series for calendar effects, then the USER, START, FILE, and USERTYPE arguments will not be used. The main function of the ARIMA spec is to specify the ARIMA part of a regARIMA model. The CES implementation employs 1 option with the ARIMA spec. MODEL mdash Specifies the actual ARIMA model to be used. In this example, the model is (2 1 0) (0 1 1) . The main function of the ESTIMATE spec is to estimate the regARIMA model specified by the REGRESSION and ARIMA specs. The CES implementation employs 1 argument with the ESTIMATE spec. MAXITER mdash Specifies the maximum number allowed of autoregressive moving average (ARMA) nonlinear iterations. ARMA is a time-series model that includes both autoregressive (AR) and moving average (MA) nonlinear components. In this example, the maximum number allowed of ARMA iterations is 1000 . The main function of the FORECAST spec is to generate forecasts (andor backcasts) for the time series model given in the SERIES spec using the estimated regARIMA model. The CES implementation employs 1 argument with the FORECAST spec. MAXLEAD mdash Specifies the number of forecasts produced. In this example, the number of forecasts specified is 24 months . The main function of the OUTLIER spec is to perform automatic detection of point outliers, temporary change outliers, level shifts, or any combination of the three. The CES implementation uses this spec to automatically detect point outliers only. CES employs 2 arguments with the OUTLIER spec. CRITICAL mdash Specifies the value to which the absolute values of the outlier t-statistics are compared to detect outliers. In this example, the critical value is 3.5 . TYPES mdash Specifies the types of outliers to detect. The CES implementation uses the OUTLIER spec to automatically detect point outliers only. In this example, the outlier type is AO (which signifies point outliers). PRINT (BRIEF - SPECSA - SPECIRR) SAVE (D10 D11 D16) SAVELOG (Q Q2 M7 FB1 FD8 MSF) The function of the X11 spec is to control certain aspects of the seasonal adjustment process. For example, the CES implementation uses the X11 spec to control the type of seasonal adjustment decomposition calculated (mode). CES employs 6 arguments with the X11 spec. MODE mdash Specifies the mode of the seasonal adjustment decomposition to be performed. There are four choices: multiplicative, additive, pseudo-additive, and log-additive. In the CES implementation, only the multiplicative or additive modes are employed. In this example, the mode specified is multiplicative ( MULT ). PRINT mdash Specifies output to be printed. In this example, BRIEF specifies that only certain tables or plots are printed. The minus sign in front of a name means that particular table or plot should be suppressed. In this example, - SPECSA specifies that a spectral plot of differenced, seasonally adjusted series be suppressed, while - SPECIRR specifies that a spectral plot of outlier-modified irregular series be suppressed. Without these options, both plots would be printed under the BRIEF option by default. SAVE mdash Specifies output to be saved. In this example, final seasonal factors with associated dates will be saved in an output file called AE10113310.D10 the final seasonally adjusted series with associated dates will be saved in an output file called AE10113310.D11 and combined seasonal and trading day factors with associated dates will be saved in an output file called AE10113310.D16 . APPENDFCST mdash Determines if forecasts of seasonal factors will be included in the X820913 output files and tables that were selected in the SAVE option. If APPENDFCST yes, then forecasted seasonal factors will be stored. In this example, the APPENDFCST value is YES . FINAL mdash Specifies the types of prior adjustment factors (obtained from the REGRESSION and OUTLIER specs) that are to be applied to the final seasonally adjusted series. In this example, FINAL USER . which means that factors derived from user-defined regressors (or in this example, the dummy variables) are to be applied to the final seasonally adjusted series, removing significant effects associated with calendar related events. SAVELOG mdash Specifies the diagnostic statistics to be printed to the log file. In this example, the following diagnostics will be printed: Q . which is the overall index of the acceptability of the seasonal adjustment. The adjustment may be poor if Q 1. Q2 . which is the Q statistic computed without the M2 Quality Control Statistic. The M2 values can sometimes be misleading if the trend shows several changes of direction. M7 . which measures the moving seasonality relative to the stable seasonality found in the series. Any M 1 indicates a source of potential problems for the adjustment procedure. FB1 . which is an F-test for stable seasonality, performed on the original series. FB8 . which is an F-test for stable seasonality, performed on the final ratio of the seasonal-to-irregular components. MSF . which is an F-test for moving seasonality. As previously mentioned, the CES program generally seasonally adjusts published series at the 3-digit NAICS level and aggregates to the higher levels. However, there are a few cases in which CES seasonally adjusts published series at a level lower than the 3-digit NAICS level. In these instances, CES seasonally adjusts the 3-digit NAICS level indirectly i. e. all of the component or lower level series are seasonally adjusted directly and then aggregated up to the 3-digit level. When this happens, the SERIES spec is replaced by the COMPOSITE spec in the specification file of the 3-digit series. TITLE quotConstruction of buildingsquot SAVE (ISF ISA CMS) NAME 20236000 - AE SAVELOG (INDTEST INDQ) The COMPOSITE spec is used as part of the procedure for obtaining both indirect and direct adjustments of a composite series data series. This spec is required for obtaining composite adjustments and is used in place of the SERIES spec. The COMPOSITE spec can also specify details about the input data series such as the name of the series and which tables are to be printed or stored. The CES implementation employs five options or arguments with the COMPOSITE spec. TITLE mdash A descriptive title for the series. In this example, the title is quotConstruction of buildingsquot . SAVE mdash Specifies output to be saved. In this example, the aggregated time series data with associated dates will be saved in an output file called AE20236000.CMS . the final seasonal factors for the indirect adjustment with associated dates will be saved in an output file called AE20236000.ISF . and the final indirect seasonally adjusted series with associated dates will be saved in an output file called AE20236000.ISA . PRINT mdash Specifies output to be printed. In this example, BRIEF specifies that only certain tables are printed. NAME mdash The name of the time series. In this example, the name is quot20236000 ndash AEquot . SAVELOG mdash Specifies the diagnostic statistics to be printed to the log file. In this example, the following diagnostics will be printed: IND TEST . which is a test for adequacy of composite adjustment. IND Q . which is an overall index of the acceptability of the indirect seasonal adjustment. Sample-based Revisions Effect of sample receipts CES data users typically are most concerned with revisions to over-the-month changes. This section profiles these monthly revisions of CES seasonally adjusted over-the-month changes and the sample collection rates that underlie the revisions. CES begins collecting sample reports for a reference month as soon as the reference period, the establishments pay period that includes the 12th of the month, is complete. Collection time available for first preliminary estimates ranges from 9 to 15 days, depending on the scheduled date for the Employment Situation news release. The Employment Situation is scheduled for the third Friday following the week including the 12th of the prior month, with an exception for January. (For January, the news release is delayed a week if the third Friday following the week of the 12th occurs on January 1, 2, or 3.) Given this short collection cycle for the first preliminary estimates, many establishments are not able to provide their payroll information in time to be included in these estimates. Therefore, CES sample responses for the reference month continue to be collected for 2 more months and are incorporated into the second preliminary and final sample-based estimates published in subsequent months. (Second preliminary estimates for a reference month are published the month following the initial release, and final sample-based estimates are published 2 months after the initial release.) Additional sample receipts are the primary source of the monthly CES employment revisions. Sample-based estimates remain final until employment levels are reset to universe employment counts, or benchmarks, for March of each year the benchmarks are primarily derived from Unemployment Insurance (UI) tax records. The annual benchmarking process results in revised data back to the last annual benchmark for not seasonally adjusted series and back 5 years for seasonally adjusted series. Monthly revisions Revisions to CES over-the-month changes are calculated by comparing each months second preliminary over-the-month change to the first preliminary over-the-month change, the final sample-based over-the month change with the second preliminary over-the-month change, and the final sample-based over-the-month change to the first preliminary over-the-month change. See bls. govwebempsitcesnaicsrev. htm for a table of revisions to seasonally adjusted total nonfarm over-the-month changes from January 1979 forward. The monthly employment change figures shown in the table do not reflect subsequent changes due to the introduction of benchmark revisions, seasonal adjustment, or other updates. Mean revisions and mean absolute revisions for each calendar year are included in the table. Mean absolute revisions indicate the overall magnitude of change to the estimates, while the mean revisions are a measure of whether there is a bias in direction of the revisions. The closer the mean revision is to zero, the less indication that revisions are predominantly either upward or downward. For example, if in a given year there were 6 upward revisions of 50,000 and 6 downward revisions of 50,000, the mean revision would be 0 however, the mean absolute revision would be 50,000. Collection rates Collection rates are defined as the percent of reports received for a monthly estimate compared to the total number of actively-reporting sample units on the sample registry. CES collection rates back to 1981 can be found on bls. govwebempsitcesregrec. htm . Much of the month-to-month variation in the first preliminary collection rates is a function of the number of collection days in the individual months. The overall upward trend over time is attributable to replacing decentralized mail collection with automated techniques. For more information about the methods used to calculate CES estimates of employment, hours, and earnings at all closings, see the section on Monthly Estimation in this documentation. Benchmarks For the establishment, or CES, survey, annual benchmarks are constructed in order to realign the sample-based employment totals for March of each year with the Unemployment Insurance (UI) based population counts for March. These population counts are much less timely than sample-based estimates and are used to provide an annual point-in-time census for employment. For national series, only the March sample-based estimates are replaced with UI counts. For state and metropolitan area series, all available months of UI data are used to replace sample-based estimates. State and area series are based on smaller samples and are therefore more vulnerable to both sampling and non-sampling errors than national estimates. Population counts are derived from the administrative file of employees covered by UI. All employers covered by UI laws are required to report employment and wage information to the appropriate Labor Market Information Agency (LMI) four times a year. Approximately 97 percent of private and total nonfarm employment within the scope of the establishment survey is covered by UI. A benchmark for the remaining 3 percent is constructed from alternate sources, primarily records from the Railroad Retirement Board (RRB) and County Business Patterns (CBP). This 3 percent is collectively referred to as noncovered employment and is explained further in the calculating noncovered employment section of this document. The full benchmark developed for March replaces the March sample-based estimate for each basic cell. The monthly sample-based estimates for the year preceding and the year following the benchmark are also then subject to revision. Each annual benchmark revision affects 21 months of data for not seasonally adjusted series and 5 years of data for seasonally adjusted series. Monthly estimates for the year preceding the March benchmark are readjusted using a quotwedge backquot procedure. The difference between the final benchmark level and the previously published March sample estimate is calculated and spread back across the previous 11 months. The wedge is linear eleven-twelfths of the March difference is added to the February estimate, ten-twelfths to the January estimate, and so on, back to the previous April estimate, which receives one-twelfth of the March difference. This assumes that the total estimation error since the last benchmark accumulated at a steady rate throughout the current benchmark year. Estimates for the 7 months following the March benchmark (April through October) also are recalculated each year. These post-benchmark estimates reflect the application of sample-based monthly changes to new benchmark levels for March and the re-computation of business birthdeath factors for each month. Following the revision of basic employment estimates, all other derivative series also are recalculated. New seasonal adjustment factors are calculated and all data series for the previous 5 years are re-seasonally adjusted before full publication of all revised data in February of each year. Estimates for the November and December following the March benchmark revise due to both impacts of benchmarking and additional sample. Additionally, new sample units are rotated into the survey starting with November. As an example of benchmark effects, the March 2014 benchmark revisions (published in February 2015) resulted in revised series from April 2013 through December 2014 on a not seasonally-adjusted-basis and revised series from January 2010 through December 2014 on a seasonally-adjusted-basis for all series except seasonally adjusted AE hours and earnings, which were revised back to January 2006. Annual CES benchmark revisions are published along with January first preliminary estimates in February of each year. For example, the annual CES benchmark revisions for March 2014 were published along with the January 2015 first preliminary estimates on February 6, 2015. The benchmark revision is the difference between the universe count of employment for March and its corresponding sample-based estimate after removing the effect of any changes in employment scope. A table of benchmark revisions from 1979 forward is included in Table 16 below. See bls. govwebempsitcesbmart. htm for more details about the benchmarking process. Table 16. CES total nonfarm benchmark revisions (1) Footnotes (1) The table reflects the benchmark revisions after removing the effect of any changes in employment scope. (2) With the conversion from SIC to NAICS, support activities for animal production (NAICS 1152) was removed from CES scope. Also, the federal government employment level derivations were changed from end-of-month counts provided by the Office of Personnel Management that excluded some workers, mostly employees of U. S. Department of Defense-owned establishments such as military base commissaries, to QCEW derived benchmark employment levels. (3) A review of industries for the possible presence of noncovered employment yielded 13 additional industries. As a result of including these industries, employment in the amount of 95,000 was added to the nonfarm level. The final difference between the benchmarked and published March estimate levels was 162,000. (4) A large non economic code change related to state-run programs brought 466,000 employment into the CES scope from outside of the CES scope. The final difference between the benchmarked and published March estimate levels was 347,000. (5) With the 2015 benchmark, CES reconstructed the national employment series for CES series 65-624120 services for the elderly and persons with disabilities back to January 2000. CES previously reconstructed this series with the 2013 benchmark however, between the 2013 and 2015 benchmark, a better source of information for the employment within NAICS 62412 for the state of California was found. The inclusion of the reconstructed series resulted in total nonfarm and total private employment that was 27,000 less than the originally published March 2015 estimate level. This table displays March 2015 data after accounting for the decrease of 27,000 from the reconstructed series. Similarly, for the education and health services supersector, this table displays March 2015 data after incorporating the reconstructed series. Calculating noncovered employment Noncovered employment results from a difference in scope between the CES program and the Quarterly Census of Employment and Wages (QCEW) program. The QCEW employment counts are derived from UI tax reports that individual firms file with their State Employment Security Agency (SESA). Most firms are required to pay UI tax for their employees however, there are some types of employees that are exempt from UI tax law, but are still within scope for the CES estimates. Examples of the types of employees that are exempt are students paid by their school as part of a work study program interns of hospitals paid by the hospital for which they work employees paid by State and local government and elected officials independent or contract insurance agents employees of non-profits and religious organizations (this is the largest group of employees not covered) and railroad employees covered under a different system of UI administered by the Railroad Retirement Board (RRB). This employment needs to be accounted for in order to set the benchmark level for CES employment. No single source of noncovered data exists therefore, CES uses a number of sources to generate the employment counts, including County Business Patterns (CBP) and the Annual Survey of Public Employment and Payroll (ASPEP) both from the US Census Bureau, the RRB, and the Labor Market Information Agencies (LMIs). The majority of noncovered employment is calculated using CBP data. Industries for which noncovered employment is derived from the CBP are provided in Table 17. The CBP mdash which draws from Social Security filings and other records which do include those employees not covered by UI tax laws mdash is lagged in its publication by approximately 2 years (e. g. in 2014 the 2012 CBP data was published). To adjust for this lag, CES assumes that the noncovered portion of employment grows or declines at the same rate as the covered portion and trends the CPB data forward using the QCEW trend. The current QCEW employment level is subtracted from the trended CBP figure, and the residual is the noncovered employment level. Noncovered employment for all CBP based industries, with the exception of religious organizations, is calculated as follows: Equation 14. Noncovered employment for CBP-based industries, except religious organizations N Noncovered employment estimate C CBP employment data for North American Industry Classification System (NAICS) code E QCEW employment for NAICS code t Benchmark year Noncovered employment for religious organizations is calculated by: Equation 15. Noncovered employment for Religious organizations N Noncovered employment estimate C CBP employment data for NAICS 813110 E QCEW employment for NAICS 813110 t Benchmark year Table 17. Noncovered industries calculated using CBP data All other transit and ground passenger transportation Over time some sources from which CES draws input data have become unreliable. Where possible CES has tried to find new sources of input data, but for series that no longer have reliable input data, CES trends forward the previous years noncovered employment levels using a ratio derived from QCEW employment data. These industries are contained in Table 20 and are calculated using the following method. Equation 17. Noncovered employment for QCEW-trend-based industries N noncovered employment estimate E QCEW employment t Benchmark year Table 20. Noncovered industries calculated using QCEW trend Footnotes (1) Indicates that noncovered employment is calculated only for firms owned by state and local government. Corporate officers are one of the largest exemptions outside of the industries listed. In several states, corporate officers are exempt from UI coverage and as a result noncovered employment exists in most NAICS industries in those states. Corporate officers and other state specific employment exemptions outside of those listed above are collected from state offices annually by CES. Noncovered employment industries are reviewed and refined periodically. This review is done to identify any changes in state UI coverage, as well as to ensure that CES captures all exempted employment within the scope of the CES survey and that our methodology and external data sources are as accurate as possible. When additions and changes are identified during review, they are incorporated with the following March benchmark. Changing data ratios for education and religious organizations Due to the small sample in religious organizations (NAICS 8131) and definitional exclusions in the collection of data for educational services (NAICS 611), certain ratios for these series are recalculated with each benchmark to allow for the creation of aggregate totals. Production or nonsupervisory employee (PE) and women employee (WE) ratios, all employee (AE) average hourly earnings (AHE) and average weekly hours (AWH), and PE AHE and AWH for these series are calculated based on the weighted average of the previous years professional and technical services, education and health services, leisure and hospitality, and other services supersectors annual averages. This year the March 2014 values were set based on the 2013 annual averages. The education services series uses the PE ratio, AHE, and AWH calculated from the weighted average. The religious organizations series uses the PE ratio, WE ratio, AHE, and AWH calculated from the weighted average. In both cases, the ratios, AHE, and AWH for AE and PE are held constant through the next benchmark. Historical Reconstructions Beyond the monthly revisions and the benchmark revisions, CES employment, hours, and earnings estimates have been reconstructed several times in order to avoid series breaks and to provide users with continuous, comparable employment time series suitable for economic analysis when incorporating methodological changes. The major reconstruction efforts are briefly described below. Improvement to seasonal adjustment methodology With the release of the 1995 benchmark revision (in June 1996), CES refined its seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. This improvement mitigated the effects that a variable number of weeks between surveys had on the measurement of employment change, thus improving the measurement of true economic trends. At that time, data for 1988 forward were revised to incorporate this new methodology. CES sample redesign Over a 4-year period, CES introduced a new probability-based sample design it replaced an outmoded and less scientific quota sample-based design. The new design was phased in by major industry division with the June 2000 through June 2003 benchmark releases (see Table 21 ). As each industry was phased in, the post-benchmark estimates for that year were affected by the new sample composition. Table 21.CES sample redesign phase-in schedule Industries converted to new sample design Industry reclassification CES periodically updates the national nonfarm payroll series to revised NAICS structures. This update usually occurs every 4 to 5 years. For all NAICS updates, affected series are reconstructed back to at least 1990, and in some cases, where longer histories are available, they are reconstructed back further. With the release of the 2011 benchmark in February 2012, CES converted from NAICS 2007 to NAICS 2012. The conversion to NAICS 2012 resulted in minor content changes within the manufacturing and the retail trade sectors, as well as minor coding changes within the utilities and the leisure and hospitality sectors. Several industry titles and descriptions were also updated. Prior to the NAICS 2012 structure, CES estimates were classified under NAICS 2007 system, preceded by the NAICS 2002 system. The NAICS system was updated from NAICS 2002 to NAICS 2007 in early 2008. Before switching to NAICS 2002, the CES estimates were classified under the Standard Industrial Classification (SIC) system. CES estimates were converted from SIC to NAICS 2002 in mid-2003. For more information about NAICS in the CES program, see bls. govcescesnaics. htm . Other Factors Contributing to Revisions Over the time period covered by the revision and collection rate tables, CES has introduced many program improvements some of these affect the revision patterns observed over time. Monthly revisions As noted above, the overall magnitude of these revisions has trended down over time mainly due to automated and improved data collection techniques which raised the collection rates for the first and second preliminary estimates. Other factors of note include: Timing of benchmark revisions Between 1980 and 2003, annual benchmark revision updates were introduced in June of each year, concurrent with the March final sample-based estimates and the April second preliminary estimates. The monthly revisions for March and April for these years were often larger than for other months, because the March final and April second preliminary estimates were incorporating not only additional sample but also other benchmark-related changes. Beginning with the 2003 benchmark revision (published in 2004), CES reduced the time required to produce the annual revisions by 4 months and thus began publishing benchmark revisions in February rather than June. Therefore from 2004 forward, the November final and December second preliminary estimates are affected by benchmark revision updates, rather than the March final and April second preliminary estimates. Timing of seasonal adjustment updates Between 1980 and June 1996 seasonal factors were updated on an annual basis along with the benchmark revisions. Thus March final and April second preliminary were affected by the recomputation of seasonal factors as well as other benchmarking procedures and additional sample receipts. Between November 1996 and November 2002, CES updated seasonal factors on a semi-annual basis, meaning that September final and October second preliminary estimates as well as March final and April second preliminary revisions were affected by seasonal factor updates. Since June 2003 the CES program has used a concurrent seasonal adjustment procedure, meaning that seasonal adjustment is rerun every month using all available months of estimates including the month currently being estimated for first preliminary. This technique yields the best possible seasonal adjustment for the current month and reduces benchmark revisions to over-the-month changes. In the application of the concurrent procedure, the previous 2 months are revised to incorporate not only additional sample receipts but also new seasonal factors. Thus there are no longer individual months that are more affected than others by seasonal factor updates. However, this practice does mean that revisions from second preliminary to final sample-based estimates for each month are affected by the CES replacement policy. Because CES revises only 2 months of estimates each month, the fourth month back from the current first preliminary estimate is adjusted using a different set of seasonal factors than the third month back. For example, with the release of October first preliminary data, factors are revised for September and August, but not July. Table of Figures Use the links below to skip to specific equations, tables, and figures describing the CES sample, data collection, available statistics, estimation, and revisions.