New Evidence on Earnings Volatility in Survey and Administrative Data

2018 ◽  
Vol 108 ◽  
pp. 287-291 ◽  
Author(s):  
Michael D. Carr ◽  
Emily E. Wiemers
Keyword(s):  
Survey Data ◽  
Administrative Data ◽  
Panel Study ◽  
Program Participation ◽  
Income Dynamics ◽  
Cross Sectional ◽  
Earnings Volatility ◽  
Apparent Inconsistency ◽  
New Evidence

Despite the rise in cross-sectional inequality since the late 1990s, there is little consensus on trends in earnings volatility during this period. Using consistent samples and methods in administrative earnings data matched to the Survey of Income and Program Participation (SIPP GSF) and survey data from the Panel Study of Income Dynamics (PSID), we examine earnings volatility for men from 1978 through 2011. In contrast to the apparent inconsistency in trends across administrative and survey data in the existing literature, we find recent increases in volatility in the SIPP GSF and the PSID, though increases are larger in the PSID.

Recent Trends in Earnings Volatility: Evidence from Survey and Administrative Data

2012 ◽  
Vol 12 (2) ◽  
Author(s):  
Sule Celik ◽  
Chinhui Juhn ◽  
Kristin McCue ◽  
Jesse Thompson
Keyword(s):  
Panel Study ◽  
Household Survey ◽  
Program Participation ◽  
Income Dynamics ◽  
Earnings Volatility ◽  
Administrative Dataset ◽  
Household Dynamics ◽  
Clear Explanation ◽  
Recent Trends ◽  
Household Survey Data

Abstract Recent papers find that earnings volatility is again on the rise (Dynan et al. 2008, and Shin and Solon 2011). Using household survey data—the matched Current Population Surveys and Survey of Income and Program Participation—and the newly available Longitudinal Employment and Household Dynamics administrative dataset, we find that earnings volatility was remarkably stable in the 1990s and through the mid 2000s. This evidence is in contrast to that from the Panel Study of Income Dynamics (PSID) which registers a sharp increase in the early 2000s. We investigate whether adjusting measures based on our sources to more closely match the characteristics of the PSID can reconcile this divergence in trends, but do not find a clear explanation for the divergence. We also find little evidence of a rise over this period in the components of volatility: volatility among job leavers, volatility among job stayers, and the fraction of workers who are job leavers.

scholarly journals NEPS Starting Cohort 6 survey data linked to administrative data of the IAB (NEPS-SC6-ADIAB)

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Nadine Bachbauer
Keyword(s):  
Survey Data ◽  
Administrative Data ◽  
Linked Data ◽  
Panel Study ◽  
Data Access ◽  
Data Sources ◽  
Employment Agency ◽  
Educational Trajectories ◽  
Analytical Potential ◽  
Site Access

BackgroundNEPS-SC6-ADIAB is a new linked data product containing survey data of Starting Cohort 6 of the German National Educational Panel Study (NEPS) and administrative employment data from the Institute for Employment Research (IAB), the research institute of the Federal Employment Agency. NEPS is provided by the Leibniz Institute for Educational Trajectories (LIfBi). Starting Cohort 6 of this panel survey includes adults in their professional life, the survey focuses on education in adulthood and lifelong learning. The administrative data in NEPS-SC6-ADIAB consist of comprehensive information on the employment histories. ObjectivesCombining these two data sources increases for example the information about individual employment history. Overall, the data volume is increased by the linkage between the survey data and the administrative data. MethodsA record linkage process was used to link the two data sources. The data access is free for the whole scientific community. In addition to a large number of On-site access locations within Germany, there are also international On-site access locations. Including London and Colchester. In addition a Remote Data Access is offered. ConclusionsThis data linkage project is very innovative and creates an extensive database, which results in extensive analytical potential. A short application example is made to exemplify the comprehensive analytical potential of NEPS-SC6-ADIAB. This ongoing project deals with nonresponse in survey data. The linked data has a variety of variables collected in both data sources, administratively and through the NEPS survey, allowing for comparative analyses. In this case an idea to compensate nonresponse in income data with administrative data is drawn.

scholarly journals Earnings Losses of Displaced Workers Revisited

2010 ◽  
Vol 100 (1) ◽  
pp. 572-589 ◽  
Author(s):  
Kenneth A Couch ◽  
Dana W Placzek
Keyword(s):  
Administrative Data ◽  
Unemployment Insurance ◽  
Fixed Effects ◽  
Panel Study ◽  
Displaced Workers ◽  
Income Dynamics ◽  
Matching Estimators ◽  
Earnings Losses ◽  
Random Growth ◽  
Mass Layoff

Earnings losses of Connecticut workers affected by mass layoff are calculated using administrative data. Estimated reductions are initially more than 30 percent and six years later, as much as 15 percent. The Connecticut estimates are smaller than comparable ones from Pennsylvania administrative data but similar to those from the Panel Study of Income Dynamics (PSID) and Department of Workforce Services (DWS). Earnings reductions in Connecticut and Pennsylvania are concentrated among Unemployment Insurance recipients. An unusually high proportion of Unemployment Insurance beneficiaries in Pennsylvania explains the larger estimated losses relative to other studies. Fixed-effects, random growth, and matching estimators produced similar earnings loss estimates suggesting each is relatively unbiased in this context.

The Dynamics of Low Income and Persistent Poverty Among U.S. Families

2007 ◽  
pp. 115
Author(s):  
John J. Hisnanick
Keyword(s):  
Low Income ◽  
Family Income ◽  
Descriptive Analysis ◽  
Program Participation ◽  
Income Dynamics ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Variance Model ◽  
Persistent Poverty ◽  
Insight Into

Are those in poverty likely to remain there or can they move out of this situation without help from other sources? Our understanding of those in or near poverty is primarily based upon the analysis of either annual income or the income distribution from cross-sectional survey data. It has been argued in the literature that this type of data can be misleading when faced with questions pertaining to transitions in and out of poverty. Studies of the persistence of poverty should focus on individuals and their families, in conjunction with labor market situations, in order to provide an insight into why the situation continues. Using the Survey of Income and Program Participation (SIPP) 1996 panel, it was possible to investigate low-income dynamics and to model family incomes for the years 1996-1999. This article provides a descriptive analysis that evaluates the lowincome dynamics of families and their exit and re-entry rates into low income. It also investigates family income and poverty experiences based upon a componentsof- variance model that identifies permanent and transitory factors and provides insight into low-income dynamics.

scholarly journals Validating Earnings in the German National Educational Panel Study – Do Interviewers Have an Impact on Measurement Accuracy?

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Manfred Antoni ◽  
Basha Vicari ◽  
Daniel Bela
Keyword(s):  
Survey Data ◽  
Administrative Data ◽  
Measurement Accuracy ◽  
Research Question ◽  
Panel Study ◽  
Item Nonresponse ◽  
Birth Cohorts ◽  
Average Deviation ◽  
Starting Point ◽  
The Impact

ABSTRACTObjectivesWe investigate characteristics of respondents and interviewers influencing the accurateness of reported income by comparing survey data with administrative data. Questions on sensitive topics like respondents' income often produce relatively high rates of item nonresponse or measurement error. In this context several analyses have been done on item nonresponse, but little is known about accuracy of reporting. Existing evidence shows that it is unpleasant for respondents to report very low or very high income. In presence of an interviewer income questions might produce incorrect responses due to social desirability bias. On the other hand side interviewers can create a trustful atmosphere in which respondents give more accurate answers. ApproachUsing linked survey and administrative data we are able to measure the extent of deviation between reported and recorded incomes and explore the influence of respondent and interviewer characteristics on it. The starting point for the linkage is data from the German National Educational Panel Study (NEPS), Starting Cohort 6, which surveys adults from birth cohorts 1944 to 1986. More than 90% of the respondents consented to a linkage of their survey information with administrative data from the German Federal Employment Agency. These longitudinal earnings data are highly reliable as they are based on mandatory notifications of employers to the social security system.We include interviewer and respondent characteristics as well as their interactions into our model to estimate their respective impact on the incidence and size of any bias in reported incomes. This allows us to control for latent interviewer traits that might have influenced the respondent's answering behavior during each interview of a given interviewer. ResultsThe average deviation of reported from administrative earnings is relatively small (less than 10% of median earnings). Descriptive evidence shows only small variation of deviation across subgroups. Most importantly, female respondents show higher report accuracy. Multivariate results hint at a negligible influence of interviewer characteristics. The major predictors for deviation in respondents' characteristics are their sex, their absolute monthly personal income, their educational level and being born abroad. ConclusionAlthough the average measurement accuracy is rather high, there are some differences in deviations by subgroups. The impact of these deviations depends on the research question at hand. Research with a strong focus on the respondent’s earnings, e.g. when using them as a dependent variable, should use the linked data rather than only the NEPS survey data.

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