I examine the learning process that economic agents use to update their expectation of an uncertain and infrequently observed event. I use a new nation-wide panel dataset of large regional floods and flood insurance policies to show that insurance take-up spikes the year after a flood and then steadily declines to baseline. Residents in nonflooded communities in the same television media market increase take-up at one-third the rate of flooded communities. I find that insurance take-up is most consistent with a Bayesian learning model that allows for forgetting or incomplete information about past floods. (JEL D12, D83, D84, G22, Q54)

https://pubs.aeaweb.org/doi/pdfplus/10.1257/app.6.3.206

Learning about an Infrequent Event: Evidence from Flood Insurance Take-Up in the United States

This paper examines the long-term impacts of early childhood exposure to air pollution on adult outcomes using US administrative data. We exploit changes in air pollution driven by the 1970 Clean Air Act to analyze the difference in outcomes between cohorts born in counties before and after large improvements in air pollution relative to those same cohorts born in counties that had no improvements. We find a significant relationship between pollution exposure in the year of birth and later-life outcomes. A higher pollution level in the year of birth is associated with lower labor force participation and lower earnings at age 30.

/pdf/every-breath-you-take-every-dollar-you-ll-make-the-long-term-t6y9nmq7j8.pdf

Every Breath You Take—Every Dollar You’ll Make: The Long-Term Consequences of the Clean Air Act of 1970

This article uses linked worker-firm data in the United States to estimate the transitional costs associated with reallocating workers from newly regulated industries to other sectors of the economy in the context of new environmental regulations. The focus on workers rather than industries as the unit of analysis allows me to examine previously unobserved economic outcomes such as nonemployment and long-run earnings losses from job transitions, both of which are critical to understanding the reallocative costs associated with these policies. Using plant-level panel variation induced by the 1990 Clean Air Act Amendments (CAAA), I find that the reallocative costs of environmental policy are significant. Workers in newly regulated plants experienced, in aggregate, more than $5.4 billion in forgone earnings for the years after the change in policy. Most of these costs are driven by nonemployment and lower earnings in future employment, highlighting the importance of longitudinal data for characterizing the costs and consequences of labor market adjustment. Relative to the estimated benefits of the 1990 CAAA, these one-time transitional costs are

/pdf/the-transitional-costs-of-sectoral-reallocation-evidence-4ylvqx7ndj.pdf

The Transitional Costs of Sectoral Reallocation: Evidence From the Clean Air Act and the Workforce*

This paper analyzes the role of establishments in the upward trend in dispersion of earnings that has become a central topic in economic analysis and policy debate. It decomposes changes in the variance of log earnings among individuals into the part due to changes in earnings among establishments and the part due to changes in earnings within establishments. The main finding is that much of the 1970s–2010s increase in earnings inequality results from increased dispersion of the earnings among the establishments where individuals work. Our results direct attention to the role of establishment-level pay setting and economic adjustments in earnings inequality.

/pdf/it-s-where-you-work-increases-in-the-dispersion-of-earnings-5bsq8rny8d.pdf

It’s Where You Work: Increases in the Dispersion of Earnings across Establishments and Individuals in the United States.

U.S. labor markets became much less fluid in recent decades. Job reallocation rates fell more than a quarter after 1990, and worker reallocation rates fell more than a quarter after 2000. The declines cut across states, industries and demographic groups defined by age, gender and education. Younger and less educated workers had especially large declines, as did the retail sector. A shift to older businesses, an aging workforce, and policy developments that suppress reallocation all contributed to fluidity declines. Drawing on previous work, we argue that reduced fluidity has harmful consequences for productivity, real wages and employment. To quantify the effects of reallocation intensity on employment, we estimate regression models that exploit low frequency variation over time within states, using state-level changes in population composition and other variables as instruments. We find large positive effects of worker reallocation rates on employment, especially for young workers and the less educated. Similar estimates obtain when dropping data from the Great Recession and its aftermath. These results suggest the U.S. economy faced serious impediments to high employment rates well before the Great Recession, and that sustained high employment is unlikely to return without restoring labor market fluidity.

/pdf/labor-market-fluidity-and-economic-performance-42ptghh7q6.pdf

Labor Market Fluidity and Economic Performance

The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau, with the support of several national research agencies, has built a set of infrastructure files using administrative data provided by state agencies, enhanced with information from other administrative data sources, demographic and economic (business) surveys and censuses. The LEHD Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their interaction in the U.S. economy. Beginning in 2003 and building on this infrastructure, the Census Bureau has published the Quarterly Workforce Indicators (QWI), a new collection of data series that offers unprecedented detail on the local dynamics of labor markets. Despite the fine detail, confidentiality is maintained due to the application of state-of-the-art confidentiality protection methods. This article describes how the input files are compiled and combined to create the infrastructure files. We describe the multiple imputation methods used to impute in missing data and the statistical matching techniques used to combine and edit data when a direct identifier match requires improvement. Both of these innovations are crucial to the success of the final product. Finally, we pay special attention to the details of the confidentiality protection system used to protect the identity and micro data values of the underlying entities used to form the published estimates. We provide a brief description of public-use and restricted-access data files with pointers to further documentation for researchers interested in using these data.

/pdf/the-lehd-infrastructure-files-and-the-creation-of-the-4r1ufdcm4k.pdf

The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators

The QuarterlyWorkforce Indicators are new estimates developed by the Census Bureau’s Longitudinal Employer-Household Dynamics Program as a part of its Local Employment Dynamics partnership with 37 state Labor Market Information offices. These data provide detailed quarterly statistics on employment, accessions, layoffs, hires, separations, full-quarter employment (and related flows), job creations, job destructions, and earnings (for flow and stock categories of workers). The data are released for NAICS industries (and 4-digit SICs) at the county, workforce investment board, and metropolitan area levels of geography. The confidential microdata - unemployment insurance wage records, ES-202 establishment employment, and Title 13 demographic and economic information - are protected using a permanent multiplicative noise distortion factor. This factor distorts all input sums, counts, differences and ratios. The released statistics are analytically valid - measures are unbiased and time series properties are preserved. The confidentiality protection is manifested in the release of some statistics that are flagged as "significantly distorted to preserve confidentiality." These statistics differ from the undistorted statistics by a significant proportion. Even for the significantly distorted statistics, the data remain analytically valid for time series properties. The released data can be aggregated; however, published aggregates are less distorted than custom postrelease aggregates. In addition to the multiplicative noise distortion, confidentiality protection is provided by the estimation process for the QWIs, which multiply imputes all missing data (including missing establishment, given UI account, in the UI wage record data) and dynamically re-weights the establishment data to provide state-level comparability with the BLS’s Quarterly Census of Employment and Wages.

Confidentiality Protection in the Census Bureau Quarterly Workforce Indicators

The Quarterly Workforce Indicators are new estimates developed by the Census Bureau’s Longitudinal Employer-Household Dynamics Program as a part of its Local Employment Dynamics partnership with 37 state Labor Market Information offices. These data provide detailed quarterly statistics on employment, accessions, layoffs, hires, separations, full-quarter employment (and related flows), job creations, job destructions, and earnings (for flow and stock categories of workers). The data are released for NAICS industries (and 4-digit SICs) at the county, workforce investment board, and metropolitan area levels of geography. The confidential microdata – unemployment insurance wage records, ES-202 establishment employment, and Title 13 demographic and economic information – are protected using a permanent multiplicative noise distortion factor. This factor distorts all input sums, counts, differences and ratios. The released statistics are analytically valid – measures are unbiased and time series properties are preserved. The confidentiality protection is manifested in the release of some statistics that are flagged as ”significantly distorted to preserve confidentiality.” These statistics differ from the undistorted statistics by a significant proportion. Even for the significantly distorted statistics, the data remain analytically valid for time series properties. The released data can be aggregated; however, published aggregates are less distorted than custom postrelease aggregates. In addition to the multiplicative noise distortion, confidentiality protection is provided by the estimation process for the QWIs, which multiply imputes all missing data (including missing establishment, given UI account, in the UI wage record data) and dynamically re-weights the establishment data to provide state-level comparability with the BLS’s Quarterly Census of Employment and Wages.

http://www2.vrdc.cornell.edu/news/3/20051205-Abowd-Stephens-Vilhuber-2005.pdf

Confidentiality Protection in the Census Bureau’s Quarterly Workforce Indicators

The Longitudinal Employer-Household Dynamics (LEHD) Program at the U.S. Census Bureau, with the support of several national research agencies, has built a set of infrastructure files using administrative data provided by state agencies, enhanced with information from other administrative data sources, demographic and economic (business) surveys and censuses. The LEHD Infrastructure Files provide a detailed and comprehensive picture of workers, employers, and their interaction in the U.S. economy. Beginning in 2003 and building on this infrastructure, the Census Bureau has published the Quarterly Workforce Indicators (QWI), a new collection of data series that offers unprecedented detail on the local dynamics of labor markets. Despite the fine detail, confidentiality is maintained due to the application of state-of-the-art confidentiality protection methods. This article describes how the input files are compiled and combined to create the infrastructure files. We describe the multiple imputation methods used to impute in missing data and the statistical matching techniques used to combine and edit data when a direct identifier match requires improvement. Both of these innovations are crucial to the success of the final product. Finally, we pay special attention to the details of the confidentiality protection system used to protect the identity and micro data values of the underlying entities used to form the published estimates. We provide a brief description of public-use and restricted-access data files with pointers to further documentation for researchers interested in using these data.(This abstract was borrowed from another version of this item.)

The Census Bureau's Quarterly Workforce Indicators (QWI) provide detailed quarterly statistics on employment measures such as worker and job flows, tabulated by worker characteristics in various combinations. The data are released for several levels of NAICS industries and geography, the lowest aggregation of the latter being counties. Disclosure avoidance methods are required to protect the information about individuals and businesses that contribute to the underlying data. The QWI disclosure avoidance mechanism we describe here relies heavily on the use of noise infusion through a permanent multiplicative noise distortion factor, used for magnitudes, counts, differences and ratios. There is minimal suppression and no complementary suppressions. To our knowledge, the release in 2003 of the QWI was the first large-scale use of noise infusion in any official statistical product. We show that the released statistics are analytically valid along several critical dimensions { measures are unbiased and time series properties are preserved. We provide an analysis of the degree to which confidentiality is protected. Furthermore, we show how the judicious use of synthetic data, injected into the tabulation process, can completely eliminate suppressions, maintain analytical validity, and increase the protection of the underlying confidential data.

Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series

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The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators

Confidentiality Protection in the Census Bureau Quarterly Workforce Indicators

Confidentiality Protection in the Census Bureau’s Quarterly Workforce Indicators

The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators

Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series