This paper demonstrates the difficulty of forecasting labor force participation (LFP) rates by showing that a random walk does just as well as select sophisticated econometric models in predicting short-term aggregate LFP. Most efforts to improve forecasts of LFP focus on fine-tuning predictions of determinants (i.e., demographics and labor market conditions). However, we show that even perfect knowledge of future demographic trends and labor market conditions is not enough to overcome the additional difficulty posed by changes in behavior over time. Behavior in this paper refers to the way in which demographics and labor market conditions impact labor supply decisions (i.e., parameter coefficients).
The authors investigate the financial disincentives to career advancement caused by benefits cliffs, which occur when earnings gains are offset by the loss of means-tested public benefits.
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Means-tested public programs and progressive tax incentives offer critical financial support to millions of low-income families and individuals. However, small changes in employment income can lead to an abrupt or gradual loss of benefits, creating a possible disincentive to enroll in training and obtain a higher-paying occupation. To help clients advance in their careers, caseworkers and other employment service providers must consider a complex set of benefits eligibility rules, support services to address barriers to employment, and labor market information to guide training and employment decisions.
In this paper we develop a novel method to project location specific life cycle wages for all occupations listed in the Bureau of Labor Statistics Occupational Outlook Handbook. Our method builds on the commonly used Mincer equation and improves it by providing a more nuanced relationship between years of experience and wages while also incorporating occupation and location specific factors. Our method consists of two steps. In the first step, we use individual level data from the Current Population Survey (CPS) to estimate the average number of years of experience associated with each percentile of the wage distribution. In the second step, we map this estimated average years of experience to the wage level percentiles reported in the Occupational Employment and Wage Statistics (OEWS) data for each occupation and area. Finally, we develop a model capable of projecting the trajectory of wages across all possible years of experience for each occupation in the OEWS data.
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