Evolution of Behavior, Uncertainty, and the Difficulty of Predicting Labor Force Participation

Abstract

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).

Millennials: Maligned or miscreants?

“…For both Boomers and Millennials at the same age (overall and for men and women separately), their labor supply responds similarly to changes in the economic environment. A one percentage point increase in the unemployment relates to about a 0.003 percentage point decrease in usual weekly hours (this estimated elasticity is consistent with others' estimates in the literature, e.g., see Higgins et al, 2019). Across the generations, women's labor supply is more sensitive to changes in the economic environment than men's, reflecting the greater likelihood that women are “added workers” during economic downturns (Guner et al, 2021).…”

“…For both Boomers and Millennials at the same age (overall and for men and women separately), their labor supply responds similarly to changes in the economic environment. A one percentage point increase in the unemployment relates to about a 0.003 percentage point decrease in usual weekly hours (this estimated elasticity is consistent with others' estimates in the literature, e.g., see Higgins et al, 2019). Across the generations, women's labor supply is 5 Estimated wage and nonlabor income elasticities for different measures of intensive margin labor supply (log vs. level, weekly vs. annual), different empirical estimation (mean vs. median), and OLS versus IV specification are found in Table A3, showing similar comparisons across generations to those seen in Figure 3.…”

“…The importance of accounting for these differences in behavior across cohorts in forecasting labor supply has been well-documented in the literature (e.g., see S. Aaronson et al, 2006;D. Aaronson & Brave, 2013;Higgins et al, 2019). This is all well and good for an understanding of how Boomers and Millennials differ from one another, but what is the implication of this for policy considerations?…”

“…However, if Millennials had Boomer characteristics, they would work less than we now observe. The importance of accounting for these differences in behavior across cohorts in forecasting labor supply has been well‐documented in the literature (e.g., see S. Aaronson et al, 2006; D. Aaronson & Brave, 2013; Higgins et al, 2019).…”

Labor Force Participation Rate and Expected Length of Retirement 2001-2047: A Case Study in Malaysia

“…To forecast LFPR, it is essential to consider the challenges and complexities associated with predicting such behavior. Previous studies have highlighted the difficulty of accurately forecasting LFP rates, with some demonstrating that even sophisticated econometric models perform similarly to random walk models in short-term predictions [40] Furthermore, the evolution of labor supply behavior, particularly among married women, has been studied extensively, revealing significant changes in participation elasticities over time [41] However, there is a scarcity of stochastic forecasts for LFPR, with existing studies primarily relying on time series modeling, indicating a gap in the literature for more advanced forecasting methods [42]. Moreover, the use of credible regression approaches with random coefficients and Kou-Modified Lee-Carter model, has been proposed for modeling and forecasting mortality dynamics, suggesting the potential applicability of similar methods in forecasting LFPR [43].…”