Abstract
Discouraged and marginally attached workers have received increasing attention from policy makers over the past several years. Through slackness in the labor market, periods of high unemployment should reduce the likelihood of receiving a job offer and thus create more discouraged workers. However, the existing literature generally fails to find evidence of such pro-cyclicality in search intensity. Surprisingly, search appears to be acyclical. We hypothesize the observed acyclicality may be the result of coarse measurement of search intensity in previous studies and the failure to account for changes in individuals’ wealth across the business cycle. In this paper we use daily time use dairies from the American Time Use Survey 2003–2011 to examine the cyclicality of search intensity to explain this apparent contradiction between theory and data. Results indicate that workers do reduce their search in response to deteriorating labor market conditions, but these effects appear to be offset by the positive effects on search that are correlated with declines in household wealth.
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Notes
Calculated using Table A-1 and A-15 from Monthly Employment Situation Report.
In his paper, he uses data from the Current Population Survey (CPS) from 1994 to 2004. However, there are limitations to using the CPS in this regard. The CPS records worker responses regarding the types of search they used. Search intensity is defined somewhat narrowly as the number of search tasks undertaken in efforts to secure employment, such as searching for employment in the newspaper, or interviewing for a job. Moreover, the CPS does not measure the actual time spent on these search activities.
If individual search intensity is non-decreasing in unemployment, u, then aggregate search intensity is increasing in unemployment. The implication is that the matching function, m(u, v) is increasing in u, ceteris paribas. This ignores how changes in wealth, opportunity costs, or other factors many influence the search intensity decision.
An alternative would be to allow the arrival rate, λ, dependent on the duration of individual i’s current unemployment spell at time t. See Eckstein and van den Berg (2007) for a survey of models which incorporate duration. Because of limitations in the ATUS data, however, we eschew this approach.
Job search technology also affects the probability of obtaining an offer conditional on search. The internet has reduced the cost to post and apply for a particular vacancy. The total amount of help-wanted ad space has decreased during the last 10 years as the unemployed and firms substitute away from the relatively more expensive newspaper ads to electronic ones. The economic recovery from the 2001 recession has virtually no increase in the column square inches of ad space. This is likely due to the increase in internet penetration rates from 1992 recession to the 2001 recession. Internet penetration rates increased from 1.7% of Americans with Internet access to 59.8% (World Bank 2012). Both of these features support an increasing efficiency of job search over time but not cyclicality over the business cycle. Before 2001, newspapers ads were negatively correlated with business cycles.
In general, the probability of receiving a job offer is also dependent on idiosyncratic differences across workers. Most specifically, the length of one’s unemployment spell is likely to affect their search intensity, even holding conditions in the labor market constant. In other words, there are non-idiosyncratic as well as idiosyncratic shocks that affect an individual’s search intensity. The natural way to model this would be to include the observed duration of unemployment for each worker. As is discussed later in the paper, however, the data is limited on this account. But even when added to the model, it has no effect on search intensity in our sample. See footnote 11.
ATUS codes for these activities are t050481, t050403, t050404, t050405 and t050499.
Though not shown in Figure 1, average search intensity for all workers follows the same general trends over time.
Unfortunately, using the respondent’s unemployment duration to proxy for idiosyncratic shocks of an individual's arrival rate is not easily done with the data. While the CPS interview records the duration of unemployment, the ATUS survey, which is conducted 2–5 months following the final CPS interview, does not. While we can accurately infer those workers who lost their jobs between the CPS and ATUS interview dates, we have no way of knowing when they lost their job. Given that our data encompasses the first years of the recent recession, a large number of the unemployed in our sample turn out to be exactly these “recently unemployed” workers. This study has the same limitations in this regard as Krueger and Mueller (2010). Nevertheless, when duration is added to the model it is insignificant and does not affect the sizes or significance of any of the other variables. The inference we take from this is that all the important effects proxying the probability of receiving a job offer are captured in the VU ratio.
In a previous version of the paper we also included stock market fluctuations. However, the ATUS data does not include information on stock ownership. In addition, aggregate stock market fluctuations are highly correlated with labor market conditions. For this reason, we estimate a model where we use stock prices as an instrument for the VU ratio.
While our results indicate Steward’s critique is valid in our case, we do report Tobit estimates from various model specifications for completeness (see Appendix, Table 3).
It is important to control for these because each individual is interviewed only on one day. As a result of the survey design, there is tremendous variation in activities depending on the day of the week and whether or not the diary day was a holiday. Likewise, there are also seasonal differences, which is why controlling for month of the year is important.
In fact, the Tobit estimates are slightly more significant in the statistical sense. However, the elasticities are roughly half of the size of those found in OLS (see Table 6 in the Appendix). This is precisely what previous research suggests should happen, as Stewart (2009) shows that when the proportion of censored observations exceeds .80 (as ours does) the bias in Tobit estimates grows to over 50%.
In our model, wealth effects are proxied by maximum weekly UI benefits and regional housing prices. Across all the specifications, UI benefits appear to have a small, but positive effect on search intensity. In fact, the elasticity of search with respect to UI benefits is less than 0.10. However, if we run the model only for those workers who are eligible for UI benefits following Krueger and Mueller (2010), we do get a negative coefficient on UI benefits, though it is not statistically significant.
Note that this holds even though we have controlled for other characteristics such as predicted wage, marital status, the presence of children, and other demographics that would be positively correlated with homeownership.
The fact that we are able to identify a significant wealth effect through housing prices is especially noteworthy given the relatively crude proxy we have for changes in housing wealth. All we know is whether a worker owns a home or not. Neither the CPS nor the ATUS provides information regarding the amount of equity individuals have in their home or other the existence of other assets. Presumably, better information about each individual’s wealth would result in more precision in these estimates. Nevertheless, the results appear to support the hypothesis that wealth affects search intensity. Further interpretation of these wealth effects are discussed in the following sections.
For example, a 30% drop in housing values results in a 0.30*32/53.04 = 49.26% increase in search time. Using the weighted average of 32 min a day, this is 16 additional minutes per day, or 111 min per week of search time.
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DeLoach, S.B., Kurt, M. Discouraging Workers: Estimating the Impacts of Macroeconomic Shocks on the Search Intensity of the Unemployed. J Labor Res 34, 433–454 (2013). https://doi.org/10.1007/s12122-013-9166-0
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DOI: https://doi.org/10.1007/s12122-013-9166-0