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Who benefits from job placement services? A two-sided analysis

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Abstract

In light of additional information market agents would achieve better outcomes, for example, a lower ask price for the buyer and a higher offer price for the seller. I examine this notion in a labor market, where employers and employees do not possess perfect information about wages, and address the question of who benefits from the information provided by job placement services? The empirical strategy considers the two-sided nature of the labor market. Estimates of employee and employer incomplete information are contrasted between users and non-users of placement services provided by Job Corps, America’s largest and most important job training program for youths. Findings suggest that employees that use placement services don’t have more information about better offer wages, relative to non-users. Interestingly, firms that employed users of placement services are better informed about reservation wages relative to firms that employed non-users.

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Notes

  1. The labor market search and matching literature is extremely vast. For a review, see Mortensen and Pissarides (1999) and Petrongolo and Pissarides (2001).

  2. An alternative branch of the search equilibrium literature is based on the so-called matching function, which is employed in a myriad of modern macroeconomics studies. This literature introduces two-sided frictions in the process of matching trading partners, where agents on both sides of a market make investments in overcoming them (for a review see Petrongolo and Pissarides 2001). Although my study is empirical and micro in nature, the idea of less than complete information in both sides of the market is taken into account.

  3. In a typical labor market, the observed equilibrium wage is derived after equating the quantity of labor demand and supply. A formal derivation yielding the observed equilibrium as depicted in (3) can be found in Polachek and Yoon (1987). Consistently, in my simplified illustration above, I add u and subtract w from the equality W p−u = W o = W r + w, which yields W p−w = W o + u − w = W r + u. The expression W o + u − w represents a full information labor market price, which is equal to the function W(X s , X b ) + v. It follows that under incomplete information then W o = W(X s , X b ) + v − u + w. In a reduced form version, models based on bargaining and search (see Mortensen 1986) would yield a similar wage specification as in (3) after allowing for interactions between supply and demand. The latter, for example, was used by Kumbhakar and Parmeter (2009).

  4. Hofler and Polachek (1985) and Polachek and Robst (1998) estimated a model of employee ignorance based on Eq. (4) with w i  = 0 (i.e., employers have perfect information) by using the conventional stochastic frontier analysis in Aigner et al. (1977). Polachek and Robst (1998) further extended their one-sided analysis by using the technique in Jondrow et al. (1982), to estimate individual specific measures of employee incomplete information.

  5. Note that the illustration in the preceding section suggests that u and w are not independent on each other, however, identification is only possible if one employs this assumption. As pointed out by one anonymous referee, in general this is an inherent identification problem of the 2TSF formulation.

  6. An earlier version of the current paper considered a two-stage residual inclusion technique, which is a consistent instrumental variable-based approach for correcting endogeneity in non-linear models estimated via non-linear least squares (Terza et al. 2008).

  7. Other approaches for dealing with sample selection in a stochastic frontier model includes Kumbhakar et al. (2009) and Lai (2015). The former approach is similar to the one proposed by Greene (2010), the difference is that the selection equation is also affected by inefficiency. Lai (2015) extends the model in Greene (2010) by replacing the half-normal distribution for a truncated normal and also considers endogenous switching. While these could be potentially extended and employed in the present context, extending the model in Greene (2010) for the 2TSF is the more straightforward approach. For a recent review of sample selection in stochastic frontier models see Parmeter and Kumbhakar (2014).

  8. Greene (2010) used the same type of test in the context of a single tiered stochastic frontier model.

  9. This question is not to be confused with questions about the overall effect of program participation on the probability of employment, which has been reported to be positive, with a magnitude of 4 percentage points (Schochet et al. 2001).

  10. Eligibility is based on several criteria, including age, legal US residency, economically disadvantage status, living in a disruptive environment, and in need of additional education or training, among others (see Schochet et al. 2001). From a randomly selected research sample of 15,386 first time eligible applicants, approximately 61 percent were assigned to the treatment group (9409) and 39 percent to the control group (5977).

  11. Another option is including control group individuals categorized as non-users, however, one has to be cautious since it is not possible to learn from the data whether they used job placement services outside of JC.

  12. Ideally, this two-sided analysis would benefit from including actual local economic condition indicators, which are important demand side determinants (Hoynes 2000). However, such measures are not available.

  13. The negative coefficient on education of users becomes positive in regression without the square term for education, however it would still be insignificant.

  14. Most of the estimated parameters in the deterministic portion of the frontier are qualitatively similar to the OLS results reported in Table 2. It is worth noting that one striking difference, relative to the regression results, is that the estimated coefficient on education is now positive for users, however it remained statistically insignificant.

  15. Confidence intervals for the difference were calculated with a bias-corrected bootstrap technique (see Efron 1982; Kim et al. 2007). For an alternative, where confidence intervals are obtained based on the estimated conditional distributions in (7) and (8), see Horrace and Schmidt (1996).

  16. Previously reported levels of employees’ information range from 70 to 85 percent (Polachek and Yoon 1987, 1996; Groot and Oosterbeek 1994; Sharif and Dar 2007; Kumbhakar and Parmeter 2009). In general, the populations studied in these papers are comprised of more educated, older and experienced workers, hence relatively more informed than the population of JC participants.

  17. Other recent studies within this literature finding similar results include: Autor and Houseman (2010) and Autor et al. (2012). Both analyzed the effect of Detroit’s welfare-to-work job placement on earnings and employment, and concluded that job placements with “direct-hire” employers raise earnings due to a single and continuous job spell. They also find evidence indicating that “temporary-help” job placements do not improve earnings.

  18. Other studies not focusing on labor markets report similar mean effects of buyers’ incomplete information on prices, for example, Kumbhakar and Parmeter (2010) report that real estate buyers pay, on average, 30 percent more relative to a perfectly informed buyer.

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Acknowledgements

I would like to thank useful comments by Alfonso Flores-Lagunes, Solomon Polachek, Subal Kumbhakar, and the Binghamton University’s Labor Group. In addition, I thank the conference participants at the 2013 Midwest Economics Association meetings.

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  1. Department of Economics, Illinois State University, Normal, IL, 61790-4200, USA

    German Blanco

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Blanco, G. Who benefits from job placement services? A two-sided analysis. J Prod Anal 47, 33–47 (2017). https://doi.org/10.1007/s11123-016-0489-8

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