Estimating labor force joiners and leavers using a heterogeneity augmented two-tier stochastic frontier
Tirthatanmoy Das and
Journal of Econometrics, 2017, vol. 199, issue 2, 156-172
In a seminal paper, Basmann (1985) introduced a serial correlation structure based on an intertemporal adjustment mechanism. Basmann’s 1985 paper of course was built on his previous pioneering work on estimation and identifiability in structural equations leading to 2SLS (Basmann, 1957, 1960). In this paper, we follow a similar path. We derive a non-standard unit root serial correlation formulation for intertemporal adjustments in the labor force participation rate. This leads to a tractable three-error component model, which in contrast to other models embeds heterogeneity into the error structure. Unlike in the typical i.i.d. three-error component two-tier stochastic frontier model, our equation’s error components are independent but not identically distributed. This leads to a complex nonlinear likelihood function requiring identification through a two-step estimation procedure, which we estimate using Current Population Survey (CPS) data. By transforming the basic equation linking labor force participation to the working age population, this paper devises a new method which can be used to identify labor market joiners and leavers. The method’s advantage is its parsimonious data requirements, especially alleviating the need for survey based longitudinal data.
Keywords: Two-tier stochastic frontier; Identification; Labor force dynamics (search for similar items in EconPapers)
JEL-codes: C23 C51 J21 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Estimating Labor Force Joiners and Leavers Using a Heterogeneity Augmented Two-Tier Stochastic Frontier (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:199:y:2017:i:2:p:156-172
Access Statistics for this article
Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson
More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().