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Employment Reallocation and Unemployment Revisited: A Quantile Regression Approach

This research explores how intersectoral labor reallocation influences unemployment fluctuations. It investigates how shocks impact job reallocation across sectors and the magnitudes of (un)employment changes. By utilizing quantile regression, the study reveals asymmetric relationships and significant findings on the effects of unemployment on various sectors. This paper fills a gap by analyzing the time-consuming processes of job searching, retraining, and relocating in the context of unemployment dynamics.

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Employment Reallocation and Unemployment Revisited: A Quantile Regression Approach

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  1. Employment Reallocation and Unemployment Revisited: A Quantile Regression Approach Theodore Panagiotidis Department of Economics, University of Macedonia, Greece and Rimini Centre for Economic Analysis, Italy. Gianluigi Pelloni Department of Economics, University of Bologna, Italy; Department of Economics, Wilfrid Laurier University, Canada; and Rimini Centre for Economic Analysis, Italy.

  2. Introduction • Intersectoral labour reallocation as a triggering force of aggregate unemployment fluctuations. • An aggregate shock → firms to lay off workers temporarily → changes in aggregate employment and unemployment. • Sector-specific shocks, affecting the allocation of demand across sectors, → intersectoral movements of workers → the time-consuming processes of searching, retraining and relocating → could also alter the levels of (un) employment (Lilien, 1982a).

  3. Introduction • Up to 1980’s aggregate shocks seen the driving force of unemployment cycles. • Lilien(1982a) Sect. Shifts Hypothesis. : reallocation shocks →macroeconomic effects. • Changes in demand composition operate as the driving force of unemployment fluctuations. Idiosyncratic shocks bring flows of job reallocation from declining sectors to expanding ones

  4. Literature • Lilien (1982a): reduced form equation with dispersion index: σt = [j (N j,t /N t) ( Δ ln N j,t - Δln Nt)2]1/2 • Criticism: Lilien (1982b, WP); Abraham and Katz (1986). • Literature Review: Gallipoli & Pelloni (2008)

  5. Methodology • Most of the existing literature focuses on the conditional mean response (LRM). The latter assumes symmetry and linearity. • Literature approached the issues by employing nonlinear models both at univeriate and multivariate level. • This paper is the first attempt to investigate this issue by quantile regression (QR): assymetry

  6. QR (Koenker and Basset, 1978) is a tool that allows us to model distributions. • Starting point for QRM → conditional quantile function (CDF). • The CDF of Yi at quantile τgiven a vector of covariates Xi is given by:

  7. Where is the distribution function of Yi at y, conditional on Xi. When τ=0.10 describes the lower decile of Yi given Xi. • Reduced form of the estimated model: • ut = ln(Ut/(1- Ut)) logistic transfromation

  8. What happens if we replace the logistic transformation with unemployment rate?

  9. Conclusions • Assymetry in the relationship revealed. • The higher the unemploeyment the more reallocation is taking place. • Deficit: upward sloping, negative and significant • Money: singificant at the 12 month lag only when unemployment is high. • Energy: not singificant

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