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Employment Reallocation and Unemployment Revisited: A Quantile Regression Approach

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;

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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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