Labor Reallocation: Panel Evidence from U.S. States

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Dimitrios Bakas ; Theodore Panagiotidis ; Gianluigi Pelloni (2013)
  • Publisher: The Rimini Centre for Economic Analysis
  • Subject: Unemployment, Sectoral Shifts, Employment Fluctuations, Dynamic Panel Data, Parameter Heterogeneity, Cross-Sectional Dependence
    • jel: jel:E32 | jel:C33 | jel:R23 | jel:E24 | jel:J21

This paper re-examines Lilien’s sectoral shifts hypothesis for U.S. unemployment. We employ a monthly panel that spans from 1990:01 to 2011:12 for 48 U.S. states. Panel unit root tests that allow for cross-sectional dependence reveal the stationarity of unemployment. Within a framework that takes into account dynamics, parameter heterogeneity and cross-sectional dependence in the panel, we show that sectoral reallocation is significant not only at the aggregate level but also at the state level. The magnitude and the statistical significance of the latter as measured by Lilien’s index increases when both heterogeneity and cross-sectional dependence are taken into account.
  • References (47)
    47 references, page 1 of 5

    1. For an extensive survey see Gallipoli and Pelloni (2008).

    2. In the wake of Long and Plosser (1987) and Blanchard and Katz (1992) much attention in this field has been paid to multivariate settings such as the VAR of Campbell and Kuttner (1996) or VAR-GARCH-M of Pelloni and Polasek (1999; 2003) or the semiparametric spatial auto-regressive set up of Basile et al. (2012).

    4. There is wide variation across papers in the choice of variables included in z˜t . Here, the vector of aggregate variables z˜t is exactly the same as the vector zt . For a full discussion of the issue see Gallipoli and Pelloni (2008).

    5. In older days this decomposition would have been introduced to capture the money surprises of Lucas misperception model (Lucas, 1972; 1973).

    6. Caporale et al. (1996) follow a similar approach.

    7. Chudik and Pesaran (2013) provide evidence on the estimation of heterogeneous panel data models with lagged dependent variable and show that the CCEMG estimator continues to be valid asymptotically when dealing with dynamics.

    8. Bond and Eberhardt (2009) provide evidence that both the CCEMG estimators and the AMG approach perform very well and with similar results in recent Monte Carlo studies.

    9. We exclude from our analysis the no-adjoining states of Alaska and Hawaii.

    10. All sectoral series were seasonally adjusted using Eviews Census X12 program.

    Abraham, K. G. and L. F. Katz (1986), “Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances?” Journal of Political Economy, 94, 507-522. (p. 3, 8)

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