1. Sectoral Shifts or Aggregate Shocks? A New Test of Sectoral Shifts Hypothesis Yanggyu Byun Korea Economic Research Institute Hae-shin Hwang Texas A&M University

2. Sectoral Shifts Hypothesis Two components of unemployment - unemployment caused by aggregate shocks - unemployment caused by aggregate shocks - unemployment caused by sectoral shocks - unemployment caused by sectoral shocks Hypothesis: sectoral shifts of labor demand have - a significant effect on aggregate unemployment rate - a significant effect on aggregate unemployment rate - and frictional U-rate (NRU) fluctuates significantly - and frictional U-rate (NRU) fluctuates significantly

3. Empirical Tests Past empirical studies –Contradicting results: depend on the model specification –Two types of model Lilien type - supports the hypothesisLilien type - supports the hypothesis Abraham & Katz type - rejects the hypothesisAbraham & Katz type - rejects the hypothesis

4. Empirical Models and Tests Major empirical issues –How to separate the effects of aggregate shocks and sectoral shocks - most controversial issue in past studies –How to measure the size of layoffs caused by sectoral shocks

5. Measure of Average Layoff Rate Employment growth of sector j : sector specific shocks : sector specific shocks : effect of aggregate shocks : effect of aggregate shocks additive and common additive and common Layoffs caused by a large negative sectoral shocks – for which

6. Measure of Average Layoff Rate Average Layoff Rate This depends on the properties of distribution function of sectoral shocks - How do we capture them?

7. Measure of Average Layoff Rate (ALR) Lilien proposed the dispersion of the distribution –Lilien's example of symmetric MPS of f 1 to f 2 –f 1 : both sectors grow at 2%, ALR=0% –f 2 : -4% and +8%, average=2%, ALR=2%, higher unemployment rate

8. Measure of Average Layoff Rate Dispersion & Skewness Dispersion σ may be sufficient for symmetric location-scale distributions. But it may not be sufficient for asymmetric distribution.Dispersion σ may be sufficient for symmetric location-scale distributions. But it may not be sufficient for asymmetric distribution. Example: Mean-Variance Preserving TransformationExample: Mean-Variance Preserving Transformation

9. Effects of Skewness on Approximation of L

10. Three-equation empirical models Purging equation → estimate sectoral shifts variables fromPurging equation → estimate sectoral shifts variables from –most controversial specification –AS t include monetary variables (anticipated & unanticipated), time trend, and 'unobservable' aggregate non-monetary factors (AK) Monetary equation → estimate unanticipated (DMR) and anticipated (DMF) monetary factorsMonetary equation → estimate unanticipated (DMR) and anticipated (DMF) monetary factors Unemployment rate equation → test hypothesis and compute NRUUnemployment rate equation → test hypothesis and compute NRU

11. Purging Equation Lilien TypeLilien Type Abraham-Katz typeAbraham-Katz type

12. Purging Equation Lilien TypeLilien Type Abraham-Katz typeAbraham-Katz type

13. Purging Equation Estimation of dispersion and skewnessEstimation of dispersion and skewness –Lilien Type: from –Abraham-Katz type: from Theoretically, estimates of sectoral shocks are more compatible with the purpose than the innovation termsTheoretically, estimates of sectoral shocks are more compatible with the purpose than the innovation terms –because we wish to capture the relationship between average layoff rate (ALR) and distributional properties of sectoral shocks

14. Monetary Equations DMF t =predicted value of DM t DMF t =predicted value of DM t DMR t =residuals DMR t =residuals

15. Unemployment Rates Equation Lilien - ARDL model; AK - DL model with AR(1) error termLilien - ARDL model; AK - DL model with AR(1) error term Different number of lags (4 vs 8) for σ and skDifferent number of lags (4 vs 8) for σ and sk Coefficient of time trend is predetermined in AK model from a linear detrendingCoefficient of time trend is predetermined in AK model from a linear detrending

16. Estimation of in AK Model AK's purging equationAK's purging equation AK's estimator ofAK's estimator of –Estimate the purging equation by OLS without – Let be the OLS residuals. AK's estimator is

17. Estimation of in AK Model Alternative EstimatorAlternative Estimator The estimator of g is the first principal component of the least squares residual matrixThe estimator of g is the first principal component of the least squares residual matrix The estimator isThe estimator is

18. Table 2. Estimates and Tests of Sectoral Shifts (1955Q1 – 2011Q1) ModelVariable sum of coefs SDp-valuejointp-value Lilien σ  only 0.2570.1050.015n.a. σ  sk  0.2060.1040.050 0.000 -0.3100.0870.000 AK (g ak ) σ u only 0.8580.8250.300n.a. σ u sk u 0.6750.8030.402 0.002 -2.2410.6710.001 σ u σ  and sk  in Lilien and σ u and sk u in AK

19. AK Model (σ u, sk u ) vs (σ  sk   ModelVariable sum of coefs SDp-valuejointp-value AK (g ak ) σ u only 0.8580.8250.300n.a. σ u sk u 0.6750.8030.402 0.002 -2.2410.6710.001 AK (g ak ) σ  only 1.7000.7450.024n.a. σ  sk  1.4010.6970.046 0.000 -1.5020.3680.000

20. Comparison of Lilien and AK σ  and sk  in both models p-values are very similar p-values are very similar ModelVariable sum of coefs SDp-valuejointp-value Lilien σ  only 0.2570.1050.015n.a. σ  sk  0.2060.1040.050 0.000 -0.3100.0870.000 AK (g ak ) σ  only 1.7000.7450.024n.a. σ  sk  1.4010.6970.046 0.000 -1.5020.3680.000

21. Table 3. Various Results of AK(g ak ) Model p-values of the hypothesis tests 1955-1982: AK's sample period, 1955-2003: no adjustment of data DMF is not included in AK's original purging equation Sample Period DMF estimate from  estimate from u σ & sk σ only σ & sk σ only 1955Q1- 2011Q1 included 0.0000.0240.0020.300 excluded0.0000.0050.0080.037 1955Q1- 2003Q1 included0.0000.0060.0120.176 excluded0.0000.0040.0150.038 1955Q1- 1982Q1 included0.0000.1370.0130.250 excluded0.0000.0580.0260.092

23. Unemployment Rates Equation

27. Conclusion Hypothesis: sectoral shifts of labor demand causeHypothesis: sectoral shifts of labor demand cause –layoffs → job Search → frictional U (NRU) –a significant effect on aggregate unemployment rate –Natural (frictional) unemplyment rate fluctuates significantly Macro-policy implication of the hypothesis:Macro-policy implication of the hypothesis: –if a large portion of the UR is frictional rate, aggregate demand management policy may be ineffective.

28. Conclusion Empirical TestsEmpirical Tests –conflicting results –Lilien type models support the hypothesis and Abraham-Katz type models reject it. Past studies used the cross-sectional dispersion of net employment growth rates as the proxy for sectoral shifts (size of layoffs).Past studies used the cross-sectional dispersion of net employment growth rates as the proxy for sectoral shifts (size of layoffs). But, the skewness plays an important role.But, the skewness plays an important role.

29. Conclusion When both dispersion and skewness of the distribution of sectoral shifts are usedWhen both dispersion and skewness of the distribution of sectoral shifts are used –the sectoral shifts hypothesis is supported strongly in both Lilien type and Abraham-Katz type models –supports are robust to sample period and other model differences

30. Conclusion Estimates of NRUEstimates of NRU –fluctuates significantly in Lilien type model –almost flat in AK type models The difference in the estimates of NRUThe difference in the estimates of NRU –is not due to the difference in the estimates of dispersion or/and skewness –is due to the difference in the specification of UR equation –disappears almost when the same structure of UR equation is used.