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Monetary shocks and job flows: evidence from disaggregated data

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This paper uses a structural near-VAR to examine the effect of monetary shocks on industry-level job creation and job destruction. I find asymmetry in the job flows’ responses to a positive monetary shock for disaggregated industries in the manufacturing, mining and services sectors. These findings indicate that monetary shocks trigger changes in job reallocation and point that monetary policy has important allocative effects. Yet, a test for the absence of job reallocation reveals that monetary shocks have a significant effect on job reallocation for a limited number of industries within these sectors. Moreover, this effect becomes largely insignificant after accounting for data mining.

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  1. The aggregate effects can be attributed to the traditional interest rate channel, where a reduction in interest rate will increase aggregate demand, whereas the allocative effects mainly refer to the mismatch between the actual and desired allocation of labor and capital.

  2. see Davis and Haltiwanger (2001) and Herrera and Karaki (2015) for a discussion on the effect of oil price shocks on job flows and how to attribute the responses of job flows to the transmission mechanism of oil price shocks.

  3. See Jackman and Sutton (1982) and Garibaldi (1997).

  4. Note that this test is inspired by the impulse response function-based tests developed by Kilian and Vigfusson (2011) and implemented by Herrera et al. (2011, 2015) and Alsalman and Herrera (2015).

  5. Note that quarterly sectoral job creation and job destruction rates are computed based on the changes in employed workers in the middle month of each quarter. Also note that I avoid interpolating quarterly data to monthly because the data-generating process at the monthly frequency for job creation and job destruction is unknown.

  6. Both the job reallocation rate and the excess job reallocation rate infer on labor market fluidity (see Davis and Haltiwanger 2014).

  7. I use 4 quarterly lags as in Christiano et al. (1999), Davis and Haltiwanger (2001) and Giordani (2004).

  8. Results not reported herein but available upon request show that all the variables are stationary and that the residuals are serially uncorrelated.

  9. Note that I use a structural VAR to account for the contemporaneous effects of output and inflation on the policy interest rate. These contemporaneous effects imply that the Fed will react directly to changes in output or inflation by altering its policy interest rate as suggested by theoretical models (see, e.g., Romer 2000) and empirical work (see, e.g., Christiano et al. 1999; Davis and Haltiwanger 2001; Giordani 2004; Herrera et al. 2017).

  10. Theoretical models on the transmission of monetary policy include a three equation structure, an IS equation, a Phillips curve and a Taylor rule (see, Svensson 1997; Ball 1999; Svensson 2000, 2001; Romer 2000). These models imply that monetary policy affects output and inflation with a lag. Giordani (2004), uses a three variable structural VAR that incorporates the monetary policy transmission implied by theoretical models. The variables in his model take the following order: output gap, inflation rate, federal funds rate. Note that the first three equations in our model are in line with Giordani (2004) model. In the fourth and fifth equation, we assume that job destruction is Wold-causality prior to job creation due to the staggering of labor contracts (see Herrera and Karaki 2015; Herrera et al. 2017).

  11. To compute the confidence intervals, I first bootstrap the distribution of the impulse response functions, and then I compute the point-wise critical values.

  12. I focus on the one year cumulative effects on job flows given that there is ample evidence that monetary shocks have quick effects on real output (see Sims 2012). Furthermore, Campbell (1997) found that following a monetary shock, the largest change in employment occurs one year after the shock. Note that the 1-year cumulative effect is based on summing up the impulse responses from horizon 0 to horizon 4.

  13. See Table A.1a–A.1i and Fig. A.1a–A.1x.

  14. See for instance Hamilton (1988), Edelstein and Kilian (2007, 2009), Ramey and Vine (2010), and Herrera (2015).

  15. See Fig. A.3a–A.3d and Table A.4.

  16. See, e.g., Herrera and Karaki (2015), Karaki (2018), Kilian and Vigfusson (2017).

  17. Table A.4a–A.4i reports the results for the test of the absence of job reallocation for the remaining 4-digit SIC industries.

  18. Test results for the remaining industries are reported in Table A.6.

  19. Note also that the lack of asterisks in Tables A.5a–A.5i, A.6 and A.7 of the online appendix indicates that the effect of monetary shocks on job reallocation is not significant using data mining robust critical values.

  20. In the online appendix, I refer to these 4 alternative specifications as “robustness model 1”, “robustness model 2”, “robustness model 3” and “robustness model 4,” respectively. Figure A.4–A.7 show the impulse response functions and Table A.7–A.10 report the results for the test of the absence of job reallocation.


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Correspondence to Mohamad B. Karaki.

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Mohamad B. Karaki declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by the author.

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I am thankful to Ana María Herrera and conference participants at the 2014 Eastern Economic Association annual meetings, and the RCEA Macro-Money-Finance Workshop “Advances in Macroeconomics and Finance” for helpful comments and suggestions. The online appendix is available at:

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Karaki, M.B. Monetary shocks and job flows: evidence from disaggregated data. Empir Econ 58, 2911–2936 (2020).

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