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Offshoring and labor income risk: an empirical investigation

Abstract

This paper analyzes how increased offshoring affects labor income risk. Dealing with the variability of incomes, it is therefore distinct from a large number of studies explaining the level effects of globalization on income in the labor market. It provides an assessment that directly connects labor income risk and offshoring trends in a panel setting at the industry level using German data. Importantly, we distinguish between transitory and permanent risk to individual income. Permanent income risk is defined as the variance of unpredictable shocks to income that do not fade out over time. Different from transitory short-term fluctuations, it has a particular welfare relevance. Our findings suggest that, at the industry level, permanent labor income risk decreases with offshoring. This effect is particularly strong for offshoring to low-income destinations.

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Notes

  1. 1.

    Yet, the opposite holds true for the receiving country. Volatility abroad (e.g., in Mexico for the case of US offshoring) is amplified.

  2. 2.

    Note that our approach, which is standard in the literature, does not allow us to link offshoring to income risk from switching industries. For studies on level effects on displaced worker’s income, among others, see Hummels et al. (2014) and Ebenstein et al. (2014).

  3. 3.

    Given that individuals affected by offshoring could be credit constrained and hence less able to smooth such shocks, we investigated whether there is any relation of offshoring and transitory income risk in our setting. We find no such effect.

  4. 4.

    It is supported by existing empirical evidence that consumption responds to permanent income shocks. For example, using PSID and CEX data, Blundell et al. (2008) find that a 10 % permanent income shock induces a 6.4 % permanent change in consumption.

  5. 5.

    The degree of permanent shocks to be smoothed out depends on the size of precautionary savings, that is, depends on the parametrization of the model, say, the risk aversion degree, the utility function, etc.

  6. 6.

    Individuals have a log utility function with degree of risk aversion 2, and the time discount factor is 0.96.

  7. 7.

    Leaving out these industry fixed effects in the first stage does not alter much our results on the link between offshoring and income risk.

  8. 8.

    The random walk assumption is not the only possible structure underlying the income process. For instance, other papers have suggested including a third, MA(1), component. Yet, as Krebs and Yao (2009) show, the permanent component of income risk is hardly affected by different assumptions on the income process. We therefore stick to the random walk assumption.

  9. 9.

    Storesletten et al. (2004) argue that the conditional variance of these permanent income shocks is counter-cyclical, increasing during contractions and decreasing during expansions.

  10. 10.

    We drop the industry subscript \(j\) for expositional reasons in this subsection.

  11. 11.

    This study uses the factually anonymous BA-Employment Panel (Years 1998–2007). Data access was provided via a Scientific Use File supplied by the Research Data Centre (FDZ) of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB). For detailed information on the database, see Schmucker and Seth (2009).

  12. 12.

    Krishna and Senses (2014) estimate income risk to be higher for individuals experiencing a transition from one industry to another when compared to individuals staying in one industry. We thus regard our estimations of income risk as a lower bound. If one were to link individual income risk arising from switching industries to offshoring, one would have to be able to clearly and causally identify offshoring as the reason for the switch—a task that we cannot accomplish with our data at hand.

  13. 13.

    The few remaining assumed imputed incomes, which are reported in the absence of a report by the employer, so-called Forschreibefälle, are deleted. In a robustness exercise further below, we show our results to be very similar when using the June waves.

  14. 14.

    The estimates in Table 1 are somewhat lower than those found in other studies, e.g., in Fuchs-Schuendeln et al. (2010). Note, however, that this latter study estimates permanent income risk of all West German individuals, while our study concentrates on fulltime employed males, which are characterized by a more stable income process. Furthermore, some studies rely on total household income, which inherently has higher risk since it includes the outcomes of labor-leisure choice and substitution effects between household members. With regard to the international evidence, it is plausible to us that the German labor market features lower income risk due to stronger institutions such as employment protection and wage bargaining coordination.

  15. 15.

    The data on imports and industry output are deflated using an aggregate manufacturing import price deflator and industry-specific producer price indices, respectively. The import price index is taken from the German Statistical Office Destatis and the industry-specific indices for producer prices come from the EU KLEMS database (March 2008) release.

  16. 16.

    Here, we again draw on the OECD STAN database and multiply the imports in (9) by the industries’ share of imports coming from non-OECD countries. When calculating import shares for non-OECD countries, we had to rely on aggregates for industries 15–16, 17–19 and 21–22 and thus use the same non-OECD share for each industry within the respective group. Note, however, that this only applies to the non-OECD trade share and neither to total imports \(IMP_{j^*t}\) nor \(\Omega _{j^*jt}\). Furthermore, this region-specific calculation of offshoring entails the assumption of identical \(\Omega _{j^*jt}\) for the two groups of countries, since the input–output tables do not hold any region-specific information.

  17. 17.

    All these data are retrieved from the OECD STAN database. Industry 36 includes industry 37 (recycling) in the OECD data. Dropping this industry leaves the results almost identical.

  18. 18.

    We present results for 1-year lagged values of offshoring as explanatory variables, because we do not find any significant contemporaneous correlation.

  19. 19.

    This does not say anything about the possible effects of displacements at the margin of offshoring. Yet, according to some recent studies, offshoring does not seem to be a major cause of aggregate job loss at the industry level (OECD 2007; Harrison and McMillan 2011), which might be due to offshoring-related productivity effects Wright (2014). The possibility that some specific groups of workers are being displaced while employment grows for others remains evident, however (Harrison and McMillan 2011). In this sense, our results have an aggregate interpretation.

  20. 20.

    Note that the (unweighted) average increase between 1999 and 2005 was 1.6 % points for worldwide offshoring and 1 % point for non-OECD offshoring, respectively, the latter coming from a lot lower values.

  21. 21.

    The data are available at (http://www.uni-goettingen.de/en/99958.html). Further information on their exact construction can be found in Geishecker (2006).

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Correspondence to Yao Yao.

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Hogrefe, J., Yao, Y. Offshoring and labor income risk: an empirical investigation. Empir Econ 50, 1045–1063 (2016). https://doi.org/10.1007/s00181-015-0966-3

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Keywords

  • International trade
  • Offshoring
  • Labor income
  • Income risk

JEL Classification

  • F14
  • F16
  • E24