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Using repeated cross-sectional data to examine the role of immigrant birth-country networks on unemployment duration: an application of Guell and Hu (2006) approach

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Abstract

Guell and Hu (J Econom 133:307–341, 2006) propose a robust econometric estimator using repeated cross-sectional data in the context of duration/survival analysis where otherwise limited or no results are possible because reliable panel data at an individual level are missing. We present a detailed exposition of the Guell and Hu (GH) strategy and show a Monte Carlo simulation for unemployment duration where the GH method produces better estimates compared to panel data with individual matching errors. We further apply the GH model to examine the immigrant unemployment duration in the USA using the Current Population Survey data from 2001 to 2013 and focus on the role of the birth-country networks on the unemployment duration around the Great Recession. We find that birth-country networks measured at the state level significantly lower unemployment duration for all immigrants, and this effect is stronger during the pre- and post-recession periods than during the recession. We also find that networks are more effective in lowering duration for immigrants unemployed for 1–2 months than for immigrants who are unemployed for longer periods, and this effect is the strongest during the post-recession period. The findings are robust to different specifications and measures of networks including those measured at the local MSA level.

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Notes

  1. This scenario is typical in surveys like the Current Population Survey (CPS) in the USA, where up to 75% of individuals interviewed each month can be followed from one month to the next. However, due to administrative reasons, there is certain degree of mismatch when linking individuals from one month to the next. See Rivera-Drew et al. (2014) for a recent discussion regarding linking records in the Current Population Survey.

  2. We use P(.) for probability distribution for a discrete variable and f(.) and g(.) for continuous variables.

  3. This functional form is chosen to guarantee that \(\frac{{m_{0} }}{{m_{1} }}P\left( {y = 1} \right)\) is strictly positive.

  4. For example, this could imply using the constraints \(\delta_{7} = \delta_{8} = \delta_{9} = \delta_{7,8,9}\), assuming that individuals with characteristics X = x who were unemployed for 7, 8 or 9 periods have the same risk of remaining unemployed for one additional period.

  5. For example, because unemployment duration spells are censored to 25 periods, observations in \({\mathbb{S}}_{c}\) with unemployment spells longer than 24 periods cannot be unambiguously assigned to a specific cohort in \({\mathbb{S}}_{b}\). Also, individuals with 11 and 12 periods of unemployment duration in \({\mathbb{S}}_{c}\) are also excluded from the estimation.

  6. See Rivera-Drew et al. (2014) for a recent discussion regarding linking records in the Current Population Survey.

  7. There are other measures typically used to evaluate the overall performance of estimators, including the root-mean-square error (RMSE) and the absolute mean squared error (AMSE). LMSE is a simple monotonic transformation of the RMSE \(\left( {{\text{LMSE}} = 0.5*{\text{Log}}\left( {\text{RMSE}} \right)} \right)\) that was chosen to avoid problems with the scale of the statistic, but has no impact on its interpretation.

  8. For the logit model, the coefficients for the grouped cohorts can be used to estimate the average relative risk of exiting unemployment between t and t + 1, for all individuals in that cohort. For the GH model, the coefficients for the grouped cohorts capture the relative risk of exiting unemployment between t & t + 1, t + 1 & t + 2 and t + 2 & t + 3.

  9. Focused on Germany, Uhlendorff and Zimmermann (2014) find that migrants are more likely to experience longer unemployment duration despite staying at their jobs for similar lengths of time when compared to natives with similar observable and unobservable characteristics. Diop-Christensen and Pavlopoulus (2016) find similar results for 12 European countries, but conclude that immigrants benefit more from increase in demand for low-skilled workers.

  10. According to the Congressional Budget Office, federal budget spending on unemployment insurance benefits increased almost five times from 33 billion in 2004 to 155 billion in 2011. For households, increased unemployment not only lowered their income and hence their standard of living, but also reduced their chance of reintegrating back into the labor market.

  11. See Mundra and Uwaifo-Oyeler (2018) for more details.

  12. According to the National Bureau of Economic Research, the Great Recession is defined as the period between December 2007 to June 2009, the pre-recession covers the period from January 2001 to November 2007, and the post-recession covers the period from July 2009 to December 2013.

  13. Unemployed immigrant workers are identified using self-reported unemployment status based on their activities during the week previous to the interview. For this individuals, the current length of unemployment spell is measured using the reported number of consecutive weeks that individual has been looking for work.

  14. Similar measures have been used in the literature by McConnell and Akresh (2008), Munshi (2003). Rauch and Trindade (2002), and Mundra (2005), to name a few.

  15. For more discussion on how networks operate at the state level, see Mundra and Uwaifo-Oyeler (2018).

  16. E-Verify is a Govt. policy which allows enrolled employers to verify the eligibility of their employees to work in the USA. Studying the impact of 2007 Legal Arizona Workers Act (LAWA), the first E-Verify law to be passed, Bohn et al. (2014) show that in response to this Government policy there was a substantial decrease in the state’s unauthorized population and that LAWA failed to improve the labor market outcome of legal low-skilled workers who compete with undocumented immigrants in the state.

  17. Data descriptive is given in the Supplementary Material.

  18. The marginal effects are given in the Supplementary Material.

  19. Bartik results are given in the Supplementary Material

  20. Bartik method and the results are given in the Supplementary Material.

  21. Figures 1 and 2 provide the predicted probability that an average person in the sample would remain unemployed for any given network size. Standard errors and confidence intervals are available upon request.

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Authors and Affiliations

  1. Department of Economics, Rutgers University, Newark, USA

    Kusum Mundra

  2. IZA, Bonn, Germany

    Kusum Mundra

  3. Levy Economics Institute, Bard College, Annandale-on-Hudson, USA

    Fernando Rios-Avila

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  1. Kusum Mundra
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  2. Fernando Rios-Avila
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Correspondence to Kusum Mundra.

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Helpful comments were received from the participants at the Southern Economic Association Meeting, Population Association of America Meeting, and the RGS/RWI Workshop on the Economics of Migration.

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Mundra, K., Rios-Avila, F. Using repeated cross-sectional data to examine the role of immigrant birth-country networks on unemployment duration: an application of Guell and Hu (2006) approach. Empir Econ 61, 389–415 (2021). https://doi.org/10.1007/s00181-020-01855-x

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  • DOI: https://doi.org/10.1007/s00181-020-01855-x

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