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A new career in a new town. Job search methods and regional mobility of unemployed workers

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Abstract

Labour mobility is critical for adjusting imbalance between local labour markets. Yet, labour markets appear still very localized. Existing studies on job search report that the choice of search methods influences job outcomes, with social contacts accounting for a substantial fraction of job matches. Whether search methods are conducive to local or national jobs has not been examined yet. This paper establishes a link between job search and regional mobility, investigating the impact of search methods on unemployment exits within and across local labour markets. The effect of search methods is estimated by a Propensity Score Matching approach, using data from the British Household Panel Survey. Results show that only direct approach to employers enhances the job hazard with regional move. Conversely, social contacts and advertisements are found to increase the hazard to local employment, although the effect of social contacts wears off as the unemployment spell prolongs. No impact is found by Employment Agencies on either exit. These findings suggest that the widespread use of social contacts, while enhancing job matches in the local labour market, might contribute to restrict labour mobility. Therefore, they bear support to policies promoting diffusion and efficacy of alternative methods, particularly when the target is long-term unemployment. Results also point out the opportunity of reforms of the job search assistance and placement service offered by Employment Agencies, taking these limitations into account.

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Notes

  1. This prediction rests on the assumption that, while employed workers share information about job opportunities with unemployed workers in their network, unemployed workers keep the information for themselves.

  2. See Loury (2006), Pellizzari (2010) and Bentolila et al. (2010) for explanations of the mixed results for the wage effect of SOCNET.

  3. Population figures are drawn from Nomis, Office for National Statistics, ONS UK (www.nomisweb.co.uk).

  4. The boundary definition of LADs in use in the present data is the one in place before the local government changes of 2009, with a total of 434 LADs. In 2000 the population of LADs was on average 135,682, ranging between 2,100 (Isles of Scilly) and 985,100 (Birmingham). Population estimates are drawn from Nomis, Office for National Statistics, ONS UK (www.nomisweb.co.uk).

  5. A PSM analysis was not performed for SEMP (steps to start business) because this method is used by a very small fraction of unemployed (9%); note that this method has received only scant attention by previous literature (see Section 2.1)

  6. The probit specification was preferred over the logit because it provided better matching diagnostics.

  7. Information on employment growth was not available at the TTWA level.

  8. Defining the hazard ratios reported in Table 5 for one possible method/exit combination as HR0, HR3 (t > 3), and HR12 (t > 12), the absolute hazard ratio for the interval 3–12 can be derived as \(\exp (\log (HR_{0})+\log (HR_{3}))\), and the absolute hazard ratio for the interval 12–\(\infty \) as \(\exp (\log (HR_{0})+\log (HR_{3})+\log (HR_{12}))\). Note that the absolute hazard ratio for the interval 0–3 correspond to HR0.

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Acknowledgments

I gratefully acknowledge the UK Data Service and the Department for Work and Pensions for access and support to data. I thank Euan Phimister and Ada Ma for sharing their code on the creation of the duration data set. I thank also seminar participants at the 56th ERSA Congress in Vienna and at the CPB Netherlands Bureau for Economic Policy Analysis. Finally, I thank David Bowie for inspiring me the title of this article.

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    Andrea Morescalchi

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Appendices

Appendix A: Description of variables

This section lists and describes the variables used in estimation.

  • Search methods dummies. DAE: applied directly to an employer. ADS: studied or replied to advertisements. EA: contacted a private employment agency or Job Centre. SOCNET: asked friends or contacts. SEMP: took steps to start own business.

  • Age. 4 age bands: 16–24, 25–34, 35–44, 45–65.

  • Female. Binary variable identifying females.

  • Unemployment benefit. Binary variable identifying whether the individual has received unemployment benefit or income support as an unemployed person in the last year.

  • Highest education. Categorical variable identifying the highest educational qualifications, with the following states: No qualifications; O Levels or equivalent; A Levels of equivalent; nursing and other qualifications; first degree or above (including teaching).

  • Marital status. Binary variable identifying married people (or living as a couple).

  • Children 0–15 years. Binary variable indicating whether the individual has own children under age of 16 in the household.

  • Housing tenure. Categorical variable identifying the following categories: homeowners; social renters; private renters.

  • Last wage. Monthly net wage earned in the last job. Expressed in 2008 real GBP. Missing cases were imputed by estimating a wage equation with the count of search methods and unemployment duration in addition to covariates used in the analysis.

  • Reservation wage. Self-reported amount in response to the following question: “What is the lowest weekly take-home pay you would consider accepting for a job?” Normalized to monthly value and expressed in 2008 real GBP. Missing cases were imputed similarly to last wage.

  • Employment growth. Growth rate (%) in employment at the Local Authority District level. This information was not available for LADs of Northern Ireland for the period under investigation, hence the national value was used. The series is drawn from Nomis, Office for National Statistics (ONS), UK (www.nomisweb.co.uk).

  • Last job occupation. Defined by the 1990 Standard Occupational Classification (SOC), with the following possible categories: managers and administrators; professional, associate professional and technical occupations; clerical and secretarial occupations; craft and related occupations; personal and protective service occupations; sales occupations; plant and machine operatives; other occupation; no previous job.

  • Last job industry sector. Industrial sectors are defined using the 1992 Standard Industrial Classification (SIC). SIC 1992 is divided in the following sectors: (A) Agriculture, Hunting and Forestry; (B) Fishing; (C) Mining and Quarrying; (D) Manufacturing; (E) Electricity, Gas and Water Supply; (F) Construction; (G) Wholesale and Retail Trade: Repair of Motor Vehicles, Motorcycles and Personal Household Goods; (H) Hotels and Restaurants; (I) Transport, Storage and Communication; (J) Financial Intermediation; (K) Real Estate, Renting and Business Activities; (L) Public Administration and Defence: Compulsory Social Security; (M) Education; (N) Health and Social Work; (O) Other Community, Social and Personal Service Activities; (P) Private Households with Employed Persons; (Q) Extra-Territorial Organisations and Bodies. For the present analysis, the following categories have been aggregated into a residual category called “Others”, due to their limited representation: (A), (B), (C), (E), (J), (L), (P) and (Q). For waves before the 12th, industry sector is recorded with the 1980 classification, therefore codes have been converted to the 1992 classification using Jennifer Smith’s one-to-one mapping. The table is downloadable at https://www2.warwick.ac.uk/fac/soc/economics/staff/jcsmith/sicmapping/resources/direct/.

  • Region dummies. Defined as follows: Yorkshire and Humberside, and North East; North-West; Midlands; East Anglia; South East; South West; Wales; Scotland; Northern Ireland.

  • Year dummies. 1996–2008

Appendix B: Full results for treatment selection models

Table 7 Treatment selection models. Full set of coefficients for estimates in Table 3
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Appendix C: Diagnostics on PS matching procedure

Fig. 2
figure 2

QQ plots for difference-in-means t-tests within-PS blocks. Notes: Plots report t-statistics of covariate difference-in-means tests by treatment status against the corresponding quantiles of the Normal distribution. Tests were performed within blocks of the PS with no significant differences in PS means. Tests behave approximately as if they were independent draws from a Normal distribution

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Fig. 3
figure 3

Common support region. Overlap check between PS distributions

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Fig. 4
figure 4

Standardized differences (%) in covariate means between treated and untreated; suggested cutoff is 10% (Austin and Stuart 2015) . Rubin’s B suggested cutoff is 25% (Rubin 2001)

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Appendix D: Estimates of competing-risks unemployment duration model without matching

Table 8 Effect of search methods on cause-specific hazards. Unmatched sample
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Appendix E: Robustness Checks

Table 9 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting. The common support has been restricted to the range .05–.95 of the Propensity Score
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Table 10 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Nearest Neighbour Matching (radius algorithm on caliper 0.05)
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Table 11 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting. Time window for moves is between 6 months before and 12 months after exit
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Table 12 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting. Time window for moves is between 9 months before and 12 months after exit
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Table 13 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting. Local Labour Markets are defined by Local Authority Districts
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Table 14 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting. An additional weighting factor was used to take into account unobserved spells
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Fig. 5
figure 5

Unemployment duration distribution. Comparison between estimation sample (cross-wave spells) and all spells (cross-wave and in between-waves spells)

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Table 15 Effect of search methods on cause-specific hazards with time-varying treatment. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting
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Table 16 Effect of search methods on cause-specific hazards with time-varying treatment and covariates. Marginal Structural Model estimates with Inverse Probability of Treatment Weighting
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Table 17 Effect of search methods on cause-specific hazards with time-varying treatment and covariates. Unobserved heterogeneity is allowed for. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting
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Morescalchi, A. A new career in a new town. Job search methods and regional mobility of unemployed workers. Port Econ J 20, 223–272 (2021). https://doi.org/10.1007/s10258-020-00175-3

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