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

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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Fig. 1

Notes

  1. 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. 2.

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

  3. 3.

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

  4. 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. 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. 6.

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

  7. 7.

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

  8. 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.

References

  1. Abadie A, Imbens GW (2008) On the failure of the bootstrap for matching estimators. Econometrica 76(6):1537–1557

    Google Scholar 

  2. Abadie A, Imbens GW (2016) Matching on the estimated propensity score. Econometrica 84(2):781–807

    Google Scholar 

  3. Addison JT, Portugal P (2002) Job search methods and outcomes. Oxf Econ Pap 54(3):505–533

    Google Scholar 

  4. Ali MS, Groenwold RHH, Pestman WR, Belitser SV, Hoes AW, de Boer A, Klungel OH (2013) Time-dependent propensity score and collider-stratification bias: An example of beta 2-agonist use and the risk of coronary heart disease. Eur J Epidemiol 28(4):291–299

    Google Scholar 

  5. Ali MS, Groenwold RHH, Belitser SV, Souverein PC, Martin E, Gatto NM, Huerta C, Gardarsdottir H, Roes KCB, Hoes AW, de Boer A, Klungel OH (2016) Methodological comparison of marginal structural model, time-varying Cox regression, and propensity score methods: the example of antidepressant use and the risk of hip fracture. Pharmacoepidemiol Drug Saf 25(Suppl. 1):114–121

    Google Scholar 

  6. Austin PC (2013) The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med 32(16):2837–2849

    Google Scholar 

  7. Austin PC (2014) The use of propensity score methods with survival or time-to-event outcomes: Reporting measures of effect similar to those used in randomized experiments. Stat Med 33(7):1242–1258

    Google Scholar 

  8. Austin PC (2016) Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med 35(30):5642–5655

    Google Scholar 

  9. Austin PC, Fine JP (2019) Propensity-score matching with competing risks in survival analysis. Stat Med 38(5):751–777

    Google Scholar 

  10. Austin PC, Stuart EA (2015) Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med 34(28):3661–3679

    Google Scholar 

  11. Bachmann R, Baumgarten D (2013) How do the unemployed search for a job? Evidence from the EU Labour Force Survey. IZA J Eur Labor Stud 2(22):1–25

    Google Scholar 

  12. Battu H, Ma A, Phimister E (2008) Housing tenure, job mobility and unemployment in the UK. Econ J 118(527):311–328

    Google Scholar 

  13. Battu H, Seaman P, Zenou Y (2011) Job contact networks and the ethnic minorities. Labour Econ 18(1):48–56

    Google Scholar 

  14. Bayer P, Ross SL, Topa G (2008) Place of work and place of residence: informal hiring networks and labor market outcomes. J Polit Econ 116(6):1150–1196

    Google Scholar 

  15. Beaman LA (2012) Social networks and the dynamics of labor market outcomes: Evidence from refugees resettled in the U.S. Rev Econ Stud 79(1):128–171

    Google Scholar 

  16. Becker SO, Egger PH (2013) Endogenous product versus process innovation and a firm’s propensity to export. Empir Econ 44(1):329–354

    Google Scholar 

  17. Bentolila S, Michelacci C, Suarez J (2010) Social contacts and occupational choice. Economica 77(305):20–45

    Google Scholar 

  18. Bertola G (1999) Labor markets in the European Union. Working Paper RSC 99/24, European University Institute

  19. Blanchard OJ, Katz LF (1992) Regional evolutions. Brook Pap Econ Act 1992(1):1–75

    Google Scholar 

  20. Blau D, Robins F (1990) Job search outcomes for the employed and unemployed. J Polit Econ 98(3):637–655

    Google Scholar 

  21. Böheim R, Taylor MP (2001) Job search methods, intensity and success in britain in the 1990s. ISER Working Paper 7, University of Essex

  22. Bonin H, Eichhorst W, Florman C, Hansen MO, Skiöld L, Stuhler J, Tatsiramos K, Thomasen H, Zimmermann K F (2008) Geographic mobility in the European Union: Optimising its economic and social benefits. IZA Research Report, 19

  23. Bover O, Muellbauer J, Murphy A (1989) Housing, wages and UK labour markets. Oxf Bull Econ Stat 51(2):97–136

    Google Scholar 

  24. Bramoullé Y, Saint-Paul G (2010) Social networks and labor market transitions. Labour Econ 17(1):188–195

    Google Scholar 

  25. Brookhart M A, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T (2006) Variable selection for propensity score models. Am J Epidemiol 163(12):1149–1156

    Google Scholar 

  26. Brown M, Setren E, Topa G (2016) Do informal referrals lead to better matches? Evidence from a firm’s employee referral system. J Labor Econ 34(1):161–209

    Google Scholar 

  27. Bryson A, Dorsett R, Purdon S (2002) The use of propensity score matching in the evaluation of labour market policies. Working Paper 4, Department for Work and Pensions

  28. Caliendo M, Kopeinig S (2008) Some practical guidance for the implementation of propensity score matching. J Econ Surv 22(1):31–72

    Google Scholar 

  29. Caliendo M, Schmidl R, Uhlendorff A (2011) Social networks, job search methods and reservation wages: Evidence for Germany. Int J Manpow 32(7):796–824

    Google Scholar 

  30. Caliendo M, Cobb-Clark DA, Uhlendorff A (2015) Locus of control and job search strategies. Rev Econ Stat 97(1):88–103

    Google Scholar 

  31. Calvo-Armengol T, Jackson M (2004) The effects of social networks on employment and inequality. Am Econ Rev 94(3):426–454

    Google Scholar 

  32. Calvo-Armengol T, Jackson M (2007) Networks in labor markets: Wage and employment dynamics and inequality. J Econ Theory 132(1):27–46

    Google Scholar 

  33. Cappellari L, Tatsiramos K (2015) With a little help from my friends? Quality of social networks, job finding and job match quality. Eur Econ Rev 78:55–75

    Google Scholar 

  34. Cattaneo MD (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability. J Econ 155(2):138–154

    Google Scholar 

  35. Cingano F, Rosolia A (2012) People I know: Workplace networks and job search outcomes. J Labor Econ 30(2):291–332

    Google Scholar 

  36. Coombes MG, Wymer C, Charlton M, Bailey S, Stonehouse A, Openshaw S (1997) Review of travel-to-work areas and small area unemployment rates. Labour Market Trends 16(105):9–12

    Google Scholar 

  37. Coulson NE, Fisher LM (2002) Tenure choice and labour market outcomes. Hous Stud 17(1):35–49

    Google Scholar 

  38. Coulson NE, Fisher LM (2009) Housing tenure and labor market impacts: The search goes on. J Urban Econ 65(3):252–264

    Google Scholar 

  39. D’Agostino RB, Lee ML, Belanger AJ (1990) Relation of pooled logistic regression to time dependent Cox regression analysis: The Framingham Heart Study. Stat Med 9(12):1501–1515

    Google Scholar 

  40. Decressin J, Fatas A (1995) Regional labor market dynamics in Europe. Eur Econ Rev 39(9):1627–1655

    Google Scholar 

  41. DellaVigna S, Paserman MD (2005) Job search and impatience. J Labor Econ 23(3):527–588

    Google Scholar 

  42. Devine T, Kiefer N (1991) Empirical labour economics: The search approach. Oxford University Press, Oxford

    Google Scholar 

  43. Dohmen TJ (2005) Housing, mobility and unemployment. Reg Sci Urban Econ 35(3):305–325

    Google Scholar 

  44. Dustmann C, Glitz A, Schönberg U, Brücker H (2015) Referral-based job search networks. Rev Econ Stud 83(2):514–546

    Google Scholar 

  45. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, New York

    Google Scholar 

  46. European Commission (2001) High level task force on skills and mobility Final report, Directorate-General for Employment and Social Affairs, Brussels

  47. European Commission (2010) An agenda for new skills and jobs: A European contribution towards full employment, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Strasbourg

  48. Fewell Z, Hernán M A, Wolfe F, Tilling K, Choi H, Sterne J A (2004) Controlling for time-dependent confounding using marginal structural models. Stata J 4(4):402–420

    Google Scholar 

  49. Flatau P, Forbes M, Hendershott PH, Wood G (2003) Homeownership and unemployment: The roles of leverage and public housing, NBER Working Paper, 10021

  50. Frijters P, Shields MA, Price SW (2005) Job search methods and their success: A comparison of immigrants and natives in the UK. Econ J 115(507):F359–F376

    Google Scholar 

  51. Galeotti A, Merlino L (2014) Endogenous job contact networks. Int Econ Rev 55(4):1201–1226

    Google Scholar 

  52. Gayat E, Resche-Rigon M, Mary JY, Porcher R (2012) Propensity score applied to survival data analysis through proportional hazards models: A Monte Carlo study. Pharm Stat 11(3):222–229

    Google Scholar 

  53. Goss EP, Phillips JM (1997) The impact of home ownership on the duration of unemployment. Rev Regional Stud 27(1):9–27

    Google Scholar 

  54. Green A, Owen D (1990) The development of a classification of travel-to-work areas. Prog Plan 34(105):1–92

    Google Scholar 

  55. Green CP (2012) Employed and unemployed job search methods: Australian evidence on search duration, wages and job stability Working Papers 50029416, Lancaster University Management School, Economics Department

  56. Gregg P, Wadsworth J (1996) How effective are state employment agencies? Jobcentre use and job matching in Britain. Oxf Bull Econ Stat 58(3):443–467

    Google Scholar 

  57. Gregg P, Machin S, Manning A (2004) Mobility and joblessness. In: Seeking a premier economy: The economic effects of British economic reforms, 1980-2000, University of Chicago Press, pp 371–410

  58. Hassler J, Mora JVR, Storesletten K, Zilibotti F (2005) A positive theory of geographical mobility and social insurance. Int Econ Rev 46(1):263–303

    Google Scholar 

  59. Heckman JJ, Ichimura H, Todd PE (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Rev Econ Stud 64(4):605–654

    Google Scholar 

  60. Hellerstein JK, McInerney M, Neumark D (2011) Neighbors and coworkers: The importance of residential labor market networks. J Labor Econ 29(4):659–695

    Google Scholar 

  61. Hellerstein JK, Kutzbach MJ, Neumark D (2014) Do labor market networks have an important spatial dimension? J Urban Econ 79:39–58

    Google Scholar 

  62. Hernán MA, Brumback B, Robins JM (2000) Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11(5):561–570

    Google Scholar 

  63. Hirano K, Imbens GW, Ridder G (2003) Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71(4):1161–1189

    Google Scholar 

  64. Holzer H (1988) Search method use by unemployed youth. J Labor Econ 6(1):1–20

    Google Scholar 

  65. Hughes G, McCormick B (1981) Do council housing policies reduce migration between regions? Econ J 91(364):919–937

    Google Scholar 

  66. Hughes G, McCormick B (1987) Housing markets, unemployment and labour market flexibility in the UK. Eur Econ Rev 31(3):615–645

    Google Scholar 

  67. Imbens G, Lynch L (2006) Re-employment probabilities over the business cycle. Port Econ J 5(2):111–134

    Google Scholar 

  68. Imbens GW (2000) The role of the propensity score in estimating dose-response functions. Biometrika 87(3):706–710

    Google Scholar 

  69. Imbens GW, Rubin DB (2015) Causal inference in statistics, social and biomedical sciences. Cambridge University Press, Cambridge

    Google Scholar 

  70. Ioannides YM, Loury LD (2004) Job information networks, neighborhood effects, and inequality. J Econ Lit 42(4):1056–1093

    Google Scholar 

  71. Jimeno JF, Bentolila S (1998) Regional unemployment persistence (Spain, 1976-1994). Labour Econ 5(1):25–51

    Google Scholar 

  72. Kramarz F, Nordström Skans O (2014) When strong ties are strong: Networks and youth labour market entry. Rev Econ Stud 81(3):1164–1200

    Google Scholar 

  73. Kroft K, Lange F, Notowidigdo MJ (2013) Duration dependence and labor market conditions: Evidence from a field experiment. Q J Econ 128(3):1123–1167

    Google Scholar 

  74. Lancaster T (1990) The econometric analysis of transition data. Cambridge University Press, Cambridge

    Google Scholar 

  75. Laschever R (2009) The doughboys network: Social interactions and the employment of World War I veterans, university of Illinois at Urbana-Champaign, unpublished manuscript

  76. Lechner M (2001) Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In: Econometric evaluation of labour market policies, Springer, pp 43–58

  77. Lechner M (2002) Program heterogeneity and propensity score matching: An application to the evaluation of active labor market policies. Rev Econ Stat 84 (2):205–220

    Google Scholar 

  78. Lechner M (2008) A note on the common support problem in applied evaluation studies. Annales d’Économie et de Statistique 91(/92):217–235

    Google Scholar 

  79. Ljungqvist L, Sargent TJ (1998) The European unemployment dilemma. J Polit Econ 106(3):514–550

    Google Scholar 

  80. Longhi S, Taylor M (2011) Explaining differences in job search outcomes between employed and unemployed job seekers. IZA Discussion Paper, 5860

  81. Lopez MJ, Gutman R (2017) Estimation of causal effects with multiple treatments: A review and new ideas. Stat Sci 32(3):432–454

    Google Scholar 

  82. Loury LD (2006) Some contacts are more equal than others: Informal networks, job tenure, and wages. J Labor Econ 24(2):299–318

    Google Scholar 

  83. Machin S, Manning A (1999) The causes and consequences of long-term unemployment in Europe. In: Ashenfelter OC, Card D (eds) Handbook of labour economics, vol 3. Elsevier, Amsterdam, pp 3085–3139

  84. Manning A (2009) You can’t always get what you want: The impact of the Jobseeker’s Allowance. Labour Econ 16(3):239–250

    Google Scholar 

  85. Manning A, Petrongolo B (2017) How local are labor markets? Evidence from a spatial job search model. Am Econ Rev 107(10):2877–2907

    Google Scholar 

  86. McCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF (2013) A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med 32(19):3388–3414

    Google Scholar 

  87. McCulloch A (2003) Local labour markets and individual transitions into and out of poverty: evidence from the british household panel study waves 1 to 8. Environ Plan A 35(105):551–568

    Google Scholar 

  88. McGee AD (2015) How the perception of control influences unemployed job search. Int Labor Relation Rev 68(1):184–211

    Google Scholar 

  89. Monchuk DC, Kilkenny M, Phimister E (2014) Rural homeownership and labour mobility in the United States. Reg Stud 48(2):350–362

    Google Scholar 

  90. Morescalchi A (2016) The puzzle of job search and housing tenure. A reconciliation of theory and empirical evidence. J Reg Sci 56(2):288–312

    Google Scholar 

  91. Munch JR, Rosholm M, Svarer M (2006) Are home owners really more unemployed? Econ J 116(514):991–1013

    Google Scholar 

  92. Nickell S, Layard R (1999) Labor market institutions and economic performance. In: Ashenfelter O, Card D (eds) Handbook of labor economics, vol 3. Elsevier, pp 3029–3084

  93. Osberg L (1993) Fishing in different pools: Job-search strategies and job-finding success in Canada in the early 1980s. J Labor Econ 11(2):348–386

    Google Scholar 

  94. Oswald AJ (1996) A conjecture on the explanation for high unemployment in the industrialized nations: Part 1, Working paper, University of Warwick

  95. Oswald AJ (1997) Thoughts on NAIRU. J Econ Perspect 11(1):227–228

    Google Scholar 

  96. Oswald AJ (1999) The housing market and Europe’s unemployment: A non-technical paper. Working paper, University of Warwick

  97. Partridge M, Rickman D (1997) The dispersion of US state unemployment rates: The role of market and non-market equilibrium factors. Reg Stud 31(6):593–606

    Google Scholar 

  98. Pellizzari M (2010) Do friends and relatives really help in getting a good job? Ind Labor Relations Rev 63(3):494–510

    Google Scholar 

  99. Petrongolo B (2009) The long-term effects of job search requirements: Evidence from the UK JSA Reform. J Public Econ 93(11–12):1234–1253

    Google Scholar 

  100. Plug E, van der Klaauw B, Ziegler L (2018) Do parental networks pay off? Linking children’s labor-market outcomes to their parents’ friends. Scand J Econ 120(1):268–295

    Google Scholar 

  101. Puhani PA (2001) Labour mobility: An adjustment mechanism in Euroland? empirical evidence for Western Germany, France and Italy. German Econ Rev 2(2):127–140

    Google Scholar 

  102. Robins JM, Hernán MA, Brumback B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11(5):550–560

    Google Scholar 

  103. Rosenbaum P, Rubin D (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–50

    Google Scholar 

  104. Rosenbaum P, Rubin D (1985) Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 39(1):33–38

    Google Scholar 

  105. Rouwendal J, Nijkamp P (2010) Homeownership and labour market behaviour: Interpreting the evidence. Environ Plan A 42(2):419–433

    Google Scholar 

  106. Rubin D B (2001) Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology 2(3–4):169–188

    Google Scholar 

  107. Sansale R, DeLoach SB, Kurt M (2019) Unemployment duration and the personalities of young adults workers. J Behav Experimen Econ 79:1–11

    Google Scholar 

  108. Schmitt J, Wadsworth J (1993) Unemployment benefit levels and search activity. Oxf Bull Econ Stat 55(1):1–24

    Google Scholar 

  109. Schmutte IM (2015) Job referral networks and the determination of earnings in local labor markets. J Labor Econ 33(1):1–32

    Google Scholar 

  110. Shimer R, Werning I (1998) Liquidity and insurance for the unemployed. Am Econ Rev 98(5):1922–1942

    Google Scholar 

  111. Siddique AA, Schnitzer ME, Bahamyirou A, Wang G, Holtz TH, Migliori GB, Sotgiu G, Gandhi NR, Vargas MH, Menzies D et al (2019) Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis. Stat Methods Med Res 28(12):3534–3549

    Google Scholar 

  112. Topa G (2001) Social interactions, local spillovers and unemployment. Rev Econ Stud 68(2):261–295

    Google Scholar 

  113. Topa G (2011) Labor markets and referrals. In: Benhabib J, Bisin A, Jackson MO (eds) Handbook of social economics, vol 1. Elsevier, Amsterdam, pp 1193–1221

  114. Topa G (2019) Social and spatial networks in labour markets. Oxf Rev Econ Policy 35(4):722–745

    Google Scholar 

  115. Upward R (1999) Constructing data on unemployment spells from the PSID and the BHPS. Tech. rep. Centre for Research on Globalisation and Labour Markets. School of Economic Studies, Nottingham

    Google Scholar 

  116. Uysal SD, Pohlmeier W (2011) Unemployment duration and personality. J Econ Psychol 32(6):980–992

    Google Scholar 

  117. Van den Berg G, van der Klaauw B (2006) Counseling and monitoring of unemployed workers: Theory and evidence from a controlled social experiment. Int Econ Rev 47(3):895–936

    Google Scholar 

  118. Van den Berg GJ, Gorter C (1997) Job search and commuting time. J Business Econ Stat 15(2):269–281

    Google Scholar 

  119. Van den Berg GJ, Van Ours IC (1996) Unemployment dynamics and duration dependence. J Labor Econ 14(1):100–125

    Google Scholar 

  120. Van Vuuren A (2009) A the impact of homeownership on unemployment in The Netherlands. In: Van Ewijk C, Van Leuvensteijn M (eds) Homeownership and the labour market in Europe. Oxford University Press, Oxford

  121. Viinikainen J, Kokko K (2012) Personality traits and unemployment: Evidence from longitudinal data. J Econ Psychol 33(6):1204–1222

    Google Scholar 

  122. Weber A, Mahringer H (2008) Choice and success of job search methods. Empir Econ 35(1):153–178

    Google Scholar 

  123. Wooldridge JM (2010) Econometric analysis of cross section and panel data. MIT Press, Cambridge

    Google Scholar 

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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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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

Appendix C: Diagnostics on PS matching procedure

Fig. 2
figure2

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

Fig. 3
figure3

Common support region. Overlap check between PS distributions

Fig. 4
figure4

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)

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

Table 8 Effect of search methods on cause-specific hazards. Unmatched sample

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
Table 10 Effect of search methods on cause-specific hazards. Propensity Score Matching Estimates with Nearest Neighbour Matching (radius algorithm on caliper 0.05)
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
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
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
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
Fig. 5
figure5

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

Table 15 Effect of search methods on cause-specific hazards with time-varying treatment. Propensity Score Matching Estimates with Inverse Probability of Treatment Weighting
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
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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Keywords

  • Local labour markets
  • Regional mobility
  • Job search methods
  • Unemployment duration
  • Social networks

JEL Classification

  • J61
  • J64
  • R23