Network-Based Job Search
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1 I Network-Based Job Search An Analysis of Monetary and Non-monetary Labor Market Outcomes for the Low-Status Unemployed Online Appendix Gerhard Krug Universität Erlangen-Nürnberg, Lehrstuhl für empirische Wirtschaftssoziologie, Findelgasse 7/9, Nürnberg, Germany; Institut für Arbeitsmarkt- und Berufsforschung (IAB), Regensburger Straße 104, Nürnberg, Germany; Martina Rebien Institut für Arbeitsmarkt- und Berufsforschung (IAB), Regensburger Straße 104, Nürnberg, Germany; To control for all necessary covariates, we identify three types of factors that are potentially important for finding a job through networks and labor market outcomes. First, we control for differences in job searching behavior between the two groups. How many search strategies and what different search strategies 1 an unemployed person uses will influence whether, in the end, the accepted job comes from his/her social network; these strategies will also influence (reservation) wages through the number of alternative job offers received during a search period. We also control for overall unemployment duration to account for a decline in reservation wages after longer periods of unemployment, and we control for any unemployment compensation received during that time. We use data on whether a 1 Two clarifications are in order. First, if job-searching method is used as an indicator for network-based searching, obviously, covariates for job-search strategies will exclude job searching via networks. Second, job-search strategies, but also job-search duration, might be an outcome of a more or less successful network-based job search. For example, some search strategies might only be employed if network searching has proven unsuccessful, or network-based job searching might influence job-search duration. If these variables are outcomes rather than covariates, they should not be controlled for. Because not controlling for them does not change the results of this paper, our results are stable with regard to this potential pitfall.
2 II respondent turned down a low-wage job offer as an indicator of a high reservation wage. We control for whether the newly employed person received an in-work benefit because the prospect of receiving such a wage subsidy might have an influence on reservation wages. Second, we take into account homophily in the genesis of social networks. Homophily is the tendency of individuals to primarily interact with those who are similar to them; wellqualified persons, for example, tend to have well-qualified friends. Well-qualified friends might be more helpful than poorly qualified friends in finding an open position. At the same time, well-qualified people tend to receive higher average wages, irrespective of how the job was found. This coincidence could result in a spurious correlation between wages and obtaining a job through networks. The problem of homophily is more pronounced when analyzing the effect of contact status (Mouw 2003: 871, 884), and it might not be as big of a problem in our context. Nevertheless, we control for several key dimensions of homophily as defined by McPherson et al. (2001). Third, we control for factors influencing an individual s access to social capital. The better the job seeker's ability to access social capital, the more likely he/she is to find a job through social networks. We identify health limitations, family income, and employment-family structure as factors that could simultaneously influence one s access to social capital and the possibility of realizing high returns from a job search (Boisjoly et al. 1995). We also include a dummy variable for East and West Germany to control for regional differences in the chances of finding a job. Table A1: Logistic regression to estimate the propensity score (job-finding method) (1) Before (2) Before (3) After (4) After Matching Matching Matching Matching Full Restricted Restricted Full Model Model Model Model Job search behavior Search effort (number of different search strategies used) 0.645*** (0.095) 0.634*** (0.066) (0.144) (0.228) Job seeker placed ad in a newspaper (0.334) (0.309) (0.283) (0.287) Asked friends, neighbors, relatives 4.689*** (1.418) 5.007*** (0.377) (0.364) Asked caseworker in employment (0.319) (0.287) (0.300) (0.291)
3 III (1) Before (2) Before (3) After (4) After Matching Matching Matching Matching Full Restricted Restricted Full Model Model Model Model agency Computer search in database of employment agency (0.339) (0.299) (0.255) (0.248) Unsolicited application (0.397) (0.374) (0.308) (0.307) Waited for job offers from the employment agency 1.857*** (0.393) 1.977*** (0.348) (0.204) (0.216) Other search strategies (0.215) (0.233) Ever turned down employment offer because wage was too low? (Ref.: no, never) (0.204) (0.207) (0.251) (0.249) duration (0.009) (0.009) (0.011) (0.011) duration (squared) 1.000* (0.000) 1.000* (0.000) (0.000) (0.000) compensation before employment (Ref.: unemployment insurance) benefit (0.203) (0.196) (0.295) (0.297) No compensation (0.184) (0.184) (0.371) (0.375) Social benefits (0.175) (0.178) (0.335) (0.326) In-work benefit (Ref.: yes) (0.179) (0.180) Dimensions of homophily Born in Germany (Ref.: yes) (0.219) (0.287) Sex (0.182) (0.212) Age (0.014) (0.018)
4 IV Formal education (none ) (1) Before (2) Before (3) After (4) After Matching Matching Matching Matching Full Restricted Restricted Full Model Model Model Model Lower secondary school (0.263) (0.350) Secondary school (0.361) (0.458) Intermediate school (0.315) (0.396) Upper secondary school (0.351) (0.337) Formal qualification (R: none) Skilled worker/technical training (0.183) (0.167) (0.186) (0.205) Vocational training/master craftsman/technici an (0.181) (0.164) (0.218) (0.268) Job experience (years) (0.014) (0.017) Family important (yes) (0.196) (0.221) Leisure important (yes) (0.183) (0.233) Work important (yes) (0.155) (0.203) Access to social capital Living with partner (married or not married) (0.238) (0.234) (0.216) (0.225) Children in Household (0.071) (0.065) (0.092) (0.098) Net household income before employment / (0.038) (0.037) (0.046) (0.048) Net household income before employment /100 (squared) Health problems (0.001) (0.001) (0.001) (0.001)
5 V (1) Before (2) Before (3) After (4) After Matching Matching Matching Matching Full Restricted Restricted Full Model Model Model Model (Ref.: none) Health problems without impact on employment chances (0.283) (0.240) Health problems with impact on employment chances (0.281) (0.530) West Germany (Ref.: yes) (0.228) (0.195) (0.224) (0.242) Pseudo R Prob > chi N Likelihood ratio (LR)- Test of model 1 vs. model 2 (based on 1107 cases from model 1): LR chi2(15) = 1.99, Prob > chi2 =1.00; * p<0.10, ** p<0.05, *** p<0.01 Table A2: Balancing table for the sample before and after propensity score matching (jobfinding method) Accepted job found via Variable Sample (before or after matching) Social network Formal job search Standardized bias (%) p- value Search effort (number of different search strategies used) Before After ,05 5,40 0,52 Job seeker placed ad in a newspaper Before After Asked friends, neighbors, relatives Before After Asked caseworker in employment agency Before After
6 VI Accepted job found via Variable Sample (before or after matching) Social network Formal job search Standardized bias (%) p- value Computer search in data base of employment agency Before After Unsolicited application Before After Waited for job offer from employment agency Before After Ever turned down employment offer because wage was too low? Before After duration Before After duration (squared) Before After insurance benefit Before After benefit Before After No unemployment compensation Before After Social benefits Before After No formal qualification Before After Skilled worker/technical training Before After Vocational training/master craftsman/technician Before After Living with partner (married or not married) Before After Children in Household Before After Net household income before employment /100 Before After
7 VII Accepted job found via Variable Sample (before or after matching) Social network Formal job search Standardized bias (%) p- value Net household income before employment /100(squared) Before After West Germany (Ref.: yes) Before Number of Cases After Before After Note: Variables from Model 3, Table A1 only; reported p-value is from a test for the equality of means; standardized bias computed as difference in means divided by the average between the respective variances of the covariate. There is no critical value but as a rule values of below 4-5 can be considered as indicating satisfactory balance (e.g. Caliendo & Hujer, 2006). This balancing table is based on the matching for the non-monetary outcomes. Due to missing values in the wage variable, the number of cases available for matching was lower. However, balancing is sufficient, even if not as good as for the non-monetary outcomes. For cases where balancing is not perfect, regression adjusted matching is recommended (Ho et al. 2007). In our analysis regression adjustment leads to no substantial changes in the results (coefficients for monetary outcomes are slightly smaller than those in Table 2, but still positive and insignificant). The respective tables and analyses are available from the authors on request. Table A3: Logistic regression to estimate the propensity score (job-searching method) Job search behavior Search effort (number of different search strategies used) Job seeker placed ad in a newspaper Computer search in database of employment agency (1) Before Matching (2) Before Matching (3) After Matching (4) After Matching Full Model Restricted Model Restricted Model Full Model 2.494*** (0.572) 2.475*** (0.564) (0.292) (0.312) 0.244*** (0.082) 0.245*** (0.082) (0.649) (0.622) (0.208) (0.210) (0.389) (0.368) Unsolicited application 0.460** (0.173) 0.467** (0.175) (0.609) (0.572) Waited for job offers from the employment agency Other search strategies 0.368*** (0.114) 0.372*** (0.114) (0.407) (0.396) 0.341*** (0.102) 0.343*** (0.103) (0.458) (0.440)
8 VIII Ever turned down employment offer because wage was too low? (Ref.: no, never) duration duration (squared) compensation before employment ((Ref.: unemployment insurance) benefit (1) Before Matching (2) Before Matching (3) After Matching (4) After Matching Full Model Restricted Model Restricted Model Full Model 0.645* (0.161) 0.649* (0.162) (0.449) (0.445) 1.028*** (0.010) 1.028*** (0.010) (0.013) (0.014) 1.000** (0.000) 1.000** (0.000) (0.000) (0.000) (0.213) (0.212) (0.244) (0.243) No compensation (0.225) (0.227) (0.272) (0.287) Social benefits (0.333) (0.332) (0.317) (0.325) In-work benefit (Ref.: yes) Dimensions of homophily Born in Germany (Ref.: yes) (0.147) (0.146) (0.330) (0.337) 1.500* (0.357) 1.499* (0.344) (0.287) (0.282) Sex (0.255) (0.242) (0.279) (0.307) Age (0.016) (0.022) Formal education (none) Lower secondary school (0.338) (0.337) (0.525) (0.507) Secondary school (0.411) (0.414) (0.607) (0.582) Intermediate school (0.515) (0.519) (0.586) (0.573) Upper secondary school Formal qualification (R: none) Skilled worker/technical training (0.668) (0.667) (0.628) (0.648) (0.182) (0.182) (0.265) (0.263)
9 IX Vocational training/master craftsman/technician (1) Before Matching (2) Before Matching (3) After Matching (4) After Matching Full Model Restricted Model Restricted Model Full Model (0.251) (0.251) (0.262) (0.265) Job experience (years) (0.017) (0.010) (0.013) (0.022) Family important (yes) Leisure important (yes) 1.500* (0.312) 1.451* (0.288) (0.228) (0.240) (0.247) (0.246) (0.279) (0.293) Work important (yes) (0.168) (0.220) Access to social capital Living with partner (married or not married) (0.264) (0.377) Children in Household 0.790*** (0.065) 0.781*** (0.064) (0.119) (0.123) Net household income before employment /100 Net household income before employment /100 (squared) Health problems (Ref.: none) Health problems without impact on employment chances Health problems with impact on employment chances West Germany (Ref.: yes) (0.029) (0.028) (0.018) (0.063) (0.001) (0.001) (0.002) (0.287) (0.281) (0.550) (0.563) 0.562** (0.157) 0.554** (0.154) (0.398) (0.400) (0.222) (0.325) Constant (0.604) (0.356) (0.608) (0.799) Pseudo R Prob > chi N Likelihood ratio (LR)- Test of model 1 vs. model 2: LR chi2(4) = 0.67, Prob > chi2 = ; * p<0.10, ** p<0.05, *** p<0.01
10 X Table A4: Balancing table for the sample before and after propensity score matching (jobsearching method) Variable Sample (before or after matching Job search via Social networks and formal methods Formal methods only Standardized bias (%) p-value Search effort (number of different Before search strategies used) After Job seeker placed ad in a Before newspaper After Computer search in data base of Before employment agency After Unsolicited application Before After Waited for job offer from Before employment agency After Other search strategies Before After Ever turned down employment Before offer because wage was too low? After duration duration (squared) insurance benefit benefit No unemployment compensation Social benefits In-work benefit (Ref.: yes) Born in Germany (Ref.: yes) Sex Before After Before After Before After Before After Before After Before After Before After Before After Before After
11 XI Variable Formal education (none) Lower secondary school Secondary school Intermediate school. Upper secondary school No formal qualification Skilled worker/technical training Sample (before or after matching Job search via Social networks and formal methods Formal methods only Standardized bias (%) p-value Before After Before After Before After Before After Before After Before After Before After Vocational training/master Before craftsman/technician After Job experience (years) Family important (yes) Before After Before After Leisure important (yes) Children in Household Before After Before After Net household income before Before employment /100 After Net household income before Before employment /100(squared) After Health problems (Ref.: none) Before After Health problems without impact Before on employment chances After Health problems with impact on Before employment chances After
12 XII Job search via Variable Sample (before or after matching Social networks and formal methods Formal methods only Standardized bias (%) p-value Number of Cases Before After Note: Variables from Model 3, Table A3 only; reported p-value is from a test for the equality of means; standardized bias computed as difference in means divided by the average between the respective variances of the covariate. There is no critical value but as a rule values of below 4-5 can be considered as indicating satisfactory balance (e.g. Caliendo & Hujer, 2006). This balancing table is based on the matching for the non monetary outcomes. Due to missing values in the wage variable, the number of cases available for matching was lower. However, balancing is sufficient, even if not as good as for the nonmonetary outcomes. For cases where balancing is not perfect, regression adjusted matching is recommended (Ho et al. 2007). In our analysis regression adjustment leads to no substantial changes in the results (coefficients for monetary outcomes are similar to results from Table 3, they remain near zero and insignificant). The respective tables and analyses are available from the authors on request. Table A5: Summary Statistics Variable Share Mean Std. Dev. n Treatment variables Job searching via networks Job finding via networks Outcome variables Hourly gross wages (in Euros) Monthly gross wages (in Euros) Satisfied with wage (Dummy, 1 if yes) Satisfied with job (Dummy, 1 if yes) Satisfied with task (Dummy, 1 if yes) Permanent contract (Dummy, 1 if yes) Control variables Search effort (number of different search strategies used) Job seeker placed ad in a newspaper (Dummy, 1 if yes) Asked friends, neighbors, relatives (Dummy, 1 if yes) Asked caseworker in employment agency (Dummy, 1 if yes) Computer search in data base of employment agency (Dummy, 1 if yes) Unsolicited application (Dummy, 1 if yes) Waited for job offer from employment agency (Dummy, 1 if yes) Other search strategies Ever turned down employment offer because wage was too low? (Dummy, 1 if yes) duration (month) insurance benefit (Dummy, 1 if yes)
13 XIII Variable Share Mean Std. Dev. n benefit (Dummy, 1 if yes) No unemployment compensation (Dummy, 1 if yes) Social benefits (Dummy, 1 if yes) In-work benefit (Ref.: yes) Born in Germany (Ref.: yes) Sex (Dummy, 1 if female) Age (Years) No formal education (Dummy, 1 if yes) Lower secondary school (Dummy, 1 if yes) Secondary school (Dummy, 1 if yes) Intermediate school. (Dummy, 1 if yes) Upper secondary school (Dummy, 1 if yes) No formal qualification (Dummy, 1 if yes) Skilled worker/technical training (Dummy, 1 if yes) Vocational training/master craftsman/technician (Dummy, 1 if yes) Job experience (years) Family important (Dummy, 1 if yes) Leisure important (Dummy, 1 if yes) Work important (Dummy, 1 if yes) Living with partner (married or not married) (Dummy, 1 if yes) Number of Children in Household Net household income before employment /100(in Euros) No health problems (Dummy, 1 if yes) Health problems without impact on employment chances (Dummy, 1 if yes) Health problems with impact on employment chances (Dummy, 1 if yes) West Germany (Dummy, 1 if yes)
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