Rukhsana Kausar 1, Stephen Drinkwater 2

Transcription

1 Who is Better off? Employment Differentials between Refugees/ Asylum Seekers and Economic Immigrants in the UK Rukhsana Kausar 1, Stephen Drinkwater 2 Labour Force Survey user meeting Thursday 2 December 2010 Royal Statistical Society, London 1 University of Surrey 2 Swansea University

2 Difference Between Economic Immigrants and Refugees Refugee Definition according to 1951 UN Convention The word refugee refers to a person who owing to a well founded fear of being persecuted for reasons of race, religion, nationality or membership of a particular social group or political opinion is outside the country of his nationality and is unable or owing to such fear, is unwilling to avail himself of the protection of that country. Asylum Seeker: Someone who is fleeing persecution in his or her country and has arrived in another country and exercises the legal right to apply for asylum/ individuals claim to be refugees who are waiting for a decision from the Home Office on their cases. Economic Migrant: Someone who migrates purely on economic motives with the expectation of increasing their lifetime income. Their economic behaviour differs in terms of their work effort, consumption, savings (remittances) and human capital investment.

3 Empirical Literature Review Many studies on the labour market performance of immigrants but relatively few that focus on refugees/asylum seekers including: Lindley (2002) Analysis of labour market performance of different immigrants groups using quarterly Labour Force Survey data ( ) for the UK. Findings - Larger earnings penalties and higher unemployment propensities for individuals from refugee sending countries and significant unexplainable ethnic penalties. Cortes (2004) Analysis of implicit time horizon differences and effects on human capital investment for US refugees and economic immigrants in the US. Findings-Faster earning growth for refugees over time due to the higher country-specific human capital investment of refugees.

4 Objectives of this paper To Analyze labour market performance of Refugees & Asylum Seekers in the UK, focusing specifically on their Economic activity and employment. By attempting to make a clear distinction between Refugees/Asylum Seekers and Economic To examine the labour market assimilation of asylum seekers/refugees (relative to other immigrants) in terms of their employment and returns to education as well as ethnicity.

5 Data Sources For labour market and socio-economic variables Quarterly Labour Force Survey For the construction of different migrant categories various sources used: Asylum Statistics United Kingdom Home Office Statistical Bulletin Control of Immigration Statistics Home Office, for the years 2000, 2003 and 2006 The State of the World s Refugees- UNHCR A Humanitarian Agenda The State of the World s Refugees- UNHCR-2000

6 Methodology for Categorization of Asylum Seekers and Refugees Asylum Seekers and Refugees category is defined using the following data Data for number of asylum applications made Refugees and Business Acceptance Ratio for settlement Refugee- Business Ratio Rb-ratio >5 : Category I 1 5 : Category II 0< & < 1 : Category III 0 : Category IV Asylum Seekers and Refugees are divided in to four categories using these sources Categories of Asylum seekers and Refugees I Refugees and asylum seekers II Mixed Refugees and Economic III Mainly Economic IV Economic

7 Table 1: Example of Countries in each Category LFS Code County No. of Asylum Applications Refugee - Business Ratio Category I,II, III,IV 11 Australia <1 IV 14 Kenya High in Mid 1990s 1-5 II 16 Tanzania High I: II:>= Jamaica Nothing until 1996, High in early 2000 >5: 2000 <1:2003& 2006 IV: III:>= Iraq High in late 1990s & early 2000s >5 I: >=1989

8 Economic Activity/Inactivity by Immigrant Category; Males Category Refugees & Asylum Seekers Mixed Refugees & Eco. Mainly Economic Economic Total Employed % % % % % Unemployed % 9.38 % 4.80 % 5.69 % 7.52 % Students 3.33 % 1.51 % 1.15 % 1.20 % 1.67 % Looking after Family/ 1.11 % 0.82 % 0.79 % 0.63% 0.79% Home Temporarily Sick/ Injured/Disabled 2.34 % 2.05 % 0.86 % 0.79 % 1.34 % Long Term Sick/ Injured/Disabled 4.99 % 3.08 % 1.22 % 1.58 % 2.47 % Not Looking Jobs % 7.12 % 3.37 % 3.90 % 6.74 % No. of Observations

9 Economic Activity/Inactivity by Immigrant Category; Females Category Refugees & Asylum Seekers Mixed Refugees& Eco. Mainly Economic Economic Total Employed % % % % % Unemployed 5.95 % 4.91 % 5.44 % 4.60 % 5.04 % Students 3.62 % 1.89 % 1.47 % 1.29 % 1.83 % Looking after Family/ Home % % % % % Temporarily Sick/ Injured/Disabled 1.81 % 1.70 % 0.98 % 0.85 % 1.18 % Long Term Sick/ Injured/Disabled % % 6.79 % 5.55 % 8.13 % Not Looking Jobs % 7.88 % 5.57 % 5.38 % 6.78 % No. of Observations

10 Employment Job Type by Immigrant Category Category Refugees & Asylum Seekers Mixed Refugees & Eco. Mainly Economic Economic Total Males Permanent % % % % % Temporary % 8.16 % % 9.97 % % Self-Employed % % % % % No. of Observations Females Permanent % % % % % Temporary % % % % % Self-Employed 6.29 % 5.93 % 7.62 % 8.08 % 7.55 % No. of Observations

11 Empirical Methodology A formal regression analysis is used to explore the determinants of employments for immigrants and to compare the earnings of refugees/asylum seekers relative to other immigrants = 0 if not employed and = 1 if employed Z i = A Set of Control Variables γ = Associated vector of Coefficients for Z i Im mig i = Set of Dummies for Immigrants category δ = Associated vector of coefficients for Co ε = Constant & = Error Term Im mig i

12 Table 4: Regression Estimates for Employment for Males and Females Males Medium Education 0.096*** (0.012) High Education 0.124*** (0.010) Asians ** (0.012) Black *** (0.017) Chinese & Others *** (0.013) No. of Dependent Children *** (0.004) Mixed Refugees & Economic 0.141*** (0.016) Mainly Economic 0.227*** (0.015) Economic 0.206*** (0.014) Years since Migration 0.009*** (0.001) Females 0.021*** (0.012) 0.129*** (0.011) *** (0.014) * (0.016) *** (0.014) 0.119*** (0.005) 0.087*** (0.016) 0.219*** (0.013) 0.169*** (0.013) 0.012*** (0.001) No. of Obs Adj R-squared Note: Robust standard errors are in parentheses and default categories are low educated, white and refugees and asylum seekers (Category 1). *p<0.1; ** p <0.05; *** p<0.01 (two-tailed tests)

13 Table 5: Regression Estimates for Employment for Immigrant Category; Males Refugees & Asylum Seekers Mixed Refugees & Economic Mainly Economic Economic Medium Education 0.155*** (0.028) 0.060* (0.029) 0.037** (0.029) 0.021*** (0.012) High Education 0.183*** (0.028) 0.085** (0.027) 0.082*** (0.023) 0.129*** (0.011) East (0.062) 0.061*** (0.041) (0.034) 0.081*** (0.019) (0.037) (0.031) (0.028) (0.014) South 0.151** (0.047) 0.067* (0.37) 0.062* (0.027) 0.086*** (0.016) Asians * * *** (0.029) (0.039) (0.017) (0.014) Black *** * (0.034) (0.039) (0.051) (0.016) Chinese & Others 0.122*** ** ** *** (0.031) (0.046) (0.030) (0.014) No. of Dependent *** ** ** Children (0.011) (0.009) (0.010) (0.005) Years since 0.029*** 0.006*** 0.008*** Migration (0.003) (0.003) (0.002) (0.001) No. of Obs Adj R-squared Note: Robust standard errors are in parenthesis. Default categories are low educated and white. *p<0.1; ** p <0.05; *** p<0.01 (two-tailed tests).

14 Table 6: Regression Estimates for Employment by Immigrant Category; Females Refugees & Asylum Seekers Mixed Refugees & Economic Mainly Economic Economic Refugees & Asylum Seekers Mixed Refugees & Economic Mainly Economic Economic Medium Education 0.168*** (0.026) 0.102** (0.027) 0.121*** (0.032) 0.074*** (0.018) High Education 0.193*** (0.025) 0.076** (0.026) 0.138*** (0.030) 0.074*** (0.017) Asians (0.031) *** (0.044) *** (0.029) *** (0.022) Black * (0.030) 0.091* (0.045) (0.045) *** (0.027) Chinese & Others *** (0.028) *** (0.049) ** (0.035) *** (0.019) No. of Dependent Children *** (0.010) *** (0.008) *** (0.004) *** (0.009) Years since Migration 0.026*** (0.003) 0.008** (0.003) 0.011** (0.003) 0.012*** (0.001) No. of Obs Adj R-squared Note: Robust standard errors are in parenthesis. Default categories are low educated and white. *p<0.1; ** p <0.05; *** p<0.01 (two-tailed tests).

15 Table 7: Multinomial Logit Estimates for Different Job Types of Employment Males Females Temporary Self-Employed Temporary Self-Employed Medium Education 0.362* (0.130) (0.100) 0.410** (0.134) High Education 0.514*** *** (0.118) (0.094) (0.128) London ** 0.264** *** (0.128) (0.118) (0.133) South ** *** (0.149) (0.135) (0.151) Asians *** (0.128) (0.105) (0.135) Black 0.366** *** (0.152) (0.165) (0.153) Chinese & Others 0.323** *** (0.127) (0.129) (0.129) No. of Dependent ** Children (0.035) (0.038) (0.047) Mixed Refugees & ** Economic (0.162) (0.129) (0.190) Mainly Economic ** (0.146) (0.135) (0.163) Economic ** ** * (0.137) (0.114) (0.146) Years since Migration *** 0.089*** *** (0.014) (0.008) (0.014) No. of Obs (0.158) (0.139) 0.517** (0.193) (0.211) ** (0.171) *** (0.277) ** (0.165) 0.248** (0.078) 0.495* (0.264) 0.533** (0.225) 0.389** (0.192) 0.050*** (0.013) Pseudo R-squared Note: Robust standard errors are in parentheses. Default categories are single, low education, living in North, white, year 2006, in permanent employment and refugees and asylum seekers (Category 1). * p<0.1; ** p <0.005; *** p<0.01 (two-tailed tests

16 Conclusions Refugees/Asylum Seekers do worse than other immigrants in terms of having a job, both for males and females. Education, location, ethnicity and years since migration are important determinants of the labour market performance of immigrants. Returns to years since migration are the greatest for refugees and asylum seekers, showing faster assimilation over time compared to economic migrants. The separate results for education and migrant categories show that the highest returns to education for employment are experienced by refugees/asylum seekers.

17 Policy Implications: Policies should target the welfare of different migrant and ethnic groups because of the diversity in labour market outcomes. Ethnicity can not be ignored when analyzing the labour market performance of immigrants, therefore measures are required to further discourage discrimination. Investment in human capital is to be encouraged for migrants, including through the provision of English language training.