Saving Profiles of Ethnic Minorities: a Life Cycle Analysis Gough, O., Sharma, A., Carosi, A., Adami, R. London, 10/05/2013 Pensions Research Network

14-2 Overview Saving profiles of ethnic minorities Data source: Family Resources Survey (FRS) Framework of analysis: Life-cycle (Modigliani and Brumberg, 1954; Friedman, 1957) Findings: significant heterogeneity amongst ethnic groups in terms of socio-economic characteristics and saving accumulation 2

14-3 Background Improved race relations and multiculturalism. Disadvantage still exists in the job market: poorer opportunities, greater self-employment and unemployment (Berthoud, 2000; Barnes and Taylor, 2006, Mawhinney, 2010). Risk of poverty in retirement, especially for ethnic minority women (Ginn and Arber, 2001) 3

14-4 The Data Family Resources Survey (FRS) 14 waves of cross-sectional individual-year observations from 1994 to 2008 (compatible data) FRS: extensive, nationally representative, annual dataset, detailed financial and demographic data for a large sample of UK residents The initial sample included: Indian, Pakistani, Bangladeshi, Chinese, Black Caribbean, Black African, Any Other Asian, Any Other Black and the white population (as the control group) 4

14-5 The Data (cont’d) The sample was also examined by: gender age cohorts: 16-24, 25-34, 35-44, 45-54, and 65+ And divided according to employment status, including: those in ‘full time employment’, ‘full time self-employment’, ‘part-time employment’, ‘unemployed’ and ‘not working for other reasons’ 5

14-6 Preliminary results on employment Employment: white men up to the age of 55 show highest percentages of those in full time employment. High percentages of full time employment also amongst Indian and Chinese men. Pakistani and Bangladeshi lowest full time employment rates, highest percentages of part-time and self-employment. Significant proportions of ethnic minorities women are in employment, with the exceptions of Pakistani and Bangladeshi women. Highest part time employment, self employment and not in work amongst Pakistani and Bangladeshi women. 6

14-7 Methodology Life-cycle framework to explain saving profiles of ethnic minorities (Jappelli, 1999). We use 2 Equations : Equation 1: the probability of an individual to save is a function of a polynomial in age, a matrix consisting of total income (in linear and quadratic terms), employment and education; a cohort polynomial specified by the respondent’s year of birth, and a set of time effects. Equation 2: we regress the logarithm of total private savings (the sum of an individual’s financial assets) as a function of factors used in first regression 7

14-8 Methodology (cont’d) Equation 1: (1) p (s a,b ) = g(a) + X  + h(b) + t a+b  +  p (s a,b ): probability of an individual to save g(a): a polynomial in age, X: matrix of socio-economic variables such as total labour income, employment and education, h(b) a cohort polynomial specified by the respondent’s year of birth t a+b a set of time effects. where ‘a + b’ shows the sampling year as age + year of birth  is the error term. 8

14-9 Notes on Methodology 1. Employment status is re-coded into three dummy variables: Full Time employment (x = 1 if employed or self-employed full time, x = 0 otherwise); Part Time employment (x = 1 if employed or self- employed part time, x = 0 otherwise) and Not in Employment (x = 1 if unemployed, x = 0 otherwise) 2. We use dummy variables to code Education and divide our sample into those in Low Education and those in High Education (FRS). We classify as Low Education school and sandwich course certificates. High Education is defined as university or college degrees, qualification in nursing or similar, open college courses, open- university, correspondence course. 9

14-10 Methodology (cont’d) Equation 2: (2) ln (S a,b ) = g(a) +X  + h(b) + t a+b  +  Total individual private saving or the sum of financial assets (S a,b ) as a function of the same factors specified in equation 1 10

14-11 Methodology (cont’d) Data on savings for some ethnic groups is not available until 2000, the regression analysis is restricted to those groups for which we have full coverage. Savers are defined as those respondents with at least one type of savings amongst basic accounts, national savings, saving for retirement and investments in any given year. Two separate sets of regressions are run separately for men and women, both regressions are run on a minimum of 50 observations of savers with complete information. 11

14-12 Methodology (cont’d) As a result, we confine our analysis to a sub-sample of three ethnic groups: Indian, Pakistani, Bangladeshi and the control group. We report only the results obtained from regressions where the coefficients are significant. 12

14-13 Summary statistics Summary Statistics – Control Group MALE FEMALE MeanMedian MeanMedian AGE YEAR-OF-BIRTH TOTAL INCOME Summary Statistics – Indian MALE FEMALE MeanMedian MeanMedian AGE YEAR-OF-BIRTH TOTAL INCOME Summary Statistics - Pakistani MALE FEMALE MeanMedian MeanMedian AGE YEAR-OF-BIRTH TOTAL INCOME Summary Statistics - Bangladeshi MALE FEMALE MeanMedian MeanMedian AGE YEAR-OF-BIRTH TOTAL INCOME

14-14 Regression 1 results, men MALES WHITE Indian Pakistani Bangladeshi Dependent Variable: Pr (SAVE) Independent Variables (1) (2) (3) (4) (5) (6) (7) (8) AGE (0.75) (-0.17) (-0.05) (-1.31) (-0.28) (-0.21) (-0.19) (-1.33) AGE^ *** *** (-6.91) (1.50) (-1.24) (-1.13) (-0.34) (0.17) (-2.62) (-0.97) AGE^ *** * ** (7.93) (5.95) (1.20) (1.66) (-1.52) (-2.10) (-1.10) (-0.53) AGE^ *** * (-1.54) (-5.30) (1.24) (1.38) (1.25) (0.89) (1.87) (0.60) AGE^ *** *** (-3.51) (-3.47) (-0.75) (-0.84) (2.60) (2.73) (1.61) (0.52) TOTAL INCOME *** (-6.11) (-0.32) (0.82) (-0.52) FT EMPLOYED 0.160*** 0.073** *** (60.71) (2.46) (1.24) (8.02) PT EMPLOYED 0.049*** (9.77) (-0.27) (-1.21) (0.20) HIGH EDUCATION 0.027*** 0.082** 0.099** 0.084*** (7.52) (2.32) (2.35) (3.13) Observations179,337157, ,7181,513 Adjusted R- Squared

14-15 Regression 1 results, women 15 FEMALES WHITE Indian Pakistani Bangladeshi Dependent Variable:TOTAL SAVINGS_D Independent Variables (1) (2) (3) (4) (5) (6) (7) (8) AGE *** *** (-0.04) (-3.32) (1.23) (0.78) (0.16) (-0.12) (-1.46) (-2.80) AGE^ *** 0.000*** * ** (-6.59) (2.70) (1.23) (1.94) (0.20) (0.85) (-2.14) (-1.32) AGE^ *** * (8.86) (5.66) (0.59) (0.80) (1.92) (1.42) (0.97) (1.11) AGE^ *** * * (-1.29) (-6.91) (-1.82) (-1.37) (-0.33) (-0.43) (1.83) (1.20) AGE^ *** (-2.99) (-0.13) (-1.59) (-1.10) (-1.23) (-1.03) (-0.61) (-1.00) TOTAL INCOME *** (-7.51) (-0.06) (-0.16) (-0.04) FT EMPLOYED 0.154*** 0.132*** *** (51.75) (2.62) (0.49) (3.22) PT EMPLOYED 0.097*** 0.106* *** (33.93) (1.82) (1.08) (3.37) HIGH EDUCATION 0.046*** 0.066* 0.070* 0.102*** (14.22) (1.76) (1.73) (4.08) Observations202,116177, ,0531,832 Adjusted R-Squared

14-16 Regression 2 results, men 16 MALES WHITE Indian Pakistani Bangladeshi Dependent Variable: LN(1+TOTAL SAVINGS) Independent Variables (1) (2) (3) (4) (5) (6) (7) (8) AGE 0.075*** 0.062*** ** *** (9.49) (6.34) (-0.26) (-0.37) (-2.06) (-2.75) (1.52) (-0.10) AGE^ ** * ** ** (-1.18) (-0.05) (-2.07) (-1.91) (-2.38) (-2.28) (0.17) (0.65) AGE^ *** 0.001* ** * 0.000** (-2.65) (-2.83) (1.80) (1.61) (-2.38) (-1.72) (2.01) (2.26) AGE^ ** (1.41) (1.18) (0.06) (0.22) (2.30) (2.53) (-0.44) (-1.29) AGE^ ** ** *** (0.93) (0.87) (-1.44) (-0.82) (2.46) (2.62) (-2.17) (-2.77) TOTAL INCOME 0.000*** ** (8.65) (0.94) (0.33) (2.18) FT EMPLOYED (-0.78) (-0.07) (-1.14) PT EMPLOYED (-0.45) (0.94) (-0.08) (-0.25) HIGH EDUCATION 0.080** (2.53) (0.59) (1.11) Observations45,72440, Adjusted R- Squared

14-17 Regression 2 results, women 17 FEMALES WHITE Indian Pakistani Bangladeshi Dependent Variable:LN(1+TOTAL SAVINGS) Independent Variables (1) (2) (3) (4) (5) (6) (7) (8) AGE 0.050*** * ** (6.78) (0.93) (-1.83) (-2.28) (-0.02) (0.22) (1.19) (-0.12) AGE^ * (-1.04) (1.89) (0.24) (0.31) (-1.03) (-0.33) (0.67) (0.07) AGE^ * 0.001** 0.002** (0.01) (-1.75) (2.32) (2.59) (-0.97) (-0.37) (-0.31) (-0.13) AGE^ (1.37) (1.21) (-0.36) (-0.31) (0.98) (0.34) (-0.41) (0.27) AGE^ (-0.64) (-0.48) (-1.01) (-0.95) (0.92) (0.35) (0.07) (-0.19) TOTAL INCOME 0.002*** 0.006*** 0.006** 0.003* (18.96) (3.77) (2.40) (1.75) FT EMPLOYED (-1.25) (-1.16) (-0.77) (-0.22) PT EMPLOYED (-0.62) (-1.08) (-1.05) (-0.16) HIGH EDUCATION 0.314*** (11.28) (-0.46) (0.30) (1.40) Observations49,49943, Adjusted R-Squared

14-18 Results: saving participation ratios Male sampleFemale sample 18

14-19 Results: saving profiles Male sample Female sample 19

14-20 Concluding Remarks  Significant heterogeneity amongst ethnic groups in terms of employment and saving patterns  Pakistanis and Bangladeshis experience substantial disadvantage in work, while Indians show employment rates close to the control group  Employment, education and income are significant in determining how individuals save for retirement  Probability to save depend on employment and education, levels saved strongly depend on income and education  Lack of sufficiently long-term private saving within some ethnic minorities, especially women, is concerning 20