Family, Community and Long-Term Earnings Inequality Paul Bingley, SFI Copenhagen Lorenzo Cappellari, Università Cattolica Milano Konstantinos Tatsiramos, University of Nottingham Winter School on Inequality and Social Welfare Theory Canazei, 10 January 2017

Motivation and Research Question Two circumstances that can influence individuals’ earnings potential beyond their effort: Family Community (schools and youth neighborhoods) What are the contributions of family, school and neighborhood in the income generation process? Is family more important than community? Do schools and/or neighborhoods matter? What are the long-term effects of these factors? Do they vary over the life-cycle? Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 This paper Using administrative data for 700,000 Danish men we model the joint earnings dynamics of siblings, neighbors and schoolmates between ages 24 and 51. We use the model to decompose for the first time the sibling correlation of earnings into family, neighborhood and school effects. We exploit two sources of variation: Differences of communities within families: mobility and its timing Partially overlapping peer groups within communities Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Findings Family is by far the most relevant factor that shapes the inequality of long-term earnings. Overall, the impact of community is limited. But there is a significant effect in the initial phase of the working life (up to age 30), which fades away afterwards. Not properly accounting for sorting of families into communities leads to upwardly biased community effects. Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Outline of talk Literature Data Model and identification Results Parameter estimates Decompositions Alternative identification strategies Robustness Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Literature Bingley, Cappellari and Tatsiramos 2017

Sibling correlations Omnibus measure of family and community effects (Corcoran et al., 1976; Solon et al., 1991; Altonji and Dunn, 1991). Models of sibling effects in permanent incomes: 𝑦 𝑖𝑗 = 𝑎 𝑖𝑗 + 𝑓 𝑗 , 𝑎 𝑖𝑗 ~ 0, 𝑣𝑎𝑟 𝑎 ; 𝑓 𝑗 ~ 0, 𝑣𝑎𝑟 𝑓 𝑟 𝑆 = 𝑣𝑎𝑟 𝑓 𝑣𝑎𝑟 𝑎 +𝑣𝑎𝑟 𝑓 Share of inequality in permanent incomes accounted for by factors shared by siblings (loosely speaking inequality ‘between families’). 𝑟 𝑆 =0.35 - 0.40 in the US (Solon et al. 1991; Mazumder, 2008), 0.35 in SWE (Björklund et al., 2009), 0.23-0.26 in DK (Björklund and Jannti, 2012), 0.43 in GER (Schnitzlein, 2014). Bingley, Cappellari and Tatsiramos 2017

Decomposing sibling correlations The idea of unpacking the sibling correlation goes back to Page and Solon (2003) PSID: in 1968 households were sampled in clusters (blocks of houses) 𝑦 𝑖𝑗𝑛 = 𝑎 𝑖𝑗𝑛 + 𝑓 𝑗𝑛 + 𝑐 𝑛 Siblings: 𝑐𝑜𝑣 (𝑦 𝑖𝑗𝑛 ;𝑦 𝑖 ′ 𝑗𝑛 )=𝑣𝑎𝑟 𝑓 +𝑣𝑎𝑟 𝑐 +2𝑐𝑜𝑣 𝑓,𝑐 Neighbors: 𝑐𝑜𝑣( 𝑦 𝑖𝑗𝑛 ;𝑦 𝑖 ′ 𝑗 ′ 𝑛 )= 𝑣𝑎𝑟 𝑐 +2𝑐𝑜𝑣 𝑓,𝑐 Because of the PSID design they could not separate neighborhood (𝑣𝑎𝑟 𝑐 ) from sorting (𝑐𝑜𝑣 𝑓,𝑐 ). 2 moment restrictions and 3 parameters They compare neighbors vs sibling correlations. 50%, upper bound Bingley, Cappellari and Tatsiramos 2017

Decomposing sibling correlations Oreopolous (2003) looks at randomized neighbors from a social housing experiment in Toronto 𝑐𝑜𝑣 𝑓,𝑐 =0 by design Finds 𝑐𝑜𝑣 (𝑦 𝑖𝑗𝑛 ;𝑦 𝑖 ′ 𝑗 ′ 𝑛 )=0, i.e. neighborhoods don’t matter Rauum et al. (2006) use the model of Page and Solon on Norwegian population data. They residualize on neighborhood fixed effects to limit sorting. Find sizeable neighborhood effects (30% of the overall sibling correlation). Bingley, Cappellari and Tatsiramos 2017

Neighborhoods and schools Focus on mean outcomes Experimental evidence from the MTO project suggests little impact on economic outcomes (e.g. Ludwig et al., 2013). Recent evidence instead finds effects that depend on the exposure to neighborhoods when young (Chetty et al. 2016; Chetty and Hendren, 2016) Effect of class size: Chetty et al. (2011): no effect on earnings at age 27 from the Tennessee STAR experiment. Fredriksson et al. (2013): positive effect on adult earnings at ages 27 to 42 using class size rule in Sweden. Leuven and Kokken (2015) find zero effect in Norway for earnings 20-48 Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Data Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Brothers Danish register: Brothers born 1960-1983 First two brothers sharing both biological parents 1-12 yrs age spacing No twins; no adoptees 120k sibling pairs 380k singletons Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Peers We link brothers and singletons to their youth peers Schoolmates: born in the same year and attending the same school on 31° October in the year they turn 15 (normally grade 9). Exploit information from the National Pupil database which reports school identifier for 15 years old since 1973 1858 schools, with average 19.5 boys from the same birth cohort. Neighbors: born in the same year and living in the same parish on 31° October in the year they turn 15. Parish is the only place identifier recorded consistently throughout 2123 parishes, with average 14.6 boys from the same cohort Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Peers We use age 15 because we do not have information at earlier ages for schools For selected cohorts we can retrieve information on parish back to age 10 Use it for robustness checks and to exploit differential timing of mobility for identification Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Sources of variation Brothers do not share the school or neighborhood when they attended different schools or lived in different neighborhoods at age 15 70% same school and neighborhood, 15% only same neighborhood, 6% only same school, 9% entirely different community Among those with same school and neigborhood: Moved between 10° and 15° birthday of brother 1 (15%) Never moved or moved before brother 1 turned 10 (85%) No full overlap between neighborhoods and public school catchment areas Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Earnings Gross annual income from labor 1984 – 2011 (2005 prices) Life-cycle earnings age 24 – 51 Trim 0.25% at each tail Require at least 3 consecutive observations Otherwise fully unbalanced panel Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Corr(B1,B2|Age B1=35) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Corr(B1,B2|Age B1=Age B2) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Corr(B1,B2|Age B1=Age B2) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Corr(Peers) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Corr(Unrelated) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Model Bingley, Cappellari and Tatsiramos 2017

Joint earnings dynamics for groups of individuals Individual earnings dynamics (Moffitt and Gottschalk, 1995; Meghir and Pistaferri, 2004) Earnings dynamics of couples (Hyslop, 2001) Bingley and Cappellari (2014) extend to earnings dynamics of triads (1 father; 2 sons) This papers extends to joint earnings dynamics for groups of arbitrary size Bingley, Cappellari and Tatsiramos 2017

Heterogeneous Income Profiles (HIP) 𝑦 𝑖𝑡 = 𝑎 𝑖 + 𝑏 𝑖 𝐴 𝑖𝑡 ; 𝑣𝑎𝑟 𝑎 , 𝑣𝑎𝑟 𝑏 , 𝑐𝑜𝑣 𝑎𝑏 If Cov(ab)<0 y Var(y) Age Age Bingley, Cappellari and Tatsiramos 2017

HIP on the shared components Human capital models predict investments to induce a trade-off between starting earnings and life-cycle earnings growth (Mincer, 1958; Ben-Porath, 1967). Earnings variance u-shaped in age. Families determine human capital investments (Becker and Tomes, 1986). Also communities might play a role. Then both family and community correlation could be u-shaped in age. Robustness to life-cycle bias (Haider and Solon, 2006) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Permanent Earnings HIP + RIP: 𝑦 𝑖𝑓𝑠𝑛𝑎 = 𝛿 𝑐 𝜋 𝑡 𝜇 𝑓 + 𝜇 𝑠 + 𝜇 𝑛 + 𝛾 𝑓 + 𝛾 𝑠 + 𝛾 𝑛 𝑎+ 𝜔 𝑖𝑎 ; 𝜔 𝑖𝑎 = 𝜔 𝑖(𝑎−1) + 𝜉 𝑖𝑎 ; 𝑡=𝑐 𝑖 +24+𝑎 Shared components correlated both between and within dimension. 𝜇 𝑓 , 𝛾 𝑓 ~ 𝜎 𝜇Φ 2 , 𝜎 𝛾Φ 2 ,𝜎 𝜇𝛾Φ ; 𝜇 𝑠 ,𝛾 𝑠 ~ 𝜎 𝜇Σ 2 , 𝜎 𝛾Σ 2 , 𝜎 𝜇𝛾Σ 𝜇 𝑛 ,𝛾 𝑛 ~ 𝜎 𝜇N 2 , 𝜎 𝛾N 2 , 𝜎 𝜇𝛾N ; 𝜇 𝑓 , 𝜇 𝑠 , 𝜇 𝑛 ~ 𝜎 ΦΣ , 𝜎 ΦN , 𝜎 ΣN The model allows for the sorting of families into communities Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Moment restrictions Brothers: 𝑐𝑜𝑣 𝑦 𝑖𝑓𝑠𝑛𝑎 ; 𝑦 𝑖 ′ 𝑓 𝑠 ′ 𝑛 ′ 𝑎 ′ = [ 𝜎 𝜇Φ 2 + 𝜎 𝛾Φ 2 𝑎 𝑎 ′ + 𝜎 𝜇𝛾Φ 𝑎+ 𝑎 ′ +𝐼 𝑠= 𝑠 ′ 𝜎 𝜇Σ 2 + 𝜎 𝛾Σ 2 𝑎 𝑎 ′ + 𝜎 𝜇𝛾Σ 𝑎+ 𝑎 ′ +𝐼 𝑛= 𝑛 ′ 𝜎 𝜇Ν 2 + 𝜎 𝛾Ν 2 𝑎 𝑎 ′ + 𝜎 𝜇𝛾N 𝑎+ 𝑎 ′ + 2 𝜎 ΦΣ +2 𝜎 ΦN +2 𝜎 ΣN ] 𝜋 𝑡 𝜋 𝑡 ′ 𝛿 𝑐 𝛿 𝑐 ′ The difference of sibling correlations between ‘staying’ and ‘moving’ families identifies community effects (moving between the 15° birthday of B1 and B2) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Moment restrictions Peers: 𝑐𝑜𝑣 𝑦 𝑖𝑓𝑠𝑛𝑎 ; 𝑦 𝑖 ′ 𝑓 ′ 𝑠 ′ 𝑛 ′ 𝑎 ′ = [𝐼 𝑠= 𝑠 ′ 𝜎 𝜇Σ 2 + 𝜎 𝛾Σ 2 𝑎 𝑎 ′ + 𝜎 𝜇𝛾Σ 𝑎+ 𝑎 ′ + 2 𝜎 𝜇ΦΣ +𝐼 𝑛= 𝑛 ′ 𝜎 𝜇Ν 2 + 𝜎 𝛾Ν 2 𝑎 𝑎 ′ + 𝜎 𝜇𝛾N 𝑎+ 𝑎 ′ +2 𝜎 𝜇ΦN +2 𝜎 𝜇ΣN ] 𝜋 𝑡 𝜋 𝑡 ′ The difference between sibling correlations and peers correlations identifies family effects The difference between peers correlation and community effects identifies sorting effects Bingley, Cappellari and Tatsiramos 2017

Identification of variance components Three (sets of) moment conditions ‘Staying’ families ‘Moving’ families Peers for three (sets of) parameters Family Community Sorting Bingley, Cappellari and Tatsiramos 2017

Results

Idiosyncratic components (RIP - Random Walk)   Initial condition (age 24) Brother 1 ( 𝜎 𝜔24,1 2 ) 0.0822 (0.0133) Brother2 ( 𝜎 𝜔24,2 2 ) 0.0731 (0.0129) Variance of innovations Brother 1 ( 𝜎 𝜉1 2 ) 0.0729 (0.0127) Brother 2 ( 𝜎 𝜉2 2 ) 0.0593 (0.0101) Bingley, Cappellari and Tatsiramos 2017

Shared components (1) (HIP - Random Growth) Variance of intercepts Family ( 𝜎 𝜇Φ 2 ) 0.1482 (0.0241) School ( 𝜎 𝜇Σ 2 ) 0.0173 (0.0082) Neighborhood ( 𝜎 𝜇N 2 ) 0.0203 (0.0095)   Variance of slopes Family ( 𝜎 𝛾Φ 2 ) 0.0026 (0.0004) School ( 𝜎 𝛾Σ 2 ) 0.0002 (0.0001) Neighborhood ( 𝜎 𝛾N 2 ) 0.0005 Bingley, Cappellari and Tatsiramos 2017

Shared components (2) HIP – Random Growth Covariance between components Family-School ( 𝜎 𝜇ΦΣ ) 0.0074 (0.0033) Family-Neighborhood ( 𝜎 𝜇ΦN ) 0.0101 (0.0038) School- Neighborhood ( 𝜎 𝜇ΣN ) 0.0050 (0.0008)   Covariance intercepts-slopes Family ( 𝜎 𝜇𝛾Φ ) -0.0117 (0.0019) School ( 𝜎 𝜇𝛾Σ ) -0.0030 (0.0009) Neighborhood ( 𝜎 𝜇𝛾N ) -0.0052 (0.0012) Bingley, Cappellari and Tatsiramos 2017

Decomposition: Life cycle average coeff. s.e. Brothers (F + C) 0.319 0.010 Family 0.298 0.012 Community (N + S) 0.021 0.009 Neighborhood School 0.011 Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Decomposition: Age 24 coeff. s.e. Brothers 0.75   Family 0.48 Community 0.12 Sorting 0.15 Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Decomposition Bingley, Cappellari and Tatsiramos 2017

Ignoring sorting effects Only stayers   No family effects coeff. s.e. Brothers 0.313 0.011 Family 0.268 School 0.029 0.001 0.044 Neighborhood 0.015 0.026 Community (N+S) 0.045 0.071 Bingley, Cappellari and Tatsiramos 2017

Larger community effects before age 30   Baseline 25 27 30 Coeff. s.e. Brothers 0.319 0.010 0.661 0.013 0.525 0.011 0.397 Family 0.298 0.012 0.483 0.019 0.399 0.015 0.323 Parish 0.095 0.020 0.063 0.033 School 0.084 0.017 0.041 0.009 Community 0.021 0.179 0.126 0.074 C/B 0.06 0.270 0.240 0.187 Bingley, Cappellari and Tatsiramos 2017

Comparison with the Literature Page and Solon (2003): Siblings=0.31, Neighbors=0.16 Oreopoulos (2003): All Toronto: Siblings=0.28, Neighbors=0.054 Public Housing: Siblings=0.26, Neighbors=0.00 Rauum et al (2006): Siblings =0.20, Neighbors=0.06 This paper: Only community: Peers=0.071 Ignoring sorting: Siblings =0.31, Peers =0.045 Joint model: Siblings =0.31, Peers= 0.021 Bingley, Cappellari and Tatsiramos 2017

Identification: Brothers «Moving» families: change of community in the time window between brothers’ 15° birthday «Staying» families: never changed community, or did change community before older brother turned 15 An issue if these groups differ in relevant unobservables We tackle the issue on a subset of younger cohorts (>1965) for whom we know parish since age 10, by either: Use only movers and limit the comparison between movers and those moving parish between B1’s 10° and 15° birthday (early movers), thus exploiting variation in timing of move Use only stayers 10-20, and compare sibling couples with high vs low degree of peers’ turnover (in-place mobility) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Timing of mobility Brothers Family Neighborhood School Community (N+S) C/B Baseline 0.319 0.298 0.01 0.011 0.021 0.066 (0.01) (0.012) (0.009) Cohorts > 1965 0.299 0.272 0.004 0.023 0.027 0.089 (0.008) (0.011) (0.010) 0.316 0.271 0.034 0.045 0.142 Only movers (0.013) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Timing of mobility Full sample Only movers Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 In-place mobility Brothers Family Community C/B Baseline 0.319 0.298 0.021 0.066 (0.01) (0.012) (0.009) Inplace mobility 0.324 0.290 0.034 0.105   (0.020) (0.018) (0.008) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 In-place mobility Bingley, Cappellari and Tatsiramos 2017

Robustness: measurement error from point-in-time definition of peers Bingley, Cappellari and Tatsiramos 2017

Robustness: Early neighborhood exposure   Brothers Family Neighborhood School Community C/B Baseline 0.299 0.272 0.004 0.023 0.027 0.089 (Cohorts > 1965) (0.008) (0.011) (0.010) Parental parish 0.329 0.307 0.018 0.022 0.066 when 15 (0.012) (0.016) (0.013) 0.356 0.325 0.011 0.020 0.031 0.088 when 14 (0.019) (0.014) 0.030 0.086 when 13 0.349 0.312 0.019 0.037 0.106 when 12 (0.015) (0.018) 0.289 0.261 0.017 0.028 0.097 when 11 (0.009) 0.294 0.267 0.009 0.094 when 10 Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Robustness Brothers Family Neighborhood School Community Baseline 0.319 0.298 0.01 0.011 0.021 (0.010) (0.012) (0.009) Up to 2 children 0.344 0.318 0.017 0.009 0.026 (0.016) (0.021) (0.015) Up to 3 children 0.296 0.286 0.020 -0.010 0.010 (0.013) Excluding 0.329 0.308 singletons (0.011) Peers at 14 and 15 0.299 Main parish of 0.300 0.006 0.013 0.019 residence 14-18 Excluding private 0.311 0.291 0.005 0.015 schools ZIP codes 0.322 0.313 -0.015 0.024 0.008 (0.014) Bingley, Cappellari and Tatsiramos 2017

Bingley, Cappellari and Tatsiramos 2017 Summary Demonstrate the value of analysing joint earnings dynamics of siblings and peers. Sibling correlation u-shaped in age, consistent with human capital models. Family is most of sibling correlation. Community effects are relevant before age 30. Ignoring family effects leads to substantial overestimation of community effects. Sorting. Similar results when exploit variation in the timing of residential mobility or in-place mobility for identification Bingley, Cappellari and Tatsiramos 2017

Appendix

Transitory shocks Coef. s.e. Initial condition (age 24)   Coef. s.e. Initial condition (age 24) Brother 1 ( 𝜎 24,1 2 ) 0.6385 0.0298 Brother 2 ( 𝜎 24,2 2 ) 0.6062 0.0293 Variance of innovations at 25 Brother 1 ( 𝜎 𝜀1 2 ) 0.4785 0.0246 Brother 2 ( 𝜎 𝜀2 2 ) 0.4654 0.0247 Age splines in variance of innovations Brother 1 26-28 -0.1595 0.0047 29-33 -0.1318 0.0035 34-38 0.0447 0.0060 39-43 0.0507 0.0045 44-51 0.0255 0.0041 Brother 2 -0.1575 0.0067 -0.1290 0.0057 0.0294 0.0084 0.0580 0.0314 0.0117 Bingley, Cappellari and Tatsiramos 2017

Within and between person covariance of transitory shocks Autoregressive coefficient Brother 1 ( 𝜌 1 ) 0.4418 0.0034 Brother 2 ( 𝜌 2 ) 0.4510 0.0045   Cross-person associations in transitory earnings Sibling covariance of innovations ( 𝜎 𝑓 ) 0.0009 0.0013 Peers covariance of transitory earnings (catch-all components) Sharing both school and neighborhood ( 𝜆 𝑠𝑛 ) -0.0010 0.0008 Sharing only school ( 𝜆 𝑠 ) 0.0019 Sharing only neighborhood ( 𝜆 𝑛 ) -0.0009 0.0010 Bingley, Cappellari and Tatsiramos 2017