Revisiting the Great Gatsby Curve · Great Gatsby Curve, initially introduced by Alan Krueger in...

Revisiting the Great Gatsby Curve

Andros Kourtellos∗ Ioanna Stylianou†

Charalambos Tsangarides‡

Preliminary and incomplete

Abstract

The main of this paper is to uncover empirically robust determinants of incomeinequality considering theories proposed by the literature focusing particularly onthe role of innovation and intergenerational mobility. In addition, following Chettyet al. (2014) we examine if and how the same set of theories simultaneously affectintergenerational mobility and what is the role of innovation and income inequality,investigating the theory proposed by Hassler et al. (2007). We assess the abovetheories using within-country data and particularly data based on commuting zoneswhich are geographical aggregations of counties originally introduced by Tolbert andKillian (1987). Following Agnion et al. (2016) we consider the role of innovationand using patents data from Lai (2013) at the zip code level we first pair them toa county, and finally, to a commuting zone. For our analysis we consider alternativemeasures of intergenerational mobility (Absolute Upward Mobility using income andenrollments, and Relative Mobility) and income inequality (Top 1% income share,Gross and Net Gini). Finally, our empirical methodology allows us to deal withparameter heterogeneity by employing the Threshold Regression of Hansen (2000).

Keywords: innovation, social mobility, income inequality, threshold regression.

JEL Classification Codes: C59, O40, Z12.

∗Department of Economics, University of Cyprus, P.O. Box 537, CY 1678 Nicosia, Cyprus, e-mail:[email protected].

†Department of Economics, University of Cyprus, P.O. Box 537, CY 1678 Nicosia, Cyprus, e-mail:[email protected].

‡Research Department, International Monetary Fund, 700 19th Street NW, Washington DC 20431, USA,e-mail: [email protected].

1 Introduction

Over the past decades wage inequality has dramatically increased particularly in the United

States. According to the Economic Policy Institute (2016), the documented rise in real

hourly wages in 2015 is due to the sharp decrease in inflation whereas real hourly wage

growth in 2015 was fastest at the top of the wage distribution. The gap between the middle

and bottom has remained stable since 2000 but the gap between the top and everyone else

has grown. Considering the distribution of income between men and women from 2014

to 2015, the strongest wage growth was at the top of the mens wage distribution and at

the bottom of the womens wage distribution. High and sustained levels of inequality have

significant social, as well as, economic costs ( Stiglitz(2012), Ostry et al.(2014) Berg and

Ostry (2011), Galor and Moav(2004), Aghion et al.(1999)). The causal forces behind the

dramatic increase in inequality worldwide in the past decades have led to a considerable

amount of theoretical, as well as, empirical research in order to uncover the sources of income

inequality (for example, Machin and Van Reenen (1998), Katz and Murphy (1992), Berman

et al. (1994), Autor et al. (2008), Card and Lemieux (2001), DiNardo et al.(1996), Blau and

Kahn (1996), Aghion et al.(2016)). The contribution of this paper is multifold: First, we

extend the literature and we uncover empirically robust determinants of income inequality

considering theories proposed by the literature (income and population growth, racial, income

and geographical segregation, taxation policies and education, labor market conditions,

migration, social capital and family structure) focusing particularly on the role of innovation

and intergenerational mobility. Second, following Chetty et al. (2014) we examine if and how

the same set of theories simultaneously affect intergenerational mobility and what is the role

of innovation and income inequality. Krueger (2012) and Corak (2013) suggest that there is

a negative relationship between income inequality intergenerational mobility. In this paper

however, we investigate empirically the Great Gatsby Curve considering the theory proposed

by Hassler et al. (2007). In particular, the theory supports that income inequality affects

negatively intergenerational mobility when the economy is very unequal (distance effect),

and positively, when inequality is low (incentive effect). Third, we assess the above theories

using within-country data instead of cross-country which exhibits significant advantages since

the analysis is based on the same data sources and methods (Solon 2002). In particular,

our analysis is based on the commuting zones level which are geographical aggregations

of counties originally introduced by Tolbert and Killian (1987). There are approximately

741 CZs in the US and on average, each CZ contains 4 counties. There are significant

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advantages estimating the model at the commuting zone level since we are able to examine

the effect of regional characteristics and policies. Fourth, following Agnion et al. (2016) we

consider the role of innovation and using patents data from Lai (2013) at the zip code level

we first pair them to a county, and finally, to a commuting zone. Fifth, for our analysis

we consider alternative measures of intergenerational mobility (Absolute Upward Mobility

using income and enrollments, and Relative Mobility) and income inequality (Top 1% income

share, Gross and Net Gini) using pre-tax, pre-transfer and after-tax, after-transfer income,

based on data from the US Census Bureau. Finally, our empirical methodology allows us to

deal with parameter heterogeneity which refers to the idea that the data generating process

that describes the stochastic phenomenon of intergenerational mobility or income inequality

is not common for all observations (commuting zones). We address parameter heterogeneity

by employing the Threshold Regression introduced by Hansen (2000).

2 The Linear Intergenerational Mobility and Income

Inequality Models

Following Chetty et al.(2014), for each commuting zone i we assume that the

intergenerational mobility between parents and offspring is determined by the following linear

regression model,

Mobilityi = α + x′iβ + ei (2.1)

where α is an intercept, xi is a p× 1 vector of intergenerational mobility determinants and

ei is an i.i.d. error term for i = 1, 2, ..., n. Commuting zones are a spatial measure of local

labor markets consisting of one or more counties or county equivalents, originally introduced

by Tolbert and Killian (1987) and Tolbert and Sizer (1996) based on commuting patterns in

the 1980 and 1990 Census, respectively. In 1980, 768 commuting zones were delineated for

all U.S. counties and county equivalents, and 741 in 1990.

As Chetty et al.(2014) point out, using the classic intergenerational elasticity of income

(IGE) by regressing log child income on log parent income, exhibits significant disadvantages:

First, observations with zero income are not included leading to biased mobility estimates

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and second, the relationship between log parent income and log child income is non-linear.

These obstacles are properly addressed by Chetty et al.(2014) using a rank-rank LS regression

between children’s percentile rank based on their position in the distribution of child income

within their birth cohorts and the percentile rank of the parents based on their position

in the distribution of parent income. In particular, for each commuting zone i Chetty et

al.(2014) estimate the following regression:

ycji = θ0i + θ1iypji + ϵji (2.2)

where ycji denotes the national income rank of child j among children in his birth cohort in

commuting zone i, and ypji is the corresponding rank of the parent in the income distribution

of parents in the core sample.

Following Chetty et al.(2014) we measure intergenerational mobility using the Absolute

Upward Mobility. Absolute mobility in general, is defined as the expected child rank of

children born to a parent whose national income rank is p in commuting zone i, and Absolute

Upward Mobility is specifically focused on children from families with below median parent

income.

Chetty et al.(2014) calculate parent and child income using data from 1040 federal

income tax records from the IRS Databank and their baseline analysis is focused on a core

sample of 1980-1982 birth cohorts. The children’s income is defined as the mean total family

income in 2011 and 2012, when they are approximately 30 years old and the their parents’

income is defined as the mean family income between 1996 and 2000, when the children are

between the ages of 15 and 20. Chetty et al.(2014) show that estimates of intergenerational

mobility stabilize when children reach their late twenties and thus, the choice of the particular

birth cohort tackles successfully any problems of lifecycle bias due to measuring income at

early or late ages. They also support that, mobility estimates are robust to the age of parents

given that parent income is measured between age 30 and 55.

Following the economics and sociology literature we consider determinants of

intergenerational mobility from eleven broad categories or theories: Segregation, Income

and Income Inequality, Tax, Education, College, Labor Market, Migration, Social Capital,

Family Structure, Innovation and Population Growth.

Following Chetty et al.(2014) we start with the racial, income, and geographical

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segregation variables. For racial segregation we use the number of individuals who are

black divided by the total population within a commuting zone and a multi-group Theil

Index calculated at the census-tract level over four racial groups (white, black, hispanic

and other). Income segregation is captured by a two-group Theil index and reflects the

degree which individuals below the pth percentile of the local household income distribution

are segregated from individuals above the pth percentile in each commuting zone. For

geographical segregation we use the Fraction with Commute < 15 mins, which is the number

of workers that commute less than 15 minutes to work divided by the total number of

workers. All variables are from the 2000 Census and according to Chetty et al.(2014) there

is a significant negative relationship between Absolute Upward Mobility, racial and income

segregation whereas areas with shorter commutes have higher rates of upward mobility.

The connection between income inequality and upward mobility is portrayed by the

Great Gatsby Curve, initially introduced by Alan Krueger in 2012. In particular, countries

with greater levels of income inequality also have lower levels of intergenerational mobility.

To examine therefore if this relationship is empirically supported at the commuting zone

level, we consider two income inequality measures: The Gini coefficient of parent income

within each commuting zone from Census Bureau calculated over the period 2006-2010 and

the Top 1% Income Share, which is the fraction of income going to the top 1% defined within

the commuting zone in 2010 from the Economic Policy Institute. In addition, we include

the mean level of Household Income per Capita for working-age adults in a commuting zone

measured in the 2000 Census and Income per Capita for the period 2006-2010 from the

Bureau of Economic Analysis. Notably, both inequality variables and Income per Capita

from the Bureau of Economic Analysis were initially available at the county level which we

have carefully grouped into commuting zones. It is important to mention that the empirical

findings of Chetty et al.(2014) show a negative relationship between the Gini coefficient and

mobility, but a limited association between mean income levels, Top 1% Income Share and

mobility.

The significant impact of tax policies on intergenerational mobility has also been

documented in the literature (Becker and Tomes (1979, 1986), Mulligan (1997)). For

example, Becker and Tomes (1979) who developed a general equilibrium model of income

distribution across family generations show that even a progressive tax and public

expenditure system may widen the inequality of disposable income. Following this literature,

we include Local Tax rates, Tax Progressivity, state Earned Income Tax Credit and Local

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government expenditures per capita. Local Tax rates reflect total tax revenues per capita

divided by mean household income per capita for working age adults in 1992. Tax

Progressivity is the difference between the state income tax rate for incomes above $100,000

and incomes in the bottom tax income bracket in 2008, whereas the state Earned Income

Tax Credit is the the mean state Earned Income Tax Credit top-up rate between 1980-2001,

with the rate coded as zero for states with no state Earned Income Tax Credit. Furthermore,

government expenditures per capita are the total local government expenditures per capita

in 1992. Chetty et al.(2014) find that commuting zones that provide more public goods and

with larger tax credits for low income families tend to have higher levels or intergenerational

mobility.

We examine further the effect of local public goods on intergenerational mobility by

considering also the role of school quality (Card and Krueger (1992), Hanushek (2003)). In

particular, we include the School Expenditure per Student which is the average expenditures

per student in public schools, the High School Dropout Rate which is the residual from a

regression of high school dropout rates on household income per capita in 2000, the Student-

Teacher Ratio which is the average student-teacher ratio in public schools in 1996-1997

and the Test Score Percentile which is the residual from a regression of mean Math and

English test scores (in 2004, 2005 and 2007) appropriately standardized using the household

income per capita in 2000. The first three variables are from the National Center for

Education Statistics, whereas the test score Percentile is from the Global Report Card.

As expected, Chetty et al.(2014) show that upward mobility is positively associated with

School Expenditure per Student and the Test Score Percentile, but negatively with High

School Dropout Rate and the Student-Teacher Ratio.

Since the quality of local schools appears significant we extend the analysis considering

the role of higher education. Specifically, we include the Number of Colleges per Capita in

2000, the College Tuition level which is the mean in-state tuition and fees for first-time, full-

time undergraduates in 2000 and finally, the College Graduation Rate which is the residual

from a regression of graduation rate in 2009. All variables are calculated using data from the

Integrated Postsecondary Education Data System (IPEDS). Chetty et al.(2014) find that

Colleges per Capita and College Graduation Rate affect positively upward mobility but the

College Tuition level, negatively. However, the effect of these variables is rather small and

insignificant.

Following the literature we also consider the effect of the local labor market structure on

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income distribution by including the Labor Force Participation which is the share of people at

least 16 years old that are in the labor force and the Share Working in Manufacturing which

is the Share of employed persons 16 and older working in manufacturing. Both variables are

from 2000 Census. Furthermore, we include the Growth in Chinese Imports which is the

share of growth in imports from China per worker between 1990 and 2000 from Autor et

al. (2013) and the Teenage Labor Force Participation which is the share of children born

between 1985-1987 who received a W2 when they were age 14-16, computed from the 2000

Census. Chetty et al.(2014) identify a rather weak association with upward mobility for all

the variables apart from the Teenage Labor Force Participation which exhibits a positive

relationship.

A number of papers in the literature suggest also a connection between immigration

rates and labor market outcomes (Altonji and Card, 1989). In our analysis migration is

measured using the Migration Inflow and Outlflow Rate which is the migration in to the

commuting zone and out of the commuting zone respectively, between 2004 and 2005. Both

variables are based on data from the IRS Statistics of Income. In addition, we include the

Fraction of Foreign Born which is the share of commuting zone residents born outside the

United States based on data from the 2000 Census. Chetty et al.(2014) find a negative, yet,

small and insignificant connection between all the migration variables and upward mobility.

Another important factor that affects social as well as economic outcomes is the presence

of social capital which reflects the level of social networks. To consider the role of the

social capital we include three variables: First, we include the Social Capital Index taken

from Rupasingha and Goetz (2008), which is a standardized index combining measures of

voter turnout rates, the fraction of people who return their census forms, and measures of

participation in community organizations in 1990. Second, we also consider the Religious

Fraction which is the share of religious adherents in 2000 based on data from the Association

of Religion Data Archives. Third, we include the Violent Crime Rate which reflects the

number of arrests for serious violent crimes per capita in 2000 based on the FBI’s Uniform

Crime Reports. Notably, as Chetty et al.(2014) document, the Social Capital Index and

Religiosity are strongly and positively associated with upward mobility, whereas the Violent

Crime Rate negatively.

Many scholars have also emphasize the importance of the family environment on

children’s outcomes (Becker, 1991). In order to examine this possibility we include the

Fraction of Children with Single Mothers which is the number of single female households

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with children divided by the total number of households with children, the Fraction of

Adults Divorced which is the Fraction of people 15 or older who are divorced, and the

Fraction of Adults Married which is the share of people 15 or older who are married and not

separated. All variables are from the 2000 Census and according to Chetty et al.(2014) there

is a significant negative relationship between the Fraction of Children with Single Mothers,

the Fraction of Adults Divorced and upward mobility, but the effect of Fraction of Adults

Married, is positive.

The relationship between innovation and social mobility has never been examined in the

literature until recently from Aghion et al.(2016). In particular, using data at the commuting

zone level they find that innovation is positively correlated with upward social mobility driven

mostly by entrant innovators and less so by incumbent innovators, and it is dampened in

states with higher lobbying intensity. Following Aghion et al.(2016), we proxy innovation

using the average Utility Patents per capita over the period 2006-2010. In particular, using

zip code level data from Lai et al. (2013) based on the Patent Inventor Database, we assigned

each inventor to a county, and finally, to a commuting zone.

Finally, following Aghion et al.(2016) we include population growth over the period

2006-2010 from the Bureau of Economic Analysis (BEA) which was initially available at

the county level and we grouped into commuting zones. Aghion et al.(2016) show that

population growth affects positively and strongly upward mobility.

One of the most important contributions of this paper is not only to uncover empirically

robust determinants of upward mobility but also, to examine if the same set of theories

simultaneously affect income inequality focusing particularly on the role of upward mobility.

In particular, for each commuting zone i we assume that income inequality is determined by

the following linear regression model,

Inequalityi = b+ z′iδ + ui (2.3)

where b is an intercept, zi is a p × 1 vector of income inequality determinants and ui is an

i.i.d. error term for i = 1, 2, ..., n.

Income inequality is measured using the Gini coefficient of parent income within each

commuting zone from Census Bureau calculated over the period 2010-2014, and the Top

1% Income Share, which is the fraction of income going to the top 1% defined within the

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commuting zone in 2013, from the Economic Policy Institute. Both variables were initially

available at the county level which we have grouped into commuting zones. In this model we

essentially extend the work of Aghion et al.(2016) in two key directions: First, we consider

a larger set of determinants (Segregation, Income, Tax, Education, College, Labor Market,

Migration, Social Capital, Family Structure, Innovation and Population Growth) and second,

we pay particular attention to the role of absolute upward mobility. Aghion et al.(2016) find

a positive effect of innovation and income on income inequality (Top 1% Income Share

and Gini coefficient) but a negative impact of labor force participation, school expenditure,

college per capita and employment manufacturing.

Table 1 presents summary statistics while a detailed description of the data and the

related sources is given in Table A1.

3 The Threshold Intergenerational Mobility and

Income Inequality Models

One of the main objectives of this paper is to identify robust determinants of Absolute

Upward Mobility and Income inequality taking into account the presence of parameter

heterogeneity by estimating the threshold intergenerational mobility and income inequality

models.

The threshold intergenerational mobility and income inequality models generalize the

linear models in (2.1) and (2.3) respectively, by allowing for the presence of multiple regimes.

In particular, we employ the threshold regression model that sorts the data into two groups

of observations based on a particular threshold variable qi. An important feature of this

model is that it allows for an estimation of the threshold parameter (sample split) as well

as the regression coefficients of the two regimes. The threshold intergenerational mobility

model can be described by the following regression equations

Mobilityi = α1 + x′iβ1 + ei, qi ≤ γ (3.4a)

Mobilityi = α2 + x′iβ2 + ei, qi > γ (3.4b)

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Similarly, the threshold income inequality model can be defined as follows

Inequalityi = b1 + z′iδ1 + ui, qi ≤ γ (3.5a)

Inequalityi = b2 + z′iδ2 + ui, qi > γ (3.5b)

where γ is the scalar threshold parameter or sample split value and (α′1, α

′2)

′, (b′1, b′2)

′,

(β′1, β

′2)

′ and (δ′1, δ′2)

′, are the vectors of the regression coefficients (constant and slope) for

the low and high regime, respectively.

The statistical theory for this problem is provided by Hansen (2000) who proposed

a concentrated least squares method for the estimation of the threshold parameter.

The regression coefficients for the two regimes are obtained using LS on the two sub-

samples, separately. Under certain assumptions the asymptotic distribution of the threshold

parameter γ is nonstandard as it involves two independent Brownian motions. Finally, the

confidence intervals for γ are obtained by an inverted likelihood ratio approach.

Estimation of the threshold intergenerational mobility model requires decisions on the

choice of the threshold variable qi. Chetty et al.(2014) show that Income Inequality and

Racial Segregation exhibit a strong and robust correlation with intergenerational mobility.

Therefore, we consider Income Inequality( Gini over the period 2006-2010 or Top 1% in 2010)

and Racial Segregation in 2000, as candidate threshold variables. Also, based on the recent

significant empirical findings of Aghion et al.(2015) regarding the impact of innovation on

upward mobility, we include the Average Patents per capita over the period 2006-2010.

Hassler et al. (2003) suggest that inequality creates incentives to become skilled, and

consequently, has a significant positive effect on upward mobility. Therefore, to test for

this particular theory we include School expenditure per student over the period 1996-97, as

an additional threshold variable. We examine also the effect of income and consider Income

Segregation in 2000 and Household Income per capita (2006-2010). For the estimation of

the threshold income inequality model we consider the same set of threshold variables apart

from the income inequality measures which are appropriately substituted by the Absolute

Upward Mobility.

In practise, we test the null hypothesis of a linear model against the alternative of a

threshold and discard threshold variables that do not reject the null of the linear model at

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10%. We do so by employing the heteroskedasticity-consistent Lagrange multiplier (LM)

test for a threshold of Hansen (1996). It is worth noting that inference in this context is

not standard since the threshold parameter, γ, is not identified under the null hypothesis

of a linear model (i.e. no threshold effect), and therefore the p-values are computed by a

bootstrap method.

4 Results

Table 2 shows the results of the threshold test for Absolute Upward Mobility. The table

includes four different model specifications: The first and the second are the baseline models

where the set of the explanatory variables include Gini (2006-2010) or Top 1% in 2010,

Patents (2006-2010) and Income (2006-2010). In the third and fourth model we include

the full set of the explanatory variables. The first column in Table 2 shows the threshold

variable under consideration, then the corresponding P-value for the null hypothesis of a

linear model against the alternative of a threshold, the threshold estimate, the confidence

interval for the threshold parameter,the joint sum of squares (JSSE), and the sample sizes of

the two regimes. According to the results, for almost all the models (apart from one case),

the linear model null hypothesis is strongly rejected. Tables 4 and 5 show the estimation

results (baseline and extended) for the best model in terms of joint sum of squares (JSSE).

Table 3 shows the threshold test for Income Inequality. As previously, we consider four

different model specifications. In the first and the second which are the baseline models, the

set of the explanatory variables includes Absolute upward mobility, Patents (2006-2010) and

Income (2006-2010). The dependent variable in the first model is Gini (2010-2014) and in

the second, Top 1% in 2013. In the third and fourth model we include the full set of the

explanatory variables. Notably, the null hypothesis for almost all the models (apart from

seven cases) is strongly rejected. Tables 6 and 7 show the estimation results (baseline and

extended) for the best model in terms of joint sum of squares (JSSE).

To be completed....

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5 Robustness

To test the robustness of our results we estimate four different model variations. Fist, we

consider an alternative income variable. In particular, we substitute income per capita over

the period 2006-2010 from BEA (Bureau of Economic Analysis) with Household Income Per

Capita in 2000 from Chetty et al. (2014). Tables 2 and 4 in the Appendix present the

results from the estimation of the corresponding threshold regression models for Absolute

Upward Mobility and Income Inequality, respectively. As expected, the results for the

baseline models, as well as, for the extended models, remain robust. Second, we extend

the set of the explanatory variables by including also the Student-Teacher Ratio and High

School Dropout Rate for Education, College Tuition, College Graduation Rate and Number

of Colleges per Capita for College, and finally, Violent Crime Rate for Social Capital. The

addition of these variables has reduced the number of the observations from 686 to 417

and as expected, we have chosen to exclude them from the initial regressors set. Notably,

the threshold regression model results remain robust for both, Absolute Upward Mobility

and Income Inequality (Table 3 and 5, respectively in the Appendix). Third, we consider

alternative intergenerational mobility variables. In particular, we consider Relative Mobility

and Absolute Upward Mobility based on college enrollments. Relative Mobility measures the

difference in income between the expected ranks of children born to parents at the top and

bottom of the income distribution within a commuting zone. Given that there is a strong

association between higher education and subsequent earnings we include Absolute Upward

Mobility based on OLS regressions of an indicator for being enrolled in college at age 19

on parent income rank in 1996-2000 from Chetty et al. (2014). Finally, for the first time,

we estimate and examine the role of Net Gini at the commuting zone level, instead of the

typical market (gross) Gini. Specifically, we calculate Net Gini using pre-tax, pre-transfer

and after-tax, after-transfer income using data from the US Census Bureau.

To be completed....

6 Conclusion

To be completed.

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Table 1: Descriptive StatisticsVariable Obs Mean Std. Dev. Min MaxIntergenerational MobilityAbsolute Upward Mobility 686 43.9900 5.6418 26.6717 64.0192Income Inequality and IncomeGini Coefficient, 2006-2010 686 0.4328 0.0262 0.3527 0.5535Gini Coefficient, 2010-2014 686 0.4416 0.0244 0.3727 0.5471Top1% Income Share, 2010 686 12.8958 4.0048 7.4000 48.8000Top1% Income Share, 2013 686 12.9011 3.5696 7.5000 41.5000Income Per Capita 686 11.6901 0.5786 9.8947 13.6091Household Income Per Capita 686 32728.1 5582.1 17378.6 58628.4InnovationPatents per capita 686 0.0610 0.0946 0.0000 1.2673OtherPopulation Growth 686 -2.9038 0.1481 -3.4146 -2.4522SegregationFraction Black 686 0.0825 0.1250 0.0002 0.6583Racial Segregation 686 0.1358 0.0993 0.0000 0.5537Income Segregation 686 0.0410 0.0315 0.0000 0.1379Fraction with Commute < 15 Mins 686 0.4426 0.1300 0.1561 0.7666TaxLocal Tax Rate 686 0.0229 0.0092 0.0081 0.0823Local Govt Expenditures Per Capita 686 2236.3 822.4 952.2 11529.1Tax Progressivity 686 0.7791 1.4542 0.0000 6.3000State EITC Exposure 686 1.3877 3.9137 0.0000 21.3333EducationSchool Expenditure per Student 686 5.9527 1.1147 3.9202 11.9063Test Score Percentile (Income adjusted) 686 0.1823 7.9598 -31.8367 20.0705Student Teacher Ratio 417 16.4594 1.7613 10.6852 23.3418High School Dropout Rate (Income adjusted) 417 0.0017 0.0195 -0.0338 0.0993CollegeNumber of Colleges per Capita 417 0.0239 0.0204 0.0044 0.2432College Tuition 417 4123.9 3716.1 0.0000 24619College Graduation Rate (Income Adjusted) 417 -0.0021 0.1311 -0.2770 0.4734Labor MarketLabor Force Participation 686 0.6141 0.0586 0.3641 0.7818Share Working in Manufacturing 686 0.1474 0.0806 0.0085 0.4370Growth in Chinese Imports 686 1.2222 1.8127 -0.0027 25.4053Teenage (14-16) Labor Force Participation 686 0.0048 0.0014 0.0017 0.0081MigrationMigration Inflow Rate 686 0.0168 0.0104 0.0000 0.0770Migration Outlflow Rate 686 0.0168 0.0075 0.0033 0.0525Fraction Foreign Born 686 0.0405 0.0496 0.0034 0.3968Social CapitalSocial Capital Index 686 0.0980 1.2151 -3.1990 5.2660Fraction Religious 686 0.5450 0.1577 0.1715 1.0490Violent Crime Rate 417 0.0016 0.0009 0.0000 0.0050Family StructureFraction of Children with Single Mothers 686 0.2048 0.0519 0.0821 0.4337Fraction of Adults Divorced 686 0.0971 0.0170 0.0395 0.1562Fraction of Adults Married 686 0.5739 0.0448 0.3729 0.6947

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Table 2: Threshold Tests and Threshold Estimates-Absolute Upward Mobility

Threshold variable p-value Threshold 95% CI jsse n1 n2

Model 1Gini 0610 0.5252 0.4095 [0.409000, 0.457875] 16992 105 581Patents per capita 2006 to 2010 0.0020 0.0182 [0.010400, 0.026700] 16238 193 493Income pc 2006 to 2010 0.0004 122876 66028.60, 125878.00] 16584 353 333Income segregation 0.0000 0.0177 [0.016920, 0.019647] 14132 191 495Racial segregation 0.0000 0.0718 [0.043901, 0.098412] 14712 202 484School expenditure per student 0.0000 6.2566 [5.912070, 6.914530] 16253 456 230

Model 2Top 1% in 2010 0.0050 15.2000 [11.43300, 15.20000] 20811 583 103Patents per capita 2006 to 2010 0.0242 0.0104 [0.010400, 0.048800] 20333 102 584Income pc 2006 to 2010 0.0000 107261 [66028.60, 200433.00] 20539 283 403Income segregation 0.0000 0.0177 [0.017511, 0.019578] 17575 191 495Racial segregation 0.0000 0.0718 [0.048758, 0.098412] 17528 202 484School expenditure per student 0.0000 6.2566 [5.917000, 6.291440] 19390 456 230

Model 3Gini 0610 0.0000 0.4483 [0.448000, 0.448333] 2636 506 180Patents per capita 2006 to 2010 0.0000 0.0246 [0.010400, 0.075100] 2740 269 417Income pc 2006 to 2010 0.0006 110864 [97554.80, 112356.00] 2743 309 377Income segregation 0.0000 0.0196 [0.019634, 0.019634] 2559 217 469Racial segregation 0.0028 0.0576 [0.040798, 0.098412] 2731 153 533School expenditure per student 0.0000 6.4154 [6.242820, 6.537840] 2730 485 201


13

Table 3: Threshold Tests and Threshold Estimates-Income Inequality


Model 1Absolute upward mobility 0.0000 49.1870 [45.9863, 50.2682] 0.313 566 120Patents per capita 2006 to 2010 0.0000 0.0293 [0.021200, 0.071300] 0.317 304 382Income pc 2006 to 2010 0.1036 67968 [66028.60, 69845.80] 0.330 107 579Income segregation 0.0182 0.0118 [0.011547, 0.012073] 0.333 117 569Racial segregation 0.0338 0.0427 [0.040798, 0.043901] 0.331 109 577School expenditure per student 0.0000 6.8957 [4.825330, 6.961240] 0.326 573 113

Model 2Absolute upward mobility 0.4882 39.5395 [39.41620, 40.38380] 7789 160 526Patents per capita 2006 to 2010 0.1308 0.0218 [0.021200, 0.021800] 8214 235 451Income pc 2006 to 2010 0.4690 69965 [66028.60, 73729.00] 7951 119 567Income segregation 0.0008 0.0326 [0.032628, 0.039053] 8040 343 343Racial segregation 0.2586 0.0416 [0.040798, 0.044338] 7399 105 581School expenditure per student 0.0116 6.8384 [4.825330, 6.961240] 8213 561 125



14

Table

4:Thresh

old

Regression-M

obility

Method

Lin

ear

Threshold

Lin

ear

Threshold

Expla

natory

Varia

ble

sLow

Hig

hLow

Hig

hcoef

se

coef

se

coef

se

coef

se

coef

se

coef

se

Constant

110.626

5.2114

61.6280

11.3690

100.596

5.7035

60.2662

5.9291

68.1596

9.4802

59.1372

6.7069

Gin

i2006-2

010

-95.3878

7.4978

-106.086

14.3046

-78.4358

7.9619

-6.4706

4.8044

-6.4093

7.3467

-11.0102

5.4193

Pate

nts

pc2006-2

010

-1.0243

1.9790

4.6217

4.7609

2.0540

1.3461

-1.5536

1.0118

1.4156

1.8291

-1.4158

0.8214

Log

Incom

epc2006-2

010

-2.1634

0.3089

2.7648

0.8001

-2.0409

0.3152

0.3448

0.2402

0.1512

0.4099

0.3921

0.2540

Pop

gro

wth

2006-2

010

--

--

--

0.6952

1.0424

2.8438

1.5997

0.6789

1.0962

Fra

ction

Black

--

--

--

4.0843

1.8749

5.3749

2.9459

3.6460

1.9819

RacialSegre

gation

--

--

--

-4.7702

1.4527

-2.1136

1.9455

-7.0292

1.5367

IncomeSegre

gation

--

--

--

-3.0599

5.8379

97.6600

41.5476

-5.9546

5.8675

Fra

ction

with

Com

mute

<15

Min

s-

--

--

-8.8429

1.7209

11.8481

3.7307

8.7533

1.6705

LocalTax

Rate

--

--

--

-5.8994

18.0674

-22.4060

29.5009

41.6731

17.8085

LocalGovtExpenditure

sPerCapita

--

--

--

0.0003

0.0001

0.0002

0.0002

0.0001

0.0001

Tax

Pro

gre

ssivity

--

--

--

0.4407

0.0720

0.9525

0.1730

0.2010

0.0634

Sta

teEIT

CExposu

re-

--

--

-0.0350

0.0247

0.0389

0.0445

0.0329

0.0284

SchoolExpenditure

perStu

dent

--

--

--

0.0343

0.1190

0.0811

0.1975

-0.0315

0.1134

Test

Score

Perc

entile

--

--

--

0.0469

0.0182

0.0702

0.0349

0.0215

0.0168

LaborForc

eParticip

ation

--

--

--

-7.0825

3.3462

1.6515

5.9707

-9.0881

3.4592

Share

Work

ing

inM

anufactu

ring

--

--

--

-10.3421

1.4859

-11.4605

2.8984

-9.3352

1.7474

Gro

wth

inChin

ese

Imports

--

--

--

-0.0651

0.0395

-0.0603

0.0883

-0.0366

0.0346

Teenage(1

4-1

6)LaborForc

eParticip

ation

--

--

--

160.176

148.942

-79.3435

253.526

201.191

154.830

Migra

tion

Inflow

Rate

--

--

--

-47.1043

15.5859

-167.184

55.8257

-17.4162

16.7853

Migra

tion

Outlflow

Rate

--

--

--

43.1477

23.1019

136.783

62.2070

22.2681

22.7381

Fra

ction

Fore

ign

Born

--

--

--

4.3117

3.0329

-21.5785

6.9729

12.7259

2.6777

SocialCapitalIn

dex

--

--

--

0.6102

0.1689

-0.0268

0.2819

0.9943

0.1908

Fra

ction

Religious

--

--

--

5.5015

0.7998

4.0006

1.2299

6.5722

0.8645

Fra

ction

ofChildre

nwith

Sin

gle

Moth

ers

--

--

--

-56.8699

6.1430

-68.6856

9.0326

-46.3095

6.4592

Fra

ction

ofAdultsDivorc

ed

--

--

--

-27.2714

8.7532

-39.7041

15.5133

-15.6322

9.1734

Fra

ction

ofAdultsM

arried

--

--

--

-6.4471

3.9581

-7.8915

6.6144

-7.6119

4.3023

15

Table

5:Thresh

old

Regression-M

obility

Method

Lin

ear

Threshold

Lin

ear

Threshold

Expla

natory

Varia

ble

sLow

Hig

hLow

Hig

hcoef

se

coef

se

coef

se

coef

se

coef

se

coef

se

Constant

61.1051

3.8542

30.1204

7.2497

51.9952

4.6401

53.9019

5.2285

62.9338

8.4509

50.4429

5.9244

Top

1%

in2010

0.0319

0.0693

-0.0020

0.0679

0.0407

0.0872

0.0852

0.0290

0.0889

0.0399

0.0305

0.0234

Pate

nts

pc2006-2

010

1.3912

2.0414

-9.4181

2.8440

5.4985

2.3737

-2.2725

1.1200

0.0898

2.3192

-1.7483

0.8579

Log

Incom

epc2006-2

010

-1.5065

0.3193

1.5789

0.6521

-0.8826

0.3824

0.3526

0.2366

0.1731

0.4087

0.4479

0.2516

Pop

gro

wth

2006-2

010

--

--

--

0.4936

1.0048

2.3207

1.6090

0.6869

1.1103

Fra

ction

Black

--

--

--

3.6989

1.8127

5.4211

2.9455

3.2288

1.9880

RacialSegre

gation

--

--

--

-4.5771

1.3800

-2.3974

1.8268

-6.6395

1.5126

IncomeSegre

gation

--

--

--

-0.4931

5.6247

90.3556

41.5973

-3.6162

6.1722

Fra

ction

with

Com

mute

<15

Min

s-

--

--

-9.3562

1.6992

11.3699

3.6723

9.3606

1.6918

LocalTax

Rate

--

--

--

-9.9094

18.0334

-24.8446

29.3026

41.7985

18.3781


sPerCapita

--

--

--

0.0003

0.0001

0.0002

0.0001

0.0001

0.0001

Tax

Pro

gre

ssivity

--

--

--

0.4598

0.0715

0.9650

0.1641

0.2155

0.0638

Sta

teEIT

CExposu

re-

--

--

-0.0396

0.0246

0.0364

0.0457

0.0396

0.0282

SchoolExpenditure

perStu

dent

--

--

--

0.0365

0.1160

0.0973

0.1956

-0.0224

0.1157

Test

Score

Perc

entile

--

--

--

0.0432

0.0176

0.0623

0.0336

0.0195

0.0171

LaborForc

eParticip

ation

--

--

--

-4.7721

3.3125

3.2937

5.8979

-7.0147

3.5345

Share

Work

ing

inM

anufactu

ring

--

--

--

-9.6365

1.4650

-10.9526

2.8536

-8.5413

1.7081

Gro

wth

inChin

ese

Imports

--

--

--

-0.0628

0.0406

-0.0450

0.0852

-0.0439

0.0361

Teenage(1

4-1

6)LaborForc

eParticip

ation

--

--

--

177.788

147.793

-7.8516

249.996

210.899

156.658

Migra

tion

Inflow

Rate

--

--

--

-49.0498

15.0470

-174.312

54.6847

-17.5002

16.7235

Migra

tion

Outlflow

Rate

--

--

--

41.6486

22.4400

141.193

58.3505

25.2808

22.5196

Fra

ction

Fore

ign

Born

--

--

--

2.0066

2.9787

-22.0337

6.9206

11.0019

2.7966

SocialCapitalIn

dex

--

--

--

0.5380

0.1710

-0.0503

0.2728

0.9795

0.1953

Fra

ction

Religious

--

--

--

5.1345

0.7927

3.7306

1.2238

6.2644

0.8903

Fra

ction

ofChildre

nwith

Sin

gle

Moth

ers

--

--

--

-58.0281

6.0207

-71.1501

8.8644

-46.9550

6.5387

Fra

ction

ofAdultsDivorc

ed

--

--

--

-29.6506

8.6542

-40.5404

15.4205

-16.4598

9.1840

Fra

ction

ofAdultsM

arried

--

--

--

-5.0718

4.0183

-9.3451

6.5218

-5.0795

4.5130

16

Table

6:Thresh

old

Regression-Inequality

Method

Lin

ear

Threshold

Lin

ear

Threshold

Expla

natory

Varia

ble

sLow

Hig

hLow

Hig

hcoef

se

coef

se

coef

se

coef

se

coef

se

coef

se

Constant

0.6113

0.0235

0.6875

0.0270

0.2713

0.0722

0.6701

0.0535

0.7511

0.0741

0.5622

0.0695

Abso

lute

upward

mobility

-0.0016

0.0002

-0.0026

0.0003

0.0021

0.0006

-0.0001

0.0004

-0.0013

0.0007

0.0011

0.0005

Pate

nts

pc2006-2

010

-0.0192

0.0113

-0.0069

0.0107

-0.0423

0.0222

0.0241

0.0100

0.0552

0.0194

0.0085

0.0075

Log

Incom

epc2006-2

010

-0.0086

0.0017

-0.0114

0.0018

0.0042

0.0053

-0.0030

0.0017

-0.0078

0.0022

0.0002

0.0025

Pop

gro

wth

2006-2

010

--

--

--

0.0022

0.0079

0.0113

0.0113

-0.0142

0.0099

Fra

ction

Black

--

--

--

0.0133

0.0164

0.0115

0.0235

0.0238

0.0415

RacialSegre

gation

--

--

--

-0.0096

0.0109

-0.0099

0.0143

-0.0147

0.0138

Incom

eSegre

gation

--

--

--

-0.1305

0.0502

-0.0452

0.0660

-0.1508

0.0774

Fra

ction

with

Com

mute

<15

Min

s-

--

--

--0

.0201

0.0136

-0.0027

0.0224

-0.0392

0.0180

LocalTax

Rate

--

--

--

-0.0001

0.1077

0.0341

0.1600

0.1599

0.1399


sPerCapita

--

--

--

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Tax

Pro

gre

ssivity

--

--

--

0.0005

0.0005

0.0000

0.0006

0.0003

0.0008

Sta

teEIT

CExposu

re-

--

--

--0

.0004

0.0002

-0.0002

0.0005

-0.0001

0.0002

SchoolExpenditure

perStu

dent

--

--

--

-0.0019

0.0009

0.0012

0.0014

-0.0035

0.0011

Test

Score

Perc

entile

--

--

--

0.0003

0.0002

0.0001

0.0002

0.0005

0.0002

LaborForc

eParticip

ation

--

--

--

-0.1144

0.0300

-0.1468

0.0411

-0.1166

0.0390

Share

Work

ing

inM

anufactu

ring

--

--

--

-0.0546

0.0137

-0.0417

0.0175

-0.0903

0.0185

Gro

wth

inChin

ese

Imports

--

--

--

0.0005

0.0004

0.0009

0.0005

-0.0003

0.0004

Teenage(1

4-1

6)LaborForc

eParticip

ation

--

--

--

-4.1451

1.2461

-4.8029

1.8429

-4.3283

1.5859

Migra

tion

Inflow

Rate

--

--

--

0.0248

0.1646

-0.0102

0.2134

-0.0704

0.2062

Migra

tion

Outlflow

Rate

--

--

--

-0.2189

0.2233

-0.4469

0.2724

0.3396

0.3234

Fra

ction

Fore

ign

Born

--

--

--

0.0752

0.0227

0.0817

0.0319

0.0929

0.0296

SocialCapitalIn

dex

--

--

--

0.0011

0.0014

-0.0019

0.0018

0.0039

0.0016

Fra

ction

Religious

--

--

--

0.0217

0.0074

0.0320

0.0109

0.0065

0.0105

Fra

ction

ofChildre

nwith

Sin

gle

Moth

ers

--

--

--

0.0857

0.0535

0.0424

0.0709

0.1753

0.0757

Fra

ction

ofAdultsDivorc

ed

--

--

--

-0.0889

0.0805

-0.0431

0.0961

-0.1572

0.1199

Fra

ction

ofAdultsM

arried

--

--

--

-0.1385

0.0374

-0.0571

0.0487

-0.1763

0.0470

17

Table

7:Thresh

old

Regression-Inequality

Method

Lin

ear

Threshold

Lin

ear

Threshold

Expla

natory

Varia

ble

sLow

Hig

hLow

Hig

hcoef

se

coef

se

coef

se

coef

se

coef

se

coef

se

Constant

16.3200

4.2440

24.6188

6.4560

10.8462

4.6561

13.4155

8.6791

27.6294

22.2682

7.1542

8.5691

Abso

lute

upward

mobility

0.0074

0.0226

0.0115

0.0278

0.0568

0.0379

0.1312

0.0576

0.1804

0.1508

0.1268

0.0541

Pate

nts

pc2006-2

010

7.6538

3.1354

19.5183

12.1732

3.0937

1.3573

8.0730

3.5641

43.0998

4.2180

3.7692

1.4022

Log

Incom

epc2006-2

010

-0.3604

0.3269

-1.1606

0.5399

-0.0248

0.3210

-0.0320

0.2944

-0.0904

0.8020

-0.1281

0.3364

Pop

gro

wth

2006-2

010

--

--

--

2.2824

1.6658

6.0764

3.7765

-0.1557

1.4721

Fra

ction

Black

--

--

--

3.4500

2.8128

-9.5692

5.9087

4.9925

3.0854

RacialSegre

gation

--

--

--

-1.8195

1.7255

-4.9614

34.6479

-1.3205

1.8396

IncomeSegre

gation

--

--

--

-13.3960

10.9313

14.8334

33.2060

0.2127

8.0801

Fra

ction

with

Com

mute

<15

Min

s-

--

--

--4

.6089

2.6927

-10.7967

4.4698

-2.9263

2.2317

LocalTax

Rate

--

--

--

81.9209

24.5189

21.5393

31.6392

71.4073

32.2345


sPerCapita

--

--

--

0.0001

0.0002

0.0003

0.0005

0.0000

0.0002

Tax

Pro

gre

ssivity

--

--

--

-0.2365

0.1122

-0.8482

0.3215

-0.0803

0.0812

Sta

teEIT

CExposu

re-

--

--

--0

.0694

0.0350

-0.0043

0.0959

-0.0541

0.0349

SchoolExpenditure

perStu

dent

--

--

--

0.0144

0.1744

0.3956

0.3411

0.0761

0.1798

Test

Score

Perc

entile

--

--

--

0.0445

0.0561

-0.0922

0.0936

0.1050

0.0317

LaborForc

eParticip

ation

--

--

--

-14.8224

5.5380

-8.4308

8.8990

-16.9481

5.8830

Share

Work

ing

inM

anufactu

ring

--

--

--

-2.1426

2.2088

-4.7397

4.8839

-0.8186

2.1037

Gro

wth

inChin

ese

Imports

--

--

--

-0.0779

0.0501

-0.0194

0.0564

-0.0482

0.0537

Teenage(1

4-1

6)LaborForc

eParticip

ation

--

--

--

-34.2222

190.412

330.485

371.104

-53.6701

205.742

Migra

tion

Inflow

Rate

--

--

--

48.0218

39.9880

-29.0418

36.8995

77.9208

41.7167

Migra

tion

Outlflow

Rate

--

--

--

8.3511

44.6641

-45.0542

77.1523

-16.0091

43.4039

Fra

ction

Fore

ign

Born

--

--

--

23.4226

5.6391

40.7644

21.6010

24.3300

5.8336

SocialCapitalIn

dex

--

--

--

0.7165

0.3535

0.9867

0.5284

0.3347

0.2346

Fra

ction

Religious

--

--

--

3.5180

1.3783

-0.2544

2.3380

5.4962

1.4316

Fra

ction

ofChildre

nwith

Sin

gle

Moth

ers

--

--

--

15.9159

8.8515

36.8832

16.5659

10.7065

9.4613

Fra

ction

ofAdultsDivorc

ed

--

--

--

43.8581

14.6365

-45.3262

30.0733

48.4406

16.8527

Fra

ction

ofAdultsM

arried

--

--

--

-1.4617

5.7350

-2.4695

11.0014

-1.8589

5.7339

18

References

19

Table

A1:Data

Appendix

Variable

Description

Interg

enera

tionalM

obility

Absolute

UpwardMob

ility

Theexpectedchildrankof

childrenbornto

aparentwhosenational

incomerankispin

commuting

zonei.

Absolute

Upward

Mob

ility

isspecically

focu

sed

onchildren

from

familieswith

below

med

ianparentincome.

Parentan

dchildincomeiscalculatedusingdatafrom

1040

federal

income

taxrecordsfrom

theIR

SDatab

ankan

dthebaselinean

alysisis

focu

sedon

the19

80-1982birth

cohorts.Thechildren’sincomeisdefi

ned

asthemeantotalfamilyincomein

2011

and2012,when

they

areap

proxim

ately30

years

oldan

dthetheirparents’incomeis

defi

ned

asthemeanfamily

incomebetween19

96an

d20

00,when

thechildrenarebetweentheag

esof

15an

d20

.Sou

rce:

Chetty

etal.(20

14).

IncomeIn

equality

and

Income

GiniCoeffi

cient

TheGinicoeffi

cientof

parentincomewithin

each

commutingzonecalculatedover

theperiod2006-

2010

and20

10-201

4.Thevariab

lewas

initiallyavailableat

thecounty

levelwhichwehavecarefully

grou

ped

into

commutingzones.Sou

rce:

Cen

susBureau

.Top

1%

IncomeShare

TheTop

1%IncomeShare,

whichisthefraction

ofincomego

ingto

thetop1%

defi

ned

within

the

commutingzonein

2010

andin

2013

.Thevariab

lewas

initiallyavailable

atthecounty

level

which

wehavecarefullygrou

ped

into

commutingzones.Sou

rce:

Econom

icPolicyInstitute.

IncomePer

Cap

ita

Incomeper

Cap

itafortheperiod20

06-201

0,initiallyavailable

atthecounty

level

whichwehave

carefullygrou

ped

into

commutingzones.Sou

rce:

Bureau

ofEconom

icAnalysis.

Hou

seholdIncomePer

Cap

ita

Meanlevel

ofHou

seholdIncomeper

Cap

itaforworking-ag

ead

ultsin

acommutingzonemeasured

inthe20

00Cen

sus.

Sou

rce:

Chetty

etal.(201

4).

Innovation

Patents

per

capita

Patents

per

capitaover

theperiod20

06-201

0,usingzipcodelevel

datawhichwefirstpairthem

toacounty,an

dfinally,to

acommutingzone.

Sou

rce:

Lai

etal.(201

3).

Oth

er

Pop

ulation

Growth

Pop

ulation

grow

thover

theperiod20

06-201

0initiallyavailableat

thecounty

levelan

dthen

grou

ped

into

commutingzones.Sou

rce:

Bureau

ofEconom

icAnalysis(B

EA).

Segre

gation

FractionBlack

Thenumber

ofindividualswhoareblack

divided

bythetotalpop

ulation

within

acommutingzone.

Sou

rce:

Cen

sus20

00.

Racial

Segrega

tion

Multi-grou

pTheilIndex

calculatedat

thecensus-tractlevel

over

fourracial

grou

ps(w

hite,

black,

hispan

ican

dother).

Sou

rce:

Cen

sus20

00.

IncomeSegrega

tion

Atw

o-grou

pTheilindex

andreflects

thedegreewhichindividualsbelow

thepth

percentile

ofthe

localhou

seholdincomedistribution

aresegregated

from

individualsab

ovethepth

percentile

ineach

commutingzone.

Sou

rce:

Cen

sus20

00.

FractionwithCom

mute

<15

Mins

Thenumber

ofworkers

that

commute

less

than

15minutesto

workdivided

bythetotalnumber

ofworkers.Sou

rce:

Cen

sus20

00.

Tab

lecontinued

onnextpag

e...

20

Tab

leA1continued

Variable

Description

Tax

LocalTax

Rate

Total

taxrevenues

per

capitadivided

bymeanhou

seholdincomeper

capitaforworkingagead

ults

in19

92.Sou

rce:

1992

Cen

susof

Governmentcounty-level

summaries.

LocalGov

tExpen

dituresPer

Cap

ita

Thetotallocalgovernmentexpen

dituresper

capitain

1992

.Sou

rce:

1992

Censusof

Government

county-level

summaries.

Tax

Progressivity

Thedifferen

cebetween

thestateincometax

rate

forincomes

above$1

00,000

and

incomes

in

thebottom

taxincomebracket

in20

08.

Sou

rce:

State

incometaxratesin

2008

from

theTax

Fou

ndation.

State

EIT

CExposure

ThemeanstateEarned

IncomeTax

Credittop-uprate

between19

80-200

1,withtherate

coded

as

zero

forstates

withnostateEarned

IncomeTax

Credit.Sou

rce:

Hotzan

dScholz(2003)

Education

SchoolExpen

diture

per

Student

Average

expen

dituresper

studentin

publicschoolsin

1996

-199

7.Sou

rce:

National

Centerfor

Education

Statistics.

TestScore

Percentile

(Incomead

justed

)Theresidual

from

aregression

ofmeanMathan

dEnglishtest

scores

(in2004,2005

and2007)

appropriatelystan

dardized

usingthehou

seholdincomeper

capitain

2000

.Sou

rce:

Global

Rep

ort

Card.

StudentTeacher

Ratio

Average

student-teacher

ratio

inpublic

schoolsin

1996

-199

7.Sou

rce:

National

Centerfor

Education

Statistics.

HighSchool

Dropou

tRate(Incomead

justed

)Theresidual

from

aregression

ofhighschool

dropou

trateson

hou

seholdincomeper

capitain

2000

.Sou

rce:

National

CenterforEducation

Statistics.

College

Number

ofCollegesper

Cap

ita

Number

ofCollegesper

Cap

itain

2000

.Sou

rce:

Integrated

Postsecon

daryEducation

DataSystem

(IPEDS).

CollegeTuition

Themean

in-state

tuition

and

fees

forfirst-time,

full-tim

eundergrad

uates

in2000.

Sou

rce:

Integrated

Postsecon

daryEducation

DataSystem

(IPEDS).

CollegeGraduationRate(IncomeAdjusted

)Theresidual

from

aregression

ofgrad

uation

rate

in20

09.

Sou

rce:

Integrated

Postsecon

dary

Education

DataSystem

(IPEDS).

Tab

lecontinued

onnextpag

e...

21

Tab

leA1continued

Variable

Description

LaborM

ark

et

Lab

orForce

Participation

Theshareof

people

atleast16

years

oldthat

arein

thelabor

force.

Sou

rce:

Census2000.

ShareWorkingin

Man

ufacturing

Shareof

employed

persons16

andolder

workingin

man

ufacturing.

Sou

rce:

Cen

sus2000.

Growth

inChineseIm

ports

Shareof

grow

thin

imports

from

Chinaper

workerbetween19

90an

d20

00.Sou

rce:

Autoret

al.

(201

3).

Teenag

e(14-16

)Lab

orForce

Participation

Shareof

childrenbornbetween19

85-198

7whoreceived

aW

2when

they

wereag

e14-16.

Sou

rce:

Cen

sus20

00.

Migra

tion

Migration

Inflow

Rate

Migration

into

thecommutingzonefrom

other

commutingzones

between20

04an

d2005.Sou

rce:

IRSStatisticsof

Income.

Migration

Outlflow

Rate

Migration

outof

thecommutingzonefrom

other

commutingzones

between20

04an

d2005.Sou

rce:

IRSStatisticsof

Income.

FractionForeign

Born

Shareof

commutingzoneresidents

bornou

tsidetheUnited

States.

Sou

rce:

Cen

sus2000.

SocialCapital

Social

Cap

ital

Index

Astan

dardized

index

combiningmeasuresof

voter

turnou

trates,thefraction

ofpeople

whoreturn

theircensusform

s,an

dmeasuresof

participationin

communityorga

nizationsin

1990.Sou

rce:

Rupasingh

aan

dGoetz(200

8).

FractionReligious

Shareof

religiou

sad

herents

in20

00.Sou

rce:

Associationof

ReligionDataArchives.

ViolentCrimeRate

Number

ofarrestsforseriou

sviolentcrim

esper

capitain

2000

.Sou

rce:

FBI’sUniform

Crime

Rep

orts.

FamilyStructu

re

Fractionof

ChildrenwithSingleMothers

Thenumber

ofsinglefemalehou

seholdswithchildrendivided

bythetotalnumber

ofhou

seholds

withchildren.Sou

rce:

Cen

sus20

00.

Fractionof

AdultsDivorced

Fractionof

people

15or

older

whoaredivorced.Sou

rce:

Cen

sus20

00.

Fractionof

AdultsMarried

Shareof

people

15or

older

whoaremarried

andnot

separated

.Sou

rce:

Cen

sus2000.

22

Table A2: Threshold Tests and Threshold Estimates-Absolute Upward Mobility


Model 1Gini 0610 0.0006 0.4399 [0.430500, 0.445500] 17570 437 249Patents per capita 2006 to 2010 0.0000 0.0290 [0.029000, 0.029000] 15971 301 385Household income per capita 2000 0.0000 36561 [35647.90, 37445.10] 16812 553 133Income segregation 0.0000 0.0189 [0.018852, 0.018852] 13936 207 479Racial segregation 0.0000 0.0921 [0.069107, 0.110686] 14825 275 411School expenditure per student 0.0000 6.2566 [5.945260, 6.921530] 16984 456 230

Model 2Top 1% in 2010 0.0002 11.4170 [10.10000, 15.20000] 20660 256 430Patents per capita 2006 to 2010 0.0000 0.0290 [0.029000, 0.029000] 18547 301 385Household income per capita 2000 0.0000 36561 [35395.80, 36670.90] 19305 553 133Income segregation 0.0000 0.0177 [0.017511, 0.018852] 16296 191 495Racial segregation 0.0000 0.0921 [0.092149, 0.092149] 17348 275 411School expenditure per student 0.0000 6.2566 [5.945260, 6.291440] 19623 456 230



23

Table A3: Threshold Tests and Threshold Estimates-Absolute Upward Mobility










24

Table A4: Threshold Tests and Threshold Estimates-Income Inequality


Model 1Absolute upward mobility 0.0000 48.6473 [44.84280, 50.33630] 0.292 554 132Patents per capita 2006 to 2010 0.0000 0.0653 [0.063800, 0.065300] 0.297 499 187Household income per capita 2000 0.0000 33836 [31564.5, 37765.4] 0.289 417 269Income segregation 0.0500 0.0175 [0.017511, 0.017511] 0.313 190 496Racial segregation 0.6068 0.0439 [0.040798, 0.044338] 0.312 113 573School expenditure per student 0.0000 6.8715 [4.825330, 6.951410] 0.305 569 117

Model 2Absolute upward mobility 0.2182 44.7158 [38.20230, 50.33630] 7865 420 266Patents per capita 2006 to 2010 0.0098 0.0653 [0.065300, 0.065300] 7442 499 187Household income per capita 2000 0.0028 37765 [33735.60, 37765.40] 6929 583 103Income segregation 0.1200 0.0243 [0.012165, 0.077187] 7860 275 411Racial segregation 0.3034 0.0408 [0.040798, 0.041649] 7245 102 584School expenditure per student 0.0030 6.7436 [4.825330, 6.961240] 7917 550 136



25

Table A5: Threshold Tests and Threshold Estimates-Income Inequality










26

Revisiting the Great Gatsby Curve · Great Gatsby Curve, initially introduced by Alan Krueger in...

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