Geography and Economic Mobility

03/19/2019

Roadmap for Today

Economic Geography perspective on explaining success!
- Geography of intergenerational economic mobility
- Exposure to bad (good) neighborhoods on upward economic mobility
- Policy Experiment: Moving to a better neighborhood

A Key Question:

Does geography actually nurture success (or failure)?… Or
- Do certain locations just attract those who would succeed (or fail)?
Given born in Tuscon, would your adult earnings have been higher had you been born in Phoenix?

Economic Mobility Background

What is Economic Mobility?

Ability to earn higher income (syn. move up the economic latter of success)

Generational: Earn $5/hr as a teenager, but earn $20/hr at 30 years old
Inter-generational: Father lifetime earnings is $100,000, daughter lifetime earnings is $400,000

Focus on Intergenerational Mobility

A worker's adult earnings depends on early family earnings
- Probability of moving from different quintiles
Upward Mobility: 10 people born into bottom income quintile:
1. how many grow up to become rich (top income quintile) in US? ______
2. how many grow up to become rich (top income quintile) in China? ______

Intergenerational Mobility: US, China

	U.S. Parent (p) Q1	U.S. Parent (p) Q5	China Parent (p) Q1	China Parent (p) Q5
Child (c) Q1	33.7%	…	45.1%	…
Child (c) Q5	7.5% ($\uparrow$ Mobility)	36.5%	3.64% ($\uparrow$ Mobility)	45.4%

In general, less Upward and Downward mobility in China than in U.S.
- Fewer people trapped in bottom quintile in U.S.
- More people experience rags to riches in U.S.
- Fewer individuals born rich stay rich in U.S.

Life's Race - Understanding IG Mobility

Chetty et al., 2014

"Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the U.S. (105 pages)
Administrative records of 40+ million Children and parents
Study intergenerational mobility
- Effects of parents income on child's earnings as an adult

administrative records on the incomes of more than 40 million children and their parents to describe three features of intergenerational mobility in the United States. First, we characterize the joint distribution of parent and child income at the national level. The conditional expectation of child income given parent income is linear in percentile ranks. On average, a 10 percentile increase in parent income is associated with a 3.4 percentile increase in a child's income. Second, intergenerational mobility varies substantially across areas within the U.S. For example, the probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 4.4% in Charlotte but 12.9% in San Jose. Third, we explore the factors correlated with upward mobility. High mobility areas have (1) less residential segregation, (2) less income inequality, (3) better primary schools, (4) greater social capital, and (5) greater family stability. While our descriptive analysis does not identify the causal mechanisms that determine upward mobility, the publicly available statistics on intergenerational mobility developed here can facilitate future research on such mechanisms. 1. 10% increase in parent income –> 3.4% increase in child's income 2. Considerable spatial heterogeneity 3. High mobility areas characterized by - less residential segregation - less income inequality - better primary schools - greater family stability

Modeling Intergenerational Mobility

Rank-Rank Form

\[rank_{it} = \beta_0 + \beta_1 rank_{i,t-1} + \beta_x X_i + \epsilon_{it}\]

$rank_{it}$: Percentile ranks of children in income distribution
$rank_{i,t-1}$: Percentile ranks of parents in income distribution
$\beta_1$: Parameter of interest (IGE)
- measures the change in the percentile rank in the child’s income distribution if the income of his/her parents changes by one percentile rank
- a higher estimate again implies lower intergenerational mobility

Intergenerational Elasticity (IGE): U.S.

Column 1: IGE between {0.344,0.618}
- Childs adult earnings significantly related to parents' income
- Implies declining integenerational mobility
Columns (2) and (3), IGE 0.264 to 0.697
- No clear gender differences though

Notes: Each column reports regression coefficients from an OLS regression with all variables normalized to have mean 0 and standard deviation 1 in the estimation sample, so univariate regression coefficients are equal to correlation coefficients. Columns 1-5 are estimated using data for the 709 CZs with at least 250 children in the core sample. The dependent variable in Columns 1-4 is our baseline CZ-level measure of absolute upward mobility, the expected rank of children whose parents are at the 25th national percentile in the core sample. In Column 5, the dependent variable is relative mobility, the rank-rank slope within each CZ. In Column 3, we restrict to CZs that intersect MSAs. In Columns 1-5, the Gini coefficient is defined as the Gini coefficient of family income for parents in the core sample in each CZ; the top 1% income share is defined as the fraction of total parent family income in each CZ accruing to the richest 1% of parents in that CZ; the Gini Bottom 99% is defined as the Gini coefficient minus the top 1% income share; and the fraction between p25 and p75 is the fraction of parents in each CZ whose family income is between the 25th and 75th percentile of the national distribution of parent family income for those in the core sample. In Columns 6-8, the dependent variable is the log-log IGE estimate by country from Corak (2013, Figure 1). The Gini coefficients across countries are obtained from the OECD Income Distribution Database (series "Income Distribution and Poverty: by country"). We interpret these coefficients as applying to the bottom 99% because the surveys on which they are based are typically topcoded. The top 1% income share across countries is from the World Top Income Database (series "Top 1% Income Share"). The independent variables are measured in 1985 in Columns 6 and 7 and in 2005 in Column 8.

Rank-Rank Plot

Intergenerational Elasticity (IGE): China

Columns 1-2: IGE between {0.237,0.442}
- Childs adult earnings significantly related to parents' income
- Implies declining integenerational mobility, esp. in post-reform China
Across subsample, clear gender differences
- IGE estimates larger for girls in late cohort
- Implies less intergenerational mobility for girls in post-reform China

Measuring Absolute Mobility

Select the 25th percentile rank to study upward mobility of children from poor households.
Change Rank-Rank equation to the following,

\[rank_{25} = \beta_0 + 25\beta_1\]

$rank_{25}$: measures the expected percentile rank in income distribution of an adult that was born into the bottom quartile of parents' income distribution.
A large $\beta_1$ indicates a higher expected rank of a child from poor families and a more mobile society.

Map of U.S. Absolute Mobility

Lowest mobility in southeast and midwest: 26-39th percentile
Highest mobility west and northeast: 45.9-65th percentile

Absolute Mobility: City Rankings

Column 4: Abs. Upward Mob Coefficient: 35.8 percentile-46.2 percentile
Column 5: Prob. from bottom 20% to top 20%: 4.4%-10.8%
Column 7: Relative Mobility (IGE) coefficents: 0.264-0.397

Explanatory Drivers?

High mobility areas characterized by less income/residential segregation

Summary of findings

Intergenerational mobility declines sharply over time
- Future economic success depends increasingly on family background
Considerable differences across locations:
- southwest & midwest low intergenerataional mobility vs. west & northeast

Our results show that intergenerational mobility in both income and education declines sharply. This trend is particularly significant for females and residents from economically disadvantaged regions, such as rural and western parts. We show an increase from 0.315 to 0.442 in the intergenerational income elasticity (IGE) between cohorts born before and after 1970, using data from the Chinese Household Income Projects (CHIPs) in 1995 and 2002. Following Chetty et al. (2014a,b), we also examine the change in the percentile rank in the income distribution of a child if the parents’ rank increases by one percentile rank. The rank-rank estimate increases from 0.273 to 0.347 across cohorts. Moreover, the rank estimates for daughters and residents of western region reach 0.39 and 0.455 in the late cohort. Since education is a major determinant of income, we also examine the intergenerational education mobility. We find that the rank-rank estimate increases from 0.235 to 0.292 across the two cohorts using the 2010 Chinese Family Panel Studies (CFPS), thereby indicating a declining trend in mobility. As with income, the result is especially significant for girls and residents from less developed regions.

Adolescent Exposure to Bad (Good) Neighborhood and Future Income

Chetty and Henderson (2016)

Look at earnings records of five million families that moved with children over 17 year time-period
Quasi-experimental design that relies on differences in the timing of when families move across areas.
Can estimate the effect of spending an additional year of childhood in each county.

Better than compare families in different areas
- bias: because some families choose to live in certain areas
Based on the earnings records of five million families that moved with children, track the careers and neighborhoods of five million people over 17 years.

Instead of contrasting the outcomes of families in different areas — which may simply reflect different families choosing to live in different areas — they can track what happens to families when they move. In fact, their analysis is based on more fine-grained comparisons. In particular, they can track families that moved from, say, Cincinnati to Pittsburgh when their children were young and compare them with families that made the same move, but when their children were a few years older.

We use a quasi-experimental approximation to this experiment that relies on differences in the timing of when families move across areas.

it finds that poor children who grow up in some cities and towns have sharply better odds of escaping poverty than similar poor children elsewhere.

The study defined low income as the 25th percentile of the income distribution — a household earning less than three-quarters of other households nationally, or about $30,000 a year for families with children. But the analysis covered the full income distribution, and the geographic patterns at the 25th percentile were very similar to those for poorer and somewhat less poor households.

Map Results

$Go to US Map!$

Best 10 and Worst 10 places to Live

DuPage:
- 1 extra year of childhood $\uparrow$ household income in adulthood by 0.76%
- Birth-18yrs: $\uparrow$ adult earnings by 14% relative to national average.
Baltimore
- 1 extra year of childhood $\downarrow$ household income in adulthood by 0.86%
- Birth-18yrs: $\downarrow$ adult earnings by 16% relative to the average.

Table 1 shows the causal effects of the top 10 and bottom 10 counties among the 100 largest counties in the U.S for children growing up in families at the 25th percentile of the national income distribution. The estimates represent the percentage change in earnings from spending an additional year of one’s childhood in the relevant county relative to the national average.

For example, each additional year that a child spends growing up in DuPage County, IL raises her household income in adulthood by 0.76%. This implies that growing up in DuPage County from birth – i.e., having about 20 years of exposure to that environment – would raise a child’s earnings by 15% relative to the national average. In contrast, every extra year spent in the city of Baltimore reduces a child’s earnings by 0.86% per year of exposure, generating a total earnings penalty of approximately 17% for children who grow up there from birth.1

2015 Baltimore Riots

Does low chance for economic success help add perspective on understanding social protests against policy brutality?

How did Chetty get these estimates?

Family A is poor and moves from Cincinnati to Pittsburgh when child is 5-years old
Family B is poor makes same move, but child is 14-years old
Compare adulthood earnings for the 5-year old & 14-year old

Example: Cincinnati vs. Pittsburgh

Income at age 26 if from a poor family AND
- Always lived in Cincinnati: $23,000; Always lived in Pittsburgh: $28,000
Two families moved from Cincinnati to Pittsburgh; and the child is
- 9 years old –> earn 50% of difference: $25,500
- 23 years old –> no gain relative to those who never left Cincinnati

Compare adulthood earnings by comparing families that moved from one city to another when their children were young versus older. Figure 1 illustrates our approach and results. As an example, consider a set of families who move from Cincinnati to Pittsburgh. Children who grow up in low-income families (at the 25th percentile of the national distribution) in Cincinnati from birth have an income of $23,000 on average at age 26, while those in Pittsburgh have an income of $28,000. Now consider the incomes of children whose families moved from Cincinnati to Pittsburgh at some point in their childhood. Figure 1 plots the fraction of the difference in income between Pittsburgh and Cincinnati that a child will on average obtain by moving at different ages during childhood. Children who were nine years old at the time of the move (the earliest age we can analyze given available data) capture 50% of this difference, leading to an income of approximately $25,500 as adults. Children who move from Cincinnati to Pittsburgh at later ages have steadily declining incomes, relative to those who moved at younger ages. Those whose families moved after they were 23 experience no gain relative to those who stayed in Cincinnati permanently. Figure 1 shows that every extra year a child spends in a better environment – as measured by the outcomes of children already living in that area – improves her outcomes, a pattern we term a childhood exposure effect. We find equal and opposite exposure effects for children whose families moved to worse areas. Further, we find analogous exposure effects for a broad range of other outcomes, including college attendance and the probability of having a teenage birth.

We implement a series of tests to assess the validity of this assumption and evaluate the robustness of our quasi-experimental methodology. First, we compare siblings within the same family, and show that the difference in siblings’ outcomes is proportional to the difference in their exposure to better environments. When a family with two children moves from Cincinnati to Pittsburgh, the younger child does better than the older child on average. Second, we show that one obtains similar estimates of exposure effects when analyzing families displaced by events outside their control, such as natural disasters or local plant closures. Finally, we exploit differences in cities’ effects across subgroups to develop sharper tests for exposure effects. For example, some areas – such as those with high crime rates – generate significantly worse outcomes for boys than girls. We find that when a family with a boy and a girl moves to such an area, their son’s outcomes worsen in proportion to the number of years he grows up there, but their daughter’s outcomes change much less. Similarly, some areas are particularly good at producing “superstars” – children who reach the top 10% of the income distribution – even though they don’t produce better outcomes on average. We find that children who move to such areas when young are themselves more likely to become superstars, but do not have higher incomes on average.

The main innovation of the new paper is its focus on children who moved. Doing so allows the economists to ask whether the places themselves actually affect outcomes. The alternative is that, say, Baltimore happens to be home to a large number of children who would struggle no matter where they grew up.

The data suggests otherwise. The easiest way to understand the pattern may be the different effects on siblings, who have so much in common. Younger siblings who moved from a bad area to a better one earned more as adults than their older siblings who were part of the same move. The particular environment of a city really does seem to affect its residents.

Mr. Chetty and Mr. Hendren also subject their analyses to more demanding tests. For instance, rather than making comparisons between families that move at different times, they can also make comparisons within the same family, comparing the results for older and younger siblings after a move. And indeed, when a family moves to a better city, the younger sibling — who will experience more years of exposure to a good neighborhood than an older brother or sister — enjoys better long-run outcomes. Again, the younger sibling enjoys a better future in proportion to the extra years of exposure he or she has.

They also explore what happens in those cities where boys tend to do particularly poorly — typically those with more crime, inequality and segregation. When families with a son and a daughter move to these cities, the son’s outcomes worsen relative to his sister. This gap is larger the longer they both spend in that neighborhood.

Chetty's remarks

“The data shows we can do something about upward mobility…Every extra year of childhood spent in a better neighborhood seems to matter.” ~ Chetty, Harvard Economist

Their findings are clear: The earlier a family moved to a good neighborhood, the better the children’s long-run outcomes. The effects are symmetric, too, with each extra year in a worse neighborhood leading to worse long-run outcomes. Most important, they find that each extra year of childhood exposure yields roughly the same change in longer-run outcomes, but that beyond age 23, further exposure has no effect. That is, what matters is not just the quality of your neighborhood, but also the number of childhood years that you are exposed to it.

“The data shows we can do something about upward mobility,” said Mr. Chetty, a Harvard professor, who conducted the main study along with Nathaniel Hendren, also a Harvard economist. “Every extra year of childhood spent in a better neighborhood seems to matter.”

offers perhaps the most detailed portrait yet of upward mobility — and the lack of it. The findings suggest that geography does not merely separate rich from poor but also plays a large role in determining which poor children achieve the so-called American dream.

First, it shows that the neighborhood environment during childhood is a key determinant of a child’s long-term success. This suggests that policy makers seeking to improve mobility should focus on improving childhood environments (e.g., by improving local schools) and not just on the strength of the local labor market or availability of jobs. Second, Figure 1 shows that the incremental benefits of exposure to a better area do not vary with a child’s age. Moving to a better area at age 9 instead of 10 produces the same incremental improvement in earnings as moving to that area at age 15 instead of 16. This finding is particularly important in light of recent discussions about early childhood interventions, as it is shows that there are significant returns to improving children’s environments even at older ages.

Questions to Consider

Should public policies offer incentives to encourage poorer households to migrate to richer areas?
What are some potential pros/cons?

Moving to Opportunity (MTO)

MTO

Economic Experiment: 1994-1998 in five large U.S. cities.
Approximately 4,600 families living in high-poverty areas
Random Assignment into 3 groups based on lottery
- MTO Voucher Experiment group: Required to move to area with < 10% poverty
- Section 8 voucher: provided public housing, but no requirement to move
- Control Group: No Voucher

It gave vouchers to help poor families move to better neighborhoods and awarded them on a random basis, so researchers could study the effects. In the Moving to Opportunity experiment in the mid-to-late 1990s, 4,600 families living in public housing entered a lottery in which the winners were offered a voucher that enabled them to move to better neighborhoods.

The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing).

Because any differences between lottery winners and losers are random, many social scientists view experiments like this as the gold standard for evidence. Consequently, the results from this experiment have been closely watched.

Backdrop

Backdrop Los Angeles

Los Angeles, 1991 Race Riots

Brought Awareness to Race, Neighborhoods and Urban Poverty
Following Race riots, Congress created an anti-poverty experiment called Moving to Opportunity (MTO).

Initial MTO Program Results

Disappointing
- MTO Voucher no effect on the employment and earnings of parents,
- some positive effects physical and mental health,
- few notable effects on children.
Led to conclusion that neighborhood environments are not an important component of economic success.

A New Hypothesis and New Findings

Initial studies too early, failed to take into account enough time to track effects on younger children that moved
New hypothesis: Chetty et al. (2016):
- Years of exposure to a good neighborhood is what matters
- Subsequent effects during adulthood to be different for toddlers who moved versus teenagers
- Toddlers enjoy more time in good neighborhood, benefit more.

Early findings had been disappointing, with no effect on the employment and earnings of parents, some positive effects seen on their physical and mental health, and few notable effects on their children. But the new studies provide a far more optimistic lens for interpreting this evidence.

Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults (e.g., Ludwig et al 2013). However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success.

The original research had not been able to follow the economic outcomes of younger children, because not enough time had passed, Mr. Katz said. - Yet if what matters are years of exposure to a good neighborhood — a hypothesis strongly suggested by the second of these two studies — then the effects might be very different, as those who moved as toddlers enjoyed most of their childhood in better neighborhoods, while those who moved as teens received few such benefits yet still had to deal with the disruption of moving.

Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market.

Led to NEW HYPOTHESIS: What matters are years of exposure to a good neighborhood

Findings: Annual Adult Income (Kids < 13 Moved)

The MTO experiment increased the earnings of children who moved to low-poverty areas before age 13 by 31%

Cost-Effective Policy:

The sharpest test comes from those who won an experimental housing voucher that could be used only if they moved to low-poverty areas. Here the findings are striking, as those who moved as a result of winning this voucher before their teens went on to earn 31 percent more than those who did not win the lottery. Other families were awarded Section 8 housing vouchers, which subsidize renting a house or apartment. But because they did not require the winners to move to better parts of the city, people typically moved to neighborhoods that were better but perhaps by only half as much. As a result, the eventual income gains to the preteen children who won this the lottery were about half as large.

The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls.

Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains.

The girls raised in better neighborhoods are also more likely to grow up to marry, and when they have children, are more likely to maintain a relationship with the father. They are also more likely to live in better neighborhoods as adults. This suggests that the next generation — the grandchildren of the winners of this lottery — are more likely to be raised by two parents, to enjoy higher family incomes and to spend their entire childhood in better neighborhoods. That is, the gains from this policy experiment are likely to persist over several generations.

The Moving Age Matters!

13+ who moved experienced lower adulthood income
WHY is this?

The Moving Age Matters!

13+ who moved experienced lower adulthood income
WHY is this?
1. Disruptive move?
2. Few benefits from spending only a short time in a better neighborhood?

Benefits of MTO Beyond Income

Policy Implications

Geography plays key role in who achieves "American dream"
- Geographic-based preferential treatment

*. To improve economic mobility, policy makers should focus on - improving childhood environments (e.g., by improving local schools) - not just on the strength of the local labor market or availability of jobs.

Tax incentives given to developers who build in poor areas
- Incentivize to build affordable housing in non-poor areas