Economic Inequality in the US - AC - Hyman - S26 - Final Review

"The Fading American Dream" (Chetty et al., 2017): Research Question

What is the effect of changing economic growth and income distribution on absolute income mobility since 1940?

"The Fading American Dream" (Chetty et al., 2017): Aim

To measure how likely children are to earn more than their parents over time.

"The Fading American Dream" (Chetty et al., 2017): Method

Quantitative analysis using tax records and historical income data; intergenerational mobility estimation and counterfactual simulations.

"The Fading American Dream" (Chetty et al., 2017): Procedure

Link children's adult incomes to parents' incomes across birth cohorts (1940 onward) and simulate effects of growth and inequality on mobility.

"The Fading American Dream" (Chetty et al., 2017): Key Finding

Rising income inequality explains about 25% of the changes in absolute mobility.

"The Fading American Dream" (Chetty et al., 2017): Analysis

S: Large administrative dataset; separates growth and inequality effects. L: Relies on income as primary measure; models depend on assumptions.

"The Race Between Education and Technology" (Goldin & Katz, 2008): Research Question

What is the effect of technological change and educational attainment on wage inequality?

"The Race Between Education and Technology" (Goldin & Katz, 2008): Aim

To explain historical changes in inequality in the United States.

"The Race Between Education and Technology" (Goldin & Katz, 2008): Method

Historical trend analysis and economic interpretation using labor market and education data.

"The Race Between Education and Technology" (Goldin & Katz, 2008): Procedure

Examine long-run expansion of education and technological change; compare wage inequality across historical periods.

"The Race Between Education and Technology" (Goldin & Katz, 2008): Key Finding

Inequality is rising because technological change has outpaced educational attainment since 1975.

"The Race Between Education and Technology" (Goldin & Katz, 2008): Analysis

S: Comprehensive long-run perspective; strong empirical documentation. L: Primarily descriptive; limited causal testing.

"Gains and Gaps" (Bailey & Dynarski, 2011): Research Question

How has income inequality in educational attainment changed over time, particularly for college entry and completion?

"Gains and Gaps" (Bailey & Dynarski, 2011): Aim

To find out why educational attainment has risen significantly more among women than among men.

"Gains and Gaps" (Bailey & Dynarski, 2011): Method

Quantitative analysis using census data, measuring educational attainment across income quartiles and race.

"Gains and Gaps" (Bailey & Dynarski, 2011): Procedure

Compare cohorts born in different decades; separate results by family income quartile and race.

"Gains and Gaps" (Bailey & Dynarski, 2011): Key Finding

There is a strong correlation between a parent's income and a child's educational attainment, especially in the top quartile.

"Gains and Gaps" (Bailey & Dynarski, 2011): Analysis

S: Large national datasets; long-term trend analysis. L: Mostly correlational, not causal.

"Falling Behind" (Fryer & Levitt, 2004): Research Question

What is the effect of race and early school experiences on Black-White student achievement gaps?

"Falling Behind" (Fryer & Levitt, 2004): Aim

To determine when Black-White achievement gaps emerge.

"Falling Behind" (Fryer & Levitt, 2004): Method

Quantitative longitudinal analysis using early childhood education data.

"Falling Behind" (Fryer & Levitt, 2004): Procedure

Analyze standardized test scores over time while controlling for socioeconomic and family background variables.

"Falling Behind" (Fryer & Levitt, 2004): Key Finding

The gap between Black and White students increases over time regardless of school quality, suggesting factors outside schools affect the gaps.

"Falling Behind" (Fryer & Levitt, 2004): Analysis

S: National longitudinal dataset; strong socioeconomic controls. L: Correlational; narrow focus on test scores.

"Where is the Land of Opportunity?" (Chetty et al., 2014): Research Question

What is the effect of geographic location on intergenerational economic mobility?

"Where is the Land of Opportunity?" (Chetty et al., 2014): Aim

To identify how mobility differs across U.S. regions and what factors are associated with higher mobility.

"Where is the Land of Opportunity?" (Chetty et al., 2014): Method

Measures mobility at the commuting zone level, using tax records for over 40 million children and their parents born 1980-1991

"Where is the Land of Opportunity?" (Chetty et al., 2014): Procedure

Link children's adult income to parent income and analyze local characteristics like segregation and school quality.

"Where is the Land of Opportunity?" (Chetty et al., 2014): Key Finding

There is significant geographic variation in absolute mobility across the United States.

"Where is the Land of Opportunity?" (Chetty et al., 2014): Analysis

S: Massive administrative dataset; strong empirical evidence. L: Correlational; regional averages may mask local variation.

"Race and Economic Opportunity" (Chetty et al., 2018): Research Question

What is the effect of race on intergenerational economic mobility in the United States?

"Race and Economic Opportunity" (Chetty et al., 2018): Aim

To examine racial differences in upward mobility across generations.

"Race and Economic Opportunity" (Chetty et al., 2018): Method

Quantitative analysis using linked Census and tax data.

"Race and Economic Opportunity" (Chetty et al., 2018): Procedure

Compare adult outcomes of Black and White children from similar-income families; examine neighborhood and gender differences.

"Race and Economic Opportunity" (Chetty et al., 2018): Key Finding

Significant racial gaps in mobility persist even when controlling for parental income, particularly for Black men.

"Race and Economic Opportunity" (Chetty et al., 2018): Analysis

S: Detailed racial and geographic comparisons. L: Limited direct measures of discrimination; focuses mainly on Black-White differences.

"Who Becomes an Inventor?" (Bell et al., 2019): Research Question

What is the effect of childhood exposure to innovation on the likelihood of becoming an inventor?

"Who Becomes an Inventor?" (Bell et al., 2019): Aim

To understand why innovation rates differ by class, race, and gender.

"Who Becomes an Inventor?" (Bell et al., 2019): Method

Quantitative analysis using patent and tax records.

"Who Becomes an Inventor?" (Bell et al., 2019): Procedure

Link patent records to childhood income and demographic data; examine exposure to inventors during childhood.

"Who Becomes an Inventor?" (Bell et al., 2019): Key Finding

Innovation is highly correlated with childhood exposure to inventors and parental income ("Lost Einsteins").

"Who Becomes an Inventor?" (Bell et al., 2019): Analysis

S: Innovative linked administrative dataset; strong evidence on exposure effects. L: Exposure measures may be indirect; correlational.

"Income and Life Expectancy" (Chetty et al., 2016): Research Question

What is the effect of income on life expectancy in the United States?

"Income and Life Expectancy" (Chetty et al., 2016): Aim

To measure the relationship between income and mortality across different regions.

"Income and Life Expectancy" (Chetty et al., 2016): Method

Quantitative analysis linking tax data with death records (2001-2014).

"Income and Life Expectancy" (Chetty et al., 2016): Procedure

Compare life expectancy across income groups and cities; examine health behaviors and local conditions.

"Income and Life Expectancy" (Chetty et al., 2016): Key Finding

Higher income is associated with longer life expectancy, but the gap varies significantly by location.

"Income and Life Expectancy" (Chetty et al., 2016): Analysis

S: Extremely large dataset; precise mortality estimates. L: Correlation does not prove causation; doesn't show quality of life.

"Mobility Report Cards" (Chetty et al., 2017): Research Question

What is the effect of college attendance on intergenerational income mobility?

"Mobility Report Cards" (Chetty et al., 2017): Aim

To evaluate which colleges promote upward mobility for low-income students.

"Mobility Report Cards" (Chetty et al., 2017): Method

Quantitative analysis using college attendance and tax data.

"Mobility Report Cards" (Chetty et al., 2017): Procedure

Measure "Access" (bottom 20% attendance) and "Success" (reach top 20%) rates for low-income students at different institutions.

"Mobility Report Cards" (Chetty et al., 2017): Key Finding

Some mid-tier public universities have higher mobility rates than elite Ivy League schools due to higher access.

"Fading American Dream" (Chetty et al., 2017): Analysis

S: Comprehensive national data; institution-level comparisons. L: Potential selection bias; focused mainly on earnings outcomes.

"Emily and Greg" (Bertrand & Mullainathan, 2004): Research Question

What is the effect of perceived racial identity on employer callback rates?

"Emily and Greg" (Bertrand & Mullainathan, 2004): Aim

To test whether racial discrimination affects hiring decisions.

"Emily and Greg" (Bertrand & Mullainathan, 2004): Method

Randomized Controlled Trial (RCT) field experiment using fictitious resumes.

"Emily and Greg" (Bertrand & Mullainathan, 2004): Procedure

Send identical resumes with racially distinct names to employers and compare callback rates.

"Emily and Greg" (Bertrand & Mullainathan, 2004): Key Finding

Resumes with "White-sounding" names received 50% more callbacks than those with "Black-sounding" names.

"Emily and Greg" (Bertrand & Mullainathan, 2004): Analysis

S: Strong causal design; real-world hiring context. L: Measures callbacks, not hiring; names may signal class as well as race.

"How Racist Are We?" (Stephens-Davidowitz, 2012): Research Question

What is the effect of racial prejudice on political and social outcomes?

"How Racist Are We?" (Stephens-Davidowitz, 2012): Aim

To measure hidden racial attitudes using internet search behavior.

"How Racist Are We?" (Stephens-Davidowitz, 2012): Method

Quantitative analysis of Google search data and voting patterns.

"How Racist Are We?" (Stephens-Davidowitz, 2012): Procedure

Analyze racist search terms by region and compare with voting outcomes.

"How Racist Are We?" (Stephens-Davidowitz, 2012): Key Finding

Hidden racial prejudice (as measured by search terms) significantly affects voting behavior and outcomes.

"How Racist Are We?" (Stephens-Davidowitz, 2012): Analysis

S: Captures socially hidden attitudes; large-scale regional data. L: Search behavior may not perfectly reflect beliefs; correlational.

"Orchestrating Impartiality" (Goldin & Rouse, 2000): Research Question

What is the effect of blind auditions on women's hiring outcomes in orchestras?

"Orchestrating Impartiality" (Goldin & Rouse, 2000): Aim

To determine whether anonymous auditions reduce gender discrimination.

"Orchestrating Impartiality" (Goldin & Rouse, 2000): Method

Natural experiment setting using orchestra audition personnel records. Fixed-effects looking at an individual’s outcomes when they audition with and without the screen.

"Orchestrating Impartiality" (Goldin & Rouse, 2000): Procedure

Use Difference-in-Differences (DID) to compare audition outcomes before and after screens were introduced.

"Orchestrating Impartiality" (Goldin & Rouse, 2000): Key Finding

Blind auditions increased the probability that a female musician would advance to the final round by 50%.

"Orchestrating Impartiality" (Goldin & Rouse, 2000): Analysis

S: Strong evidence of reduced gender bias; real hiring outcomes. L: Limited to orchestras; historical context may reduce generalizability.

"Ban the Box?" (Mullainathan, 2016): Research Question

What effect does removing criminal history questions have on racial discrimination in hiring?

"Ban the Box?" (Mullainathan, 2016): Aim

To evaluate unintended consequences of "Ban the Box" policies.

"Ban the Box?" (Mullainathan, 2016): Method

field experiment using fictitious job applications - triple-differences technique comparing race, criminal record status, and policy timing,

"Ban the Box?" (Mullainathan, 2016): Procedure

Send applications with and without criminal records before and after policy changes; vary race-coded names.

"Ban the Box?" (Mullainathan, 2016): Key Finding

Employers became less likely to call back any Black applicant, assuming they may have a criminal record (statistical discrimination).

"Ban the Box?" (Mullainathan, 2016): Analysis

S: Highlights unintended consequences; policy-relevant. L: Difficult to isolate policy effects; motives not directly observed.

"Neighborhood Impacts Mover Study" (Chetty & Hendren, 2015): Research Question

What is the effect of neighborhood exposure on adult economic outcomes?

"Neighborhood Impacts Mover Study" (Chetty & Hendren, 2015): Aim

To estimate how neighborhoods influence intergenerational mobility.

"Neighborhood Impacts Mover Study" (Chetty & Hendren, 2015): Method

Quasi-experimental analysis using tax and migration records of families who move between counties.

"Neighborhood Impacts Mover Study" (Chetty & Hendren, 2015): Procedure

Compare children exposed to better neighborhoods for different lengths of time (fixed effects) and measure adult earnings.

"Neighborhood Impacts Mover Study" (Chetty & Hendren, 2015): Key Finding

Every year spent in a "better" neighborhood during childhood improves adult income (the exposure effect).

"Neighborhood Impacts Mover Study" (Chetty & Hendren, 2015): Analysis

S: Strong quasi-experimental design; estimates causal impact. L: Movers may differ from non-movers.

"Moving to Opportunity" (Chetty et al., 2016): Research Question

What is the effect of moving to lower-poverty neighborhoods on children's long-term outcomes?

"Moving to Opportunity" (Chetty et al., 2016): Aim

To determine whether better neighborhoods improve economic mobility for children.

"Moving to Opportunity" (Chetty et al., 2016): Method

Experimental analysis using randomly offered housing vouchers (MTO vs. Control).

"Moving to Opportunity" (Chetty et al., 2016): Procedure

Compare families offered section 8/MTO vouchers to control groups; measure adult income and education by age at move.

"Moving to Opportunity" (Chetty et al., 2016): Key Finding

Children who moved before age 13 had significantly higher earnings as adults; those moving after 13 saw slight negative impacts.

"Moving to Opportunity" (Chetty et al., 2016): Analysis

S: Randomized experimental design; strong causal evidence. L: Effects differ by child age; mechanisms remain unclear.

"Public Housing Demolition" (Eric Chyn, 2018): Research Question

What is the effect of moving to lower-poverty neighborhoods (following demolition) on children's long-term outcomes?

"Public Housing Demolition" (Eric Chyn, 2018): Aim

To determine whether better neighborhoods improve economic outcomes for displaced children.

"Public Housing Demolition" (Eric Chyn, 2018): Method

Project fixed effects and empirical approach comparing displaced children to non-displaced within housing projects.

"Public Housing Demolition" (Eric Chyn, 2018): Procedure

Compare long-term outcomes for displaced children (natural experiment in Chicago) vs. non-displaced children.

"Public Housing Demolition" (Eric Chyn, 2018): Key Finding

Children who moved were more likely to be employed and had greater earnings; large effect for kids of all ages.

"Public Housing Demolition" (Eric Chyn, 2018): Analysis

S: Externally valid for urban low-income families; more convincing than MTO for older children. L: Relies on random demolition assumption.

"The Oregon Experiment" (Baicker et al., 2013): Research Question

What is the effect of Medicaid coverage on health outcomes and financial well-being?

"The Oregon Experiment" (Baicker et al., 2013): Aim

To evaluate the consequences of gaining Medicaid insurance.

"The Oregon Experiment" (Baicker et al., 2013): Method

Randomized controlled trial / RCT using Oregon Medicaid lottery data.

"The Oregon Experiment" (Baicker et al., 2013): Procedure

Compare lottery winners and non-winners; measure health outcomes, usage, and financial strain via surveys and screenings.