The paper I hope to extend is “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States” by Chetty et al. 2014. This paper measures the intergenerational mobility in the whole United States as well as in every commuting zones (Chetty et al. 2014). It compares intergenerational mobility across the United States. In addition, the paper estimates the correlation (not causal effects) between some factors and intergenerational mobility (Chetty et al. 2014). It shows that a commuting zone’s racial segregation, inequality, social capital, school quality, and fraction of single parents are correlated with intergenerational mobility (Chetty et al. 2014).
I hope to extend this paper by estimating the causal effects of those factors (and perhaps also other factors) on intergenerational mobility and detecting the internal mechanism of intergenerational transmission. That is, I will not only find factors that are correlated with intergenerational mobility as Chetty et al. 2014 did, but also try to estimate the causal effects as well as find possible mechanisms determining intergenerational mobility as much as possible. I will do this by answering the following two questions separately. The first is what factors influence individuals’ education level and the second is how education level determines one’s income in the future.
Regarding the first question, (Chetty et al. 2017, Chetty et al. 2015) have found that there exist causal effects of neighborhood on intergenerational mobility by analyzing the data from the “Moving to Opportunity (MTO) experiment” (Chetty et al. 2015) and the records of families who moved from one neighborhood to another (Chetty et al. 2017). Chetty et al. 2017, Chetty et al. 2015 showed that on average, if children from poor family moved to a good neighborhood at an early age, they would have higher income when they are older (i.e., the intergenerational mobility increases). To have a deeper understanding on this finding, I want to detect what factors in those “good” neighborhoods make the intergenerational mobility high and what factors in those “bad” neighborhoods result in low intergenerational mobility. Then I could also utilize family move when children are young to identify what factors impact intergenerational mobility by regarding family move as an IV variable. In addition to the data of family move, I also need the data regarding these children’s education performance (e.g. test grades, highest education level), as well as the features of neighborhoods. Then, I could utilize these data to identify what factors in neighborhood impact individuals’ education level in the future if they move to another neighborhood. To detect the mechanism of one’s education level, I use the data regarding students’ school performance in each education level. Since different children move to another neighborhood at different ages, using the school performance data in each education level, we could detect a more dynamic impact of those factors on people’s education performance. That is, how these factors influence students’ school performance in different education stages and how those factors play a role in children at different ages.
Regarding the second question, I could extend this finding by using individuals’ education level and income data to analyze how education level influences one’s future income. I will make the parents’ income and children’s education level as independent variables. Therefore, the model not only measures how education level will influence people’s income controlling for their parents’ income, but also would tell us how the gap between the income of people from poor family and wealthy family can be explained by the difference between their education level (i.e., how people’s income depends on their parents’ wealth after controlling their education level). It also provides insight for children from poor families that how much their income in the future could be if they can have a high education level.
Combing these two steps, I might be able to detect factors that determine people’s income from poor family and find an internal mechanism of intergenerational transmission.
Reference:
Raj Chetty, Nathaniel Hendren, “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates”, Quartely Journal of Economics, 2018, 133(3): 1163-1228
Raj Chetty, Nathaniel Hendren, Lawrence Katz, “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment”, American Economic Review, 2015, 106(4): 855-902
Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States”, Quartely Journal of Economics, 2014, 129(4): 1553-1623
The paper I hope to extend is “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States” by Chetty et al. 2014. This paper measures the intergenerational mobility in the whole United States as well as in every commuting zones (Chetty et al. 2014). It compares intergenerational mobility across the United States. In addition, the paper estimates the correlation (not causal effects) between some factors and intergenerational mobility (Chetty et al. 2014). It shows that a commuting zone’s racial segregation, inequality, social capital, school quality, and fraction of single parents are correlated with intergenerational mobility (Chetty et al. 2014). I hope to extend this paper by estimating the causal effects of those factors (and perhaps also other factors) on intergenerational mobility and detecting the internal mechanism of intergenerational transmission. That is, I will not only find factors that are correlated with intergenerational mobility as Chetty et al. 2014 did, but also try to estimate the causal effects as well as find possible mechanisms determining intergenerational mobility as much as possible. I will do this by answering the following two questions separately. The first is what factors influence individuals’ education level and the second is how education level determines one’s income in the future. Regarding the first question, (Chetty et al. 2017, Chetty et al. 2015) have found that there exist causal effects of neighborhood on intergenerational mobility by analyzing the data from the “Moving to Opportunity (MTO) experiment” (Chetty et al. 2015) and the records of families who moved from one neighborhood to another (Chetty et al. 2017). Chetty et al. 2017, Chetty et al. 2015 showed that on average, if children from poor family moved to a good neighborhood at an early age, they would have higher income when they are older (i.e., the intergenerational mobility increases). To have a deeper understanding on this finding, I want to detect what factors in those “good” neighborhoods make the intergenerational mobility high and what factors in those “bad” neighborhoods result in low intergenerational mobility. Then I could also utilize family move when children are young to identify what factors impact intergenerational mobility by regarding family move as an IV variable. In addition to the data of family move, I also need the data regarding these children’s education performance (e.g. test grades, highest education level), as well as the features of neighborhoods. Then, I could utilize these data to identify what factors in neighborhood impact individuals’ education level in the future if they move to another neighborhood. To detect the mechanism of one’s education level, I use the data regarding students’ school performance in each education level. Since different children move to another neighborhood at different ages, using the school performance data in each education level, we could detect a more dynamic impact of those factors on people’s education performance. That is, how these factors influence students’ school performance in different education stages and how those factors play a role in children at different ages. Regarding the second question, I could extend this finding by using individuals’ education level and income data to analyze how education level influences one’s future income. I will make the parents’ income and children’s education level as independent variables. Therefore, the model not only measures how education level will influence people’s income controlling for their parents’ income, but also would tell us how the gap between the income of people from poor family and wealthy family can be explained by the difference between their education level (i.e., how people’s income depends on their parents’ wealth after controlling their education level). It also provides insight for children from poor families that how much their income in the future could be if they can have a high education level. Combing these two steps, I might be able to detect factors that determine people’s income from poor family and find an internal mechanism of intergenerational transmission.
Reference:
Raj Chetty, Nathaniel Hendren, “The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates”, Quartely Journal of Economics, 2018, 133(3): 1163-1228
Raj Chetty, Nathaniel Hendren, Lawrence Katz, “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment”, American Economic Review, 2015, 106(4): 855-902
Raj Chetty, Nathaniel Hendren, Patrick Kline, Emmanuel Saez, “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States”, Quartely Journal of Economics, 2014, 129(4): 1553-1623