Income Taxation and Intergenerational Mobility

There is a large literature on the Intergenerational Correlation of Income (ICI) focused on obtaining estimates of correlation across generations. However, there are few studies that have emphasized on estimating the causal relationship and analyzed the relationship between parental and children income. These studies estimating the causal relationship generally do not consider households decisions such as parental investment and fertility behavior. To find the causal impact of policy changes on the ICI, both parents and childrens economic decisions should be analyzed in detail. In particular, any policy change can directly impact child-related parental investment decisions as well as the fertility behavior. This project will significantly contribute to literature by identifying the causal inferences of policies. One of my recent papers studies the causal impact of existence and type of taxation on the ICI (currently R&R at Quantitative Economics). The analysis shows that income taxes significantly impact social mobility. Although the results of this paper are a substantial contribution to the literature, the paper focuses only on income taxation and lacks other important policies, such as parental leave and child tax credits. The proposed study extends the analysis in my current paper. Another significant contribution will be to the literature on macroeconomic analysis. This study will embed two macroeconomic policies, one at a time, into a micro-founded model, i.e. a more detailed model of households with different characteristics such as gender, race, and education. Therefore, the proposed study will be in line with Lucas Critique, which states that the effects of a change in economic policy entirely on the basis of relationships observed in highly aggregated historical data cannot accurately be predicted. Many macroeconomic studies are challenged by the critique since they are not micro-founded. In addition, many micro-founded studies do not focus on the effects of macroeconomic policies. The bridge that will be created by this proposed study between two strands of the literature will be a significant contribution. To be able to success aforementioned objectives, I will study an economic model, in which households optimally decide how many hours to work, how many children to have, and how much childcare to perform to maximize happiness of their lives. This happiness consists of two components- the happiness from the households choices and the happiness from the success of children. The happiness will be measured by mathematical formulations of household choices. The parameters of the mathematical formulations will be estimated with the data. Using the estimated parameters, hypothetical data will be generated to test whether the choices of households, on average, are matched with the real data. This analysis is different than most of the studies on the ICI which mostly uses regression analysis. However, this type of analysis is not capable to observe all mechanisms that impact the ICI. Especially, to be able to get healthy results from counterfactual exercises, an analysis using a mathematical model is crucial. To measure the ICI correctly, first, the sources of income should be clarified. The literature shows that individuals with higher degrees earn more, on average. This fact raises the importance of parental investment in childrens education. To make an efficient investment in childrens educational attainments, the crucial ingredients of the education should be investigated. The literature finds that two important parental decisions significantly impact their childrens educational outcome: goods purchased for child-rearing and parental childcare. On the other hand, the interaction of these two decisions is controversial. All else equal, if parents want to spend more money on their children, they have to work more, which would reduce parental childcare. This observation suggests the causal impacts cannot be obtained with classical regression methods. Therefore, an instrument variable linear probability model will be used to find causal relationships. Moreover, the results of the project can be shared with policymakers who want to control and reduce income inequality within and across generations will benefit from this analysis. In this aspect, the results of this study will also enlighten the central government as well as local policy designers.