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Undoing the Chilean Reform (DSI-SRP)

Posted by on Sunday, August 15, 2021 in College of Arts and Science, Completed Research, DSI-SRP, DSI-Supported Research, Social and Behavioral Sciences.

This DSI-SRP fellowship funded Janet Stefanov to work with Professor Kathleen McKiernan in the Department of Economics during the summer of 2021. Janet is a senior with majors in Economics and Mathematics and minors in Russian and Data Science.

The project funded by this fellowship aimed to understand the macroeconomic and welfare implications of proposed social security reforms in Chile. The performance of Chile’s system of individual retirement accounts has long been of interest, and under performance of payments relative to other OECD countries contributes to growing calls for reform. The case of Chile’s social security is interesting on a number of dimensions. Although Chile’s initial privatization inspired a wave of privatizations across Latin America and Eastern Europe, full reversals of privatization have not occurred, which makes both the technical details of reform and the outcome of this project relevant and interesting for researchers and policymakers alike. Moreover, because Chile has undertaken both directions of reform, work on this subject can shed light on the transition costs associated with undertaking these reforms. In other words, what are the welfare costs of each direction of transition and who bears these costs? Which groups benefit most from either transition?

To address these questions, this project fits a heterogeneous-agent overlapping generations model to Chile’s economy. Initial estimates use data from 2019 to calibrate a model where households differ in both age and income. Model performance is evaluated using the technique of targeted moments, where a set of moments that come out of the model are compared against the same moments in the data. This technique gives insight into the performance and fit of the model, and can direct where adjustments in calibration are necessary. Computation of the balanced growth path is performed via Gauss-Seidel fixed-point iteration with backward and forward induction along a grid to first determine the optimal decision (policy) function at each node and then using the initial conditions from data, store the specific decisions at each node going forward. These steps occur based on each initial vector guess of an interest rate and a government transfer, and the process continues until both fixed points are found.

An important aspect of this project is using available micro and census data to understand the initial conditions of the Chilean economy prior to the Covid pandemic and this new social security reform. Exploratory analysis and visualization of microdata on income and taxable income, as well as consumption spending, informal employment, wage income vs transfer income informs the decisions on relevant inputs to the model. This type of analysis informs decisions to reduce computational intensity by decreasing the heterogeneity amongst agents without losing the precision and equity associated with having more specific groups.

In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.

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