Synthetic Control and Dynamic Panel Estimation: A Case Study of Iran

International sanctions imposed on Iran, targeting primarily Iran’s key energy sector and its ability to access the international financial system, have harmed Iran’s economic growth, specifically from 2011 to 2014. This thesis uses this case to study and compare the applicability of two different popular approaches used in comparative case studies exploring the effect of a policy intervention. In the Chapter 1 we study the synthetic control method. Using this method, we estimate the effect of the intensification of sanctions on Iran’s GDP during the period 2011 to 2014. The year of 2011 was Iran’s first full year under these heavy sanctions, and in 2015, the Iran nuclear deal framework was established. Prior to this time, in spite of the ongoing U.S. sanctions, Iran’s GDP had a positive trend from 1990 to 2011. However, our estimates show that the GDP suffered a hit of more than 17 percent over the period under question. We find that these effects were particularly severe in 2012 – the same year of the enforcement by the European Union of an oil embargo and added financial boycotts against Iran. In Chapter 2, we take a different approach to the same case, and incorporate a more structural and traditional framework. We use a Difference-in-Difference model as well as a dynamic panel data model to estimate the effect of sanctions. According to the dynamic panel data estimation, the cumulative effect of sanctions on the country’s GDP is −11.40,−18.12, and −18.62 percent for 2012, 2013, and 2014. In this chapter, we also compare the synthetic control method with the dynamic panel data regression framework. First, we show that the synthetic control method provides an unbiased estimator if the underlying model of the outcome variable of interest is a dynamic panel data model. Second, we compare the prediction power of these two methods. In Chapter 3 we design a Monte Carlo study to discuss the performance of the methods used in previous chapters over many replications. In this chapter, we examine the robustness of the method. We conclude that the dynamic panel data model seems to be performing well with the macro level aggregate data, and the assumptions are appropriate. However, for the synthetic control method we observe large standard error in the estimated values. If we translate that to a significance analysis, this means that even though we observe meaningful values reported as the effect of the intervention, they are not statistically significantly different from 0

A Simple Measure to Study Multinational Income Inequality

Using the Big Mac Index, we offer a simple measure to study the real income inequality. We provide a multidimensional real income inequality analysis by exploring the Coefficient of Variation and the Big Mac Affordability of households across all income deciles of 28 countries for the years 2000 to 2013. We look more into a few of the most interesting countries in our analysis in order to have explanations for the wide range of income inequality we observe. We compare Denmark and Mexico as representatives of the “more equal” and “less equal” countries in our analysis, and we find a visible difference in the share of each decile to the top decile of income between the two countries. However, we observe that, although a more equal country, Denmark has been exp eriencing a rise in income inequality while a less equal country (Mexico) has been experiencing a reduction in income inequality. We also focus on the United States and study how it compares to Russia, a country that shows a different direction of income inequality compared to the U.S.A. We find that while the wage income inequality in Russia has been correlated inversely with its growth, in the U.S.A., the overall growth and wage income inequality have been positively correlated

Money Supply and Inflation after COVID-19

The core personal consumption expenditure (PCE) price index, the Federal Reserve’s preferred inflation gauge, rose to 5.2 percent on January 2022, which is the highest rate of increase since 40 years ago. Our estimates show that the annualized quarterly core PCE prices could reach 5.45% in the second quarter of 2022 and are as high as 8.57% in a longer time horizon unless corrected with restrictive monetary policies. Thus, the inflation shock since COVID-19 is not transitory, but it is persistent. As economists expect the Federal Reserve to tighten the money supply in March 2022, the insufficient policy responses may be attributed to a failure to incorporate a unique macroeconomic shock to unemployment during the pandemic. We propose a modified vector autoregression (VAR) model to examine structural shocks after COVID-19, and our proposed model performs well in forecasting future price levels in times of a pandemic

Money Supply and Inflation after COVID-19

The core personal consumption expenditure (PCE) price index, the Federal Reserve’s preferred inflation gauge, rose to 5.2 percent on January 2022, which is the highest rate of increase since 40 years ago. Our estimates show that the annualized quarterly core PCE prices could reach 5.45% in the second quarter of 2022 and are as high as 8.57% in a longer time horizon unless corrected with restrictive monetary policies. Thus, the inflation shock since COVID-19 is not transitory, but it is persistent. As economists expect the Federal Reserve to tighten the money supply in March 2022, the insufficient policy responses may be attributed to a failure to incorporate a unique macroeconomic shock to unemployment during the pandemic. We propose a modified vector autoregression (VAR) model to examine structural shocks after COVID-19, and our proposed model performs well in forecasting future price levels in times of a pandemic

Relevance of Education to Occupation: A New Empirical Approach Based on College Courses

We introduce a new approach to measuring the match between education and occupation by using the number of college courses related to one’s occupation. Previous studies have only considered the match between college “major” and occupation. This approach ignores the content of education and the courses taken in college. We find that taking courses in college that are relevant to one’s occupation is significantly associated with higher wages, which can be taken as evidence against the notion that returns to college are principally a matter of signaling. We further find that performing well in these courses is associated with an even higher wage premium. A student’s wage increases, on average, by 1.6–2.9 percent for each matched course. This effect increases to 2.3–3.8 percent when we use a grade weighted measure of match