Essays in applied economics and data analysis

Applied Microeconomics connects economics theories with detailed micro-level data in order to test the theoretical foundation. My dissertation is comprised of two chapters, which include four essays, in applied microeconomics to define new modeling approach for cultural behaviors and energy economics.
The first chapter demonstrates a proper model to measure the effect of culture on economic behaviors, particularly on women’s decisions to work and fertility. This chapter shows that the best way to measure the cultural effect is assessing second-generation immigrants’ behaviors in a host country. Each immigrant has a specific culture that he/she brings to the host country and may transmit to his/her own second generation. Accordingly, economic behaviors of second-generation immigrants may depend on the historical characteristics of their ancestors’ heritage countries. This study indicates that an ancestor’s female labor force participation and total fertility rate are more likely to have an impact on the number of hours a woman works and the number of children she has in the United States. This study suggests that the second-generation immigrant who kept their heritage languages can represent their heritage cultures. This study proposes a general model for quantifying cultural impact on individuals’ decisions that can be extended for other economic and financial behaviors such as saving behaviors, risk behaviors, investment behaviors, and so on. The second chapter displays two main studies for modeling residential energy demand. The two main approaches in energy-economy modeling exist: disaggregated studies and aggregated studies. The first study of this chapter uses disaggregated approach for modeling residential energy demand. This study employs detailed data for the energy consumption of more than 560,000 households in the U.S. to focus on gas and electricity consumption by using main factors including socio-economics and demographics, building characteristics, location situation, temperature, and energy prices. The findings show that all five main factors such as social and demographical compositions, building characteristics, location, temperatures, and energy prices have impact on the household energy demand. The second study of this chapter uses aggregated approach for estimating residential energy demand. This study uses the static panel estimation approach as well as the dynamic panel estimation approach with several robustness checks to estimate residential electricity and gas demand. The results show the impact of socio-economic and demographic characteristics, building age, energy prices, and weather conditions on the residential energy demand at the state level for both static and dynamic estimation models. The results indicate that short-run own price elasticities are negative and lower than 1 for both electricity and gas demand, meanwhile only long-run own price elasticities for electricity demand are lower than 1. Next, this study proposes two alternative scenarios to reduce residential energy demand based on the most precise model. For every 10,000 dollars of per capita income in each state: (1) increasing residential electricity price by 1 cent per kWh and (2) decreasing average building age by 1%. In the first scenario, the findings indicate that annual residential electricity demand would decrease by 7.3% on average and in the second scenario, residential gas demand would decrease by an average of 15.8% annually. These proposed scenarios assist policymakers in optimizing decisions and investments to reduce residential energy consumption.