Part I: Case Study One (This analysis does not need to coding) Consider a 2020 academic article "Does Household Electrification Supercharge Economic Development?" (https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.34.1.122). Excerpt: While launching the Sustainable Energy for All program to promote rural electrification in 2011, then-United Nations Secretary-General Ban Ki-moon described energy as "the golden thread that connects economic growth, increased social equity, and an environment that allows the world to thrive" (SEFA 2012). Reinforcing this perspective is the strong, positive cross-country correlation between electricity consumption and GDP per capita documented in the macroeconomic literature (for example, Burke, Stern, and Bruns 2018), which we present in Figure 1. Today, nearly a billion people still live without access to electricity (IEA 2018). Thus, access to energy has re-emerged as a key priority for policymakers and donors in low-income countries. Electrification could allow poor households to have easy access to lighting for evening chores or studying and power for phone charging and possibly for a range of new small business activities, both on and off the farm.

Figure 1 The Positive Correlation between Electricity Consumption and GDP per Capita 100,000- United States Brazil 10,000- . South Africa GDP per capita (USD) Philippines Chana Vietnam Kenya. Nicaragua . India 1,000= Bangladesh 100 - 100 1,000 10,000 100,000 Electricity consumption per capita (kWh) Source: 2014 data obtained from the World Bank DataBank. Note: Both variables are presented on a logarithmic scale. GDP per capita data are in current US dollars.Regressions #1 and #2 below include all 180 observations in Figure 1. Note the OLS regression results In the titles. Regression #1 Regression #2 n = 180, R2 = 0.4942 n = 180, R2 = 0.8135 y-hat = 5523.0059 + 2.6371*x y-hat = 2.4300 + 0.8572*x 200000- 12 150000- 10 GDP per capita (USD) 100000 Ln(GDP per capita) 8 50000- 20000 40000 6000( 6 8 10 12 Energy consumption per capita (KWh) Ln(Energy consumption per capita) Regressions #3 and #4 below use only the 138 observations in the World Bank DataBank that are actual countries (hence, they are a subset of the 180 observations in Figure 1 and Regressions #1 and #2 above). These exclude observations like "North America," "Euro area," "Arab World," "Sub-Saharan Africa," and "OECD members." Regression #3 Regression #4 = 138, R2 = 0.4608 n = 138, R2 = 0.7975 y-hat = 6773.9336 + 2.4785*x y-hat = 2.6308 + 0.8357*x 200000- 12- 150000 3 10 GDP per capita (USD 100000- Ln(GDP per capita) 8- 50000 0 20000 40000 6000 6 8 10 12 Energy consumption per capita (kWh) Ln(Energy consumption per capita) Regressions #5 and #6 below are like Regressions #3 and #4 except that they exclude Iceland, which has energy consumption per capita of 53,832.48 kWh.Regression #5 Regression #6 n = 137, R2 = 0.6825 n = 137, R2 = 0.7974 y-hat = 475.0389 + 4.2324*x y-hat = 2.5606 + 0.8457*x 150000- 121 100000 10- GDP per capita (USD) Ln(GDP per capita) 50000 8 0 5000 10000 15000 20000 2500( 6 8 10 Energy consumption per capita (KWh) Ln(Energy consumption per capita) Regressions #7 and #8 below are like Regressions #3 and #4 except that they exclude 10 countries - Bahrain, Canada, Finland, Iceland, Kuwait, Luxembourg, Norway, Qatar, Sweden, and the United States - with energy consumption per capita above 12,500 kWh. Regression #7 Regression #8 n = 128, R2 = 0.6764 n = 128, R2 = 0.7706 y-hat = -1667.5774 + 5.0007*x y-hat = 2.6952 + 0.8252*x 121 80000 60000 10- GDP per capita (USD) 40000 Ln(GDP per capita) 8 20000 O- 6 0 5000 10000 5 6 7 8 9 m Energy consumption per capita (KWh) Ln(Energy consumption per capita) of 6Supplement for Question: Household Electrification and Development. (a) Among the 138 countries in the World Bank DataBank, what is the coefficient of correlation between energy consumption per capita (kWh) and GDP per capita (USD)? Answer with a quantitative analysis. (b) For Regression #5, what is the interpretation of 475.0389 and 4.2324? Answer with 2 - 3 sentences. (c) For Regression #4, what is the interpretation of 0.8357? Answer with 1 precise sentence. (d) For Regression #7, the Se equals 9529.3. In plain English, what does the Se measure in this context? Include the units of measurement of the S. in this case. Is it big or small? For Regression #7, explain why the S is a misleading measure in this context. Answer with 4 - 5 sentences