Economics Differences in Pre Tax Incomes in The US Worksheet
Professor: Julien Lafortune Economics 130 Fall 2023 PROBLEM SET #1 [DUE 09/20/2023 BY 11:59PM] Student Name (bCourses official and preferred): Student ID: Collaborators (if any): Space is provided for answers below. You are not required or expected to fill all of the space; should you require extra space, please attach additional pages. You may submit typed, scanned, or handwritten answers. Please submit on bCourses, or in person during lecture. 1. MULTIPLE CHOICE. Choose the correct answer. You do not need to explain your answer. (1) Technological change that favors high-skilled work, or “skill-biased technical change”, has led to increasing inequality in the United States since 1980 because: (a) Firm owners can capture larger returns from global markets (b) Demand for high-skilled workers has increased faster than supply of these workers (c) The earnings premium for work requiring a college degree has not changed (d) Federal tax policy has become less progressive over time, especially for top incomes (e) All of the above 2. TRUE / FALSE / UNCERTAIN. Determine whether each statement is true, false, or uncertain, AND provide a brief explanation (1-3 sentences will typically be sufficient) for your answer. To receive full points, you must provide an explanation. 1 (a) Inequality is higher in the United States because of differences in pre-tax incomes rather than differences in taxes and transfers. (b) There are no economic justifications for government intervention in the economy. (c) A lack of correlation between two variables implies that there is no causal link between them. 2 (d) A proposal to send every California resident $1,000 for “inflation relief” is progressive even though every resident receives the same amount. 2. POTENTIAL OUTCOMES. Coding skills are often highly rewarded in the labor market, and companies frequently claim there are a shortage of qualified workers. Meanwhile, employment in the leisure and hospitality sector (e.g. restaurants) is still below pre-COVID levels, and wages are often relatively lower. Consider a program to offer a one-month coding “boot camp” to former restaurant workers to help them transition to a career in software engineering. The program’s effects can be summarized via the following potential outcomes table. Here, Yi(Di) are potential earnings in the month after the program for each individual i, where enrollment in the “boot camp” training is given by Di = {0,1} (i.e. Di = 1 means they enrolled in the “boot camp”). In other words, Yi(0) is the potential earnings one month later for individual i if they did not receive the training, while Yi(1) is their income one month later if they do receive training. For parts (a)–(d), we will only consider the earnings one month after the program, outlined in Table 1 below. Table 1: Earnings in month after boot camp program: i Person 1 Person 2 Person 3 Person 4 Person 5 Person 6 Yi (0) 5000 3500 3500 2500 4500 6000 Yi (1) 6000 4000 3000 5000 6000 7000 Di 1 0 1 0 1 0 3 (a) Explain the “fundamental problem of causal inference.” Using all of the potential outcomes in the table, what is the average treatment effect? (b) Do all individuals benefit from the program? Which individual would benefit the most? Which individual does benefit the most? (c) Now suppose – as is the case when working with real data – that you only have access to realized outcomes (i.e. you no longer observe all of the information in Table 1). Make an assumption, write it down, and under this assumption use only realized outcomes to calculate the average treatment effect. (Hint: think about the experimental ideal) 4 (d) Why might the assumption you made in (c) not hold in this context? Now suppose we are able to obtain data on the earnings of individuals in the two months prior to the program. Here, we define t-2 as two months prior and t-1 as one month prior to the program. Time t+1 is one month after the program: Y i,t+1 is therefore equivalent to the realized Yi from Table 1. (Note: We do not consider earnings at time t, as that is when individuals enroll in the program.) Table 2: Earnings two months before, one month before, and one month after program: i Yi,t-2 Y i,t-1 Y i,t+1 Di Person 1 3500 4000 6000 1 Person 2 2500 2500 3500 0 Person 3 4000 3500 3000 1 Person 4 3000 2500 2500 0 Person 5 4500 4500 6000 1 Person 6 5000 5500 6000 0 (e) Calculate the average difference in earnings between those who did and did not partake in the program in the month prior to the program (t-1). In light of these findings, discuss the validity of the assumption you made in part (c). 5 (f) Using only information from t-1 and t+1, calculate a differences-in-differences (DiD) estimate of the program’s effect. (g) Now, calculate the average difference in earnings from t-2 to t-1 for those who did and did not partake in the program. Does this support or refute a key assumption necessary to interpret your answer from part (f) as causal? 6 (h) As is sometimes the case, you meet an economist who is skeptical about the DiD estimate from part (f). Describe another way you could estimate the program’s effect if you had the resources to design a new study and collect new data. 3. STATE VS FEDERAL FISCAL POLICY Describe how revenues and expenditures differ between the federal government vs state and local governments. Identify at least two taxes and two expenditure categories that differ between state/local and the federal levels. 7 4. INCOME INEQUALITY AND UPWARD MOBILITY (a) Define income inequality and intergenerational mobility, and give an example of a measure used for each one. (b) Propose and describe a policy that could help address income inequality. Does it also address intergenerational mobility? (c) Propose and describe a policy that could help address intergenerational mobility. Does it also address income inequality? 8 5. SHORT DATA “ESSAY” (less than one page) Visit the Opportunity Atlas (https://www.opportunityatlas.org/ ). If you grew up in the US, find the neighborhood where you grew up. If you grew up elsewhere, pick another neighborhood (e.g. could be somewhere in Berkeley, or somewhere else you find interesting). First, consider the child outcome in adulthood of “household income at age 35”: describe (a) the level of income, and how it differs with nearby neighborhoods; (b) how it varies by parent income, race, and/or gender. Then, (c) considering “household income at age 35” and other outcomes (such as employment or incarceration rates), comment on whether you think this neighborhood offers good opportunities for children (Note: you can rely on data from the Atlas and/or your personal experience). 9
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