ECONOMICS THESIS PROPOSALS

Note: December 2020 1.PDF bookmarks are included (show left bookmark panel) 2. Zoom links for each day are embedded in underlined bold blue text

TUESDAY 12/1/20 3:00 TO 5:00 Sean Gao The Effects of Medicaid Patient Populations on Hospital Reimbursement and Care Quality: Evidence from Washington Andrew Swenson Stackelberg Advantages in Land Allocation: How Timing Affects Tradeoffs between Agglomeration and Local Market Power Kevin Ma The Impact of CARES Act Benefits on Retail Trade Employment

Trishala Roy Political Affiliations, Investment in Public Health, and Effects on Covid-19 Mortality

Sirig Gurung Spillovers of India's Demonetization to the Nepali Economy through Migrants from Nepal to India Ahliaa Moore The Effects of FDI on the Socioeconomic Outcomes of Jamaican Citizens

THURSDAY 12/3/20 3:00 TO 5:00 Angela Zhao Health, Race, and Place: The Effects of Segregation on Racial Disparities in Health Outcomes Arnav Parikh Assessing the Effectiveness of Affirmative Action In India using Fertility Rates Emily Kiernan The Venmo Effect: The Impact of Digital Payment Platforms on Consumer Willingness to Pay Rafael Gonzalez An Exploration of the Role of Social Connections in Job Placement Pedro Morais The Economics of Misinformation: Informational Signals and Citizen Responses in the COVID-19 Pandemic Claire Holleman School Based Health Centers and Their Impact on Student Achievement

FRIDAY 12/4/20 9:00 TO 11:00 Erik March Implicit Discount Rates in Solar Investments: Implications for Environmental Policy Thai Nguyen Do Vietnam’s State-Owned Enterprises Improve their Performance after Equitization? Evidence from the Last Decade Dana Frishman Athletic Participation and Academic Outcomes in NYC Public High Schools Crystal Yujing Zhou Migrate for A Brighter Future? The Impact of Parental Migration Decisions on Children’s Education in China Seamus Lawton A Model of Grade Inflation as a Collective Action Problem

Page 1 TUESDAY

TUESDAY 12/1/20 3:00 TO 5:00 Sean Gao The Effects of Medicaid Patient Populations on Hospital Reimbursement and Care Quality: Evidence from Washington Andrew Swenson Stackelberg Advantages in Land Allocation: How Timing Affects Tradeoffs between Agglomeration and Local Market Power Kevin Ma The Impact of CARES Act Benefits on Retail Trade Employment

Trishala Roy Political Affiliations, Investment in Public Health, and Effects on Covid-19 Mortality Sirig Gurung Spillovers of India's Demonetization to the Nepali Economy through Migrants from Nepal to India Ahliaa Moore The Effects of FDI on the Socioeconomic Outcomes of Jamaican Citizens

Page 2 Economics Thesis Proposal Name: Sean Gao Title: The Effects of Medicaid Patient Populations on Hospital Reimbursement and Care Quality: Evidence from Washington Field: Health Economics, Industrial Organization Version: Version 3 on 11/17/2020 Advisor(s): Prof. Jun Ishii, Prof. Jessica Reyes

Question Does a hospital’s Medicaid patient percentage have an effect on reimbursement rates from private insurers; do those reimbursement differences affect care quality? Area This topic lies in the intersection of health economics and industrial organization. Specifically, the question addresses the competition dynamics of the healthcare industry between hospitals and insurance companies, with the subsequent investigation of care quality and health outcomes falling in line with many studies in health economics. Motivation Medicaid is a public health insurance program that covers patients with limited income or resources. Bargaining determines private insurance reimbursement rates, but Medicaid rates are set by individual state governments. These rates are generally substantially lower than private insurer reimbursement rates; in many cases, hospitals are losing money on these reimbursements. Medicaid patient populations can affect hospital financials through their own lower reimbursement, but may also impact hospital-private insurer bargaining and subsequent private insurance reimbursement. Many states have or are in the process of expanding Medicaid coverage through the Affordable Care Act. As a result, Medicaid reimbursement concerns have become especially salient in recent years. To my knowledge, there is not much literature on the impact of this expansion or Medicaid patients on reimbursement rates and care quality. Theory Negotiations between hospitals and insurers generally center around the inclusion of a hospital in an insurer’s network. Hospitals in a network offer lower prices to patients insured by that insurer. Hospital-insurer bargaining has generally been modeled using a Nash bargaining framework. Let the hospital’s and insurer’s utility functions be given by h(x) and n(x), respectively. The Nash bargaining solution for the allocation of surplus is the pair of values, (x,y ), that maximizes (h(x)(− h d))(n(y)(− n e)), where (d,e ) are the hospital’s and insurer’s outside options, respectively. Using the framework of Baron and Berman (2014), let α [0,1 ] be the bargaining power of the hospital, and 1 − α be that of the insurer. The bargaining solution maximizes (h(x)(− h d))α (n(y)(− n e))1−α . Among others, Lewis and Pflum (2015) demonstrate that market share and power affect reimbursement through bargaining power and position. Hospitals have explicit and implicit capacity constraints; explicit capacity constraints include bed capacity, while implicit capacity constraints include reduced care quality or longer wait times. Medicaid patients treated at hospitals use resources and fill capacity. An increase in the Medicaid patient population at a hospital then reduces the “capacity” available to privately insured patients. This has two effects. Privately insured patients, if they face greater wait times or lower-quality care, may choose to go to hospitals that do not suffer from high admission. This reduced patient traffic decreases the attractiveness of the constrained hospital in an insurer’s

Page 3 network. Hospitals with capacity concerns due to greater Medicaid patient populations thus may also see declines in their market shares in the market for privately insured healthcare; as market share is a determinant of bargaining outcomes, we would expect that this would result in lower reimbursement rates for those hospitals in negotiations with private insurers. Given that a hospital may face lowered private insurance reimbursement rates with increases in Medicaid patients, whose reimbursements are already low and sometimes below cost, hospitals may face financial difficulties. Accordingly, it is intuitive to expect cost-cutting measures in these hospitals. Decreases in care quality may occur; decreasing care quality for privately insured patients may further drive substitution away from these hospitals, so care quality changes may differ between privately insured and publicly insured patients, as the utilization of this hospital by the latter group may not be as valued. Methods Estimating the effects of increasing Medicaid patient populations requires exogenous variation in these populations. This variation must be uncorrelated with hospitals’ capacity decision-making. While Medicaid expansion in Washington state in 2014 may have impacted hospital capacity decision making, the over enrollment of new patients was exogenous. I use Washington hospitals from 2010-2017 as the basis for my proposed specification:

Markupht = β1XHHIht + β2 rt + β3InsNumrt + β4 Med%ht + β5RivalMed%ht + β6SystemMed%ht + εhrt where Markup is the difference between average price and cost for a service, X is hospital ​ ​ characteristics, HHI and InsNum are measures of hospital and insurer market concentration, and ​ ​ ​ ​ Med% variables denote the percentage of Medicaid patients in the patient population for the ​ hospital, its rivals, and its hospital system. The observations are indexed by hospital h, region r, ​ ​ ​ and year t. I will also include time and system fixed effects. I may remove system Medicaid ​ ​ percentage and include it in a separate specification as system competition has a large impact on bargaining outcomes; in that case, I will define system rivals separately as well. An important consideration here is potential interactions between HHI and the Medicaid share variables. I am also exploring the inclusion of Med%-RivalMed% as an explanatory variable with a spline ​ ​ specification that separates positive and negative values for that variable, negative and positive shares relative to rivals may result in differing coefficients and effects on reimbursement. My proposed regression equations to investigate care quality is :

Qualityit = γ1Xht + γ2InsT ypeit + γ3 P redMarkuphit + γ4 InsT ypeit × P redMarkuphit + εhit . Quality may include hospital readmission rate, use of imaging procedures, and risk-adjusted ​ mortality, InsType is the patient insurance, and PredMarkup is the predicted markup from the ​ ​ ​ ​ previous regression for a hospital. The observations are indexed by patient i, hospital h, and year ​ ​ ​ ​ t. There are factors that affect both predicted markup and care quality; hospital Medicaid ​ percentage may impact both. PredMarkup should only include the care effects of Medicaid ​ ​ patient percentage that occur through effects on markup; so, one possible plan is to use the PredMarkup values from the specification that uses Medicaid percentage differences between ​ the hospital and its rivals and include the hospital’s own Medicaid percentage as a control. Issues ● Bargaining is over surplus; is markup a suitable proxy for this allocation? ● Hospital differences may result in different surplus calculations. I may want to include interaction terms or group hospitals by similar characteristics before the regression. ● Selection bias in patient outcomes: I only observe patient outcomes and care quality measures for a patient at the hospital they chose, not other counterfactual hospitals.

Page 4 References

Binmore, Ken, Ariel Rubinstein, and Asher Wolinsky. “The Nash Bargaining Solution in Economic Modelling.” The RAND Journal of Economics 17, no. 2 (1986): 176–88. ​ ​ https://doi.org/10.2307/2555382. Bitler, Marianne, and Madeline Zavodny. “Medicaid: A Review of the Literature.” NBER Working Paper 20169, Cambridge, MA: National Bureau of Economic Research, May 2014. https://doi.org/10.3386/w20169. Currie, Janet, and Jonathan Gruber. “Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant Women.” Journal of Political Economy 104, no. 6 ​ ​ (1996): 1263–96. Dafny, Leemore, Mark Duggan, and Subramaniam Ramanarayanan. “Paying a Premium on Your Premium? Consolidation in the US Health Insurance Industry.” American Economic ​ Review 102, no. 2 (April 1, 2012): 1161–85. https://doi.org/10.1257/aer.102.2.1161. ​ Dafny, Leemore, Kate Ho, and Robin Lee. “The Price Effects of Cross-Market Hospital Mergers.” NBER Working Paper 22106, Cambridge, MA: National Bureau of Economic Research, March 2016. https://doi.org/10.3386/w22106. ​ ​ Duggan, Mark, Atul Gupta, Emilie Jackson. “The Impact of the Affordable Care Act: Evidence from California’s Hospital Sector.” NBER Working Paper 25488, Cambridge, MA: National Bureau of Economic Research, January 2019. https://doi.org/10.3386/w25488. ​ ​ Einav, Liran, and Amy Finkelstein. “Selection in Insurance Markets: Theory and Empirics in Pictures.” Journal of Economic Perspectives 25, no. 1 (February 1, 2011): 115–38. ​ ​ https://doi.org/10.1257/jep.25.1.115. Gowrisankaran, Gautam, Aviv Nevo, and Robert Town. “Mergers When Prices Are Negotiated: Evidence from the Hospital Industry.” American Economic Review 105, no. 1 (January 1, ​ ​ 2015): 172–203. https://doi.org/10.1257/aer.20130223. ​ ​ Greaney, Thomas L. “Antitrust and Hospital Mergers: Does the Nonprofit Form Affect Competitive Substance?” Journal of Health Politics, Policy and Law 31, no. 3 (June ​ ​ 2006): 511–29. https://doi.org/10.1215/03616878-2005-004. ​ ​ Ho, Kate, and Robin S. Lee. “Equilibrium Provider Networks: Bargaining and Exclusion in Health Care Markets.” American Economic Review 109, no. 2 (February 1, 2019): ​ ​ 473–522. https://doi.org/10.1257/aer.20171288. ​ ​ Katona, Katalin, and Marcel Canoy. “Welfare Standards in Hospital Mergers.” The European ​ Journal of Health Economics 14, no. 4 (August 2013): 573–86. ​ https://doi.org/10.1007/s10198-012-0403-x. Kessler, Daniel P., and Mark B. McClellan. “Is Hospital Competition Socially Wasteful?” The Quarterly Journal of Economics 115, no. 2 (2000): 577–615. ​ Lewis, Matthew S., and Kevin E. Pflum. “Diagnosing Hospital System Bargaining Power in Managed Care Networks.” American Economic Journal: Economic Policy 7, no. 1 ​ ​ (February 1, 2015): 243–74. https://doi.org/10.1257/pol.20130009. ​ ​ ​ Lewis, Matthew S., and Kevin E. Pflum. “Hospital Systems and Bargaining Power: Evidence from out-of-Market Acquisitions.” The RAND Journal of Economics 48, no. 3 (August ​ ​ 2017): 579–610. https://doi.org/10.1111/1756-2171.12186. ​ ​ ​ Luft, Harold S., and Robert H. Miller. “Patient Selection in a Competitive Health Care System.” Health Affairs 7, no. 3 (January 1, 1988): 97–119. https://doi.org/10.1377/hlthaff.7.3.97. ​

Page 5 Robinson, James C. “Hospital Quality Competition and the Economics of Imperfect Information.” The Milbank Quarterly 66, no. 3 (1988): 465–481. ​ ​ ​ ​ Sorensen, Alan T. “Insurer-Hospital Bargaining: Negotiated Discounts in Post-Deregulation Connecticut.” The Journal of Industrial Economics 51, no. 4 (2003): 469–90. ​ ​ Town, Robert, Douglas Wholey, Roger Feldman, and Lawton Burns. “The Welfare Consequences of Hospital Mergers.” NBER Working Paper 12244, Cambridge, MA: National Bureau of Economic Research, May 2006. https://doi.org/10.3386/w12244. Young, Gary J., Kamal R. Desai, and Fred J. Hellinger. “Community Control and Pricing Patterns of Nonprofit Hospitals: An Antitrust Analysis.” Journal of Health Politics, ​ Policy and Law 25, no. 6 (December 2000): 1051–81. ​ https://doi.org/10.1215/03616878-25-6-1051.

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Economics Thesis Proposal Name: Andrew Swenson Title: Stackelberg advantages in land allocation: how timing affects trade-offs between agglomeration and local market power. Field: Urban Economics and Industrial organization Version: Version 3 Advisor(s): Jun Ishii, Jake Blackwood Question ​ How does a landowner choose how many/what establishments to have on their land while taking into account agglomeration and competition effects? Does modeling landowners’ choices as sequential rather than simultaneous affect this choice? Area My topic resides at the intersection of urban economics and industrial organization. In analyzing spatial structure’s effect on competition I will use techniques from urban economics. In analyzing competition between landowners I will use techniques from industrial organization. In modeling these strategic interactions as sequential and simultaneous move games I will use techniques from game theory. Motivation My dad used to own a coffee shop. After owning it for about a year, his landlord informed him that it was against the terms of his lease to sell food. This term was put in his lease with encouragement from the restaurant owner next door, who had the same landlord. I see this as a prime example of a landowner transferring their market power downstream by vertically foreclosing their land input and insulating one firm on their land from price competition. Conversely, a few blocks away there is a small area of land, also owned by one landowner, with a McDonald’s, Taco Bell, and Burger King all right next to each other. I see this as a prime example of a landowner using their market power to coordinate agglomeration economies by bringing many like-establishments together. These two examples motivated me to look into what factors a landowner considers when deciding which of these two uses of land to pursue. My theory is that once the fast food center was established, my dad’s landowner expected that building many restaurants in order to attract customers would be fruitless as his center would need to compete with the other center for ​ customers. Instead, the landowner chose to extract more surplus from consumers who strongly preferred his center due to differences in transportation costs between the centers since having fewer like-establishments in a center will lessen competition within the center, and thus lead to higher profits per customer. Theory I begin by modelling a landowner’s choice between transferring their market power downstream and coordinating agglomeration economies. In this model, I will consider two landowners at either end of a line of length x. Between these landowners, there is a distribution of consumers. Consumers value retail centers based on the number of establishments in the center, the average price of goods in the center, and the transportation costs they must incur in order to travel to the center. Therefore, a consumer a’s expected utility function when visiting center i is E[U ai] = W a + f a(ni) − p(ni)− t (|λa − λi |) where W a is consumer a’s wealth, ni is the number of firms in center i, f a(ni) is consumer a’s expected marginal benefit of visiting center i (which is a function of ni) ,p(ni) is the average price of goods at center i (which is also a

Page 7 function of ni) , and t(|λa − λi |) is consumer a’s transportation costs for visiting center i, which is a function of the difference between consumer a’s endowed home on the line, λa , and center i’s endowed home on the line, λi . For simplicity, I will assume that for any two consumers a and b, W a = W b and f a = f b . Furthermore, I will assume consumers travel to at most one center and buy one good at that center. Now, each establishment k, on landowner i’s land will have before-rent expected profit equal to E[πki ]= (Di (ni)/ni)(pk(ni))− ck where Di is the number of consumers going to center i (which is a function of ni based on above consumer demands), pk is the price establishment k charges (which is also a function of ni because establishments price compete in a center) and ck is the marginal cost of producing establishment k’s good. For simplicity, and in order to focus on landowner strategic behavior we will assume ck = 0 and that landowners extract full surplus from their tenants. Moving on to the landowners, if we assume that all establishments within a center will set the same price, landowner i’s expected profit function is now E[πi(ni)]= Di (ni)(* pk ni) . Here, we can now see the essential trade-off a landowner makes. If pk(ni) is a decreasing function of ni (which we would expect under normal competition), and Di is an increasing function of ni (which we know it will be from our consumers’ utility functions) a landowner essentially has the choice of charging high prices to consumers who visit their center (by having few firms on their land) or attracting more consumers (by having a lot of firms on their land). If we model this with two landowners picking their establishments sequentially, we may expect that if the first picking landowner (landowner i) picks a high ni the second landowner (landowner j) will face a Dj that increases less with increases in nj then it would in the case where landowner i picked a low ni , as competition for consumers between landowner j and landowner i will be more intense in the case where landowner i has many establishments.

Because of this, landowner j may pick a low nj in order to extract more surplus from the consumers who strongly prefer their center due to transportation cost differences. Furthermore, anticipating this effect, landowner i may pick a higher ni than they would if this game were simultaneous, or if they were a monopoly landowner. Methods For consumers, I will need to look into the literature on search goods and agglomeration economies in order to find or construct consumer demand primitives that result in consumers’ expected utility rising with the number of goods in a center. For establishments, I will use the Dixit-Stiglitz model of competition where there are infinite symmetric and differentiated firms in order to abstract away from establishment-level choices of location and focus on landowner choices. This will also allow me to justify my assumptions that all establishments in a center set the same price and that landowners extract full surplus from tenants. After solving this model, I will perform comparative static analysis on my exogenous parameters to see if the distance between centers, consumer density, and transportation cost structure affect landowners tradeoffs. Issues - What types of consumer demand primitives should I use? - Is it possible to incorporate a case-study element to this thesis. If so, how would I collect this data?

Page 8 References Capozza, Dennis R., and Robert Van Order. "Pricing under Spatial Competition and Spatial Monopoly: Reply." Econometrica 48, no. 5 (1980): 1329. doi:10.2307/1912190. ​ ​

Coase, R. H. "Durability and Monopoly." The Journal of Law and Economics 15, no. 1 ​ ​ (1972): 143-49. doi:10.1086/466731.

Deng, Feng. "Comparative Urban Institutions and Intertemporal Externality: A Revisit of the Coase Conjecture." Journal of Institutional Economics 5, no. 2 (2009): 225-50. ​ ​ doi:10.1017/s1744137409001313.

Evans, Alan W. "On Monopoly Rent." Land Economics 67, no. 1 (1991): 1. ​ ​ doi:10.2307/3146481.

Machlup, Fritz, and Martha Taber. "Bilateral Monopoly, Successive Monopoly, and Vertical Integration." Economica 27, no. 106 (1960): 101. doi:10.2307/2550895. ​ ​

Ohta, Hiroshi, Yasushi Asami, and Janet E. Kohlhase. "Land, Labour and Product Markets under Spatial Monopoly and Spatial Competition." New Frontiers in Regional Science, ​ ​ 1990, 95-111. doi:10.1007/978-1-349-10633-2_8.

Waldman, Michael. "Durable Goods Theory for Real World Markets." Journal of Economic ​ Perspectives 17, no. 1 (2003): 131-54. doi:10.1257/089533003321164985. ​

West, Douglas S., Balder Von Hohenbalken, and Kenneth Kroner. "Tests of Intraurban Central Place Theories." The Economic Journal 95, no. 377 (1985): 101. ​ ​ doi:10.2307/2233471.

Page 9 Name: Kevin Ma Title: Impact of CARES Act Benefits on Retail Trade Employment ​ Field: Unemployment, Macroeconomics Version: Version 3 11/17/20 Advisors(s): Neil White, Christopher Kingston

Question Can the behavior of the small-business retail labor market during the pandemic be explained? Did higher unemployment insurance (UI) replacement rates from the Coronavirus Aid, Relief, and Economic Security ​ (CARES) Act negatively impact employment? If so, was the effect immediate or was there a delay? Did ​ coronavirus shutdowns decrease retail employment? As COVID-19 deaths increased, were workers afraid of contracting coronavirus and did staying on unemployment become a more attractive option?

Area The questions of this proposal reside in the fields of labor economics and public economics. The main focus is on the microeconomic decision-making of workers in the retail sector that were heavily affected by the pandemic. My inquiry will also gauge the macroeconomic impacts of increasing unemployment benefits and enacting shutdowns to battle the pandemic on the retail labor market.

Motivation The COVID-19 pandemic created the fastest-moving economic crisis in U.S. history, with millions of jobs lost within weeks. Congress responded by passing the CARES Act, the largest economic relief package in history. Currently, Congress is debating whether to pass a second stimulus package. A sharp point of contention is enhanced UI benefits, specifically the Federal Pandemic Unemployment Compensation (FPUC), as some fear that high UI benefits discourage people from working. The FPUC program, which expired July 31st, allowed people eligible to collect unemployment insurance benefits to receive an extra $600 in federal benefits per week. Many workers earned more money from FPUC than from their previous job; the median replacement rate for workers receiving benefits was 134% and 68% of workers qualified for a replacement rate higher than 100% (Ganong et al., 2020). Research has been conducted analyzing the effect of high replacement rates on the job market during the pandemic, and preliminary evidence suggests that employers have not experienced greater difficulty finding applicants for vacancies after the CARES Act (Marinescu et al., 2020). I would like to examine retail workers as many are deemed “essential” but also have a strong incentive to stay home as they have both a high replacement rate from FPUC and a high risk of contracting COVID-19.

Theory My model is based on the work of Petrosky-Nadeau (2020) and describes whether a risk-neutral job seeker will accept a job opportunity or remain unemployed. I compare the present value of having a job WE(w), to the value ​ ​ ​ ​ of remaining unemployed with t remaining weeks of eligibility WU(b,t). The values of being employed and ​ ​ ​ ​ ​ remaining unemployed are expressed as: 1 W E(w) = w + 1 + r [(1− s )[(1− c )W E(w) + cCov(t)] + sW U (b,T )] 1 Cov(t) = w − k + 1+r [h * H + ( 1− h )Cov(t − 1)] for t ≤ 2 W (b,t ) = b + 1 [(1− f )W (b,t − 1 ) + fm ax[W (w),W (b,t − 1 )]] for 1 < t ≤ T U 1 + r U * E U ​ ​ If a person is employed, she receives a wage w and has the discounted future value of three possibilities. She ​ could become unemployed with probability s, stay employed but contract COVID-19 with probability c, or stay ​ ​ ​ ​ employed and not contract COVID-19. The “value” of contracting COVID-19 is denoted by Cov(t) . Workers that contract COVID-19 still receive their wage but incur a disutility k of becoming sick. Once contracting ​

Page 10 COVID-19, she has a probability h of becoming hospitalized, which results in a large disutility H . If a person ​ is unemployed, she receives UI benefits b and has the discounted future value of having a probability f of ​ ​ finding a job and having the option to take the job or remain unemployed. I assume that workers do not think ​ they can contract COVID-19 while remaining unemployed because they will stay at home during the period. I r can then solve for the reservation benefit b (t,​ w) which denotes the benefit level needed for a person to be ​ ​ r indifferent from staying unemployed and taking a job offering wage w. b (t,​ w) can be written as: r ​ ​ ​ ​ ​ r b (1,w) b (t,w)​ = t−1 for 0 < t ≤ T ​ 1 − f i ∑()1 + r i=0 T −1 r r[w + c(1 − s)Cov(t) + sB(T )] 1−f i where b (1,w ) = r(r + f + s) + c(1 − s)(r + f) and B(T ) = ∑ b (1+r ) i=0

Thus, I can now express a retail worker’s reservation benefit as a function of k and WH. All other variables in the ​ ​ ​ ​ equations are available as tangible numbers, which means that if I can provide a reasonable estimate for k and ​ WH, I will be able to solve for a retail worker’s reservation benefit level. ​ ​

Methods I am attempting to answer a moral hazard problem where the government increases UI benefits as a response to rising unemployment, but increasing UI benefits may cause increased unemployment in the future. In order to find the causal effect of an increase/decrease in replacement rate at week t on the change in retail employment ​ ​ during a future week t+h , I can take advantage of the fact that the raw number increase/decrease in replacement ​ rate was equal across the nation, but the percentage increase varied across states. Thus, regressing the change in ​ retail employment in one state on the change in replacement rate deriving from an across-the-board flat national increase while controlling for other explanatory variables should provide a causal effect. However, there is a second issue: state UI policies during the pandemic may also vary. This creates an endogenous problem where the variance in state unemployment is the cause of variance in state policy. I will address this issue by using the change in a state’s replacement rate during the Great Recession as an instrument for the change in replacement rate during the pandemic. The assumption is that states that increased benefits less during the Great Recession would also increase benefits less during the pandemic. My︿ regression model will be: ΔRetEmp = α + β ΔRR + γ′ X + u ︿ t+h h h t h t t+h where ΔRR denotes the predicted change in replacement rate. X represents the vector of control variables t ​ ​t which are the following: a state’s stay-at-home status, a state’s weekly COVID-19 deaths per 100,000, a state’s overall unemployment rate, and the partisan lean of the branches of a state’s government. A higher COVID-19 death rate would imply a higher danger to working, while a state’s stay-at-home order status affects the number of businesses allowed to operate. The overall unemployment rate and the partisan lean of a state’s government act as proxies for how difficult it is to find a job and the generosity of a state’s social safety net respectively.

Issues ● I would like to expand on the theory section and iron out more of the details. How do I find reasonable

values for k and WH? ​ ​ ​ ​ ​ ● Control variables still need to be finalized, would like to reduce omitted variable bias ● I have managed to obtain city-level data for retail employment, do I use this data and if so how? Cities may have a drastically different labor market compared to states ● ID validation still needs work, causal effect still needs to be more fully established

Page 11 References

Bartik, Alexander, et al. “Measuring the Labor Market at the Onset of the COVID-19 Crisis.” 2020, doi:10.3386/w27613.

Federal Reserve Bank of San Francisco, and Nicolas Petrosky-Nadeau. “Reservation Benefits: Assessing Job Acceptance Impacts of Increased UI Payments.” Federal Reserve Bank of San Francisco, Working Paper Series, August 4, 2020, 1.000-24.000. https://doi.org/10.24148/wp2020-28. ​ ​

Forsythe, Eliza, et al. “Labor Demand in the Time of COVID-19: Evidence from Vacancy Postings and UI Claims.” 2020, doi:10.3386/w27061.

Ganong, Peter, et al. “US Unemployment Insurance Replacement Rates During the Pandemic.” 2020, doi:10.3386/w27216.

Hagedorn, Marcus, et al. “Unemployment Benefits and Unemployment in the Great Recession: The Role of Macro Effects.” 2013, doi:10.3386/w19499.

Lee, Jasmine C., et al. “See How All 50 States Are Reopening (and Closing Again).” The New York Times, The New York Times, 25 Apr. 2020, www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html.

Marinescu, Ioana Elena, et al. “Job Search, Job Posting and Unemployment Insurance During the COVID-19 Crisis.” SSRN Electronic Journal, 2020, doi:10.2139/ssrn.3664265.

Mortensen, D. T., and C. A. Pissarides. “Job Creation and Job Destruction in the Theory of Unemployment.” The Review of Economic Studies, vol. 61, no. 3, 1994, pp. 397–415., doi:10.2307/2297896.

“Table A. Job Openings, Hires, and Total Separations by Industry, Seasonally Adjusted.” U.S. Bureau of Labor Statistics, U.S. Bureau of Labor Statistics, 9 Sept. 2020, www.bls.gov/news.release/jolts.a.htm.

Page 12 ECONOMICS THESIS PROPOSAL Name: Trishala Roy Title: Political Affiliations, Investment in Public Health, and Effects on Covid -19 Mortality Field: Applied Microeconomics, Health Economics Version: Version 3 on 11/17/20 Advisors: Jessica Reyes, Mesay Gebresilasse

Question: Does political affiliation at the county level affect Covid-19 mortality outcomes in the US? If so, through what mechanism? Is there a clear causal link between political affiliation and mortality through pre-pandemic public healthcare expenditure? How does this link differentially impact demographically different communities?

Area: These questions are situated at the intersection of Applied Microeconomics, Health Economics, and Econometrics. I will use economic theories and methodologies relevant to these areas in order to thoroughly investigate the relationship between local political affiliation and Covid mortality rates through the mechanism of pre-pandemic public healthcare expenditure. The existing literature regarding the relationship between Covid-related outcomes and politics has yet to consider this mechanism, thus creating space for this inquiry.

Motivation: The coronavirus pandemic has had an undeniably profound impact on the world and specifically the US during this tumultuous year. Despite its well-developed infrastructure, robust economy, and expansive access to resources, the US continues to struggle to get a handle on the pandemic. The daily number of new cases are increasing at an alarming rate while the death count also steadily increases, now having surpassed a quarter of a million deaths. Such a high volume of deaths, especially amongst elderly and Black and Hispanic populations with underlying conditions, begs a consideration of what more could have been done to prevent this tragic loss of life. Much of the economics research conducted in this area since the beginning of the pandemic has focused on establishing causality between various explanatory variables and people’s behavior with regard to Covid restrictions. For example, “rugged individualism”, or “the combination of individualism and opposition to government”, differences in civic capital, and misalignment of risk perception between counties with a high Trump voter share (TVS) and those with low TVS have been shown to have hindered the collective action public health response to Covid-19 (Bazzi et al., Barrios et al., Barrios and Hochberg 2020). While each of these inquiries have indeed contributed much to our understanding of micro-level responses to Covid-19, they have yet to investigate the institutions or infrastructures that could have been essential to reducing the deadly impact of the virus.

Theory: I will use state fixed effects and time lags in order to identify the causal relationship between Republican Vote Share (RVS) and mortality in a given county. Covid-19 disproportionately affected densely populated urban areas at the beginning of the pandemic due to the nature of how the virus spreads. As a result, a direct comparison between urban counties (which tend to be lean Democratic) and rural counties (which tend to lean Republican) would result in a biased

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Page 13 coefficient. Therefore, I will model a lagged regression similar to that employed by Philip, Ray, and Subramanian in their research regarding India’s low Case Fatality Rate (CFR). I will establish a “number of cases” and “number of deaths” threshold, that when surpassed by a county, will constitute time period t = 0. Each subsequent week will increase t by one for that county. In this way, I will account for the urban areas’ “head-start” in case and death count. Including state fixed effects will address non-observable factors that could potentially confound the regression results.

Method: After running a preliminary regression, regressing CFR on RVS during the presidential election years of 2008, 2012, and 2016, I found that 2016 RVS maintains a significant negative correlation with CFR even after including fixed effects and controls while 2008 and 2012 RVS do not. As such, rather than just using RVS from each of the mentioned years, I will construct a 2016 RVS’ that is relative to the 2012 RVS in order to identify the true relationship between RVS and mortality. In other words, RVS’ will no longer represent the share of Republican votes in a county in 2016, but rather the percent increase or decrease of Republican votes relative to 2012 in that county. This will allow me to isolate what is essentially the “Trump” effect. Now, constructing a clear path between RVS’ and CFR will involve four regression equations:

1) ���!" = ∝ + �#���′!" + ��������� + � 2) ��!" = ∝ + �#���′!" + ��������� + � 3) ���!" = ∝ + �#��!" + ��������� + � 4) ���!" = ∝ + �#��!" + �$���′!" + �%(���′!" ∗ ��!") + ��������� + � Where s represents a given state, t represents the time at t = 0 + n weeks (explained above), HS represents Health Spending (pre-pandemic). Public health expenditure (HS) will be calculated as a percentage of total budget at the county level. I will control for population density, income level, demographics, social distancing levels, and testing levels in each of these specifications. As a robustness check, I will replace CFR with 1) number of cases and 2) mortality per capita in each of these specifications and compare the results to those from the regressions using CFR. I will use publicly available data regarding infection rates and number of deaths at the local level from John’s Hopkins University, Census Bureau data regarding local governments’ public healthcare expenditure as well as population and demographics over the years, mobility data from the Google Mobility Report in order to control for individual adherence to social distancing guidelines, and publicly available county-level voting data in order to establish the political affiliations of counties over the last three presidential elections (pre-2020).

Issues: Trump notoriously denied federal funding access to state governments scrambling to provide healthcare workers and hospitals with adequate equipment during the beginning of the pandemic. This is a threat to identification for my analysis for two reasons. First, as mentioned above, the states that were most deeply affected during the early pandemic were the more populous and Democratic leaning states. Second, little was known about the virus and how it spread at the time, so the most affected states were already caught off guard and unprepared. Denial of federal funding at such a crucial time could have exacerbated the problem and resulted in more cases and deaths than might have occurred otherwise. This could potentially result in a negative bias in the regression analysis.

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Page 14 References:

Angrist, Joshua D. and Pischke, Jorn-Steffen. 2014. Mastering Metrics: The Path from Cause to Effect. 1st ed. New Jersey: Princeton University Press. Aum, Sangmin, Lee, Sang Yoon (Tim), and Shin, Yongseok. 2020. “COVID-19 Doesn't Need Lockdowns to Destroy Jobs: The Effect of Local Outbreaks in Korea.” NBER Working Paper 27264, National Bureau of Economic Research, Cambridge, MA. Barrios, John M., Benmelech, Efraim, Hochberg, Yael V., Sapienza, Paola, and Zingales, Luigi. 2020. “Civic Capital and Social Distancing during the Covid-19 Pandemic.” NBER Working Paper 27320, National Bureau of Economic Research, Cambridge, MA. Bavel, Jay J. Van, Katherine Baicker, Paulo S Boggio, Valerio Capraro, Aleksandra Cichocka, Mina Cikara, Molly J Crockett, Alia J Crum, Karen M Douglas, James N Druckman et al. 2020. “Using social and behavioural science to support COVID-19 pandemic response,” Nature Human Behaviour, pp. 1–12. Bazzi, Samuel, Fiszbein, Martin and Gebresilasse, Mesay. 2020. “Rugged Individualism and Collective (In)action During the COVID-19 Pandemic.” NBER Working Paper 27776, National Bureau of Economic Research, Cambridge, MA. Campbell, Angus, P., W. Miller, and D. Stokes. 1960. “The American Voter.” New York: Wiley Dave, Dahval M., Friedson, Andrew I., Matsuzawa, Kyutaro, Sabia Joseph J., and Safford, Samuel. 2020. “Black Lives Matter Protests, Social Distancing, and COVID-19.” NBER Working Paper 27408, National Bureau of Economic Research, Cambridge, MA. Desmet, Klaus and Wacziarg, Romain. 2020. “Understanding Spatial Variation in COVID-19 across the United States.” NBER Working Paper 27329, National Bureau of Economic Research, Cambridge, MA. Dincer, Oguzhan C. and Robert Gillanders. 2020. “Shelter in Place? Depends on the Place: Corruption and Social Distancing in American States,” Depends on the Place: Corruption and Social Distancing in American States (May 28, 2020). Fuchs-Schündeln, Nicola, Krueger, Dirk, Ludwig, Alexander, and Popova, Irina. 2020. “The Long-Term Distributional and Welfare Effects of Covid-19 School Closures.” NBER Working Paper 27773, National Bureau of Economic Research, Cambridge, MA. Goolsbee, Austan, and Chad Syverson. 2020. “Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline,” NBER Working Paper 27432, National Bureau of Economic Research, Cambridge, MA. Khan, Jahidur Rahman, Nabil Awan, Md Mazharul Islam, and Olav Muulrink. 2020. “Healthcare Capacity, Health Expenditure, and Civil Society as Predictors of Covid-19 Case Fatalities: A Global Analysis,” Frontiers in Public Health. Accessed October 4, 2020. https://www.frontiersin.org/articles/10.3389/fpubh.2020.00347/full?report=reader. McLaren, John. 2020. “Racial Disparity in COVID-19 Deaths: Seeking Economic Roots with Census data.” NBER Working Paper 27407, National Bureau of Economic Research, Cambridge, MA. Philip, Minu, Debraj Ray, and S. Subramanian. 2020. “Decoding India’s Low Covid-19 Case Fatality Rate.” NBER Working Paper 27696, National Bureau of Economic Research, Cambridge MA. Wright, Austin L., Geet Chawla, Luke Chen, and Anthony Farmer. 2020 “Tracking Mask Mandates During the Covid-19 Pandemic,” University of Chicago, Becker Friedman Institute for Economics Working Paper 104.

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Page 15 ECONOMICS THESIS PROPOSAL Name: Sirig Gurung Title: Spillovers of the India’s Demonetization to the Nepali Economy through Migrants from Nepal to India Field: International spillovers of unexpected policy changes; Analysis of migrant links Version: Version 3 Advisors: Prof. Blackwood and Prof. Honig Question: What are the effects of the Indian Demonetization policy of 2016 on household expenditure and aggregate economic activity in Nepal? Area This topic broadly relates to international spillovers of unexpected shocks. Specifically, my thesis will utilize international migrants as a transmission mechanism for currency shocks. Motivation Cash plays an integral role in developing economies. With the lack of well-developed and widely available electronic payment and banking infrastructure, economic agents turn to cash as their primary liquid asset for transactions. In such a context, the demonetization policy of India poses an interesting policy experiment: in 2016, Prime Minister Narendra Modi demonetized all highest denomination notes of rs. 500 and rs. 1000 (~86% of the currency in circulation in India). While formal channels to exchange demonetized bills were available in India until the end of 2016, individuals and businesses in Nepal holding Indian currency for transactional and operational purposes did not have access to such facilities1. My thesis will attempt to add to the literature by exploring the implications of the policy on the Nepali economy. Since migrants contribute to their households and home economies by remitting or returning with foreign currency, they facilitate the transmission of foreign currency shocks at the household and aggregate-level. Although there is no official record of Nepali migrants to India2, it is estimated that 1 million (out of 28 million) Nepalese are migrants to India due to the low cost of migration, open border and currency peg between the two countries (World Bank, 2010). Official estimates state that 14.2% of Nepal’s $8.1 billion remittance economy was sustained by migrants to India in 20193 (Kafle, 2020). Thus, migration from Nepal to India promises to be a prominent transmission mechanism for India’s demonetization policy. Theory Households in Nepal use Indian and Nepali currency as liquid assets. Let Li denote the total liquid asset household i uses for consumption. A fraction of Li, ∝푖 퐿푖 for ∝푖∈ [0,1], is in Indian currency. 푈푖(퐶푑,푖, 퐶푛,푖) is a utility function for household i exhibiting diminishing returns, where 퐶푑,푖 and 퐶푛,푖 are consumption of durable and non-durable goods. This subject to the budget constraint 푝푑퐶푑,푖 + 푝푛퐶푛,푖 ≤ 퐿푖, where 푝푑 and 푝푛 are the respective prices. In the absence of credit constraints, households could cope with the unanticipated, income shock of demonetization by borrowing. However, households facing credit constraints

1 Estimated holdings by Nepali individuals and informal sector are Indian Rs. 9,500,000,000 ($146 million, 2018 exchange rate). 2 Indian and Nepali citizens do not need work authorization to be employed in the either country. 3 Likely a lower bound as remittances through informal channels, popular amongst migrants to India, are not tracked.

Page 16 can only allocate (1−∝푖)퐿푖 to consumption after demonetization, shifting the budget constraint to the left and lowering the maximum utility that household i can attain. At the aggregate-level, if households substantially reduce their consumption post-demonetization, lower aggregate demand and production in Nepal could follow. Methods I will conduct an analysis at the household-level using data from the Nepal Household Vulnerability and Risk survey, 2016-2018. This dataset includes annually collected data on ~6,000 rural households in Nepal. I will estimate the effect of the demonetization policy using a Difference-in-Differences (DD) analysis. The first difference will be before and after the demonetization policy. The second difference will be differences in a dependency on Indian currency measure for household i:

#푀𝑖𝑔푟푎푛푡푠 푡표 퐼푛푑𝑖푎푖 푑푒푝푖 = #퐻표푢푠푒ℎ표푙푑 푚푒푚푏푒푟푠 (푎𝑔푒: 15 − 64)푖 Which is the fraction of the number of migrants to India out of the total number of working age (15-64 years) members of household i calculated using the 2016 sample. The specification as a regression equation is:

푦푖,푡 = 훽0 + 훽1퐼푃표푠푡.푑푒푝푖+훽2퐼푃표푠푡 + 훽3푑푒푝푖 + 휌푑 + 휖푖,푡

Where 푦푖,푡 is the outcome in household i in year t. Outcomes of interest include household expenditure, (formal and informal) borrowing and employment status of household members. Using household borrowing as an outcome could help analyze the salience of credit constraints. The variable IPost is an indicator for observations after demonetization and 휌푑 are district fixed effects. The coefficient of interest is 훽1. Standard errors will be clustered at the district-level. For this specification to be valid, in the absence of the demonetization policy, there need to be parallel trends in the outcomes for households in the sample with varying dependency measures. A similar analysis at the district-level will estimate the effects of the policy on aggregate economic activity, using variation in time and the pre-demonetization share of migrants to India. District-level migrant share data is sourced from the Nepal Demographic Health Survey (NDHS) of 2011. Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light intensity at the district-level will proxy for aggregate economic activity in Nepal. This data is available at high- frequencies, is strongly predictive of economic activity and human development indicators in developing countries and attempts to cover the real informal economy (Beyer, et. al., 2018; Bruederle and Hodler, 2018). The following is the specification as a regression equation:

ln(푙𝑖𝑔ℎ푡푠푑,푚푦) = 훾0 + 훾1퐼푃표푠푡 푠ℎ푎푟푒푑+ 휌푑 + 훿푑,푚푦 + 휖푑,푚푦

Where ln(푙𝑖𝑔ℎ푡푠푑,푚푦) is the natural logarithm of average night-time light intensity of district d in month-year my, 푠ℎ푎푟푒푑 is the migrant to India share for district d in 2011, IPost is an indicator for observations after the announcement of demonetization, 휌푑 and 훿푑,푚푦 are district 4 and development region-month-year fixed effects . The coefficient of interest is 훾1. This specification is only valid if there are parallel trends in night-time light intensity over time for districts in the sample with varying migrant shares in the absence of the policy.

4 The variables 푠ℎ푎푟푒푑and 퐼푃표푠푡 are not included in the specification due to the inclusion of these fixed effects.

Page 17 Issues • Ideally, I would use a reliable measure of migrant income from India by total household income for household dependency on Indian currency. Since the household dataset does not have income data, one improvement could be to weigh each household member by the average wages in the sector they work in. This would have to be sourced from a different dataset. • Since the household dataset does not contain data before 2016, a separate dataset will have to be used to check for parallel trends for the household-level analysis. The Annual Household Survey of Nepal could be used, and is publicly available for the years 2012/2013 to 2015/2016 (before the demonetization). • It will be difficult to establish a causal link between migration to India and changes in household outcomes and aggregate economic activity at the district-level. As an example, districts or households with higher share of migrants could be closer to the border and, consequently, be more reliant on trade with India. Migration to India could capture exposure to demonetization that is more comprehensive than micro-level migrant household behavior. For this reason, my causal question and theory section do not reference migration to India, and I only use it as part of my identification strategy for the household and district-level analyses.

Page 18 References

Beyer, Robert, Esha Chhabra, Virgilio Galdo, and Martin Rama. 2018. “Measuring Districts’ Monthly Economic Activity from Outer Space.” SSRN Scholarly Paper ID 3238366. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=3238366.

Bruederle, Anna, and Roland Hodler. 2018. “Nighttime Lights as a Proxy for Human Development at the Local Level.” PLoS ONE 13 (9). https://doi.org/10.1371/journal.pone.0202231.

Chodorow-Reich, Gabriel, Gita Gopinath, Prachi Mishra, and Abhinav Narayanan. 2018. “Cash and the Economy: Evidence from India’s Demonetization.” Working Paper 25370. Working Paper Series. National Bureau of Economic Research. https://doi.org/10.3386/w25370.

Karmakar, Sudipto, and Abhinav Narayanan. 2020. “Do Households Care about Cash? Exploring the Heterogeneous Effects of India’s Demonetization.” Journal of Asian Economics 69 (August): 101203. https://doi.org/10.1016/j.asieco.2020.101203.

Koning, Jp. 2017. “Moneyness: No Rupees Left Behind.” Moneyness (blog). September 5, 2017. http://jpkoning.blogspot.com/2017/09/no-rupees-left-behind.html.

Kose, Ayhan, and et. al. n.d. “The Global Role of the U.S. Economy: Linkages, Policies and Spillovers.” Text/HTML. World Bank. Accessed September 11, 2020. https://documents.worldbank.org/en/publication/documents-reports/documentdetail.

Lahiri, Amartya. 2020. “The Great Indian Demonetization.” Journal of Economic Perspectives 34 (1): 55–74. https://doi.org/10.1257/jep.34.1.55.

McKenzie, David, Caroline Theoharides, and Dean Yang. 2014. “Distortions in the International Migrant Labor Market: Evidence from Filipino Migration and Wage Responses to Destination Country Economic Shocks.” American Economic Journal: Applied Economics 6 (2): 49–75. https://doi.org/10.1257/app.6.2.49.

Wadhwa, Sagar. 2019. “Impact of Demonetization on Household Consumption in India,” November, 70.

Yang, Dean. 2008. “International Migration, Remittances And Household Investment: Evidence From Philippine Migrants’ Exchange Rate Shocks.” The Economic Journal, 40.

Page 19 Economics Thesis Proposal Name: Ahliaa Moore ​ Title: The impacts of FDI on the Socioeconomic Outcomes of Jamaican Citizens ​ ​ Field: Economic Development and Foreign Affairs ​ Version: Version 3 on 11/17/2020 ​ Advisor(s): Mesay Gebresilasse and Adam Honig ​

Question: What are the impacts of Foreign Direct Investment on the socioeconomic outcomes, ​ ​ such as education, unemployment, and incidence of poverty, of Jamaica’s population? Do government policies meant to encourage foreign businesses result in sustainable development in affected communities? How has recent investment by Chinese businesses altered FDI impacts?

Area: This research would join the field of research on the effects of FDI in developing nations, ​ ​ with a focus on post-independence Jamaica and the socioeconomic outcomes of individuals. Though the literature on FDI impacts is vast, there is little research done on impacts of FDI within the Caribbean basin. This paper will fill the gap in literature by observing how individual outcomes change in relation to increasing FDI presence within Jamaica. This paper will utilize geographic specific policies passed by the Jamaican government to highlight outcome differences between different regions. Additionally, this paper will address the current mass investments by China’s Belt and Road Initiative. ​ ​

Motivation: Foreign Direct Investment is considered the preferable form of foreign investment ​ ​ because it invests directly into domestic markets, but despite the assumption that FDI results in positive impacts, empirical research has found little evidence of domestic gains. Many nations, including Jamaica, adopt costly foreign policies to incentivize and encourage more foreign investment. These attempts can result in nations taking on insurmountable debt and losing ownership of their major economic hubs and ports, such as with Sri Lanka. To avoid this issue, it is important for Jamaica to determine whether the effects of FDI are worth the cost of incentivizing foreign investment. Some incentives of Jamaican foreign policy include tax reliefs and duty-free imports for foreign investors. For Chinese investments, the Belt and Road ​ Initiative allows China to import $660 million worth of imports duty-free, while Jamaica exports ​ only ~$20 million worth of goods to the Chinese markets and holds an $80 billion debt to China. Identifying FDI impacts will help determine whether Jamaica’s FDI policies are sustainable.

Theory: In theory, Foreign Direct Investments are beneficial through the lens of the ​ neoclassical growth model, in which a country’s GDP growth rate can increase through the accelerated growth of GDP inputs, specifically investments and consumption. The theory assumes that increases in firms producing goods for consumers and increased total savings to use for investments will increase output in the short run, while expedited growth in technological advancement, skilled labour employment opportunities, and skilled labourers will help maintain accelerated growth in the long term. With the understanding that better and more skilled job opportunities have a positive relationship on the average income of a population and average

Page 20 education achieved, economists can connect a higher presence of FDI to higher socioeconomic outcomes. I will be challenging this theory by determining whether higher presence of FDI in various Jamaican cities actually correlates with increased socioeconomic outcomes within the community, and if so, to what extent FDI played a role in the socioeconomic trends. By using the presence of geographically-specific government policy as a proxy for increased FDI presence, I will determine whether policy-affected cities display different trends than unaffected cities. If policy affected cities display positive and accelerated trends relative to their control counterparts, then the neoclassical growth model will be supported. Otherwise, the neoclassical model will be unsupported and/or the costs of incentivizing outweigh possible benefits.

Methods: Using microdata from national annual surveys, such as the Survey for Living ​ Conditions, Labour Force Survey, and Production Survey, I will discern trendlines in educational achievement, employment levels, and incidence of poverty between various cities within the 14 parishes of Jamaica. I will be using the geographically specific Free Zones and Special ​ ​ ​ Economic Zones policies to create the conditions for a Difference in Difference regression. These ​ policies create unique conditions that encourage FDI in specific locations. I will be comparing the socioeconomic outcome trends of cities affected by these policies against the trends of cities unaffected. The Difference in Difference structure will be: Average change of cities impacted by specific policies over time: E(Yt | Policy_Effects = 1, Pt = 1) - E(Y | Policy_Effects = 1, Pt = 0) Average change of the control cities over time: E(Yt | Policy_Effects = 0, Pt = 1) - E(Y | Policy_Effects = 0, Pt = 0) DID: Avg △ in policy Affected Cities - Avg △ in Control Cities ​ ​ Y = outcome over time Policy_Effects = dummy variable indicating 1 if the city was impacted by the Free Zone or SEZ policies, 0 otherwise Pt = Dummy variable indicating 1 if the time period is post when these policies went into effect, 0 otherwise

I will be using regression equations that highlight the impact of FDI inflow, GDP growth, city/geographical location control and policy effect controls over time, on education, unemployment and incidence of poverty. I will also account for the correlation between FDI and GDP through interaction variables between them. Additionally, I will include a variable to take into account the lagged effects of FDI inflow and previous levels of the socioeconomic trend. An example of the regression is as follows: UnEmp_Perc = β₀ + β₁FDIinflowPerc + β₂Year + β₃GDPgrowthPerc + β₄City_Location + β₅Free_Zone + β₆SEZ + β₇FDIpercXGDPperc + β₈Ft +∊

Ft = ӨFDIinflowPerc ₋ ₁ + ӨUnEmp_Perc ₋ ₁ By identifying where socioeconomic trends have accelerated or changed their patterns over years in relation to the locations where FDI has been most concentrated, I can help indicate whether FDI impacts socioeconomic outcomes. Issues: ● Acquiring very specific firm information on the businesses approved under the SEZ Authority (since there is not a public database) ● Identifying Chinese specific FDI presence outside of investments to specific business sectors

Page 21 ● Conveying an acceptable level of relationship between these government policies and FDI presence, for readers to accept policy presence as a proxy for FDI presence ● There is a lag between Jamaica’s publication of data, so getting access to the most recent years has been difficult

Bibliography: Aitken, Brian J. and Ann E. Harrison. 1999. “Do Domestic Firms Benefit from Direct Foreign Investment? Evidence from Venezuela.” American Economic Review (89): 605-618. Bornschier, V., Chase-Dunn, C., and Rubinson, R. 1978. “Cross-national evidence of the effects of foreign investment and aid on economic growth and inequality: A survey of findings and a reanalysis.” American Journal of Sociology 84 (3): 651–83. ​ ​ Ekholm, Caroline (2017) “Foreign Direct Investment’s Effect on Economic Growth in Developing Countries: Cross-Border Mergers and Acquisitions versus Greenfield Investments.” Lund University: School of Economics and Management. ​ Foxcroft, Tiffany. “Jamaica has China to thank for much-needed infrastructure - but some locals say it has come at a price.” CBC, November 28, 2019. ​ ​ Fry, Maxwell. J. 1993. Foreign direct investment in a macroeconomic framework: Finance, efficiency, incentives, and distortions. Working Paper 1141. Washington, DC: World Bank International Economics Department. Goncalves, Reinaldo. (1986) “Technological Spillovers and Manpower Training: A Comparative Analysis of Multinational and National Enterprises in Brazilian Manufacturing.” Journal of Development Economics 11(1) 119-132. ​ Herzer, Dierk; Klasen, Stephan; Nowak-Lehmann D., Felicitas (2006) : In search of FDI-led growth in developing countries, IAI Discussion Papers, No. 150, Georg-August-Universität Göttingen, Ibero-America Institute for Economic Research (IAI), Göttingen Hope, Kempe R. (1989). “Private Direct Investment and Development Policy in the Caribbean: Nationalism and Nationalization Scared Away Foreign Investors but Reagan Initiative’s Luring Them.” The American Journal of Economics and Sociology 48(1). 69-78. ​ ​ Jorge Bermejo Carbonell & Richard A. Werner (2018) “Does Foreign Direct Investment Generate Economic Growth? A New Empirical Approach Applied to Spain”. Economic Geography, 94:4, 425-456, Liu, Zhiqiang. 2008. “Foregin Direct Investment and Technology Spillovers: Theory and Evidence.” Journal of Development Economics (85): 176 - 193 Mohan, Preeya S., Patrick K. Watson, and Patick K. Watson (2014) “CARICOM Foreign Direct Investment Flows” Social and Economic Studies 63(3) 281-306. ​ ​ Sultanuzzaman, Md Reza, Hongzhong Fan, Mahamud Akash, Banban Wang, Uddin Sarker Md Shakij (2018). “The role of FDI inflows and export on economic growth in Sri Lanka: An ARDL approach.” Cogent Economics & Finincance 6(1). ​ ​ Worrell, DeLisle (1993) “Investment in the Caribbean”. Social and Economic Studies 42(2). 243-259. ​ ​ Zhang, Kevin H. (2001). “Does Foreign Direct Investment Promote Economic Growth? Evidence from East Asia and Latin America.” Contemporary Economic Policy (19): 175 - 185.

Page 22 THURSDAY

THURSDAY 12/3/20 3:00 TO 5:00 Angela Zhao Health, Race, and Place: The Effects of Segregation on Racial Disparities in Health Outcomes Arnav Parikh Assessing the Effectiveness of Affirmative Action In India using Fertility Rates

Emily Kiernan The Venmo Effect: The Impact of Digital Payment Platforms on Consumer Willingness to Pay Rafael Gonzalez An Exploration of the Role of Social Connections in Job Placement

Pedro Morais The Economics of Misinformation: Informational Signals and Citizen Responses in the COVID-19 Pandemic Claire Holleman School Based Health Centers and Their Impact on Student Achievement

Page 23 Economics Thesis Proposal Name: Angela Zhao Title: Health, Race, and Place: The Effects of Segregation on Racial Disparities in Health Outcomes Field: Health Economics Version: Version 3 on 11/17/2020 Advisor(s): Professor Jessica Wolpaw Reyes and Professor Brian Baisa

Question How do health outcomes (complications and mortality) differ between Black and white adult patients with diabetes? Can levels of racial segregation at the census tract level explain any such differences?

Area The questions of this proposal reside in health economics and public economics, focused on inequality along racial lines in health care, access, treatment, and outcomes in the United States, ​ using diabetes as an example. Specifically for diabetes, Black individuals are more likely than white counterparts to experience diabetes-related complications and have higher incidence and mortality rates. Current economic literature highlights disparities in health care and health status amongst socioeconomic and racial lines through examination at the individual (physician-patient interactions) and community (residential segregation and neighborhood investment) levels.

Motivation The COVID-19 pandemic has highlighted inequalities and systemic racism in the United States, disproportionately affecting more vulnerable populations such as Black, Indigenous and people of color (BIPOC) and residents in low-income, urban neighborhoods. Along with the Black Lives Matter and the George Floyd protests in the summer of 2020, systemic racism in U.S. institutions has been underscored, including in the U.S. health system. Such systemic issues can be seen through the underrepresentation of Black physicians and higher pregnancy-related mortality rates for Black women. In understanding disparities between Black and white patients, we must take into consideration the economic and social forces influencing disease conditions, vulnerability, access, quality of care, and outcomes. Racial segregation has been previously linked to increasing economic inequalities between racial groups in the United States. As a result, it may influence racial disparities in health and healthcare as well. Understanding disparities and segregation in a causal manner is important for further applications to health policy and more general public policy, in hopes of improving existing health inequalities.

Theory Michael Grossman constructed a model of the demand for health as a commodity, understanding health as a durable capital stock (1972). Individuals inherit an initial stock of health that can be increased by investments, including medical care, healthy eating, and exercise. However, neighborhood inequalities create an unequal distribution, along socioeconomic and racial lines, of private and public resources to invest in health and the initial stock of health. Additionally the initial stock of health can be impacted due to factors such a prenatal care and the birthing mother’s health, showing an inter-generational impact of health investments as well. To explore what impacts and magnifies these neighborhood inequalities, I intend to incorporate the levels of

Page 24 segregation in these neighborhoods. To incorporate racial residential segregation, I intend view it as a contributor to these inequalities through socioeconomic inequalities. Segregation levels in a neighborhood can be calculated through dissimilarity indexes where: N 1 | black i nonblack i | Index of dissimilarity = 2 ∑ | black − nonblack | i=1 | total total | The dissimilarity index is acquired through census data with i indicating each census tract in a ​ ​ city/metropolitan area. Ranging from 0 to 100, its interpretation measures the percentage of one racial group that would have to move to a different census tract for the proportion of that group in each neighborhood to equal the overall city’s proportion. I intend to examine the relationship between segregation and racial differences in diabetes-related complications as well as between segregation and racial differences in diabetes mortality. For example, the racial difference in diabetes morality rates is calculated via: ∆ = E[Mortality|Black]- E[Mortality|White] The racial differences in the health measures are calculated for each unique census tract.

Methods I intend to show a causal impact of (1) racial residential segregation on diabetes-related complication and of (2) racial residential segregation on diabetes mortality, both by differences-in-differences in race. The primary health outcome I am analyzing is diabetes-related complications, with diabetes mortality as a secondary. Data at the individual-level is available at request from the Medical Expenditure Panel Survey (MEPS). Respondents with diabetes can be identified through the Diabetes Care Supplement. The survey provides further information on diabetes expenditure, severity, and treatment. MEPS also provides information on race, census tract, and health insurance. The CDC provides data for diabetes incidence rates, complications, and mortality. Census tracts are defined by the U.S. Census Bureau and their demographic and ecological factors can be found from the U.S. Census as well as the Integrated Pulic Use Microdata Series (IPUMS). This includes variables such as population density, median household income by race, poverty rates by race, and the Gini index. Segregation levels can be calculated through dissimilarity indexes as defined above and are available from the U.S. Census and CensusScope. These multiple data sources will require the merging of datasets with census tracts as unique observations. To make any causal inferences, I intend to run an instrumental variables strategy, similar to that of Ananat’s (2011). This would entail that segregation-induced Black poverty in neighborhoods impacts diabetes outcome disparities. I hope to see how segregation levels impact socioeconomic status within census tracts, mainly focusing on poverty rates of the Black population in the first stage. Assuming the validity of segregation as an instrumental variable holds, I will proceed by measuring the impacts of the dissimilarity index on my two health measures defined above. Additionally, controls to account would include neighborhood factors such as population density but also diabetes treatment and expenditure by race. For the regression on diabetes mortality, I will include diabetes complications, as it could impact mortality. Falsification checks also need to be conducted to ensure that segregation is not directly impacting diabetes outcome disparities.

Issues ● I may want to further explore and consider within-race diabetes disparities. ● I need to read more economic literature to better understand how to build a causal model.

Page 25 References: Ananat, Elizabeth Oltmans. "The wrong side (s) of the tracks: The causal effects of racial segregation on urban poverty and inequality." American Economic Journal: Applied Economics 3, no. 2 (2011): 34-66. Bridges, Khiara M. 2018. “Implicit Bias and Racial Disparities in Health Care.” Human Rights, ​ ​ ​ August 1. https://www.americanbar.org/groups/crsj/publications/human_rights_magazine_home/the -state-of-healthcare-in-the-united-states/ Chan, Kitty S., Darrell J. Gaskin, Gniesha Y. Dinwiddie, and Rachael McCleary. "Do diabetic patients living in racially segregated neighborhoods experience different access and quality of care?." Medical care 50, no. 8 (2012): 692. Chandra, Amitabh, and Jonathan Skinner. Geography and racial health disparities. No. w9513. National bureau of economic research, 2003. Chetty, Raj, John N. Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. The opportunity atlas: Mapping the childhood roots of social mobility. No. w25147. National Bureau of Economic Research, 2018. Glasmeier, Amy K., and Tracey L. Farrigan. "Landscapes of inequality: Spatial segregation, economic isolation, and contingent residential locations." (2007): 221-229. Grossman, Michael. "On the concept of health capital and the demand for health." Journal of Political economy 80, no. 2 (1972): 223-255. Hill, Andrew, Daniel Jones, and Lindsey Woodworth. "A Doctor Like Me: Physician-Patient Race-Match and Patient Outcomes." Available at SSRN 3211276 (2018). ​ ​ LaVeist, Thomas A., Roland J. Thorpe, Jessica E. Galarraga, Kelly M. Bower, and Tiffany L. Gary-Webb. "Environmental and socio-economic factors as contributors to racial disparities in diabetes prevalence." Journal of general internal medicine 24, no. 10 (2009): 1144. Rosenstock, Summer, Steve Whitman, Joseph F. West, and Michael Balkin. "Racial disparities in diabetes mortality in the 50 most populous US cities." Journal of Urban Health 91, no. 5 (2014): 873-885. Scheffler, Richard M., and Timothy T. Brown. "Social capital, economics, and health: new evidence." Health Economics, Policy and Law 3, no. 4 (2008): 321-331. Schulz, Amy J., David R. Williams, Barbara A. Israel, and Lora Bex Lempert. "Racial and spatial relations as fundamental determinants of health in Detroit." The Milbank Quarterly 80, no. 4 (2002): 677-707. Whiteis, David G. "Hospital and community characteristics in closures of urban hospitals, 1980-87." Public health reports 107, no. 4 (1992): 409. Yearby, Ruqaiijah. "Racial disparities in health status and access to healthcare: the continuation of inequality in the United States due to structural racism." American Journal of Economics and Sociology 77, no. 3-4 (2018): 1113-1152.

Page 26 ECONOMICS THESIS PROPOSAL Name: Arnav Parikh Title: Assessing the Effects of Affirmative Action in India Using Fertility Rates Field: Developmental Economics Version: Version 3 on 11/17/2020 Advisor(s): Caroline Theoharides and Jessica Reyes Question: Are affirmative action policies in India reducing the differences, in terms of socio- economic outcomes including income, literacy, education, life expectancy etc., between lower and upper castes in Indian society? Area: My inquiry resides in the broader research area of development economics and the economics of inequality in India. My study also focusses on the area of affirmative action and adds to growing literature on this topic worldwide. Motivation: Growing up in India, I was always curious as to why inequality still existed in modern India. To give a little background, Hindu society is divided into a regressive caste system. The two lowest rungs of the caste system, Dalits (untouchables) and Shudras (manual laborers), have faced significant discrimination and oppression over the years, which has contributed to persisting inequality in India. Inequality in India is further exacerbated because the indigenous people of India (Adivasis) have lost most of their land and livelihoods as a result of encroachment and deforestation and have relocated to urban areas with no income and education. To combat persisting inequality, affirmative action policies have been constitutionally guaranteed to certain subpopulations. However, these policies have been contentious and have always been hotly debated in public discourse. Therefore, I was motivated to provide empirical evidence to understand the effectiveness of affirmative action policies in reducing inequality in India. Theory: To achieve the aim of not only eliminating inequality but also redressing the effects of past discrimination, Scheduled Castes (Dalits and Shudras), Scheduled Tribes (indigenous people) and Other Backward Classes benefit from state-sponsored affirmative action (Chhetri, 2012). Constitutionally guaranteed affirmative action policies attempt to increase the representation of historically disadvantaged castes by regulating the allocation of scarce and coveted seats in higher education, government and public-sector employment (Loury, 2005). By guaranteeing access to higher education, pubic-sector jobs and government, affirmative action policies intend to improve the socio-economic outcomes of marginalized groups and reduce between caste differences. The existing literature on affirmative action in India has predominantly used changes in income to test whether affirmative action policies have been effective. However, in India, measuring income accurately is difficult because people do not report income from business and farming activities and income is affected by household size. Therefore, this paper uses fertility rates to identify the effects of affirmative action on its beneficiaries. The advantages of fertility rates are that they are easy to calculate and hard to misreport. They are also holistic measures of developmental progress because they are indicators for poverty alleviation, literacy rates, women empowerment, life expectancy and a growing GDP per capita among other developmental outcomes. An analysis of fertility rates in India in 2018 shows that upper castes have the lowest fertility rates of 1.9 while OBCs, SCs and STs have fertility rates of 2.2, 2.3 and 2.5 respectively. The poorest of families in India have an average fertility rate of 3.2 while the richest families had

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Page 27 the least fertility rate at 1.5. (Nagarajan 2018). Therefore, if affirmative action has had its desired effects we expect to see a closing of the gap between the fertility rates of lower and upper castes. Methods: This paper uses two natural experiments to investigate changes in fertility rates. The first natural experiment focusses on the 1976 Area Restriction Removal Act that removed all intra- state restrictions, increasing the population of Scheduled Castes that received affirmative action by 2.4 million (Cassan, 2019). The second natural experiment, much larger in scale, introduced a new Other Backward Class caste category, comprising of 27% of the population, that was allowed to benefit from affirmative action. This proposal outlines the methodology for the 1993 natural experiment but a similar methodology will be carried out for the 1976 natural experiment. The panel data used for the natural experiments are collected at three time points: 1993, 2005 and 2012. The data collected in 1993 comes from a survey conducted by the Human Development Profile of India (HPDI) that interviewed 13,900 households. The 2005 and 2011 time points come from the Indian Human Development Survey (IHDS). The IHDS dataset is a nationally representative, multi-topic survey of 42,152 households, including the 13,900 households that were interviewed in 1993. For the empirical model, a categorical ‘Eligible’ variable with two levels is constructed. Women born after 1973, get a ‘Eligible’ value of 1 while women born before 1973 get a ‘Eligible’ value of 0. The year 1973 is chosen because women born in or before 1973 would have been more than 20 years of age and, if they belonged to the Other Backward Category (OBC), would have been too old to benefit from the new policy that gave access to affirmative action to OBCs in 1993. Using, this constructed ‘Eligible’ women variable the model used is:

� = � + ���� + ��������� + ���� � �������� + � + � where � is the predicted fertility rate for a women in State �. ��� is binary variable that determines if the woman is from the OBC group or from the upper caste and �������� is a binary variable that determines if the woman has had access to affirmative action. The main coefficient of interest is ��� � �������� which is an interaction term between ��� and ��������. � represents that state fixed effects are included in the model. This difference in differences approach is appropriate because the overall fertility rates in India have been on a downward trend largely because of urbanization, liberalization and increased access to contraceptives. Assuming these factors have affected all castes equally, the interaction term tests if affirmative action has affected the parallel trend for OBCs by reducing the gap between the fertility rates of OBCs and higher castes after 1993. Threats to this identification strategy include policies that have disproportionately affected OBCs compared to the upper castes in the 1990s and 2000s. For example, during the same time period, several policies have been successful in significantly reducing poverty in India. Since more OBCs lived below the poverty line compared to upper castes, poverty reduction policies could have also played a role in reducing fertility rates for OBCs. Issues: • Using the IHDS data, I have to try and include appropriate control variables that could potentially reduce the effects of the threats to identification. • At the moment, with this identification strategy, I can compare only two castes of interest. I am figuring out a way to use mixed models to compare the trends for several castes. • The data includes women who are young and could have more children in the future. I have to decide whether I am going to use ‘Number of Children’ a woman has had and exclude younger women from the experiment or include all women by using their reported ‘Ideal Number of Children’ as the dependent variable.

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Page 28 References:

Asher, Sam, Paul Novosad, and Charlie Rafkin. “Intergenerational Mobility in India: New Methods and Estimates Across Time, Space, and Communities” (July 2020). Bagde, Surendrakumar, Dennis Epple, and Lowell Taylor. “Does Affirmative Action Work? Caste, Gender, College Quality, and Academic Success in India.” American Economic Review 106, no. 6 (June 1, 2016): 1495–1521. https://doi.org/10.1257/aer.20140783. Bains, Karan. “Delayed Gratification and Parental Decision-Making: Investigating the Impact of Poverty on Child Marriage in India,” Submitted to the Department of Economics at Amherst College (April 16, 2014) Bertrand, Marianne, Rema Hanna, and Sendhil Mullainathan. “Affirmative Action in Education: Evidence from Engineering College Admissions in India.” Journal of Public Economics 94, no. 1 (February 1, 2010): 16–29. https://doi.org/10.1016/j.jpubeco.2009.11.003. Cassan, Guilhem. “Affirmative Action, Education and Gender: Evidence from India.” Journal of Development Economics 136, no. C (2019): 51–70. Chhetri, Durga P. “Politics of Social Inclusion and Affirmative Action: Case of India,” The Indian Journal of Political Science Vol 73, No. 4 (December 2012): 587 – 600 Deshpande, Ashwini. “Quest for Equality: Affirmative Action in India.” Indian Journal of Industrial Relations 44, no. 2 (2008): 154–63. Deshpande, Ashwini, and Thomas E. Weisskopf. “Does Affirmative Action Reduce Productivity? A Case Study of the Indian Railways.” World Development 64 (December 2014): 169–80. https://doi.org/10.1016/j.worlddev.2014.05.024. Dunn, Christopher E. “The Intergenerational Transmission of Lifetime Earnings: Evidence from Brazil.” The B.E. Journal of Economic Analysis & Policy 7, no. 2 (October 9, 2007). https://doi.org/10.2202/1935-1682.1782. Haq, Rana, and Abhoy K. Ojha. “Affirmative Action in India: Caste-Based Reservations,” International Handbook on Diversity Management at Work. Edward Elgar Publishing, 2010. https://www.elgaronline.com/view/9781847208903.00013.xml. Jayal, Niraja Gopal. “Affirmative Action in India: Before and after the Neo-Liberal Turn.” Cultural Dynamics 27, no. 1 (March 2015): 117–33. https://doi.org/10.1177/0921374014564652. Jr, Roland G Fryer, and Glenn C Loury. “Affirmative Action and Its Mythology,” Journal of Economic Perspectives, Vol 19, no. 3 (Summer 2005): 147 – 162. Khanna, Gaurav. “Does Affirmative Action Incentivize Schooling.” The Review of Economics and Statistics, Vol CII, no. 2 (May 2020): 219 – 233. Konso, Julia Mbakire. “Empowering Women in India: How Does Political Representation Interact with Progressivity,” Submitted to the Department of Economics at Amherst College (May 1, 2018). Mosse, David. “Caste and Development: Contemporary Perspectives on a Structure of Discrimination and Advantage.” World Development 110 (October 2018): 422–36. https://doi.org/10.1016/j.worlddev.2018.06.003. Munshi, Kaivan, and Mark Rosenzweig. “Traditional Institutions Meet the Modern World: Caste, Gender, and Schooling Choice in a Globalizing Economy.” The American Economic Review 96, no. 4 (2006): 1225–52. Nagarajan, Rema. "Fertility Rates below Replacement Level for all but Hindus and Muslims." Times of India (January 12, 2018)

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Page 29 Pande, Rohini. “Can Mandated Political Representation Increase Policy Influence for Disadvantaged Minorities? Theory and Evidence from India.” American Economic Review 93, no. 4 (August 1, 2003): 1132–51. https://doi.org/10.1257/000282803769206232. Prakash, Nishith. “Improving the Labor Market Outcomes of Minorities: The Role of the Employment Quota” Munich Personal RePEc Archive, Paper No. 11010 (October 11, 2008). Shariq Mohammed, A.R. “Does a Good Father Now Have to Be Rich? Intergenerational Income Mobility in Rural India.” Labour Economics 60 (October 2019): 99–114. https://doi.org/10.1016/j.labeco.2019.06.005. Sowell, Thomas. Affirmative Action around the World : An Empirical Study. Yale University Press, 2004. Tilak, Jandhyala B.G. “Equalizing Access: Affirmative Action in Higher Education in India, United States, and South Africa, Edited by Zoya Hasan and Martha C. Nussbaum (New Delhi: Oxford University Press, 2012).” Contributions to Indian Sociology 48, no. 2 (June 1, 2014): 289–93. Weisskopf, Thomas E. Affirmative Action in the United States and India : A Comparative Perspective. Routledge Frontiers of Political Economy: 56. Routledge, 2004. Zacharias, Ajit, and Vamsicharan Vakulabharanam. “Caste and Wealth Inequality in India.” SSRN Electronic Journal, 2009. https://doi.org/10.2139/ssrn.1410660.

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Page 30 ECONOMICS THESIS PROPOSAL Name: Emily Kiernan Title: The Venmo Effect: The Impact of Digital Payment Platforms on Consumer Willingness to Pay Field: Behavioral Economics Version: Version 3 on 11/17/20 Advisor(s): Debnam Guzman, Baisa Question Does using Venmo as form of payment lead to higher willingness to pay (WTP) compared to debit and credit cards? If there is a higher willingness to pay with Venmo, is it driven by mental accounting or by social factors like conspicuous consumption and priming? Area My inquiry resides in the area of behavioral economics. More specifically, my topic falls under the umbrellas of consumer decision making and consumer spending. Motivation Venmo is a digital payment platform that allows users to transfer money to other users or retailers. Existing literature finds that consumers are more willing to spend with credit cards than cash (Hirschman, 1979; Feinberg, 1986; Prelec and Simester, 2001) and digital payment platforms lead to a higher WTP than cash (Huang and Savary, 2018). However, in my experience, college students are more willing to spend when using Venmo because it does not seem like “real” money. On the other hand, debit cards feel very real and credit cards would fall somewhere in the middle. I want to do a deep dive into Venmo (rather than digital payment platforms in general) as it has a social aspect that has not yet been explored. Additionally, the existing literature only compares digital payment platforms to cash, which isn’t popular with college students (even more so due to the current pandemic) so I’d like to expand the analysis to include debit/credit cards. Theory The Venmo Effect is our theory that consumers have a higher WTP when using Venmo than debit or credit cards. We propose three different mechanisms that contribute to this higher WTP with Venmo: 1) mental accounting, 2) conspicuous consumption, and 3) priming. First, mental accounting is the idea that people mentally segment their wealth into different accounts with different purposes (Shefrin and Thaler, 1988). Using this framework, Venmo would be a separate subcategory of wealth that is valued lower than money in a checking account, savings, or cash. Using Prelec and Loewenstein (1998)’s double-entry mental accounting model, we assume that for every transaction, a consumer gets some positive amount of utility from acquiring the good (the same regardless of form of payment) and some negative amount of utility (or “pain”) from spending money, which varies depending on form of payment. With Venmo, pain of payment is less than with cash or credit cards, meaning that consumers have a higher WTP when using Venmo. Venmo is unique from other digital payment platforms as it has a social aspect. In the app, you can see descriptions or captions of transactions (but not the dollar value) that other users have made. Given that Venmo is used primarily for peer to peer transactions, this social aspect could be influential in two ways. First, conspicuous consumption: the idea that consumption is not only driven by the value of a good but also by what it signals to others (Heffetz, 2011). On Venmo, users may increase spending in anticipation of other users seeing

Page 31 their transactions. Second, priming: users may see others’ transactions, priming spending in their minds. This priming would contribute to the Venmo Effect as it would encourage spending. Methods We will use an online experiment that measures WTP across payment types. Participants will be randomly assigned to one of five payment groups: 1) debit card, 2) credit card, 3) Venmo on the “private” setting, 4) Venmo on the “friends” setting, and 5) Venmo on the “public” setting. Within the Venmo groups, participants will then be randomly assigned to either priming (browsing through their Venmo feed for 2 minutes) or no priming. Then, all participants will have the opportunity to enter a series of Becker, DeGroot, Marshack (BDM) lotteries. BDM lotteries are used to elicit accurate WTP estimates by having participants name their maximum price for an item and then drawing a random price; if the random price is less than their stated max price, the participant is required to buy the item at the randomly drawn price (but if the random price is greater, then there is no transaction). This incentivizes participants to name their true WTP and ensures that any transactions result in consumer surplus. Participants will undergo separate BDM lotteries for ten different low-cost goods (like pens, gum, etc.). Though participants will participate in ten lotteries, only one randomly selected winning lottery will actually be transacted upon (for simplicity and cost purposes) which will allow us to collect more observations. After the lotteries, we will ask participants questions about demographics and Venmo usage, then debrief them. After conducting the experiment, first we will use the data collected to evaluate our overarching research question: does Venmo lead to higher spending than credit or debit cards? We will use the following OLS regression: WTPi,j = 0 + 1 Treati + 2 Itemj + 3 Controlsi +  where WTPi,j is the max price listed by the participant, Treati is a binary variable for whether the individual belongs to the treatment (Venmo, No Priming) group, Itemj is item-level fixed effects, and Controlsi are control variables (Venmo balance, item rating, and income). Here, i represents an individual respondent and j represents the lottery item. If we find the item fixed effects to be significant, then we will run the regression (omitting the item fixed effects) for each item. Next, we will use the same regression to measure each of our proposed mechanisms: mental accounting, conspicuous consumption, and priming. For each of these analyses, we will change the definition of our Treat variable depending on the specific payment forms that are relevant to the mechanism. 1) To evaluate mental accounting, Treat will equal 1 if the participant was assigned to Venmo Private (No Priming) and 0 if assigned to debit card; by using the private setting in Venmo, we are isolating the mental accounting mechanism by eliminating the influence of any social factors. We expect WTPVPr > WTPD, which is consistent with Huang and Savary (2018). 2) To evaluate the effect of conspicuous consumption, Treat will be a categorical variable with values for the Friends, Public, and Private settings. We expect WTPVFr > WTPVPu > WTPVPr because people care most about impressing their friends. 3) To evaluate the effect of priming, Treat will be a binary variable that equals 1 if the participant underwent the priming treatment. We expect WTPPriming > WTPNoPriming, with the largest impact being on the Public and Friends groups. Issues -We’re concerned about attrition, specifically when participants are asked to enter their payment information. We’ll assess attrition levels with an initial test of our survey with about 50 participants so we can adjust the randomization if there are certain groups with higher levels of attrition. -Show up fee may cause an income effect, but people won’t participate if they aren’t compensated.

Page 32 References Feinberg, Richard A. 1986. “Credit Cards as Spending Facilitating Stimuli: A Conditioning Interpretation.” Journal of Consumer Research 13 (3): 348–56. https://doi.org/10.1086/209074. Heffetz, Ori. 2011. “A Test of Conspicuous Consumption: Visibility and Income Elasticities.” Review of Economics and Statistics 93 (4): 1101–17. https://doi.org/10.1162/REST_a_00116. Hirschman, Elizabeth C. 1979. “Differences in Consumer Purchase Behavior by Credit Card Payment System.” Journal of Consumer Research 6 (1): 58–66. https://doi.org/10.1086/208748. Huang, Liang and Jennifer Savary. 2018. "Attenuating Endowment Effect With Venmo: Online Payment Systems Make It a Pleasure to Pay." Advances in Consumer Research (46), eds. Andrew Gershoff, Robert Kozinets, and Tiffany White, Duluth, MN : Association for Consumer Research, Pages: 600-602. Prelec, Drazen, and George Loewenstein. 1998. “The Red and the Black: Mental Accounting of Savings and Debt.” Marketing Science 17 (1): 4–28. https://doi.org/10.1287/mksc.17.1.4. Prelec, Drazen, and Duncan Simester. 2001. “Always Leave Home Without It: A Further Investigation of the Credit-Card Effect on Willingness to Pay.” Marketing Letters 12 (1): 5–12. https://doi.org/10.1023/A:1008196717017. Shefrin, Hersh M., and Richard H. Thaler. 1988. “The Behavioral Life-Cycle Hypothesis.” Economic Inquiry 26 (4): 609–43. https://doi.org/10.1111/j.1465-7295.1988.tb01520.x.

Page 33 Economics Thesis Proposal

Name: Rafael Gonzalez Title: An exploration of the role of social connections in job placement. Field: Personnel Economics Version: 3 Advisors: Daniel Barbezat and Neil White

Question: What role do social connections play in hiring practices? How do different hiring conditions affect this connection and to what extent does it vary under differing market conditions? When does this connection constitute discrimination? When is discrimination the product of market circumstances and when does it originate outside of the market? What are the broader implications of this association and how can this help explain hiring institutions? Area: The majority of my research falls under the branch of labor economics with a sizeable quantity also coming from the subfield of personnel economics. Later in the process, I intend to draw from game theory to develop a theoretical model. Motivation: I think that social connections have a large impact on how people find jobs either through nepotism, discrimination, or other mechanisms. By extension it has a large impact on both the lives of workers and the economy at large. Last spring while doing research for my Institutions and Governance final paper, I was surprised to realize that the literature (or at least what I found) on the economics of nepotism and discrimination seemed relatively thin. Further, most of what I read focused on taste discrimination which explains nepotism and discrimination as originating outside of the market. I find this approach limiting. Fortunately, the literature in labor economics is more thorough and is brimming with theories of hiring and job searches. I want to link these two literatures by expanding on the existing literature on nepotism and discrimination to think about the impact of hiring practices rooted in social connections more broadly. I then hope to connect that to the existing literature in labor economics. I believe this approach is filled with opportunity and has the potential to yield compelling results. Theory: Models from the literature on discrimination and nepotism often begin with Gary Becker’s The Economics of Discrimination (1957), in which Becker proposed the idea that discrimination would die out in a competitive market because it is inefficient. More recent studies have modified this work. One author who has done such work is Matthew Goldberg who wrote an article titled Discrimination, Nepotism and Long-Run Wage Differentials (1982). In the article Goldberg argued that discrimination could persist if it was small enough and only practiced by a minority of firms. This work is valuable but generally fails to identify the significance of social connections in the hiring process, instead focusing on outright discrimination. Labor economics is full of applicable literature, unfortunately, it too fails to relate hiring to social Fortunately the subfield is able to fill in some of these gaps by incorporating concepts such as search costs, talent screening, and hiring structures. By combining these two

Page 34 literatures I hope to create a compelling model that draws on past understandings of nepotism and discrimination to augment the current understanding of hiring practices in labor economics. Though I haven’t yet developed the full theory I intend to work with, the following equation gets at some of the fundamental tensions I seek to explore.

푈푡𝑖푙푡푦푒푚푝푙표푦푒푟 = 푠푘𝑖푙푙 − 푤푎푔푒 ∗ 푑𝑖푠푐푟𝑖푚𝑖푛푎푡𝑖표푛 푐표푒푓푓𝑖푐𝑖푒푛푡 In this equation the discrimination coefficient is 1 for members of the employer’s ingroup, but some number greater than 1 for members of the employer’s outgroup. This reflects the fact that when hiring this employer will act as if outgroup members must be paid extra. This equation reflects the concept known as taste discrimination. Outright discrimination is practiced based on the biases of the employer and market conditions. Compare this to similar equations such as this one.

푈푡𝑖푙𝑖푡푦푒푚푝푙표푦푒푟 = √푠푘𝑖푙푙 ± 푒 − 푤푎푔푒 In this equation the employer does not practice taste discrimination, but that does not mean that ingroup membership doesn’t matter. If this error term is smaller for members of the employer’s ingroup and the employer is risk averse, then they will still be motivated to hire ingroup members over outgroup members. This is not taste discrimination and many wouldn’t consider this outright discrimination. However, the consequence is essentially equivalent. When given the choice between otherwise equal candidates the employer chooses the ingroup option and in order for the employer to be indifferent between the two potential employees of the same skill level, they must be receiving different wages.

푈푡𝑖푙𝑖푡푦푒푚푙표푦푒푟 = 푠푘𝑖푙푙 + 푐푢푙푡푢푟푎푙 푓𝑖푡 푐표푒푓푓𝑖푐𝑖푒푛푡 − 푠푒푎푟푐ℎ 푐표푠푡 − 푤푎푔푒 This equation represents two other mechanisms by which non-discriminatory firms may display a preference for ingroup members. The cultural fit coefficient represents the benefit to having employees who work well together. The idea being that having many employees from the same ingroup makes a workplace more productive. In this equation cultural fit is represented by a constant value that is high for ingroup members and low for non-ingroup members. Search costs refer to the cost an employer incurs by finding an employee. It can be thought of as the combination of flyers printed, newspaper ads posted, and labor hours devoted to employee search. Search costs can be significant for firms and importantly ingroup members can often be found with a lower search cost than can non-ingroup members. As was the case with the idea of unknown skill levels, the cultural fit and search cost mechanisms both allow discriminatory outcomes to be produced without a discriminating employer. Significantly, because these discriminatory outcomes were produced because of and not in spite of market forces, no level of market competition will eradicate them. This critical tension will form the foundation of this thesis. Methods: I hope to develop this topic by expanding on the theory described above. I want to build on it mostly be developing theory, although hopefully with a factual basis. I hope to carefully curate

Page 35 the terms that I use so that there are no superfluous terms and that each term interacts with the others in insightful ways. It is my hope that from these interactions I will be able to draw conclusions that are broadly applicable and that can help us understand socially-oriented hiring in the world at large as well. Issues: • I need to do the work to make sure that my thesis is grounded in strong empirical observations so that the assumptions I’ve made are convincing. • I am worried about being able to write a theoretical thesis that is sufficiently interesting. • I am concerned that too much of the math I will employ in this thesis will be excessively rudimentary and not substantial enough. • I am not sure how to narrow down my topic without losing the value that some of the more tangential ideas I have had bring.

Page 36 References: Gagliarducci, Stefano; Manacorda, Marco. Politics in the Family: Nepotism and the

Hiring Decisions of Italian Firms. American Economic Journal: Applies Economics. Vol. 12, No

2. 2020.

Becker, Gary. Economics of Discrimination. University of Chicago Press. 1957.

Goldberg, Matthew S. Discrimination, Nepotism, and Long-Run Wage Differentials. The

Quarterly Journal of Economics. Vol. 97, No. 2. 1982.

Raitano, Michele; Vona, Francesco. Nepotism vs Specific Skills: the effect of professional liberalization on returns to parental background of Italian lawyers. Science Po. No. 36 2018.

Lentz, Bernard F., Laband, David N. Why So Many Children of Doctors Become

Doctors: Nepotism vs. Human Capital Transfers. The Journal of Human Resources. Vol. 24, No.

3. 1989.

Borjas, George. Labor Economics. Douglas Reinier. 2013.

Lazear, Edward. Personnel Economics. MIT Press. 1994.

Page 37 Economics Thesis Proposal

Name: Pedro Morais Title: The Economics of Misinformation: Informational Signals and Citizen Responses in the COVID-19 Pandemic Field: Institutional Economics Version: Version 3 Preliminary Advisors: Professor Christopher Kingston and Professor Brian Baisa

Question The primary goal of this thesis is to answer two key questions: How do governmental institutions shape the behavior of government officials in deciding to share accurate or inaccurate informational signals to their citizens? How do citizens respond to these signals when there is ambiguity around the accuracy of the information? When the government is the sole collector and distributor of a certain type of information and data, government officials can abuse this “monopoly” of information by concealing certain pieces of data or manipulating information before it is shared. Identifying the factors that affect the behavior of government officials in sharing information will inform what measures and policies will minimize occurrences of misinformation in the future.

Area This thesis is situated in the field of institutional economics; more specifically, it deals with questions of corruption and governance. There is extensive literature on the field of institutional economics that deals with a variety of forms of corruption, but few papers make an attempt to address the information gap between governments and their citizens. With this thesis, I hope to add to the literature on corruption and governance by looking at the abuse of the asymmetric information by governments.

Motivation The role of information is more critical than ever in the worldwide pandemic we find ourselves in. Ministries of health are gathering and distributing crucial medical information, and the discourses of governors are heavily influencing the daily decisions of citizens on matters essential to their own and their nation’s well-being. Accurate depiction of information can be the difference between an effective and a tragic response to the pandemic. For example, the presidents of US and Brazil, the two countries most affected by the coronavirus, have heavily questioned data around the potency of the virus and have adopted an unwary discourse towards the pandemic. Bolsonaro even fired two health ministers over disagreements on the effectiveness of hydroxychloroquine as a treatment for the virus. It doesn’t seem coincidental, then, that both countries now currently face some of the worst case and death counts around the world. This specific scenario is indicative of the broader fact that what government officials say might matter just as much as what they do. Information signals from government officials can have a strong impact citizen behavior, and we need to strive to better understand the mechanisms through which this happens. Creating further clarity around the institutional factors that enable and create incentives for misinformation can allow us to more effectively curb this phenomenon.

Theory

Page 38 The asymmetric information gap between governments and citizens can be modelled by a dynamic Bayesian signaling game, in which the government sends a signal to its citizens about some piece of information. The game below models the behavior of a government in a pandemic, signaling to its citizens whether a virus is “potent” or “mild”. After nature determines the potency of the virus, the government observes this information and signals it to its citizens, who then decide to stay at home or go about business as usual. At a baseline, governments receive the same payoff as citizens, which is determined by the potency of the pandemic and whether the behavior chosen was appropriate for the potency. This baseline payoff is modified by c, which captures the cost of sending a misinforming signal; and b, which captures any additional benefit to the government for short-run economic performance. As an example, c might be high if there is a strong, free media presence in the country, and b might be high if a government official is up for re-election in the near future and wants to give a strong impression to the electorate.

If both c and b are zero, the baseline perfect Bayesian equilibrium is that the government will always send a truthful signal. However, as benefits for prioritizing the short-run economy increase, so do incentives for governments to send a misinforming signal to its citizens in a bid to get them to leave their homes. This incentive is counteracted by the cost of misinforming, which creates barriers for governments to send such signals. As such, sufficiently high values of the benefit b relative to the cost c leas to a perfect Bayesian equilibrium in which a government sometimes “bluffs”, sending a positive signal to its citizens when the situation is negative, which is what I contend happened in the US and Brazil during this pandemic. This model serves as the rationale for the econometric analysis presented in the subsequent section, which tests the impact of the behavior specified in the model on health outcomes in the current pandemic.

Methods

The main methodology of my thesis will involve a game-theoretic analysis of the model described above, driving insights out of the equilibria that are generated by the game. I then seek to back up the insights of the model with an empirical econometric analysis, by testing whether the two endogenous variables in my model, c and b, had a significant impact on health outcomes in the current pandemic, indicating they have influenced government and citizen behavior. Specifically, I plan to use country-level data in order to discern the effect of proxies for c and b

Page 39 in COVID-19 infection and death rates. As a proxy for c, I plan on using the World Press Freedom Index, with the rationale that freer press makes it more difficult and costly for governments to misinform. As a proxy for b, I plan on using the time until the next election of head of state in a country. The rationale here is that government officials that are closer to an election have a higher incentive to prioritize the short-run economy, in order to maximize their chances of re-election. This analysis can be summarized in the following specification:

퐶푖 = 훼0 + 훼1푃퐹퐼푖 + 훼2퐷퐸푖 + 훼3 푋푖 + 휖푖 Where C is cumulative COVID-19 cases by million inhabitants in country i, PFI is the Press Freedom Index, DE is days to next head of state election, X is a matrix representing our control variables, and ε is the error term. Through a series of regression models, I hope to discern the marginal effects of PFI and DE on C, 훼1 and 훼2 respectively.

Issues • Right now, in my econometric analysis, I am looking at the impact of misinformation cost and short-run economic benefit on COVID-19 outcomes. However, there is a missing gap in the logic, which is connecting these worse COVID-19 outcomes with government misinformation. How do I go about this? o Tricky because misinformation is inherently difficult to measure, as by its very nature it tries to disguise itself. What proxies could I think about using as a measure for misinformation? • I have panel data on COVID-19 outcomes on a country-by-day level, but some of my variables of interest do not vary by day during the pandemic (i.e. press freedom). What econometric techniques can I use to account for this and still use the complete country- by-day COVID-19 dataset for maximum statistical power?

References

Ashan, A. F. M. Mainul, Mohammad Osman Gani, and Bokhtiar Hasan. 2013. “Effects of Misinformation on the Stock Return: A Case Study.” Advances in Economics and Business 1 (3): 282–89. Baines, Darrin, and Robert J. R Elliott. 2020. “Defining Misinformation, Disinformation and Malinformation: An Urgent Need for Clarity during the COVID-19 Infodemic,” 32 pages. doi:ftp://ftp.bham.ac.uk/pub/RePEc/pdf/20-06.pdf. Brown, Etienne. 2018. “Propaganda, Misinformation, and the Epistemic Value of Democracy.” Critical Review 30 (3–4): 194–218. Budish, Eric B.. 2020. “R < 1 as an Economic Constraint: Can We 'Expand the Frontier' in the Fight Against Covid-19?” University of Chicago, Becker Friedman Institute Working Paper 2020-31. Bullock, David S., Klaus Mittenzwei, and Timothy E. Josling. 2019. “Social Welfare Effects of Transparency and Misinformation in a Political Economy.” Journal of Agricultural and Applied Economics 51 (3): 485–94. Bursztyn, Leonardo, Aakaash Rao, Christopher Roth, and David Yanagizawa-Drott. 2020.

Page 40 “Misinformation During a Pandemic.” University of Chicago, Becker Friedman Institute Working Paper 2020-44. Chan, Nathan W. “Misinformation and Its Implications for Green Markets.” Strategic Behavior and the Environment 5, no. 3–4 (2015): 301–16. Freelon, Deen and Chris Wells. 2020. “Disinformation as Political Communication.” Political Communication 37(2): 145-156. doi:10.1080/10584609.2020.1723755. Flynn, D.J. and Yanna Krupnikov. 2019. “Misinformation and the Justification of Socially Undesirable Preferences.” Journal of Experimental Political Science, 6(1): 5-16. Glaeser, Edward L., and Gergely Ujhelyi. 2010. “Regulating Misinformation.” Journal of Public Economics 94 (3–4): 247–57. Li, Hao and Wei Li. 2013. “Misinformation.” International Economic Review 54(1): 253-277. Social Value of Public Information Morris, Stephen, and Hyun Song Shin. 2002. “Social Value of Public Information.” American Economic Review 92 (5): 1521–34. Polak, Mateusz. 2012. “The Misinformation Effect in Financial Markets--An Emerging Issue in Behavioural Finance.” E-Finanse 8 (3): 55–61.

Page 41 ECONOMICS THESIS PROPOSAL

Name: Claire Holleman Title: The Impact of School-Based Health Centers on Education Outcomes Field: Health and Education Economics Version: Version 3 on 11/12/2020 Advisor(s): Professor Hyman and Professor Reyes

Question I am interested in the ways in which improving access to healthcare can improve educational outcomes for students. Specifically, how does having access to a school-based health center (SBHC) affect test scores, particularly for young children? Area My question resides at the intersection of health and education economics. There is a well- established link between low socioeconomic status, poor health, and poor academic outcomes (Case et al 2002, Case et al 2005, Currie 2009). School-based health centers are one proposed intervention that may help break this cycle of poverty. Much of the existing literature on school- based health comes from cross-sectional case-study analyses in the public health and medical fields. There is a dearth of longitudinal, causal analysis with regards to school-based health centers which I hope to fill with this thesis. Motivation Much of the existing research that has been done in the area of child health has focused on the fetal origins hypothesis (Currie and Almond 2011), which shows the long term effects of events during pregnancy, as well as the importance of early childhood education (Currie 2001, Ludwig et al 2007). There is less research focusing on children in their grade school years, and even less in the context SBHCs. Lovenheim Reback, and Wedenoja (2016) is the only longitudinal, large- scale, causal evaluation of the impacts of school-based health. Using a difference-in-differences approach, they find there is a significant decrease in teen pregnancy after a school-based health center opens but find no impact on high school dropout rates- the only measure of educational attainment they study. They hypothesize this is due to the intervention coming too late to have any meaningful change on the habits that would keep a student in school. I hope to build upon the work done by Lovenheim et al (2016) by focusing on elementary and middle school students. Younger children are at a critical age when the two most common childhood chronic conditions – asthma and diabetes – are diagnosed and they may be more malleable in forming healthy habits. Looking at different academic outcome for a different group of students is a promising avenue of inquiry for measuring the success of school-based health centers. Theory The microeconomic theory behind SBHCs sits at the intersection of the “Health as Human Capital” model proposed by Grossman in 1972, and Eric Hanushek’s Education Production Function (1979). Grossman proposes three possible roles of health, one of which considers health as a form of capital which acts as a stock that can be carried from one period to the next and can accumulate or depreciate over time. This health stock can then be thought of as an input to the education production function, where the inputs are investments in the child and the output is their academic achievement. The

Page 42 education production function is often thought of with respect to traditional educational elements, such as class size, as well as other characteristics that affect a student’s outcomes. The following model, based on the 1979 model proposed by Hanushek, for an individual, i, is: Ai=f(Bi, Pi, Si, Ii, Hi) where A is achievement and B, P S, I, and H represent vectors of family influences, peer effects, school inputs, innate abilities, and health respectively. While H doesn’t appear in the Hanushek model, there is substantial research suggesting that a student’s health plays an essential role in academic success (Prinz et al 2018). For the purposes of this paper, access to a school-based health center can be thought of as raising the level of H for a given student, which in turn should increase A. Methods I’ve collected data from school-based health center organizations in New York, California, West Virginia, Colorado, Louisiana, Delaware, Arkansas, Oregon, and Georgia. The data include opening dates for every center in the state, as well as the services they provide. I’ve also collected school-level test score data from every state department of education website for which I have the health center data, and I use the NCES Common Core of Data collection for other school-level descriptive data, such as enrollment and free and reduced lunch statistics. Each observation describes the average test score for a given grade and ethnic or socioeconomic subgroup in a given year. I utilize difference-in-differences and event study methodologies to estimate the effect of the effect of the opening of a health center on subsequent test scores. My baseline difference-in-differences regression, based on the Lovenheim et al (2016) paper is: Y=β0+β1SBHC+β2Post+β3SBHC*Post + ε where Y indicates the academic outcome, SBHC is a dummy variable for ever having a SBHC in the school, Post is the dummy variable for the time period post-intervention, and SBHC*Post is the interaction term. The coefficient of interest is β3, which estimates the causal effect of the SBHC on test scores in the period after it opened. I anticipate adding both state and year fixed effects, a vector of student qualities (race, ethnicity, free and reduced lunch status, gender) and dummy variables for the location (urban vs rural) and services (dental care, mental health, etc.). The identifying assumptions are that SBHCs are arriving in the schools exogenously and that any change in test scores after the opening is attributable to the SBHC. There are two large potential threats to validity. First, there is overlap between students that need healthcare and students who attend lower quality schools, so SBHCs are opened in schools with lower test scores. To address this problem, I run the regression against a control group of schools that are similar to the treated schools in terms of baseline characteristics, but that never receive a health center. Second, if test scores are trending prior to the opening of a center, I would be concerned a change in scores is not due to the SBHC, but rather due to the existing trend. I intend to use the event study to confirm there are parallel pre-trends between the treatment and control groups prior to the opening of the health center. If the parallel trends assumption holds, then this suggests that a change in student achievement was caused by the opening of the health center. Issues • It would be interesting to have some sort of measure of the neighborhoods which SBHCs are in, especially with respect to existing health services- potentially heterogeneous treatment based on what is already available, however not sure how to accomplish this • I would love to try to talk to a doctor that works in a SBHC- I feel like that would give me a better standing of how it actually fits into the school/whether they feel like it works

Page 43 References

Almond, Douglas, and Janet Currie. “Killing Me Softly: The Fetal Origins Hypothesis.” Journal of Economic Perspectives 25, no. 3 (2011): 153–72.

Case, Anne, Darren Lubotsky, and Christina Paxson. “Economic Status and Health in Childhood: The Origins of the Gradient.” American Economic Review, 2002.

Case, Anne, Angela Fertig, and Christina Paxson. “The Lasting Impact of Childhood Health and Circumstance.” Journal of Health Economics 24, no. 2 (March 2005): 365–89. https://doi.org/10.1016/j.jhealeco.2004.09.008.

Currie, Janet. “Early Childhood Education Programs.” Journal of Economic Perspectives 15, no. 2 (2001): 213–38.

Currie, Janet. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development.” Journal of Economic Literature 47, no. 1 (March 2009): 87–122. https://doi.org/10.1257/jel.47.1.87.

Geierstanger, Sara Peterson, Gorette Amaral, Mona Mansour, and Susan Russell Walters. “School- Based Health Centers and Academic Performance: Research, Challenges, and Recommendations.” Journal of School Health 74, no. 9 (2004): 347–52. https://doi.org/10.1111/j.1746-1561.2004.tb06627.x.

Grossman, Michael. “On the Concept of Health Capital and the Demand for Health.” Journal of Political Economy 80, no. 2 (1972): 223–55.

Hanushek, Eric A. “Conceptual and Empirical Issues in the Estimation of Educational Production Functions.” Journal of Human Resources 14, no. 3 (1979): 351–88.

Lovenheim, Michael, Randall Reback, and Leigh Wedenoja. “How Does Access to Health Care Affect Teen Fertility and High School Dropout Rates? Evidence from School-Based Health Centers.” Cambridge, MA: National Bureau of Economic Research, February 2016. https://doi.org/10.3386/w22030.

Ludwig, Jens, and Douglas L. Miller. “Does Head Start Improve Children’s Life Chances? Evidence from a Regression Discontinuity Design.” Quarterly Journal of Economics 122, no. 1 (February 2007): 159–208.

Prinz, Daniel, Michael Chernew, David Cutler, and Austin Frakt. “Health and Economic Activity Over the Lifecycle: Literature Review.” National Bureau of Economic Research, July 30, 2018.

Walker, Sarah Cusworth, Suzanne E.U. Kerns, Aaron R. Lyon, Eric J. Bruns, and T.J. Cosgrove. “Impact of School-Based Health Center Use on Academic Outcomes.” Journal of Adolescent Health 46, no. 3 (March 2010): 251–57. https://doi.org/10.1016/j.jadohealth.2009.07.002.

Page 44 FRIDAY

FRIDAY 12/4/20 9:00 TO 11:00 Erik March Implicit Discount Rates in Solar Investments: Implications for Environmental Policy Thai Nguyen Do Vietnam’s State-Owned Enterprises Improve their Performance after Equitization? Evidence from the Last Decade Dana Frishman Athletic Participation and Academic Outcomes in NYC Public High Schools

Crystal Yujing Zhou Migrate for A Brighter Future? The Impact of Parental Migration Decisions on Children’s Education in China Seamus Lawton A Model of Grade Inflation as a Collective Action Problem

Page 45 ECONOMICS THESIS PROPOSAL

Name: Erik March Title: Peer Effects in Solar Adoption: The Role of Solar Community Organizations Field: Environmental Economics Version: Version 3 on 11/17/20 Advisor(s): Professor Debnam Guzman

Question The question I am pursuing is generally, do peer effects play a causal role in increasing solar adoption in Massachusetts? Within that question, I specifically plan to examine whether Solar Community Organizations (SCOs) in MA towns (the Solarize MA program) significantly increase the impact of peer effects. Another possible sub-question may be whether SCOs are more effective at increasing adoption than traditional solar rebate programs.

Area My research question lies at the intersection of two literatures. The first of these is a classic issue in Environmental Economics: the energy efficiency gap. This literature examines the low uptake of energy efficient technology, generally attributing low rates of adoption to high discount rates and a plethora of external barriers. This energy efficiency gap is especially relevant for solar panels, with low adoption rates despite significant net benefits. The second primary literature focuses on peer effects in technology diffusion. This literature focuses on the impact of social networks and the spread of information through those networks to explain technology adoption. A small section of that literature has also begun to examine these effects the adoption of solar panels, a literature that I am aiming to expand.

Motivation Peer effects play a crucial role in technology diffusion. The flow of information through social networks has been shown to significantly impact the adoption of new technologies. Peer effects have been shown to be particularly critical for experience goods (goods where characteristics such as quality or price are best ascertained through experience) and technologies that require a large initial investment. Solar panels not only require high upfront capital but are also goods that can best be understood and advocated for by those who have experience with them (i.e., those who have purchased and used solar panels). The complexity surrounding solar regulations (net metering, solar energy renewable credits etc.) and general ambiguity about the operation and true benefits of solar panels introduce substantial uncertainty into the adoption decision. As such, peer effects and the spread of information through local social networks can likely play a large role in reducing the uncertainty surrounding adoption and, in doing so, increase the diffusion of solar. Given that the world is teetering on the edge of climate disaster, understanding the diffusion of solar panels and, specifically, the role that SCOs can play is essential to moving towards a clean energy future.

Theory In theory, if peer effects exist for the diffusion of a certain technology, there should be a distinct relationship between the number of people in a social network or geographical area who have adopted a technology and the likelihood of others in that network adopting that technology. In the small literature examining peer effects in solar adoption, the installed base of solar panels (cumulative number of previous adoptions) has traditionally been used to predict the probability of someone in the same geographical area choosing to adopt. As such, there is a distinct link between the installed base and the adoption rate for solar panels in the presence of peer effects. Although the mechanism underlying these peer effects is relatively unstudied, the spread of information and the resulting reduction in perceived risk and uncertainty for potential adopters is a likely possibility.

Methods Using data from the MA Clean Energy Center, I plan to examine how the installed base of solar panels in a specific town (by zip code) predicts future installations. The data includes date of installation, zip code, size of the installation, rebate received, as well as the system’s associated solar program. Given that the data that I have now, I can only perform peer effects analyses at a zip code level. With these limitations I plan on using a model based one used by Bollinger & Gillingham (2012). In this model, the probability that a household in a zip code z adopts solar at time t is modeled as 푃푅푧푡 = ∝푧 log(푏푧푡) + 푋푧푡훽 + 훿푡 + 휁푧푡 + 휀푧푡 where 푏푧푡 is the installed base, 푋푧푡 includes time-varying potential explanatory variables (such as incentives), 훿푡 includes time indicator variables, and 휁푧푡 is zip-code fixed effects. Using this model, I should be able to calculate a relationship between the installed base of solar panels and the probability of adoption for all zip codes in MA. Using this information, it should be relatively easy to compare the strength of peer effects in specific zip codes, essentially using the Solarize MA program (which only lasts one year) as a policy shock. I should be able to do a similar comparison if I choose to examine the impact of other solar programs (rebates etc.) as well. Overall, as I altered my idea late in the process, I am still trying to figure out the best method to use given the data that I have available. If the MA CEC has more detailed information available, Bollinger & Gillingham (2012) also do a street-level analysis that I could use as the basis for my model.

Issues • I’m waiting to hear from the MA Clean Energy Center about the possibility of more specific locational data (street level). If I do receive that data, I should be able to do a street level peer effects analysis or construct a social network as an instrument, but I still need to wait on the CEC. • Peer effects analysis has three common problems: self-selection, correlated unobservable, and a type of simultaneity called reflection. I need to be careful to ensure that my methodology can properly account for these issues (using rich fixed effects etc.). • In general, I altered my idea a bit late in the game so I am still trying to make sure that I have the methods fully nailed down so that I can show a causal relationship without any of the normal pitfalls of peer effects analysis.

References Alberini, Anna, Silvia Banfi, and Celine Ramseier. 2013a. “Energy Efficiency Investments in the Home: Swiss Homeowners and Expectations about Future Energy Prices.” Energy Journal 34 (1): 49–86. Bollinger, Bryan, and Kenneth Gillingham. 2012. “Peer Effects in the Diffusion of Solar Photovoltaic Panels.” Marketing Science 31 (6): 900–912. https://doi.org/10.1287/mksc.1120.0727. Carson, Richard T., and Brigitte Roth Tran. 2009. “Discounting Behavior and Environmental Decisions.” Journal of Neuroscience, Psychology, and Economics 2 (2): 112–30. https://doi.org/10.1037/a0017685. Gerarden, Todd D, Richard G Newell, and Robert N Stavins. n.d. “Assessing the Energy-Efficiency Gap,” 65. Graziano, Marcello, Maurizio Fiaschetti, and Carol Atkinson-Palombo. 2019. “Peer Effects in the Adoption of Solar Energy Technologies in the United States: An Urban Case Study.” Energy Research & Social Science 48 (February): 75–84. https://doi.org/10.1016/j.erss.2018.09.002. Hassett, Kevin A., and Gilbert E. Metcalf. 1993a. “Energy Conservation Investment.” Energy Policy 21 (6): 710–16. https://doi.org/10.1016/0301-4215(93)90294-P. Houston, Douglas A. 1983. “Implicit Discount Rates and the Purchase of Untried, Energy-Saving Durable Goods.” Journal of Consumer Research 10 (2): 236–46. McCollough, John. 2010. “Consumer Discount Rates and the Decision to Repair or Replace a Durable Product: A Sustainable Consumption Issue.” Journal of Economic Issues 44 (1): 183–204. https://doi.org/10.2753/JEI0021- 3624440109. Meier, A. 1983. “Consumer Discount Rates Implied by Purchases of Energy-Efficient Refrigerators.” Energy 8 (12): 957– 62. https://doi.org/10.1016/0360-5442(83)90094-4. Moezzi, Mithra, Aaron Ingle, Loren Lutzenhiser, and Benjamin O. Sigrin. 2017. “A Non-Modeling Exploration of Residential Solar Photovoltaic (PV) Adoption and Non-Adoption.” NREL/SR--6A20-67727, 1379469. https://doi.org/10.2172/1379469. Noll, Daniel, Colleen Dawes, and Varun Rai. 2014. “Solar Community Organizations and Active Peer Effects in the Adoption of Residential PV.” Energy Policy 67 (April): 330–43. https://doi.org/10.1016/j.enpol.2013.12.050. Palm, Alvar. 2017. “Peer Effects in Residential Solar Photovoltaics Adoption—A Mixed Methods Study of Swedish Users.” Energy Research & Social Science 26 (April): 1–10. https://doi.org/10.1016/j.erss.2017.01.008. Shrum, Trisha. n.d. “The Salience of Future Climate Impacts and the Willingness to Pay for Climate Change Mitigation,” 58. Train, K. 1985. “Discount Rates in Consumers’ Energy-Related Decisions: A Review of the Literature.” Energy 10 (12): 1243–53. https://doi.org/10.1016/0360-5442(85)90135-5. Tsukayama, Eli, and Angela Lee Duckworth. 2010. “Domain-Specific Temporal Discounting and Temptation.” Judgment and Decision Making 5 (2): 72–82. Ubfal, Diego. 2016. “How General Are Time Preferences? Eliciting Good-Specific Discount Rates.” Journal of Development Economics 118 (January): 150–70. https://doi.org/10.1016/j.jdeveco.2015.07.007. Xiong, Hang, Diane Payne, and Stephen Kinsella. 2016. “Peer Effects in the Diffusion of Innovations: Theory and Simulation.” Journal of Behavioral and Experimental Economics 63 (August): 1–13. https://doi.org/10.1016/j.socec.2016.04.017.

ECONOMICS THESIS PROPOSAL

Name: Thai Nguyen Title: Do Vietnam’s state-owned enterprises improve their performance after equitization? Evidence from the last decade. Field: Development Economics Version: Version 3 on 11/17/2020 Advisor(s): Mesay M. Gebreselasse Adam D. Honig Question Do equitized firms perform better than fully state-owned enterprises? What are the factors that influence the performance of equitized firms? Does structural complexity of an enterprise affect the effectiveness of its equitization process? Is equitizing state-owned enterprises a viable strategy for long-term economic growth of Vietnam?

Area This research topic lands itself at the intersection between microeconomic and macroeconomic theory. Measurements of private sector and companies’ productivity, efficiency and profits relate to firm theory and industrial organization, while quantifying the impact of privatization as a national strategy to economic growth in developing economies makes it an important macro and development economic question.

Motivation Vietnam’s growth in the last two decades has been largely driven by foreign investments and exports of manufactured goods, both of which have propelled it to the status of a middle-income economy. As the third decade of the century arrives, problems of falling labor productivity, bureaucracy and firms’ inefficiency threaten to stunt its uninterrupted growth by lowering manufacturing outputs and deterring investments. These events warrant a careful reconsideration of Vietnam’s macroeconomic strategy. One possible strategy that Vietnam has employed since the opening of its economy is privatizing state-owned enterprises (SOEs). Through divestments of government’s claims to these enterprises, the private sector is allowed into industries that were traditionally considered entirely public. At the moment, Vietnam has equitized many of its smaller SOEs, yet the biggest ones are only starting to join the process. While there has been research done to assess the effectiveness of the strategy on smaller equitized firms, none has been done for the larger ones, which are arguably more important to the economy as a whole. As such, I will look into the data for the firms equitized in the last decade, which are in the third stage in Vietnam’s equitization program focusing on complex and nationally important SOEs.

Theory Proponents of state ownership often argues that SOEs are established to cure market failures in non-competitive industries or areas where significant externalities exist. Private firms operating in monopolistic industries will charge higher price than the socially optimal price level to maximize profit, leading to under-consumption of the goods. Meanwhile, in industries with huge externalities, private firms will either under-produce when there are positive externalities or

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Page 46 over-produce in market with negative externalities. SOEs provide solutions to both aforementioned issues as they are controlled by governments in order to achieve social welfare objectives, thus thereby improve on the strictly profit-seeking decisions of private enterprises, bridging the divergence between private and social objectives with pricing policies closer to social marginal costs. Conversely, critics of state ownership often pointed to inefficiency and value destruction as reasons for the unsustainability of SOEs. From the political perspective, SOEs might not follow objectives of profit maximization, but rather other political agenda established by the government, leading to inefficient practices like over-hiring or poor investment decisions to secure political support. From the managerial perspective, as managers are not evaluated based on the performance of the firm but rather on their allegiance to the political party, they lack the incentive to maximize profit and minimize cost. Privatizing these SOEs will alleviate both concerns by removing the government’s role in deciding firms’ performances. For Vietnam, the dominant method of privatization is actually equitization, which differs from the privatization as the government doesn’t fully divest its shares but still holds some control in the previously state-owned firms. This might be a double-edged sword, as the government might still influence firms’ decisions, preventing them from achieving economic efficiency by maintaining political agendas.

Methods Vietnam’s equitization campaign is entirely determined by a state committee, so their decision serves as the identification strategy by being the exogenous force determining which firms equitize. Using data on Vietnam’s SOEs, equitized and privatized firms’ performance indicators from 2010 to now, I will conduct a difference-in-difference (DID) study with control variables to identify the effect of equitization on firms’ performance. With this specification, the following equation represents my intended empirical approach: ����!" = � + ��! ∗ ���� + ��! + ����� + ��!" + �!" where ����!" is performance indicator of firm i at time t (which will include return on total assets, return on equity and turnaround), time is the period dummy (which will be coded 0 for pre-equitization and 1 for post-equitization), Ti is the equitization dummy (which will be coded 0 for SOEs that do not equitize at all, 1 for SOEs that equitize but not fully privatize, and 2 for SOEs that fully privatize), Ti*time is the interaction term of equitization dummy and period dummy, and Xit is a vector of control variables (which will include firm age, firm size and industry dummy). The DID estimators will be reflected in the coefficient of b, which represents the performance changes post-equitization. There is definitely selection bias that influenced which firms to equitize at a certain time. I am gathering information on the selection criteria that the committee used and hope to shed light on their selection bias.

Issues • I am facing issues with obtaining data from the government sources. I have contacted the equitization committee a few times but have no responses. • While the micro approach provides better identification strategy, I want to also discuss the implications of equitization as a macro strategy, and I do not currently know how to do this.

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Page 47 References D’Souza, Juliet, and William L. Megginson. 1999. “The Financial and Operating Performance of Privatized Firms during the 1990s.” The Journal of Finance 54 (4): 1397–1438.

Estrin, Saul, and Adeline Pelletier. 2018. “Privatization in Developing Countries: What Are the Lessons of Recent Experience?” The World Bank Research Observer 33 (1): 65–102. https://doi.org/10.1093/wbro/lkx007.

Gasmi, F., A. Maingard, P. Noumba, and L. Recuero Virto. 2013. “The Privatization of the Fixed-Line Telecommunications Operator in OECD, Latin America, Asia, and Africa: One Size Does Not Fit All.” World Development 45 (May): 189–208. https://doi.org/10.1016/j.worlddev.2012.11.005.

Hakkala, Katariina. n.d. “THE STATE AND THE PRIVATE SECTOR IN VIETNAM,” 42. Loc, Truong Dong, Ger Lanjouw, and Robert Lensink. 2006. “The Impact of Privatization on Firm Performance in a Transition Economy: The Case of Vietnam 1.” Economics of Transition 14 (2): 349–89. https://doi.org/10.1111/j.1468-0351.2006.00251.x.

Malesky, Edmund, and Jonathan London. 2014. “The Political Economy of Development in China and Vietnam.” Annual Review of Political Science 17 (1): 395–419. https://doi.org/10.1146/annurev-polisci-041811-150032.

Megginson, William L., Robert C. Nash, and Matthias Van Randenborgh. 1994. “The Financial and Operating Performance of Newly Privatized Firms: An International Empirical Analysis.” The Journal of Finance 49 (2): 403–52. https://doi.org/10.2307/2329158.

Ngoc, Pham Quang, and Pierre Mohnen. 2012. “Privatization and Poverty Reduction in Vietnam Optimal Choices and Its Potential Impacts.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2348133.

Ramstetter, Eric D., and Phan Minh Ngoc. 2013. “Productivity, Ownership, and Producer Concentration in Transition: Further Evidence from Vietnamese Manufacturing.” Journal of Asian Economics 25 (April): 28–42. https://doi.org/10.1016/j.asieco.2012.12.001.

Tran, Ngo My. 2014. “Government Ownership and Firm Performance: The Case of Vietnam” 4 (3): 23.

Tran, Ngo My, Walter Nonneman, and Ann Jorissen. 2015. “Privatization of Vietnamese Firms and Its Effects on Firm Performance.” Asian Economic and Financial Review 5 (2): 202–17. https://doi.org/10.18488/journal.aefr/2015.5.2/102.2.202.217.

Boubakri, Narjess, Jean-Claude Cosset, and Omrane Guedhami. 2008. “Privatisation in Developing Countries: Performance and Ownership Effects.” Development Policy Review 26 (3): 275–308. https://doi.org/10.1111/j.1467-7679.2008.00411.x.

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Page 48 ECONOMICS THESIS PROPOSAL

Name: Dana Frishman Title: Athletic Participation and Academic Outcomes in NYC Public High Schools Field: Educational Inequality Version: Version 3 on 11/16/20 Advisor(s): Professor Joshua Hyman, Professor Caroline Theoharides

Question What effect does formal high school athletic participation have on short-term, at-risk students’ outcomes? More specifically, how does removal from this form of athletic participation affect the attendance, suspension, and graduation rates of these former athletes? How does this effect vary by student body demographics?

Area The larger area in which these research questions resides is the study of educational inequality. Most studies in this area investigate various forms of the education production function. In particular, these studies aim to define the most significant inputs of the function in order to guide policy by providing a systematic and efficient way of equalizing outputs, or student outcomes (Monk 1989). The typical inputs that research focuses on are those which promote the development of either students’ cognitive or non-cognitive skills. My study plans on investigating the importance of non-cognitive skill development and the role it plays in the education production function. More specifically, my study plans on evaluating whether athletic participation can affect academic outcomes through the development of non-cognitive skills.

Motivation My study hopes to shed light on potential mitigants of educational inequality in New York City (NYC). Substantial literature highlights the correlation between educational inequality and educational segregation, and concludes that educational inequality is most pervasive in urban areas which tend to have higher levels of segregation (Chen 2018; Roscigno, Tomaskovic- Devey, and Crowley 2006). Thus, given the persistent severity of this issue in NYC in particular, it is critical that attempts be made to better understand how we can improve the outcomes of at- risk students sitting at the low end of academic achievement within the city (BrightBeam 2020). Furthermore, while there is substantial literature highlighting the importance of cognitive skill development in improving student outcomes, there is much less economic evidence on the importance of non-cognitive skill development and even less on the impact it has on short-term outcomes specifically. One reason for this is that non-cognitive skills, such as confidence and self-control, have a more direct impact on long-term labor outcomes (e.g. interview performance, promotion status) than they do on short-term academic outcomes (e.g. test scores). Another reason is that they are simply harder to observe and measure. Thus, my research will address the issue of educational inequality by evaluating the importance of a potentially undervalued input in the education production function of at-risk students. As such, it will simultaneously assess what role athletic participation plays in the development of non-cognitive skills.

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Page 49 Theory The core economic theory relevant to my research is the education production function (EPF). The EPF uses various school-related and student-related inputs to produce student outcomes (Hanushek 2008). My paper hopes to highlight the importance of non-cognitive skill development in the EPF, and specifically within the EPFs of at-risk students. Currently, there is disagreement as to how early non-cognitive skills affect outcomes, as well as which non- cognitive skills are most necessary. However, heterogeneity in treatment effect across studies suggests that non-cognitive skills are significant determinants of at-risk students’ outcomes in particular (Crispin 2017; Melnick, Sabo, and Vanfossen 1992), through the improvement of skills such as self-discipline, self-esteem and the value of teamwork, all of which are most commonly gained from extracurricular activity participation (Eide and Ronan 2001). While these skills may not directly affect test scores, they are more likely to directly affect attendance habits and in-school behavior which subsequently affect achievement outcomes. Thus, my paper will investigate the importance of extracurricular activities as an input in the education production function, and the effect it has on student outcomes as measured by attendance, suspension, and graduation rates.

Methods My identification strategy uses a policy which created eligibility requirements to participate in the Public School Athletic League (PSAL) in NYC. I use school-level data from the NYS DOE on high school attendance, suspension, and graduation rates for NYC public high schools, and data from the PSAL website to identify which schools participated in the PSAL during the time of the policy. I then perform a quasi-experiment using a difference-in-difference model to capture the treatment effect of removal from athletic participation. Thus, the key estimating equation is: � = �! + �"(�����) + ∑ �# �# + ∑ �$ �$ + �#$ “Treat” is binary variable equal to 1 if an observation is in the treatment group (i.e. if they were a low-achieving school in the PSAL during the 2012-2013 school year). Theta and Pi represent strings of dummy variables for each school and year respectively. These behave as school- and year-fixed effects. I run additional regressions which include interaction terms to identify any differential impact treatment has on schools based on student demographics (e.g. over 50% FRL status). Lastly, I include interaction terms between these covariates and Treat to account for any bias caused by heterogeneity across time and treatment (Dynarski 2003). Finally, I cluster my standard errors to account for any serial correlation in my data, since I will be tracking the same student bodies over a period of time. Issues My ID strategy relies on a few assumptions. First, it requires that the decision for a school to participate in the PSAL versus a different league is random. If not, there could be unobservables affecting both a school’s decision to enter the PSAL and the quality of the school (and consequently, those students’ outcomes). Second, my ID strategy assumes that students who were removed from the PSAL did not join other formal sports leagues. Otherwise, this would cause them to lose their treatment of removal from athletics participation. Since there is no participation level data available, I will account for this issue by using an intent to treat effect. My hope is that if the treatment effect is truly statistically significant, the dilution of the effect by using an ITT will not cause the effect to lose its significance.

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Page 50 REFERENCES BrightBeam. 2020. “The Secret Shame :How America’s Most Progressive Cities Betray Their Commitment to Educational Opportunity for All.” https://brightbeamnetwork.org/cities/.

Chen, Michelle. 2018. “New York’s Separate and Unequal Schools,” February 20, 2018. https://www.thenation.com/article/archive/new-yorks-separate-and-unequal-schools/.

Crispin, Laura M. 2017. “Extracurricular Participation, ‘At-Risk’ Status, and the High School Dropout Decision.” Education Finance and Policy 12 (2): 166–96.

Dynarski, Susan M. 2003. “Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion.” American Economic Review 93 (1): 279–88. https://doi.org/10.1257/000282803321455287.

Eide, Eric R., and Nick Ronan. 2001. “Is Participation in High School Athletics an Investment or a Consumption Good?: Evidence from High School and Beyond.” Economics of Education Review 20 (5): 431–42. https://doi.org/10.1016/S0272-7757(00)00033-9.

Hanushek, Eric A. 2008. “Education Production Functions.” In The New Palgrave Dictionary of Economics, edited by Steven N. Durlauf and Lawrence E. Blume, Second Edition. http://hanushek.stanford.edu/publications/education-production-functions.

Lipscomb, Stephen. 2007. “Secondary School Extracurricular Involvement and Academic Achievement: A Fixed Effects Approach.” Economics of Education Review 26 (4): 463– 72. https://doi.org/10.1016/j.econedurev.2006.02.006.

Melnick, M. J., D. F. Sabo, and B. Vanfossen. 1992. “Educational Effects of Interscholastic Athletic Participation on African-American and Hispanic Youth.” Adolescence 27 (106): 295–308.

Monk, David H. 1989. “The Education Production Function: Its Evolving Role in Policy Analysis.” Educational Evaluation and Policy Analysis 11 (1): 31–45. https://doi.org/10.2307/1163714.

Roscigno, Vincent J., Donald Tomaskovic-Devey, and Martha Crowley. 2006. “Education and the Inequalities of Place.” Social Forces 84 (4): 2121–45.

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Page 51 Economics Thesis Proposal Name: Crystal Zhou Title: Migrate for A Brighter Future? The Impact of Parental Migration Decisions on Children’s Education in China Field: Migration & Human Capital Development Version: Version 3 Advisors: Professor Theoharides & Professor Barbezat Question What factors influence China’s internal migrant parents’ decisions to migrate with or leave their children behind? How does this decision affect the education of their children? Area My research topic is at the intersection of the field of migration and human capital development of children. There is rich empirical scholarship on the impact of international and domestic migration on children who are being left behind but the empirical evidence shows mixed results. The phenomenon of left-behind children in China is attracting more attention, but the educational development of children who migrate with their parents has not seen much empirical study in either English journals or Chinese journals. Motivation China’s rapid economic development and urbanization were made possible by millions of migrant workers who traveled back and forth between the developed urban centers and the poorer rural hometowns. There are approximately 288 million migrant workers in China, 61 million children left-behind by migrant parents, and 36 million children who migrated along with their parents. Many of the migrant workers are parents who face a difficult decision concerning their children’s education: migrate with their children or leave their children behind? Children who are left behind typically attend local schools and live with remaining family members. Although migrating with one’s children may lead to better education for their children, institutional barriers such as limited enrollment, entrance tests, and additional fees for students with rural Hukou (registered residency) make migration with children a less desirable option. With more cities relaxing constraints and opening up public school enrollment, more migrant workers bring their children along. How rural youth are impacted differently by their family’s migration decisions is still an area with limited empirical evidence. I hope to bring the two groups of children together and consider the welfare implications of their parents’ decisions. Theory I assume that migrant parents decide whether to bring their children by maximizing their utility over two periods, 1 and 2. In period 1, parents either bring their child or not, which impacts the consumption of the parent and education attainment of the child. In period 2, parents retire and the child becomes a grown up and cares for her parents by transferring some wealth which is a function of her education attainment. Each household is assumed to have one child which is the norm given the history of one-child policy. Parents’ utility function is the following: 푝 푝 푝 푝 푝 푚 푝 푝 푚 푚 푚 푈푝 = U(퐶1 ) + 훽 U⁡(퐶2 ) subject to 퐼푝 = 퐶1 + 푆1 + 푡푐,1;⁡퐶2 = (1 + 푟)푆1 + 푡푝,2; 푡푝,2 = 푓(퐸(퐴, 푔 )) 푝 푝 where 훽 is the discounting of period 2; 퐼푝is the parental income and 푆1 is parents’ saving in 푝,푚 푝,푚 period 1; 퐶1 , 퐶2 denote parents’ level of consumption in period 1 and 2, with m indicating 푚 “migrate with” or “leave behind” children(w⁡or⁡l), 푡푐,1 is the wealth transferred to the child in 푤 푙 푚 period 1 with 푡푐,1 > 푡푐,1 > 0, due to higher cost of living in the city; 푡푝,2 is the transfer of wealth from the grown up child to parents in period 2 and is a function of the child’s education 퐸 determined by innate ability 퐴 and education investment by government 푔푚. Urban centers have

Page 52 better education infrastructure compared to rural areas, therefore 푔푤 ≥ 푔푙, and we also assume 퐸퐴 > 0, 퐸푔 > 0. The trade-off for the parents between bringing their child or not therefore is between bearing a lower level of consumption now in exchange for a potentially higher level of education attainment by the child and greater wealth transfer to the parents in the future. Existing empirical studies add complications to some of the assumptions. The absence of parents may negatively impact left-behind children’s psychological health, causing an under- utilization of educational resources. Despite continued reforms, surveys show that extra fees and filing of various paperwork make enrollment in high-quality public schools difficult and some migrant children have to attend make-shift private schools that are not better than schools back home. Some studies show that migrant children face discrimination and social exclusion in public schools which also negatively impacts their education attainment. Methods I draw data from the China Family Panel Studies dataset, a nationally representative survey which began in 2010 and has been conducted every other year till 2018. Given that social policies in China, including education policies, originate from directives issued by the central government and are then tailored by regional governments, the timing of policies on opening public school enrollment is arguably exogenous to regional conditions such as a bottom-up pressure for policy changes. I exploit the different timing in policy announcements by the provincial governments as a result of political decentralization to instrument for whether migrants bring their children since these policies lower enrollment cost and increase government investment, inducing more families to migrate with children. Measuring performance and persistence in education, the outcome variables are z scores for test scores standardized by grade and province and middle/high school enrollment. To estimate the impact of parental decision on the education of the child and whether different regional policy setups led to heterogeneity, I estimate the following model:푦푖푝푡 = 훽0 + ′ 훽1푚𝑖푔푟푎푛푡⁡푤𝑖푡ℎ⁡푐ℎ𝑖푙푑푟푒푛푖푝푡 + 훽2푚𝑖푔푟푎푛푡⁡푤𝑖푡ℎ⁡푐ℎ𝑖푙푑푟푒푛푖푝푡 ∗ 𝑖푛푡푒푛푠𝑖푡푦푖푝푡 + 훽3푿 푖훿푡 + 풅푖 ∙ 풕 + 푂푙훿푡 + 푒푝푐 where 푚𝑖푔푟푎푛푡⁡푤𝑖푡ℎ⁡푐ℎ𝑖푙푑푟푒푛푖푝푡is an indicator variable of whether migrant family i in province p migrate with their children by year t; 𝑖푛푡푒푛푠𝑖푡푦푝푡 is a discrete measure of how liberalized the reform is; 푿′푖 is vector of children’s and household’s characteristics including: age, gender, birth order; household income, family composition, parental education and occupation, within or across province migration; 훿푡is a linear time trend; 풅푖 ∙ 풕 is a destination- year dummy variable to control for macroeconomic shocks and other time-varying factors at the migration destination.⁡푂푖 is migration origin characteristics including distance to the provincial capital, number of schools weighted by population, mean income, % of migrants. The first stage 5 regression is as follows: migrant⁡with⁡children푖푝푡 = 훼0 + (∑푡=1 훼푡 ∗ 푝표푙𝑖푐푦푝푡) + 풅푖 ∙ 풕 + 푂푖훿푡 + 휀푖푝푡 where 푝표푙𝑖푐푦푝푡 is a dummy, indicating whether province p announced a reform policy in year t and the following years. Although the timing of policy announcements should be driven by directives from the center, there may still be some endogeneity. For a robustness check, I can use exogenous shocks to cities’ budgets to instrument decisions to bring children or not since the cities’ budget directly influences the public school funding and therefore additional enrollment fee in public schools for migrant children. Issues -How to consistently evaluate the “intensity” of different policy reforms implemented by local government? Since the data is anonymized at the county level and policies published by local government below the provincial governments cannot be matched to the county, can the number

Page 53 of policies weighted by province population be used as a proxy for the degree of intensity at the province level? -CFPS data is recorded every other year suggesting that the effects observed will be lagged in some instances and may be compounded if multiple policies are announced and implemented before the gathering of the data. References Abarcar, Paolo, and Caroline Theoharides. 2020. “Medical Worker Migration and Origin-Country Human Capital: Evidence from U.S. Visa Policy.” Preprint. SocArXiv. https://doi.org/10.31235/osf.io/m79h2. Antman, Francisca M. “The Impact of Migration on Family Left Behind.” International Handbook on the Economics of Migration, 2013, 293–308. https://doi.org/10.4337/9781782546078.00025. Antman, Francisca M. 2012. “Gender, Educational Attainment, and the Impact of Parental Migration on Children Left Behind.” Journal of Population Economics 25 (4): 1187–1214. https://doi.org/10.1007/s00148-012-0423-y.

Brown, Philip H., and Albert Park. 2002. “Education and Poverty in Rural China.” Economics of Education Review 21 (6): 523–41. https://doi.org/10.1016/S0272-7757(01)00040-1.

Chan, Aris, and Edited Geoffrey Crothall. 2009. “Paying the Price for Economic Development:” China Labor Bulletin.

Chen, X., Huang, Q., Rozelle, S., Yaojiang, Y., & Linxiu, Z. 2009. “Effect of migration on children’s educational performance in rural China.” Comparative Economic Studies, 51, 323–343. Chen, Yiwen, et al. “To Migrate With or Without Ones’ Children in China—That Is the Question.” Annals of Economics and Statistics, no. 135, [GENES, ADRES], 2019, pp. 69–88. JSTOR.

Chen, Yiwen. 2018. “School Performance of Chinese Internal Migrants’ Children.” 18–02. CREA Discussion Paper Series. CREA Discussion Paper Series. Center for Research in Economic Analysis, University of Luxembourg. https://ideas.repec.org/p/luc/wpaper/18-02.html. Chen, Yuanyuan, and Shuaizhang Feng. 2013. “Access to Public Schools and the Education of Migrant Children in China.” China Economic Review 26 (September): 75–88. https://doi.org/10.1016/j.chieco.2013.04.007 de Brauw, Alan, and John Giles. “Migrant Opportunity and the Educational Attainment of Youth in Rural China.” Journal of Human Resources 52, no. 1 (2016): 272–311. https://doi.org/10.3368/jhr.52.1.0813-5900r. de Brauw, and Giles. 2008. “Migrant Labor Markets and the Welfare of Rural Households in the Developing World: Evidence from China.” Policy Research Working Paper, no. 4585. https://openknowledge.worldbank.org/handle/10986/6493. Kinnan, Cynthia, Shing-Yi Wang, and Yongxiang Wang. 2015. “Relaxing Migration Constraints for Rural Households.” Working Paper 21314. Working Paper Series. National Bureau of Economic Research. https://doi.org/10.3386/w21314. Liang, Zai, and Yiu Por Chen. “Migration and Gender in China: An Origin‐Destination Linked Approach.” Economic Development and Cultural Change, vol. 52, no. 2, The University of Chicago Press, 2004, pp. 423–43. JSTOR, JSTOR, doi:10.1086/380594.

Page 54 Liang, Zai, Zhongshan Yue, Yuanfei Li, Qiao Li, and Aihua Zhou. 2020. “Choices or Constraints: Education of Migrant Children in Urban China.” Population Research and Policy Review 39 (4): 671–90. https://doi.org/10.1007/s11113-019-09564-9.

Lu, Shuang, Yi-Ting Lin, Juliann H. Vikse, and Chien-Chung Huang. 2016. “Well-Being of Migrant and Left-behind Children in China: Education, Health, Parenting, and Personal Values.” International Journal of Social Welfare 25 (1): 58–68. https://doi.org/10.1111/ijsw.12162.

Meng, Xin, and Chikako Yamauchi. 2017. “Children of Migrants: The Cumulative Impact of Parental Migration on Children’s Education and Health Outcomes in China.” Demography 54 (5): 1677– 1714. Meyerhoefer, Chad D., and C. J. Chen. 2011. “The Effect of Parental Labor Migration on Children’s Educational Progress in Rural China.” Review of Economics of the Household 9 (3): 379–96. https://doi.org/10.1007/s11150-010-9105-2.

Song J, Li S (宋锦, 李实) (2014). “An analysis on the determinants of rural migrant workers’ decision on migration of their children” (农民工子女随迁决策的影响因素分析). Chinese Rural Economy (中国农村经济), 30(10): 48–61

Taylor, J. Edward, Scott Rozelle, and Alan de Brauw. 2003. “Migration and Incomes in Source Communities: A New Economics of Migration Perspective from China.” Economic Development and Cultural Change 52 (1): 75–101. https://doi.org/10.1086/380135. Theoharides, Caroline. “Manila to Malaysia, Quezon to Qatar: International Migration and its Effects on Origin-Country Human Capital” Journal of Human Resources 53, no. 4 (2018): 1022–49. https://doi.org/10.3368/jhr.53.4.0216-7714r1. Wang, Feng, and Xuejin Zuo. “Inside China’s Cities: Institutional Barriers and Opportunities for Urban Migrants.” The American Economic Review, vol. 89, no. 2, American Economic Association, 1999, pp. 276–80. JSTOR.

Wang, Yafang, and Diqing Jiang. 2016. “Educational Inequality in Migrant Children in China: From Visible Exclusion to Invisible Discrimination.” In Childhood, Youth and Migration: Connecting Global and Local Perspectives, edited by Christine Hunner-Kreisel and Sabine Bohne, 115–32. Children’s Well-Being: Indicators and Research. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-31111-1_8.

Zhou, Chengchao, Sean Sylvia, Linxiu Zhang, Renfu Luo, Hongmei Yi, Chengfang Liu, Yaojiang Shi, et al. 2015. “China’s Left-Behind Children: Impact Of Parental Migration On Health, Nutrition, And Educational Outcomes.” Health Affairs 34 (11): 1964–71. https://doi.org/10.1377/hlthaff.2015.0150.

Page 55 ECONOMICS THESIS PROPOSAL

Name: Seamus Lawton Title: A Model of Grade Inflation as a Collective Action Problem Field: Economics of Education, Game Theory Version: Version 3 on 11/17/20 Advisor(s): Joshua Hyman and Christopher Kingston

Question: Numerous studies have documented a steady increase in American college students’ GPAs over the past several decades—a phenomenon known as “grade inflation.” In my thesis, I plan to develop a model that takes into account the incentives of students, colleges, and employers in order to explain why grade inflation occurs, and why grades are not just high but are continuing to rise. Area: This paper lies at the intersection of education economics and game theory. I propose to address an important issue in higher education (specifically, at four-year colleges and universities) by constructing a game theoretic model. The analysis will be primarily theoretical, although I may include an empirical component that tests some of the implications of my analysis. Motivation: Grade inflation represents a serious problem at four-year colleges nationwide. Lenient grading policies can discourage student effort and, if applied unevenly across departments, distort course selection. Even more concerningly, a compression of grades at the upper bound of the grade distribution (an A or an A+) makes it challenging to distinguish between students of varying abilities. This inhibits the efficient matching of students with employers, who observe grades but struggle to discern the actual intellectual capabilities of a potential hire. As a result, understanding why colleges inflate their students’ grades becomes an important endeavor, especially if it points to potential solutions. Theory and Methods: While the precise details of my model remain to be worked out, I have sketched a broad outline of it. The model starts with high school students, whose intellectual capabilities are distributed according to a probability density function characterized by a mean and a variance (probably a normal or truncated normal distribution). After observing her intellectual ability, each student must decide whether or not to go to college; she attends college if and only if the marginal benefit—her expected wage after graduating minus the wage she could earn by entering the labor market immediately—exceeds the marginal cost, which is the effort she must exert by studying in college. For each student, this cost is a decreasing function of her intelligence, so students with higher intelligence find it less costly to attend college. Every student who attends college enrolls at one of N identical colleges, each of which has the same distribution of students (I will relax this assumption later). Colleges want to maximize their graduates’ earnings since this increases the prestige of the institution and generates future alumni donations. Each college independently chooses a grading policy. An “honest” grading policy would be to set each student’s grade equal to her intelligence, but colleges also have an opportunity to inflate grades by giving each student a grade of her intelligence plus some constant, up to a maximum grade. This ensures that for any two students at the same college, the student with higher intelligence has a weakly higher grade than the student with lower intelligence.

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Page 56 Employers observe each student’s grade but not her intelligence. There are M jobs available, where M is less than the total population of college students. Employers’ payoffs increase with the quality of the students they hire, so they hire the top M students (according to their grades) across all colleges. This creates an incentive for every college to inflate its students’ grades in order to give them an advantage in the hiring process over their peers at rival institutions. At this point the game begins again, with one key difference: Since grades are now higher for all students, the level of effort required in college is lower than before. This induces more students to attend college, which increases the competition for the same M jobs, which leads to further grade inflation, which causes more students to attend college in the next period… at which point the cycle begins anew, leading to an escalating sequence of enrollment increases and grade inflation, eventually making it difficult for employers to distinguish between students as more and more receive the maximum grade. This would have disastrous welfare implications. Treating colleges as identical is helpful for understanding situations in which employers hire from only a small subset of relatively homogeneous colleges. But to capture the dynamics at work in the higher education industry writ large, the model has to incorporate college heterogeneity and reputation. I propose to incorporate college heterogeneity into the model by dividing colleges into two types: selective and less selective. Selective colleges have a distribution of students with a high mean intelligence and a low variance, while less selective colleges have a distribution with a lower mean but a higher variance. It would be interesting to investigate whether each of type of college has a different strategy in the game described previously. To incorporate reputation into the model, I will have employers take into account the history of their past hires from each college. If a college gives unintelligent students high grades and they get hired, their employer might be reluctant to hire from that college in the future. As a result, when a firm is deciding which students to hire from a certain college, they should consider both the students’ grades and the past performance of hires from that college (perhaps the average quality of the students hired). Issues: The final version of my proposal faces challenges both new and familiar: • My analysis implies that grade inflation will be highest where competition for jobs is fiercest (I think this is why grade inflation is generally higher in the humanities than in STEM subjects). I would like to test this implication, but I still need to find good data. • The assumption that the number of jobs available to college graduates is less than the number of college graduates does not accurately reflect reality. I might instead introduce two types of jobs open to college graduates: high-paying, high-status “elite” jobs for which there is intense competition, and other, “regular” jobs. If any college graduate can get a “regular” job but not necessarily an “elite” job, then the same pressures for grade inflation will exist. I haven’t added this to the model because I think it adds complication without changing the conclusions, but I could incorporate it. • Making grades dependent only on intelligence ignores the value employers place on non- cognitive abilities, particularly conscientiousness (in many jobs, a hard-working person of average intelligence will make a better employee than a smart but lazy person). As a result, it might be fruitful to instead make a student’s grade a function of her intelligence and her effort. Then the model would better represent employers’ desires, and it would also allow an examination of grade inflation’s effect on student effort. Presumably, grade inflation could cause students to exert less effort in their coursework, which would be another downside of the policy.

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Page 57 References

Butcher, Kristin F., Patrick J. McEwan, and Akila Weerapana. 2014. “The Effects of an Anti- Grade Inflation Policy at Wellesley College.” Journal of Economics Perspectives 28(3): 189-204.

Chan, William, Li Hao, and Wing Suen. 2007. “A Signaling Theory of Grade Inflation.” International Economic Review 48(3): 1065-090.

Costrell, Robert M. 1994. “A Simple Model of Educational Standards.” The American Economic Review 84(4): 956-71.

Cukierman, Alex, and Allan H. Meltzer. 1986. “A Theory of Ambiguity, Credibility, and Inflation under Discretion and Asymmetric Information.” Econometrica 54(5): 1099- 1128.

Hershbein, Brad. 2013. “Worker Signals Among New College Graduates: The Role of Selectivity and GPA.” Upjohn Institute Working Paper 13-190.

Kolevzon, Michael S. 1981. “Grade Inflation in Higher Education: A Comparative Study.” Research in Higher Education 15(3): 195-212.

Kuh, George and Shouping Hu. 1999. “Unraveling the Complexity of the Increase in College Grades from the Mid-1980s to the Mid-1990s.” Educational Evaluation and Policy Analysis 21(3): 297-320.

Pressman, Steven. 2007. “The Economics of Grade Inflation.” Challenge 50(5): 93-102.

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Rojstaczer, Stuart and Christopher Healy. 2012. “Where A is Ordinary: The Evolution of American College and University Grading, 1940-2009.” Teachers College Record 114(7): 1-23.

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