Updated 5/6/2021 to list Summer 2021 projects
Updated 5/7/2021 to add project #15
Updated 5/10/2021 to add projects #16-21
We believe that conducting research is an important part of your training as an economist. Learning how to define a question, locate and review related literature, identify, download and clean data, and conduct empirical analysis develops a skill set highly valued by graduate programs and future employers alike. The Economics Department has two programs:  the general undergraduate student researcher program, and  the Lynde grad student mentoring program. The mentoring program is a subset of the more general program. You sign up only once. . . Read on.
The undergrad student researcher program is designed for Economics majors at any stage of your education. If you have not yet taken econometrics -- which Prof. Olney says you should take as soon as you can! -- we still encourage you to apply. Some graduate students or faculty need RAs who can do coding in Stata or R or Python, but others are looking for RAs to do literature searches or other tasks that do not require econometrics or CS/DS toolsets.
The grad student mentoring program adds in several components that ensure the undergraduate student researcher experience provides you not only with an experience to list on your resume, but also with a graduate student mentor who will teach you about research and who is available to discuss courses, work or graduate school plans, the life of an economist, and whatever else may come up. The mentoring program is funded by a generous donation from Cal alumnus, Dr. Matthew Lynde.
About the Lynde Mentoring Program
The features of the mentoring program are listed below. Items that are
struck out are not relevant to summer term.
- You work with a graduate student who has expressed interest in and received training in serving as a mentor for undergraduates. Mentoring means that the graduate student has made a commitment to discussing the research process with the RA, helping the RA see the big picture for the research project and the specific tasks the RA is working on, talking with the RA about future opportunities in economics or in research (including graduate school options), and ensuring that the RA acquires research skills as part of the RA process. If you are considering graduate school in economics, or just want to know more about what it means to be an economist, the mentoring program may be for you.
A welcoming Zoom-reception for all RAs hired as part of the Mentoring Program will be held on Monday evening, February 8, 6:30-8:00, on Zoom. Link will be provided by email. The welcoming reception will give you the opportunity to meet all the other undergraduate RAs serving this term, learn about all of this term's projects, and ask questions about research in economics. A pair of zoom-workshops conducted by Economics graduate student will offer you tips & training in the use of R and LaTeX. The workshops will be scheduled for the weeks of February 8. More information will be on the RSVP form for the welcoming reception. An end-of-term Zoom-poster reception scheduled for Wednesday evening April 28, 6:30 - 8:00 pm, on Zoom, at which you and the grad student you're working with will display a poster that describes the project and the work you've contributed to the project. When we get back to in-person life, posters will be on display in the 6th floor of Evans Hall for at least a semester. A small financial incentive ($50) will be paid in May to undergrad RAs who participate in the mentoring program by working with a grad student mentor, attending the welcoming reception on February 8, and attending the poster session on April 28.
There are about 40 graduate students who have completed the training to serve as mentors. Their projects cover a wide range of fields and the research tasks they are looking for help with also cover a wide range. The second column of the table below indicates whether or not the grad student is part of the mentoring program. If you are interested in participating in the mentoring program, be sure to check the appropriate box at the bottom of the application for serving as an undergrad RA.
There are also Economics, ARE, and BPP (part of Haas's PhD program) grad students who are not part of the mentoring program, postdocs, and ARE & Economics faculty who will look at the applications in order to find RAs. So you can be an RA without being in the mentoring program.
About the Application
The application form for undergraduate RA positions is available at https://www.econ.berkeley.edu/undergrad/economics-majors-interested-ra-positions. The Economics faculty and graduate students seeking RA assistance will reach out to those students they are interested in interviewing. Watch your email!
On the application form, you can signal up to four (4) projects that particularly interest you. Use the project number in the left most column when completing the "signalling" part of the application. The second column tells you whether or not the person in charge of the project is a faculty member or a graduate student who is part of the Mentoring Program.
About Earning Econ 199 Credits
Two important points about compensation:  You must be an enrolled student. Due to labor laws, we cannot allow people who have already graduated to serve as an unpaid RA.  Being an RA for a graduate student or faculty member is considered an educational or academic endeavor. In most cases, these are unpaid positions, much like an academic internship.
If you are not being paid, you must receive 1-3 Econ 199 units for being an RA. These will be P/NP units that count toward graduation but not as an upper-division elective. Because the deadline to sign up for Econ 199 is Friday of the 3rd week of classes, all unpaid positions must be filled by then. (Because of the cost of enrolling in summer units, Econ 199 enrollment not required in summer term.)
Non-Economics Faculty & Graduate Students Seeking RA Assistance
Graduate students or faculty from other departments can also hire our Economics undergraduates! We invite grad students, post-docs, and faculty who are not in the Economics Department to submit a description of their position directly to the undergraduate office (firstname.lastname@example.org) for inclusion in the weekly bCourses e-blast that goes to all Econ majors. Please include the following information: who you are, contact information, deadline for applying; title & description of the project; expected RA tasks and anticipated RA skill set; whether compensation is $ or units; anticipated # of hours per week. Send your info by email to email@example.com with an indication that it is for the weekly e-blast to Economics majors.
List of Projects for this Term
|Project #||In Mentoring Program?||Grad Student or Faculty||# of RA's needed||Project Title||Project Abstract||RA tasks|
|1||yes||Econ grad student||2 new||Misperceptions, Promotion and Turnover: Evidence from Ethiopia||High turnover rates are a common issue in manufacturing firms in developing countries. In one of the main industrial parks in Ethiopia, the government has speculated that misperceptions regarding job characteristics among new hires contribute to high turnover rates. We will implement a new information treatment, with particular focus on long-run promotion incentives, and analyze the effect on belief update and turnover decisions.||Monitor field work data, data cleaning and preliminary analysis|
|2||yes||Econ grad student||4 new||Gender bias in USPTO patent applications||Women are grossly underrepresented in the patenting system. In 2016, only 12% of US patent inventors were women, only 21% of granted patents had at least one woman inthe inventors’ team (Lissoni et al., 2018), and patent granting rates for women are significantly lower than for men (Jensen et al., 2018). This project intends to ask to what extend is this gender gap a result of gender bias in the examination process, and whether there is a gender bias heterogeneity.||The first task would be digitization and data cleaning of the patent data. Next, and only if interested, we will work together on writing a Python script to scrape examiner data.|
|3||Econ grad student||1 new||Employer - employee cooperation and payments under the table: evidence from Brazil||We are studying a particular margin of evasion which has been unexplored in the literature: firms can have employees hired formally but pay part of their salaries under the table. We are exploiting breaks in the employees' incentive to cooperate as well as institutional and our own survey data.||The RA would be working with Brazilian survey data (PNAD). RA's duties will involve cleaning the data and building some descriptive statistics. This should be carried out in Stata, R or Python. Latin American students are particullarly encouraged to apply, since they may find this setting very interesting.|
|4||yes||Econ grad student||2 new||Fiscal Multipliers from the Perspective of Stock Market||This project aims to use the return of US defense industry to re-evaluate the effect of temporary government spending.
The goals of the project are threefold: 1) To form a new measure of fiscal policy shocks by constructing the return of US defense industry during the Cold War and connecting it with the existing data, 2) to estimate fiscal multipliers using the new measure and compare them with the existing literature, and 3) to test competing model predictions on how government spending will affect consumer welfare.
Current progress of the project reveals an underestimation in the effect of government spending from previous studies due to unaccounted expectation formation processes. This point is important for present policy concern given the increasing obstacles faced by the monetary policy from a prolonged global low interest rate environment and the on-going trade war.
|1) Collect and organize data from official sources on consumption, employment, CPI, and wages in the United States during the Cold War and WWII era.
2) Review historical evidence (e.g., government supply contracts, firms’ annual reports, congressional budget records, etc.) on the military vs. civilian sales of major US government contractors during the Cold War, especially during the Korean War.
3) Perform preliminary analysis if necessary (optional).
4) Preferred skill sets: Stata, Excel, basic knowledge in econometrics, and a love for finding data about the Cold War era.
|5||yes||Econ grad student||2 new||Monetary Spillovers through Multinational Enterprises||Do multinational enterprises (MNEs) play a special role in transmitting monetary shocks cross borders? The past decades have witnessed the establishment of vast production networks and deep internal capital markets of MNEs, yet little is known if MNEs have affected the transmission of global shocks and the subsequent financial flows. In this project, I cooperate with a research team in Switzerland to link confidential records with detailed firm-level data to investigate the role of MNEs in transmitting global monetary shocks. In addition, I explore the interaction between MNEs and global banks and ask whether such interaction challenge or enhance the autonomy of central banks. Based on the results of initial findings, counterfactual analysis will be performed on the improvement of policy making and the welfare consequences of potential policy actions on domestic and foreign households.||1) Helps are needed to collect, maintain, and organize financial data from several proprietary databases. Candidates with familiarity in firm balance sheet and income statements are preferred. Candidates with research experience in Stata are strongly preferred.
2) CS background in natural language processing and/or name matching is a plus, as some datasets may need to be merged by firm names.
3) Preferred background: Econometrics, International finance/macro/trade, CS.
|6||yes||Econ grad student||2 new||Financial Market Implications of Trade War||Current abstract: The trade war between the United States and China has a significant impact on high-yield spreads, long-term interest rates, and stock prices. However, the 10-year-minus-2-year Treasury yield spread, whose inversion generated significant media chatter about a looming recession, does not seem to be influenced by news about the trade war. These results are consistent with the relatively modest macroeconomic impact of the trade war predicted by previous studies and suggest that the financial-market impact is primarily driven by changes in risk premia.
The next step: Several highly interesting, yet unexpected results are discovered while working on the project in the past summer. I will perform empirical analysis on these results. Meanwhile, a theoretical model will be developed to guide and discipline the potential explanations.
|1) Helps are needed to maintain and organize professional financial databases, some of them are large and high frequency. Candidates with some familiarity in firm balance sheet and income statements are preferred, but these knowledges are not required.
2) CS background in natural language processing and/or name matching is a plus, as some datasets may need to be merged by firm names.
3) Occasional data cleaning in Stata or Excel + preliminary econometric analysis (optional).
4) Preferred background: Financial economics, International macro/trade, CS.
|7||yes||Econ grad student||2 new||Priors vs. Desires||How do people update their beliefs when presented with a new signal? In what ways do they deviate from the Bayesian benchmark, especially when they have preferences over the truth? How is their updating behavior related to cognitive ability?
I present new results on these old questions using data from Tappin (2020). I recreate the authors’ analyses, but differentiate between a signal’s concordance (alignment with a subject’s politics) and polarity (alignment with a subject’s priors). I find that polarity mediates the impact of political alignment: subjects are unmoved by a signal consistent with their priors irrespective of its political alignment, but update considerably upon receiving an inconsistent (surprising) signal. If it's a pleasant (politically aligned) surprise, subjects eagerly update to a posterior of 50/50; but if it's an unpleasant surprise, participants still update more than a Bayesian, but heterogeneously by cognitive ability.
|- Work with existing data and papers; be able to summarize and compare regression estimates and experiment designs
- Run regressions and write clean code in R. Be familiar with fixed effects, logits, etc.
- Produce publication quality figures and tables.
(Happy to guide in all of these tasks. Sample code, etc. for most tasks is already available.)
|8||Econ faculty||1 new||simulate oligopoly when firms match average of others' prices||Model price dynamics in an oligopoly in which (exogenously) firms take turns having opportunities to adjust price, and at each such opportunity the firm whose turn it is sets its price to the mean of others’ prices. We first analyze the equilibrium of the model, then turn to the effects of mergers.
Given that behavioral dynamic, it is a steady state (or “equilibrium,” in a dynamic but not incentive-based sense) if all firms charge the same price: p_0. We consider moves away from such a steady state.
We first study the dynamics of convergence to a (new) steady state when an initiating firm moves its price away from an initial steady state, and reactions thereafter consist of each firm (including the initiator) in turn matching the mean price of all others.
We are interested in the tradeoff that faces the initiating firm when it can lead the industry to a new and higher uniform steady-state price but during the readjustment or re-equilibration process is generally higher-priced than are its competitors. Equivalently (changing sign), we are concerned with the tradeoff when an initiating firm cuts its price and consequently is temporarily lower-priced than are its competitors during re-equilibration, but in the end the uniform market-wide price is lower than it had been. (Implicitly, therefore, we are considering prices below the monopoly price, so that it is more profitable for all to all have higher prices within this range.) We do not explicitly model the initiator’s preferences over that tradeoff, but instead consider how structure affects the available “terms of trade” that it faces.
|Simulation in Mathematica, and guiding/tutoring me (on zoom) toward being able to work with the Mathematica model myself|
|9||Econ faculty||1 new||Vehicle pollution standards and taxes||This project studies the effectiveness and efficiency of policies to regulate pollution from passenger transportation. We are using hundreds of millions of pollution tests, several research designs, and analytical and quantitative models of the new and used vehicle markets.||We need help creating a database listing local and state government registration taxes and fees for vehicles. This involves visiting local and state government websites, possibly calling a few sources, and creating a spreadsheet in excel listing local tax policies.|
|10||Econ grad student||1 new||The effects of (un)fair wages for public sector workers||This research project will examine how the federal government’s wage policies affect public sector worker selection, recruitment, and separations. One main line of research might look at testing behavioural economics theories of labor supply by looking at wage raises that were reneged on. Another aims to estimate the overall elasticity of labor supply to the government, which will help determine the amount of labor market power that the government has, and the relationship between wages and the quality of that labor supply.||Researching historical government documents and newspapers. Conducting literature reviews. Checking code and data for issues. Writing well-documented code in Stata to conduct secondary analyses of data, e.g. descriptive statistics, making graphs.|
|11||Econ faculty||1 new||The Effects of Automation on Employment, Inequality and Livelihoods and Policy Evaluation in Advanced Industrial Economies||In the US and other advanced industrial economies, new technologies that automate work hold the promise of higher productivity and efficiency. But these technologies also threaten major disruptive changes in labor markets that will displace workers and trigger changes in the skill, occupational and sectoral composition of employment and in the level and distribution of wages. Concerns over these disruptive changes raise many questions about the future of labor markets and the future of work. As intelligent machines take on more work currently done by humans, will there be enough jobs for human workers seeking employment? How will automation affect the educational and skill requirements of jobs? Will automation displace low and middle-skill workers while increasing demand for more skilled and educated workers, polarizing labor markets, and resulting in greater wage and income inequality? Will available jobs in the future have adequate wages and social protections to provide meaningful livelihoods to workers and their families?||Candidate should be comfortable doing literature reviews of papers from journals such as the AER and the QJE. Should be comfortable with econometrics and data collection and analysis (e.g. Stata, Python, etc.; at least have completed Econ 140 or some upper division data science classes). Only self-motivated candidates with strong research, writing and editing skills should apply.|
|12||yes||BPP grad student||4 new||From Rivals to Partners: The Alignment of Capital and State Coercion in the Rise of Modern Economic Growth||The conventional wisdom on the source of economic growth emphasizes inclusive institutions: constraints on state elites, which allow open access to political and economic power. Yet, in many contexts where economic growth has emerged, we see the partnership of powerful, coercive (often nondemocratic) states and private enterprise: from Industrial Revolution Britain, to the post-WWII Asian economic tigers, to post-Mao China. Focusing on the emergence of modern growth in Britain, we propose a model of a partnership between coercive states and private actors: merchants lend money to the state, which is used to develop coercive capacity. In our model, good institutions are an outcome, rather than a cause of increased economic activity, and the good outcome is driven by aligned incentives, not political inclusion. To study the openness of the British political elite in the 17th and 18th centuries, we collect data on the social origins of high officials, MP's, and Privy Councilors. We also collect information from the Canterbury Probate Records to examine the composition of the economic elite. Our preliminary results suggest an alignment between traditional political elites and new economic elites, which we hope to examine further using data on financial interests of the political elite, and on the relationship between international trade, wars, and government revenues.||Our goal is to match RAs to tasks based on interest and skills. We expect RAs to mainly contribute to the empirical and data-intensive aspects of the project. We use Python, R and Stata. Tasks include, for example:
1) Collecting and digitizing novel archival data
2) Web scraping and Optical Character Recognition of historical records
3) Combining data from multiple sources to construct new data sets
4) Working with “text as data” using machine learning and natural language processing
5) Analyzing the data using econometric methods
6) Visualizing the results in graphs and maps
|13||yes||Haas grad student||3 new||Can Political Machines Promote Social Mobility? Evidence from Tammany Hall||Political machines are hierarchical organizations with a dense network of local brokers that mobilize votes for their candidates with promises of patronage, pork, or other rewards and threats. Machines dominate elections in many developing countries and are commonly associated with corruption and bad governance. Existing research emphasizes how brokers often target poorer voters. By targeting patronage and public services at poorer citizens, do machine politicians contribute to more equal outcomes and greater social mobility? Can disenfranchised groups like immigrants improve their situation by rising through the machine hierarchy? I study these questions in the context of the archetypical machine in US history: New York City’s Tammany Hall. I am currently digitizing archival records on the machine’s personnel, yearly registers of city employees, and local public good provision. I plan to link this information to individual-level census data and newly collected data on house prices to document who profits from machine politics. I would then compare the economic outcomes of immigrants living close to local machine operatives vs. those living in the same district but further away from operatives. To overcome potential selection, I plan to estimate a difference-in-difference specification and leverage the variation in the proximity to machine officials induced by unexpected changes in the machine’s personnel.||The goal is to match RAs to tasks based on interest and skills. I expect RAs to mainly contribute to the empirical and data-intensive aspects of the project. We use Python, R, Stata, and GIS applications like QGIS or ArcGIS.
I am especially looking for someone with GIS experience to create new shapefiles of district boundaries for historical Manhattan. But these skills can also be learned while working on the project.
Other tasks include, for example:
1) Collecting and digitizing novel historical data
2) Web scraping and Optical Character Recognition of historical records
3) Working with geo-spatial data and digitized maps
4) Combining data from the census and other sources to construct new data sets
5) Analyzing the data using econometric methods
6) Visualizing the results in graphs and maps
|14||Econ grad student||3 new||Are lawsuits bad for business? Litigiousness and workers' compensation||We study how litigiousness affects labor markets. We analyze a large reform in the workers' compensation system in Argentina, which limited the possibilities of lawsuits between employers and workers. We explore effects on employment, business development, and wages.||We have various tasks we need help with. First, we need to transcribe information from some pdfs into datasets. This could be done with OCR (the pdfs have a very standard format) or manually. Second, we need to do some cleaning and organizing of some existing datasets (this would be done using Stata). Third, we need to search for more literature related to this topic. Some knowledge of Spanish is preferred (although not strictly required) for some of these tasks (like transcribing some information from the pdfs).|
|15||Econ faculty||2 new||Neuroscience foundations and economic applications of experience-based learning||Experience effects (or, experience-based learning) is a relatively new concept in economics, which postulates that our past lifetime experience shape our beliefs and risk attitudes for years and decades to come. Several theoretical and empirical papers have been published, and we will looking to further our understanding of its (1) neuroscience foundations (synaptic tagging, neuroplasticity) and (2) broader applications, e.g., to gender differences, ractial discrimination, education, childhood experience.||(1) Finding data and establish baseline facts (summary stats, correlations). (2) Read advanced ecnomic papers, possibly replicate (here, R or STATA or Python knowledge needed). (3) Help identify new applications of "experience effects" beyond finance and macro.|
|16||yes||Econ grad student||2 new||Exchange rate fluctuations and Overshooting Inflation Expectations||I am currently working on inflation expectations in emerging markets. I am testing a widely held view among policymakers and central bankers that inflation expectations are very sensible to exchange rate movements. Preliminary results show that indeed inflation expectations rise when exchange rates depreciate, even after controlling the pass-through from the exchange rate to consumer prices. Moreover, these results show that depreciations are systematically followed by periods in which inflation expectations exceed realized inflation, a phenomenon I label "overshooting inflation expectations".||
The project is very data intensive, so I need RAs helping me to automate the process of data retrieval and putting it into a nice format that I'm using. Experience with web scraping with Python and APIs necessary.
|17||Econ grad student||2 new||How much can we fight income tax evasion in developing countries?||Income tax non-compliance is a major issue in developing countries. The project studies the limits to enforcing compliance with the income tax with typical enforcement policies (like audits and fines). We combine various sources of information on the degree of non-compliance with income taxes and study how it is affected by stricter (or weaker) government enforcement.||
RAs would be involved with managing various datasets, both from administrative and survey data. This includes data cleaning, merging, and analysis (such as producing summary statistics, graphs, and regression tables). Knowledge of Stata and R is needed.
|18||yes||Econ grad student||3 new||Unpacking Intergenerational (Im-)mobility: Child vs. Parent Career Preferences||In German panel data, high school students and their parents frequently disagree about post-secondary career plans and many students end up adjusting their career choices to their parents’ preferences. I design a field experiment with 1,200 students and 800 parents to test whether this adjustment is due to the public nature of actual decisions: I experimentally vary whether students’ incentivized career aspirations are shared with their parents or not. Making students’ aspirations observable by parents causes an increased share of students with at least one college-educated parent to state an aspiration to attend college and more students to aspire to high earning fields. As a result, the socio-economic gap in college aspirations doubles to 27 percentage points and similarly widens for aspirations to enroll in a high earning major at university. Parents’ preferences and their children’s tendency to adjust to these even when transitioning to adulthood can therefore contribute to intergenerational immobility. This, in turn, has important implications for our understanding of intergenerational mobility and for policies designed to increase mobility.||
There are several types of tasks I need help with (you don’t need to help with all of them, but should be able to help with a subset):
- Structural model estimation using Python
- Data cleaning
- Replicate analysis as done with German panel data using very similar US panel data sets
- Screen survey/data documentation to identify variables of interest
- Literature review of related strands of literature in Econ and/or sociology
|19||yes||Econ grad student||1 new||Motivated Memory and the Intergenerational Transmission of Fertility Norms||How do people form beliefs about long-term processes in life that they later pass on to the next generation? This paper studies the long-term memory of 4,000 Kenyan women and men when it comes to their past reproductive desires 10 years ago and intergenerational transmission of their beliefs to the next generation. Respondents’ memory is inaccurate and biased, at least partly so for motivated reasons. Those who have more children than they initially desired are likely not to remember so and to avoid information that could suggest otherwise. What is more, those who are not aware of. Their excess fertility would also advice the next generation to have more children than those with accurate memory. This combination of motivated memory and intergenerational transmission has the potential to contribute to cultural persistence and might also be behind the persistence of traditions such as female genital mutilation.||
I anticipate needing help with a variety of tasks:
- Data cleaning and data analysis (STATA), on questions such as which individual reasons are behind excess fertility, what are the consequences for (undesired) children, which motivations are behind selective forgetting and information avoidance?
- Literature review of related literature on memory, belief formation, cultural transmission and cultural norms around fertility in Kenya
|20||yes||Econ grad student||2 new||Missing Markets in Burundi||We are working on three projects in Burundi at the intersection of development and behavioral economics that we are hiring RAs to help with. In the first, we are working with farmers to understand the barriers they face in accessing input and output markets. In the second, we are working with casual laborers to understand how their recollection of their past job search behavior impacts their future job search. In the final project, we are looking at why their is particularly high unemployment among recent university graduates, and whether behavioral biases impact how these graduates search for jobs.||
This is a good job for someone who wants to work with data and improve their data skills. We will be collecting new survey data over the summer, and the RA will work on cleaning and constructing this new data, as well as on other datasets from Burundi, and performing preliminary analysis on this data, preferably using stata. In addition, we may also ask the RA to assist with programming surveys (no previous experience required).
|21||yes||Econ grad student||2 new||The Long-Term Effects of Soviet Policies||The goal of the project is to understand how Soviet policies have affected modern economic outcomes. I am specifically interested in policies regarding 1) female emancipation, and 2) the man-made famine of 1932-33 that took the lives of more than 4 million persons. In addition to modern outcomes, I want to examine the information about districts’ economic development prior to and during Soviet rule. The work will contribute to the broader literature about the long-term effects of socialism, collectivist culture, and planned economy.||
I need RA help in three main areas:
1) Finding historical data (e.g., browsing through websites of libraries and archives)
2) Digitizing historical data (manually or using OCR technologies)
3) Preliminary data processing.
Desired skill set:
- Ability to understand written text in Ukrainian/Russian/Polish would be very useful for some tasks.
- Familiarity with Python is a plus.
Prepared by Martha Olney (firstname.lastname@example.org)
Last update 5/10/2021 4:00 pm