In this installment of the Goldman School of Public Policy’s “Five Questions for Faculty” series, O-Lab Faculty Director Hilary Hoynes, Associate Dean and Chancellor’s Professor of Economics and Public Policy, discusses how the upcoming general election could effect the US social safety net. Read the article here.
The impact of Medicaid expansion on disability benefit take-up
New open-source volume on the effects of the 2021 Expanded Child Tax Credit
Newly released in the Annals of The American Academy of Political and Social Science, “Evaluating the Effects of the 2021 Expansion of the Child Tax Credit” brings together some of the strongest research on the 2021 CTC expansion. Edited by O-Lab Faculty Director Hilary Hoynes, Megan Curran of Center on Poverty and Social Policy at Columbia University, and Zachary Parolin, the volume frames the policy change in historical and international contexts, presents the expansion’s near-term impacts, and explores the potential, long-term effects of a permanently expanded credit, in addition to alternative policy designs. Explore the full volume here.
The long-term effects of entering the workforce during a recession
Economic recessions create an array of immediate and widespread challenges for workers, exerting labor market pressures that lead to layoffs and make it difficult to find a job and earn enough to make ends meet. These conditions can also lead to long-term consequences for those whose first entry into the workforce coincides with a recession or economic downturn. With increasing access to high-quality administrative data spanning decades, there is a growing body of research documenting the scale of these consequences (referred to as “economic scarring”) on one’s long-term life trajectory.
Cesia Sanchez, who received her PhD from UC Berkeley’s Economics Department in May 2024, is making important new contributions to this literature. In part, her interest stems from a desire to understand the experiences that shaped her upbringing, including the impact the subprime mortgage crisis had on her family. During her undergraduate studies at Texas A&M, Sanchez realized, “I need to keep up with econ because it's going to help me understand my life experiences. In my principles of macroeconomics course, I learned what being seasonally employed meant. We studied the bursting of the real estate bubble. That class taught me that all of my life experiences could inform research questions that I’ve studied.”
Sanchez’s dissertation, The Effect of Early Economic Conditions on Young Adults’ Transition Into Adulthood and their Occupational Characteristics, offers new ways of looking at the long-term impacts of exposure to economic downturns early in life. Specifically, while previous work in this field has largely examined how labor market trends affect college graduates, Sanchez focuses additionally on high school graduates. Her analysis also considers a wider array of variables (in addition to standard outcomes related to earnings and employment) in order to understand how recessions impact the transition into adulthood more broadly, and what consequences economic downturns have for the quality of employment one can access down the road.
In the first part of her paper, Sanchez uses data from the American Community Survey, covering 19 to 30 year olds between 2006 and 2021, to estimate the effect of economic conditions on a variety of variables indicative of transition to adulthood – including employment rates, earnings, living arrangements, marriage, and educational attainment. As a measure of exposure to recessionary conditions, she takes the unemployment rate at age 18 for each member of her sample, rather than focusing on a labor market entry point of age 22, or post-college graduation. By beginning her analysis at the point of high school graduation, rather than college graduation, Sanchez can estimate how recessions influence the way young people make decisions – about pursuing careers, post-secondary education, and alternatives like trade school – and how these choices impact future job quality.
Corroborating findings from past research, Sanchez finds that those who experience high unemployment rates at age 18 are less likely to be employed up to age 23, and are more likely to have very low earnings throughout their twenties, relative to their counterparts who come of age during a period of low unemployment. In a new contribution to the evidence on scarring effects, Sanchez’s paper also documents that these same individuals are much more likely to live with their parents throughout their twenties. Consequences for marriage and college attendance, however, are a bit more mixed. High unemployment rates at age 18 have a polarizing effect on the decision to marry: those who would’ve otherwise married early do so even earlier, whereas those who would’ve married later delay. Similarly, high unemployment rates accelerate college enrollment, but do not spur enrollment from students who would not have attended college in more favorable economic conditions.
Source: Autor, David H., and David Dorn. 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market." American Economic Review, 103 (5): 1553–97.
If young people’s employment and earnings are negatively affected by recessions, what consequences might economic conditions have for the quality of jobs they access? The second part of Sanchez’s dissertation examines this question, with a focus on what kinds of skills are required of jobseekers. While past research on scarring effects has investigated outcomes for job quality, Sanchez takes a somewhat different approach in her measurements of job quality. “[In past literature] working in a higher quality job was typically defined as working in a job within the top five highest-paying categories of your college major – again, thinking about those people who graduated college,” she said. Rather than follow this definition, Sanchez draws from research on the “job polarization phenomenon,” which describes why economic downturns tend to result in job losses in middle-skilled occupations. Sanchez employs “task intensity measures” from this literature, designating occupations as primarily routine, manual, or abstract.
Sanchez expected to find that graduating from high school in a time of high unemployment would lead to (a) an increased likelihood of working in a primarily manual or abstract occupation, and (b) a decreased likelihood of working in routine occupations. This hypothesis aligns with the job polarization phenomenon, which suggests that recessionary conditions put the most pressure on availability of middle-skilled, or “routine” occupations, as companies can most easily automate these job functions. Sanchez’s analysis confirmed these hypotheses for routine jobs (decreased likelihood) and abstract jobs (increased likelihood), but it also suggested that graduating under recessionary conditions reduced employment in manual jobs as well – a surprising finding to her, given that high school graduates are relatively low-skilled. “That's a puzzle that I'd like to understand – why do those effects not match the job polarization phenomenon?”
In August 2024, Sanchez joined Baylor University’s Department of Economics as a Clinical Assistant Professor, where she’ll continue her research on the long-term consequences of economic and labor market conditions on young people’s lives. In particular, Sanchez hopes to more thoroughly investigate why recessions seem to lead to higher-quality jobs for high school graduates. She takes her findings as suggestive evidence of an acceleration effect that recessions might produce for high school graduates: facing the labor market consequences of a recession, new graduates rapidly pursue education - in particular, trade certificates - that quickly result in employment in abstract-oriented, high-quality jobs. By comparison, jobseekers who graduate from college in a recession may not have anticipated struggling to find a job, and may have therefore chosen career paths that are less well-suited to recessionary conditions. “Experiencing a recession at the age of 21, you hadn't been in the job search at the age of 18 to really think through, ‘What is the economy going to demand by the time that I graduate?’ And I think that's what's happening. It’s something that I want to study more to fully complete this paper.”
Building evidence on new pathways for economic opportunity
What the quality of bureaucracy can tell us about government effectiveness
Guo Xu, Associate Professor at the Haas School of Business, joined the O-Lab Faculty Network in May 2024. Xu joined Haas in 2017, where he researches the organization and development of personnel in both public and private-sector organizations, working at the intersection of political economy, economic development, and economic history. Much of his recent research has examined aspects of bureaucracies, exploring topics including the interplay of political ideology and bureaucrat performance, how large shocks influence norms in the civil service, and the role of bureaucracies in economic development.
We sat down with Guo and discussed key takeaways from his work on bureaucracies and personnel, how he has leveraged data to inform multiple investigations into economic history, and where he’d like to take his research next.
How did you become interested in economics research? Were there any particular policy problems or phenomena that you witnessed that initially caught your attention?
I'm a trained development economist, so the question I always had was: why are some places rich, and others poor? When you travel around, you notice these huge income differences – what's driving that? During my undergraduate studies, it was all about institutions – there were all these papers on colonial legacy, and the impact stemming from differences in institutions that were set up. I found that really fascinating. At the same time, I wanted to dig deeper, which led me down the rabbit hole of looking at public organizations and their personnel.
Your primary research fields of interest are political economy, economic history, and development economics. Can you describe your specific research interests within these fields, personnel being one of them?
My work is really at the intersection of these fields. The questions I have in mind tend to be development questions. There's no denying that you need some basic set of public goods for a country or society to function. So a natural question is, “How can you improve public service delivery to stimulate economic development and growth?”
The intersection with political economy comes into play when thinking about policy. Sometimes you know what the right policy choices are, and yet they are not implemented for political reasons. And even if policies get implemented, there remains a huge amount of variation in the quality of implementation. You might see the same policy being rolled out within the very same country - and yet there will be differences depending on how effective the local administration is.
The historical part comes in because these studies often rely on natural experiments such as big reforms or large-scale changes in governance that happened in the past. What's very appealing with the study of history is that you can rewind time and study big events after the dust has settled.
Much of your recent research centers on studying bureaucracy, with evidence from the US Federal Government. Can you describe your interest in studying workplaces of the public sector, as compared to the private sector?
A lot of the toolkits that I use are also used in the study of private sector organizations. Instead of studying senior bureaucrats, you study CEOs and managers. There are many similarities, but what I find particularly appealing about the study of public organizations is simply that they're so big and important for the lives of many. In the U.S., for example, the federal government has been the biggest civilian employer for a long time. The public sector is a huge part of the economy.
Could you go over some of the key takeaways or main findings that you've found from looking at the public sector?
There's often this notion that management in the public sector is different from the private sector. But most personnel practices in the private sector also apply in the public sector. For example, some might say that monetary incentives in the public sector don't work, or that paying people more is not necessarily a good thing – you might crowd out highly motivated people. But most of the research seems to suggest that those differences are not really there once you look closely. Many personnel policies that we've seen in the private sector seem to work, to some extent, in the public sector as well.
To give you an example, there’s typically a notion that bureaucracies are very rigid, and can't really motivate people, because you don't have the same type of systems - performance pay or up-or-out promotions - in place as in the private sector. In a study where we looked at the Indian federal bureaucracy, which is particularly rigid, we see that incentives matter quite a bit, and that there are always margins for incentives to kick in. In that particular setting, it turned out it was not about providing monetary incentives, but promotion incentives that allow individuals to reach the most prestigious positions - the “glittering prizes” - before the mandatory retirement age. This implicit retirement incentive governs how people exert effort throughout their career. There are many indirect ways of providing incentives – for example, by transferring employees to more desirable locations or more prestigious postings. Even rotations – you don't have to promote people, you can rotate them across different jobs – can get you quite far.
I expect that one key advantage of doing research on the US government is the availability of data, but in the case of your paper on federal employment segregation, you undertook an extensive digitization project. Can you describe what this process looks like? It seems like you've been able to use that data to study a couple of different questions.
One major part of doing economic history is trying to unearth old data. At that time, we wanted to study the economic costs of the introduction of racial segregation within the federal government under Woodrow Wilson. We were lucky to find these personnel records that go far back in time.
Getting historical data into a workable format is a lot of work – back then, we relied on optical character recognition, and then processed the data in a semi-automated way. We did a lot of manual work too – we had teams of people check the data line-by-line. That was only a couple of years ago - I’m curious to see how AI will help speed up these digitization efforts.
Back then, however, it was a pretty big investment in terms of data collection. It took us more than a year to digitize and clean it all. Luckily, we were also able to use the data to analyze other questions. One question that we got from the study of federal employment segregation was: “Why are there so many black civil servants in the first place?” It seemed very unusual because of the ongoing discrimination at that time. That led us to study the introduction of the Pendleton Act a couple of decades earlier.
Before Pendleton, the President could appoint public servants at will - everyone served “at the pleasure of the President.” The Pendleton Act put in controls by introducing rule-based selection, and employees couldn't be fired at will anymore. We studied that rollout, and looked at whether it actually changed the composition of workers and their performance. The idea was that the introduction of rule-based selection would remove potential biases, increasing the entry of individuals from underrepresented groups. One surprising thing we found was that the Act didn't do much to change the demographic makeup of the civil service. So what we thought might be the primary effect of the Pendleton Act turned out to be much less important.
But it turned out that we still saw large performance gains, which we traced to the fact that the Act was reducing disruption in the organization. In the old system of appointments, with every new presidential election, the President would replace existing civil servants with their own political supporters, creating huge layoff cycles every 4 years. With service protections in place, these political cycles began to diminish and ultimately disappear. That, in turn, made the organization more stable, creating a civil service that more closely resembles the stable careers we associate with public sector work today – you can transmit knowledge more, and there's more on-the-job learning. We found that this increased the performance of the public sector, as measured through reductions in postal delivery errors and increased cost efficiency.
We focused on post offices for the study for several reasons: first, the Pendleton Act was gradually rolled out across post offices, allowing us to compare post offices with newly introduced civil service protections to those without. Second, rich administrative data on post offices was plentiful, allowing us to apply modern statistical methods to estimate the effects of the reform. And of course, the post office was a crucial infrastructure through which communication flowed, connecting far-flung areas of the country.
Initially, we expected the reform to have a large impact on getting individuals from underrepresented backgrounds into the government - but it turned out that the question of whether exams would increase the amount of Black employees in the civil service was not that clear, even in the historical record. It was a case where doing empirical analysis yielded some interesting surprises.
How do you see retrospective research as informing future policy?
It's perhaps a little cliché to say, “Oh, you can always learn from the past” - but that’s true. We can’t run experiments all the time, and many policies and events take a long time to fully unfold, making them difficult to study. In that sense, history offers useful episodes and lessons we can look at from a more distant perspective.
That said, I think economists tend to be wary of extrapolating, unless we fully understand the underlying mechanisms through which certain effects unfold. All empirical research is retrospective and in a sense historical. Historical work is not about the age of the data, but taking a deep dive - a careful study of the context and institutional setting. This also helps to get a sense of how comparable different events in time are and the extent one can extrapolate.
Personally, I get a lot out of studying history, because it's a great way to learn – when I moved to the US, I didn't know much about US administrations. The good thing about this job is that you can choose what you study, and that's very enjoyable.
What kinds of new questions have your research projects raised that you see as important areas for future projects?
The nice thing about studying public organizations is that you can study different functions of the government. One dimension I'm hoping to do more work on is the study of environmental regulation – a big challenge that is tightly linked to implementation. There are federal environmental programs that are implemented in different places, with a lot of heterogeneity. You have that in the US, and there’s a similar structure in India, so there's a lot to be looked at!
One land, many promises: the unequal consequences of childhood location
Research increasingly shows that opportunities for economic mobility are not equally distributed across different geographical areas; rather, in many countries, opportunities are becoming increasingly concentrated in a small number of regions. And a growing body of evidence is demonstrating the powerful impact that living in disadvantaged neighborhoods can have on one’s long-term, socioeconomic well-being. But to what degree do these “place effects” differ depending on other aspects of one’s upbringing, such as family income, or immigrant status? And how should policymakers consider demographic-specific impacts when designing policies aimed at promoting economic mobility?
Hadar Avivi, a recent PhD graduate of the Berkeley Economics Department, has spent the last few years investigating these granular aspects of place effects on children’s long-run economic outcomes, with support from O-Lab’s Place-Based Policy Initiative. As part of her job market paper, “One Land, Many Promises,” Avivi examines how place has impacted high-income and low-income families differently, and how it has affected immigrant and non-immigrant families differently. Her dataset includes over 1 million Soviet Jews who emigrated to Israel between 1989 and 2000, about 20% of whom were children under the age of 19 when they moved, a sample size large enough to allow Avivi to conduct a robust comparison of how neighborhood effects in childhood differ for different groups of children.
In order to carry out her analysis, Avivi leveraged comprehensive administrative data on the entirety of her sample - children and parents - collected by different Israeli ministries and consisting of tax records, education records, and national census data. Unlike in the United States, where statutes restrict data sharing across government departments, the Israeli Bureau of Statistics serves as a centralized body that is legally allowed to merge data from different ministries, contingent upon approval of a detailed proposal. “Very few countries have the possibility to merge different data sources like education records, tax records,” noted Avivi. This detailed dataset allowed her to incorporate demographic variables, like gender and country of origin, with information about date of immigration, place of residence, educational attainment, and income.
Strikingly, Avivi found substantial variation in the consequences of childhood location of residence across different cities and for different groups - children from high-income and low-income families and children who were born in Israel and those who emigrated during childhood. For high-income families, location effects are strongly and positively correlated: places with higher effects for immigrants are associated with higher effects for Israeli-born children. Among lower-income families, though, there was far more variation in which locations benefited immigrant children (and by how much) and which places benefited Israeli-born children. “The main, surprising finding is that… the correlation between the location effects for low-income immigrants versus Israeli-born children is practically zero,” said Avivi. This suggests that there is no single ladder of location quality, and the benefits from a childhood location of residence vary substantially across families.
For example:
Among immigrant families in the 25th percentile of the income distribution, relocating to the average Israeli city one year earlier increases their children's earnings at age 28 by $90 USD. By comparison, an additional year after relocating to the average Israeli city increases the income rank of children born in Israel by only $77 USD compared to spending one more year in Jerusalem.
Avivi’s paper also finds that location effects are more consequential for immigrant children at the higher end of the income distribution as well; overall, there is less variation in the way location impacts outcomes for Israeli-born children than for immigrant children.
For children from low-income families, moving at birth to a city with one standard deviation higher location effects increases the income of Israeli-born children by $1,370 at age 28, while increasing the income of immigrant children by $1,602 a year.
In order to better understand the disparate effects of location on immigrants and Israeli-born children of low- and high-income backgrounds, Avivi also investigated the relationships between location effects and city-level characteristics. On average, she found that larger cities are associated with more substantive long-run effects on children’s income, especially for immigrant families. And, for immigrant children, cities with very large or very small shares of immigrants were less likely to increase adult incomes. Cities with higher crime rates and welfare expenditures were more likely to be detrimental to children born in Israel, with more muted effects for low-income immigrants.
Finally, Avivi addresses the question of how policymakers could make use of these findings through a theoretical “moving to opportunity” policy, in which the government incentivizes low-income families to move to high-opportunity housing and neighborhoods. “While the first-best policy is personalized,” she says, “ethical and practical considerations [rule out] individually-targeted strategies because policymakers are not allowed to discriminate based on ethnic identity.” However, a “second-best” policy that simply ranks locations based on pooled weighted average estimates of outcomes can lead to worse outcomes for immigrant children (as they are a minority group, and therefore, the weighted average city effect puts, by construction, lower weight on their gains). To minimize the gap between the “first best” and “second best” policies, then, Avivi develops a novel “minimax” model that minimizes the potential harm to any group that might arise due to the inability to provide a targeted policy.
By expanding our understanding of how the long-term effects of location vary across groups, Avivi’s paper contributes to a growing body of evidence on the role of place in determining economic outcomes. The project, which received the 2024 O-Lab and Stone Center Prize for Excellence in Research on Economic Opportunity, also offers new insights into how policymakers can develop sophisticated and equitable policies in settings where the personalized first-best policy is infeasible. In September 2025, Avivi will continue her work as an Assistant Professor in the Department of Economics at University College London, after spending one year as a postdoctoral fellow in the Industrial Relations Section at Princeton University.
Racial discrimination in the New Deal’s Agricultural Adjustment Act
In the early years of the Great Depression, US farmers faced an emergency, as the prices for their goods plummeted due to excess supply. In order to restore farmers’ purchasing power and stabilize prices, President Roosevelt passed the Agricultural Adjustment Act (AAA) of 1933, which, among other things, offered incentives to farmers who did not plant basic crops.
While the AAA was successful in achieving some of its goals, its design and implementation heavily favored white farmers and landowners, while preventing many Black farmers and sharecroppers from collecting benefits they were owed. This has led historians to suggest that discrimination within the program helped drive many Black farmers out of agriculture entirely, although there have been no empirical analyses documenting the impact of the program on Black families’ economic outcomes.
Sheah Deilami-Nugent, a third year student in Berkeley’s Department of Agricultural and Resource Economics, is aiming to fill this gap in the literature by investigating how the design of the AAA impacted occupational outcomes and decisions to relocate among Black farmers. Deilami’s interest in the topic was motivated in part by a podcast episode, in which a Black farmer described his struggles in accessing credit. As she dug deeper into this history, Deilami found the AAA repeatedly cited as one of the major sources of discrimination that led to land losses for Black farmers.
The Agricultural Adjustment Act was passed in 1933 to reduce the supply of key crops – corn, cotton, milk, peanuts, rice, tobacco, and wheat – by providing direct payments to farmers who agreed to limit their production of these crops. And while there were no explicitly discriminatory elements in the language of the act itself, its implementation opened two critical doors for discrimination against Black farmers.
First, AAA payments were processed through an existing structure of county-level agricultural extension offices. Extension agents were responsible for both educating farmers on how to claim their benefits and appointing the members of the county committees – consisting of primarily wealthy, white landowners – which processed appeals and complaints from farmers. White extension agents notoriously did not work with Black farmers and sharecroppers – and, while some counties had Black extension agents, the role of Black agents was focused almost entirely on education of Black farmers, and they generally did not have the same power as white agents to appoint committee members. So Black farmers were less likely to be informed about the act and their eligibility, and were less likely to receive a fair hearing when complaints arose.
The process of distributing payments also created incentives for discrimination. Specifically, payments were made exclusively to landowners (often white), who in many cases did not pass on any benefits to sharecroppers and tenant farmers (often Black). Because the complaint process was effectively closed to Black farmers, there was little recourse for Black tenant farmers and sharecroppers to claim the benefits that they were owed.
Deilami hypothesizes that differences in the racial makeup of county-level extension offices may have led to different rates of farmers successfully receiving AAA payments. Specifically, she is exploring whether Black farmers with access to Black extension agents in their counties might have known more about the policy, and had more recourse to receive funds. To investigate this, her project is leveraging agricultural and individual-level census data, AAA spending data, and data on county extension agents and committees. Deilami is still in the process of gathering and analyzing this data – an undertaking which led her to the National Archives in Maryland earlier this year. “I have extension agent data from Louisiana, but I need it for the rest of the Southeast. I might have to go to a few more places to get that data — but it’s amazing to work with archival data, and hold documents that only a few people have held. As a graduate student, you have the privilege to work on these kinds of projects that take a longer amount of time.” Deilami’s goal is to use data on the demographics of extension agents as a source of variation that may be correlated with occupational exit and migration, in addition to long-run outcomes like wealth and income.
While previous work has examined the general effects of the AAA, Deilami’s focus on its racial impacts will contribute important new evidence on the drivers of the Great Migration and how local institutions like the AAA’s county committees can impact long-term outcomes for the people they govern. Deilami also sees her project as well-positioned to catalog the scale of damages done by discriminatory policies, as she notes “This land back in the 1900s that could’ve generated a lot of intergenerational wealth…[that opportunity] is just gone now…the damage has been done.” By contributing to the growing evidence base on the long-term harms of federal discrimination, Deilami’s project provides essential perspective on the importance of equity-oriented policy and implementation.
O-Lab Cash Assistance Webinar featured in Spotlight on Poverty
O-Lab’s May 23 webinar on cash assistance for children featured compelling remarks from Senator Michael Bennet, insightful conversations between policy practitioners, and exciting new evidence from researchers on how income support can support socioeconomic well-being for families with children. Check out this feature in Spotlight on Poverty to learn more.
Gabe Zucman advocates for a global minimum wealth tax in the New York Times
In response to wealth taxes on the national level, billionaires often respond to relocating to low-tax countries. In this New York Times opinion piece, Gabe Zucman makes the case for a global minimum tax to address wealth inequality.
Cailin Slattery on cost drivers of US transportation infrastructure
Assistant Professor Cailin Slattery joined the faculty of Haas School of Business in the Fall of 2023. Her work focuses on the relationship between firms and local governments, and on the role state and local incentives play in driving local economic development.
Slattery is also one of the leaders of O-Lab’s new initiative on US Transportation & Infrastructure Development, which is building a new body of research on what kinds of policies and strategies governments can deploy in efficiently and equitably bringing large-scale infrastructure and transportation projects to fruition. As part of this initiative, Slattery is leading new work on the drivers of transportation costs in the United States. In particular, she and her co-authors are examining how much of the cost of road maintenance and resurfacing costs are influenced by the competence of the managing agency staff. The research builds on work she conducted with co-authors Zach Liscow and Will Nober to survey procurement practices across all 50 states to understand what those working on procurement and maintenance on the ground think about what’s driving the costs, as well as what the actual variation looks like across states.
We recently sat down with Cailin to learn more about her research and the potential lessons for policymakers that could result from her ongoing work into the drivers of high costs in US road maintenance and resurfacing projects.
Cailin, your work focuses on identifying what’s driving up costs in US road projects - before we get into it, it would be great if you could provide some background on what we know more generally about the costs associated with infrastructure development in the United States. How does the United States compare to other high-income countries?
You're starting off with a hard question – we don't know a ton. There's really good research on transit costs across countries – subways and light rails – but not on resurfacing and road building, which my paper looks at. The US does look like an outlier, in terms of how much it costs to build a mile of subway in New York, versus in Paris or in Rome.
We focus on roads because it’s the bulk of the work that state DOTs are doing – every state is maintaining their roads and their bridges. But not many states at this point are building new roads, and not all states have big transit projects. We wanted to study a type of project that was common across states, so we took the bread and butter, resurfacing, as our case study.
Do we know anything about how infrastructure costs are related to different regulations at the state or federal levels, or to different political processes?
If you look at highway infrastructure, my co-author Zachary Liscow has a nice paper on the cost of road building over time in the United States. It ends in 1980 or 1990, because that's when we stopped building a lot of new roads, but the authors find a significant increase in cost per mile between the 1950s and the end of the century, even after controlling for all the variables they can – things like weather and geography. They also show that increases in labor costs don’t explain the bulk of the road building cost increase. Their main interpretation of this is that citizen voice is making it more expensive to build roads; there were a series of regulations in the 1970s that made it easier for people to get involved in the government process. That’s not necessarily a bad thing – neighborhoods can organize to prevent a road being built near them, or require more environmental review - but that greatly slows the process, which indirectly makes it more expensive.
Your most recent paper - Procurement and Infrastructure Costs - focused on the role of the procurement process in driving costs. You built a large database of state-level procurement rules and interviewed those working on projects on the ground. Can you start by giving a high-level overview on how the procurement process for US infrastructure projects work? And how this could increase costs?
The basic way that it works is that each state DOT (who manage the majority of infrastructure projects)writes up a plan about which projects they want to complete in the next couple of years. They submit that plan, with a budget, to the federal government as an application for federal funds for infrastructure. They'll get some federal funds based on that, which are earmarked to those projects in the proposal. So a lot of money is coming from the federal government going to the state level to build and maintain infrastructure, and then states have their own sources that they complement this funding with.
At the next stage - the procurement stage - the DOT says, “We have this money to resurface this part of the highway – we're going to put out a call for bidders.” For the most part, the government is not the one building and maintaining infrastructure – the private sector is.
The state’s question is, “How do we find the right contractor for the job, at the lowest cost to the government?” So they'll run an auction, where all the prospective contractors look at the project plans and submit bids privately. The government then looks at all the bids; generally, they pick the lowest bid, and that's who will build the project. Along the way though, there are many inputs that can drive costs.
For example, before a project is even posted, engineers at the DOT estimate how much it's going to cost, and give very detailed plans to the contractor – because if the contractor doesn't know exactly what you want them to do, how are they supposed to bid on it? And the more detailed the plans, the better – because as you provide more details to the contractor, they have a better idea of exactly how much it's going to cost them, and they can make a more informed bid on the project. If the contractor has an incorrect expectation of what the project is going to look like, they break ground and things are not as they seemed; the contract has to be renegotiated, which causes delays and increases the project’s cost. So putting plans together is a big part of the job of the DOT, and is potentially where you could see costs increase.
Another decision that DOTs make is how to advertise the projects. How do you get people to bid on your project? Are you actually having all the contractors in the market that could potentially do the project?
Once the contractor wins the contract, they actually have to complete the work. Now, the DOT has to oversee and manage the private contractor on the project. Here, communication is really important – for instance, being quick to respond to changes that might need to be made to the plans or to the contract. Anything to avoid delays is potentially something that can keep costs down. Anything that has to do with permitting and traffic rerouting is where the DOT is going to get involved. So it's not simply, “we have a procurement auction and someone wins.” When someone wins, there's months or years of repeated interaction as the project is actually constructed.
What were some of your biggest findings from the survey and the review of state-level rules?
The motivation for the data collection exercise was that we couldn't say, in the aggregate, how much differences in procurement processes— how states procure projects — explain differences in costs. At the end of the survey, we had around 80 variables that we correlated with costs, – and very few of them showed any significant correlation. For instance, how do states differ in deciding how to throw out a bid? It doesn't seem like [they do] at all. The way I think about it now is that there are a lot of rules in the United States, and a lot of standardization across states.
But the [variables] most correlated with costs [from survey and administrative data] were all about the capacity of the DOT, or the competition for the contract. So I think of it as inputs into the procurement process that matter – the capacity of the DOT, as measured by employment, employee quality, and things that are correlated with those two things, like plan specification.
Another factor seems to be competition in the market – how many contractors there are actually bidding on projects.
What I liked about the survey was that it featured free responses. So you can ask the DOT official, or ask the contractor, “What do you think drives costs?” “What do you think of your DOT colleagues?” “What do you think of the DOT procurement process?”
They're very descriptive about these issues. There are many state DOT engineers that are super knowledgeable on all of the rules. Because there’s so many rules, you have to be there for a while to understand all the ins and outs, and what to do in any given situation. If you're new, then you struggle with that. If a bunch of people retire, and we can't fill those spots, then engineers face this knowledge gap and have to learn from scratch.
Does California’s unique position as a state, and specific infrastructure procurement processes, present any challenges for external validity?
I think the capacity story is a universal one. We know from the survey that a lot of states are dealing with this. I talked to some industry groups when I was in New York, when we first started this project and, and they were saying the same things, even coming from outside of the DOT. So I don't think it's a California specific issue – it's just that California is kind enough to share detailed data with me.
Your new paper goes beyond correlations and focuses on how much the capacity of DOT staff impacts costs. It seems difficult to isolate the effect of specific staff members on the overall costs of these projects. Can you explain a bit about how you’re doing this?
Our plan is to do a fixed effects analysis, which is pretty standard in the literature. You see it applied to studies of teachers, judges – you know, how do you measure a teacher’s value added? Well, you look at all of the students a teacher has, and then students switch teachers, and you can measure what the teacher effect is. So the idea is to do that with the engineers who manage the projects.
This type of fixed effects analysis has been done in other procurement settings, mostly in developing countries, to try and understand how much a bureaucrat matters. What is interesting about our setting is that we have two levels of bureaucrat. We know who the person is that is managing the project – and they’re supposed to be managing the project from the inception of the project to when it is completed. They manage putting together the plans, doing the environmental review, dealing with permits – all this stuff that happens outside of the actual building of the road. But for each project, we also know the engineer on the ground, who’s managing the road building. Having these two layers in our setting will help us understand what matters more for driving costs.
Are there particular methodological challenges that come with studying infrastructure projects in this way? If so, how has your paper dealt with these challenges?
What’s difficult in our setting, which we're still struggling with, is that in any project, things can go wrong that are unobservable to the researcher – like, maybe it just rained for three weeks straight, and that's why it costs so much – not just because the engineer was doing a bad job managing the project. Examining many projects that the same engineer has worked on is an attempt to control for that. But if that's happening all of the time, it adds a ton of noise to the data, and that makes it a lot harder to measure the true value of the engineer.
The descriptions we have of the projects are also pretty short. There's a “project type” category, but I wish we knew more about exactly what each project entailed to be able to, to the best of our knowledge, say we're comparing apples to apples here. So that's something we're still working on.
Addressing this involves a lot of going back and learning more about how Caltrans categorizes projects – going into project plans and seeing what they look like, to see how much variation there is across types of projects.
The second part of your project investigates the impact of a program Caltrans has developed to increase staff capacity in local project management. Can you talk about the motivation behind this program, and describe some potential effects of a more or less centralized management process for these types of projects?
Caltrans has a program, the Local Assistance Program, which aims to help localities who have their own Departments of Public Works with some of their projects. Caltrans is interested in knowing how much that helps. One aspect they're interested in studying is having a Caltrans engineer oversee a project, instead of a local engineer. It's not clear in theory what effect that would have.
You might think the local engineer has local expertise, and maybe has a better idea of what the exact specifications need to be – and you'd think that the state isn't going to add much value there. On the other hand, if you think the state engineers have more experience just in general, maybe they've seen a particular type of project before that a local engineer hasn’t, it could help reduce costs. It's an interesting empirical question they want to study.
You can find two very similar looking projects, but one is managed by a state engineer, and one stays local, and compare them. We do that for many projects, and then we could dig into the mechanisms of why getting state support might help or or hurt. I could come up with good reasons in both directions, so I think that that's what makes it interesting. Maybe what we'll find is that sometimes it really works, and sometimes it really doesn't. I think it's a more general question in economics – how centralized do we want to do things? How centralized should we procure, not even roads, but say, pharmaceuticals? If the government was going to procure pharmaceuticals, you might think, the bigger the buyer the better, because they have a lot of buying power and can achieve a lower cost. But with roads, the amount of knowledge it takes, and the fact that there's supervision on the ground – all of these things make it much more complicated.
If procurement rules don’t seem to have a substantive effect on the cost and timeliness of infrastructure projects, what other policy tools might states use to address these issues?
I think that there are creative policies to be had on the labor market side of how to recruit high quality employees. It used to be that being an engineer for the state was a prestigious and well-paying position, but now the private sector dominates, in terms of wages, and state benefits are not what they used to be. That's more of a structural change related to government jobs versus the private sector.
The other thing that we haven't talked about as much is the contractor's market. Contractors complain there's not enough firms for competition on the subcontracting market. DOTs complain there's not enough competition on the primary contracting market. I think in many cases, firms exist that do this type of work, but perhaps don't know how to apply for government contracts and go through the process. In some situations, the forms that firms have to fill out are hundreds of pages long. One avenue for policy might focus on ways that the state can grow the market of contractors that take on public sector work. There are contractors out there that don't compete for government contracts, and we want to understand why. That’s a place where there's potential for more research.
Faculty Director Hilary Hoynes inducted into National Academy of Sciences
Congratulations to O-Lab Faculty Director Hilary Hoynes on her induction to the National Academy of Sciences! Hoynes was recognized for her important work advancing the evidence base around tax and transfer programs in the United States – and how these programs can generate long-term health and human capital benefits for recipient children.
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The Case for Place-Based Policies
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The minimum wage for California’s fast food workers increased to $20/hour, with many critics predicting that firms will respond with mass layoffs and price increases. In this CalMatters article, Michael Reich unpacks the evidence against these predictions and explains how monopsony power has allowed firms to set artificially low wages.
Adam Leive on the effects of work requirements in SNAP
Over the last several years, there has been renewed attention to how the United States promotes economic security for low-income families. During the height of the COVID-19 pandemic, expansions to the Child Tax Credit - combined with direct cash assistance and increased unemployment insurance - demonstrated the powerful capacity of the government to deliver robust support that reduced poverty and protected families’ ability to access food, housing, and other basic needs. The immediate and dramatic impact of those policies have lent additional momentum to efforts to strengthen the safety net by increasing our investments in nutritional support and tax transfers.
While the evidence continues to grow on both the short and long-term benefits of programs like the CTC, the Earned Income Tax Credit (EITC), and the Supplemental Nutrition Assistance Program (SNAP), there is ongoing debate around who is most deserving of accessing the safety net, and how strictly aid should be tied to work.
Adam Leive, Assistant Professor at the Goldman School of Public Policy, has produced valuable evidence to inform the debate around the impact of work requirements in safety net programs. His 2023 paper, “Employed in a SNAP? The Impact of Work Requirements on Program Participation and Labor Supply” evaluated how the introduction of work requirements in Virginia’s SNAP program affected overall recipients’ employment and earnings, as well as how it affected their participation in the program itself.
The team found that the introduction of work requirements for adults 50 and under led to no increases in employment or earnings, but did lead large numbers of people to leave the SNAP program entirely. Leive and his research team also found that homeless populations were disproportionately impacted by the change.
In this O-Lab Q&A, we sat down with Adam Leive, to discuss his findings, the background behind SNAP work requirement policies, his experience collaborating with Virginia’s Department of Social Services, and his perspectives on how research can inform policy.
Source: Social Security Administration
Can you start by talking a bit about the motivation for your study? The question of if (and how much) to make public benefits conditional on work has been around for a long time. Can you give a little background and talk about how this debate has evolved?
To start with the pros and cons, proponents [of work requirements] have been really concerned that providing government assistance ends up discouraging work. So their argument for policies like work requirements is that by incentivizing work, people develop a stronger attachment to the labor market, and then eventually people will earn enough through their job to get by without government assistance.
On the other hand, critics see work requirements as a policy that prevents people who are vulnerable from receiving assistance, ultimately weakening the safety net. So opponents argue that if the reason that people aren't working is something other than the work requirements, then this policy is just going to cut people's benefits without improving their labor market outcomes.
In the US, the safety net has been heavily tied to employment, both incentivizing employment as well as tying benefits to employment. The biggest expansion in terms of work requirements was in 1996 under president Clinton. The official name of that bill was the "Personal Responsibility and Work Opportunity Act," which is better known as welfare reform, and the major change that that bill introduced in terms of work requirements was to make sure that people who are in SNAP who are childless adults and didn't have dependents or disabilities were working for some amount of time in order to receive benefits for more than just a few months.
I think the other thing that's worth mentioning with the '96 reform is that [it] received bipartisan support. Today, work requirements are much more divided, with Democrats largely being against, and Republicans being in favor. In terms of how the debate has evolved, I think there's been a view that some of these conditions can be harmful, and are now something that are not as widely supported the way they were 30 years ago.
What kind of evidence do we have on work requirements, and what new contributions does your study make to this literature?
There's actually been relatively little research on work requirements, when you think about how politically fraught they are. One challenge with looking at the 1996 reform is that so much else changed along with [the work requirements] that it was difficult to tease apart the work requirements from everything else that was going on.
Source: Grey, Leive et al. 2023
There were studies from the Welfare to Work experiments in the 70s and 80s that were really good in that they were randomized controlled trials (RCTs), but they also bundled work requirements with other types of labor market policies, and for the same reasons, also struggled to distinguish the effects of work requirements from some of these other policies.
More recently, and around the same time we wrote our study, there were other studies on SNAP that used different sources of data that also focused on able-bodied adults without dependents, or ABAWDs. These studies tended to find mixed results on participation and labor market outcomes.
Perhaps the best evidence on work requirements comes from Arkansas's experience with Medicaid. One state implemented work requirements in Medicaid for a roughly 9 month period in 2018, and the researchers studied what happened in Arkansas and surrounding states using survey data — they found that the Medicaid work requirements, which were much more onerous than SNAP work requirements, led to large drops in program participation. People also didn't realize what was required to meet them — many people were already meeting them, but just weren't reporting that they were. That experience was a case study of bureaucratic obstacles, as well as the work requirements themselves.
Source: Grey, Leive et al. 2023
Our study examined Virginia - one state - but used detailed administrative data to try to provide a new, focused look on the topic. We took advantage of the fact that work requirements had been suspended during the Great Recession. Like many states, Virginia suspended them and then reintroduced them well after the recovery, in October of 2013. So, we studied what happened before and after work requirements resumed. Importantly, we could study outcomes at the individual level, using administrative records on people's participation in SNAP and their earnings in Virginia. We linked – through a partnership with the Virginia government – these detailed earnings records with SNAP participation records, and that allowed us to see what happened in a world without work requirements and in a world with work requirements.
At a high level, what we did is we took advantage of the fact that people under age 50, if they don't have dependents or disabilities, have to meet work requirements, whereas those over age fifty don't. So we compared outcomes for those just below age 50 with those just above age 50. The only difference between these people should be their age when work requirements came back. What we then found was that people just below age 50 had much larger reductions in SNAP participation. We looked at people who were on SNAP after the work requirements resumed, and much higher rates of that group left SNAP compared to those just above age 50 who didn't have to meet work requirements. And yet those large participation declines did not translate into any improvements in earnings or employment on average. So if a major goal of the work requirements is to improve labor market outcomes – we didn't see that. We also found that people who are homeless experience disproportionately large reductions in SNAP participation, so they were especially impacted by the policy.
Effect of work requirements on SNAP retention, 18 months after work requirements
Source: Grey, Leive et al. 2023
This is such a political issue. How do you think about the prospect for your findings to meaningfully inform policy in Virginia or elsewhere? Are there enough legislators who are motivated by research like this?
I wish I knew what politicians were thinking, or motivated by, or how they make decisions. But I do think there's an important role for research in terms of generating evidence that can inform these debates. There are often so many studies out there on any particular topic, but I think a very small number of studies have the potential to influence policy. And the studies that do, I think, are really high quality, often RCTs – if it's not an RCT, it has really compelling evidence. So my focus is generating that kind of research, where there may still be limitations, but it's much more with things like external validity, rather than "I don't believe the study itself," or the internal validity of the research. I see value in trying to produce those types of studies that can have an impact, and the way that I try to go about doing so is by testing some main theory or some motivation, for these work requirements. But often what the research does is rule out some explanations, rather than to “rule in” a particular explanation – that's what the social sciences are well equipped to do.
So what we've done is say, "Okay, work requirements don't lead to large improvements in employment or earnings. We can feel pretty confident statistically about this, for this population." If we saw a big improvement in employment and a decline in participation, that would arguably be seen as a good thing, or at least it would be consistent with the intent of the policy. So our results indirectly suggest that maybe there are other barriers people are facing. For example, people don't have a stable job that offers hours that don't fluctuate. Other people might have challenges getting transportation to a job. What our study would point to is that looking at those barriers may be important for future work.
Another motivation that is sometimes discussed is that imposing some kind of hurdle can be efficient from an economic perspective, because only the people who need benefits the most will go through the hassles of overcoming those hurdles. So if the government has a limited amount of resources to provide as part of safety net benefits, then these hurdles can perhaps better target assistance by getting people to self-select into the policy. That has been more of a theoretical point that some economists began making in research decades ago. And our results that homeless people are disproportionately screened out, I would say, is strong evidence against that theory. If the most vulnerable people face a higher cost to overcoming these barriers, even if they have higher benefits from the safety net, they might be screened out.
Your paper finds that the reestablishment of work requirements in Virginia disproportionately caused homeless individuals to leave the program. What role does SNAP play in supporting homeless people, and in what direction should safety net debates shift in light of the homelessness crisis?
Homeless people are some of the most vulnerable people in society, and SNAP and other safety net programs can provide lifelines for them. So I think efforts to make it easier for people who are eligible to receive and maintain their benefits are extremely important. I'm not an expert in this particular aspect of SNAP, but I think that making sure that information is communicated via different modalities, like alternatives to mailing addresses, can be important, especially when benefits require a lot of reporting from the client side. In terms of the mechanics of benefit calculations, there's a shelter deduction for people without stable housing that can allow them to increase their benefits, and that's important for people who have some income. People who don't have any income are getting the max benefit, but many homeless people are working, so shelter deductions can help increase those benefits.
Taking a step back on this issue of homelessness, safety net policies that provide assistance for healthcare, for utilities, for rent can be important for preventing homelessness, but those deal with the problems caused by a lack of affordable housing. So ultimately, I really think the issue is much more about increasing the supply of affordable housing as the crucial policy lever. SNAP and the safety net are very important, but we should also be focused on making upstream investments in increasing housing supply.
Virginia State Capitol building. Source: faungg via Flickr.
How would you describe your experience working with the Virginia state government? How did the collaboration arise? Has the partnership offered any insights into working with governments that might be useful for researchers conducting similar work?
Overall, it's been an absolutely fantastic experience. Before coming to Berkeley, I spent 6 years at The University of Virginia’s Public Policy School and I wanted to produce research on topics relevant to the local community and the state. I'm very lucky to have great partners in Virginia – and as someone who's in a policy school, it's kind of a dream come true. You get to work with government collaborators to study a question that they're interested in answering using tools that you have specialized training in. I was very fortunate that my counterparts in Virginia were people who understood research and its value, and were interested in results regardless of what the outcome was, and who also had the training to be able to articulate why certain data was necessary. There were a lot of “asks” that had to be made in terms of trying to get more information from various government officials, or understand certain parts of a program. To have partners who have themselves been involved in research, many of whom have PhDs, is really helpful because they can advocate for the importance of research, as there can understandably be skepticism about an outside scholar coming in to study a program. I think what you learn is the importance of finding someone who has the decision-making power to say yes, in terms of helping you get the access you need to do the study, and who's genuinely interested in the answer, regardless of what it is.
Do you have any plans to examine how work requirements affect people in the long term? If not, what avenues could you expect other researchers to take?
I'm continuing to collaborate with Virginia's Department of Social Services to help them design ways that the program can best support the SNAP population, and simultaneously improve economic outcomes for recipients. Some outcomes that I think are important to look at in the context of work requirements, that we don't know much about, are food insecurity and health, the most fundamental aspect of what SNAP is supposed to be about. Taking a broader lens, on the benefits side, I think it's possible that some of the effects on health might be very important for long-term outcomes.
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Evaluating "Imperfectas pero Buenas," Walmart Chile's new initiative combatting food waste
Within the United States and other high income countries, most food waste happens at the household level. In low- and middle-income countries, however, the bulk of food waste occurs at the supplier level, before it ever reaches the consumer. As global food waste accounts for 12% of overall greenhouse gas emissions, the development of scalable strategies for reducing waste along the supply chain is crucial. Can new approaches to food waste simultaneously promote better health outcomes for people with limited resources?
This dual question is the focus of new research led by Daniela Paz Cruzat through the Initiative on Equity in Energy and Environmental Economics, which supports PhD students and undergraduate research apprentices in collaboratively investigating critical questions of energy, environment, and economic opportunity. With the help of two undergraduate mentees, Vanessa Perez and Jessica Yu, Paz Cruzat is working to evaluate “Imperfectas pero Buenas,” or “Imperfect but Good,” an initiative from Walmart Chile aimed at improving customers’ access to nutritional foods while also reducing food waste. By selling produce that does not meet Walmart’s standard shape and color requirements at lower costs, the initiative intends to reduce food waste from agricultural suppliers, who would otherwise waste the 5-15% of their produce considered noncompliant. Selling produce at a reduced price might also reduce barriers to adequate nutrition for low-income consumers, supporting improved health outcomes.
Figure 1: Section 3 of Daniela’s questionnaire for customers asks about environmentalism, while Section 4 features questions eliciting preferences around grocery shopping.
“Imperfect but Good” has rolled out gradually in Latin America: the program was piloted in 7 Chilean grocery stores in September 2022, and Walmart has since expressed intentions to scale the program to additional stores and countries. To empirically measure the program’s effects, Daniela’s will leverage this variation to compare the outcomes of similar consumers and firms who were exposed to the initiative at different points in time. Merging scanner-level product data provided by Walmart Chile (which documents details including product name, brand, price, and discounts) with data on customers enrolled in the Walmart loyalty program will allow Paz and the research team to link purchases to individual characteristics and control for variables including age and gender. Daniela will then supplement data from Walmart with a survey of 2,000 loyalty program members to elucidate the effects of nutritional knowledge, environmentalism, and individual preferences on consumption and program success. At the firm level, Daniela is also working with Walmart to access data from agricultural suppliers that spans firm-level characteristics and transactions, to better understand how the initiative has affected their growth and experience.
Working with a large, private entity like Walmart has presented Daniela with challenges, but also tremendous opportunities for impact at scale. As Walmart had no plans for an impact evaluation of “Imperfect but Good,” Daniela saw an opportunity to offer help – but had to secure buy-in from Walmart leadership. “We started following people on LinkedIn…trying to get our first contact to make other contacts. After a long time, there was one person who clicked with our idea, [and] that was the person who manages the data.” Despite facing initial hurdles related to data access, Daniela is optimistic about the potential impact of the project, due to Walmart’s position as a large, multinational firm, with a growing presence in Latin America. “The size of Walmart…the environmental impact they could have is huge.
Through the Initiative on Equity in Energy and Environmental Economics, Daniela is also guiding her undergraduate mentees as they explore options for graduate school, internships, and careers in energy and environmental research and policy. For Jessica Yu, a senior beginning the graduate school application process, Daniela shared insights on important factors like grades, recommendation letters and exams required. Senior Vanessa Perez, who supported the project by collecting novel data about produce suppliers and translating materials from Spanish to English, entered the mentorship program aspiring to work on sustainability in the private sector, making Daniela’s project a natural fit. While Daniela’s mentees have developed their technical skillsets throughout the project, Paz emphasized the importance of a mentor-mentee relationship that caters to the bigger picture. “I feel that undergraduates are a bit alone…with decisions, where to work, how to ask people for a meeting, what to ask in the meeting, how to write an email…For one of our mentees, it was her first experience coding with real data. That’s great, but it’s also the people you know, the contacts, the networking…that [support] can be extremely valuable.”
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