Publications

This paper studies the effects of automation in a task-based economy in which some jobs pay workers rents—wages above their outside option. We show that automation targets high-rent tasks, dissipating rents, amplifying wage losses, and reducing within-group wage dispersion in exposed groups. This form of rent dissipation is inefficient and offsets the productivity gains from automation. Using US data from 1980 to 2016, we find evidence of sizable rent dissipation and reduced within-group wage dispersion due to automation. Automation accounts for 52% of the increase in between-group inequality since 1980, with rent dissipation explaining one-fifth of this total. Our estimates imply that inefficient rent dissipation has offset 60–90% of the productivity gains from automation over this period.

We investigate the efficiency of a market relative to a non-market institution—an auction relative to a quota—as allocation mechanisms in the presence of frictions. We use data from water markets in southeastern Spain and explore a specific change in the institutions to allocate water. On the one hand, frictions arose because poor farmers were liquidity constrained. On the other hand, farmers who were part of the wealthy elite were not liquidity constrained. We estimate a structural dynamic demand model by taking advantage of the fact that water demand for both types of farmers is determined by the technological constraint imposed by the crop’s production function. This approach allows us to differentiate liquidity constraints from unobserved heterogeneity. We show that the institutional change from an auction to a quota increased total efficiency for the farmers considered. Welfare increased by 23.4 real pesetas per farmer per tree, a 6 % increase in total production relative to the market.

During adolescence, peer interactions become increasingly central to children’s development, whereas the direct influence of parents wanes. Nevertheless, parents can continue to exert leverage by shaping their children’s peer groups. We construct and estimate a model of parenting with peer and neighborhood effects where parents intervene in peer formation and show that the model captures empirical patterns of skill accumulation, parenting style, and peer characteristics among US high school students. We find that interventions that move children to better neighborhoods lose impact when they are scaled up, because parents’ equilibrium responses push against successful integration with the new peer group.

This paper estimates the consumer surplus from using alternative payment methods. We use evidence from Uber rides in Mexico, where riders have the option to use cash or cards to pay for rides. We design and conduct three large-scale field experiments, which involved approximately 400,000 riders. We also build a structural model which, disciplined by our new experimental data, allows us to estimate the loss of private benefits for riders when a ban on cash payments is implemented. We find that Uber riders who use cash as means of payment either sometimes or exclusively suffer an average loss of approximately 40–50% of their total trip expenditures paid in cash before the ban. The magnitude of these estimates reflects the intensity with which cash is used in the application, the shape of the demand curve for Uber rides, and the imperfect substitutability across means of payments. Welfare losses fall mostly on the least-advantaged households, who rely more heavily on the cash payment option.

We exploit a unique event to study the extent to which popular attitudes towards trade are driven by economic fundamentals. In 2007, Costa Rica put a free trade agreement (FTA) to a national referendum. With a single question on the ballot, 59% of Costa Rican adult citizens cast a vote on whether they wanted an FTA with the U.S. to be ratified or not. We merge disaggregated referendum results, which break new ground on anonymity-compatible voting data, with employer–employee, customs, and firm-to-firm transactions data, and data on household composition and expenditures. We document that a firm’s exposure to the FTA, directly and via input–output linkages, significantly influences the voting behaviour of its employees. This effect dominates that of sector-level exposure and is greater for voters aligned with pro-FTA political candidates. We also show that citizens considered the expected decrease in consumer prices when exercising their vote. Overall, economic factors explain 7% of the variation in voting patterns, which cannot be accounted for by non-economic factors such as political ideology, and played a pivotal role in this vote.

We exploit a unique event to study the extent to which popular attitudes towards trade are driven by economic fundamentals. In 2007, Costa Rica put a free trade agreement (FTA) to a national referendum. With a single question on the ballot, 59% of Costa Rican adult citizens cast a vote on whether they wanted an FTA with the U.S. to be ratified or not. We merge disaggregated referendum results, which break new ground on anonymity-compatible voting data, with employer–employee, customs, and firm-to-firm transactions data, and data on household composition and expenditures. We document that a firm’s exposure to the FTA, directly and via input–output linkages, significantly influences the voting behaviour of its employees. This effect dominates that of sector-level exposure and is greater for voters aligned with pro-FTA political candidates. We also show that citizens considered the expected decrease in consumer prices when exercising their vote. Overall, economic factors explain 7% of the variation in voting patterns, which cannot be accounted for by non-economic factors such as political ideology, and played a pivotal role in this vote.

Substantial advances toward global decarbonization have been made in areas such as electricity generation and the electrification of building heat and road transport, yet the decarbonization of energy-intensive industries remains a formidable but crucial challenge. Decarbonization of the industrial sector, whose direct emissions account for about 25% of global carbon dioxide, is essential for transitioning the world economy toward a sustainable growth path. With present technologies and policies, such decarbonization appears technically possible, but difficult and costly. Here, we highlight the pressing need for new lines of research on two emerging frontiers. The first quantifies how industrial decarbonization technologies and policies interact with the broader economy. The second builds on growing data availability and policy experience with industrial decarbonization to provide broad-scale ex post quantifications of its impacts as an essential empirical complement to a largely modeling-based literature to date.

We examine how labor market disruptions following childbirth relate to intra-household consumption inequality in the long run. Novel survey data from Germany shows that women less educated than their partners are more likely to report child-related career interruptions and receive a smaller share of household consumption, relative to women more educated than their spouses. Moreover, conditioning on partners’ relative education, female career disruptions correlate with higher male consumption, suggesting that child-rearing may shape gender disparities not only in labor outcomes but also in long-term consumption—an overlooked aspect of the “motherhood penalty.”

This paper presents the first numbers on Spanish migration to Spanish America for the colonial period (1492-1830). We analyze quantitative patterns, geographic origins and destinations, gender, and migrant human capital. Drawing on a wide array of primary and secondary sources, we provide the first comprehensive dataset covering for the entire colonial period. This dataset opens new avenues for research on migrant networks, elite formation, social mobility, and the links between migration and long-run economic development.

The extent to which women participate in the labor market varies greatly across the globe. If such differences reflect distortions that women face in accessing good jobs, they can reduce economic activity through a misallocation of talent. In this paper, we build on Hsieh et al. (2019) to provide a methodology to quantify these productivity consequences. The index we propose, the "Global Gender Distortions Index (GGDI)", measures the losses in aggregate productivity that gender-based misallocation imposes. Our index allows us to separately identify labor demand distortions (e.g., discrimination in hiring for formal jobs) from labor supply distortions (e.g., frictions that discourage women’s labor force participation) and can be computed using data on labor income and job types. Our methodology also highlights an important distinction between welfare-relevant misallocation and the consequences on aggregate GDP if misallocation arises between market work and non-market activities. To showcase the versatility of our index, we analyze gender misallocation within countries over time, across countries over the development spectrum, and across local labor markets within countries. We find that misallocation is substantial and that demand distortions account for most of the productivity losses.

As businesses increasingly rely on granular consumer data, the public has increasingly pushed for enhanced regulation to protect consumers’ privacy. We provide a perspective based on the academic marketing literature that evaluates the various benefits and costs of existing and pending government regulations and corporate privacy policies. We make four key points. First, data-based personalized marketing is not automatically harmful. Second, consumers have heterogeneous privacy preferences, and privacy policies may unintentionally favor the preferences of the rich. Third, privacy regulations may stifle innovation by entrepreneurs who are more likely to cater to underserved, niche consumer segments. Fourth, privacy measures may favor large companies who have less need for third-party data and can afford compliance costs. We also discuss technology platforms’ recent proposals for privacy solutions that mitigate some of these harms but, again, in a way that might disadvantage small firms and entrepreneurs.

Current estimates of child labour often rely on questions such as, “How many hours did you work last week?” While biases in adult self-reports are well-documented in high-income countries, there is limited evidence on the accuracy of children’s responses in low- and middle-income countries (LMICs). Using data from nine LMICs, including China and India, this paper shows that time diaries report more than twice as many work hours as standard questionnaires. This discrepancy suggests that current estimates may significantly understate child labour. Moreover, certain forms of work—such as collecting water or firewood—appear to contribute to these measurement gaps.

Two opposed interested parties (IPs) compete to influence citizens with heterogeneous priors which receive news items produced by a variety of sources. The IPs fight to capture the coverage conveyed in these items. We characterize the equilibrium level of capture of item as well as the equilibrium level of information transmission. Capture increases the prevalence of the ex ante most informative messages and can explain the empirical distribution of slant at the news-item level. Opposite capturing efforts do not cancel each other and instead undermine social learning as rational citizens discount informative messages. Citizen skepticism makes efforts to capture the news strategic substitutes. Because of strategic substitution, competition for influence is compatible with horizontal differentiation between successful media. In equilibrium, rational citizens choose to consume messages from aligned sources despite knowledge of the bias in a manner consistent with recent empirical evidence.

We present a model of digital advertising with three key features: (1) advertisers can reach consumers on and off a platform, (2) additional data enhances the value of advertiser–consumer matches, and (3) the allocation of advertisements follows an auction-like mechanism. We contrast data-augmented auctions, which leverage the platform’s data advantage to improve match quality, with managed-campaign mechanisms that automate match formation and price-setting. The platform-optimal mechanism is a managed campaign that conditions the on-platform prices for sponsored products on the off-platform prices set by all advertisers. This mechanism yields the efficient on-platform allocation but inefficiently high off-platform product prices. It attains the vertical integration profit for the platform and the advertisers, and it increases off-platform product prices while decreasing consumer surplus, relative to data-augmented auctions.

Urban air pollution hotspots pose significant health risks, yet their detection and analysis remain limited by the sparsity of public sensor networks. This paper addresses this challenge by combining predictive modeling and mechanistic approaches to comprehensively monitor pollution hotspots. We enhanced New Delhi’s existing sensor network with 28 low-cost sensors, collecting PM2.5 data over 30 months from May 1, 2018, to Nov 1, 2020. Applying established definitions of hotspots to this data, we found the existence of additional 189 hidden monthly hotspots in addition to confirming the 660 detected by the public network. Using predictive techniques like Space-Time Kriging, we identified monthly hotspots with 95% precision and 88% recall with 50% sensor failure rate, and with 98% precision and 95% recall with 50% missing sensors. The projections of our predictive model were further compiled into policy recommendations for public authorities. Additionally, we developed a Gaussian Plume Dispersion Model to understand the mechanistic underpinnings of hotspot formation, incorporating an emissions inventory derived from local sources. Our mechanistic model is able to explain 65% of observed transient hotspots. Our findings underscore the importance of integrating data-driven predictive models with physics-based mechanistic models for scalable and robust air pollution management in resource-constrained settings.