Publications
Lower tariffs typically raise productivity, production, and trade, increasing the benefits from building infrastructure. Infrastructure spending by governments should therefore increase after countries open up to trade. I test this hypothesis empirically using a trade reform in India and find that a 1 percentage point reduction in tariffs increased states’ infrastructure spending by 0.5% between 1991 and 2001. To understand the mechanisms behind my empirical findings, I develop and calibrate a multi-region model of international trade, private capital accumulation, and infrastructure spending, in which each government chooses such spending to maximize their state’s welfare. I find if governments choose infrastructure following the reform optimally, infrastructure would have increased by 60% on average. The actual increase, based on my empirical findings, was about 29%. Counterfactual exercises show that raising aggregate infrastructure towards its optimal following the trade reform will result in state GDP to increase by 7% points on average.
Recent papers use regression discontinuity designs (RDDs) based on age discontinuity to evaluate social assistance (SA) and unemployment insurance (UI) extension policies. Job search theory predicts that such designs generate biased estimates of the policy-relevant treatment effect. Owing to market frictions, people below the age threshold modify their search behavior in expectation of future eligibility. We use a job search model to quantify the biases on various datasets in the literature. The impacts of SA benefits on employment are underestimated, whereas those of UI extensions on nonemployment duration are overestimated. The article provides insights for RDD evaluations of age-discontinuous policies.
We expand the theory of politician quality in electoral democracies with citizen candidates by supposing that performance while in office sends a signal to the voters about the politician's valence. Individuals live two periods and decide to become candidates when young, trading off against type-specific private wages. The valence signal increases the reelection chances of high valence incumbents (screening mechanism of reelection), and thus their expected gain from running for office (self-selection mechanism). Since self-selection improves the average quality of challengers, voters become more demanding when evaluating the incumbent's performance. This complementarity between the self-selection and the screening mechanisms may lead to multiple equilibria. We show that more difficult and/or less variable political jobs increase the politicians' quality. Conversely, societies with more wage inequality have lower quality polities. We also show that incumbency advantage blurs the screening mechanism by giving incumbents an upper-hand in electoral competition and may wipe out the positive effect of the screening mechanism on the quality of the polity.
This paper describes an empiric study of aggregation and deliberation—used during citizens’ workshops—for the elicitation of collective preferences over 20 different ecosystem services (ESs) delivered by the Palavas coastal lagoons located on the shore of the Mediterranean Sea close to Montpellier (S. France). The impact of deliberation is apprehended by comparing the collectives preferences constructed with and without deliberation. The same aggregation rules were used before and after deliberation. We compared two different aggregation methods, i.e. Rapid Ecosystem Services Participatory Appraisal (RESPA) and Majority Judgement (MJ). RESPA had been specifically tested for ESs, while MJ evaluates the merit of each item, an ES in our case, in a predefined ordinal scale of judgment. The impact of deliberation was strongest for the RESPA method. This new information acquired from application of social choice theory is particularly useful for ecological economics studying ES, and more practically for the development of deliberative approaches for public policies.
Mexican cities along the US-Mexico border, especially Cd. Juarez, became notorious due to high femicide rates supposedly associated with maquiladora industries and the NAFTA. Nonetheless, statistical evaluation of data from 1990 to 2012 shows that their rates are consistent with other Mexican cities’ rates and tend to fall with increased employment opportunities in maquiladoras. Femicide rates in Cd. Juarez are in most years like rates in Cd. Chihuahua and Ensenada and, as a share of overall homicide rates, are lower than in most cities evaluated. These results challenge conventional wisdom and most of the literature on the subject.
This paper highlights how technology can contribute to reaching the 2015 Paris Agreement goals of net zero carbon dioxide (CO2) emissions and global warming below 2°C in 2100. It uses the Advanced Climate Change Long-term model (ACCL), particularly adapted to quantify the consequences of energy price and technology shocks on CO2 emissions, temperature, climate damage and Gross Domestic Product (GDP). The simulations show that without climate policies the warming may be +5°C in 2100, with considerable climate damage. An acceleration in ‘usual’ technical progress not targeted at reducing CO2- even worsens global warming and climate damage. According to our estimates, the world does not achieve climate goals in 2100 without ‘green’ technologies. Intervening only via energy prices, e.g. a carbon tax, requires challenging hypotheses of international coordination and price increase for polluting energies. We assess a multi-lever climate strategy combining energy efficiency gains, carbon sequestration, and a decrease of 3% per year in the relative price of ‘clean’ electricity with a 1 to 1.5% annual rise in the relative price of polluting energy sources. None of these components alone is sufficient to reach climate objectives. Our last and most important finding is that our composite scenario achieves the climate goals.
This paper combines a database on non-tariff measures (NTMs) with Morocco’s firm-level census to explore the effect of regulatory harmonization with the E.U. on firms’ outcomes. Exploiting cross-sectoral variation in the timing and extent of regulatory harmonization, we find that harmonization waves correlate with rises in productivity, with higher markups and with greater numbers of exporting firms. These effects were reinforced by an induced market-structure change: harmonization temporarily protected the Moroccan market from competition from low-end producers in other developing countries, who took time to adapt. We identify these effects through changes in both trade patterns and firm-level outcomes.
Wine experts' ratings provide quality information and reduce the information asymmetry for the consumer. We hypothesize that consumers' ratings will come to dominate expert ratings in the wine expertise market. We employ a hedonic regression framework on the attributes of 36,970 French red wines to determine the relative impacts of expert and Vivino community ratings on wine prices. Average consumer ratings are found to have a larger effect on price than expert scores. These results are found to be robust to outliers and the general conclusion that peers matter more than experts holds when we exclude the top-end wines.
Many problems ask a question that can be formulated as a causal question: what would have happened if...? For example, would the person have had surgery if he or she had been Black? To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as the skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by $$\boldsymbol{x}$$x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of $$\boldsymbol{x}$$x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable whose effect we are trying to quantify. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).
The paper examines the question of non-anonymous Growth Incidence Curves (na-GIC) from a Bayesian inferential point of view. Building on the notion of conditional quantiles of Barnett (1976. “The Ordering of Multivariate Data.” Journal of the Royal Statistical Society: Series A 139: 318–55), we show that removing the anonymity axiom leads to a complex and shaky curve that has to be smoothed, using a non-parametric approach. We opted for a Bayesian approach using Bernstein polynomials which provides confidence intervals, tests and a simple way to compare two na-GICs. The methodology is applied to examine wage dynamics in a US university with a particular attention devoted to unbundling and anti-discrimination policies. Our findings are the detection of wage scale compression for higher quantiles for all academics and an apparent pro-female wage increase compared to males. But this pro-female policy works only for academics and not for the para-academics categories created by the unbundling policy.
