Abstract: This paper examines the determinants of public procurement prices using comprehensive data on pharmaceutical purchases by the public sector in Chile. We first document sizable price differences between buyers for the same product and quantity purchased: the difference between the average prices paid by buyers at the 90th and 10th percentiles of the distribution is 16 percent. Our main results are related to the importance of market structure in explaining the dispersion in procurement prices. We find that market structure explains three times more dispersion than buyer effects. Moreover, we leverage exogenous variation in market structure due to patent expirations to estimate that the entry of an additional seller decreases average procurement prices by 11.7 percent, which is 72 percent of the price differences implied by the gap between the 90th and 10th percentiles of estimated buyer effects. These results suggest that supply-side factors are relevant determinants of public procurement prices and that their quantitative importance may exceed that of demand-side factors previously emphasized in the literature.

Abstract: Attempts to curb undesired behavior through regulation get complicated when agents can adapt to circumvent enforcement. We test a model of enforcement with learning and adaptation, by auditing vendors selling illegal fish in Chile in a randomized controlled trial, and tracking them daily using mystery shoppers. Conducting audits on a predictable schedule and (counter-intuitively) at high frequency is less effective as agents learn to take advantage of loopholes. We observe the specific defensive actions vendors adopt to circumvent fines, and their pattern of adoption over time is consistent with the model of learning. A consumer information campaign proves to be almost as cost-effective at curbing illegal sales, and obviates the need for complex monitoring and policing. The Chilean government subsequently chose to scale up the information campaign.  

Media: Yale Insights, VoxDev, J-PAL [Summary], [Blog], [Case Study]

Abstract: The latest resurgence in the U.S. of policies aimed at reducing imports and bolstering domestic production has included the expansion of Buy American provisions. While some of these are new and untested, in this paper we evaluate long-standing procurement limitations on the purchase of foreign products by the U.S. Federal Government. We use procurement micro-data to first map and measure the positive employment effects of government purchases. We then calibrate a quantitative trade model adapted to include features relevant to the Buy American Act: a government sector, policy barriers in final and intermediate goods, labor force participation, and external economies of scale. We show that current Buy American provisions on final goods purchase have created up to 100,000 jobs at a cost of between $111,500 and $137,700 per job. However, the recently announced tightening of the policy on the use of foreign inputs will create fewer jobs at a higher cost of $154,000 to $237,800 per job. We also find scant evidence of the use of Buy American rules as an effective industrial policy.

Media: Bloomberg, VoxEU, NBER Digest, ProMarket

Abstract: Product complementarities can shape market patterns, influencing the demand for related products and their accessories. This study examines complementarities in the demand for rooftop solar and an accessory, battery energy storage. Using nationwide administrative data, we estimate a dynamic nested-logit model of solar and storage adoption. We quantify the demand complementarity between solar and storage, and find that if storage was not available, 20% of households who coadopt solar and storage would not adopt anything. We find that the demand for solar and storage bundles increases with power outages, with a larger effect in California.

Abstract: This paper studies regression discontinuity (RD) designs in settings where the assignment variable is mismeasured. In addition to the standard sources of classical measurement error, we allow the assignment variable to be affected by the treatment. We first establish how to recover the RD parameters of interest in this case, given the distributions of measurement error and treatment effects. We then show that these objects can be nonparametrically recovered from the densities of the mismeasured running variable conditional on treatment status. In the absence of mismeasurement, the conditional densities of the running variable for treatment and control units should each be sharply discontinuous at the threshold. The difference between these sharp benchmarks and the observed densities reveals the extent of mismeasurement, and can be used to adjust the RD estimates. Imposing further assumptions, identification is possible even in the presence of “manipulation” of the running variable. We develop a method to estimate treatment effects in this context based on our results. We discuss examples of settings that fit within our framework, and illustrate our method with a particular empirical application on the effect of a policy to increase competition for public procurement contracts.