Knowledge that Transforms

This article considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. There is a variety of economic applications where solving this problem allows to carry out inference on causal and structural parameters; a notable example is the market structure model of Ciliberto and Tamer (2009) where p=2^m+1 with m being the number of firms that could possibly enter the market. We consider the test statistic given by the maximum of p Studentized (or t-type) inequality-specific statistics, and analyse various ways to compute critical values for the test statistic. Specifically, we consider critical values based upon (1) the union bound combined with a moderate deviation inequality for self-normalized sums, (2) the multiplier and empirical bootstraps, and (3) two-step and three-step variants of (1) and (2) by incorporating the selection of uninformative inequalities that are far from being binding and a novel selection of weakly informative inequalities that are potentially binding but do not provide first-order information. We prove validity of these methods, showing that under mild conditions, they lead to tests with the error in size decreasing polynomially in n while allowing for p being much larger than n; indeed p can be of order $\exp (n^c)$ for some $c > 0$. Importantly, all these results hold without any restriction on the correlation structure between p Studentized statistics, and also hold uniformly with respect to suitably large classes of underlying distributions. Moreover, in the online supplement, we show validity of a test based on the block multiplier bootstrap in the case of dependent data under some general mixing conditions.

We use 130 years of data on historical migrations to the U.S. to show a causal effect of the ancestry composition of U.S. counties on foreign direct investment (FDI) sent and received by local firms. To isolate the causal effect of ancestry on FDI, we build a simple reduced-form model of migrations: Migrations from a foreign country to a U.S. county at a given time depend on (1) a push factor, causing emigration from that foreign country to the entire U.S., and (2) a pull factor, causing immigration from all origins into that U.S. county. The interaction between time-series variation in origin-specific push factors and destination-specific pull factors generates quasi-random variation in the allocation of migrants across U.S. counties. We find that doubling the number of residents with ancestry from a given foreign country relative to the mean increases the probability that at least one local firm engages in FDI with that country by 4 percentage points. We present evidence that this effect is primarily driven by a reduction in information frictions, and not by better contract enforcement, taste similarities, or a convergence in factor endowments.

We develop a new methodology to estimate the elasticity of urban costs with respect to city population using French house and land price data. After handling a number of estimation concerns, we find that the elasticity of urban costs increases with city population with an estimate of about 0.03 for an urban area with 100,000 inhabitants to 0.08 for an urban area of the size of Paris. Our approach also yields a number of intermediate outputs of independent interest such as the share of housing in expenditure, the elasticity of unit house and land prices with respect to city population, and within-city distance gradients for house and land prices.

Dynastic models common in macroeconomics use a single parameter to control the willingness of individuals to substitute consumption both intertemporally, or across periods, and intergenerationally, or across parents and their children. This article defines the concept of elasticity of intergenerational substitution (EGS), and extends a standard dynastic model in order to disentangle the EGS from the EIS, or elasticity of intertemporal substitution. A calibrated version of the model lends strong support to the notion that the EGS is significantly larger than one. In contrast, estimates of the EIS suggests that it is at most one. What disciplines the identification is the need to match empirically plausible fertility rates for the U.S. We illustrate the potential role of the EGS in macroeconomics.

© The Author(s) 2018. Low adoption of agricultural technologies holds large productivity consequences for developing countries. Many countries hire agricultural extension agents to communicate with farmers about new technologies, even though a large academic literature has established that information from social networks is a key determinant of product adoption. We incorporate social learning in extension policy using a large-scale field experiment in which we communicate to farmers using different members of social networks. We show that communicator own adoption and effort are susceptible to small performance incentives, and the social identity of the communicator influences others' learning and adoption. Farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions. Exploring the incentives for injection points in social networks to experiment with and communicate about new technologies can take the influential social learning literature in a more policy-relevant direction.

Unemployed households’ access to unsecured revolving credit more than tripled over the last three decades. This article analyses how both cyclical fluctuations and trend increases in credit access impact the business cycle. The main quantitative result is that credit expansions and contractions have contributed to moderately deeper and more protracted recessions over the last 40 years. As more individuals obtained credit from 1977 to 2010, cyclical credit fluctuations affected a larger share of the population and became more important determinants of employment dynamics. Even though business cycles are more volatile, newborns strictly prefer to live in the economy with growing, but fluctuating, access to credit markets.

Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than via randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people’s knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third data set that nominated seeds are central in a network sense, and are not just those with many friends or in powerful positions.

We study the implications of including many covariates in a first-step estimate entering a two-step estimation procedure. We find that a first-order bias emerges when the number of included covariates is “large” relative to the square-root of sample size, rendering standard inference procedures invalid. We show that the jackknife is able to estimate this “many covariates” bias consistently, thereby delivering a new automatic bias-corrected two-step point estimator. The jackknife also consistently estimates the standard error of the original two-step point estimator. For inference, we develop a valid post-bias-correction bootstrap approximation that accounts for the additional variability introduced by the jackknife bias-correction. We find that the jackknife bias-corrected point estimator and the bootstrap post-bias-correction inference perform excellent in simulations, offering important improvements over conventional two-step point estimators and inference procedures, which are not robust to including many covariates. We apply our results to an array of distinct treatment effect, policy evaluation, and other applied microeconomics settings. In particular, we discuss production function and marginal treatment effect estimation in detail.

The rules and procedures of legislatures often provide legislators with information bearing on the identities of upcoming proposers. For a broad class of legislative bargaining games, we establish that Markovian equilibria necessarily deliver all economic surplus to the first proposer whenever the information structure permits the legislators to rule out some minimum number of proposers one round in advance. This result holds regardless of the recognition process and even if players vary in patience and risk aversion. It raises the possibility that procedures adopted in the interest of transparency may contribute to the imbalance of political power.

Panel and experimental data are used to analyse the economic outcomes in the extended warranty market. We establish that the strong demand and high profits in this market are driven by consumers distorting the failure probability of the insured product, rather than standard risk aversion or sellers’ market power. Providing information to consumers about failure probabilities significantly reduces their willingness to pay for warranties, indicating the important role of information, or lack of, in driving consumers’ purchase behaviour. Such information provision is shown to be more effective in enhancing consumer welfare than additional market competition.