Knowledge that Transforms

Abstract Our study analyses 6 million civil judgments in China from 2014 to 2018, documenting gender disparities that disfavour female litigants. We investigate the impact of an open justice reform that mandated courts to broadcast legal proceedings live on a centralized online platform. By exploiting variations in its implementation across courts and over time and employing both difference-in-differences and Bartik IV approaches, we find that gender disparities in chances of winning decrease as broadcast intensity increases. Analysis of the textual content of judicial decisions provides further evidence that these changes in judicial outcomes stem from altered judge behaviours (i.e. attention and effort) under enhanced judicial transparency. Our results demonstrate how information technology shapes judges’ conduct, underscoring its broader potential to improve accountability in public institutions.

Abstract We study attention utility, the hedonic pleasure or pain derived purely from paying attention to information, which differs from the news utility that arises from gaining new information. The main, field, study examines brokerage account login data to show that investors pay disproportionate attention to already-known positive information on their stocks. Through its effect on logins, this selective attention affects their trading activity. Three experimental studies then show that (1) investors are more likely to engage in a paid task that will involve attention to a prior investment if that investment has gained value; (2) paying attention to a winning stock is more motivating than a doubling of monetary incentives; and that (3) attention has value independent of information acquisition.

Abstract For standard statistical problems, we provide new evidence documenting (1) multimodality and (2) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of Bayes' rule.

Abstract This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under classical assumptions on the data, provided that the controlled lag order diverges. Thus, the commonly perceived efficiency loss of LPs can become asymptotically negligible with many controls. Second, we propose LP-based inference procedures for (level and cumulated) impulse responses that possess robustness properties not shared by existing methods. Inference methods using two distinct standard errors are considered. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while that of the second holds more generally for conditionally heteroskedastic martingale-difference shocks. We propose a bootstrap procedure that improves finite-sample performance and extend the standard error construction to structural responses.

Abstract We show that the financial sector’s asset supply elasticities are sufficient statistics summarizing its macroeconomic effects for a large class of financial frictions. These elasticities are crucial for a wide range of policy questions, ranging from the size of fiscal multipliers to the relative effectiveness of asset purchases targeting the financial sector versus tax cuts targeting households. Workhorse macroeconomic models imply different values of these elasticities, generating output responses to policies that differ by orders of magnitude. We construct empirical measures of these elasticities and evaluate their policy implications in a quantitative model with household heterogeneity and illiquidity.

Abstract 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.

Abstract How do cities grow in the process of structural transformation? To answer this question, we develop a multi-sector spatial equilibrium model with endogenous land use: land is used either for agriculture or housing. Urban land, densely populated due to commuting frictions, expands out of agricultural land. With low productivity and high subsistence needs, farmland is expensive, households cannot afford large homes and cities are very dense. Increasing productivity reallocates factors away from agriculture, freeing up land for urban expansion and limiting the increase in land values despite higher income and urban population. With the area of cities growing faster than urban population, urban density can persistently decline, as in the data over a long period. The quantitative evaluation calibrated to historical data assembled for France over 180 years explains a large fraction of the joint evolution of urban areas, population density and land values across time and space.

Abstract An important class of structural models studies the determinants of skill formation and the optimal timing of interventions. In this article, I provide new identification results for these models and investigate the effects of seemingly innocuous scale and location restrictions on parameters of interest. To do so, I first characterize the identified set of all parameters without these additional restrictions and show that important policy-relevant parameters are point identified under weaker assumptions than commonly used in the literature. The implications of imposing standard scale and location restrictions depend on how the model is specified, but they generally impact the interpretation of parameters and may affect counterfactuals. Importantly, with the popular constant elasticity of substitution (CES) production function, commonly used scale restrictions fix identified parameters and lead to misspecification. Consequently, simply changing the units of measurements of observed variables might yield ineffective investment strategies and misleading policy recommendations. I show how existing estimators can easily be adapted to solve these issues. As a byproduct, this article also presents a general and formal definition of when restrictions are truly normalizations.

Abstract We investigate how technologies that reduce the costs of monitoring by central authorities have shaped the historical transition from small patrimonial states to large bureaucratic organizations. Our analysis is based on a novel dataset that traces changes in the organizational structure and geographic presence of the U.S. federal government over the nineteenth century. To identify causal effects, we develop a new identification strategy that exploits the expansion of the railroad network as a source of variation in the travel time–and thus monitoring costs–between Washington D.C. and other locations. We present three main findings. First, reductions in travel time to Washington D.C. significantly increased the likelihood of federal government presence in a location. Second, this effect is stronger for occupations and tasks characterized by more severe agency problems. Third, decreases in travel time to Washington D.C. are associated with a decline in patrimonial features of the federal government in the location, in line with enhanced monitoring capacity reducing dependence on personal trust and connections.

Abstract I develop a unified theoretical framework with schools and residential choices to study the welfare consequences of public schools’ switching from the traditional neighbourhood assignment to the deferred acceptance mechanism. I find that when families receive higher priorities at neighbourhood schools, the deferred acceptance mechanism creates higher aggregate or utilitarian welfare than neighbourhood assignment. Under a common school ranking assumption, I also show that the deferred acceptance creates higher aggregate welfare with neighbourhood priorities than without them.