Knowledge that Transforms

We construct an endogenous growth model with random interactions where firms are subject to distortions. The TFP distribution evolves endogenously as firms seek to upgrade their technology over time either by innovating or by imitating other firms. We use the model to quantify the effects of misallocation on TFP growth in emerging economies. We structurally estimate the stationary state of the dynamic model targeting moments of the empirical distribution of R&D and TFP growth in China during the period 2007–2012. The estimated model fits the Chinese data well. We compare the estimates with those obtained using data for Taiwan and perform counterfactuals to study the effect of alternative policies. R&D misallocation has a large effect on TFP growth.

This paper provides evidence that exposure to illegal labor markets during childhood leads to the formation of industry‐specific human capital at an early age, putting children on a criminal life path. Using the timing of U.S. antidrug policies, I show that when the return to illegal activities increases in coca suitable areas in Peru, parents increase the use of child labor for coca farming, putting children on a criminal life path. Using administrative records, I show that affected children are about 30% more likely to be incarcerated for violent and drug‐related crimes as adults. No effect in criminality is found for individuals that grow up working in places where the coca produced goes primarily to the legal sector, suggesting that it is the accumulation of human capital specific to the illegal industry that fosters criminal careers. However, the rollout of a conditional cash transfer program that encourages schooling mitigates the effects of exposure to illegal industries, providing further evidence on the mechanisms.

We use household‐level panel data from China and a quantitative framework to document the extent and consequences of factor misallocation in agriculture. We find that there are substantial within‐village frictions in both the land and capital markets linked to land institutions in rural China that disproportionately constrain the more productive farmers. These frictions reduce aggregate agricultural productivity by affecting two key margins: (1) the allocation of resources across farmers (misallocation) and (2) the allocation of workers across sectors, in particular the type of farmers who operate in agriculture (selection). Selection substantially amplifies the productivity effect of distortionary policies by affecting occupational choices that worsen average ability in agriculture.

We study the problem of aggregating private information in elections with two or more alternatives for a large family of scoring rules. We introduce a feasibility condition, the linear refinement condition , that characterizes when information can be aggregated asymptotically as the electorate grows large: there must exist a utility function, linear in distributions over signals, sharing the same top alternative as the primitive utility function. Our results complement the existing work where strong assumptions are imposed on the environment, and caution against potential false positives when too much structure is imposed.

Sanctioned by its constitution, India is home to the world's most comprehensive affirmative action program, where historically discriminated groups are protected with vertical reservations implemented as “set asides,” and other disadvantaged groups are protected with horizontal reservations implemented as “minimum guarantees.” A mechanism mandated by the Supreme Court in 1995 suffers from important anomalies, triggering countless litigations in India. Foretelling a recent reform correcting the flawed mechanism, we propose the 2SMG mechanism that resolves all anomalies, and characterize it with desiderata reflecting laws of India. Subsequently rediscovered with a high court judgment and enforced in Gujarat, 2SMG is also endorsed by Saurav Yadav v. State of UP (2020) , in a Supreme Court ruling that rescinded the flawed mechanism. While not explicitly enforced, 2SMG is indirectly enforced for an important subclass of applications in India, because no other mechanism satisfies the new mandates of the Supreme Court.

We study the optimal design of tests with manipulable inputs. Tests take a unidimensional state of the world as input and output, an informative signal to guide a receiver's approve or reject decision. The receiver wishes to only approve states that comply with her baseline standard. An agent with a preference for approval can covertly falsify the state of the world at a cost. We characterize receiver‐optimal tests and show they rely on productive falsification by compliant states. They work by setting a more stringent operational standard, and granting noncompliant states a positive approval probability to deter them from falsifying to the standard. We also study how falsification‐detection technologies improve optimal tests. They allow the designer to build an implicit cost of falsification into the test, in the form of signal devaluations. Exploiting this channel requires enriching the signal space.

The agent is modeled as a current self that optimally incurs a cognitive cost of empathizing with future selves. The model unifies well‐known experimental and empirical findings in intertemporal choice and enriches the multiple selves model with a notion of self‐control. The defining feature of the model is magnitude‐decreasing impatience: greater patience toward larger rewards. Two behavioral definitions of magnitude‐decreasing impatience are provided and the model is characterized under each of them.

We evaluate and partially challenge the household leverage view of the Great Recession. In the data, employment and consumption declined more in U.S. states where household debt declined more. We study a model of a monetary union composed of many regions in which liquidity constraints shape the response of employment and consumption to changes in debt. We estimate the model with Bayesian methods combining state and aggregate data. Changes in household credit explain 40% of the differential rise and fall of employment across states, but a small fraction of the aggregate employment decline in 2007–2010. Nevertheless, since household deleveraging was gradual, credit shocks greatly slowed the recovery.

Nudge interventions have quickly expanded from academic studies to larger implementation in so‐called Nudge Units in governments. This provides an opportunity to compare interventions in research studies, versus at scale. We assemble a unique data set of 126 RCTs covering 23 million individuals, including all trials run by two of the largest Nudge Units in the United States. We compare these trials to a sample of nudge trials in academic journals from two recent meta‐analyses. In the Academic Journals papers, the average impact of a nudge is very large—an 8.7 percentage point take‐up effect, which is a 33.4% increase over the average control. In the Nudge Units sample, the average impact is still sizable and highly statistically significant, but smaller at 1.4 percentage points, an 8.0% increase. We document three dimensions which can account for the difference between these two estimates: (i) statistical power of the trials; (ii) characteristics of the interventions, such as topic area and behavioral channel; and (iii) selective publication. A meta‐analysis model incorporating these dimensions indicates that selective publication in the Academic Journals sample, exacerbated by low statistical power, explains about 70 percent of the difference in effect sizes between the two samples. Different nudge characteristics account for most of the residual difference.

Smooth ambiguity preferences (Klibanoff, Marinacci, and Mukerji (2005)) describe a decision maker who evaluates each act f according to the twofold expectation <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi>V</a:mi> <a:mo stretchy="false">(</a:mo> <a:mi>f</a:mi> <a:mo stretchy="false">)</a:mo> <a:mo>=</a:mo> <a:msub> <a:mrow> <a:mo>∫</a:mo> </a:mrow> <a:mrow> <a:mi mathvariant="script">P</a:mi> </a:mrow> </a:msub> <a:mi>ϕ</a:mi> <a:mo stretchy="true" maxsize="5.2ex" minsize="5.2ex">(</a:mo> <a:msub> <a:mrow> <a:mo>∫</a:mo> </a:mrow> <a:mrow> <a:mi mathvariant="normal">Ω</a:mi> </a:mrow> </a:msub> <a:mi>u</a:mi> <a:mo stretchy="false">(</a:mo> <a:mi>f</a:mi> <a:mo stretchy="false">)</a:mo> <a:mspace width="0.2em"/> <a:mi mathvariant="normal">d</a:mi> <a:mi>p</a:mi> <a:mo stretchy="true" maxsize="5.2ex" minsize="5.2ex">)</a:mo> <a:mspace width="0.2em"/> <a:mi mathvariant="normal">d</a:mi> <a:mi>μ</a:mi> <a:mo stretchy="false">(</a:mo> <a:mi>p</a:mi> <a:mo stretchy="false">)</a:mo> </a:math> defined by a utility function u , an ambiguity index ϕ , and a belief μ over a set <u:math xmlns:u="http://www.w3.org/1998/Math/MathML" display="inline"> <u:mi mathvariant="script">P</u:mi> </u:math> of probabilities. We provide an axiomatic foundation for the representation, taking as a primitive a preference over Anscombe–Aumann acts. We study a special case where <x:math xmlns:x="http://www.w3.org/1998/Math/MathML" display="inline"> <x:mi mathvariant="script">P</x:mi> </x:math> is a subjective statistical model that is point identified, that is, the decision maker believes that the true law <ab:math xmlns:ab="http://www.w3.org/1998/Math/MathML" display="inline"> <ab:mi>p</ab:mi> <ab:mo>∈</ab:mo> <ab:mi mathvariant="script">P</ab:mi> </ab:math> can be recovered empirically. Our main axiom is a joint weakening of Savage's sure‐thing principle and Anscombe–Aumann's mixture independence. In addition, we show that the parameters of the representation can be uniquely recovered from preferences, thereby making operational the separation between ambiguity attitude and perception, a hallmark feature of the smooth ambiguity representation.