IN the Far East, the overall volume of intraregional trade is not as substantial as that of Europe, but much larger than that of Latin America.2 One rather unique and very interesting feature of Far Eastern intraregional trade is that one country, namely, Japan, stands out prominently, from the point of view of both the character and magnitude of its trade. Japan contributed about one-third of the Far Eastern intraregional trade in the immediate prewar years (32.3 per cent for 1935, 34.5 per cent for 1937, and 38.o per cent for I938). Immediately after the cessation of hostilities, its share suffered a sharp reduction, but again became a significant percentage of the total in 1949 (I6.5 per cent). The ratio of Far Eastern intraregional trade to its total export, and Japan's share in intraregional trade showed a tendency to fluctuate together.3 Since Japan's share was about one-third in the prewar period and its intraregional imports were related to the volume of total intraregional exports, several questions may be raised with regard to the future role of Japan in the intraregional trade of the Far East. What are the initial and secondary effects of Japan's imports from the Far East on the intraregional exports of the countries of this region? Does such relationship in the prewar period remain true in the postwar period? If there is a change of preand postwar relationships, what are some of the reasons for the change? How are Japan's imports from the Far East related to the over-all exports of the region to all countries? This paper is a preliminary attempt at an analysis of the above questions.

We analyze a bias in transaction-based price indexes due to the presence of seller reservation prices. We develop a model in which the ratio of sellers' reservation prices to the market value affects trading volume and biases of observed transaction prices: when trading volume decreases (increases), index returns are estimated with an upward (downward) bias. We propose a new econometric procedure to mitigate the bias, and use simulations to demonstrate the effectiveness of the procedure. We construct a reserve-conditional unbiased index for the Los Angeles housing market, which substantially differs from a traditional repeat sale index.

We propose a restrictiveness measure for economic models based on how well they fit predefined synthetic data. This measure, together with a measure for how well the model fits real data, outlines a Pareto frontier, where models that rule out more regularities, yet capture the regularities that are present in real data, are preferred. To illustrate our approach, we evaluate the restrictiveness of models in two laboratory settings—certainty equivalents and initial play—and one field setting—takeup of microfinance in Indian villages. The restrictiveness measure reveals insights about each, including that some economic models with only a few parameters are very flexible.

A model of racial discrimination provides testable implications for two features of statistical discriminators: differential treatment of signals by race and heterogeneous experience that shapes perception. We construct an experiment in the U.S. rental apartment market that distinguishes statistical discrimination from taste-based discrimination. Responses from over 14,000 rental inquiries with varying applicant quality show that landlords treat identical information from applicants with African American– and white-sounding names differently. This differential treatment varies by neighborhood racial composition and signal type in a manner consistent with statistical discrimination and in contrast to patterns predicted by a model of taste-based discrimination.

We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probabilities of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.

After growing steadily for decades, average U.S. household energy consumption began declining in the mid-2000s. Using household-level data from the Residential Energy Consumption Survey and Current Population Survey between 1990 and 2020, we decompose overall changes in per household consumption into three components: average income, cross-household income distribution, and consumption habits, which include energy efficiency. Growth of average income caused consumption to increase by 11%, and rising income inequality reduced consumption by 8%, nearly entirely offsetting the effect of income growth. Changes in habits also reduced consumption. Back-of-the-envelope calculations indicate an unexpected effect of rising income inequality: climate and air quality improvements valued at $9 billion in 2020 due to lower electricity consumption. The results indicate the importance of coordinating policies that address inequality and pollution.

Self-control problems constitute a potential explanation for the underinvestment in preventive health in low-income countries. Behavioral economics offers a tool to solve such problems: commitment devices. We conduct a field experiment to evaluate the effectiveness of different types of theoretically motivated commitment contracts in increasing preventive doctor visits by hypertensive patients in rural India. Despite achieving high take-up of such contracts in some treatment arms, we find no effects on actual doctor visits or individual health outcomes. A substantial number of individuals pay for commitment but fail to follow through on the doctor visit, losing money without experiencing health benefits. We develop and structurally estimate a prespecified model of consumer behavior under present bias with varying levels of naiveté. The results are consistent with a large share of individuals being partially naive about their own self-control problems: sophisticated enough to demand some commitment but overly optimistic about whether a given level of commitment is sufficiently strong to be effective. The results suggest that commitment devices may in practice be welfare diminishing, at least in some contexts, and serve as a cautionary tale about their role in health care.

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). The standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair, and thus, is conservative. The analytical inference involves estimating multiple functional quantities that requires several tuning parameters. In this paper, we propose two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. In particular, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.