Knowledge that Transforms

March 01 2019 Review of Economics and Statistics over the Past 100 Years: Content Explorer Asim I. Khwaja, Asim I. Khwaja Search for other works by this author on: This Site Google Scholar Kunal Mangal Kunal Mangal Search for other works by this author on: This Site Google Scholar Author and Article Information Asim I. Khwaja Kunal Mangal Online Issn: 1530-9142 Print Issn: 0034-6535 © 2019 The President and Fellows of Harvard College and the Massachusetts Institute of Technology2019The President and Fellows of Harvard College and the Massachusetts Institute of Technology The Review of Economics and Statistics (2019) 101 (1): i–iii. https://doi.org/10.1162/rest_e_00816 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Asim I. Khwaja, Kunal Mangal; Review of Economics and Statistics over the Past 100 Years: Content Explorer. The Review of Economics and Statistics 2019; 101 (1): i–iii. doi: https://doi.org/10.1162/rest_e_00816 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThe Review of Economics and Statistics Search Advanced Search This content is only available as a PDF. © 2019 The President and Fellows of Harvard College and the Massachusetts Institute of Technology2019The President and Fellows of Harvard College and the Massachusetts Institute of Technology Article PDF first page preview Close Modal You do not currently have access to this content.

IN an article published in this review (Antman & Duncan, 2015), we document how racial identity responds to state affirmative action policy. A coding error was recently brought to our attention that resulted in 0.55% of our sample being misclassified in terms of their African ancestry.1 We regret and apologize for this error. Although the error affected only a tiny percent of the overall sample, the correction changes the conclusion of how individuals with multiracial African ancestry respond to state affirmative action bans, from a negative and statistically significant effect to a positive and statistically significant effect. The corrected table 3 shows the updated results. The coefficients for college-aged individuals with African ancestry reported in table 5 are also now positive, but are no longer statistically significant at conventional levels. Correcting the error does not change the conclusions for individuals with only African ancestry or no African ancestry. None of the Asian ancestry classifications, and thus none of the results for individuals with Asian ancestry (table 4 and the last two columns of table 5), were affected by the coding error. There are no meaningful changes to the summary statistics in table 2 except in the column for those with multiracial black ancestry. The most notable change is the fraction of individuals with multiracial black ancestry who self-identify as black: 49.37% in the original table and 90.86% in the updated table.2 (For further explanation and a complete set of updated results, see Antman & Duncan, 2019.) We continue to find that racial identity responds to state affirmative action policy, albeit with a different conclusion for multiracial blacks, and we are now able to distinguish stronger effects for multiracial individuals with more distant connections to their minority group.This is the original abstract: We link data on racial self-identification with changes in state-level affirmative action policies to ask whether racial self-identification responds to economic incentives. We find that after a state bans affirmative action, multiracial individuals who face an incentive to identify under affirmative action are about 30% less likely to identify with their minority group. In contrast, multiracial individuals who face a disincentive to identify under affirmative action are roughly 20% more likely to identify with their minority group once affirmative action policies are banned.We modify this original abstract as follows: We link data on racial self-identification with changes in state-level affirmative action policies to ask whether racial self-identification responds to economic incentives. We find that after a state bans affirmative action, multiracial individuals who face an incentive to identify under affirmative action are about 2% to 5% more likely to identify with their minority group. In contrast, multiracial individuals who face a disincentive to identify under affirmative action are roughly 20% more likely to identify with their minority group once affirmative action policies are banned.

I introduce an empirical auction model where in addition to the private value that each bidder receives upon winning the auction, losing bidders incur a negative externality that depends on the identity of the rival winner. I show how the externalities and private value distributions can be identified and estimated in such a model. I then apply the model to U.S. Forest Service timber auctions. I find that mill bidders impose significant externalities on one another of between 10% and 22% of the heterogeneous portion of their valuation. This leads the timber tracts to be misallocated in 5.2% of the auctions in my sample.

This paper considers moment-based tests applied to estimated quantities. We propose a general class of transforms of moments to handle the parameter uncertainty problem. The construction requires only a linear correction that can be implemented in sample and remains valid for some extended families of nonsmooth moments. We reemphasize the attractiveness of working with robust moments, which lead to testing procedures that do not depend on the estimator. Furthermore, no correction is needed when considering the implied test statistic in the out-of-sample case. We apply our methodology to various examples with an emphasis on the backtesting of value-at-risk forecasts.

Research has focused on why and when firms access external technology markets. Less is known about the reliability of patents attached to licensed technologies during litigation. Unreliable patents expose a firm to loss of downstream revenues. We address this by constructing a data set of patent litigation in the pharmaceutical industry and exploit a change in patent law that exogenously increased the probability of litigation. We find that licensed patents are more likely to fall during litigation. This effect is isolated to firms with fewer intellectual property capabilities and less patenting experience, suggesting that benefits from external technology are not shared equally.

Governments have used vouchers to spend billions of dollars on private education; much of this has gone to religiously affiliated schools. We explore the possibility that vouchers could alter the financial outcomes of religious organizations that are operating schools and thus have an impact on the spiritual, moral, and social fabric of communities. Using a data set of Catholic parish finances from Milwaukee, we show that vouchers are a dominant source of funding for many churches. Vouchers appear to offer financial stability for congregations as voucher expansion prevents church closures and mergers. However, voucher expansion causes significant declines in church donations and church revenue from noneducational sources.

Survey nonresponse has risen in recent years, which has increased the share of imputed and underreported values found on commonly used data sets. While this trend has been well documented for earnings, the growth in nonresponse to government transfers questions has received far less attention. We demonstrate analytically that the underreporting and imputation of transfer benefits can lead to program impact estimates that are substantially overstated when using instrumental variables methods to correct for endogeneity or measurement error in benefit amounts. We document the importance of failing to account for these issues using two empirical examples.

We propose to use economic theories to construct shrinkage estimators that perform well when the theories' empirical implications are approximately correct but perform no worse than unrestricted estimators when the theories' implications do not hold. We implement this construction in various settings, including labor demand and wage inequality, and estimation of consumer demand. We provide asymptotic and finite sample characterizations of the behavior of the proposed estimators. Our approach is an alternative to the use of theory as something to be tested or to be imposed on estimates. Our approach complements uses of theory for identification and extrapolation.

How high is unemployment? How low is labor force participation? Is obesity more prevalent among men? How large are household expenditures? We study the sources of the relevant official statistics—the Current Population Survey, the Behavioral Risk Factor Surveillance System, and the Consumer Expenditure Survey—and find that the answers depend on whether we look at easy- or at difficult-to-reach respondents, measured by the number of call and visit attempts made by interviewers. A challenge to the (conditionally-)random-nonresponse assumption, these findings empirically substantiate the theoretical warning against making population-wide estimates from surveys with low response rates.

This study examines the effect of chain store entry on drug quality and prices in India. In contrast to prevailing mom-and-pop pharmacies, chains exploit scale economies in distribution and signaling to offer high-quality drugs at lower cost. We show that chain entry leads to a 5% improvement in drug quality and a 2% decrease in prices at incumbent retailers. Effects are larger for locally distributed drug brands but do not depend on consumer SES. Our findings suggest that in markets with asymmetric information, organizational technologies such as chains can play an important role translating market expansion into higher quality.