Quantitative Economics July 2020, Volume 11, Issue 3 is now online

TABLE OF CONTENTS, July 2020, Volume 11, Issue 3
Full Issue

Articles
Abstracts follow the listing of articles.

Bounds on treatment effects in regression discontinuity designs with a manipulated running variable
François Gerard, Miikka Rokkanen, Christoph Rothe

Policy discontinuity and duration outcomes
Gerard J. van den Berg, Antoine Bozio, Mónica Costa Dias

Estimating local interactions among many agents who observe their neighbors
Nathan Canen, Jacob Schwartz, Kyungchul Song

Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization
Yichong Zhang, Xin Zheng

A nondegenerate Vuong test and post selection confidence intervals for semi/nonparametric models
Zhipeng Liao, Xiaoxia Shi

Testing jointly for structural changes in the error variance and coefficients of a linear regression model
Pierre Perron, Yohei Yamamoto, Jing Zhou

Eligibility, experience rating, and unemployment insurance take‐up
Stéphane Auray, David L. Fuller

Household risk‐sharing channels
Pierfederico Asdrubali, Simone Tedeschi, Luigi Ventura

Asymmetric information in secondary insurance markets: Evidence from the life settlements market
Daniel Bauer, Jochen Russ, Nan Zhu

Bounds on treatment effects in regression discontinuity designs with a manipulated running variable
François Gerard, Miikka Rokkanen, Christoph Rothe

Abstract

The key assumption in regression discontinuity analysis is that the distribution of potential outcomes varies smoothly with the running variable around the cutoff. In many empirical contexts, however, this assumption is not credible; and the running variable is said to be manipulated in this case. In this paper, we show that while causal effects are not point identified under manipulation, one can derive sharp bounds under a general model that covers a wide range of empirical patterns. The extent of manipulation, which determines the width of the bounds, is inferred from the data in our setup. Our approach therefore does not require making a binary decision regarding whether manipulation occurs or not, and can be used to deliver manipulation‐robust inference in settings where manipulation is conceivable, but not obvious from the data. We use our methods to study the disincentive effect of unemployment insurance on (formal) reemployment in Brazil, and show that our bounds remain informative, despite the fact that manipulation has a sizable effect on our estimates of causal parameters.

Regression discontinuity manipulation bounds partial identification unemployment insurance C14 C21 J65
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Policy discontinuity and duration outcomes
Gerard J. van den Berg, Antoine Bozio, Mónica Costa Dias

Abstract

Causal effects of a policy change on hazard rates of a duration outcome variable are not identified from a comparison of spells before and after the policy change if there is unobserved heterogeneity in the effects and no model structure is imposed. We develop a discontinuity approach that overcomes this by considering spells that include the moment of the policy change and by exploiting variation in the moment at which different cohorts are exposed to the policy change. We prove identification of average treatment effects on hazard rates without model structure. We estimate these effects by kernel hazard regression. We use the introduction of the NDYP program for young unemployed individuals in the UK to estimate average program participation effects on the exit rate to work as well as anticipation effects.

Policy evaluation hazard rate identification causality regression discontinuity selectivity kernel hazard estimation local linear regression average treatment effect job search assistance youth unemployment C14 C25 J64
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Estimating local interactions among many agents who observe their neighbors
Nathan Canen, Jacob Schwartz, Kyungchul Song

Abstract

In various economic environments, people observe other people with whom they strategically interact. We can model such information‐sharing relations as an information network, and the strategic interactions as a game on the network. When any two agents in the network are connected either directly or indirectly in a large network, empirical modeling using an equilibrium approach can be cumbersome, since the testable implications from an equilibrium generally involve all the players of the game, whereas a researcher's data set may contain only a fraction of these players in practice. This paper develops a tractable empirical model of linear interactions where each agent, after observing part of his neighbors' types, not knowing the full information network, uses best responses that are linear in his and other players' types that he observes, based on simple beliefs about the other players' strategies. We provide conditions on information networks and beliefs such that the best responses take an explicit form with multiple intuitive features. Furthermore, the best responses reveal how local payoff interdependence among agents is translated into local stochastic dependence of their actions, allowing the econometrician to perform asymptotic inference without having to observe all the players in the game or having to know the precise sampling process.

Strategic interactions behavioral modeling information sharing games on networks cross‐sectional dependence C12 C21 C31
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Quantile treatment effects and bootstrap inference under covariate‐adaptive randomization
Yichong Zhang, Xin Zheng

Abstract

In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard error underrejects. But for method (2), its asymptotic size equals the nominal level. We also show that, for both methods, the asymptotic size of the Wald test using a covariate‐adaptive bootstrap standard error equals the nominal level. We illustrate the finite sample performance of the new estimation and inference methods using both simulated and real datasets.

Bootstrap inference quantile treatment effect C14 C21
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A nondegenerate Vuong test and post selection confidence intervals for semi/nonparametric models
Zhipeng Liao, Xiaoxia Shi

Abstract

This paper proposes a new model selection test for the statistical comparison of semi/non‐parametric models based on a general quasi‐likelihood ratio criterion. An important feature of the new test is its uniformly exact asymptotic size in the overlapping nonnested case, as well as in the easier nested and strictly nonnested cases. The uniform size control is achieved without using pretesting, sample‐splitting, or simulated critical values. We also show that the test has nontrivial power against all √n‐local alternatives and against some local alternatives that converge to the null faster than √n. Finally, we provide a framework for conducting uniformly valid post model selection inference for model parameters. The finite sample performance of the nondegenerate test and that of the post model selection inference procedure are illustrated in a mean‐regression example by Monte Carlo.

Asymptotic size model selection/comparison test post model selection inference semi/nonparametric models C14 C31 C32
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Testing jointly for structural changes in the error variance and coefficients of a linear regression model
Pierre Perron, Yohei Yamamoto, Jing Zhou

Abstract

We provide a comprehensive treatment for the problem of testing jointly for structural changes in both the regression coefficients and the variance of the errors in a single equation system involving stationary regressors. Our framework is quite general in that we allow for general mixing‐type regressors and the assumptions on the errors are quite mild. Their distribution can be nonnormal and conditional heteroskedasticity is permitted. Extensions to the case with serially correlated errors are also treated. We provide the required tools to address the following testing problems, among others: (a) testing for given numbers of changes in regression coefficients and variance of the errors; (b) testing for some unknown number of changes within some prespecified maximum; (c) testing for changes in variance (regression coefficients) allowing for a given number of changes in the regression coefficients (variance); (d) a sequential procedure to estimate the number of changes present. These testing problems are important for practical applications as witnessed by interests in macroeconomics and finance where documenting structural changes in the variability of shocks to simple autoregressions or vector autoregressive models have been a concern.

Change‐point variance shift conditional heteroskedasticity likelihood ratio tests C22
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Eligibility, experience rating, and unemployment insurance take‐up
Stéphane Auray, David L. Fuller

Abstract

In this paper, we investigate the causes and consequences of “unclaimed” unemployment insurance (UI) benefits. A search model is developed where the costs to collecting UI benefits include both a traditional “fixed” administrative cost and an endogenous cost arising from worker and firm interactions. Experience rated taxes give firms an incentive to challenge a worker's UI claim, and these challenges are costly for the worker. Exploiting data on improper denials of UI benefits across states in the U.S. system, a two‐way fixed effects analysis shows a statistically significant negative relationship between the improper denials and the UI take‐up rate, providing empirical support for our model. We calibrate the model to elasticities implied by the two‐way fixed effects regression to quantify the relative size of these UI collection costs. The results imply that on average the costs associated with firm challenges of UI claims account for 41% of the total costs of collecting, with improper denials accounting for 8% of the total cost. The endogenous collection costs imply the unemployment rate responds much slower to changes in UI benefits relative to a model with fixed collection costs. Finally, removing all eligibility requirements and allowing workers to collect UI benefits without cost shows these costs to be 4.5% of expected output net of vacancy costs. Moreover, this change has minimal impact on the unemployment rate.

Unemployment insurance take‐up rate experience rating matching frictions search E61 J32 J64 J65
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Household risk‐sharing channels
Pierfederico Asdrubali, Simone Tedeschi, Luigi Ventura

Abstract

This paper aims to fill the gaps in the analysis of risk‐sharing channels at the microlevel, both within and across households. Using data from the Bank of Italy's Survey on Household Income and Wealth covering the financial crisis, we are able to quantify in a unified and consistent framework several risk‐sharing mechanisms that so far have been documented separately. We find that Italian households were able to smooth on average about 85% of shocks to household head's earnings in both 2008–2010 and 2010–2012 spells. The most important smoothing mechanisms turn out to be self‐insurance through savings/dissavings (40% and 47% in 2008–2010 and 2010–2012, respectively), and within‐household risk‐sharing (16% and 14%). Interestingly, risk‐sharing through portfolio diversification and private transfers is rather limited, but the overall percentage of shock absorption occurring through private risk‐sharing channels hovers around four‐fifths, as opposed to around one‐fifth of a shock cushioned by taxes and public transfers, excluding pensions. In addition, by exploiting subjective expectations on the following year's household income, we find significant evidence of a lower degree of smoothing of persistent shocks.

Household risk‐sharing precautionary savings consumption smoothing income smoothing C31 D12 E21
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Asymmetric information in secondary insurance markets: Evidence from the life settlements market
Daniel Bauer, Jochen Russ, Nan Zhu

Abstract

We use data from a large US life expectancy provider to test for asymmetric information in the secondary life insurance—or life settlements—market. We compare realized lifetimes for a subsample of settled policies relative to all (settled and nonsettled) policies, and find a positive settlement‐survival correlation indicating the existence of informational asymmetry between policyholders and investors. Estimates of the “excess hazard” associated with settling show the effect is temporary and wears off over approximately 8 years. This indicates individuals in our sample possess private information with regards to their near‐term survival prospects and make use of it, which has economic consequences for this market and beyond.

Asymmetric information life settlements life expectancy secondary insurance market D12 G22 J10

 

 

 

Sincerely,
Christopher Taber
Editor, Quantitative Economics

Publication Date: 
Thursday, July 16, 2020

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