pwc-archive/papers-with-abstracts huggingface dataset:
Heckman-Selection or Two-Part models for alcohol studies? Depends
Authors: Reka Sundaram-Stukel
Date: 2021-12-20 00:00:00
Tasks: Selection bias, Survey, Vocal Bursts Valence Prediction

Aims: To re-introduce the Heckman model as a valid empirical technique in alcohol studies. Design: To estimate the determinants of problem drinking using a Heckman and a two-part estimation model. Psychological and neuro-scientific studies justify my underlying estimation assumptions and covariate exclusion restrictions. Higher order tests checking for multicollinearity validate the use of Heckman over the use of two-part estimation models. I discuss the generalizability of the two models in applied research. Settings and Participants: Two pooled national population surveys from 2016 and 2017 were used: the Behavioral Risk Factor Surveillance Survey (BRFS), and the National Survey of Drug Use and Health (NSDUH). Measurements: Participation in problem drinking and meeting the criteria for problem drinking. Findings: Both U.S. national surveys perform well with the Heckman model and pass all higher order tests. The Heckman model corrects for selection bias and reveals the direction of bias, where the two-part model does not. For example, the coefficients on age are upward biased and unemployment is downward biased in the two-part where the Heckman model does not have a selection bias. Covariate exclusion restrictions are sensitive to survey conditions and are contextually generalizable. Conclusions: The Heckman model can be used for alcohol (smoking studies as well) if the underlying estimation specification passes higher order tests for multicollinearity and the exclusion restrictions are justified with integrity for the data used. Its use is merit-worthy because it corrects for and reveals the direction and the magnitude of selection bias where the two-part does not.
Robustness, Heterogeneous Treatment Effects and Covariate Shifts
Authors: Pietro Emilio Spini
Date: 2021-12-16 00:00:00

This paper studies the robustness of estimated policy effects to changes in the distribution of covariates. Robustness to covariate shifts is important, for example, when evaluating the external validity of quasi-experimental results, which are often used as a benchmark for evidence-based policy-making. I propose a novel scalar robustness metric. This metric measures the magnitude of the smallest covariate shift needed to invalidate a claim on the policy effect (for example, $ATE \geq 0$) supported by the quasi-experimental evidence. My metric links the heterogeneity of policy effects and robustness in a flexible, nonparametric way and does not require functional form assumptions. I cast the estimation of the robustness metric as a de-biased GMM problem. This approach guarantees a parametric convergence rate for the robustness metric while allowing for machine learning-based estimators of policy effect heterogeneity (for example, lasso, random forest, boosting, neural nets). I apply my procedure to the Oregon Health Insurance experiment. I study the robustness of policy effects estimates of health-care utilization and financial strain outcomes, relative to a shift in the distribution of context-specific covariates. Such covariates are likely to differ across US states, making quantification of robustness an important exercise for adoption of the insurance policy in states other than Oregon. I find that the effect on outpatient visits is the most robust among the metrics of health-care utilization considered.
Lassoed Boosting and Linear Prediction in the Equities Market
Authors: Xiao Huang
Date: 2021-12-16 00:00:00
Tasks: Prediction, regression

We consider a two-stage estimation method for linear regression. First, it uses the lasso in Tibshirani (1996) to screen variables and, second, re-estimates the coefficients using the least-squares boosting method in Friedman (2001) on every set of selected variables. Based on the large-scale simulation experiment in Hastie et al. (2020), lassoed boosting performs as well as the relaxed lasso in Meinshausen (2007) and, under certain scenarios, can yield a sparser model. Applied to predicting equity returns, lassoed boosting gives the smallest mean-squared prediction error compared to several other methods.
Uniform Convergence Results for the Local Linear Regression Estimation of the Conditional Distribution
Authors: Haitian Xie
Date: 2021-12-16 00:00:00
Tasks: regression

This paper examines the local linear regression (LLR) estimate of the conditional distribution function $F(y|x)$. We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both $x$ and $y$ and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. We demonstrate the usefulness of these uniform results with two examples: the stochastic equicontinuity condition in $y$, and the estimation of the integrated conditional distribution function.
Testing Instrument Validity with Covariates
Authors: Thomas Carr, Toru Kitagawa
Date: 2021-12-15 00:00:00

We develop a novel test of the instrumental variable identifying assumptions for heterogeneous treatment effect models with conditioning covariates. We assume semiparametric dependence between potential outcomes and conditioning covariates. This allows us to obtain testable equality and inequality restrictions among the subdensities of estimable partial residuals. We propose jointly testing these restrictions. To improve power, we introduce distillation, where a trimmed sample is used to test the inequality restrictions. In Monte Carlo exercises we find gains in finite sample power from testing restrictions jointly and distillation. We apply our test procedure to three instruments and reject the null for one.
Behavioral Foundations of Nested Stochastic Choice and Nested Logit
Authors: Matthew Kovach, Gerelt Tserenjigmid
Date: 2021-12-14 00:00:00

We provide the first behavioral characterization of nested logit, a foundational and widely applied discrete choice model, through the introduction of a non-parametric version of nested logit that we call Nested Stochastic Choice (NSC). NSC is characterized by a single axiom that weakens Independence of Irrelevant Alternatives based on revealed similarity to allow for the similarity effect. Nested logit is characterized by an additional menu-independence axiom. Our axiomatic characterization leads to a practical, data-driven algorithm that identifies the true nest structure from choice data. We also discuss limitations of generalizing nested logit by studying the testable implications of cross-nested logit.
Semiparametric Conditional Factor Models in Asset Pricing
Authors: Qihui Chen, Nikolai Roussanov, Xiaoliang Wang
Date: 2021-12-14 00:00:00

We introduce a simple and tractable methodology for estimating semiparametric conditional latent factor models. Our approach disentangles the roles of characteristics in capturing factor betas of asset returns from ``alpha.'' We construct factors by extracting principal components from Fama-MacBeth managed portfolios. Applying this methodology to the cross-section of U.S. individual stock returns, we find compelling evidence of substantial nonzero pricing errors, even though our factors demonstrate superior performance in standard asset pricing tests. Unexplained ``arbitrage'' portfolios earn high Sharpe ratios, which decline over time. Combining factors with these orthogonal portfolios produces out-of-sample Sharpe ratios exceeding 4.
Identifying Marginal Treatment Effects in the Presence of Sample Selection
Authors: Otávio Bartalotti, Désiré Kédagni, Vitor Possebom
Date: 2021-12-13 00:00:00

This article presents identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three increasingly restrictive sets of assumptions. The first result imposes standard MTE assumptions with an unrestricted sample selection mechanism. The second set of conditions imposes monotonicity of the sample selection variable with respect to treatment, considerably shrinking the identified set. Finally, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. Our analysis extends to discrete instruments. The results rely on a mixture reformulation of the problem where the mixture weights are identified, extending Lee's (2009) trimming procedure to the MTE context. We propose estimators for the bounds derived and use data made available by Deb, Munking and Trivedi (2006) to empirically illustrate the usefulness of our approach.
Robust Algorithmic Collusion
Authors: Nicolas Eschenbaum, Filip Mellgren, Philipp Zahn
Date: 2022-01-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms' strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust.
COVID Lessons: Was there any way to reduce the negative effect of COVID-19 on the United States economy?
Authors: Mohammadreza Mahmoudi
Date: 2022-01-02 00:00:00

This paper aims to study the economic impact of COVID-19. To do that, in the first step, I showed that the adjusted SEQIER model, which is a generalization form of SEIR model, is a good fit to the real COVID-induced daily death data in a way that it could capture the nonlinearities of the data very well. Then, I used this model with extra parameters to evaluate the economic effect of COVID-19 through job market. The results show that there was a simple strategy that US government could implemented in order to reduce the negative effect of COVID-19. Because of that the answer to the paper's title is yes. If lockdown policies consider the heterogenous characteristics of population and impose more restrictions on old people and control the interactions between them and the rest of population the devastating impact of COVID-19 on people lives and US economy reduced dramatically. Specifically, based on this paper's results, this strategy could reduce the death rate and GDP loss of the United States 0.03 percent and 2 percent respectively. By comparing these results with actual data which show death rate and GDP loss 0.1 percent and 3.5 percent respectively, we could figure out that death rate reduction is 0.07 percent which means for the same percent of GDP loss executing optimal targeted policy could save 2/3 lives. Approximately, 378,000 persons dead because of COVID-19 during 2020, hence reducing death rate to 0.03 percent means saving around 280,000 lives, which is huge.
They Still Haven't Told You
Authors: Bruce Knuteson
Date: 2022-01-01 00:00:00

The world's stock markets display a decades-long pattern of overnight and intraday returns seemingly consistent with only one explanation: one or more large, long-lived quant firms tending to expand its portfolio early in the day (when its trading moves prices more) and contract its portfolio later in the day (when its trading moves prices less), losing money on its daily round-trip trades to create mark-to-market gains on its large existing book. In the fourteen years since this extraordinary pattern of overnight and intraday returns was first noted in the literature, no plausible alternative explanation has been advanced. The main question remaining is therefore which of the few firms capable of profitably trading in this manner are guilty of having done so. If any of this is news to you, it is because the people you trust to alert you to such problems still haven't told you.
On income inequality and population size
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2022-01-01 00:00:00

The pursuit of having an appropriate level of income inequality should be viewed as one of the biggest challenges facing academic scholars as well as policy makers. Unfortunately, research on this issue is currently lacking. This study is the first to introduce the theoretical concept of targeted level of income inequality for a given size of population. By employing the World Bank's data on population size and Gini coefficient from sixty-nine countries in 2012, this study finds that the relationship between Gini coefficient and natural logarithm of population size is nonlinear in the form of a second-degree polynomial function. The estimated results using regression analysis show that the majority of countries in the sample have Gini coefficients either too high or too low compared to their appropriate values. These findings could be used as a guideline for policy makers before designing and implementing public policies in order to achieve the targeted level of income inequality.
A simple method for estimating the Lorenz curve
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2021-12-31 00:00:00
Tasks: Form

Given many popular functional forms for the Lorenz curve do not have a closed-form expression for the Gini index and no study has utilized the observed Gini index to estimate parameter(s) associated with the corresponding parametric functional form, a simple method for estimating the Lorenz curve is introduced. It utilizes 3 indicators, namely, the Gini index and the income shares of the bottom and the top in order to calculate the values of parameters associated with the specified functional form which has a closed-form expression for the Gini index. No error minimization technique is required in order to estimate the Lorenz curve. The data on the Gini index and the income shares of 4 countries that have different level of income inequality, economic, sociological, and regional backgrounds from the United Nations University-World Income Inequality Database are used to illustrate how the simple method works. The overall results indicate that the estimated Lorenz curves fit the actual observations practically well. This simple method could be useful in the situation where the availability of data on income distribution is low. However, if more data on income distribution are available, this study shows that the specified functional form could be used to directly estimate the Lorenz curve. Moreover, the estimated values of the Gini index calculated based on the specified functional form are virtually identical to their actual observations.
A simple method for measuring inequality
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2021-12-31 00:00:00

To simultaneously overcome the limitation of the Gini index in that it is less sensitive to inequality at the tails of income distribution and the limitation of the inter-decile ratios that ignore inequality in the middle of income distribution, an inequality index is introduced. It comprises three indicators, namely, the Gini index, the income share held by the top 10%, and the income share held by the bottom 10%. The data from the World Bank database and the Organization for Economic Co-operation and Development Income Distribution Database between 2005 and 2015 are used to demonstrate how the inequality index works. The results show that it can distinguish income inequality among countries that share the same Gini index but have different income gaps between the top 10% and the bottom 10%. It could also distinguish income inequality among countries that have the same ratio of income share held by the top 10% to income share held by the bottom 10% but differ in the values of the Gini index. In addition, the inequality index could capture the dynamics where the Gini index of a country is stable over time but the ratio of income share of the top 10% to income share of the bottom 10% is increasing. Furthermore, the inequality index could be applied to other scientific disciplines as a measure of statistical heterogeneity and for size distributions of any non-negative quantities.
The International Monetary Funds intervention in education systems and its impact on childrens chances of completing school
Authors: Adel Daoud
Date: 2021-12-30 00:00:00

Enabling children to acquire an education is one of the most effective means to reduce inequality, poverty, and ill-health globally. While in normal times a government controls its educational policies, during times of macroeconomic instability, that control may shift to supporting international organizations, such as the International Monetary Fund (IMF). While much research has focused on which sectors has been affected by IMF policies, scholars have devoted little attention to the policy content of IMF interventions affecting the education sector and childrens education outcomes: denoted IMF education policies. This article evaluates the extent which IMF education policies exist in all programs and how these policies and IMF programs affect childrens likelihood of completing schools. While IMF education policies have a small adverse effect yet statistically insignificant on childrens probability of completing school, these policies moderate effect heterogeneity for IMF programs. The effect of IMF programs (joint set of policies) adversely effect childrens chances of completing school by six percentage points. By analyzing how IMF-education policies but also how IMF programs affect the education sector in low and middle-income countries, scholars will gain a deeper understanding of how such policies will likely affect downstream outcomes.
Thirty Years of Academic Finance
Authors: David Ardia, Keven Bluteau, Mohammad Abbas Meghani
Date: 2021-12-30 00:00:00
Tasks: Articles, Topic coverage

We study how the financial literature has evolved in scale, research team composition, and article topicality across 32 finance-focused academic journals from 1992 to 2021. We document that the field has vastly expanded regarding outlets and published articles. Teams have become larger, and the proportion of women participating in research has increased significantly. Using the Structural Topic Model, we identify 45 topics discussed in the literature. We investigate the topic coverage of individual journals and can identify highly specialized and generalist outlets, but our analyses reveal that most journals have covered more topics over time, thus becoming more generalist. Finally, we find that articles with at least one woman author focus more on topics related to social and governance aspects of corporate finance. We also find that teams with at least one top-tier institution scholar tend to focus more on theoretical aspects of finance.
Institutional Quality and the Wealth of Autocrats
Authors: Christopher Boudreaux, Randall Holcombe
Date: 2021-12-29 00:00:00

One frequently given explanation for why autocrats maintain corrupt and inefficient institutions is that the autocrats benefit personally even though the citizens of their countries are worse off. The empirical evidence does not support this hypothesis. Autocrats in countries with low-quality institutions do tend to be wealthy, but typically, they were wealthy before they assumed power. A plausible explanation, consistent with the data, is that wealthy individuals in countries with inefficient and corrupt institutions face the threat of having their wealth appropriated by government, so have the incentive to use some of their wealth to seek political power to protect the rest of their wealth from confiscation. While autocrats may use government institutions to increase their wealth, autocrats in countries with low-quality institutions tend to be wealthy when they assume power, because wealthy individuals have the incentive to use their wealth to acquire political power to protect themselves from a potentially predatory government.
Technology, Institution, and Regional Growth: Evidence from Mineral Mining Industry in Industrializing Japan
Authors: Kota Ogasawara
Date: 2021-12-29 00:00:00

Coal extraction was an influential economic activity in interwar Japan. At the initial stage, coal mines had used not only males but also females as the miners worked in the pits. The innovation of labor-saving technologies and the renewal of traditional extraction methodology, however, induced the institutional change through the revision of labor regulations on the female miners in the early 1930s. This dramatically changed the mines as the place where skilled males were the principal miners engaged in the underground works. I investigate the impact of coal mining on the regional growth and assess how the institutional change induced by the labor regulations affected its process. By linking the location information of mines with registration- and census-based statistics, I found that coal mines led to the remarkable population growths. The labor regulations did not stagnate but accelerated the local population growth as it had forced female miners to exist from labor market and to form their families. The regulations prohibited risky underground works by the female miners. This reduction in the occupational hazards also led to the improvements in the early-life mortality via the mortality selection mechanism in utero.
Analysis of Performance of Drivers and Usage of Overtime Hours: A Case Study of a Higher Educational Institution
Authors: K. C. Sanjeevani Perera
Date: 2021-12-29 00:00:00

This study attempted to analyze whether there is a relationship between the performance of drivers and the number of overtime hours worked by them. The number of overtime hours worked by the drivers in the pool for the years 2017 and 2018 were extracted from the overtime registers and feedback received on the performance of drivers from staff members who frequently traveled in the University vehicles were used for this study. The overall performance of a driver was decided by taking the aggregate of marks received by him for the traits: skillfulness, patience, responsibility, customer service and care for the vehicle. The type of vehicle the driver is assigned for is also taken into account in the analysis of this study. The study revealed that there is no significant relationship between the performance of the drivers and the number of overtime hours worked by them but the type of vehicle and the condition of the vehicle affects attracting long journeys to them which enable them to earn more overtime hours.