1,721,042 research outputs found
Maximum Likelihood Estimation of a Binary Choice Model with Random Coefficients of Unknown Distribution
No full textIchimura, Hidehiko; Thompson, T. Scott. (1993). Maximum Likelihood Estimation of a Binary Choice Model with Random Coefficients of Unknown Distribution. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/55580
Retirement Process in Japan: New evidence from Japanese Study on Aging and Retirement (JSTAR)
Get PDFWhile the average retirement age is higher in Japan, the retirement process has not been in-depth explored from multiple factors including economic, health and family statuses. We examine the transition of work status and working hours for Japanese males and females using JSTAR (Japanese Study on Aging and Retirement) in 2007 and 2009. We provide some empirical patterns of retirement. First, those who are aged 60 or over and retired stay retired two years later, either male or female, while some portion of those who are aged in 50s come back to work. Second, the probability to retire in 2009 for those who were not retired in 2007 ranges 20-30%. Higher index workers in their 60s are less likely to retire but quickly retire if working hours are reduced. Third, higher index workers seem to keep working at the current working hours than lower index counterparts.
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Yixuan Li's Thesis of Three Chapters
No full textThe first chapter examines how Ban-the-box policies affect employment outcome of females. While the previous literature finds that minority males are hurt by Ban-the-box policies, no one has focused on females. I fill this gap by using individual level employment status data to examine the influence of Ban-the-box policies on females by initial employment status. I find that relative to non-Hispanic white females, Hispanic females who started outside the labor force experience a lower probability of being employed or entering the labor force after pubic bans.The second chapter examines whether changes in temperature affects gender differentials in time allocation and the potential mechanisms through which this response might operate. Based on data from the American Time Use Survey (ATUS), we find that, relative to men, women decrease their labor supply by approximately one hour during days with temperatures of 100 degrees Fahrenheit or higher, despite having fewer working hours than men over the entire distribution of temperature. However, the differences in the time allocated to housework and leisure between men and women vary little with temperature. Our further investigation suggests that a substantial part of the gender gap in response to temperature is attributed to family status and fertility status.
The third chapter investigates whether higher housing prices cause parents to be less willing to have boys. Over the past two decades, Chinese males have been competing in marriage markets by offering to purchase homes when getting married. The rise in housing prices has made this practice increasingly expensive, and may help explain why the sex ratio of new-born babies in China has declined since 2008. Using aggregated data at the city level, I find that higher housing price are associated with lower male-to-female ratio of new-born babies, confirming that higher housing price do weaken Chinese parents’ son preference. The mechanism is that higher housing price combined with the custom for the bride to provide a house significantly increases the cost of nurturing a son while the return does not increase much. So parents are less willing to having a son compared with having a daughter
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Economics of Search Design on E-Commerce Platforms
No full textOne of the fundamental distinctions between online and conventional retailing is the widespread use of recommendation systems and search tools that assist buyers and sellers in searching for trading partners. Growing research interests in marketing, economics, and computer science have been directed toward the design of ranking and recommendation algorithms to increase search precision and make the search results more customized to consumers’ preferences. However, most of the existing studies focus on (1) the short-run effects of platform designs on consumer search and purchase behaviors and (2) the changes from the demand side, abstracting away from the possibility of changes from the supply side in the long run that can affect market equilibrium outcomes. To fill these gaps, I take advantage of a 2019 quasi-experiment in Alibaba – one of the world’s largest e-commerce platforms - to examine the value of improving search precision in shaping consumer behaviors and firm competition strategies. In 2019, Alibaba refined some product categories into finer subgroups in order to return more targeted search results to online shoppers. As a result, the matching outcomes between consumers’ preferences behind the search query and relevant products in the search results are substantially improved after the category refinement. Since consumers are not aware of these behind-the-scenes adjustments of search algorithms, I can causally identify how consumers respond to the improvement in search precision (See Chapter 1). With the estimates from the demand side, I further examine the supply side responses to the changes in search algorithms (See Chapter 2 and 3).
In the Chapter 1 of my dissertation, “Exploitation and Exploration: Improving Search Precision on E-commerce Platforms,” I examine the impacts of improving search precision on consumer search and purchase behaviors. A more precise search algorithm may improve search precision, however it may also reduce cross-selling and up-selling opportunities because of less time that consumers spend exploring different products. I empirically quantify these tradeoffs through the 2019 quasi-experiment on Alibaba that refined some broader categories into narrow ones. Using a flexible difference-in-differences design, I provide estimates of the effects of improving search precision by examining the effect of the category refinement. The estimated results suggest that increasing search precision can improve click-through rates and purchase rates in the short run, especially for “goal-directed” searchers who care about the efficiency of gathering information in the search process. However, too precise search results may take away the pressure of exploration, thus decreasing continued engagement and unplanned spending of “exploratory” searchers in the long run. As the first empirical study of the long-run impacts of search algorithms, my study indicates that e-commerce platforms should balance the short-term gains from increasing the search precision with the long-term benefits of encouraging consumer exploration. Most of the existing search algorithms can immediately boost search engine revenues but may be unsustainable in a longer time horizon. My results imply that e-commerce platforms can increase consumer engagement and generate long-term impacts of user value by encouraging consumers to explore diverse, novel, and serendipitous product categories.
In the Chapter 2 of my dissertation, “Query Markets and Seller Strategies: Some Stylized Facts,” I look at the supply side of the market and investigate the value of improving search precision in helping entrepreneurs to succeed in decentralized online marketplaces. E-commerce platforms guide consumers’ search traffic toward online retailers that are classified into different product categories. An online retailer can either list itself under a broad category to reap larger search traffic, or choose a narrow category, often a subcategory of a broad category, to target a niche audience. Reduced-form evidence suggests that the platform helps match the demand and the supply through market segmentation and search traffic allocation. By refining a broader category into narrow subcategories, the e-commerce platform gives retailers the flexibility to forgo higher volumes of search traffic in order to gain a better conversion rate. As a result, small and new sellers are more likely to enter the narrow category after the category refinement.
In the Chapter 3 of my dissertation, “Capturing Sellers’ Responses: A Model of Search Traffic Allocation, Revenue Conversion, and Entry Strategies,” I estimate a structural model of online retailors’ location decisions. In my framework, each market is defined by a search query, which matches an online retailer’s product either closely or distantly. The platform allocates search traffic into different categories, and online retailers compete for the search traffic in each product category with heterogeneous abilities to convert search traffic into revenue. With detailed data on search queries, search exposure, and seller revenue from Alibaba, I recover the parameters in the revenue function and use “revealed preference” inequalities to estimate the cost structure when firms making entry decisions. I find that online retailer faces a tradeoff between market size and competition intensity, and a retailer is better at converting closely matched search traffic into revenues. Further counterfactual experiments show that eliminating category refinement would lead to about 21% revenue losses for sellers in product categories I study, with incidence mostly on sellers that specialize in niche products. My findings suggest that e-commerce platforms as entrepreneurial incubators can help small business owners thrive on the platform through targeted search traffic allocation.Dissertation not available (per author's request
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Anomalies, Roll’s Critique, and Proxy Error
No full textAnomalies generate alphas. Roll (1977) notes that these alphas contain biases that result from using the equity market portfolio as a proxy for the unobserved aggregate wealth portfolio of CAPM – the portfolio of all risky assets. While the existence of the bias is well understood, the size of the bias is not. I develop a formal test that uses Ross’ (1976) absence of arbitrage bound to quantify the size of the bias. I find that 12 of 99 documented anomalies have a statistically significant bias, which ranges from .6% to 1.5%, annualized. I show that this bias is a manifestation of an anomaly’s correlation with the omitted variable – the assets that the proxy omits. In fact, when I add an anomaly with a significant bias as a second factor, to make a new proxy, the biases across all the anomalies are removed. Using these anomalies, my test provides a new proxy that may be closer to the aggregate wealth portfolio
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Essays on Microeconometric Analysis
No full textThis dissertation comprises three essays in micro econometrics that explore issues of social justice and individual decision-making.In the first chapter, I examine how judges’ decisions affect defendants' reoffending behavior. I consider the issue of selection when a judge decides on sentencing and probation and builds a novel method using many instruments to identify the compound effects of the verdict (including sentence and probation) on recidivism. In the second, more theoretical chapter, I propose a novel method for evaluating treatment effects when both the instrument and controls have high dimensionalities. In the third, more empirical chapter, I assess the moral hazard issue in HIV preventative medication.
In the first chapter, I consider an important social issue - the problem of reoffending and its determinants. I address the question, ``How do judges make a series of decisions, and how does this affect the future criminal behavior of defendants?`` using judge randomization with a selection model. In my model, the judge makes numerous decisions, such as whether or not to impose probation, the duration of probation, and the (potential) length of the sentence. To address the selection issue inherent in these decisions, a novel methodology is developed that use multiple instruments to identify the compound effects of the verdict on reoffending behavior. Using data on new offenders in North Carolina from 1995 to 2010, the analysis reveals that neglecting the selection process for probation can introduce severe estimation biases. The findings suggest that longer probation periods lead to a marginal increase in recidivism, while both active and suspended sentences exhibit a marginal deterrent effect. This chapter contributes to the literature by providing a comprehensive understanding of the criminal population and identifying the effectiveness of judicial decisions.
The second chapter proposes a new method for estimating treatment effects when both the instrument and control variables have high dimensionalities. This approach is particularly relevant in many empirical settings where exogenous shocks interact with covariates to capture heterogeneity. Existing solutions, such as Lasso, only address high dimensionality in either instruments or controls and rely on sparsity assumptions that may not be compatible with various econometric applications. The proposed technique can simultaneously handle both high-dimensional controls and instruments, making it especially useful when researchers are uncertain about asymptotic constraints. The inference protocol developed in this chapter yields more robust results compared to alternative methods.
The third chapter investigates the potential moral hazard associated with HIV pre-exposure prophylaxis (PrEP) medication among high-risk individuals. While PrEP is highly effective in preventing HIV infection, concerns have been raised regarding its potential to encourage riskier sexual behaviors. Using panel data from the Multicenter AIDS Cohort Study and employing a difference-in-differences approach, the analysis examines the impact of PrEP on hazardous sexual behaviors and the incidence of other sexually transmitted diseases (STDs). The findings indicate that PrEP increases the likelihood of engaging in unprotected sexual activity, having a higher number of sexual partners, and contracting syphilis.Release after 08/20/202
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Essays on Labor Economics
No full textLife cycle decisions regarding education, marriage, divorce, and labor supply form the foundation of individual economic outcomes. These interconnected individual choices also determine the evolution of the economy in various aspects. Understanding the mechanisms underlying these decisions presents substantial empirical challenges. The dynamic nature of life cycle choices, combined with unobserved heterogeneity, selection effects, and complex interactions between individual decisions and market equilibrium, makes it difficult to identify causal relationships and evaluate policy interventions using reduced-form approaches alone. Structural labor economics provides a methodological framework for addressing these challenges by explicitly modeling the behavioral foundations of life cycle decision-making. By combining economic models with detailed data and modern econometric techniques, structural estimation enables researchers to recover deep parameters governing individual preferences, account for dynamic selection and general equilibrium effects, and conduct counterfactual policy analyses where individuals respond optimally under the hypothetical conditions. This dissertation contributes to the structural labor economics literature by developing and estimating dynamic models of life cycle decisions and family outcomes across three empirical contexts. In the first chapter, I examine how socioeconomic and institutional changes influenced life cycle decisions in the United States from 1968 to 2018. I develop a general equilibrium overlapping generations model that incorporates educational, marital, and female labor supply choices, allowing for rich interactions between labor and marriage markets during periods of transition. The model addresses the empirical challenge of disentangling the effects of multiple sequential underlying driving forces—including shifting social attitudes toward marriage, rising college premiums, narrowing gender wage gaps, increasing wage volatility, and divorce law reforms. By proposing a methodology that estimates the model through fitting connected equilibrium transitional paths to time-series data and exploiting variation in state-level divorce law changes, the analysis decomposes the relative importance of these driving forces. The findings reveal that rising college premiums drove increased educational attainment for both genders and contributed to marriage delay, while declining social attitudes toward marriage emerged as the primary driver of both delayed marriage and increased divorce risk. The results document a shift from marriage market to labor market motivations in educational decisions among younger generations. This chapter provides an innovative framework for studying economic transitions initiated by multiple driving forces with complex interactions among them. The second chapter (joint work with Sebastian Galiani and Juan Pantano) focuses on how cash transfer programs affect life cycle decisions among adolescent girls in developing countries. We estimate a dynamic discrete choice model of schooling and marriage decisions using data from a randomized controlled trial in Malawi. The structural approach addresses the challenge of dynamic selection arising from program eligibility requirements (unmarried, enrolled girls) in the presence of unobserved heterogeneity in household preferences for girls' education. By estimating the model using data from control and unconditional transfer groups while reserving conditional transfer outcomes for out-of-sample validation, the analysis demonstrates how structural methods can complement experimental designs to enhance policy inference. The model enables evaluation of the effects of counterfactual policies that have never been implemented in reality. This chapter demonstrates how the dual-use of randomized controlled trials data and structural modeling helps develop more reliable and useful tools for policy evaluation. In the third chapter, I develop a life cycle model of marriage market participation that features an endogenous, limited-size matching market. The model incorporates search effort decisions and rational expectations about future partner availability, allowing individual choices to jointly determine population dynamics and equilibrium marriage market outcomes. Applied to Japanese demographic data, the model successfully replicates observed marriage timing patterns and reveals how delays in family formation can become self-reinforcing through expectation formation and behavioral adjustments. When fewer individuals marry at young ages, the increased availability of potential partners in future periods raises the option value of remaining single, creating incentives for further delay. Counterfactual analyses indicate that negative shocks to match quality and wage declines amplify marriage delay through belief updates and search effort adjustments, while monetary child support policies prove more effective due to these endogenous market dynamics. This chapter demonstrates how structural modeling can capture complex equilibrium market effects and predict individual responses to policy changes more accurately. Together, these chapters advance the structural labor economics literature by providing methodological tools and empirical insights for understanding life cycle decision-making and family outcomes. The work demonstrates how structural modeling can be used to conduct policy evaluation and counterfactual analysis across diverse economic contexts.Release after 07/17/202
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Essays in Industrial Organization
No full textThis dissertation studies environmental and health policy-relevant issues using cutting-edge applied microeconomic, especially industrial organization, tools and related econometric developments. The first chapter considers policies incentivizing renewable power investors to achieve the policymaker’s environmental objectives. The second chapter relaxes econometric assumptions in dynamic games—a workhorse in industrial organization—to enhance the robustness of policy suggestions implied by the structural estimates. The third chapter explores how physicians and patients interact and how these organic interactions affect the productivity and disparities of the healthcare system.
In the first chapter, I propose a structural framework of policymakers providing financial incentives to support renewable investors in building new capacity. Widespread investment in renewable energy is seen as a critical tool in mitigating the impacts of climate change. However, renewable energy projects face substantial risk because they sell their electricity into volatile wholesale electricity markets, and this risk may hinder investments that otherwise appear profitable. Policymakers looking to encourage renewable investment can choose between direct subsidies for renewable investment and power purchase agreements that assume the risk of future electricity sales. The relative value of these two approaches depends critically upon investors’ risk premium. This chapter investigates this trade-off in the context of Brazilian wind energy actions that award winners purchase agreements for a share of their production. I develop and estimate a structural auction model that separately recovers the investors’ risk aversion and private costs. I find that investors are substantially risk averse: investors require an additional risk premium of 5.44/MWh. For 3% of Brazil’s generation capacity auctioned, full share purchase agreements will be expected to cost $20 billion less than subsidies.
In the second chapter, my coauthors and I study the identification of dynamic games when the underlying information structure is unknown to the researcher. To tractably characterize the set of Markov perfect equilibrium predictions while maintaining weak assumptions on players’ information, we introduce Markov correlated equilibrium, a dynamic analog of Bayes correlated equilibrium. The set of Markov correlated equilibrium predictions coincides with the set of Markov perfect equilibrium predictions that can arise when the players might observe more signals than assumed by the analyst. We propose computational strategies for estimation and counterfactual analysis to deal with non-convexities that arise in dynamic environments.
In the third chapter, my coauthor and I study the role of racial concordance among doctors, specialists, and patients in doctors’ referral decisions. Using a 20% random sample of Medicare beneficiaries from 1999–2010 in the United States, we find that doctors refer to the same race specialist more than otherwise, especially when the patient is also the same race. We propose a structural framework to identify the racial concordance effect net of outcome benefits on doctors’ referral decisions, accounting for endogenous specialist choice set within the local market
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