In my dissertation, I investigate the relationship between household consumption expenditures and transitory income shocks. In the first two essays, I pay particular attention to household expenditures in the aftermath of natural disasters, which are becoming more frequent and costly in the U.S. since 1980. Additionally, I study specialty farm producers' risk attitudes after an income shock due to natural disasters. Although the permanent income hypothesis predicts that households smooth consumption over their lifetimes, credit-constrained households may find consumption smoothing impractical. This dissertation brings forth evidence regarding heterogeneity in the effect of income shocks on household expenditures. First, I find that floods and hurricanes affect food-at-home (FAH) spending in different ways. The average 15-day decrease in FAH spending is about $2 in the 90 days after a flood and about $7 in the 30 days after a hurricane. In other words, floods have a prolonged effect and hurricanes have an immediate effect. I find that floods and hurricanes remain a threat to the FAH expenditures of vulnerable households, for instance, low-income households and households in coastal states. Second, Indiana specialty farm households reduce their monthly expenses of food and miscellaneous categories by about $119 and $280, respectively, after an income loss of 20%-32%. I also find that Indiana specialty producers are less willing to take financial risk after an income loss experience, i.e., they have a decreasing absolute risk aversion. Finally, in the third essay, I show that Australian households exhibit loss aversion in consumption expenditures which also means that they behave asymmetrically in their consumption response to income shocks. However, it is only working-age younger households that show asymmetric consumption behavior as opposed to the symmetric behavior of retirement-age households. The main message of these various findings is clear: after an income shock, the magnitude of change in consumption expenditures and the saliency of certain expenditure categories for adjustment are context- and population-dependent. Hence, income support policies and post-disaster relief programs may benefit from a better understanding of the consumption behavior of beneficiary population, to achieve maximum impact through better targeting.

<p>  </p> <p>My dissertation is composed of three independent chapters in the field of labor economics and international trade. </p> <p><br></p> <p>The first chapter studies marriage market signaling and women’s occupation choice. Despite the general closure of gender disparities in the labor market over the past half century, occupational segregation has been stubbornly persistent. I develop a new model that explains these occupational outcomes through marriage market signaling. Vertically differentiated men have preference over women’s unobservable caregiving ability. Heterogenous women choose caregiving occupations to signal their ability to be caregivers. My model generates unique predictions on the influence of marriage market conditions on women’s occupational choices. I find empirical support for these predictions using longitudinal data on marriage rates, policy shocks to divorce laws, and shocks to the marriage market sex ratio driven by waves of immigration. </p> <p><br></p> <p>The second chapter investigates Covid19 and consumer animus towards Chinese products. Covid19 has tremendously affected all areas of our lives and our online shopping behaviors have not been immune. China is the first country to report cases of Covid19 and suffers from rising animus in the U.S. In this paper, we study consumer animus towards Chinese products post Covid19 using Amazon data. We tracked all face masks sold on Amazon between Sep. 2019 to Sep. 2020, and collect product information that is available to a real consumer, including reviews. By analyzing both seller-generated (e.g., product name, description, features) and user-generated (e.g., reviews and customer Q&A) content, we collect information on the country-of-origin as well as consumer animus for the products. Under a fully-dynamic event study design, we find that the average rating drops significantly after a product is identified as made in China for the first time, while no such drop is found for products with other countries-of-origin. This negative impact is U-shaped, which quickly expands in the first five weeks, and then gradually fades out within six months. An informative-animus review affects the average rating of a Chinese product both directly (through its own rating) and indirectly (through other future ratings), with both mechanisms supported in data. We also provide strong evidence that the drop in average rating is driven by consumer animus instead of product quality. </p> <p><br></p> <p>The third chapter explores how cultural transmission through international trade affects gender discrimination. In this paper, I propose that international trade helps alleviate gender discrimination. With imperfect information on workers’ ability, there is statistical discrimination towards female workers. Through international trade, culture transmits asymmetrically between firms located in countries with different gender cultures. This cultural transmission benefits women because it transmits only in one direction from more gender-equal cultures to less gender-equal cultures. I prove this by linking the Customs data to the Industrial Firms data of China in 2004, and find that Chinese firms trading with more gender-equal cultures hire a higher fraction of female workers and enjoy higher profits. Similar patterns are not found in Chinese firms trading with less gender-equal cultures. The impact of cultural transmission goes beyond the firms engaged in international trade to have spillover effects onto purely domestic firms. Comparing across skill groups, cultural transmission benefits high-skill female workers more.</p>

<p>Platforms are now everywhere in our society. Some platforms share real-time information such that people can refer to many aspects, i.e., transportation, weather, news, etc. For example, online learning platforms can play a significant role in accelerating learning through things like providing more real-time feedback loops. Due to the recent innovation in mobile devices as well as faster networks, live streaming platforms become a new trend. Several usages of live streaming platforms are gaming experience sharing such as Twitch, or shopping experience like Amazon Live. My dissertation studies the strategic designs of different online platforms, especially how information affects users’ strategic behaviors and how it creates<br> different market outcomes.</p>

<p dir="ltr">An inquiry into the additionality of the income tax credit program for solar purchasing in Ohio, where aggregation electric purchasing programs exist.</p><p dir="ltr">In the State of Ohio, a unique feature of the electric market regulatory landscape permits local governments to become energy suppliers to their residents and small businesses through programs known as community choice aggregation (CCA). Some of these programs guarantee 100% renewable electricity to all enrollees. Concurrently, the federal government offers an income tax credit (ITC) for the purchase of a solar array. When policy incentives are offered, it is important to ensure they impact their target audience to act in ways that would not be observed in the scenario without the tax incentive. This is known as “additionality.” In the context of carbon emissions reduction goals, individuals who claim the ITC while already having 100% renewable electricity would violate additionality. In other words, these renewable aggregation programs may crowd out the benefits of the ITC. This paper seeks to assess the additionality of the ITC in the context of Ohio’s CCA program. The actual additionality can depend on whether renewable energy is already being supplied to the site that constructs a solar array. Hence, we study the relationship between CCA and solar adoption probability to determine whether tax incentives are additional. Using non-parametric survival analysis, panel data methods, and post-estimation simulations, this paper seeks to discern if additionality is violated using the ITC in areas where a supply of renewable energy is already guaranteed. We find that aggregation programs increase the probability of solar adoption and that on average, in Ohio, roughly $0.44 of every dollar spent on the income tax credit is non-additional. This will help policymakers determine the efficacy of funds allocated to their respective programs.</p>

資本資產定價模型 (CAPM) 由Sharp (1964)、Lintner (1965)及Black (1972)發展出後，近年來已被廣泛的應用於衡量證券之預期報酬率與風險間之關係。一般而言，衡量結果之估計有兩個階段，首先由時間序列分析估計出貝它(beta)係數，然後再檢定廠商或投資組合之平均報酬率與貝它係數之關係。 Fama與MacBeth (1973)利用最小平方法估計貝它係數，再將由橫斷面迴歸方法所得出之斜率係數加以平均後，以統計t-test檢定之。然而以最小平方法估計係數，其估計值很容易受離群值之影響，因此本研究考慮以穩健估計 (robust estimator)來避免此一問題。另外，本研究亦將長期資料分析 (longitudinal data analysis) 引入CAPM裡，期望能檢定貝它係數是否能確實有效地衡量出系統性風險。 論文中以台灣股票市場電子業之實證分析來比較上述不同方法對CAPM的結果，資料蒐集期間為1998年9月至2001年12月之月資料。研究結果顯示出，穩健估計相對於最小平方法就CAPM有較佳的解釋力。而長期資料分析模型更用來衡量債券之超額報酬部分，是否會依上、中、下游或公司之不同而不同。 / The Capital Asset Pricing Model (CAPM) of Sharp (1964), Lintner (1965) and Black (1972) has been widely used in measuring the relationship between the expected return on a security and its risk in the recent years. It consists of two stages to estimate the relationship between risk and expected return. The first one is that betas are estimated from time series regressions, and the second is that the relationship between mean returns and betas is tested across firms or portfolios. Fama and MacBeth (1973) first used ordinary least squares (OLS) to estimate beta and took time series averages of the slope coefficients from monthly cross-sectional regressions in such studies. However it is well known that OLS is sensitive to outliers. Therefore, robust estimators are employed to avoid the problems. Furthermore, the longitudinal data analysis is applied to examine whether betas over time and securities are the valid measure of risk in the CAPM. An empirical study is carried out to present the different approaches. We use the data about the Information and Electronic industry in Taiwan stock market during the period from September 1998 to December 2001. For the time series regression analysis, the robust methods lead to more explanatory power than the OLS results. The linear mixed-effect model is used to examine the effects of different streams and companies for the security excess returns in these data.

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La thèse analyse les effets de la régulation sur le risque systématique d’un panel de 17 grands opérateurs de télécommunications européens entre 1997 et 2012. La relation régulation/risque est étudiée sous trois angles : (i) via la modification de la distribution des revenus de la firme, (ii) la promotion de l’intensité concurrentielle, et (iii) les choix de structure financière des opérateurs. Une modélisation de l’impact de la régulation sur la structure financière de la firme met en évidence que la hausse stratégique de l’endettement peut accroitre ou décroitre le risque systématique. Trois méthodologies d’estimations du risque sont utilisées : les MCO et le filtre de Kalman sans et avec effet TGARCH. Les trois aspects de la relation risque/régulation sont ensuite abordés via une étude en données de panel (couvrant les ratios financiers, l’intensité concurrentielle, l’intensité et le régime de régulation) puis en évaluant la réaction du risque aux annonces d’évolutions du cadre réglementaire européen (étude d’évènements). / The thesis analyzes the effects of regulation on the systematic risk of a panel of 17 leading European telecommunications operators between 1997 and 2012. The regulation / risk relationship is studied through: (i) the changes in the firm income distribution, (ii) the promotion of competitive intensity, and (iii) the operators' choice of financial structure. Modeling the impact of regulation on the financial structure of the firm highlights that strategic increase in debt may increase or decrease the systematic risk. Three estimation methodologies of risk are used: OLS and Kalman filter technics with and without TGARCH effect. The three aspects of risk / regulation relationship are then addressed through a panel data study (covering financial ratios, competitive intensity, and regulatory regime index) and then by an events study evaluating market reactions to announcements of changes in the European regulatory framework.

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<p dir="ltr">Seafood plays a pivotal role in global economies, livelihoods, and nutritional security. However, climate change and global pandemics pose significant threats to seafood harvests, production, supply chains, and marketing channels. The focus of my thesis is to understand the impact of external factors on our seafood resources and explore adaptive strategies in the face of uncertainties. We utilize economics techniques to study human-nature systems by zooming into social elements (government agencies, industry stakeholders, and fish farmers/fishermen) and aquatic resources. The three essays of my thesis delve into this inquiry from the perspectives of government, industry, and market, accordingly.</p><p dir="ltr">The first chapter in my thesis, <i>Climate Change and Snow Crab Harvest - Applying Random Effect Estimators with Instrumental Variable</i>, estimates the snow crab harvest function with unbalanced panel data of eastern Bering Sea snow crab, Canadian snow crab, Japanese snow crab, and Barents Sea snow crab. Specifically, we analyze the relationship between snow crab biomass, stock, and catch. To address the endogeneity of stock in the harvest function, climate change indicators are selected as instrumental variables. We identify that the Arctic Sea ice extent is effective in addressing the endogeneity and the random effects instrumental variable model with error components two stage least squares estimator performs the best to control heterogeneity. We find that a 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock-harvest elasticity and provide supporting evidence for government fishery agencies to prioritize stock enhancement in policy designs.</p><p dir="ltr">The second chapter, <i>Online Media Sentiment Analysis of Shrimp and Salmon in the United States</i>, employs online media analytics on shrimp and salmon in the US to provide insights into consumer perceptions and potential demand signals for seafood. Search hits and mentions are quantified for top sources, domains, and prevalent terms. In addition, sentiment drivers and sentiment values are identified and calculated using natural language processing tools. The results reveal that the occurrence of peak mentions does not necessarily coincide with the peak of net sentiment, and farmed seafood consistently exhibits lower net sentiments compared to their wild counterparts. Autoregressive modeling is conducted to predict the dynamics of seafood’s net sentiments. The regional analysis demonstrates that public attitudes toward both farmed shrimp and salmon in the East North Central region exhibit a more positive net sentiment, while the New England and Middle Atlantic regions tend to have a lower net sentiment for farmed shrimp and salmon, respectively. The fitted forecast model serves as a supplementary tool for industry stakeholders to quickly respond to future public perceptions. Regional statistics also help the seafood industry tailor business strategies to different regions.</p><p dir="ltr">In the third chapter, <i>Comparative Case Study of Small-Scale Fish Processing for Local Seafood Supply</i><i>,</i> we examine the feasibility of utilizing a shared-use commercial kitchen and on-farm kitchen to support small-scale local fish processing, which helps diversify marketing channels in the US Midwest and supply seafood to local food systems. A case study of each facility type is assessed for economic viability for fish farmers. The financial analysis suggests farmers interested in processing tilapia or rainbow trout from 2,500 lbs to 5,000 lbs per year utilize rental commercial kitchens. A minimum of 15% markup and processing of 10,000 lbs/year tilapia is required to make the on-farm kitchen option more viable. For farmers who process rainbow trout, 10,000 lbs/year with a 10% markup using an on-farm kitchen is a better choice. Factoring in the stochastic variability of raw product prices, rental rates, and set-up costs, we provide simulated ranges for economic metrics including profitability index, payback period, and net present values. The reports of estimated costs, revenues, and breakeven prices, provide fish farmers with suggested selling prices, kitchen choices, and production levels to achieve optimum profits under risks.</p>

<p dir="ltr">The National Flood Insurance Program (NFIP), which is run by the U.S. Federal Emergency Management Agency (FEMA), is presently under huge debt to the U.S. treasury. The debt is primarily caused by low flood insurance take-up rate, low willingness to pay for flood insurance, and large payouts after major disasters. Addressing this insolvency problem requires the NFIP to understand (1) what drives the demand for flood insurance so that it can be increased, (2) how risk factors contribute towards large flood insurance payouts so that effective risk reduction policies can be planned, and (3) how to predict the future flood insurance payouts so that the NFIP can be financially prepared. This research has answered these three fundamental questions by developing empirical models based on historical data. To answer the first question, this research has developed a propensity score-based causal model that analyzed one of the key components that influences the demand for flood insurance – the availability of post-disaster government assistance. It was found that the availability of the federal payout in a county in a year increased the number of flood insurance policies by 5.2% and the total insured value of the policies by 4.6% in the following year. Next, this research has developed Mixed Effects Regression model that quantified the causal relationships between the annual flood insurance payout in a county and flood related risk factors such as flood exposure, infrastructure vulnerability, social vulnerability, community resilience, and the number of mobile homes in the county. Based on the derived causal estimates, it was predicted that climate change, which is expected to increase flood exposure in coastal counties, will increase the annual NFIP payout in New Orleans, Louisiana by $2.04 billion in the next 30 years. Lastly, to make the NFIP financially prepared for future payouts, this research has developed a predictive model that can predict the annual NFIP payout in a county with adequate predictive accuracy. The predictive model was used to predict the NFIP payout for 2021 and it was able to predict that with a 9.8% prediction error. The outcomes of this research create new knowledge to inform policy decisions and strategies aimed at fortifying the NFIP. This includes strategies such as flood protection infrastructure, tailored disaster assistance, and other interventions that can bolster flood insurance uptake while mitigating the risk of substantial payouts. Ultimately, this research contributes to sustaining the NFIP's ability to provide vital flood insurance coverage to millions of Americans.</p>