Three Essays in Environmental Economics

This dissertation consists of three essays in environmental economics. The first essay examines the effectiveness of air quality information designed to reduce the public health risks associated with air pollution exposure. Using daily bike-share trip data for the metro DC area, I estimate the causal effect of air quality alerts on avoidance behavior in a regression discontinuity analysis, assigning a cutoff for treatment that triggers air quality alerts. Air quality alerts cause less bike-share trip counts and duration. Results for heterogeneous treatment effects indicate that air quality alerts mainly reduce weekend trips in the central DC, which implies that bike share-reducing effects are driven by leisure trips rather than commuting trips. The second essay investigates whether a gasoline tax can be a useful policy tool to reduce air pollutants emitted from automobiles. Using a difference-in-differences and synthetic control method, I explore variation differences in air quality across New Jersey and the other states, before and after New Jersey’s gasoline tax increase in 2016. Althoughestimates suggest that New Jersey’s air pollution levels were lower than those of the other states with the gasoline tax increase, none of these differences are statistically significant. Moreover, the gasoline tax increase was not successful in reducing gasoline consumption and vehicle miles traveled. The third essay examines how the effect of temperature on crime varies across urban and rural areas. Using a 10-year panel of monthly crime and temperature data for California cities, I identify the impact of higher temperatures on violent and property crimes in urban and rural areas. Results show that higher temperatures are correlated with more violent crimes. Urban areas have a higher number of violent crimes than rural areas, holding temperature constant. The number of violent crimes tends to increase in proportion to temperature across both areas, but the marginal effects of temperature are smaller in urban than in rural areas

Growth of Global Over-the-Top and Korean Media Market: Competition and Regulatory Policy Issues

Global markets for OTT (Over-the-Top) services are growing. Focusing on OTT services in Korean media markets, this study explores technological, industrial, and policy factors in OTT markets. Specifically, platform competition and regulatory classification for market entry regulation, global OTT players’ negative impacts on national economy, reverse discrimination against domestic players and user protection issues are discussed. Based upon the discussion on competition and policy issues for OTT players, this study suggests future policy directions for Korean media markets

Fiscal sustainability in the light of aging trend : finding the patterns among aged OECD countries

Title from PDF of title page (University of Missouri--Columbia, viewed on November 9, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Ronald Ratti.Vita.Ph. D. University of Missouri--Columbia 2008.This study is an investigation into the fiscal sustainability of countries in the light of the aging trend and finds that it runs downhill in the countries that are classified as high-level age and have experienced the trend of high-speed aging. This conclusion, and the others that follow, come from the empirical results of the 18 OECD countries, using the annual financial data from 1971 [about] 2005. First, this study finds that the reaction of the primary surplus against increasing the debt - GDP ratio, as the indicator of the fiscal sustainability, is negative or changing from positive to negative in seven countries. Included in this group is Japan, the highest age and aging speed country in the world. Second, it identifies the distinctive properties within groups that are classified by both the age level and the aging speed. In particular, high-age level countries with the high-speed aging trend show signs of financial difficulty with rapidly increasing debt-GDP ratios, whereas the relatively low-age level countries with slow-speed aging trend exhibit fiscal sustainability with decreasing debt-GDP ratios. Third, it concludes that the U.S. has a chance to prepare for future fiscal difficulty, whereas Korea has a high probability that it will suffer from fiscal sustainability problems over the next two decades or more. The debt-GDP ratios for the main OECD countries are expected to double when compared to 2005 levels.Includes bibliographical references (p. 141-143)

Conspiracy Beliefs, Misinformation, Social Media Platforms, and Protest Participation

Protest has long been associated with left-wing actors and left-wing causes. However, right-wing actors also engage in protest. Are right-wing actors mobilized by the same factors as those actors on the left? This article uses cross-national survey data (i.e., US, UK, France, and Canada) gathered in February 2021 to assess the role of misinformation, conspiracy beliefs, and the use of different social media platforms in explaining participation in marches or demonstrations. We find that those who use Twitch or TikTok are twice as likely to participate in marches or demonstrations, compared to non-users, but the uses of these platforms are more highly related to participation in right-wing protests than left-wing protests. Exposure to misinformation on social media and beliefs in conspiracy theories also increase the likelihood of participating in protests. Our research makes several important contributions. First, we separate right-wing protest participation from left-wing protest participation, whereas existing scholarship tends to lump these together. Second, we offer new insights into the effects of conspiracy beliefs and misinformation on participation using cross-national data. Third, we examine the roles of emerging social media platforms such as Twitch and TikTok (as well as legacy platforms such as YouTube and Facebook) to better understand the differential roles that social media platforms play in protest participation

Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor

Despite recent progress in language models, generating constrained text for specific domains remains a challenge, particularly when utilizing black-box models that lack domain-specific knowledge. In this paper, we introduce ScoPE (Score-based Progressive Editor) generation, a novel approach for controlled text generation for black-box language models. We employ ScoPE to facilitate text generation in the target domain by integrating it with language models through a cascading approach. Trained to enhance the target domain score of the edited text, ScoPE progressively edits intermediate output discrete tokens to align with the target attributes throughout the auto-regressive generation process of the language model. This iterative process guides subsequent steps to produce desired output texts for the target domain. Our experimental results on diverse controlled generations demonstrate that ScoPE effectively facilitates controlled text generation for black-box language models in both in-domain and out-of-domain conditions, which is challenging for existing methods

Inference of stochastic time series with missing data

Inferring dynamics from time series is an important objective in data analysis. In particular, it is challenging to infer stochastic dynamics given incomplete data. We propose an expectation maximization (EM) algorithm that iterates between alternating two steps: E-step restores missing data points, while M-step infers an underlying network model of restored data. Using synthetic data generated by a kinetic Ising model, we confirm that the algorithm works for restoring missing data points as well as inferring the underlying model. At the initial iteration of the EM algorithm, the model inference shows better model-data consistency with observed data points than with missing data points. As we keep iterating, however, missing data points show better model-data consistency. We find that demanding equal consistency of observed and missing data points provides an effective stopping criterion for the iteration to prevent overshooting the most accurate model inference. Armed with this EM algorithm with this stopping criterion, we infer missing data points and an underlying network from a time-series data of real neuronal activities. Our method recovers collective properties of neuronal activities, such as time correlations and firing statistics, which have previously never been optimized to fit