Microfinance institutions are considered pivotal in addressing poverty in low- and middle-income countries and providing financial inclusion through loans, savings, and insurance to those typically excluded from traditional banking. Most microloan recipients are women who own small businesses, and thus microfinance is regarded as pivotal to increasing women’s economic status. Some studies, however, highlight the rather lacking ability of microloans to improve household and business outcomes, as well as measures of women’s empowerment. My research explores if targeting such an agenda is the most effective way to uplift women and therefore help economic conditions in middle-income countries. I do this by examining the impact of microfinance loans on women’s autonomy in Vietnam by constructing a panel dataset from the 2006 and 2010 Vietnam Household Living Standards Surveys and designing a causal model. One channel through which microfinance empowers a woman is through her ability to financially contribute to the household, which in turn increases her decision-making ability, and thus changes gender relations in the household. In order to test increased decision-making, I calculate the share of resources in the household that are allocated to each woman and estimate changes in resource shares as a result of receiving a microloan. I expect results to show little to no increase in resource shares as the outcome of a household obtaining a microloan. In order to test changes in gender relations, I compare the previous results across Vietnam’s eight regions, which display varying levels of intimate partner violence. I expect regions with high rates of intimate partner violence to be negatively correlated with increased resource shares for women as compared to regions that have low rates of intimate partner violence. By addressing both the economic and social status of women, I illustrate a more complete picture of the efficacy of microfinance.

Millions of people go hungry and are insecure about their access to food. Charities and non-profit organizations run numerous food assistance programs to help those in need. Previous research suggests that public services are positively associated with housing prices, demonstrating that intra-jurisdictional differences are capitalized through market processes. However, food assistance services bring negative externalities, such as attracting non-residents to gather in the community, disrupting our previous understanding of housing prices. In this research, we employed a hedonic price modeling approach to estimate the impact of the presence of food assistance centers on housing sales, controlling for property attributes, neighborhood, and city characteristics in Baltimore City, Maryland. Two main measurement methods were used to label the attributes of a property: marking all properties within a neighborhood where the food pantry is present as accessible properties and marking all properties within a one-kilometer radius around the food pantry as accessible properties. We tested two hypotheses in this study. The first hypothesized that the presence of a food assistance center is an important indicator of housing prices. The second hypothesized that the impact of food assistance centers varies by the type of property: condo, townhouse, and single-family residents. Under the one-kilometer radius measurement method, the overall effect of the food pantry on house prices is negative, showing that housing prices are lower for accessible properties than properties without food pantry service. Single-family residents and townhomes are the types of properties that are most affected by the presence of food pantries. These findings have the potential to inform urban economics research devoted to the nature of public services. In particular, they show that the food pantry, as one kind of public service, is important not only in the public health field but also plays a role in affecting housing markets and urban planning.

Urban trees are an increasingly essential part of the city environment, as lining streets with greenery has been shown to benefit residents’ health and quality of life. Trees especially impact air quality, which has implications for chronic illnesses like asthma, and existing literature suggests a relationship between air quality and children's health. This paper examines the impact of increasing urban tree canopies in a large, evolving city like Chicago on children’s asthma rates over a decade. I use tree census data from the Morton Arboretum to evaluate change in tree cover from 2010 to 2017, and use Chicago health records to track rates of emergency department visits due to pediatric asthma. Testing the strength of correlation through a regression model can determine the existence and strength of a correlation and relationship between tree canopies and health. The results suggest a need for increased tree planting policies to improve local health outcomes, especially in low-income neighborhoods with sparse canopy cover. As the climate gets warmer and air quality decreases, infrastructure that addresses both climate change and public health will become increasingly essential. By continuing to study the impacts of urban forests, we can develop strategies for mitigating these global issues in a local context.

This study examines the impact of ease of public transport access on unemployment. Commuting is a significant barrier to work because of its time commitment, expense, and reliance on car ownership. The average commute time in the United States is increasing, rising by 10% from 2006 to 2019. This effect is especially prominent in major cities. As workers travel further to reach their jobs, suburbs far from work centers continue to grow, and affordable urban housing is becoming a rarity, public transportation ridership has seen a decline. Past studies focused on a singular city area showed significant differences in employment between those reliant on public transportation and those with automobiles. With my study I want to see if this applies on a nationwide scale. This study uses a linear regression run on the 2017 US Demographic Census data using the software RStudio for now, as more research is conducted alternate sources may be used. The impacts of variables such as race, economic health of the county, geographical differences, and immigration are isolated to determine the extent to which ease of access to public transport is a cause of unemployment. Tests run on the coefficients of the regression determine statistical significance. A second regression run on the 2020 US Demographic Census determines whether the COVID-19 pandemic had an effect. With the rise in remote workers, it is possible that the impact of ease of access is mitigated. A negative correlation between the ease of public transportation access for a county and its unemployment rate is expected. This study could shed light on another cause of unemployment and the impacts of car-reliant transportation.