Analysis of the Impact of Gender and Age of Protagonists in Top-Grossing Films from 2000-2019 on Film Success
By Daniella Welton
Abstract
The gender wage gap is prominent in many fields of work, but it is especially prevalent among actors in the film industry. According to the U.S. Department of Labor, as of 2019 female annual workers were earning about 82.3% of their male counterparts. In a study of feature films released from 1980 to 2015, females were making only 56% of their male counterparts on average; this gap also has been shown to increase as female actors get older (Blau & Kahn, 2017; De Pater et al., 2014; Izquierdo Sanchez & Navarro Paniagua, 2017). In this paper I investigate the relationship between the gender and age of protagonists in the film industry and film success through a series of three regressions with film success defined as film total gross, critic reviews, and audience reviews. My data set is composed of 100 top-grossing films from each year 2000-2019. Through my statistical analysis I did not find any evidence that the gender or age of the protagonist influences film success. Thus my results do not show any evidence that the gender wage gap could be related to differences in film success.
Professor Genna Miller, Faculty Advisor
Professor Kent Kimbrough, Seminar Advisor
JEL Codes: J16, J30, J70, J71
Predictors of Student Loan Repayments: A Comparison Between Public, Private For-Profit and Private Nonprofit Schools
by Mannat Bakshi and Arjun Ahluwalia
Using a sample of over 3,500 colleges from the College Scorecard Dataset , we investigate the association of average federal student loan repayment rates with institutional, regional, and student demographic characteristics of colleges. We consider educational cohorts from 2010 to 2016 at public, private for-profit, and private non-profit institutions. Our data do not allow us to see individual student characteristics, hence we control for traits of the average student in each college and focus on institutional traits that impact repayment rates. Our controls for demographics are consistent with prior research on student loan repayment rates (Lochner and Monge-Naranjo, 2014; Kelchen and Li, 2017).
We ran a Random Effects panel regression to determine how institutional, regional, and student demographic characteristics impact repayment rates. We see an important influence of the institution attended. Institution selectivity (lower admission and withdrawal rates) is associated with higher average repayment. Furthermore, the highest degree awarded is a more significant variable when it comes to describing the variation in repayment rates for public schools; private for-profit schools exhibit lower repayment rates and private nonprofit schools exhibit higher repayment rates regardless of the highest degree awarded. This could be due to a combination of signaling and screening effects. Local income and unemployment impact repayment for the average student in public and for-profit schools, but not in private non-profit schools.
A noticeable institutional finding is that, even after controlling for average school demographics, for-profit schools exhibit lower repayment rates across all types of degree-granting programs. Attending a for-profit school may be a negative signal of ability or value to potential employers. Median family income positively affects repayment twice as much for for-profit schools compared to other school types. These finding on for-profit institutions help explain Obama’s “crack down on for-profit career training colleges” (Simon & Emma, 2014).
Advisor: Professor Genna Miller | JEL Codes: I2, I22, I26
The Impact of Population Mobility on repayment Rates in Microfinance Institutions
By Allison Vernerey
Several studies have attempted to model the determinants of repayment rates for group-based loans administered by micro-finance institutions (MFIs). One of the main variables that have been identifies as playing a role in determining the repayment rate is social capital. Empirical research however has struggled with quantifying this qualitative variable, resulting in vast inconsistencies across studies, aggravating cross-comparison and objective interpretation. Instead, we argue that the use of quantitative, cross-country comparable proxy that is intuitively linked to social capital would yield more consistent and reliable results. We hypothesize that population mobility is such a proxy, and that lower population mobility correlates positively with higher social capital and thus higher repayment rates. Using population mobility as a proxy for social capital would allow MFIs to lower their cost of data collection for performance assessments and simplify the process for policy makers trying to evaluate the programs success. At the village level, we find significant evidence that higher emigration within a community is strongly linked to lower repayment rates in micro-finance. These results provide micro-finance institutions with a new and more cost effective way to monitor their performance as well as improve their capacity to make well-informed lending decisions.
Advisor: Genna Miller | JEL Codes: G, G2, G21 | Tagged: Bangladash, Microfinance Institutions, Population Mobility, Repayment Rates, Social Capital
