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  • Thumbnail for The economics of delisting: the case of European football
    The economics of delisting: the case of European football by Burbano, Santiago Alejandro

    Professional football teams that once chose to list their stock in the exchange markets have started to delist in the last few years. This study presents a modified version of Altman’s 1968 bankruptcy model and applies multivariate discriminant analysis to predict which financial and socioeconomic factors affect a team’s decision to delist from a stock market. Our non-metric dependent variable is listed/delisted teams, while our independent variables include a number of Altman’s financial ratios, GDP per capita, winning percentage, and two measures specific to soccer franchises–broadcasting and sponsorship revenues. Data are obtained for a total of 37 European teams, out of which 21 remained listed, while 16 were delisted at the time this study was written. Results suggest that the two main variables affecting a delisting decision are broadcasting revenues and working capital. Wealthier football teams that remain listed could benefit from our results by focusing on maintaining a positive working capital, while for smaller teams it might be wise to find alternative revenue sources other than TV revenues.

  • Thumbnail for Determinants of child mortality across states in India
    Determinants of child mortality across states in India by Barker, Leah

    India accounts for one in four of the under-five deaths in the world. Almost 300 million of its people live on less than 25 cents a day. This paper examines the determinants of child mortality in order to aid development strategies that aim to decrease mortality and increase human capital. I use a multivariate regression model examine the effect on child mortality of fertility, female literacy, health expenditure, education expenditures, GDP growth, per capita income, male literacy and vaccination rates across all 28 states in India. The majority of data used is from the third National Family Health Survey of India. The initial results were mixed and further testing shows influences of severe multicollinearity on the data. Due to the large range in child mortality rates across states, a dummy variable test examines the variation in two groups of states caused be either “high” or “low” child mortality.