[pt] Este artigo investiga o impacto da provisão pública na concorrência dentro do setor de ensino superior brasileiro. Desenvolvemos e estimamos um modelo empírico de demanda por ensino superior que incorpora instituições gratuitas e restrições de escolha do consumidor. Nosso modelo produz padrões de substituição mais realistas do que um modelo logit sem restrições, indicando que a seletividade é uma dimensão essencial da diferenciação de produtos. Mostramos que os programas públicos mais seletivos exercem pressão competitiva comparável aos programas privados mais seletivos, mas os programas públicos menos seletivos exercem mais pressão competitiva do que os programas privados menos seletivos. Nossas estimativas da resposta de oferta das instituições privadas sugerem que, na ausência de programas públicos, as mensalidades seriam cerca de 7 por cento mais altas. Esses resultados fornecem informações importantes sobre a dinâmica competitiva do ensino superior brasileiro e destacam o papel da provisão pública na promoção da concorrência nesse setor. / [en] This paper investigates the impact of public provision on competition within the Brazilian higher education sector. We develop and estimate an empirical model of demand for higher education that incorporates tuition-free institutions and consumer choice constraints. Our model produces more realistic substitution patterns than a logit model without constraints, indicating that selectivity is an essential dimension of product differentiation. We find that the most selective public programs exert comparable competitive pressure to the most selective private programs, but the least selective public programs exert more competitive pressure than the least selective private programs. Our estimates of the supply response of private institutions suggest that, in the absence of public programs, tuitions would be about 7 percent higher. These findings provide important insights into the competitive dynamics of Brazilian higher education and highlight the role of public provision in promoting competition in this sector.

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Background and Objectives: I investigated the use of sensitivity analyses in assessing statistical results or analytical approaches in three different statistical issues: (1) accounting for within-subject correlations in analyzing discrete choice data, (2) handling both-armed zero-event studies in meta-analyses for rare event outcomes, and (3) incorporating external information using Bayesian approach to estimate rare-event rates. Methods: Project 1: I empirically compared ten statistical models in analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening. Logistic and probit models with random-effects, generalized estimating equations or robust standard errors were applied to binary, multinomial or bivariate outcomes. Project 2: I investigated the impacts of including or excluding both-armed zero-event studies on pooled odds ratios for classical meta-analyses using simulated data. Five commonly used pooling methods: Peto, Mantel-Haenszel fixed/random effects and inverse variance fixed/random effects, were compared in terms of bias and precision. Project 3: I explored the use of Bayesian approach to incorporate external information through priors to verify, enhance or modify the study evidence. Three study scenarios were derived from previous studies to estimate inhibitor rates for hemophilia A patients treated with rAHF-PFM: 1) a single cohort of previously treated patients, 2) individual patient data meta-analysis, and 3) an previously unexplored patient population with limited data. Results and Conclusion: Project 1: When within-subject correlations were substantial, the results from different statistical models were inconsistent. Project 2: Including both-armed zero-event studies in meta-analyses increased biases for pooled odd ratios when true treatment effects existed. Project 3: Through priors, Bayesian approaches effectively incorporated different types of information to strengthen or broaden research evidence. Through this thesis I demonstrated that when analyzing complicated health research data, it was important to use sensitivity analyses to assess the robustness of analysis results or proper choice of statistical models. / Dissertation / Doctor of Philosophy (PhD)

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京都大学 / 新制・課程博士 / 博士(農学) / 甲第24681号 / 農博第2564号 / 新制||農||1100(附属図書館) / 学位論文||R5||N5462(農学部図書室) / 京都大学大学院農学研究科生物資源経済学専攻 / (主査)教授 栗山 浩一, 教授 浅見 淳之, 教授 伊藤 順一 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM