Browsing by Author "Ottoni-Wilhelm, Mark"
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Item Arts and Culture Giving(2009) Ottoni-Wilhelm, MarkItem Arts and Culture Giving 2007(2007) Ottoni-Wilhelm, MarkItem Avoiding Bad Control in Regression for Partially Qualitative Outcomes, and Correcting for Endogeneity Bias in Two-Part Models: Causal Inference from the Potential Outcomes Perspective(2021-05) Asfaw, Daniel Abebe; Terza, Joseph; Ottoni-Wilhelm, Mark; Tennekoon, Vidhura; Tan, FeiThe general potential outcomes framework (GPOF) is an essential structure that facilitates clear and coherent specification, identification, and estimation of causal effects. This dissertation utilizes and extends the GPOF, to specify, identify, and estimate causally interpretable (CI) effect parameter (EP) for an outcome of interest that manifests as either a value in a specified subset of the real line or a qualitative event -- a partially qualitative outcome (PQO). The limitations of the conventional GPOF for casting a regression model for a PQO is discussed. The GPOF is only capable of delivering an EP that is subject to a bias due to bad control. The dissertation proposes an outcome measure that maintains all of the essential features of a PQO that is entirely real-valued and is not subject to the bad control critique; the P-weighted outcome – the outcome weighted by the probability that it manifests as a quantitative (real) value. I detail a regression-based estimation method for such EP and, using simulated data, demonstrate its implementation and validate its consistency for the targeted EP. The practicality of the proposed approach is demonstrated by estimating the causal effect of a fully effective policy that bans pregnant women from smoking during pregnancy on a new measure of birth weight. The dissertation also proposes a Generalized Control Function (GCF) approach for modeling and estimating a CI parameter in the context of a fully parametric two-part model (2PM) for a continuous outcome in which the causal variable of interest is continuous and endogenous. The proposed approach is cast within the GPOF. Given a fully parametric specification for the causal variable and under regular Instrumental Variables (IV) assumptions, the approach is shown to satisfy the conditional independence assumption that is often difficult to hold under alternative approaches. Using simulated data, a full information maximum likelihood (FIML) estimator is derived for estimating the “deep” parameters of the model. The Average Incremental Effect (AIE) estimator based on these deep parameter estimates is shown to outperform other conventional estimators. I apply the method for estimating the medical care cost of obesity in youth in the US.Item Basic Facts about Charitable Giving from the Center on Philanthropy Panel Study(2005-09-16) Ottoni-Wilhelm, MarkBasic facts about the charitable giving of families are presented using the first wave of the Center on Philanthropy Panel Study, a new module in the Panel Study of Income Dynamics (PSID). The basic facts are about the relationship between giving and income and the distribution of giving.Item Basic Needs Giving 2007(2007) Ottoni-Wilhelm, MarkItem Basic Needs Giving 2009(2009) Ottoni-Wilhelm, MarkItem Building Civic Infrastructure Organizations: The Lilly Endowment's Experiment to Grow Community Foundations(2019-05) Wang, Xiaoyun; Benjamin, Lehn; Burlingame, Dwight; Guo, Chao; Ottoni-Wilhelm, Mark; Steensland, BrianIn the past 50 years, we have seen significant public and philanthropic investment in building civil society in countries around the globe. This includes initiating community foundations to support the development of vibrant communities and civic life. Yet we have little knowledge about why some initiatives bear fruit and others fail to do so. More specifically, why some community foundations initiated by institutional funders are able to garner local giving necessary to sustain themselves and others are not. This dissertation contributes to our knowledge about such initiatives by researching the Lilly Endowment’s GIFT Initiative (Giving Indiana Funds for Tomorrow), a project providing incentives to start nearly 60 new community foundations and revive 17 existing community foundations in Indiana since 1990. I employed mixed methods and three sources of data: historical archives, statistics of community foundations’ financial information and community demographics, and case studies of four community foundations. First, I found two existing explanations offered in the literature did not account for the lack of local support for the community foundations I studied. More specifically, I found that high level of income and wealth does not necessarily lead to high level of giving to community foundations and the lack of community identity is not the primary reason explaining community foundations’ struggles in attracting local donations. Rather the study shows that social capital is crucial for garnering local giving through the mechanism of facilitating information sharing. Second, I examined the long-term effects of matching grants, a key strategy used by Lilly Endowment to leverage local giving. I found that long-term provision of matching grants might reduce organizations’ incentives to seek funding sources on their own. My dissertation lends further insight into the sustainability of civic infrastructure organizations, a popular institutional model for building local civil society even today.Item Can too much similarity to self backfire? The effects of different levels of similarity on charitable donations(2018-02-22) Tian, Yuan; Konrath, Sara; Tempel, Gene; Ottoni-Wilhelm, Mark; Mesch, DebraHow is charitable giving influenced by other donors’ charitable giving? Do people give more in the presence of other donors who are similar to themselves? Most research suggests that individuals are positively influenced by others who are similar across a variety of behaviors. In the charitable giving contexts, people are more likely to donate (or donate more) to the same cause if others who are similar donate. Yet, prior research has paid little attention to potential non-linear effects of similarity on charitable giving. Is there a certain amount of similarity that is too much? My dissertation investigates this research question through two different methodological approaches, a systematic literature review and an experimental study. The findings suggest the curvilinear effects of similarity on charitable giving (i.e. self-other oversimilarity hypothesis); that is, individuals are more likely to donate (and donate more) in the presence of other generous donors who are moderately similar to themselves. Yet, individuals are less likely to donate (and donate) less in the presence of other generous donors who are in high similarity to themselves. In other words, too much similarity between donors may actually backfire in charitable giving contexts when others give generously. This dissertation consists of a brief overview of similarity (Chapter 1), a systematic literature review (Chapter 2), an experimental study (Chapter 3) and a research proposal (Chapter 4). Chapter 1 in this dissertation identifies the importance of similarity in social relationships. Chapter 2 investigates the effects of similarity on charitable giving and identifies the literature gap. Chapter 3 attempts to fill the gap via developing and testing self-other oversimilarity hypothesis. It further offers practical implications for nonprofit fundraising practices on how to apply similarity between donors to motivate more funding. In order to provide additional empirical evidence that may contribute to theory and practice, and to address certain limitations of the current experimental study, Chapter 4 proposes a new research project to further test self-other oversimilarity hypothesis in the presence of a stingy donor.Item Casual analysis using two-part models : a general framework for specification, estimation and inference(2018-06-22) Hao, Zhuang; Terza, Joseph V.; Devaraj, Srikant; Liu, Ziyue; Mak, Henry; Ottoni-Wilhelm, MarkThe two-part model (2PM) is the most widely applied modeling and estimation framework in empirical health economics. By design, the two-part model allows the process governing observation at zero to systematically differ from that which determines non-zero observations. The former is commonly referred to as the extensive margin (EM) and the latter is called the intensive margin (IM). The analytic focus of my dissertation is on the development of a general framework for specifying, estimating and drawing inference regarding causally interpretable (CI) effect parameters in the 2PM context. Our proposed fully parametric 2PM (FP2PM) framework comprises very flexible versions of the EM and IM for both continuous and count-valued outcome models and encompasses all implementations of the 2PM found in the literature. Because our modeling approach is potential outcomes (PO) based, it provides a context for clear definition of targeted counterfactual CI parameters of interest. This PO basis also provides a context for identifying the conditions under which such parameters can be consistently estimated using the observable data (via the appropriately specified data generating process). These conditions also ensure that the estimation results are CI. There is substantial literature on statistical testing for model selection in the 2PM context, yet there has been virtually no attention paid to testing the “one-part” null hypothesis. Within our general modeling and estimation framework, we devise a relatively simple test of that null for both continuous and count-valued outcomes. We illustrate our proposed model, method and testing protocol in the context of estimating price effects on the demand for alcohol.Item Combined Purposes Giving(2009) Ottoni-Wilhelm, Mark