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This dissertation introduces advanced statistical and econometric methods in two distinct areas of non-market valuation: benefit transfer (BT) and hedonic analysis. While the first and the third chapters address the challenge of estimating the societal benefits of prospective environmental policy changes by adopting locally weighted regression (LWR) technique in an environmental valuation context, the second chapter combines the output from traditional hedonic regression and matching estimators and provides guidance on the choice of model with low risk of bias in housing market studies. The economic and societal benefits associated with various environmental conservation programs, such as improvement in water quality, or increment in wetland acreages, can be directly estimated using primary studies. However, conducting primary studies can be highly resource-intensive and time-consuming as they typically involve extensive data collection, sophisticated models, and a considerable investment of financial and human resources. As a result, BT offers a practical alternative, which involves employing valuation estimates, functions, or models from prior primary studies to predict the societal benefit of conservation policies at a policy site. Existing studies typically fit one single regression model to all observations within the given metadata and generate a single set of coefficients to predict welfare (willingness-to-pay) in a prospective policy site. However, a single set of coefficients may not reflect the true relationship between dependent and independent variables, especially when multiple source studies/locations are involved in the data-generating process which, in turn, degrades the predictive accuracy of the given meta-regression model (MRM). To address this shortcoming, we employ the LWR technique in an environmental valuation context. LWR allows an estimation of a different set of coefficients for each location to be used for BT prediction. However, the empirical exercise carried out in the existing literature is rigorous from a computational perspective and is cumbersome for practical adaptation. In the first chapter, we simplify the experimental setup required for LWR-BT analysis by taking a closer look at the choice of weight variables for different window sizes and weight function settings. We propose a pragmatic solution by suggesting "universal weights" instead of striving to identify the best of thousands of different weight variable settings. We use the water quality metadata employed in the published literature and show that our universal weights generate more efficient and equally plausible BT estimates for policy sites than the best weight variable settings that emerge from a time-consuming cross-validation search over the entire universe of individual variable combinations. The third chapter expands the scope of LWR to wetland meta-data. We use a conceptually similar set of weight variables as in the first chapter and replicate the methodological approach of that chapter. We show that LWR, under our proposed weight settings, generates substantial gain in both predictive accuracy and efficiency compared to the one generated by standard globally-linear MRM. Our second chapter delves into a separate yet interrelated realm of non-market valuation, i.e., hedonic analysis. Here, we explore the combined inferential power of traditional hedonic regression and matching estimators to provide guidance on model choice for housing market studies where researchers aim to estimate an unbiased binary treatment effect in the presence of unobserved spatial and temporal effects. We examine the potential sources of bias within both hedonic regression and basic matching. We discuss the theoretical routes to mitigate these biases and assess their feasibility in practical contexts. We propose a novel route towards unbiasedness, i.e., the "cancellation effect" and illustrate its empirical feasibility while estimating the impact of flood hazards on housing prices. / Doctor of Philosophy / This dissertation introduces novel statistical and econometric methods to better understand the value of environmental resources that do not have an explicit market price, such as the benefits we get from the changes in water quality, size of wetlands, or the impact of flood risk zoning in the sales price of residential properties. The first and third chapters tackle the challenge of estimating the value of environmental changes, such as cleaner water or more wetlands. To figure out how much people benefit from these changes, we can look at how much they would be willing to pay for such improved water quality or increased wetland area. This typically requires conducting a primary survey, which is expensive and time-consuming. Instead, researchers can draw insights from prior studies to predict welfare in a new policy site. This approach is analogous to applying a methodology and/or findings from one research work to another. However, the direct application of findings from one context to another assumes uniformity across the different studies which is unlikely, especially when past studies are associated with different spatial locations. To address this, we propose a ``locally-weighting" technique. This places greater emphasis on the studies that closely align with the characteristics of the new (policy) context. Determining the weight variables/factors that dictate this alignment is a question that requires an empirical investigation. One recent study attempts this locally-weighting technique to estimate the benefits of improved water quality and suggests experimenting with different factors to find the similarity between the past and new studies. However, their approach is computationally intensive, making it impractical for adaptation. In our first chapter, we propose a more pragmatic solution---using a "universal weight" that does not require assessing multiple factors. With our proposed weights in an otherwise similar context, we find more efficient and equally plausible estimates of the benefits as previous studies. We expand the scope of the local weighting to the valuation of gains or losses in wetland areas in the third chapter. We use a conceptually similar set of weight variables and replicate the empirical exercise from the first chapter. We show that the local-weighting technique, under our proposed settings, substantially improves the accuracy and efficiency of estimated benefits associated with the change in wetland acreage. This highlights the diverse potential of the local weighting technique in an environmental valuation context. The second chapter of this dissertation attempts to understand the impact of flood risk on housing prices. We can use "hedonic regression" to understand how different features of a house, like its size, location, sales year, amenities, and flood zone location affect its price. However, if we do not correctly specify this function, then the estimates will be misleading. Alternatively, we can use "matching" technique where we pair the houses inside and outside of the flood zone in all observable characteristics, and differentiate their price to estimate the flood zone impact. However, finding identical houses in all aspects of household and neighborhood characteristics is practically impossible. We propose that any leftover differences in features of the matched houses can be balanced out by considering where the houses are located (school zone, for example) and when they were sold. We refer to this route as the "cancellation effect" and show that this can indeed be achieved in practice especially when we pair a single house in a flood zone with many houses outside that zone. This not only allows us to accurately estimate the effect of flood zones on housing prices but also reduces the uncertainty around our findings.

Submitted by Orlando Laércio de Souza Cavalcante Vilar (orlandovilar@gmail.com) on 2016-01-08T12:59:42Z No. of bitstreams: 1 Dissertação - Orlando Vilar 07 jan 2016_v5.pdf: 707346 bytes, checksum: 65a8488da2068fb61203197572d6ed19 (MD5) / Rejected by Renata de Souza Nascimento (renata.souza@fgv.br), reason: Orlando, boa tarde Por gentileza, realizar as alterações mencionadas abaixo para que possamos aceitar seu trabalho: CAPA: Seu nome deve estar um pouco acima do título (centralizado). O título deve estar em letra maiúscula na CAPA e CONTRACAPA. Em seguida realizar uma nova submissão do trabalho. At on 2016-01-08T14:23:54Z (GMT) / Submitted by Orlando Laércio de Souza Cavalcante Vilar (orlandovilar@gmail.com) on 2016-01-08T16:43:13Z No. of bitstreams: 1 Dissertação - Orlando Vilar 08 jan 2016_v6.pdf: 707109 bytes, checksum: 4877b3b357324b1b8da8a7cb63f818ff (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2016-01-08T16:45:01Z (GMT) No. of bitstreams: 1 Dissertação - Orlando Vilar 08 jan 2016_v6.pdf: 707109 bytes, checksum: 4877b3b357324b1b8da8a7cb63f818ff (MD5) / Made available in DSpace on 2016-01-08T16:50:42Z (GMT). No. of bitstreams: 1 Dissertação - Orlando Vilar 08 jan 2016_v6.pdf: 707109 bytes, checksum: 4877b3b357324b1b8da8a7cb63f818ff (MD5) Previous issue date: 2015-12-14 / This paper studies whether the introduction and utilization of interest on equity (IOE) payments affected the leverage ratios of Brazilian companies. To test the hypothesis that IOE led companies to use less debt, a matching estimator procedure is used, considering as control group a pool of companies that did not pay IOE and as treatment group the ones that did pay it, on a sample comprised of 40 companies during the years of 1995 to 1998. As expected from previous studies, the empirical evidence supports the argument that the average treatment effect (ATE) for companies that pay IOE will result in a reduction of their leverage ratio. However, there were no significant results to leverage reduction when we analyze the average treatment effect on the treated (ATET or ATT). / Este trabalho tem como proposta estudar como a introdução e a utilização do pagamento de juros sobre capital próprio (JSCP) afetaram o nível de endividamento das empresas brasileiras. Para testar a hipótese que os JSCP levaram as empresas a usarem menos capital de terceiros, são utilizados matching estimators, estabelecendo como grupo de controle as empresas que não pagaram JSCP e como grupo de tratamento as que o fizeram, em uma amostra composta de 40 companhias durante os anos de 1995 até 1998. Em linha com estudos anteriores, foi encontrada evidência empírica indicando que o efeito médio do tratamento (ATE) para empresas que decidem pagar JCSP é o de reduzir o seu nível de endividamento. Contudo, não houve resultado significativo de redução de endividamento ao analisarmos o efeito médio do tratamento dentre o grupo de empresas tratadas (ATET ou ATT).