Today a standard procedure to analyze the impact of environmental factors on productive efficiency of a decision making unit is to use a two stage approach, where first one estimates the efficiency and then uses regression techniques to explain the variation of efficiency between different units. It is argued that the abovementioned method may produce doubtful results which may distort the truth data represents. In order to introduce economic intuition and to mitigate the problem of omitted variables we introduce the matching procedure which is to be used before the efficiency analysis. We believe that by having comparable decision making units we implicitly control for the environmental factors at the same time cleaning the sample of outliers. The main goal of the first part of the thesis is to compare a procedure including matching prior to efficiency analysis with straightforward two stage procedure without matching as well as an alternative of conditional efficiency frontier. We conduct our study using a Monte Carlo study with different model specifications and despite the reduced sample which may create some complications in the computational stage we strongly agree with a notion of economic meaningfulness of the newly obtained results. We also compare the results obtained by the new method with ones previously produced by Demchuk and Zelenyuk (2009) who compare efficiencies of Ukrainian regions and find some differences between the two approaches. Second part deals with an empirical study of electricity generating power plants before and after market reform in Texas. We compare private, public and municipal power generators using the method introduced in part one. We find that municipal power plants operate mostly inefficiently, while private and public are very close in their production patterns. The new method allows us to compare decision making units from different groups, which may have different objective schemes and productive incentives. Despite the fact that at a certain point after the reform private generators opted not to provide their data to the regulator we were able to construct tree different data samples comprising two and three groups of generators and analyze their production/efficiency patterns. In the third chapter we propose a semiparametric approach with shape constrains which is consistent with monotonicity and concavity constraints. Penalized splines are used to maintain the shape constrained via nonlinear transformations of spline basis expansions. The large sample properties, an effective algorithm and method of smoothing parameter selection are presented in the paper. Monte Carlo simulations and empirical examples demonstrate the finite sample performance and the usefulness of the proposed method.

This doctoral thesis consists of three essays. In the first essay I investigate the presence of productivity convergence in eight regional pulp and paper industries of U.S. and Canada over the period of 1971-2005. Expectation of productivity convergence in the pulp and paper industries of Canadian provinces and of the states of its southern neighbour is high since they are trading partners with fairly high level of exchanges in both pulp and paper products. Moreover, they share a common production technology that changed very little over the last century. I supplement the North-American regional data with national data for two Nordic countries, Finland and Sweden, which provides a scope to compare the productivity performances of four leading players in global pulp and paper industry. I find evidence in favour of the catch-up hypothesis among the regional pulp and paper industries of U.S. and Canada in my sample. The growth performance is at the advantage of Canadian provinces relative to their U.S. counterparts. However, it is not good enough to surpass the growth rates of this industry in the two Nordic countries. It is well-known that econometric productivity estimation using flexible functional forms often encounter violations of curvature conditions. However, the productivity literature does not provide any guidance on the selection of appropriate functional forms once they satisfy the theoretical regularity conditions. The second chapter of my thesis provides an empirical evidence that imposing local curvature conditions on the flexible functional forms affect total factor productivity (TFP) estimates in addition to the elasticity estimates. Moreover, I use this as a criterion for evaluating the performances of three widely used locally flexible cost functional forms - the translog (TL), the Generalized Leontief (GL), and the Normalized Quadratic (NQ) - in providing TFP estimates. Results suggest that the NQ model performs better than the other two functional forms in providing TFP estimates. The third essay capitalizes on newly available high frequency energy consumption data from commercial buildings in the District of Columbia (DC) to provide novel insights on the realized energy use impacts of energy efficiency standards in commercial buildings. Combining these data with hourly weather data and information on tenancy contract structure I evaluate the impacts of energy standards, contractual structure of utility bill payments, and energy star labeling on account level electricity consumption. Using this unique panel dataset, the analysis takes advantage of detailed building-level characteristics and the heterogeneity in the building age distribution, resulting in buildings constructed before and after mandatory energy standards came into effect. Estimation results suggest that in commercial buildings constructed under a code, electricity consumption is lower by about 0.48 kWh per cooling degree hour. When tenants pay for their own utilities, consumption is lower by 0.82 kWh per cooling degree hour. The Energy Star effect is a 0.31 kWh reduction per cooling degree hour. Finally, peak savings for all three variables of interest occur at 2pm in the summer months, whereas peak summer marginal prices at DC's local electric utility occur at 5pm.