Abstract
The dissertation consists of three essays on industrial organization. The first two chapters study the shale gas firms’ choices of lease locations in Pennsylvania. The third chapter is an independent study of two-sided matching games, with an emphasis on empirical methods.
The motivation of the first two chapters originates from a policy carried out by the Penn- sylvania government. In November 2010, the Pennsylvania Department of Environmental Protection began posting well-specific production data of the Marcellus Shale. The informa- tion potentially reveals the shale gas reserve distribution. The first two essays seek to study whether the production information disclosure has affected the shale gas operators’ choices of lease locations. In the first chapter, a simple theoretical model is proposed, in which firms sequentially choose between two locations to make their lease investment. The firms con- duct Bayesian updating of the locations’ output and maximize their expected return. The disclosure of the production information is assumed to affect the firms’ prior beliefs, with a higher productivity leading to a greater prior probability that the location has higher poten- tial. Analyses of the two-firm case shows that, when the prior beliefs are corrected adjusted by the production information, the expected number of firms choosing the location with higher output potential goes up. For the multiple-firm case, simulation is run and generates results that are consistent with the two-firm case.
The second chapter examines the model implication using the production and the lease ii
data from the Marcellus Shale in Pennsylvania. A Tobit model is assumed for the monthly incremental acreages of an operator at a municipality. The model is used to study how the acreage is determined by the signals observed by an operator, and how the correlations be- tween the acreage and the signals are affected by the production information. The acreages an operator obtains at the municipality in the past three months is used as a proxy for its pri- vate signal. Estimation shows that without the production information, a one acre increase in the acreages obtained by an operator at a municipality in the past three months leads to a 0.542 acre increase in the latent acreage the operator obtains at the same municipality in the current month. Once the production information is available, for each one percent increase in the municipality’s productivity, that number goes up by 0.013 acres. The estimation also finds that every one percent increase in productivity is associated with a 24.823 acre direct increase in the latent acreages. The findings are consistent with the prediction of the the- oretical mode, which shows that the production information improves the shale gas firms’ resource allocation efficiency.
The third chapter extends the marriage matching model in Choo and Siow (2006) by proposing two empirical models of one-to-one matching games. The first model considers a static one-to-one transferable utility matching game with search frictions, and studies its identification using a nonparametric approach. The second model examines a static one-to- one non-transferable utility matching game. The model is parametrically estimated, allowing heterogeneity by imposing random effects. In particular, a finite mixture model is employed and estimated using the Expectation Maximization algorithm.
The motivation of the first two chapters originates from a policy carried out by the Penn- sylvania government. In November 2010, the Pennsylvania Department of Environmental Protection began posting well-specific production data of the Marcellus Shale. The informa- tion potentially reveals the shale gas reserve distribution. The first two essays seek to study whether the production information disclosure has affected the shale gas operators’ choices of lease locations. In the first chapter, a simple theoretical model is proposed, in which firms sequentially choose between two locations to make their lease investment. The firms con- duct Bayesian updating of the locations’ output and maximize their expected return. The disclosure of the production information is assumed to affect the firms’ prior beliefs, with a higher productivity leading to a greater prior probability that the location has higher poten- tial. Analyses of the two-firm case shows that, when the prior beliefs are corrected adjusted by the production information, the expected number of firms choosing the location with higher output potential goes up. For the multiple-firm case, simulation is run and generates results that are consistent with the two-firm case.
The second chapter examines the model implication using the production and the lease ii
data from the Marcellus Shale in Pennsylvania. A Tobit model is assumed for the monthly incremental acreages of an operator at a municipality. The model is used to study how the acreage is determined by the signals observed by an operator, and how the correlations be- tween the acreage and the signals are affected by the production information. The acreages an operator obtains at the municipality in the past three months is used as a proxy for its pri- vate signal. Estimation shows that without the production information, a one acre increase in the acreages obtained by an operator at a municipality in the past three months leads to a 0.542 acre increase in the latent acreage the operator obtains at the same municipality in the current month. Once the production information is available, for each one percent increase in the municipality’s productivity, that number goes up by 0.013 acres. The estimation also finds that every one percent increase in productivity is associated with a 24.823 acre direct increase in the latent acreages. The findings are consistent with the prediction of the the- oretical mode, which shows that the production information improves the shale gas firms’ resource allocation efficiency.
The third chapter extends the marriage matching model in Choo and Siow (2006) by proposing two empirical models of one-to-one matching games. The first model considers a static one-to-one transferable utility matching game with search frictions, and studies its identification using a nonparametric approach. The second model examines a static one-to- one non-transferable utility matching game. The model is parametrically estimated, allowing heterogeneity by imposing random effects. In particular, a finite mixture model is employed and estimated using the Expectation Maximization algorithm.
Original language | English |
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Type | PhD Thesis |
Publisher | University of California, Los Angeles |
Publication status | Published - 2018 |
Externally published | Yes |