ASSOCIATE PROFESSOR OF ECONOMICS
Richard S. Gearhart III, Ph.D.
RESEARCH PAPER DESCRIPTIONS AND LINKS TO MANUSCRIPTS
"Oil Price Fluctuations and Employment in Kern County: A Vector Error Correction Approach" (with Nyakundi Michieka) Energy Policy, 87: 584-590, 2015.
Kern County is one of the country’s largest oil producing regions where a significant fraction of the labor force works in the oil industry. In this study, the short- and long-run effects of oil price fluctuations on employment in Kern County are investigated. First, we investigate whether the West Texas Intermediate (WTI) and Brent Spot prices have an effect on employment in Kern County. This relationship is explored using a modified version of the Granger Causality test proposed by Toda and Yamamoto (1995), before a VECM is used to examine the employment-oil price linkage both in the short- and long-run. Empirical results over the period 1990:01-2015:03 suggest long-run causality running from both WTI and Brent to employment. No causality is detected in the short-run. Results reveal that Kern County should formulate appropriate policies, taking into account the fact that changes in oil prices have long term effects on employment rather than short term.
"The Robustness of Cross-Country Healthcare Rankings Among Homogeneous OECD Countries" Journal of Applied Economics, XIX(1): 113-144, 2016.
This paper examines cross-country healthcare efficiency rankings using modern, non-parametric estimator. It re-examines analyses of cross-country healthcare efficiency using the hyperbolic direction, extending the dataset to include more years to estimate efficiency rankings and Malmquist indices to determine productivity changes over the panel. This paper finds that cross-country heterogeneity leads to different efficiency rankings than previously thought, and that the newer hyperbolic order-α estimator leads to more robust efficiency scores when looking across different output measures when looking at the more homogeneous OECD countries only. It also finds that the United States, if excluding the percent of healthcare expenditures that are publicly financed, is one of the more inefficient healthcare delivery systems in the world, across a variety of output measures. This highlights the need for reform in the United States. However, what are commonly thought of as well-run healthcare systems (Austria and France) are either inefficient themselves or have variation in their efficiency rankings, showcasing difficulties in using other countries’ healthcare systems as models for reform. It also finds that there has been productivity regression in all countries except the United States, which has no statistically significant productivity change. These highlight the difficulties in cross-country efficiency comparisons, and the need for reliable estimates that policy can be derived from.
"No Theory: An Explanation of the Lack of Consistency in Cross-Country Healthcare Comparisons Using Non-Parametric Estimators" Health Economics Review 6(40): 1-12, 2016.
Since 2000 several papers have examined the efficiency of healthcare delivery systems worldwide. These papers
have extended the literature using drastically different input and output combinations from one another, with little
theoretical or empirical support backing these specifications. Issues arise that many of these inputs and outputs are
available for a subset of OECD countries each year. Using a common estimator and the different specifications
proposed leads to the result that efficiency rankings across papers can diverge quite significantly, with several
countries being highly efficient in one specification and highly inefficient in another. Broad input-output measures
that are collected annually provide consistent efficiency rankings across specifications, compared to specifications
that utilize specific measures collected infrequently. This paper also finds that broad output measures that are not
quality-adjusted, such as life expectancy, seem to be a suitable alternative for infrequently collected quality-adjusted
output measures, such as disability adjusted life years.
"Non-parametric Frontier Estimation of Health Care Efficiency Among US States, 2002-2008" Health Systems 6(1): 15-32 2017.
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This paper examines cross-state health care efficiency rankings using modern non-parametric estimators. Cross-state efficiency rankings are robust to minor modifications in the input–output combinations used for estimation. This paper finds that there is no clear relationship between health care efficiency rankings and per capita health care expenditures in that state in the models used for this paper, even though this is a key variable that policymakers target. It also finds that Massachusetts, in one dataset, has shown significant productivity improvement from 2005 to 2008, the time period during which its health care reform was launched. In a second dataset, from 2002 to 2007, productivity regressed in Massachusetts. This may hint that efficiency gains from structural health care reform can outweigh population behavioral inefficiencies from using the ER as a source of primary care with insurance coverage expansion. I also find that states that chose to expand Medicaid were less efficient, on average, than states that did not choose to expand Medicaid. Simple variable comparisons suggest that this is an artifact of the data and political decision making, rather than people migrating for Medicaid or productive inefficiency.
"The Financial State of Municipalities and the Effect on Housing Values" (with Anne Anders) Journal of Housing Research 27(1): 17-44, 2018.
This study investigates the impact of municipalities' financial condition on the housing values within that municipality. The data consist of 68,882 housing units located in 175 cities throughout 115 metropolitan statistical areas (MSAs), across 42 states.
Information on the housing units and owner's characteristics are drawn from the 2011 Integrated Public Use Microdata Series (IPUMS) dataset and supplemented with MSA level economic condition variables. The municipal financial information is drawn from the 2010 government census and consists of detailed information of every local government's finances. The empirical results provide evidence that the financial state of a municipality affects local housing values. In particular, engaging in long-term capital projects lead to higher housing values, while not spending revenues on public goods will not entice individuals to move to the area.
"A comparison of the robust conditional order-m estimation and two stage DEA in measuring healthcare efficiency among California counties" (with Nyakundi Michieka) Economic Modelling 73: 395-406, 2018.
This paper examines cross-county healthcare efficiency rankings using modern non-parametric estimators, while taking into account secondary environmental variables. Results indicate that output-direction efficiency estimates yield counties producing inefficiently for both order-alpha and order-m estimators. After accounting for a variety of secondary environmental variables, unconditional efficiency estimates improve by anywhere between 7.5 and 10-percentage points. Results show that there is little correlation between the highly visible Robert-Woods-Johnson Foundation estimates with those derived here. We also find that counties are more efficient when they possess lower rates of obesity, unemployment, and preventable hospital readmissions. In addition, demographic variables do not play much of a role in explaining cross-county inefficiency. The analysis shows that the two stage DEA is inappropriate and violates several assumptions in comparison to the conditional order-m estimation.
"The Impact of Secondary Environmental Variables on OECD Healthcare Efficiency: A Robust Conditional Approach" https://doi.org/10.1515/bejeap-2018-0063, The B.E. Journal of Economic Analysis and Policy, 2019.
In this paper, I estimate country-level efficiency using a newer order-m estimator where I condition efficiency estimates on secondary environmental variables. This allows me to identify which variables influence the effectiveness of a healthcare delivery system. I find that not controlling for secondary environmental variables leads to the average OECD country being 11-percent inefficient; after controlling for demographics and economic (social protection) environmental variables, inefficiency reduces to 7-percent (5-percent). This provides evidence that a substantial part of the inefficiencies of a healthcare system is related to demographics, socioeconomics, and the structure of the healthcare delivery system. Using the second-stage results, I find lower healthcare spending, both as a percent of GDP and total out-of-pocket, as well as more of the population covered by public health insurance, is related to better efficiency. Lower fertility rates, lower immigration rates, higher incomes, and lower pharmaceutical doses are also consistent with better healthcare efficiency. Lastly, a healthcare system that provides a basic benefits package but allows for purchase of private health insurance, with moderate gatekeeping and flexibility to increase the budget for healthcare through public and private financing, are the most efficient healthcare systems.
"Oil Price Dynamics and Sectoral Employment in the US" (with Nyakundi Michieka) Economic Analysis and Policy 62: 140-149, 2019.
This paper examines the long- and short-run relationships between oil prices and employment in four sectors of the top oil producing counties in the U.S. In the long-run, findings from a Panel Auto Regressive Distributed Lag (ARDL) model illustrate causality running from oil prices to employment in every sector in the county panel. Findings from the state panel indicate the presence of long- and short-run causality from oil prices to mining and trade employment, while service employment is not affected. Natural resource and mining employment reverts fastest to equilibrium following shocks in oil prices.
"Efficiency of American States After Implementation of the PPACA From 2014 to 2017" (with Nyakundi Michieka) Applied Economics 52(18): 1959-1972, 2020.
We assess the impact of healthcare efficiency among U.S. states after implementation of the PPACA. A Malmquist
Index decomposes productivity changes of state healthcare systems since 2014. Results indicate productivity
regression of 0.14-percentage points from 2014 to 2017. In 2017 the average state is 4.9-percentage points
inefficient. Using the conditional order-m estimator and non-parametrically regressing the ratio of conditional to
unconditional order-m efficiency scores on secondary environmental variables, we find that behavioral,
socioeconomic, and healthcare utilization factors play a role in explaining state inefficiency. The average state
becomes nearly fully efficient after controlling for behavior (efficiency improves by 3.7-percentage points),
socioeconomic (4.1-percentage points), and healthcare utilization (3.2-percentage points) variables. Using a second-stage
regression framework, we find that higher levels of obesity, adult smoking, and diabetes lead to lower
healthcare efficiency. Likewise, we see that low and high levels of unemployment are associated with improved
healthcare efficiency, which is in line with contradictory studies. We also find further support for the link between
income and health outcomes, through the vehicle of improved health efficiency. Lastly, we find that ensuring that
low-cost populations engaging in health treatments have improved health outcomes, as they prevent high cost
morbidities in the future.
Natural Resource Abundance and Healthcare Efficiency in Appalachia: A Robust Conditional Approach” (2019) (with Nyakundi Michieka) Energy Policy 129: 985-996, 2019.
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This study investigates the role of natural resource abundance on healthcare efficiency in Appalachia. Unconditional efficiency measures are calculated from a production process where health inputs are turned into health outcomes. We utilize a non-parametric robust order-m estimator where we condition our efficiency measures on secondary environmental variables that do not directly impact health in a production process. Unlike the unconditional estimation procedure which impacts the shape of the production frontier, as well as the distance to the frontier, the secondary environmental variables alter the location of the production frontier, with corresponding effects on efficiency. We find that taking into account the relatively worse socioeconomic and healthcare conditions in Appalachia improves healthcare efficiency by 1.6- and 1.9-percentage points, respectively. The presence of natural resource extraction measured by oil, natural gas or coal production, worsens healthcare efficiency by 0.14-percentage points, another vehicle of the resource curse. Regressing the conditional efficiency estimates on secondary environmental variables shows that counties with natural resource production are further from the production frontier and therefore more inefficient. Findings suggest that policymakers can improve health efficiency by incentivizing positive health behaviors, such as reduced obesity and smoking rates, which would mimic health-improving inputs in a production process.
"A Non-Parametric Investigation of Supply-Side Factors and Healthcare Efficiency in the U.S." (with Nyakundi Michieka) Journal of Productivity Analysis 54: 59-74.
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This study investigates the role of natural resource abundance on healthcare efficiency in Appalachia. Unconditional efficiency measures are calculated from a production process where health inputs are turned into health outcomes. We utilize a non-parametric robust order-m estimator where we condition our efficiency measures on secondary environmental variables that do not directly impact health in a production process. Unlike the unconditional estimation procedure which impacts the shape of the production frontier, as well as the distance to the frontier, the secondary environmental variables alter the location of the production frontier, with corresponding effects on efficiency. We find that taking into account the relatively worse socioeconomic and healthcare conditions in Appalachia improves healthcare efficiency by 1.6- and 1.9-percentage points, respectively. The presence of natural resource extraction measured by oil, natural gas or coal production, worsens healthcare efficiency by 0.14-percentage points, another vehicle of the resource curse. Regressing the conditional efficiency estimates on secondary environmental variables shows that counties with natural resource production are further from the production frontier and therefore more inefficient. Findings suggest that policymakers can improve health efficiency by incentivizing positive health behaviors, such as reduced obesity and smoking rates, which would mimic health-improving inputs in a production process.
"Oil Prices, the Housing Market and Spillover Effects: Evidence from California’s Central Valley" (with Nyakundi Michieka and Yiannis Ampatzidis) Forthcoming, Journal of Housing Research, 2020.
This paper examines the effects of oil prices on home values in Kern County, which is California's top oil producer. Using monthly data from 1990:01 to 2018:03, results from an ARDL model indicate that there is a long-run equilibrium relationship between oil prices, unemployment, interest rates and home values. In the short run, a one percent increase in unemployment and interest rates will decrease home values by 2.06 and 0.82 percent, respectively. VEDC and GIRFs imply that changes in Kern's home values will influence home prices in San Bernardino County. Los Angeles has the greatest effect on home sales in Kern County.