Regime-dependent wheat price volatilities

Applied Economics Letters · 2024 · 1 citations

• Political Science
• Economics
• Econometrics

Correlated discrete and continuous outcomes with endogeneity and lagged effects: past season yield impact on improved corn seed adoption

Journal of Applied Statistics · 2020

• Computer Science
• Econometrics
• Economics

Farmers in Sub-Saharan Africa have lower agricultural technology adoption rates compared to the rest of the world. It is believed that the past season yield affects a farmer's capacity to take on the riskier improved seed variety; but this effect has not been studied. We quantify the effect of past season yield on improved corn seed use in future seasons while addressing the impact of the seed variety on yield. We develop a maximum likelihood method that addresses the fact that farmers self-select into a technology resulting in its effect on yield being endogenous. The method is unique since it models both lagged and endogenous effects in correlated discrete and continuous outcomes simultaneously. Due to the prescence of the lagged effect in a three year dataset, we also propose a solution to the initial conditions problem and demonstrate with simulations its effectiveness. We used survey longitudinal data collected from Kenyan corn farmers for three years. Our results show that higher past season yield increased the likelihood of adoption in future seasons. The simulation and empirical studies indicate that ignoring the self selection of improved seed use biases the results; we obtain a different sign in the covariance.

Beef Producer Alliance Preferences for Vertical Coordination: A Bivariate Nested Panel Probit Approach

AgEcon Search (University of Minnesota, USA) · 2019-01-01

Using a nested bivariate panel probit model, we quantify the perceived attribute values (PAV) that beef producers place on different information flows and alliance attributes. Our framework allows us to quantify the monetary value of individual rather than fixed sets of attributes. Results indicate that young producers are most likely to join an alliance, and high participation fees are a significant deterrent to joining an alliance. A PAV of $12.64/head is attached to an alliance that enforces restrictions on vaccinations and antibiotic use. For small producers, not having a required minimum number of animals has a PAV of $9.65/head.

Modeling regime‐dependent agricultural commodity price volatilities

Agricultural Economics · 2017-07-25 · 29 citations

Abstract In stark contrast to financial markets, relatively little attention has been given to modeling agricultural commodity price volatility. In recent years, numerous methodologies with various strengths have been proposed for modeling price volatility in financial markets. We propose using a mixture of normals with unique GARCH processes in each component for modeling agricultural commodity prices. While a normal mixture model is quite flexible and allows for time varying skewness and kurtosis, its biggest strength is that each component can be viewed as a different market regime and thus estimated parameters are more readily interpreted. We apply the proposed model to ten different agricultural commodity weekly cash prices. Both in‐sample fit and out‐of‐sample forecasting tests confirm that the two‐state NM‐GARCH approach performs better than the traditional normal GARCH model. A significant and state‐dependent inverse leverage effect is detected only for pork in the regime where the price is expected to drop, indicating the volatility in this regime tends to increase more following a realized price rise than a realized price drop.

A NOTE ON THRESHOLD FACTOR LEVEL(S) AND STONE-GEARY TECHNOLOGY

Journal of Agricultural and Applied Economics · 2015-07-21 · 4 citations

Abstract This article proposes the parsimonious Stone-Geary utility function from consumer choice theory as a production function model. The viability of the threshold input idea is empirically demonstrated for irrigation water (and in the case of nitrogen, a “gratis” threshold) using two field trials from the famous Hexem-Heady data sets. The implications of the Stone-Geary model for tractable U-shaped average variable cost and for factor demand and product supply are explored.

Public School Open Enrollment and Housing Capitalization

2014-01-01

Economic literature on real estate markets, especially that on house prices, shows that houses cost more in better school districts. This paper evaluates the effect of open-enrollment (OE) in public school districts on house prices. We demonstrate a way for removing unobserved heterogeneity for the fixed effects by using a difference in sales model. In addition to estimating the mean effects using a difference model, we also estimate the effects of OE on median and quartile house prices. Many studies have used hedonic models for explaining house prices. Typically, these models use house and neighborhood characteristics, and school and school district characteristics for explaining house prices. Others have relied on cross-sectional identification of relationship between house prices and variables that can be used as a proxy for perceived school quality in different school districts. The most important feature of these studies has been to disentangle the effects of schools on house prices from other implicit characteristics. Bogart and Cromwell (2000) compared the sale prices of houses on either side of a school-district boundary to attribute the differences in prices to better schools. Since the variation in house prices might also be due to some other unobserved neighborhood quality, the results might be biased. Black (1999) used boundary dummies to control for unobserved neighborhood characteristics and found that there is a premium for schools with better test scores, attendance rates and other unobserved school quality characteristics. Some other studies have also used school level data to attribute the premium in single family home prices using distance to school as an explanatory variable and in some cases assigning each house a school level data. Under OE, children are not restricted to attend public schools in their own school district. Instead, OE allows students from anywhere to attend schools in a district that adopts OE. With the introduction and popularity of OE in many states in the U.S., an immediate question concerns the price premium for houses in better school districts. Because enrollment in schools is no longer restricted to homeowners in that particular district, one might expect the premium for better schools to depreciate over time. This effect could be magnified with the advent of charter schools, magnet schools and the expansion of private schools. The literature on the impact of OE varies widely, and has focused on issues like impact on parental decision making, difference in education deliverance, equity in forms of economic outcomes and other ethnic outcomes, mobilization of homebuyers (Goldhaber, 1999), goals of integration and OE (Smith, 1995), supply of and demand for educational choice (Funkhouser and Colopy, 1994), early effects of OE on significant changes in district open enrollments (Rubenstein, 1992). Reback's (2005) work is the first attempt to evaluate the effect of OE on house prices. He found that residential properties appreciated significantly in those districts from where students were able to transfer and declined in those which accepted transfer students. He controlled for the unobserved heterogeneity for the fixed effects by considering the effect on percentage change between the assessed price and actual sale prices in two different years of the percentage changes in explanatory variables. This paper evaluates the effect of school characteristics on house values capitalization via the impact of OE at the district level. Using district dummies for school characteristics, this paper assesses the impact of OE on single family home prices. As is standard in the hedonics literature, we have used the log-linear models for estimation. We explore several model specifications and the results are quite robust to the different specifications. The dataset used is from 6 school districts in and around Tucson Metropolitan area in Pima County, Arizona for 2001-2012, and draws on data from the Pima County Assessor's Office, Pima County GIS, Arizona Department of Education Research and Evaluation, along with proprietary OE numbers from the Catalina Foothills School District (CFSD) which is considered the best school district in the study region. It contains information on all single-family houses sold in this time period, their characteristics, school district dummies, boundary dummies and other variables characterizing the economy. For the houses in the boundary outside of CFSD, we consider separately the effects on the two school districts which share their boundary with CFSD. The dataset also contains houses that were sold more than once, and we use these houses for the difference model which controls for the fixed effects in differences. We control for unobserved heterogeneity by using a difference model. Specifically, we only consider houses that are sold more than once in the time period and use differences of log of sales prices as the dependent variable. Regressors include differences in OE numbers and differences in other time-varying explanatory variables. The differencing washes out time invariant house characteristics and the unobserved heterogeneity by controlling for the differences in fixed effects. The intuition behind the results is that it identifies the mean effect of differences in OE numbers on differences in sale prices by controlling for other observed and unobserved house characteristics. We also identify the houses on boundaries of the school districts and use boundary dummies to capture the effect of OE on the houses which share the boundary but are otherwise identical. In this paper we also explored the effects on median-priced houses by quantile regression as we expect the housing market to be segregated by price. Preliminary results show that OE significantly increases house prices for school districts bordering the CFSD but this effect is not same for the two different neighboring districts. However, the house prices within the CFSD boundary are not significantly affected by OE, on an average. This is mostly attributed to the capacity constraint on OE numbers in school districts. All these analyses also show that houses along the boundaries are significantly different from those that are closer to the center of a district. This validates that OE does not have similar effects on all houses in a school district. This paper also presents the marginal effects of OE for different specifications. Evidence of the impact of school characteristics on real estate markets, anecdotal and empirical, is critical for reassessment with the expansion of OE in public school districts. It also remains to be seen whether parents are still willing to pay a premium for better school districts with the advent of OE. This will have non-trivial policy implications for public school decision makers, realtors and individuals. While this paper does not attempt to identify other school characteristics which people are willing to pay for, it does evaluate the impact of OE in house values. The difference approach used in this paper controls for unobserved heterogeneity. It also looks in detail at how the houses on the school district boundary differ from the ones that are away from the boundary. Finally, this paper considers different segments of the housing market and emphasizes on the median effects. The overall results obtained are robust to the model specifications explored, lending strength to our findings.

Public School Open Enrollment and Housing Capitalization

2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota · 2014-01-01

Economic literature on real estate markets, especially that on house prices, shows that houses cost more in better school districts. This paper evaluates the effect of open-enrollment (OE) in public school districts on house prices. We demonstrate a way for removing unobserved heterogeneity for the fixed effects by using a difference in sales model. In addition to estimating the mean effects using a difference model, we also estimate the effects of OE on median and quartile house prices. Many studies have used hedonic models for explaining house prices. Typically, these models use house and neighborhood characteristics, and school and school district characteristics for explaining house prices. Others have relied on cross-sectional identification of relationship between house prices and variables that can be used as a proxy for perceived school quality in different school districts. The most important feature of these studies has been to disentangle the effects of schools on house prices from other implicit characteristics. Bogart and Cromwell (2000) compared the sale prices of houses on either side of a school-district boundary to attribute the differences in prices to better schools. Since the variation in house prices might also be due to some other unobserved neighborhood quality, the results might be biased. Black (1999) used boundary dummies to control for unobserved neighborhood characteristics and found that there is a premium for schools with better test scores, attendance rates and other unobserved school quality characteristics. Some other studies have also used school level data to attribute the premium in single family home prices using distance to school as an explanatory variable and in some cases assigning each house a school level data. Under OE, children are not restricted to attend public schools in their own school district. Instead, OE allows students from anywhere to attend schools in a district that adopts OE. With the introduction and popularity of OE in many states in the U.S., an immediate question concerns the price premium for houses in better school districts. Because enrollment in schools is no longer restricted to homeowners in that particular district, one might expect the premium for better schools to depreciate over time. This effect could be magnified with the advent of charter schools, magnet schools and the expansion of private schools. The literature on the impact of OE varies widely, and has focused on issues like impact on parental decision making, difference in education deliverance, equity in forms of economic outcomes and other ethnic outcomes, mobilization of homebuyers (Goldhaber, 1999), goals of integration and OE (Smith, 1995), supply of and demand for educational choice (Funkhouser and Colopy, 1994), early effects of OE on significant changes in district open enrollments (Rubenstein, 1992). Reback's (2005) work is the first attempt to evaluate the effect of OE on house prices. He found that residential properties appreciated significantly in those districts from where students were able to transfer and declined in those which accepted transfer students. He controlled for the unobserved heterogeneity for the fixed effects by considering the effect on percentage change between the assessed price and actual sale prices in two different years of the percentage changes in explanatory variables. This paper evaluates the effect of school characteristics on house values capitalization via the impact of OE at the district level. Using district dummies for school characteristics, this paper assesses the impact of OE on single family home prices. As is standard in the hedonics literature, we have used the log-linear models for estimation. We explore several model specifications and the results are quite robust to the different specifications. The dataset used is from 6 school districts in and around Tucson Metropolitan area in Pima County, Arizona for 2001-2012, and draws on data from the Pima County Assessor's Office, Pima County GIS, Arizona Department of Education Research and Evaluation, along with proprietary OE numbers from the Catalina Foothills School District (CFSD) which is considered the best school district in the study region. It contains information on all single-family houses sold in this time period, their characteristics, school district dummies, boundary dummies and other variables characterizing the economy. For the houses in the boundary outside of CFSD, we consider separately the effects on the two school districts which share their boundary with CFSD. The dataset also contains houses that were sold more than once, and we use these houses for the difference model which controls for the fixed effects in differences. We control for unobserved heterogeneity by using a difference model. Specifically, we only consider houses that are sold more than once in the time period and use differences of log of sales prices as the dependent variable. Regressors include differences in OE numbers and differences in other time-varying explanatory variables. The differencing washes out time invariant house characteristics and the unobserved heterogeneity by controlling for the differences in fixed effects. The intuition behind the results is that it identifies the mean effect of differences in OE numbers on differences in sale prices by controlling for other observed and unobserved house characteristics. We also identify the houses on boundaries of the school districts and use boundary dummies to capture the effect of OE on the houses which share the boundary but are otherwise identical. In this paper we also explored the effects on median-priced houses by quantile regression as we expect the housing market to be segregated by price. Preliminary results show that OE significantly increases house prices for school districts bordering the CFSD but this effect is not same for the two different neighboring districts. However, the house prices within the CFSD boundary are not significantly affected by OE, on an average. This is mostly attributed to the capacity constraint on OE numbers in school districts. All these analyses also show that houses along the boundaries are significantly different from those that are closer to the center of a district. This validates that OE does not have similar effects on all houses in a school district. This paper also presents the marginal effects of OE for different specifications. Evidence of the impact of school characteristics on real estate markets, anecdotal and empirical, is critical for reassessment with the expansion of OE in public school districts. It also remains to be seen whether parents are still willing to pay a premium for better school districts with the advent of OE. This will have non-trivial policy implications for public school decision makers, realtors and individuals. While this paper does not attempt to identify other school characteristics which people are willing to pay for, it does evaluate the impact of OE in house values. The difference approach used in this paper controls for unobserved heterogeneity. It also looks in detail at how the houses on the school district boundary differ from the ones that are away from the boundary. Finally, this paper considers different segments of the housing market and emphasizes on the median effects. The overall results obtained are robust to the model specifications explored, lending strength to our findings.

Environmental Justice and Federalism

Edward Elgar Publishing eBooks · 2012-12-28 · 7 citations

The authors discuss two case studies in their investigation of the complex interactions between environmental justice and government. These analyses offer a comprehensive view of both the siting and regulation of polluting activities, as well as a discussion of the effects on major natural resources such as clean air and drinking water. In each case, the authors both describe current government responses to the problem and offer specific recommendations regarding what actions should be taken in the future.

Contents

Edward Elgar Publishing eBooks · 2012-12-28

Copyright

Edward Elgar Publishing eBooks · 2012-12-28