Abdoul G. Sam

Voluntary Pollution Reductions and the Enforcement of Environmental Law: An Empirical Study of the 33/50 Program

This paper studies determinants and effects of firms’ participation in the 33/50 program, which is a voluntary pollution reduction (VPR) program initiated by government regulators. We examine a wide range of explanations for voluntary corporate environmentalism and find evidence in support of an enforcement theory that predicts that (1) VPR participation is rewarded by relaxed regulatory scrutiny, (2) the anticipation of this reward spurs firms to participate in the program, and (3) the program rewards regulators with reduced pollution. We also find that 33/50 participation was more likely for firms operating in states with larger environmentalist constituencies.

Voluntary Pollution Reduction Programs, Environmental Management, and Environmental Performance: An Empirical Study

This paper examines, empirically, the mechanism by which a voluntary pollution reduction program (VPR) achieves pollution reductions. We find that participation in the 33/50 program, the U.S. Environmental Protection Agency’s first VPR, spurred the adoption of total quality environmental management (TQEM), an environmental management system that views pollution as a quality defect to be continuously reduced through the development of products and processes that minimize waste generation at source. We find in turn that TQEM had a significant negative effect on 33/50 releases and that 33/50 participation produced additional direct pollution reduction benefits both during and after the program years. (JEL C23, Q58)

Semiparametric Estimation of Consumer Demand Systems with Micro Data

Maximum likelihood and two-step estimators of censored demand systems yield biased and inconsistent parameter estimates when the assumed joint distribution of disturbances is incorrect. This paper proposes a semiparametric estimator that retains the computational advantage of the two-step approach but is immune to distributional misspecification. The key difference between the proposed estimator and the two-step estimator is that the parameters of the binary censoring equations are estimated using a distribution-free single-index model. We implement the proposed estimator using household-level data obtained from the Hainan province in China. specification test lends support to our approach.

Nonparametric Estimation of the Short Rate Diffusion Process from a Panel of Yields

In this paper, we propose a nonparametric estimator of the short rate diffusion process using observations of a panel of yields. The proposed estimator can greatly reduce the bias of the nonparametric estimator proposed in Stanton (1997) that uses a single time series of short rate observations. Simulations confirm that the new method significantly attenuates the spurious nonlinearity of the drift function as documented in Chapman and Pearson (2000). We apply the method to estimate the U.S. short rate process using a panel of six Treasury yields. With 42 years’ daily observations of the panel of yields, the proposed drift function estimator achieves the same efficiency as the Stanton (1997) estimator based on 145 years of daily short rate observations. Finally, we show that the proposed estimator also has significant economic implications on the pricing of bonds and interest rate derivatives.

Geographically and temporally weighted likelihood regression: Exploring the spatiotemporal determinants of land use change

Urban areas possess complex spatial configurations. These patterns are produced by cumulative changes in land use and land cover as human and natural environments are influenced by markets forces, policy, and changes in the natural landscape. To understand the mechanisms underlying these complex patterns, it is important to develop models that can capture the complexity of the underlying economic process. This includes spatiotemporal variation in the variables as well as spatiotemporal heterogeneity or non-stationarity in the model. The objective of this paper is to build on previous work in spatial nonparametric modeling and propose a spatiotemporal technique for nonlinear panel data models. Using a series of Monte Carlo experiments, we demonstrate how extending a geographically weighted likelihood regression (GWLR) model to account for temporal heterogeneity can improve the performance of the model when heterogeneity exists in the spatial and temporal dimension. We also show how the technique can be used in modeling real world land use changes by applying our proposed technique to a panel of historical subdivision development from an urbanizing county in the Baltimore/Towson Metropolitan Statistical Area (MSA). Our results demonstrate that the method provides better performance than a standard parametric model. We also demonstrate how the spatiotemporal marginal effects from the model can be used to conduct policy analysis at multiple spatial and temporal scales, which is not possible using the standard global parameter estimates. Our proposed technique is simple to execute and can be implemented using any statistical software package.

Agricultural Technology Adoption and Nonfarm Earnings in Uganda: A Semiparametric Analysis

Household diversification into nonfarm work activities is a major rural livelihood strategy in many developing economies. In this paper, we explore empirically if rural households in Uganda leverage their nonfarm earnings to overcome credit constraints and invest in high yielding maize seed varieties. We use a semiparametric estimator of binary outcomes that accommodates endogenous regressors straightforwardly to estimate the effect of nonfarm income on technology adoption decisions. Our results show that nonfarm income has a positive and significant effect on the adoption of improved maize seed.

Nonparametric estimation of market risk: an application to agricultural commodity futures

Purpose - While the extant literature is replete with theoretical and empirical studies of value at risk (VaR) methods, only a few papers have applied the concept of VaR to quantify market risk in the context of agricultural finance. Furthermore, papers that have done so have largely relied on parametric methods to recover estimates of the VaR. The purpose of this paper is to assess extreme market risk on investment in three actively traded agricultural commodity futures. Design/methodology/approach - A nonparametric Kernel method was implemented which accommodates fat tails and asymmetry of the portfolio return density as well as serial correlation of the data, to estimate market risk for investments in three actively traded agricultural futures contracts: corn, soybeans, and wheat. As a futures contract is a zero-sum game, the VaR for both short and long sides of the market was computed. Findings - It was found that wheat futures are riskier than either corn or soybeans futures over both periods considered in the study (2000-2008 and 2006-2008) and that all three commodities have experienced a sharp increase in market risk over the 2006-2008 period, with VaR estimates 10-43 percent higher than the long-run estimates. Research limitations/implications - Research is based on cross-sectional data and does not allow for dynamic assessment of expenditure elasticities. Originality/value - This paper differs methodologically from previous applications of VaR in agricultural finance in that a nonparametric Kernel estimator was implemented which is exempt of misspecification risk, in the context of risk management of investment in agricultural futures contracts. The application is particularly relevant to grain elevator businesses which purchase grain from farmers on a forward contract basis and then turn to the futures markets to insure against falling prices.

Corporate environmentalism and environmental innovation

Several papers have explored the effect of tighter environmental standards on environmental innovation. While mandatory regulation remains the central tenet of US environmental policy, the regulatory landscape has changed since the early 1990s with the increased recourse by federal and state agencies to corporate environmentalism--voluntary pollution prevention (P2) by firms--to achieve environmental improvements. We therefore estimate the effects of voluntary P2 activities on the patenting of environmental technologies by a sample of manufacturing firms. With our panel data of 352 firms over the 1991-2000 period, we adopt an instrumental variable Poisson framework to account for the count nature of patents and the endogeneity of the P2 adoption decision. Our results indicate that the adoption of voluntary P2 activities in the manufacturing sector has led to a statistically and economically significant increase in the number of environmental patents, suggesting that corporate environmentalism can act as a catalyst for investments in cleaner technologies. Our findings are internationally relevant given the increasing ubiquity of corporate environmentalism in both developed and developing economies.

Country of origin advertising and US demand of imported wine: an empirical analysis

We investigate the impact of media advertising on the US consumption of imported wine. Panel data from six countries over 15 years (1994–2008) are used to estimate an aggregate demand function for US wine imports. Our empirical analysis reveals evidence of important effects of advertising of domestic and imported wines on imported quantities; the advertising of imported wines significantly increases the quantity of imports for most countries while the advertising of domestic wines has a mixed effect on imported wine volumes. Other determinants such as price and real income are also found significant.

Heterogeneous Effects of Maternal Labor Market Participation on the Nutritional Status of Children: Empirical Evidence from Rural India

We use a dataset of rural Indian households to investigate the effects of maternal participation in labor markets on child nutrition (the standardized height-for-age). Our study differs methodologically from previous research in this realm in that we are using an instrumental variable quantile regression framework in order to estimate the causal effects of maternal work at various locations of the height-for-age distribution in rural India. The quantile estimates provide evidence of large heterogeneity in the effect of a mother’s work on child nutrition. In particular, the results suggest that it is children in the lower tail of the distribution who experience more sizable ‘nutritional premiums’ due to maternal labor market participation; the effects are small and insignificant for children in the rest of the distribution.