Estimating returns to soil conservation adoption in the northern Ethiopian highlands

Land degradation in the form of soil erosion and nutrient depletion presents a threat to food security and sustainability of agricultural production in many developing countries. Governments and development agencies have invested substantial resources to promote soil conservation practices as part of an effort to improve environmental conditions and reduce poverty. However, limited rigorous empirical work has been done on the economics of soil conservation technology adoption. This article investigates the impact of stone bunds on value of crop production per hectare in low and high rainfall areas of the Ethiopian highlands using cross-sectional data from more than 900 households, with multiple plots per household. We have used modified random effects models, stochastic dominance analysis (SDA), and matching methods to ensure robustness. The parametric regression and SDA estimates are based on matched observations obtained from the nearest neighbor matching using propensity score estimates. This is important, because conventional regression and SDA estimates are obtained without ensuring that there actually exist comparable conserved and nonconserved plots on the distribution of covariates. We use matching methods, random effects, and Mundlak's approach to control selection and endogeneity bias that may arise due to correlation of unobserved heterogeneity and observed explanatory variables.
We find that the three methods tell a consistent story. Plots with stone bunds are more productive than those without such technologies in semi-arid areas but not in higher rainfall areas, apparently because the moisture conserving benefits of this technology are more beneficial in drier areas. This implies that the performance of stone bunds varies by agro-ecology type, suggesting the need for designing and implementing appropriate site-specific technologies.     

The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients

The US housing market has experienced significant cyclical volatility over the last twenty-five years due to major structural changes and economic fluctuations. In addition, the housing market is generally considered to be weak form inefficient. Houses are relatively illiquid, exceptionally heterogeneous, and are associated with large transactions costs. As such, past research has shown that it is possible to predict, at least partially, the time path of housing prices. The ability to predict housing prices is important such that investors can make better asset allocation decisions, including the pricing and underwriting of mortgages. Most of the prior studies examining the US housing market have employed constant coefficient approaches to forecast house price movements. However, this approach is not optimal as an examination of data reveals substantial sub-sample parameter instability. To account for the parameter instability, we employ alternative estimation methodologies where the estimated parameters are allowed to vary over time. The results provide strong empirical evidence in favor of utilizing the rolling Generalized Autoregressive Conditional Heteroskedastic (GARCH) Model and the Kalman Filter with an Autoregressive Presentation (KAR) for the parameters time variation. Lastly, we provide out-of-sample forecasts and demonstrate the precision of our approach.     

A heuristic programming approach to the employee scheduling problem and some thoughts on “managerial robots”

We describe a system for the automatic scheduling of employees in the particular setting in which: the number of employees wanted on duty throughout the week fluctuates; the availabilities of the employees varies and changes from week to week; and a new schedule must be produced each week, by virtue of the changing demand for service.The problem which we address appears in a variety of settings, including: airline reservation offices; telephone offices; supermarkets; fast food restaurants; banks and hotels.Previous approaches to the problem have relied chiefly on formal methods, generally involving one or another variation of linear or integer, mathematical programming. We suggest that except in cases involving very small problems (only a handful of employees) that those approaches have not proven promising, especially where union rules and management requirements impose complex constraints on the problem, and that a heuristic approach has proven to be substantially superior.We set forth the general features of our heuristic approach, which we see as an application of artificial intelligence; we show how, in contrast to other approaches, which design shifts as if employees were always available and try to fit those shifts to employees who are not always available, our system design shifts with deference to the employees' limited availabilities; we suggest that, for a given service level, our system produces schedules with a better “fit”—number of employees actually on duty comparing more favorably with the number wanted; and we state that while, for a given service level, a ‘manual scheduler’ may take up to 8 hours each week to prepare a good schedule, our system, on most micro computers, routinely produces better schedules involving up to 100 employees in about 20 minutes.The scheduling of employees is generally considered to be a managerial function, in the setting of the problem we address. When a craft employee is replaced on an assembly line by a machine which performs the same function, we speak of the replacing mechanism as an industrial robot.We suggest that systems like that which we describe deserve a name, to distinguish them from comparable, computer based systems which do not replace, but rather supplement a manager, and we suggest the name ‘managerial robot’ for such systems.We set forth the characteristics which we feel would justify applying the term ‘managerial robot’ to a computer based system, and suggest that classification is basic to understanding and communication and that just as terms such as decision support systems and expert systems prove useful in our increasingly advanced, technological society, so also the term managerial robot has a place in our scheme of things.Decision support systems do not qualify as managerial robots for the reason that managerial robots don't simply support the decision making process, but rather replace the manager in his performance of a function which, when performed by a human being, is considered a managerial function.Nor do we consider managerial robots to qualify as expert systems. While our scheduling system contains an inference mechanism, and could be enhanced to improve the quality of its schedules thru ‘experience’ (and thus to ‘learn’?), that—lacking a knowledge base in the sense of expert systems-and most of all in replacing rather than supporting the decision maker, the managerial robot needs a term of its own.We elaborate, in this paper, a specific application of our system, and show how the design of shifts, and the placement of breaks, serve to yield a fit whose quality no human scheduler can duplicate.     

Skills and job commitment in high technology industries in the US

This article, based on a study of workers, engineers and managers in a sample of high technology firms in the US, suggests extensive skill dislocation, labour market segmentation and a lack of participation in production decisions. There is a crisis of organisation and commitment among employees in such firms.     

Income, Money Ethic, Pay Satisfaction, Commitment, and Unethical Behavior: Is the Love of Money the Root of Evil for Hong Kong Employees?

This study examines a model involving income, the love of money, pay satisfaction, organizational commitment, job changes, and unethical behavior among 211 full-time employees in Hong Kong, China. Direct paths suggested that the love of money was related to unethical behavior, but income (money) was not. Indirect paths showed that income was negatively related to the love of money that, in turn, was negatively related to pay satisfaction that, in turn, was negatively associated with unethical behavior. Pay satisfaction was positively related to organizational commitment. Thus, the love of money is the root of evil, but money is not.     

An Examination of Adverse Selection in the Public Provision of Insurance

Using a unique data set from Florida's residual property insurer, we test for adverse selection in the public provision of homeowners’ insurance in Florida. We find a significant relationship between the losses and deductible choices of insureds in Florida's residual homeowners’ insurance market. This relationship provides strong evidence of the existence of an adverse selection problem in Florida's residual property insurance market. While this relationship is important to Florida regulators (and taxpayers) specifically, a finding of an adverse selection problem in residual markets in general has implications more broadly for government providers of insurance as an adverse selection problem in these settings will impact the public policy debates and decisions involving these markets.     

An Empirical Investigation of Herding Behavior in the U.S. REIT Market

Our study investigates the market-wide herding behavior in the U.S. equity REIT market. Utilizing the quantile regression method, we find that herding is more likely to be present in the high quantiles of the REIT return dispersion. This implies that REIT investors tend to herd under turbulent market conditions. Our results also support the asymmetry of herding behaviors, that is, herding is more likely to occur and becomes stronger in declining markets than in rising markets. In addition, our findings show that the current financial crisis has caused a change in the circumstances under which herding can occur, as we find that during the current crisis REIT investors may not start to herd until the market becomes extremely turbulent whereas during the relatively normal period before the crisis, investors tend to herd when the market is moderately turbulent. Finally, we find that compared with the case of the ‘pre-modern’ era, REIT investors are more likely to herd in the ‘modern’ era, during which herding usually occurs when the market becomes tumultuous. This implies that the switch of REITs from passive externally managed entities into active self-managed ones has made the investors more responsive to market sentiment.