Global Financial and Macroeconomic Fluctuations: Implications for African Economic Development

In the light of dampening effects of the global financial melt-down, the paper examines the trends in financial flows, particularly foreign direct investment (FDI) and the possible effects of the global financial crisis and macroeconomic fluctuations on economic development in Africa. The paper employs simple panel data approach which links panel data methodology that allows for individual heterogeneity, while the method of estimation is the Fixed and Random Effects regression. The method of panel VAR is also used in the paper with a view to capturing the dynamic effects of FDI inflows for policy analysis using the impulse response functions. The number of countries (27) included in the paper and the period of estimation, 1987-2007, are informed by data availability. With some suggestions on the direction of policy to stimulate increased financial flows, the paper opines that there is the need for comparative dynamics of African economies in order to return to the path of sustainable growth and development

THE CAUSAL STRUCTURE OF LAND PRICE DETERMINANTS

This paper investigates causation contemporaneously and over time to elucidate the persistent lack of agreement about what "causes" changes in farmland prices. Using recently developed causal modeling framework of directed acyclic graphs (DAGs) and cointegrated (VAR) techniques, the assumed causal structures of existing structural and empirical models are tested directly. The results validate concerns about the nonstationarity of these series. Land price changes are found to respond to a small subset of the oft-cited causes of price change, including macroeconomic variables.Land Economics/Use,

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Marketing,

The Nature of Risk Preferences: Evidence from Insurance Choices

The authors use data on insurance deductible choices to estimate a structural model of risky choice that incorporates standard risk aversion (diminishing marginal utility for wealth) and probability distortions. They find that probability distortions--characterized by substantial overweighting of small probabilities and only mild insensitivity to probability changes--play an important role in explaining the aversion to risk manifested in deductible choices. This finding is robust to allowing for observed and unobserved heterogeneity in preferences. They demonstrate that neither Kőszegi-Rabin loss aversion alone nor Gul disappointment aversion alone can explain our estimated probability distortions, signifying a key role for probability weighting

Estimating Risk Preferences in the Field

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences—including both expected utility (EU) theory and non-EU models—that have been estimated using field data, and we highlight issues related to identification and estimation of such models using field data. We then survey the literature, giving separate treatment to research that uses individual-level data (e.g., property insurance data) and research that uses aggregate data (e.g., betting market data). We conclude by discussing directions for future research

Distinguishing Probability Weighting from Risk Misperceptions in Field Data

The paper outlines a strategy for distinguishing rank-dependent probability weighting from systematic risk misperceptions in field data. Our strategy relies on singling out a field environment with two key properties: (i) the objects of choice are money lotteries with more than two outcomes and (ii) the ranking of outcomes differs across lotteries. We first present an abstract model of risky choice that elucidates the identification problem and our strategy. The model has numerous applications, including insurance choices and gambling. We then consider the application of insurance deductible choices and illustrate our strategy using simulated data