Are hyperinflation paths learnable?

Earlier studies of the seigniorage inflation model have found that the high-inflation steady state is not stable under learning. We reconsider this issue and analyze the full set of solutions for the linearized model. Our main focus is on stationary hyperinflationary paths near the high-inflation steady state. These paths are shown to be stable under least-squares learning if agents can utilize contemporaneous data. In an economy with a mixture of agents, some of whom only have access to lagged data, stable hyperinflationary paths emerge only if the proportion of agents with access to contemporaneous data is sufficiently high.     

Recovering Hicksian Consumer Surplus within a Collective Model: Hausman's Method for the Household

The purpose of this paper is to describe the implications of the collective model of household behavior for the methods used to estimate the economic value of non-marketed environmental resources. After demonstrating how the separability restrictions inherent in the collective model allow individual preference and household income allocation choices to be distinguished, the paper demonstrates how the framework can be used to recover Hicksian consumer surplus. An algebraic example is used to illustrate how the framework can be used in valuing environmental resources.     

Pricing of the time-change risks

We develop an equilibrium endowment economy with Epstein-Zin recursive utility and a Lévy time-change subordinator, which represents a clock that connects business and calendar time. Our setup provides a tractable equilibrium framework for pricing non-Gaussian jump-like risks induced by the time-change, with closed-form solutions for asset prices. Persistence of the time-change shocks leads to predictability of consumption and dividends and time-variation in asset prices and risk premia in calendar time. In numerical calibrations, we show that the risk compensation for Lévy risks accounts for about one-third of the overall equity premium.     

Emerging managerial competencies of professionals in the Tanzanian construction industry

The managerial competencies required by professionals in the construction industry in Tanzania to operate effectively in a business environment which is changing as a result of a transition from a socialist to a free-enterprise economy, economic liberalization, globalization and regionalization are explored through a qualitative study. The study reveals that local Tanzanian construction firms are finding it difficult to compete with foreign companies for projects. This is mainly because Tanzanian professionals lack the competencies required to compete and manage projects in a liberalized market economy.

The study thus highlights the need for Tanzanian construction professionals to acquire a better grasp of both occupational and organizational competencies. In this respect, it is suggested that they need post-experience training in order to acquire the competencies required to function successfully in a commercial environment.     

The Law of 1/n Revisited: Distributive Politics,Legislature Size,and the Costs of Collective Action

The foundational model of distributive politics predicts a positive relationship between the number of legislative districts and the level of inefficiency of projects approved by the legislature—Weingast, Shepsle, and Johnsen's “Law of 1/n.” This relationship has been tested extensively in the empirical literature, with mixed results. This article presents a model wherein passing the omnibus legislation typical of distributive politics is a costly process. The model predicts a nonlinear relationship between legislature size and spending as increasing the size of the legislature also increases the costs of collective action. Results from an empirical exercise based on U.S. state legislatures (1962–2014) are consistent with the proposed model, showing a 1/n effect which diminishes at the margin as the legislature's size increases, especially in the lower chamber.     

Extending the BG/NBD: A simple model of purchases and complaints

Extant customer-base models like the beta geometric/negative binomial distribution (BG/NBD) predict future purchasing based on customers' observed purchase history. We extend the BG/NBD by adding an important non-transactional element that also drives future purchases: complaint history. Our model retains several desirable properties of the BG/NBD: it can be implemented in readily available software, and estimation requires only customer-specific statistics, rather than detailed transaction-sequence data. The likelihood function is closed-form, and managerially relevant metrics are obtained by drawing from beta and gamma densities and transforming these draws to a sample average. Based on more than two years of individual-level data from a major U.S. internet and catalog retailer, our model with complaints outperforms both the original BG/NBD and a modified version. Even though complaints are rare and non-transactional events, they lead to different substantive insights about customer purchasing and drop-out: customers purchase faster but also drop out much faster. Furthermore, there is more heterogeneity in drop-out rates following a purchase than a complaint.