Eduardo Ley

Tax Incentives for Business Investment: A Primer for Policy Makers in Developing Countries

Abstract The use of tax incentives is widespread even though the available empirical evidence on the cost-effectiveness of such incentives in stimulating investment is highly inconclusive. This paper is primarily intended as a primer on the use of tax incentives for policy makers, especially those in developing countries. It discusses the objectives, cost-effectiveness, and transparency of implementing tax incentives; assesses the comparative merits of alternative forms of such incentives; and provides a review of the empirical literature. Its main conclusions are that the justification for the use of tax incentives should be limited to the rectification of market failures, and that the preferred form of tax incentives are those that provide for faster recovery of investment costs.

Linear programming approaches to the measurement and analysis of productive efficiency

An important and rapidly growing empirical application of operations research techniques involves the measurement and analysis of the efficiency with which goods are produced and services are provided. The production activities whose efficiency has been the subject of investigation have varied widely, from profit-oriented industrial manufacturing enterprises all the way to public and private service providers operating in a not-for-profit environment. A similarly wide variety of operations research techniques has been utilized in the measurement and analysis of productive efficiency, ranging from stochastic parametric regressionbased methods to nonstochastic nonparametric mathematical programming methods. Foremost among the latter is a family of linear programming models collectively referred to as Data Envelopment Analysis (DEA). The purpose of this paper is to provide a selective overview of some of the most useful DEA models. I analyze these models in terms of their ability to accurately reflect the structure of the underlying production technology, their ability to accurately measure the productive efficiency of the producers being analyzed, and their data requirements and their sensivity to shortcomings in the data that form the basis of the analysis. The selectivity of the overview reflects my talents and my interests. Thus the orientation of the overview is toward the user of existing DEA models, not toward the developer of new models. In particular, the review is intended to inform practitioners in the fields of management science, economics and public administration who want to learn and

Bayesian analysis of long memory and persistence using ARFIMA models

This paper provides a Bayesian analysis of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. We discuss in detail inference on impulse responses, and show how Bayesian methods can be used to (i) test ARFIMA models against ARIMA alternatives, and (ii) take model uncertainty into account when making inferences on quantities of interest. Our methods are then used to investigate the persistence properties of real U.S. GNP.

A tax–based approach to slowing global climate change

This paper begins by comparing CO2 taxes and emissions trading from a domestic perspective across a broad range of criteria of potential concern to policy makers. Next the authors briefly discuss some further issues in the practical design of a domestic CO2 tax. Following that the authors turn to issues in implementing CO2 taxes at the global level. The authors then offer some concluding remarks.

Conceptual issues related to carbon sequestration: Uncertainty and time

Abstract Global climate change is about uncertainty related to ecological and economic processes, and political responses. It is about fairness and income distribution among nations, both now and in the future. It is a dynamic problem that involves national carbon transition functions, damage functions and discount rates. These issues form the basis of the current paper, which examines them from a conceptual point of view.

Validation and generalization of DEA and its uses

SummaryRapidly expanding uses of DEA have been accompanied by developments which have enhanced its power and enlarged its utility for additional applications. Developments covered in the present paper include simulation studies comparing DEA with competing forms of statistical regressions. Other studies covered show how these two approaches can be combined in complementary fashion. Another part of this paper deals with Chance Constrained Programming formulations which incorporate probabilistic elements into DEA. Included also are discussions of statistical characterizations with accompanying tests of statistical significance for DEA efficiency evaluations. This paper concludes with uses of DEA in “discovery processes”-processes that need strengthening (and encouragemnt) in contemporary social science and management science research. Suggestions are made for additional research on further developments which extend to uses of DEA to provide new approaches in economics (including econometrics), management and psychology and an Appendix introduces new or recently developed efficiency measures for use in DEA.

Bayesian modelling of catch in a north‐west Atlantic fishery

We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used, the mesh size of the nets, etc.), are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and four categorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log of catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to prediction of catch for single and aggregated ships.

The Plutocratic Gap in the CPI: Evidence from Spain

The plutocratic gap is defined as the difference between the inflation measured according to the current official consumer price index (CPI) and a democratic index in which all households receive the same weight. During 1992-97, the plutocratic gap in Spain averaged 0.055 percentage points a year. Since positive and negative gaps cancel out, however, the average absolute gap is significantly larger: 0.090 percentage points a year. For the purposes of accounting for the plutocratic gap, a 53-dimensional commodity space can be conveniently reduced to two dimensions: a luxury index and a necessities index.

We Just Averaged over Two Trillion Cross-Country Growth Regressions

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian model averaging (BMA). We find that the posterior probability is distributed among many models, suggesting the superiority of BMA over any single model. Out-of-sample predictive results support that claim. In contrast with Levine and Renelt (1992), our results broadly support the more “optimistic” conclusion of Sala-i-Martin (1997b), namely, that some variables are important regressors for explaining cross-country growth patterns. However, the variables we identify as most useful for growth regression differ substantially from Sala-i-Martin’s results.

The Laspeyres bias in the Spanish consumer price index

The CPI compares the cost of acquiring a reference quantity vector at current and base prices. Such reference vector is the vector of mean quantities actually bought by a reference population, whose consumption patterns are investigated during a period τ prior to the index base period 0. This paper shows that unless the price change between these two dates is taken into account, the CPI ceases to be a proper statistical price index of the Laspeyres type. Among several negative consequences, the most important is that this omission produces a bias in the measurement of inflation: the ‘Laspeyres bias’. Using Spanish data, the size of the Laspeyres bias is estimated at -0.061% per year, during 1992–1998. The Laspeyres bias in shorter time periods reached -0.122% per year in 1992, and -0.108 in 1997.