On the real effects of inflation and inflation uncertainty in Mexico

We estimate an augmented multivariate GARCH-M model of inflation and output growth for Mexico at business cycle frequencies. The main findings are: (1) inflation uncertainty has a negative and significant effect on growth; (2) once the effect of inflation uncertainty is accounted for, lagged inflation does not have a direct negative effect on output growth; (3) However as predicted by Friedman and Ball, higher average inflation raises inflation uncertainty, and the overall net effect of average inflation on output growth in Mexico is negative. That is, average inflation is harmful to Mexican growth due to its impact on inflation uncertainty. (4) The Mexican Presidential election cycle significantly raises inflation uncertainty both during the year of the election and the year following the election which has correspondingly negative effects on output growth.     

Disequilibrium dynamics in a simple macroeconomic model

The dynamic behavior of a simple macroeconomic disequilibrium model is analyzed in which consumers' changes in money holdings constitute the dynamic link between any two periods. It is shown that, under constant government consumption, a constant production function (no investment), and fixed prices and wages, stationary states of Keynesian unemployment are stable whereas those of repressed inflation are globally unstable. Possibilities of unemployment and output cycles are indicated for fixed wages as well as for some very simple class of wage and price adjustment mechanisms.     

KIDNEY TRANSPLANTS AND THE SHORTAGE OF DONORS: IS A MARKET THE ANSWER?

This article examines the problem of kidney shortages for transplant in the United States. Following a study by Kaserman and Barnett, we reexamine the viability of allowing a market for cadaveric kidneys and estimate the implied equilibrium price based on our survey responses. In sharp contrast to the findings of Kaserman and Barnett, we estimate that a market equilibrium price for cadaveric kidneys may be prohibitively high. Consequently, we support other policy alternatives to increase supply, particularly presumed consent and mandated choice. Our findings also highlight the importance of obtaining data through experiments, rather than a survey, to estimate the impact of financial incentives. ( JEL I18, I12, I00)     

Experimental evidence of gender effects in arbitration decisions

The study investigates arbitrator gender and arbitrator-grievant gender effects on the decisions of 146 arbitrators rendered on a hypothetical sexual harassment case. Gender was not found to have a statistically significant effect on the decisions examined. However, other characteristics of the arbitrator—level of experience and level of education—were found to influence the arbitral decision.     

Some Determinants of Property Crime: Economic Factors Influence Criminal Behavior But Cannot Completely Explain the Syndrome

Abstract . An empirical analysis of the property crimes, robbery, burglary and larceny, is presented for all 120 counties in Kentucky. While this analysis is based on an economic model of crime, certain sociological and legal variables are included as well in the system of equations. Overall, the empirical results support prior studies’findings with the exception that a quadratic relationship is found to exist between urbanization and each of the property crimes. Furthermore, neither the economic nor the non economic influences measured appear more important for affecting crime rates. Specifically, results indicate that the level of poverty, the degree of tourism, the presence of police, the unemployment rate and the apprehension rate all affect property crimes. In contrast, the length of sentence, the degree of industrialization, the level of public assistance payments and the proportion of youth in the county have no affect on property crime rates in these areas.     

Monotonicity of Rank Statistics in some Non–parametric: Testing Problems

For some non–parametric testing problems (one–sided two–sample problem, k –sample trend problem, testing independence against positive dependence) a partial ordering, denoted by ≥, over the alternatives is defined. This partial ordering expresses the strength of the deviation from the null–hypothesis. All familiar rank tests turn out to become more powerful under "increasing" alternatives; that is, all familiar rank statistics preserve the ordering stochastically in samples whenever it is present between underlying distributions. As a tool, the sample equivalence of ≥ is introduced as a partial ordering over pairs of permutations. Functions, defined on pairs of permutations, which preserve this ordering are studied.     

Analytic derivation of sociometric dimensions

Dimensional analysis has been defined variously as the synthesis of dimensionless variables and as a way of spatially representing data. While in sociometrics, one may not be able to devise models resembling those of the physical sciences because of the complexity of many social situations, one may in some cases successfully apply the techniques of classic dimensional analysis and get empirically valid results. More often we derive with analytic methods from the social sciences different kinds of dimensional frameworks in which to represent data. This paper will compare these approaches to the study of relationships and give examples of where each might be appropriate. Finally, a classification of the analytical methods will be presented.     

Predicting corporate financial distress: Reflections on choice-based sample bias

Financial distress precedes bankruptcy. Most financial distress models actually rely on bankruptcy data, which is easier to obtain. We obtained a dataset of financially distressed but not yet bankrupt companies supplying a major auto manufacturer. An early warning model successfully discriminated between these distressed companies and a second group of similar but healthy companies. Previous researchers argue the matched-sample design, on which some earlier models were built, causes bias. To test for bias, the dataset was partitioned into smaller samples that approach equal groupings. We statistically confirm the presence of a bias and describe its impact on estimated classification rates.