When is the Pareto optimal amount of public goods independent of income distribution? Subject to some regularity conditions, the answer is when preferences of every individual i can be represented by a utility function of the form U(X_i,Y)=A(Y)X_i+B_i(Y) where X_i is i's consumption of private goods and Y is the amount of public goods.
Let Y=(Y 1 ,Y 2 , .Yn) -0 be an income distribution pattern ton-"income receiving units" which may be n-persons, n-farnilies, n-states of the same country or n-countries.Abstractly, a fiscal program is a course of action, undertaken by social conseasus, under which portions of the incomes of certain receiving units are transferred to other receiving units to render the income distribution more equitable.The most familiar example of such a fiscal program is the collection of taxes from individuals (or individual families) with the revenue being paid out as.welfare payments by the government.As another example, the Federal government may collect taxes from the states only to give some of the revenue back to the states under a "revenue sharing" program.An international consortium or the World Bank may work out a formula under which contributions will be solicited from the wealthy countries or "donors" to provide foreign aid or make concessionary loans to the poor countries.This paper is concerned with the principles governing the design of such equity oriented fiscal programs.The first general principle concerns the "rationality" of the fiscal program.Suppose the income level of "i" is higher that that of "j".On the one hand, a principle of "minimally progressive" suggests that, in case "i" and "j" are taxpayers, "i" should pay no less taxes than "j" and, in case "i" and "j" are recipients of welfare payments "i" should receive no more than "j".On the other hand, a principle of "incentive perservation" suggests that the disposable income of "i" should be no less than that of "j"--i.e. the fiscal program clearly should not reverse their relative income ranks in order to preserve the incentive for the individuals .,. ..
Is one distribution (of income, consumption, or some other economic variable) among families or individuals more or less equal in relative terms than another? Despite the seeming straightforwardness of this question, there has been and continues to be considerable debate over how to go about finding the answer. There are two points of contention. One is the issue of cardinality vs. ordinality. Practitioners of the cardinal approach compare distributions by means of summary measures such as a Gini coefficient, variance of logarithms, and the like. For purposes of ranking the relative inequality of two distributions, the cardinality of the usual measures is not only a source of controversy, but it is also redundant. Accordingly, some researchers prefer an ordinal approach, adopting Lorenz domination as their criterion. The difficulty with the Lorenz criterion is its incompleteness, affording rankings of only some pairs of distributions but not others. Current practice in choosing between a cardinal or an ordinal approach is now roughly as follows: Check for Lorenz domination in the hope of making an unambiguous comparison; if Lorenz domination fails, calculate one or more cardinal measures. This raises the second contentious issue: which of the many cardinal measures in existence should one adopt? The properties of existing measures have been discussed extensively in several recent papers. Typically, these studies have started with the measures and then examined their properties. In this paper, we reverse the direction of inquiry. Our approach is to start by specifying as axioms a relatively small number of properties which we believe a ?good? index of inequality should have and then examining whether the Lorenz criterion and the various cardinal measures now in use satisfy those properties. The key issue is the reasonableness of the postulated properties. Work to date has shown the barrenness of the Pareto criterion. Only recently have researchers begun to develop an alternative axiomatic structure. The purpose of this paper is to contribute to such a development.
We study games of incomplete information as both the information structure and the extensive form vary. An analyst may know the payoff‐relevant data but not the players' private information, nor the extensive form that governs their play. Alternatively, a designer may be able to build a mechanism from these ingredients. We characterize all outcomes that can arise in an equilibrium of some extensive form with some information structure. We show how to specialize our main concept to capture the additional restrictions implied by extensive‐form refinements.
We use the control function approach to identify the average treatment effect and the effect of treatment on the treated in models with a continuous endogenous regressor whose impact is heterogeneous. We assume a stochastic polynomial restriction on the form of the heterogeneity, but unlike alternative nonparametric control function approaches, our approach does not require large support assumptions.
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This paper analyzes the conditions under which consistent estimation can be achieved in instrumental variables (IV) regression when the available instruments are weak and the number of instruments, Kn, goes to infinity with the sample size. We show that consistent estimation depends importantly on the strength of the instruments as measured by rn, the rate of growth of the so-called concentration parameter, and also on Kn. In particular, when Kn→∞, the concentration parameter can grow, even if each individual instrument is only weakly correlated with the endogenous explanatory variables, and consistency of certain estimators can be established under weaker conditions than have previously been assumed in the literature. Hence, the use of many weak instruments may actually improve the performance of certain point estimators. More specifically, we find that the limited information maximum likelihood (LIML) estimator and the bias-corrected two-stage least squares (B2SLS) estimator are consistent when , while the two-stage least squares (2SLS) estimator is consistent only if Kn/rn→0 as n→∞. These consistency results suggest that LIML and B2SLS are more robust to instrument weakness than 2SLS.
We investigate the impact of job loss on crime and the mitigating role of unemployment benefits, exploiting detailed individual‐level data linking employment careers, criminal records, and welfare registries for the universe of male workers in Brazil. The probability of committing crimes increases on average by 23% for workers displaced by mass layoffs, and by slightly less for their cohabiting sons. Using causal forests, we show that the effect is entirely driven by young and low‐tenure workers, while there is no heterogeneity by education and income. Regression discontinuity estimates indicate that unemployment benefit eligibility completely offsets potential crime increases upon job loss, but this effect vanishes completely immediately after benefit expiration. Our findings point to liquidity constraints and psychological stress as the main drivers of criminal behavior upon job loss, while substitution between time on the job and leisure does not seem to play an important role.
J. Tinbergen, Tjalling C. Koopmans, Jacob Marschak, Francois Divisia, Jacques Rueff, B. Chait, La loi de divergence: Discussion, Econometrica, Vol. 17, Supplement: Report of the Washington Meeting (Jul., 1949), pp. 124-128