Wealth inequality and efficiency in the Commons, part II: the regulated case

Does more inequality lead to more efficiency in the managementof common property resources? To answer this question, an attemptis made to develop relevant theoretical models and to articulatethem with empirical evidence drawn mainly from social sciencestudies. The paper is divided into two parts. In this, the secondpart, it is shown that inequality tends to amplify the distributiveeffects of regulation when the latter is carried out throughthe use of second best instruments. As a resuh efficiency gainsfrom regulation must decrease as inequality increases, for allusers to gain from such regulation.     

Are labels effective against child labor?

In this paper, we investigate the impact of introducing a label certifying the absence of child labor in the export production of the South. When most eligible producers in the South can obtain the label, its impact is considerably reduced by a displacement effect whereby adult workers replace children in the export sector while children replace adults in the domestic sector. The label is then unable to create a price differential in the South between goods produced under the label and those produced without it.When only a small fraction of eligible producers have access to the label, the South exports both labeled and unlabeled goods to the North. In this case, labeled producers generally gain while those without a label generally loose from the introduction of the label. Ex ante welfare may fall in the South if the probability of getting a label when one qualifies is small. The impact on child labor is in general ambiguous.     

Some robust exact results on sample autocorrelations and tests of randomness

Several exact results on the second moments of sample autocorrelations, for both Gaussian and non-Gaussian series, are presented. General formulae for the means, variances and covariances of sample autocorrelations are given for the case where the variables in a sequence are exchangeable. Bounds for the variances and covariances of sample autocorrelations from an arbitrary random sequence are derived. Exact and explicit formulae for the variances and covariances of sample autocorrelations from a Gaussian white noise are given. It is observed that the latter results hold for all spherically symmetric distributions. A simulation experiment, with Gaussian series, indicates that normalizing each sample autocorrelation with its exact mean and variance, instead of the usual approximate moments, can improve considerably the accuracy of the asymptotic N(0,1) distribution to obtain critical values for tests of randomness. The exact second moments of rank autocorrelations are also studied.