A note on coherent invariant distributions as non-informative priors for exponential and location-scale families

The problem of finding a non-informative prior distribution for a parameter is approached using the notion of context-invariance. This concept is revisited and discussed with the aim of applying it to finding context-invariant non-informative priors for the one-parameter exponential family (suitably redefined) and the location-scale family. Our approach, carried-out in a finitely-additive framework, generally leads to a class of non-informative priors with respect to any given problem. For most common statistical models such a class does not always contain the corresponding Jeffreys' prior, but does contain the so-called ALI prior by Hartigan.     

Weighting Elementary Prices in Consumer Price Index Construction Using Spatial Autocorrelation

The Consumer Price Indexes (CPI) are used in current economic systems to measure inflation. When constructing CPIs, however, official institutions have systematically overlooked the spatial dimension of elementary prices. Ignoring the fact that prices are collected at geographical locations implicitly implies considering prices as spatially independent, when in fact they are not. To solve this problem, this article proposes to weight basic price data by taking into account the spatial correlation they display. The weighted geometric and arithmetic means suggested generalize and improve the simple geometric and arithmetic means currently in use.     

Overeducated Workers as an Insurance Device

Empirical findings show that firms frequently employ overeducated workers. Since overeducated workers earn more than adequately educated ones working in similar jobs these findings seem to be puzzling. In this paper, we introduce an insurance approach to explain the employment of overeducated workers. Referring to this approach, overeducated workers are employed by a firm to avoid high losses in the case of a crisis (e.g. when the production process breaks down). Contrary to adequately educated workers, overeducated ones may be helpful in this situation by quickly offering improvisatorial solutions. First, we use a simple model to demonstrate the insurance argument. Second, we test the major implication of the model empirically by using industry panel data: if and only if high‐skilled workers are employed for insurance purposes, the average wage of high‐skilled workers in firms that need insurance will be lower compared with firms that do not need insurance. The data confirm this theoretical result.     

Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings

This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on quantiles. The quantile treatment effects (QTE) estimator reduces to quantile regression when selection for treatment is exogenously determined. QTE can be computed as the solution to a convex linear programming problem, although this requires first‐step estimation of a nuisance function. We develop distribution theory for the case where the first step is estimated nonparametrically. For women, the empirical results show that the JTPA program had the largest proportional impact at low quantiles. Perhaps surprisingly, however, JTPA training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution.     

Testing noninferiority in three‐armed clinical trials based on likelihood ratio statistics

Clinical noninferiority trials with at least three groups have received much attention recently, perhaps due to the fact that regulatory agencies often require that a placebo group be evaluated along with a new experimental drug and an active control. The authors discuss likelihood ratio tests for binary endpoints and various noninferiority hypotheses. They find that, depending on the particular hypothesis, the test reduces asymptotically either to the intersection‐union test or to a test which follows asymptotically a mixture of generalized chi‐squared distributions. They investigate the performance of this asymptotic test and provide an exact modification. They show that this test considerably outperforms multiple testing methods such as the Bonferroni adjustment with respect to power. They illustrate their methods with a cancer study to compare antiemetic agents. Finally, they discuss the extension of the results to other settings, such as Gaussian endpoints.     

Repossession through sharing of and access to seeds: different cases and practices

Seeds are both the means and product of agricultural production. The corporate appropriation of seeds affects farmers’ autonomy and has been contested and resisted by farmers worldwide through practices of repossession. This article investigates different practices of the repossession of seeds emphasising the micro-structure and recent developments in agricultural practices that lead to a commonisation of seeds. Various practices of seed repossession present in India are analysed and compared with open-source initiatives to present examples of the diversity of singular initiatives aimed at the commonisation of seeds in the Global South. The article shows that each initiative applies a multitude of concrete practices to counter what we will refer to as metabolic rift, but without a single generic strategy, each seeking in its own way to repossess seeds and (re)locate them in a social space of commons.     

Large Sample Properties of Matching Estimators for Average Treatment Effects

Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results. We focus on matching with replacement with a fixed number of matches. First, we show that matching estimators are not N^1/2‐consistent in general and describe conditions under which matching estimators do attain N^1/2‐consistency. Second, we show that even in settings where matching estimators are N^1/2‐consistent, simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound. Third, we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions. Software for implementing these methods is available in Matlab, Stata, and R.     

SMUGGLING HUMANS: A THEORY OF DEBT‐FINANCED MIGRATION

We introduce financial constraints in a theoretical analysis of illegal immigration. Intermediaries finance the migration costs of wealth‐constrained migrants, who enter temporary servitude contracts to repay the debt. These debt/labor contracts are easier to enforce in the illegal than in the legal sector of the host country. Hence, when moving from the illegal to the legal sector becomes more costly—for instance, because of stricter deportation policies—fewer immigrants default on debt. This reduces the risks for intermediaries, who are then more willing to finance illegal migration. Stricter deportation policies may thus, ex ante, increase rather than decrease the flow of illegal migrants. Furthermore, stricter deportation policies worsen the skill composition of immigrants. While stricter border controls decrease overall immigration, they may result in an increase of debt‐financed migration. We also show that there are complementarities between employer sanctions and deportation policies. We use available evidence to check the empirical consistency of the theory. (JEL: J61, K42, O17)