Just-in-time purchasing: an empirical study of operational practices, supplier development and performance

This paper examines the role of supplier development in establishing and managing efficient buyer–supplier operational links. The paper develops and assesses a measurement instrument for “operational” and “supplier development” just-in-time purchasing practices, followed by an examination of the relationships between the two sets, and an investigation into whether the use of “operational” and “supplier development” practices has a bearing on higher plant performance. A plant-level survey was carried out on a sample of electronics and machinery plants. The study empirically:

• documents the close connection between the buyer–supplier operational link and the buyer’s practices for supplier development;

• demonstrates that recourse to supplier development programs and their nature depend on the kind of vendor–vendee operational connection;

• test whether different plant performance outcomes result from the implementing of different “operational” and “supplier development” practices. It demonstrates that better-performing plants exhibit more advanced design and logistic links with sources, more formalised vendor-rating and ranking procedures, greater use of organisational devices for supplier-organisational integration and place greater importance on supplier assistance and training.

STICKY PRICES: A NEW MONETARIST APPROACH

Why do some sellers set nominal prices that apparently do not respond to changes in the aggregate price level? In many models, prices are sticky by assumption; here it is a result. We use search theory, with two consequences: prices are set in dollars, since money is the medium of exchange; and equilibrium implies a nondegenerate price distribution. When the money supply increases, some sellers may keep prices constant, earning less per unit but making it up on volume so profit stays constant. The calibrated model matches price‐change data well. But, in contrast to typical sticky‐price models, money is neutral.     

The dual network structure of organizational problem solving: A case study on open source software development

We reconstruct the dual network structure generated by the association between 72 contributors and 737 software bugs engaged during a full development cycle of the free/open source software project Epiphany. Estimates of structural parameters of Exponential Random Graph Models for two-mode networks reveal the structural logics shaping activities of collaborative problem solving. After controlling for contributor-specific and software bug-specific characteristics, we find that contributors (“problem solvers”) tend to distribute their activity over multiple software bugs. At the same time, however, we find that software bugs (“problems”) tend not to share multiple contributors. This dual tendency toward de-specialization and exclusivity is sustained by specific local network dependencies revealed by our analysis which also suggests possible organizational mechanisms that may be underlying the puzzling macro-structural regularities frequently observed, but rarely explained, in the production of open source software. By combining these mechanisms with the influence of contributors characterized by different levels of involvement in the project, we provide micro-level evidence of structural interdependence between “core” and “peripheral” members identified exclusively on the basis of their individual level of contribution to the project.     

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.     

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.     

Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs

It is common in regression discontinuity analysis to control for third, fourth, or higher-degree polynomials of the forcing variable. There appears to be a perception that such methods are theoretically justified, even though they can lead to evidently nonsensical results. We argue that controlling for global high-order polynomials in regression discontinuity analysis is a flawed approach with three major problems: it leads to noisy estimates, sensitivity to the degree of the polynomial, and poor coverage of confidence intervals. We recommend researchers instead use estimators based on local linear or quadratic polynomials or other smooth functions.     

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)