Strategic and Operational Determinants of Relationship Outcomes With New Venture Suppliers

While the vast majority of the supplier selection and development literature has focused on relationships between mature, established firms, significantly less attention has been paid to relationships between established firms and new, entrepreneurial ventures. This study addresses this important topic and, using an interdisciplinary lens, investigates the question of how established buying firms can work with new ventures to achieve desired relationship outcomes. Drawing on the literature from the disciplines of entrepreneurship and supply chain management, we propose a theoretical model that links buying firms' strategic orientation in supplier selection (innovation and cost in strategic supplier selection) and operational approach in supplier development (direct and indirect development of new venture suppliers) with new venture‐specific relationship outcomes (purchasing volume and realized innovations). The model's predictions are tested on cross‐sectional survey data from 136 buying firms. Our results contribute to the emerging research stream at the supply chain management–entrepreneurship interface and enhance the understanding of relationships between established firms and new ventures.     

Nonparametric estimation in case of endogenous selection

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Pointwise asymptotic distribution of the estimators is established. In addition, bootstrap uniform confidence bands are obtained. Finite sample properties are illustrated in a Monte Carlo simulation study and an empirical illustration.     

Goodness-of-fit tests based on series estimators in nonparametric instrumental regression

This paper proposes several tests of restricted specification in nonparametric instrumental regression. Based on series estimators, test statistics are established that allow for tests of the general model against a parametric or nonparametric specification as well as a test of exogeneity of the vector of regressors. The tests’ asymptotic distributions under correct specification are derived and their consistency against any alternative model is shown. Under a sequence of local alternative hypotheses, the asymptotic distributions of the tests are derived. Moreover, uniform consistency is established over a class of alternatives whose distance to the null hypothesis shrinks appropriately as the sample size increases. A Monte Carlo study examines finite sample performance of the test statistics.     

Market Structure and the Environmental Implications of Trade Liberalization: Russia's Accession to the World Trade Organization

We investigate the environmental impacts of Russia's World Trade Organization (WTO) accession with a computable general equilibrium model incorporating imperfectly competitive firms, foreign direct investment and endogenous productivity. WTO accession increases CO₂ emissions through technique (?), composition (+) and scale (+) effects. We consider three complementary policies to limit CO₂ emissions: cap and trade, emission intensity standards and energy efficiency standards. With imperfectly competitive firms, gains from WTO accession result with any of these policies. If we assume perfectly competitive market structures, the negative environmental impacts of WTO accession are smaller and no net gains arise when environmental regulation involves energy intensity or efficiency standards.     

Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda

Journal of Business Ethics - Companies increasingly deploy artificial intelligence (AI) technologies in their personnel recruiting and selection process to streamline it, making it faster and more...     

Correction to: Improving decisions with market information: an experiment on corporate prediction markets

Experimental Economics - A correction to this paper has been published: https://doi.org/10.1007/s10683-021-09711-0     

Multiple Testing for No Cointegration under Nonstationary Volatility

With cointegration tests often being oversized under time‐varying error variance, it is possible, if not likely, to confuse error variance non‐stationarity with cointegration. This paper takes an instrumental variable (IV) approach to establish individual‐unit test statistics for no cointegration that are robust to variance non‐stationarity. The sign of a fitted departure from long‐run equilibrium is used as an instrument when estimating an error‐correction model. The resulting IV‐based test is shown to follow a chi‐square limiting null distribution irrespective of the variance pattern of the data‐generating process. In spite of this, the test proposed here has, unlike previous work relying on instrumental variables, competitive local power against sequences of local alternatives in 1/T‐neighbourhoods of the null. The standard limiting null distribution motivates, using the single‐unit tests in a multiple testing approach for cointegration in multi‐country data sets by combining P‐values from individual units. Simulations suggest good performance of the single‐unit and multiple testing procedures under various plausible designs of cross‐sectional correlation and cross‐unit cointegration in the data. An application to the equilibrium relationship between short‐ and long‐term interest rates illustrates the dramatic differences between results of robust and non‐robust tests.     

Comparing supply‐side specifications in models of global agriculture and the food system

This article compares the theoretical and functional specification of production in partial equilibrium (PE) and computable general equilibrium (CGE) models of the global agricultural and food system included in the AgMIP model comparison study. The two model families differ in their scope—partial versus economy‐wide—and in how they represent technology and the behavior of supply and demand in markets. The CGE models are “deep” structural models in that they explicitly solve the maximization problem of consumers and producers, assuming utility maximization and profit maximization with production/cost functions that include all factor inputs. The PE models divide into two groups on the supply side: (1) “shallow” structural models, which essentially specify area/yield supply functions with no explicit maximization behavior, and (2) “deep” structural models that provide a detailed activity‐analysis specification of technology and explicit optimizing behavior by producers. While the models vary in their specifications of technology, both within and between the PE and CGE families, we consider two stylized theoretical models to compare the behavior of crop yields and supply functions in CGE models with their behavior in shallow structural PE models. We find that the theoretical responsiveness of supply to changes in prices can be similar, depending on parameter choices that define the behavior of implicit supply functions over the domain of applicability defined by the common scenarios used in the AgMIP comparisons. In practice, however, the applied models are more complex and differ in their empirical sensitivity to variations in specification—comparability of results given parameter choices is an empirical question. To illustrate the issues, sensitivity analysis is done with one global CGE model, MAGNET, to indicate how the results vary with different specification of technical change, and how they compare with the results from PE models.