The Economics of Counterfeiting

We develop a strategic theory of counterfeiting as a multi‐market large game. Bad guys choose whether to counterfeit, and what quality to produce. Opposing them is a continuum of good guys who select a costly verification effort. In equilibrium, counterfeiters produce better quality at higher notes, but verifiers try sufficiently harder that verification still improves. We develop a graphical framework for deducing comparative statics. Passed and counterfeiting rates vanish for low and high notes. Our predictions are consistent with time series and cross‐sectional patterns in a unique data set assembled largely from the Secret Service.     

Time and Organizational Learning: A Review and Agenda for Future Research

This paper examines the time dimensions of organizational learning. While several recent studies have addressed aspects of time in relation to organizational learning, the topic of time has received little attention in reviews of the field, and this promising domain of research is fragmented. The objective of this paper is to bring these dispersed conceptualizations and findings together and to provide a more solid conceptual foundation for the time dimensions of organizational learning as a new research avenue. Three sets of mechanisms are discerned: concerning time as duration; the timing of organizational learning; and the role of the past, present and future in organizational learning. Each of these perspectives offers unique insights, which when integrated can help map new directions for future research.     

Disability and work intensity in Italian households

Review of Economics of the Household - The 2030 Agenda of the United Nations clearly sets the inclusion of persons with disabilities in the labour market as a main goal. However, especially in care...     

Perturbation by multiplicative noise and the Simulation Extrapolation method

While most of the literature on measurement error focuses on additive measurement error, we consider in this paper the multiplicative case. We apply the Simulation Extrapolation method (SIMEX)—a procedure which was originally proposed by Cook and Stefanski (J. Am. Stat. Assoc. 89:1314–1328, 1994) in order to correct the bias due to additive measurement error—to the case where data are perturbed by multiplicative noise and present several approaches to account for multiplicative noise in the SIMEX procedure. Furthermore, we analyze how well these approaches reduce the bias caused by multiplicative perturbation. Using a binary probit model, we produce Monte Carlo evidence on how the reduction of data quality can be minimized. For helpful comments, we would like to thank Helmut Küchenhoff, Winfried Pohlmeier, and Gerd Ronning. Sandra Nolte gratefully acknowledges financial support by the DFG. Elena Biewen and Martin Rosemann gratefully acknowledge the financial support by the Federal Ministry of Education and Research (BMBF). The usual disclaimer applies.     

"A Salary is not Important Here": The Professionalization of Social Work in Contemporary Russia

Helping professions address the growing number of social problems which have emerged, along with contextual changes in Russian society, culture and state social policy. Social work only emerged in Russia in 1991 and it still lacks recognition by public opinion and by other caring professions. This paper addresses the current development of the social work profession in Russia, considers its context and reviews the main issues affecting processes within social work practice and education. Many social work agencies are in search of new forms of organization and are trying to develop new philosophies of service, in order to build positive relations with communities. However, given the 70 years of the Soviet era when social protection was highly centralized and bureaucratized, the organizational cultures of the new social services sometimes reproduce old patterns of bureaucracy, especially where employees lack professional education. The reflective practitioner type of professionalism is here argued to be more appropriate for social workers in today's Russia. The paper includes an analysis of interviews with social workers and administrators in a large Russian city and considers the results of an ethnographic study of social services in the same city.     

Large sample results for tests of association ii. multinomial and stratified sampling

This paper deals with the asymptotics of a class of tests for association in 2-way contingency tables based on square forms in cell frequencies, given the total number of observations (multinomial sampling) or one set of marginal totals (stratified sampling). The case when both row and column marginal totals are fixed (hypergeometric sampling) was studied in Kulinskaya (1994), The class of tests under consideration includes a number of classical measures for association, Its two subclasses are the tests based on statistics using centralized cell frequencies (asymptotically distributed as weighted sums of central chi-squares) and those using the non-centralized cell frequencies (asymptotically normal). The parameters of asymptotic distributions depend on the sampling model and on true marginal probabilities. Maximum efficiency for asymptotically normal statistics is achieved under hypergeometric sampling, If the cell frequencies or the statistic as a whole are centralized using marginal proportions as estimates for marginal probabilities, the asymptotic distribution does not differ much between models and it is equivalent to that under hypergeometric sampling. These findings give an extra justification for the use of permutation tests for association (which are based on hypergeometric sampling). As an application, several well known measures of association are analysed.     

Quantifying the Effects of Expert Selection and Elicitation Design on Experts’ Confidence in Their Judgments About Future Energy Technologies

Expert elicitations are now frequently used to characterize uncertain future technology outcomes. However, their usefulness is limited, in part because: estimates across studies are not easily comparable; choices in survey design and expert selection may bias results; and overconfidence is a persistent problem. We provide quantitative evidence of how these choices affect experts’ estimates. We standardize data from 16 elicitations, involving 169 experts, on the 2030 costs of five energy technologies: nuclear, biofuels, bioelectricity, solar, and carbon capture. We estimate determinants of experts’ confidence using survey design, expert characteristics, and public R&D investment levels on which the elicited values are conditional. Our central finding is that when experts respond to elicitations in person (vs. online or mail) they ascribe lower confidence (larger uncertainty) to their estimates, but more optimistic assessments of best‐case (10th percentile) outcomes. The effects of expert affiliation and country of residence vary by technology, but in general: academics and public‐sector experts express lower confidence than private‐sector experts; and E.U. experts are more confident than U.S. experts. Finally, extending previous technology‐specific work, higher R&D spending increases experts’ uncertainty rather than resolves it. We discuss ways in which these findings should be seriously considered in interpreting the results of existing elicitations and in designing new ones.     

DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA

Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social Security for a growing population of elderly adults. We consider an extract of functional disability data from the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) model. We first describe GoM as an individual-level mixture model that allows individuals to have partial membership in several mixture components simultaneously. We then prove the equivalence between individual-level and population-level mixture models, and use this property to develop a Markov Chain Monte Carlo algorithm for Bayesian estimation of the model. We use our approach to analyze functional disability data from the NLTCS.