A simple model of crime waves, riots, and revolutions

Standard economic models of criminal behavior analyze the criminal's decision in a partial equilibrium context. The standard model does not recognize that the probability of being punished is a function of the total amount of crime that occurs. As the total amount of crime increases, police resources become strained, courts become congested, and prisons become overcrowded. As a result, proportionately fewer criminals are apprehended, convicted, and imprisoned. The feedback effects from one criminal's decision to participate in crime to another criminal's decision can be highly significant. In one parameterization of the model developed here, the individual commits twice as many crimes for a given parameter shift than is implied by the standard model. The model also sheds light on other areas where criminal actions are interdependent such as riots, crime waves, and revolutions.     

Money and banking in a mature Walrasian economy

All financial transactions in the original Walrasian economy, as written in the édition définitive of the éléments, occur in the markets for short-term money services (the short-term bond market) and commodity E (the equity market). However, introduction of the long-term bond and the deposit markets requires only minor surgery to the original and they are now supplied, inspiring the name mature Walrasian economy. Transactions in numeraire then entail all institutions of the banking system and provide completely for money and credit. Into this economy the condition of inconvertibility, a discretionary numeraire with reserve requirements, and a central bank are introduced and clarified. Finally, multiple monetary subsystems in the same global economy are recognized and reconciled, thus establishing a global economy with a single numeraire commodity.     

Do Off‐Label Drug Practices Argue Against FDA Efficacy Requirements? A Critical Analysis of Physicians' Argumentation for Initial Efficacy Requirements

The amended Food, Drug and Cosmetics Act requires efficacy certification for a drug's initial uses (“on‐label”), but does not require certification before physicians may prescribe the drug for subsequent uses (“off‐label”). Does it make sense to require FDA efficacy certification for new drugs but not for new uses of old drugs? Using a sequential online survey, we carried on a “virtual conversation” with some 500 physicians. The survey asked whether efficacy requirements should be imposed on off‐label uses; almost all physicians said no. It asked whether the efficacy requirements for initial uses should be dropped, and most physicians said no. We then asked respondents whether opposing efficacy requirements in one case but not the other involved an inconsistency. In response, we received hundreds of written commentaries. We organize and discuss these commentaries with an eye to understanding how the medical market certifies off‐label drug uses and how this compares to FDA certification. Does off‐label medicine use suggest that efficacy requirements should be placed on new uses of old drugs? Does it suggest that efficacy requirements on new drugs should be lifted? We explore these questions, and ask whether the response of many of the doctors exhibits the familiar behavior bias toward the status quo.     

Supply response of U.K. milk producers

Short‐run own‐price response elasticities are estimated from two samples of panel data for specialist dairy farms in the Northwest and Southwest of England. Although these farms are subject to milk marketing quotas, the free tradability of quotas enables them individually to adjust the output quite rapidly. Model specifications have been chosen to test the ability of panel data to directly reveal behavioral responses without the imposition of theoretical restrictions. Significantly, the two independent samples generate quite similar results, especially in terms of supply response measures. Stable and promising estimates are obtained with simultaneous models giving higher elasticities than single equation models.     

Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data

This article explores how data envelopment analysis (DEA), along with a smoothed bootstrap method, can be used in applied analysis to obtain more reliable efficiency rankings for farms. The main focus is the smoothed homogeneous bootstrap procedure introduced by Simar and Wilson (1998) to implement statistical inference for the original efficiency point estimates. Two main model specifications, constant and variable returns to scale, are investigated along with various choices regarding data aggregation. The coefficient of separation (CoS), a statistic that indicates the degree of statistical differentiation within the sample, is used to demonstrate the findings. The CoS suggests a substantive dependency of the results on the methodology and assumptions employed. Accordingly, some observations are made on how to conduct DEA in order to get more reliable efficiency rankings, depending on the purpose for which they are to be used. In addition, attention is drawn to the ability of the SLICE MODEL, implemented in GAMS, to enable researchers to overcome the computational burdens of conducting DEA (with bootstrapping).     

Telling the data story the right way

Effective data-driven analyses of service-line performance require: Buy-in and agreement at the outset from all parties (hospital and physicians) on the validity of the data used to evaluate service-line performance, Actionable data and metrics relevant to physicians, with financial goals tangibly linked to clinical improvement, Transparent sharing of data with physicians to build their trust and support the case for change, A physician champion who can help validate findings and guide how data are presented, Willingness of physicians to acknowledge that the opportunity for improved margin depends largely on the variable costs that they control as individuals.     

Brain drain or brain bank? The impact of skilled emigration on poor-country innovation

The development prospects of a poor country or region depend in part on its capacity for innovation. In turn, the productivity of its innovators, whom are often concentrated around urban centers, depends on their access to technological knowledge. The emigration of highly skilled individuals weakens local knowledge networks (brain drain) but may also help remaining innovators access valuable knowledge accumulated abroad (brain bank). We develop a model in which the size of the optimal innovator Diaspora depends on the competing strengths of co-location and Diaspora effects for accessing knowledge. Then, using patent citation data associated with inventions from India, we estimate the key co-location and Diaspora parameters. The net effect of innovator emigration is to harm domestic knowledge access, on average. However, knowledge access conferred by the Diaspora is particularly valuable in the production of India’s most important inventions as measured by citations received. Thus, our findings imply that the optimal emigration level may depend, at least partly, on the relative value resulting from the most cited compared to average inventions.     

Infinite-dimensional VARs and factor models

This paper proposes a novel approach for dealing with the ‘curse of dimensionality’ in the case of infinite-dimensional vector autoregressive (IVAR) models. It is assumed that each unit or variable in the IVAR is related to a small number of neighbors and a large number of non-neighbors. The neighborhood effects are fixed and do not change with the number of units (N), but the coefficients of non-neighboring units are restricted to vanish in the limit as N tends to infinity. Problems of estimation and inference in a stationary IVAR model with an unknown number of unobserved common factors are investigated. A cross-section augmented least-squares (CALS) estimator is proposed and its asymptotic distribution is derived. Satisfactory small-sample properties are documented by Monte Carlo experiments. An empirical illustration shows the statistical significance of dynamic spillover effects in modeling of US real house prices across the neighboring states.     

Antecedents to Decision‐Making Quality and Agility in Innovation Portfolio Management

Innovation portfolio management (IPM) is a dynamic decision‐making process, in which projects are evaluated and selected, and resources are allocated. Previous research has developed an understanding of IPM success and its influencing factors. However, little research investigated the quality of the decision‐making process and the ability to quickly adapt the portfolio. This study focuses on the antecedents of decision‐making quality and agility (i.e., responsiveness to changes in the environment). Based on a decision‐making framework, five structural and cultural IPM components are derived as important antecedents of decision‐making quality, which in turn influences agility. The structural components (1) clarity of strategic goals, (2) formality of the IPM processes, and (3) controlling intensity serve a coordinating function. The cultural components (4) innovation climate and (5) risk climate serve a motivating function in IPM. An analysis of a sample of 179 firms and their innovation portfolios through structural equation modeling using a double‐informant design documents that these five components all positively influence portfolio decision‐making quality, which in turn positively influences agility. Results further show that environmental turbulence moderates some of these relationships. While the positive effect of process formality is weakened under increasing turbulence, the effects of controlling intensity and climate for innovation are strengthened by environmental turbulence. The findings have theoretical implications for the understanding of IPM as a dynamic capability and practical implications for the management of portfolios in turbulent environments.     

The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.