Daniel Shapira

Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data

We show how networks modify the diffusion curve by affecting its symmetry. We demonstrate that a networks degree distribution has a significant impact on the contagion properties of the subsequent adoption process, and we propose a method for uncovering the degree distribution of the adopter network underlying the dissemination process, based exclusively on limited early-stage penetration data. In this paper we propose and empirically validate a unified network-based growth model that links network structure and penetration patterns. Specifically, using external sources of information, we confirm that each network degree distribution identified by the model matches the actual social network that is underlying the dissemination process. We also show empirically that the same method can be used to forecast adoption using an estimation of the degree distribution and the diffusion parameters at an early stage (15%) of the penetration process. We confirm that these forecasts are significantly superior to those of three benchmark models of diffusion. Our empirical analysis indicates that under heavily right-skewed degree distribution conditions (such as scale-free networks), the majority of adopters (in some cases, up to 75%) join the process after the sales peak. This strong asymmetry is a result of the unique interaction between the dissemination process and the degree distribution of its underlying network.

Zooming In: Self-Emergence of Movements in New Product Growth

In this paper, we propose an individual-level approach to diffusion and growth models. By zooming in, we refer to the unit of analysis, which is a single consumer instead of segments or markets and the use of granular sales data daily instead of smoothed e.g., annual data as is more commonly used in the literature. By analyzing the high volatility of daily data, we show how changes in sales patterns can self-emerge as a direct consequence of the stochastic nature of the process. Our contention is that the fluctuations observed in more granular data are not noise, but rather consist of accurate measurement and contain valuable information. By stepping into the noise-like data and treating it as information, we generated better short-term predictions even at very early stages of the penetration process. Using a Kalman-Filter-based tracker, we demonstrate how movements can be traced and how predictions can be significantly improved. We propose that for such tasks, daily data with high volatility offer more insights than do smoothed annual data.

From Research to Practice: Which Research Strategy Contributes More to Clinical Excellence? Comparing High-Volume versus High-Quality Biomedical Research.

The question when and to what extent academic research can benefit society is of great interest to policy-makers and the academic community. Physicians in university hospitals represent a highly relevant test-group for studying the link between research and practice because they engage in biomedical academic research while also providing medical care of measurable quality. Physicians’ research contribution to medical practice can be driven by either high-volume or high-quality research productivity, as often pursuing one productivity strategy excludes the other. To empirically examine the differential contribution to medical practice of the two strategies, we collected secondary data on departments across three specializations (Cardiology, Oncology and Orthopedics) in 50 U.S.-based university hospitals served by 4,330 physicians. Data on volume and quality of biomedical research at each department was correlated with publicly available ratings of departments’ quality of care, demonstrating that high-quality research has significantly greater contribution to quality of care than high-volume research.

Uncovering Social Network Structures through Penetration Data

We show how networks modify the diffusion curve by affecting its symmetry. We demonstrate that a networks degree distribution has significant impact on the contagion properties of the subsequent adoption process, and propose a method for uncovering the degree distribution of the adopter network underlying the dissemination process, based exclusively on limited early-stage penetration data. In this paper we propose and empirically validate a unified network-based growth model that links network structure and penetration patterns. Specifically, using external sources of information, we confirm that each network degree distribution identified by the model matches the actual social network that is underlying the dissemination process. We also show empirically that the same method can be used to forecast adoption using an estimation of the degree distribution and the diffusion parameters, at an early stage (15%) of the penetration process. We confirm that these forecasts are significantly superior to those of three benchmark models of diffusion. Our empirical analysis indicates that under heavily right-skewed degree distribution conditions (such as scale-free networks), the majority of adopters (in some cases, up to 75%) join the process after the sales peak. This strong asymmetry is a result of the unique interaction between the dissemination process and the degree distribution of its underlying network.

Practical modified Gini index

The Gini index is the most common method for estimating the level of income inequality in countries. In this article, we suggest a simple modification that takes into account the moderating effect of in-kind government benefits. Unlike other studies that use micro-level data that are rarely available for many countries or over a period of time, the proposed Modified Gini (MGINI) index could be calculated using just the regularly available data for each country. Such data include the original Gini coefficient, government consumption expenditures, Gross Domestic Product (GDP) and total tax revenue as a percentage of GDP. This modified version of the Gini index allows us to calculate the level of inequality more precisely and make better comparisons between countries and over time.

Traps and incentives

Abstract Many theories of development traps rely on coordination failures. In this paper we develop a theory of incentive traps in organizations, which demonstrates how the provision of incentives may itself become reinforcing for the emergence of traps. Our theory marks the dynamic interplay between incentives and performance in teams where peer-effects are present, the returns to which accrue far beyond the career horizon of current cohort of agents, while taking into account both intergenerational learning dynamics and the existence of markets for talent. The theory may help explain why high-quality research is rewarded less in those institutions where it is mostly scarce, why relative wages of professors in some developing countries are significantly lower than in developed economies, or why governments expenditure on education as percent of GDP is substantially lower in LDCs as compared to developed economies.

The Break of the Linkage between Monetary Incentives and Effort

The average worker in the US needs to work more than a year to earn his or her CEO’s daily wage. The well-accepted justification among economists for these huge wage gaps is the necessity to achieve economic efficiency, in terms of efficient allocation in the labor market and incentivizing employees to put forth maximum effort. Building on the idea that the level of effort any individual can invest is bounded, we provide a compact mathematical proof that challenges the well-accepted economic notion that extremely high gaps in net wages and wealth distribution are the lesser evil because they ensure economic efficiency. We conclude suggesting an economically efficient solution that does not limit gross wages, but still reduces socioeconomic inequalities.

Less (Model's Weight) is More (Population Overweight): Fashion Models and the Overweight Epidemic

Advertisements that present the image of the slim figures of models, movie actors and celebs are often blamed in triggering anorexia. In this paper we suggest that the slim model effect on consumers’ weight is substantially greater than previously estimated due to its role in the emergence of overweight spread. We develop an economic model of rational consumer food consumption showing that the growing gap between the ideal and average figure has made the former irrelevant for most individuals. Thus, consumers now refer to the median weight rather than to the ideal one, as the latter has lost its restraining ability. We show that the lower weight of the ideal beauty figure interplayed with other factors (such as the increasing level of food industrialization) exhibits a form of overweight epidemic dynamics that is dominated by a multiplier effect. Based on large-scale historical datasets, we support our theoretical assertions by analyzing US population BMI’s over the past five decades vis-a-vis ideal beauty body-size proxies, while controlling for large sets of demographic variables and food industry data.