Does it Make a Difference? Comparing Growth Effects of European and North American FDI in Latin America

FDI from the European Union (EU) ranks before FDI from North America (NA) in some of the Latin American countries. We investigate the impact of EU- versus NA-FDI on the growth rate including about 50 controls. Country specific effects and parameter heterogeneity are incorporated in our estimation. We use Bayesian Model Averaging to address model uncertainty and to select the best models and most robust parameters. Our results indicate that positive effects of FDI are dependent on the functioning of legal frameworks and the quality of infrastructure. EU-FDI is an important, robust growth determinant whereas NA-FDI is not. --Growth determinants,FDI,model uncertainty,Bayesian Model Averaging,Latin America

Data Science for Entrepreneurship Research:Studying Demand Dynamics for Entrepreneurial Skills in the Netherlands

The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science --- data science methods --- we are now able to analyze data sets that can be huge, multidimensional, unstructured, and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012-2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population --- outside the domain of entrepreneurs --- is a rewarding subject for future research

Data Science for Institutional and Organizational Economics

To which extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory

A comparison of two model averaging techniques with an application to growth empirics

Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present

Semi-Public Competitions *

Abstract The process of innovation is driven by two main factors: new inventions and institutions supporting the transformation of inventions into marketable innovations. This paper proposes a new institution, called a semi-public competition, that has been neglected by the economic literature but exists frequently in practice. I show how semi-public competitions can mitigate a dilemma that arises at an early stage of innovative activity and specify the conditions under which a semi-public competition can increase welfare. The results suggest that governments promote knowledge about the semi-public competition mechanism but refrain from direct public funding of competitions

Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands

The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science—data science methods—we are now able to analyze data sets that can be huge, multidimensional, and unstructured and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012–2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population—outside the domain of entrepreneurs—is a rewarding subject for future research

Data science for entrepreneurship research:studying demand dynamics for entrepreneurial skills in the Netherlands

The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science—data science methods—we are now able to analyze data sets that can be huge, multidimensional, and unstructured and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012–2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population—outside the domain of entrepreneurs—is a rewarding subject for future research

Data Science for Institutional and Organizational Economics

To which extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational governance structures. We elaborate on several examples applying data science to analyze legal, political, and social institutions and sketch how specific data science techniques can be used to study important research questions that could not (to the same extent) be studied without these techniques. We conclude by comparing the main strengths and limitations of computational social science with traditional empirical research methods and its relation to theory