The Role of Open Data in the Digital Economy: A Machine Learning and Econometric Perspective

Abstract

The recent adoption of Open Data (OD) policies by Governments and the increased release of Open Government Data (OGD) around the world is a phenomenon being studied by different disciplines trying to understand and estimate its potential benefits, limitations, and dimensions. The purpose of this research is to analyze the relationship and effect of open (government) data on entrepreneurship at the country level. The framework of this research is based on a theoretical model explaining how different levels of open (government) data could affect the decision of individuals of becoming an entrepreneur. This work also tests this relationship and effect, developing an empirical model through a selection of macroeconomic variables, this selection process is supported by a literature review approach based on entrepreneurship theory. The sample of this analysis is a panel data composed of 137 economies that includes indicators from The Global Entrepreneurship Index (GEDI), Open Data Barometer (ODB), Global Open Data Index (GODI), Economic Freedom Index (EFI), The Global Competitiveness Report (GCR), and the Global Innovation Index (GII) from 2013 to 2016. A multiple linear regression analysis adopting an econometric and machine learning approach is used in order to produce a more robust analysis and estimate the relationship between open data and an index of entrepreneurship at the country level. Our estimates suggest that open (government) data has a positive and statistically significant impact on entrepreneurship and its potential benefits are that open (government) data gives access to information and the identification of new business opportunities, more effective strategic planning, and more efficient evaluation of investment projects. All these concepts are closely related to the formation of new entrepreneurs and new businesses.