# An Analysis of the Supply of Open Government Data
## 1 Introduction
This work pretends to fill this gap and contribute to the state of the art of open government data, providing a statistical analysis explaining countries' variabilities of the release of open government data through economic, social, and institutional factors. According to the Global Open Data Index published in 2016 by the Open Knowledge Foundation (OKF) ([[https://okfn.org/](https://okfn.org/)]([https://okfn.org/](https://okfn.org/))), there are significant differences across countries in the supply of open government data. In particular, Australia, the United Kingdom, and France obtain the highest GODI scores reported by the Open Knowledge Foundation (meaning that these countries contribute the most to the supply of open government data), while countries such as Myanmar, Barbados, Malawi, and Botswana obtained the lowest records on the GODI score (meaning that, in a global comparison, these countries contribute the least to the supply of open government data). This leads us to the following question: What explains the high heterogeneity in the global supply of open government data? The objective of this paper is to provide an answer to this question by extending a single academic perspective, due to this research being based on an interdisciplinary approach aligned by the fields of data science and economics. The intersection point of these disciplines involves analyzing and estimating the determinants of the heterogeneity in the supply of global open government data by means of gathering information from different sources, featuring extraction and variable selection, modelling through the implementation of statistical methods, and explaining the effect and relationship of this heterogeneity. On the one hand, the data science approach is implemented in order to systematically create a data pipeline collected from different portals. This task is executed following a process of obtaining, scrubbing, exploring, modelling, and interpreting (OSEMN) the information collected from several sources. Then, we apply feature engineering in order to extract and analyze the data by way of a regression model that seeks to analyze the statistical association between some political and economic determinants of open government data. To estimate our model of regression analysis, we develop a sample with country cross-section data with data of the Global Open Data Index (GODI) for the year 2016. In this process, we solve empirical issues that arise in the regression analysis, such as multicollinearity, heteroscedasticity, missing data, outliers, and high dimensionality with our target variable (open government data). On the other hand, economic theory is adopted to develop an empirical analysis (using our data pipeline) for the analysis of variables and their justification based on domain knowledge. Open government data is considered as a pure public good [8]; that is to say, we consider open government data as satisfying two important properties: it is a non-excludable (once open government data is provided, then any person who seeks access can have access to that good) and it is a non-rival good (the consumption of open government data by some agent does not preclude the consumption of the same good by everyone else). Applying this theoretical framework, we test if political and social institutions such as civil rights, transparency, quality of democracy, and political participation, as well as economic and sociodemographic characteristics at the country level (such as the size of the economy, the efficiency of the government, the demand for Internet services, the median age of the population of a country, and the size of the population), can explain the global variability in the supply of open government data. Using a cross-country regression model, our analysis provides evidence that cross-country differences in governance and social institutions such as civil liberties, government transparency, and the quality of democracy are statistically significant predictors of cross-country differences in the supply of open government data.