_Note_ : Data on GDP, population and SPI are retrieved from the World Bank’s World Development Indicators For GDP per capita, the figure indicates an elasticity of 0.5, meaning that a 10% increase in GDP is associated with a 5% increase in academic articles using data. The elasticity with respect to population is 0.67, and population alone explains 59% of the variation in academic articles using data. The overall SPI score is also predictive of academic output using data. A 10-point increase in SPI scores, which is approximately the same as moving from the median SPI score to the 65th percentile, translates into around a 0.6% increase in the number of articles using data.

Table 2 shows coefficients from a regression of the log number of academic papers using data on log GDP per capita, log population, and the SPI scores. The first column shows a specification including just log GDP per capita and log population. These two indicators alone explain roughly 75% of the variation in the production of articles using data. The second column includes the SPI overall score as an additional control. The performance of a country’s statistical system is associated with greater academic output. Conditional on GDP per capita and population, a tenpoint increase in SPI overall scores (on a scale of 0-100) translates into a 0.2% increase in academic output.

It is possible that omitted variables are behind this result. For example, it could be the case that countries that are of general interest to the research community also happen to have high statistical performance. Das et al. (2013) and Porteous (2022) control for a range of variables which may influence the relationship sought after, such as whether English is a first language, international tourist arrivals, etc. We leverage that we have an estimate of the number of articles not using data – referred to as qualitative articles – to control for all factors that are likely to influence overall research output in a country unrelated to statistical performance. The number of qualitative articles is calculated as the number of total articles minus any articles using data, based on our NLP model.

Countries that tend to be over-represented in quantitative articles also tend to be overrepresented in qualitative articles, conditional on GDP per capita and population (Figure A.3). The elasticity is around 0.6, suggesting that a 1% increase in the number of qualitative articles associated with a 0.6% increase in the number of articles using data. We take this to imply that quantitative articles do not crowd out qualitative articles and vice versa. Returning to Equation (2), one prediction from the model is that countries with particularly high costs related to using data, (such as a lower statistical performance score), should see reductions in the amount of quantitative research, but it would not follow that it would see lower levels of qualitative ii XX research. This would predict the share of quantitative articles to total articles increasing in SPI score. Contrary to this, there is almost no relationship between SPI overall scores and the share of qualitative papers to total papers produced overall (Figure A.4). This suggests to us that some omitted variable may in part be driving the impact of statistical performance on academic articles using data, and that the number of qualitative articles can control for such possibly omitted factors.

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