To answer the second question – how countries can increase their national evidence base – we first establish that a country’s statistical capacity is predictive of data research even after controlling for population and GDP, and articles not using data (which we use as a proxy for the general research interest in the country). To understand which part of a country’s statistical capacity is most important for increasing data-driven research, we explore the data sources most related to academic data use. We find that the availability of geospatial data at the first administrative level is associated with a 1.1% increase in data use, that a population census in the past decade is associated with a 0.3% increase in data use, and that two or more labor force (agriculture) surveys over the past 10 years is associated with a 0.4% (0.2%) increase in data use. Though we are unable to establish these links causally, there are concrete data products that governments could supply to likely increase the evidence-base at its disposal.

Boosting the supply of data is one way for a country to increase the data-driven research it is subject to, another is to increase the demand for its data. This is particularly relevant for countries that have already invested in relevant data products but are nonetheless subject to relatively little data-driven research. These are cases where existing data are underutilized and where it may be relevant to make existing data more accessible to researchers and possibly boost data literacy in the country. To explore this distinction between boosting data supply and data demand, and building upon Porteous (2020), we classify countries into four groups: _Deserts_ have little data demand and little data supply, _swamps_ have high data supply but little data demand, _oases_ have high data demand but little data supply, and _lakes_ have high data demand and high data supply. Nearly two-thirds of low-income countries and countries in Sub-Saharan Africa are oases, suggesting that these countries are getting relatively large evidence from their data supply and that they have relatively little issue with lack of demand, but that they could benefit from increasing the data available to researchers. By contrast, nearly half of countries in Europe are data swamps, suggesting a priority on increasing the use of existing data.

Previous research has highlighted gaps between countries in economic research output and noted that richer countries are the subject of more economic research. For instance, Robinson, Hartley, and Schneider (2006), Das et al. (2013), and Porteous (2020) examine which countries are studied most by economists using the EconLit database. Cameron, Mishra, and Brown (2016) and Sabet and Brown (2018) extend this to note that impact evaluations are highly uneven across countries as well. Phillips and Greene (2022) show conflict research is biased towards Western countries, while Courtioux et al. (2022) show that academic research is highly related to public investments in scientific research.

We contribute to the literature by applying NLP to improve our understanding of what countries are under-researched. Using NLP allows us to go beyond the existing literature in three ways: (1) scale up the sample size and look at all fields of interest rather than only economics, (2) identify papers that use data, which is key to understanding whether data demand or data supply could be explaining a country’s lack of research, and (3) indicate measures countries can take to increase data research.

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