literacy among researchers, low trust in data from the national statistical system, and lack of infrastructure to access and use the data, which may be particularly an issue of low- and middleincome countries.

The policies needed to boost data supply differ from those needed to boost data demand. Boosting the data supply of the national statistical system requires more financing for national statistical offices, better technical capacity of its staff, and at times better statistical laws to ensure their independence from other government bodies. Policies to boost data demand, on the other hand, involve financially cheap (but not necessarily politically cheap) wins, such as making data more accessible for researchers. It also involves increasing the data literacy of academics. Given the non-overlapping policy responses, it is useful for countries to know whether data demand or data supply is their weakest link in the data value chain. Below, we classify countries by whether they are primarily constrained by data supply, data demand, neither or both.

Building on the semantics introduced by Porteous (2020) who classified countries into research deserts and research oases, we classify countries into four groups: _Data deserts_ have little data demand and little data supply, _data swamps_ have high data supply but little data demand, _data oases_ have high data demand but little data supply, and _data lakes_ have high data demand and high data supply. For assessing data supply, we again turn to the availability of various data sources, such as recent population and business censuses, household and health surveys, Civil Registration and Vital Statistics (CRVS), and sub-national geospatial data (for details see Dang et al., 2023). We measure data use as the residual from the regression of number of articles on the country using data on log population, log GDP per capita, and the average number of qualitative papers. The control for the number of qualitative papers once again serves as a proxy for general research interest and research environment unrelated to data.

We classify countries by whether they are below/above the median of our indicators of data supply and data use (Figure 7). Since we use median values in the two dimensions to create the categories, there are about as many countries belonging to each group. Yet there are large discrepancies by region and income group (Table 3). The poorest countries and poorest regions are more likely to be data deserts or data oases. These categories apply to more than 80% of lowand lower-middle income countries, 93% of countries in Sub-Saharan Africa, and only 7% of highincome countries. Thus, poorer countries and poorer countries tend to have lower data supply.

Low-income countries and countries in Sub-Saharan Africa are twice as likely to be oases than deserts. In fact, when looking at the map, it is evident that a large part of the world’s data lakes, are to be found in Sub-Saharan Africa. This suggests that, despite data scarcity, a significant volume of quantitative articles is already produced on these countries, when factoring in their population size, income, and attention in non-quantitative scholarship. These countries may benefit most from bolstering their data supply. This applies for example to Uganda, Ghana, Nepal, and Malawi. By contrast, nearly all upper-middle income countries and countries in Latin America who have low data supply are deserts rather than oasis. That is, they face both data 16