# **1 Introduction**

In recent decades, the amount of data produced has exploded, generating boundless opportunities for policies to improve people’s lives (World Bank 2021). Though data can be valuable in their raw form, the full value of data is only realized when they are analyzed to create insights, and these insights are converted to public policies or increased accountability.

Researchers have a vital role to play in this regard. Many researchers spend countless hours digesting data, using data to create new knowledge, and communicating this knowledge with the intent of impacting public discourse and public policies. There are numerous examples of data-driven analyses having real and important impacts on people’s lives (Jolliffe et al. 2023). One example from Brazil explicitly looks at researchers’ ability to influence policy outcomes. There, evidence from 2,150 municipalities found that informing municipal mayors of research findings on the effectiveness of a simple policy change increased the probability that their municipality implemented the policy by 10 percentage points (Hjort et al. 2021).

Without research, there is a risk that the return of data to society will be reduced, and policies to improve lives unrealized. Yet very little is known about where there is missing data-driven evidence and how governments can best stimulate an evidence base for local decision makers. This paper attempts to fill these gaps by addressing two questions: (1) Which countries are the subject of research papers using data? (2) How can countries increase their national evidence base? We focus on data-driven research due to the increasing importance of data for policy making and the specific policies that are needed to increase the supply and demand of data, such as boosting statistical capacity and improving data literacy.

To answer the first question, we introduce a new method for measuring data use in research articles based on 1 million English-language articles spanning 216 countries and various academic fields. These articles are made available by Semantic Scholar Open Research Corpus (s2orc), which has digitized millions of research papers worldwide and made its raw text accessible via APIs (Lo et al. 2020). With the aid of Amazon Mturk workers, we manually code 900 of these articles as using data or not, upon which we train a natural language model to predict the coding of the Mturk workers (Devlin et al. 2018). The model achieved an 87% out-of-sample accuracy rate, and when the articles are aggregated to the country level, the model had a correlation of 0.99 with the number of articles classified by Mturk workers. The model was then applied to 1 million academic articles from the s2orc database from 2000-2020. The model estimates the amount of data-driven research on a country regardless of where the researchers may be located, _not_ the amount of data-driven research by the citizens of a country. We argue that the former is the relevant quantity to understand the evidence base available to national decision makers.

We find that data-driven research is strongly correlated with GDP per capita and population, which together account for around 75% of the variation across countries. High-income countries are the subject of nearly 50% of all papers using data despite representing only around 15% of the world's population, while low-income countries, comprising approximately 10% of the world population, account for only about 5% of articles using data.

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