- that the predictions match well with those from costlier traditional surveys of the population.<sup>97</sup> Similar approaches have been used to produce granular maps of crop yields and malnutrition.<sup>98</sup>

- **Improved targeting of public health interventions and natural disaster relief.** Understanding who is most affected by natural disasters and predicting relocation patterns is key to effective humanitarian relief operations, public health interventions, and long‐term reconstruction efforts. Mobile phone data have been used to reveal who is most affected by natural disasters,<sup>99</sup> where people relocate in response to a disaster,<sup>100</sup> and the implications of relocation for the spread of disease.<sup>101</sup>

- **Natural Language Processing methods: text as data.** Traditional qualitative surveys require in‐ depth analysis of interviews coupled with participant observation in small groups.  These have recently been supplemented with the analysis of narratives from much larger samples by applying Natural Language Processing methods to existing texts. Such methods have been used to analyze Wikipedia pages to predict levels of economic development<sup>102</sup> , and to study discourse within village meetings in rural India.<sup>103</sup>

# **Challenges in harnessing digital data for development**

Governments have been at the center of traditional data collection efforts. In contrast, digital data is typically collected by firms, which focus on its commercial value rather than its potential social value. Moreover, data are “non‐rival”, meaning that a person’s call data records, location history, internet usage and medical records can be used by many firms (and governments) at the same time and for different purposes. Recent theoretical research shows that this may lead firms to respect data privacy less than is socially desirable, and they may also have incentives to inefficiently hoard data.<sup>104</sup> In short, when firms own data, they may over‐use and under‐share it while not adequately respecting consumer privacy. Policy priorities for making digital data work for development include: 97 Blumenstock, Joshua, Gabriel Cadamuro, Robert On. 2015. “Predicting Poverty and Wealth from Mobile Phone Metadata.” _Science_ 350 (6264): 1073‐1076. _._ 98 Burke, Marshall and David A. Lobell. 2017. “Satellite‐Based Assessment of Yield Variation and Its Determinants in Smallholder African Systems.” _Proceedings of the National Academy of Sciences_ (USA) 114(9): 2189‐2194. Osgood‐ Zimmerman, Aaron, et al. 2018. “Mapping Child Growth Failure in Africa Between 2000 And 2015.” _Nature_ 555 (41): 41‐47.

99 Blumenstock, Joshua, Nathan Eagle, and Marcel Fafchamps, 2016. “Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters.” _Journal of Development Economics_ 120: 157‐ 181.

100 Lu, Xin, Linus Bengtsson, and Petter Holme, 2012. “Predictability of Population Displacement after the 2010 Haiti Earthquake.” _Proceedings of the National Academy of Sciences_ (USA) 109: 11576‐11581.

101 Wesolowski, Amy, et al., 2015. “Impact of Human Mobility on the Emergence of Dengue Epidemics in Pakistan.” _Proceedings of the National Academy of Sciences_ (USA) 112: 11887‐11892.

102 Evan Sheehan et al. 2019. “Predicting Economic Development Using Geolocated Wikipedia Articles,” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2698‐ 2706.

103 Ramya Parthasarathy, Vijayendra Rao and Nethra Palaniswamy “Deliberative Democracy in an Unequal World: A Text‐As‐Data Analysis of South India’s Village Assemblies” _American Political Science Review_ , 113 (3, August 2019): 623‐640 104 Jones, Charles I. and Christopher Tonetti, 2019. “Nonrivalry and the Economics of Data.” Stanford University Graduate School of Business, mimeo.

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