# 9. New Data for Development Policy

_<mark>Robert Cull, Dean Jolliffe, Vijayendra Rao</mark>_ Until recently, most of development economics research relied on traditional data types such as household and firm surveys and national accounts. Governments (or agents authorized by governments) have typically been central to the data collection efforts and data typically have been collected for specific purposes, often to foster development. For example, the earliest social surveys were undertaken in England by Charles Booth and Seebohm Rowntree in the 1890s to measure poverty, to describe the deplorable living conditions of the poor, and to bring about social policy reform.

Traditional data methods require strong statistical capacity. Trained staff, budgetary autonomy for agencies that collect data, adequate installations, connected databases, and international partnerships are important factors in shaping successful national data systems.<sup>92</sup> These resources are often scarce in low‐income countries,<sup>93</sup> leaving them least equipped to collect the data necessary to assess and understand the scope and nature of development problems and make inroads to solving them. Enhancing the statistical capacity of client countries therefore has, and will continue to be, a point of emphasis for the World Bank Group.

## **Moving beyond traditional data**

At the same time, traditional data collection methods can be relatively costly.<sup>94</sup> Surveys are therefore performed infrequently,<sup>95</sup> and they lack the granularity necessary to make meaningful inferences about sub‐populations of interest. In contrast, so‐called “digital data” from mobile phones, satellites, and other sources can be collected cost‐effectively, with high frequency, and at fine levels of granularity. Digital data therefore may offer new insights for understanding and resolving some development challenges. Recent examples from empirical research include:

- **Measuring poverty, agricultural productivity, and malnutrition.** Influential recent work has used satellite imagery of nighttime lighting to measure poverty by using it as a proxy for wealth.<sup>96</sup> In Rwanda, researchers have mapped personal data from mobile networks to individual phone subscriber wealth. They applied their model to predict wealth throughout the country, showing

> 92 World Bank. 2018. _Data for Development: An Evaluation of World Bank Support for Data and Statistical Capacity._ Independent Evaluation Group, Washington, DC: World Bank.

> 93 Cameron, Grant J., Hai‐Anh H. Dang, Mustafa Dinc, James Foster, and Michael M. Lokshin. 2019. “Measuring the Statistical Capacity of Nations.” World Bank Policy Research Working Paper 8693.

> 94 Kilic, Talip, Umar Serajuddin, Hiroki Uematsu, and Nobuo Yoshida. 2017. “Costing Household Surveys for Monitoring Progress toward Ending Extreme Poverty and Boosting Shared Prosperity.” WPS7951. The World Bank. <u>http://documents.worldbank.org/curated/en/260501485264312208/Costing‐household‐surveys‐for‐monitoring‐ progress‐toward‐ending‐extreme‐poverty‐and‐boosting‐shared‐prosperity.</u>

> 95 Serajuddin, Umar, Hiroki Uematsu, Christina Wieser, Nobuo Yoshida, and Andrew L. Dabalen. 2015. “Data Deprivation : Another Deprivation to End.” WPS7252. The World Bank. <u>http://documents.worldbank.org/curated/en/700611468172787967/Data‐deprivation‐another‐deprivation‐to‐end</u>

> 96 Neal Jean et al. (2016), “Combining Satellite Imagery and Machine Learning to Predict Poverty,” _Science_ l 353 (6301): 790‐794.

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