These sources of information combined with traditional data allow new approaches for empirical investigation. For example, Blumenstock et al. (2018) combine firms’ mobile phone and administrative data with geocoded violent events data to investigate how Afghanistan’s private sector reacts to insecurity. Using existing cell phone network data in this case was particularly fruitful because of the unsafe and violent context of the country.

In countries where data availability is constrained, or where data have not been collected, mobile phone metadata can provide important insights into population behavior and characteristics. This source of information not only reveals an individual’s intricate social network, but can reveal communications frequency, intensity, directionality, and timing; travel patterns; migration patterns and specific locations; and consumption and expenditure histories with much more frequency than traditional survey information.

Cell phone technology uses base transceiver stations to connect cell phones with the network, and ranges for stations vary from less than 1 km<sup>2</sup> in dense urban areas to more than 3 km in rural areas. Thus, mobile phone data permit a much more disaggregated geographic level of analysis compared to household surveys, which are limited to state or district levels (Soto et al. 2011). In theory, the frequency and locational advantages of mobile phone network data could be combined with new methods for real-time policy evaluation and population monitoring in remote, violent, and inaccessible regions.

# <u>Data requirements and pitfalls</u>

While some studies have shown that an individual’s footprint can predict her socioeconomic level, and therefore substitute for census or household data (Blumenstock, Cadamuro & On, 2015; Soto et al., 2011), most studies use network data to complement more traditional data sources. For example, Gutierrez, Krings, and Blondel (2013) created a predictive model to extract indicators of wealth, inequality, and segregation from mobile phone data. They suggest that network data sets represent a starting point to understand socioeconomic variables in countries with no resources to conduct large surveys. At the same time, these researchers are also cautious, warning that traditional surveys are still needed to build more accurate models or to assess the quality of mobile phone data.

Data quality can vary according to the specific scope of a study. For example, in the previous Blumenstock et al. (2018) paper using private firms’ mobile phone data in Afghanistan, the authors disclaim that registered corporate lines are unlikely to represent all firms, particularly small and informal firms operating outside urban areas. In developing countries, the large size of the informal economy can skew results interpretation. Similarly, mobile phone information is limited to people able to afford a cell phone. Even though mobile phone use is increasingly widespread, it is important to note the limitations of these data sets A last drawback is related to data availability and individuals’ privacy. Since private firms usually operate cell phone networks, it can be problematic to obtain user data due to commercial, ethical, and regulatory concerns. It is hard to estimate the cost of these data sets, but the main obstacle for obtaining them may be regulatory and administrative hurdles. Researchers must prove that they will use the information only for research purposes.

# **Geospatial data**

# <u>Method</u>

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