significant misreporting by transfer recipients in national surveys; and Nicholas and Wiseman (2010) used administrative records to evaluate and estimate better measures of poverty for social security recipients.

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

The first pitfall related to use of “big data” is the quality and discontinuity of the data. Administrative records can be severely fragmented. Censuses and surveys rarely change, because the data should allow comparisons over time; if they do change, modifications respond to specific needs. In governmental programs, changes may occur due to legislative, regulatory, or political matters affecting the scope of the data and their use. Furthermore, in some administrative systems, individual records remain at the local source and only aggregate data are assembled centrally. In other systems, data might not even be centralized. Having several institutions or agencies responsible for data can interfere with quality control that a central agency or researcher can impart. In other words, these are obstacles to collection of quality, complete, and continuous data.

Coverage and content represent further drawbacks. With administrative data, target populations, topics, and all specific variables of interest are defined by administrative requirements or purposes. Contrary to surveys were the researcher can design the questions of interest, administrative records offer a limited source of information and variables. For example, when using tax records to analyze businesses or industries, databases need to be complemented with another source to better estimate production statistics (Brackston, 1987).

Administrative records might not be representative of the national population. Most governmental programs cover only a portion of the population with very specific beneficiary characteristics. Records can also suffer from misreporting; for example, tax reports can lack information on the many informal economic actors. Weaknesses in administrative data coverage and content are the main reasons why many empirical studies use data imputation based on surveys, or other alternative sources of data and information on sample populations.

Lastly, another pitfall is access and confidentiality. Depending on the regulatory premises of each country, access to administrative data may be limited to confidentiality-protected aggregate data or to disaggregated individual data. Open governments can be a great source of information, but in many cases data access is limited due to unwillingness of governmental administrations to fully share program beneficiary databases or other information that could hold administrators accountable.

Confidentiality can also prevent governments from providing full access to its records. Since some statistical techniques require individual identifiers to manipulate intermediate data, there could be public privacy concerns related to how databanks could be used against respondents.

# **Social networks**

# <u>Method</u>

Communications within an individual ́s social network can reveal a lot of information about individual interactions, mobility, and habits that correlate with socioeconomic levels. Bluemenstock, Cadamuro and On (2015) showed that an individual’s mobile phone history, for example, can infer socioeconomic status. Furthermore, they showed that it is possible to derive national and subnational wealth estimates from patterns in cellular phone trace data.

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