enough information to create a targeting tool based on expenditures of households, the World Bank and UNCHR created a targeting algorithm that mapped directly to all individuals in the ProGres data (Verme et al. 2015). This algorithm revealed that UNHCR’s targeting efficiency could be improved markedly. As a result, this targeting model has been embedded into the UNHCR operational portal and used to rank refugee households for targeting cash assistance. For humanitarian organizations, this has been a paradigm shift that revolutionized targeted programs in North Africa and the Middle East and beyond, and the tool has been applied to displaced populations in Egypt, Lebanon by the UNCHR and the WFP.

Interoperability can also induce cost savings for the private sector, particularly data containing key national identifiers of companies, individuals, geographical units, and other entities, which allow for easy linking with the company’s own data. National ID systems, for example, can increase private sector efficiency through cost savings such as removing the need for companies to create their own ID systems, and increased revenues by expanding the potential customer base (World Bank 2020a and World Bank 2018c). In India, Aadhar, the unique 12-digit identity number that can be obtained by residents and citizens of India, has reduced the need for identity verification for firms. As a result, it is estimated that a firm’s typical onboarding costs for new employees could decrease from 1,500 rupees to 10 rupees – a reduction of 99.3% (World Bank 2020a and World Bank 2018c).

# **Impartiality**

When data are not impartial, their value falls apart due to potential misuse. Lack of data impartiality can seriously affect countries’ credibility and creditworthiness. For example, in 2007 the Argentine government began interfering with the consumer price index and other official statistics. This led to inaccurate and non-credible reported inflation figures (Todesca 2017). Similarly, Greece’s debt statistics appear to have deliberately misrepresented the country’s financial situation in the lead-up to the 2009-euro crisis (Katsimi and Moutos 2010). In both cases, the deliberate manipulation of statistics was the precursor to larger crises, revelations and fiscal austerity measures that triggered social unrest and had significant social welfare implications.

Assuring that data cannot be manipulated by producers can have positive consequences on government budgets. India saved 19 percent on a rural employment guarantee scheme—the world’s largest workfare program—after introducing e-governance to the payments system. Most of the savings were incurred because local officials were no longer tasked with distributing the funds and thus could not mismanage them and misreport the data. The change decreased officials’ personal wealth by 10 percent (Banerjee et al. forthcoming).

In some cases when data are not impartial, third-party sources may notice inconsistencies. In China, for instance, media and watchdog organizations in Beijing noted inconsistencies between official government data on air quality and data from independent air quality monitoring systems. Heightened concerns about air quality fueled a dramatic expansion in publicly available, real-time data from thousands of air quality monitoring locations (Yin et al.

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