percentage points in urban areas between 2009/10 and 2011/12. Conversely, in the middle of Argentina’s debt crisis, the poverty headcount rate rose by nearly 10 percentage points in urban areas within a single year. Therefore, in line with the GDDS upper bound for survey periodicity, the World Bank Household Survey Strategy recommends that data on poverty and inequality be produced every 3 years at a minimum. Given the requirement of reporting poverty and shared prosperity statistics on an annual basis as part of global monitoring, increasing the frequency and availability of household survey data in IDA countries becomes even more critical to improving the reliability and precision of SDG monitoring by reducing reliance on projections.<sup>10</sup> <mark>Furthermore, the IDA status of a given country is important to understanding the underpinnings of the WBG’s commitment to alleviate data deprivation over the period of 2016-2030. Sixty-eight percent of the countries that are classified as data deprived by Serajuddin et al. (2015) have a lending status of either IDA (40 countries) or Blend (i.e. 12 IDA/IBRD countries). If a minimum of 3 poverty data points in a 10 year window is considered a necessary condition for avoiding data deprivation, we observe 82 percent of IDA-funding eligible countries to be data deprived. The remaining countries with IDA or Blend lending status could easily fall into deprivation if any funding problems were to arise. India, a special case which graduated from Blend to IBRD lending status in FY14 but which can also borrow under IDA terms through FY17, would also be classified as data deprived under the broadened definition of the concept.</mark><sup>11</sup> <mark>Since prior funding is no guarantee of future funding, all 77 countries with either IDA (59 countries) or Blend (18 countries) lending status as well as India constitute the focus of the WBG’s commitment to alleviate data deprivation over the period of 2016-2030.</mark><sup>12</sup>

# **III. Approach to survey implementation and technical assistance cost projections**

# **a. Survey implementation unit cost compilation**

The first step in our cost projections was to compile a comprehensive, cross-country database of actual survey implementation unit costs (i.e. costs per sampled household). In compiling this database, we focused on surveys that 1) have a multi-topic focus while collecting information towards the construction of a comprehensive, often consumption-based, welfare aggregate and 2) are implemented by NSOs and integrated into NSSs. This decision was informed by the World Bank Shared Strategy for Household Surveys and its specific recommendations that Bank-

> 10 Since household consumption surveys for countries do not always coincide with the reference year for which global poverty is estimated, the World Bank fills data gaps based on projections, which are far less reliable than poverty and inequality statistics directly estimated from household survey data.

> 11 The broadened definition would necessitate multi-topic survey data production every 3 years.

> 12 <mark>Regarding the non-IDA countries that are considered data deprived under the broadened definition of the concept, we assume that their data deprivation is not a result of a lack of funding but is instead due to other prohibitive factors such as a lack of prioritization of data collection, and limitations to data access.</mark>

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