# **3. Data and descriptive statistics**

# **a. Data and key variables**

World Bank lending data come from the Business Intelligence (BI) unit.<sup>10</sup> For each fiscal year 19612016, the data list all loans made by the Bank (including DPFs, IPFs and P4Rs). For each loan, the data also reports the borrowing country, the Global Practice (GP) that administered the loan, the loan amount (including if the loan is from IDA or IBRD funds), and the date the loan was committed. For this paper, we map the loans to fiscal years, and construct a yearly data set where each observation is a country-year.

Although the organizational structure of Bank changed over time (with two major reorganizations in 1997 and 2013), the purview of three HD units – Education (EDU), Health, Nutrition and Population (HNP) and Social Protection (SPL) – has remained intact. For the remaining units, we could map current and historical units into current vice-presidencies (VPs – Human Development (HD), Sustainable Development (SD), and Economic and Financial Institutions (EFI)) with accuracy. We use the GP and VP mapping of loans to construct several variables. Our first variable of interest is if a country borrows at all in a given year and if it borrows for HD. Similarly, for both overall lending and lending by HD GPs, we compute the number of projects and total lending for each country-year. We also use the GP mapping to compute the “share of HD GPs” variable, which is defined as total lending for HD in a year divided by total lending for all GPs. Finally, we construct the “share the HD projects” variable, which is the ratio of number of HD projects to number of total projects. These variables are the key outcomes for our analysis.

Two issues warrant caution in the interpretation of the “share of HD GPs.” First, some fraction of HD loans might, in fact, be allocated to other areas. For example, if part of lending is used to improve the statistical capacity of the National Institutes of Statistics (with the goal of informing HD issues), we could overstate the true share of HD lending. Conversely, countries can finance DPFs through the Macro-Fiscal Management (MFM) GP with the specific goal of supporting an HD sector.<sup>11</sup> Such instances would understate the share of HD lending. The data tag each project to (up to) five sectors and report the fraction of the loan amount committed to each sector. We use this to compute the “share of HD sectors”, which captures lending for HD that may have happened through other GPs. So, for instance, if a rural roads project (delivered by the Transport GP) allocates 20 percent of the loans to connecting rural clinics, we can capture those as HD investments.

It is also worth distinguishing the difference between these two share variables. The variable “share of HD GPs” captures total lending that occurs through HD GPs and the variable “share of HD sectors” captures the share of total Bank lending to HD sectors. Finally, note that interpretation of share variables is also complicated by the fact that the values are undefined for years when a country

> 10 Internal users at the World Bank can access the data from: http://reports.worldbank.org/BIPORTAL/.

> 11 However, note that the Bank’s rules stipulate that DPF financing cannot be earmarked; and the amounts of financing are typically determined by the borrowing countries’ fiscal needs more than direct support for policy reforms. Sector tags in DPFs, thus, relate to the content of countries’ policy programs. Thus, it is possible that IPF and DPF funds are used in completely different ways.

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