metadata: authors: 0: author: Gatti,Roberta V.
1: author: Mohpal,Aakash
count: World
docna: 0: docna: Investing in Human Capital : What Can We
            Learn from the World Bank's Portfolio Data ?
docty: Policy Research Working Paper
subtopic: Educational Sciences,Health Care Services Industry,Social Protections & Assistance,Financial Crisis Management & Restructuring
teratopic: Education,Industry,Social Protections and Labor,Finance and Financial Sector Development
abstracts: cdata!: This paper compiles project-level data
            from the World Bank's lending history to describe
            patterns and the composition of its portfolio. The paper
            focuses particularly on the effect of countries'
            transition from International Development Association to
            International Bank for Reconstruction and Development
            status, which marks the point when countries start borrowing
            at near market rates, on lending for human development
            sectors (education, health and social protection). Using
            country and year fixed effects, which account for
            unobservable country characteristics (for example, national
            priorities) and time effects (for example, market interest
            rates), the paper finds that human development lending
            decreases when countries graduate from the International
            Development Association. The average difference in the
            binary indicator of lending for any sector is 27 percent
            while it is 60 percent for human development sectors. The
            share of human development lending (lending by human
            development Global Practices over total lending) is also 6.9
            percentage points (30 percent) lower. This decline in human
            development lending in International Bank for Reconstruction
            and Development countries is accompanied by a greater use of
            budget support. The results are robust to controlling for
            non-World Bank aid, as well as various alternative
            specifications and estimation samples.
repnme: repnme: Investing in Human Capital : What Can We
            Learn from the World Bank's Portfolio Data ?
display_title: Investing in Human Capital : What
            Can We Learn from the World Bank's Portfolio Data
listing_relative_url: /research/2019/01/30779212/investing-human-capital-can-learn-world-banks-portfolio-data
topicv3: Education m1327048 470,Social Protection and Labor m1327194 697,Health, Nutrition and Population m1327755 621,Health m1504661 3480,Credit Risk m1505083 3885
publishtoextweb_dt: 2019-01-23T00:00:00Z
historic_topic: Education,Industry,Social Protections and Labor,Finance and Financial Sector Development
no_of_pages: 33
author_keys: 0: author_key: 321051
1: author_key: 1122649
model_extractions: WPS8716 Policy Research Working Paper 8716 Investing in Human Capital What Can We Learn from the World Bank’s Portfolio Data?

_Roberta Gatti Aakash Mohpal_ Human Development Global Practice January 2019
Policy Research Working Paper 8716

# **Abstract**

This paper compiles project-level data from the World Bank’s lending history to describe patterns and the composition of its portfolio. The paper focuses particularly on the effect of countries’ transition from International Development Association to International Bank for Reconstruction and Development status, which marks the point when countries start borrowing at near market rates, on lending for human development sectors (education, health and social protection). Using country and year fixed effects, which account for unobservable country characteristics (for example, national priorities) and time effects (for example, market interest rates), the paper finds that human development lending decreases when countries graduate from the International Development Association. The average difference in the binary indicator of lending for any sector is 27 percent while it is 60 percent for human development sectors. The share of human development lending (lending by human development Global Practices over total lending) is also 6.9 percentage points (30 percent) lower. This decline in human development lending in International Bank for Reconstruction and Development countries is accompanied by a greater use of budget support. The results are robust to controlling for non-World Bank aid, as well as various alternative specifications and estimation samples.

This paper is a product of the Human Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at rgatti@worldbank.org and amohpal@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Investing in Human Capital: What Can We Learn from the World Bank’s Portfolio Data?**

Roberta Gatti<sup>1</sup> and Aakash Mohpal<sup>2</sup> JEL: O10, O15, O19 Keywords: Aid allocation, World Bank lending, Human Development, IDA, IBRD Acknowledgements: We thank Aart Kraay, Keith Hansen, Amit Dar, Celine Ferre, Amer Hasan, Mahesh Shrestha, and Michel Welmond for their useful comments, and our colleagues Sadaf Alam, Milagros Delgado, Anton Dobronogov, Stephane Guimbert, Steven Knack, and Tihomir Stucka for useful conversations. This work does not necessarily reflect the views of the World Bank or its Executive Directors. All errors are our own.

> 1 Chief Economist for Human Development, the World Bank Group, rgatti@worldbank.org

> 2 Young Professional, Health, Nutrition and Population, the World Bank Group, amohpal@worldbank.org
# **1. Introduction**

With the launch of the Human Capital Project (HCP), the World Bank has called on countries to accelerate progress in the human capital of their populations (World Bank, 2018). There are many reasons why human capital outcomes might fall short of expectations. For example, countries may underinvest in the skills, health, and resilience of their citizens because the returns on these investments can take a long time to materialize. In comparison, investments in physical capital – bridges and roads – could materialize more quickly. Moreover, differently from investment in human capital, they often generate financial returns (for example, tolls). These arguments led development practitioners to conjecture that countries borrow less for human development interventions in the face of increased borrowing costs. For instance, when countries graduate from IDA (International Development Association) status to IBRD (International Bank for Reconstruction and Development)<sup>3</sup> status in their engagement with the World Bank, they also face an increase in borrowing costs. Countries with per capita income below $1,215 have access to IDA funds at concessional terms, while richer countries have access to IBRD funds (which typically charge LIBOR plus a fixed spread).

The discrete change in financing terms when countries graduate from IDA to IBRD could discourage countries from borrowing for “soft sectors” like Education, Health and Social Protection (together grouped as the Human Development group in the World Bank) in favor of sectors where investments have more immediate and tangible returns. However, other mechanisms might be at play. Take total Bank borrowing for example. On one hand, as interest rates go up when countries graduate IDA, the demand for Bank loans should decline. In addition, IDA graduation also operates as a signal of creditworthiness for other lenders (bilateral lenders as well as the market), thereby permitting crowding in other lenders, which could further lower borrowing from the Bank. On the other hand, IDA graduation alleviates borrowing limits imposed on the IDA pool and countries face fewer restrictions on how much they can borrow. Finally, the creditworthiness signal could work in the opposite direction as well – observing the signal, bilateral donors may reduce aid amounts, thus increasing governments’ demand for Bank loans.<sup>4</sup> What is even less clear theoretically is how HD lending would respond to graduation vis-à-vis total lending.

In this work, we use the Bank’s portfolio data to understand and describe the patterns of total and HD lending at the World Bank. First, we describe the patterns of lending in IDA and IBRD countries. We then examine how total Bank lending and lending for HD sectors respond to countries’ transition from IDA to IBRD. To do this, we combine the Bank’s portfolio data with countries’ borrower status and analyze the effect of IDA graduation in a regression framework. Although, in principle, countries graduate from IDA status exogenously when they cross a prespecified income threshold, in practice, there are exceptions and additional factors that affect graduation eligibility, which makes it impossible to treat graduation as an exogenous event. As a second-best approach, we use fixed-effects regressions (country and year) to isolate the effect of graduation. Year fixed effects capture economic climate and

> 3 Interest rates for IBRD flexible loans with a variable spread were LIBOR + 0.99% as of October 1, 2018 (Group A countries, >18-20 years maturity). The USD LIBOR – 6 months average for 2018 was 2.487%. The interest rate on regular IDA loans was 3.125% with 38 years maturity, and 6 years grade period.

> 4 See Knack et al. (2014) for further discussion. The authors study how IDA graduation affects other donors’ allocation of aid, thereby generating direct evidence on the signaling effect.

2
the effect of graduating in different time periods thus subsuming the effect of market interest rates (which are assumed common to all countries, but which have differed markedly over the period covered by this study). Country fixed effects allow us to capture country-specific characteristics that may affect lending and ensure that the identifying variation comes solely from within those countries that transition from IDA to IBRD.

Our results lend broad support to the hypothesis that countries borrow less for HD sectors when they graduate from IDA. We find that although overall lending and the share of HD lending as a fraction of total lending increased between 1988-2016, IBRD countries (a) finance fewer projects, (b) are disproportionately less likely to finance an HD project, and (c) have a lower share of HD borrowing. Relative to IDA countries, IBRD countries, on average, finance 0.57-0.68 fewer projects (25-30 percent), and they finance 0.19-0.34 fewer HD projects (36-65 percent). The share of HD lending (as a fraction of total lending) is also 3.9-5.5 percentage points (17-24 percent) lower in IBRD countries relative to IDA countries. Note that the share variable is defined only when countries borrow at all, and since IBRD countries are less likely to borrow for HD, these estimates represent a lower bound on the true effect.

Although our results support the popular hypotheses, they need to be interpreted with caution. First, while the fixed-effect analysis boosts confidence in our results, the estimates cannot be interpreted as causal. Ideally, we would find an instrument (a variable that is correlated with graduation but not the outcomes) and use an instrumental variable framework to identify causal effects. Second, our results assume no supply side constraints in borrowing, but this is not always the case. If the supply of funds declines when countries graduate, the observed decline in total lending would be simply be explained by reduction in supply rather than IDA graduation.<sup>5</sup> This, however, does not explain why lending for HD would decline disproportionately more than overall lending (which is what we observe, and which is the focus of this paper). To be sure, throughout the paper, we present results for overall Bank lending patterns and patterns for HD areas.

Our results are robust to the inclusion of various controls (population, GNI per capita, other aid flows, fiscal spending on HD, Polity Score) and a range of alternative specifications and samples (shorter sample period and exclusion of large countries like India and China). Taken together these results suggest that lending for HD is negatively correlated with IDA graduation. The rest of the paper proceeds as follows. Section 2 describes the institutional details surrounding IDA graduation, and why it matters for lending. Section 3 describes the data, the key variables of interest, and presents the descriptive statistics for lending patterns. Section 4 and Section 5 present the regression framework and results, respectively. Section 6 reports the results from several robustness checks. Section 7 discusses potential explanations as well as the limitations of the research. Section 8 concludes the paper with further avenues for future research.

> 5 While it is possible to obtain data on total supply of funds for IDA countries, we could not obtain this information for IBRD countries.

3
# **2. IDA graduation and institutional details**

# **a. What is IDA?**

IDA is one of the largest sources of assistance for the world’s 77 poorest countries, 39 of which are in Africa, and is the single largest source of donor funds for basic social services in these countries. IDA lends money on concessional terms. This means that IDA credits have a zero or very low-interest charge and repayments are stretched over 25 to 40 years, including a 5- to 10-year grace period. IDA also provides grants to countries at a risk of debt distress. Eligibility for IDA support depends first and foremost on a country’s relative poverty, defined as GNI per capita below an established threshold and is updated annually.<sup>6</sup> Other parameters that are considered are the country’s creditworthiness, and broader country characteristics.

In practice, graduation from IDA extends over several stages – countries move from (1) IDA nongap to (2) IDA gap to (3) IDA blend, and eventually to (4) IBRD status.<sup>7</sup> Countries that have been over the income threshold for two years but not creditworthy enough are categorized as IDA gap countries. With creditworthiness, they are passed on to IDA blend status. IDA blend countries are eligible for both IDA and IBRD financing; the former is phased out and the latter is phased in over time. To graduate to IBRD status, countries need to have been over the threshold and maintained creditworthiness for at least three years.

The intention of this policy was to facilitate the transition and avoid any fiscal cliffs that may arise when countries graduate. Under each classification, countries pay different effective interest rates on loans. IDA non-gap countries receive IDA funds that are disbursed in the form of grants, bear no interest, and are of “regular” credits. As of April 2017, regular credits were available with a 38-year maturity and 2.63 percent effective interest rate (6-year grace period, 3.125 percent interest for years 7-38). “Blend” credits, which can be accessed by IDA gap countries and IDA blend countries, had a 25-year maturity, and 3.32 percent effective interest rate (5-year grace period, 3.3 percent interest for years 6-20, 6.7 percent for years 21-25). IBRD countries have different maturities on loans, and the interest rate is a function of the LIBOR and a country-specific spread.

The difference in interest rates between IDA non-gap and IDA gap countries as well as between IDA and IBRD countries can be substantial. For that reason, even though policies are in place to smoothen the transition from IDA, we might expect countries to make some adjustment in borrowing when they graduate. However, these adjustments can take different forms. As we discussed above, IDA funds are scarce, so a country that is willing to borrow may find graduation an advantage. Conversely, IDA funds are cheaper than IBRD funds, and other countries may reduce borrowing after graduation. Seventy-seven countries (plus India) are currently eligible to receive IDA resources. As of April 2016, 30 countries have graduated from IDA. Forty-one countries have graduated since IDA’s founding in 1960; 11 of these graduates have since “reverse graduated,” or re-entered IDA.

> 6 The threshold was originally set to $580 and was intended to ration scarce IDA funds. Since 1987, the threshold has been adjusted only for inflation and is currently at $1,215.

> 7 For a complete discussion, see “Review of IDA’s Graduation Policy”, DFIRM (2016).

4
# **b. Is IDA graduation exogenous?**

Since countries receive differential support to smoothly transition from IDA to IBRD, it is difficult to argue that graduation is exogenous. First, aside from transition support, the Bank also has other policies in place to smoothen the impact of graduation. For example, “small island economies” like Cabo Verde, Tonga and Maldives can access IDA grants and regular credits, even though they meet the criteria for graduation. These countries, with population less than 1.5 million people, are granted exceptions in maintaining IDA eligibility since they face significant vulnerability due to size and geography.

Second, another exception, not implemented by the World Bank itself, takes the form of “buydowns”. Buy-downs decrease the cost of borrowing IBRD funds either through principal repayment or through interest repayment by a third party. To encourage China to maintain its successful collaboration with the World Bank in the health and education sectors, the Department for International Development (DfID) offered buy-downs of loans (RfD, 2013). These buy-downs prepaid a fixed percentage of the principal amount of IBRD loans at regular intervals to reduce the amount owed by China. DfID also implemented similar programs in Pakistan and Nigeria.

Finally, since 2014, the same year India graduated from IDA, the World Bank also created a separate pool of IDA funds called “transitional support.” Loans from this pool have the same 25-year maturity as blend credits but carry a higher effective interest rate – 4 percent (5-years grace period, 5 percent interest from years 6-25). Keeping in mind the criteria for IDA graduation and the exceptions, it is difficult to argue that IDA graduation is a truly exogenous event that occurs when countries cross a pre-specified per capita income threshold. Unfortunately, the implication is that crossing of the threshold cannot be used as an instrument for graduation either.

# **c. List of country graduates**

A total of 153 countries have a current engagement with the World Bank.<sup>8</sup> Of these, 47 countries have always been an IDA borrower, that is, they joined the Bank as an IDA country and have retained the status until now. Forty-five countries have been IBRD only members. The remaining 60 countries transition between IDA and IBRD status – 33 countries graduated once and have maintained their IBRD status for the rest of the time. Three countries – the Arab Republic of Egypt, Indonesia, and the Philippines – graduated twice. That is, they graduated from IDA to IBRD, fell back into IDA status after a period, and then graduated again. The remaining 24 countries, surprisingly, moved from IBRD to IDA status. These are primarily African countries, and the reverse graduation took place in

> 8 The Bank has 189 countries as members.

5
the 1970s and 1980s.<sup>9</sup> Table 1 below contains the list of countries and their borrowing status over the 1961-2015 period.

Table 1: List of countries and their graduation status

|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|
|**Countries that do n**|**ot change status**|**Countri**|<br>**es that change sta**|<br>**tus**|
||||Graduated IDA|Moved from|
|Always IDA|Always IBRD|Graduated IDA|twice|IBRD to IDA|
|Afghanistan; Bangladesh;|Algeria; Antigua and|Albania; Angola;|Egypt, Arab Rep.;|Burkina Faso;|
|Benin; Bhutan; Bolivia;|Barbuda; Argentina;|Armenia; Azerbaijan;|Indonesia;|Burundi; Congo,|
|Cabo Verde; Cambodia;|Bahamas, The;|Bosnia and|Philippines|Dem. Rep.; Côte|
|Central African Republic;|Barbados; Belarus;|Herzegovina; Botswana;||d'Ivoire; Ethiopia;|
|Chad; Comoros;|Belize; Brazil; Bulgaria;|Cameroon; China;||Ghana; Guinea;|
|Djibouti; Dominica;|Chile; Colombia; Costa|Congo, Rep.;||Guyana; Haiti;|
|Eritrea; Gambia, The;|Rica; Croatia; Cyprus;|Dominican Republic;||Kenya; Liberia;|
|Grenada; Guinea-Bissau;|Czech Republic;|Ecuador; El Salvador;||Mali; Marshall|
|Kiribati; Kosovo; Kyrgyz|Estonia; Fiji; Gabon;|Equatorial Guinea;||Islands;|
|Republic; Lao People's|Guatemala; Hungary;|Georgia; Honduras;||Mauritania;|
|Democratic Republic;|Iran, Islamic Rep.; Iraq;|India; Jordan; Korea,||Micronesia, Fed.|
|Lesotho; Madagascar;|Jamaica; Kazakhstan;|Rep.; Macedonia, FYR;||Sts.; Moldova;|
|Malawi; Maldives;|Latvia; Lebanon;|Mauritius; Montenegro;||Myanmar;|
|Mongolia; Mozambique;|Lithuania; Malaysia;|Morocco; Nicaragua;||Nigeria; Senegal;|
|Nepal; Niger; Pakistan;|Mexico; Namibia;|Papua New Guinea;||Sierra Leone; Sri|
|Rwanda; Samoa;|Oman; Panama; Peru;|Paraguay; Serbia; St.||Lanka; Uganda;|
|Solomon Islands;|Poland; Portugal;|Kitts and Nevis;||Uzbekistan;|
|Somalia; South Sudan; St.|Romania; Russian|Eswatini; Syrian Arab||Zambia|
|Lucia; St. Vincent and the|Federation; Seychelles;|Republic; Thailand;|||
|Grenadines; Sudan; São|Slovak Republic;|Tunisia; Turkey;|||
|Tomé and Principe;|Slovenia; South Africa;|Zimbabwe|||
|Tajikistan; Tanzania;|Trinidad and Tobago;||||
|Timor-Leste; Togo;|Turkmenistan; Ukraine;||||
|Tonga; Tuvalu; Vanuatu;|Uruguay; Venezuela, RB||||
|Vietnam; Yemen, Rep.|||||
|**Total = 47**|**Total = 46**|**Total = 33**|**Total = 3**|**Total = 24**|

Source: Credit Risk Office, World Bank

> 9 The movement from IBRD to IDA or Blend status took place in the following years: Burkina Faso (1969), Burundi (1966), Congo Dem Rep. (1969), Côte d’Ivoire (1992), Ethiopia (1963), Ghana (1968), Guinea (1975), Haiti (1963), Kenya (1965), Liberia (1972), Mali (1967), Marshall Islands (2012), Mauritania (1965), Micronesia Fed. Sts. (2012), Moldova (1997), Myanmar (1973), Nigeria (1989), Senegal (1967), Sierra Leone (1970), Sri Lanka (1968), Uganda (1967), Uzbekistan (2002), Zambia (1978). In practice, most of these countries went from IBRD to Blend status and eventually moved to IDA. For Moldova, Nigeria and Uzbekistan, which moved from IBRD to IDA in the period after 1987 (when IDA graduation criteria were established), the movement followed a period of marked decline in per capita GDP. For other countries this pattern is less clear.

6
# **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.

7
<!-- Start of picture text --> Total Lending Over Time<br>g R<br>S<br>E<br>I \<br>Lo & / \<br>88 - R |<br>S s \<br>Rs /<br>osgs # \o<br>2<br>=e B<br>= S Wa<br>So<br>a<br>o<br>x 2 x + RK ° x é 2 2 © Q x 19 2 = x<br>oa© odo ado And rte)ind Fe)im ooa ooa oyoO aD s+D iQD 3oS oe)So ooS aSo a=<br>> = = 2 2 2 = = = 2 = > & & & & &<br>IDA cycle<br>——s—— IDA/Blend — © — IBRD<br>Note: Shaded vertical lines represent cycles/years affected by major global downturns<br><!-- End of picture text -->
<!-- Start of picture text --> HD lending Over Time<br>8<br>S g<br>~ /\<br>ie \<br>ed38 A y<br>o= / \ 7 \<br>3s 7 \<br>no \<br>i 2 fr~d<br>So a $ \<br>g ©<br>Si 2 —<br>o<br>x 2 om = lal So Tt Led c=] 2 2 Q N wo oO om =<br>= re) d a o r) s rey oy a ¢ i 3 el oe a a<br>IDA cycle<br>Note: Shaded vertical lines/years represent cycles affected by major global downturns<br><!-- End of picture text -->
Table 2: Bank's portfolio over time and sector

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||.<br>TotalTending<br>(millions of<br>2010dollars)|.<br>Petcapita<br>lending (2010<br>dollars)|Numberof<br>.<br>projects|Fractionof<br>.<br>totallending|Av**e**rage<br>proj ct—<br>cae<br>=(millions of<br>2010dollars)|
|Year: 1971||||||
|Allprojects|8,796.5|10.7|112.0|100.0|78.5|
|<br>HDpractices|430.8|0.9|15.0|49|28.7|
|<br>Education|397.8|0.7|13.0|4.5|30.6|
|Health,Nutrition&Population|33.0|0.2|2.0|0.4|16.5|
|<br>Social Protection & Labor|0.0|0.0|0.0|0.0||
|SDpractices|7,126.9|8.7|89.0|81.0|80.1|
|<br>EFI practices|1,238.8|0.5|8.0|14.1|154.8|
|HD sectors|287.1|0.8|9.0|3.3|31.9|
|Year: 1985||||||
|All projects|24.836.0|14.9|235.0|100.0|105.7|
|HDpractices|1,994.4|0.9|27.0|8.0|73.9|
|<br>Education|1,656.6|0.6|20.0|67|82.8|
|Health,Nutrition&Population|337.8|0.2|7.0|14|48.3|
|<br>Social Protection & Labor|0.0|0.0|0.0|0.0||
|SDpractices|20,307.1|10.0|176.0|81.8|115.4|
|<br>EFI practices|2,534.6|3.4|32.0|10.2|79.2|
|HD sectors|1,834.1|1.1|24.0|74|76.4|
|Year: 2000||||||
|All projects|18,883.3|8.0|232.0|100.0|81.4|
|HDpractices|3,153.3|19|54.0|16.7|58.4|
|<br>Education|845.5|0.8|21.0|45|40.3|
|Health,Nutrition&Population|1,220.0|0.5|21.0|6.5|58.1|
|<br>Social Protection& Labor|1,087.8|0.5|12.0|5.8|90.6|
|SDpractices|8,143.7|3.3|111.0|43.1|73.4|
|<br>EFI practices|7,586.3|2.5|67.0|40.2|113.2|
|HD sectors|3,199.6|1.7|104.0|16.9|30.8|
|Year: 2015||||||
|Allprojects|34,069.1|18.6|280.0|100.0|121.7|
|<br>HDpractices|6,796.3|1.6|59.0|19.9|115.2|
|<br>Education|2,445.0|0.4|23.0|7.2|106.3|
|Health, Nutrition& Population|1,348.5|0.4|15.0|4.0|89.9|
|Social Protection&Labor|3,002.8|0.7|21.0|8.8|143.0|
|SD practices|18,302.6|11.1|151.0|53.7|121.2|
|EFIpractices|8,970.3|53|70.0|26.3|128.1|
|<br>HDsectors|6,861.0|17|111.0|20.1|61.8|

Source: Own calculations from Wodld Bank Portfolio data
Change in total borrowing after IDA graduation (five years after - five years before) <!-- Start of picture text --> Dominican Republic, 1972 a<br>Korea, Rep.,1972 hla<br>Turkey,1972 a<br>Botswana, 1973 '<br>Syrian Arab Republic,1Ecuador,1 973 i——ESESESEq/XwEeaSs<br>Mauritius, 1974 =<br>Morocco, 1974 flea<br>Swaziland,1974 =<br>El Salvador,1976 =—<br>Paraguay,1976 a<br>Tunisia,1976 =<br>Jordan,1977 x<br>Thailand,1978 Ee<br>Honduras,1979 I<br>Cameroon,1980 =<br>Nicaragua,1980 a<br>Congo, Rep.,1981 —<br>Papua New Guinea, 1982 =<br>St. Kitts Zimbabwe,and Nevis,1993|1982 =waSSsaSSSg<br>China,1998 a<br>Equatorial Guinea,1998| ——<br>Macedonia, FYR,2001 —<br>Montenegro,2007 SS<br>Serbia,2007 =<br>Albania,2008 —<br>Azerbaijan,2011 a<br>Angola,2014 !<br>Bosnia and Herzegovina,2014Armenia,2014 aa<br>Georgia,2014 a<br>India,2014 a<br>-100 0 100 200 300<br>Percentage change<br><!-- End of picture text -->

Note: Top-coded at 95th percentile
years after) yields similar patterns (4.35 vs. 2.33 total projects, and 0.86 vs. 0.28 HD, and both differences are statistically significant).

Total lending per year also declines by 60.4 percent in the 5 years after graduation compared to the 5 years before graduation. Analysis of 10-year periods shows that the decline is similar at 59.7 percent, thus signaling that the declines are not only transitory. The decline in HD lending is even larger and statistically significant. Comparing the 5-year periods before and after graduation, average HD GP lending per country per year declines from $87.91m to $17.9m, representing a 79.6 percent change. The pattern for 10-year periods is similar – average HD lending declines from $84.7m to $12.5m, or 72 percent.

In contrast, results for the countries graduating in the pre-1988 period are quite the opposite (Table 3B). These countries increased borrowing in the years after IDA graduation although significantly less so for the HD areas (107 percent overall vs. 77 percent for HD before and after 5-year periods). Similarly, while the overall number of projects increased, the number of HD projects did not change. These patterns are consistent with the notion that countries borrow proportionately less for the HD when they graduate from IDA.

These differences, however, do not account for other explanatory variables, including country size, and could be driven by the behavior of a few countries. For instance, if big countries like India and China stop borrowing for HD, the difference in average borrowing could be large even if the average country did not change borrowing behavior after graduation. To check for this, we look at per capita borrowing before and after graduation. Looking at the 5-year before and after periods, we see that per capita total lending and per capita HD lending also decreased, although the results are not statistically significant. These results suggest that country size is an important variable to control for; however, the results are also consistent with a reallocation away from HD.

12
Table 3A: Summary statistics for graduating countries, 1988-2016

||(1)<br>eyA <br>graduation|(2)<br> >=‘sin <br>_graduation|(3)<br> Difference<br>2)-()|(4)<br>Percentage<br>Coange|(5)<br>nee,a" <br>graduation|(6)<br> ae<br>_graduation|(7)<br>Difference<br>)-@)|(8)<br>Percentage<br>anaes|
|---|---|---|---|---|---|---|---|---|
|All lending (2010 dollars)|||||||||
|Total (millions)|784.36|310.55|-473.80*|-60.41|656.87|264.55|-392.32**|-59.73|
|Totalpercapita|25.05|17.31|-7.73|-30.87|19.42|21.53|2.12|10.90|
|<br>HDlending(2010dollars)|||||||||
|<br>Total(millions)|87.91|17.91|-69.99**|-79.62|84.72|12.45|-712.27***|-85.30|
|<br>Sectors (millions)|90.74|22.60|-68.14*|-75.09|83.84|15.60|-68.24***|-81.39|
|Totalpercapita|3.07|2.16|-0.91|-29.65|2.95|1.92|-1.03|-35.01|
|<br>Sectors per capita|2.12|2.38|0.26|12.22|1.91|3.12|122|63.93|
|HDPracticeAreas(2010dollars)|||||||||
|<br>Education(millions)|40.10|8.78|-31.32|-78.11|40.95|5.32|-35.62**|-87.00|
|<br>Health, Nutrition andPopulation (millions)|34.51|6.35|-28.17*|-81.62|34.06|4.69|-29.36**|-86.22|
|SocialProtectionand Labor(millions)|13.29|2.79|-10.50|-79.01|9.72|2.44|-7.28*|-74.90|
|<br>Numberofprojects|||||||||
|<br>All|4.64|2.60|-2.04**|-43.96|4.35|2.33|-2.02***«|-46.45|
|HD|0.87|0.38|-0.49**|-56.26|0.86|0.28|-0.58***|-67.52|
|Education|0.25|0.12|-0.13|-51.20|0.25|0.08|-0.17***|-68.96|
|Health,NutritionandPopulation|0.33|0.12|-0.21**|-63.40|0.34|0.11|-0.23***|-68.55|
|<br>SocialProtectionandLabor|0.30|0.14|-0.16|-52.56|0.27|0.10|-0.18***|-64.95|

Notes: Altogether 33 countries graduated IDA between 1988 and 2016. The data excludes the exact years in which countries graduate IDA. In columns (3) and (6), significance stars reports results from testing for statistical significance for difference in the two means. * Significant at 10%, ** Significant at 5%, ** Significant at 1%.
||Table3B:S<br>|ummarystatist<br>|icsforgradua<br>|tingcountries,<br>|1961-1987<br>||||
|---|---|---|---|---|---|---|---|---|
||<br>()<br>heretDA <br>graduation|<br>Q)<br> °a‘Hine <br>_graduation|<br>8)<br> Difference<br>2)-()|<br>4<br>Percentage <br>cnaige|<br>6)<br> _—" <br>graduation|6<br> bent<br>_graduation|0)<br>Difference<br>©)-@)|8)<br>Percentage<br>eunee|
|Alllending(2010dollars)|||||||||
|<br>Total (millions)|99.47|206.33|106.86***|107.43|71.34|216.98|145.64***|204.16|
|Total per capita|14.30|22.87|8.57%**|59.90|11.51|22.34|10.83***|94.16|
|HDlending(2010dollars)|||||||||
|<br>Total (millions)|7.62|13.50|5.88|77.21|5.62|12.67|7.05**|125.28|
|Sectors (millions)|6.38|10.05|3.67|57.48|4.98|12.26|7.28%***|145.99|
|Total per capita|1.35|2.71|1.36*|101.12|0.96|2.62|1.65***|171.88|
|Sectors per capita|1.16|2.55|1.39*|119.47|0.87|2.57|1.71***|197.32|
|HD Practice Areas (2010 dollars)|||||||||
|Education (millions)|7.40|12.28|4.88|65.94|5.51|11.46|5.95**|107.89|
|Health, Nutrition andPopulation (millions)|0.22|1.22|1.00|459.42|0.11|1.21|1.10*|975.11|
|Social Protection and Labor (millions)|0.00|0.00|0.00||0.00|0.00|0.00||
|Numberofprojects|||||||||
|<br>All|1.62|2:29|0.67***|41.14|1.23|2.29|1.06***|85.62|
|HD|0.23|0.23|0.01|3.33|0.16|0.23|0.07*|46.85|
|Education|0.22|0.20|-0.02|-7.00|0.15|0.20|0.05|31.98|
|Health, Nutrition andPopulation|0.01|0.03|0.02|210.00|0.01|0.03|0.02*|448.22|
|SocialProtectionand Labor|0.00|0.00|0.00||0.00|0.00|0.00||

results from testing for statistical significance for difference in the two means. * Significant at 10%, ** Significant at 5%, *TM* Significant at 1%.
# **4. Regression framework and specifications**

The before and after statistics reported in Tables 3A and 3B can reflect a variety of factors in addition to graduation, including structural differences across countries and the effects of graduating in different years or decades. To isolate the effect of IDA graduation, we use a country and year fixed effects approach and estimate the following regression specification: where, 1�IBRD� is an indicator, which equals 1 if the country is an IBRD country; 0 if IDA or blend country. γ� represents year fixed effects, and we include these in all specifications. X�� is a vector of controls, and we estimate regressions with and without these effects. We control for four main variables – log of population, GNI per capita, Net Aid Flow (non-Bank aid only), and Polity IV score.<sup>13</sup> In separate regressions, we add Fiscal Spending in HD as another control. τ� represents country fixed effects, and we estimate regressions with and without these effects. ε�� is the idiosyncratic error term that we cluster at the country-level. The main coefficient of interest is β, which represents the average difference between IDA and IBRD countries on the outcome of interest y.

The year fixed effects capture the effect of graduating in different years. Among these, they capture the effects of prevailing interest rates (that vary from year to year) as well as any effects due to graduating in different time periods (for example, in a boom vs. a recession). The country fixed effects capture any country-specific characteristics that may affect the outcomes of interest, including national priorities for the HD sectors and/or an appetite for HD lending. They also ensure that the identifying variation comes from countries that transition from IDA to IBRD (since the fixed effects will be collinear for countries that remain in the same status throughout). Given the fact that IDA graduation policy based on the GNI per capita cut-off came into existence in 1987, for our main results, we restrict the analysis to data from 1988-2015. In our robustness checks, we report the results from the full sample of years – 1961-2015.

# **5. Regression results**

Table 4A reports the marginal effects of estimating Probit regressions of the likelihood of any project (Columns 1-4) and the likelihood of any HD project (Columns 5-8). For each outcome variable, the first column reports the results from estimating equation 1 without controls and without country fixed effects. The second column then adds the control variables. The third column then reports results including country fixed effects but no controls. The fourth column then adds the control variables. All regressions include fiscal year fixed effects. Results for other variables are reported in the same format. We primarily focus our discussion on the fourth column, which includes year and country fixed effects, as well as the control variables, but present all results for completeness.

> 13 In other versions of the results, we also included controls for other bilateral and multilateral net flows (DAC data from OECD) as well as countries’ own spending levels in the HD sectors (data from IMF). We chose not to include these covariates because the data are incomplete (resulting in a large loss of number of observations) and because these data did not have much explanatory power. Results are available upon request.

15
Column 1 of Table 4A shows that IBRD countries are on average 14.1 percentage points less likely to borrow from the Bank relatively to IDA countries. The mean in IDA countries is 0.731 (last row) that represents a 19.3 percent decline. When country fixed effects are added so that the identifying variation only comes from the sample of graduating countries, the coefficient is even larger – 0.19 percentage points, which represents a 26.8 percent decline. The effect for HD projects is bigger – 13.1 percentage points or 34.9 percent decline without country fixed effects and 24.9 percentage points or 64.7 percent decline with country fixed effects. The coefficients are qualitatively similar and statistically significant across the four specifications. Although in the regressions without country fixed effects, the coefficient increases significantly when controls are added (Column 1 to Column 2), the increase is trivial in the regressions with country fixed effects (Column 3 to Column 4). This is because country fixed effects already capture much of the variation that occurs in the control variables. Overall, these results suggest that IBRD countries are indeed less likely to finance projects from the Bank and the decline is greater for HD projects.

Table 4B reports the results for the total number of projects (Columns 1-4), number of HD projects (Columns 5-8) and the share of HD projects (Columns 9-12). The results are similar for any project. While the number of total projects declines by 25 percent, HD projects decrease by 26 percent in IBRD countries. The share of HD projects also declines by roughly one-third (7.4 percentage points). Here too, the estimated results are similar across specifications. The results from Table 4A and 4B, taken together, strongly suggest that the likelihood of borrowing and the number of projects financed are negatively correlated with IDA graduation, and the correlation is much stronger for the HD Global Practices.

In Table 4C, we turn to lending amounts – total lending, HD GP lending, and HD sector lending. Because the lending variables have a wide spread, we use the natural logarithm of the variables as the dependent variable.<sup>14</sup> But notice that, since the main independent variable of interest is a binary indicator (dummy for an IBRD country), the OLS coefficient cannot be interpreted as percentage differences, as is standard in log-linear models. Instead, to obtain percentage differences, one needs to compute 100 ∗�exp�β��1�.<sup>15</sup> The coefficient on log of total lending is -0.651 and is statistically significant (Column 4) which represents a 47.9 percent decline from a baseline of $210m when countries graduate from IDA to IBRD. The decline in HD loans is greater – the coefficient is -0.868 and is statistically significant (Column 8) and translates into a 58.0 percent reduction. Results for HD sector loans are similar (Column 12). For completeness, we present regression results with the levels of loans as the dependent variable in Appendix Table A1. First, notice that the coefficient on total lending is positive in the base specification (Column 1) while the coefficient is negative in the specification with controls (Column 2). This is because country population and GNI per capita are evidently strongly correlated with total lending and controlling for these variables, thus, yields more precise estimates. Although the

> 14 We present regression results with the dependent variables in levels in Appendix Table A1 for completeness. 15 To see this, notice that exponentiating both sides, we have y�exp �α�β⋅x�. Now, since x is binary,<sup>dy</sup> �dx � Y� �Y� �exp�α�β��exp�α� . To obtain percentage difference, dividing the equation by Y� and multiplying by 100, we have 100 ∗�exp�β��1�. For example, the coefficient in column 4 of Table 4C is -0.651, which amounts to a -47.8 percent change.

16
coefficients on total lending (Columns 2-4) are negative, they are not statistically significant. For HD GP lending (Columns 5-8) and HD sector lending (Columns 9-12), the patterns are similar.

Finally, in Table 4D, we report regression results with the share of HD lending as outcome variables. In Columns 1-4, we first present results for HD GP share. Focusing our attention on the specifications with country fixed effects and controls, we find that IDA graduation is correlated with a 6.9 percentage points (30.9 percent) decrease the share of HD lending. Results for the HD sectors yield smaller and statistically insignificant coefficients (Columns 5-8).

Overall, our results are consistent with the hypothesis that HD lending declines disproportionately more when countries graduate from IDA to IBRD. The probit results of regressions of any HD lending as outcome provide strong evidence that the likelihood of an HD project is halved in IBRD country. Although the likelihood of the Bank lending also declines, the effect on HD lending is much stronger. Similarly, results from the share variables suggest that HD GP share declines by 5-7 percentage points. Note that, as we discussed above, the share variable is censored, and since the likelihood of HD projects decreases disproportionately, the coefficient on the share variable represents a lower bound on the true estimate. Taken together, these results suggest that IDA graduation is indeed correlated with declining HD lending.

17
|Table 4A:|Regression <br>|results from <br>|the full samp<br>|le of153 cou<br>|ntries and all<br>|years 1988-<br>|2015<br>||
|---|---|---|---|---|---|---|---|---|
||)|Q|8)|)|6)|6|0)|8)|
|||Anyprojec|t (dy/ds)|||HD projec|t (dy/dx)||
|IBRD country|-0.141***|=-0.232"*«|=-0.187**|= -0.196***«|-0.131***<br>|= -0.226%**<br>|=-0.249%**«|= -0)_ 24poxx|
|(1=yes)|(0.044)|(0.049)|(0.076)|(0.073)|(0.032)|(0.033)|(0.047)|(0.053)|
|Logpopulation||0.079***||0.148||0.080***||0.120|
|||(0.010)||(0.173)||(0.007)||(0.133)|
|LogGNIper capita||0.019||0.061||0.036**||0.054*|
|||(0.023)||(0.049)||(0.015)||(0.032)|
|Net aid flow||0.010*||0.012**||0.002||-0.002|
|(millions of2010 dollars)||(0.006)||(0.006)||(0.003)||(0.004)|
|Number ofobservations|3,894|3,402|3,391|2.844|3,894|3,402|3,660|3,140|
|R2|0.023|0.433|0.616|0.646|0.024|0.159|0.262|0.268|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|0.731|0.766|0.696|0.723|0.731|0.766|0.742|0.779|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, “TM* Significant at 95%.
<!-- Start of picture text --> Table 4B: Regression results from the full sample of 153 countries and all years 1988-2015<br>) Q) 8 @) 6) 6 0 8 ©) 1) dy) (2<br>Number of projects Number of HD projects Share of HD projects<br>IBRD country -0.571** -1.634*%** -0.680** = -0.852**« -0.189%** = -0.423*** = -0.342*** = -0.360*** -0.040*<< -0.043** -0.071** = -0.068**<br>(1=yes) (0.274) (0.286) (0.347) (0.398) (0.059) (0.070) (0.089) (0.097) (0.015) (0.021) (0.032) (0.034)<br>Log population 0.619*** 1.431** 0.142*** 0.474**« -0.004 -0.063<br>(0.085) (0.618) (0.019) (0.198) (0.004) (0.078)<br>Log GNI per capita 0.298** 0.387* 0.097*** 0.109** 0.014 0.040<br>(0.129) (0.207) (0.032) (0.055) (0.010) (0.025)<br>Net aid flow 0.178*** 0.072*** 0.015* 0.002 -0.001 -0.001<br>(millions of 2010 dollars) (0.024) (0.021) (0.008) (0.009) (0.001) (0.002)<br>Number of observations 3,894 3,402 3,894 3,402 3,894 3,402 3,894 3,402 2,602 2,365 2,602 2,365<br><!-- End of picture text -->

|||Table 4B:|Regression|results from|the full samp|le of153 cou|ntries and al|l years 1988-|2015||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||)|Q)<br>Numbero|8<br>fprojects|@)|6)|6<br>Numberof|0<br>HDprojects|8|©)|1)<br>ShareofH|dy)<br>Dprojects|(2|
|IBRD country|-0.571**|<br>-1.634*%**|<br>-0.680**|= -0.852**«|-0.189%**|<br> = -0.423***|<br>=-0.342*** =|-0.360***|-0.040*<<|<br>-0.043**|<br>-0.071**<br>|= -0.068**|
|(1=yes)|(0.274)|(0.286)|(0.347)|(0.398)|(0.059)|(0.070)|(0.089)|(0.097)|(0.015)|(0.021)|(0.032)|(0.034)|
|Logpopulation||0.619***||1.431**||0.142***||0.474**«||-0.004||-0.063|
|||(0.085)||(0.618)||(0.019)||(0.198)||(0.004)||(0.078)|
|LogGNI per capita||0.298**||0.387*||0.097***||0.109**||0.014||0.040|
|||(0.129)||(0.207)||(0.032)||(0.055)||(0.010)||(0.025)|
|Net aid flow||0.178***||0.072***||0.015*||0.002||-0.001||-0.001|
|(millionsof2010dollars)||(0.024)||(0.021)||(0.008)||(0.009)||(0.001)||(0.002)|
|<br>Numberofobservations|3,894|3,402|3,894|3,402|3,894|3,402|3,894|3,402|2,602|2,365|2,602|2,365|
|R2|0.023|0.433|0.616|0.646|0.024|0.159|0.262|0.268|0.024|0.027|0.131|0.130|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|2.282|2.443|2.282|2.443|0.526|0.558|0.526|0.558|0.238|0.235|0.238|0.235|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, *** Significant at 95%.
|||Table 4C:|Regression|results from|the full samp|le of153 coun|tries and al|l years 1988-|2015||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||)<br>Logo|Q)<br>ftotal loans (|8)<br>millions of|@)<br>2010 $)|6)<br>Logo|6<br>fHD loans (|0<br>millions of2|8<br>010 $)|9)<br>LogofH|1)<br>D sectorloa|dy<br>ns (millions|of2010 $)|
|IBRD country|-0.124|-1.053***|= -0.508*|= -0.651***|-0.270|-0.916***|-0.774***<br>|= -0.868***|-0.297|-0.933%**|-0.587***|-0.691***|
|(1=yes)|(0.311)|(0.239)|(0.275)|(0.249)|(0.172)|(0.154)|(0.239)|(0.245)|(0.184)|(0.164)|(0.212)|(0.219)|
|Logpopulation||0.767***||1.324*||0.437***||0.764||0.456***||1.164**|
|||(0.058)||(0.741)||(0.046)||(0.529)||(0.049)||(0.512)|
|LogGNIper capita||0.276**||0.441*||0.258***||0.290**||0.222**||0.298**|
|||(0.121)||(0.233)||(0.080)||(0.130)||(0.087)||(0.143)|
|Net aid flow||0.053***||0.017||0.032||-0.009||0.052***||0.006|
|(millions of 2010 dollars)||(0.020)||(0.015)||(0.020)||(0.022)||(0.016)||(0.017)|
|Number ofobservations|3,753|3,402|3,753|3,402|3,753|3.402|3,753|3,402|3,753|3,402|3,753|3,402|
|R2|0.010|0.399|0.566|0.597|0.013|0.202|0.308|0.317|0.013|0.261|0.382|0.394|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|3.185|3.376|3.185|3.376|1.372|1.458|1.372|1.458|1.454|1.546|1.454|1.546|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 3%, TM* Significant at 95%.
<!-- Start of picture text --> Table 4D: Regression results from the full sample of 153 countries and all years 1988-2015<br>() Q) 8 4) 6) 6 0 5<br>HD GP share HD sectors share<br>IBRD country -0.039** = -0.049** -0.055 -0.069* -0.048*** = -0.049** -0.031 -0.035<br>(1=yes) (0.017) (0.023) (0.037) (0.035) (0.013) (0.021) (0.031) (0.030)<br>Log population -0.005 -0.083 -0.003 -0.005<br>(0.005) (0.081) (0.004) (0.074)<br>Log GNI per capita 0.017 0.064** 0.009 0.047**<br>(0.011) (0.027) (0.011) (0.024)<br>Net aid flow -0.001 -0.001 -0.001 -0.002<br>(millions of 2010 dollars) (0.001) (0.002) (0.001) (0.001)<br>Number of observations 2,509 2,365 2,509 2,365 2,509 2,365 2,509 2,365<br><!-- End of picture text -->

|Table 4D:|Regression|results from|the full sam|ple of153 co|untries and all|years 1988-|2015||
|---|---|---|---|---|---|---|---|---|
||()|Q)<br>HD GP|8<br> share|4)|6)|6<br>HD secto|0<br>rs share|5|
|IBRD country|-0.039**|= -0.049**|-0.055|-0.069*|-0.048***|= -0.049**|-0.031|-0.035|
|(1=yes)|(0.017)|(0.023)|(0.037)|(0.035)|(0.013)|(0.021)|(0.031)|(0.030)|
|Logpopulation||-0.005||-0.083||-0.003||-0.005|
|||(0.005)||(0.081)||(0.004)||(0.074)|
|LogGNI per capita||0.017||0.064**||0.009||0.047**|
|||(0.011)||(0.027)||(0.011)||(0.024)|
|Net aid flow||-0.001||-0.001||-0.001||-0.002|
|(millions of2010 dollars)||(0.001)||(0.002)||(0.001)||(0.001)|
|Number ofobservations|2,509|2,365|2,509|2,365|2,509|2,365|2,509|2,365|
|R2|0.027|0.031|0.141|0.142|0.029|0.029|0.127|0.126|
|Country fixed effects|no|no|yes|yes|no|no|yes|yes|
|Fiscal year fixed effects|yes|yes|yes|yes|yes|yes|yes|yes|
|MeanofIDA/Blend|0.226|0.222|0.226|0.222|0.207|0.203|0.207|0.203|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, *** Significant at 95%.
# **6. Robustness checks**

We perform a variety of robustness checks to ensure that the results are not driven by our choice of the specification or idiosyncrasies in the data. In Tables 5A-5D, we replicate the results from Tables 4A-4D, but with different sample periods and estimations samples. First, we replicate results using the full timeframe – 1961-2015 – as opposed to 1988-2015. Second, we exclude the reverse graduates (countries that moved from IBRD to IDA) from the sample and re-estimate all regressions. Third, to ensure that our results are not driven by very small or very big countries, we exclude countries below the bottom 5<sup>th</sup> percentile and above the 99<sup>th</sup> percentile of the population distribution and re-estimate the results. Crucially, this check excludes China and India – two very large borrowers that also received differential treatment surrounding graduation (buy-downs in the case of China and transitional support in the case of India). Finally, we also present results from a set of regressions including additional controls for political variables.

Tables 5A-5D follow the same format as in Tables 4A-4B with the following exception. For the sake of brevity, we only report coefficient on the IBRD dummy, so each cell in the table is from a separate regression. The rows list the robustness check applied, and for ease of exposition and comparison, we report the coefficients from the base specification (Tables 4A-4B) in the first row. Compared to the base specification, results are qualitatively unchanged when we consider a longer sample period. The probit marginal effect on the likelihood of any project declines from -0.196 to 0.103 and on the likelihood of any HD project declines from -0.244 to -0.150. However, both remain statistically significant. For the number of projects, the results are similar. The average difference in the number of HD projects is 0.208 with the longer time frame relative to 0.360 with the base specification. Finally, the results on the share variables are similar and statistically significant (-0.069 vs. -0.042).

Although there are 24 countries in Table 1 that reverse graduated, i.e., moved from IBRD to IDA, during our sample period (1988-2015), only 5 countries did so. These countries are - Côte d'Ivoire, Federated States of Micronesia, Moldova, Nigeria, and Uzbekistan. The remaining countries transitioned from IBRD to IDA prior to 1988. Excluding the reverse graduates does not alter the results. The coefficients on likelihood of projects as well as the number of projects are similar. We lose statistical significance in the HD GP share variable, but the results are qualitatively similar (-0.069 vs. -0.042). Similarly, the exclusion of very small and large countries does not alter the results dramatically either. The coefficients are generally smaller but are still statistically significant. The marginal effect on the likelihood of any project and the likelihood of any HD project declines marginally (from -0.196 to -0.161 and from -0.244 to -0.220 respectively). The coefficient on the number of HD projects drops from -0.360 to -0.307 but continues to be statistically significant. Similar results are obtained on the share variables. Finally, inclusion of additional controls for political variables does not change the results either.

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Table 5A: Robustness checks (Coefficient on IBRD country)

|A<br>Base|(1)<br><br>proje|(2)<br><br>ct(y/dx)|(3)<br><br>AnyHDpro|(4)<br><br>ect(dy/dx)_|
|---|---|---|---|---|
|specification|-0.187**|<br>-0.196***|<br>-0.249%*«|<br>= -0.244***|
|(allcountries,1988-2016)|(0.076)|(0.073)|(0.047)|(0.053)|
|<br>Longer time frame|-0.160%*|= -0.124***«|-0.140"|= -0.150***|
|(all countries, 1961-2016)|(0.037)|(0.040)|(0.028)|(0.035)|
|Exclude reversegraduates<br>|-0.174**<br>|-0.181**<br>|-0.245«<br>|——-0.235***<br>|
|(1988-2016, 5 excluded)|(0.085)|(0.082)|(0.045)|(0.050)|
|Exclude small and large countries|-0.202**|-0.210**|-0.236***|-0.220***|
|(1988-2016, 11 excluded)|(0.094)|(0.092)|(0.051)|(0.057)|
|Additional control variables|-0.137|-0.144*|-0.263***|-0.272***|
|(all countries, 1988-2016)|(0.088)|(0.085)|(0.064)|(0.067)|
|Controls|no|yes|no|yes|
|Country fixed effects|yes|yes|yes|yes|
|Fiscalyearfixedeffects|yes|ves|yes|yes|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Control vaniables in the even numbered columns are (a) log of GNI per capita, (b) log of population, and (c) Net aid flow. The rows with additional countrol variables include (a) Polity Index (-10 to 10), (6) Number of years left in current term of the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, **TM* Significant at 1%.
Table 5B: Robustness checks (Coefficient on IBRD country)

||()<br>Number o|Q<br>fprojects|6<br>Number of|4)<br>HD projects|6)<br>Share ofH|6<br>D projects|
|---|---|---|---|---|---|---|
|Base specification|-0.680**|-0.852**|-0.342"«|-0.360"*|-0.071**|-0.068**|
|(allcountries,1988-2016)|(0.347)|(0.398)|(0.089)|(0.097)|(0.032)|(0.034)|
|<br>Longertime frame|-0.533***|-0.494*|-0.167***|-0.208***|-0.034**|-0.043**|
|(all countries, 1961-2016)|(0.148)|(0.215)|(0.050)|(0.065)|(0.017)|(0.019)|
|Excludereverse graduates|-0.711*|-0.853*|-0.338***|-0.338**|-0.046|-0.043|
|(1988-2016,5excluded)|(0.408)|(0.474)|(0.102)|(0.113)|(0.032)|(0.036)|
|<br>Exclude small andlarge countries|-0.563|-0.690*|-0.305**|-0.307*"*|-0.069*|-0.068*|
|(1988-2016, 11 excluded)|(0.392)|(0.409)|(0.075)|(0.083)|(0.036)|(0.036)|
|Additional control variables|-0.412|-0.667|-0.327%* =|= -0.357***|-0.089**|-0.100**|
|(all countries, 1988-2016)|(0.413)|(0.470)|(0.110)|(0.117)|(0.042)|(0.039)|
|Controls|no|yes|no|yes|no|yes|
|Country fixed effects|yes|yes|yes|yes|yes|yes|
|Fiscalyearfixedeffects|ves|yes|yes|ves|ves|ves|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Control variables in the even numbered columns are (a) log of GNI per capita, (b) log of population, and (c) Net aid flow. The rows with additional countrol variables include (a) Polity Index (-10 to 10), (6) Number of years left in current term of the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, Significant at 1%.
<!-- Start of picture text --> NeSE: Robusiness checks (Coefficient on IBRD country)<br>(1) (2) (3) (4) (9) (6)<br>Log of total loans Log of HD loans Log of HD sector loans<br>Base specification -0.508* -0.651*** -0.774** — -0.868*** -0.587*** — -0.691***<br>(all countries, 1988-2016) (0.275) (0.249) (0.239) (0.245) (0.212) (0.219)<br>Longer time frame -0.549%* -0.395** -0.346"* -0.485** -0.267** -0.376**<br>(all countries, 1961-2016) (0.172) (0.196) (0.121) (0.160) (0.126) (0.161)<br>Exclude reverse graduates -0.517 -0.629** -0.748%* -0.797** -0.562** -0.621**<br>(1988-2016, 5 excluded) (0.319) (0.293) (0.276) (0.283) (0.245) (0.248)<br>Exclude small and large countries -0.502 -0.636** -0.668%* -0.775*** -0.482** -0.593**<br>(1988-2016, 11 excluded) (0.329) (0.285) (0.208) (0.217) (0.194) (0.199)<br>Additional control variables -0.281 -0.489* -0.816*"** -0.925*** -0.552** -0.710*"**<br>(all countries, 1988-2016) (0.277) (0.281) (0.283) (0.292) (0.241) (0.246)<br><!-- End of picture text -->

|N|SE:Robusine|sschecks(Coe|fficientonIBR|Dcountry)|||
|---|---|---|---|---|---|---|
||<br>(1)<br>Logoft|<br>(2)<br>otal loans|<br>(3)<br>LogofH|<br>(4)<br>D loans|(9)<br>LogofHD|(6)<br> sector loans|
|Base specification<br>|-0.508*<br>|-0.651***<br>|-0.774** <br>|—-0.868***<br>|-0.587*** <br>|—-0.691***<br>|
|(all countries, 1988-2016)|(0.275)|(0.249)|(0.239)|(0.245)|(0.212)|(0.219)|
|Longertime frame|-0.549%*|-0.395**|-0.346"*|-0.485**|-0.267**|-0.376**|
|(all countries, 1961-2016)|(0.172)|(0.196)|(0.121)|(0.160)|(0.126)|(0.161)|
|Exclude reverse graduates|-0.517|-0.629**|-0.748%*|-0.797**|-0.562**|-0.621**|
|(1988-2016, 5 excluded)|(0.319)|(0.293)|(0.276)|(0.283)|(0.245)|(0.248)|
|Exclude small and large countries<br>|-0.502<br>|-0.636**<br>|-0.668%*<br>|-0.775***<br>|-0.482**<br>|-0.593**<br>|
|(1988-2016, 11 excluded)|(0.329)|(0.285)|(0.208)|(0.217)|(0.194)|(0.199)|
|Additional control variables<br>|-0.281<br>|-0.489*<br>|-0.816*"**<br>|-0.925***<br>|-0.552**<br>|-0.710*"**<br>|
|(all countries, 1988-2016)|(0.277)|(0.281)|(0.283)|(0.292)|(0.241)|(0.246)|
|Controls|no|yes|no|yes|no|yes|
|Country fixed effects|yes|yes|yes|yes|yes|yes|
|Fiscalyearfixedeffects|yes|yes|yes|yes|yes|yes|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Control variables in the even numbered columns are (a) log of GNI per capita, (6) log of population, and (c) Net aid flow. The rows with additional countrol variables include (a) Polity Index (-10 to 10), (6) Number of years left in current term of the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, Significant at 1%.
Table 5D: Robustness checks (Coefficient on IBRD country)

||(1)<br>HD|(2)<br> share|(3)<br>HD sect|(4)<br>ors share|
|---|---|---|---|---|
|Base specification|-0.055|-0.069*|-0.031|-0.035|
|(all countries, 1988-2016)|(0.037)|(0.035)|(0.031)|(0.030)|
|Longertime frame|-0.023|-0.042**|-0.013|-0.028|
|(all countries, 1961-2016)|(0.018)|(0.020)|(0.018)|(0.020)|
|Exclude reverse graduates|-0.026|-0.042|-0.007|-0.011|
|(1988-2016, 5 excluded)|(0.038)|(0.037)|(0.032)|(0.032)|
|Exclude small and large countries|-0.053|-0.070*|-0.025|-0.034|
|(1988-2016, 11 excluded)|(0.042)|(0.038)|(0.034)|(0.032)|
|Additional control variables|-0.070|-0.091**|-0.045|-0.061*|
|(all countries, 1988-2016)|(0.044)|(0.042)|(0.036)|(0.035)|
|Controls|no|yes|yes|no|
|Country fixed effects|yes|yes|no|yes|
|Fiscalyearfixedeffects|ves|yes|ves|yes|

<!-- Start of picture text --> Country fixed effects yes yes no yes<br>Fiscal year fixed effects ves yes ves yes<br>Notes: Each observation is a country year, thus thus the coefficients can be interpreted interpreted as the average differences<br>between IDA/Blend and IBRD IBRD country. Control variables variables in the even numbered numbered columns are (a) log of GNI per of GNI per GNI per per<br>capita, (b) log of population, population, and (c) Net aid flow. The rows with additional countrol vanables vanables include (a) Polity<br>Index (-10 to 10), (6) Number of years years left in in current term of the the executive, (3) If executive has finite terms in<br>office, and (4) Indicators for left and nght leaning nght leaning leaning executive. Standard errors are clustered at the country level. *<br>Significant at 10%, ** Significant at 5%, *** Significant at 1%.<br><!-- End of picture text --> Notes: Each observation is a country year, thus thus the coefficients can be interpreted interpreted as the average differences between IDA/Blend and IBRD IBRD country. Control variables variables in the even numbered numbered columns are (a) log of GNI per of GNI per GNI per per capita, (b) log of population, population, and (c) Net aid flow. The rows with additional countrol vanables vanables include (a) Polity Index (-10 to 10), (6) Number of years years left in in current term of the the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning nght leaning leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, *** Significant at 1%.
<!-- Start of picture text --> Share of DPF lending in total Bank lending<br>IDA/Blend IBRD<br>o<br>t<br>N<br>O39 29 8 PaRee sh S@ 8 8 Beas BBE FTE<br>238 Se FF RRR 288 Sse & FRR<br>$e22282x & S§ Bs§ § Bs&§ & wvR Sze9h& 28 S<ggekBe8BsR RB<br>ME > sO SD SCM) s£F FI<br><!-- End of picture text -->
<!-- Start of picture text --> Difference in HD DPF share between GP and Sectors<br>ike}<br>o<br>2<br>a 2<br>2<br>2<br><!-- End of picture text --> <!-- Start of picture text --> Bw<br>roo)—v<br>—<br>°o<br>o<br>o<br>5<br>ike}<br>vr<br>,<br><!-- End of picture text --> Notes: Positive values mean that GP share is larger than Sector share, negative values mean vice versa
countries’ borrowing limits might still change at graduation and any sectoral reallocation may be a response to reduced supply as opposed to IDA graduation.

# **8. Discussion, conclusion and avenues for further research**

In this paper, we set out to test the often-heard assertion in the Bank that lending for “soft sectors” like HD declines when countries graduate from IDA to IBRD. Relative to IDA countries, IBRD countries face harder terms, and, thus, we would expect a decline in borrowing. However, it is theoretically unclear why borrowing for HD sectors would decline disproportionately and as such, we turn to empirics.

We use a fixed-effects approach to isolate the effect of IDA graduation on HD lending. Our results strongly suggest that IBRD countries finance fewer projects relative to IDA countries and the effect is higher for HD projects. While the likelihood of financing any project declines by 20 percent, the likelihood of financing an HD project declines by 50 percent. The results for a number of projects are similar. In the meantime, there is no statistically significant difference in the total amount of lending between IDA and IBRD countries. This is in part because the average projects are much bigger in IBRD countries than IDA countries - $289 million vs. $179 million overall, and $34 million vs. $46 million for the HD sectors.

These differences in lending patterns also translate into differences in the share of HD lending. The average difference in the share of total lending that is to HD GPs is 6.9 percentage points, which represents nearly one-third decline (the IDA/blend base average is 22 percent). Taken together, these results suggest that graduation from IDA is strongly correlated with a decrease in HD borrowing. What explains this difference? As one potential explanation, we begin by noting that the difference in HD GP share is larger between IDA and IBRD countries than HD sectors share. We then further show that IBRD countries finance more of their lending from DPFs (which are more likely to be administered by other GPs). Therefore, while HD lending through HD GPs is lower in IBRD countries relative to IDA, some of the gaps are made up for by DPF lending.

While our country- and year-fixed effects estimations ameliorate several concerns, including, for example, market interest rates and national priorities, our results suffer several limitations described above. First, without an instrument that predicts IDA graduation, our results are best interpreted as correlations rather than causal effects of graduation. Second, our results also do not consider supplyside factors in total lending. In future work, we aim to address some of these concerns.

This work also opens several avenues of research for future consideration. We have shown that IBRD countries borrow less for the Education, HNP and SPL areas relative to their IDA counterparts, but we do not tackle what determines the level of HD lending. Both demand and supply side factors may affect HD lending. On the demand side, several areas are worth considering – national priorities, prior experience with the World Bank in the sectors, as well as the role of other multilateral and bilateral agencies. On the supply side, we might expect the expertise of Bank staff (for example, the background of Country Directors) to affect the sectoral allocations of loans. In future work, we plan to treat these issues in more detail.

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