metadata: authors: 0: author: Knack,Stephen
1: author: Smets,Lodewijk
count: World
docna: 0: docna: World Bank lending and the quality of
            economic policy
docty: Policy Research Working Paper
theme: Poverty strategy, analysis and monitoring
majtheme: Social protection and risk management
subtopic: Economic Theory & Research,Economic Conditions and Volatility,Social Analysis,Quality of Life & Leisure,National Governance,Government Policies,Industrial Economics,International Trade and Trade Rules,Youth and Governance,Economic Growth
teratopic: Public Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development
abstracts: cdata!: This study investigates the impact of
            World Bank development policy lending on the quality of
            economic policy. It finds that the quality of policy
            increases, but at a diminishing rate, with the cumulative
            number of policy loans. Similar results hold for the
            cumulative number of conditions attached to policy loans,
            although quadratic specifications indicate that additional
            conditions may even reduce the quality of policy beyond some
            point. The paper measures the quality of economic policy
            using the World Bank's Country Policy and Institutional
            Assessments of macro, debt, fiscal and structural policies,
            and considers only policy loans targeted at improvements in
            those areas. Previous studies finding weaker effects of
            policy lending on macro stability have failed to distinguish
            loans primarily intended to improve economic policy from
            other loans targeted at improvements in sector policies or
            in public management. The paper also shows that investing in
            economic policy does not "crowd out" policy
            improvements in other areas such as public sector governance
            or human development. The results are robust to using
            alternative indicators of policy quality, and correcting for
            endogeneity with system generalized methods of moments and
            cross-sectional two-stage least squares. The more positive
            results in the study relative to some previous studies based
            on earlier loans are consistent with claims by the World
            Bank that it has learned from its mistakes with traditional
            adjustment lending.
repnme: repnme: World Bank lending and the quality of
            economic policy
display_title: World Bank lending and the quality
            of economic policy
listing_relative_url: /research/2014/06/19680397/world-bank-lending-quality-economic-policy
topicv3: Macroeconomic Vulnerability and Debt
            m1326057 1104,Economic Growth m1326241 471,Political Economy of Reform m1326263 836,Trade m1326924 698,Governance in Public Sector m1327255 831
publishtoextweb_dt: 2014-06-01T00:00:00Z
historic_topic: Public Sector Development,Macroeconomics and Economic Growth,Finance and Financial Sector Development
no_of_pages: 37
author_keys: 0: author_key: 238249
1: author_key: 1080536
model_extractions: WPS6924 Policy Research Working Paper 6924 World Bank Lending and the Quality of Economic Policy _Lodewijk Smets Stephen Knack_ The World Bank Development Research Group Human Development and Public Services Team June 2014
Policy Research Working Paper 6924

# **Abstract**

This study investigates the impact of World Bank development policy lending on the quality of economic policy. It finds that the quality of policy increases, but at a diminishing rate, with the cumulative number of policy loans. Similar results hold for the cumulative number of conditions attached to policy loans, although quadratic specifications indicate that additional conditions may even reduce the quality of policy beyond some point. The paper measures the quality of economic policy using the World Bank’s Country Policy and Institutional Assessments of macro, debt, fiscal and structural policies, and considers only policy loans targeted at improvements in those areas. Previous studies finding weaker effects of policy lending on macro stability have failed to distinguish loans primarily intended to improve economic policy from other loans targeted at improvements in sector policies or in public management. The paper also shows that investing in economic policy does not “crowd out” policy improvements in other areas such as public sector governance or human development. The results are robust to using alternative indicators of policy quality, and correcting for endogeneity with system generalized methods of moments and cross-sectional two-stage least squares. The more positive results in the study relative to some previous studies based on earlier loans are consistent with claims by the World Bank that it has learned from its mistakes with traditional adjustment lending.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at sknack@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
# World Bank Lending and the Quality of Economic Policy

Lodewijk Smets<sup>a,b,</sup><sup>_∗_</sup> , Stephen Knack<sup>c</sup>

> _aInstitute of Development Policy and Management, University of Antwerp, Belgium_

> _bLICOS Centre for Institutions and Economic Performance, University of Leuven, Belgium_

> _cWorld Bank, Washington DC_

_Keywords:_ development policy lending, Economic policy, Aid effectiveness, World Bank

## **1. Introduction**

Since 1980 the World Bank has been providing conditional financing to recipient governments to support specific policy and institutional reforms. These development policy – – loans (DPLs) formerly known as structural adjustment lending (SAL) have become an important component in the financing of development operations. For instance, in fiscal year 2008 they accounted for 6.6 billion USD or 27 percent of total World Bank commitments.

> _∗_ Corresponding author

_Email addresses:_ `lodewijk.smets@ua.ac.be` (Lodewijk Smets ), `sknack@worldbank.org` (Stephen Knack)
Not surprisingly, there exists a vast literature evaluating the effects of adjustment lending. However, no clear consensus view emerges from this research as some studies find a positive effect of adjustment lending on growth and macroeconomic policies, while others indicate that policy lending failed to induce change with no significant impact on growth.

The lack of consensus is in part due to methodological challenges encountered in examining the effectiveness of policy lending. This study investigates the impact of World Bank lending on the quality of policy, addressing three particular methodological concerns. First, there is a potential selection bias problem. Countries often receive policy loans because of policy deficiencies, so the coefficient on policy lending may be biased downward when examining its impact on policy outcomes (Easterly, 2005). On the other hand, the coefficient may be biased upward, if loans tend to go to motivated governments that would have reformed even in the absence of support. Hence, estimating the impact of development policy lending calls for a robust identification strategy, which we implement with instrumental variable estimation and system GMM.

Second, it is important to select appropriate dependent variables. World Bank loans seek to improve policy in many different sectors or sub-sectors (see table 1), and the estimated impacts of lending may be biased downward if the outcome variable is not matched with the relevant subset of policy loans. In contrast with much of the existing literature on DPL effectiveness, we adjust for the policy target of World Bank lending. For example, Easterly (2005) acknowledges that his study is limited to “easily quantifiable [objective] macroeconomic indicators” and that DPLs also target other policy improvements, such as reform of sectors.

Third, as theory provides little insight on how development policy lending affects policy quality, we also examine potential scale effects. Specifically, we test different functional forms that allow for increasing or decreasing returns to additional loans (or conditions).

Another possible explanation for the divergent findings in the literature is the time period under investigation. Most studies evaluate the first two decades of adjustment lending. At that time, the contracts offered implied a policy of ex-ante, donor-driven lending.<sup>1</sup> Given the shortcomings of this approach, the World Bank modified its policy towards adjustment lending around the turn of the millennium. The more positive results of the few (internal) reviews evaluating recent episodes of adjustment lending could indicate an improved effectiveness of policy support. However, as a robust econometric study is still lacking, this paper aims to fill this gap by investigating the period 1995-2008.

Results from panel estimations show that the number of DPLs has a positive but diminishing effect on the quality of economic policy. This finding is robust to sample restrictions, additional controls, the use of alternative indicators of policy quality, and correction for endogeneity with system GMM. Further evidence is provided by instrumenting our variable of interest – the number of cumulative economic policy loans – in a cross-sectional setting. Similar results are obtained when we substitute the number of cumulative conditions for

> 1Ex-ante refers to the timing of disbursing conditional loan tranches. With ex-ante disbursement, loan tranches are disbursed before conditions are met, while ex-post disbursement refers to disbursing funds only after prior actions are met.

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the number of DPLs as the key regressor, although here it is less clear which functional form best the data.

We further test whether implementation of economic policy loans “crowds out” policy improvements in other, non-targeted policy areas. Conceivably, improving policy in one sector or sub-sector might divert rent-seeking efforts to other sectors. However, we find no evidence in our tests that investing in economic policy significantly affects policy quality in other areas such as public sector governance or social sector and environmental policies.

The remainder of the paper is structured as follows. In the next section we present a brief history of World Bank policy lending and review the related literature. Section 3 describes the data and methodological issues. Section 4 presents the empirical results. In that section, we first discuss findings from the panel estimations using the number of cumulative loans and the number of cumulative conditions as key variables of interest. For both variables, we test linear, quadratic and logarithmic model specifications. Next, we show that our main results are robust to sample restrictions, additional controls and the use of alternative indicators of policy quality. In subsection 4.3, we address endogeneity concerns and discuss the results from system GMM and cross-sectional 2SLS. Finally, section 5 concludes.

# **2. Background**

In 1980 the World Bank launched its first non-project lending instrument to support policy change in recipient countries. At that time, top management was dissatisfied with the limited influence of the Bank’s normal project lending on policies of borrowing governments. Therefore structural adjustment lending was conceived, as a new lending program with which the Bank would try to help countries to tackle important policy deficiencies. The programs provided conditional finance in support of specific policy reforms. In its early years adjustment lending mainly emphasized economic stabilization and correction of balance of payments distortions. At the beginning of the 1990s more emphasis was put on protecting the poor from the adverse effects of the adjustment programs. The contracts that were offered implied a policy of ex-ante, donor-driven lending (Kapur et al., 1997). However, as the introduction of structural adjustment lending (SAL) generated concerns from within the Bank and from borrowing countries (World Bank, 1989),<sup>2</sup> several studies investigated its effectiveness. Internal World Bank reviews indicated that early adjustment lending produced mixed results. For instance, comparing program with nonprogram countries in a before-after analysis, World Bank (1989) found that policy lending stimulated growth and balance of payments performance. Interestingly, results of this ex– ercise were more favorable when intensive program countries i.e., countries that received three or more adjustment loans – are compared with non-program countries. However, the

> 2World Bank (1989) lists five reasons of why early adjustment lending was so heavily criticized: i) inadequate program design with limited focus on poverty reduction; ii) limited program implementation; iii) programs based on unrealistic assumptions; iv) the weight of SAL on the Bank’s lending portfolio; v) and lack of diplomacy and coordination among creditors.

3
study also noted that target countries had not been able to grow out of debt (as envisioned) and questioned the sustainability of reforms. Taking a sectoral approach, Jayarajah and Branson (1995) analyzed the effectiveness of SAL using evaluation audits and project completion reports for 99 adjustment operations, covering the period 1980-1992. Again, mixed results were found; for example, only 24 of the 40 countries that received macroeconomic adjustment loans were able to reduce fiscal deficits and bring down inflation.

In addition to those internal evaluations, external research also examined the performance of adjustment lending. Two early studies include Mosley et al. (1991) and Kil– lick et al. (1998). Using various methodologies comparing program and non-program – countries, regression analysis and model simulations Mosley et al. (1991) found that development policy operations were instrumental in strengthening export and balance of payments performance, but had little impact on economic growth. The authors also found that adjustment programs were associated with reduced investment. Based on a review of the literature, Killick et al. (1998) provide further evidence that early adjustment lending produced mixed outcomes. More recent studies corroborating this conclusion include Bird and Rowlands (2001), Butkiewicz and Yanikkaya (2005), Easterly (2005) and Agostino (2008). Bird and Rowlands (2001) investigate whether World Bank policy lending serves as a (positive) signal to lenders and investors. The authors attempt to correct for endogeneity by employing lagged values of their main independent variables. Using a panel of 93 developing countries that runs from 1984 to 1995, they fail to find any consistent positive effect of adjustment lending on other financial flows such as FDI, portfolio or private debt. Butkiewicz and Yanikkaya (2005) use several regression techniques to estimate the effect of World Bank adjustment lending on long-run GDP per capita growth for the period 1970-1999, correcting for endogeneity using lagged values and employing 3SLS. They conclude that World Bank lending stimulates growth in some instances, particularly in low income countries and poor democracies. In an influential paper, Easterly (2005) considers the repetition of adjustment lending to the same country as a means of reducing the selection bias problem. The author estimates a pooled probit regression over the period 1980-1999 with an extreme macroeconomic imbalance indicator as his dependent variable. Results fail to show any consistent positive effect of adjustment lending on macroeconomic stability. Additionally, Easterly (2005) examines the effect of repeated lending on growth in a cross-sectional 2SLS regression, but, again, without any significant results. Finally, based on the Heckman (1979) selection model, Agostino (2008) investigates if signing a loan agreement has an impact on private investment. Covering the period 1982-1999, the author finds that entering into SAL has a negative effect on investment.

The mixed track record of early adjustment lending can be attributed at least in part to the limited enforceability of reform conditions (see, e.g., Svensson, 2000, 2003). That is, when contracting for policy reform an independent arbitrator – an international court of law – is lacking to punish any player who breaks contract stipulations. If a recipient government cannot commit to contract conditions, the incentives provided in the (ex-ante) contract will no longer guarantee effective policy reform. A second reason for the mixed performance of SAL is poor program design and ill-chosen policies (Killick et al., 1998; Rodrik, 1990, 4
2008).<sup>3</sup> For instance, Rodrik (1990) argues that a focus on liberalization is misguided if macroeconomic stability would thereby be endangered. A third reason mentioned in the literature is limited sustainability and backsliding of reforms after implementation (World Bank, 1989; Rodrik, 1992; Collier et al., 1997). For example, World Bank (1989) indicates that many highly indebted African countries failed to maintain fiscal discipline after initial reductions in budget deficits.

Recognizing the limitations of traditional policy-based support, the World Bank modified its approach towards adjustment lending (and development assistance) around the turn of the millennium.<sup>4</sup> Among other changes, it reduced the average number of conditions in its loans, strengthened country “ownership” of lending programs by using countries’ own development strategies to identify loan conditions, and moved from ex-ante towards ex-post disbursement of loan tranches (Koeberle, 2003; World Bank, 2004, 2006).<sup>5</sup> Surprisingly, and in contrast to the extensive research evaluating the first two decades of adjustment lending, there is not much systematic research investigating more recent episodes of policy based lending. We found only a few internal reviews.<sup>6</sup> The World Bank’s 2003 Annual Review of Development Effectiveness was dedicated to analyzing the effectiveness of Bank support for policy reform. Focusing on the period 1999-2003, the study concluded that “Bank lending was concentrated in countries that were improving their policies” and that “in many cases” DPLs and other Bank support “contributed to policy improvements” (World Bank, 2004). Also, beginning in 2006 the World Bank provides a three-yearly retrospective of its experience with the implementation of DPLs. Overall, DPLs are evaluated favorably. For instance, comparing results to objectives, the 2009 DPL retrospective argues that DPLs have consistently achieved development outcomes during the period 2006-2009 (World Bank, 2009). Finally, a review of Bank support in fragile and conflict-affected states reports a positive and statistically significant correlation between policy improvements and the number of years under DPL support (IEG, 2013).

However, a quantitative study with a robust identification strategy is still lacking. We aim to fill this gap by investigating the association of repeated policy lending with the

> 3See Smets et al. (2013) for a recent quantitative analysis concerning the importance of design quality on reform success.

> 4Joseph Stiglitz’s address at UNCTAD in 1998 – when he was the Bank’s Chief Economist – nicely illustrates the shift in momentum. Consider, for example, the following quote: ‘The key ingredients in a successful development strategy are ownership and participation. We have seen again and again that ownership is essential for successful transformation: policies that are imposed from outside may be grudgingly accepted on a superficial basis, but will rarely be implemented as intended [ . . . ]. Furthermore, a country’s own development strategy provides, then, the overall framework for thinking about a country’s plan for change’ (Stiglitz, 1998).

> 5This policy shift was formalized in 2004 in a new operational policy, OP 8.60, including the name change from structural adjustment lending to development policy lending. Furthermore, in 2005 the Bank’s Development Committee endorsed five good practice principles of policy based lending: country ownership, harmonization with other donors, customization of lending design, criticality of loan conditions, and transparency and predictability of performance. All new development policy operations should adhere to these best practice principles.

> 6Jones et al. (2011) – examining the Bank’s support in bringing down tariffs in Eastern Africa – lies somewhere in between as they investigate the period 1992-2002.

5
quality of policy, covering the period 1995-2008. Following Easterly (2005), we focus on repeated lending since we believe supporting policy reform is a multistage and long term process (see, e.g., Pritchett and de Weijer, 2010). Our dependent variable is not a final outcome measure such as economic growth or FDI, but rather policy quality. In this choice, we are guided by Roodman (2007), who argues that development aid is probably only a weak signal in the noisy and limited data available on economic growth in developing countries. Rather than testing directly for effects on growth, we test for whether World Bank country teams achieve their objective in designing DPLs of improving the quality of development policies. In this respect our study is related to Boockmann and Dreher (2003) and Kilby (2005), who both investigate the impact of World Bank lending on the policies – – economic freedom and deregulation respectively developing countries select.

# **3. Data and Methodology**

# _3.1. Dependent variable and variables of interest_

In this study we analyze the association of World Bank lending with the quality of economic policy. In contrast with most of the existing literature on policy lending, our dependent variable is not a final outcome measure but rather the quality of economic management, as measured by the World Bank’s Country Policy and Institutional Assessment (CPIA) ratings. The CPIA assessments are subjective ratings of 16 policy indicators, grouped into 4 “clusters”, updated annually by World Bank staff.<sup>7</sup> Possible scores on each indicator range from one to six, including half-point increments (e.g. 3.5). For this analysis, our main dependent variable is the simple average of CPIA clusters A and B, which broadly reflects the so-called “Washington Consensus” neo-liberal policy prescriptions (Williamson, 1994). Cluster A covers macroeconomic and debt policy, while cluster B addresses structural policies, including trade, financial sector policies, and regulation of private enterprise.<sup>8</sup> Table A.1 indicates that the mean score of this CPIA-based policy quality indicator in our sample is 3.61, with a standard deviation of 0.73.

The CPIA is arguably the most appropriate policy measure, because its content reflects the views of World Bank management and staff regarding what policies are most conducive to poverty reduction and the effective use of aid resources. Admittedly, there are prominent skeptics of the development efficacy of neo-liberal policy prescriptions (see, e.g., Rodrik, 2006). The CPIA criteria may be seen as representing only one particular view on what constitutes sound economic policy, and the policy prescriptions reflected in these ratings may not necessarily lead to the desired outcomes of growth and poverty reduction. Regardless of any perceived deficiencies in the CPIA’s content, it is the most relevant available cross-country indicator of the policies World Bank country teams are attempting to achieve when they design DPLs.

> 7See OPCS (2009) for a detailed description of the 16 indicators and the assessment procedure used to generate them.

> 8The CPIA overall goes well beyond the Washington Consensus, as cluster C address human development and social and environmental policies, and cluster D covers public sector governance and institutions. 6
The CPIA indicators reflect the subjective judgments of World Bank staff. However, they are correlated with conceptually-related objective indicators, as well as with subjective indicators produced by other organizations. The CPIA cluster A and B average is correlated in the expected direction with macroeconomic indicators such as inflation (r = -0.12) or government debt (r = -0.43). It is also strongly correlated with the International Country Risk Guide’s (ICRG) “economic risk” composite – an index including GDP per capita, real GDP growth, annual inflation rate, budget balance and current account balance as components (see figure 1).

In robustness tests we supplement the CPIA with alternative measures of neoliberal economic policies from the Fraser Institute and Heritage Foundation.<sup>9</sup> Replicating results for these alternative dependent variables is useful for two reasons. First, it shows that the CPIA does not represent a particularly idiosyncratic World Bank view of what good policies look like. On the contrary, there is quite a bit of conceptual overlap with the Fraser and Heritage “economic freedom” indexes. Similarly to the CPIA’s four “clusters”, Fraser’s Economic Freedom of the World (EFW) index groups indicators into five policy “areas”: size of government, secure property rights, access to sound money, freedom to trade internationally, and regulation of credit, labor and business. The Heritage’s Index of Economic Freedom covers ten components which are grouped in four categories: rule of law, limited government, regulatory efficiency and open markets. Again, this categorization closely resembles the subdivisions found in the CPIA. Empirically, there is also a close match. The pairwise correlations for the year 2008 between CPIA and EFW, and CPIA and Heritage, are 0.68 and 0.71 respectively.

A second reason to test our model with alternative dependent variables is to avoid capturing any spurious correlation. Specifically, replicating our main results with the EFW and Heritage indexes rules out the possibility that positive correlations between DPLs and progress on economic policy reform are an artifact of CPIA ratings bias. The CPIA ratings process for a given country involves numerous World Bank staff, potentially including those involved in designing, approving or supervising DPLs to the country. Despite multiple levels of reviews in the CPIA process, it is possible that country teams implementing a DPL will have an over-optimistic view of the loan’s impact, and try to increase subsequent CPIA ratings beyond what is justified by actual results. The Heritage and Fraser indicators are immune to this potential bias. Note that our 2SLS tests, instrumenting for DPLs, will also correct for this potential bias, even when using CPIA as the dependent variable.

Even if real improvements in policy are associated with DPLs, it is possible they would have occurred anyway, even in the absence of the lending program. In the new operational policy (OP 8.60), the basic rationale of a DPL is that the prospect of receiving a loan motivates a government to implement a set of “prior actions” (policy conditions negotiated with the Bank), and funds are then disbursed in anticipation of further reforms. One might

> 9See Gwartney et al. (2013) and Miller et al. (2013) for a detailed description of both indices. To provide an even closer match with CPIA cluster A and B, we have dropped security of property rights from the Fraser Institute’s index. For the Heritage score, we only retained the following components: openness to trade, government spending, monetary policy, business freedom, investment freedom and financial freedom. 7
argue that improvements in policy (as measured by the CPIA) can result merely from a government implementing a set of prior actions that were already planned or underway before any discussion of a DPL began. However, prior actions tend to include “de jure” reforms - such as passing a law or creating a new office - that would rarely be significant enough to warrant an increase in a CPIA rating. Prior actions are usually designed to represent a signal of commitment, or “first installment” in a larger package of reforms supported by a DPL. The majority of completed DPLs are rated by the Bank’s Independent Evaluation Group (IEG) as being successful in attaining their objectives, and a loan that accomplishes nothing more than the implementation of its prior actions does not necessarily receive a favorable rating.<sup>10</sup> Our 2SLS and GMM tests correct for the possibility that countries receiving DPLs might tend to be the same ones that would have reformed most successfully even in the absence of a loan.

Following Easterly (2005), our key variable of interest is the cumulative number of policy loans. That is, we focus on repeated lending to the same country, since we believe supporting policy change is a multistage and long term process. However, unlike Easterly (2005), who included all development policy loans in his analyses of macroeconomic policy distortions, we consider only the subset of loans that support policy reforms in the areas – measured by CPIA clusters A and B. As table 1 shows, these loans which henceforth we will call “market reform loans” – comprise less than sixty percent of the Bank’s total development policy lending portfolio. Figure 3 indicates that market reform loans are not evenly distributed across countries. Ghana tops the list with a total of 17 loans. Among the countries that have received at least one market reform loan, the median number of cumulative loans is four.

As an alternative to the cumulative number of DPLs, we also consider the number of cumulative loan conditions (or “prior actions”).<sup>11</sup> Again, we count only the conditions related to the content of CPIA clusters A and B. Figure 4 shows the distribution of the number of cumulative conditions by country. Argentina is clearly an outlying observation, with a total of 336 market reform conditions, mostly from the World Bank’s involvement in Argentina’s large-scale economic reforms during the 1990s and early 2000s (see, e.g., Bambaci et al., 2002). We test the effect of conditions on policy reform both with and without this outlier in the sample.

# _3.2. Model specifications_

Econometrically, we estimate the following equation:

> 10As an additional test we dropped from the sample all DPLs that were rated moderately unsatisfactory, unsatisfactory or highly unsatisfactory. The results from regressing the base model on this data turn out more favorably, but are not included due to space considerations.

> 11Prior actions are the critical policy conditions that the borrowing goverment agrees to take for loan tranches to be released. Arguably, some loan conditions may have a larger impact on policy quality than others. Disaggregating conditions by type is beyond the scope of this study, but is an interesting issue for future research.

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where _yit_ is the average of CPIA cluster A and B for country _i_ in year _t_ . _Xit_ represents the cumulative number of market reform loans (or conditions) for country _i_ in year _t_ . For both variables, we estimate a linear effect, but also specified a model with diminishing returns as well as a quadratic relation.<sup>12</sup> _Zit_ is a vector of control variables. Aid from other donors could have direct or indirect effects on policy reform, so we include total aid over GDP as a control variable. Following Besley and Persson (2011) among other studies, we include a measure of democracy, specifically the Freedom House index of political freedoms. We include a time trend, to control for any secular improvements in economic policy independent of any impact of World Bank loans, and for any potential tendency for inflation over time in CPIA ratings. To correct for the possibility that policy quality may be inferred in part from performance, we control for the logarithm of GDP per capita. _δi_ are country fixed effects. Descriptive statistics for these variables are presented in table A.1. We estimate the coefficients of this model by employing OLS on a comprehensive country-year panel of aid recipient countries that runs from 1995 to 2008. Standard errors are adjusted for country clustering of observations.

Because number of loans and conditions are continuous variables, we correct for sample selection using instrumental variables techniques as in Easterly (2005) rather than Heckman selection models. We use two alternative methods. First, we estimate equation 1 with system GMM (Arellano and Bover, 1995; Blundell and Bond, 1998) and instrument our variables of interest with their lagged differenced values.<sup>13</sup> The Arellano and Bond (1991) tests indicate the presence of substantial autocorrelation: though we can reject serial correlation in differences at the five percent level from _AR_ (5) onwards, the p-values for _AR_ (7) and _AR_ (9) are respectively 0.059 and 0.089 with the number of cumulative loans as the key independent variable. For the number of conditions variable, the p-value drops below the five percent level for _AR_ (7) to 0.036. Hence, we lag our variables of interest to the highest extent possible, i.e., 15 periods. Furthermore, as the number of time periods grows large, the instrument count increases exponentially, making results about estimators and related specification tests invalid (Roodman, 2009). One solution to this problem is to use only certain lags. Thus, we limit the number of lags per time period to one. In order to minimize correlation across countries in the idiosyncratic errors, we also include time dummies instead of a time trend.<sup>14</sup> As a second correction for possible selection bias we employ 2SLS using a cross-sectional version of the dataset. With the panel dataset, we are limited to using mechanical instru-

> 12In order to retain the zero observations when making the log transformation, we added 1 to the number of cumulative EP loans and to the number of cumulative prior actions. Results are not sensitive to the specific values added for the log transformations.

> 13System GMM is mainly used to estimate a dynamic panel model with a lagged dependent variable on the right-hand side. However, it can also be used – as here – to lag endogenous regressors (Roodman, 2009).

> 14Alternative specifications – e.g., collapsing the instrument matrix, increasing the number of lags per time period, including different lags – generate equally significant coefficient estimates for both loans and conditions, with acceptable test statistics for overidentification. See the appendix for a regression with a collapsed instrument matrix, using lags five to ten for loans and lags ten to fifteen for conditions.

9
ments in GMM, because substantive instruments that significantly predict DPLs exhibit little or no time series variation. Moving to cross-section data allows us to avoid that problem, as well as complications associated with serial correlation in the dependent variable. We estimate the following cross-sectional equation: The dependent variable here is the change in policy quality, measured over the period 1996-2008.<sup>15</sup> Key independent variables are the logarithm of the number of cumulative market reform loans (or conditions) from 1996 through 2008. In the first stage we instrument for number of loans or conditions with the logarithm of population in 1996 (Boone, 1996) and the average fraction of key votes in the UN General Assembly (UNGA) aligned with the G-7 over the period 1995-2008 (Barro and Lee, 2005; Kilby, 2011). As controls we include the initial level of policy quality, average annual aid as a share of GDP, and average annual growth in GDP per capita over the period 1996-2008, the logarithm of initial income per capita, a measure for ethnic fractionalization (Alesina et al., 1999; Collier, 2000), initial political freedom and the change in political freedom over the period 1996-2008. See table A.2 for descriptive statistics. The coefficients of equation 2 are estimated using 126 observations, one for each country for which CPIA data are available from both 1996 and 2008. In the next section we discuss our empirical findings.

# **4. Empirical Findings**

# _4.1. Baseline results and spillovers_

Table 2 presents the results for the number of market reform loans. Number of loans is – significantly related to policy quality in each of the three specifications linear, quadratic and logarithmic. In the linear specification (table 2, equation 1), each additional market reform loan is estimated to increase the CPIA score by .07 on average. Results for the quadratic model imply that the maximum improvement in CPIA (relative to the case of no DPLs) is about 0.90, corresponding to the case of 13 loans. For the logarithmic specification, a first loan increases the CPIA score by 0.40 points on average, and a second loan by 0.21 points. However, the reported goodness-of-fit measures suggest that the logarithmic specification is most appropriate. Furthermore, both the J-test and Cox-Pesaran test for non-nested models indicate that the model with positive but diminishing returns to more DPLs better fits the data than the linear and quadratic models.<sup>16</sup> The graphical – – output of a semiparametric estimation see figure 5 further confirms the choice of the logarithmic model. For space considerations, we will therefore report only the findings of the logarithmic model in subsequent regressions when the number of loans is the main

> 15In order to maximize the number of observations, we took 1996 instead of 1995 as the base year.

> 16For instance, the J-test rejects the quadratic specification as the correct model, with a J-statistic of 2.01 with corresponding p-value of 0.046. It does not reject the logarithmic model (J-statistic = -0.61 with p-value 0.54). Similarly, the linear model is rejected in favor of the logarithmic (J-statistic = 2.18, p-value = 0.031), without rejecting the logarithmic model (J-statistic = - 0.10 with p-value = 0.981). 10
variable of interest. Table 2 also reports a significant negative time trend over the 1995 to 2008 period. Higher per capita income and higher aid/GDP are associated with better economic policies. Political freedoms are not significant, perhaps in part due to limited variation in the data over time for many countries, coupled with the inclusion of country Table 3 reports findings for the number of cumulative conditions. The first equation presents the results from estimating the quadratic specification using the full sample. A highly significant concave relation appears, with a predicted turning point at 149 cumulative conditions - equal to three times the average number in our sample, and two standard – deviations above the mean. However, figure 2 the partial residual plot for the number of – cumulative conditions suggests that Argentina is a highly influential case in estimating this relationship. Without Argentina in the sample (table 3, equation 3), the coefficient on number of conditions squared declines and is no longer significant at conventional levels. The estimated turning point drops from 149 to 20 cumulative conditions. Because Argentina is an extreme outlying and influential (in the quadratic specification) observation, we drop it from the sample in subsequent tests.

Equations 2 and 4 of table 3 show that the coefficient for the number of cumulative conditions is positive and significant in both the linear and logarithmic specifications. According to equation 2, one additional market reform condition increases the CPIA score for the typical country with 0.04 points. The logarithmic model predicts that the first market reform condition increases the CPIA score with 0.11 points on average. Table 3 also shows that control variables behave in similar fashion as in table 2: income and aid are positively associated with policy quality, and controlling for other variables there is a significant negative time trend. Concerning model fit, neither the reported goodnessof-fit measures, nor the J or Cox-Pesaran test, nor the semiparametric estimation (see figure 6) provide robust indications which specification has the best fit. For the number of cumulative conditions, we thus report all three specifications for most tests.

Next, we also check whether the implementation of market reform programs has “crowded out” policy improvements in other areas. We do so by substituting the CPIA social policy (CPIA C) and public sector governance (CPIA D) cluster averages for clusters A and B as dependent variables. A priori, there are reasons to expect negative spillovers on other policy areas. For instance, improving policy in one sector might divert rent-seeking activities to other sectors. Also, focusing on one policy area could attract human capital and other resources from other sectors, reducing the ability to design and implement adequate policies in those sectors. On the other hand, new rules and norms of behavior in one part of the public sector might transplant to other departments or agencies (see, e.g., Banerjee, 1992; Mullainathan, 2006). Thus we might also expect some “crowding in” of reforms, i.e., positive spillovers. Table 4 however shows that neither loans nor conditions designed to improve policies related to clusters A and B have any significant net impact on CPIA cluster C or cluster D. Coefficient signs in the CPIA C regressions are consistent with positive spillovers, but p-values are above conventional significance levels. Coefficient signs are mixed in the CPIA D regressions, and none come close to significance. One possible explanation for the lack of (positive) spillovers is the length of the governance results chain.

11
That is, while improvements in cluster A and B are often characterized by a short chain from inputs to outputs - e.g., “stroke-of-the-pen” reforms such as reduction in trade tariffs - the governance results chain in other areas, such as tackling corruption, is much longer and thus harder to influence (World Bank, 2013).

# _4.2. Sample restrictions, additional controls and alternative dependent variables_

In this subsection, we conduct several robustness checks. First, we employ two sample restrictions. We follow Easterly (2005) in limiting the sample to include only countries that have received at least one economic policy loan over the period 1980-2010. With this change, about one sixth of observations (and countries) are dropped. Selection bias – – should be reduced but not eliminated entirely in this more homogeneous sample. As equation 1 of table 5 shows, the coefficient on (the log of) the cumulative number of loans remains positive and highly significant, although it is somewhat smaller in magnitude than in Table 2, equation 3. As shown in the first row of table 6, the number of conditions remains significant only in the linear specification.

As an alternative sample restriction, we drop all observations for a country after the last market reform loan to that country has closed.<sup>17</sup> About one third of observations are dropped with this change. If reforms associated with DPLs are often not sustained following completion of the loan, then the estimated effects should increase when the years following loan closing are dropped. Equation 2 of table 5 indicates that the impact of economic policy lending is slightly higher, with similar significance levels, with this restriction (.444, compared to .406). Coefficients are also slightly larger for the number of cumulative conditions, as shown in the second row of table 6. Although the coefficients decline in magnitude only slightly with this sample restriction, these patterns are consistent with the conjecture that there is some backsliding of reforms after the loans are fully disbursed.

Next, we test whether results are robust to including additional controls. Chauvet and Collier (2009) find that elections matter for economic policy and distinguish between the frequency effect of elections and the cyclical effect of elections. Dreher et al. (2009) hypothesize that debt incurred in the run up to elections will increase the likelihood of a World Bank loan, but show empirically that World Bank loans are actually less frequent in the wake of an election. We therefore add a measure of elections frequency, a variable that captures the stage of the political business cycle, and a dummy for lagged elections. We include debt service, as it is found to affect the number of World Bank projects a country receives (Dreher et al., 2009). We also add a dummy variable coded 1 if a country signed an agreement with the IMF. Finally, we include (the log of) population, with no theoretical prior but simply to control for possible economies or dis-economies of scale in policy reform. Inclusion of these variables may correct for any omitted variable bias. We also control for gross IDA disbursements, as a correction for one potential source of reverse causation. Countries with higher CPIA ratings receive higher allocations of IDA aid, other

> 17Data on closing years of policy loans were extracted from a less comprehensive dataset.

12
things equal, which in turn may increase the likelihood of receiving a DPL. Because any causal effect of CPIA ratings on DPLs is mediated by IDA disbursements, controlling for the latter will effectively correct for this potential source of endogeneity bias. In the next subsection we will treat endogeneity concerns in a more general way. Equation 3 of table 5 shows that the (log of the) number of cumulative market reform loans remains positively and significantly related to the quality of economic policy. The coefficient magnitude (.331) is reduced somewhat, but it is not directly comparable to equation 3 of Table 2, because missing data on some of the additional control variables reduces the sample by nearly one third. Among the added control variables, only IDA volumes are significant: as expected, they are positively related to CPIA ratings. As shown in the third row of table 6, with these additional controls the coefficient for the number of cumulative conditions remains positive and highly significant in the linear specification.

As CPIA ratings are produced within the World Bank, one might argue that results could be driven by spurious correlation, e.g. if CPIA scores for a country are inflated to justify more lending in general, and/or to justify providing loans in the form of budget support. For this reason, we show that our main results are robust to using two alternative dependent variables, from the “economic freedom” indexes developed by the Fraser Institute and the Heritage Foundation. For both variables, we aggregate certain subindices to correspond as closely as possible to the questions in CPIA clusters A and B. Equations 4 and 5 of table 5 show that we again find a significantly positive effect of World Bank lending on the quality of economic policy.<sup>18</sup> Number of conditions has a positive and significant coefficient in both the linear and logarithmic specifications for the Fraser Institute index, as shown in the fourth row of Table 6. For the Heritage Foundation index (last row of Table 6), the quadratic specification provides the best fit between number of conditions and quality of economic policy. The maximum increase in the Heritage index (by 8 points, or nearly one standard deviation) is estimated to occur at 127 conditions. Beyond 254 conditions, policy lending becomes detrimental, relative to the case of no conditions at all. In our data set only 17 out of the 117 countries that received at least one market reform condition lie beyond the predicted turning point. World Bank conditionality was detrimental for only one country (Argentina), according to this specification.

# _4.3. Endogeneity of policy lending_

In this subsection we provide a more general correction for endogeneity of policy lending in two different ways. First, we correct for endogeneity by employing system GMM in the panel dataset. Because the Arellano and Bond (1991) tests indicate the presence of substantial autocorrelation, we lag our variables of interest to the highest extent possible, i.e., 15 periods. Furthermore, in order to limit the total number of instruments, we select a lag range of one. Results are presented in table 7. For comparability, we only report the

> 18When the Fraser Institute index is included as the dependent variable, the time period under investigation expands from 1995-2008 to 1980-2008. This might explain the positive time trend in equation 4 of table 5.

13
findings of the logarithmic model.<sup>19</sup> Coefficients are positive and significant for both the number of loans (equation 1) and the number of conditions (equation 2). Furthermore, test statistics presented at the bottom of table 7 are reassuring. The p-values of the Hansen J statistic do not indicate reject the null that instruments are exogenous. The values reported for the Diff-in-Hansen test provide an indication whether the additional moment restrictions necessary for system GMM are met (Bond et al., 2001). With p-values of around 0.45 for both variables, we do not reject the null that the additional moment conditions are valid.

As a second robustness test, we employ 2SLS and estimate equation 2 in a crosssectional version of the data. With the panel dataset, we are limited to using mechanical instruments in GMM, because substantive instruments that significantly predict DPLs exhibit little or no time series variation. Moving to cross section data allows us to avoid that problem. The dependent variable here is the change in CPIA cluster A and B, and the endogenous regressor is the logarithm of the number of cumulative loans (or conditions), both measured over 1996 to 2008. In the first stage we instrument for number of DPLs (or conditions) with (the log of) population (in 1996) and the average fraction of the country’s key votes in the UNGA that are aligned with the votes of G-7 countries over the period 1995-2008 (Barro and Lee, 2005; Kilby, 2011). We expect larger countries, and allies of major donors, to receive more DPLs. We assume neither variable directly affects quality of economic policies; note population was not significant when added as a control variable to equation 3 of Table 5.

Results for OLS and 2SLS regressions are reported in tables 8 and 9. Equation 1, table 8 shows that the effect of loans on changes in policy quality is positive and statistically significant. Furthermore, the coefficient for initial level of policy quality is significantly negative, implying a regression toward the mean effect. Both the initial level of political rights and its change over the period are associated with improved policy quality<sup>20</sup> . This finding is consistent with Svensson (2003) and Heckelman and Knack (2008), but inconsistent with other studies suggesting that democratic institutions might actually hamper reform (see, e.g., Alesina and Drazen, 1991; Rodrik, 1996). Equations 2 and 3 present the results from 2SLS estimation. Equation 2 shows first-stage results. Population and UN voting are both highly significant predictors of more loans. The F-statistic of excluded instruments is 19.12, which indicates a strong association of our instruments with the receipt of World Bank DPLs. Furthermore, Wooldridge (1995)’s robust score test of overidentifying restrictions does not reject the null that the excluded instruments are exogenous to the quality of policy (test score = 0.21, p-value = 0.64). In equation 3, the exogenous effect of policy lending is reported. The coefficient on loans more than triples in comparison with its OLS counterpart, suggesting that the net effect of endogeneity bias was negative. The 2SLS regression confirms the regression toward the mean effect. In addition, both the initial income level and income growth now have a positive and significant effect on

> 19Other specifications generate similar results and are available upon request.

> 20“Political freedoms” varies from 1 (most democratic) to 7 (least democratic), so a negative coefficient implies that more political freedoms are associated with higher CPIA ratings. 14
changes in policy.

Table 9 presents the 2SLS results when the number of cumulative conditions is substituted for number of loans as the key regressor. Again, regression diagnostics support our identification strategy. The first-stage F-statistic is 27, and the p-value for the overidentification test is .906. As table 9 shows, findings are similar to results in table 8. The OLS coefficient on log of conditions is positive and highly significant (equation 1), but it nearly triples in magnitude when we instrument for conditions with initial population and UNGA voting. The coefficient on initial CPIA is again negative and statistically significant, implying that, on average, countries with greater initial policy quality tend to improve less over time. Furthermore, estimates suggest that increasing political rights improves economic policy. The 2SLS regression also confirms that economic policy improvements are associated with high initial income and income growth.

# **5. Summary and Concluding Remarks**

In this study we investigate the impact of World Bank policy loans on the quality of economic policy, correcting for several methodological problems and allowing for the possibility of increasing or decreasing returns to additional loans or conditions. We find that policy lending has a positive but diminishing effect on the quality of economic policy. Results are robust to sample restrictions, additional controls, the use of alternative indicators of the quality of economic policy, and correction for endogeneity with system GMM and cross-sectional 2SLS. Similar results are generally obtained when we substitute the number of cumulative conditions for the number of cumulative loans, although in this case no one functional form consistently best fits the data. There is some evidence for negative returns to additional conditions beyond some point, but the estimated inflection point is highly sensitive to the inclusion or exclusion of Argentina in the sample. The average number of conditions in DPLs declined from about 35 in the 1980s to about 12 by 2005, and our results provide some support for the Bank’s decision to make conditionality less onerous. Finally, we investigate the possibility of spillover effects on other policy areas, and show that investing in economic policy reform does not significantly affect policy quality for good or ill in the areas of public sector governance, and human development, social policy, and environmental policy.

Our main results are in contrast with most of the research examining the effectiveness of adjustment lending. Although there are many differences in data and methodology that could explain this discrepancy, four of them are particularly worthy of note. First, estimating the impact of development policy lending calls for a sound identification strategy. However, many of the early studies employed a before-after analysis or a with-without approach using strong but dubious assumptions. In contrast, our study relied on instrumental variables techniques to obtain identification. Second, our analysis distinguished – among the policy targets of DPLs many of them target sectoral policies, not economic policies. Failing to make this distinction can produce a downward bias in the estimated impact of lending on policy reform. In this respect, our study is similar in spirit to Clemens et al. (2012), who show that aid’s estimated impact on short-run growth strengthens when 15
humanitarian and other components of aid are excluded that are not intended to further short-run growth. Third, instead of looking at final outcome measures such as economic – – growth for which aid might only represent a weak signal (Roodman, 2007) we take as the dependent variable what World Bank country teams are attempting to achieve when they design DPLs, i.e., the quality of development policies. And finally, the time period under investigation is different. Most research evaluates the first two decades of adjustment lending. However, as mentioned in section 2 the practice of development policy lending evolved substantially over time, particularly since the end of the 1990s. The more positive results in our study suggest that the World Bank’s claims about learning from its mistakes with traditional adjustment lending have some validity.

# **Acknowledgement**

We would like to thank Vincenzo Verardi, Adam Wagstaff, Peter Moll, Patricia Geli and the seminar participants at the 2013 LAGV conference for useful comments and suggestions. Lodewijk is also indebted to the Institute of Development Policy and Management (IOB) and the Research Foundation Flanders (FWO) for financial support.

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18
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Table 1: sectoral distribution of all effective adjustment loans for the period 1980-2010

|sector|frequency|percentage|
|---|---|---|
|**Market Reform Loans**|||
|Economic Policy|450|44.91|
|Financial and Private Sector Development|121|12.08|
|Financial Sector|12|1.2|
|Private Sector Development|7|0.7|
|**Other DPLs**|||
|Agriculture and Rural Development|62|6.19|
|Education|29|2.89|
|Energy and Mining|46|4.59|
|Environment|14|1.4|
|Public Financial Management|1|0.1|
|Global Information/Communications Techn|2|0.2|
|Health, Nutrition and Population|8|0.8|
|Poverty Reduction|51|5.09|
|Public Sector Governance|127|12.67|
|Social Development|2|0.2|
|Social Protection|49|4.89|
|Transport|5|0.5|
|Urban Development|14|1.4|
|Water|2|0.2|
|Total|1,002|100|

22
Table 2: panel regression of CPIA clusters A and B average on cumulative loans

|equation no.|(1)|(2)|(3)|
|---|---|---|---|
|number of cumulative loans|.073|.134|.|
||(.023)<sup>_∗∗∗_</sup>|(.047)<sup>_∗∗∗_</sup>||
|number of cumulative loans (squared)|.|-.005|.|
|||(.003)<sup>_∗_</sup>||
|log of number of cumulative loans|.|.|.406|
||||(.112)<sup>_∗∗∗_</sup>|
|year|-.023|-.024|-.026|
||(.008)<sup>_∗∗∗_</sup>|(.008)<sup>_∗∗∗_</sup>|(.008)<sup>_∗∗∗_</sup>|
|log GDP per capita (PPP)|.805|.799|.814|
||(.152)<sup>_∗∗∗_</sup>|(.149)<sup>_∗∗∗_</sup>|(.148)<sup>_∗∗∗_</sup>|
|aid over GDP|1.618|1.577|1.512|
||(.531)<sup>_∗∗∗_</sup>|(.519)<sup>_∗∗∗_</sup>|(.515)<sup>_∗∗∗_</sup>|
|Political Rights|-.016|-.015|-.011|
||(.021)|(.021)|(.021)|
|_country fxed efects_|_yes_|_yes_|_yes_|
|Observations|1761|1761|1761|
|Countries|139|139|139|
|_R_<sup>2</sup>|.134|.139|.147|
|Adjusted _R_<sup>2</sup>|.131|.137|.144|
|AIC|1113.115|1103.17|1086.232|
|BIC|1140.483|1136.012|1113.601|

Note: * significance at 10%; ** significance at 5%; *** significance at 1%.

23
Table 3: panel regression of CPIA clusters A and B average on cumulative conditions

|equation no.|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|variation|quad.+Arg.|linear|quad|log|
|number of cumulative conditions|.010|.004|.007|.|
||(.003)<sup>_∗∗∗_</sup>|(.001)<sup>_∗∗∗_</sup>|(.003)<sup>_∗∗_</sup>||
|number of cumulative conditions (squared)|-.00003|.|-.00002|.|
||(.00001)<sup>_∗∗∗_</sup>||(.00001)||
|log of number of cumulative conditions|.|.|.|.111|
|||||(.061)<sup>_∗_</sup>|
|year|-.018|-.017|-.018|-.016|
||(.008)<sup>_∗∗_</sup>|(.008)<sup>_∗∗_</sup>|(.008)<sup>_∗∗_</sup>|(.008)<sup>_∗∗_</sup>|
|log GDP per capita (PPP)|.807|.792|.794|.816|
||(.155)<sup>_∗∗∗_</sup>|(.154)<sup>_∗∗∗_</sup>|(.154)<sup>_∗∗∗_</sup>|(.158)<sup>_∗∗∗_</sup>|
|aid over GDP|1.697|1.682|1.690|1.689|
||(.520)<sup>_∗∗∗_</sup>|(.535)<sup>_∗∗∗_</sup>|(.526)<sup>_∗∗∗_</sup>|(.521)<sup>_∗∗∗_</sup>|
|Political Rights|-.016|-.019|-.016|-.016|
||(.021)|(.021)|(.021)|(.021)|
|_country fxed efects_|_yes_|_yes_|_yes_|_yes_|
|Observations|1761|1748|1748|1748|
|Countries|139|138|138|138|
|_R_<sup>2</sup>|.14|.124|.127|.123|
|Adjusted _R_<sup>2</sup>|.137|.122|.124|.12|
|AIC|1102.59|1090.29|1085.58|1092.87|
|BIC|1135.43|1117.46|1118.38|1120.12|

Note: * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample for equations (2) through (4).

24
Table 4: spillover effects on other policy areas

|dependent variable|CPIA C|CPIA D|
|---|---|---|
|log of number of cumulative loans|.153<br>(.114)|.103<br>(.088)|
|cumulative conditions, linear spec.|.0005<br>(.001)|-.0003<br>(.001)|
||.001|-.0004|
|cumulative conditions, quadratic spec.|(.004)|(.004)|
||-.000003|.0000005|
||(.00002)|(.00002)|
|cumulative conditions, logarithmic spec.|.037<br>(.058)|-.024<br>(.052)|

Note: Regression results from estimating equation 1 with CPIA C and CPIA D as dependent variables. CPIA cluster C average measures the quality of policies for social inclusion and equity and CPIA cluster D average measures the quality of policies for public sector governance. Only coefficient estimates and clustered standard errors of loans and conditions variables reported. Argentina excluded from the sample when the number of conditions is used as variable of interest. When the number of loans is used as variable of interest, 1761 observations are used covering 139 countries. When the number of conditions is used as variable of interest, 1748 observations are used covering 138 countries.

25
Table 5: robustness tests: cumulative loans

|variation|DPL_>_0|closing year|controls|EFW|Heritage|
|---|---|---|---|---|---|
|equation no.|(1)|(2)|(3)|(4)|(5)|
|log of number of cumulative loans|.341|.444|.331|.394|2.623|
||(.115)<sup>_∗∗∗_</sup>|(.127)<sup>_∗∗∗_</sup>|(.129)<sup>_∗∗_</sup>|(.134)<sup>_∗∗∗_</sup>|(1.413)<sup>_∗_</sup>|
|year|-.018|-.039|-.036|.045|-.130|
||(.009)<sup>_∗∗_</sup>|(.011)<sup>_∗∗∗_</sup>|(.019)<sup>_∗_</sup>|(.009)<sup>_∗∗∗_</sup>|(.101)|
|log GDP per capita (PPP)|.727|.980|.899|.442|12.193|
||(.148)<sup>_∗∗∗_</sup>|(.260)<sup>_∗∗∗_</sup>|(.263)<sup>_∗∗∗_</sup>|(.204)<sup>_∗∗_</sup>|(2.566)<sup>_∗∗∗_</sup>|
|aid over GDP|1.564|1.458|2.019|1.961|-34.705|
||(.556)<sup>_∗∗∗_</sup>|(.576)<sup>_∗∗_</sup>|(.706)<sup>_∗∗∗_</sup>|(.973)<sup>_∗∗_</sup>|(11.410)<sup>_∗∗∗_</sup>|
|Political Rights|-.008|-.012|.010|-.079|-.751|
||(.021)|(.026)|(.021)|(.042)<sup>_∗_</sup>|(.358)<sup>_∗∗_</sup>|
|log of gross IDA|.|.|.077|.|.|
||||(.023)<sup>_∗∗∗_</sup>|||
|IMF arrangement|.|.|-.022|.|.|
||||(.025)|||
|debt service (% of GNI)|.|.|-.004<br>(.004)|.|.|
|log of population|.|.|.941|.|.|
||||(.734)|||
|lagged election|.|.|-.009|.|.|
||||(.019)|||
|election cycle|.|.|-.011|.|.|
||||(.014)|||
|election frequency|.|.|.008|.|.|
||||(.010)|||
|_country fxed efects_|_yes_|_yes_|_yes_|_yes_|_yes_|
|Observations|1443|1182|1235|1040|1607|
|Countries|114|121|105|95|132|
|_R_<sup>2</sup>|.143|.136|.191|.547|.195|

Note: Panel regression results from several robustness tests. Dependent variable: CPIA clusters A and B average. Variable of interest: (log of) number of cumulative market reform loans. * significance at 10%; ** significance at 5%; *** significance at 1%. Standard errors clustered by country.

26
Table 6: robustness tests: cumulative conditions

|robustness test|linear|quadratic|logarithmic|
|---|---|---|---|
|DPL_>_0|.002|.|.|
||(.001)<sup>_∗_</sup>|||
||.|.006<br>(.003)<sup>_∗_</sup>|.|
||.|-.00002<br>(.00002)|.076<br>(.060)|
|closing year|.004<br>(.001)<sup>_∗∗∗_</sup>|.|.|
||.|.008|.|
|||(.003)<sup>_∗∗_</sup>||
||.|-.00002|.119|
|||(.00002)|(.068)<sup>_∗_</sup>|
|controls|.005|.|.|
||(.002)<sup>_∗∗∗_</sup>|||
||.|.009|.|
|||(.004)<sup>_∗∗∗_</sup>||
||.|-.00002|.126|
|||(.00002)|(.078)|
|EFW|.007|.|.|
||(.002)<sup>_∗∗∗_</sup>|||
||.|.008|.|
|||(.004)<sup>_∗_</sup>||
||.|-.000006|.096|
|||(.00002)|(.057)<sup>_∗_</sup>|
|Heritage|.031|.|.|
||(.020)|||
||.|.127|.|
|||(.049)<sup>_∗∗∗_</sup>||
||.|-.0005|.757|
|||(.0002)<sup>_∗∗_</sup>|(.766)|

Note: Panel regression results from several robustness tests. Dependent variable: CPIA clusters A and B average. Variable of interest: number of cumulative market reform conditions. Only coefficient estimates and clustered standard errors of conditions variable are reported. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample. Taking into account the exclusion of Argentina, observations and number of countries are similar to table 5.

27
Table 7: System GMM

|equation no.|(1)|(2)|
|---|---|---|
|log of number of cumulative loans|.194<br>(.091)<sup>_∗∗_</sup>|.|
|log of number of cumulative conditions|.|.090|
|||(.043)<sup>_∗∗_</sup>|
|log GDP per capita (PPP)|.221|.219|
||(.063)<sup>_∗∗∗_</sup>|(.062)<sup>_∗∗∗_</sup>|
|aid over GDP|-1.089|-.947|
||(.528)<sup>_∗∗_</sup>|(.572)<sup>_∗_</sup>|
|Political Rights|-.104|-.106|
||(.027)<sup>_∗∗∗_</sup>|(.027)<sup>_∗∗∗_</sup>|
|_country fxed efects_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|
|Observations|1761|1748|
|Countries|139|138|
|Number of instruments|44|44|
|Wald statistic|159.88|153.02|
|_p-value_|0.0001|0.0001|
|Hansen J-test|23.10|25.01|
|_p-value_|0.627|0.518|
|Dif-in-Hansen test|13.99|13.89|
|_p-value_|0.45|0.451|

Note: Dependent variable: CPIA cluster A and B average. For equation (1), the log of the number of cumulative loans is the variable of interest. For equation (2), the log of the number of cumulative conditions is the variable of interest. Cluster-robust standard errors are reported. Coefficients estimated with forward orthogonal deviations and level equations for IV style instruments. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample when the number of conditions is used as variable of interest.

28
Table 8: cross-sectional 2SLS number of loans

|equation no.|(1)|(2)|(3)|
|---|---|---|---|
||OLS|First stage|Second Stage|
|log of cumulative loans 1996-2008|.224|.|.770|
||(.065)<sup>_∗∗∗_</sup>||(.175)<sup>_∗∗∗_</sup>|
|CPIA 1996|-.586|.061|-.636|
||(.063)<sup>_∗∗∗_</sup>|(.088)|(.075)<sup>_∗∗∗_</sup>|
|average annual GDP per capita growth|.016|.0002|.018|
||(.009)<sup>_∗_</sup>|(.005)|(.008)<sup>_∗∗_</sup>|
|log of GDP per capita 1996|.076|-.210|.256|
||(.056)|(.079)<sup>_∗∗∗_</sup>|(.087)<sup>_∗∗∗_</sup>|
|ethnic fractionalization|.124|.013|.068|
||(.176)|(.228)|(.205)|
|Political Rights 1996|-.058|-.056|-.011|
||(.029)<sup>_∗∗_</sup>|(.040)|(.038)|
|change in Political Rights|-.102|-.071|-.043|
||(.033)<sup>_∗∗∗_</sup>|(.047)|(.042)|
|average annual aid over GDP|-1.635|1.840|.386|
||(1.081)|(1.969)|(1.295)|
|log of 1996 population|.|.160|.|
|||(.041)<sup>_∗∗∗_</sup>||
|average fraction of votes with G-7|.|.896|.|
|||(.358)<sup>_∗∗_</sup>||
|No. observations|126|126|126|
|_R_<sup>2</sup>|.552|.347|.261|
|F test of excluded instruments|.|19.1276|.|
|_p-value_|.|0.00001|.|
|test of endogeneity|.|.|13.2256|
|_p-value_|.|.|0.0003|
|Overidentifcation test|.|.|0.2125|
|_p-value_|.|.|0.6449|

Note: Dependent variable is the change in policy quality over the period 1996-2008, as measured by the CPIA cluster A and B average. Robust standard errors in parentheses. * significance at 10%; ** significance at 5%; *** significance at 1%.

29
Table 9: cross-sectional 2SLS number of conditions

|equation no.|(1)|(2)|(3)|
|---|---|---|---|
||OLS|First stage|Second Stage|
|log of cumulative conditions 1996-2008|.097|.|.274|
||(.027)<sup>_∗∗∗_</sup>||(.057)<sup>_∗∗∗_</sup>|
|CPIA 1996|-.588|.146|-.630|
||(.062)<sup>_∗∗∗_</sup>|(.220)|(.068)<sup>_∗∗∗_</sup>|
|average annual GDP per capita growth|.016|.003|.017|
||(.009)<sup>_∗_</sup>|(.012)|(.007)<sup>_∗∗_</sup>|
|log of GDP per capita 1996|.047|-.160|.129|
||(.053)|(.195)|(.066)<sup>_∗∗_</sup>|
|ethnic fractionalization|.120|.086|.070|
||(.177)|(.571)|(.200)|
|Political Rights 1996|-.063|-.048|-.035|
||(.029)<sup>_∗∗_</sup>|(.093)|(.033)|
|change in Political Rights|-.118|.031|-.103|
||(.033)<sup>_∗∗∗_</sup>|(.112)|(.037)<sup>_∗∗∗_</sup>|
|average annual aid over GDP|-1.706|7.420|-.316|
||(1.126)|(5.021)|(1.289)|
|log of 1996 population|.|.419<br>(.096)<sup>_∗∗∗_</sup>|.|
|average fraction of votes with G-7|.|2.963|.|
|||(.847)<sup>_∗∗∗_</sup>||
|No. observations|126|126|126|
|_R_<sup>2</sup>|.56|.331|.368|
|F test of excluded instruments|.|27.0558|.|
|_p-value_|.|0.00001|.|
|test of endogeneity|.|.|12.8265|
|_p-value_|.|.|0.0003|
|Overidentifcation test|.|.|.141|
|_p-value_|.|.|0.906|

Note: Dependent variable is the change in policy quality over the period 1996-2008, as measured by the CPIA cluster A and B average. Robust standard errors in parentheses. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample.

30
# **Appendices**

# **Appendix A. Descriptive Statistics, variable definitions and sources**

Table A.1: Summary statistics panel model

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|
|CPIA cluster A and B average|3.613|0.73|1|5.850|
|CPIA cluster C|3.422|0.700|1|6|
|CPIA cluster D|3.226|0.717|1|5.5|
|EFW|6.313|1.026|2.027|8.932|
|Heritage|60.09|9.591|20.833|88.183|
|number of cumulative loans|2.86|2.763|0|16|
|number of cumulative conditions|50.368|49.784|0|210|
|year|2001.646|3.883|1995|2008|
|log GDP per capita (PPP)|7.981|1.018|5.076|10.352|
|aid over GDP|0.042|0.064|-0.019|0.806|
|Political Rights|3.777|1.989|1|7|
|log of gross IDA (current million USD)|2.31|2.26|0|8.311|
|arrangement with IMF|0.15|0.357|0|1|
|debt service (% of GNI)|5.021|6.28|0.053|138.888|
|log of population|15.926|1.756|11.515|20.854|
|election|0.194|0.396|0|1|
|election cycle|1.841|2.513|0|24|
|election frequency|5.151|2.202|1|22|

31
Table A.2: Summary statistics cross-sectional model

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|
|change in CPIA cluster A and B|0.046|0.661|-1.731|1.819|
|log of cumulative loans 1996-2008|.8286652|.7132909|0|2.30259|
|log of cumulative conditions 1996-2008|2.091848|1.694547|0|4.82831|
|CPIA A and B 1996|3.699|0.856|1|5.231|
|average annual GDP per capita growth|4.770|8.168|-2.486|82.035|
|log of GDP per capita 1996|6.834|1.168|4.191|9.031|
|ethnic fractionalization|0.473|0.252|0|0.930|
|Political Rights 1996|3.738|2.06|1|7|
|change in Political Rights|-0.079|1.312|-5|3|
|average annual aid over GDP|0.035|0.045|0|0.277|
|log of population 1996|15.588|1.958|10.618|20.92|
|average fraction of votes with G-7|0.478|0.203|0.113|0.943|

32
|Bank<br> Bank<br> Bank<br> Bank<br>raser Institute<br>eritage Foundation<br> Bank<br> Bank<br> Bank<br> Development Indicators<br> on WDI<br>omhouse<br> on WDI<br> and Sturm (2011)<br>et al. (2001)<br> on Beck et al. (2001)<br> on Beck et al. (2001)<br> on Beck et al. (2001)<br>a et al. (2003)<br>r and Sturm (2012)|
|---|
|source<br>World<br>World<br>World<br>World<br>The F<br>the H<br>World<br>World<br>World<br>World<br>based <br>Freed<br>based <br>Moser<br>WDI<br>WDI<br>Beck <br>based <br>based <br>based <br>Alesin<br>Drehe|
|defnition<br>assessment of the quality of a country’s economic management<br>assessment of the quality of a country’s structural policies<br>assessment of the quality of a country’s policies for social inclusion<br>assessment of the quality of a country’s public sector management and<br>institutions<br>average of Economic Freedom of the World Index area 1, 3, 4 and 5<br>Index of Economic Freedom based on trade freedom, business freedom<br>and investment freedom<br>cumulative count of loans with sector codes corresponding to CPIA clus-<br>ter A and B<br>cumulative counts of conditions with theme codes corresponding to CPIA<br>cluster A and B<br>year trend<br>logarithm of GDP per capita, PPP<br>Net ODA and ofcial aid over GDP (international $)<br>measure for Political Rights<br>logarithm of one added to gross IDA disbursements, current million USD<br>dummy coded 1 if an IMF arrangement was signed<br>Total debt service, as % of GNI<br>logarithm of population<br>dummy coded 1 if an election occurred at t+1<br>number of years that separate year t from the nearest election<br>number of years between election in year t and the previous election<br>average annual per capita growth over the period 1996-2008<br>measure for ethnic fractionalization<br>average fraction of votes on key issues aligned with the G-7 over the<br>period 1995-2008<br>Table A.3: variable defnitions and sources|
|uster A<br>uster B<br>uster C<br>uster D<br><br> of cumulative loans<br> of cumulative conditions<br>DP per capita<br> GDP<br> Rights<br>oss IDA<br>ment with IMF<br>vice<br>pulation<br> cycle<br> frequency<br>annual per capita growth<br>actionalization<br>fraction of votes with G-7|
|variable<br>CPIA cl<br>CPIA cl<br>CPIA cl<br>CPIA cl<br>EFW<br>Heritage<br>number <br>number <br>year<br>log of G<br>aid over <br>Political<br>log of gr<br>arrange<br>debt ser<br>log of po<br>election<br>election <br>election <br>average <br>ethnic fr<br>average <br>33|
# **Appendix B. Country Policy and Institutional Assessment**

The CPIA scores are designed to measure government policies and institutions, rather than outcomes. The set of criteria are revised periodically to reflect changes in the collective knowledge of practitioners and specialists - both inside and outside the World Bank – regarding policies and public sector management institutions that matter for these outcomes. The criteria are grouped into 4 “clusters” as follows:

- A. Economic Management

1. Macroeconomic Management

2. Fiscal Policy 3. Debt Policy

- B. Structural Policies

4. Trade 5. Financial Sector 6. Business Regulatory Environment

- C. Policies for Social Inclusion/Equity

7. Gender Equality 8. Equity of Public Resource Use 9. Building Human Resources 10. Social Protection and Labor 11. Policies and Institutions for Environmental Sustainability

- D. Public Sector Management and Institutions

12. Property Rights and Rule-based Governance 13. Quality of Budgetary and Financial Management 14. Efficiency of Revenue Mobilization 15. Quality of Public Administration 16. Transparency, Accountability, and Corruption in the Public Sector For each criterion, countries are rated on a scale of 1 (low) to 6 (high). A 1 rating corresponds to a very weak performance, and a 6 rating to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5 and 5.5 may also be given. For the years 1995-1997, countries were rated on a scale of 1 to 5. Scores have been rescaled for this research to a scale of 1 to 6. See OPCS (2009) for a detailed elaboration of the scoring procedure.

34
# **Appendix C. Additional System GMM regression**

Table C.1: Additional System GMM regression

|equation no.|(1)|(2)|
|---|---|---|
|log of number of cumulative loans|.389|.|
||(.10)<sup>_∗∗∗_</sup>||
|log of number of cumulative conditions|.|.146|
|||(.034)<sup>_∗∗∗_</sup>|
|log GDP per capita (PPP)|.278|.251|
||(.054)<sup>_∗∗∗_</sup>|(.053)<sup>_∗∗∗_</sup>|
|aid over GDP|-.485|-.481|
||(.524)|(.510)|
|Political Rights|-.089|-.099|
||(.025)<sup>_∗∗∗_</sup>|(.026)<sup>_∗∗∗_</sup>|
|_country fxed efects_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|
|Observations|1761|1748|
|Number of instruments|24|24|
|Wald statistic|194.05|178.86|
|_p-value_|0.0001|0.0001|
|Hansen J-test|7.31|5.77|
|_p-value_|0.293|0.449|
|Dif-in-Hansen test|0.66|2.73|
|_p-value_|0.417|0.10|

Note: cluster-robust standard errors are reported. Coefficients estimated with forward orthogonal deviations and level equations for IV style instruments. Collapsed instrument matrix. Lags 5 to 10 used for loans and 10 to 15 for conditions. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample when the number of conditions is used as variable of interest.

35