WPS6130 Policy Research Working Paper 6130 Political Ideology, Quality at Entry and the Success of Economic Reform Programs Lodewijk Smets Stephen Knack Nadia Molenaers The World Bank Development Research Group Human Development and Public Services Team July 2012 Policy Research Working Paper 6130 Abstract This study investigates how government ideology matters measures of ideology, different estimation techniques and for the success of World Bank economic policy loans, the inclusion of additional control variables. Next, robust which typically support market-liberalizing reforms. A findings from estimating a recursive triangular system simple model predicts that World Bank staff will invest of equations indicate that leftist governments comply more effort in designing an economic policy loan when more fully with loan agreements. Results also suggest that faced with a left-wing government. Empirically, estimates World Bank resources are more productive—in terms of from a Heckman selection model show that the quality reform success—in the design of policy operations than at entry of an economic policy loan is significantly higher in their supervision. Anecdotal evidence from several for governments with a left-wing party orientation. This country cases is consistent with the finding that left-wing result is robust to changes in the sample, alternative governments receive higher quality loans. 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 author 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 Political ideology, quality at entry and the success of economic reform programs Lodewijk Smetsa,∗, Stephen Knackb , Nadia Molenaersa a Institute of Development Policy and Management, University of Antwerp, Belgium b World Bank, Washington DC Abstract This study investigates how government ideology matters for the success of World Bank economic policy loans, which typically support market-liberalizing reforms. A simple model predicts that World Bank staff will invest more effort in designing an economic policy loan when faced with a left-wing government. Empirically, estimates from a Heckman selection model show that the quality at entry of an economic policy loan is signiï¬?cantly higher for governments with a left-wing party orientation. This result is robust to changes in the sample, alternative measures of ideology, different estimation techniques and the inclusion of additional control variables. Next, robust ï¬?ndings from estimating a recursive triangular system of equations indicate that leftist governments comply more fully with loan agreements. Results also suggest that World Bank resources are more productive – in terms of reform success – in the design of policy operations than in their supervision. Anecdotal evidence from several country cases is consistent with the ï¬?nding that left-wing governments receive higher quality loans. Keywords: development policy lending, World Bank, political ideology, Heckman selection model, triangular system of equations 1. Introduction In 1980 the World Bank launched its ï¬?rst non-project lending instrument to support policy change in recipient countries. At that time, top management was dissatisï¬?ed with the limited influence of the Bank’s normal project lending on policies of borrowing gov- ernments. Therefore structural adjustment lending was conceived, a new lending program with which the Bank would help countries to tackle important policy deï¬?ciencies. In its early years adjustment lending mainly emphasized economic stabilization and correction of balance of payments distortions (Kapur et al., 1997). These loans, now called development ∗ Corresponding author Email addresses: lodewijk.smets@ua.ac.be (Lodewijk Smets), sknack@worldbank.org (Stephen Knack), nadia.molenaers@ua.ac.be (Nadia Molenaers) policy loans (DPLs), have become an important component in the ï¬?nancing of develop- ment operations. For instance, in ï¬?scal year 2008 they accounted for 6.6 billion USD or 27 percent of total World Bank commitments. Currently, policy lending covers a wide range of thematic areas, from economic policy to public sector governance. In this study we investigate whether and how the ideology of the ruling party in govern- ment matters for the success of World Bank economic policy loans. In contrast with other studies on the performance of policy based lending, we consider both donor and recipient factors at four different stages of the lending process: program quality at entry, World Bank supervision during implementation, borrower compliance, and the overall outcome of the program. We speciï¬?cally focus on the World Bank effort in ensuring quality at entry. We begin by reviewing the related literature on the impact of ideology on economic policy choice, and on the relevant strands of research investigating World Bank lending decisions and their outcomes. In Section 3 we present a simple model predicting that World Bank staff exert more effort on average when faced with a left-wing government, which (we assume) tends to be more skeptical of the beneï¬?ts of market-liberalizing reforms supported by economic policy loans. This prediction is empirically tested in section 4 where we estimate a Heckman selection model using a dataset of 182 (uncensored) observations running from 1985 until 2008. We ï¬?nd robust evidence that the quality at entry of economic policy loans is signiï¬?cantly higher for countries with left-wing governments. Incumbent tenure is also positively associated with quality at entry. Geopolitical motives also appear to matter, as countries voting in line with the G-7 in the UN General Assembly (UNGA) tend to receive more and higher-quality programs. In the selection model voting congruence is associated with a higher likelihood of receiving an economic policy loan. Moreover, we ï¬?nd evidence that the loans these countries receive are rated more highly on quality at entry. In Section 4 we also examine how political ideology and quality at entry affect the other stages of an economic policy loan. Estimating a recursive triangular system of regression equations, we ï¬?nd that left-wing governments tend to comply more fully with loan arrangements. Looking at overall outcomes of reform programs, we ï¬?nd that short- term growth and initial macroeconomic conditions influence reform success. In addition to these recipient country variables, quality at entry and World Bank supervision also matter. Higher quality at entry is positively associated with ratings on quality of supervision, borrower compliance and overall outcome success. To a lesser extent, supervision ratings are positively related to reform success. In Section 5 we discuss several illustrative country cases that are consistent with the argument that World Bank staff need to invest more effort preparing and negotiating an economic policy loan when working with left-wing governments. Evidence on these country cases was gathered through structured interviews with World Bank team task leaders who managed economic policy loans in Mozambique, Zambia, Mexico and Malawi1 . The Mozambican case points to cognitive processes and attitude change – in addition to informational asymmetries and political economy considerations – as a way of explaining increased compliance by left-wing borrowers. Finally, Section 6 summarizes and concludes. 1 Interviews were conducted in August 2011 at the World Bank Headquarters in Washington D.C. 2 2. Related Literature In examining the effect of ideology on reform, our study is related to the political econ- omy of policy change. Much work in this ï¬?eld2 investigates the impact of party orientation on economic policy choice. Predictions range from equilibria where parties are locked in their ideological position (e.g. Glazer and Grofman, 1989) to studies indicating that in certain circumstances policy switches occur, i.e. the implementation of policies by parties whose ideological position runs against such policies. For instance, Cukierman and Tom- masi (1998) show that under certain conditions a left-wing government is more likely to implement a right-wing reform since the public has less reason to suspect that ideological preferences are determining policy choice. The empirical literature3 on the association between political ideology and economic outcomes concerns mainly – but not exclusively – developed countries. Most ï¬?ndings are consistent with the view that governments pursue policies in line with their ideological preferences. If we consider economic ideology as an interconnected set of beliefs on the functioning of the economy, this paper is also associated with the growing body of literature that links (cognitive) characteristics of political leaders with economic and policy outcomes. For instance, Jones and Olken (2005) ï¬?nd that unexpected changes in leadership affect monetary policy as well as economic growth rates. Besley et al. (2011) provide evidence that more educated leaders are associated with higher growth rates. Dreher et al. (2009a) show that market-liberalizing economic reforms are more likely to occur during the tenure of a chief executive who is a former entrepreneur - particularly if he or she heads a left-wing government - suggesting that personal capabilities matter for reform success. Meseguer (2006) empirically demonstrates that country leaders learn from others as well as past experience in determining economic policy. Chai (1998) offers an interesting insight in how beliefs affect economic policy choice in developing countries by relating the formation of ideology to cognitive dissonance4 . He argues that in Western colonies indigenous pro- independence elites internalized oppositional ideology – i.e. socialism – as a way of reducing cognitive dissonance aroused by engaging in conflict with colonial rulers. By investigating the determinants of successful development policy operations, this paper is also related to the aid effectiveness literature. Several authors have pointed out 2 A number of interesting studies in this respect are Wittman (1983), Alesina (1988), Glazer and Grof- man (1989), Alesina and Cukierman (1990), Roemer (1997), Roemer (1999), Terai (2006). Both Drazen (2000) and Persson and Tabellini (2000) provide an extensive overview on the political economy of economic reform. 3 A partial listing includes Beck (1982), Bjornskov (2005), Bjornskov (2008), Bortolotti and Pinotti (2008), Dutt and Mitra (2005), Dutt and Mitra (2006) Frey and Schneider (1978), Hibbs (1977), Pitlik (2007), Potrafke (2010). 4 According to the theory of cognitive dissonance, a person who behaves in a way contrary to his beliefs is motivated by resulting discomfort or anxiety to reduce this “cognitive dissonanceâ€?, e.g. by changing his beliefs. Cognitive dissonance theory has been widely applied in social science research. For applications in the ï¬?eld of economics, see among other Akerlof and Dickens (1982) and Gilad et al. (1987). Hirschman (1965) and James and Gutkind (1985) have applied it to claim that the coercive nature of conditional aid hinders reform. 3 that without the commitment of the recipient country the implementation of conditional aid programs is bound to fail (see among other Dijkstra, 2002; Killick, 1997; Murshed, 2009; Svensson, 2000, 2003). With a more speciï¬?c focus on recipient country characteristics, Dollar and Svensson (2000) assert that successful reform is associated with democratically elected governments and non-linearly with the time an incumbent has been in power and ethnic fractionalization. Noorbakhsh and Paloni (2007) provide evidence that initial macro- economic conditions and economic performance during the reform program influence the probability of compliance with World Bank conditionality. In contrast with Dollar and Svensson (2000), Malesa and Silarszky (2005) claim that factors under donor control also matter, and ï¬?nd that devoting more resources to the preparation of a development policy loan raises the probability of success. Note that the ï¬?ndings on the determinants of DPL success are generally conï¬?rmed by the literature investigating the success factors of all World Bank operations, whether investment projects or adjustment programs. For an overview of that literature, see Denizer et al. (2011). Finally, another related literature examines the influence of powerful donors on World Bank decision making. For instance, Dreher et al. (2009b) demonstrate that countries with a temporary seat in the UN Security Council receive a higher number of World Bank projects on average. Kilby (2011) ï¬?nds that project preparation time tends to be shorter for projects in strategically important countries, potentially at the expense of project quality. Furthermore, Andersen et al. (2006) ï¬?nd evidence that IDA countries voting in line with the U.S. in the UN General Assembly receive higher aid volumes on average. Similarly Kilby (2009) asserts that DPL disbursements are less dependent on macroeconomic outcomes for countries ‘friendly’ with the United States. The above ï¬?ndings suggest that important donors use the Bank’s resources to seek support of strategically relevant countries. In the next section we elaborate on the design process of policy lending and provide a simple formalization on how political ideology affects the quality at entry of economic reform programs. 3. A Simple Model In the case of World Bank development policy lending, the extension of a loan is pre- ceded by a process of identiï¬?cation, preparation, appraisal and negotiation. At the start of a development policy operation a consultation round is held where the IMF, relevant Bank units, key donors and the concerned regional development bank discuss the policy objectives of the borrowing country with a World Bank task team. Afterwards a concept document is drafted explaining the rationale for the proposed operation. The program out- line is discussed with recipient government officials and after an internal review a program document is prepared. It sets out the objectives of the policy loan in terms of the speciï¬?c outcomes and design provisions. During an appraisal mission, the task team assesses the adequacy of the proposed program to achieve its stated objectives. Afterwards World Bank staff sit together with recipient government officials to discuss and negotiate the different loan modalities. It is only after the representatives of the borrower have signed the loan agreement that a development policy loan becomes effective. During the implementation 4 process Bank staff supervise the progress of the operation by preparing Implementation Supervision Reports on a frequent basis5 . Within six months after completion of the op- eration an initial evaluation takes place. In this section we adapt the model from Wane (2004) to formalize the design process of an economic policy loan. Wane showed in a two-period setup that the quality at entry of a project is endogenous to the staff incentive system in the World Bank on the one hand and the government’s capacity – i.e. its ability to screen projects – and accountability on the other. In adapting this model we abstract from government capacity and electoral accountability and focus instead on another characteristic of government, namely economic ideology. Speciï¬?cally, we assume that ruling parties and executives associated with the political “leftâ€? are more skeptical (compared to those further to the “rightâ€?) of the beneï¬?ts of market-liberalizing reforms supported by economic policy loans. Our model, like Wane’s, is based on a two-period game where a World Bank staff team is matched6 with a government with reservation utility U i . In the ï¬?rst period the staff team designs the economic reform program which the recipient country can accept or decline. In the second period the staff’s promotion is decided upon and the recipient country implements the program, if accepted. We model the borrowing government to be one of two different types, L or R. The government will sign the loan agreement if the net beneï¬?t of the program, Ï€(e), is at least as high as the government’s reservation utility U i , with i = L, R. We assume that the reservation utility for a government of type i is drawn from a Normal distribution, i.e. U L ∼ N (µL , σ) and U R ∼ N (µR , σ) with E[U L ] > E[U R ] > 0 (3.1) According to 3.1, governments of type L have on average a higher reservation utility for accepting the economic reform program. This is interpreted as reflecting their policy preferences/beliefs towards the adjustment operation. Intuitively, World Bank staff must work harder to “sellâ€? an economic policy loan to government officials that tend to be more skeptical regarding the likelihood of favorable impacts from market-liberalizing reforms. By exerting costly effort e in designing the program, World Bank staff is able to affect the net beneï¬?t of the DPL: Ï€(e) = g(e).(B − C) (3.2) with 0 ≤ e ≤ 1, g(0) = 0, g(1) = 1 and g(e) an invertible and concave function on the interval [0, 1]. We model the incentive system for promotion of World Bank staff based on 5 For multi-tranche operations a Tranche Release Document is prepared on the basis of which the decision is taken to release subsequent tranches. 6 A DPL task team leader interviewed for this study suggested that matching World Bank staff with governments might not be random; for example, more capable staff members might be assigned to work with more ‘difficult’ governments. On the other hand, to the extent higher-quality staff have more leverage in choosing their assignments, they might opt to work with less difficult governments, where their advice is more welcome. Data limitations prevent us from measuring staff quality. Denizer et al. (2011) control for the impact of task leader ï¬?xed effects on project success, but our sample of policy loans is too small to implement this approach. 5 acceptance of a loan agreement, i.e. staff will receive a salary7 S > s in period 2 if: g(e∗ ).(B − C) ≥ U i (3.3) The salary increase is assumed to be high enough for the staff to be willing to exert maximum effort if it entails promotion8 . Following Wane (2004), the staff team aims for promotion and maximizes: max{u(s) + δ[u(S).I(g(e).(B − C) ≥ U i ) + u(s).(1 − I(g(e).(B − C) ≥ U i )] − ψ(e)} (3.4) e with u(.) a concave utility function, s the entry salary, δ a discount rate, I(.) a 0-1 indicator function and ψ(.) a convex function representing the disutility from exerting effort. Based on 3.2, 3.3 and 3.4 the staff team exerts effort e∗ in equilibrium: U ∗ g −1 ( B−C ) if (B − C) ≥ U i > 0 i e = (3.5) 0 if (B − C) < U i or U i ≤ 0 Proposition 1. On average, World Bank staff exerts higher effort in designing a program when faced with governments of type L for which (B − C) ≥ U i . Proof. This result follows directly from 3.1 and 3.5. Note that when (B − C) < U i World Bank staff exerts 0 effort and the government declines the program9 . In other words, proposition 1 states that, for all accepted economic policy loans, left-wing governments on average end up with higher quality programs. In the next section we test this prediction. 4. Statistical Testing 4.1. Models and data To assess the effect of political ideology on quality at entry of economic policy loans, we ï¬?rst estimate the following selection equation (Heckman, 1979): ∗ P (zi,t > 0) = Φ(γ wi,t ) (4.1) ∗ with zi,t the latent selection variable, wi,t a vector of factors influencing the access to a DPL and Φ(.) the standard normal distribution. As selection covariates10 we have included 7 The salary incentive can be generalized to non-pecuniary sources of utility, such as the utility World Bank staff receive from continued policy dialogue with high-level government officials. Arguably, the policy dialogue with a left-wing government would be damaged more by a failed policy reform program, so staff have an added incentive to design a higher-quality loan. 8 This assumption holds if u(s) − δ.u(S) − ψ(1) > 0. 9 A case in point is post-apartheid South Africa, where the COSATU-backed ANC government for a long time disagreed with the World Bank’s lending proposals. 10 We have also included party orientation as a selection variable, yet it did not turn out to influence receipt of DPL. 6 a variable indicating the extent to which a country voted in line with the G-7 on key issues the year prior to selection, the inflation rate the year before selection into the program, the logartihm of outstanding World Bank debt the year before the program became effective, the overall CPIA score11 , the logarithm of population, GDP per capita. As an additional exclusion restriction - so we are not relying only on nonlinearity of the selection model for identiï¬?cation - we use distance of the country’s capital to the World Bank’s headquarters in Washington D.C. Distance is commonly found to affect aid Stromberg (2007) and has been used as an exogenous instrument for aid by Chauvet et al. (2006) and Knack (2012) among others. The coefficients of equation 4.1 were obtained by maximum likelihood estimation using dataset running from 1985 until 2008 that contains 1966 censored and 182 uncensored observations. The selection variable zi,t is coded 1 if country i received an economic policy loan in year t. World Bank DPLs also support reform in other policy areas, but we exclude them from the analysis as they are less strongly identiï¬?ed than economic policy with left-right ideological distinctions. Speciï¬?cally, we examined the sectoral codings of DPLs and retained economic policy, private sector development and ï¬?nancial sector loans, while dropping loans in other sectors such as education or health. The analysis thus focuses on reform programs targeting economic policy issues such as privatization, macroeconomic management, debt reduction and ï¬?nancial and private sector development, i.e. the so- called “Washington Consensusâ€? set of policies (Williamson, 1994). In robustness tests, we both expanded the sample to include loans in other policy areas, and, alternatively, restricted it to include only economic policy loans. The outcome equation is speciï¬?ed as: E[qi,t |zi,t = 1] = x β + Ï?σe λ(γ w) (4.2) with qi,t the quality at entry, x a vector of covariates and λ the non-selection hazard com- puted as φ(ˆ wi,t )/Φ(ˆ wi,t ). The dependent variable of the outcome equation, quality at entry, γ γ is derived from the assessment of the World Bank’s Independent Evaluation Group (IEG). The IEG rates performance at each stage of a program – Bank quality at entry, Bank supervision, borrower compliance and overall outcome – into one of six categories, rang- ing from highly unsatisfactory to highly satisfactory12 . This provides us with a six-point ordered indicator for quality at entry (see table 13 in Appendix for a detailed deï¬?nition 11 The Country Policy and Institutional Assessments (CPIA) are subjective ratings of 16 indicators updated annually by World Bank staff, and used for allocating IDA loans. 12 Although the validity of these measures might be questioned, we follow the convincing argument of Dollar and Svensson (2000, pp. 897-899) and Denizer et al. (2011, pp. 7-10) as to why the IEG outcome assessments are acceptable measures of program success. The following arguments have been put forth: (i) the IEG is independent of the Bank’s senior management; (ii) the outcome variables are highly correlated with improvements in observed economic performance; (iii) IEG ratings do not signiï¬?cantly differ from the more in-depth and detailed “Project Performance Audit Reportsâ€?. Unfortunately, the latter ratings are available only for a very limited sample of DPLs. While there surely is still remaining measurement error, there is no reason to believe the IEG ratings are biased, particularly with respect to government ideology. This view is supported by interviews of experienced IEG staff members. 7 of the IEG ratings). From this information a binary indicator of program quality can be constructed, to ensure that ï¬?ndings are not overly sensitive to subjective and possible un- reliable distinctions between (for example) “satisfactoryâ€?, “moderately satisfactoryâ€? and “highly satisfactoryâ€?. The key independent variable in the outcome equation is the executive party’s orienta- tion with respect to economic policy. As a source we have used the Database of Political Institutions (DPI) (Beck et al., 2001). The authors identify the party orientation either as “Leftâ€?, “Centerâ€?, “Rightâ€? or “Other/No informationâ€? – coded respectively as 3, 2, 1 and 0 – based on different criteria including cross-checking with multiple sources. Using this variable we created a dummy coded one if the party orientation of the executive party was rated as left-wing and zero for right and center governments. As a robustness test we also considered the three-point categorical version as well as an index of political ideology created by Bjornskov (2005). Other political covariates include a dummy for democracy and the time the incumbent has been in power. As a proxy for political favoritism, we have added a variable indicating whether the country voted in line with the G-7 in the UNGA on key issues the year prior to selection into the program13 . We include the log of the loan amount to control for any effect of loan size on program quality. Finally, we included a time trend, the log of population, the log of GDP per capita and regional dummies as supplementary controls. As a robustness test we added the current account balance, inflation rate, ethnic fractionalization, and CPIA score at the start of the program as ad- ditional controls. The coefficients of equation 4.2 were estimated with OLS and standard errors were adjusted for country clustering of observations. Variable deï¬?nitions, sources and summary statistics are found in the Appendix. In order to test how party orientation and quality at entry matter at the other stages of an economic policy loan, we have estimated a recursive14 triangular system of regression equations:  y1 = qδ1 + x β1 + 1  y = y1 β12 + qδ2 + x β2 + 2 (4.3)  2 y3 = y1 β13 + y2 β23 + qδ3 + x β3 + 3  with q quality at entry, y1 quality of loan supervision, y2 borrower compliance and y3 overall outcome of the program15 . Next to the abovementioned covariates, we followed Noorbakhsh and Paloni (2007) and included the growth rate the year after the loan agreement was 13 Following Dreher and Sturm (2012), we consider the G-7 as a country group. The variable reflects the average G-7 vote by weighing each G-7 countries’ vote with its quota in the IMF. 14 For the triangular model to be fully recursive, we assume a diagonal variance covariance matrix Σ. It is conceivable that this assumption is violated as an omitted variable may simultaneously affect the left-hand side variables. However, a likelihood ratio test of uncorrelated errors yields a χ2 statistic of 2.87 with corresponding p-value of 0.4123. 15 Triangular systems assume a certain ordering of endogenous variabes in a sense that the ith equation may contain endogenous variables whose position is lower than i (Lahiri and Schmidt, 1978). As the World Bank formulates monitoring and evaluation arrangements in the preparation stage of the program, they signal their supervision intentions before the start of the program. Next, part of the supervision effort includes an evaluative review to identify problems during implementation and recommendations to resolve 8 signed, to test whether initial growth affects our outcome measures. We included the estimated nonselection hazard as an additional control. The coefficients of model 4.3 were estimated with maximum likelihood using a dataset of 161 observations running from 1985 until 2007. Standard errors were adjusted for country clustering of observations. In the remainder of this section we present our empirical ï¬?ndings. 4.2. Political ideology and quality at entry In table 1, the coefficients of most included selection covariates come in signiï¬?cantly when estimating equation 4.1. Countries voting in line with the G7 have a higher proba- bility of receiving World Bank policy lending. This ï¬?nding is consistent with the literature on donor influence in international organizations. Table 1 also shows that countries with higher outstanding World Bank debt are more likely to receive ï¬?nancial assistance through policy lending. This result is in line with the earlier ï¬?ndings of Svensson (2003) and reflects the reasoning of Mosley et al. (1991) who claim that policy-based lending also serves to prevent default on outstanding obligations. Countries with macroeconomic distortions are more likely to receive an economic policy loan as the coefficient on the inflation rate is sig- niï¬?cantly positive. The positive coefficient on the CPIA score is consistent with the view that aid is more effective in better policy environments. Table 1 also indicates that richer countries are less likely to engage with the World Bank for policy support. Finally, the excluded instrument, distance from a country’s capital to Washington D.C., is associated with a signiï¬?cantly lower likelihood of selection, as expected. Table 2 presents estimation results of the outcome equation. Equation 1 shows that the quality at entry of an economic policy loan is signiï¬?cantly related to ideology of the governing party. The quality-at-entry rating is .42 points higher (on the 1-6 scale) on average for left-wing governments. Incumbent tenure also enters the model signiï¬?cantly, as higher-quality loans are associ- ated with longer time in office. In terms of the above model, long-lived regimes on average have a higher reservation utility for engaging in policy reform. A variety of reasons could serve to explain this result16 . As leadership tenure also captures the ability to build ï¬?rm political networks and transfer mechanisms to remain in power (Bienen and Van De Walle, 1989; Bueno de Mesquita et al., 2002), negotiating costly policy reform with such regimes could require substantial time and resources.17 The proxy for donor influence is also associated with quality at entry: countries that voted in line with the G7 on key issues received higher quality loans on average. This result them. For these reasons we include Bank supervision on the right hand side of the second (compliance) equation. On the other hand, it is standard operating procedure to supervise loan agreements irrespective of the extent of borrower compliance. Furthermore, Malesa and Silarszky (2005) ï¬?nd no evidence that supervision costs (and other donor variables) are endogenous to the probability of DPL success and explain this by the fact that the resources for supervision are budgeted in advance. That is why we have opted to exclude compliance from the ï¬?rst (quality-at-entry) equation. 16 See Besley and Case (2003) for a detailed literature review on how political factors affect (economic) policy choice. 17 Dreher et al. (2009a) ï¬?nd that market-liberalizing policy reforms are inversely related to the chief executive’s time in office. 9 appears to conflict with Kilby (2011), who ï¬?nds that project preparation time is shorter in such countries, possibly to the detriment of project quality. However, Kilby (2011) does not directly test for links between project preparation time and project quality. Moreover, the samples in the two studies differ: we consider only DPLs, while DPLs comprise only about one-ï¬?fth of the projects in Kilby (2011). We tested the stability of our results in several ways. First, we checked whether results are robust to changes in the sample of DPLs. We restricted the sample by dropping all private sector and ï¬?nancial sector loans, leaving only the 115 observations coded as eco- nomic policy loans - i.e. those arguably most associated with ideological differences. Table 2, equation 2 shows that the effect of left-wing ideology on quality at entry remains signif- icant and positive, with a larger coefficient than in equation 1. As a second modiï¬?cation we expanded the sample by adding loans from other economic sectors: agriculture, energy and mining, transport and urban development. This change increased the sample size to 215 observations. For this expanded sample of loans, the coefficient for the left-ideology dummy remains positive and signiï¬?cant (at the 5 % level). The association of ideology and quality at entry is also robust to alternative constructs of our ideology variable. In place of our binary variable for left-wing governments, we tested an index of political ideology, created by Bjornskov (2005). The index is constructed by considering the three largest government parties, instead of only the chief executive’s party, and weighing their ideology with the share of seats they hold in parliament. The resulting variable is distributed between -1 and 1, with negative values indicating a more left-wing party orientation. Equation 4 of table 2 indicates that high quality loans remain positively correlated with left-wing party orientation: one standard deviation decrease in the Bjornskov index (towards the left) is associated with an average increase of .324 points in quality at entry. Testing our model with DPI’s three point categorical variable did not affect our results as equation 5 shows. As a third robustness test we constructed binary versions of the dependent variable. Speciï¬?cally, we collapsed the three lowest-performance categories “highly unsatisfactoryâ€?, “unsatisfactoryâ€? and “moderately unsatisfactoryâ€? into one, and similarly for the three highest-performance categories. Using this information we estimated equation 4.2 as a probit model. As shown in equation 6 of table 2 the result for political ideology remains unchanged. A marginal effects analysis reveals that the likelihood a DPL is well-designed is 18 percent higher, other things equal, for left-wing governments. Finally, our results are robust to the inclusion of several additional control variables. Added regressors include ethnic fractionalization, the current account balance (as % of GDP), inflation of consumer prices (annual %) and the overall CPIA score at the start of the program. The latter three variables collectively control for the stage of a country’s reform process, which otherwise could bias the relationship between quality at entry and political ideology. In the early stages of reform, economic deï¬?ciencies are more apparent and programs are fairly straightforward to design – e.g. privatization of state-owned en- terprises, removal of trade barriers, etc. – resulting in loans with higher quality at entry. At later stages, additional reforms are likely to be more complex. If left-wing governments are more prevalent in the early stages of economic reform, when state control over the 10 economy remains high, the coefficient on left ideology would be biased upward. Equation 7 shows that the coefficient for left ideology remains positive and signiï¬?cant at the 5 % level, controlling for stage of reform. Table 2 also conï¬?rms the role of tenure and donor influence as determinants of loan quality. In the next subsection we discuss how political ideology and quality at entry affect the other stages of a reform program. 4.3. Ideology, quality at entry and the success of economic policy lending Results for model 4.3 are presented in table 3. Equation 1 shows that quality at entry is positively associated with quality of World Bank supervision. A possible explanation is that World Bank staff may be more committed to keeping a DPL on track after having invested substantial time and resources in preparing the program18 . Other than quality at entry, none of the other covariates enter signiï¬?cantly. Turning to borrower compliance (table 3, equation 2), both quality at entry and su- pervision quality are positively related to the recipient country’s effort in complying with loan agreements. Equation 2 also shows that left-wing governments comply more fully with DPL provisions. This result is in line with the prediction of Cukierman and Tommasi (1998) that under certain circumstances19 left-wing governments enjoy more leeway in im- plementing market reforms. Other examples of ‘policy reversals’ by left-wing governments (most notably the economic reforms in Spain during 1982-1986) are found in Haggard and Webb (1994). Equation 3 presents the coefficient estimates of the overall outcome equation. The included covariates are able to explain more than 60 percent of the variation in reform outcome. The results provide further evidence that donor effort does matter for reform success: a decomposition-of-effects analysis reveals that a one point increase in quality at entry increases the overall outcome rating more than half a point (0.574) on average. Similarly, a one point jump in quality of Bank supervision (indirectly) augments the overall outcome by a ï¬?fth of a point (0.225) on average. These estimates suggest that the payoff – in terms of reform success – is greater in investing scarce resources in project identiï¬?cation and preparation rather than in supervision of a DPL. For a given level of total resources invested, overall outcome ratings for DPLs could be improved by shifting resources at the margin from supervision to preparation. Unsurprisingly, borrower compliance also contributes to successful reform. Income growth in the year following DPL approval is also positively associated with DPL success. This ï¬?nding is in line with Noorbakhsh and Paloni (2007), who argue that short term economic success helps increase credibility of the reforms and sustain adjustment programs by generating additional resources and muting political 18 However, an omitted variable such as staff capacity may well affect quality at entry and supervision in the same way. Yet, it is not uncommon in World Bank practice to have various staff teams working on the different stages of one DPL. 19 The authors identify three conditions for such a policy switch to occur: ï¬?rstly, the desirability of the policy switch should be considerable and relatively rare. Second, policy reversals are more prone to occur when voters are uncertain about the government’s preferences. Finally, the outcomes of the policies under consideration take place in the future. However, Cowen and Sutter (1998) have shown that similar policy reversals also hold under less stringent conditions. 11 opposition. The time trend variable in equation 3 is signiï¬?cantly negative, suggesting that it has become increasingly difficult to achieve the major objectives of economic policy loans. a The political difficulties and increasing complexity of second-generation reforms vis-`-vis the more self-implementing ï¬?rst generation programs provides a possible explanation for this result (see Navia and Velasco, 2003; Rodrik, 2006). Finally, equation 3 shows that the nonselection hazard comes in signiï¬?cantly negative, indicating that there are unobserved factors for which countries are selected into the program which negatively affect the overall outcome of the program. Tables 4 through 9 present the results from applying the abovementioned robustness tests to the triangular model. Generally, ï¬?ndings from the base model are conï¬?rmed: higher quality at entry is positively associated with ratings on quality of supervision, bor- rower compliance and overall outcome success; left-wing governments comply more fully with loan arrangements; and overall outcomes of reform are positively related to short-term growth and negatively affected by increasing complexity – as proxied by the time trend – and unobserved selection factors. In four of the robustness tests supervision quality comes in signiï¬?cantly positive in the overall outcome equation. Notice that three out of the six robustness tests show that the World Bank puts in more effort on average supervising loan agreements in long-lived regimes. Also, table 9 indicates that the coefficient for the CPIA score is signiï¬?cantly negative in the supervision equation. This negative effect may be explained as a substitution of World Bank supervision by partner country institutions. That is, when operating in a country where Bank staff rate policies and institutions more favorably, they may be more inclined to trust country systems to implement loan agree- ments instead of investing heavily in supervision activities (Knack, 2012). Finally, table 9 supports the view that deteriorating economic conditions can motivate governments to re- form (see e.g. Krueger, 1993; Ranis and Mahmood, 1992), as the coefficient on the current account balance comes in signiï¬?cantly negative in the compliance and outcome equations. 5. Anecdotal Evidence In this section we present concrete examples – based on interviews with World Bank team task leaders managing economic policy loans – consistent with the above econometric ï¬?nding that World Bank staff put in more effort designing an economic policy loan when faced with a left-wing government. We describe two cases where the World Bank was faced with a left-wing government (Mozambique and Zambia) and two cases of economic policy reform with governments without an outspoken leftist ideology (Mexico and Malawi) 20 . A ï¬?rst telling case concerns the successful privatization of the telecommunications sec- tor in Mozambique. Analytical work during the mid 1990s indicated a clear need for 20 In three other cases, interviews suggested that ideology was less salient. The four cases discussed, and the 7 selected in total, are not necessarily representative of all DPLs. A form of “convenience samplingâ€? was employed, in which the authors selected task team leaders who were judged mostly likely to respond favorably to interview requests. Whether representative or not, the cases are illustrative of the main arguments and intuition behind the model. 12 privatizing the telecommunication sector: by the year 2000 there were 85,000 landlines and 51,000 cellphones in Mozambique with high telephone charges and poor quality cell- phones (World Bank, 2005). Faced with the Marxism-Leninism inspired FRELIMO21 , at that time led by former pro-independence crusader Joaquim Chissano, it took the World Bank considerable effort to convince the Ministry of Planning to allow a second player into the telecommunications market. A number of political advisors to the Minister of Planning openly questioned the beneï¬?ts of privatization and claimed that only a state monopoly would serve the people. An extensive World Bank staff team – 12 World Bank staff members were involved in the negotiation process including the country director, the country economist, a World Bank telecommunications expert and a number of economists with experience in Mozambique – succeeded in convincing the Mozambican government officials. The then team task leader recalls that a presentation showing the positive impact of telecom privatization – with examples all over the globe – was instrumental in shifting the mindset. Also, in-country analytical work and a ï¬?ne-tuning of conditionality served to convince the Government of Mozambique to sign the loan agreement. According to the task leader, after the negotiations the government of Mozambique expressed a clear and ï¬?rm commitment to implement the program: it created an independent regulatory body, revised the telecommunications sector law and attracted a new mobile operator. By 2005 there were 800,000 mobile phone subscribers and the quality of service improved substan- tially (World Bank, 2005). This case provides another reason – in addition to informational asymmetries and political economy considerations – why leftist governments perform better on DPL compliance: the extensive negotiation and preparation process clearly facilitated attitude change22 concerning the beneï¬?ts of market reform with increased commitment as a consequence (Hirschman, 1965; James and Gutkind, 1985). Another case in point involves the restructuring of state-owned non-bank ï¬?nancial institutions in Zambia. The early 1970s nationalization programs by Kenneth Kuanda left Zambia with a number of insolvent non-bank ï¬?nancial institutions (NBFIs). A 2004 World Bank economic policy loan aimed to address the weaknesses in those enterprises. However, public opinion was opposed to further market reforms: when the World Bank staff team came to negotiate the loan, an editorial in an influential Zambian newspaper titled ‘The Devils of Washington have arrived’. Nor was the center-left government party, the Movement for Multi-party Democracy, convinced of the beneï¬?ts of privatization23 . In this context the World Bank had to come well-prepared to the negotiation table: 92.5 staff 21 Note that at that time FRELIMO already discarded its radical Marxist-Leninist ideology – yet still situated ï¬?rmly on the left – which allowed the World Bank the opportunity to discuss market reforms. 22 During the interviews a team task manager mentioned that attitude change was also crucial in suc- cessfully supporting the economic reform programs in Vietnam. 23 Within the MMD there were outspoken differences concerning the impact of market reforms. President Levy Mwanawasa opposed privatization arguing that market policies “had brought untold suffering to the workersâ€? while his Finance Minister Emmanuel Kasonde maintained that it was inevitable to redress the situation where state-owned enterprises continue to depend on the state for subventions. Following these differences over privatization, Emmanuel Kasonde was relieved of his ministerial position by the Mwanawasa Government (World Bank, 2004). 13 weeks – at an estimated cost of $ 547,000 – were spent preparing the economic reform program. Furthermore, the Bank had to invest considerable resources to conclude the loan agreement. The then team task leader recalled that much back and forth was required to meet the Government of Zambia’s demands. Only after 8 months of negotiating and ï¬?ne-tuning prior actions was it agreed that the non-bank ï¬?nancial institutions would fall under the supervision of the Bank of Zambia and that the operations of the NBFIs were restricted until they were sufficiently privatized. The ï¬?scal reform program in Mexico provides an example of the World Bank engaging with a right-wing government. After ruling Mexico for more than 70 years, the Institutional Revolutionary Party lost the presidency in 2000 to Vincente Fox, a member of the National Action Party (PAN). During the Fox administration much emphasis was put on market- based approaches in economic policy making (Shirk, 2004). The PAN-led government also succeeded in bringing down inflation to a record low of 3.6%. With the support of the World Bank, the government decided in 2001 to engage in a tax reform program in order to reduce its dependence on the oil sector. The reform program was quickly prepared – in less than two months – as the policy objectives of the Mexican government were in line with World Bank prescriptions. The initial setup was based on a World Bank study (World Bank, 2002) and suggested tax reform in ï¬?ve areas – Value Added Tax, Personal Income Tax, Corporate Income Tax, Excise Tax and federalization of ï¬?scal revenues – with the reforms to the VAT envisioned to bring the most additional resources. However, due to staunch opposition in Congress and lack of public support the VAT reforms were abandoned (Shirk, 2004). As a consequence the scope, funding and relevance of the program were considerably reduced. The economic reforms in Malawi during the 1990s offer a second illustration of the World Bank supporting policy change in a country with no outspoken leftist ideology. Prior to 1994, Malawi’s economy was highly regulated with a few publicly ï¬?nanced conglomerates dominating production. In 1994 Bakili Muluzi from the United Democratic Front (UDF) rose to power. The reform strategy of the newly elected government included privatization and deregulation as well as an increased focus on the poor (IEG, 2006). The World Bank aimed to support these reforms with a series of economic policy loans. In 1998, the second Fiscal Restructuring and Deregulation program (FRDP II) sought to address a number of bottlenecks: reducing the ï¬?scal deï¬?cit and strengthening budget discipline, privatizing a publicly owned agricultural conglomerate (ADMARC), reducing government interventions in the maize market, reforming tax and utility policy reform. However, the outcome of most of these reforms remained below expectations. For instance, the ï¬?scal deï¬?cit increased from 9.0 percent in 1997 to 12.4 percent in 1999 and during the implementation of FRDP II the government backtracked on the privatization of ADMARC (IEG, 2006, pp. 31-32). The then team task leader concurred that the Malawian government had difficulties strongly enforcing conditionalities. An assessment by the Bank’s Independent Evaluation Group rated the quality at entry as inadequate and concluded that the program design was too broad in scope without taking into account the lessons learned from past experience (IEG, 2006, p. xiii). 14 6. Summary and Conclusion In this paper we investigated how party orientation matters at the different stages of economic reform programs. We ï¬?nd robust evidence that World Bank staff put in more effort designing an economic policy loan when faced with a left-wing government. We explain this result in terms of the interaction between the prevailing staff incentive system in the World Bank and the (initial) partner country skepticism towards reform. In addition to party orientation, incumbent tenure is also positively associated with quality at entry. Results from estimating a triangular system of equations show that left-wing governments also comply more fully with loan arrangements. Although political economy considerations and informational asymmetries offer a reasonable explanation, interviews with team task leaders also point to cognitive processes and attitude change as a way of interpreting this ï¬?nding. Looking at the overall outcome of a reform program, we ï¬?nd that short-term growth and initial macroeconomic conditions influence reform success. In addition to recipient country variables, donor effort matters as well. For instance, ensuring a high quality at entry increases supervision, borrower compliance and overall outcome. To a lesser extent, higher-quality supervision of the implementation of loan covenants positively affects reform success. Finally, geopolitical motives appear to matter, as countries voting in line with the G-7 in the UN General Assembly are favored in two ways. First, the selection model indicates that such countries have easier access to DPL ï¬?nancing. Second, we ï¬?nd evidence that loans to countries voting in line with the G-7 tend to receive higher quality-at-entry ratings. What can be learned from this study? In line with previous papers, our ï¬?ndings point to the importance of domestic political variables for successful reform. On the other hand, factors under donor control also matter. Our estimates suggest that the returns to resources invested at the design stage exceed the returns to supervision in development policy operations. A re-allocation of resources at the margin can potentially increase the number of successful DPOs for a given level of total resources devoted to design and supervision. As the World Bank’s country units tend to design higher-quality economic reform programs for leftist governments, the lending reviews conducted by the Bank’s central units and Executive Board should devote extra scrutiny to DPLs proposed for other borrowers. Our analysis considered only policy lending conducted by the World Bank. We would welcome similar studies of lending and aid programs intended to promote policy reform, by the regional development banks or large bilateral donors, using their own internal datasets where available. Ideology of government parties may conceivably have a differing effect on design quality, when countries work with other agencies that are not so strongly identiï¬?ed as the World Bank is with “Washington Consensusâ€? or market- liberalizing policies. 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(in 100 km) -0.002∗ (0.001) ∗∗∗ voting in line with G7 t-1 0.591 (0.203) inflation t-1 .00008∗ (.00004) log of World Bank debt t-1 0.214∗∗∗ (0.057) ∗∗ GDP per capita (PPP) -.00003 (.00001) log of population -0.079 (0.053) ∗∗ CPIA score 0.182 (0.074) Intercept -5.092∗∗∗ (0.601) N 2148 LR statistic 58.73 Pseudo R2 0.053 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% 19 Table 2: Heckman model, outcome equation equation no. (1) (2) (3) (4) (5) (6) (7) variation base model sample I sample II bjornskov 3-point var. probit controls left-wing ideology .421 .598 .461 -.400 .237 .874 .432 (.232)∗ (.328)∗ (.230)∗∗ (.216)∗ (.121)∗ (.358)∗∗ (.215)∗∗ year .034 .026 .037 .045 .033 .057 .032 (.026) (.032) (.026) (.028) (.025) (.031)∗ (.027) log of loan amount -.051 -.083 -.029 .029 -.055 -.085 .010 (.134) (.177) (.175) (.158) (.136) (.181) (.140) democracy .281 .692 -.002 .763 .283 1.035 .023 (.382) (.628) (.374) (.482) (.379) (.434)∗∗ (.385) tenure .073 .092 .068 .090 .074 .107 .053 (.025)∗∗∗ (.037)∗∗ (.024)∗∗∗ (.031)∗∗∗ (.025)∗∗∗ (.034)∗∗∗ (.026)∗∗ log of per capita GDP .272 .614 .229 .290 .294 .325 .087 (.301) (.367)∗ (.275) (.294) (.297) (.363) (.358) 20 log of population .124 .234 .088 .142 .129 .253 .065 (.158) (.170) (.167) (.176) (.155) (.201) (.156) voting in line with G7 t-1 1.557 .807 1.299 1.483 1.581 1.361 1.757 (.632)∗∗ (.788) (.672)∗ (.665)∗∗ (.620)∗∗ (.737)∗ (.716)∗∗ CPIA score . . . . . . .141 (.368) Current account balance . . . . . . .011 (.023) inflation . . . . . . .00008 (.0008) ELF85 . . . . . . -.749 (.764) regional ï¬?xed effects yes yes yes yes yes yes yes No. of uncensored observations 182 115 215 161 182 177 167 R2 .209 .208 .131 .238 .21 .259 .211 * signiï¬?cance at 10%; ** signiï¬?cance at 5%; *** signiï¬?cance at 1%; constant not reported. Table 3: triangular system, base model Variable Coefficient (Std. Err.) Equation 1 : banksupervision quality at entry 0.508∗∗∗ (0.096) left -0.035 (0.229) growth t+1 0.021 (0.028) year -0.003 (0.022) log of loan amount -0.041 (0.094) democratic regime -0.019 (0.282) incumbent tenure 0.034 (0.024) log of GDP per capita (PPP) -0.008 (0.199) log of population 0.096 (0.116) voting in line with G7 t-1 -0.564 (0.700) nonselection hazard 0.053 (0.417) regional ï¬?xed effects yes - Equation 2 : borrcompliance bank supervision 0.239∗ (0.126) quality at entry 0.369∗∗∗ (0.106) left 0.330∗ (0.177) growth t+1 0.035 (0.026) year -0.010 (0.024) log of loan amount -0.110 (0.107) democratic regime -0.261 (0.340) incumbent tenure 0.020 (0.025) log of GDP per capita (PPP) -0.030 (0.196) log of population 0.093 (0.108) voting in line with G7 t-1 -0.249 (0.635) nonselection hazard -0.697 (0.502) regional ï¬?xed effects yes - Equation 3 : outcome bank supervision 0.143 (0.093) ∗∗∗ borrower compliance 0.343 (0.089) ∗∗∗ quality at entry 0.334 (0.102) left -0.108 (0.154) ∗∗ growth t+1 0.043 (0.019) year -0.042∗∗∗ (0.013) log of loan amount -0.194 (0.129) democratic regime 0.212 (0.234) incumbent tenure 0.001 (0.017) log of GDP per capita (PPP) 0.086 (0.172) Continued on next page... 21 ... table 3 continued Variable Coefficient (Std. Err.) log of population 0.061 (0.102) voting in line with G7 t-1 -0.216 (0.390) nonselection hazard -0.824∗ (0.444) regional ï¬?xed effects yes - N 161 Log-likelihood -2904.116 R2 equation 1 0.382 R2 equation 2 0.394 R2 equation 3 0.603 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. Table 4: triangular system, reduced sample Variable Coefficient (Std. Err.) Equation 1 : banksupervision quality at entry 0.441∗∗∗ (0.108) left 0.108 (0.289) growth t+1 0.015 (0.035) year 0.008 (0.028) log of loan amount -0.063 (0.129) democratic regime 0.113 (0.381) incumbent tenure 0.042 (0.031) log of GDP per capita (PPP) -0.102 (0.220) log of population 0.025 (0.137) ∗ voting in line with G7 t-1 -1.353 (0.767) nonselection hazard -0.727 (0.768) regional ï¬?xed effects yes - Equation 2 : borrcompliance bank supervision 0.372∗∗∗ (0.142) ∗∗∗ quality at entry 0.309 (0.115) left 0.280 (0.245) growth t+1 0.069∗∗ (0.028) year -0.024 (0.026) log of loan amount -0.129 (0.149) democratic regime -0.106 (0.488) incumbent tenure 0.001 (0.035) log of GDP per capita (PPP) 0.374 (0.265) Continued on next page... 22 ... table 4 continued Variable Coefficient (Std. Err.) log of population 0.175 (0.115) voting in line with G7 t-1 -0.795 (0.901) nonselection hazard -1.088 (0.824) regional ï¬?xed effects yes - Equation 3 : outcome bank supervision 0.235∗∗ (0.118) borrower compliance 0.239∗∗ (0.097) quality at entry 0.346∗∗∗ (0.114) left 0.007 (0.177) growth t+1 0.072∗∗∗ (0.026) year -0.075∗∗∗ (0.020) log of loan amount -0.209 (0.128) democratic regime 0.121 (0.215) incumbent tenure -0.014 (0.018) log of GDP per capita (PPP) 0.510∗∗ (0.219) log of population 0.088 (0.095) voting in line with G7 t-1 -0.326 (0.515) nonselection hazard -1.678∗∗ (0.699) regional ï¬?xed effects yes - N 103 Log-likelihood -1803.271 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. Table 5: triangular system, expanded sample Variable Coefficient (Std. Err.) Equation 1 : banksupervision quality at entry 0.423∗∗∗ (0.069) left 0.113 (0.225) growth t+1 0.029 (0.024) year -0.006 (0.018) log of loan amount -0.067 (0.098) democratic regime 0.134 (0.274) incumbent tenure 0.037∗ (0.022) log of GDP per capita (PPP) -0.054 (0.187) log of population 0.113 (0.121) Continued on next page... 23 ... table 5 continued Variable Coefficient (Std. Err.) voting in line with G7 t-1 -0.578 (0.654) nonselection hazard 0.060 (0.400) regional ï¬?xed effects yes - Equation 2 : borrcompliance bank supervision 0.305∗∗ (0.119) quality at entry 0.353∗∗∗ (0.086) left 0.366∗∗ (0.175) growth t+1 0.032 (0.024) year -0.010 (0.023) log of loan amount -0.188∗ (0.114) democratic regime -0.161 (0.314) incumbent tenure 0.015 (0.026) log of GDP per capita (PPP) -0.193 (0.176) log of population 0.154 (0.117) voting in line with G7 t-1 0.180 (0.594) nonselection hazard -0.482 (0.498) regional ï¬?xed effects yes - Equation 3 : outcome bank supervision 0.166∗∗ (0.071) borrower compliance 0.338∗∗∗ (0.074) quality at entry 0.343∗∗∗ (0.076) left -0.033 (0.131) growth t+1 0.042∗∗ (0.016) year -0.045∗∗∗ (0.010) log of loan amount -0.145 (0.097) democratic regime 0.165 (0.189) incumbent tenure -0.009 (0.016) log of GDP per capita (PPP) 0.067 (0.153) log of population 0.022 (0.082) voting in line with G7 t-1 -0.279 (0.385) nonselection hazard -0.598∗ (0.306) regional ï¬?xed effects yes - N 191 Log-likelihood -3487.396 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. 24 Table 6: triangular system, index of ideology Variable Coefficient (Std. Err.) Equation 1 : banksupervision quality at entry 0.518∗∗∗ (0.093) bjornskov index of ideology 0.094 (0.149) growth t+1 0.020 (0.027) year -0.004 (0.020) log of loan amount -0.041 (0.095) democratic regime -0.083 (0.306) incumbent tenure 0.032 (0.024) log of GDP per capita (PPP) 0.002 (0.196) log of population 0.087 (0.118) voting in line with G7 t-1 -0.583 (0.686) nonselection hazard 0.096 (0.424) regional ï¬?xed effects yes - Equation 2 : borrcompliance bank supervision 0.247∗∗ (0.123) quality at entry 0.355∗∗∗ (0.096) bjornskov index of ideology -0.295∗∗∗ (0.099) growth t+1 0.033 (0.027) year -0.003 (0.024) log of loan amount -0.096 (0.113) democratic regime -0.132 (0.336) incumbent tenure 0.029 (0.025) log of GDP per capita (PPP) -0.026 (0.176) log of population 0.085 (0.109) voting in line with G7 t-1 -0.254 (0.628) nonselection hazard -0.912∗∗ (0.458) regional ï¬?xed effects yes - Equation 3 : outcome bank supervision 0.131 (0.091) ∗∗∗ borrower compliance 0.360 (0.087) ∗∗∗ quality at entry 0.351 (0.098) bjornskov index of ideology 0.253∗∗ (0.103) growth t+1 0.042∗∗ (0.018) year -0.050∗∗∗ (0.013) ∗ log of loan amount -0.212 (0.124) democratic regime 0.002 (0.229) incumbent tenure -0.011 (0.017) log of GDP per capita (PPP) 0.112 (0.153) Continued on next page... 25 ... table 6 continued Variable Coefficient (Std. Err.) log of population 0.064 (0.098) voting in line with G7 t-1 -0.207 (0.407) nonselection hazard -0.666∗ (0.361) regional ï¬?xed effects yes - N 159 Log-likelihood -2920.272 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. Table 7: triangular system, 3-point categorical variable Variable Coefficient (Std. Err.) Equation 1 : banksupervision quality at entry 0.512∗∗∗ (0.096) ideology -0.048 (0.139) growth t+1 0.020 (0.028) year -0.003 (0.022) log of loan amount -0.040 (0.094) democratic regime -0.050 (0.283) incumbent tenure 0.033 (0.024) log of GDP per capita (PPP) -0.004 (0.195) log of population 0.092 (0.116) voting in line with G7 -0.569 (0.709) nonselection hazard 0.044 (0.423) regional ï¬?xed effects yes - Equation 2 : borrcompliance bank supervision 0.243∗ (0.127) quality at entry 0.360∗∗∗ (0.104) ideology 0.219∗∗ (0.103) growth t+1 0.036 (0.027) year -0.009 (0.024) log of loan amount -0.112 (0.108) democratic regime -0.221 (0.345) incumbent tenure 0.023 (0.025) log of GDP per capita (PPP) -0.023 (0.188) log of population 0.101 (0.105) voting in line with G7 -0.225 (0.637) Continued on next page... 26 ... table 7 continued Variable Coefficient (Std. Err.) nonselection hazard -0.688 (0.505) regional ï¬?xed effects yes - Equation 3 : outcome bank supervision 0.143 (0.093) borrower compliance 0.342∗∗∗ (0.089) quality at entry 0.333∗∗∗ (0.102) ideology -0.047 (0.088) growth t+1 0.044∗∗ (0.019) year -0.042∗∗∗ (0.013) log of loan amount -0.194 (0.129) democratic regime 0.225 (0.234) incumbent tenure 0.001 (0.018) log of GDP per capita (PPP) 0.078 (0.176) log of population 0.061 (0.104) voting in line with G7 -0.220 (0.392) nonselection hazard -0.819∗ (0.448) regional ï¬?xed effects yes - N 161 Log-likelihood -2997.979 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. Table 8: triangular system, binary LHS variables Variable Coefficient (Std. Err.) Equation 1 : banksupervision dummy bankquality 0.442∗∗∗ (0.089) left 0.033 (0.068) growth t+1 0.006 (0.007) year 0.004 (0.007) log of loan amount -0.025 (0.028) democratic regime 0.079 (0.077) incumbent tenure 0.016∗∗∗ (0.006) log of GDP per capita (PPP) -0.026 (0.049) log of population 0.042 (0.032) voting in line with G7 t-1 -0.182 (0.204) nonselection hazard 0.050 (0.122) Continued on next page... 27 ... table 8 continued Variable Coefficient (Std. Err.) regional ï¬?xed effects yes - Equation 2 : dummy borrcompliance dummy supervision 0.140 (0.126) ∗∗∗ dummy bankquality 0.386 (0.098) left 0.074 (0.049) growth t+1 0.010 (0.008) year 0.002 (0.007) log of loan amount -0.020 (0.033) democratic regime -0.125 (0.103) incumbent tenure 0.005 (0.007) log of GDP per capita (PPP) -0.044 (0.059) log of population 0.013 (0.034) voting in line with G7 t-1 -0.074 (0.200) nonselection hazard -0.193 (0.160) regional ï¬?xed effects yes - Equation 3 : dummy outcome dummy supervision 0.311∗∗∗ (0.114) dummy compliance 0.271∗∗ (0.109) ∗ dummy bankquality 0.184 (0.100) left 0.011 (0.046) growth t+1 0.010∗ (0.005) ∗ year -0.009 (0.005) log of loan amount -0.034 (0.046) democratic regime 0.006 (0.093) incumbent tenure -0.002 (0.006) log of GDP per capita (PPP) -0.066 (0.050) log of population -0.003 (0.034) voting in line with G7 t-1 0.042 (0.152) ∗∗ nonselection hazard -0.303 (0.143) regional ï¬?xed effects yes - N 161 Log-likelihood -2144.738 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. 28 Table 9: triangular system, additional covariates Variable Coefficient (Std. Err.) Equation 1 : banksupervision quality at entry 0.459∗∗∗ (0.067) left -0.016 (0.190) ∗ growth t+1 0.041 (0.022) year 0.012 (0.018) log of loan amount -0.119 (0.150) democratic regime 0.203 (0.309) ∗ incumbent tenure 0.044 (0.024) log of GDP per capita (PPP) 0.154 (0.238) log of population 0.138 (0.122) voting in line with G7 t-1 -0.885 (0.580) nonselection hazard -0.228 (0.516) CPIA score -0.360∗ (0.214) Current account balance (% of GDP) -0.003 (0.017) Inflation, consumer prices (annual %) -0.001 (0.001) ELF85 (Roeder) 0.169 (0.431) regional ï¬?xed effects yes - Equation 2 : borrcompliance bank supervision 0.171∗ (0.092) ∗∗∗ quality at entry 0.375 (0.086) left 0.317 (0.212) growth t+1 0.055∗∗ (0.025) year -0.012 (0.020) log of loan amount -0.254 (0.167) democratic regime 0.133 (0.344) incumbent tenure 0.042 (0.027) log of GDP per capita (PPP) 0.303 (0.266) ∗ log of population 0.238 (0.137) voting in line with G7 t-1 0.022 (0.652) nonselection hazard -0.034 (0.575) CPIA score 0.155 (0.241) ∗∗∗ Current account balance (% of GDP) -0.061 (0.019) Inflation, consumer prices (annual %) 0.001 (0.001) ELF85 (Roeder) 0.255 (0.481) regional ï¬?xed effects yes - Equation 3 : outcome bank supervision 0.148∗∗ (0.069) ∗∗∗ borrower compliance 0.323 (0.061) Continued on next page... 29 ... table 9 continued Variable Coefficient (Std. Err.) quality at entry 0.365∗∗∗ (0.068) left -0.147 (0.159) growth t+1 0.052∗∗∗ (0.019) year -0.033∗∗ (0.015) log of loan amount -0.226∗ (0.125) democratic regime 0.184 (0.257) incumbent tenure -0.001 (0.020) log of GDP per capita (PPP) 0.307 (0.199) log of population 0.077 (0.103) voting in line with G7 t-1 -0.446 (0.485) nonselection hazard -0.848∗∗ (0.428) CPIA score -0.191 (0.179) Current account balance (% of GDP) -0.035∗∗ (0.015) Inflation, consumer prices (annual %) 0.001 (0.001) ELF85 (Roeder) -0.063 (0.358) regional ï¬?xed effects yes - N 147 Log-likelihood -579.011 Signiï¬?cance levels : ∗ : 10% ∗∗ : 5% ∗ ∗ ∗ : 1% ; no intercept reported. Model estimated with FGLS due to maximization problems with MLE. 30 Appendices Table 10: Summary statistics selection model Variable Mean Std. Dev. Min. Max. selection dummy 0.072 0.258 0 1 distance from Washington D.C. (in 100 km) 89.935 38.314 22.86 163.37 voting in line with G7 t-1 0.478 0.231 0 1 inflation t-1 59.537 636.282 -13.057 23773.131 log of World Bank debt t-1 19.823 1.953 9.903 24.189 GDP per capita (PPP) 4076.83 3486.904 185.299 19652.381 log of population 15.809 1.996 10.6 21.004 CPIA score 3.265 0.714 1 5.5 31 Table 11: Summary statistics outcome model Variable Mean Std. Dev. Min. Max. quality at entry 4.495 1.329 1 6 left 0.484 0.501 0 1 bjornskov index -0.045 0.811 -1 1 3-point variable 2.088 0.936 1 3 year 1997.588 5.617 1985 2008 log of loan amount 4.726 1.096 2.303 7.834 democratic regime 0.676 0.469 0 1 incumbent tenure 7.912 5.841 1 32 log of per capita GDP (PPP) 8.036 0.911 5.931 9.475 log of population 16.558 1.577 12.91 20.827 voting in line with G7 t-1 0.54 0.221 0 1 CPIA score 3.458 0.52 2 4.4 Current account balance (% of GDP) -3.666 7.055 -44.841 17.605 Inflation, consumer prices (annual %) 26.541 86.537 -1.167 1058.374 ELF85 (Roeder) 0.515 0.255 0.05 0.984 32 variable deï¬?nition source distance to Washington D.C. distance to Washington D.C. in 100 km Gleditsch (2011) voting in line with G7 t-1 fraction of votes on key issues aligned with the G-7 the year prior to Dreher and Sturm (2012) selection inflation t-1 inflation in consumer prices (annual %) the year prior to selection World Development Indicators (WDI) log of World Bank debt t-1 logarithm of IBDR loans and IDA credits the year prior to selection WDI GDP per capita (PPP) GDP per capita, PPP (constant 2005 international USD) WDI log of population logarithm of population WDI CPIA score assessment of the quality of a country’s policy and institutional frame- World Bank work left dummy coded 1 if the economic party orientation of the executive party Beck et al. (2001) is considered left-wing 33 Bjornskov index of ideology weighted index of government ideology Bjornskov (2005) ideology three point categorical variable of executive party ideology Beck et al. (2001) year the year the loan became effective World Bank log of GDP per capita (PPP) logarithm of GDP per capita, PPP (constant 2005 international USD) WDI growth t+1 GDP growth (annual %) the year after the loan became effective WDI log of loan amount logarithm of loan size (current million USD) World Bank democratic regime dummy coded 1 if the country regime classiï¬?es as democratic Cheibub et al. (2010) incumbent tenure total number of years the effective head of government is in power Cheibub et al. (2010) current account balance Current account balance as % of GDP WDI inflation inflation in consumer prices (annual %) WDI ELF85 ethnolinguistic fractionalization 1985 Roeder (2001) Table 12: variable deï¬?nitions and sources variable deï¬?nition Quality at entry The extent to which the World Bank identiï¬?ed, facilitated preparation of and appraised the operation to achieve relevant development outcomes. The following criteria are taken into account: Bank inputs and processes, strategic relevance and approach, macro and socio-economic aspects, im- plementation arrangements, M&E arrangements, institutional and ï¬?du- ciary aspects. Bank supervision The extent to which the World Bank proactively identiï¬?ed and resolved threats to the achievement of relevant development outcomes. The fol- lowing criteria are taken into account: focus on development impact and ï¬?duciary aspects, adequacy of supervision and quality of performance reporting. Borrower compliance The extent to which the borrower complied with covenants and agree- ments. The following criteria are taking into account: government owner- ship and commitment to achieving objectives, adequacy of stakeholder in- volvement, timely resolution of implementation issues, adequacy of M&E arrangements and relationship with donors/partners. Outcome The extent to which the operation’s major relevant objectives are achieved. Shortcomings in objectives have to do with either the number of objectives and/or the extent to which one or more objectives are not achieved. Shortcomings in relevance have to do with the extent to which the operation is inconsistent with the country’s development priorities. Table 13: IEG performance ratings 34