Mobilization Effects of Multilateral Development Banks

Abstract

1. Introduction

In 2015, 193 countries adopted the 2030 Sustainable Development Agenda, which set ambitious targets for poverty reduction and inclusive development. The United Nations estimates that achieving the Sustainable Development Goals (SDGs) will require investment of up to US|${\$}$|9 trillion per year. At current investment levels, the estimated annual investment gap in developing countries is about US|${\$}$|2.5 trillion (UNCTAD 2014; Gaspar et al. 2019). At the same time, official development assistance (ODA) totalled US|${\$}$|143 billion in 2016, an order of magnitude smaller than the amount needed. This leaves open a key role for the private sector.

An important question—for both policy and research—is how the international community can mobilize additional resources for investment. Multilateral development banks (MDBs) are international institutions that provide financial assistance (such as loans and grants) to developing countries with the clear mandate of promoting economic and social development.¹

MDBs have different motivations than private lenders, reflected in the extent to which they select projects that maximize the expected development impact.² In addition, their investment decisions are driven by the explicit aim of mobilizing domestic and foreign capital; but, like commercial lenders, they are also constrained by the need to maintain financial sustainability. Because of their specific mandate, MDBs can play an important role in helping to fund the investment gap, by providing direct financial assistance and also mobilizing additional private sector resources in developing countries.

The provision of direct financial support to member countries is part of the mandate of MDBs, which are expected to step in when private financing is scarce (Humphrey and Michaelowa 2013), possibly mitigating the pro-cyclicality of private capital inflows (Galindo and Panizza 2018). But direct financing is constrained by the loan capacity of MDBs, which is small compared to countries’ needs: the demand for financing well exceeds the supply of what MDBs can finance directly (United Nations 2015). For this reason, MDBs have recently reaffirmed their pledge to catalyze more investment from private investors (World Bank 2018). There are multiple examples from around the world of projects with MDB participation that had important catalytic effects in different settings. A prominent example is the expansion of the Panama Canal. The project of expanding the canal to allow transit of larger ships and avoid market losses followed a referendum in 2006 and was financed by the Inter-American Development Bank Group, the European Investment Bank, the Corporación Andina de Fomento, the International Finance Corporation, and the Japan Bank for International Cooperation. It is the largest infrastructure investment in the country since the canal opened, amounting to 30 percent of GDP, and in the five years after the project’s announcement it is estimated to have attracted almost US|${\$}$|10 billion in private investment, 1.8 times the project cost (Lanzalot et al. 2018).

This study formally investigates whether MDBs can crowd-in private sector resources to finance investment by examining MDB participation in syndicated lending, which is a key source of funding for private corporations in developing and emerging markets (Bruche, Malherbe, and Meisenzahl 2019; Cortina, Didier, and Schmukler 2018). MDBs can leverage additional resources from the private sector through various channels. For example, their entrance into a given country-sector could signal future investment opportunities and hence mobilize private finance. Also, thanks to their long-term perspective, MDBs could promote macroeconomic stability, growth, and an investment-friendly environment,³ all factors that are attractive to private creditors (Eichengreen and Mody 2000; Kidwelly 2017). In a similar vein, the presence of MDBs itself can signal to the private market the donors’ trust in the country’s institutional capacity and its commitment to reform, raising creditworthiness and consequently private capital inflows (Morris and Shin 2006; Basílio 2014). MDBs can also mobilize private resources through the reduction of political and credit risks; MDBs can use their leverage to influence governmental decisions and deter adverse events that could negatively affect project outcomes (Hainz and Kleimeier 2012). Credit risk could be reduced through multilateral guarantees and extension of the MDBs’ preferred creditor status, which implies that their loans are excluded from debt reschedulings (Arezki et al. 2017; Pereira dos Santos and Kearney 2018; Gurara, Presbitero, and Sarmiento 2020). In addition, to overcome or mitigate information asymmetries, private creditors may be willing to co-invest in a loan syndication with an MDB to take advantage of its technical expertise, monitoring capacity, and better knowledge of the country-sector (Chelsky, Morel, and Kabir 2013; Ratha 2001; Gurría, Volcker, and Birdsall 2001).

However, it could be that MDB lending substitutes for rather than complements private finance, leading to crowding-out rather than crowding-in of private capital inflows (Basílio 2014; Bird and Rowlands 2007). Lack of additionality could simply be the result of MDB lending displacing private investors who would have invested anyway. Moreover, private inflows might also be discouraged if multilateral lending creates incentives for moral hazard, with borrowing governments financing low-return projects, delaying reforms, or using lending to repay old debt (Ratha 2001; Swaroop and Devarajan 1999). Crowding-out could also arise from MDBs imposing higher environmental, social, and governance standards, monitoring development outcomes—which is costly—and possibly interfering with corporate strategy. Finally, countries may need to borrow from MDBs when they are excluded from private markets, and MDB lending could signal severe economic distress, discouraging private investment.

As studies on the catalytic effect of international financial institutions make clear, estimating the effect of the presence of MDBs on private capital flows requires dealing with selection bias and the endogeneity of MDB lending (Carter, Van de Sijpe, and Calel 2018). In particular, since an MDB’s choice to invest in a given country-sector is not exogenous, the identification of causal effects is impaired by the fact that macro data do not allow determination of whether private lending would have happened even without MDB involvement. The approach taken in this article, which is based on more granular loan-level data, has the advantage of absorbing all time-varying country- and sector-specific factors that could drive MDB and private sector lending into a large set of fixed effects. In particular, by exploiting data at the country-sector level, this approach is able to control for not only country and year fixed effects but also country-sector, country-year, and sector-year fixed effects. Thus, this approach greatly reduces the possibility of omitted variable bias and increases the accuracy in the estimation of the mobilization effects.

The results, based on regressions at the country-sector-year level, indicate that the volume of syndicated lending, the average number of lending banks per loan, and the average loan maturity all increase in the years following the presence of a syndicated loan with MDB participation in a given country-sector pair.

To mitigate concerns of reverse causality, this article presents (i) descriptive evidence that MDBs tend to be among the first lenders to enter a given country-sector, and (ii) formal evidence of the lack of anticipation effects. Then, the authors run a number of robustness tests to address any remaining concerns about omitted variable bias, which could potentially affect the results whenever there are confounding factors driving both MDB and private bank lending. In particular, including a large set of confounding factors in the baseline model—the presence of the largest global banks, Chinese lending, aid flows, corporate bond issuances, and value added growth in the country-sector pair—does not affect the significance or size of the estimated MDB mobilization effects. In addition, the analysis controls for sector-specific linear and quadratic trends, and identifies the effects in deviation from trend; further, it controls for unobserved heterogeneity by computing the Oster (2019) bounds.

Several extensions are considered. First, to gain a better sense of the macroeconomic implications of the main results, the analysis information on the volume of MDB lending to estimate the MDB lending multiplier. The paper shows that for each dollar that MDBs invest through syndicated loans, they are able to mobilize about seven dollars in syndicated lending by private banks over a three-year period. These are economically meaningful effects, suggesting that MDBs could actively contribute to the mobilization of resources toward meeting the ambitious goals of the 2030 Sustainable Development Agenda. Second, by estimating total mobilization effects, which include both direct and indirect effects, the analysis finds evidence suggesting that MDBs can attract private flows both directly and indirectly.⁴ Third, this article addresses the concern that the mobilization effects on lending could be partially or completely offset by a reduction in other debt flows; there is no evidence that corporate bond financing declines after an MDB starts lending in a given country-sector pair.⁵ Fourth, a concern is that the MDB mobilization effects estimated at the sector level (within a country) could be (partially) offset if the presence of MDBs in a given sector crowds out private bank lending to other sectors. This is shown not to be the case, and the results hold even when the data are aggregated at the country-year level. Finally, the study finds differences in the mobilization effects across countries. In particular, some of the effects are weaker in low-income countries, suggesting that MDBs still face challenges mobilizing resources in weak macroeconomic contexts.⁶

The existing literature on mobilization effects focuses mostly on IMF lending, but its catalytic effect—the capacity to attract private investment after the provision of official assistance (Giannini and Cottarelli 2002; Morris and Shin 2006)—cannot easily be generalized to MDBs, given the crisis-lending nature of IMF financing.⁷ A smaller strand of literature focuses explicitly on MDBs and, using aggregate macroeconomic data, finds mixed results. Rodrik (1995) tests whether net transfers from multilateral sources to a country are a predictor of subsequent net private capital inflows, controlling for past private flows. Using country data averaged over four periods of six years (from 1970 to 1993), Rodrik (1995) does not find a significant association between past multilateral lending and current private flows. Within a similar framework, Dasgupta and Ratha (2000) and Ratha (2001) instead find evidence suggesting that private capital flows to a large sample of developing countries respond positively to multilateral lending.

The contribution of this article is twofold. First, it departs from the existing literature on catalytic finance, which has primarily discussed the catalytic role of the IMF, by focusing specifically on MDBs, which have been greatly overlooked so far but could play an important role in light of the 2030 Sustainable Development Agenda. Second, while the existing literature has mainly considered case studies or country-aggregate data, the present analysis is based on loan-level data from the international market of syndicated loans and covers a large sample of more than 100 countries over 25 years. By analyzing such data, it is easier to: (i) isolate the effects of MDB participation on subsequent bank flows (as well as loan terms); (ii) run a series of additional tests dealing with the omitted variable bias to provide a convincing estimate of the mobilization effects; (iii) test for crowding-out effects with respect to other flows; and (iv) estimate country-level mobilization effects. To the best of our knowledge, this study provides the first assessment and quantification, based fully on loan-level data, of MDB mobilization effects, a key channel for stimulating the private sector and financing investment and growth in developing countries.

2. Data

The main source of data for the analysis is the Dealogic Loan Analytics database, which contains micro data at the level of tranches on all syndicated loans to 127 developing countries from 1993 to 2017. These data are widely used for studying the international syndicated loan market (see, e.g., Esty and Megginson 2003; Carey and Nini 2007; Giannetti and Laeven 2011), and their coverage is comparable to flows coming from aggregate statistics; for instance, Cerutti, Hale, and Minoiu (2015) compare syndicated loan exposures with loan claims reported by the Bank of International Settlements, finding a very good match between the two series between 1995 and 2012.

Syndicated loans are provided by a syndicate, a group of lenders that share risks by pooling together capital. Such loans have been used for decades and are now becoming a dominant way to tap banks, finance companies, and institutional investors (Miller 2006). Their relevance has been expanding dramatically, and they have become a key source of funding for corporations in both developing countries and advanced economies (Bruche, Malherbe, and Meisenzahl 2019; Cortina, Didier, and Schmukler 2018).

In line with existing studies (e.g., Nini 2004; Carey and Nini 2007; Cortina, Didier, and Schmukler 2018), the analysis in this article excludes loans to public authorities, as these are likely driven by different factors than loans to non-sovereign entities (private and public sector firms). The resulting dataset contains 21,373 syndicated loans to 117 countries: 51 percent of these loan deals are destined to Asia, 27 percent to the Americas, 15 percent to Europe, 7 percent to Africa, and 0.3 percent to Oceania. The countries receiving the majority of syndicated loans over the period are India, Brazil, Russia, Indonesia, Turkey, and Mexico, indicated by the largest bubbles in fig. 1. The countries with the greatest share of loans supported by MDBs are Afghanistan, Tajikistan, Vanuatu, Belize, Kyrgyzstan, and Moldova, represented by the darker bubbles in fig. 1. Over time the number of syndicated loans has increased, despite some drops during major financial crises, as shown in fig. 2.

Figure 1.

Number of Syndicated Loans and Share with MDB Support by Country, 1993–2017

Source: Authors’ calculations based on data from Dealogic Loan Analytics. Note: The bubbles on the map represent the number of syndicated loans by country in 1993–2017. The area of each bubble represents the total number of syndicated loans in the period: the larger the bubble, the more syndicated loans. The shade of color of a bubble represents the share of syndicated loans supported by at least one multilateral development bank (MDB) in the period: the darker the blue, the greater the share of syndicated loans receiving MDB support.

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Figure 2.

Number of Syndicated Loans

Source: Authors’ calculations based on data from Dealogic Loan Analytics. Note: The chart shows the trend in the total number of syndicated loans over time. MDB = multilateral development bank.

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The information contained in the data is comprehensive, including signing date, nationality of the borrower, total value of the deal in USD, maturity of each tranche, the general industry group of the deal, and the names of the lending banks. Information on deal type is also available—specifically whether the syndicated loan is an investment-grade, a leveraged, or a highly leveraged loan.⁸ Information on the lender allows those syndicated loans that have participation of at least one MDB to be identified. The MDBs participate in the syndicated loan markets on commercial terms through their private sector windows to mobilize financing from domestic and foreign creditors. MDBs operate under a joint financial and development mandate, which differentiates them from commercial banks. In the sample, the largest players are the European Investment Bank (EIB), the European Bank for Reconstruction and Development (EBRD), and the International Finance Corporation (IFC); see table S1.1 in the supplementary online appendix. Deals that involve MDBs account for 9.2 percent of the sample and, on average, are significantly larger, have longer maturities, and have fewer non-MDB banks involved (table 1).

The general industries of the syndicated loans are grouped into nine sectors: agriculture, construction and real estate, finance, government, infrastructure, manufacturing, mining and metals, oil and gas, and services. Most of the loans are in the finance, infrastructure, and manufacturing sectors, which are also the sectors having the majority of loans with MDB participation.

To prevent large countries from driving the results, nominal GDP in USD from the World Economic Outlook is used as a scaling factor for the total value of each syndicated loan. Scaling by GDP also enables a better understanding of the magnitude of a loan relative to the country’s economy. For data-cleaning purposes, the analysis winsorizes the top and bottom 1 percent of the total amount of syndicated loans, scaled by GDP.

With this information, the study builds a balanced panel at the country-sector-year level. In total, there are 26,325 observations corresponding to 1,053 country-sectors (117 countries and 9 sectors) over 25 years. By constructing a balanced panel, zeros are imputed for country-sector-years in which no syndicated lending takes place. Consequently, the averages of the main variables of interest become smaller in magnitude. For example, although the average number of participating banks per syndicated loan is around 6.2 (table 1), the average number of banks participating in the syndicated lending market per country-sector-year is only 1.6 (table 2). To test whether syndicated loans with MDB participation crowd out corporate bond financing, the study uses information on corporate bond issuances provided by Dealogic for 67 developing countries over the period 1993–2017. As for the loans, the bond data are transformed into a balanced panel at the country-sector-year level.

The main outcome variables of interest are: (i) the number of syndicated loans in each country-sector, excluding deals with MDB participation; (ii) the total size of the syndicated loans as a share of GDP, excluding the amounts lent by MDBs and by their partners;⁹ (iii) the number of banks per loan, excluding banks partnering only in deals with MDB participation; and (iv) the average loan maturity.¹⁰ To explore direct mobilization effects, the analysis considers also the following variables: (v) the number of banks participating in syndicated lending per loan; and (vi) the total size of the syndicated loans as a share of GDP, excluding the amount lent by MDBs themselves but including the amount lent by banks co-investing in a syndicated loan with MDB participation.

Descriptive Statistics

Table 2 reports the summary statistics for the country-sector panel data. On average there are 0.7 syndicated loans in each country-sector-year (panel A). The average total value of loans in a country-sector-year is 0.11 percent of GDP, which is only slightly higher than the average total value of loans excluding those supported by MDBs. The average number of banks (excluding MDBs) involved in a syndicated loan is 1.6, similar to the average number without counting those banks partnering in a syndicated loan with MDBs. The average maturity of loans, excluding those with MDB participation, is 8.7 months. Panel B reports the summary statistics for corporate bond issuances, which refer to a smaller sample than that for syndicated loans. On average, bond issuances in a country-sector pair are more numerous than syndicated loans, but their size (as a percentage of GDP) is smaller.

Consistent with the idea that mobilization effects could be driven by signaling and demonstration effects as well as better information, the descriptive evidence shows that MDBs are among the first lenders to enter a given country-sector through the syndicated loan market. If, for each country-sector pair, the “first” lending year is defined to be the first year since 1995 in which the country-sector receives a syndicated loan, then in 32 percent of these “first” years there is at least one MDB joining the first syndicated loans; by contrast, this share becomes substantially lower (23 percent) in subsequent years.¹¹

3. Empirical Strategy

The lagged dependent variable, y_cs,t−1, is also included, as lagged syndicate lending in the country-sector cs could be an omitted variable that is time-varying within the sector, and this could represent a threat to the identification strategy.

The key explanatory variable, MDB_cs,t, is a dummy that equals 1 if there is at least one deal supported by MDBs in the country-sector pair cs at time t.¹² The coefficient β₀ of MDB_cs,t measures the contemporaneous MDBs’ (indirect) mobilization effect. However, to allow for the possibility that mobilization effects show up with a lag, the analysis includes up to two lags (i.e., MDB_cs,t−k with k = 1 and 2), and then computes the cumulative effect between year t − 2 and year t (β₀ + β₁ + β₂). To rule out any possible increase in the outcome variables before MDB participation, the robustness analysis tests for the presence of an anticipation effect, by including up to two leads of the MDB dummy variable (see section 4).

The model is saturated with a large set of fixed effects to absorb unobserved factors that could drive lending by both MDBs and commercial banks and potentially bias the estimates of the β coefficients. In theory, the bias could go in either direction. On the one hand, since MDBs have a different mandate from private lenders, in the sense that project selection is aimed at maximizing the expected development impact (Engen and Prizzon 2018), their lending would go to countries and sectors that are otherwise poorly served by private lenders; in this case, the omitted variable bias would be negative. On the other hand, MDBs have taken explicit steps to mobilize domestic and foreign capital and to make investment decisions subject to financial sustainability considerations (World Bank 2018); in this respect, both MDBs and private lenders would seek out country-sector pairs with strong investment opportunities, and this profit-driven behavior would lead to overestimation of the mobilization effect. Time-varying country (δ_c,t) and sector (ζ_s,t) fixed effects absorb any time-varying global shock as well as country- and sector-specific unobserved factors, such as changes in credit demand and local economic conditions. The country|$\, \times \,$|sector fixed effects (α_cs) further reduce the threat of omitted variable bias by controlling for all time-invariant differences in observables and unobservables between country-sector pairs. Thus, the strategy here relies on the assumption that there are no unobservable factors that vary over time within each country-sector pair and are also correlated with changes in the MDB dummy variable; that is, one has to assume that all the time-changing characteristics of country-sector pairs that cannot be observed are uncorrelated with the presence of syndicated loans involving MDBs. However, changes in local economic conditions, changes in global commodity prices, and policy changes and reforms that affect some sectors in a country more than others could affect both MDB and private lending to a country-sector, potentially biasing the estimates. To deal with this threat to identification, in section 4 the baseline model is augmented to control for a set of variables that vary at the country-sector-year level, including the presence of the top 10 banks in the syndicate market, aid flows, Chinese lending, corporate bond issuances, and value added growth. In addition, the size of the potential omitted variable bias is estimated by computing the Oster (2019) bounds. Finally, the results may pick up a spurious correlation if different country-sector pairs follow specific trends. This concern is addressed by augmenting the model with linear or quadratic trends specific to the country-sector pairs.

Following Abadie et al. (2017), the standard errors (ε_cs,t) are clustered at the country level, under the conservative assumption that MDBs’ support is assigned at the country level and that mobilization effects vary by country. As a robustness check, standard errors are also clustered at the country-sector level to account for the possibility that in reality MDBs’ support is assigned at the country-sector level; see section 4.

4. Results

Main Findings

Table 3 shows, in separate panels, the results of estimating equation (1) for the different outcome variables. Each panel reports the results from adding sequentially a different set of fixed effects to investigate how the coefficients of the MDB dummy variables change as unobserved heterogeneity is controlled for at the country|$\, \times \,$|sector, country|$\, \times \,$|year, or sector|$\, \times \,$|year level. A general trend observed is that saturating the model with fixed effects significantly increases its explanatory power (as illustrated by the increase in the R² value) and attenuates the estimated correlation between MDB participation and private lending. For example, the R² more than doubles moving from a model without any fixed effects to the fully saturated one when looking at loan size (panel B) and almost doubles when looking at loan maturity (panel D). At the same time, the cumulative effect of the MDB dummies (⁠|$\sum$|MDB_cs,t−k) is halved (for loan size) or reduced by two-thirds (in the case of loan maturity). A similar reduction is seen in the cumulative effect on the number of banks (panel C). Interestingly, the inclusion of the fixed effects not only reduces the coefficients of the MDB dummies when explaining the number of loans (panel A), but also makes the cumulative effect no longer statistically significant.

A similar pattern is shown by the coefficients of the lagged dependent variables, which become smaller as more granular fixed effects are added, but remain statistically significant even when they are identified within country-sector pairs and when one controls for unobserved heterogeneity at the country and sector levels. In particular, in the most demanding specification, the coefficient of the lagged dependent variable is 0.87 when looking at the number of loans (this high persistence could explain the lack of significant results for MDB participation), but it is close to 0.2 when considering loan size and the number of lending banks, and further decreases to 0.08 in the case of loan maturity.

Overall, these findings suggest that in a simple bivariate regression, estimated coefficients would be biased. In fact, the inclusion of granular fixed effects absorbs a large component of unobserved heterogeneity and leads to smaller effects of MDB participation on private lending. The fact that the size of cumulative effects only marginally decreases (if anything) upon adding the last set of sector-year fixed effects suggests that the most saturated model absorbs most of the unobserved heterogeneity. The rest of the analysis takes the specification saturated with country|$\, \times \,$|year, sector|$\, \times \,$|year, and country|$\, \times \,$|sector fixed effects as the preferred one, and focuses on the size of the cumulative effects. However, section 4 reports a set of additional tests performed to rule out the possibility that the estimates are biased by observed confounding factors, country-sector-specific trends, and some residual unobserved heterogeneity.

Moving to specific results, the first column of table 3 shows that having at least one syndicated loan supported by at least one MDB is associated with an increase of 0.66 syndicated loans in the same year. The cumulative effect from year t to year t + 2 is 0.9. As discussed above, adding the fixed effects reduces the estimated coefficient β_t and halves the cumulative effect and, more importantly, makes it not statistically different from zero. However, when considering the total amount lent to private borrowers in a country-sector pair (measured as a percentage of GDP and excluding the amount lent by the MDBs themselves and by their partners in the syndicated loans), the results indicate that MDB participation is associated with more private sector lending, even upon adding the full set of fixed effects (panel B). The preferred (most demanding) specification indicates that the cumulative indirect mobilization effect between year t − 2 and year t is 0.125 percent of GDP, which corresponds to about 120 percent of the average size of syndicated loans to private creditors in a country-sector-year (which is equal to 0.11 percent of GDP; see table 2). If this effect is scaled by the size of the average syndicated loan (also equal to 0.11 percent of GDP; see table 1), it can be seen that the cumulative effect remains economically large.

Panel C of table 3 assesses whether MDB lending attracts other banks as lenders to the country-sectors. The preferred specification shows that MDB participation is associated with an average increase of 0.46 banks participating in syndicated lending to a country-sector pair in the same year. The effect persists over time, with similar magnitudes each year. Hence, the cumulative effect over three years—equivalent to 1.6 additional lending banks per loan—is economically large, given that on average there are 1.5 banks per syndicated loan per year at the country-sector level and that the average syndicated loan in the sample involves 6.1 banks (table 1).

Finally, the results of the preferred model on loan maturity (table 3, panel D, column 8) indicate that when MDBs participate in syndicated loans, the average weighted loan maturity in a country-sector pair increases by 0.64 years in the same year. The cumulative effect is estimated precisely and is slightly greater than one year. Again, these results are economically meaningful, given the average loan maturity of 5.5 years (table 1).

Robustness

Anticipation effects

Even though the empirical setting controls for a large set of unobserved factors that could drive syndicated lending, reverse causality remains a concern; in other words, MDBs could follow private lending and enter country-sector pairs that had already been receiving more and larger syndicated loans.

The lack of significant anticipation effects suggests that MDBs do not enter country-sectors following private lenders. By contrast, the results indicate that MDBs are the first lenders to enter a given country-sector pair, in line with the descriptive evidence discussed in section 2. This evidence could be explained in part by MDBs and private sector lenders having different objective functions: while commercial banks maximize profits, MDBs select projects that maximize expected development impact subject to financial sustainability at the project and portfolio level.

Trends

The inclusion of country-year and sector-year fixed effects allows to control for trends common to all countries or all sectors. But this specification might still be vulnerable to a different problem. For example, consider the oil and gas sector in two countries. In one country, because of a recent oil discovery, both private and MDB lending trend up exogenously, while in the other country, where oil reserves are depleting, they both trend down exogenously. As these trends are specific to the country-sector pair, the set of fixed effects included in the baseline specification does not control for this possibility, and the estimated β coefficients would spuriously pick up these trends. To control for this possibility, parametric trends are added to each country-sector pair, and the effect of MDB participation is identified as a deviation from trend. As shown in table 5, the results are robust to the inclusion of linear or quadratic trends. An interesting finding is that the cumulative effect on the number of loans becomes positive and significantly different from zero, suggesting that MDB participation has a mobilization effect also on the number of loans.¹³