metadata: authors: 0: author: Broccolini,Chiara
1: author: Lotti,Giulia
2: author: Maffioli,Alessandro
3: author: Presbitero,Andrea F.
4: author: Stucchi,Rodolfo Mario
count: World
docna: 0: docna: Mobilization Effects of Multilateral
            Development Banks
docty: Policy Research Working Paper
subtopic: Business Cycles and Stabilization Policies,Common Carriers Industry,Pulp & Paper Industry,Construction Industry,Plastics & Rubber Industry,Food & Beverage Industry,General Manufacturing,Textiles, Apparel & Leather Industry,Oil & Gas,Banks & Banking Reform,Private Sector Economics
teratopic: Macroeconomics and Economic Growth,Industry,Private Sector Development,Energy,Finance and Financial Sector Development
abstracts: cdata!: This study uses loan-level data on
            syndicated lending to a large sample of developing countries
            between 1993 and 2017 to estimate the mobilization effects
            of multilateral development banks (MDBs), that is, their
            ability to crowd-in capital from private creditors.
            Controlling for a large set of fixed effects, the paper
            shows evidence of positive and significant mobilization
            effects of multilateral lending on the size of bank inflows.
            The number of lenders and the average maturity of syndicated
            loans also increase. These effects are present not only on
            impact but last for up to three years and are not offset by
            a decline in bond financing. There is no evidence of
            anticipation effects, and the results are robust to numerous
            tests controlling for the role of confounding factors and
            unobserved heterogeneity. Finally, the results are
            economically sizable, indicating that MDBs can mobilize
            about seven dollars in bank credit over a three-year period
            for each dollar invested.
repnme: repnme: Mobilization Effects of Multilateral
            Development Banks
display_title: Mobilization Effects of Multilateral
            Development Banks
listing_relative_url: /research/2020/02/31811964/mobilization-effects-multilateral-development-banks
topicv3: Commercial Banking m1326069 1236,Manufacturing Sectors m1326521 1281,Oil and Gas Systems m1327342 549,Oil and Gas Industry m1327562 539,Blended Finance m1583858 4699
publishtoextweb_dt: 2020-02-25T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Private Sector Development,Energy,Finance and Financial Sector Development
origu: DEC-Knowledge and Strategy (DECKS)
no_of_pages: 62
author_keys: 0: author_key: 1492842
1: author_key: 1337835
2: author_key: 941274
3: author_key: 792084
4: author_key: 933987
model_extractions: Policy Research Working Paper 9163

# Mobilization Effects of Multilateral Development Banks

_Chiara Broccolini Giulia Lotti Alessandro Maffioli Andrea Presbitero Rodolfo Stucchi_ Development Economics Knowledge and Strategy Team February 2020
Policy Research Working Paper 9163

# **Abstract**

This study uses loan-level data on syndicated lending to a large sample of developing countries between 1993 and 2017 to estimate the mobilization effects of multilateral development banks (MDBs), that is, their ability to crowd-in capital from private creditors. Controlling for a large set of fixed effects, the paper shows evidence of positive and significant mobilization effects of multilateral lending on the size of bank inflows. The number of lenders and the average maturity of syndicated loans also increase.

These effects are present not only on impact but last for up to three years and are not offset by a decline in bond financing. There is no evidence of anticipation effects, and the results are robust to numerous tests controlling for the role of confounding factors and unobserved heterogeneity. Finally, the results are economically sizable, indicating that MDBs can mobilize about seven dollars in bank credit over a three-year period for each dollar invested.

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at broccolini.chiara@gmail.com, glotti@iadb.org, alessandrom@iadb.org, apresbitero@imf.org, and rstucchi@iadb.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
# Mobilization Effects of Multilateral Development Banks<sup>_∗_</sup>

Chiara Broccolini<sup>_†_</sup> Giulia Lotti<sup>_‡_</sup> Alessandro Maffioli<sup>_§_</sup> Andrea Presbitero<sup>_¶_</sup> Rodolfo Stucchi<sup>_∥_</sup> February 24, 2020

## **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 up to USD 3.9 trillion per year. At current investment levels, the estimated annual investment gap in developing countries is at about USD 2.5 trillion (UNCTAD, 2014; Gaspar et al., 2019). At the same time, total official development assistance (ODA) amounted to USD 143 billion in 2016, one order of magnitude smaller than the needs. This leaves a key role for the private sector.

An important question—for both policy and research—is how the international community can mobilize those additional resources for investment. Multilateral Development Banks (MDBs) are international institutions that provide financial assistance (e.g., loans, grants, etc.) to develop-

> _∗_ We thank Tito Cordella, Daniel Gurara, Sotirios Kokas, Aart Kraay (the editor), three anonymous referees, and participants at the “International Conference on Blended Development Finance and the New Industrial Policy” (Geneva 2018), “ABCDE World Bank Conference” (Washington, DC 2019), “Georgetown Center for Economic Center Alumni Conference” (Washington, DC 2019), “EFiC Conference in Banking and Corporate Finance” (University of Essex 2019), “Sustainability and Development Conference” (University of Michigan, Ann Arbor 2019), “LACEA-LAMES Annual Meeting” (Puebla 2019), and at seminars at the IDB and the IMF for helpful comments on earlier drafts. We also thank Norah Sullivan for editing the manuscript. This paper is part of a project on Macroeconomic Research in Low-Income Countries (project id: 60925) supported by the U.K.’s Department for International Development. The views expressed in this paper are those of the authors and do not necessarily represent those of the IDB Group, IMF, DFID, their Executive Boards, or their management.

> _†_ Consultant; broccolini.chiara@gmail.com

> _‡_ Inter-American Development Bank and CAGE Warwick; glotti@iadb.org

> _§_ IDB Invest; alessandrom@iadb.org

> _¶_ International Monetary Fund and MoFiR; apresbitero@imf.org

> _∥_ IDB Invest; rstucchi@iadb.org
ing countries with the clear mandate of promoting economic and social development.<sup>1</sup> MDBs have different motivations than private lenders, to the extent to which they select projects that maximize the expected development impact.<sup>2</sup> In addition, their investment decisions are driven by the explicit aim of mobilizing domestic and foreign capital but, in the same way as commercial lenders, they are also constrained by the need to preserve financial sustainability. Because of their specific mandate, MDBs can play an important role to help fund the investment gap, directly providing financial assistance, but also mobilizing additional private sector resources to developing countries. The first role, 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 fact that MDBs’ loan capacity is small compared to countries’ needs: demand for financing exceeds supply well beyond 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 Panama Canal expansion. The project to expand the canal and allow larger ships to transit to avoid market losses followed a referendum in 2006 and was financed by the Inter-American Development Bank Group, the European Investment Bank, the Corporaci ́on 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% of GDP—and in the 5 years after its announcement it is estimated to have attracted almost USD 10 billion in private investment, 1.8 times the project cost (Lanzalot et al., 2018).

We formally test whether MDBs can crowd-in private sector resources to finance investment by looking at MDB participation in syndicated lending, which is a key source of funding for private corporations in developing and emerging markets (Bruche et al., 2017; Cortina et al., 2018). There are different channels through which MDBs can leverage additional resources from the private

> 1See Table A1 for a list of the MDBs included in the analysis. See Engen and Prizzon (2018) for an overview of MDBs, their mandate, operations and financial activities.

> 2In 2012, MDBs endorsed the _Principles to Support Sustainable Private Sector Operations_ —additionality, crowdingin, commercial sustainability, reinforcing markets, promoting high standards—which aim to guide their engagement with the private sector to achieve the development goals they pursue as part of their mandate. These principles were reinforced in the 2013 _DFI Guidance for Using Investment Concessional Finance in Private Sector Operations (Private Sector Development Institutions Roundtable, 2013)_ , the 2017 _Enhanced Principles for Blended Finance (Multilateral Developlment Banks, 2018a)_ , and the 2018 _Multilateral Development Banks’ Harmonized Framework for Additionality in Private Sector Operations (Multilateral Developlment Banks, 2018b)_ .

1
sector. For example, MDBs can mobilize private finance as their entrance in a given country-sector could signal future investment opportunities. Also, thanks to their long-term perspective, MDBs could promote macroeconomic stability, growth and an investment-friendly environment,<sup>3</sup> all factors that can attract 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; Bas ́ılio, 2014). MDBs can also mobilize private resources thanks to the reduction of political and credit risks. MDBs can use their leverage to influence governmental decisions and deter adverse events that would negatively affect the project outcome (Hainz and Kleimeier, 2012). Credit risk could be reduced through multilateral guarantees and the 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 et al., 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 et al., 2013; Ratha, 2001; Gurr ́ıa et al., 2001).

However, MDB lending could be a substitute rather than a complement to private finance, leading to crowding-out rather than crowding-in of private capital inflows (Bas ́ılio, 2014; Bird and Rowlands, 2007). Lack of additionality could simply be the result of the fact that MDB lending may displace private investors which 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 originate from the fact that MDBs impose higher environmental, social, and governance standards, monitor development outcomes—which is costly—and may interfere 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 et al., 2018). In particular, since an MDB’s choice to invest

> 3Through advisory and knowledge work MDBs can help governments identify and implement reforms to improve the investment environment and remove barriers to investments. Moreover, by encouraging countries experiencing balance of payments crises to pursue an IMF-supported program, or offering emergency financing to address macroeconomic vulnerabilities only after the IMF has assessed that an appropriate macroeconomic framework is in place, they can help restore macroeconomic stability (Group of Twenty, 2018).

2
in a given country-sector is not exogenous, the identification of causal effects is impaired by the fact that macro data do not allow for determining if private lending would have happened even without MDB involvement. Our approach, based on more granular loan-level data, has the advantage of absorbing all time-varying country- and sector-specific factors which could drive MDB and private sector lending via a large set of fixed effects. In particular, by exploiting country-sector level data, we are able to control not only for country- and year- fixed effects, but also for countrysector, country-year, and sector-year fixed effects; hence, we greatly diminish the possibility of omitted variable bias and increase accuracy in the estimation of the mobilization effects.

Our 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 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, we show (i) descriptive evidence that MDBs tend to be among the first to enter a given country-sector, and (ii) formal evidence of the lack of anticipation effects. Then, we run a number of robustness tests to address lingering concerns about the omitted variable bias, which could potentially affect our results, as long as there are confounding factors driving both MDB and private bank lending. In particular, we show that 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 the size of the estimated MDB mobilization effects. In addition, we control for sector-specific linear and quadratic trends and identify the effects in deviation from trend, and we further control for unobserved heterogeneity computing the Oster (2019) bounds.

We perform a number of extensions. First, to have a better sense of the macroeconomic implications of our results, we exploit the information on the volume of MDB lending to estimate the MDB lending multiplier. We find 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 threeyear period. These are economically meaningful effects, which suggest that MDBs could actively contribute to mobilizing resources towards meeting the ambitious goals of the 2030 Development Agenda. Second, by estimating total mobilization effects, which include direct and indirect effects, we find evidence suggesting that MDBs can attract private flows both directly and indirectly.<sup>4</sup> Third, we deal with the concern that the mobilization effects on lending could be partially or com-

> 4 We consider direct mobilization as the financing from a private entity that directly participates in a syndicated loan with the MDB, and indirect mobilization as the financing from a private entity mobilized in connection with a specific MDB activity but lent to the borrower through other syndicated loans.

3
pletely offset by a reduction in other debt flows, finding no evidence that corporate bond financing declines after an MDB starts lending in a given country-sector pair.<sup>5</sup> Fourth, 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. We show that this is not the case and our results hold even when aggregating the data at the country-year level. Finally, we find differences in the mobilization effects across countries. In particular, some of these effects are weaker in low-income countries, suggesting that MDBs still face challenges with mobilizing resources in weak macroeconomic contexts.<sup>6</sup> The existing literature on mobilization effects is mostly focused 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 be easily generalized to MDBs, given its nature of crisis lending.<sup>7</sup> A smaller strand of literature has explicitly focused 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), he does not find a significant association between past multilateral lending and current private flows. With 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.

Our contribution to this strand of literature is twofold. First, we depart from the literature on catalytic finance that has mainly discussed the catalytic role of the IMF by focusing specifically on the role of MDBs, which have been greatly overlooked to date and can instead be an important player in light of the 2030 Development Agenda. Second, while the literature has mainly focused on case studies or country-aggregate data, our 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

> 5We focus on the potential substitution between corporate bonds and syndicated loans, as they constitute two similar sources of financing from a firm’s perspective (Altunbas ̧ et al., 2010), while the role of the equity market in developing countries is still relatively limited (World Bank, 2015; Cortina et al., 2018). We also look at potential displacement effects on aid and other development finance flows, finding no evidence that MDB mobilization effects crowd-out these flows. 6The last set of results is discussed in Appendix A.

> 7Theoretically, Corsetti et al. (2006) and Morris and Shin (2006) show that IMF catalytic financing can reduce the incidence of panic-driven liquidity crises. Empirically, Eichengreen and Mody (2001), Mody and Saravia (2006) and Eichengreen et al. (2006) use transaction data of individual bond issuances and find the IMF programs can have a catalytic effect, conditional on country fundamentals being only moderately bad. In a review of economics and politics of the IMF, Bird (2007) warns against any generalization from the empirical evidence, as results do not appear robust and consistent across methodologies, samples and economic conditions. More recently, Erce and Riera-Crichton (2015) use aggregate data on gross capital flows to show that while the IMF does not appear able to catalyze foreign capital, there is substantial evidence that it does affect the behavior of resident investors, who are less likely to place their savings abroad and more likely to repatriate their foreign assets.

4
years. In this way, we can: (i) better isolate the effect of MDB participation on subsequent bank flows (as well as loan terms), (ii) run a series of additional tests to deal with the omitted variable bias to provide a convincing estimate of the mobilization effects, (iii) test for crowding-out effects to other flows, and (iv) estimate country-level mobilization effects. To the best of our knowledge, we provide the first assessment and quantification, fully based on loan-level data, of MDB mobilization effects, a key channel to catalyze the private sector and finance investment and growth in developing countries.

# **2 Data**

Our main source of data is the Dealogic Loan Analytics database, which contains micro data at the level of tranches of all syndicated loans to 127 developing countries from 1993 to 2017. These data are widely used for studying the international syndicated loan market (see, for instance, 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 et al. (2015) compare syndicated loan exposures with loan claims as 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, e.g., a group of lenders, that share risks by pooling together capital. They 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 et al., 2017; Cortina et al., 2018).

In line with existing studies (e.g., Nini, 2004; Carey and Nini, 2007; Cortina et al., 2018), we exclude loans to public authorities, as they are likely driven by different factors compared to loans to non-sovereign entities (private and public sector firms). As a result, we are left with 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 with the majority of syndicated loans across the period are India, Brazil, Russia, Indonesia, Turkey and Mexico, as shown by the largest bubbles in Figure 1. The countries with the greatest share of loans supported by MDBs are Afghanistan, Tajikistan, Vanuatu, Belize, Kyrgyzstan and Moldova, as shown by the darker bubbles in the same chart. Over time the number of syndicated loans has increased, despite some drops during major financial crises, as shown in Figure 2.

The information in the data is comprehensive, including signing date, nationality of the bor5
rower, total value of the deal in USD, maturity of each tranche, the general industry group of the deal and the name of the lending banks. We also have information on deal type, specifically whether the syndicated loan is an investment grade, leveraged or highly leveraged loan.<sup>8</sup> Thanks to the information on the lender, we are able to distinguish the syndicated loans in which there is the participation of at least one MDB. The MDBs participate in the syndicated loans markets at 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 our 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 A1. Deals that involve MDBs are 9.2 percent of the sample and they are, on average, significantly larger, with longer maturities, and with fewer non-MDB banks involved (Table 1).

We group the information on the general industry of the syndicated loans into 9 sectors: agriculture, construction and real estate, finance, government, infrastructure, manufacturing, mining and metals, oil and gas, and services. The majority of loans belong to the finance, infrastructure, and manufacturing sectors, which are also the sectors with the majority of loans with MDB participation.

To prevent large countries from driving the results, we use nominal GDP in USD from the World Economic Outlook (WEO) as a scaling factor for the total value of the syndicated loan. Scaling by GDP also gives us a better understanding of the relative magnitude of the loan size with respect to the country’s economy. For data cleaning purposes we winsorize the top and bottom 1 percent of the total amount of syndicated loans, scaled by GDP .

With this information we build a balanced panel at the country-sector-year level. In total, we have 26,325 observations corresponding to 1,053 country-sectors (117 countries and 9 sectors) that we observe for 25 years. By constructing a balanced panel, we impute zeros to country-sectoryears where no syndicate lending takes place. Consequently, the averages of the main variables of interest become smaller in magnitude. For example, while the average number of participating banks per syndicate loan is around 6.2 (Table 1), the average number of banks participating in the syndicate lending market per country-sector-year is 1.6 (Table 2). To test whether syndicated loans with MDB participation crowd-out corporate bond financing, we also use the information

> 8This characterization refers to the borrower’s credit rating, with investment grade loans being issued to higherrated borrowers, while leveraged loans are for below-investment-grade borrowers. Most are investment grade loans (62.9 percent), the rest are leveraged. There is a small percentage (4.6 percent) of syndicated loans that are highly leveraged.

6
on corporate bond issuances provided by Dealogic for 67 developing countries over the period 1993-2017. As for loans, we transform the data into a balanced panel at the country-sector-year level.

Our main outcome variables of interest are: 1) the number of syndicated loans in the countrysector, excluding deals with MDB participation; 2) the total size of the syndicated loans (as a share of GDP), excluding both the amount lent by the MDBs and by their partners<sup>9</sup> ; 3) the number of banks per loan, excluding those partnering only in deals with MDB participation; and 4) the average loan maturity.<sup>10</sup> To explore direct mobilization effects we also look at: 5) the number of banks that participate in syndicated lending per loan; and 6) 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 the ones supported by MDBs. The average number of banks (excluding MDBs) involved in syndicated loans is 1.6, similar to the average number of banks without counting the ones partnering in a syndicated loan with MDBs. The average maturity of loans, without counting loans 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 of syndicated loans. On average, bond issuances in the country-sector pair are more numerous than syndicated loans, but their size (as a percent 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 to enter a given country-sector through the syndicated loan market. If, for each countrysector pair, we define the “first” lending year as the first year since 1995 in which a country-sector receives a syndicate loan, we find that in 32 percent of these “first” years there is at least an MDB joining the first syndicated loans; by contrast, this share becomes substantially lower (23 percent) in subsequent years.<sup>11</sup>

> 9That is, we exclude the full amount of any syndicated loan the MDB is involved in, i.e., we exclude the partners’ contribution to all syndicated loans with an MDB.

> 10The dataset provides the maturity of each tranche of syndicated loans. We calculate the weighted average of the maturities of syndicated loans by weighting the maturity of each tranche by its relative size within the loan. Once we collapse the dataset at the country-sector-year level, we calculate the average of weighted maturities of syndicated loans in the country-sector-year.

> 11As our sample begins in 1993 and we do not have information of the presence of syndicated loans before that

7
# **3 Empirical Strategy**

A key advantage of using loan-level data is that we can set up our dataset at the country-sectoryear level and look at new loans in a given country-sector pair after an MDB has entered that country-sector co-financing a syndicate loan, controlling for time-varying unobservable factors both at the country and sector level. Thus, we evaluate whether the presence of an MDB attracts private capital by estimating the following equation: where _ycs_ , _t_ is the outcome variable in country-sector pair _cs_ at time _t_ . As anticipated in the previous section, in the paper we explore effects on different outcome variables defined at the countrysector-year level: 1) the number of syndicated loans; 2) the total size of syndicated loans, scaled by GDP; 3) the average number of banks involved per syndicated loan; and 4) the average loan maturity of syndicated loans, in years. When measuring the outcome variables, in the baseline analysis we focus on indirect mobilization and we always exclude the loans with MDB participation. In additional analyses, we exploit the richness of the dataset to disentangle direct and indirect mobilization effects by computing the number of banks and loan size including also the partners of the MDBs and the amount they lend in the loans with MDB participation; see Section 5.2.

We also include a lagged dependent variable, _ycs_ , _t−_ 1, as lagged syndicate lending in the countrysector _cs_ could be an omitted variable that is time-varying within sector and that represents a threat to our identification strategy.

Our key explanatory variable ( _MDBcs_ , _t_ ) is a dummy equal to one if there is at least one deal supported by MDBs in the country-sector pair _cs_ at time _t_ .<sup>12</sup> The coefficient _β_ 0 of _MDBcs_ , _t_ measures the contemporaneous MDBs’ (indirect) mobilization effect. However, to allow for the possibility that mobilization effects show up with a lag, we include up to two lags of _MDBcs_ , _t−k_ and then calculate the cumulative effect between year _t −_ 2 and _t_ ( _β_ 0 + _β_ 1 + _β_ 2). To rule out that there is an increase in the outcome variables before MDB participation, in the robustness section we also test for the presence of any anticipation effect including up to two leads of the MDB dummy variable.

We saturate the model with a large set of fixed effects to absorb unobserved factors which

> date, we compute the shares by allowing the “first” to begin in 1995, to leave at least two years with no syndicated loans before (under the assumption that there were no loans before 1993). If we relax this assumption, and we start considering any year between 1996 and 2000 as the “first” year, the results are qualitatively similar.

> 12However, we also employ a continuous indicator to measure the intensive margin of MDB participation and compute the MDB lending multiplier; see Section 5.1.

8
could drive lending by both MDBs and commercial banks and potentially bias the estimate of the _β_ coefficients. In theory, the bias could go in both directions. 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 also taken explicit steps to mobilize domestic and foreign capital and make investment decisions subject to financial sustainability (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 overestimating the mobilization effect. Time-varying country ( _δc_ , _t_ ) and sector ( _ζs_ , _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 _×_ sector fixed effects ( _αcs_ ) further reduce the threat of omitted variable bias by controlling for all time-invariant differences in observables and unobservables between country-sector pairs. Thus, our strategy 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, we have to assume that all the time-changing characteristics of country-sector pairs that we cannot observe are uncorrelated to the presence of syndicated loans with MDB participation. However, changes in local economic conditions, changes in global commodity prices, policy changes and reforms that affect some sectors in a country more than others could affect both MDB and private lending to a countrysector, potentially biasing our estimates. To deal with this threat to identification, in Section 4.2 we augment the baseline model to control for a set of variables which 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, we estimate the size of the potential omitted variable bias computing the Oster (2019) bounds. Finally, our results may pick up a spurious correlation if different country-sector pairs follow specific trends. We deal with this concern by augmenting our model with linear or quadratic trends specific to the country-sector pairs.

We follow Abadie et al. (2017) and cluster the standard errors ( _εcs_ , _t_ ) 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 we also cluster standard errors at the country-sector level, to account for the possibility that in reality MDBs’ support is assigned at the country-sector level; see Section 4.2.

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# **4 Results**

## **4.1 Main Findings**

Table 3 shows the results of estimating equation 1 for the different outcome variables in separate panels. Each panel reports the results adding sequentially a different set of fixed effects to document how the coefficients of the MDB dummy variables change as we control for unobserved heterogeneity at the country _×_ sector, country _×_ year, and sector _×_ year level. As a general trend, we observe that saturating the model with fixed effects significantly increases its explanatory power (as illustrated by the increase in the _R_<sup>2</sup> ) and attenuates the estimated correlation between MDB participation and private lending. For instance, the _R_<sup>2</sup> 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 2 MDB dummies (∑ _MDB_ cs,t-k) is halved (for loan size) or reduced by two thirds (in case of loan _k_ =0 maturity). We find a similar reduction for the cumulative effect on the number of banks (Panel C). Interestingly, the inclusion of the fixed effects does not only reduce the coefficients of the MDB dummies when explaining the number of loans (Panel A), but it also makes the cumulative effect no more statistically significant.

A similar pattern is present when looking at the coefficients of the lagged dependent variables, which become smaller as we add more granular fixed effects, but remain statistically significant even when they are identified within country-sector pairs and controlling for unobserved heterogeneity at the country and sector level. 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) adding the last set of sector-year fixed effects would suggest that the most saturated model absorbs most of the unobserved heterogeneity. In the rest of the analysis we take the specification saturated with country _×_ year, sector _×_ year and country _×_ sector fixed effects as the preferred one and we 10
focus our attention on the size of the cumulative effects. However, in Section 4.2 we perform a set of additional tests to rule out that our estimates are biased by observed confounding factors, country-sector specific trends, and some residual unobserved heterogeneity.

Moving to the specific results, the first column 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 _t_ to _t+2_ is equal to 0.9. As discussed above, adding the fixed effects reduces the estimated coefficient _βt_ and reduces the cumulative effect by half and, more importantly, makes it not statistically different from zero. However, when considering the total amount lent to private borrowers in the country-sector pair (measured in percent of GDP and excluding the amount lent by the MDBs themselves and by their partners in the syndicated loans), results indicate that MDB participation is associated with more private sector lending, even when adding the full set of fixed effects (Panel B). Our preferred (most demanding) specification indicates that the cumulative _indirect_ mobilization effect between year _t −_ 2 and year _t_ is equal to 0.125% of GDP, which corresponds to about 120% of the average size of syndicated loans to private creditors in a country-sector-year (which is equal to 0.11% of GDP, see Table 2). If we scale this effect by the size of the average syndicated loan (also equal to 0.11% of GDP, see Table 1), we still observe that the cumulative effect is economically large.

Results shown in Panel C assess 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 the country-sector pair in the same year. The effect is persistent over time, with similar magnitudes each year. Thus, the cumulative effect over three years—equal 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 is formed by 6.1 banks (Table 1).

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

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# **4.2 Robustness**

**Anticipation effects.** Even though our empirical setting controls for a large set of unobserved factors that could drive syndicated lending, reverse causality remains a concern, as MDBs could follow private lending and enter country-sector pairs which have been receiving more and larger syndicated loans. To mitigate this concern, we test for anticipation effects by adding up to 2 leads of the variable _MDBcs_ , _t_ to equation 1, so that we estimate the following model:

Our results are robust to the inclusion of leads of the MDB participation dummy and there are no anticipation effects. The coefficients of _MDBcs_ , _t_ +1 and _MDBcs_ , _t_ +2, reported in Table 4, are never significantly different from zero for all four dependent variables, indicating that MDBs do not enter in markets where the number, value of loans, number of banks per loan, and maturity are already increasing. However, some caution should be used when interpreting these results, as some of the point estimates, while not significant, are positive and large in magnitude (although much smaller than the contemporaneous and lagged coefficients, with the exception of loan size). Moreover, even though cumulative anticipation effects are mostly not significantly different from zero, when we estimate equation 2 on size, the cumulative anticipation effects are marginally significant.

The lack of significant anticipation effects suggests that MDBs do not enter country-sectors following private lenders. By contrast, our results indicate that MDBs are the first 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 the fact that MDBs and the private sector have 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 us to control for trends common to all countries or to all sectors. But this specification might still be vulnerable to a different problem. Let us consider, for example, the same sector (e.g., oil and gas) 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 control for this possibility and the estimated _β_ coefficients would spuriously pick up these trends. To control for this possibility, we add parametric trends in each country-

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sector pair and identify the effect of MDB participation as a deviation from trend. As shown in Table 5, results are robust to the inclusion of linear or quadratic trends. Interestingly, we also find 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.<sup>13</sup>

**Sensitivity to outliers.** Given that our dependent variables are characterized by a skewed distribution with a large share of zeros, we run several tests to make sure that our results are not driven by outliers. We start by identifying countries or sectors where there are few syndicated loans and exclude them from the analysis. First, we calculate the number of syndicated loans per country in the 1993-2017 period and exclude countries that have fewer than 100 syndicated loans in total. This selection leaves us with a sample of 32 countries. Even though the number of observations in the sample drops significantly, the results—shown in Table A2—are mostly robust: the estimated cumulative effects when the outcome variable is the number of loans are not significantly different from zero (column 1), and the effects on loan size (column 2), banks per loan (column 3), and loan maturity (column 4) are not significantly different from the ones presented in Table 3 either. Second, we exclude from the sample sectors with fewer than 1,000 syndicated loans in 1993-2017. We are left with 5 sectors: oil and gas, construction and real estate, manufacturing, infrastructure, and finance. As shown in Table A3, results are generally not significantly different from Table 3. Finally, we combine the previous two exercises and exclude the outliers defined both in terms of countries and sectors. As shown in Table A4, even though the sample size is about 15% of the whole sample, results are consistent with the baseline, suggesting that a large presence of zeros in the balanced dataset is not biasing our results.

**Clustering of standard errors.** In our fixed effects setting, a clustering adjustment is necessary if the treatment assignment mechanism is clustered and if there is heterogeneity in the treatment effects (Abadie et al., 2017). Throughout the paper we cluster standard errors at the country level, in line with the idea that MDB support is assigned at the country level and that mobilization effects are heterogeneous across countries. However, if the decision to provide MDB syndicated loans is made at the country-sector level, clustering at a more aggregate level gives estimates that are too conservative. As the level at which MDB support is assigned cannot be determined with certainty, we opted for the most conservative approach and clustered at the country level. Table A5 provides a robustness check with standard errors clustered at the country-sector level, in line with MDB

> 13As the identification is in deviation from trend, the effect of the lagged dependent variable is significantly smaller than in the baseline (Table 3, panel A), a fact which could explain why the effect of MDB participation turns significant.

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support assigned at the country-sector level. Coefficients are more precisely estimated and more likely to be significantly different from zero compared to the baseline results in Table 3.<sup>14</sup>

**Confounding factors.** In all our estimates we are able to control for unobserved time-varying country- and sector- level unobserved variables that can drive private lending. Although we also include country _×_ sector fixed effects, our baseline set of fixed effects cannot control for the possibility that other time-varying factors attract resources from both MDBs and private creditors in a given country-sector pair. To address this concern, we run a set of additional tests, reported in Appendix A.1, in which we augment the baseline model with a set of variables that vary over time and between country-sector pairs. Specifically, we show that our results are not driven by the presence of large global banks, Chinese lending, official development assistance, corporate bond financing, and value added growth. The idea that controlling for a large set of fixed effects reduces the scope for omitted variable bias is further reinforced by the estimation of Oster (2019) bounds.

# **5 Extensions**

## **5.1 The MDB Lending Multiplier**

So far we have focused our attention on the presence of MDBs in a given sector through their participation in a syndicated loan. However, we can exploit all the available information on the size of MDB lending to capture a sort of multiplier, which could give a better sense of the economic relevance of the mobilization effects of MDBs.

To this end, we regress total dollar lending at the country-sector-year level (excluding the loans in which MDBs take part) on the dollar amount of MDB lending, and we run two exercises, which should be interpreted keeping in mind the limitations of our research design in terms of identification. In the first exercise, we take the logarithms of the dollar flows and directly estimate the elasticity of private bank lending to MDB lending (Table 6, columns 1-3). Scale factors are absorbed by country-sector fixed effects, so that we are looking at the mobilization effects within a country-sector pair. However, we further strengthen our identification with the inclusion of linear and quadratic country-sector trends. The results are robust to the inclusion of trends and consistently show that the cumulative elasticity is about 0.17. Given the average values of total and MDB lending (reported at the bottom of the table), the implied marginal effect is around 7, that is, for

> 14We run an additional exercise and use the two-way clustering (Cameron et al., 2011) to account for cross-country correlation within sector in addition to cross-sector correlation within country. Results are reported in Table A6 and show that our baseline results are robust to this alternative treatment of the correlation structure of the standard errors.

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each dollar that the MDB puts in a country-sector in the previous 3 years, the private sector lends about 7 dollars.

Alternatively, in the second exercise we scale the bank flows by (country-level) population and regress total dollar lending per capita at the country-sector-year level on the dollar amount per capita of MDB lending (Table 6, columns 4-7). In the full sample (column 4), we do not find significant cumulative effects, even though the p-value is 0.2. However, once we reduce the noise and restrict the sample to countries with at least 50 (column 6) or 100 (column 7) loans over the sample period, the cumulative marginal effect becomes significantly different from zero. Moreover, the point estimates are very close to those found through the log-log regressions shown in columns 1-3, confirming that the MDB lending multiplier is in the order of 7.

# **5.2 Direct and Indirect Effects**

Thanks to the richness of the data, we are able to observe the total mobilization effects, which include both the direct and indirect mobilization effects (see footnote 4 for their definition). To disentangle indirect from direct effects, we re-estimate equation 1, but the outcome variables now measure total mobilization effects and include the volume of the loan in which MDBs participate (excluding their share) and the number of private creditors partnering in the loan itself. Results are reported in the first two columns of Table 7, while columns 3 and 4 report only the indirect mobilization effects as estimated in the baseline, where the outcome variables exclude the loans with MDB participation. The table reports mobilization effects at time _t_ only to make a clearer comparison between direct and indirect effects, as anything that happens from _t+1_ onward is pure indirect mobilization. Results on total mobilization effects in columns 1 and 2 show that a countrysector with MDB lending experiences a volume of syndicated loans— including both the amount lent by MDB partners (direct mobilization) and the amounts lent through other loans (indirect mobilization)—which is 0.3% of GDP higher than in country-sectors without MDB participation.<sup>15</sup> Moreover, in country-sectors with MDB participation there are 2.6 more banks, either partnering with the MDB (direct mobilization) or forming part of other deals (indirect mobilization). Comparing these findings with the indirect mobilization effects as estimated in the baseline (columns 3 and 4, where outcome variables exclude the loans with MDBs’ participation) shows that the total

> 15We cannot isolate direct mobilization effects, as direct mobilization effects will also include part of the indirect effects if an MDB was previously present in the country-sector. Indeed, when we restrict our analysis to 2000-2017, and we control for MDB participation in syndicated loans in the country-sector in 1993-1999, we find that MDB mobilization effects are even stronger if MDBs were already present. Findings are robust to reducing the sample period to 2005-2017 and controlling for MDB presence in the country-sector in 1993-2004.

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mobilization effects are larger than the indirect effects, indicating that MDBs can attract private lending both directly and indirectly.

# **5.3 Infrastructure Lending**

We also assess whether mobilization effects take place in the infrastructure sector alone. We focus on infrastructure given its relevance for development and the urgent need for resources in this sector (Dobbs et al., 2013; Gurara et al., 2018; World Bank, 2018). According to UN estimates, total investment in economic infrastructure is currently under USD 1 trillion per year, but will need to reach between USD 1.6-2.5 trillion a year over the period 2015-30 (UNCTAD, 2014). Infrastructure investments can provide relatively high total returns with low correlations to traditional asset classes (e.g., equities, real estate), but are characterized by high perceived risks (JPMorgan, 2017; Blended Finance Taskforce, 2018). Moreover, given its long-term financing needs, the sector is more vulnerable to resource scarcity (Chelsky et al., 2013), making the effect of MDB lending on attracting private flows particularly relevant. Hence, we limit our analysis to a subsample of syndicated loans in infrastructure only (Table A7). Perhaps due to a loss of power, we do not observe any significant effects on the number of loans or size of loans. The effect on the number of creditors is similar to our baseline results. Given that investments in infrastructure are long-term, what is perhaps most interesting to note is the larger mobilization effect on maturity: MDB participation in the infrastructure sector of a country increases the average maturity of syndicated loans by 0.81 years, and results are substantial even in the following years.

# **5.4 Is There a Crowding-Out of Corporate Bond Financing?**

Our baseline analysis looks exclusively at syndicated lending. In this respect, one may argue that the positive mobilization effects that we have documented so far could be partially (or fully) offset by a reduction in other capital inflows. To address this concern, we look at corporate bond issuances, which represent the closest substitute to syndicated loans, as they have similar size and maturity (Altunbas ̧ et al., 2010). Aggregate flows to developing countries show that corporate bonds and syndicated loans are the largest source of long-term finance in developing countries, given the still relatively limited size of equity markets (World Bank, 2015; Cortina et al., 2018).

To test for any substitution effect, we estimate equation 1 by taking the number of bond issuances and the total amount of bond issuances (in percent of GDP) in the country-sector pair _cs_ at 16
time _t_ as dependent variables.<sup>16</sup> Results are reported in Table 8 and show no significant effect on the number of bonds or in the size of corporate bond financing at the same time as, and in the two years after, MDB participation in a syndicated loan (columns 1 and 5). In the remaining columns we show that results are robust to the inclusion of controls for the presence of the top 10 banks and ODA in the previous 2 years. Only when we control for Chinese lending (columns 3 and 7) do we observe a marginally significant increase in the size of bond financing at _t+1_ . The cumulative effects are never significantly different from zero.<sup>17</sup> Overall, there is no evidence of crowding-out in the corporate bond market.

A further concern could be that MDBs’ entrance in a given country-sector pair may attract the private sector, but at the same time, crowd-out other sources of development financing. To mitigate this concern, we run the same exercise explained above but using either the number of projects financed by ODA flows or the number of officially-financed Chinese projects as dependent variables. In both cases, we do not find any statistical association between MDB participation and changes in official aid flows by OECD’s Development Assistance Committee (DAC) countries and official Chinese lending (see Table A9).

# **6 Conclusions**

Filling the investment gap to achieve the SDGs by 2030 is a major development challenge. As foreign aid and domestic revenue mobilization alone cannot meet the target, leveraging private investment is essential to make progress towards inclusive growth. In this respect, MDBs can play an important role in attracting private capital flows towards investment in developing countries.

We use granular data on international syndicated lending to evaluate whether MDBs can mobilize private capital flows. Our results indicate that once an MDB enters a country-sector pair through the participation in a loan syndication, the number of total syndicated loans and the associated bank lending flows increase. In addition, access to credit improves, since the average number of lending banks per loan and the average loan maturity also increase. These effects last over time and are economically sizable: for each dollar that MDBs invest in a country-sector pair over a three-year period, commercial banks lend almost 7 dollars.

These results are robust to a large set of tests controlling for the potential role of several con-

> 16As for the size of syndicated loans, we winsorize the bond size variable at 1%.

> 17Since the sample used to analyze effects on bonds is smaller, we test whether the absence of effects is driven by the sample construction, and re-estimate mobilization effects on syndicated loans in this same sample. Results are robust—see Table A8.

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founding factors and unobserved heterogeneity that may drive lending by MDBs and private banks at the same time. We also find that MDB mobilization effects in the syndicated loan market do not crowd-out corporate bonds and other sources of external financing. Furthermore, our main results are confirmed when we aggregate the data at the country level, suggesting that there are no crowding-out effects across sectors.

Finally, our findings indicate that the mobilization effects are not homogeneous across countries. In particular, MDB lending could be less effective in mobilizing private bank flows to lowincome countries, suggesting that MDBs may still face significant constraints to attract private resources, especially in countries with larger financing needs.

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# **Figures**

Figure 1: Number of Syndicated Loans and Share with MDB Support by Country, 1993-2017 <!-- Start of picture text --> IBRD 44692 | OCTOBER 2019<br><!-- End of picture text -->

_Notes_ : The bubbles in the map represent the number of syndicated loans by country in 1993-2017. The area represents the number of total syndicated loans in the period: the larger the bubble, the more syndicated loans. The color represents the share of syndicated loans supported by at least one MDB in the period: the darker the blue, the more the syndicated loans receive MDB support.

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<!-- Start of picture text --> 1200<br>1000<br>c<br>oo<br>2<br>iE 800<br>[un<br>2<br>ez<br>~ 600<br>—<br>o<br>g<br>E 400<br>=<br>=<br>200 =”<br>-_-" “ ‘‘<br>— aa Neen<br>= - ‘et es a a —<br>0<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>—— Allsyndicated loans -—-—-—- Syndicated loans with MDBs' participation<br><!-- End of picture text -->
# **Tables**

Table 1: Summary Statistics of Syndicated Loans

||**Mean**|**SD**|**Min**|**Max**|**N**|
|---|---|---|---|---|---|
|Panel A._All Syndicated Loans_||||||
|Size (% GDP)|0.108|0.319|0.001|4.295|21,164|
|Number of participating banks|6.153|7.498|1|80|21,373|
|Maturity (years)|5.458|4.476|0.083|35.083|18,603|
|Loans with MDB participation (%)|9.202|28.908|0|100|21,373|
|Panel B._Syndicated Loans with MDB Parti_|_cipation_|||||
|Size (% GDP)|0.169|0.403|0.001|3.938|1,946|
|Number of participating banks|4.487|5.976|1|59|1,967|
|Maturity (years)|7.489|4.499|0.2|30|1,111|
|Panel C._Corporate Bonds_||||||
|Size (% GDP)|0.030|0.060|0.001|0.304|18,760|

_Notes_ : The table provides loan-level (bond-level in Panel C) summary statistics for the main outcome variables in the analysis: (i) size of syndicated loans (% of GDP), (ii) number of banks participating in syndicated loans, and (iii) average weighted maturity (in years, averaged across tranches, where weights are the relative sizes of the tranches) of syndicated loans. Panel A refers to the full sample of syndicated loans at the individual level. Panel B refers only to those syndicated loans where there is at least one MDB that supports a syndicated loan. Panel C refers to corporate bonds.

Table 2: Summary Statistics in the Aggregate Data

||**Mean**|**SD**|**Min**|**Max**|**N**|
|---|---|---|---|---|---|
|Panel A._Syndicates Loans_||||||
|Number of Loans|0.737|4.518|0|188|26,325|
|Size (% GDP)|0.109|0.693|0|41.514|26,325|
|Size w/o Loan w/MDB (% GDP)|0.101|0.672|0|41.514|26,325|
|Banks|1.474|5.185|0|80|26,325|
|Banks w/o MDB partners|1.562|5.309|0|80|26,325|
|Maturity (years)|0.724|2.174|0|35|26,096|
|Panel B._Corporate Bonds_||||||
|Number of Bonds|1.245|8.801|0|336|15,075|
|Bond Size (% GDP)|0.037|0.18|0|6.187|15,075|

_Notes_ : The table provides summary statistics for the main outcome variables in the analysis in the full sample of countrysector-years. Panel A reports summary statistics for: (i) number of loans, (ii) size of syndicated loans (% of GDP) including the amount brought by MDB partners, (iii) size of syndicated loans (% of GDP) excluding the amount brought by MDB partners, (iv) average banks per loan including MDB partners, (v) average banks per loan excluding MDB partners, and (vi) maturity of syndicated loans (in years) in country-sector-years. Loan size is winsorized at the 1% level. Loan maturity is the average weighted maturity (averaged across tranches, where weights are the relatives sizes of the tranches) of syndicated loans. Panel B reports summary statistics for number and the size of corporate bonds (% of GDP) at the country-sector-year level. Bond size is winsorized at the 1% level.

26
Table 3: MDB Mobilization Effects: Baseline Results

||**Panel A.**|Number of|Loans||**Panel B.**S|ize (% GDP|)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MDBcs,t|0.6553***|0.5502**|0.3791**|0.3846**|0.1099***|0.0835***|0.0656***|0.0637***|
||(0.222)|(0.224)|(0.169)|(0.161)|(0.025)|(0.022)|(0.022)|(0.020)|
|MDBcs,t-1|0.3006*|0.2396|0.1245|0.1235|0.0493*|0.0349|0.0192|0.0125|
||(0.156)|(0.161)|(0.152)|(0.158)|(0.025)|(0.023)|(0.024)|(0.024)|
|MDBcs,t-2|-0.0528|-0.1072|-0.0583|-0.0508|0.0792***|0.0559***|0.0509***|0.0492***|
||(0.106)|(0.179)|(0.155)|(0.159)|(0.020)|(0.015)|(0.016)|(0.018)|
|Number of Loanscs,t-1|0.9521***<br>(0.053)|0.8712***<br>(0.073)|0.8688***<br>(0.078)|0.8685***<br>(0.078)|||||
|Size (%GDP)cs,t-1|||||0.4382***|0.2282***|0.2214***|0.2155***|
||||||(0.078)|(0.069)|(0.071)|(0.071)|
|2<br>∑<br><br>_MDB_cs,t-k|0.903***|0.683|0.445|0.457|0.238***|0.174***|0.136***|0.125***|
|_k_=0<br>_Wald test_ : _p value_|0.008|0.124|0.228|0.214|0.000|0.000|0.002|0.005|
|Observations|24,219|24,219|24,219|24,219|24,219|24,219|24,219|24,219|
|R-squared|0.779|0.792|0.833|0.835|0.197|0.319|0.401|0.410|
|Average MDB|0.048|0.048|0.048|0.048|0.048|0.048|0.048|0.048|
|Average Dep. Var.|0.760|0.760|0.760|0.760|0.105|0.105|0.105|0.105|
||**Panel C.**B|anks|||**Panel D.**|Maturity|||
||(1)|(2)|(3)|(4)|<br>(5)|<br>(6)|(7)|(8)|
|MDBcs,t|1.5789***|0.8945***|0.4796**|0.4634**|1.3678***|0.8443***|0.6855***|0.6371***|
||(0.259)|(0.262)|(0.206)|(0.200)|(0.149)|(0.133)|(0.133)|(0.125)|
|MDBcs,t-1|1.3708***|0.8162***|0.5634***|0.6026***|0.7080***|0.3605***|0.1762|0.1345|
||(0.238)|(0.249)|(0.188)|(0.180)|(0.149)|(0.121)|(0.125)|(0.125)|
|MDBcs,t-2|1.2071***|0.5697**|0.4287*|0.5157**|0.9294***|0.5888***|0.3672***|0.3096***|
||(0.218)|(0.219)|(0.223)|(0.226)|(0.114)|(0.095)|(0.095)|(0.092)|
|Banks per Loancs,t-1|0.6377***<br>(0.031)|0.3016***<br>(0.033)|0.2310***<br>(0.032)|0.2221***<br>(0.031)|||||
|Maturitycs,t-1|||||0.4430***|0.1310***|0.0856***|0.0751***|
||||||(0.032)|(0.022)|(0.017)|(0.017)|
|2<br>|||||||||
|∑<br>_k_=0<br>_MDB_cs,t-k|4.157***|2.280***|1.472***|1.582***|3.005***|1.794***|1.229***|1.081***|
|_Wald test_ : _p value_|0.000|0.000|0.003|0.001|0.000|0.000|0.000|0.000|
|Observations|24,219|24,219|24,219|24,219|23,439|23,439|23,439|23,439|
|R-squared|0.479|0.592|0.670|0.676|0.284|0.439|0.522|0.531|
|Sector-country FE|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|<br>Country-year FE|No|No|Yes|Yes|No|No|Yes|Yes|
|Sector-year FE|No|No|No|Yes|No|No|No|Yes|
|<br>Average MDB|0.048|0.048|0.048|0.048|0.038|0.038|0.038|0.038|
|<br>Average Dep. Var.|1.526|1.526|1.526|1.526|0.752|0.752|0.752|0.752|

_Notes_ : The table presents the estimates of equation 1. The dependent variables are: (i) the number of loans (Panel A, columns 1-4), (ii) the size of syndicated loans (% of GDP, Panel B, columns 5-8), (iii) the average number of banks per loan (Panel C, columns 1-4), and (iv) the average maturity of syndicated loans (in years) (Panel D, columns 5-8). MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . At the bottom of each panel, the table reports the cumulative effect of MDBcs,t between year _t −_ 2 and _t_ , with the associated p-value of a Wald test, and the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

27
Table 4: MDB Mobilization Effects: Controlling for Anticipation Effects

||Number<br>of loans|Size (% GDP)|Banks|Maturity|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|MDBcs,t|0.4378***|0.0628***|0.4477**|0.6169***|
||(0.144)|(0.024)|(0.217)|(0.136)|
|MDBcs,t-1|0.3278**|0.0112|0.5824***|0.0868|
||(0.135)|(0.024)|(0.183)|(0.132)|
|MDBcs,t-2|0.1437|0.0515***|0.4831**|0.2512***|
||(0.107)|(0.018)|(0.236)|(0.092)|
|MDBcs,t+1|0.1878|0.0277|0.2271|0.0986|
||(0.258)|(0.029)|(0.202)|(0.109)|
|MDBcs,t+2|0.1553|0.0450|0.2789|0.1153|
||(0.135)|(0.048)|(0.257)|(0.106)|
|Number of Loanscs,t-1|0.7307***<br>(0.077)||||
|Size (%GDP)cs,t-1||0.2036***<br>(0.072)|||
|Banks per Loancs,t-1|||0.2151***<br>(0.032)||
|Maturitycs,t-1||||0.0706***<br>(0.018)|
|2<br>∑<br>_k_=0<br>_MDB_cs,t-k|0.909***|0.126***|1.513***|0.955***|
|_Wald test_ : _p value_<br>|0.000|0.008|0.003|0.000|
|2<br>∑<br>_k_=1<br>_MDB_cs,t+k|0.343|0.072*|0.506|0.214|
|_Wald test_ : _p value_|0.356|0.083|0.178|0.168|
|Observations|22,113|22,113|22,113|21,388|
|R-squared|0.829|0.404|0.680|0.531|
|Sector-country FE|Yes|Yes|Yes|Yes|
|<br>Country-year FE|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|
|<br>Average MDB|0.049|0.049|0.049|0.039|
|Average Dep. Var.|0.730|0.105|1.545|0.736|

_Notes_ : The table presents the estimates of equation 2. The dependent variables are: (i) the number of loans (column 1), (ii) the size of syndicated loans (% of GDP, column 2), (iii) the average number of banks per loan (column 3), and (iv) the average maturity of syndicated loans (in years) (column 4). MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . The table also reports, in the bottom rows, the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

28
Table 5: MDB Mobilization Effects: Including Sector-Level Trends

||**Panel A.**N<br>|umber of loans<br>||**Panel B.**Siz<br>|e (% GDP)<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|MDBcs,t|0.3846**|0.4931***|0.5126***|0.0637***|0.0693***|0.0665***|
||(0.161)|(0.149)|(0.154)|(0.020)|(0.025)|(0.024)|
|MDBcs,t-1|0.1235|0.2825**|0.3205**|0.0125|0.0171|0.0145|
||(0.158)|(0.137)|(0.127)|(0.024)|(0.027)|(0.026)|
|MDBcs,t-2|-0.0508|0.1246|0.1656|0.0492***|0.0537***|0.0518***|
||(0.159)|(0.129)|(0.113)|(0.018)|(0.019)|(0.019)|
|Dep. Var.cs,t-1|0.8685***|0.7006***|0.6519***|0.2155***|0.1334*|0.1366*|
||(0.078)|(0.068)|(0.062)|(0.071)|(0.071)|(0.070)|
|2<br>∑<br><br>_MDB_cs,t-k|0.457|0.900***|0.999***|0.125***|0.140**|0.133**|
|_k_=0<br>_Wald test_ : _p value_|0.214|0.002|0.000|0.005|0.016|0.015|
|Observations|24,219|24,219|24,219|24,219|24,219|24,219|
|R-squared|0.835|0.858|0.865|0.410|0.453|0.452|
|Average MDB|0.0483|0.0483|0.0483|0.0483|0.0483|0.0483|
|Average Dep. Var.|0.760|0.760|0.760|0.105|0.105|0.105|
||**Panel C.**Ba|nks||**Panel D.**M|aturity||
||(1)|(2)|(3)|(4)|(5)|(6)|
|MDBcs,t|0.4634**|0.5331***|0.4522**|0.6371***|0.5609***|0.5386***|
||(0.200)|(0.203)|(0.205)|(0.125)|(0.127)|(0.127)|
|MDBcs,t-1|0.6026***|0.7400***|0.6596***|0.1345|0.0966|0.0693|
||(0.180)|(0.208)|(0.200)|(0.125)|(0.131)|(0.131)|
|MDBcs,t-2|0.5157**|0.7416***|0.6660***|0.3096***|0.2784***|0.2605***|
||(0.226)|(0.199)|(0.198)|(0.092)|(0.090)|(0.090)|
|Dep. Var.cs,t-1|0.2221***|0.1193***|0.1244***|0.0751***|-0.0097|-0.0117|
||(0.031)|(0.035)|(0.034)|(0.017)|(0.018)|(0.018)|
|2<br>∑<br>_k_0<br>_MDB_cs,t-k|1.582***|2.015***|1.778***|1.081***|0.936***|0.868***|
|=<br>_Wald test_ : _p value_|0.001|0.000|0.000|0.000|0.000|0.000|
|Observations|24,219|24,219|24,219|23,439|23,439|23,439|
|R-squared|0.676|0.708|0.706|0.531|0.570|0.571|
|Sector-country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Country-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-sector Trends|No|Yes|Quadratic|No|Yes|Quadratic|
|Average MDB|0.0483|0.0483|0.0483|0.0483|0.0483|0.0483|
|<br>Average Dep. Var.|1.526|1.526|1.526|0.752|0.752|0.752|

_Notes_ : The table presents the estimates of equation 1 controlling for linear and quadratic country-sector trends. The dependent variables are: (i) the number of loans (Panel A, columns 1-3), (ii) the size of syndicated loans (% of GDP, Panel B, columns 4-6), (iii) the average number of banks per loan (Panel C, columns 1-3), and (iv) the average maturity of syndicated loans (in years) (Panel D, columns 4-6). MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . For each panel, the table reports, in the bottom rows, the cumulative effect of MDBcs,t between year _t −_ 2 and _t_ , with the associated p-value of a Wald test, and the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

29
|Countries<br>with at least<br>100 loans<br>(7)<br>3.8881***<br>(1.138)<br>3.8701<br>(2.595)<br>0.0121<br>(0.384)<br>0.1736***<br>(0.045)<br>7.770*<br>0.0563<br>7.770*<br>6,606<br>0.483<br>Yes<br>Yes<br>Yes<br>No<br>44.800<br>0.680<br>or_cs_at time_t_, excluding<br>r_cs_at time_t_. In columns<br>d 7 the sample is limited<br>ed by MDBs in country-<br>ered at country level are|
|---|
|Countries<br>with at least<br>50 loans<br>(6)<br>3.5868***<br>(1.157)<br>3.4742<br>(2.507)<br>-0.0370<br>(0.283)<br>0.1949***<br>(0.048)<br>7.024*<br>0.0697<br>7.024*<br>8,469<br>0.471<br>Yes<br>Yes<br>Yes<br>No<br>24.300<br>0.3860<br>(USD) in country-sect<br>MDBs in country-secto<br>te. In columns 5, 6 an<br>SD) per capita provid<br>. Standard errors clust|
|**apita)**<br>Countries<br>with at least<br>25 loans<br>(5)<br>2.4487*<br>(1.400)<br>2.9621<br>(2.336)<br>0.3248<br>(0.592)<br>0.0695***<br>(0.005)<br>5.736<br>0.176<br>5.736<br>13,230<br>0.346<br>Yes<br>Yes<br>Yes<br>No<br>30.700<br>0.3610<br>of syndicated loans<br>(USD) provided by<br>hich MDBs participa<br>syndicated loans (U<br>000,000 individuals)|
|**Size ($ per c**<br>Full Sample<br>(4)<br>1.8918<br>(1.224)<br>2.3253<br>(1.963)<br>0.2704<br>(0.405)<br>0.0818***<br>(0.019)<br>4.487<br>0.203<br>4.487<br>24,147<br>0.339<br>Yes<br>Yes<br>Yes<br>No<br>18.000<br>0.2450<br>rithm of the total size<br>e of syndicated loans<br>the size of loans in w<br>est is the total size of<br>size is meaured per 1,|
|(3)<br>0.0824***<br>(0.018)<br>0.0451**<br>(0.021)<br>0.0414<br>(0.025)<br>0.0515***<br>(0.018)<br>0.169***<br>0.000<br>7.071***<br>24,219<br>0.747<br>Yes<br>Yes<br>Yes<br>Quadratic<br>0.859<br>0.175<br>158.5<br>3.788<br>le is the natural loga<br>rithm of the total siz<br>_s_at time_t_, excluding<br>ain variable of inter<br>iables (average loan|
|**nt)**<br>(2)<br>0.0862***<br>(0.018)<br>0.0484**<br>(0.021)<br>0.0443*<br>(0.025)<br>0.0472**<br>(0.018)<br>0.179***<br>0.000<br>7.490***<br>24,219<br>0.748<br>Yes<br>Yes<br>Yes<br>Yes<br>0.859<br>0.175<br>158.5<br>3.788<br>the dependent variab<br>erest is the natural loga<br>ita in country-sector_c_<br>d, respectively. The m<br>Bcs,t and outcome var|
|**Size (ln $ le**<br>(1)<br>0.0873***<br>(0.020)<br>0.0415**<br>(0.020)<br>0.0353<br>(0.025)<br>0.1860***<br>(0.022)<br>0.164***<br>0.000<br>6.862***<br>24,219<br>0.714<br>Yes<br>Yes<br>Yes<br>No<br>0.859<br>0.175<br>158.5<br>3.788<br>n 1. In columns 1-3,<br>main variable of int<br>loans (USD) per cap<br>ver the sample perio<br>e averages of the MD|
|MDB sizecs,t<br>MDB sizecs,t-1<br>MDB sizecs,t-2<br>Sizecs,t-1<br>2<br>∑<br>_k_=0<br>_MDB_cs,t-k<br>_Wald test_ : _p value_<br>Marginal Effect<br>Observations<br>R-squared<br>Sector-country FE<br>Country-year FE<br>Sector-year FE<br>Country-sector Trends<br>Average Size (ln $/per capita)<br>Average MDB size (ln $/per capita)<br>Average Size ($ lent)<br>Average MDB size ($ lent)<br>_Notes_: The table presents the estimates of equatio<br>the size of loans in which MDBs participate. The<br>4-7, the dependent is the total size of syndicated<br>to countries with at least 25, 50, and 100 loans o<br>sector_cs_at time_t_. The last rows show the sampl<br>in parentheses: * p_<_0.1, ** p_<_0.05, *** p_<_0.01.|

30
# Table 7: Direct and Indirect MDB Mobilization Effects

||**Direct + Indire**|**ct Effects**|**Indirect Effect**|**s**|
|---|---|---|---|---|
||Size (%GDP)<br>including<br>MDB loan<br>(1)|Banks<br>including<br>MDB loan<br>(2)|Size (%GDP)<br>excluding<br>MDB loan<br>(3)|Banks<br>excluding<br>MDB loan<br>(4)|
|MDB cs,t|0.2952***<br>(0.043)|2.6323***<br>(0.275)|0.0651***<br>(0.021)|0.5328**<br>(0.207)|
|Size (%GDP) cs,t-1|0.2151***<br>(0.069)||||
|Banks per Loan cs,t-1||0.2205***<br>(0.029)|||
|Size (%GDP) cs,t-1|||0.2252***<br>(0.072)||
|Banks per Loan cs,t-1||||0.2394***<br>(0.031)|
|Observations|25,272|25,272|25,272|25,272|
|R-squared|0.407|0.675|0.405|0.672|
|Sector-country FE|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|Yes|
|Average MDB|0.0478|0.0478|0.0478|0.0478|
|Average Dep. Var.|0.109|0.112|0.103|1.503|

_Notes_ : The table presents the estimates of equation 1, without additional lags of MDBcs,t. Columns 1-2 report total mobilization effects (direct + indirect); columns 3-4 report indirect mobilization effects only. The dependent variables are: (i) the size of syndicated loans (% of GDP, columns 1-3), and (ii) the average number of banks per loan (columns 2-4). In columns 1-2 the outcome variables include the amount lent by MDBs’ partners and the number of those partners, respectively. In columns 3-4 the outcome variables exclude the amount brought by MDBs’ partners and the number of the banks. MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . The bottom rows show the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

31
|(8)<br>0.0112<br>(0.012)<br>0.0086<br>(0.007)<br>0.0090<br>(0.009)<br>0.0035<br>(0.004)<br>0.5582***<br>(0.065)<br>0.0289<br>0.221<br>11,718<br>0.690<br>Yes<br>Yes<br>Yes<br>0.0621<br>0.0368<br>mns 1-4), and (ii) the<br>g a syndicated loan<br>ountry-sector in the<br>ntry-sector received<br>ssociated p-value of<br> _<_0.1, ** p_<_0.05, ***|
|---|
|(7)<br>0.0201<br>(0.017)<br>0.0226**<br>(0.009)<br>0.0062<br>(0.013)<br>0.0053<br>(0.007)<br>0.5109***<br>(0.078)<br>0.0489<br>0.134<br>6,318<br>0.743<br>Yes<br>Yes<br>Yes<br>0.0954<br>0.0538<br>ate bonds (colu<br>e MDB providin<br>present in the c<br>al to 1 if the cou<br>and_t_, with the a<br>parentheses: * p|
|% GDP)<br>(6)<br>0.0080<br>(0.010)<br>0.0085<br>(0.006)<br>0.0038<br>(0.008)<br>0.0107<br>(0.008)<br>0.5550***<br>(0.061)<br>0.0203<br>0.279<br>13,869<br>0.693<br>Yes<br>Yes<br>Yes<br>0.0713<br>0.0396<br>mber of corpor<br>re is at least on<br>top banks was<br>a dummy equ<br>een year_t −_2<br>try level are in|
|Bond Size (<br>(5)<br>0.0082<br>(0.010)<br>0.0096<br>(0.007)<br>0.0049<br>(0.008)<br>0.5562***<br>(0.061)<br>0.0227<br>0.237<br>13,869<br>0.693<br>Yes<br>Yes<br>Yes<br>0.0713<br>0.0396<br>les are: (i) the nu<br>y equal to 1 if the<br>f at least 1 of the<br>ODA) we include<br>of MDBcs,t betw<br>lustered at coun|
|(4)<br>-0.0498<br>(0.213)<br>0.0681<br>(0.249)<br>-0.5706<br>(0.635)<br>0.0160<br>(0.073)<br>0.8958***<br>(0.061)<br>-0.552<br>0.451<br>11,718<br>0.898<br>Yes<br>Yes<br>Yes<br>0.0621<br>1.080<br>pendent variab<br>cs,t is a dumm<br>my equal to 1 i<br>nt assistance (<br>mulative effect<br>ndard errors c|
|(3)<br>0.1422<br>(0.282)<br>0.2141<br>(0.230)<br>-0.7618<br>(0.755)<br>0.1453<br>(0.230)<br>0.8046***<br>(0.092)<br>-0.406<br>0.672<br>6,318<br>0.905<br>Yes<br>Yes<br>Yes<br>0.0954<br>1.934<br>e bonds. The de<br>sector-year. MDB<br>e include a dum<br>fcial developme<br>om rows, the cu<br>me variables. Sta|
|Bonds<br>(2)<br>-0.0772<br>(0.257)<br>0.1187<br>(0.230)<br>-0.4171<br>(0.475)<br>0.0195<br>(0.095)<br>0.8966***<br>(0.057)<br>-0.376<br>0.585<br>13,869<br>0.904<br>Yes<br>Yes<br>Yes<br>0.0713<br>1.331<br>on 1 on corporat<br>) in the country-<br>top 10 banks, w<br>fnancing and of<br>ports, in the bott<br>DBcs,t and outco|
|Number of<br>(1)<br>-0.0768<br>(0.257)<br>0.1208<br>(0.227)<br>-0.4152<br>(0.473)<br>0.8966***<br>(0.057)<br>-0.371<br>0.584<br>13,869<br>0.904<br>Yes<br>Yes<br>Yes<br>0.0713<br>1.331<br>estimates of equati<br>GDP, columns 5-8<br> To control for the<br>or Chinese offcial<br>years. The table re<br>verages of the M|
|MDBcs,t<br>MDBcs,t-1<br>MDBcs,t-2<br>Top 10 Banks<br>Chinese lending<br>ODA<br>Number of Bondscs,t-1<br>Bond Size (%GDP)cs,t-1<br>2<br>∑<br>_k_=0<br>_MDB_<br>_Support_cs,t-k<br>_Wald test_ : _p value_<br>Observations<br>R-squared<br>Sector-country FE<br>Sector-year FE<br>Country-year FE<br>Average MDB<br>Average Dep. Var.<br>_Notes_: The table presents the<br>size of corporate bonds (% of<br>in country-sector_cs_at time_t_. <br>previous 2 years. To control f<br>these fows in the previous 2<br>a Wald test, and the sample a<br>p_<_0.01.|

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