Gross Capital Flows by Banks, Corporates and Sovereigns


BIS Working Papers

No 760


Gross Capital Flows by Banks, Corporates and Sovereigns

by Stefan Avdjiev, Bryan Hardy, Şebnem Kalemli-Özcan and Luis Servén


Monetary and Economic Department

December 2018


JEL classification: demography, ageing, inflation, monetary policy

Keywords: E31, E52, J11


This publication is available on the BIS website (


© Bank for International Settlements 2017. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated.

ISSN 1020-0959 (print)

ISSN 1682-7678 (online)


Gross Capital Flows by Banks, Corporates and Sovereigns

by Stefan Avdjiev,

Bryan Hardy,

Şebnem Kalemli-Özcan and Luis Servén

December 2018∗


We construct a new data set of quarterly international capital flows by sector, with an emphasis on debt flows. Using our new data set, we establish four facts. First, the co-movement of capital inflows and outflows is driven by inflows and outflows vis-a-` vis the domestic banking sector. Second, the procyclicality of capital inflows is driven by banks and corporates, whereas sovereigns’ external liabilities move acyclically in advanced and countercyclically in emerging countries. Third, the procyclicality of capital outflows is driven by advanced countries’ banks and emerging countries’ sovereigns (reserves). Fourth, capital inflows and outflows decline for banks and corporates when global risk aversion (VIX) increases, whereas sovereign flows show no response. These facts are inconsistent with a large class of theoretical models.

JEL-Codes: F21, F41, O1

Keywords: Quarterly Capital Flows, Business Cycles, External Corporate and Bank Debt, Sovereign Debt, VIX, Systemic Risk, Emerging Markets

  • We thank Luis Catao, Eugenio Cerutti, Stijn Claessens, Branimir Gruic, Gian Maria Milesi-Ferretti, and Philip ˜ Wooldridge for useful comments and suggestions and Bat-el Berger for excellent assistance with the BIS IBS data. We are grateful to seminar participants at the NBER Summer Institute 2017, CEPII, and the BIS. All errors are our own. The views expressed here are ours only and do not necessarily reflect those of the Bank for International Settlements or the World Bank. This work was partly funded by the World Bank’s Knowledge for Change and Strategic Research programs.
  1. Introduction

It is widely recognized that international capital flows have nontrivial consequences for macroeconomic outcomes. The history of financial crises has taught us that the vulnera­bility to external shocks can vary greatly depending on which economic sector(s) are on the receiving side of capital inflows. For example, sovereign debt proved to be the Achilles' heel in the Latin American crises, while private sector debt financed by capital inflows was the key source of fragility in the Asian financial crises. During the latest global financial crisis, in the US, the culprit was the domestic household debt held by US and global banks. By contrast, in the European countries, sovereigns' and banks' external borrowing played the central role, which culminated in a sudden stop. Still, gross capital flows by sector have re­ceived little attention in the empirical literature due to lack of data for a large set of countries and long time periods at the business cycle frequency. Our paper fills this gap.

Our paper's contributions are twofold. First, we introduce a new comprehensive dataset on gross capital inflows and outflows at the quarterly frequency starting 1996 for a balanced panel of 85 countries for inflows and 31 countries for outflows, decomposing both inflows and outflows by domestic sector (i.e. inflows into the corporate sector of a country, out­flows from the banking sector of the country, etc.). Second, using this dataset we document four new stylized facts. First, the co-movement of capital inflows and outflows is primar­ily driven by flows to and from banks. Second, procyclicality of capital inflows is driven by banks and corporates in all the countries, whereas sovereigns' external liabilities move acyclically in advanced countries and countercyclically in emerging markets. Third, pro­cyclicality of capital outflows is driven by advanced countries' banks and emerging coun­tries' sovereigns. Fourth, capital inflows and outflows decline for banks and corporates, when global risk aversion (VIX) increases, whereas neither advanced country nor emerging market sovereigns respond to such global shocks. These facts are inconsistent with many theoretical models that seek to explain the co-movement of inflows and outflows together with the procyclicality (or fickleness) of capital flows.

Our dataset combines data from several publicly available sources and offers a distinct advantage over existing datasets from single institutions such as the IMF and/or World Bank, namely its much broader coverage of developing countries and emerging markets at the quarterly frequency, which is the preferred frequency to study the relationship between capital flows and the business cycle. While we focus mainly on debt flows by sector, our analysis also includes official reserves and FDI debt inflows.

Why decompose only debt inflows and outflows by sector? Debt flows are generally the largest component in total capital flows, both for advanced economies and emerging markets, in spite of the advances made in increasing equity flows in the last decade. It is important to analyze both portfolio debt (e.g., bonds) and other investment debt (e.g. loans, deposits, trade credit, etc.), as their relative importance varies considerably across countries and sectors. Our dataset reveals that banks owe the lion's share of the external debt for ad­vanced countries, but in emerging markets the outstanding external debt stocks are roughly split equally between banks, corporates and sovereigns. Our data further reveals that while most of the portfolio debt in advanced economies is due to corporate borrowing and most of the non-portfolio debt is due to bank borrowers, as is the conventional wisdom, this pat­tern changes when examining emerging markets. There, sovereigns account for most of the portfolio debt owed, while banks and corporates roughly split the other debt.

From our stock data on the asset side, we find that sovereigns are the main lending sector for emerging markets, due mainly to their accumulation of reserve assets, while corporates in all countries typically lend externally via portfolio debt. Advanced economy banks do most of the lending in other investment debt, but in emerging markets the figure is again split between banks and corporates. These data patterns, and others we discuss throughout the paper, highlight the importance of separating external debt liabilities and debt assets by sector for a more complete understanding of the drivers of capital flows and lead us to a re-evaluation of conventional stylized facts on capital flows.

Most of the literature focuses on net capital flows defined as the purchase of domes­tic assets by foreign agents minus purchase of foreign assets by domestic agents. There have been recent papers, such as Forbes and Warnock (2012), Broner, Didier, Erce, and Schmukler (2013), and Davis and van Wincoop (2017), that focus on gross inflows and out­flows separately—that is capital inflows by foreign agents and capital outflows by domestic agents—but no paper separated these gross inflows by foreigners and gross outflows by domestics into borrowing and lending sectors. These papers show a high degree of cor­relation between capital inflows and outflows and an increase in this correlation over time. Some of these works show that both capital inflows and outflows are procyclical. Our con­jecture is that depending on which foreign agent and which domestic agent are involved in external borrowing and lending, there will be further differences in the response of capital flows to countries' own business cycles and global shocks.

We document that the positive correlation between aggregate capital inflows and out­flows is driven mainly by the borrowing and lending patterns of advanced country banks. This results holds both for unconditional correlations and correlations conditional on the global financial cycle (or global risk appetite), proxied by the VIX, and countries' own GDP growth. Regressing inflows on outflows also delivers similar results. While the behavior of cross border activities of banks has been extensively studied, to our knowledge we are the first to show that the overall correlation between capital inflows and outflows is primarily due to the banking sector.

In order to investigate procyclicality of capital inflows and outflows, we run separate quarterly panel regressions of capital inflows and outflows on the lagged value of the VIX and countries' own lagged GDP growth. These regressions include country fixed effects, so the identifying information is drawn from the within variation, that is, from changes in the VIX, GDP growth, and capital flows. The regressions allow us to ask whether during ex­pansions (contractions) foreign agents increase (decrease) their purchase of domestic assets, whether domestic agents increase (decrease) their purchase of foreign assets, and whether these patterns differ by sector. The same regressions also allow us to evaluate the response of inflows and outflows to global shocks, proxied by changes in VIX.

We find that, during domestic economic downturns (expansions), capital inflows to do­mestic banks and corporates decline (increase) in all countries. Capital inflow procyclicality

For capital outflows, the case is quite different. Capital outflows are procyclical only for the advanced country banks and emerging markets sovereigns, where the rest of sec­tors' outflows are acyclical. Hence during expansions advanced country banks invest more abroad, whereas during contractions they retrench. In a similar fashion, EM sovereigns' outflows behave procyclically, where they run down reserves during downturns and ac­cumulate reserves during booms. This is an important result since it means that during a downturn/crisis in a given emerging market, domestic private agents do not bring their in­vestment back (retrench) to their own country, as argued by other researchers. During those bad times when foreigners flee from the emerging market, sovereigns provide the much needed risk sharing. In a similar vein, during a downturn in advanced economies, banks bring the money back helping to smooth out the bust.

The results on procyclicality of inflows and outflows supports our earlier finding on the co-movement of inflows and outflows being driven by advanced country banks since the only sector that is procyclical both in terms of capital inflows and capital outflows is the banking sector in advanced countries, as banking and corporate sector outflows in emerging markets are acyclical and sovereign and corporate sector outflows in advanced countries are also acyclical. Emerging market sovereign sector capital outflows are procyclical but the same sovereign sector's capital inflows are countercyclical and hence cannot create/

What about global conditions? Capital inflow and outflow responses to global cycles differ from their responses to their own business cycles. In response to an adverse change in global financial conditions, such as an increase in the VIX, inflows to banks and corporates decline, while domestic banks and corporates invest less abroad, decreasing their outflows. Sovereigns do not respond to such global movements on average. Several papers document that gross flows respond systematically to changes in global conditions. Our results are consistent with these works and explain that another potential source responsible for the co-movement of capital inflows and outflows might be the response in all countries of both banking and corporate sector inflows and outflows to the global financial cycle.

These four facts stand in contrast to standard international macroeconomic models, which treat domestic and foreign investors symmetrically. Further, the evidence we provide helps to discriminate among several classes of models that try to explain the comovement of in­flows and outflows. As we explain in section 5, our findings are consistent with models with financial shocks or financial frictions and a role for a banking sector and sovereigns, but not with models featuring only productivity shocks and/or asymmetric information and sovereign risk as the sole sources of friction.

The rest of the paper is organized as follows: Section 2 describes the construction and coverage of our data; Section 3 illustrates descriptive patterns; Section 4 presents the results from our empirical analysis; Section 5 discusses the theoretical implications and Section 6 concludes.

  1. A New Dataset for Capital Flows Research

We construct a new dataset for capital flows research that disaggregates inflows to and out­flows from a country by sector in the domestic economy. We focus mainly on debt flows, which account for a substantial portion of international capital flows as we document be­low. We construct the dataset by taking the existing BOP data and performing internal and external data filling exercises. This enables us to expand the coverage of our dataset, relative to publicly available statistics, dramatically, in terms of both countries and time. The capi­tal flight literature also uses techniques of internal filling with the BOP and external filling with other datasets in order to identify unreported private capital flows (Chang, Claessens, & Cumby, 1997; Claessens & Naude, 1993).

As a preview of our dataset and to illustrate the importance of our analysis, Figure 1 illustrates the size of debt in total external liabilities, as well as the breakdown of outstanding stocks by sector. The figure shows time series of the composition of external liability stocks to illustrate the relative importance of the different components. Panel (a) shows the share of total debt in total external liabilities. Debt represents the majority of external liabilities globally and in advanced economies (AE). In emerging markets (EM), debt and non-debt liabilities are of similar magnitude. Panel (b) highlights that other investment debt (usually bank loans) accounts for the bulk of external debt stocks. Portfolio debt (bonds) in panel (c)

represents nearly half of AE external debt and around a third of EM external debt. Thus, it

Upload pictures - get urlis important to consider both types of external debt.

Employing our new dataset, panels (d)-(i) highlight the sectoral share of external debt stocks for each flow type and country group. In AE, banks account for the lion's share of external debt liabilities, whereas in EM, corporates, banks and sovereigns have more or less equal shares. This is interesting since in general it is thought that firms and governments
would directly access international capital markets more in AE than in EM. One interpreta­tion is that banks do most of the intermediation of external funds in AE, while corporates and sovereigns might be borrowing more domestically. Perhaps more surprising, the con­ventional wisdom that other investment debt is primarily owed by banks and portfolio debt is primarily owed by corporates holds for AE but not for EM. In the latter, most of the portfo­lio debt is attributable to sovereigns, while banks and corporates have equal shares in other investment debt.

The composition of external debt is remarkably stable over time, with few exceptions. The share of other investment debt in total external liabilities is decreasing and the share of portfolio debt is increasing in AE over time. This seems to be partly driven by the global financial crisis: in these countries, the share of bank-held debt (mostly other investment debt) declines and that of sovereign debt (mostly portfolio debt) increases following the crisis. For EM, sector shares are more stable over time, although during the pre-crisis period there is a small decline in the share of debt in total inflows.

Figure 2 shows the counterpart of Figure 1 for the composition of external asset stocks in debt instruments, including reserves. Panel (a) shows the share of debt in total external assets. Debt assets represents the majority of external assets; 70 percent in EM and 60 per­cent in AE on average during 2000s, though the share of debt assets in total external assets is on a declining trend for both set of countries. Panel (b) highlights that other investment debt accounts for the bulk of debt asset stocks in AE, whereas portfolio debt assets in panel (c) represents only 40 percent of the AE economies external debt assets. For EM, other in- vestment debt assets represent half of the external debt assets, portfolio debt assets are not important, and the remainder consists of reserves.

Image 26

Panels (d)-(i) highlight the sectoral share of external debt asset stocks for each flow type and country group. In EM the public sector is overwhelmingly the main lender to other countries. This is primarily driven by their accumulation of reserve assets, which are in­
eluded in the total debt figure. In AE, as is the case for borrowing, banks do the lion's share of external lending in loans, while corporates also have a big share of AE lending in portfolio debt assets. For EM, banks and corporates do about an equal share of lending in other in­vestment debt, while corporates lead in terms of portfolio debt. The composition of external debt assets is also very stable over time, as in the case of debt liabilities.

Other papers and datasets examining capital flows by sector are much more limited in terms of coverage, frequency, or sectoral breakdown. Milesi-Ferretti and Tille (2011) and Cerutti et al. (2015) separate out the banking sector within the other investment debt cate­gory of the BOP to analyze it on its own, but not in tandem with the other sectors and other capital flow asset classes. Other studies examining gross capital inflows using only BOP data sometimes exclude official reserves and IMF credit in order to focus on private inflows (see Forbes and Warnock (2012), Bluedorn, Duttagupta, Guajardo, and Topalova (2013), and Milesi-Ferretti and Tille (2011) for example). Milesi-Ferretti and Tille (2011) additionally exclude central bank loans and deposits. Bluedorn et al. (2013) analyze private flows by removing from total flows reserves, IMF credit, and most government-related components included under the other investment debt category. However, there is also a substantial amount of public sector debt under portfolio securities, which these studies do not remove. We separate this category and examine both public and private capital flows. Arslanalp and Tsuda (2014b) and Arslanalp and Tsuda (2014a) decompose sovereign/government loan and bond debt by creditor, both foreign and domestic. They employ the IMF and World Bank's Quarterly External Debt Statistics (QEDS) data to distinguish between foreign and domestic creditors. They also use BIS data to identify external bank lenders, similar to our approach (described below and in Appendix B), but only for the sovereign starting from 2005, where we consider all three sectors since 1996: banks, corporates and the sovereigns.

We do not break down portfolio (non-FDI) equity flows by sector, due to the lack of avail­able external datasets with which to fill in the missing data. We do however consider FDI debt inflows in our sector decomposition and country total FDI and portfolio equity flows. Galstyan, Lane, Mehigan, and Mercado (2016) use data starting only after 2013 from the IMF's Coordinated Portfolio Investment Survey (CPIS) to examine portfolio debt and port­folio equity stocks by the sectoral identity of the issuer and holder of the security. While this data has a more granular breakdown, it is only available for recent years, only for portfolio instruments, and only at a semi-annual frequency. In contrast, we focus on all the compo­nents of debt, that is the flow of portfolio debt and other investment debt by sector, over a much longer time horizon in quarterly data.

Due to its large coverage of countries, long time series, coverage of multiple instruments (asset classes), and quarterly frequency, our dataset is an important contribution to capital flows research. We next detail more background on the relevant capital flow definitions used in the data and research, the various datasets covering international capital flows, and how we construct our dataset.

2.1 Data Construction

What is commonly called "gross flows" in the literature is actually more accurately de­scribed as "net inflows" and "net outflows", which are broadly defined as follows:

Image 27

Thus, although these measures are often called "gross", they can be positive or negative. The separation of flows into asset and liability flows allows interpreting liability flows as inflows from foreign agents, and asset flows as outflows by domestic agents. This is the primary working definition of capital flows in the BOP and elsewhere, which we use across all data sources for consistency.

The focus of this paper is on the differentiation of capital flows by source or destination sector in the domestic economy. The domestic economy refers to entities that are resident in that economy, a rule known as the "Residence Principle" , regardless of the nationality of the entity. This is the basis upon which the BOP data is compiled, which we match when performing our filling exercise. The term "sector" is used here to refer to institutional sectors: general government, central banks, depository corporations except the central bank ("banks"), and other sectors ("corporates").

To build our dataset, we combine and harmonize several publicly available sources: Bal­ance of Payments (BOP) and International Investment Position (IIP) statistics of the Interna­tional Monetary Fund (IMF), Locational Bank Statistics (LBS) and Consolidated Bank Statis­tics (CBS) from the Bank for International Settlements (BIS), International Debt Securities (IDS) Statistics from the BIS, Quarterly External Debt Statistics (QEDS) of the IMF and World Bank (WB), and Debt Reporting System (DRS) data of the WB.

The cornerstone of our dataset is the Balance of Payments (BOP) data produced by the

IMF, which is the most comprehensive source of international capital flow data across coun­tries. The BOP data, which is reported to the IMF by country statistical offices, captures capital flows into and out of a given country. The accompanying stock measures of external assets and liabilities are captured in the IMF's International Investment Position (IIP) data. Capital flows are measured as asset flows (outflows), liability flows (inflows), and net flows (inflows - outflows). We focus on the financial account portion of the data and the latest (6th) version of the balance of payments manual (BPM6). More details on the BOP data, along with its different presentations and versions, are given in Appendix A.2.

Figure 3 illustrates the structure of the BOP data. In simple terms, capital flows in the BOP are split into three main categories: direct investment, portfolio investment, and other investment; and an important public sector outflow category, official reserves. Each of these categories, except reserves, can be split into debt and equity components, though other investment equity is negligible. Thus, inflows and outflows can be summarized as:

Image 32

where DIE is direct investment equity, DID is direct investment debt, PE is portfolio equity, PD is portfolio debt, OID is other investment debt, and Res is reserves. For portfolio invest­ment debt and equity and other investment debt, the flows can be further subdivided by domestic sector. Other investment debt can also be decomposed by instrument and then by sector.

Image 35

While in theory each type of capital flow can be disaggregated by the domestic sector, in practice, however, the coverage of such disaggregated information in the BOP tends to be sparse, especially for EM/developing countries and earlier years. To be absolutely clear, capital flow types (asset classes) are generally very well reported in aggregate terms in the BOP data, and the reporting of the sectoral breakdowns has improved in recent years. Nev­ertheless, for most emerging/developing countries and years before 2005 the reporting of the data by sector is much less exhaustive.

To construct our capital inflows dataset, we start with BOP data by sector, and incorpo­rate data from the BIS and the WB on external bond and loan flows to expand the limited quarterly sectoral coverage available in the BOP. We similarly construct our dataset for outflows, and incorporate data from the BIS to complement coverage for portfolio debt and other investment debt outflows by banks.

Given the extent of missing observations in the BOP data, we proceed with a "filling" ex­ercise. Assuming missing data is zero may or may not be accurate depending on the country under consideration, as it is difficult to tell a true zero from a missing observation in the BOP data, so we fill missing values internally if possible and with data from other sources. We start by identifying the appropriate variables from the BOP data. This is not as easy as it sounds since, unfortunately, in the public download of the BOP data the sector breakdown of the other investment debt category is shown under the other investment equity cate- gory. Other investment debt flows are important since the vast majority of external bank flows are in this category. Crucially, this category also includes some cross-border loans to corporates and loans to sovereigns, such as IMF credit. In most countries, sovereigns tend to borrow externally primarily via bonds, which appear under the portfolio debt cat­egory. When bond financing to emerging market borrowers, including governments, dries up, emerging market sovereigns rely more on loans.

In order to get a larger, longer, and balanced panel of countries with debt flows split by sector, we proceed with the following methodology for our data filling exercise. When the BOP data contains the total for the category and for three out of the four sectors, we take the total and subtract the 3 reported sectors in order to obtain the fourth sector. For the remain­ing observations where the sector data is still missing, we construct measures of portfolio debt and other investment debt inflows by sector from several alternative datasets. The data that fills in the most observations in our dataset is from the BIS. We use the BIS International Debt Secutiries dataset (IDS), which captures securities issued in international markets, to fill in the portfolio debt flows series. The other important BIS dataset is the International Banking Statistics (IBS), capturing cross-border bank flows, which we use to fill the missing data under other investment debt. Here, we only use loan lending by BIS reporting banks, so as not to capture direct investment flows or debt securities holdings. We then comple­ment these loans with any other non-missing data from the BOP for particular instruments within other investment debt (trade credit, IMF credit, etc.) to get a more complete and accurate measure of other investment debt flows for each sector. While there may be rea­sons why the sectoral break down of debt inflows was not reported by particular countries in particular years, the BIS data has the benefit of being collected from the main lending countries instead of the borrower country (or in the case of debt securities, directly from the issued security itself). Thus, it avoids whatever underlying problems with data construction and reporting that may have generated the missing observation in the first place.

While the BIS data has extensive coverage and captures a vast amount of capital flows, in some cases it may not match well with the BOP data. An important example is that of ad­vanced economy government bonds, which are issued domestically and then traded abroad. These flows would not be captured by the BIS debt securities data, which captures exclu­sively bonds that are issued in international markets. Thus for public sector debt generally, and for corporate sector portfolio debt in AE, we rely first on measures derived from IIP, compiled concurrently with the BOP data by the IMF, and the QEDS data produced jointly by the IMF and World Bank. These data have the same sectoral and capital flow definitions and breakdowns, making them comparable to the BOP data. These are stock measures, which we first difference with a simple currency adjustment to approximate flows. While imperfect, these stock-derived measures often line up very well with reported BOP flow data and allow us to be more accurate as we fill missing data.

We deflate GDP and all capital flows to 1996 USD and express them in billions. Addi­tionally, we construct accompanying stock measures of external debt by sector, which were previewed earlier. To do so, we rely first on the IIP data as the main source. When this is missing after the internal fill within the IIP dataset, we rely on QEDS data on external debt by sector. We fill any remaining observations with our BIS estimates.

A detailed description of the datasets and our construction of the data to fill missing observations can be found in Appendix A.3. Here, we briefly illustrate the validity of our approach. To gauge how well our estimates capture the true inflows, we undertake a coun­terfactual exercise. We take a sample of countries where BOP data by sector is non-missing over 2006q1-2013q4. Then we compare this data to our estimates done for this period as if the BOP data were missing. Then, for each country group, we plot the aggregate flows for each sector and capital flow type using non-missing BOP data, and our constructed esti­mates. Figures A3 and A4 in Appendix A.3 report these plots for both other investment debt flows and portfolio debt flows for each sector. The match is close, with a correlation for total debt inflows over 0.86, even though the period includes the volatile capital flows around the 2008 crisis. It thus speaks to the quality of our constructed estimates to fill missing data over the entire sample. On the whole, our filled series capture most of the volume and vari­ation of inflows for most countries and allow us to extend substantially the coverage of our dataset.

There are few important details to note. We remove exceptional financing flows to banks and corporates, within portfolio debt and other investment debt, and reassign them to the central bank. Exceptional financing captures financial flows made or fostered by the author­ities for balance of payments needs. Thus, they can be seen as a substitute for reserves or IMF Credit.

Direct investment contains both debt and equity flows and is split by debt and equity components in the BOP data. However, it is not disaggregated by sector in the BOP data. Nevertheless, debt flows between related enterprises are recorded as direct investment debt only when at least one counterparty is a non-financial firm. Direct investment debt flows between two financial firms (including banks) are instead classified as either portfolio in­vestment debt or other investment debt (depending on the instrument type). If direct in­vestment debt flows from non-financial firms to financial firms are negligible, then we can attribute all direct investment debt as flows either from financial firms to non-financial firms or flows from non-financial firms to non-financial firms. In either case, the borrowing sector is the non-financial sector and hence direct investment debt inflows can be assigned in full to the corporate sector. We include direct investment debt in total debt and corporate debt inflows in our regression analysis. More details on the contribution of direct investment debt are given in Appendix C.1.

To complement our extensive dataset on capital inflows, we also construct a dataset of capital outflows. Due to a comparative lack of complementary external datasets, we do very little external filling of data for capital outflows, and hence describe them in less detail. As with inflows, we primarily use the BOP data and first do an internal filling exercise. We combine the general government and central bank sectors into a single (public) sector, so we can fill the missing sector if two sectors and the total are non-missing. Note that combining government and central banks into a single sector makes the internal filling exercise more fruitful, as only banks and corporates need to be non-missing in order to fill missing data for the public sector. The one external fill that we do for outflows is for the banking sector. We fill in portfolio debt asset flows and other investment debt asset flows using the BIS banking data (Locational Banking Statistics by Residency, LBS/R), which has information on bank cross-border claims in each instrument. This data only covers banks resident in BIS reporting countries, and so is more limited in terms of coverage than the BIS data used for inflows. Additionally, most BIS reporting countries have decent reporting of the sectoral breakdown in the BOP data. Hence, this filling exercise complements a few gaps in the BOP data, but largely the outflows dataset is derived solely from the BOP.

2.2 Coverage of the New Dataset

We divide the countries into three groups by level of development: advanced, emerging, and developing. In our sample of annual capital inflows, we have 89 countries (25 ad­vanced, 34 emerging, 30 developing). We exclude financial centers (e.g. Panama, Hong Kong, Bermuda) to avoid distorting the patterns in the data for the typical country, but cap­ital flows between financial centers and the economies in our sample are still captured by the respective counterparty country's flows. At the quarterly frequency, our inflow sample drops to 85 countries, leaving off El Salvador, Mongolia, Montenegro, and Serbia. For the re­gression and correlation analysis below where we use quarterly GDP, our sample is further limited due to unavailability of quarterly GDP for many emerging/developing countries.

The outflow sample consists of 31 countries (15 advanced, 16 emerging) at a quarterly fre­quency spanning 2004q1-2014q4. For the annual data, we have 31 countries (13 advanced and 18 emerging) spanning 2002-2014. Details on the samples are given in Appendix A.4. We are unable to make the outflow sample as large as the inflow sample because data on liabilities owed is more widely reported than data on assets owned, so we do not have many comparable filling series to replace missing outflows values in the BOP. Thus, while our ef­forts do improve our coverage of outflows, we focus on the contribution to inflow coverage in this section.

Table A5 in the appendix illustrates the impact of our data filling exercise on sample cov­erage for inflows. For each capital flow type, sector, and country group, the table shows the percentage of observations in our balanced panel that come from the raw BOP data, from our internal filling procedure, and from our filling from external data sources. Generally speak­ing, developing countries, central banks, and portfolio debt tend to have less data available in the original BOP. Our internal filling procedure makes a large difference for the coverage of central banks, but otherwise does not provide many more observations for portfolio debt and/or developing countries. Our external filling procedure, on the other hand, makes a large difference, especially for the quarterly data, where it fills 25-40 percent of observations for EM and 75-90 percent of observations for developing countries that were missing under portfolio debt. In the case of other investment debt, only 11 percent of observations are filled for EM, but for developing countries 40-50 percent of observations are filled. A sizable num­ber of observations are filled by external data also for advanced economies: 20-30 percent for portfolio debt observations, and 15-18 percent of other investment debt.

Our filling exercise has a dramatic impact on the time and country coverage of the inflow data. A balanced sample requires that portfolio debt and other investment debt not be missing for any of the 4 sectors in any period for each country. With 8 components required to be non-missing in each period, the probability that at least one is missing is high. With no adjustments to the BOP data, we have 0 countries in our sample (12 in the annual data). After our internal BOP fill, our sample of countries increases to 10 (16 in the annual data). After incorporating the BIS, IIP, and QEDS datasets, our balanced sample increases to 85 countries (89 in the annual data). Given the advantages of a balanced country sample for cross-section and panel regression analysis, the impact of our data filling on sample size can be very consequential.

Figure A1 in the appendix compares aggregate inflows as measured by our filled data and from the BOP alone, for total external debt of banks and corporates in our samples of AE and EM. We plot annual flows here for clarity. These graphs show that generally both series tell the same story, but there are periods in which accounting for the missing data makes a significant difference. For advanced economy corporates, a significant expansion leading up to the 2008 crisis and a the subsequent contraction are missed. This is due primarily to filling in portfolio debt data for the US and Spain for the 2008 surge, as well as a few other AE for the earlier 2001 peak. For EM, both banks and corporates had much larger flows relative to the BOP measure following the 2008 collapse, driven primarily by filling data for other investment debt inflows for China.

Figure A2 in the appendix plots total external debt inflows for government and central bank sectors. Missing U.S. government portfolio debt drives the difference for the AE in panel (a). EM governments and AE central banks are fairly well represented in terms of

volume. Note that net inflows can be negative as well as positive, which is the case for EM central banks, where some missing data consists of negative net inflows, which brings our filled data below the raw BOP total. The surge at the end of the sample for EM central banks is driven by China.

In summary, our dataset captures a large volume of capital inflows by sector that would otherwise be missed. Additionally, our data increases the number of both large and small countries with debt inflow data by sector over a long time horizon at the quarterly frequency.

  1. Descriptive Patterns

In this section, we present patterns and trends observed in our data over time. We use the annual version of the dataset for clarity in the figures.

Figure 4 (a)-(c) plots the aggregate debt inflows by sector for each country group. The buildup and collapse surrounding the 2008 global financial crisis (GFC) is the most striking feature in all of these figures. An interesting distinction between AE and EM is the response following the crisis. While flows to advanced economies collapse and remain fairly low, flows to emerging and developing countries rebound and increase across all sectors. An important difference in flows by sector is in the evolution of debt inflows to governments. Across all country groups, governments see an increase in debt inflows precisely when pri­vate flows collapse, with an especially large and sustained increase for developing nations relative to their private flows. Advanced-country central banks also see a small increase as private flows collapse.

Panels (d)-(i) plot portfolio debt and other investment debt flows. They reveal that the increase in inflows for governments comes primarily in the form of bonds, with the excep­tion of developing country governments, which also see an increase in other investment

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debt funding (i.e. loans). Advanced economy corporates also have a significant share of their inflows coming in the form of portfolio debt. Although emerging market banks and corporates see an increase in bond flows in the wake of the GFC, the aggregate pattern of their flows is driven primarily by other investment debt. Advanced country banks get the lion's share of capital inflows prior to 2008, the majority of which is in the form of other investment. However, they see consistent negative net inflows for several years following the GFC, reflecting the deleveraging of these institutions. Developing country banks and corporates are also primarily receiving inflows in the form of other investment debt.

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Much of the increase in emerging-market private debt after 2008 is attributable to a few large EM. Foremost among these is China, whose debt inflows are shown in Figure 5. China has poor sector coverage in the BOP data, so much of the measured effect is derived from our data filling series. Both bank and corporate inflows increase substantially, but bank in­flows to China have been much larger. In India, the corporate sector has been the dominant recipient of debt flows, though bank flows increased considerably after 2010. Brazil saw a sustained increase in corporate debt inflows, and volatile increases in bank and government flows.

The result that public sector gross inflows increase when private gross inflows are falling, at the business cycle frequency, is an important finding that complements existing work on long-term movements in public vs private net flows (Aguiar & Amador, 2011; Alfaro, Kalemli-Ozcan, & Volosovych, 2014; Gourinchas & Jeanne, 2013). The public sector is often able to borrow from abroad even as such funding dries up for the private sector. Thus, the public sector acts as a countervailing force to the private sector, smoothing the total debt inflows into the country

Turning to outflows, Figure 6 plots the debt asset flows for our sample of 31 countries over 2002-2014. The public sector is the sum of central banks and general government sec­tors, and total debt asset flows for the public sector include the flow of reserves.

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For advanced countries, we see the same pattern for total and other investment debt as we see with inflows. More concretely, the landscape of flows is dominated by the buildup of private flows in the mid-2000s, led by the banking sector, followed by a sharp contraction at the time of the global financial crisis. The public sector plays a relatively small role for AE

outflows. Portfolio debt outflows for AEs show a sharp contraction for banks at the time of

the crisis. Nevertheless, there is actually an increase in external portfolio debt investment by the corporate sector, followed by a brief contraction coinciding more closely to the Eurozone crisis.

Emerging market banks and corporates show a contraction in their other investment debt outflows, followed by a much stronger rebound than that seen in AEs. However, the decline in corporate other investment debt is offset by an increase in corporate portfolio debt outflows. EM public sector sees a drop in both portfolio and other investment outward investment around the crisis, but portfolio debt recovers robustly in the following years. However, public sector outflows, and total EM debt outflows, are clearly dominated by reserves, as seen in panel (d), with a large buildup and collapse mirroring the private sector inflow and outflows pattern.

  1. Empirical Analysis

4.1 Comovement of Capital Inflows and Outflows

So far, we have have documented and discussed the patterns in our data. These dynamic patterns can be due to inflows and outflows by sector responding to domestic and external shocks differentially. In this section, we will analyze how these responses work in detail.

Table 1 presents correlations of inflows and outflows across sectors. These are partial cor­relations of debt flows/country GDP, conditional on country fixed effects, lagged log of VIX, and lagged GDP growth. The sample is our asset flow sample detailed in Appendix A.4, consisting of 31 countries (15 advanced and 16 emerging) over 2004q1-2014q4. The public sector consists of general government and central bank sectors. Debt is the sum of portfolio debt and other investment debt, and also reserves in the case of public sector outflows.

The strength of the inflow-outflow correlation within the bank sector is striking. In fact, the only positive correlation that is more than 50 percent for inflows and outflows is the correlation between banking inflows and outflows. Conditioning on countries' own GDP growth and the VIX, both of which can drive capital flow behavior as we show below, is important in terms of getting at the true co-movement between inflows and outflows and we see that there is a high degree of correlation between bank inflows and bank outflows. This is clearly the case in AEs; furthermore, banks still have the strongest positive inflow-outflow correlation in EMs, though with lower magnitude. As a result, the key to understanding the inflow-outflow comovement is the banking sector. All of the negative correlations in this table involve the public sector, reinforcing the point that the public sector often behaves differently than the private sector.

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To see visually that banking flows are the key to capital inflow and outflow co-movement, we plot in Figure 7 inflows and outflows, after demeaning them from the common time ef­fects, over time stopping before the global financial crisis. It is clear that while the series sometimes match well, the correlations for non-bank flows are very low (the corresponding bank inflow-outflow correlations for Figure 7 are 0.85, 0.91, 0.89 respectively).

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Table 2 plots the correlations for AE and EM while distinguishing by instrument. The correlations are presented as a heatmap, with blue values indicating positive correlations, red values indicating negative correlations, and darker shading indicating stronger corre­lations. Examining these heatmaps makes it clear that the strongest comovement at this disaggregation is among AE banks, particularly within other investment debt flows. AE banks are in general global banks and these banks' borrowing and lending patterns within their internal capital markets combined with hedging-related trades produce a strong corre­lation between capital inflows and outflows, especially for AE. In AE, corporates' other in­vestment debt inflows and outflows also appear to be highly correlated, presumably due to
financial arms of large corporates in such countries, while public sector inflows are broadly negatively correlated with other inflows.

EMs do not display correlations as strong as those of AEs at this level of disaggregation, but it is still easy to see that the strongest positive correlation is that of other investment debt outflows of banks with bank inflows in either portfolio debt or other investment debt. An interesting feature of the emerging markets panel is that outflows of public other investment debt have a strong negative correlation with inflows of other investment to banks. This suggests that there is more to understand about the relationship between the banking sector and the public sector, particularly when it comes to EM capital flows.

4.2 Panel Regressions: Capital Inflows by Sector

We next examine the response of sectoral capital inflows to the global financial cycle/global risk apetite, measured by the VIX (push factor), and to the domestic business cycle, mea­sured by countries' own GDP growth (pull factor). We do so in a panel regression setup with our quarterly data. We focus on a very simple specification to illustrate our results:

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Our dependent variable is capital flows as a percent of GDP. We run each regression sep­arately for each sector and capital flow type. INFLOW.jt is a measure of capital inflows (in total or by instrument) to sector s £ {Public, Banks, Corp., All} for country i in quarter t. GDP.t is quarterly GDP from Datastream and national sources. The dependent variables are capital flows expressed as a percent of GDP. The regressions are run separately by sector, so that for each sector, a is a country fixed effect. VIXt-1 is the option-implied volatility of the

S&P 500 index, which enters into the regression in logged values. As already mentioned, the VIX is often used as a measure of global risk aversion or a proxy for the global finan­cial cycle and global financial conditions, and represents a standard push factor for capital inflows, particularly to EM. GDPGrowthi— is real year-on-year GDP growth for country i in the previous period, which is a standard pull factor driving foreign capital to a partic­ular country. Our standard errors are clustered at the country level. Using quarterly GDP data significantly restricts our sample along both country and time dimensions. We use a balanced sample (detailed in Appendix A.4) of 55 countries (23 advanced, 28 emerging, 4 developing) over 2002q4-2014q4.

As a baseline, Table 3 reports regressions at the country level by instrument (e.g. direct investment, portfolio debt, etc.).

As we would expect, capital inflows are negatively associated with the VIX across all cap­ital flow types, and with significance on total flows and other investment debt flows. GDP growth is likewise positively associated with capital inflows, with significance for total and other investment flows. Results are similar in Panels A and B with all countries and just AEs. For EMs in Panel C, we see additionally that both portfolio debt and direct investment are significantly related to the VIX. The significant relationship for direct investment is notable as it is generally thought of as less volatile than debt flows. Direct investment also has a significant positive coefficient on GDP growth. These results confirm the importance of examining debt flows more carefully, including our extension to include direct investment debt.

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Table 4 shows our regressions for total debt inflows, separated by sector. Column (1) reports the results with total debt inflows for all sectors, where debt is the sum of portfolio debt and other investment debt. Column (2) shows results on sovereign inflows, column (3) for banks, and column (4) for the corporate sector. Columns (5)-(6) add direct investment debt (DID) to total debt and corporate debt inflows, respectively, to obtain a more complete measure of debt inflows

For the full set of countries in Panel A, total debt inflows respond negatively to increases in the VIX. This response is driven by the private sector (banks and corporates), and holds (with an even larger magnitude) when DID is included in columns (5) and (6). The public sector flows' response to the VIX is positive but not significant. On the business cycle front, the total and private sector flows respond positively to a domestic boom, while the public sector flows are countercyclical, but not significant. This pattern is largely the same for the AE countries (Panel B), but with larger coefficients. An exception is that AE corporates do not respond to VIX shocks, once we include DID, which is the internal market debt of corporates in AE and may play a smoothing role in some cases, resulting in the larger standard errors on the response.

Inflows for EM countries in Panel C also follow a similar pattern, with the exception of the sovereign sector. As the VIX rises or as GDP falls, total and private inflows fall. This is in contrast to total debt flows to the public sector, which run counter-cyclical to domestic

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growth. These results are the gross inflows analog to the results found in Alfaro, Kalemli-for net debt flows, who show, using the annual DRS data explored in Appendix C, that net flows to the public sector are counter-cyclical, due primar­ily to sovereign-to-sovereign flows, while debt flows to the private sector are procyclical. Our results thus complement theirs and contribute to our understanding of upstream gross capital flows in addition to net flows, at the quarterly frequency.

The global financial crisis (GFC) has generated a lot of discussion about how the nature of capital flows may have changed in its wake. Tables C1 and C2 in Appendix C show our regressions for total debt for advanced and emerging economies, split into pre-GFC (2002q4-2007q4) and post-GFC (2008q1-2014q4) periods. For advanced economies, flows are significantly associated with the VIX before the GFC with the expected negative sign, but after the crisis they are more strongly driven procyclically by GDP growth. EM flows similarly have a stronger correlation with the VIX prior to the GFC and stronger correlation with GDP growth after it, with the expected signs. Banking flows to EM move opposite to the VIX during both the pre- and post-GFC periods.

In Tables 5-6, we focus on separate asset classes and show regressions by sector for other investment debt and portfolio debt. In Table 5, we see a negative relationship with the VIX and a positive relationship with GDP growth for total other investment debt inflows, as also shown by other researchers. As panel B and C show, these effects are driven by AE and EM

banks and corporates. Panel C also shows that in EM total other investment debt flows do

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not respond to VIX, as also shown by other researchers, but the rest of the Panel C explains why this is the case: the response of banks and corporates to VIX shocks is negative but the response of EM sovereigns is positive. Note that while other investment debt is usually not the primary form of financing for the public sector, it can account for a non-trivial share at times, including IMF credit and other official flows. Such flows are exactly the ones to be used in times of global stress by EM when private foreign investors were fleeing. Thus, the response of public sector flows to a global risk shock goes in the opposite direction to that of private sector flows in EM, which makes it hard to find a response in total flows as debated in the literature.

Table 6 examines portfolio debt inflows. For all countries and for advanced economies (in Panels A and B, respectively), there is not much in terms of significant relationships. To­tal and corporate portfolio debt inflows exhibit a significantly negative relationship with the VIX for the full set of countries, but as shown in Panel C, this is driven by EM corporates. As also shown in Panel C for EM, for GDP growth, we find a negative and significant rela­tionship for


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public and corporate sectors, but not for banks or for total flows. For the public sector, this drives the overall countercyclical movement in their external debt, as bonds are the primary form of borrowing for sovereigns. For corporates, on the other hand, other in­vestment debt drives their total external borrowing patterns, so this negative relationship reflects a change in the composition of external debt over the business cycle, with more bonds when the domestic economy is declining.

One remark from the results on inflows is that researchers using a mixed sample of de­veloping and advanced countries, employing the standard data sources, may have their re- suits driven by advanced countries, since coverage of developing and EM countries is much more restricted in those sources than in our dataset. As we show, the difference between AE and EM countries can be important, and provides valuable insight into the nature of capital flows.

4.3 Panel Regressions: Capital Outflows by Sector

For debt outflows, we use the same regression setup as the one for inflow regressions. The sample for outflows is smaller and shorter, covering 31 countries (15 advanced, 16 emerging) over 2004q1-2014q4, with the sample detailed in Appendix A.4. We also include flows of official reserves in this analysis.

Table 7 shows our regressions for total debt outflows (portfolio debt plus other invest­ment debt). Columns (1) presents results for all sectors, (2)-(4) present results separately by sector, and columns (5) and (6) add reserve flows to total and public flows, respectively. Debt outflows respond negatively to the VIX, reflecting domestic agents scaling back their external investments when global risk appetite is low (VIX is high). In terms of business cycle response, when the domestic economy is growing faster, total debt outflows increases but this is solely driven by the banking sector outflows. Thus, domestic banks invest more abroad when the domestic economy is stronger. Panel B shows that these patterns are driven by advanced countries. EM in Panel C show the same responses to the VIX as AEs. How­ever, the cyclical behavior of capital flows in EM is very different. Only reserve outflows responds procyclically to GDP growth and all else is acyclical.

Tables 8 and 9 show the relationships for other investment debt and portfolio debt out­flows separately, with reserve flows included in Table 9. Panels A and B of Table 8 reflect

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the same patterns as total debt outflows. For EM, the responses are again similar to those in Table 7, with the exception that the the responses of total and corporate sector flows to GDP growth in columns (1) and (4) are significant with a positive coefficient.

Table 9 shows the response of portfolio debt outflows by sector, with reserves excluded from columns (1)-(4) and examined in isolation in column (5). Unlike the other tables, here the results for the full set of countries (in Panel A) reflects more the behavior of the EM than the advanced economies. EM countries exhibit a significant negative response to VIX that is driven by the banking sector. Outward portfolio debt investment does not show any significant cyclicality across any of the sectors or country groups, but reserve flows are procyclical for EM. This confirms the relationship observed in Table 7 (Panel C columns (5) and (6)), that reserve accumulation is an important procyclical capital outflow for EM. Reserves are accumulated during good times and used when the domestic economy suffers.

  1. Theoretical Implications

Previous research has shown that capital inflows and outflows are positively correlated with each other and procyclical. Unsurprisingly, standard international real business cycle mod­els with a single asset cannot account for these patterns. In these models, the only shock is a shock to productivity in a single country, so capital inflows go in one direction only and hence procyclicality and co-movement cannot be accounted for. Researchers have argued that only a few models can account for these patterns, including McGrattan and Prescott (2010) and Bianchi, Boz, and Mendoza (2012).[1] In the former model, a positive produc­tivity shock generates both capital inflows and outflows. The country with the positive


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productivity shock receives inflows from multinationals. At the same time, it also experi­ences outflows as affiliates of multinationals invest in other countries given their increased productivity. These patterns can also create procyclicality.

However, our findings point to procyclical outflows only by banks in advanced countries and sovereigns in emerging markets, which means that a model based on investment by multinational companies cannot account for our findings. We can also rule out explanations based on asymmetric information, unless there is a specific reason why only the banking sector in advanced countries and the sovereign sector in emerging markets are subject to asymmetric information, while other sectors are not.

The model by Bianchi et al. (2012) can match the comovement of total capital inflows and outflows, but not its source. In that model, sovereigns borrow and accumulate re­serves. When a sudden stop occurs, capital outflows decline along with inflows since re­serves are used to smooth consumption. This model would be able to account for capital inflow-outflow comovement in EM if the comovement were driven by sovereigns, but as we show it is not. Sovereign inflows are countercyclical - in bad times, the sovereign sector borrows, increasing inflows, and runs down reserves, decreasing outflows.

We argue that, in the absence of frictions, only models with financial shocks, as in Kalemli- Ozcan, Papaioannou, and Perri (2013), can generate the positive correlation of banking in­flows and outflows found in the data. Models in which domestic financial frictions tighten for certain sectors during bad times, can also match our findings. For example, R. Caballero and Simsek (2018) assume that, during crisis times, financial frictions bind for domestic banks but not for foreign banks. Their model can provide a rationale for our findings. These authors argue that models featuring only portfolio investors ignore the important role of banks in intermediating capital flows. In their model both banks and sovereigns play a role in EM, consistent with our data.

  1. Conclusion

We construct a new data set for gross capital flows during 1996-2014 for a large set of coun­tries at a quarterly frequency, focusing primarily on debt flows. We decompose debt inflows and outflows by borrower and lender type: banks, corporates and sovereigns. We use the standard BOP data from IMF as the starting source. In order to get a larger, longer, and balanced panel of countries with debt flows split by sector, we proceed with a data filling exercise. When the BOP data by sector is missing, we use an internal filling procedure and then complement the gaps with other publicly available data from the IMF, WB, and BIS. Our data captures fairly accurately the volume and variation of aggregate flows for most countries and allows us to extend the coverage of the standard samples substantially.

We establish four facts with the new data. First, the co-movement of capital inflows and outflows is driven by the banking sector. Second, procyclicality of capital inflows is driven by banks and corporates everywhere, whereas sovereigns' external liabilities move acyclically in advanced and countercyclically in emerging countries. Third, procyclicality of capital outflows is driven by advanced countries' banks and emerging countries' sovereigns (reserves). Fourth, capital inflows and outflows decline for banks and corporates, when global risk aversion (VIX) increases, whereas sovereigns do not respond to such changes.

These facts are inconsistent with a large class of models that assume only productivity shocks and default risk as the sole friction. Our findings can be produced by models with financial shocks and/or financial frictions giving a role to sovereigns and the banking sector.

The results highlight the importance of separating capital flows by borrower and lender sec­tor to understand better their effects, as well as the systemic risks that they may pose for the borrowing country and the lending country. They also show the difficulty of establishing robust stylized facts about the business cycle properties of capital flows and their relation­ship with global push factors, especially in a sample that combines EM and AE countries. Our new dataset should prove very useful for future research on capital flows.



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A Dataset Construction

This appendix describes the construction of the dataset used in this paper, as well as the relevant background information for capital flow data generally and the underlying data sources specifically. The purpose of this dataset is to split capital inflows and outflows by capital flow type and by sector of the domestic economy, focusing primarily on debt flows. We base our dataset on the Balance of Payments (BOP) dataset, which includes capital flow data with breakdowns by flow type and sector, but also has some missing data. We fill in gaps in the data using some external datasets, such as the Quarterly External Debt Statistics (QEDS) and banking and bond data from the Bank for International Settlements (BIS).

We describe first the basics of capital flow data, the structure and coverage of the BOP data. We then explain the filling exercise and the external datasets that are used. We present comparisons to illustrate the quality of the fit of our external data and the contribution of our filling exercise. Lastly, we summarize the samples and coverage of our completed dataset. In Appendix B, we give more detail on the BIS datasets and how those series are constructed.

  1. 1 Capital Flow Data

Some of the presentations and definitions of international capital flow data can be ambigu­ous or inconsistent across data sources. In order to be clear about what we are doing, we briefly highlight some basic concepts regarding capital flow data generally.

In the literature and in the data, there is some ambiguity of terms when referring to net and gross flows. Essentially, there are three distinctions:

Gross Flows: Strictly speaking, gross inflows and outflows refer to one-way flows with­out netting out any capital flowing in the opposite direction. This definition of gross flows is generally what comes to mind when the term is used. Nevertheless, data that actually matches this definition are quite scarce.

Net Inflows and Outflows: What is commonly called "gross flows" in the literature is actually more accurately described as "net inflows" and "net outflows". There are no com­prehensive datasets on flows that are truly gross. Instead, researchers tend to use net inflows and net outflows, which can be obtained from the IMF's BOP dataset. Net inflows are gross liability flows, net of repayments. Net outflows are gross asset flows, net of disinvestment. Thus, although these measures are often called "gross", they can be positive or negative. The separation of flows into asset and liability flows allows interpreting liability flows as net inflows from foreign agents, and asset flows as net outflows by domestic agents. This is the primary working definition of capital flows, which we use across all data sources for consistency.

Net Flows: This relates to the net movement of capital into and out of a country. This is the equivalent of the negative of the current account, that is, the difference between Net Inflows and Net Outflows (or equivalently the difference between Gross Inflows and Gross Outflows).

Stock/Position Data: In general, there is no standard definition of "net" stocks, as some countries report outstanding debt net of some financial assets (Arslanalp & Tsuda, 2014b), while others do not. A more widely-agreed view is that the net stock of external wealth should be equivalent to the Net International Investment Position, which is the difference between outstanding external stock of assets and outstanding external stock of liabilities. Gross positions then refer to the outstanding stocks of assets and liabilities separately.

A.1.2 External Borrowing of Sectors

The focus of this paper is on the differentiation of capital flows by sector in the domestic economy. The term "sector" is used here to refer to institutional sectors: general govern­ment, central banks, depository corporations except the central bank ("banks"), and other sectors ("corporates").There are other ways to define the sectors of the economy, but this breakdown is the most common in the data/ For much of our analysis, and all analysis us­ing asset flows, we combine the central bank and general government sectors into a single sector called "public sector".

These broad sectors can sometimes be decomposed into various institutional subsectors (for example, other sectors are sometimes split into other non-bank financial and other non­financial sectors in the BOP data). Thus, sectors can also be defined differently depending on the dataset or measure. For instance, several datasets such as the WB DRS produce statistics on public and publicly guaranteed (PPG) debt. In this case, public refers to general govern­ment, central banks, and the public sector portions of banks and corporates. Non-publicly guaranteed private sector debt is defined precisely as its name suggests and is the comple­ment to PPG. Otherwise, most datasets using a sectoral breakdown conform to the standard definition of the main institutional sectors and subsectors given above. We consider PPG vs. PNG debt in Appendix C.2.

A.1.3 Sign of Flows

There remains some confusion about the sign of capital inflows and outflows in the data. This is primarily due to a change in sign conventions that occurred when the BOP data switched from the BPM5 to the BPM6 version. In BPM5, a negative sign indicated that capital was leaving the country on net, regardless of whether it was an asset or liability flow. In the current version of the BOP data (BPM6), a positive asset flow represents capital leaving the country on net by domestic residents, while a positive liability flow represents capital entering the country on net by foreigners. We use the updated convention, where a positive sign indicates an increase in either assets or liabilities, and adjust our interpretation accordingly.

A.2 Balance of Payments Data

The IMF's Balance of Payments (BOP) data is the most comprehensive dataset available on international capital flows and the basis for our dataset. It comprises two main accounts - the Current Account and the Financial Account. The current account records transactions from the real side, capturing imports and exports, factor income, and transfer payments. The financial account records transaction from the financial side, capturing the acquisition of financial assets and the incurrence of financial liabilities. We focus on the Financial Account portion of the BOP data.

There are several presentations of the BOP data. The standard presentation disaggre­gates the data by flow type and instrument. Figure 3 illustrates this structure, with the available breakdowns by sector. The analytic presentation, which is the one available within the IMF's International Financial Statistics (IFS), reports exceptional financing (used to meet balance-of-payments financing needs) separately from the standard presentation. The an­alytic presentation can be useful to separate some public flows from private flows, because exceptional financing can be viewed as an alternative instrument to the use of reserve assets or IMF credit to help deal with balance of payments shortfalls. We use the sectoral presen­tation, which breaks down the standard presentation by domestic institutional sector, but we also use measures of exceptional financing from the analytic presentation to allocate all exceptional financing flows to the public sector.

In theory, the structure of the BOP dataset should allow separating the flows by insti­tutional sector, but the requisite data is sometimes missing. It is difficult to determine if missing data is truly missing, or if it is zero. Data on outflows are generally more sparse than data on inflows. Further, the time coverage of the data varies greatly across countries. Especially for variables with sectoral breakdown, the coverage is weighted heavily towards recent years.

A.2.1 Types of Flows

Capital flows in the Financial Account of the BOP are disaggregated first by type of flow.

The main types are direct investment, portfolio equity, portfolio debt, other investment,

financial derivatives, and reserves. For each of these flow types, the BOP reports asset flows and liability flows. We describe each type of flow and how it can be broken down into the various institutional sectors. We focus on the debt portions of capital flows (portfolio debt, other investment debt, reserves, and sometimes direct investment debt) in our dataset, but we describe all components of capital flows here.

Direct Investment: Direct investment, commonly called FDI, captures investment in­volving at least 10% ownership. It is meant to reflect investment relationships based on control and influence. In addition to equity investment, it also captures other investments under a controlling relationship, including debt and reverse investment.

Direct investment is not broken down by sector. Unlike the BPM5 version of the data, the BPM6 data does have splits according to liability and asset flows for direct investment (consistent with other BOP flows). Direct investment does not have a split in the BOP by sector, but the debt portion of direct investment inflows can be allocated with some as­sumptions. Direct investment debt inflows between affiliated parties are only recorded as direct investment debt if at least one party is a non-financial firm. Thus for inflows, we can attribute all direct investment debt to the Corporate sector if we assume that such lending from offshore non-financial firms to onshore banks is negligible.

Portfolio Equity: Portfolio equity captures investment in equity securities not included in direct investment. It is broken down by institutional sector and, in principle, asset and liability flows are defined for all sectors. Note, however, that liability flows for central banks and general government should equal zero regardless of data reporting.

Portfolio Debt: Portfolio debt consists of all debt securities not captured under direct investment. It is separated into asset and liability flows, and then disaggregated by institu­tional sector.

Financial Derivatives: Financial derivatives tend to be a quantitatively small category of gross flows, covering derivatives and employee stock options. Financial derivatives that are associated with reserve asset management are excluded. Both asset and liability flows offer breakdowns by institutional sector. Due to its small size and sparse data, we ignore this component in our analysis.

Other Investment: Other investment captures all other investments not included in the previous categories. It is first broken into other investment equity and other investment debt. Other investment debt is then disaggregated as follows: currency and deposits, loans (including use of IMF credit and loans), insurance and pensions,trade credit and advances, other accounts payable/receivable, and SDR allocations.

Other investment debt as a whole, and each of its component instruments, is broken down into asset and liability flows, and then further broken down by institutional sector. However, there is no sectoral breakdown of Other Investment Equity.

Reserves: Reserve Assets are external assets held by the Central Bank or Monetary Au­thority that are readily available for use to meet Balance of Payments financing needs. These

include foreign currency, convertible gold, SDRs, and other reserve assets. Thus, this com­ponent is an asset flow of the public sector only.

While in principle the structure of the BOP data contains all the ingredients required to compute each type of flow for each sector, with the exception of direct investment, in practice there are some countries which do not exhaustively provide these breakdowns, especially for earlier years. Table A1 highlights the coverage by flow type and sector in the quarterly BOP data. For each component, the table displays the number of countries reporting data, the number of quarters with at least one country reporting data, the number of country-quarter observations with non-missing data, and the number of countries that have data for that component in every period over the 1996q1-2014q4 period. Next to each of these numbers, in brackets we report the implied coverage as percentage of the theoretical maximum, given by 190 countries, 144 quarters, and 27360 total observations. The direct investment and reserves lines give us an idea of the coverage of the more standard items that are not disaggregated by sector. Generally, we see that for most sectors and flow types, most countries and periods show some data. However, the data is skewed towards recent years, and few countries show coverage over the full 1996q1-2014q4 period.

Table A2 shows the coverage breakdown for Other investment Debt by instrument, with each instrument listed separately under Asset and Liability by sector. The table illustrates

how more detailed breakdowns tend to result in poorer coverage, as not all countries pro­vide such detail to the IMF. Generally, if other investment debt by sector is missing, then all of the underlying instruments (with the exception of IMF credit) are also missing. When data for instruments is reported, it can be the case that all of other investment debt is recorded under a single instrument (usually loans), despite the number representing other instruments as well (such as trade credit, etc.).

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A.3 Filling Missing Data

We proceed in two steps to fill the gaps in the BOP data. The first step is an internal fill. When the BOP data reports the total for a flow type and reports 3 out of the 4 sectors, we fill the fourth sector by subtracting the three reported sectors from the total, the residual being allocated to the missing sector. In the case of capital outflows (asset flows), we combine general government and central bank into a single public sector. So, when one or both of general government or central bank are missing data, we fill the public sector with the residual of the total minus banks and corporate sectors. After performing our internal filling exercise, we use external data to fill the remaining gaps.

We draw on 3 separate sources for data to construct measures of capital inflows that can be used when the BOP data is missing. The first is banking and bond data from the BIS, which is described in detail in Appendix B. We also draw on the International Investment Position (IIP) data that accompanies the BOP data, and the Quarterly External Debt Statistics (QEDS) data which is produced jointly by the World Bank and IMF. Both of these are stock measures, and have the same sector and capital flow type classifications as the BOP data. The QEDS data is quarterly and is compiled from a combination of data reported to the IMF via their Special Data Dissemination Standard (SDDS) and their General Data Dissemination System (GDDS), thus sometimes giving it better coverage than the reported IIP stock data. The IIP data comes either quarterly or annually.

The dataset with the broadest coverage by sector and capital flow type, and thus fills the most observations, is derived from the BIS data. The BIS produces a database on interna­tional bond issuances and databases on international banking flows (e.g. loans), which are described in more detail below and in Appendix B. While the BIS data in many cases cap­tures much of the international financial flows we are trying to measure, it is not always an appropriate fill and so we do not want to use just a single data source for our external fill­ing exercise. Specifically, bond inflows are measured in the BIS data as net issuance of debt securities in international markets. While this measure is appropriate for many countries, countries that have many foreigners buying domestically issued bonds or domestics buying international issued bonds will introduce error. An important example of this is govern­ment debt issued by advanced economies. The US has a substantial amount of sovereign debt that is traded abroad, but nearly all of the debt is issued domestically, making the BIS measure an inappropriate way to fill that missing series. Thus to increase the accuracy of our filling process, we turn first to the IIP and QEDS data. To approximate flows, we first difference the stocks with a simple correction for exchange rate valuation effects. When both IIP and QEDS data are available, we use the IIP measures for consistency with the BOP data. We use these stock measures to fill both portfolio debt and other investment debt for the government and central bank sectors. We also use these measures to fill Corporate portfolio debt in AE.

For the remaining missing data, we use our BIS constructed measures. Table A4 summa­rizes the process of constructing matching series for inflows using the BIS data.


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For the BIS data, we construct our measure of portfolio debt flows from the BIS Inter­national Debt Securities (IDS) data. It captures net issuance of debt securities (bonds) in a market other than that of the country where the borrower resides (Gruic & Wooldridge, 2012). This does not necessarily imply that the securities are held by foreigners, but can be taken as an approximation for external financing flows through debt securities. Since the IDS data are compiled on a security-by-security basis, granular sectoral splits are easy to obtain; we thus construct these net issuances by sector using the same sector definitions as the BOP data.

For other investment debt, we construct our series from our BIS estimates as follows: First, we examine the underlying components of other investment debt. The primary in­struments are loans (for corporates and governments) and currency and deposits (for banks and central banks). If loans are missing for corporates or government, or currency and de­posits is missing for banks or central banks, we rely on the BIS Locational Banking Statistics (LBS) to fill in the data. The BIS data captures cross-border lending from banks in BIS reporting countries. This lending can be broken by instrument into loans, debt securi­ties holdings, and other instruments. We use just the loan instrument in our measure, and so avoid capturing any bond holdings or equity investment made by banks. Since the BIS data will not capture official lending, we add IMF Credit to these series to capture that com­ponent of loans. The Locational Banking Statistics by Residence (LBSR) historically only break the counterparty sector for Bank lending into banks and non-banks, though recent data includes additional sector splits. We employ the BIS Consolidated Banking Statistics (CBS) and the Locational Banking Statistics by Nationality (LBSN), both of which have fur­ther counterparty breakdowns, in order to construct estimates for Bank lending flows for all 4 sectors for the entire period, as described in Appendix B.

After augmenting the Loans (or Currency and Deposits) with the BIS data, we sum them with any remaining non-missing instruments within other investment debt. This sum be­comes our estimate for other investment debt from BIS data.

Our corresponding stock measures are similarly constructed. We rely first on IIP data, with an internal fill. We next fill any missing data with QEDS measures. And finally any remaining missing observations are filled with our BIS stock estimates derived above.

Table A5 shows the percentage of observations for inflows that are filled by each step of our filling exercise for each sector-instrument category for each country group. For outflows (asset flows), there are few external datasets to do comparable filling. Thus, we rely primar­ily on our internal filling strategy and end up with a much smaller sample of countries. In one case, we can and do fill using external data. The BIS banking data has data for cross border lending of banks in countries that report to the BIS, separated into loans and bonds. Thus, we use this data to fill for the banking sector when missing, but given that most BIS member reporting countries are advanced, this does not fill many observations.

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Figure A1 compares aggregate inflows as measured by our filled data and from the BOP alone, for total external debt of banks and corporates in our samples of AE and EM. We plot annual flows here for clarity. These graphs show that generally both series tell the same story, but there are periods in which accounting for the missing data makes a significant difference. For advanced economy corporates, a significant expansion leading up to the 2008 crisis and a the subsequent contraction are missed. This is due primarily to filling in portfolio debt data for the US and Spain for the 2008 surge, as well as a few other AE for the earlier 2001 peak. For EM, both banks and corporates had much larger flows relative to the BOP measure following the 2008 collapse, driven primarily by filling data for other investment debt inflows for China.

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Missing U.S. government portfolio debt drives the difference for the AE in panel (a). EM governments and AE central banks are fairly well represented in terms of volume. Note that net inflows can be negative as well as positive, which is the case for EM central banks, where some missing data consists of negative net inflows, which brings our filled data be­low the raw BOP total. The surge at the end of the sample for EM central banks is driven by China.

To illustrate the quality of our inflow filling series, we compare it with the available BOP data. Figures A3 and A4 illustrates this match by plotting the aggregate inflows for each series by sector, capital flow type, and country group. For each sector and capital flow type, we keep only countries that had non-missing BOP data over 2006q1-2013q4.

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A.4 Samples

A.4.1 Inflow Figures

There are 89 countries in our annual data sample of capital inflows:

Advanced (25): Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States

Emerging (34): Argentina, Brazil, Bulgaria, Chile, China, Colombia, Croatia, Czech Re­public, Egypt, Estonia, Hungary, India, Indonesia, Jordan, Kazakhstan, Latvia, Lebanon, Lithuania, Macedonia, Malaysia, Mexico, Peru, Philippines, Poland, Romania, Russian Fed­eration, Slovak Republic, Slovenia, South Africa, Thailand, Turkey, Ukraine, Uruguay, Venezuela Developing (30): Albania, Angola, Bangladesh, Belarus, Bolivia, Costa Rica, Cote d'Ivoire, Dominican Republic, Ecuador, El Salvador, Gabon, Ghana, Guatemala, Jamaica, Kenya, Liberia, Mongolia, Montenegro, Morocco, Namibia, Nigeria, Pakistan, Papua New Guinea, Paraguay, Serbia, Sri Lanka, Sudan, Trinidad and Tobago, Tunisia, Vietnam

Countries dropped for the Direct Investment figures (22): Angola, Austria, Belgium,

Cote d'Ivoire, El Salvador, Gabon, Greece, India, Ireland, Jamaica, Jordan, Lebanon, Liberia, Malaysia, Montenegro, Morocco, New Zealand, Serbia, Trinidad and Tobago, Ukraine, Venezuela, Vietnam

A.4.2 Inflow Regressions

Sample was selected from countries that had data for debt flows for all 4 sectors and for GDP over 2001q3-2014q4.

Advanced (23): Australia, Austria, Belgium, Canada, Denmark, Finland, France, Ger­many, Greece, Ireland, Israel, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Por­tugal, Spain, Sweden, Switzerland, United Kingdom, United States

Emerging (28): Argentina, Brazil, Bulgaria, Chile, China, Colombia, Croatia, Czech Re­public, Egypt, Estonia, Hungary, India, Indonesia, Kazakhstan, Latvia, Lithuania, Malaysia, Mexico, Peru, Philippines, Poland, Romania, Russian Federation, Slovak Republic, Slovenia, South Africa, Thailand, Turkey

Developing (4): Bolivia, Costa Rica, Ecuador, Guatemala

Note that we drop Cyprus and Iceland due to their large debt flows relative to individual GDP.

A.4.3 Outflow Sample

Our outflow sample consists of 31 countries:

Advanced (15): Australia, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Israel, Italy, Japan, Korea, Netherlands, Norway, United Kingdom

Emerging (16): Brazil, Bulgaria, Chile, Colombia, Croatia, Czech Republic, Estonia, Hun­gary, Kazakhstan, Lithuania, Mexico, Philippines, Russian Federation, South Africa, Thai­land, Turkey

BIS Data

The Bank for International Settlements (BIS) produces datasets on international bond is­suance and bonds outstanding, by sector and by residence or nationality of the issuer. In­ternational debt securities (IDS) are defined as those issued in a market other than that of the country where the borrower resides (Gruic & Wooldridge, 2012). This does not neces­sarily imply that the securities are held by foreigners, but can be taken as an approximation for external holdings of debt securities. Since the IDS data are compiled on a security- by-security basis, granular sectoral splits are easy to obtain, unlike the data on debt from international bank creditors which requires some construction to obtain the split.

The IDS data are important for our exercise. While the BOP data relies on reporting by national statistical offices (which can result in incomplete coverage of portfolio debt secu­rities by sector), the IDS data are compiled directly on a security-by-security basis, which can result in much better coverage. The IDS data can also be presented on a residency basis or by the nationality of the issuing institution. See Avdjiev, Chui, and Shin (2014) and Shin (2013) for a more detailed discussion of this issue.

There are several options for how we allocate international debt securities to each sector. As noted earlier, bonds can be classified based on the residence of the issuer or the nation­ality of the issuer. Further, the BIS classifies IDS according to sector with several subsectors
which can be aggregated up to our public, bank, and corporate sectors: Public banks, pri­vate banks, central banks, public other financial corporations, private other financial corpo­rations, public non-financial corporations, private non-financial corporations, and general government sectors.

We keep general government and central bank sectors as they are found. Public and private banks are allocated to the bank sector. Public and private other financial and public and private non-financial corporations are allocated to the corporate sector. This aligns the bonds up with the standard institutional sector definitions in the BOP data. However, the role of public banks and corporations can be quite important in some countries.

B.2 BIS External Bank Credit Data

The BIS compiles two sets of statistics on international banking activity. The Locational Banking Statistics (LBS) capture outstanding claims and liabilities of internationally active banks located in 44 reporting countries against counterparties residing in more than 200 countries. Banks record their positions on an unconsolidated basis, including intragroup positions between offices of the same banking group. The data are compiled based on the residency principle (as done for BOP or QEDS). The LBS capture the overwhelming major­ity of cross-border banking activity. The historical LBS data breaks down counterparties in each country into banks (banks and central bank sectors) and non-banks (corporate and government sectors). The LBS reports outstanding stocks, and based on them BIS calcu­lates exchange rate- and break-adjusted flows.

The second set of banking data is the Consolidated Banking Statistics (CBS). This differs from the LBS in that the positions of banks reporting to the BIS are aggregated by the na­tionality (rather than by the residence) of the reporting bank. Currently, banking groups from 31 countries report to the CBS. We use the CBS on an immediate counterparty basis (CBS/IC). The CBS data does provide a borrower breakdown of the Non-Bank Sector into Public and Private. Since there is no currency breakdown available for the CBS, the BIS does not calculate adjusted flows.

B.3 Obtaining Borrowing Sector Splits for Bank Creditor Data

In this section, we describe our methodology for constructing gross capital inflows and debt outstanding from BIS sources. Our goal is to obtain the stocks and flows measured based on residency (consistent with the LBS data), but we also employ the CBS to obtain certain (non-bank) borrowing sector splits. We deviate from residency in some cases to gain a more complete picture of flows.

The bank loan data is from the LBS by residency (LBSR). For observations prior to 2013, the LBS only provide the breakdown between bank and non-bank debtors (where non-bank captures both the non-bank private and the public sector). We focus on cross-border bank lending in the LBS in the form of loans, for which we have data starting in 1996. However, our methodology described below can also be applied to total cross-border bank claims (in all instruments).

Next, we describe how we use the sectoral split information contained in the CBS/IC data in order to divide the Non-Bank sector in the LBS data into Non-Bank Public sector and Non-Bank Private sector. This is described next. First, we go over our methodology for constructing the split for the outstanding stocks of LBS cross-border bank loans. Then, we describe our methodology for constructing the split for exchange rate adjusted changes, which relies on currency composition information available in the LBS.

B.3.1 Borrowing Sector Splits for Outstanding Stocks

For outstanding stocks, we use the share of international bank debt for each sector from the CBS to estimate the split of the Non-Bank LBS data into Public and Private components. We calculate that as follows:

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denotes the borrowing country, and t denotes the time period. XBS is our estimated cross border bank debt, XBC denotes the cross border claims (from the LBS) of BIS reporting banks, and INTC is international claims (from the CBS on immediate counterparty basis). The CBS international claims are defined as the sum of XBC and the local claims by foreign affiliates that are denominated in foreign currencies (LCFC).

This construction of the split of bank debt makes the following assumptions: First, the sectoral shares for INTC are the same as the sectoral shares for XBC. This is reasonable since for most countries, LCFC tends to be small relative to XBC. Second, the sectoral shares for the set of banks that report LBS data (44 countries) are the same as the sectoral shares for the set of banks that report CBS data (31 countries). The 31 CBS reporting countries account for about 90% of the XBC in the LBS, and the CBS captures the activities of the subsidiaries of banks from these 31 countries worldwide. As a result, the CBS data are sufficiently representative to make the above assumption a reasonable one. Third, data for the CBS that allows us to estimate the split of Non-Bank into Public and Private is not available for advanced economies before 2000, and is only available on a semiannual basis for EM for the period before 2000. We linearly extrapolate the semiannual shares to Public and Private into a quarterly series for EM. For advanced economies, we assume constant shares from 2000 backwards

Having made these assumptions and constructed the external debt to bank creditors, we can then estimate total external debt by sector by adding XBS to IDS for each sector. This will produce a longer series of external debt estimates by sector than the Quarterly External

Debt Statistics (QEDS), and cover more countries.

Recently, the BIS has released its enhanced banking data, starting in 2013. This data contain more granular borrowing sector splits - Bank, Public, and Non-Bank Private. We use this short, recent series to judge the quality of our decomposition. Our methodology for estimating borrowing sector splits for the non-bank borrowing sector and the public sector generates estimates that are very close to the actual (reported) underlying figures.

B.3.2 Borrowing Sector Splits for Outstanding Flows

Obtaining exchange rate-adjusted flows to all sectors and to banks is straightforward since they are reported in the LBS data. However, as discussed above, the historical LBS data do not have a split of the non-banks sector into its public and private components. Thus, in order to get estimates for exchange rate-adjusted flows to the non-bank private sector and to the public sector, we rely on the estimated stocks for those sectors obtained in the previous section.We assume that the currency compositions of claims on these sectors are the same as the currency composition of claims on the non-bank sector as a whole.

Using the above assumption, we can obtain estimates of the stock of bank lending to the non-bank private Sector denominated in currency j as follows: where XBSi t is the estimated stock of claims denominated in currency j on the non-bank

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private Sector in country i at the end of period t; XBSi tis the estimated stock of claims

denominated in all currencies on the Non-Bank Private Sector in country i at the end of period t; XBS^jf* is the reported stock of claims denominated in currency j on the Non-Bank Private Sector in country i at the end of period t; and XBSalj'nb is the reported stock of claims denominated in all currencies on the Non-Bank Private Sector in country i at the end of period t.

We then estimate the flow of bank lending to the Non-Bank Private Sector in each cur­rency by converting the USD values of the estimated stocks into their corresponding values in the currency in which they are denominated using the same period USD exchange rate, differencing them, and then converting back into USD using the average exchange rate:

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where XBFi t is the estimated flow of claims denominated in currency J on the Non-Bank

Private Sector in country i during period t; FXjusd is the end-of-period t exchange rate be­—- j,usd

tween currency J and USD; and FXtis the average exchange rate during period t between

currency j and USD.

Now that we have the estimated flow for each currency, we sum these individual flows to obtain the total estimated flow:

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where nb p denotes the Non-Bank Private Sector.

Estimates of flows to the Public Sector can be obtained in an analogous fashi.

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The direct investment debt (DID) component of the data is not as extensively reported as our augmented data for portfolio debt and other investment inflows, so we limit our sample for this analysis. The balanced DID sample is a subsample of 67 countries, of which there are 20 advanced, 28 emerging, and 19 developing. Details of the 22 countries that are dropped can be found in Appendix A.4.

Direct investment debt is an important part of direct investment flows, as shown in Fig­ure C4 where we plot it against direct investment equity, in aggregate terms. The figure shows that they share the same pattern over time. However, with the rise in offshore is­suance much of direct investment debt may really be more like portfolio debt flows and hence less stable than its equity counterpart (Avdjiev et al., 2014). Direct investment debt makes up a larger share of direct investment for AE, but less so for EM and especially de­veloping countries. It is interesting to note that, for both debt and equity, direct investment has decreased substantially in advanced economies following the global financial crisis, but has leveled off somewhat in emerging and developing economies. Thus, while direct in­vestment debt plays a larger role in the advanced world prior to the crisis, its influence will be felt relatively more in other economies.

Direct investment debt is only recorded in the BOP if one of the (related) counterparties involved is a non-financial entity. Debt flows between related financial enterprises (includ­ing banks) are captured in either portfolio debt or other investment debt. We make the assumption that direct investment debt flows from offshore non-financial firms to onshore financial firms (or banks) are negligible. With this assumption, we can allocate direct invest-


We see that direct investment debt can be significant in size, relative to other capital flow types. It tends to follow the same trends as other forms of debt in the aggregate, but can have some influence on the evolution of total debt. In fact, it is larger than the other debt components in some periods.

  1. 2 PPG vs PNG Debt Inflows

We have focused in this paper on the sectoral split of inflows by government, central bank, banks, and corporates, and found important differences between public and private flows. Another way to examine the roles of the public and private sector is to split the data by Public and Publicly-Guaranteed Debt (PPG) vs Private Non-Guaranteed Debt (PNG). This allows us to capture flows nominally allocated to the private sector which should actually be considered liabilities of the public sector, such as borrowing by public and quasi-public cor­porations common in many EM.We can do this for emerging and developing economies using the World Bank's Debtor Reporting System (DRS) data found within the World Bank International Debt Statistics (WB-IDS). This data is annual going back to 1970 for many countries, but we use a balanced sample of 14 EM and 60 developing countries over 1981­2014:

Emerging (14): Brazil, Bulgaria, China, Colombia, Egypt, India, Indonesia, Jordan, Malaysia, Mexico, Peru, Philippines, Thailand, TurkeyDeveloping (60): Algeria, Bangladesh, Belize, Benin, Bhutan, Bolivia, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Republic of Congo, Costa Rica, Cote d'Ivoire, Dominica, Dominican Republic, Ecuador, El Salvador,Ethiopia, Fiji, Gabon, Ghana, Grenada, Guatemala, Guinea-Bissau, Guyana, Honduras, Ja-
maica, Kenya, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Morocco,Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Rwanda, Sene-
gal, Sierra Leone, Solomon Islands, Sri Lanka, Sudan, Swaziland, Togo, Tunisia, Uganda,Vanuatu, Zambia, Zimbabwe


Figure C6 (a)-(b) plots aggregate debt inflows from the DRS data, with flows split by PPG and PNG debt. Panels (c)-(d) plot the average of PPG and PNG debt to GDP ratio for each group of countries. According to these measures, PNG debt in EM soared leading up to the GFC, as most measures of debt inflows did. Following a brief collapse, PNG debt rebounded significantly in the aggregate, but this rebound is muted if we examine flows relative to GDP for the average country. This is consistent with what we see in Figures 4 and C2, where much of the post-2008 increase in aggregate flows is driven by large and quicklygrowing EM such as China.

In both emerging and developing economies, and in both the aggregate and average GDP figures, we see a steady decline in PPG debt until the GFC, after which it rebounds, and significantly so in the case of developing economies. This is similar to what we observe in Figures 4 and C2, but in those figures the decrease leading up to 2008 is not as pronounced as when you take the longer time horizon.

These figures also highlight how private and public capital flows can move opposite each other, consistent with our previous results. This is particularly noticeable for EM around the 2008 crisis, where we see PNG flows fall dramatically while PPG flows rise, thus smoothing out the total debt inflows.