The Role of Domestic Debt Markets in Economic Growth
An Empirical Investigation for Low-Income Countries and Emerging Markets
Author: S. M. Ali Abbas and Mr. Jakob E Christensen

Contributor Notes

Author’s E-Mail Address: sabbas@imf.org; jch@nationalbanken.dk
Publication Date:
01 Jun 2007
eISBN:
9781451866919
Language:
English
Keywords:
WP; DD market; central bank; per capita income; crowding out; issuance capacity; DD instrument; interest rate; markets increasing; private sector lending; DD database
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We develop a new public domestic debt (DD) database covering 93 low-income countries and emerging markets over the 1975-2004 period to estimate the growth impact of DD. Moderate levels of non-inflationary DD, as a share of GDP and bank deposits, are found to exert a positive overall impact on economic growth. Granger-causality regressions suggest support for a variety of channels: improved monetary policy; broader financial market development; strengthened domestic institutions/accountability; and enhanced private savings and financial intermediation. There is some evidence that, above a ratio of 35% percent of bank deposits, DD begins to undermine growth, lending credence to traditional crowding out and bank efficiency concerns. Importantly, the growth contribution of DD is higher if it is marketable, bears positive real interest rates and is held outside the banking system. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.

Abstract

We develop a new public domestic debt (DD) database covering 93 low-income countries and emerging markets over the 1975-2004 period to estimate the growth impact of DD. Moderate levels of non-inflationary DD, as a share of GDP and bank deposits, are found to exert a positive overall impact on economic growth. Granger-causality regressions suggest support for a variety of channels: improved monetary policy; broader financial market development; strengthened domestic institutions/accountability; and enhanced private savings and financial intermediation. There is some evidence that, above a ratio of 35% percent of bank deposits, DD begins to undermine growth, lending credence to traditional crowding out and bank efficiency concerns. Importantly, the growth contribution of DD is higher if it is marketable, bears positive real interest rates and is held outside the banking system. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.

I. Introduction

Public domestic debt (DD) in low-income countries (LICs) and emerging markets (EMs) remains a controversial issue in academic and policy making circles.1 The question is more pertinent than ever given the increased scope for expanding DD in many LICs and EMs following external debt reduction initiatives and a surge in international portfolio interest in local currency bonds. A meaningful policy response is, however, constrained by the lack of comprehensive empirical studies that examine DD’s impact on savings, investment, financial deepening, institutions and, hence, growth. So far, most of the vast literature on the effects of public debt on capital accumulation and growth has been derived in the context of industrialized countries (Barro, 1974). These studies find that the optimal public debt ratio for developed countries ranges from about 30-70 percent of GDP.2

Policy advice has traditionally sought to limit the accumulation of DD. Given shallow financial markets, financial repression propensities, and poor debt management capacity, which are found in many LICs and even some EMs, many observers believe that DD expansion will have significant negative implications for private investment, fiscal sustainability and, ultimately, economic growth and poverty reduction. In addition, given that most LICs have access to very cheap external finance, in the form of concessionary loans and grants from international financial institutions, governments in poor countries have been advised to avoid seemingly expensive domestic borrowing. In other words, low DD issuance is considered beneficial for economic development.

In recent years, however, research has increasingly begun to echo the positive view of many market participants regarding the importance of DD instruments for monetary and financial systems, as well as the development of political institutions. Compared to other forms of budgetary finance, market based domestic borrowing is seen to contribute more to macroeconomic stability – low inflation and reduced vulnerability to external real and domestic monetary shocks – domestic savings generation and private investment. This seems to be supported by the experience of fast growing EMs such as China, India, and Chile, which have maintained relatively low external indebtedness and avoided major financial or fiscal crises.3

Existing empirical studies on the implications of DD markets for LICs and EMs have mostly taken a fiscal sustainability view, while direct analysis of the relationship between public debt and economic growth has been limited to external debt – see Sachs (1989), Husain (1997), and Pattillo et al. (2002). This lack of interest in formally studying the impact of DD on growth could be attributed to i) data unavailability – reliable datasets on DD either do not exist or are not amenable to empirical analysis; ii) a wide-spread perception that DD is “endogenous” rather than an exogenous policy choice variable that governments can tweak to affect macro-financial outcomes: countries’ DD issuance capacity is “determined” entirely by their level of income, pool of savings and institutional quality; and iii) the relatively small size of DD relative to external public debt in most LICs and EMs. These factors have, arguably, combined over the years to “crowd out” the amount of attention paid to DD.

The objective of this paper is to fill this void in the literature, by: bringing together the various arguments for and against DD issuance currently scattered across the literatures on capital markets, public finance, debt management and fiscal sustainability (section II); compiling a new DD database spanning the period 1975-2004 for 93 LICs and EMs, as well as consolidating existing databases on DD (section III); using panel econometric techniques to examine the endogeneity of DD and its impact on growth with a view to obtaining a sense of the optimal size and quality of DD (section IV); and presenting empirically-grounded policy conclusions on DD to guide macro-financial practitioners, especially in LICs (section V).

II. Existing Theoretical and Empirical Studies

A. Pros and cons of domestic debt

Issuing DD, whether to finance the fiscal deficit or to mop up monetary liquidity, involves a complex evaluation of the costs and benefits to the economy. Although practitioners’ views on the subject abound, the academic literature on the pros and cons of DD issuance and the channels through which this type of financing can affect public finances, the financial sector, and the real economy is limited. Critics of DD are concerned with the repercussions on private sector lending, fiscal and debt sustainability, weakening bank efficiency, and inflationary risks.

The most prominent concern about DD is the crowding out effect on private investment. When governments borrow domestically, they use up domestic private savings that would otherwise have been available for private sector lending. In turn, the smaller residual pool of loanable funds in the market raises the cost of capital for private borrowers, reducing private investment demand, and hence capital accumulation, growth and welfare (Diamond, 1965). In shallow financial markets, especially where firms have limited access to international finance, DD issuance can lead to both swift and severe crowding out of private lending.

Second, critics of DD are also concerned with repercussions on fiscal and debt sustainability. DD is viewed as more expensive than concessionary external financing (Beaugrand et al, 2002).4 As a result, the interest burden of DD may absorb significant government revenues and thereby crowd-out pro-poor and growth enhancing spending. In addition, reliance on domestic financing may also delay tax mobilization efforts, which may be necessary but politically costly. Given the short-term structure of the DD portfolio in many LICs and EMs - see, for example, Christensen (2004) on Sub-Saharan Africa (SSA) – governments also face a significant liquidity risk from having to constantly roll-over large amounts of debt.

Third, the cost of DD may rise sharply due to time inconsistency problems when government credibility is low. If the state has weak (direct) tax collection, as is the case in most LICs, the state will have a strong incentive to monetise deficits and use the net domestic financing window to both, generate seigniorage, and, reduce the real burden of existing DD. Under these circumstances, the government faces a classic time inconsistency problem and, therefore, either cannot issue nominal debt at all, or has to pay a significant premium to compensate investors for the potential risk of surprise inflation.5

Fourth, high-yielding government DD held by banks can make them complacent about costs and reduce their drive to mobilise deposits and fund private sector projects. The incentive to provide credit to the private sector is often weakened by a poor credit environment. Hence, from a risk weighted perspective, government debt is highly attractive, providing a constant flow of earnings, so that banks have less incentive to expand credit to riskier private borrowers or cut their overheads (Hauner, 2006).6

Proponents of DD stress its positive impact on growth, inflation, and savings from deeper and more sophisticated capital markets, which enhance the volume and efficiency of private investment. Consequently, they question the wisdom of forever pursuing zero net domestic financing (NDF) policies in countries where marketable DD is already small and capital markets underdeveloped. Such policies, by reducing the size of DD relative to GDP and deposits, could exert a negative impact on financial market development, and complicate the exit from foreign aid dependency.

DD markets can help strengthen money and financial markets, boost private savings, and stimulate investment. First, government securities are a vital instrument for the conduct of indirect monetary policy operations and collateralized lending in interbank markets; the latter helps banks manage their own liquidity more effectively, reducing the need for frequent central bank interventions.7 Consequently, central banks operating in well-developed DD markets do not have to rely as much on direct controls like credit ceilings, interest rate controls and high reserve requirements, all of which distort financial sector decisions and lead to financial disintermediation at the expense of private sector savings and investments (Gulde et al., 2006). Second, yields on government securities can serve as a pricing benchmark for long-term private debt issued by banks or enterprises and, hence, promote the development of a corporate bond market which boosts competition in the baking sector (Fabella and Mathur, 2003). Third, the availability of DD instruments can provide savers with an attractive alternative to capital flight as well as lure back savings from the non-monetary sector into the formal financial system (IMF, 2001). The possible benefits here can go beyond saving mobilization and extend to a reduction in the size of the black economy, widened tax base, increased financial depth, de-dollarization and improved perceptions of currency and country risk.

In addition to enhancing the volume of investment, DD can also improve the efficiency of investment and help increase total factor productivity. Banks in many developing countries face an inherently risky and unpredictable business environment, which makes them reluctant to engage with the private sector. As a result, banks play only a very limited role in providing longer term financing to important strategic sectors, such as agriculture and manufacturing, and prefer instead to finance consumption related trade activities (in the case of Africa, see Gulde et al., 2006). In providing banks with a steady and safe source of income, holdings of government securities may serve as collateral and encourage lending to riskier sectors. In other words, holdings of government debt may compensate for the lack of strong legal and corporate environments (Kumhof, 2004 and Kumhof and Tanner, 2005). The collateral function of DD may be particularly important when bank overheads cannot be reduced further, and lending risks remain high due to asymmetric information and/or weak contract enforcement (including of foreclosure laws).8

Third, in the longer-term, nominal debt contracts enhance political accountability and help governments build a track record to access international capital markets. Increasing the reliance on domestic financing may help mitigate the problems of external borrowing, which has been found to crowd out domestic institutions by weakening the state’s dependence on its citizenry and hence severing the accountability channel that forces domestic institutional reform (Moss et al, 2006; Abbas, 2005). Furthermore, developing a track record may promote access to international financial markets. Research shows that countries that have successfully issued sovereign bonds on international markets have typically had a long prior experience with issuing domestic government bonds in their own markets (Kahn, 2005).

B. Empirical Survey

Studies on DD have been constrained by a lack of reliable data, especially time series data for a large enough panel of countries. Fry (1997) is the only panel study on the impact of alternative deficit-financing strategies on economic growth in LICs and EMs. For over 66 LICs and EMs over the 1979-1993 period, Fry finds market-based DD issuance to be the least costly method of financing the budget deficit as opposed to external borrowing, seigniorage and financial repression, all of which are eventually seen to stifle growth, reduce domestic saving, and fuel inflation. Indeed, the real question, according to him, is not “whether” countries should switch to market based domestic financing, but “how” they should do so.

Several studies have examined the impact of domestic financing on bank efficiency and private sector lending. Using bank-level data on 73 middle-income countries over the post-1990 period, Hauner (2006) finds that banks, which allocate more credit to the government, are more profitable, but less efficient. However, applying aggregate country level data on commercial bank holdings of DD, the results are mixed: DD only begins to harm financial development at very high levels. Since Hauner’s sample excludes sub-Saharan Africa and other poorer LICs, which typically have low DD, his results are already somewhat biased towards finding a low residual DD capacity. Furthermore, the study does not take into account the fact that the extent to which banks can “sit on” government bond interest income or “pass them on” to depositors and borrowing firms depends on the nature of competition in the financial sector.

Moreover, Hauner (2006) does not consider the possibility that banks’ decision to hold DD may be economically efficient from a risk-diversification perspective. For instance if in the long-term, banks’ real return on private lending were negatively correlated with their income from government securities, the overall risk of the bank portfolio would fall through a risk-diversification effect. This will lower depositors’ required return, enabling banks to lower their lending rate for any given intermediation margin. Abbas (2007b) demonstrates the theoretical plausibility and empirical support for this negative correlation between private and government returns.9

Empirical evidence on the crowding out effects of DD at the macroeconomic level is mixed. In a study of the determinants of financial depth, such as loans and deposits scaled to GDP, Detragiache et al. (2005) include government domestic interest payments as a proxy for DD in 82 LICs and EMs over the 1990-2001 period. The coefficient on interest payments is found to be significantly negative, although not robustly so in regressions of bank assets scaled to GDP, thereby suggesting a standard crowding out effect, at first glance. However, domestic interest payments enter the loans to GDP and deposits to GDP regressions positively, significantly and robustly, suggesting a crowding in effect in line with Kumhof and Tanner’s (2005) collateral argument.

IMF (2005a) explicitly examines the impact of DD on private sector credit in the context of 40 LICs (including 15 mature stabilisers) over the 1993-2002 period.10 Overall, the study finds “limited evidence of government recourse to domestic financing crowding out private sector borrowing in the mature stabilizers” (p. 34). Higher “levels” of DD are found to be associated with lower levels of corporate lending, but the relationship breaks down when first differences of the variables are used. The report also finds no robust evidence of a negative correlation between real T-bill rates and changes in DD for either the mature stabilizers or the broader LIC group. However, the report notes that crowding out may occur through channels other than interest rates, such as credit rationing, and cautions against a rapid buildup in DD, especially in the context of the availability of concessionary external financing.

C. Testable hypotheses

The foregoing suggests a complex cost-benefit calculus for DD and a series of plausible hypotheses (i-iii) must be tested in order to unravel it. For instance:

  1. DD may have either a positive or negative net impact on growth. Furthermore, the impact of DD on growth may well be “non-linear”.

    DD could both have positive and negative effects on economic growth. These contrary views may be bridged by the existence of a non-linear impact of debt: at moderate levels, DD boosts growth but beyond a certain level, more traditional crowding out concerns may dominate. Hence, it may be important to identify whether such threshold exits, which could help evaluate whether debt in a given country has reached inappropriate levels.

  2. The macroeconomic impact of DD may work primarily through the investment efficiency channel rather than capital accumulation.

    DD may both boost the pool of savings and enhance the volume of investment in the economy. In addition, the positive spill-over effects from DD markets to broader capital markets may promote more risk taking and support better allocation of capital to productive sectors. If these sectors have been underfinanced in the past, DD markets will help raise total factor productivity and expand the economy’s production frontier.

  3. The institutional environment as well as the quality and span of DD markets may have a significant bearing on the growth impact of DD.

The institutional environment could have a complex interaction with DD. On one hand, better institutions can imply a competent policy framework, featuring optimal use of fiscal resources for the provision of public services, infrastructure development, maintenance of law and order, and property rights protection. This would tend to make the growth impact of DD, or any source of budgetary finance for that matter, higher. On the other hand, DD markets may be less important in stable institutional environments as the collateral or risk-diversification function that DD performs on banks’ balance sheets will play a smaller role. Furthermore, the risk-diversification benefit will become less important as the overall magnitude of risk in the economy falls. A priori, it is not clear which of two effects dominates: i.e., whether good institutions complement DD for public service provision, or whether they substitute for the collateral and risk-underwriting functions that DD performs on banks’ balance sheets.

The quality and breadth of DD markets should also be important. It is relevant to investigate how i) the composition of DD in terms of arrears and overdrafts versus auctioned securities (marketable T-bills and bonds) and ii) the holding of DD in terms of banks versus nonbank sectors, affect the growth impact of DD. Indeed, many of the benefits of DD discussed above: safe asset and collateral functions, monetary policy and liquidity management benefits, and benchmark yield curve for private lending, all clearly apply to securitized DD and not to debt issued in captive markets or accumulated due to fiscal irresponsibility.

Commercial bank holdings of DD are likely to be associated with lower financial system efficiency and greater crowding out, than when debt is held by the non-bank sector.11 As indicated in Christensen (2004) and Gulde et al. (2006), the ability of LICs, in particular in SSA, to expand DD without crowding out bank lending to the private sector partly depends on the importance of the contractual savings sector (pension and insurance companies etc.). The participation by individual and contractual savings institutions in the government securities market boosts competition in the financial sector alleviating some of the concerns by Hauner (2006).12

III. Data and Econometric Framework

A. New domestic debt database

As mentioned earlier, reliable DD data has been, and still is, a serious problem in LICs and some EMs. There are only a handful of LICs which maintain and report public DD data in an organized and regular manner. Even among this small subset, regular reporting has been instituted only recently and consistent time series on DD are not available for a decent stretch of time. The absence of such data has also effectively precluded, in our view, serious research on DD, the consequent emergence of a “total” public debt (i.e. domestic + external) view on debt management and fiscal policy, and an understanding of how debt structure choices are affected by and affect macro, fiscal, financial and institutional variables.

Researchers at IMF have recently attempted to collect DD data on subsets of LICs and EMs.13 Christensen (2004) collected annual data on central government domestic securities from 1980-2000 on 26 Sub-Saharan African countries; however the data has many gaps, effectively covering only 20 countries. Mellor collected securitized and non-securitized central government DD on 70 IMF “program” countries from 1996-2004 – see Mellor and Guscina (forthcoming). The data is also usefully disaggregated by holder (banking system vs. nonbank sectors) and securitized vs. unsecuritized. A third database introduced by Jeanne and Guscina (2006) compiles securitized central government securities on 19 emerging markets, disaggregated by maturity and currency since 1980. We make selective use of the Mellor and Christensen databases in this paper.

Our main data source, however, is Abbas (2007a) who extracts from the IFS monetary survey data a DD series spanning 93 LICs and EMs over 30 years (1975-2004).14 The definition used is commercial banks’ gross claims on the central government plus central bank liquidity paper.15 The series is then scaled to both GDP (DOMdebt) and commercial bank deposits (DD2dep). Table 1 provides a list of all the countries in the sample as well as a country-by-country breakdown of the evolution of these ratios over three decades, starting in 1975.

Table 1:

Public domestic debt trends in LICs (1975-2004)

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Notes:(i) The dark lines separate the three groups of LICs: SSA (Sub-Saharan African excluding South Africa), OTHER (some Asian, North African, Middel Eastern & Laltin American LICs) and EM (emerging markets)(ii) “Domestic debt” = [Banking sector’s claims on central government (IFS 22a+42a) + securitised claims on central bank (IFS 20c+40c)] divided by GDP at current market prices (IFS line item 99b)(iii) “Deposits” include current, time, try and saving deposits

DD, as share of GDP, appears to have risen over time from 5.5 percent to 8.4 percent, but, as a share of deposits, has remained relatively stable around 21.5 percent. The ratios for both DOMdebt and DD2dep are higher in EMs at 10 percent and 27.7 percent, respectively, compared with SSA’s 4.1 percent and 22.2 percent, respectively. Since substantial scope for economic expansion and financial deepening remains in SSA, the implied DD issuance capacity may be significant, going forward. Also, while the distribution of key DD ratios across SSA countries has been stable over time, the same in EMs has become significantly more dispersed, indicating increased heterogeneity among EMs; Figure 1 compares the ratios for the pre- and post-1990 periods for both SSA and EMs.16

FIGURE 1:
FIGURE 1:

PUBLIC DOMESTIC DEBT TRENDS: 1975-1989 vs. 1990-2004 (dashed line)

outliers removed

Citation: IMF Working Papers 2007, 127; 10.5089/9781451866919.001.A001

B. Controls and causality variables

For Granger causality regressions to investigate the endogeniety of DD and the channels through it may affect the economy, we use the following variables: per capita income, private savings rate, institutions and financial development. Since reliable series on the latter two variables do not exist in IFI databanks, we invoke suitable proxies. For institutions, we use the International Country Risk Guide (ICRG) “composite index”, which tracks countries’ political economic and financial risks over time; the index rises as the risk reduces and stability increases. The series runs from 1986 onwards, is available for most of the 93 countries, and is denoted as “stability”. Although we use it as a proxy for institutional quality, it can be construed as such only insofar as good institutions affect risk and stability.

To proxy for financial development, a “financial depth index” was constructed using the approach in Huang and Temple (2005). The index was developed from three underlying series -liquid liabilities of the financial system; private sector credit; and commercial bank share in banking system assets, using principal component analysis techniques – see Appendix II (A) for details on extraction methodology.17 With private sector credit included as an integral part of the index, the latter’s response to DD would also shed light on any crowding out effects.

Our control variables for the growth regressions are similar to those used in Pattillo et al. (2002): lagged income, population growth, investment, budget balance, openness to trade and terms of trade growth and the additional controls, inflation and external debt.18 The summary statistics, definitions and correlation matrix for the main regression variables are presented in Appendix I. Our choice and number of countries is similar to Pattillo et al. (2002): 93, but our time period is 1975-2004 divided into 10 three-year periods.

C. Econometric specification for growth regressions

Preliminary support for the non-linear relationship hypothesized (in section II C) between DD and growth is provided by the scatterplots in Figure 2.19 The plots suggest that growth may have a Laffer curve relationship with DD2dep, and a linear relationship with DOMdebt. This appears realistic in low financial depth contexts, where the financial size of a country would place a more binding constraint on DD capacity than economic size.

FIGURE 2:
FIGURE 2:

growth - domestic debt scatter plots

(country means over the period 1975-2004)

Citation: IMF Working Papers 2007, 127; 10.5089/9781451866919.001.A001

Note: Dashed line is linear prediction and unbroken line is quadratic prediction

The empirical analysis is modeled on Pattillo et al. (2002) who investigate the nonlinear growth effects of external debt on a panel of 93 developing countries over the 1969-98 period, using 5-year averaged data and a conditional convergence framework. Like them, we employ fixed effects, system GMM (generalized method of moments) and pooled OLS regressions of PPP per capita real income growth on linear and non-linear debt terms and an elaborate set of controls.20 For hypothesis (i) identified in section II ©, we estimate the following two equations:

git=αi+β′Xit+γDOMdebtit−1+ϕt+εit….(i)
git=αi+β′Xit+γDOMdebtit−1+δ(DOMdebt*quart_DOM)it−1+ϕt+εit….(ii)

where g is growth in PPP GDP per capita, X is a vector of control variables, DOMdebt is the domestic debt/GDP ratio, αi captures country heterogeneity and ϕt are period dummies. Similar regressions are run for the domestic debt/deposits ratio (DD2dep).

Our specification differs from Pattillo et al’s (2002) in that we work with the actual DD ratios (as opposed to logs) while using quartile dummy interaction variables (instead of squared terms) to study non-linear growth effects.21 For the case of DOMdebt, the corresponding quartile dummy is named quart_DOM, and for DD/deposits (DD2dep), quart_DD2dep. Our choice of specification is driven by the particular constraints posed by the DD database. For instance, many of the DD ratios in the sample – especially for LICs – were less than “1.00” (i.e. less than 1%). This precluded taking logs (which would have produced negative values), or squaring the non-logged ratios (as the squaring numbers less than 1.00 yields smaller not larger values). Moreover, given the high dispersion of the DD ratios – see Appendix Table A1a – squaring the ratios would have increased outlier problems.

For hypothesis (ii) of section II (C): whether DD impacts growth through investment efficiency of capital accumulation, investment is removed from the control set and the difference in results from the with-investment regressions observed.

And finally, for hypothesis (iii): the extent to which the growth impact of DD depends on the institutional environment in which the debt is issued, we have:

git=αi+β′Xit+γDOMdebtit−1+θ(DOMdebt*quart_STABILITY)it−1+ϕt+εit…(iii)

The regressions with attributes of DD were similar in structure to the above, except that, instead of quart_STABILITY, the following ratios were used for interaction:

  • Share of securities in total DD stock (SDD2DD) [range 0-100]

  • Share of DD held by banking system (shBANK) [range 0-100]

  • a period dummy (ERA) taking the value of 0 for pre-1990 observations (corresponding roughly to financial repression years) and 1 for 1990 onwards (corresponding to the financial liberalization years)

  • dummy variable (REALi) taking the value of 0 for observations where the real interest rate (deposit rate minus inflation rate) was zero or negative, and 1 when it was positive

We also run regressions using Christensen’s (2004) DD data on 20 SSA countries over 19802000. This dataset covers central government securities (unsecuritized debts excluded) and both bank and nonbank holdings of this debt, enabling us to indirectly test if these features have the expected positive impact on any observed growth effect of DD.

The priors on the coefficients of our control variable follow from the large number of empirical growth studies. GDP per capita growth should, in accordance with Solow’s convergence hypothesis, have a negative impact on growth. High inflation and population growth rates are also expected to undermine real economic growth. Robust empirical evidence (Elbadawi et al., 1997 and Pattillo et al., 2002) suggests that external debt impacts growth negatively. In contrast, gross fixed capital formation, fiscal balance, terms of trade growth, and openness should have positive effects on growth.

The primary attraction of using panel data methods in these cross-country regressions is their ability to deal with time-invariant individual effects (αi). If the effects are random, we can use the random effects (RE) estimator for unbiased and efficient estimation. However, if the effects are fixed, or if they are correlated with the regressors, RE is inconsistent, and fixed effects (FE) methods, which wipe out the individual heterogeneity altogether, must be employed to recover consistent estimates of β, γ and δ.

FE methods, however, are biased and inconsistent in dynamic panel data models of the type we are estimating. In particular, the coefficient on the lagged dependent variable (lnY_1) will be severely downward biased (numerically).22 Reverting to OLS and RE for estimating this coefficient is also unhelpful as both are severely upward biased, as discussed in Bond et al. (2002). Secondly, FE models (like OLS or RE) cannot deal with endogenous regressors, a key concern in the present context. For these reasons, we rely, in the main, on system GMM23 estimation of our regressions, which can simultaneously address the problems of endogeneity and lagged dependent variable.

IV. Empirical Results

A. Granger-causality tests on the endogeneity of DD

As a precursor to the growth regressions, we run a battery of Granger-causality panel regressions to study the extent to which DD is endogenous to, or drives, income, private savings, institutions (politico-economic stability) and financial development. Although these tests are very widely used in a range of contexts, it must be acknowledged that they are judgments on statistical causality and may not necessarily imply economic causality. This disclaimer applies equally to the causality inferences derived below.

Appendix II (B) details the econometric methodology underpinning the Granger causality regressions while Table 2 presents the results. The latter are also summarized in the following causality map, and seem to suggest support for two-way statistical causality links between DD and the other variables.24 Institutions are not causal, income and financial depth are weakly causal, while private savings are strongly causal for DD. Evidence on reverse-causality suggests that DD is an important explanatory variable for private savings and institutions and, to a lesser extent, for financial development and income. Overall, this appears to weaken the case for approaching DD as a purely endogenous variable.25

UF1
[An arrow from X to Y implies that the null of “X does not Granger-cause Y” is rejecteed at the 5% level of significance; at 10% in case of broken arrow.]
Table 2:

Granger causality tests for domestic debt, financial depth, stability, income & saving

(system GMM regressions; all variables are log-normalised; coefficients of interest in bold)

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Notes:(i) INCOME is PPP GDP per capita from WEO; DOMdebt is 100*domestic debt/GDP (see text); FINDEPTH index is developed using Beck et al(2000) data (see text); prSAVING rate is from IMF: WEO database; STABILITY is ICRG composite risk index capturing a country’s political, economic and financial risk (higher value denotes lower risk)(ii) z-statistics in italics, unless otherwise stated; constant and time dummies included in all regressions (iii)** significant at 5%, * = at 10% (iv) data spans 93 countries and 10 three-year time periods constructured from 30-year annual data (1975-2004) (v) GMM results reported here were obtained using the xtabond2 two-step command in STATA; z-statistics are based on heteroskedasticity-consistent errors and the finite-sample adjustment of Windmeijer (2005). AR(1) and AR(2) are tests for first-order and second-order serial correlation. First order (negative) serial correlation is expected due to first-differencing, but identification of the models relies on the absence of second-order correlation. Hansen’s chi-squared tests the additional moment conditions used by the system GMM estimator (vi) GMM instrumentation: for the difference equation, levels dated t-2 and t-3 are used as instruments; while for the moment conditions in levels, first differences dated t-1 are used as instruments (vii) the joint test for significance of β s relates, in any given column, to the coefficients appearing in bold in that column (viii) for regressions in panel c, variables enter in non-normalised form (ix) βLR captures the LR effect of the explantory variable on the regressand; where βLR >1, the beta and the associated β1+ β2 =0 test are not reported; the coefficients on regressand lags are dentoed by α1 and α2 (x) Stability requires that the roots of the polynomial 1 - α1L - α2L2 = 0 are outside the unit circle (xi) The test H0: β1+ β2 =0 checks if there is support for the long-run “levels” relationship (captured by βLR). Failure to reject the null implies β1=- β2, so that βit= β1(Xit-1-Xit-2); in other words Y depends on changes in X, not the level of X; the relationship, therefore, is not long-term but short-term. A rejection of the null implies the opposite, and provides support for a long-run “levels” relationship.

DD and private savings were found to be closely associated. Higher private savings increase the scope for DD issuance while a larger supply of DD instruments provides incentives to increase private savings. Strengthening and expanding DD markets can, therefore, form a potentially virtuous cycle of higher private savings and stronger capital markets. Note that the size of the long-run marginal effect of DD on private saving: βLR [= 0.82] in panel c of Table 2 far exceeds the rather low estimates [around 0.5] for the Ricardian offset ratios floating in the savings literature, ruling out a pure Ricardian explanation for the positive association – see Masson et al. (1998).

DD was found to weakly and positively Granger-cause financial depth positively. However, financial depth had a surprisingly weak causal contribution to income (panel e, Table 2), which seems at odds with other empirical studies that find a significant impact of financial development on economic growth.26 The inconsistency can be resolved, partly, by noting that financial depth – which is highly sensitive to short-term credit and deposit booms – is only a crude proxy for financial development, which may be regarded as a “longer-term” concept. In that context, insofar as expanding DD markets are also a long-term phenomenon (especially when measured in relation to GDP), DD can serve as a better proxy for financial development than financial depth. Indeed, Beck et al.’s (2006) financial development dataset includes both data on financial depth and local bond market capitalization. To the extent that the latter is driven primarily by outstanding government bonds in LICs and EMs – see World Bank (2006: Fig. 2.2) – this seems like an implicit acknowledgement that the development of DD markets is, in and by itself, an integral part of the process of financial development.27

B. The behavior of control variables in growth regressions on DD

Coefficient signs and magnitudes for the control variables all appear to be empirically plausible, and broadly in line with our stated priors (Tables 3-4). For lagged income (lnY_1) and population growth (gPOP), a one percentage point rise corresponds to a decrease in per capita growth of about ½ of a percentage point. The INFLATION coefficient is negative and significant (although not in all regressions), confirming conventional wisdom that low inflation is a pre-condition for lasting growth. Gross fixed capital formation or investment (lnINVEST) is highly significant in all regressions and has the usual high semi-elasticity of around 3.5 – similar to Pattillo et al. (2002), but significantly higher than Mankiw et al.’s (1992) range of 2.1-2.2. The coefficient on fiscal balance (FISBAL) is, expectedly, significant and positive in all regressions hovering in the 0.1 to 0.2 range, and virtually identical to the range found in Pattillo et al. (2002). The benefits of fiscal austerity underlined here will inform the policy implications on DD derived in section V. Finally, the results on EXTdebt are also in line with expectations to the extent that the sign on all significant coefficients thereof is negative - consistent with the finding in Pattillo et al (2002).28

Table 3:

Growth regressions on DOMdebt (domestic debt/GDP)

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Notes:(i) t -statistics (FE & OLS) and z -statisitcs (RE & GMM) in italics.(ii) constant; time dummies included in all regressions.(iii) ** sig. at 5%, * = at 10%.(iv) data spans 93 countries and 10 three-year time periods constructed from 30-year annual data (1975-2004).(v) Results obtained using STATA’s reg (OLS), xtreg (RE; FE) and xtabond2 (GMM-system; two-step) commands.(vi) Domestic debt regressors are lagged one period.(vii) z-statistics for GMM are heteroskedasticity-consistent.(viii) Arellano-Bond AR(1) and AR(2) tests are for 1st and 2nd-order serial correlation in errors. 1st order negative serial correlation is expected due to first-differencing; model identification requires absence of 2nd-order correlation.(ix) Hansen’s chi-squared test checks if the moment conditions used by the system GMM estimator are valid(x) GMM instrumentation:gTOT and gPOP were assumed exogenous, while the domestic debt variables, lnlNVEST, InOPEN and FISBAL were treated as endogenous.instruments used for the difference equation: Xt-2, where X denotes an endogenous variable.additional instruments used for the levels equation: ∆Xt-1, where X is an endogenous variable.(xi) “quart_DOM” dummy = 0 if DOMdebt falls in 1st quartile, 1 if DOMdebt falls in 2nd quartile, 2 if DOMdebt falls in 3rd quartile, and 3 if DOMdebt falls in the 4th quartile; relevant DOMdebt percentiles are: p25 = 1.26%, p50 = 4.61% & p75 = 7.36%.
Table 4:

Growth regressions on ‘domestic debt to deposits ratio’ [DD2dep: domestic debt/(current, time and saving deposits)]

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Notes:(i) t -statistics (FE & OLS) and z -statistics (RE & GMM) in italics.(ii) constant; time dummies included in all regressions.(iii) ** sig. at 5%, * = at 10%.(iv) data spans 93 countries and 10 three-year time periods constructed from 30-year annual data (1975-2004).(v) Results obtained using STATA’s reg (OLS), xtreg (RE; FE) and xtabond2 (GMM-system; two-step) commands.(vi) Domestic debt regressors are lagged one period.(vii) z-statistics for GMM are heteroskedasticity-consistent.(viii) Arellano-Bond AR(1) and AR(2) tests are for 1st and 2nd-order serial correlation in errors. 1st order negative serial correlation is expected due to first-differencing; model identification requires absence of 2nd-order correlation.(ix) Hansen’s chi-squared test checks if the moment conditions used by the system GMM estimator are valid(x) GMM instrumentation:gTOT and gPOP were assumed exo genous, while the domestic debt variables, lnlNVEST, lnOPEN and FISBAL were treated as endogenous.instruments used for the difference equation: Xt-2/ where X denotes an endo genous variable.additional instruments used for the levels equation: ∆Xt-1, where X is an endo genous variable.(xi) “quart_DD2dep” dummy = 0 if DD2dep falls in 1st quartile, 1 if DD2dep falls in 2nd quartile, 2 if DD2dep falls in 3rd quartile, and 3 if DD2dep falls in the 4th quartile; relevant DD2dep percentiles are:p25   8.54%p50   17.43%p75   29.93%

C. The growth impact and optimal size of DD

Results here suggest broad support for a positive overall contribution of “moderate” DD levels to economic growth. For the first DD ratio measured in percent of GDP, DOMdebt, we find a positive significant linear coefficient. The range for the coefficient was between 0.04 with OLS, 0.06 for FE and 0.07 for RE and GMM (Table 3A). Taking 0.06 as the average, increasing DD by one standard deviation (9.70%), implies an increase in the growth rate by 0.58 percentage points (0.13 standard deviations). The non-linear specification in this case (Table 3B) neither adds to overall explanatory power, nor throws up significant non-linear effects. That said, the linear coefficients are higher and the sign on the DOMdebt*(quart_DOM) term consistently negative in all four regressions, so that the possibility of a Laffer curve relationship between growth and DD, perhaps in a slightly richer group of countries, cannot be ruled out. In the current sample, however, with the fourth DOMdebt quartile beginning at 7.36%, there does not appear to be any evidence of diminishing returns to public DD.

However, regressions with DD/deposits (DD2dep) suggest a non-linear growth impact. The linear specification in Table 4A produces positive and significant coefficients for DD2dep in all but the FE regression, with the coefficients being smaller than those obtained for DOMdebt. This may partly be because of stronger in-sample non-linearities in the growth-DD2dep relationship compared with DOMdebt; in line with the scatterplots in Figure 3. The results on the non-linear specification (Table 4B) do indeed suggest support for this hypothesis. The coefficient of the linear DD2dep term strengthens noticeably compared with its counterpart in Table 4A, while the non-linear interaction term DD2dep*quart_DD2dep is negative and significant in all regressions. The turning points, or growth-maximizing levels of DD2dep, in the OLS and RE regressions are out-of-sample, but for the FE and GMM regressions are 35.7% and 65.4%, respectively. The FE maxima also appear closer to the 35-40% turning point suggested by the growth-DD2dep scatterplot in Figure 2.29

D. The channels of influence: Investment volume vs. efficiency

The foregoing raises important questions about the channels through which DOMdebt might affect growth. A causality-centered treatment of this question proffered important causal links from DD to institutions, savings and financial depth. As far as growth is concerned, these channels could feature both volume effects that work primarily through the quantity of investment, and efficiency (quality of investment) effects that work through total factor productivity.

By including both investment and DD in our growth specifications, we have thus far been focusing on the efficiency contribution of DD, rather than its investment volume effect, currently picked up by the investment coefficient. To establish the relative weight of the volume and the efficiency contributions, we run regressions excluding investment as a regressor and study the difference. As can be seen from Table 5, the DOMdebt coefficient is consistently higher in such regressions. The ratio of the with-investment to the without-investment DOMdebt coefficients is 78.6% (average across all four regressions), indicating that the primary contribution of DD is through investment efficiency; mirroring Pattillo et al.’s (2002) conclusions on external debt.30 31 This, in turn, suggests, that should other determinants that constrain the quality of investment improve, such as private sector risk, the contribution of DD to growth will weaken. Some evidence of this emerges below.

Table 5:

Linear specifications “excluding” investment (lnlNVEST)

Table 6: GMM regressions including DOMdebt interactions with STABILITY

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Notes:(i) t-statistics (for OLS and FE) and z-statistics (for RE and GMM) in italics.(ii) Constant; time dummies included in all regressions.(iii) ** sig. at 5%, * = at 10%.(iv) Data spans 93 countries and 10 three-year time periods constructed from 30-year annual data (1975-2004).(v) Results obtained using STATA’s reg (OLS) xtreg (FE; RE) and xtabond2 (GMM-system; two-step) commands.(vi) Domestic debt regressors are lagged one period.(vii) z-statistics for GMM are heteroskedasticity-consistent.(viii) Arellano-Bond AR(1) and AR(2) tests are for 1st and 2nd-order serial correlation in errors. 1st order negative serial correlation is expected due to first-differencing; model identification requires absence of 2nd-order correlation.(ix) Hansen’s chi-squared test checks if the moment conditions used by the system GMM estimator are valid(x) GMM instrumentation:(a) gTOT and gPOP were assumed exogenous, while the domestic debt variables, lnINVEST, lnOPEN & FISBAL were treated as endogenous.(b) instruments used for the difference equation: Xt-2, where X denotes an endogenous variable.(c) additional instruments used for the levels equation: ∆Xt-1, where X is an endogenous variable.(xi) The “quart_STABILITY” dummy = 0 if the STABILITY variable (ICRG composite index) falls in the 1st quartile, 1 if it falls in 2nd quartile, 2 if it falls in 3rd quartile, and 3 if it falls in the 4th quartile; the relevant “STABILITY” percentiles are as follows:  p25   p50   p75STABILITY (ICRG composite index)   50.8   59.15   66.9

E. Does DD complement, or substitute for, good institutions?

Results on the interaction of DD with institutions (STABILITY), suggest a substitutive rather than complementary relationship. For our preferred GMM estimation (regression 3, Table 6), the marginal growth effect of DD becomes negative at the 60th percentile (ICRG index = 62), indicating a non-linear relationship in the variable. Interpreted in economic terms, this suggests that the collateral and risk-diversification functions of DD might be more relevant in high-risk countries where banks cannot lend to the private sector as freely as they would wish to. To further understand this result, we look at the composition of the sub-sample for which the STABILITY index was greater than its optimal threshold of 62.

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SSA countries make up 43 percent of the total observations in the full sample (930=93*10), but only 19 percent of the sub-sample for which STABILITY>62. By contrast, EMs are significantly over-represented in this sub-sample. This seems to confirm Kumhof and Tanner’s (2005) argument that DD has more of a positive role to play when there are structural and institutional factors constraining good quality lending. If one of these factors is the high undiversifiable risk arising from politico-economic instability, then a country that reduces such instability through improved governance and stronger domestic institutions is less likely to need, and/or benefit from, DD.

F. The impact of DD quality on its optimal size

The signs on the relevant interaction regressors employed here appear to underscore the importance of DD quality for its growth impact. Debt that is securitized, bears positive real interest rates and is diversely held is found robustly friendlier to growth. Some of these results, summarized in Table 7 (a-b), are obtained from data spanning 70 IMF program countries over the 1996-2004 period (the Mellor database). Similarly, regressions (e-f) employ data on 20 SSA countries since 1980 (Christensen’s 2004 database). Less than 200 observations were available for each of these four regressions, so the results, especially the coefficient “sizes”, should be interpreted with caution.

Table 7:

Interactions with attributes of domestic debt quality

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Notes:(i) FE regressions reported only (due to space considerations); t -statistics in italics.(ii) constant; time dummies included in all regressions, (iii)** sig. at 5%, * = at 10%.(iv) for regressions a & b, data spans 70 IMF programme countries over 1996-2004, i.e. four 3-year periods (using Mellor’s 2006 database); for regressions c & d, the full 93 country (1975-2004) sample was used; for regressions e-f, Christensen’s (2004) DD data on 20 SSA countries (1980-2000) was used.(v) ALLonCG = 100*[all banking sytem claims on central government/GDP]

Regression (a) tests the interaction of the share of securitized DD in total central government DD (SDD2DD) with all banking system claims on the central government (ALLonCG). The linear ALLonCG term, which includes the central government’s inflationary overdrafts from the central bank is negative and almost significantly so. However, the securitized component of central government DD [ALLonCG*SDD2DD] has a strongly positive coefficient, indicating the benefits of issuing DD as marketable securities. The positive coefficients on the interaction terms with ERA (financial liberalization post-1990 =1) and REALi (positive real interest rates =1) in regressions (c) and (d), respectively, provide further confirmatory evidence of this result.32 Further as can be seen from the summary statistics on SDD2DD (right panel, Table 7), only about 27% of central government DD in IMF program countries is securitized, representing substantial scope for marketization, going forward.

The careful selection and interaction of terms in regression (b) test the hypothesis that the growth contribution of DOMdebt decreases in the share of DD held by the banking-system.33 Summary statistics on this shBANK series indicate a median share of 50% and an interquartile range of 5.3 percent to 85.2 percent. The results indicate that DOMdebt becomes less growth-enhancing as shBANK rises.34 The most obvious policy implication of this result is that public debt issuers should attempt to diversify debt holdings beyond commercial banks by encouraging participation from institutions (pension funds etc.), the retail sector, and if appropriate, foreign investors. Fortunately, with private domestic savings rebounding in LICs and EMs, contractual saving institutions expanding and foreign interest in their DD markets increasing, the conditions are quite conducive for undertaking such diversification.

Regressions (e) and (f) suggest that the result on positive overall growth payoff of DD documented earlier remains robust to the SSA subgroup and with an important alternative definition of DD, i.e. “all central government securities”.35 The estimated linear marginal effect for the DDSSA/GDP ratio is 0.16 and for DDSSA/deposits is 0.02, matching the earlier pattern of higher growth payoffs to DOMdebt compared with DDdep. The quadratic terms are negative but insignificant in both regressions, suggesting that current perceptions of DD capacity in SSA may be unnecessarily bearish. The fact that the quadratic term is not significant – even for DDSSA/deposits – may partly reflect i) the exclusion from the DDSSA measure of a less desirable component of DD: unsecuritized liabilities, overdrafts of the central bank etc.; and ii) the inclusion in the measure of a relatively desirable component: nonbank-held DD.

Indirectly, therefore, these results support the same hypotheses that regressions (a) and (b) lean towards: DD is more growth-friendly when issued as marketable securities and, to a diverse investor base, including the nonbank sectors.

G. Selected robustness tests

Before discussing any policy conclusions, the results reported here should be tested for robustness i) across estimation methods, ii) over different horizons and country sub-groups; and iii) after removing outliers. By using OLS, random effects, fixed effects and system GMM and establishing the stability of the results over this broad range of estimation techniques, (i) has already been addressed. For (ii), the regressions (e) and (f) on the SSA sub-sample partly addresses the issue of robustness over country-groups. Further, to test for robustness over horizon length, 3-year data is aggregated into 6-year data, to make sure that any residual cyclical effects are also smoothed out. As a result of the conversion, the total number of observations halves. Table 8 summarizes the results of OLS, FE and system GMM regressions of growth on DD/GDP (DOMdebt, linear, panel a) and DD/deposits (DD2dep, non-linear, panel b) using 6-year data. As can be seen, the DOMdebt coefficient strengthens compared with the 3-year case. By contrast, the evidence for a non-linear growth impact of DD2dep weakens: all the non-linear terms are insignificant while the linear coefficients are lower than their 3-year counterparts in two of the three regressions.

Table 8:

Robustness check 1 Selected regressions with 6 year data

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Notes:(i) t -statistics (FE & OLS) and z -statisitcs (GMM) in italics.(ii) constant; time dummies included in all regressions.(iii) ** sig. at 5%, * = at 10%.(iv) data spans 93 countries and 5 six-year time periods constructed from 10 3-year period data (1975-2004).(v) results obtained using STATA’s reg (OLS), xtreg (RE; FE) and xtabond2 (GMM-system; two-step) commands.(vi) regressor Domestic debt means DOMdebt (domestic debt/GDP) for panel a regressions, and DD2dep (domestic debt/deposits) for panel b regressions; quartile means quartiles of DD2dep.(vii) z-statistics for GMM are heteroskedasticity-consistent.(viii) Arellano-Bond AR(1) and AR(2) tests for GMM regressions check for 1st and 2nd-order serial correlation in errors. 1st order negative serial correlation is expected due to first-differencing; model identification requires absence of 2nd-order correlation.(ix) Hansen’s chi-squared test checks if the moment conditions used by the system GMM estimator are valid(x) GMM instrumentation:(a) gTOT and gPOP were assumed exogenous, while the domestic debt variables, lnINVEST, lnOPEN and FISBAL were treated as endogenous.(b) instruments used for the difference equation: Xt-2, where X denotes an endogenous variable.(c) additional instruments used for the levels equation: ∆Xt-1, where X is an endogenous variable.

The final robustness check – sensitivity to outliers – based on the DFBETA post-estimation command in STATA is also green.36 The command works with OLS and LSDV (FE) regressions, and computes the influence of each observation (country-period) on the coefficient of interest. DFBETA series for all three measures of DD were generated after running their corresponding OLS and FE regressions. Observations with |DFBETA| > √2/N (N being the total number of observations) were then dropped from the sample and the regressions re-run on the new smaller samples. Table 9 reports the results from these outlier-cleansed regressions. As can be seen, in the case of DOMdebt, the coefficient size rises significantly for both the OLS and FE cases. For DD2dep (panel b), evidence for a non-linear growth impact endures, with a turning point for DD2dep at 35.4% for the FE case, very similar to the result obtained earlier.37

Table 9:

Robustness check 2-Selected regressions with DFBETA outliers removed

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Notes:(i) t -statistics in italics.(ii) constant; time dummies included in all regressions.(iii) ** sig. at 5%, * = at 10%.(iv) data spans 93 countries and 10 three-year time periods constructed from 30-year annual data (1975-2004).(v) results obtained using STATA’s reg (OLS) command; since DFBETA is a post-estimation command that works only after reg, the FE regressions had to be simulated using least squared dummy variable specifications (i.e. by including country dummies).(vi) DFBETA series for each domestic debt coefficient in a particular regression were generated; DFBETA outliers were then defined as those observations for which |DFBETA | > (2/√n) where n is the total number of observations in the original regression.For example, take regression a1 above: the original regression (Table 3: A1) corresponding to regression a1 had 618 observations, implying a DFBETA threshold of ± 2/√618 = ± 0.0805. All observations with DFBETA outside this range (24 in this case) were construed as “outliers”. Regression a1 (reported here) was run after dropping these outlier observations.(vii) regressor Domestic debt means DOMdebt (domestic debt/GDP) for panel a regressions and DD2dep (domestic debt/deposits) for panel b regressions; quartile means quartiles of DD2dep.