I. Introduction

The African Development Bank recently announced that it plans to launch a new bond program for infrastructure to raise up to US$40 billion for investments in projects such as ports and airports, highlighting the growing role for bond markets in financing development in sub-Saharan Africa. ² Yet bond markets in these countries are at a nascent stage of development and there is a strong need to promote their development.

First, sub-Saharan Africa has been heavily dependent on external grants and concessional loans for funding capital spending and government deficits. Only a small number of countries have limited access to global capital markets. ³ Additionally, western donors are now facing substantial fiscal challenges and consequently donor flows to sub-Saharan Africa may be scaled back significantly. Without access to alternative sources of finance, including bond markets, many African countries could find it difficult to finance critical needs.

Second, well-functioning bond markets help sustain economic stability. The Asian experience supports this point. Since the 1997 Asian financial crisis, many Asian economies have made significant progress in strengthening bond market development. This has in turn helped these Asian economies weather the recent global financial crisis because deeper financial markets generated valuable funding sources for these countries to finance fiscal stimulus packages.

Third, the development of bond markets in sub-Saharan Africa can improve the intermediation of savings. Although Africa needs money, Africa is a net capital exporter to the rest of the world (IMF, 2012). This is mainly because there is a lack of effective intermediate channels to absorb this capital. Bond markets are an effective way to intermediate capital savers with capital users.

Fourth, promoting bond market development in sub-Saharan Africa can improve the structure of the African financial system. The African financial sector is dominated by banks. The non-banking sector and bond markets, both public and private, are still in their infancy. Bond markets and bank finance are complementary rather than incompatible. While banks tend to be more adept at providing short-term (working) capital, bond markets enjoy a comparative advantage in financing government deficits and infrastructure investment, and providing longer-term capital to companies for growth.

Fifth, deeper bond markets will enable central banks in sub-Saharan Africa to conduct monetary policy more effectively. At present, many banks have few domestic fixed-income instruments to use for sterilization other than short-term government debt. Deeper bond markets would provide a wider, more effective range of instruments for monetary policy implementation.

This paper investigates empirically the determinants of local currency bond markets in sub-Saharan Africa. ⁴ Although a number of countries have issued sovereign bonds in foreign currencies, we focus on local currency bond markets because of the importance of the local currency markets compared to international sovereign bonds and because of the need to focus on African countries’ ability to overcome what is referred to in the literature as “original sin,” that is, the inability to issue debt in local currency. ⁵

We use data for local currency government securities market capitalization for 36 countries, over the years 1980–2010, along with a newly developed database for corporate bond market capitalization. This sample makes the study the largest of its kind in terms of both number of countries included and number of years covered. To investigate the determinants of bond markets, we draw upon an econometric approach used in Eichengreen and Luengnaruemitchai (2004), Claessens, Klingebiel, and Schmukler (2007), and Adelegan and Radzewicz-Bak (2009), among others. We use generalized method of moments estimation, in view of possible endogeneity among variables relevant to bond market development.

This research aims to achieve three purposes. First, it outlines the current situation of local currency bond markets including both government securities and corporate bond markets in sub-Saharan African countries. Second, it discusses and estimates key determinants of bond market development in sub-Saharan Africa. Finally, it offers policy advice for enhancing bond market development in sub-Saharan Africa.

The structure of this paper is as follows. Section II reviews the relevant literature. Section III provides an overview of the government securities and corporate bond markets in sub-Saharan Africa. Section IV sets out the analytical framework and discusses the econometric methodology underpinning the empirical analysis. Section V presents and discusses the results from the estimation. Section VI draws out the policy implications of the findings from the previous section and concludes.

II. Literature Review

The research on African financial sector development is growing. Most of the literature has so far focused on financial development of the banking sector and stock markets (e.g., Detragiache et al., 2005; McDonald and Schumacher, 2007; Yartey and Adjasi, 2007; Andrianaivo and Yartey, 2009; Anayiotos and Toroyan, 2009; Kablan, 2010; and Beck et al., 2011). Relatively little attention has focused on development of public and private bond markets. ⁶

Several studies have examined the determinants of bond markets in more developed economies. Eichengreen and Luengnaruemitchai (2004) consider a broad set of determinants of bond market development, using panel data from 1990 to 2001, for a sample of 41 developing and developed countries, with a focus on emerging Asia. They regress several measures of domestic currency bond market capitalization on various explanatory variables, and estimate these equations using generalized least squares, with heteroskedasticity and panel-specific autocorrelation correction. For determinants of the stock of public bonds, they find that GDP at purchasing power parity, exports, English origin, distance from the equator, a positive investment profile, and an open capital account are positive and significant while GDP per capita at purchasing power parity, banking sector concentration, bureaucracy quality, the interest rate spread, exchange rate volatility, and the fiscal balance are negative and significant (Table 6, column 7 of their paper). For private bonds, they find that GDP at purchasing power parity, exports, Asia dummy, distance from the equator, corruption, accounting standards, domestic credit, and bureaucracy quality are positive and significant while English origin, the interest rate spread, and exchange rate volatility are negative and significant (Table 6, column 4 of their paper). They conclude that market size matters; poor accounting standards hinder development of private debt markets, along with corruption and low bureaucratic quality. Well-capitalized bank systems promote bond markets. Stability of exchange rates encourages bond market development, and an absence of need for public financing discourages public bond markets. Capital controls also discourage bond market development.

Table 1.

Countries in the Sample, Definition of Variables and Sources

Table 1.

Countries in the Sample, Definition of Variables and Sources

Countries in the Sample Angola Congo, Rep. Kenya Senegal Botswana Cote d’Ivoire Lesotho Seychelles Burundi Eritrea Madagascar Sierra Leone Cameroon Ethiopia Malawi South Africa Cape Verde Gabon Mali Swaziland Central African Rep. Gambia, The Mauritius Tanzania Chad Ghana Namibia Togo Comoros Guinea-Bissau Nigeria Uganda Congo, Dem. Rep. Guinea Rwanda Zambia
Definition of Variables and Sources
Variable	Description	Sources
Dependent Variable
Gdebt	Government securities market capitalization (percent of GDP)	International Monetary Fund, WEO and IFS; World Bank, ADI
Cdebt	Corporate bond market capitalization (percent of GDP)	As listed in Table 2
Independent Variables
Econsize	GDP, PPP (US$ billions)	World Bank, ADI
Tradeopen	Total exports including goods and services (percent of GDP)	World Bank, ADI
Credit	Domestic credit to private sector (percent of GDP)	World Bank, ADI
Gdpcap	GDP per capita, PPP (US$ thousands)	World Bank, ADI
Spread	Bank interest rate spread (lending rate minus LIBOR)	World Bank, ADI and British Bankers’ Association
Intvol	Standard deviation of log of interbank rate	International Monetary Fund, IFS
Xrvol	Standard deviation of log first difference of nominal exchange rate (local currency per US$)	International Monetary Fund, IFS
Capopen	Chinn-Ito Index integrated IMF measure for capital account openness	Chinn and Ito (2008)
Fiscal	Three year moving average of overall fiscal balance including grants (percent of GDP)	World Bank, ADI
Area	Country size (square kilometers in thousands)	World Bank, ADI
Comprisk	Composite political, economic and financial risk	ICRG
Bureaucracy	Quality of bureaucracy	ICRG
Corruption	Corruption within political system	ICRG
Invprofile	Risk to investments	ICRG
Laworder	Strength of legal system	ICRG
Dummy Variables
Legalorigin	Value of 1 for country with an English legal origin, 0 otherwise	CIA World Factbook 2010

View Table

Table 1.

Countries in the Sample, Definition of Variables and Sources

Countries in the Sample Angola Congo, Rep. Kenya Senegal Botswana Cote d’Ivoire Lesotho Seychelles Burundi Eritrea Madagascar Sierra Leone Cameroon Ethiopia Malawi South Africa Cape Verde Gabon Mali Swaziland Central African Rep. Gambia, The Mauritius Tanzania Chad Ghana Namibia Togo Comoros Guinea-Bissau Nigeria Uganda Congo, Dem. Rep. Guinea Rwanda Zambia
Definition of Variables and Sources
Variable	Description	Sources
Dependent Variable
Gdebt	Government securities market capitalization (percent of GDP)	International Monetary Fund, WEO and IFS; World Bank, ADI
Cdebt	Corporate bond market capitalization (percent of GDP)	As listed in Table 2
Independent Variables
Econsize	GDP, PPP (US$ billions)	World Bank, ADI
Tradeopen	Total exports including goods and services (percent of GDP)	World Bank, ADI
Credit	Domestic credit to private sector (percent of GDP)	World Bank, ADI
Gdpcap	GDP per capita, PPP (US$ thousands)	World Bank, ADI
Spread	Bank interest rate spread (lending rate minus LIBOR)	World Bank, ADI and British Bankers’ Association
Intvol	Standard deviation of log of interbank rate	International Monetary Fund, IFS
Xrvol	Standard deviation of log first difference of nominal exchange rate (local currency per US$)	International Monetary Fund, IFS
Capopen	Chinn-Ito Index integrated IMF measure for capital account openness	Chinn and Ito (2008)
Fiscal	Three year moving average of overall fiscal balance including grants (percent of GDP)	World Bank, ADI
Area	Country size (square kilometers in thousands)	World Bank, ADI
Comprisk	Composite political, economic and financial risk	ICRG
Bureaucracy	Quality of bureaucracy	ICRG
Corruption	Corruption within political system	ICRG
Invprofile	Risk to investments	ICRG
Laworder	Strength of legal system	ICRG
Dummy Variables
Legalorigin	Value of 1 for country with an English legal origin, 0 otherwise	CIA World Factbook 2010

Table 2.

Sub-Saharan Africa Corporate Bond Market Database, 1980–2010

Source: IMF staff compilations.

¹BVMAC is the Bourse Régionale des Valeurs Mobilières d’Afrique Centrale.

²CAR is the Central African Republic.

³BRVM is the Bourse Régionale des Valeurs Mobilières.

Table 2.

Sub-Saharan Africa Corporate Bond Market Database, 1980–2010

Country	Securities Exchange	Activity	Source
Botswana	Botswana Stock Exchange	1997–2010	MCM (2009), Botswana Stock Exchange
Cameroon	Dar es Salaam Stock Exchange, BVMAC1	2005–2010	African Development Bank
Cape Verde	Bolsa de Valores de Cabo Verde	2007–2010	Bolsa de Valores de Cabo Verde
CAR2	BVMAC	2007–2010	African Development Bank
Chad	BVMAC	2007–2010	African Development Bank
Congo, Rep.	BVMAC	2007–2010	African Development Bank
Cote d’Ivoire	BRVM3	1999–2010	BRVM Regional Securities Exchange
Ethiopia	None	2006–2010	National Bank of Ethiopia
Gabon	BVMAC	2007–2010	African Development Bank
Ghana	Ghana Stock Exchange	1996–2010	Ghana Securities Exchange Commission
Guinea-Bissau	BRVM	1999–2010	BRVM Regional Securities Exchange
Kenya	Nairobi Stock Exchange	1996–2010	Kenya Capital Markets Authority
Mali	BRVM	1999–2010	BRVM Regional Securities Exchange
Mauritius	Stock Exchange of Mauritius	1990–2006	Mauritius Stock Exchange
Namibia	Namibia Stock Exchange	2001–2010	Bank of Namibia, Namibian Stock Exchange
Nigeria	Nigerian Stock Exchange	1981–2010	MCM (2009), Securities Exchange Commission
Rwanda	Rwanda Stock Exchange	2008–2010	Rwanda Stock Exchange
Senegal	BRVM	1999–2010	BRVM Regional Securities Exchange
South Africa	Bond Exchange of South Africa	1989–2010	Bank for International Settlements
Swaziland	Swaziland Stock Exchange	1990–2010	Central Bank of Swaziland
Tanzania	Dar es Salaam Stock Exchange	2002–2010	MCM (2009), Dar es Salaam Stock Exchange
Togo	BRVM	1999–2010	BRVM Regional Securities Exchange
Uganda	Uganda Securities Exchange	1998–2010	Uganda Capital Markets Authority
Zambia	Lusaka Stock Exchange	2000–2010	Lusaka Stock Exchange

Source: IMF staff compilations.

¹BVMAC is the Bourse Régionale des Valeurs Mobilières d’Afrique Centrale.

²CAR is the Central African Republic.

³BRVM is the Bourse Régionale des Valeurs Mobilières.

View Table

Table 2.

Sub-Saharan Africa Corporate Bond Market Database, 1980–2010

Country	Securities Exchange	Activity	Source
Botswana	Botswana Stock Exchange	1997–2010	MCM (2009), Botswana Stock Exchange
Cameroon	Dar es Salaam Stock Exchange, BVMAC1	2005–2010	African Development Bank
Cape Verde	Bolsa de Valores de Cabo Verde	2007–2010	Bolsa de Valores de Cabo Verde
CAR2	BVMAC	2007–2010	African Development Bank
Chad	BVMAC	2007–2010	African Development Bank
Congo, Rep.	BVMAC	2007–2010	African Development Bank
Cote d’Ivoire	BRVM3	1999–2010	BRVM Regional Securities Exchange
Ethiopia	None	2006–2010	National Bank of Ethiopia
Gabon	BVMAC	2007–2010	African Development Bank
Ghana	Ghana Stock Exchange	1996–2010	Ghana Securities Exchange Commission
Guinea-Bissau	BRVM	1999–2010	BRVM Regional Securities Exchange
Kenya	Nairobi Stock Exchange	1996–2010	Kenya Capital Markets Authority
Mali	BRVM	1999–2010	BRVM Regional Securities Exchange
Mauritius	Stock Exchange of Mauritius	1990–2006	Mauritius Stock Exchange
Namibia	Namibia Stock Exchange	2001–2010	Bank of Namibia, Namibian Stock Exchange
Nigeria	Nigerian Stock Exchange	1981–2010	MCM (2009), Securities Exchange Commission
Rwanda	Rwanda Stock Exchange	2008–2010	Rwanda Stock Exchange
Senegal	BRVM	1999–2010	BRVM Regional Securities Exchange
South Africa	Bond Exchange of South Africa	1989–2010	Bank for International Settlements
Swaziland	Swaziland Stock Exchange	1990–2010	Central Bank of Swaziland
Tanzania	Dar es Salaam Stock Exchange	2002–2010	MCM (2009), Dar es Salaam Stock Exchange
Togo	BRVM	1999–2010	BRVM Regional Securities Exchange
Uganda	Uganda Securities Exchange	1998–2010	Uganda Capital Markets Authority
Zambia	Lusaka Stock Exchange	2000–2010	Lusaka Stock Exchange

Source: IMF staff compilations.

¹BVMAC is the Bourse Régionale des Valeurs Mobilières d’Afrique Centrale.

²CAR is the Central African Republic.

³BRVM is the Bourse Régionale des Valeurs Mobilières.

Table 3.

Descriptive Statistics, 1980–2010¹

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

¹This is based on the full 36 country sample as in Table 1.

²Higher values for bureaucracy, comprisk, corruption, invprofile, and laworder provide a more favorable indication for institutional quality than lower values.

Table 3.

Descriptive Statistics, 1980–2010¹

Variable	Mean	Std. Dev.	Min	Max
Gdebt (percent of GDP)	17.36	16.74	0.06	127.79
Cdebt (percent of GDP)	2.31	4.65	0.00	24.82
Econsize (US$ billions)	22.24	57.46	0.19	524.20
Tradeopen (percent of GDP)	31.88	20.13	3.21	119.30
Credit (percent of GDP)	18.73	20.73	0.00	161.91
Gdpcap (US$ thousands)	2.21	3.26	0.21	23.07
Spread	12.83	17.05	−8.50	210.61
Intvol (standard deviation of the log of the interbank rate)	0.31	0.22	0.00	1.66
Xrvol (standard deviation of the log change in the nominal exchange rate)	0.20	0.19	0.06	1.49
Capopen (Chinn-Ito Index)	−0.75	1.06	−1.86	2.46
Fiscal (three-year moving average, percent of GDP)	−5.76	7.77	−45.11	17.34
Area (square kilometers in thousands)	468.77	522.71	0.46	2267.05
Bureaucracy (range 0–4)2	1.50	0.91	0.00	4.00
Comprisk (range 0–100)	57.51	10.43	25.50	84.70
Corruption (range 0–6)	2.56	0.96	0.00	6.00
Invprofile (range 0–12)	6.56	1.97	0.00	6.00
Laworder (range 0–6)	2.91	1.14	0.00	6.00
Legalorigin (1 is English origin and 0 is non-English)	0.37	0.48	0.00	1.00

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

¹This is based on the full 36 country sample as in Table 1.

²Higher values for bureaucracy, comprisk, corruption, invprofile, and laworder provide a more favorable indication for institutional quality than lower values.

View Table

Table 3.

Descriptive Statistics, 1980–2010¹

Variable	Mean	Std. Dev.	Min	Max
Gdebt (percent of GDP)	17.36	16.74	0.06	127.79
Cdebt (percent of GDP)	2.31	4.65	0.00	24.82
Econsize (US$ billions)	22.24	57.46	0.19	524.20
Tradeopen (percent of GDP)	31.88	20.13	3.21	119.30
Credit (percent of GDP)	18.73	20.73	0.00	161.91
Gdpcap (US$ thousands)	2.21	3.26	0.21	23.07
Spread	12.83	17.05	−8.50	210.61
Intvol (standard deviation of the log of the interbank rate)	0.31	0.22	0.00	1.66
Xrvol (standard deviation of the log change in the nominal exchange rate)	0.20	0.19	0.06	1.49
Capopen (Chinn-Ito Index)	−0.75	1.06	−1.86	2.46
Fiscal (three-year moving average, percent of GDP)	−5.76	7.77	−45.11	17.34
Area (square kilometers in thousands)	468.77	522.71	0.46	2267.05
Bureaucracy (range 0–4)2	1.50	0.91	0.00	4.00
Comprisk (range 0–100)	57.51	10.43	25.50	84.70
Corruption (range 0–6)	2.56	0.96	0.00	6.00
Invprofile (range 0–12)	6.56	1.97	0.00	6.00
Laworder (range 0–6)	2.91	1.14	0.00	6.00
Legalorigin (1 is English origin and 0 is non-English)	0.37	0.48	0.00	1.00

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

¹This is based on the full 36 country sample as in Table 1.

²Higher values for bureaucracy, comprisk, corruption, invprofile, and laworder provide a more favorable indication for institutional quality than lower values.

Table 4.

Bond Market Capitalization Comparison, 2010

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; BIS; and national sources for corporate bond market capitalization as set out in Table 2.

Table 4.

Bond Market Capitalization Comparison, 2010

Region	Country	Market Capitalization (percent of GDP)		Contribution to Total Domestic Debt (percent)
		Government	Corporate	Government	Corporate
Developing Countries and Emerging Markets
Africa	All	14.8	1.8	89.2	10.8
	South Africa (SA)	31.2	20.0	60.9	39.1
	All excluding SA	14.2	1.3	91.8	8.2
	CEMAC	10.5	0.7	93.8	6.3
	WAEMU	14.1	2.3	86.0	14.0
	Oil exporters	7.7	1.1	87.5	12.5
	Fragile countries	18.4	1.2	93.9	6.1
	Low income	15.3	1.1	93.3	6.7
	Middle income	15.1	3.5	81.2	18.8
Asia	China	27.3	22.8	54.5	45.5
	Hong Kong	35.9	13.8	72.2	27.8
	Malaysia	57.3	57.0	50.2	49.8
	South Korea	43.8	59.5	42.4	57.6
	Thailand	50.5	12.8	79.7	20.3
Latin America	Argentina	13.3	2.6	83.7	16.3
	Brazil	39.4	22.7	63.4	36.6
	Chile	13.1	17.0	43.5	56.5
	Mexico	22.6	17.1	56.9	43.1
Central Europe	Czech Republic	23.3	11.2	67.5	32.5
	Hungary	57.3	7.0	89.1	10.9
	Poland	42.6	1.8	95.9	4.1
Developed Countries
Global	Australia	27.4	51.0	35.0	65.0
	Canada	63.2	26.5	70.5	29.5
	Japan	205.4	37.8	84.5	15.5
	United States	75.7	98.6	43.4	56.6
	Europe	55.8	46.4	54.6	45.4

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; BIS; and national sources for corporate bond market capitalization as set out in Table 2.

View Table

Table 4.

Bond Market Capitalization Comparison, 2010

Region	Country	Market Capitalization (percent of GDP)		Contribution to Total Domestic Debt (percent)
		Government	Corporate	Government	Corporate
Developing Countries and Emerging Markets
Africa	All	14.8	1.8	89.2	10.8
	South Africa (SA)	31.2	20.0	60.9	39.1
	All excluding SA	14.2	1.3	91.8	8.2
	CEMAC	10.5	0.7	93.8	6.3
	WAEMU	14.1	2.3	86.0	14.0
	Oil exporters	7.7	1.1	87.5	12.5
	Fragile countries	18.4	1.2	93.9	6.1
	Low income	15.3	1.1	93.3	6.7
	Middle income	15.1	3.5	81.2	18.8
Asia	China	27.3	22.8	54.5	45.5
	Hong Kong	35.9	13.8	72.2	27.8
	Malaysia	57.3	57.0	50.2	49.8
	South Korea	43.8	59.5	42.4	57.6
	Thailand	50.5	12.8	79.7	20.3
Latin America	Argentina	13.3	2.6	83.7	16.3
	Brazil	39.4	22.7	63.4	36.6
	Chile	13.1	17.0	43.5	56.5
	Mexico	22.6	17.1	56.9	43.1
Central Europe	Czech Republic	23.3	11.2	67.5	32.5
	Hungary	57.3	7.0	89.1	10.9
	Poland	42.6	1.8	95.9	4.1
Developed Countries
Global	Australia	27.4	51.0	35.0	65.0
	Canada	63.2	26.5	70.5	29.5
	Japan	205.4	37.8	84.5	15.5
	United States	75.7	98.6	43.4	56.6
	Europe	55.8	46.4	54.6	45.4

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; BIS; and national sources for corporate bond market capitalization as set out in Table 2.

Table 5.

Sub-Saharan Africa Bond Market Capitalization, 2006–10

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

Table 5.

Sub-Saharan Africa Bond Market Capitalization, 2006–10

	Group	Year
		2006	2007	2008	2009	2010
Government Securities Market Capitalization (percent of GDP)	All	18.7	15.4	14.6	14.1	14.8
	South Africa (SA)	27.3	23.9	22.4	27.0	31.2
	All excluding SA	18.4	15.1	14.3	13.7	14.2
	CEMAC	15.5	13.8	11.3	10.4	10.5
	WAEMU	14.8	14.7	15.6	12.7	14.1
	Oil exporters	9.7	8.9	9.1	9.1	7.7
	Fragile countries	20.1	18.5	19.0	18.4	18.4
	Low income	22.6	17.2	16.5	16.5	15.3
	Middle income	19.9	14.7	12.7	12.2	15.1
Corporate Bond Market Capitalization (percent of GDP)	All	1.0	1.5	1.5	1.7	1.8
	South Africa	18.2	19.4	19.2	19.9	20.0
	All excluding SA	0.5	0.9	1.0	1.2	1.3
	CEMAC	0.0	0.2	0.6	0.9	0.7
	WAEMU	1.6	2.2	2.3	2.5	2.3
	Oil exporters	0.2	0.3	0.6	0.8	1.1
	Fragile countries	0.6	0.9	1.0	1.1	1.2
	Low income	0.5	0.8	0.8	0.9	1.1
	Middle income	2.3	3.2	3.1	3.4	3.5
Contribution of Corporate Bonds to Total Bonds (percent)	All	5.1	8.9	9.3	10.8	10.8
	All excluding SA	2.7	5.9	6.7	7.8	8.2
	South Africa	39.9	44.7	46.1	42.4	39.1
	CEMAC	0.0	1.4	5.0	8.0	6.3
	WAEMU	9.8	13.0	12.8	16.4	14.0
	Oil exporters	2.0	3.3	6.2	8.1	12.5
	Fragile countries	2.9	4.6	5.0	5.6	6.1
	Low income	2.2	4.4	4.6	5.2	6.7
	Middle income	10.4	17.9	19.6	21.8	18.8

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

View Table

Table 5.

Sub-Saharan Africa Bond Market Capitalization, 2006–10

	Group	Year
		2006	2007	2008	2009	2010
Government Securities Market Capitalization (percent of GDP)	All	18.7	15.4	14.6	14.1	14.8
	South Africa (SA)	27.3	23.9	22.4	27.0	31.2
	All excluding SA	18.4	15.1	14.3	13.7	14.2
	CEMAC	15.5	13.8	11.3	10.4	10.5
	WAEMU	14.8	14.7	15.6	12.7	14.1
	Oil exporters	9.7	8.9	9.1	9.1	7.7
	Fragile countries	20.1	18.5	19.0	18.4	18.4
	Low income	22.6	17.2	16.5	16.5	15.3
	Middle income	19.9	14.7	12.7	12.2	15.1
Corporate Bond Market Capitalization (percent of GDP)	All	1.0	1.5	1.5	1.7	1.8
	South Africa	18.2	19.4	19.2	19.9	20.0
	All excluding SA	0.5	0.9	1.0	1.2	1.3
	CEMAC	0.0	0.2	0.6	0.9	0.7
	WAEMU	1.6	2.2	2.3	2.5	2.3
	Oil exporters	0.2	0.3	0.6	0.8	1.1
	Fragile countries	0.6	0.9	1.0	1.1	1.2
	Low income	0.5	0.8	0.8	0.9	1.1
	Middle income	2.3	3.2	3.1	3.4	3.5
Contribution of Corporate Bonds to Total Bonds (percent)	All	5.1	8.9	9.3	10.8	10.8
	All excluding SA	2.7	5.9	6.7	7.8	8.2
	South Africa	39.9	44.7	46.1	42.4	39.1
	CEMAC	0.0	1.4	5.0	8.0	6.3
	WAEMU	9.8	13.0	12.8	16.4	14.0
	Oil exporters	2.0	3.3	6.2	8.1	12.5
	Fragile countries	2.9	4.6	5.0	5.6	6.1
	Low income	2.2	4.4	4.6	5.2	6.7
	Middle income	10.4	17.9	19.6	21.8	18.8

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

Table 6.

Sub-Saharan African Bond Market Capitalization, 1990–2000 and 2001–10¹

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

¹This table excludes certain countries for which bond market capitalization data are not available over either 1990–2000 or 2001–10.

Table 6.

Sub-Saharan African Bond Market Capitalization, 1990–2000 and 2001–10¹

	Government Securities Market Capitalization (percent of GDP)		Corporate Bond Market Capitalization (percent of GDP)
Country	1990–2000	2001–10	1990–2000	2001–10
Angola	0.09	5.17	0.00	0.00
Botswana	12.97	4.88	0.07	2.02
Burundi	7.76	6.46	0.00	0.00
Cameroon	19.71	11.78	0.00	0.10
Central African Republic	0.00	14.03	0.00	0.21
Chad	1.49	5.73	0.00	0.05
Congo, Rep.	26.91	6.73	0.00	0.04
Cote d’Ivoire	19.13	12.33	0.01	0.40
Eritrea	57.77	54.62	0.00	0.00
Ethiopia	21.78	28.42	0.00	1.71
Gabon	4.49	8.62	0.00	0.52
Gambia	26.78	29.84	0.00	0.00
Guinea-Bissau	17.53	17.27	0.01	0.08
Ghana	13.74	28.17	0.04	3.47
Guinea	9.63	8.44	0.00	0.00
Kenya	15.02	20.84	0.03	0.46
Lesotho	4.46	9.25	0.00	0.00
Mali	2.38	2.05	0.02	1.42
Mauritius	22.76	32.39	0.23	0.20
Namibia	16.41	18.21	0.00	1.54
Nigeria	21.61	7.44	0.29	1.11
Rwanda	18.81	10.88	0.00	0.01
Senegal	0.11	2.54	0.05	0.99
Seychelles	55.02	81.18	0.00	0.00
Sierra Leone	7.83	28.50	0.00	0.00
South Africa	39.22	29.38	16.55	15.94
Swaziland	0.95	1.84	0.76	0.85
Uganda	39.99	40.30	0.04	0.20
Sub-Saharan Africa	17.30	18.83	0.65	1.12

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

¹This table excludes certain countries for which bond market capitalization data are not available over either 1990–2000 or 2001–10.

View Table

Table 6.

Sub-Saharan African Bond Market Capitalization, 1990–2000 and 2001–10¹

	Government Securities Market Capitalization (percent of GDP)		Corporate Bond Market Capitalization (percent of GDP)
Country	1990–2000	2001–10	1990–2000	2001–10
Angola	0.09	5.17	0.00	0.00
Botswana	12.97	4.88	0.07	2.02
Burundi	7.76	6.46	0.00	0.00
Cameroon	19.71	11.78	0.00	0.10
Central African Republic	0.00	14.03	0.00	0.21
Chad	1.49	5.73	0.00	0.05
Congo, Rep.	26.91	6.73	0.00	0.04
Cote d’Ivoire	19.13	12.33	0.01	0.40
Eritrea	57.77	54.62	0.00	0.00
Ethiopia	21.78	28.42	0.00	1.71
Gabon	4.49	8.62	0.00	0.52
Gambia	26.78	29.84	0.00	0.00
Guinea-Bissau	17.53	17.27	0.01	0.08
Ghana	13.74	28.17	0.04	3.47
Guinea	9.63	8.44	0.00	0.00
Kenya	15.02	20.84	0.03	0.46
Lesotho	4.46	9.25	0.00	0.00
Mali	2.38	2.05	0.02	1.42
Mauritius	22.76	32.39	0.23	0.20
Namibia	16.41	18.21	0.00	1.54
Nigeria	21.61	7.44	0.29	1.11
Rwanda	18.81	10.88	0.00	0.01
Senegal	0.11	2.54	0.05	0.99
Seychelles	55.02	81.18	0.00	0.00
Sierra Leone	7.83	28.50	0.00	0.00
South Africa	39.22	29.38	16.55	15.94
Swaziland	0.95	1.84	0.76	0.85
Uganda	39.99	40.30	0.04	0.20
Sub-Saharan Africa	17.30	18.83	0.65	1.12

Sources: IMF staff compilations based on data from IMF IFS, WEO and World Bank, ADI; and national sources for corporate bond market capitalization as set out in Table 2.

¹This table excludes certain countries for which bond market capitalization data are not available over either 1990–2000 or 2001–10.

Eichengreen, Panizza, and Borensztein (2008) extend this analysis, using panel data on a range of developing and developed countries, with a focus on Latin America. They construct separate measures of the dependent variable for government bonds, private bonds (corporate plus financial), corporate bonds, and financial bonds. Their results (Table 9, columns 1–4 of their paper) confirm many of those of Eichengreen and Luengnaruemithai (2004). They find that country size is positive and significant, with a concave relationship. GDP per capita is also positive and concave and trade openness is positive and significant. The domestic interest rate is negative and significant only for government bonds. Interest rate volatility is positively correlated with the private bond market and negatively with the government bond market. Domestic credit is positively and concavely related to financial bonds. The interest rate spread is positively correlated with the corporate but not public bond market. The opposite is found for financial bonds. Stricter capital controls are correlated with large public bond markets, but do not influence private bond markets. Larger public debt is linked to large public bond markets but is not significant with regard to private bond market determinants.

Table 7a.

Correlation Matrix¹

Source: IMF staff estimates.

¹Government securities sample.

Table 7a.

Correlation Matrix¹

	Gdebt	Econsize	Tradeopen	Gdpcap	Credit	Intvol	Spread	Xrvol	Fiscal	Capopen	Area	Legalorigin	Invprofile	Laworder	Corruption	Bureaucrac	Comprisk
	Gdebt	1.000
Econsize	0.069	1.000
Tradeopen	0.051	0.010	1.000
Gdpcap	0.287	0.175	0.627	1.000
Credit	0.201	0.682	0.122	0.346	1.000
Intvol	0.176	0.079	−0.045	−0.080	−0.042	1.000
Spread	−0.095	0.018	0.142	−0.042	−0.137	0.460	1.000
Xrvol	−0.072	0.009	−0.041	−0.087	−0.128	0.265	0.243	1.000
Fiscal	−0.201	0.169	0.293	0.257	0.043	−0.137	−0.062	−0.122	1.000
Capopen	0.238	−0.062	0.194	0.340	0.079	0.076	0.113	−0.104	0.001	1.000
Area	−0.163	0.342	−0.139	−0.091	0.076	0.229	0.142	0.433	0.086	−0.119	1.000
Legalorigin	0.096	0.310	−0.157	−0.068	0.112	0.303	0.086	0.050	0.058	0.110	0.134	1.000
Invprofile	0.127	0.226	0.073	0.379	0.379	−0.136	−0.085	−0.306	0.288	0.279	−0.053	0.189	1.000
Laworder	0.156	−0.135	−0.065	0.110	0.057	0.061	−0.021	−0.187	−0.033	0.199	0.073	0.259	0.341	1.000
Corruption	−0.030	−0.100	−0.261	−0.137	0.059	−0.170	−0.085	0.102	−0.136	0.052	0.152	−0.100	−0.067	0.207	1.000
Bureaucracy	0.108	0.087	−0.035	0.257	0.245	−0.143	−0.217	−0.105	−0.124	0.244	−0.001	0.092	0.245	0.237	0.225	1.000
Comprisk	−0.015	0.184	0.244	0.548	0.351	−0.245	−0.182	−0.304	0.390	0.248	0.053	0.125	0.753	0.550	0.105	0.392	1.000

Source: IMF staff estimates.

¹Government securities sample.

View Table

Table 7a.

Correlation Matrix¹

	Gdebt	Econsize	Tradeopen	Gdpcap	Credit	Intvol	Spread	Xrvol	Fiscal	Capopen	Area	Legalorigin	Invprofile	Laworder	Corruption	Bureaucrac	Comprisk
	Gdebt	1.000
Econsize	0.069	1.000
Tradeopen	0.051	0.010	1.000
Gdpcap	0.287	0.175	0.627	1.000
Credit	0.201	0.682	0.122	0.346	1.000
Intvol	0.176	0.079	−0.045	−0.080	−0.042	1.000
Spread	−0.095	0.018	0.142	−0.042	−0.137	0.460	1.000
Xrvol	−0.072	0.009	−0.041	−0.087	−0.128	0.265	0.243	1.000
Fiscal	−0.201	0.169	0.293	0.257	0.043	−0.137	−0.062	−0.122	1.000
Capopen	0.238	−0.062	0.194	0.340	0.079	0.076	0.113	−0.104	0.001	1.000
Area	−0.163	0.342	−0.139	−0.091	0.076	0.229	0.142	0.433	0.086	−0.119	1.000
Legalorigin	0.096	0.310	−0.157	−0.068	0.112	0.303	0.086	0.050	0.058	0.110	0.134	1.000
Invprofile	0.127	0.226	0.073	0.379	0.379	−0.136	−0.085	−0.306	0.288	0.279	−0.053	0.189	1.000
Laworder	0.156	−0.135	−0.065	0.110	0.057	0.061	−0.021	−0.187	−0.033	0.199	0.073	0.259	0.341	1.000
Corruption	−0.030	−0.100	−0.261	−0.137	0.059	−0.170	−0.085	0.102	−0.136	0.052	0.152	−0.100	−0.067	0.207	1.000
Bureaucracy	0.108	0.087	−0.035	0.257	0.245	−0.143	−0.217	−0.105	−0.124	0.244	−0.001	0.092	0.245	0.237	0.225	1.000
Comprisk	−0.015	0.184	0.244	0.548	0.351	−0.245	−0.182	−0.304	0.390	0.248	0.053	0.125	0.753	0.550	0.105	0.392	1.000

Source: IMF staff estimates.

¹Government securities sample.

Table 7b.

Correlation Matrix¹

Source: IMF staff estimates.

¹Corporate bond sample.

Table 7b.

Correlation Matrix¹

	Cdebt	Econsize	Tradeopen	Gdpcap	Credit	Intvol	Spread	Xrvol	Fiscal	Capopen	Area	Legalorigin	Invprofile	Laworder	Corruption	Bureaucrac	Comprisk
Cdebt	1.000
Econsize	0.730	1.000
Tradeopen	−0.199	−0.183	1.000
Gdpcap	0.392	0.262	0.374	1.000
Credit	0.830	0.733	−0.083	0.515	1.000
Intvol	−0.116	0.072	−0.288	−0.259	−0.095	1.000
Spread	−0.154	−0.078	−0.053	−0.136	−0.159	0.290	1.000
Xrvol	0.149	0.158	0.158	0.137	0.138	−0.088	0.002	1.000
Fiscal	−0.057	0.085	0.217	0.226	−0.049	−0.068	−0.127	0.040	1.000
Capopen	−0.212	−0.173	−0.053	0.209	−0.067	0.306	0.489	−0.080	−0.096	1.000
Area	0.442	0.582	−0.376	0.032	0.364	0.155	−0.074	0.092	0.153	−0.153	1.000
Legalorigin	0.224	0.440	−0.403	0.047	0.207	0.427	0.097	0.240	−0.026	0.298	0.539	1.000
Invprofile	0.206	0.090	−0.023	0.570	0.324	−0.215	−0.238	0.213	0.027	0.277	−0.019	0.263	1.000
Laworder	−0.125	−0.240	−0.108	0.121	−0.056	0.176	0.107	0.059	−0.119	0.277	0.067	0.261	0.282	1.000
Corruption	0.510	0.166	−0.135	0.365	0.379	−0.225	−0.039	−0.037	−0.041	0.055	0.224	0.120	0.122	0.118	1.000
Bureaucracy	0.447	0.313	−0.331	0.312	0.432	0.074	−0.325	0.217	−0.192	0.175	0.277	0.653	0.343	−0.024	0.420	1.000
Comprisk	0.239	0.157	0.120	0.686	0.368	−0.188	−0.186	0.119	0.224	0.231	0.126	0.295	0.721	0.477	0.378	0.333	1.000

Source: IMF staff estimates.

¹Corporate bond sample.

View Table

Table 7b.

Correlation Matrix¹

	Cdebt	Econsize	Tradeopen	Gdpcap	Credit	Intvol	Spread	Xrvol	Fiscal	Capopen	Area	Legalorigin	Invprofile	Laworder	Corruption	Bureaucrac	Comprisk
Cdebt	1.000
Econsize	0.730	1.000
Tradeopen	−0.199	−0.183	1.000
Gdpcap	0.392	0.262	0.374	1.000
Credit	0.830	0.733	−0.083	0.515	1.000
Intvol	−0.116	0.072	−0.288	−0.259	−0.095	1.000
Spread	−0.154	−0.078	−0.053	−0.136	−0.159	0.290	1.000
Xrvol	0.149	0.158	0.158	0.137	0.138	−0.088	0.002	1.000
Fiscal	−0.057	0.085	0.217	0.226	−0.049	−0.068	−0.127	0.040	1.000
Capopen	−0.212	−0.173	−0.053	0.209	−0.067	0.306	0.489	−0.080	−0.096	1.000
Area	0.442	0.582	−0.376	0.032	0.364	0.155	−0.074	0.092	0.153	−0.153	1.000
Legalorigin	0.224	0.440	−0.403	0.047	0.207	0.427	0.097	0.240	−0.026	0.298	0.539	1.000
Invprofile	0.206	0.090	−0.023	0.570	0.324	−0.215	−0.238	0.213	0.027	0.277	−0.019	0.263	1.000
Laworder	−0.125	−0.240	−0.108	0.121	−0.056	0.176	0.107	0.059	−0.119	0.277	0.067	0.261	0.282	1.000
Corruption	0.510	0.166	−0.135	0.365	0.379	−0.225	−0.039	−0.037	−0.041	0.055	0.224	0.120	0.122	0.118	1.000
Bureaucracy	0.447	0.313	−0.331	0.312	0.432	0.074	−0.325	0.217	−0.192	0.175	0.277	0.653	0.343	−0.024	0.420	1.000
Comprisk	0.239	0.157	0.120	0.686	0.368	−0.188	−0.186	0.119	0.224	0.231	0.126	0.295	0.721	0.477	0.378	0.333	1.000

Source: IMF staff estimates.

¹Corporate bond sample.

Table 8a.

Determinants of Government Securities Markets

Source: IMF staff estimates.*,**,*** indicates significance at 10%, 5% and 1% respectively. Results based on heteroskedasticity robust standard errors.

¹The specific effects row in the table indicates whether country and time effects are included in the estimation. Since there are no specific effects in the pooled least squares model, a ‘NO’ is indicated, whereas a ‘YES” is indicated for the two-way random and fixed effects models due to the inclusion of specific effects. The outcomes of the Breusch Pagan and F tests for random and fixed effects respectively are presented.

²The random effects vs. fixed effects row presents the outcome of a Hausman test for the correct specific effects model (see Hausman, 1978). If the fixed effects model is preferred, then fixed effects is indicated, along with the statistical significance of the test statistic, otherwise random effects is reported.

Table 8a.

Determinants of Government Securities Markets

	Least Squares			Random Effects			Fixed Effects
	1	2	3	1	2	3	1	2	3
Econsize	0.010	0.011	0.022	−0.052*	−0.018	−0.009	−0.065*	−0.053*	−0.052*
	(0.011)	(0.013)	(0.014)	(0.025)	(0.022)	(0.020)	(0.032)	(0.025)	(0.024)
Tradeopen	−0.079*	−0.063	−0.052	−0.038	−0.148*	−0.123	−0.121	−0.270**	−0.309**
	(0.038)	(0.041)	(0.042)	(0.059)	(0.071)	(0.067)	(0.068)	(0.094)	(0.093)
Gdpcap	1.526***	−0.691***	−0.669***	1.318**	−0.201	−0.358	0.763	−0.172	0.129
	(0.383)	(0.195)	(0.187)	(0.431)	(0.457)	(0.448)	(0.574)	(0.717)	(0.759)
Credit	0.083*	0.158***	0.155***	0.153*	0.005	0.077	0.231*	−0.117	−0.235*
	(0.038)	(0.041)	(0.046)	(0.071)	(0.071)	(0.073)	(0.093)	(0.115)	(0.119)
Intvol	21.380***	10.470**	9.985**	1.045	−0.082	−5.797	−9.219	−7.364	−7.505
	(3.482)	(3.163)	(3.035)	(4.354)	(4.125)	(4.201)	(5.093)	(4.721)	(4.750)
Spread	−0.284***	−0.175***	−0.175**	−0.259***	−0.166**	−0.274***	−0.371***	−0.268***	−0.309***
	(0.071)	(0.052)	(0.053)	(0.066)	(0.052)	(0.055)	(0.075)	(0.060)	(0.057)
Xrvol	−4.183	−8.605	−9.247	−21.610***	−26.600***	−17.530**	−13.320*	−24.730***	−23.170***
	(7.470)	(4.754)	(4.841)	(5.687)	(5.106)	(5.530)	(6.435)	(5.930)	(5.926)
Fiscal	−0.380**	−0.442***	−0.429***	−0.388***	−0.626***	−0.688***	−0.484***	−0.853***	−0.809***
	(0.129)	(0.121)	(0.122)	(0.099)	(0.100)	(0.111)	(0.113)	(0.113)	(0.115)
Capopen	2.154***	0.824	1.092*	−1.671*	−1.862*	−1.558*	−2.429**	−3.281***	−2.900**
	(0.552)	(0.506)	(0.481)	(0.720)	(0.769)	(0.783)	(0.794)	(0.914)	(0.986)
Area	−0.010***	−0.011***	−0.012***	−0.004	−0.006	−0.008	−0.005**	−0.001	0.001
	(0.001)	(0.002)	(0.002)	(0.006)	(0.005)	(0.004)	(0.002)	(0.002)	(0.003)
Legalorigin	1.946	2.716*	1.317	7.423	8.145*	4.960	6.546***	9.935***	8.343***
	(1.306)	(1.300)	(1.266)	(4.698)	(4.012)	(3.397)	(1.372)	(1.555)	(1.900)
Comprisk		0.087			0.341***			0.258*
		(0.096)			(0.082)			(0.104)
Invprofile			0.521			0.092			−0.076
			(0.420)			(0.383)			(0.400)
Laworder			1.659*			2.791***			4.098***
			(0.664)			(0.775)			(0.848)
Corruption			−0.524			0.138			0.099
			(0.689)			(0.726)			(0.825)
Bureaucracy			−1.559			−1.523			−1.006
			(0.917)			(1.015)			(1.105)
_Cons	11.520**	9.860	12.500*	14.560***	3.777	12.950	17.060**	15.320	21.620**
	(4.397)	(7.470)	(5.841)	(4.101)	(6.626)	(9.384)	(6.093)	(8.237)	(6.512)
N	506	328	328	506	328	328	506	328	328
Specific effects1	NO	NO	NO	YES***	YES***	YES***	YES***	YES***	YES***
Random effects vs							Fixed effects***	Fixed effects**	Fixed effects***
fixed effects2

Source: IMF staff estimates.*,**,*** indicates significance at 10%, 5% and 1% respectively. Results based on heteroskedasticity robust standard errors.

¹The specific effects row in the table indicates whether country and time effects are included in the estimation. Since there are no specific effects in the pooled least squares model, a ‘NO’ is indicated, whereas a ‘YES” is indicated for the two-way random and fixed effects models due to the inclusion of specific effects. The outcomes of the Breusch Pagan and F tests for random and fixed effects respectively are presented.

²The random effects vs. fixed effects row presents the outcome of a Hausman test for the correct specific effects model (see Hausman, 1978). If the fixed effects model is preferred, then fixed effects is indicated, along with the statistical significance of the test statistic, otherwise random effects is reported.

View Table

Table 8a.

Determinants of Government Securities Markets

	Least Squares			Random Effects			Fixed Effects
	1	2	3	1	2	3	1	2	3
Econsize	0.010	0.011	0.022	−0.052*	−0.018	−0.009	−0.065*	−0.053*	−0.052*
	(0.011)	(0.013)	(0.014)	(0.025)	(0.022)	(0.020)	(0.032)	(0.025)	(0.024)
Tradeopen	−0.079*	−0.063	−0.052	−0.038	−0.148*	−0.123	−0.121	−0.270**	−0.309**
	(0.038)	(0.041)	(0.042)	(0.059)	(0.071)	(0.067)	(0.068)	(0.094)	(0.093)
Gdpcap	1.526***	−0.691***	−0.669***	1.318**	−0.201	−0.358	0.763	−0.172	0.129
	(0.383)	(0.195)	(0.187)	(0.431)	(0.457)	(0.448)	(0.574)	(0.717)	(0.759)
Credit	0.083*	0.158***	0.155***	0.153*	0.005	0.077	0.231*	−0.117	−0.235*
	(0.038)	(0.041)	(0.046)	(0.071)	(0.071)	(0.073)	(0.093)	(0.115)	(0.119)
Intvol	21.380***	10.470**	9.985**	1.045	−0.082	−5.797	−9.219	−7.364	−7.505
	(3.482)	(3.163)	(3.035)	(4.354)	(4.125)	(4.201)	(5.093)	(4.721)	(4.750)
Spread	−0.284***	−0.175***	−0.175**	−0.259***	−0.166**	−0.274***	−0.371***	−0.268***	−0.309***
	(0.071)	(0.052)	(0.053)	(0.066)	(0.052)	(0.055)	(0.075)	(0.060)	(0.057)
Xrvol	−4.183	−8.605	−9.247	−21.610***	−26.600***	−17.530**	−13.320*	−24.730***	−23.170***
	(7.470)	(4.754)	(4.841)	(5.687)	(5.106)	(5.530)	(6.435)	(5.930)	(5.926)
Fiscal	−0.380**	−0.442***	−0.429***	−0.388***	−0.626***	−0.688***	−0.484***	−0.853***	−0.809***
	(0.129)	(0.121)	(0.122)	(0.099)	(0.100)	(0.111)	(0.113)	(0.113)	(0.115)
Capopen	2.154***	0.824	1.092*	−1.671*	−1.862*	−1.558*	−2.429**	−3.281***	−2.900**
	(0.552)	(0.506)	(0.481)	(0.720)	(0.769)	(0.783)	(0.794)	(0.914)	(0.986)
Area	−0.010***	−0.011***	−0.012***	−0.004	−0.006	−0.008	−0.005**	−0.001	0.001
	(0.001)	(0.002)	(0.002)	(0.006)	(0.005)	(0.004)	(0.002)	(0.002)	(0.003)
Legalorigin	1.946	2.716*	1.317	7.423	8.145*	4.960	6.546***	9.935***	8.343***
	(1.306)	(1.300)	(1.266)	(4.698)	(4.012)	(3.397)	(1.372)	(1.555)	(1.900)
Comprisk		0.087			0.341***			0.258*
		(0.096)			(0.082)			(0.104)
Invprofile			0.521			0.092			−0.076
			(0.420)			(0.383)			(0.400)
Laworder			1.659*			2.791***			4.098***
			(0.664)			(0.775)			(0.848)
Corruption			−0.524			0.138			0.099
			(0.689)			(0.726)			(0.825)
Bureaucracy			−1.559			−1.523			−1.006
			(0.917)			(1.015)			(1.105)
_Cons	11.520**	9.860	12.500*	14.560***	3.777	12.950	17.060**	15.320	21.620**
	(4.397)	(7.470)	(5.841)	(4.101)	(6.626)	(9.384)	(6.093)	(8.237)	(6.512)
N	506	328	328	506	328	328	506	328	328
Specific effects1	NO	NO	NO	YES***	YES***	YES***	YES***	YES***	YES***
Random effects vs							Fixed effects***	Fixed effects**	Fixed effects***
fixed effects2

Source: IMF staff estimates.*,**,*** indicates significance at 10%, 5% and 1% respectively. Results based on heteroskedasticity robust standard errors.

¹The specific effects row in the table indicates whether country and time effects are included in the estimation. Since there are no specific effects in the pooled least squares model, a ‘NO’ is indicated, whereas a ‘YES” is indicated for the two-way random and fixed effects models due to the inclusion of specific effects. The outcomes of the Breusch Pagan and F tests for random and fixed effects respectively are presented.

²The random effects vs. fixed effects row presents the outcome of a Hausman test for the correct specific effects model (see Hausman, 1978). If the fixed effects model is preferred, then fixed effects is indicated, along with the statistical significance of the test statistic, otherwise random effects is reported.