Benin
Selected Issues and Statistical Appendix

The strategy adopted by Benin to strengthen its macroeconomic stability and promote private-investment-led growth has been detailed in the note. The statistical data on gross domestic product by sector of origin at current prices and at constant supply, production and producer prices of cotton, products, production, and cultivated area of principal food crops in Benin has been given. The retail price of major petroleum products, consumer price index in urban areas, industrial minimum legal wage, central government revenue and expenditure, and related statistical information is detailed.

Abstract

The strategy adopted by Benin to strengthen its macroeconomic stability and promote private-investment-led growth has been detailed in the note. The statistical data on gross domestic product by sector of origin at current prices and at constant supply, production and producer prices of cotton, products, production, and cultivated area of principal food crops in Benin has been given. The retail price of major petroleum products, consumer price index in urban areas, industrial minimum legal wage, central government revenue and expenditure, and related statistical information is detailed.

Basic Data

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Sources: Beninese authorities; and IMF staff estimates and projections.

The 2006 projections incorporate the MDRI resources for the IMF, IDA and AfDF in stock operations.

Cotton production for T-1/T season. Production of cotton seed in crop year T-1/T affects agricultural production in year T-1, industry, services, and exports of ginned cotton in year T.

In percent of broad money at the beginning of the period.

Total revenue minus all expenditure, excluding interest due.

Total revenue minus all expenditure, excluding foreign-financed capital expenditure and interest due.

Interest payment only.

After HIPC relief and before MDRI.

I. Private Investment Dynamics and Growth in Benin1

Summary

Benin has been undergoing democratization since the early 1990s, a process that has been furthered by the recent 2006 presidential elections. With program engagement with the Fund virtually uninterrupted since the 1990s, the country reached the completion point under the enhanced HIPC Initiative in March 2003 and began receiving assistance under the Multilateral Debt Relief Initiative (MDRI) in 2006. The current three-year Poverty Reduction and Growth Facility arrangement was approved in August 2005. In this context, the government that took office in April 2006 has adopted a strategy to strengthen macroeconomic stability and promote private-investment-led growth.

The following study uses econometric techniques to investigate the short- and long-run behavior of private investment and its contribution to growth in Benin. It finds that besides developments in the institutional and regulatory framework and in the terms-of-trade, public investment and private capital formation facilitated by credit to the private sector have a strong impact on growth performance. The analysis also confirms that slow improvement in Benin’s economic freedom, reflecting weak institutions and limited progress in implementing structural reforms, impedes private investment. Speed-of-adjustment analysis indicates that a 1 percent increase in private investment leads to a 0.2 percent increase in long-run GDP growth. Conversely, a 1 percent shock to private investment would entail a 16-percent yearly correction, requiring about 4 years to close half of the deviation of real GDP from its long-run equilibrium.

On the basis of these findings, the study underscores that:

  • By promoting private sector development and directing fiscal resources to public investment in infrastructure and institutional building, the authorities could help Benin realize higher long-run growth. The additional fiscal space created by MDRI-related aid should be allocated to high-return projects.

  • Structural reforms and the gradual formalization of informal economic activities through appropriate market incentives could help improve Benin’s economic competitiveness and strengthen its fiscal position.

  • A segmented public-private sector partnership could deepen the private sector’s involvement in Benin’s infrastructure development.

  • Finally, broadening access to credit would boost private investment and support growth.

A. Introduction

1. This study analyzes the relationship between private investment and growth, and their key determinants. To the extent that private investment is an important determinant of long-run growth, a comprehensive assessment of their relationship is essential to identify and appropriately address related policy issues. Such an assessment is of particular importance to Benin given that, despite its macroeconomic stability, the country has so far failed to attract significant private investment flows.

2. The analysis uses econometric techniques to investigate the short- and long-run behavior of private investment and its links to growth. Consistent with earlier growth accounting studies, it establishes that private investment is indeed a critical determinant of growth in Benin. Moreover, public investment appears to have an important role in supporting private investment and growth. This suggests that channeling part of the additional fiscal space in debt relief to public investment could benefit Benin’s medium term growth rate. The analysis also shows that adverse shocks, e.g., the terms of trade, can have long-lasting effects, while credit to the private sector has a short-lived impact on private investment and growth. And there is significant potential for institutional reforms to improve the business environment, raise private investment and invigorate growth.

3. The rest of the chapter is organized as follows: Section B provides background and stylized facts on Benin’s economic performance. Section C briefly describes the theoretical basis of the econometric model and discusses the estimation results. Section D provides insights on private investment behavior and its impact on growth through Variance Decomposition, Impulse Response Function, and Historical Decomposition Analysis. Section E offers concluding remarks and policy prescriptions for sustained growth through private investment.

B. Background and Stylized Facts

4. Benin has recorded modest private investment rates and per capita GDP growth despite its relatively good position within the WAEMU. Between 1965 and 2005, real GDP grew by an annual average of 3.4 percent, with per GDP capita increasing by only 0.3 percent per annum. After the 1994 CFA franc devaluation, real GDP growth rebounded to a yearly average of 4.4 percent during 1994–2005, and real GDP per capita increased by 1.7 percent per year.

5. Interestingly, since 1994, real private investment has tended to slow relative to historical rates (See Figure 1). The pace of real private investment growth decelerated from an annual average of 14 percent during 1980–93 to 6.1 percent in 1994–2006. Similarly, in the key cotton sector (see Box 1), investment has been insufficient to cover capital depreciation. The stock of foreign direct investment (FDI) in the cotton sector2 declined from US$441 million in 1998 to US$291 million in 2004, while total FDI inflows nearly doubled during the same period (US$ 60 millions in 2004, from US$33 millions in 1998).

Figure 1.
Figure 1.

Benin: Constant GDP and Private Investment, 1965–2005

(Natural log of constant value in CFA franc terms)

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Source: Benin authorities.

Cotton Production in Benin

  • The cotton sector and trade explain most of Benin’s output fluctuations. The cotton sector remains a dominant economic sector, though cotton seed production has a relatively small share of nominal GDP (about 3 percent on average during 2000–05) and is subject to large swings.

  • Production. Of the approximately 550,000 Benin families who run small farms to generate their main income, about 310,000 grow cotton and sell cotton seed. About 14,000 new small farmers (in net terms) enter the agricultural sector each year. Average family size in rural areas is 10 people. Ginning industry processing capacity is estimated at 600,000 tons.

  • The sectoral implication of cotton production. The production of cotton seed in crop year T-1/T affects agricultural production in year T-1, industry, services, and exports of cotton lint in year T.

  • The growth of cotton seed production in crop year T/T+1 affects agricultural production in year T and industry and services in year T+1. Noncotton-related sectors grow as the same rate as in the benchmark case. Authorities assumed a one-to-one impact. For instance a 1 percent increase in cotton seed production would imply a 1 percent increase in cotton-related activities in secondary and tertiary sectors. It was estimated in 2002 that the production of seed cotton represented about 15 percent of agricultural production, 10 percent of cotton-related industry, and 10 percent of cotton-related services.

  • Marketing. Cotton marketing, including the allocation of seed cotton to all ginning companies, is partly controlled by the parastatal ginning company (SONAPRA). The cotton sector is overregulated. Prices for inputs, transport, and seed cotton are centrally determined by institutions representing stakeholders (farmers, suppliers of inputs, and ginning companies).

  • Risks. Persistent sectoral risks pertain to (i) production (e.g., rainfall and high input costs); (ii) ginning quality; (iii) marketing and international price changes, (iv) transportation; and (v) credit allocation (e.g., liquidity constraints and incidence of nonperforming loans).

6. Private investment matters for growth. Over the last five years, economic growth has been more robust in CEMAC than in WAEMU countries. Text table 13 indicates that this can partly be explained by differing private investment performance in the two regions. High returns in the oil sector that is associated with greater private investment, have helped the CEMAC countries enjoy relatively higher real GDP growth. Interestingly, the Central African Republic, the only non-oil country in the CEMAC, achieved a real GDP growth of -0.4 percent on average during 2000–05, together with the lowest private investment ratio in the region.

Text table 1.

Comparative Private Investment, 2000–05

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Source: I.M.F. - World Economic Outlook, 2006.

PPP weighted.

WAEMU: West African Economic and Monetary Union.

CEMAC: Communauté Economic des Etats d’Afrique Centrale.

7. Benin’s institutions, regulatory system, and financial sector developments likely impede private investment (Text Table 2). The World Bank’s 2006 “Doing Business” report ranks Benin 137 out of 175 countries in terms of ease of doing business. This relatively lackluster performance partly reflects cumbersome licensing requirements, difficult labor market conditions, scarce credit, and high factor costs. Casero and Varoudakis (2004) found similar impediments to private investment in Tunisia.

Text table 2.

Selected Countries: Comparative Rankings of Doing Business 1/

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Sources: World Bank, 2006. www.doinbusiness.org

Countries are ranked from 1 to 175 with 1 being the best performer.

Except Guinea-Bissau.

8. Progress in improving the business environment has been slow. Among the WAEMU countries Benin has made the least progress in improving the business environment in the last couple of years. The country’s “ease of doing business” indicators gained only 2 points from 2005 to 2006 while the WAEMU best performer during the period, Côte d’Ivoire, improved by 15 points. Excessive regulations and other institutional factors may have contributed to the laggard performance. The relatively subdued progress may also reflect delays in implementing reforms and in addressing various institutional weaknesses, especially in the cotton sector.

C. The Empirical Model

9. The background discussion suggests that understanding the impact of private investment on Benin’s long-run growth requires an understanding of institutional factors and the regulatory framework. There is not much empirical research in this area, particularly on developing countries. Most studies have to some extent looked separately at financial development or trade liberalization and growth, or have emphasized the role of public or fiscal policy, or institutions. Also, less attention has been paid to the relationship between private sector dynamics and growth while controlling for public investment, financial sector development, external factors, and institutional changes.

10. The analysis here uses the time-to-build approach to estimate the potential impact of private investment on growth. According to this approach, capital stock becomes productive once the investment projects are completed in sequence. It acknowledges that lags in investment returns depend on production technology (see Altug 1989, 1993 and Kydland and Prescott 1982), as opposed to the cost-of-adjustment model4 or irreversible investment under uncertainty models.5 The empirical model used here is a structural vector autoregression model (SVECM) that captures the time lag needed for initial investment to contribute to future growth,6, as well as endogeneity problems among the system variables.

11. The model includes the following set of endogenous variables:

Yt=(LTRADt,LGINVt,LCREDtLPINVtLGDPt),7

Where:

12. The background discussion above and existing empirical literature motivate the choice of variables 8. Including public capital (LGINV) in the model helps to assess whether there is a crowding-in or a crowding-out effect of fiscal policy on private investment. The choice of LCRED, credit to the private sector, is justified as it is well known from the empirical literature that the interest rate channel is less effective in, and the credit channel better suited for, capturing the effectiveness of monetary policy.9 However, LCRED can also be viewed as a structural variable given that Benin’s financial sector has been growing in response to ongoing sector reforms. Furthermore, as shown in Figure A, the LCRED would give a sense of the extent to which financial deepening may be conducive to growth.

13. An institutions variable, denoted INSTt is assumed to be exogenous in the system. It is measured using the Fraser Institute’s Economic Freedom Index10 results for 1996–2006, extrapolated back to 1965 using the average scores derived from individual correlation to (only) trade, fiscal burden, foreign investment, banking, and wages and prices. The ranking ranges from 5 to 111; the better the institution is, the lower the ranking.

14. The model set up. The full representation of the model is as follows:

Yt=Σi=1pπiYti+øINSTt+εt(1)

where πi (i = 1…, p), P is the number of lag, and φ unknown parameter vectors. As variables in (1) are in log terms, by subtracting and adding various lags of Yt one gets the following dynamics of (1): basis for cointegration analysis. By subtracting and adding various lags of Yt, (1) can be re-written as:

ΔYt=ΠYt1+Σi1p1ΓiΔYti+Σi=0p1χiΔINSTti+εt(2)12

A variable preceeded by the operator Δ can be interpreted as the percentage change; (2) can also be rewritten as:

ΔYt=α(βYt1)+Σi1p1ΓiΔYti+Σi=0p1χiΔINSTti+εt(3)

This equation, the “error correction” representation, has the merit of representing the dynamics for each individual (system) variable in terms of its deviation from its long-run equilibrium (the first term13) and in terms of its year-to-year or short-term change (the last two terms).

D. Empirical Evidence14 15

15. The investment equation supports the conclusion that the trade index and public investment have a positive impact on private investment in the long-run. It appears that credit to the private sector does not have a significant long-run impact on private investment or on GDP growth. However, public investment and the export index have a direct impact on GDP growth and an indirect output effect via private investment. This result is consistent with the empirical literature, which suggests that public investment stimulates private investment (Oshikoya 1994, Odedokun 1997, and Ramirez 2000).

The long-run equilibrium equations for private investment and GDP are16:

PINV=0.316[2.003]*TOT+0.431[3.987]*GINV+ECTPINVGDP=0.422[4.191]*TOT+0.204[3.693]*GINV+0.219[2.288]*PINV+ECTGDP

ECT_PINV and ECT_GDP are the error correction terms for private investment and GDP equations respectively. They are the residual terms between actual value (left-hand side) and estimated values (right-hand side)

The short-run dynamic equations are:17

16. It follows from the long- and short-run equations above that:

17. The SVECM approach provides a clearer picture of the relationship between the selected economic variables and their dynamic behavior across time. By contrast, descriptive statistics or ordinary least square regressions do not account for endogeneity problems or for contemporaneous and dynamic interactions between variables. For instance, simple descriptive statistics do not account for the silent dynamics of the direct and indirect contribution of private investment to the business cycle. Text table 3 below shows the linkages between the issues we are addressing and the econometric methodology we are applying. 20

Text table 3.

Study Objective and Econometric Methodology

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Notes:

Depending on unit root results that may lead to SVAR analysis for I(0) series or SVECM for I(1), most recent studies employ simultaneously HD, VD and IRF because of their complementarity.

HD, VD, and IDF provide complementary insights to the analysis.

Text table 4.

Error Correction Model, 1965 - 2005

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t-statistics in [], 41 observations, optimal lag length = 2,ECT_PINV = Error correction model for private investment;ECT_GDP = Error correction model for GDP;TOT = Terms of trade; GINV=Public investment; CRED=Credit to the private sectorPINV = Private investment; INST=Economic freedom index (institution variable).

The joint test weak exogeneity test (WETS) of TOT for the two cointegrating vectors, PINV for GDP equation and GDP for PINV failed to reject the null hypothesis at χ 2 (3) = 4.160 [0.2447]

Variance decomposition

18. Our variance decomposition analysis estimates the relative significance of each random innovation to the system variable by subjecting all endogenous variables in the SVAR model to standard deviation shocks. That is, for each period, the resulting simulated error, in a given endogenous variable, is decomposed into the error arising from its own innovation and the error stemming from the shock to the rest of endogenous variables.

Text Table 5.

Forcast-Error Variance Decomposition 1/

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Based on structural factorization.

Historical decomposition

19. Historical decomposition analysis is another way to assess the importance of different shocks over time. It evaluates the portion of the forecast error attributable to each of the structural shocks. The advantage of historical decomposition is its ability to reveal the relative importance of shocks in different periods.

20. Interestingly, the results support the previous findings on variance decomposition analysis. The results show that since the 1990s, public investment has become the most significant of the determinant of private investment.21 Supply factors 22 shocks were more marked during 1975’1990, and the impact of credit was significant but relatively limited.

21. Supply factors drive private investment. Private investment would have been higher if it had been driven by supply factors.23 Supply factors mostly influenced private investment until early in 1990, when the centrally planned economic regime gave way to a new market-oriented economy.

Figure 2.
Figure 2.

Benin: Historical Decomposition of Private Investment, 1975–2005

(Annual change)

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Source: IMF staff estimates.

Impulse Response Function

22. We use impulse response function (IRF) analysis to further investigate private investment and GDP dynamics.24 IRF provides the time path of change in private investment and real GDP in response to a one-unit shock to terms of trade, public investment, credit to private sector, private investment, and GDP.

23. The IRF analysis confirms most of the variance decomposition results. The GDP response to private investment shocks holds. The first year after the shock, GDP growth picks up; however, in the sixth year after a slowdown, the impact of private investment is positively correlated with some marginal comovement of output response to shock to public investment. This result confirms the public and private nexus to the output effect of private investment.

24. Private investment overreacts to its own shock. Except for shocks to private investment, the GDP and private investment response is less than one to one. Because investment is irreversible, any adverse shock to private investment could lead to a sudden halt in private investment followed by disinvestment. Responses of a change in private investment to a terms-of-trade shock is limited; however, there is a persistent response to a private sector credit shock. However, the GDP change in response to a terms-of-trade shock is more pervasive and persistent.

Figure 3.
Figure 3.

Impulse Response to a “Shock” in Terms of Trade, Public Investment, Credit to Private Sector, Private Investment, and GDP

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Source: Fund staff estimates.

E. Conclusion and Policy Issues

25. The econometric analysis finds that— besides Benin’s institutional and regulatory framework and terms-of-trade developments—public investment and credit to the private sector positively affect growth through increased private investment. The analysis also confirms that an obstacle to private investment in Benin is the country’s slow improvement in economic freedom owing to weak institutions and a slow pace of structural reforms. Speed of adjustment analysis shows that while a 1 percent increase in private investment leads to a 0.2 percent increase in GDP in the long run, a 1-percent shock to private investment would entail a 16-percent correction each year, requiring about 4 years to close 50 percent of the deviation of real GDP from its long-run equilibrium.

26. Promoting private sector development and directing fiscal resources to public investment would likely shift Benin to a higher long-run growth path. Given fiscal constraints, it remains imperative that any additional space created in the post-MDRI era is allocated to high-return projects.

27. Institutions also appear to be an important determinant of investment and growth; however, slow institutional changes do little to improve private investment and growth trends. Since 1996, cumbersome trade and informal market property rights and regulations have significantly affected the economic freedom index, with Benin’s performance rating on the Fraser Institute’s Economic Freedom Index increasing only 0.3 percent per year, on average, in 1996–2006. Structural reforms and the gradual elimination of informal economic activities through market incentives would therefore likely help improve Benin’s economic competitiveness and fiscal stance.

28. A segmented public-private partnership could deepen the private sector’s involvement in Benin’s infrastructure development. The partnership could be achieved in large companies, small and medium enterprises, as well as in the informal sector.

29. Finally, greater access to credit would likely boost private investment and support growth. As is evident from the historical decomposition analysis, supply shocks, for example through changing labor market and investment conditions, have a strong impact on private investment forecast error variance. Credit to the private sector indirectly affects growth via its impact on private sector investment; this effect, however, is short lived, apparently because in Benin such credit has tended to be short term in nature.

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Appendix A: Time Series Analysis

Figure A.
Figure A.

Benin: Selected Economic Indicator, 1965 - 2005

(Natural log of constant value)

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Source: Benin authorities.

Appendix B: Econometric Model

Variables ordering

Because the variance decomposition and impulse response function through the SVAR methodology are sensitive to the ordering of variables, following the SVAR literature our system variables are ordered according to assumed decreasing exogeneity.25 In other words, the terms of trade variable is assumed to be the most exogenous variable, while GDP is considered the most endogenous. Public investment and credit to the private sector are considered two policy instruments; however, credit to the private sector is assumed to be endogenous to the fiscal instrument.26

Cointegration rank

The model below

ΔYt=ΠYt1+Σi=1p1ΓiΔYti+Σi=0p1χiΔINSTti+εt(B1)

indicates that the rank of Π and Π determines how the process Yt-1 enters the system.

If we assume that rank (Π)≤ r, Π can be written as Π = αβ’, where α and β are n x r vectors. The statistical hypothesis of cointegration is based on the rank of Π. It is known that:

If rank (Π) =n, all variables Yt-1 are stationary (I(0)).

If rank (Π) = 0, ΔYt is stationary.

If 0 ≺rank (Π) =r ≺ n, there are r cointegrating relations.

Data and modeling steps

The analysis uses annual data for 1965–2005 from the World Bank Development Indicators 2006 and the IMF International Financial Statistics of June 2006. The data in these series (except for terms of trade and institutions) are at constant prices (2000=100). Before specifying and estimating the model, we apply a series of Augmented Dickey-Fuller (ADF) tests to analyze the generation process of each variable (see Appendix Table C1).

Table C1.

Augmented Dickey-Fuller Test, 1965–2005

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T=37, constant term, 5%=-2.94; 1%=-3.62

The second step of the analysis specifies the VAR model, including the model-checking procedure. This includes optimal lag choice, stability, normality, AR, and heteroschedascity tests (see Appendix Table C2 and C3; and Figure C1 and C2). Because the system variables are integrated of order 1, a crucial step is to draw its information content through the cointegration analysis. We apply the Johansen cointegration technique (Johansen 1988; Johansen and Juselius 1990).

Figure C1.
Figure C1.

Benin: Model Stability Test (with two lags)

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Figure C2.
Figure C2.

Benin: Residual Normality Tests

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Table C2.

Optimal Lag Length

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F-Statistics, F(25,20)

F-Statistics from descendant order F(25,20), F(50,26), F(75,28), F(100,29), and F(125,29).

Table C3.

AR, Normality, and Heteroschedasticity Tests

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Structural Model and Identification Issues

The empirical analysis, in estimating a structural vector autoregressive (SVAR), imposes a number of restrictions for identification purposes. The structural representation of Equation is:27

AΔYt=α(βYt1)+Σi=1p1ΓiΔYti+Σi0p1χiΔINSTti+Bεt(2)

Assuming two cointegration vectors, the long-run effects of εt shocks can be written as Φ and C = φA-1 B28 are of rank 3 that is =5-2, as we have 5 endogenous variables and 2 cointegration relationships. A and B are nonsingular. A meaningful impulse response function will require at least 5 (5-1)/2=10 restrictions.

  • Given that there are 2 cointegration relations, at most 2 have transitory effects or zero have long-run impacts.

  • There are at least 3=5-2 shocks with permanent effects.

  • These two transitory shocks will induce 6=2*(5-2) independent restrictions.

  • Following King et al. (1991), 2*(2-1)/2=1 contemporaneous restriction identities will be imposed.

  • Using a data-oriented approach by degree of significance, we impose 3*(3-1)/2 additional restrictions.

  • Together, the total number of restrictions for the identified system will be [2*(5-2)] + [2*(2-1)/2] + [3*(3-1)/2] = 10.

Appendix C: Diagnostic Tests and Structural Model Long-run Matrix

Data generation processes indicate that most of the economic variables are nonstationary. Although the F-test indicates one lag, we extend the number of lags to 2, as suggested by Park (1994), to account for more memory effect. At lag 2, the model passed the residual normality and heteroschedasticity tests while the null hypothesis of non–AR 1-2 is rejected at 10 percent. Further, the stability of the model variables at lag 2 passed the 1-up and N-down Chow tests, indicating that the system has perfect stability.

Table C4.

Unrestricted Cointegration Rank Test (Maximum Eigenvalue) 1/, 2/, 3/,

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Max-eigenvalue test indicates 2 cointegrating eqn (s) at the 0.05 level

denotes rejection of the hypothesis at the 0.05 level

MacKinnon-Haug-Michelis (1999) p-values

41 observations from 1965 - 2005.

Test assumes no intercepts and no trend.

the test is carried with two lags in VAR.

Table C5.

Structural Model/Long-Run Matrix.

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Included observations: 36 after adjustmentsModel: Ae = Bu where E[uu’] = I, and B = I

Long-run Matrix

C=[10000C211C2300C31C32100C41C42C4310C51C520C541]
Figure C3.
Figure C3.

Benin: Historical Decomposition of GDP, 1975 - 2005

(Annual change)

Citation: IMF Staff Country Reports 2008, 084; 10.5089/9781451803525.002.A001

Source: Staff estimate.

Statistical Appendix

Table 1.

Benin: Gross Domestic Product by Sector of Origin at Current Prices, 2000–2005

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Sources: Institut National de la Statistique et de l’Analyse Economique (INSAE); and staff estimates.
Table 2.

Benin: Gross Domestic Product by Sector of Origin at Constant 1985 Prices, 2000–2005

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Sources: Institut National de la Statistique et de l’Analyse Economique (INSAE); and staff estimates.
Table 3.

Benin: Supply and Use of Resources at Current Prices, 2000–2005

(Billions of CFA francs)

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Sources: Beninese authorities; and staff estimates.