EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS 471 Public Expenditures and Agricultural Growth in Burkina Faso Souleymane Ouedraogo Agro-economist; Institute of Environment and Agricultural Research (INERA) 476, Ouagadougou 03, Burkina Faso Daman Bako Statistician Economist; Millennium Challenge Account, 01 BP 6443 Ouagadougou 01, Burkina Faso Abstract This study analyzes public funding in the agricultural sector in Burkina Faso and assesses its impact on agricultural growth. Based on data collected from several sources (finance acts over the period 1983-2008, Automated Prediction Instrument (IPA), World Bank and National agricultural statistics over 26 years (from 1983 to 2008), the agricultural production has been modelled by using an error correction model and Cobb-Douglas function. The econometric analysis results show that public funding has a positive impact on agricultural production in the short term. A 9% growth rate of public funding over the period 2009-2015, causes an average agricultural production of 6.75% over the period. So, it is necessary for the State to increase funding in the agricultural sector to achieve a better growth of the domestic production and to meet the Millennium Development Goals regarding hunger reduction over the period 2009-2015. Keywords: Public funding, Maputo, agricultural growth, Burkina Faso Introduction 1 The agricultural sector (including, livestock, fisheries and forestry) has long been neglected by African countries and has not been allocated the funding required for its development. According to Food and Agriculture Organization (FAO, 2012) the proportion of public expenditures allocated to agriculture in 2007 was estimated at 3% in average for Sub-Saharan Africa against 7% for East Asia and the Pacific. Agriculture is so important in controlling hunger and poverty that the African Union Heads of States and Governments ratified in July 2003 in Maputo the Declaration on Agriculture and Food Security. This Corresponding author’s Name: Souleymane Ouedraogo Email address: kouedsouley144@outlook.fr declaration commits the States to allocate at least 10% of their budgets to the agricultural sector so as to achieve a 6% agricultural growth required to reduce poverty by 50% by 2015. From 1983 to 1990, Burkina Faso significantly invested in the rural sector. Through programs such as the “mass Development Program” (1983-1985) and the “Five Year Development Plan” (1986-1990), the country allocated 44% and 41% of its investments to agriculture and water (Somé, 2004). But the following structural adjustment program (1991-2002) was characterized by the withdrawal of state funding from the agricultural sector. Most of the public subsidies were cancelled and State support to the sector became occasional and limited. Furthermore, the international funding for this sector was also reduced (FAO, 1996) over this period. Asian Journal of Agriculture and Rural Development journal homepage: http://aessweb.com/journal-detail.php?id=5005 mailto:kouedsouley144@outlook.fr Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 472 Since the 2008 food crisis and the subsequent violent riots, the State is increasingly focused on the agricultural production improvement. That is translated into considerable subsidies granted to help purchase fertilizers and improved seeds in order to boost production. According to the World Bank (2012), over the period 2004-2011, the spending in the agricultural sector, as defined by the New Partnership for African Development (NEPAD), accounted for 10.2% of the national budget. The rate is slightly above the Maputo commitment requirement. The volume of agriculture expenditures, estimated as per NEPAD Classification of the Function of Government (COFOG) method, doubled from 65 billion FCFA in 2004 to 129 billion FCFA in 2011. Meanwhile, the agricultural growth reached 3% in average from 2004 to 2011. The International Food Policies Research Institute (IFPRI 2006) shows that, this rate must be raised to 6.8% if the first Millennium Development Goals (MDG 1) "reducing extreme poverty rates by half by the 2015 deadline" is to be achieved. This study intends to analyze public funding to the agricultural sector in Burkina Faso and its impact on agricultural growth. It also addresses the public investment required to achieve the MDG 1 on reducing poverty. This paper encompasses four sections: the first section enlightens on the agricultural background in Burkina Faso. The second one describes the study methodology; Section 3 presents and discusses the main outcomes of the study and the last section highlights the main conclusions drawn. Background of agriculture in Burkina Faso Characteristics of agriculture in Burkina Faso Agriculture is one of the key sectors of Burkina Faso economy. It accounts for 85% of Burkina Faso’s active labor force. Most agricultural activities are organized and implemented by family farms, which stands as the main form of production in Burkina Faso. Farming sometimes faces harsh conditions (poor rainfall, poor soils, funding problems) limiting severely the production growth and resulting in a fall in the agricultural export revenues and food insecurity. Yet, the agricultural sector is still the leading sector of the country economy since; for years to come, the economic growth will be based on dynamic of agricultural exports (cotton, fruits and vegetables) and agri-industry (fruits and vegetable processing, skin tannery and cotton spinning). Good export prospects in the agro- pastoral sector will however be dependent on the capacities of the sector to meet high demands from coastal countries in terms of meat and cattle, and cereals for the ECOWAS and WAEMU countries, and fruits and vegetables for the European market. Agriculture in Burkina Faso is subsistence farming, producing mainly cereals that accounts for 77% of the surface areas cultivated and 71% of the total production over the 2001-2010 periods. The production growth rate over the period from 1984 to 2010 is estimated at 6% which is far beyond the population growth rate (about 3.1%). The cash crops consisting mainly of cotton are also important as they constitute the main export agricultural products in Burkina Faso. Most cotton growers (80% in 2000) are illiterate. The fruit and oleaginous plant production sectors are suffering from poor trading organization and lack of grading and cleaning infrastructure. Agriculture in Burkina Faso is highly dependent on climate and will need irrigation to develop (FAO, 2005). Considering the increasing food needs and the high pressure on land, irrigated agriculture has to play an increasingly important role in Burkina Faso economy. The country has a huge potential of irrigable land area which is estimated at 255 000 ha with only 25% of it exploited and 500 000 ha of lowland appropriate for development. Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 473 The livestock sector also contributes to food security and animal traction, transportation and farm fertilization. Livestock population is large and varied. Nevertheless, the per capita productivity needs to be significantly improved if the country livestock production is to fully cover the country demand for milk and milk products. This effort has to be supported by the establishment of large and/or small scale processing plants and an efficient distribution chain (Ministry of Animal Resources, 2008). Along these potentials, a number of natural, technical, financial, economic and organizational constraints affect the development of the sector. The rainfall is scarce, irregular and inadequately distributed and the soils are poor and not suitable for cultivation. Most of the farmers are illiterate, inefficient and lack the adequate means to modernize the production systems. This results in a limited modernization of agriculture and a poor use of fertilizers in the production. Poor rural roads and inadequate promotion of agricultural products affect the marketing in this sector. Funding of agricultural sector in Burkina Faso The agriculture sector in Burkina Faso is in need of funding; only 10% of the need is hardly covered. Financial institutions, for various reasons, are reluctant to granting farmers the necessary credit that will allow them to properly invest in production activities. For the 2009-2010 campaign, only 19.6% of agricultural households had access to agricultural credit to purchase inputs and 2.1% of them for the equipments. According to studies conducted in the sector, agricultural training, extension and monitoring also lack adequate funding. Most of the farmers have a low educational level (71.7% of the population was illiterate 2007 (INSD, 2007)). It is proven that literacy increases the efficiency of Burkina farmers by 31.4%; agricultural production could be increased in the same rate if all the farmers were literate (Zonon, 2003). The agricultural sector, like any other sector, needs funding to better contribute to the economic growth. The access to inputs and equipments which is required to foster for the adoption of an intensive production system is highly dependent on the financial resources available. Yet currently, the FAO (1996) pointed out a reduction in agricultural funding these years in developing countries. Therefore, the African Heads of States undertook in 2003 through the Maputo declaration to allocate 10% of public expenditures to agriculture, with the view to help achieve the 6.8% agricultural growth rate assumed necessary to reduce extreme poverty and hunger by half by the 2015 (MDG 1). The reason for State intervention in the agricultural sector is that private funding is inadequate. Therefore, the State and the public sector have to play an important role in agriculture development to address this issue. The Maputo declaration clearly shows the leading role of agriculture in the economic development of African countries and the return of public interventions as a result of funding the agricultural sector. National expenditure in agriculture in Burkina Faso has been weak until the 2003s (fig1). From 1997 to 2001 the trend went downward. The 1990s were marked by the structural adjustment programs with the liberalization of the agricultural sector through the withdrawal of all state subsidies granted to the farmers. Since 2003, the Governments committed to abide by the Maputo declaration. The national spending in agriculture has increased from 65 billion in 2004 to more than 129 billion in 2011 representing a 98.46% increase over the period. This additional funding was used to fund agricultural service development programs, agricultural sector support programs and hydro-agricultural developments. Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 474 Theoretical framework and methodology of the study The theoretical framework of the analysis of the agricultural sector funding Economists have always value the contribution of agriculture to economic growth. For Lewis (1954), Agriculture contributes to capital development, frees poorly productive workforce for others sectors such as industry. For Gillis (1990) agriculture, considering its potential, attracts foreign direct investment, creates jobs and provides new investment opportunities for local entrepreneurs to increase local production. Agriculture contributes to the development as an economic activity, livelihood and environmental service. Historically, the national expenditure has been one of the main instruments of agricultural policy. In all countries, budgets are allocated to the agricultural sector for various purposes: irrigation, product storage and transportation, and marketing infrastructure, loans to farmers, research, extension, and improved seeds production. Another heading allocated by national budgets to the agricultural sector include funding post-harvest programs (for purchasing cereal from farmers at high price and selling them to consumers at lower price). However, empirical evidence of the nature of relationship between national spending and economic growth has always been debated. Devarajan et al. (1996) could not show the significant relationship between growth and the level of expenditures. The outcome of empirical literature on the effects of expenditures composition was also debated. Barro (1997) found that public consumption expenditures as a percentage of the GDP were negatively correlated to growth. On the other hand, Devarajan et al. (1996), revealed a positive relationship between national consumption spending and economic growth. Caselli et al. (1996) have also pointed out the positive effect of national spending on growth. Easterly et al. (1997) found no significant effect of national consumption spending in the GDP on growth in Latin America. Morley and Perdikis (2000) in Egypt (following the 1974 and 1991fiscal reforms) found that there was a long-term positive effect of the total national spending on growth; however there was no significant effect in the short-term. Nubukpo (2003) about the WAEMU countries found that except from Senegal and Togo, for the long- term, the total national spending has no positive effect on UEMOA economic growth. Coulbaly (2013) in Côte d’Ivoire came to the conclusion that spending in education has a significant contribution to economic growth, with a competition between the education sector and the other economic sectors for the effective allocation of public financial resources. According to Ben and Hassad (2006) national spending in education and health can lead to economic growth provided this spending is made efficiently. Kane (2004) in Senegal came to 0 20 40 60 80 100 120 140 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 Fig. 1: Public agricultural spendings Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 475 the conclusion that national capital spending has a positive and significant impact on economic growth. Model selection The nature of the variables requires the use of the error correction model (ECM) that helps in modeling both the ongoing dynamic (represented by the variables in first difference) and the long-term dynamic (represented by level variables). Indeed to see the stochastic features of a chronological series, it has to be stationary meaning that its mathematical expectation and variance are finite constants and that its co-variance is a finite function independent from the time dimension. Theoretical model This paper analyzes the impact of public expenditures on agricultural growth. The functional form, to establish the link between agricultural production and factors underpinning its growth is a Cobb-Douglas type function. Barro and Sala-i-Martin (1996) and Guillaumont (2003) resorted to this type of function in identifying the determinants of production in the Sahel. And so did Mundlak et al. (2002) in analyzing the agricultural growth determinants in Indonesia, Philippines and Thailand. We will adapt this function to our study. Let 𝑌 be the agricultural production, 𝐾 capital, L labour, 𝐹, funding we have : 𝑌 = 𝐴𝐾𝛼𝐿𝛽 With 𝐴 = 𝑒𝑥𝑝(𝑎 + 𝛾𝐹) Agriculture is characterized by a production function with constant returns to scale. Therefore we assume that 𝛼 + 𝛽 = 1 The application of the logarithm gives: log 𝑌 = 𝑎 + 𝛾𝐹 + 𝛼𝑙𝑜𝑔(𝐾) + 𝛽𝑙𝑜𝑔 𝐿 After differentiation: ∆𝑌 𝑌 = 𝛾∆𝐹 + 𝛼 ∆𝐾 𝐾 + 𝛽 ∆𝐿 𝐿 We will determine the value of the coefficient 𝛾 in the short and long-terms using error correction model. Speciation of the variables As suggested by the theoretical model: we will use the following variables: public expenditures allocated to the agricultural sector, agricultural production, capital and agricultural labor. Public funding allocated to agricultural sector (FIN) Variable FIN represents the share of the national budget allocated to agricultural sector. Or the cumulative public expenditures in this sector: spending in salary, equipment, transfers, agricultural research. Data collected for this variable are from the finance act of the study period (1983-2008). Agricultural production (PROD) PROD is the total agricultural production in value (livestock and crop production) over the study period. The data used are from the data base of the automated forecasting tool. Agricultural labor force (TRV) TRV is the agricultural workforce. For raison of simplification, rural working population is considered as agricultural workforce. Data for this variable are from the World Bank indicators (2007). Capital (CAP) The capital is determined based on agricultural investments. The following traditional relationship between capital and investment: 𝐾𝑡 = 𝐼𝑡 + 1 − 𝛿 𝐾𝑡−1 The literature suggests several methods to determine the initial capital. Yet the most commonly used method is the one proposed by Harberger (1978), subject to the hypothesis of a balanced growth, through the relationship: 𝐾𝑡−1 = 𝐼𝑡 𝑔 + 𝛿 𝑎𝑛𝑑 𝐾0 = 𝐼1 𝑔 + 𝛿 Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 476 With 𝑔 representing the long term economic growth rate that is estimated roughly with the actual growth rate (4.2% in Burkina Faso). The parameter 𝛿 describes the capital depreciation rate. The depreciation can be assessed through survey within industrial plants but for the aggregated capital stock a depreciation rate ranging from 4 to 6% is acceptable (Aamer and Suleiman, 2007). Limits of the study Because of the lack of data, we could not cover private funding (micro-finance institutions, commercial banks credits) in the econometric analysis. Thus, the issue of the impact of private funding on agricultural production growth in Burkina Faso still remains to be addressed. Results and discussion Estimation and validation of the model As suggested in the theoretical model, LPROD, LTRV and LCAP respectively represent the logarithms of PROD, TRV and CAP. Tests such as stationarity, co- integration, and residual tests were conducted to validate the model. Stationarity tests The stationarity tests considered herein are unit root tests of Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS). The null hypothesis in ADF and PP tests is the presence of unit root (no stationarity). The KPSS test is generally less used and the PP test is known to have a weak power (Villemot, 2004). Both tests were then used mainly to confirm the results of the ADF test. According to the results, the variables FIN, LPROD, LTRV are stationary in first difference and LCAP is stationary in second difference (see. annex I). Optimal number of lags and co- integration test The selection of the optimal number of lags is essential since an inadequate number may encourage auto-correlation of the residuals of model and a high number of lag can lead to an over estimate of the number of co- integration (Keho, 2006). The number of optimal lags shows that the common criteria by Akaike (AIC) and Hannan-Quinn are minimal when considering two lags (see annex II). The result of the co-integration test by Johansen, taking into account the nature of the data, shows the co-integration relationship through the trace statistics (see annex II). Estimation of the model using the ordinary least squares method The ordinary least squares method in one step as suggested by Banerjee et al. (1993) was use to estimate the model, because of the small size of our sample. In fact, one of the weaknesses of the two step method by Engle and Ganger is that long-term estimate does not take into account potential information from the short-term dynamics (Keho, 2006). Banerjee et al. (1993) shown that this case leads to a considerable bias for small samples. Banerjee et al. estimate method consists in making an estimate through ordinary least squares of the following equation: 𝐷 𝐿𝑃𝑅𝑂𝐷𝑡 = 𝛽0 + 𝛽1𝐷(𝐹𝐼𝑁𝑡) + 𝛽2𝐷(𝐷 𝐿𝐶𝐴𝑃𝑡 ) + 𝛽3𝐷(𝐿𝑇𝑅𝑉𝑡) + 𝛽4𝐿𝑃𝑅𝑂𝐷𝑡−1 + 𝛽5𝐿𝐹𝐼𝑁𝑡−1 + 𝛽6𝐷(𝐿𝐶𝐴𝑃𝑡−1) + 𝛽7𝐿𝑇𝑅𝑉𝑡−1 + 𝛽8𝑡𝑒𝑛𝑑 + 휀𝑡 The coefficients 𝛽𝑖 are real parameters and 휀𝑡 represents the errors terms in the linear regression. The variable 𝑡𝑒𝑛𝑑 represents the trend that we add because of some variables trends significance in the model. The table 2 below shows the results of model estimate. Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 477 Table 2: Results of the model estimate (OLS) Variables Coefficient Std. Error t-Statistic Prob. C -7.250760 7.059068 -1.027155 0.3196 D(D(LCAP)) 0.068706 0.176376 0.3 89545 0.7020 D(FIN) 0.008048 0.004138 1.944748* 0.0696 D(LTRV) 15.90428 7.210288 2.205776** 0.0424 LPROD(-1) -1.455197 0.255645 -5.692251** 0.0000 D(LCAP(-1)) 0.115000 0.162966 0.705667 0.4905 FIN(-1) 0.005866 0.004942 1.187072 0.2525 LTRV(-1) 1.688146 0.508818 3.317783** 0.0044 R-squared = 0.,7058 Adjusted R-squared = 0.5489 Prob (F_statistic) = 0.0059 Number of observations = 24 Source: Authors' own results LPROD (-1) coefficient, which is the adjustment coefficient, is negative. It is also significant at 5% level (with a probability of Student p of 0.0000). There is obviously a correction mechanism; meaning that on the long-term, the gaps between our variables are filled. The correction method is therefore valid. The model is significant at 5% level (Fisher statistics probability is 0,0027 < 0,05) and its explanatory power is quite high (R² adjusted=0.54). The coefficient of the variable public funding is statistically significant at 10% level in the short term. An increase of 1% of the national budget allocated to agriculture results in an increase of the production value of 0.008%, suggesting that public funding is still not enough to have a significant impact on production. On the other hand, the coefficients of the variable agricultural labor are significant in short and long term. An increase of 1% of the agricultural labor force leads to an increase in the value of production by 15.9% in the short term and 1.69% in the long term. Coefficients of the variable capital are positives but not significant. Public Funding has therefore a positive influence on agricultural growth in the short- term whereas agricultural labor has a long term and short-term positive impact on agricultural growth. Residuals tests and constant returns to scale assumption The results of these tests are presented in annex III. Jarque-Bera test shows that errors follow a normal distribution (JB=1.64<5.99). Since this test is not appropriate for series with limited number of observations, we used the Shapiro-Wilk test which has confirmed the results of the normality of our residuals (W= 0, 95170; Wcritic= 0,916 (W >Wcritic)). Based on the White's test, the heteroskedasticity hypothesis of errors can be rejected: Hence, they are homoskedastic. The hypothesis of error autocorrelation can be rejected based on the Breuch-Godfrey test. Furthermore, the CUSUM test shows that the model is structurally stable (the CUSUM curve remains within the confidence interval). To test the hypothesis of increasing returns to scale of the production function, the Wald's test was used. The null hypothesis of the test conducted is 𝛽2 + 𝛽3 = 1. With a p- value higher than 5% (p≈0.4), we accept the null hypothesis. The yields are then constant. Simulation results According to the International Food Policy Research Institute (IFPRI), the production growth required to achieve the MDG 1 is set at 6.8%, with regard to that aspect, we made simulations to assess the increase rate in public funding required to achieve this level. Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 478 The results of the simulations are presented in table 3. Table 3: Simulation results In cr e a se i n a g ri cu lt u ra l p ro d u ct io n ( in % ) Increase in funding (%) Years 1% 5% 8% 9% 9.5% 10% 2009 8.36 9.96 11.16 11.57 11.77 11.97 2010 1.38 2.33 3.11 3.39 3.53 3.67 2011 4.84 6.07 7.13 7.51 7.71 7.91 2012 3.19 4.38 5.49 5.90 6.11 6.34 2013 3.97 5.29 6.57 7.06 7.32 7.58 2014 3.61 4.97 6.38 6.94 7.24 7.55 2015 3.78 5.24 6.83 7.47 7.82 8.18 Mean 3.71 5.06 6.29 6.75 6.98 7.23 Source: Estimate by the authors These results reveal that a 9% increase in public funding over the period 2009-2015, brings about a 6.75% average increase in agricultural growth over the same period. With a 0.19% estimation error in this study, the production growth rate will range between 6.56% and 6.93%. Considering that the IFPRI rate (6.8%) is included in this confidence range, we can conclude that with a 9% increase in public funding for agriculture, the country can achieve the MDG 1. Conclusion Agriculture is a key sector for Burkina Faso economy. In this regard, there is a need to deeply consider the conditions for a better growth of the country agricultural production. In addressing the issues of agriculture funding in Burkina Faso, this study is in line with this effort. The study was actually intended mainly to highlight the issue of agricultural sector funding, while assessing its impact on agricultural production. The economical analysis shows that the public funding has a significant impact on agricultural production growth. The simulations also evidence that a 9% increase of public funding over the period 2009-2015 would help Burkina Faso achieve the MDG 1 (of halving poverty and hunger). It is then obvious that public funding has a positive impact on agricultural production in the short and long terms. The State should therefore increase funding for the agricultural sector if it wants to strengthen its economy. In other words, the objective will be to increase the share of the budget allocated to agriculture. This increase is expected to reach at least 9% yearly over the period from 2009 to 2015 in order to achieve MGD No. 1. This increase could be achieved through the promotion of granting subsidies to producers to purchase agricultural inputs (fertilizers, pesticides, farm machinery). It can also materialize through marketing infrastructure development or rehabilitation (rural roads, cold storage facilities, slaughterhouses) and promoting processing plants to increase the production added value. Furthermore, agricultural training could be enhanced to improve farmer’s performance. The State could strive to invest more in the irrigation sector in order to increase food security and increase exports revenues. References Amir, Abu-Qarn, & Suleiman, Abu-Bader (2007). Getting income shares right. A panel data investigation for OECD countries. Economics department, Ben-Gurion University, Beer-Sheva, Israel, p. 34. Banerjee, A., Dolado J. J., Galbraith, J. W., & Hendry, D. F. (1993). 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Annexs Annex I: Results of stationnarity tests Agriculture production Public expenditure Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 481 Agricultural labour Capital Annex ii: Results of co integration test Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 482 Annex iii: Residual and constant yield Outcome of normality test Results of the homoskedasticity test Results of the error correlation test Results of the stability test Asian Journal of Agriculture and Rural Development, 4(10)2014: 471-483 483 Results of the constant yields test Table 1: Spending in agriculture estimated as per the NEPAD’s COFOG method, 2004-2011 (in billion of FCFA) MAH - MRA - MEDD Other ministries and Dept. Interm. Comm Non state budget projects Other 1 Total spending COFOG Execution State budget 2 % Maputo 2004 40.2 12.2 8.8 4.0 65.3 640.3 10.2% 2005 42.7 11.1 9.8 2.3 66.0 716.7 9.2% 2006 56.2 11.3 11.8 2.2 81.5 835.1 9.8% 2007 53.5 32.3 34.7 1.6 122.0 944.2 12.9% 2008 59.2 11.1 25.7 1.5 97.6 886.1 11.0% 2009 56.5 38.3 21.0 2.3 118.0 1.083,1 10.9% 2010 71.2 8.0 15.1 2.9 97.2 1.121,1 8.7% 2011 89.1 14.2 22.1 3.8 129.2 1.357,1 9.5% Total 468.7 138.5 148.9 20.8 776.9 7.583,7 10.2% Source: World Bank, SP/CPSA 2012, IAP 2013