 Good afternoon everybody. I will present my joint paper with Mathilde Morel the impact of education and electricity access on TFP on the Senegalese economy. The aim of this paper is to investigate the relationship between total factor productivity electricity access and education level in Senegal. For this proposal, we estimated the TFP at the firm level over the period 1998 until 2011. After we estimate the long-run relationship between these variables using the Pullman group estimator and the dynamic ordinary less squares. By the following, I will present the intervention plan. So first, I will summarize the different industrial policies in Senegal based on the scoping paper on Senegal, written by Troy and Fatou Cicé and Mathilde Morel. After, I will identify the main obstacles of industrial development in Senegal. And finally, I will introduce the empirical approach and conclude. From the independence until now, the Senegalese economy were enrolled in different economic programs and industrial policies to sustain the economic growth in the long run and to promote the industrial sector. The first graph compared the per capita GDP on Senegal and the other Western African economies and we can identify four major periods and three major events. From the independence till the 70s, we can see that the per capita GDP rise in Senegal but still remains below the level of other Western African economies. In 1980, the government adopted the first adjustment program to cope with the decline of macroeconomic aggregates and to achieve a balance of budgetary budgetary, sorry. But this program failed and the main reason identified for this is the currency misalignment. After the devaluation of 1994 and with the support of International Monetary Fund, the government launched a series of economic reforms and an industrial policy to sustain the economic growth in the long run. So over the period 2000 until the financial crisis, the per capita GDP grows but remains unstable. In association with these economic reforms, the government adopted several industrial policies. From the independence until the 70s, these policies were defined by a high level of state intervention and the main objectives were the import substitution and to create a heavy industry plant and also to replace the private sector in key industries like water and electricity. In 1980 and with the launch of the structural adjustment program, the government adopted a new industrial policy and by now the objective is to remove the import restriction and to arise the contribution of industrial, to arise the contribution in exports of high value added activities. However, this policy failed. Indeed, we can see, we can state that 50% of the firms fade out and some major companies didn't survive to the opening of the domestic market to foreign competitors. After the devaluation and since 2000, the government adopted a new accelerated growth strategy with a new industrial redeployment policy and the objective was to increase the contribution of manufacturing sector in the economic growth at least by 25% at the end of 2010. Overall, we can see that the industrial sector in Senegal suffers from many obstacles and while the literature defined these obstacles like geography or political risks, etc., the African Bank of Development identified two major obstacles namely the quality of electric power supply and the education. Considering the education, an efficiency would be happened by a balance between supply and demand in the job market. In Senegal, the job market is more open for job seekers from primary, secondary or even vocational level of education. The table shows the gross secondary enrollment rate in Senegal in comparison with other sub-Saharan Africa and we can see that this rate is below the level of sub-Saharan African countries whereas the level of higher education enrollment is much higher in Senegal in comparison with other sub-Saharan African countries. This situation denotes a mismatch between supply and demand in the job market in Senegal and the rising and important supply of higher graduated workers and this situation will cause at the long term the choice of some of higher educated workers to return to unemployment or even to emigrate. For instance, 50% of Senegal doctors and 27% of nurse have been emigrated from 1995 until 2005 and mainly to France. The first issue identified for the Senegalese economy is to try to better match the need of the firms in the Senegal by focusing on primary, secondary and enrollment and the technical training rather than in higher education. The African of development identified one other issue for the industrial sector namely the quality of electricity access. The table showed this access in the urban area and rural and the comparison between middle income, low income and the Senegalese access. We can see that this level is at the same of middle income countries considering urban access but in the rural access the level is much lower than the middle income countries. Moreover, a recent firm study highlights electricity access as the major problem identified by firms. About 58% state that is a major problem and 85% of the firms state that they had a power shortage during the last month and for information each power shortage, its duration is around 8 hours which represents a waste of time and activity for the firms. So from this first part we can identify two major obstacles to the growing, to the growth of the productivity in the industry sectors. The mismatch between demand and supply in the job market and the electricity access. So to assess this relationship between productivity and these two variables we are going to estimate the TFP at the firm level and to estimate the long relationship between these three variables. The QC database, the single information collection center database provide firm level data on four hand 4,000 firms across 23 manufacturing sectors. The data span is from 1998 until 2011. The time series on value added capital and labor are provided by the same database. For the determinants of TFP we identified two main determinants namely the human capital measured by the sectoral employment shares of the different qualification degree and the electricity access. For the electricity access we have this data only at the economic aggregated level but we make the assumption that there is a proportional relationship between the size of tangible assets and the electricity access so we calculate the electricity access at the sectoral level. Then the first part is the estimation of the TFP and for this we consider a standard cobb-duckless production function and we estimated using ordinarily less squads with fixed effects and the main results are the part, the chair of labor around 54% and the chair of capital is around 46% which is in line with the finding of recent study made by the OCDU. The graph shows the distribution of annual average growth through the 23 sector and we can see that for the extractive industries the productivity is much higher whereas for food industry and food production industry and footwear the productivity is much negative. After the estimation of the TFP we try to assess if there are long run relationship between the productivity factor at the sectoral level and the main determinant identified in this paper namely electricity access and human capital. So before proceeding to the estimation we need to make some tests. The first one is to test if there is some cross-section dependence between the sector. That means we try to control if there is some unobserved factor which can affect all the sector such financial crisis or some economic reforms decides and which could affect all the sector. The test rejects the the hypothesis of no cross-sectional dependence and therefore we can test for the order of integration of the variables. For this we use three unit truth tests, two didn't account for the cross-section dependence and the third one take into account this cross-section. The results show that all the variables are integrated of order one which means that we have just two differentiated one time to have the to have the variable stationary. The second step is to check if there is a long run equilibrium relationship between these three variables. For this we run the Westerland Cointegration Test which is computable under the exception of cross-section dependence and we can see that all the statistic of the test rejects the new hypothesis of no cointegration which means that there is a long run relationship between our three variables. Once we have assessed if there is a long run relationship we now estimated the parameter of this relationship using two estimators. The first one is the dynamic ordinary list squares and the second one is the PMG groups. For those estimators we use a regression in which we introduce lag and lead of the first explanatory variables as regressor of the TFP. The results from this estimation showed that shows that one percent increase of skate, technician and electricity access rise the TFP in the long run by 24 percent and the 21 percent respectively. However the impact of senior managers in the long run is significantly negative. The second estimator used is the Pullman group estimator and we used two estimators to check the robustness of our results. The PMG estimator gives the coefficient in the short run and the long run and also gives the speed of adjustment toward the long run relationship and overall we find the same results in the long run. Skate, technician and electricity access have a positive effect whereas senior managers have a negative effect. This relationship is different if we consider the short run in sense of senior managers have a positive effect which means that high skilled workers impact positively the TFP only in the short run but in the long run this effect wears off. Instead of technician where we have a negative impact in the short run but in the long run the impact is positive. Considering electricity access we have the same effect in the short run and in the long run the difference is instead of the coefficient in the short run the access will increase the TFP by 12 percent and in the long run the impact is more higher 29 percent. The results we can conclude this presentation by saying that the determinant the main determinant of TFP identified by the African Bank of Development namely electricity access and education or human capital have a positive effect on the long run and have a positive effect on the long run and we can summarize by saying that the Senegalese economy need more to focus on technical skills to match the need of the firms and to improve the quality of electricity access to improve in the long run the productivity at the industrial sector. Thank you.