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Dr. Mouhamed MBOUANDI_2023 AGRODEP Annual Conference

  1. University of Douala- Cameroon Institutional quality and trade openness in Sub- Saharan Africa: Empirical evidence Dr. Mouhamed MBOUANDI
  4. #2023 AGRODEP CONFERENCE CONTEXT Over the past five decades, SSA has experienced a significant decline in its contribution to world trade. Between 1960 and 2019, the region's trade in goods fell from 4.38% to 1.72% (UNCTAD, 2020). East Asia's contribution to world trade increased from 5.77 percent in 1960 to 24.46 percent in 2019. Two non-exhaustive explanations for the decline in Sub-Saharan Africa's trade (WTO, 2018)  Exports dominated by primary products, with fuels 60% and agricultural products for more than 25%.  Trade agreements between African countries and the rest of the world do not allow trade to flow smoothly.  Although Economic Partnership Agreements (EPAs) have been signed between some countries in the region and the EU, less than 20% of trade benefit from this legal framework.
  5. #2023 AGRODEP CONFERENCE INTERESTS OF THE SUDY (1/3) The conclusions of the literature on the two-way relationship between institutions and trade remain poorly substantiated. Recently, Liu et al (2018) found that corruption and lack of law enforcement severely hamper trade for at least two reasons.  First, corruption increases transaction costs and customs clearance procedures, thereby stimulating a small increase in trade volume.  Second, unenforced laws and regulations discourage investors and reduce trade.
  6. #2023 AGRODEP CONFERENCE INTERESTS OF THE SUDY (2/3) The analysis of stylized facts in SSA countries has led to the following major observations: 1. Trade Openness remains low in Sub-Saharan Africa Graph 1: Evolution of trade openness in Sub-Saharan Africa Source : Authors, from WDI (2019) 43.5 44 44.5 45 45.5 46 46.5 47 47.5 48 48.5 1996 2000 2005 2010 2015 2018
  7. #2023 AGRODEP CONFERENCE INTERESTS OF THE SUDY (3/3) The quality of institutions is poor in Sub-Saharan Africa Graph 2. Evolution of governance indicators in Sub-Saharan Africa Source : Authors, from WGI (2019) -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 Controle of Corruption Government effectiveness Political Stability Quality of Regulation Role of the law Voice and Responsibility 1996 2018
  9. #2023 AGRODEP CONFERENCE BRIEF LITTERATURE REVIEW From the empirical formalization of optimistic and pessimistic visions, three groups of work emerge.  First, confirm the positive effect of institutions on trade openness (Ades and Di Tella, 1999; Wei, 2000; Mansfield et al. 2002; Dollar and Kraay, 2003; Maurel, 2004; 2005; De Sousa and Pitlik, 2008; Méon and Sekkat, 2008; Milner and Mukherjee, 2009; Galiani and Torrens, 2014).  Second, those that attest to the negative effect of institutions on trade openness (Garett, 2000; Anderson and Marcouiller, 2002; Rodrik, 2002; Kono, 2008).  Second, those that attest to the negative effect of institutions on trade openness (Garett, 2000; Anderson and Marcouiller, 2002; Rodrik, 2002; Kono, 2008).
  11. #2023 AGRODEP CONFERENCE The empirical model The estimated model is an extension of work by Candau and Gbandi (2019), Liu et al (2018), Álvarez et al (2018) and Wuetal. (2012). The reduced version is presented by equation (1) below : Ouv_com represents the trade openness approximated by the flow of exports and imports of goods and services as a percentage of GDP. I is the vector of institutional variables that incorporates the indicators of Kaufmann et al, (2010).
  12. #2023 AGRODEP CONFERENCE The empirical model (2/ 3) We also consider a composite index of institutional quality that has three advantages. First, it provides information on a fairly large panel of countries, particularly those in Sub-Saharan Africa. Second, the time horizon of the index's calculation is long term. Thirdly, the indicators selected for this purpose are multidimensional. X is the vector of control variables consisting of: (i) Gross Fixed Capital Formation (GFCF), (ii) Foreign Direct Investment (FDI); FDI is expected to drive trade openness (Anyanwu and Yameogo, 2015); (iii) private investment (Inv_pri), (Choong et al., 2010);(iv) the rate of internet use, it reduces transaction costs and increases the productivity of industries (Cadot et al., 2016).
  13. #2023 AGRODEP CONFERENCE The empirical model (3/ 3) Indexes i and t provide information on countries and periods; ν_i captures unobserved country fixed effects; μ_t takes into account the temporal fixed effect common to all countries, and ε_it is the error term. The estimated model is specified by equation (2) below:
  14. #2023 AGRODEP CONFERENCE The estimation Technique (1/2) The estimate is made by three methods:  firstly, the ordinary least squares which are justified by the linear nature of the relationship between trade openness and the quality of institutions.  We specify that the stationarity tests have led to the validation of the non-presence of the stationarity of the variables. Then the method of instrumental variables, which makes it possible to estimate the causal relations between the variables. It is also used for estimations when a problem of exogeneity of the variables is suspected.  Finally, we use the Generalised Moment Method (GMM).
  15. #2023 AGRODEP CONFERENCE Data Macroeconomic data (FDI, Internet, private investment, infrastructure, trade openness) are taken from World Development Indicators (2019). Data on institutional variables are from World Governance Indicators (2019). The sample comprises 48 African countries (Table 1). The study covers the period 1996-2018 dictated by data availability.
  17. #2023 AGRODEP CONFERENCE Dependent variable : Trade openness Estimation Technique: Ordinary Least Squares (1) (2) (3) (4) (5) (6) (7) Infrastructures -000881*** -0.0969*** -0.0851*** -0.0103*** -0.0873*** -0.0874*** -0.0910*** (0.000145) (0.000159) (0.000145) (0.000178) (0.000146) (0.000143) (0.000160) Inves_ privé 0.449*** 0.533*** 0.361** 0.578*** 0.425** 0.455** 0.425** (0.172) (0.165) (0.176) (0.142) (0.183) (0.177) (0.177) Internet 0.0608 0.0646 0.0294 0.118 0.000991 0.0501 0.0249 (0.0903) (0.0900) (0.0916) (0.0844) (0.0955) (0.0862) (0.0850) IDE 1.169*** 1.167*** 1.101*** 1.255*** 1.063*** 1.145*** 1.115*** (0.235) (0.244) (0.254) (0.222) (0.274) (0.249) (0.265) Corruption -6.268** (2.849) Efficacité_Gouv. -7.224*** (2.059) Stabilité_Pol. -10.78*** (4.040) Qualité_Regl. -7.397*** (2.544) Rôle de la loi 0.352 (1.842) Voix_et_Resp. -4.439*** (1.579) Constante 25.09*** 20.06*** 32.99*** 17.45*** 31.40*** 25.38*** 29.09*** (5.675) (4.395) (7.391) (3.301) (7.840) (6.415) (6.743) Observations 816 816 816 816 816 816 816 Nombre de pays 48 48 48 48 48 48 48 R2 0.45 0.61 0.51 0.37 0.52 0.46 0.52
  18. #2023 AGRODEP CONFERENCE Interpretations Infrastructure has negative and statistically significant effects on trade openness in SSA. The plausible explanations are the lack of quality infrastructure, which does not allow for positioning on international value chains and logistics chains to become major players in world trade. Private investment as well as foreign direct investment have a positive and statistically significant effect on trade openness.
  19. #2023 AGRODEP CONFERENCE Robustness analysis To test our results, we consider a composite index of institutions that takes into account all governance indicators. The composite index is calculated by the geometric mean of all the indicators of the institutional variables. The model is always estimated by Least Ordinary Squares, the Instrumental Variable Method and the Generalized System Moment Method. The estimated model is specified by equation (3) below.
  20. #2023 AGRODEP CONFERENCE • Table 7: Results of robustness analysis
  21. #2023 AGRODEP CONFERENCE Robustness analysis interpretation Overall, the results reinforce the memory effect. The trade opening observed is positively and statistically influenced by the previous trade opening. Regardless of the estimator considered, institutions have negative and statistically significant effects on trade openness in SSA. The control variables used are infrastructure, private investment and FDI. Taking into account a composite index of institutions does not erode the effect of the control variables (Infrastructure, FDI, private investment) on trade openness. Their effects remain negative, significant and are more accentuated.
  22. #2023 AGRODEP CONFERENCE CONCLUSION AND SUGGESTIONS Three main but non-exhaustive suggestions: Firstly, to improve the institutional environment by putting in place legislation that supports business activity with foreign countries, promotes good governance policies and stimulates competitiveness in market systems, and guarantees the protection of property rights. Second, governments could consolidate industrilisation strategies to modernise the processing of natural resources, in order to invest financial resources from natural resource rents in meeting basic needs. Thirdly, governments should strengthen security in high-risk and transboundary areas to protect households from financed armed attacks that create unrest in resource-rich areas.