The document summarizes a study on the relationship between institutional quality and trade openness in Sub-Saharan Africa. The study finds that institutional quality has a negative and statistically significant impact on trade openness based on analyses using ordinary least squares, instrumental variables, and generalized method of moments estimations. Private investment and foreign direct investment are found to have a positive effect on trade openness. The study concludes by recommending improvements to institutional environments, industrialization strategies, and security to help increase trade openness in Sub-Saharan Africa.

1. University of Douala- Cameroon
Institutional quality and trade openness in Sub-
Saharan Africa: Empirical evidence
Dr. Mouhamed MBOUANDI

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OUTLINE
•CONTEXT AND INTERESTS OF THE STUDY
•EMPIRICAL STRATEGY
•KEY FINDINGS AND INTERPRETATIONS
•CONCLUSION AND SUGGESTIONS

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CONTEXT AND INTERESTS OF THE STUDY

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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.

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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.

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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

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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

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BRIEF LITTERATURE REVIEW

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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).

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EMPIRICAL STRATEGY

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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).

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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).

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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:

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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).

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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.

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KEY FINDINGS AND INTERPRETATIONS

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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

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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.

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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.

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• Table 7: Results of robustness analysis

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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.

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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.

23. THANK YOU