INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3200
www.rsisinternational.org
The Role of Trade in Achieving Sustainable Development Goals: A
Case Study of Agricultural Exports in Africa
1
C Nathaniel Willie II,
2
Queenetta D. Johnson,
3
Allenton D. Allen
1,2
Department of Economics, Sichuan Agricultural University, Chengdu City, Sichuan Province, China
3
Department of Agriculture, Hohai University, Nanjing City, Jiangsu Province, China
DOI:
https://dx.doi.org/10.47772/IJRISS.2025.910000258
Received: 17 September 2025; Accepted: 30 September 2025; Published: 10 November 2025
ABSTRACT
This study employs a fixed-effects panel regression model with Driscoll-Kraay standard errors to analyze the
relationship between agricultural export performance and Sustainable Development Goal (SDG) indicators
across 45 African nations from 2000 to 2022. Export performance is measured through the per capita value of
agricultural exports and a Herfindahl-Hirschman Index (HHI) of export product concentration, while controlling
for GDP per capita, institutional quality, and population dynamics.
The empirical results reveal statistically significant yet dualistic associations. A 1% increase in agricultural
exports per capita is associated with a 1.25 percentage point reduction in poverty (p<0.05) and a 0.89 percentage
point increase in annual GDP growth (p<0.01), suggesting a positive link to SDGs 1 and 8. However, this export
growth is also correlated with a 0.83 point increase in the Gini coefficient (p<0.05), a 2.15% rise in agricultural
COâ‚‚ emissions (p<0.01), and a 1.98 percentage point loss in forest cover (p<0.01), highlighting severe trade-offs
for SDGs 10, 13, and 15. Furthermore, a one-unit increase in export concentration (HHI) is associated with a
2.18 percentage point increase in poverty and a 1.12 percentage point decrease in forest area (p<0.05). The
findings indicate that the scale and composition effects of export expansion dominate, overwhelming any
potential technique effect from economic gains.
We conclude that while agricultural exports are a significant correlate of macroeconomic growth in Africa, their
benefits are not automatically inclusive or sustainable. The results robustly indicate that export diversification
and strong institutions are critical mitigating factors against these trade-offs. Policy must therefore pivot from
promoting raw commodity volume to strategically fostering value-added, diversified, and sustainably certified
export sectors to align trade with the holistic 2030 Agenda.
Keywords: Agricultural Exports, Sustainable Development Goals (SDGs), Africa, Panel Data Analysis, Export
Diversification, Environmental Degradation, Trade Policy
INTRODUCTION
Background of the Study
The 2030 Agenda for Sustainable Development, with its 17 Sustainable Development Goals (SDGs), presents a
universal blueprint for peace and prosperity for people and the planet (United Nations, 2015). For African
nations, achieving these goals is a paramount challenge that requires harnessing all available economic engines.
International trade, long enshrined in economic theory as a driver of growth, is explicitly recognized as a key
means of implementation (Target 17.11) for the SDGs. The agricultural sector is of particular strategic
importance; it employs over 60% of the sub-Saharan African workforce and accounts for a significant share of
GDP and export earnings (World Bank, 2023). The continent possesses vast tracts of arable land and is a leading
global exporter of commodities like cocoa, coffee, tea, and cotton. This positions agricultural exports as a
potential contributor to progress across multiple SDGs, including:
Problem Statement
Despite the theoretical promise, the reality of agricultural trade-led development in Africa is complex and often
inconsistent. The classical Ricardian and Heckscher-Ohlin models predict gains from specialization based on
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3201
www.rsisinternational.org
comparative advantage. However, Africa's historical specialization in primary commodity exports has frequently
been associated with vulnerabilities: price volatility, deteriorating terms of trade (as postulated by Prebisch and
Singer), and limited value addition (Addison & Levin, 2012). This has created a situation where export growth
does not automatically translate into broad-based, sustainable development. Furthermore, the expansion of
agricultural land for export crops is a primary driver of deforestation, biodiversity loss, and environmental
degradation (Hosonuma et al., 2012), creating a direct tension between economic objectives (SDG 8) and
environmental goals (SDGs 13 and 15). Thus, a critical research gap exists in understanding the net effect of
agricultural exports on the multi-dimensional spectrum of sustainable development in the African context.
Research Questions and Objectives
This study is guided by the following primary research question: How do agricultural exports influence the
achievement of Sustainable Development Goals in Africa?
To answer this, the study pursues the following specific objectives:
1. To analyze the impact of the volume and value of agricultural exports on key economic SDG indicators
(SDG 1, SDG 8).
2. To investigate the relationship between agricultural export patterns and social SDG indicators (SDG 2,
SDG 10).
3. To assess the environmental consequences of agricultural export-led growth on SDG indicators (SDG
13, SDG 15).
4. To examine the role of export diversification in mitigating trade-offs and enhancing synergies between
these dimensions.
5. To derive evidence-based policy recommendations for aligning agricultural trade policies with the 2030
Agenda.
Significance of the Study
This research makes significant contributions to academic and policy debates in several ways. It provides a new,
holistic assessment of trade impacts by moving beyond a purely GDP-centric analysis to a multi-dimensional
SDG framework. It presents updated empirical evidence from a large panel of African nations, utilizing rigorous
econometric techniques. The findings will be invaluable for policymakers in African governments, regional
economic communities (e.g., AfCFTA Secretariat), and international organizations (e.g., AU, FAO, UNCTAD)
seeking to design trade and agricultural policies that genuinely foster sustainable development.
Scope and Limitations
The study focuses on 45 African countries over the period 2000-2022, capturing the era of the Millennium
Development Goals and the transition to the SDGs. The analysis is limited to merchandise trade in agricultural
goods (SITC Sections 0, 1, 2, 4 minus 27, 28).
This methodological approach presents certain limitations. First, while the econometric model identifies robust
associations, establishing definitive causality is challenging due to potential endogeneity. Future research could
address this by employing an instrumental variables (IV) approach, for instance, using global commodity price
shocks as an instrument for export performance.
Second, measurement constraints must be acknowledged. The export concentration index (HHI) is calculated at
the product category level, which may mask finer-grained concentration within sub-categories. Furthermore,
inequality data, sourced from the Standardized World Income Inequality Database (SWIID), relies on estimation
and imputation to ensure cross-country comparability. While it is the best available source for a broad panel
study, its estimated nature necessitates cautious interpretation.
Finally, while institutional quality is controlled for, the deep-seated political economy of trade and resource
distribution is a limitation that qualitative studies could address more fully. Data availability for some SDG
indicators also presents constraints.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3202
www.rsisinternational.org
LITERATURE REVIEW AND THEORETICAL FRAMEWORK
Classical and Neo-Classical Theories of Trade
The theoretical foundation for advocating free trade stems from the classical theories of David Ricardo (1817),
who introduced the law of comparative advantage. This principle states that countries should specialize in
producing goods where they have a lower opportunity cost, leading to efficiency gains and aggregate welfare
improvements for all trading partners. The Heckscher-Ohlin model (1919, 1933) extended this, positing that a
country's comparative advantage is determined by its factor endowments (land, labor, capital). For land-abundant
Africa, this theory justifies specialization in agricultural and raw material exports. The expected outcomes are
increased incomes, economic growth, and poverty reduction—directly contributing to SDGs 1 and 8.
The Structuralist Critique and the Dependency School
In contrast, the structuralist school, pioneered by Raúl Prebisch (1950) and Hans Singer (1950), argued that the
classical model perpetuates global inequality. The Prebisch-Singer hypothesis suggests a long-term decline in
the terms of trade for primary commodities relative to manufactured goods. This implies that African countries
must export increasingly more agricultural products to import the same quantity of manufactured goods, leading
to a structural development deficit and hindering progress towards SDG 9 (Industry and Innovation). This
dependency on a narrow range of primary commodities also creates vulnerability to external price shocks,
undermining economic stability (SDG 8.1).
Modern Trade Theory and Endogenous Growth
New trade theory (Krugman, 1979) and endogenous growth models (Romer, 1990) introduced economies of
scale, product differentiation, and knowledge spillovers as key drivers of trade and growth. Applied to
agriculture, this suggests that trade can facilitate the transfer of sustainable technologies and management
practices, potentially improving productivity and environmental outcomes (SDG 2. a and 12.a). However, the
"Resource Curse" literature (Sachs & Warner, 2001) warns that export booms can lead to Dutch Disease
(appreciation of the real exchange rate that hurts other exports) and weaken institutions, potentially corroding
governance (SDG 16).
Trade and Sustainable Development: Conceptual Nexus
The relationship between trade and sustainability is framed by the Environmental Kuznets Curve (EKC)
hypothesis (Grossman & Krueger, 1995). It suggests that environmental degradation initially increases with
income per capita but eventually declines after a certain turning point, due to a shift from industrial to service-
based economies and increased demand for environmental regulation. In the trade context, the EKC decomposes
the effect into:
Scale Effect: Increased economic activity leads to greater resource use and pollution.
Technique Effect: Higher incomes lead to the adoption of cleaner, more efficient technologies.
Composition Effect: The structure of the economy changes towards less polluting sectors.
For African agricultural exports, the scale and composition effects may currently dominate, posing risks to
environmental SDGs.
The African Agricultural Export Context: Empirical Evidence
Empirical studies show mixed results. Maertens & Swinnen (2009) demonstrated that horticulture exports in
Senegal and Kenya created wage employment and reduced poverty (SDG 1). However, Carrère & Strauss-Kahn
(2017) found that export product concentration is associated with lower growth. Environmentally, studies
consistently link export-oriented agriculture (e.g., palm oil, cocoa) to deforestation in Africa (Hosonuma et al.,
2012). The role of value addition and diversification is highlighted as crucial for resilience and capturing more
value within the continent (Cadot et al., 2011).
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3203
www.rsisinternational.org
Conceptual Framework and Hypotheses
Based on the literature, we develop a conceptual framework (Figure 1) linking agricultural exports to SDGs
through economic, social, and environmental channels.
Figure 1: Conceptual Framework: The Agricultural Trade-SDG Nexus
This diagram illustrates the hypothesized relationships tested in this study. The central concept, Agricultural
Export Performance, is characterized by its two key dimensions: The Value of exports and their Diversification.
This performance influences sustainable development outcomes through three primary channels:
Economic Channel: Increased and diversified exports generate foreign exchange, government revenue, GDP
growth, and employment, directly contributing to SDG 1 (No Poverty) and SDG 8 (Decent Work and Economic
Growth).
Social Channel: The nature of export-led growth affects incomes, food security, and social equity. This channel
links performance to SDG 2 (Zero Hunger), SDG 5 (Gender Equality), and SDG 10 (Reduced Inequalities).
Environmental Channel: The scale and methods of agricultural production for export impact natural resources.
This channel connects export performance to SDG 6 (Clean Water), SDG 13 (Climate Action), and SDG 15
(Life on Land).
METHODOLOGY
Research Design
This study employs a quantitative research design using panel data for 45 African countries from 2000 to 2022.
The panel approach allows us to control for unobserved time-invariant country heterogeneity.
Model Specification
We specify a panel regression model based on the EKC and standard growth models:
SDGit = β0 + β1 ln (AgExportValuePC)it + β2 Diversificationit + β3 Xit + μi + λt + ϵit
Where:
1. SDGit: Sustainable Development Goal indicator for country ii in year tt.
2. Ln (AgExportValuePC)it: Natural log of agricultural exports per capita (constant US$).
3. Diversificationit: Herfindahl-Hirschman Index (HHI) of agricultural export products.
4. Xit: Vector of control variables (GDP per capita, population growth, institutional quality, arable land per
capita).
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3204
www.rsisinternational.org
5. μi: Country-fixed effects.
6. λt: Year-fixed effects.
7. ϵit: Error term.
Variable Description and Measurement
Variable Type
Variable Name
Measurement
Expected Sign
SDG Link
Dependent
Poverty Headcount
% pop. below $2.15/day
SDG 1
GDP per capita growth
Annual %
SDG 8
Gini Index
Index (0-100)
SDG 10
Agri-CO2 emissions
kg per capita
SDG 13
Forest Area
% of land area
SDG 15
Independent
Ln (Ag Exports PC)
Constant US$ (log)
+/-
Export Concentration
HHI Index
+
Controls
Ln (GDP PC)
Constant US$ (log)
Institutional Quality
WGI Rule of Law Index
-
Arable Land PC
Hectares per person
Data Sources
Data is sourced from:
1. World Development Indicators (WDI, 2023)
2. UN Comtrade Database (via WITS)
3. World Governance Indicators (WGI)
4. Standardized World Income Inequality Database (SWIID)
5. FAO STAT
Estimation Technique
We use the Fixed Effects (FE) estimator with Driscoll-Kraay standard errors to account for heteroskedasticity,
autocorrelation, and cross-sectional dependence.
EMPIRICAL RESULTS, ANALYSIS, AND DISCUSSION
Descriptive Statistics and Trends
The descriptive statistics reveal Africa's significant economic dependence on agricultural exports, which average
32.5% of total merchandise exports. Initial trends suggest a complex relationship between export growth and
development outcomes: while increasing export values coincide with declining poverty rates, they also correlate
with substantial forest cover loss.
Table 4.1: Descriptive Statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
Poverty Headcount (%)
990
45.6
23.1
3.1
91.6
GDP Growth (%)
1,035
3.8
4.9
-62.1
35.8
Gini Index
900
42.1
8.3
26.2
65.8
Forest Area (% land)
1,035
27.5
22.1
0.2
91.3
Ln (Ag Exports PC)
1,020
4.12
1.56
-0.91
7.88
Export Concentration (HHI)
1,020
0.38
0.24
0.06
0.99
The data shows considerable variation across countries: poverty levels range dramatically (3.1%-91.6%),
economic growth is volatile (-62.1%-35.8%), and forest coverage varies widely (0.2%-91.3%). The moderate
mean export concentration (HHI: 0.38) indicates most countries rely on multiple agricultural exports rather than
extreme specialization. These patterns set the stage for our multivariate analysis examining these relationships
while controlling for key factors.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3205
www.rsisinternational.org
Regression Results and Interpretation
Table 4.2 presents the core findings of our empirical analysis, quantifying the relationship between agricultural
export performance and key Sustainable Development Goal (SDG) indicators across Africa. The results, derived
from a fixed-effects panel regression with Driscoll-Kraay standard errors, reveal a complex and dualistic set of
associations.
Table 4.2: Fixed Effects Regression Results (Dependent Variables in Columns)
*(Driscoll-Kraay standard errors in parentheses; * p<0.05, ** p<0.01)*
Independent Variables
(1) Poverty Rate
(2) GDP Growth
(3) Gini Index
(4) Agri-CO2
(5) Forest Area
Ln (Ag Exports PC)
-1.25 (0.51) *
0.89 (0.31) **
0.83 (0.32)*
2.15 (0.78) **
-1.98 (0.65) **
Export Concentration
2.18 (0.87) *
-1.02 (0.41) *
1.45 (0.59) *
0.41 (0.92)
-1.12 (0.48) *
Observations
900
990
850
1,000
1,020
R-squared
0.67
0.42
0.58
0.51
0.63
Note: All models include control variables (Ln (GDP PC), Rule of Law, and others as specified in Chapter 3),
country fixed effects, and year fixed effects.
The analysis reveals a dualistic relationship between agricultural exports and sustainable development in Africa.
A 1% increase in agricultural exports per capita correlates with significant economic benefits (+0.89% GDP
growth; -1.25% poverty) but substantial social and environmental costs (+0.83 Gini points; +2.15% COâ‚‚
emissions; -1.98% forest cover). Export concentration exacerbates these trade-offs: higher specialization
associates with increased poverty (+2.18%), reduced growth (-1.02%), and greater forest loss (-1.12%). The
findings demonstrate that strong institutions (Rule of Law) are crucial for mitigating negative outcomes,
highlighting the need for diversified, well-regulated export strategies to achieve balanced, sustainable
development.
Illustrative Case Study: The Cocoa Paradox in Côte d'Ivoire and Ghana
Dual-Axis Plot (Economic vs. Environmental Trend)
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3206
www.rsisinternational.org
Table 4.3: The Dual Edges of Cocoa: Key Indicators for Côte d'Ivoire and Ghana
Indicator Category
Metric
Value (Approx.)
Source
Implications for SDGs
Economic Edge
Contribution to National
Export Earnings
40% (CIV),
25% (GHA)
World Bank
(2022)
Positive: Strong contributor to
SDG 8 (Economic Growth).
GDP Contribution
~15% (CIV),
~8% (GHA)
World Bank
(2022)
Positive: Significant driver of
national income.
Social Edge
% of Cocoa Farmers
Living in Poverty
>55%
World Bank
(2021)
Negative: Contributes to SDG 1
(Poverty) and SDG 10
(Inequality).
Estimated Children in
Hazardous Labor
1.5 Million
(CIV & GHA)
NORC (2020)
Negative: Major hurdle for
SDG 8.7 (Decent Work).
Environmental
Edge
Forest Loss since 1960
>80% (CIV)
FAO (2021)
Negative: Direct conflict with
SDG 15 (Life on Land).
Primary Driver of
Deforestation
Cocoa Farm
Expansion
FAO (2021)
Negative: Direct link between
export agriculture and
environmental degradation.
Value Capture
% of Global Chocolate
Value captured locally
<5%
Author's calc.
based on ICCO
Negative: Highlights the lack of
diversification, hindering SDG 9
(Industry).
Cocoa production in West Africa, especially in Côte d'Ivoire and Ghana, illustrates the trade-offs highlighted by
the regression analysis. Côte d'Ivoire, the world’s top cocoa producer, relies on the sector for 15% of GDP and
over 40% of export earnings, fueling strong economic growth (SDG 8). However, this has driven severe
deforestation, with over 80% of forest cover lost since 1960 (SDG 15).
Socially, the sector generates billions in export revenue, yet 55% of cocoa farmers live in poverty, with persistent
issues of child labor and inequality (SDG 10). The main value is captured by corporations, not smallholders. To
address this, both Côte d'Ivoire and Ghana are promoting export diversification and value addition through local
cocoa processing. This shift aims to capture more value, create better jobs, and reduce trade-offs, aligning with
SDG 9.
DISCUSSION OF FINDINGS
The empirical results reveal a complex, dualistic relationship between agricultural export performance and
sustainable development outcomes in Africa. While confirming the role of trade as an engine of macroeconomic
growth, our findings challenge the assumption that export revenues automatically translate into broad-based,
sustainable development. Instead, they reveal significant trade-offs across the economic, social, and
environmental dimensions of sustainability.
Economic and Social Trade-Offs
The strong positive association between agricultural export value and economic growth aligns with classical
trade theories that predict gains from specialization based on comparative advantage (Ricardo, 1817; Heckscher
& Ohlin, 1919/1933). The observed correlation between export growth and poverty reduction suggests that
agricultural exports can indeed contribute to achieving SDGs 1 and 8. However, the simultaneous positive
association with income inequality reveals a fundamental limitation of this growth model. The benefits of export
expansion appear to be captured disproportionately by a narrow segment of the population—typically large
landowners and export conglomerates—while smallholder farmers and rural laborers experience more modest
gains. This finding resonates with the structuralist critique of Prebisch (1950) and Singer (1950), who argued
that specialization in primary commodities perpetuates global inequality. The case of cocoa production in Côte
d'Ivoire and Ghana exemplifies this paradox, where billions in export revenue coexist with widespread poverty
among smallholder farmers.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3207
www.rsisinternational.org
Environmental Implications and the EKC Paradox
The environmental findings present a particular challenge to the Environmental Kuznets Curve (EKC)
hypothesis. Rather than observing the anticipated technique effect—whereby economic gains lead to improved
environmental outcomes—our results show the scale and composition effects dominating. The positive
association between export growth and both COâ‚‚ emissions and deforestation suggests that the expansion of
agricultural land for export production is overwhelming any potential environmental benefits that might arise
from increased incomes.
Several factors may explain the absence of the technique effect in this context. First, the nature of the exported
commodities themselves—primarily land-intensive crops like cocoa, coffee, and palm oil—creates inherent
pressure for forest conversion. Second, weak environmental regulations and enforcement in many African
countries fail to incentivize the adoption of sustainable technologies. Third, limited technology transfer and
access to sustainable agricultural practices among smallholder farmers further constrain the potential for greener
production methods. This combination of factors creates a scenario where export revenues are not automatically
reinvested in sustainable techniques, leading to a direct trade-off between economic gains and environmental
integrity.
The Critical Role of Export Structure and Institutions
Perhaps the most significant finding concerns the mediating role of export structure. The negative associations
between export concentration and multiple development outcomes powerfully support the structuralist critique
that over-specialization creates vulnerability. Countries dependent on a narrow range of primary commodities
experience more volatile and less inclusive growth patterns, exacerbating both poverty and environmental
degradation.
Conversely, the strong negative coefficients for the Rule of Law variable underscore that robust institutions are
crucial for ensuring that the benefits of trade are widely shared and its negative impacts mitigated. Effective
governance appears essential for translating export revenues into inclusive development and enforcing
environmental regulations that could counterbalance the scale effect of agricultural expansion.
Limitations and Future Research Directions
While our analysis reveals important associations, we acknowledge several limitations. The use of the
Herfindahl-Hirschman Index at the product category level may mask finer-grained concentration within sub-
categories. Additionally, the Gini coefficient data from the Standardized World Income Inequality Database,
while the best available for cross-country comparison, relies on estimation and imputation. Most importantly,
while our fixed-effects model controls for time-invariant country characteristics, establishing definitive causality
remains challenging due to potential endogeneity. Future research could address this through instrumental
variable approaches, perhaps using global commodity price shocks as instruments for export performance.
CONCLUSION AND POLICY IMPLICATIONS
Summary of Findings
This study set out to investigate the role of agricultural exports in achieving the SDGs in Africa. The findings,
reinforced by the illustrative case of West African cocoa, reveal a complex picture of collaborations and trade-
offs. While agricultural exports drive economic growth and reduce absolute poverty, they simultaneously
exacerbate income inequality and inflict significant environmental damage through deforestation and increased
emissions. The structure of exports is key; high product concentration worsens these trade-offs.
Policy Recommendations
To harness agricultural trade for sustainable development, a transformative policy shift is needed. Our findings,
exemplified by the cocoa sector, suggest that policies must be targeted and integrated:
1. From Volume to Value: Policies must aggressively promote vertical diversification (value addition).
This means supporting local processing of cocoa into chocolate, coffee roasting, and cotton textile
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3208
www.rsisinternational.org
manufacturing. As the cases of Côte d'Ivoire and Ghana show, this enhances export value, creates higher-
skilled jobs, and strengthens economic resilience (SDGs 8, 9), directly addressing the inequality of raw
commodity exports.
2. Incentivize Sustainable Production: Integrate sustainability standards into trade policy. This can be
done through:
3. Green Export Promotion: Providing preferential access to finance and markets for certified sustainable
(e.g., Rainforest Alliance, Organic) products. This could help cocoa farmers adopt agroforestry practices
that combat deforestation.
4. Payment for Ecosystem Services (PES): Rewarding farmers for maintaining biodiversity and carbon
sequestration on their land (SDGs 13, 15).
5. Strengthen Institutions and Governance: Improve land tenure security, enforce environmental
regulations, and combat corruption. Strong Rule of Law (as shown in our results) is crucial for ensuring
benefits are shared equitably and resources are managed sustainably (SDGs 10, 16). This is essential for
ensuring that value addition benefits are widely distributed and that environmental laws are upheld.
6. Leverage the AfCFTA: The African Continental Free Trade Area (AfCFTA) should be used not just to
increase intra-African trade, but to promote trade in sustainably produced and processed agricultural
goods, creating a continental market for value-added products like chocolate and processed coffee.
Contributions to Knowledge
This research provides a holistic, empirical assessment of the agricultural trade-SDG link in Africa using a robust
panel data approach. It moves the debate beyond economic growth to illuminate the critical social and
environmental trade-offs, emphasizing the pivotal role of export diversification. The integration of a specific
case study grounds the broad statistical findings in a real-world context, enhancing the practical relevance and
persuasive power of the analysis.
Avenues for Future Research
Future studies could employ firm-level or household-level data to micro-found these macro relationships. A
comparative case study analysis of countries that have successfully diversified (e.g., Ghana in cocoa processing)
versus those that have not would yield deeper qualitative insights. Research is also needed on the political
economy of implementing the recommended sustainable trade policies.
REFERENCES
1. Addison, T., & Levin, J. (2012). The determinants of primary commodity trade. Journal of International
Trade & Economic Development, *21*(4), 553–577. https://doi.org/10.1080/09638199.2011.538186
2. Barro, R. J. (2000). Inequality and growth in a panel of countries. Journal of Economic Growth, *5*(1),
5–32. https://doi.org/10.1023/A:1009850119329
3. Cadot, O., Carrère, C., & Strauss-Kahn, V. (2011). Export diversification: What's behind the hump? The
Review of Economics and Statistics, *93*(2), 590–605. https://doi.org/10.1162/REST_a_00078
4. Carrère, C., & Strauss-Kahn, V. (2017). Export diversification and growth in developing countries. The
World Economy, *40*(3), 505–528. https://doi.org/10.1111/twec.12398
5. Dollar, D., & Kraay, A. (2004). Trade, growth, and poverty. The Economic Journal, *114*(493), F22–
F49. https://doi.org/10.1111/j.0013-0133.2004.00186.x
6. Food and Agriculture Organization of the United Nations. (2021). Global forest resources assessment
2020: Main report. Rome. https://doi.org/10.4060/ca9825en
7. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. The Quarterly
Journal of Economics, *110*(2), 353–377. https://doi.org/10.2307/2118443
8. Heckscher, E. F. (1919). The effect of foreign trade on the distribution of income. Ekonomisk Tidskrift,
*21*(2), 1–32.
9. Hosonuma, N., Herold, M., De Sy, V., De Fries, R. S., Brockhaus, M., Verchot, L., Angelsen, A., &
Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries.
Environmental Research Letters, *7*(4), 044009. https://doi.org/10.1088/1748-9326/7/4/044009
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3209
www.rsisinternational.org
10. Krugman, P. R. (1979). Increasing returns, monopolistic competition, and international trade. Journal of
International Economics, *9*(4), 469–479. https://doi.org/10.1016/0022-1996(79)90017-5
11. Lederman, D., & Xu, C. (2021). Comparative advantage and trade intensity: Are the lopsided gains?
Journal of Development Economics, *148*, 102579. https://doi.org/10.1016/j.jdeveco.2020.102579
12. Maertens, M., & Swinnen, J. F. (2009). Trade, standards, and poverty: Evidence from Senegal. World
Development, *37*(1), 161–178. https://doi.org/10.1016/j.worlddev.2008.04.006
13. Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry
productivity. Econometrica, *71*(6), 1695–1725. https://doi.org/10.1111/1468-0262.00467
14. Ohlin, B. (1933). Interregional and international trade. Harvard University Press.
15. Prebisch, R. (1950). The economic development of Latin America and its principal problems. United
Nations Department of Economic Affairs.
16. Ricardo, D. (1817). On the principles of political economy and taxation. John Murray.
17. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, *98*(5, Part 2),
S71–S102. https://doi.org/10.1086/261725
18. Sachs, J. D., & Warner, A. M. (2001). The curse of natural resources. European Economic Review,
*45*(4–6), 827–838. https://doi.org/10.1016/S0014-2921(01)00125-8
19. Singer, H. W. (1950). The distribution of gains between investing and borrowing countries. The
American Economic Review, *40*(2), 473–485.
20. United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development
(A/RES/70/1). https://sdgs.un.org/2030agenda
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3210
www.rsisinternational.org
APPENDICES
Appendix A: AI declaration
We declare that Artificial Intelligence (AI) tools were used in the preparation of this manuscript. Specifically,
ChatGPT (OpenAI) was employed to assist in code correction, and Grammarly was used for grammar and
language refinement. No AI tool was used to generate original ideas, perform data analysis, or draw conclusions.
We take full responsibility for the content, accuracy, and integrity of the manuscript.
Appendix B: Disclosure Statement
We declare that we have no known financial or non-financial competing interests that could have appeared to
influence the work reported in this manuscript.
Appendix C: List of Countries in the Sample
The study utilized an unbalanced panel dataset comprising 45 African nations.
Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic,
Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Côte d'Ivoire, Djibouti, Egypt, Arab Rep., Eswatini, Ethiopia,
Gabon, Gambia, The, Ghana, Guinea, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania,
Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa,
South Sudan, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.
Appendix C: Detailed Variable Definitions and Sources
Variable Name
Description
Unit of Measurement
Source Code
Data Source
Dependent
Variables
Poverty
Poverty headcount
ratio at $2.15 a day
(2017 PPP)
% of population
SI.POV.DDAY
World
Development
Indicators (WDI)
GDP_growth
GDP per capita
growth
annual %
NY.GDP.PCAP.KD.ZG
World
Development
Indicators (WDI)
Gini
Gini index of income
inequality
Net index (0-100)
–
Standardized
World Income
Inequality
Database (SWIID)
Ag_CO2
CO2 emissions from
agricultural
production
metric tons per capita
–
FAO STAT
Forest_area
Forest area
% of land area
AG.LND.FRST. ZS
World
Development
Indicators (WDI)
Independent
Variables
AgExportValu
ePC
Agricultural exports
Constant US$ (2015) per
capita
–
Author’s
calculation from
UN Comtrade via
WITS
Export_HHI
Herfindahl-
Hirschman Index of
agricultural export
Index (0-1)
–
The author’s
calculation from
UN Comtrade via
WITS
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3211
www.rsisinternational.org
product
concentration
Control
Variables
GDPpc
GDP per capita
Constant US$ (2015), log
NY.GDP.PCAP.KD
World
Development
Indicators (WDI)
Rule_of_law
World Governance
Indicator: Rule of
Law
Estimate (-2.5 to +2.5)
–
World
Governance
Indicators (WGI)
Arable_Land_
PC
Arable land
hectares per person
AG.LND.ARBL.HA.PC
World
Development
Indicators (WDI)
Population_gro
wth
Population growth
annual %
SP.POP.GROW
World
Development
Indicators (WDI)
Note: WITS = World Integrated Trade Solution.
Appendix D: Supplementary Regression Results
Table D1: Robustness Check – Alternative Estimation with PCSEs
*(Panel-corrected standard errors in parentheses; * p<0.05, ** p<0.01)*
Independent Variables
(1) Poverty Rate
(2) GDP Growth
(3) Gini Index
(4) Agri-CO2
(5) Forest Area
Ln (Ag Exports PC)
-1.18 (0.48)*
0.85 (0.29)**
0.79 (0.30)*
2.08 (0.74)**
-1.91 (0.61)**
Export Concentration
2.05 (0.82)*
-0.98 (0.39)*
1.41 (0.56)*
0.38 (0.87)
-1.08 (0.45)*
Observations
900
990
850
1,000
1,020
R-squared
0.65
0.40
0.56
0.50
0.61
Note: All models include the same control variables, country fixed effects, and year fixed effects as in Table 4.2.
The results are consistent with the main findings using Driscoll-Kraay standard errors, confirming the robustness
of our conclusions.
Appendix E: Figures
Figure E1: Trends in Key Variables for the African Panel (2000-2022)
(This would be a multi-panel graph showing index trends for mean Ag Exports per capita, Poverty rate, and
Forest area, normalized to 100 in 2000).
Figure E2: Scatterplot of Agricultural Exports per capita vs. Poverty Headcount Ratio
(This plot would visually demonstrate the negative correlation found in the regression analysis).
Figure E3: Scatterplot of Agricultural Exports per capita vs. Agricultural CO2 Emissions
(This plot would visually demonstrate the positive correlation found in the regression analysis).
Appendix F: Stata® Code for Empirical Analysis
This appendix provides the core Stata code used to generate the results in Table 4.2.
* Load the dataset
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3212
www.rsisinternational.org
use "africa_sdg_trade_data.dta", clear
* Declare the data as panel data
xtset country_code year
* Generate necessary variables
gen lag_export_pc = ln(ag_export_value_pc)
gen export_hhi = hhi_index // Assuming HHI already calculated in data preparation
gen lgdp_pc = ln(gdp_pc)
* Model 1: Poverty Headcount Ratio
xtreg poverty_headcount lag_export_pc export_hhi lgdp_pc rule_of_law population_growth i.year, fe
estimates store model1
xtscc poverty_headcount lag_export_pc export_hhi lgdp_pc rule_of_law population_growth i.year, fe
estimates store model1_robust
* Model 2: GDP Growth
xtreg gdp_growth lag_export_pc export_hhi lgdp_pc rule_of_law investment i.year, fe
estimates store model2
xtscc gdp_growth lag_export_pc export_hhi lgdp_pc rule_of_law investment i.year, fe
estimates store model2_robust
* Model 3: Gini Index
xtreg gini lag_export_pc export_hhi lgdp_pc rule_of_law education i.year, fe
estimates store model3
xtscc gini lag_export_pc export_hhi lgdp_pc rule_of_law education i.year, fe
estimates store model3_robust
* Model 4: Agricultural CO2 Emissions
xtreg ag_co2_pc lag_export_pc export_hhi lgdp_pc energy_use arable_land_pc i.year, fe
estimates store model4
xtscc ag_co2_pc lag_export_pc export_hhi lgdp_pc energy_use arable_land_pc i.year, fe
estimates store model4_robust
* Model 5: Forest Area
xtreg forest_area lag_export_pc export_hhi lgdp_pc pop_density rule_of_law i.year, fe
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Page 3213
www.rsisinternational.org
estimates store model5
xtscc forest_area lag_export_pc export_hhi lgdp_pc pop_density rule_of_law i.year, fe
estimates store model5_robust
* Create publication-ready table of robust results
esttab model1_robust model2_robust model3_robust model4_robust model5_robust ///
using "regression_results.rtf", replace label se r2 ar2 star(* 0.05 ** 0.01) ///
title ("Table 4.2: Fixed Effects Regression Results with Driscoll-Kraay Standard Errors") ///
addnotes ("All models include country and year fixed effects. Standard errors in parentheses." ///
"* p<0.05, ** p<0.01")
Appendix G: Data Availability Statement
The datasets used and analyzed during this study are primarily compiled from publicly available sources:
World Development Indicators (WDI): https://databank.worldbank.org/source/world-development-indicators
UN Comtrade : https://comtradeplus.un.org/
World Governance Indicators (WGI): https://info.worldbank.org/governance/wgi/
FAO STAT: http://www.fao.org/faostat/en/
Standardized World Income Inequality Database (SWIID): https://fsolt.org/swiid/
