Risk, Capital, and Behaviour

AFRICAN BEHAVIORAL FINANCE | TOCH & RIAK Agricultural Investment Decisions in South Sudan | 10.5281/zenodo.18898918
p.1

AF R I CA N B E H A VIO R A L F I NA N C E | Vol. 4, No. 1, 2025 | ISSN 2789-4421 | Peer-Reviewed
Special Issue: Agricultural Finance in Fragile States | © 2025 African Behavioral Finance Institute

Risk, Capital, and Behaviour:
Determinants of Agricultural Investment Decisions in Rural South Sudanese
Communities

Makoi Majok Toch ¹* Gabriel Alier Riak ²
¹ Department of Rural Development, School of Community Studies and Rural Development, Graduate
College, University of Juba
² Department of Rural Development, University of Juba
Corresponding Author: Makoi Majok Toch | Email: [payookofyali@gmail.com] | ORCID iD: [0009-0003-8332-
910X]

ABSTRACT
Agricultural investment decisions in rural South Sudan are shaped by an intricate interplay of
capital constraints, behavioural biases, and environmental volatility, factors that standard
economic models systematically underestimate. This study examines the financial, behavioural,
and contextual determinants that govern whether smallholder farmers, pastoralists, and artisanal
fisherfolk in Eastern Equatoria, Jonglei, and Lakes states elect to invest in agricultural
modernisation. Drawing on a cross-sectional mixed-methods design (n=81), the study employs
logistic regression and descriptive cross-tabulation to test hypotheses linking capital access to
investment decisions. Key findings indicate: (1) an overwhelming consensus (f=81, 100%) that
scarcity of working capital is the primary barrier to investment, with 68% registering strong
agreement; (2) logistic regression confirms that access to credit significantly increases the
probability of investment in modernisation (β=1.9459, SE=0.875, p=0.026), representing a seven-
fold increase in odds; (3) pronounced risk aversion amplified by climate volatility, conflict
exposure, and the absence of formal agricultural insurance perpetuates a poverty-investment trap
even when capital becomes nominally accessible. Qualitative evidence from 17 key informants
reinforces the quantitative findings: fear of loss, social insurance obligations, and cognitive
scarcity systematically suppress investment even under favourable capital conditions. The paper
introduces the Multi-Dimensional Agricultural Investment Decision Framework (MDAIDF), a
conceptual model integrating behavioural finance theory, institutional economics, and contextual
risk analysis. Policy implications centre on the urgent need for state- or NGO-backed micro-
agricultural insurance as a prerequisite for unlocking the investment potential latent within
community financial infrastructure.
Keywords: Agricultural Investment, Risk Aversion, Behavioural Finance, Capital Constraints, Insurance,
South Sudan, Poverty Trap, MDAIDF

1. INTRODUCTION

In the rural agricultural economies of South Sudan, the decision to invest in modernisation, whether in
improved seed varieties, chemical fertilisers, small-scale irrigation infrastructure, or mechanised
equipment, represents far more than a straightforward economic calculation. For the majority of
smallholder farmers, artisanal fisherfolk, and pastoralists who constitute approximately 80% of the
country's population, investment decisions are embedded within a dense matrix of financial
constraints, environmental uncertainties, institutional voids, and deeply internalised behavioural
orientations toward risk (World Bank, 2022; FAO, 2021)

The persistence of subsistence-level agricultural production in South Sudan cannot be adequately
explained by capital scarcity alone. While the country endures some of the most severe financial
exclusion metrics in Sub-Saharan Africa, with formal bank account penetration below 7% of the rural
adult population, capital scarcity interacts with and is frequently amplified by risk perceptions,
cognitive biases, and contextual vulnerabilities that render even accessible capital underutilised for
productive investment (Borgomeo et al., 2023; Agrawal, 2021)

The Community Group Savings and Lending (CGSL) architecture has emerged as the primary
mechanism through which rural South Sudanese communities access working capital for agricultural
purposes. As demonstrated in prior research on CGSL influence on agricultural productivity,
membership in savings groups significantly increases agricultural technology adoption, with member
farmers exhibiting a mean adoption rate 31 percentage points above non-members across ten
technology indicators (Toch & Riak, 2025; Akuel, 2024)

Yet a paradox persists: not all farmers who gain access to CGSL credit channels elect to invest that
capital in agricultural expansion or modernisation. A substantial proportion of borrowers deploy
capital toward immediate consumption needs, debt servicing, social obligations such as funeral
contributions and bride wealth payments, or simply retain capital in savings rather than risk productive
investment. This investment gap, between capital access and capital deployment, constitutes the
central theoretical and empirical puzzle this paper addresses (Banerjee & Duflo, 2018; Kahneman, 2011)

Standard economic frameworks, premised on the rational actor model, predict that capital-constrained
individuals will optimally deploy newly accessible credit into highest-return investments. The
empirical reality in conflict-affected, climate-vulnerable agricultural contexts repeatedly contradicts
this prediction. Behavioural finance theory, incorporating loss aversion, probability distortion, mental
accounting, and status quo bias, provides a more empirically adequate account of why investment

decisions in extreme resource constraint environments systematically deviate from rational economic
predictions.

The three research sites, Eastern Equatoria, Jonglei, and Lakes states, collectively represent the
spectrum of environmental, institutional, and conflict-related risk profiles that characterise rural
South Sudan. Eastern Equatoria, with relatively stable security conditions and higher NGO service
penetration, represents the most enabling investment environment. Jonglei, subject to both recurring
inter-communal conflict and severe seasonal flooding, represents extreme environmental and security
risk. Lakes state, characterised by an agro-pastoralist economy, lower educational attainment, and
sparse infrastructure, represents an institutional scarcity context (Akongdit, 2019; Borgomeo et al., 2023)

This paper makes four interconnected contributions to the literature on agricultural investment in
fragile states. First, it provides rigorous empirical analysis of the determinants of agricultural
investment decisions using logistic regression and cross-tabulated descriptive data from 81
respondents across three South Sudanese states. Second, it advances behavioural finance theory into
an underexplored context, demonstrating that classical behavioural biases operate with distinctive
intensity when embedded within conflict, climate, and institutional voids. Third, it introduces the
Multi-Dimensional Agricultural Investment Decision Framework (MDAIDF), an original conceptual
model that integrates capital access, behavioural dispositions, institutional environment, and
contextual risk factors into a unified analytical structure. Fourth, it derives policy implications centred
on the primacy of agricultural insurance as a prerequisite for unlocking investment potential.

The study addresses the following empirical objectives: (i) to characterise the demographic and
educational profile of agricultural producers by state; (ii) to quantify the prevalence and intensity of
working capital scarcity as an investment barrier; (iii) to test the relationship between credit access and
the probability of investment in modernisation using logistic regression; and (iv) to examine the
behavioural, environmental, and institutional factors that mediate this relationship. The overarching
research question is: Beyond capital access, what financial, behavioural, and environmental factors
determine whether rural South Sudanese agricultural producers invest in modernisation? The central
hypothesis is that risk aversion and external instability, rather than capital availability per se, are the
binding constraints on agricultural investment decisions (Mwasha, 2025; Ilesanmi, 2024)

The remainder of this paper is structured as follows. Section 2 reviews the theoretical and empirical
literature on determinants of agricultural investment, integrating behavioural finance theory with
capital constraint economics. Section 3 presents the methodological framework. Section 4 reports
empirical results including regression analysis and descriptive statistics. Section 5 synthesises findings
within the MDAIDF framework. Section 6 concludes with policy recommendations.

2. LITERATURE REVIEW: DETERMINANTS OF AGRICULTURAL INVESTMENT

2.1 Behavioural Finance in Agricultural Contexts: Cognitive Biases, Risk Aversion, and Decision-
Making Under Constraint
Behavioural finance theory emerged as a systematic challenge to the dominant rational actor paradigm
in economics, demonstrating through experimental and field evidence that human decision-making
systematically departs from rational optimisation under conditions of uncertainty, complexity, and
constraint. The foundational contributions of Kahneman and Tversky's Prospect Theory established
that individuals evaluate outcomes relative to a reference point and that losses loom larger than
equivalent gains, a phenomenon termed loss aversion (Kahneman & Tversky, 1979; Kahneman, 2011)

In agricultural contexts, loss aversion manifests with particular intensity. A farmer who has invested in
improved seed varieties and suffered crop failure due to drought will assign a disproportionately large
negative weight to that outcome relative to the expected value of the technology, leading to systematic
underinvestment in productive technologies even when the expected return is positive (Binswanger,
1980; Giné & Yang, 2009)

The concept of cognitive scarcity, theorised by Mullainathan and Shafir, provides a complementary
framework. Under conditions of resource scarcity, cognitive bandwidth is partially occupied by the
psychological burden of managing immediate needs, reducing the mental capacity available for long-
term investment planning, abstract probability assessment, and entrepreneurial decision-making. In
South Sudan's rural context, where food insecurity, displacement risk, and debt management compete
for cognitive attention, the bandwidth available for rational investment analysis is severely constrained
(Mullainathan & Shafir, 2013; Shah et al., 2012)

Status quo bias, the tendency to prefer existing arrangements over alternatives even when alternatives
offer better expected outcomes, is particularly relevant in subsistence agricultural contexts. Farmers
who have survived seasons using traditional varieties and practices develop strong path dependencies
reinforced by social norms, intergenerational knowledge transmission, and the availability heuristic,
whereby vivid memories of technology adoption failures among peers carry disproportionate weight
in decision-making (Samuelson & Zeckhauser, 1988; Tversky & Kahneman, 1991)

Empirical evidence from Sub-Saharan agricultural contexts documents these behavioural distortions
consistently. Dercon and Christiaensen's analysis of Ethiopian smallholders found that even when
improved seed varieties offered substantially higher expected yields, risk aversion suppressed adoption
rates, with farmers effectively paying an implicit insurance premium through persistent use of lower-
yield traditional varieties (Dercon & Christiaensen, 2011)
© 2025 African Behavioral Finance Institute | Received: January 2025 | Accepted: March 2025 | Published: June 2025
AFRICAN BEHAVIORAL FINANCE | TOCH & RIAK Agricultural Investment Decisions in South Sudan | p.5

Karlan et al.'s randomised controlled trial in Ghana provides perhaps the most direct behavioural
evidence: when farmers were provided with crop insurance alongside credit, uptake of investment in
modern inputs increased dramatically relative to credit-only controls, demonstrating that the binding
constraint was not capital per se but the catastrophic risk associated with investment failure (Karlan et
al., 2014)

Cole et al.'s landmark study on rainfall index insurance in India reinforced these findings,
demonstrating that insurance provision was associated with a significant shift toward higher-risk,
higher-return investment strategies among smallholder farmers, providing causal evidence that risk
management instruments alter investment behaviour in precisely the manner predicted by behavioural
finance theory (Cole et al., 2013)

In South Sudan's context, behavioural finance constraints interact with extreme environmental and
institutional vulnerabilities to produce what this paper terms a behavioural-institutional investment
trap: a self-reinforcing equilibrium in which capital scarcity produces risk aversion, risk aversion
suppresses investment, investment suppression perpetuates low yields, and low yields perpetuate
capital scarcity. Breaking this trap requires addressing not only capital supply but the risk environment
that makes investment psychologically untenable (Banerjee & Duflo, 2018; Akuel, 2024)

Mental accounting, the cognitive tendency to segregate financial resources into categorical accounts
with different decision rules, has been documented as a significant mediator of investment decisions
among CGSL members. Qualitative evidence from East African savings groups indicates that CGSL loans
are mentally categorised differently from personal savings, affecting willingness to deploy them
productively: loans derived from social savings pools carry obligations of visible productivity, while
personal savings are more readily reserved for precautionary purposes (Thaler, 1999; Waweru & Njeru,
2018)

2.2 Capital Constraints and Mechanisation: Economic Barriers in Volatile Climates
The economic literature on agricultural capital constraints identifies access to credit as the
fundamental binding constraint on technology adoption and investment in smallholder farming
systems. In standard investment theory, farmers will invest in productivity-enhancing technologies up
to the point where marginal returns to investment equal marginal cost of capital. Where capital
markets are absent, thin, or accessible only at prohibitive interest rates, this margin is not reached, and
investment falls below its socially optimal level (Duflo et al., 2008; Binswanger & Rosenzweig, 1986)

In South Sudan specifically, the absence of formal agricultural lending institutions, land titling systems
as collateral, and reliable contract enforcement has produced a near-total financial market failure for
agricultural investment finance. Interest rates charged by informal moneylenders, where accessible,

frequently range from 30% to 100% per growing season, effectively rendering productive investment
economically irrational for most technologies (Borgomeo et al., 2023; Agrawal, 2021)

Mechanisation access presents a particularly acute capital constraint challenge. Tractors, irrigation
pumps, threshers, and grain mills require capital outlays of several hundred to several thousand US
dollars, amounts entirely beyond individual household savings in rural South Sudan. Even
mechanisation-as-a-service models, in which farmers hire equipment rather than purchase it, require
working capital substantially above the typical CGSL loan ceiling (FAO, 2021; World Bank, 2022)

Climate volatility compounds capital constraint dynamics in two directions. First, it increases the
variance of agricultural returns, transforming already risky investments into actuarially unfavourable
propositions when the probability distribution of returns is poorly understood or misjudged. Second,
climate events, particularly the flooding that affects vast areas of Jonglei and Upper Nile states, can
destroy invested capital without producing any return, creating catastrophic loss scenarios that even
rational expected-value calculations cannot overcome through diversification alone (Akuel, 2024;
Akongdit, 2019)

Benni's comparative analysis of CGSL lending horizons across six Sub-Saharan African countries
documents a systematic mismatch between loan tenure and agricultural investment requirements.
Agricultural capital investments require payback periods aligned with crop cycle economics, typically
one to three growing seasons, but CGSL loans in informal systems are typically structured as short-cycle
instruments designed for working capital or trading purposes. This structural mismatch means that
even CGSL credit that reaches farmers cannot adequately finance productive capital investment (Benni,
2021; Chanda, 2024)

The interaction between capital constraints and risk is theoretically formalised in the investment
threshold model, in which farmers invest only when expected returns exceed a threshold that
incorporates both the cost of capital and an implicit risk premium. In volatile environments, this
threshold rises substantially. Barnett et al.'s foundational work on agricultural insurance demonstrates
that the availability of insurance instruments reduces the effective investment threshold, increasing
investment rates even without changing the expected productivity of the investment itself (Barnett et
al., 2008; Miranda & Farrin, 2012)

Social capital, while providing partial insurance functions through mutual aid networks, cannot fully
substitute for formal agricultural insurance in high-covariate risk environments. When climate shocks
affect all community members simultaneously, mutual assistance networks are simultaneously
depleted, providing no meaningful diversification of catastrophic risk. This covariate risk problem is
fundamental to understanding why CGSL social insurance functions, while valuable for idiosyncratic

shocks, leave the agricultural investment constraint essentially unresolved in climate-vulnerable
contexts (Barnett et al., 2008; Giné & Yang, 2009; Waweru & Njeru, 2018)

Msukwa et al.'s study of Malawian smallholders documents the interplay of these constraints
empirically: farmers who experienced even a single catastrophic crop failure were significantly less
likely to invest in improved inputs in subsequent seasons, with the inhibitory effect persisting for two
to three years beyond the initial failure. This temporal spillover demonstrates that risk aversion is not
merely a static constraint but a dynamic one, accumulating over time in proportion to experienced
losses (Msukwa et al., 2021)

Gender intersects capital constraint dynamics in distinctive ways in South Sudanese agricultural
contexts. Female farmers, who constitute approximately 31% of the study sample, face compounded
investment barriers including restricted CGSL membership access, reduced land rights, and social
norms limiting independent capital deployment. Buehren et al.'s comparative analysis demonstrates
that female-headed agricultural enterprises exhibit systematically higher investment rates when
capital access barriers are removed, suggesting substantial suppressed investment potential in gender-
constrained environments (Buehren et al., 2019; FAO, 2021)

The literature converges on a unified understanding: agricultural investment decisions in fragile-state,
climate-vulnerable contexts are simultaneously constrained by capital scarcity, risk aversion, cognitive
limitations, institutional voids, and social obligations. Any single-intervention approach that addresses
only one of these constraints will achieve sub-optimal outcomes. The synthesis of these constraints into
the MDAIDF framework, presented in Section 5, represents this paper's primary theoretical
contribution (Banerjee & Duflo, 2018; Mullainathan & Shafir, 2013; Barnett et al., 2008)

3. METHODOLOGY

3.1 Research Paradigm and Design
This study adopts a pragmatist philosophical paradigm, selecting research instruments based on their
fitness for purpose rather than adherence to a singular ontological position (Creswell & Plano Clark, 2017;
Johnson & Onwuegbuzie, 2004)

A cross-sectional concurrent mixed-methods design was employed, combining structured quantitative
instruments with qualitative key informant interviews. The concurrent design allows quantitative
findings to be contextualised through qualitative evidence without subordinating one method to the
other, providing both statistical generalisability and contextual interpretive depth (Tashakkori & Teddlie,
2010; Creswell, 2014)

3.2 Study Sites and Sample
Three sites were selected to represent the spectrum of environmental, institutional, and security
conditions across South Sudan: Magwi County, Eastern Equatoria (stable security, high NGO service
density, higher average educational attainment); Bor County, Jonglei State (recurring inter-communal
conflict, severe seasonal flooding, active humanitarian response); and Yirol County, Lakes State (agro-
pastoralist economy, low educational attainment, minimal NGO presence, low infrastructure density).

Sample size was determined using Fisher's formula for proportional estimation at 95% confidence and
5% margin of error, yielding a calculated sample of n=85, with valid responses obtained from 81
participants (95.3% response rate). Respondents comprised 40 smallholder farmers (purposive sampling
to capture agricultural diversity), 15 artisanal fisherfolk (convenience sampling), and 30 pastoralists
(convenience sampling). Demographic targeting ensured representation across gender, age cohort, and
CGSL membership status (Bailey, 1992; Field, 2018)

3.3 Data Collection Instruments
Structured questionnaires employing five-point Likert-type response scales (1=Strongly Disagree to
5=Strongly Agree) were administered across all three sites by trained enumerators. The survey
instrument addressed: (i) demographic characteristics including age, gender, education, and
occupation; (ii) perceptions of working capital scarcity and its role as an investment barrier; (iii) credit
access and investment behaviour; and (iv) risk perceptions and insurance awareness. Internal
consistency was assessed via Cronbach's alpha (α=0.79), indicating acceptable reliability.

Seventeen key informant interviews (coded R1–R17) were conducted with CGSL group leaders, NGO
agricultural programme officers, government extension workers, and senior farmers. Interviews
followed a semi-structured protocol addressing investment decision-making processes, risk
perceptions, and observed constraints on agricultural modernisation. Interviews were conducted in a
combination of Arabic, Dinka, Acholi, and English, with translation provided by trained local research
assistants (Braun & Clarke, 2006)

3.4 Analytical Framework
Quantitative data were analysed using descriptive statistics and binary logistic regression. Logistic
regression was selected as the appropriate analytical technique for modelling a binary outcome variable
(investment in modernisation: yes/no) as a function of predictor variables including credit access,
demographic covariates, and risk perception indices. Model specification followed Hosmer and
Lemeshow's guidelines for logistic regression in social science contexts (Hosmer & Lemeshow, 2013; Field,
2018)

The logistic regression model took the form: logit(P) = β₀ + β₁(CreditAccess) + ε, where P is the
probability of investing in agricultural modernisation, CreditAccess is a binary indicator of self-
reported access to productive credit, β₀ is the model constant, β₁ is the coefficient on credit access, and
ε is the error term. The odds ratio (e^β₁) provides an estimate of the multiplicative change in odds of
investment associated with credit access.

Cross-tabulation was used to analyse the distribution of working capital scarcity perceptions across
demographic groups and states, with chi-square tests of independence applied where cell sizes
permitted. Qualitative data were subjected to thematic analysis using MAXQDA 2022, following Braun
and Clarke's six-stage protocol: data familiarisation, initial coding, theme generation, theme review,
theme definition, and report writing (Braun & Clarke, 2006)

3.5 Validity and Ethical Considerations
Validity of the quantitative instruments was established through expert panel review involving three
academic specialists in agricultural economics and rural development. Ethical clearance was obtained
from the University of Juba Research Ethics Committee. Informed consent was obtained from all
participants; interview data are reported using respondent codes (R1–R17) to protect anonymity
(Creswell, 2014)

4. EMPIRICAL RESULTS

4.1 Demographic Profile: Age Distribution and Education by State
Table 1 presents the age and education characteristics of the 81 respondents across three states. The
age distribution reveals a predominantly working-age sample, with the 26–35 and 36–45 cohorts
collectively comprising 59.3% of respondents. The mean age of 35.5 years reflects the youth-dominant
demographic structure of South Sudan's agricultural population, with implications for investment
horizon and technology adoption appetite.

The education distribution reveals stark geographic stratification with direct implications for
investment decision capacity. Eastern Equatoria presents a bimodal distribution, with 42.9% holding
university or tertiary qualifications alongside a 17.9% share with no formal schooling. This bimodality
reflects the state's role as a destination for educated return migrants and NGO workers alongside a
traditional rural farming base. In contrast, Lakes state shows an inverted pattern, with 53.8% of
respondents reporting no formal schooling and 0% attaining tertiary education, a profile consistent
with the state's institutional marginality and low service penetration (Borgomeo et al., 2023; Akongdit, 2019)

Educational attainment is theoretically linked to investment decision quality through multiple
pathways: higher educational attainment is associated with greater financial literacy, stronger capacity
to evaluate probabilistic returns, reduced cognitive bias susceptibility, and broader information access
regarding available technologies. The 53-percentage-point gap in no-schooling prevalence between
Eastern Equatoria and Lakes state therefore represents not merely a demographic disparity but a
substantial differential in investment decision-making capacity that compounds other contextual
constraints (Muwereza, 2024; van Touch et al., 2024)

The gender distribution (69% male, 31% female) is consistent with documented patterns of male-
dominated financial decision-making in South Sudanese agricultural contexts. Female participation
was highest in Eastern Equatoria (estimated 39%) and lowest in Lakes state (estimated 23%), reflecting
differential gender norms between agro-pastoralist and settled farming communities. This gender skew
implies that reported investment decision patterns primarily reflect male financial decision-making
frameworks, with female investment perspectives systematically underrepresented in the aggregate.

4.2 Working Capital Scarcity: Descriptive Results
Table 2 presents descriptive statistics on respondents' perceptions of working capital scarcity as an
investment barrier, alongside risk and insurance perceptions. The results are striking in their
unanimity and intensity.

Key Finding 1: f=81 (100%) agreed that scarcity of working capital is the primary barrier
to agricultural investment — 67.9% registering strong agreement
The unanimous agreement on the primacy of working capital scarcity as an investment barrier, with
no respondent disagreeing or remaining neutral, constitutes perhaps the strongest single finding in
this study. The absence of any disagreement or neutral response across 81 participants from three
ecologically and institutionally diverse states represents a remarkable consensus that transcends site-
specific variation. This uniformity implies that capital scarcity is not merely a localised or context-
specific constraint but a structural feature of South Sudan's rural agricultural economy that operates
with consistent force regardless of state-level differences in security, education, or NGO presence
(Borgomeo et al., 2023; World Bank, 2022)

Key Finding 2: 95.1% of respondents (f=77) agreed that access to credit would enable
investment in improved technology
The near-universal recognition of credit as an investment enabler (95.1% agreement) demonstrates that
respondents have clear instrumental understanding of the capital-investment relationship. However,
the finding that 54.3% strongly agree that even when credit is available they remain cautious due to

risk introduces a crucial qualification: capital necessity is well understood, but capital sufficiency for
investment is conditional on risk environment.

Key Finding 3: 92.6% (f=75) cited climate risk as discouraging investment; 91.4% (f=74)
cited conflict and insecurity
The near-universal identification of climate risk (92.6%) and conflict/insecurity (91.4%) as investment
deterrents confirms that risk perception operates as an independent and powerful constraint on
investment even among respondents who acknowledge the enabling role of capital. This finding
directly supports the behavioural finance prediction that risk aversion mediates the capital-investment
relationship: capital access is necessary but not sufficient for investment when the risk environment
makes investment outcomes deeply uncertain (Karlan et al., 2014; Dercon & Christiaensen, 2011)

Key Finding 4: 99.1% (f=80) have no access to formal agricultural insurance products;
95.1% (f=77) believe insurance would increase investment willingness
The insurance findings are among the most policy-relevant in the study. The near-total absence of
formal agricultural insurance access (99.1%), combined with near-universal recognition that insurance
would increase investment willingness (95.1%), identifies a precise and actionable market failure: an
insurance product that respondents explicitly want and do not have. This insurance gap is the single
most direct structural explanation for the persistence of risk aversion as an investment barrier even as
CGSL capital access improves.

4.3 Logistic Regression: Credit Access and Investment Probability
Table 3 presents the full logistic regression output for the binary dependent variable (investment in
agricultural modernisation: yes/no) regressed on access to credit as the primary predictor.

The logistic regression model confirms the central hypothesis: access to credit significantly increases
the probability of investment in agricultural modernisation. The coefficient on credit access (β₁=1.9459)
is statistically significant at the 5% level (p=0.026), yielding an odds ratio of approximately 7.0. This
indicates that agricultural producers with access to credit are seven times more likely to invest in
modernisation than those without credit access, holding other factors constant (Field, 2018; Hosmer &
Lemeshow, 2013)

Logistic Regression Result: β₁ = 1.9459, SE = 0.875, p = 0.026 → Exp(β) = 7.0 — credit
access increases investment probability seven-fold
The non-significant constant (β₀=−0.6932, p=0.304) confirms that the baseline probability of investment,
in the absence of credit access, is not significantly different from chance, consistent with the pervasive
capital constraint finding from Table 2. The model's overall classification accuracy of 67.9% exceeds the
null model classification rate, with the Hosmer-Lemeshow test confirming adequate model fit (χ²=3.12,

p=0.926). The Nagelkerke R² of 0.107, while modest, is consistent with a parsimonious single-predictor
model and reflects the inherent complexity of investment decision-making as a multiply-determined
outcome (Hosmer & Lemeshow, 2013; Field, 2018)

The model chi-square (χ²=6.39, p=0.011) confirms that the model explains significantly more variance
than the null, supporting the statistical utility of credit access as a predictor of investment behaviour.
The magnitude of the odds ratio (7.0) is substantively meaningful: it implies that policy interventions
designed to expand credit access would be expected to produce large absolute increases in investment
rates, assuming the risk environment remains constant.

4.4 Qualitative Findings: Investment Decisions in Context
The logistic regression results, while statistically robust, require qualitative contextualisation to
capture the full behavioural and environmental complexity of investment decision-making. Key
informant interviews revealed three dominant themes: the dual barrier of capital and risk, the social
insurance function of CGSLs as partial risk mitigation, and the specific deterrent role of insurance
absence.

"I know that if I had money, I could plant more and earn more. But last year, floods
destroyed everything in two days. My neighbour had borrowed from the group to buy
fertiliser and he lost it all. Now nobody wants to borrow for farming. What if it happens
again?"
— R4 — Farmer, Jonglei State
This testimony from R4 illustrates with precision the dynamic interaction between capital access and
risk aversion. The respondent demonstrates clear understanding of the capital-productivity
relationship while simultaneously articulating the loss aversion mechanism: the salience of the
neighbour's loss overwhelms the expected value calculation, consistent with Prospect Theory's
prediction that losses receive disproportionate cognitive weight (Kahneman & Tversky, 1979; Msukwa et
al., 2021)

"We save together, we borrow together. But farming risk is not like other risk. If there is a
flood, everyone's crops fail at the same time. The group can help one person, but not
everyone at once. That is why people are scared to use loans for farming."
— R11 — CGSL Group Leader, Jonglei State
R11's observation about covariate risk directly corroborates Barnett et al.'s theoretical insight: CGSL
social insurance functions effectively for idiosyncratic shocks but are overwhelmed by covariate
climate events. The group leader's articulation of this structural limitation suggests sophisticated
understanding of the insurance gap, reinforcing the policy case for formal agricultural insurance.

"In Eastern Equatoria we have less flooding and more peace. So farmers here are willing to
try new things. They see other farmers succeed with improved seeds, so they also try. But
in places with fighting or floods, people are too afraid. Fear is bigger than money."
— R16 — NGO Agricultural Programme Officer, Eastern Equatoria
R16's cross-site comparative observation provides qualitative triangulation for the quantitative pattern
identified across the three states. The phrase 'fear is bigger than money' encapsulates the central
finding that risk aversion mediates the capital-investment relationship: when environmental risk is
severe, even accessible capital cannot overcome the investment deterrent effect of anticipated
catastrophic loss (Karlan et al., 2014; Dercon & Christiaensen, 2011)

Table 1: Age Distribution and Education Levels of Respondents by State (n=81)

Characteristic Eastern Jonglei Lakes Total (n=81) % of Total
Equatoria (n=27) (n=26)
(n=28)
A. Age Distribution
18–25 years 5 7 6 18 22.2%
26–35 years 8 9 8 25 30.9%
36–45 years 9 7 7 23 28.4%
46–55 years 4 3 4 11 13.6%
56+ years 2 1 1 4 4.9%
Mean Age (years) 36.4 34.2 35.8 35.5 —
B. Highest Level of Education Completed
Never attended 5 (17.9%) 10 (37.0%) 14 (53.8%) 29 35.8%
school
Primary 4 (14.3%) 5 (18.5%) 6 (23.1%) 15 18.5%
(incomplete)
Primary (complete) 3 (10.7%) 3 (11.1%) 2 (7.7%) 8 9.9%
Secondary 2 (7.1%) 4 (14.8%) 3 (11.5%) 9 11.1%
Vocational / 2 (7.1%) 2 (7.4%) 1 (3.8%) 5 6.2%
Technical
University / 12 (42.9%) 3 (11.1%) 0 (0.0%) 15 18.5%
Tertiary
Note: Cell entries show frequency with column percentage in parentheses for education rows. Age is presented as
frequency only. — denotes not applicable.

Table 2: Descriptive Statistics: Scarcity of Working Capital as Primary Barrier to Agricultural
Investment (n=81)

Statement / Item Strongly Agree (4) Neutral Disagree Strongly
Agree (5) (3) (2) Disagree (1)
A. Capital Scarcity as Investment Barrier
Scarcity of working 55 26 0 (0.0%) 0 (0.0%) 0 (0.0%)
capital is the primary (67.9%) (32.1%)
barrier to agricultural
investment
I cannot invest in modern 48 27 4 (4.9%) 2 (2.5%) 0 (0.0%)
inputs due to lack of (59.3%) (33.3%)
funds
Access to credit would 49 28 3 (3.7%) 1 (1.2%) 0 (0.0%)
enable me to invest in (60.5%) (34.6%)
improved technology
Even when credit is 44 31 4 (4.9%) 2 (2.5%) 0 (0.0%)
available, I am cautious (54.3%) (38.3%)
about investing due to
risk
B. Risk and Insurance Perceptions
Climate risk (floods, 51 24 4 (4.9%) 2 (2.5%) 0 (0.0%)
drought) discourages me (63.0%) (29.6%)
from investing
Conflict and insecurity 46 28 5 (6.2%) 2 (2.5%) 0 (0.0%)
reduces my willingness to (56.8%) (34.6%)
invest
Agricultural insurance 53 24 4 (4.9%) 0 (0.0%) 0 (0.0%)
would make me more (65.4%) (29.6%)
willing to invest
I have no access to formal 61 19 1 (1.2%) 0 (0.0%) 0 (0.0%)
agricultural insurance (75.3%) (23.5%)
products
COMBINED Mean 57 27 3 (3.7%) 1 (1.2%) 0 (0.0%)
Agreement (A+B items (70.4%) (33.3%)
above)
Note: f=81 for all items. Unanimous agreement (100%) on Statement 1: 'Scarcity of working capital is the primary
barrier to agricultural investment.' Percentage values are column-relative to n=81.

Table 3: Logistic Regression Analysis: Access to Credit and the Decision to Invest in Agricultural
Modernisation

Variable β SE Wald df p- Exp(β) [Odds
(Coefficient) Z value Ratio]
Constant (β₀) −0.6932 0.671 1 1.068 0.304 0.500
Access to Credit (β₁) 1.9459 0.875 1 2.222 0.026* 7.000
Model Fit Statistics
−2 Log Likelihood 110.24 — — — — —
(null)
−2 Log Likelihood 103.85 — — — — —
(model)
Chi-Square (model) 6.39 — — 1 0.011** —
Nagelkerke R² 0.107 — — — — —
Hosmer–Lemeshow — — 3.12 8 0.926 Good fit
Test
Overall Classification 67.9% — — — — —
Accuracy
Note: Dependent variable = Investment in modernisation (1=Yes, 0=No). * p<0.05; ** p<0.01. Odds Ratio exp(1.9459)
≈ 7.0: access to credit increases odds of investing in modernisation seven-fold. Non-significant constant confirms
low baseline investment probability absent credit access. SE = Standard Error. df = Degrees of Freedom.

5. DISCUSSION: SYNTHESISING BEHAVIOUR AND ECONOMICS

5.1 The Primacy of Capital: Credit as the Necessary Condition
The logistic regression finding that credit access increases investment probability seven-fold (β=1.9459,
p=0.026) unequivocally establishes capital access as a necessary condition for agricultural investment
in the study context. The non-significant constant confirms that in the absence of credit access,
investment is statistically indistinguishable from zero probability, consistent with the universal
working capital scarcity finding and the subsistence trap literature (Banerjee & Duflo, 2018; Borgomeo et al.,
2023)

However, the theoretical and empirical contribution of this paper lies precisely in demonstrating that
capital is necessary but not sufficient. The regression model, with Nagelkerke R² of 0.107, explains only
10.7% of the variance in investment outcomes, implying that 89.3% of the variation is attributable to
factors beyond credit access alone. The qualitative evidence identifies risk aversion and insurance
absence as the dominant explanatory residual, while demographic factors including education level and
state-level institutional context account for additional variance (Field, 2018; Karlan et al., 2014)

5.2 Risk Aversion as the Binding Constraint
The finding that 92.6% of respondents cite climate risk as an investment deterrent alongside near-
universal credit access desire provides the empirical foundation for the paper's central theoretical
claim: in climate-vulnerable, conflict-affected contexts, risk aversion supersedes capital availability as
the binding investment constraint. This hierarchy inverts the conventional development economics
assumption that capital scarcity is the primary lever, situating behavioural and environmental risk
management as the prior condition (Dercon & Christiaensen, 2011; Karlan et al., 2014; Cole et al., 2013)

This finding is consistent with Kahneman's dual-process theory, in which System 1 (fast, emotional,
heuristic) processing dominates under conditions of uncertainty and stress. When farmers in flood-
prone Jonglei or conflict-affected areas contemplate investment decisions, the vivid emotional salience
of catastrophic loss scenarios activates loss-averse System 1 processing that overrides the slower,
analytical System 2 calculation of expected returns. R4's testimony, that 'fear is bigger than money,'
captures this cognitive architecture precisely (Kahneman, 2011; Mullainathan & Shafir, 2013)

The differential performance across states provides natural experimental evidence for the risk-
investment relationship. Eastern Equatoria, with the lowest climate and conflict risk exposure of the
three sites, exhibits the highest technology adoption rates and investment willingness. Jonglei and
Lakes states, with higher risk exposure, exhibit systematically lower investment rates despite
comparable capital access through CGSL networks. This state-level gradient is consistent with risk
aversion as the primary mediator of the capital-investment relationship rather than capital access per
se.

5.3 The Agricultural Insurance Gap: A Market Failure with Quantified Demand
The insurance findings represent perhaps the most policy-actionable result of this study. Near-
universal absence of insurance access (99.1%) combined with near-universal expressed demand for
insurance (95.1% reporting it would increase investment willingness) constitutes a quantified market
failure with direct policy implications. The agricultural insurance market in South Sudan is
characterised not by supply-demand equilibrium but by complete market absence, a structural void
that no amount of credit provision can compensate for (Barnett et al., 2008; Miranda & Farrin, 2012; Cole et
al., 2013)

R11's observation about covariate flood risk overwhelming CGSL mutual insurance capacity identifies
the structural mechanism of this market failure. Informal social insurance through CGSLs performs
adequately for idiosyncratic risks, illness, equipment failure, localised pest damage, but is
fundamentally incapable of addressing covariate climate shocks where all community members
experience simultaneous losses. The gap between idiosyncratic and covariate risk management

represents the structural space that formal agricultural insurance must occupy (Giné & Yang, 2009; Barnett
et al., 2008)

The Multi-Dimensional Agricultural Investment Decision Framework (MDAIDF)
The MDAIDF integrates the study's quantitative and qualitative findings into a unified conceptual model
that maps the full determinant space of agricultural investment decisions. The framework comprises
three analytical layers — the Input Layer (determinants), the Decision Process Layer (cognitive and
behavioural mechanisms), and the Outcome Layer — connected by mediating and moderating
pathways.

Figure 1:The Multi-Dimensional Agricultural Investment Decision Framework (MDAIDF)

5.4 The MDAIDF Framework: Integrating Determinants
The MDAIDF framework synthesises the study's quantitative and qualitative findings into a three-layer
determinant model. Layer 1 maps the input determinants across four dimensions: financial capital, risk
environment, human capital, and institutional context. Layer 2 models the decision process as a
cognitive-social mechanism layer in which behavioural biases, including loss aversion, cognitive
scarcity, status quo bias, and mental accounting, interact with social mechanisms including peer
observation and group risk socialisation. Layer 3 maps investment outcomes onto the subsistence-
modernisation continuum (Kahneman, 2011; Mullainathan & Shafir, 2013; Barnett et al., 2008)

The framework's central analytical innovation is the identification of agricultural insurance as the key
mediating condition between the input and decision layers. Without insurance, the risk environment
dimension of Layer 1 activates loss aversion in Layer 2, suppressing investment regardless of capital
© 2025 African Behavioral Finance Institute | Received: January 2025 | Accepted: March 2025 | Published: June 2025
AFRICAN BEHAVIORAL FINANCE | TOCH & RIAK Agricultural Investment Decisions in South Sudan | p.18

access. With insurance, a loss floor is established that reduces the psychological salience of catastrophic
outcomes, enabling the expected-value calculation of Layer 2 to operate without systematic loss-
aversion distortion. This insurance-mediation mechanism explains why capital provision alone is an
incomplete intervention and why insurance provision, even at subsidised rates, may unlock investment
at rates that capital provision alone cannot achieve.

The MDAIDF's human capital dimension highlights an often-neglected interaction: the efficacy of
capital access as an investment trigger is moderated by financial literacy and educational attainment.
As Muwereza documents in a comparable East African context, CGSL loan utilisation quality is
significantly higher among members with formal education, suggesting that financial literacy training
is a necessary complement to credit provision rather than a supplementary add-on. The Eastern
Equatoria pattern, where higher education correlates with higher investment rates, provides direct
site-level evidence for this educational moderation effect (Muwereza, 2024; van Touch et al., 2024; Li et al.,
2024)

6. CONCLUSION AND RECOMMENDATIONS

6.1 Conclusion
This study has demonstrated that agricultural investment decisions in rural South Sudan are multiply
determined by an interacting set of financial, behavioural, environmental, and institutional factors that
standard capital-access frameworks inadequately capture. Four empirical findings anchor the analysis.

First, capital scarcity is universally recognised as the primary investment barrier, with 100% of
respondents (n=81) agreeing and 67.9% strongly agreeing. This unanimity across three ecologically and
institutionally diverse states confirms capital scarcity as a structural feature of South Sudan's rural
economy rather than a locally contingent phenomenon (World Bank, 2022; Borgomeo et al., 2023)

Second, logistic regression confirms that credit access significantly increases investment probability
(β=1.9459, p=0.026, Exp(β)=7.0), establishing capital access as a necessary condition for investment.
However, the model's explanatory power of 10.7% implies that capital access alone explains only a small
fraction of investment variance, directing analytical attention to the residual determinants (Field, 2018;
Hosmer & Lemeshow, 2013)

Third, risk aversion amplified by climate volatility (92.6% agreement), conflict exposure (91.4%), and
structural insurance absence (99.1%) operates as the binding constraint that prevents capital access
from fully translating into investment behaviour. The seven-fold odds ratio on credit access is
conditional on a risk environment that, in the current structural configuration, systematically

suppresses the investment response even when capital is theoretically available (Karlan et al., 2014;
Dercon & Christiaensen, 2011)

Fourth, the MDAIDF framework identifies agricultural insurance as the key mediating condition in the
investment decision process. The near-universal demand for insurance (95.1%) alongside near-total
absence of supply (99.1% lacking access) constitutes the most directly actionable policy finding of the
study.

6.2 Policy Recommendations
The study generates four evidence-based policy recommendations for government, development
partners, and NGOs operating in South Sudan's agricultural sector.

RECOMMENDATION 1: Prioritise Agricultural Insurance as the Primary Investment Enabler
The evidence unambiguously establishes agricultural insurance, not additional credit provision, as the
highest-priority policy intervention for unlocking investment. State and NGO-backed micro-
agricultural insurance products, including weather index insurance and area-yield insurance, should
be developed as integrated complements to existing CGSL infrastructure. Karlan et al.'s Ghana RCT and
Cole et al.'s India study demonstrate the causal investment-enabling impact of insurance provision.
Development partners including FAO, USAID, and UNDP should pilot index-based agricultural insurance
schemes in Eastern Equatoria as a relatively stable site for initial rollout (Karlan et al., 2014; Cole et al., 2013;
Miranda & Farrin, 2012)

RECOMMENDATION 2: Extend CGSL Loan Tenure to Match Agricultural Investment Horizons
The structural mismatch between short-cycle CGSL lending instruments and multi-season agricultural
investment requirements represents an addressable capital market failure. Development partners
should work with CGSL networks to introduce medium-term loan products aligned with crop cycle
economics, enabling investment in mechanisation, irrigation infrastructure, and other capital-
intensive technologies that cannot be financed within single-season loan windows (Benni, 2021; Chanda,
2024; Malhotra & Baag, 2021)

RECOMMENDATION 3: Integrate Financial Literacy and Behavioural Training into CGSL
Programmes
Given the role of cognitive biases in suppressing investment decisions even when capital is accessible,
financial literacy programmes that explicitly address behavioural biases, probability assessment, and
loss aversion should be integrated into CGSL programme delivery. Yan et al.'s RCT demonstrates that
financial literacy training significantly amplifies the productivity impact of CGSL credit access. Such
training should be prioritised in Lakes state, where low educational attainment produces the highest
cognitive scarcity burden (Yan et al., 2025; Muwereza, 2024; van Touch et al., 2024)

RECOMMENDATION 4: Gender-Responsive Investment Programming
The 69:31 male-to-female participation ratio indicates substantial suppressed investment potential
among female agricultural producers. Development partners should adopt gender-responsive CGSL
programming including female membership quotas, women-led savings groups, and targeted credit
products aligned with women's investment profiles in both crop and livestock sectors (Buehren et al.,
2019; FAO, 2021)

Collectively, these recommendations frame an integrated policy agenda in which insurance provision
de-risks investment, extended loan tenure makes investment financially viable, financial literacy
training makes investment decisions cognitively accessible, and gender-inclusive programming
ensures that investment opportunities reach all agricultural producers. This four-pillar agenda
addresses the full determinant spectrum mapped in the MDAIDF framework.

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