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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025
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Crop Productivity and Multidimensional Poverty Reduction in
Ghana
Boahen Atta Oppong
1*
, Edward Ebo Onumah
2
, Ramatu Mahama Al-Hassan
3
, Akwasi Mensah-Bonsu
4
1
Department of Agricultural and Resource Economics, University of Energy and Natural Resources,
Sunyani-Ghana
2,3,4
Department of Agricultural Economics & Agribusiness, University of Ghana, Legon
*Corresponding Author
ABSTRACT
This study estimated crop productivity and multidimensional poverty based on health, education, and living
standards indicators in rural and urban areas and the agroecological zones of Ghana. The multidimensional
poverty index was regressed on crop productivity with instrumental variable fixed and random effects models
and pseudo panel data from the Ghana Living Standards Survey Rounds 5 and 6. The study found
multidimensional poverty headcount ratio reduced 46% in 2005/06 to 34% in 2012/13 in Ghana. The
multidimensional poverty headcount ratio reduced 49% in 2005/06 to 38% in 2012/13 rural and reduced 25%
2005/06 to 16% 2012/13 urban. The multidimensional poverty headcount ratio reduced 32%, 29%, and 60% in
2005/06 to 28%, 21%, and 45% in 2012/13 at the coastal, forest, and savannah agroecological zones,
respectively. The study further showed that 1% growth crop productivity reduced multidimensional poverty by
0.17% disaggregated into 0.09% and 0.28% coastal and savannah agroecological zones respectively but
marginally reduced at the forest zone.
Keywords Crop Productivity, Multidimensional Poverty, Capabilities, Living Standards, Instrumental
Variable Regression
JEL Classification Q18 Q5 Q210 D6 C33
INTRODUCTION
Crop productivity growth depends on the adoption of technologies such as irrigation, fertilizers, and improved
seeds. Bhutto and Bazmi (2007) observed that productivity growth depends on improved farm production
technologies, market access, and a factor shift from the agriculture to non-agricultural sectors. Poverty is
defined as a lack of basic material needs such as food, clothing, and shelter for an individual or family
(McConnell et al. 2003). Income or consumption unidimensional poverty measures, such as the World Bank’s
dollar-a-day headcount ratio have been the most prevalent measures of poverty. Households that are
consumption-poor might suffer malnutrition, are ill-educated, lack assets, and lack social amenities. Monetary
poverty indicators may provide insufficient policy guidance on deprivation in other dimensions.
Multidimensional measures of poverty or well-being identify multiple attributes or deprivations experienced
by individuals in different dimensions which may typically include some measure of income or expenditure;
they do not rely solely on the economic circumstances. Income is only one dimension of poverty; other
indicators of welfare may better show the relative well-being of women- and female-headed households
(Rogan, 2016).
Multidimensional measures indicating achievement below certain minimum levels reflect the complexity of
well-being and poverty in that they convey the extent to which a person is poor in several distinct and
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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independently important dimensions (Foster et al. 2010). Sen’s definition of capabilities potentially leads to a
long list of dimensions as functionings the lack of which defines poverty (Ravallion 2011). Sen’s capabilities
perspective informed the use of the Global Multidimensional Poverty Index based on three dimensions: health,
education, and living standards (Alkire and Santos 2010). The pattern of multidimensional poverty among crop
farmers show a decline or upward in poverty according to rural and urban disparities and agroecological zones
in Ghana.
MATERIALS AND METHODS
Estimation of Multidimensional Poverty Index
The MPI is based on the method of Alkire and Foster (2009) and uses principles of Foster et al. (1984) who
introduced a new class of poverty measures with axiomatic properties of additive decomposability and
subgroup consistency enabling coherent evaluation of poverty across population subgroups. The asset
indicators of multidimensional analysis capture the long-run household economic status and could be superior
predictors of welfare rather than consumption expenditure (Sahn & Stifel, 2003). The asset index is not
flawless because of the relatively slow change in asset holdings and it is less useful for measuring short-term
changes in household economic status.
Health, education, and living standards dimensions were assigned weight of 3.33 (10/3). Health dimension
(3.33) consists of malnutrition and wastage indicators which were assigned weight of 1.67 (3.33/2)
respectively for deprived households. The education dimension (3.33) indicators include: household member
has completed primary education and children between 6 and 12 years are not attending school were weighted
1.67 (3.33/2) for deprived households. Living standards dimension (3.33) has six indicators: lack of electricity,
lack of portable water, lack of toilet facilities, lack of clean floor material, use of dirty cooking fuel and lack
of durable assets were assigned weights of 0.56 (3.33/6) respectively for deprived households. Crop farming
household deprivation scores were counted and households with deprivation scores greater than a cut-off of 3
(30% of the indicators) become multidimensionally poor and are denoted by. The number of poor
households divided by the total farm households of gives the proportion of households
multidimensionally poor ) as shown in equation (1).
The sum of deprivation weights per total number of indicators (d) and total number of poor persons (q) is the
extent of deprivation given by in equation (2).
where c is the sum of weighted deprivations of the poor experience and d is the total number of indicators
which is 10. The product of the multidimensional poverty incidence and the extent of deprivation is the
Multidimensional Poverty Index (MPI) (Grewal et al. 2012).
(3)
MPI is the proportion of the multidimensionally poor adjusted by the intensity of deprivation. The intensity of
deprivation adjusts the multidimensional poverty headcount ratio by increasing or decreasing the deprivation.
Data limitations constrain the dimensions, indicators, and unit of analysis for MPI as well as other
methodological decisions. The decisions on the MPI parameters of deprivation cut-offs, weights, and poverty
cut-off are based on normative arguments (Alkire & Santos 2010).
Effect of Crop Productivity on Multidimensional Poverty Index
Crop productivity (CROPPROD),
, was measured as crop income per hectare of land. Crop productivity was
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regressed on the instruments of cost of chemical inputs
(fertilizer, herbicides, and insecticides) and the
cost of intermediate inputs (
) (fuel, transportation, repairs, and maintenance) as a first-stage regression
(equation 4). Multidimensional poverty deprivation which is the dependent variable was regressed on
the estimated crop productivity (CROPPROD), household size (HHS), livestock income (LIVY), and
remittance income (REMY) as a second-stage regression (equation 5). The first and second stages of the
instrumental variable regression model in equations (4) and (5) were estimated simultaneously using either a
fixed effects or random effects estimator. The fixed-effects model assumes that independent variables have
individual unobserved heterogeneity that causes endogeneity which is estimated using the fixed-effects model
for unbiased estimates. The random-effects model accommodates the correlation between the independent
variables and error term which makes the estimates both unbiased and consistent (Equation 6).
The study employed the Ghana Living Standards Surveys 2005/06 and 2012/13. The study grouped the 2910
and 8355 farm households by age range of 15-20, 21-25, 26-30, 31-35, 36-40 etc., gender being male or female
and agro ecological zones of coastal, forest, and savannah zones to constitute pseudo-panel data. Deaton
(1985) suggests creating cohorts, based on some pre-determined characteristics that are time invariant, can
substitute for panel data and have cohort means which generate unbiased and efficient estimates (Guillerm
2017).
RESULTS AND DISCUSSION
Multidimensional Poverty: Health, Education, and Living Standards Indicators
The study found that approximately 31% of farm households were malnourished in 2005/06 which decreased
to 17% in 2012/13. The African region accounted for 39.4% of stunted children, which is a form of
malnutrition. The new Sustainable Development Goals (SDGs) 1 and 2 state that extreme poverty and hunger
can be eradicated by halving the number of people living on less than $1.25 per day and the number of people
suffering from hunger (MICS, 2019). The study found that about 20% of farm households did not have
children in school in 2005/06 which increased to 22% in 2012/13. In Ghana, 19% of school-going children did
not attend primary school in 2017/18 (MICS 2019). Interventions introduced into the educational sector to
increase enrolment include the Free Compulsory Universal Basic Education and School Feeding Programme
(Owusu & Mensah 2013). Increasing access to education is vital for improving the overall health and longevity
of a society, growing economies, and even combating climate change, which are related to sustainable
development goal 4. The lowest level of deprivation was 3 percent for households that lacked at least six years
of primary education for adults which was constant between 2005/06 and 2012/13. The study found that 78%
of the households lacked access to electricity in 2005/06 which reduced to 60% in 2012/13. The study found
that access to clean drinking water reduced slightly from 30% in 2005/06 to 29% in 2012/13. Basic Drinking
Water is Sustainable Development Goal 6; drinking water from an improved source, provided collection time
is not more than 30 min for a round-trip including queuing and the drinking water sources have the potential to
deliver safe water by nature of their design and construction. These include piped water, boreholes, tube wells,
protected dug wells, protected springs, rainwater, and packaged or delivered water.
The study revealed that households lacking toilet facilities increased 44% 2005/06 to 46% 2012/13. Basic
Sanitation Services SDG 6 includes the use of improved sanitation facilities that are not shared with others
(MICS 2019). The lack of clean cooking fuel deprivation had the highest score increasing from 92% 2005/06
to 94% 2012/13. Farmers mainly use wood, charcoal, and crop residues, among other dirty cooking fuels, for
domestic purposes. The use of firewood and charcoal is less expensive but rapidly prevents regeneration of the
forest and shifts to more environmentally friendly and safer Liquefied Petroleum Gas systems; however, it can
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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save the forest and prevent respiratory diseases (EC 2006). The study found that approximately 24% of
households 2005/06 lacked clean floor material which reduced to 20% 2012/13. Ghana needs about 85,000
housing units annually to solve overcrowding and homelessness and limit the effects of rainfall (GSS 2014).
Households that lack durable assets such as mobile phones, radio, television, cars, etc. reduced 40% 2005/06
to 34% 2012/13 (Table 1).
Table 1: Multidimensional Poverty Indicator Deprivations (%)
Indicator
Deprivation
(%)
2005/06
2012/13
Malnutrition
31
17
Wasting
23
19
Lack of Adult Primary Education
3
3
Children absent from School
20
22
Lack of Electricity
78
60
Lack of Clean Drinking water
30
29
Lack of toilet facility
44
46
Lack of Clean cooking fuel
92
94
Lack of Clean Floor material
24
20
Lack of Durable Assets
40
34
Ghana Living Standards Survey Rounds 5 & 6
Multidimensional Poverty Headcount Ratio Rural and Urban and Agroecological Zone
The estimates of the multidimensional poverty headcount ratio indicate the proportion of households that are
multidimensionally poor in health, education, and living standards indicators. The product of the
multidimensional poverty headcount ratio and intensity of deprivation by the weights of the indicators of
health, education, and living standards is the estimate for the multidimensional poverty index which ranges
zero to one. Multidimensional poverty index values close to one indicate higher intensity of multidimensional
poverty. Crop farmers multidimensional poverty headcount ratio reduced 46% in 2005/06 to 34% in 2012/13
by 12 percentage points in Ghana (Table 2). The multidimensional poverty headcount ratio reduced 49%
2005/06 to 38% 2012/13 rural areas and reduced 25% 2005/06 to 16% urban areas 2012/13. The
multidimensional poverty headcount ratio reduced 32%, 29%, and 60% in 2005/06 to 28%, 21%, and 45% in
2012/13 at the coastal, forest, and savannah agroecological zones of Ghana. The multidimensional poverty
headcount ratio decreased significantly between 2005/06 and 2012/13 and was higher rural areas than urban
areas. The multidimensional poverty rates were lower in the coastal and forest zones than in the savannah
zone. The multidimensional poverty index decreased 0.21 2005/06 to 0.15 2012/13. The multidimensional
poverty index decreased 0.22 2005/06 to 0.16 2012/13 rural and decreased 0.11 2005/06 to 0.07 2012/13 urban
areas in Ghana. The multidimensional poverty index was higher rural areas than urban areas. The
multidimensional dimensional poverty index reduced 0.14, 0.13, and 0.28 2005/06 to 0.11, 0.09, and 0.20
2012/13 respectively in the coastal forest and savannah agroecological zones of Ghana. The multidimensional
poverty index was lower in coastal and forest zones than savannah agroecological zones in Ghana.
Table 2 Multidimensional Poverty Index by Rural and Urban and Agroecological Zone
Multidimensional Poverty Headcount Ratio (%)
Multidimensional Poverty Index
2005/06
2012/13
2005/06
2012/13
Rural
49
38
0.22
0.16
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Urban
25
16
0.11
0.07
Coastal
32
28
0.14
0.11
Forest
29
21
0.13
0.09
Savannah
60
45
0.28
0.20
National
46
34
0.21
0.15
Ghana Living Standards Survey Rounds 5 & 6
Effect of Crop Productivity on Multidimensional Poverty Index by Agroecology
The results indicate that 1% growth crop productivity reduced multidimensional poverty deprivation by -
0.17% among farm households in Ghana (Table 3). The findings of the study further revealed that 1% growth
crop productivity significantly reduced multidimensional poverty deprivation by -0.09% in the coastal zone
and -0.28% in the savannah zone but had no effect in the forest agroecological zone. Agricultural productivity
growth 1% reduced the Human Development Index by 0.12% in the developing world (Irz et al., 2001). The
results indicate that 1% increase household size leads to 0.27% increase multi-dimensional poverty
deprivation. The study further reveals that 1% increase household size increased the multidimensional poverty
deprivation by 0.23%, 0.21%, and 0.31% in coastal, forest, and savannah zones, respectively. The study further
reveals that increasing livestock income by 1% reduced multidimensional poverty deprivation by -0.0115% in
the forest zone but increased multidimensional deprivation by 0.0205% in the savannah zone. Furthermore 1%
increase remittance income reduced multidimensional deprivation by -0.0182% in all the agroecological zones
and by -0.0274% and -0.0147% in the forest and savannah zones respectively.
Table 3: Effect Of Crop Productivity on Multidimensional Poverty Index
Variables
All zone
Coastal
Forest
Savannah
Crop productivity
-0.170***
-0.0932**
-0.0411
-0.284***
(0.0151)
(0.0440)
(0.0262)
(0.0199)
Householdsize
0.269***
0.225***
0.210***
0.311***
(0.00957)
(0.0362)
(0.0160)
(0.0126)
Livestock Income
0.00415
-0.00850
-0.0115*
0.0205***
(0.00318)
(0.0111)
(0.00635)
(0.00395)
Remittance Income
-0.0182***
0.0115
-0.0274***
-0.0147***
(0.00250)
(0.00947)
(0.00402)
(0.00338)
Constant
-0.861***
-1.327***
-1.649***
-0.188*
(0.0895)
(0.257)
(0.158)
(0.114)
Ghana Living Standards Survey Rounds 5 & 6
CONCLUSION
The multidimensional poverty rate of crop farmers reduced between 2005/06 and 2012/13 and was lower in
urban areas than rural areas and higher savannah agroecological zone than in the coastal and forest
agroecological zones. The study further found that crop productivity reduced the multidimensional poverty
index and the effect was stronger in the savannah agroecological zone than in the coastal and forest zones. This
study recommends policy on crop productivity growth through the adoption of improved inputs, access to the
market, infrastructure, etc., to increase food provision and income to reduce multidimensional poverty in rural
and urban areas and the agroecological zones.
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ACKNOWLEDGMENT
The study received partial financial support from Alliance for Green Revolution, Africa. The study
acknowledges the support of the Ghana Statistical Service for providing the data for the study.
Competing Interest
No potential conflict of interest is reported by the authors.
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