Revista Galega de Economía
2021, 30 (2), 6862
ISSN-e 2255-5951
http://dx.doi.org/10.15304/rge.30.2.6862
ARTICLE
OPEN ACCESS
Effect of public spending on agricultural productivity in Nigeria (1981-2018)
Temidayo Apata*
Federal University Oye-Ekiti, Department of Agricultural Economics and Extension, Ikole Campus, Ikole, P.M.B. 374101, Ekiti
Satate, Nigeria
Received: 15 May 2020 / Accepted: 1 October 2020
Abstract
This study examines the effect of public spending on agricultural productivity in major agro-ecological regions in Nigeria (19812018). Using public finance data from agricultural and the non-agricultural sectors at a national level, agricultural productivity
returns were analysed. Public spending on drivers of agricultural growth such as education, farm feeder roads and health care
facilities and their effect on agricultural productivity were also examined. Data were analysed using descriptive statistics and
three-stage simultaneous equations. Descriptive statistics analysis results indicated that agricultural public spending as a part
of total public spending averaged 4.88% between 1981 and 2018 across zones in Nigeria. Less than 25% of this allocation was
spent on agricultural developmental/capital project. Elasticity results computed from the 3-stage simultaneous equation
showed that the access to moderate farm feeder roads variable was 0.045, the access to education variable was 0.071 and the
access to health care facilities (within 15-30 minutes’ walk to health facility) variable was 0.013. These variables were all
significant at 1%. Such outcomes suggest that a 1% increase in the funding of education, farm feeder roads and health care
facilities will enhance agricultural productivity per capita by 0.043. Hence, the results revealed an estimated benefit-cost-ratio
of 4.3:1. Consequently, public expenditure on education, farm feeder roads and health care facilities of 4.3% would enhance
agricultural productivity by 1%. However, the assessed marginal consequences and returns vary for four agro-ecological
regions. Hence, harmonizing along with quality public spending on access to health care facilities, education and farm feeder
roads would enhance agricultural productivity.
Keywords
Public expenditure and financings/ Marginal returns / Agricultural output / Agro-ecological zones / Nigeria.
O efecto do gasto público na produtividade agrícola en Nixeria (1981-2018)
Resumo
Este estudo examina o efecto do gasto público na produtividade agrícola nas principais rexións agroecolóxicas de Nixeria
(1981-2018). Utilizando datos das finanzas públicas nacionais procedentes tanto dos sectores agrícola coma non agrícola,
analízanse os rendementos da produtividade agrícola. Tamén se estuda o gasto público nos motores do crecemento agrícola
como a educación, as vías de acceso ás granxas e as instalacións de atención médica, e mais o seu efecto sobre a produtividade.
Os datos analizáronse mediante estatística descritiva e ecuación simultánea en tres etapas. Os resultados da análise estatística
indicaron que o gasto público agrícola, como parte do gasto público total, foi do 4,88% de media entre 1981 e 2018 en todas as
zonas de Nixeria. Menos do 25% desta asignación foi empregado en proxectos de capital/desenvolvemento agrícola. Os
resultados de elasticidade calculados a partir da ecuación simultánea de tres etapas mostraron que a variable vías de acceso ás
granxas foi de 0,045, á educación de 0,071 e ás instalacións de atención médica (a 15-30 minutos camiñando ao centro de
saúde) de 0,013. Todas estas variables resultaron significativas ao 1%. Estes resultados suxiren que un aumento do 1% no
financiamento na educación, nas vías de acceso ás granxas e nas instalacións de atención médica melloraría a produtividade
agrícola per cápita un 0,043. Polo tanto, os resultados revelaron unha relación custo-beneficio de 4,3:1. En consecuencia, un
gasto público en educación, nas vías de acceso ás granxas e nas instalacións de atención médica dun 4,3% melloraría a
produtividade agrícola nun 1%. Porén, as consecuencias marxinais avaliadas e mais os retornos varían nas catro rexións
agroecolóxicas. Xa que logo, harmonizar un gasto público de calidade nas instalacións de atención médica, na educación e nas
vías de acceso ás granxas mellorará a produtividade agrícola.
Palabras clave
Gasto público e financiamento / Rendibilidade marxinal / Produción agrícola / Zonas agroecolóxicas / Nixeria.
JEL Codes: Q10.
* Corresponding author: dayo.apata@fuoye.edu.ng
1
© 2021 Universidade de Santiago de Compostela. This is an open access article distributed under the terms of the Creative Commons
Attribution-NonComercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.
Apata, T.
Revista Galega de Economía 2021, 30 (2), 6862
1. Introduction
Public spending/expenditure is the basis of budget building and an expectation for development for
today and, also, for the future. During the scope years (1981-2018) sustainable public spending in
Nigeria has been a debatable concern in economic development (Anisimova, 2016; Arndt, Pauw &
Thurlow, 2015; Babalola, 2015; Kareem, Bakare, Ademoyewa, Ologunla & Arije, 2015; Makhtar, 2017;
Mogues, Morris, Freinkman, Adubi & Ehui, 2008). Past studies have argued that the notion of public
spending is connected to sustainable development. It is argued that a sustainability concept is in the
scope of government expenditure (Aregbeyemi & Kolawole, 2015; Baldos, Viens, Hertel & Fuglie, 2018;
Goyal & Nash, 2016). Public spending is an economic instrument that government uses to maintain the
economy and for development. Agriculture is the prime sector in terms of its contributions to Gross
Domestic Product (GDP) and employment for most developing countries (Tenaye, 2020). Moreover, the
majority of people existing in poverty globally obtained their income from agriculture and agricultural
correlated activities in rural areas (Petkovová, Hartman & Pavelka, 2020; Peón Pose, Martínez-Filgueira
& López-Iglesias, 2020; Siebrecht, 2020). Therefore, developing an effectual agricultural policy in
developing countries must be highly significant and efficient government mechanisms must be put in
place to propel agricultural growth (Alshahrani & Alsadiq, 2014; Arndt et al., 2015; Fan, Hazell & Thorat,
2000; Ojiako, Chianu, Johm & Ojukwu, 2016; Rodrik, 2016).
Karamba & Winters (2015) and Babatunde (2018) argued that cost-effective agricultural public
expenditure enhances poverty reduction. Fan & Zhang (2008) and Wu, Tang & Lin (2010) indicated
that cost-effective funding of drivers of agricultural growth like extension services, efficient
credit delivery systems, research and development among others, bring about agricultural growth.
However, evidence has shown that in developing countries, public expenditure on agriculture and on
the drivers of agricultural growth is too low to bring about meaningful development (Diao, Fan,
Kanyarukiga & Yu, 2010; Manyong et al., 2005). Zhang & Fan (2004) and Makhtar (2017) contended
that identifying the drivers of growth and funding these drivers promptly is the key to meaningful
agricultural growth.
Nigeria, since its inception, has set out policies that could transform the agricultural sector. Past
studies argued that the country’s huge agricultural resource base, which offers great potential for
growth, has not really achieved that feat, due to poor funding , hence these policies could not influence
agricultural growth (Chan, Ramly & Abdkarim, 2017; Mogues et al., 2008). Manyong et al. (2005)
revealed that in the 1960s, agriculture in Nigeria influenced about 64% of the total GDP, owing to the
substantial investment the sector enjoyed both from public and private organizations. Kareem et al.
(2015) indicated that in the 1970s agricultural contribution to GDP declined from 65% in 1986 and to
48% in 1995. There was a further decrease to 15% in 2008. This study indicated that the root cause of
this decline was the poor funding given to major drivers of agricultural growth which led to poor
agricultural outputs (Takesshima & Liverpool-Tasie, 2015). Evidence from other African countries like
Zimbabwe revealed that government spending on agriculture was extraordinarily high (1990-2010)
which yielded substantial agricultural outputs (Ansari, Gordon & Akuamoah, 2007).
Coelli & Prasada Rao (2005) examined the levels and trends in agricultural output and productivity
in 93 developed and developing countries from 1980 and 2000. The results revealed that in Asia, the
highest annual Total Factor Product (TFP) growth of 2.9% was achieved, followed by North America (US
and Canada), Australia, Europe, and South America. However, developing countries (Sub Saharan Africa
SSA, West Asia, Caribbean, and Oceania) experienced a decline of TFP because the regions (SSA)
continued to rely on resource-led agricultural activities rather than productivity which the developed
countries imbibed. Agricultural productivity returns on public spending in European countries are
enormous (De Olde, Sautier & Whitehead, 2020). The increase in enhanced agricultural productivity in
developed countries derives from more intensive input use, advanced modern use of agricultural
technology (which is highly funded), efficiency, managerial skills and organization of production
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Revista Galega de Economía 2021, 30 (2), 6862
(European Commission, 2016b; Kostlivý , Fuksová & Dubec, 2017; Quiroga, Suárez, Fernández-Haddad
& Philippidis, 2017; Svilokos, Vojinić & Tolić, 2019).
The above indicates that huge public spending brings about enhanced agricultural productivity. In
most developing countries, like Nigeria, high public spending allocated to agriculture has not enhanced
agricultural productivity. This is the gap the study has identified and the factors/drivers influencing
these outcomes need to be studied. Consequently, the study examines factors/drivers of agricultural
growth and public spending in Nigeria and whether the level of public spending prompts agricultural
productivity. The main objective of the study is to examine quantity and quality of public spending and
its effect on agricultural productivity. The study also explores the consequence of public spending on
education, farm feeder roads and health care facilities and its effect on agricultural productivity. It is
hoped that the outcome of this research would increase knowledge that can contribute to policy decision
making in agricultural development. The paper hopes to contribute to literature about the efficiency of
public spending and agricultural productivity.
Although there are large bodies of researchers that have examined the productivity effects of public
expenditure, fewer studies have examined the drivers of agricultural growth and productivity effects,
especially in Africa. The limited research on productivity effects of public agricultural expenditure in
many developing countries was largely due to the lack of extended time series expenditure data and
when they used were, it was for short periods of time (not more than 10 years). However, fewer studies
–Benin & Nin-Pratt (2015) for Africa; Benin, Mogues, Cudjoe & Randriamamonjy (2012) for Ghana;
Mogues, Fan & Benin (2015) for Ethiopa; Fan, Nyange & Rao (2012) for Tanzania; and Fan & Zhang
(2008) for Uganda– used longer period data and revealed that public expenditure influenced
productivity and economic growth thus providing established evidence of the effect of public spending
on agricultural productivity.
Understanding this effect can offer beneficial policy visions for the government to enhance the
efficiency of its public spending on agriculture. In addition to physical inputs, agricultural productivity
can also be enhanced by quality spending on health care services, educational services and farm feeder
roads among others (Baldos et al., 2018). This paper eeks to achieve this objective by examining the
effect of public funding on agricultural productivity in Nigeria across main agro-ecological regions
(1981-2018), as well as assessing marginal effects of public spending on health care facilities, education
and farm feeder roads and their returns to agricultural productivity.
The paper is ordered as follows: section one is the introduction; section two looks at the
methodological approaches used in this study, while section three presents results and discussions.
Section four concludes the paper.
2. Methodology
2.1. Area of study
The study area is major agro-ecological regions in Nigeria (Figure 1, Table 1). These are marginal/
short grass savannah, derived woodland/long grass savannah, rainforest and mangrove/swamp.
Nigeria has a geographical area of 923,768 square kilometres with an estimated population of about 170
million (Central Bank of Nigeria’s [CBN] 2016 estimates). It lies wholly within the tropics along the gulf
of Guinea on the western coast of Africa. The country has highly diversified agro-ecological conditions,
which makes it possible to produce a wide range of agricultural products. Less than 50% of the country’s
cultivable agricultural land is under cultivation. Moreover, smallholder and traditional farmers who use
rudimentary production techniques, with resultant low yields, cultivate most of these lands. The country
is divided into seven agro-ecological regions but four are distinctive and are used as the basis of analysis
for this study (Figure 1).
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Revista Galega de Economía 2021, 30 (2), 6862
Figure 1. Map of Nigeria showing agro-ecological regions. Source:
www.nigerianstat.gov.ng/nada/index.php/catalog
Table 1. Major agro-ecological regions in Nigeria
Major
s/n agro-ecological
zones
States
Major agricultural activities
Vegetation
1
Marginal/Short
grass savanna.
Bauchi, Borno, Jigawa,
Kano, Katsina, Kebbi,
Sokoto, Yobe and
Zamfara.
Cotton, groundnut, sorghum, millet, maize
and wheat. Locust bean trees (Parkia
filicoidea), tamarind tree (Tamarindus
indica) and mango (Mangifera indica).
Low average annual
rainfall of 657.3 mm and
prolonged dry season
(6-9 months).
2
Derived
woodland/Long
grass savanna.
Abuja, Adamawa,
Benue, Gombe,
Kaduna, Kogi, Kwara,
Nassarawa, Niger,
Plateau and Taraba.
Grazing livestock such as cattle, goats,
horses, sheep, camels, and donkeys. Maize,
cassava, yam and rice.
This zone experiences
lower rainfall, shorter
rainy season and long dry
period.
3
Rainforest.
Abia, Anambra, Ebonyi,
Edo, Ekiti, Enugu,
Ogun, Ondo, Osun, and
Oyo.
Staple crops like, yam, cassava, cocoyam,
sweet potatoes, melon, groundnut, rice
maize and oil palm (Elaeis guineensis),
cocoa (Theobroma cacao), rubber (Hevea
brasiliensis) banana/plantain (Musa spp.),
cotton and kola nut (Cola nitida). Cowpeas
and beans as well as several fruits. Various
timber trees such as the African mahogany,
the scented sapele wood Entandrophragma
cylindricum) and iroko (Chlorophora
excelsa).
Prolonged rainy season,
resulting in high annual
rainfall above 2000 mm.
4
Mangrove/Swamp. Akwa Ibom, Bayelsa,
Cross Rivers, Delta,
Lagos and Rivers.
Oil-palm, cocoa, cassava, maize, yam.
Various palm and fiber plants such as
Raphia spp., Raphia vinifera, the wine palm
and Raphia hookeri, the roof-mat palm.
Prolonged rainy season
and lagoons overflow
banks in the wet season
(8-9 months). Thus longer
rains, has led to badly
leached soils and severe
erosion.
Sources: [1] https://soilsnigeria.net; [11] Oyenuga (1967); [iii] Materials from https://www.fao.org; [iv] Sowunmi & Akintola
(2010).
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Revista Galega de Economía 2021, 30 (2), 6862
2.2. Method of data collection
Due to the nature of this research, public expenditure on agricultural and related non-agricultural
enterprises data were collected. Data were sourced from the Ministry for Agriculture and other
significant ministries, departments, agencies, and offices responsible for finance, budget and planning.
The study conceptualized agriculture and agricultural activities to include arable and covers crop,
livestock, forestry and fisheries. Public expenditure was deduced as annual and complementary
appropriations (budget) that support funding of direct and indirect agricultural activities. Public finance
data were also sourced from the Ministry of Finance (Nigeria), public expenditure data from other key
sectors, the Central Bank of Nigeria’s (CBN) Statistical Bulletin (2018). Public expenditure data
(1980-2018) on agriculture was obtained from the Budget and Economic Planning office (Federal
Ministry of Finance Abuja), the National Bureau of Statistics’ (NBS) annual abstract (various issues), and
the Agricultural Development Project (ADP) Offices.
While public expenditure data on the non-agricultural sector at the national level for education,
health care facilities and farm feeder roads were taken from the individual government ministries,
departments and agencies, data on agricultural production, private farm investments and other
farm-household physiognomies were sourced from the most recent National Living Standards Survey.
Data on education and health care facilities and services access were acquired from the latest report of
the Core Welfare Indicators Questionnaire (CWIQ). Data on farm feeder roads and associated
information were sourced from the Federal Ministry of Transport and Aviation and State Ministries of
Transport. These variables used in the analysis were presented in Table 2. All monetary values were
changed into year 2000 constant prices using the local consumer price index to exclude the influence of
inflation and other temporal monetary and fiscal trends.
Table 2. Description and statistical summaries of major variables used
Variable
name
Variable description
TOAGR
Total value of agricultural-output per capita of a household. It’s also the
value of total agricultural investments made and inputs used by the
household in the survey scenario (N6500 naira per capita).
PUEXP
Labelled as a function of public expenditure in agriculture. It is also based
on:
(i) developmental expenditures and
(ii) recurrent expenditures.
FACDEV
Other factors influencing public-investment that motivate enterprise
growth in agriculture, like infrastructures (good farm access roads, storage
facilities), education, health care facilities.
Access
farm
roads
This is to deduce the quality of farm-access roads to residences and
markets and its significance on income generation: rainforest/mangrove
(i) Good farm access roads: 0.5 0.25 0.0 0.0
(ii) Moderate farm access roads: 0.5 0.75 0.75 0.5
(iii) Poor farm access roads: 0.0 0.0 0.25 0.5
Education
Proportion of household members that have completed level of formal
education and its significance on income generation: rainforest/mangrove
(i) No formal-education: 0.5 0.5 0.0 0.0
(ii) Completed primary school: 0.5 0.5 0.25 0.5
(iii) Completed secondary school: 0.0 0.0 0.25 0.5
(iv) Post-secondary attempt/completed: 0.0 0.0 0.5 0.0
Mean
Standard
dev.
Data
source
5,872.27
138.26
Min. of
Agric.
43.05
148.19
6.16
13.62
2.25
0.62
Annual
Budget
(various
issues)
Annual
Budget
(various
issues)
CWIQ
1.5
1.75
2.25
0.58
0.50
0.52
Min. of
Education
1.5
2.5
3.25
2.5
0.57
0.58
0.96
0.58
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Revista Galega de Economía 2021, 30 (2), 6862
Table 2 (continuation). Description and statistical summaries of major variables used
Variable
name
Variable description
Access to Proportion of households living within vicinity of health facility:
health
(cf.: up to 15 minutes): rainforest/mangrove
care
(i) 15-29 minutes: 0.00 0.00 0.25 0.00
(ii) 30-44 minutes: 0.25 0.50 0.50 0.25
(iii) 45 minutes or more: 0.75 0.50 0.25 0.75
SOCIOXT
AGRO
ZONE
Household characteristics:
(i) Household size: Number of household members (adult equivalents)
(ii) Gender of head: Male = 1 and Female = 0
(iii) Age of head: Age of household head (years)
(iv) Adult labour: Proportion of members aged 18 to 64
(v) Male labour: Proportion of members that are male
(vi) Female labour: Proportion of members that are male
(vii) Employment: Proportion of members employed
(viii) Income diversification/strategy rainforest/mangrove
Subsistence farming only: 0.0 0.0 0.50 0.50
Semi commercial farming only: 0.0 0.0 0.0 0.0
Subsistence farming + Market-oriented crops: 0.25 0.25 0.25 0.25
Semi commercial farming + Market-oriented: 0.0 0.0 0.0 0.0
Subsistence farming + Non-farm activity: 0.50 0.25 0.25 0.25
Semi commercial farming + Non-farm activity: 0.25 0.5 0.0 0.0
(ix) Farm assets characteristics rainforest/mangrove
Population 2009 projections: 63500175 45889717 22175254 18640172
Proportion of households living below poverty line (%): 36.53 33.87 32.43 38.73
Total land area (1000 sq. km): 338,206 380,728 121,355 69,100
Farm size: Acres of farmland (%): 37.17 41.90 13.51 7.42
Livestock assets:
No of tropical livestock units: 11,936.41 5427.92 482.52 215.30
Value of crop production equipment
(N20,000 per capita): 7203.15 917.31 728.17 253.28
% of population with agriculture as main activity: 71.26 65.03 41.03 35.17
Agro-ecological zones:
(i) Marginal/Short grass savanna; public expenditures on agriculture
Marginal/Short grass savanna; agriculture contribution to GDP
(ii) Derived woodland and long grass savanna; public expenditures on agric.
Derived woodland and long grass savanna; agriculture contribution to GDP
(iii) Rainforest; public expenditures on agriculture
Rainforest; agriculture contribution to GDP
(iv) Mangrove/Swamp: Public expenditures on agriculture
Mangrove/Swamp: Agriculture contribution to GDP
Mean
Standard Data
dev.
source
CWIQ
2.0
2.5
2.75
0.82
0.58
0.50
5.93
0.62
51.23
0.32
0.47
0.52
0.35
1.65
0.31
5.28
0.17
0.21
0.43
0.08
1.57
0.0
2.0
0.0
3.25
3.0
0.48
0.0
1.41
0.0
0.96
0.82
CWIQ
1502053 4,423.85
18
2
36.39
2.80
227347
38,431
CWIQ
31.58
29.98
47.17
31.32
36.71
25.27
17.64
13.34
3.82
4.17
5.24
3.02
4.14
5.02
6.16
5.28
Source: Various federal and state government agencies.
2.3. Empirical links between public expenditure and agricultural growth
Public expenditure is a significant factor which aims at financing the incentives for development,
creating a fertile ground for the promotion of private sector investments and enterprise growth. Hence,
it could also influence enterprise growth. Several models have been used to examine this link. Fan et al.
(2000) and Benin et al. (2009) modelled a simultaneous equation approach to establish the links
between public expenditure and agricultural growth. These studies argued that the composition of
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Revista Galega de Economía 2021, 30 (2), 6862
public expenditure for major agricultural drivers should be paramount. Taking a lead from the works of
Wu et al. (2010), the composition of government-expenditure is modelled:
Vit = k (PEXPGDPt, GDP%t, DVt, Ut)
(1)
Where Vit is the share of ith sector (agricultural) in total government expenditure, t for time, PEXPGDP
is public expenditure as a percentage of GDP, GDP%t is per capita GDP, DVt is a dummy variable that is
equal to 1 when macroeconomic regulations are implemented and equal to 0 otherwise. Macroeconomic
regulations regulate monetary, fiscal, trade policies, exchange rate and inflation. Ut are unexplained
factors in the equation and can influence government expenditure efficiency. In order to avoid the
possibility of an endogeneity problem with the independent variables, the GMM instrumental variable
was adopted (Dhrymes, 1973). Moreover, GMM1 take care of any possible presence of unit roots or
non-stationarity of variables that may cause spurious regression results. Hence equation (2) was
structured to reflect this procedure and presented as:
where
TOAGR = f (PUEXP, FACDEV, PRODET, DRIVERS, IDFACT, SOCIOXT, β0 β1 β2)
(2)
TOAGR: Total value of agricultural output per capita of a household.
PUEXP: Function of public expenditure in agriculture (where PUEXPp = PUEXPca + PUEXPrc).
PUEXPca: Public capital expenditure in agriculture.
PUEXPrc: Public recurrent expenditure in agriculture.
FACDEV: Other factors that motivate agricultural enterprise growth like infrastructures, farm feeder
roads, education, access to quality health-care facilities.
PRODET: Production functions of the determinants of public spending to use.
DRIVERS: Drivers of agricultural growth that motivate enterprise-development like, research and
development, credit delivery services, extension services.
IDFACT: Indirect factors influencing agricultural enterprise growth.
SOCIOXT: Socioeconomics characteristics and institutional factors that could influence production
process.
β0 β1 β2: Are vectors of parameters to be estimated for the equation.
FACDEV = f (PUEXP, PRODET, DRIVERS, IDFACT, SOCIOXT, β1 β2)
DRIVERS = f (INTERPOL, PRODTE, IDFACT, β1 β2)
(3)
(4)
INTERPOL: Intervention policies of the government to stimulate and motivate enterprise growth in
agriculture.
β1 β2: Are vectors of parameters to be estimated for the respective equations (3) and (4).
TOAGR: Captures the level of impact of public investments for enterprise growth in agriculture (equation
2).
Equation (3) examines enterprise growth within public expenditure and the indirect effects of public
expenditure on enterprise growth. Equation (4) considers the location effects (agro-ecological zone of
the country) of public expenditure and government intervention on the drivers of enterprise growth
programs. Thus, by including public expenditure and intervention in other sectors in equation (4), the
study tried to capture possible interactions between expenditure on the non-agricultural and
agricultural sectors.
1
Dickey-Fuller approach have been used for tests of presence of unit roots or non-stationarity.
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Revista Galega de Economía 2021, 30 (2), 6862
2.4. Marginal effect of public expenditure on agricultural growth
Marginal effect of public investments on agricultural growth was estimated as:
𝑑𝑇𝑂𝐴𝐺𝑅
𝑑𝑃𝑈𝐸𝑋𝑃
∈ 𝐷𝑅𝐼𝑉𝐸𝑅𝑆 =
=
𝜕𝑇𝑂𝐴𝐺𝑅
𝜕𝑃𝑈𝐸𝑋𝑃
+
𝜕𝑇𝑂𝐴𝐺𝑅
𝜕𝐹𝐴𝐶𝐷𝐸𝑉
𝑋
𝜕𝐹𝐴𝐶𝐷𝐸𝑉
𝜕𝑃𝑈𝐸𝑋𝑃
----------
(5)
∊ DRIVERS is the marginal effects of the drivers of agricultural growth that motivate enterprise
development such as research and development, credit delivery services, extension services. Therefore,
this equation measures the direct effect of public investment in agriculture.
𝑇ℎ𝑢𝑠 =
𝑑𝑇𝑂𝐴𝐺𝑅
𝑑𝑃𝑈𝐸𝑋𝑃
𝑎𝑛𝑑
𝜕𝑇𝑂𝐴𝐺𝑅
𝜕𝑃𝑈𝐸𝑋𝑃
+
𝜕𝑇𝑂𝐴𝐺𝑅
𝜕𝐹𝐴𝐶𝐷𝐸𝑉
𝑋
𝜕𝐹𝐴𝐶𝐷𝐸𝑉
𝜕𝑃𝑈𝐸𝑋𝑃
-captured the indirect effect
Equation (5) hypothesized the typical vector of production function estimates with respect to farm
investments (i.e. factors of production and inputs). This equation captured the elasticity of agricultural
productivity with respect to public investment in the other sectors (∊ IDFACT), which is a function of βp
βk and βa, and can be obtained by:
∈ 𝐼𝐷𝐹𝐴𝐶𝑇 =
𝑑𝐹𝐴𝐶𝐷𝐸𝑉
𝑑𝐼𝐷𝐹𝐴𝐶𝑇
=
𝜕𝐹𝐴𝐶𝐷𝐸𝑉
𝜕𝐼𝐷𝐹𝐴𝐶𝑇
𝜕𝐹𝐴𝐶𝐷𝐸𝑉
+ 𝜕𝑃𝑅𝑂𝐷𝐸𝑇 𝑋
2.5. Marginal returns on public spending
𝜕𝑃𝑅𝑂𝐷𝐸𝑇
+
𝜕𝐼𝐷𝐹𝐴𝐶𝑇
∈ 𝐷𝑅𝐼𝑉𝐸𝑅𝑆 𝑋
𝑑𝑇𝑂𝐴𝐺𝑅
𝑑𝑃𝑈𝐸𝑋𝑃
(6)
Marginal returns on public investments (i.e. the benefit-cost ratio or BCR) can be computed by
multiplying equations (7) and (8) with the relevant ratio of agricultural output per capita to public
investment (Benin et al., 2009; Fan et al., 2000):
𝐵𝐶𝑅 𝐷𝑅𝐼𝑉𝐸𝑅𝑆 = ∈ 𝐷𝑅𝐼𝑉𝐸𝑅𝑆 𝑋
𝐵𝐶𝑅 𝐼𝐷𝐹𝐴𝐶𝑇 = ∈ 𝐼𝐷𝐹𝐴𝐶𝑇 𝑋
𝐹𝐴𝐶𝐷𝐸𝑉
𝐷𝑅𝐼𝑉𝐸𝑅𝑆
𝐹𝐴𝐶𝐷𝐸𝑉
𝐼𝐷𝐹𝐴𝐶𝑇
(7)
(8)
Marginal returns provide information for comparing the relative benefits of an additional unit of
public spending.
2.6. Estimation techniques and concerns
The study adopted estimation techniques of a Three-Stage Least Squares (3SLS) method to appraise
equations (1), (2), (3), and (4) simultaneously, following a previous study (Amemiya, 1977). Past studies
argued that the 3SLS method is best fit to estimate all coefficients in the equations simultaneously, while,
equations that are under-identified are disregarded in the 3SLS estimation (Gallant & Dale, 1979;
Jorgenson & Laffont, 1975). The 3SLS estimates were used to estimate the linear equations (eqns. 1-4)
with cross-equation constraints (public expenditure) imposed, but with a diagonal covariance matrix of
the disturbances across equations (Dhrymes, 1973).This process helps to obtain the parameter
estimates that form a consistent estimate of the covariance matrix of the disturbances, which was used
as a weighting matrix, this led to a model re-estimation to obtain new values of the parameters used in
the subsequent equations.
Past studies argued that when these techniques and estimations are considered, some issues and
concerns need to be clarified (Benin et al., 2009; Fan et al., 2000). Firstly, the estimation techniques
8
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Revista Galega de Economía 2021, 30 (2), 6862
require an equal number of observations for each of the independent variables and to address this
concern, each low independent variable data will be aggregated upwards to be the same as others
(Gallant & Dale, 1979). In addition, in estimating the variance and standard errors, the study emulates
the work of Hsieh & Lai (1994) who adopted the delta method (∊) for the estimation technique. Hence,
the typical form of the probable elasticities of the method:
̂0 𝛽
̂1 𝛽
̂2)
∈ = 𝑓 (𝛽
(9)
Also, the variance of the probable elasticities, adopting the delta method and the variance-covariance
matrix of the coefficients (∑ ∊^), can be achieved using the general form:
𝜕𝑓
𝜕𝑓
𝜕𝑓
𝜕𝑓
𝜕𝑓
𝜕𝑓
𝑉𝑎𝑟 (∈) = [( ) ( ) ( ) 𝑥 ∑[ ( ) ( ) ( )] 𝑇
𝜕𝛽0
𝜕𝛽1
𝜕𝛽2
𝜕𝛽0
𝜕𝛽1
𝜕𝛽2
(10)
Moreover, the identification issue in the equation that might occur during estimation especially in
the equation (1) was addressed by exploiting exclusion restrictions i.e. excluding some of the
explanatory variables (or instruments) used in estimating the equation (2). Another concern the study
dealt with was the issue of multicollinearity due to a large set of explanatory variables data. Hence, the
Variance Inflation Factor (VIF) was adopted to take care of this (Greene, 1993). The results of this study,
however (to the knowledge of the researcher), do not reflect any biased estimates.
3. Results and discussion
3.1. Public expenditure on agriculture in agro-ecological regions and contribution to Gross
Domestic Product (1981-2018)
Table 3 reviewed public spending on agriculture in main agro-ecological regions and its contribution
to GDP from 1981-2018. The results indicated that from 1981-2018, the share of statutory budget
(public spending) allocation to agricultural development was 4.88% across zones, but the
marginal/short grass savanna agro-ecological region received the highest (7.32%), while the
mangrove/swamp agro-ecological zone received 2.39%. The agricultural contribution to GDP (%) from
1981-2018 averaged 35.14% with the marginal/short grass savanna agro-ecological area reaching
29.13% while the mangrove/swamp agro-ecological area stood at 4.32%. Total funding (shares) to the
agricultural sector also indicated 32.52%, 47.16%, 37.80%, and 17.82% for marginal/short grass
savanna, derived/woodland long grass savanna, rainforest and mangrove/swamp agro-ecological areas
respectively.
The results revealed that public spending on agricultural sectors across the agro-ecological zones
had been very poor. Often, intervention of both local and foreign direct investments has been used to
augment and finance agricultural projects in Nigeria. Intervention of both local and foreign direct
investments on agriculture during the years under focus showed 63.47%, 76.51%, 80.34% and
74.69% in marginal/short grass savanna, derived/woodland long grass savanna, rainforest and
mangrove/swamp agro-ecological regions respectively (Table 3). This finding was corroborated by the
studies of Mongues et al. (2008) and Manyong et al. (2005) who acknowledged the role these
intervention agencies (local and foreign direct investments) played in agricultural development in
Nigeria.
Concerns arise about whether public funding in agriculture enhanced agricultural productivity in the
identified agro-ecological zones, particularly the marginal/short grass savanna. This may be considered
in future research.
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Revista Galega de Economía 2021, 30 (2), 6862
Table 3. Agricultural budget and expenditure appropriation for agro-ecological zones and contribution to GDP
(1981-2018)
Major agro-ecological zones
Share of States
Statutory Budget
allocation to
agricultural
development (%)
Share of Federal
Government
intervention to
agricultural
develop. (%)
Share of Local
Total funding
and International
(shares) to
Aids/Intervention agricultural
to agricultural
sector (%)
development (%)
Agriculture
contribution
to GDP (%)
07.62
06.72
05.27
02.62
05.01
06.03
04.92
02.72
26.02
41.05
24.02
08.91
38.65
53.80
34.21
14.25
40.02
24.94
23.01
12.03
07.02
05.62
03.18
02.16
06.12
05.14
03.83
02.92
29.83
42.88
22.81
07.27
42.97
53.64
29.82
12.35
23.71
39.52
25.04
11.73
05.17
04.04
03.92
02.02
05.04
05.20
03.18
02.03
13.07
29.83
45.05
12.05
23.28
39.07
52.15
16.10
24.84
27.47
29.31
18.38
06.05
05.47
03.45
02.37
05.21
05.82
03.29
02.05
12.45
31.54
41.29
14.72
23.71
42.83
48.03
19.14
26.01
31.36
27.38
15.25
07.67
06.25
04.46
02.84
04.86
05.14
03.06
02.64
11.85
28.18
35.07
24.90
24.38
39.57
42.59
30.38
31.05
26.91
27.82
14.22
8.20
7.60
4.70
2.31
04.52
04.38
02.91
01.63
27.81
42.06
17.85
12.28
40.53
54.04
25.46
16.22
32.61
34.05
22.28
11.06
6.82
6.31
3.82
1.70
03.85
03.82
02.03
01.57
23.43
37.06
26.47
13.04
34.10
47.19
32.32
16.31
30.80
33.05
23.63
12.52
7.01
6.48
5.16
3.01
04.39
04.17
04.02
02.06
24.05
37.28
28.05
13.41
36.28
47.44
36.11
18.05
33.82
36.17
28.02
14.17
1981-1985 35.10*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
1986-1990 36.58*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
1991-1995 32.66*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
1996-2000 33.08*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
2001-2005 38.42*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
2006-2010 31.72*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
2011-2015 21.35*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
2016- 2018 24.85*
Marginal/Short grass savanna
Derived/Woodland and long grass savanna
Rainforest
Mangrove/Swamp
Notes: *aggregate value for the scenarios considered. Sources: Federal Ministry of Agriculture and Rural Development
(FMARD), FAOSTAT data 2005 and 2015, World Bank, NBS: Annual abstract of statistics (various issues), Central Bank of
Nigeria - Statistical Bulletin (various issues), Federal Ministry of Finance (Budget office), Authors’ computation based on data
from SPARC (2014): Based on data from Federal Ministry of Agriculture and Rural Development, and State Ministries
(1981-2014).
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Revista Galega de Economía 2021, 30 (2), 6862
3.2. Regression estimates of the determinants of agricultural production in the agro-ecological
zones of Nigeria
Three-stage least squares (3SLS) regression results were presented in Tables 4 and 5. Analyses were
done in phases, firstly, by means of the joint total sample and then separately for the four agro-ecological
zones. The analysis was based on the data provided to update equations (2) and (3).
Table 4. Three-stage least squares regression estimates of the determinants of agricultural production in Nigeria
(Equation 2: Ln TOAGRk): Using aggregate public agricultural expenditures
Agro-ecological zone
Explanatory variables
Total value of agricultural output per capita of a
household: Ln TOAGR
Total
sample
Marginal
savanna
Derived
savanna
Rainforest Mangrove/
zone
Swamp zone
0.037***
0.015***
0.006***
0.312**
-0.436**
Public capital expenditure in agriculture: Ln PUEXPT
(i) Developmental expenditures and
(ii) Recurrent expenditures
Ln FACDEV
Access farm roads
(i) Good farm access roads
(ii) Moderate farm access
(iii) Poor farm access roads
0.251**
0.712
0.044***
0.682
0.328**
0.841
0.427**
-0.735*
-0.512*
-0.438**
0.004***
0.382**
0.885
0.062***
0.061**
0.993
0.092***
0.639*
0.841
0.037***
0.839*
-0.382*
0.731
0.829
-0.751*
Education:
(i) No formal education
(ii) Completed primary school
(iii) Completed secondary school
(iv) Post-secondary attempt/completed
-0.0716**
0.071*
0.005***
0.000***
0.917
0.091*
0.018**
0.010**
0.310
0.852
0.031**
0.049**
0.082
0.554
0.001***
0.000***
0.904
0.628
0.048*
0.006***
Access to health care:
(i) 10–30 minutes
(ii) 31-45 minutes
(iii) 46 minutes or more
-0.0716**
0.071*
0.005***
0.917
0.091*
0.018**
0.310
0.852
0.031**
0.082
0.554
0.001***
0.904
0.628
0.048*
0.028
0.082
0.175
0.098*
0.497
-0.002***
-0.028**
0.006***
0.0911*
0.583
0.091*
0.048**
0.027**
0.082*
0.073
0.852
0.073*
0.076*
0.063*
0.279
0.554
0.008***
0.005***
0.492
0.048
0.628
0.937
0.184
0.739
0.078
0.004***
0.007***
0.028**
0.059*
0.066*
0.005***
0.000***
0.018**
0.000***
0.027**
0.001***
0.073*
0.004***
0.846
0.732
7.058***
4.927***
5.924***
3.017***
-2.018*
1902.07
0.371
428.93
0.282
631.04
0.341
310.73
0.258
294.61
0.225
3.061
2.301
2.934
1.947
1.305
SOCIOXT:
(i) Gender of head: Dummy variable for head of
household: 0 = female, 1 = male
(ii) Ln Household size
(iii) Ln Age of head: Age of household head (years)
(iv) Adult labour: Proportion of members aged 18 to 64
(v) Male labour: Proportion of members that are male
(vi) Female labour: Proportion of members that are
male
(vii) Ln Employment: Proportion of members
employed
Agro-ecological zones:
Public expenditures on agriculture
Agriculture contribution to GDP
Intercept
Model estimation statistics
(i) Chi-square
(ii) R-square
Number of observations
Model identification test (exclusion restriction)
Hansen’s J chi-square statistic
Notes: See Table 2 for a detailed description of the variables. All continuous variables are transformed by natural logarithm,
which is indicated by Ln *, ** and *** means that the coefficient is statistically significant at the 10 percent, 5 percent or 1
percent level, respectively. Source: own elaboration.
11
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Revista Galega de Economía 2021, 30 (2), 6862
Table 5. 3SLS regression estimates of the determinants of agricultural production in Nigeria (Equation 3: Ln
FACDEVp): Using aggregate public agricultural expenditures
Agro-ecological zone
Explanatory variables
Total
sample
Marginal
savanna
Derived
savanna
0.005***
-0.0301*
0.0400**
0.714*
0.0410**
0.649
0.062*
-0.021**
-0.942
-0.007***
0.023**
0.016**
-0.062*
0.075*
0.032**
0.617
0.029**
0.062*
0.153
0.713
0.912
-0.032**
0.615
0.814
-0.077*
Education:
(i) No formal education
(ii) Completed primary school
(iii) Completed secondary school
(iv) Post-secondary attempt/completed
-0.005***
0.034**
0.000***
0.000***
0.013**
0.083*
0.001***
0.000***
0.418
0.401
0.004***
0.000***
0.995
0.703
0.000***
0.000***
-0.043**
0.001***
0.010**
0.000***
Access to health care:
(i) 10–30 minutes
(ii) 31-45 minutes
(iii) 46 minutes or more
0.003***
0.031**
-0.003***
0.044**
0.084*
-0.038**
0.005***
0.852
-0.048**
0.013**
0.930
-0.008***
0.037**
0.111
-0.017**
0.014**
0.000***
0.084*
0.009***
0.927
0.015**
0.000***
0.017**
0.047**
0.187
0.008***
0.005***
0.027**
0.000***
0.672
0.042**
0.006***
0.003***
0.018**
0.816
0.067**
0.910
-0.025**
0.837
0.328
0.048**
0.008***
0.008***
0.001***
0.946
0.010**
0.004***
0.002***
0.074**
0.000***
0.000***
0.004***
0.007***
0.006***
0.006***
0.027**
0.729
0.071*
0.067*
0.927
0.082*
0.962
0.091*
0.052*
0.862
6281.06
0.574
2800.14
0.497
4320.91
0.503
3006.16
0.417
1104.52
0.389
Ln PUEXP
(i) Developmental expenditures and
(ii) Recurrent expenditures
Ln Access farm roads
(i) Good farm access roads
(ii) Moderate farm access
(iii) Poor farm access roads
SOCIOXT:
(i) Gender of head
(ii) Ln Household size
(iii) Ln Age of head: Age of household head (years)
(iv) Adult labour: Proportion of members aged 18-64
(v) Employment: Proportion of members employed
Income diversification/strategy:
(i) Subsistence farming only
(ii) Subsistence farming + Market-oriented crops
(iii) Subsistence farming + Non-farm activity
(iv) Semi commercial farming + Non-farm activity
Intercept
Model estimation statistics
Chi-square
R-square
Number of observations
Rainforest Mangrove/
zone
Swamp zone
Notes: See Table 2 for a detailed description of the variables. All continuous variables are transformed by natural logarithm,
which is indicated by Ln. *, ** and *** means that the coefficient is statistically significant at the 10 percent, 5 percent or 1
percent level, respectively. Source: own elaboration.
Tables 4 and 5 clearly indicated the significant role public spending played in agricultural output and
factors influencing agricultural productivity. Public spending on the agricultural sector (1981-2018)
had a significant and positive impact on agricultural output. The Model Statistics R-square of 52.3%
indicated a moderately goodfit. Moreover, most of the variables considered had their explanatory
variables coefficients statistically significant at the 10%, 5% or 1% level, respectively. The regression
results reveal that public spending on the agricultural sector in recent years has had a substantial
positive influence on agricultural productivity, either directly or through better private farm
investments . For all the zones together, the marginal effect is assessed at 0.037 (Table 4). This means
that a one percent increase in agricultural public expenditure is related to a 0.04 percent increase in the
value of agricultural production per capita.
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Revista Galega de Economía 2021, 30 (2), 6862
3.3. Public agricultural spending and marginal agricultural productivity effects
Regression estimates of the drivers of agricultural public expenditure in Nigeria were presented in
Table 6. Model fit indicators revealed R2 of 0.54, which is 54% of the independent variables considered
and thus explained the model. The access to farm road variable was 0.045, the access to education
variable was 0.071 and access to health (within 15-30 minutes’ walk to health facility) was 0.013, all
significant at 1% level suggesting that a 1% increase in the funding of education access, farm feeder
roads and health facilities will enhance agricultural productivity per capita by 0.043. Regression
estimates of the drivers of agricultural public expenditure in Nigeria (Equation 4: Ln PUEXPT) were
presented in Table 6. Moderate access to farm roads was significant and positive (for capital
expenditure), but insignificant for recurrent expenditure. This result suggests that poor farm access
roads contributed negatively to agricultural productivity. In addition, secondary school education
completing the above variables was a significant factor enhancing human development which translates
to productivity. The access to health care variable (where the majority could walk to health facilities
centres within 45 minutes) revealed a positive significance.
Table 6. Ordinary least squares regression estimates of the drivers of agricultural public expenditure in Nigeria
(Equation 4: Ln PUEXPT) using aggregate public agricultural expenditure
PUEXPTotal
PUEXPcapital exp
PUEXPrecurrent exp
Ln Access farm roads:
(i) Good farm access roads
(ii) Moderate farm access
(iii) Poor farm access roads
0.045***
0.067*
-0.072*
0.032**
0.082*
-0.062*
0.326*
0.824
-0.007***
Education:
(i) No formal education
(ii) Completed primary school
(iii) Completed secondary school
(iv) Post-secondary attempt/completed
0.842
-0.041**
0.071***
0.000***
0.518
0.619
0.043**
0.000***
0.739
0.618
0.042**
0.007***
Access to health care:
(i) 10-30 minutes
(ii) 31-45 minutes
(iii) 46 minutes or more
0.013***
0.036**
-0.033**
0.007***
0.052*
-0.025**
0.025**
0.839
-0.046**
SOCIOXT:
Ln Population
Ln Proportion of households living below poverty line
Ln Total land area
Ln Farm size: Acres of farmland
Ln Livestock assets:
Ln Value of crop production equipment
Ln % of population with agriculture as main activity
0.583
0.937
0.617
0.056*
0.038**
0.052*
-0.071*
0.618
0.738
0.613
0.043**
0.017**
0.048**
0.005***
0.528
-0.045**
0.816
-0.068*
0.062*
-0.082*
-0.083*
Agro-ecological zones:
(i) Marginal/Short grass savanna
(ii) Derived/Woodland and long grass savanna
(iii) Rainforest
(iv) Mangrove/Swamp
0.006***
0.002***
0.032**
0.069*
0.000*
0.000*
0.015**
0.052*
0.835
0.628
-0.081*
-0.095*
Intercept
R-square
Number of observations
F-test statistic
6.045***
0.525
8500
9.717***
5.015***
0.473
8500
8.205***
4.927***
0.620
8500
7.417***
Explanatory variables
DRIVERS
Notes: See Table 1 for a detailed description of the variables. All continuous variables are transformed by natural logarithm,
which is indicated by Ln. *, ** and *** means that the coefficient is statistically significant at the 10 percent, 5 percent or 1
percent level, respectively. Source: own elaboration.
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Revista Galega de Economía 2021, 30 (2), 6862
3.4. Marginal effects (elasticities) of public expenditure on agricultural productivity in Nigeria
Table 7 indicated the effect of public spending on the value of agricultural production per capita. This
result fluctuates substantially across the four agro-ecological zones. The marginal effect of the analysis
was positive and statistically significant across all four. The marginal effects of PUEXPCE were
insignificant only for the mangrove/swamp zone but significant in the other agro-ecological areas, with
elasticities of 0.782, 0.041, 0.042 and 0.35 in mangrove savanna, derived savanna, rainforest and
mangrove/swamp zones respectively (Table 7). Access to education, access to farm roads and access to
health care variables all played a significant and positive role in enhancing agricultural productivity.
However, there were a few exceptions, particularly, the variable PUEXPRE on access to farm roads. The
resulting effect of the insignificance of PUEXPRE in the mangrove/swamp zone was due to the
neutralizing negative effects related to recurring spending. In addition, recurrent expenditure was
negative and significant in the rainforest area due to the response of the variable as an exclusive driving
force of agricultural productivity (Table 7).
Table 7. Marginal effects (elasticities) of public expenditures in Nigeria
Agro-ecological zone
Explanatory variables
Total
sample
Marginal
savanna
Derived
savanna
Rainforest Mangrove/
zone
Swamp zone
Agriculture
PUEXPTN
PUEXPCE
PUEXPRE
0.026**
0.018**
-0.047**
0.014**
0.035**
-0.028**
0.017**
0.042**
0.037**
0.037**
0.041**
-0.031**
0.071*
0.782
0.419
Education
PUEXPTN
PUEXPCE
PUEXPRE
0.064*
0.077*
0.006***
0.092*
0.062*
0.011**
0.084*
0.033**
0.025**
0.025**
0.080*
0.062*
0.626
0.506
0.371
Access farm roads
PUEXPTN
PUEXPCE
PUEXPRE
0.004***
0.007***
-0.024**
0.002***
0.005***
-0.017**
0.009***
0.002**
0.047**
0.064**
0.048**
-0.062**
0.001***
0.817
0.502
Access to health care
PUEXPTN
PUEXPCE
PUEXPRE
0.008***
0.007**
0.219
0.001***
0.002***
0.772
0.000***
0.002***
0.618
0.000***
0.000***
0.529
0.004***
0.021**
0.916
Notes: Authors’ calculations based on Tables 9 and 10 and equations and (4), (4') and (9). Estimate is
statistically significant at the 10 percent, 5 percent or 1 percent level, respectively. Source: own
elaboration.
3.5. Marginal cost of public services and agricultural productivity
Literature has revealed that public spending on drivers of agricultural growth such as infrastructural
development and provision of basic amenities - access to good roads and primary health is sine qua non
to agricultural development. Therefore, evaluating the requisite cost that would achieve this purpose is
significant. To assess marginal returns of public spending on these indicators would require accessing
information on the unit cost. Evaluating the financial implication of how much it would cost to educate
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the majority of Nigerians to attend at least primary school level entails assessing various channels that
can facilitate attendance and knowledge impartation. These include provision of primary school
institutions closer to the people, adequate teachers and motivation of teachers to provide quality
teaching among others. Hence, the financial implication was estimated based on these criteria (Table
8).
Data were sourced from the Federal Ministry of Education, non-governmental agencies and other
relevant sources. Average annual spending on public institutions was calculated and divided by the total
number of pupils enrolled in the corresponding educational system. Thus, the estimated (on average)
annual cost was computed. The result revealed N12,550.00/pupil/year ($34.86) for primary school
pupils over the years under consideration. This was then multiplied by the number of people that
completed at least primary education to arrive at the marginal cost (Table 8). The question arises: can
this cost enhance human capital development? This is a matter to be discussed in future research. Data
on access to health care were sourced through numerous outlets to estimate marginal cost using the
methodology of Benin et al. (2009) that estimated the average unit cost from previous investments,
where the accrued public capital stock is divided by total expenditure over several years. Due to data
limitation, the study modified this approach. Firstly, data were sourced to calculate the average annual
spending on provision of health facilities and second, access to health care services by most Nigerians.
These steps enabled the study to source for data on the proportion of households living within 45
minutes of health-care facilities and that have access to moderate/quality health care. For example,
access will improve when people themselves move closer to an existing facility or service or when they
invest in ways to reach the facility for prompt service delivery.
Table 8. Marginal (one-percent increase in) stock and costs of public expenditures (1981-2018)
Agro-ecological zone
Explanatory variables
Total
sample
Marginal
savanna
Derived
savanna
Rainforest Mangrove/
zone
Swamp zone
Education (N billion)
Marginal stock (population completed
at least primary education) No in % of
the population
marginal cost (%) (N billion)
902.52
65.67
206.65
38.17
213.29
56.48
234.96
89.72
247.65
78.29
12.30
3.04
3.25
2.49
1.83
Access to health care (N billion)
Marginal stock (households within 45
minutes’ walk to health centre) No in %
marginal cost (%) (N billion)
341.43
36.74
77.25
28.16
79.29
33.28
89.20
47.26
95.75
38.26
37.53
12.16
10.78
7.12
4.85
Access farm roads (N Billion)
Marginal stock (farm access of km per sq
km) No in %
marginal cost (%) (N billion)
48.49
62.77
13.45
81.06
12.61
72.92
11.78
58.36
10.05
38.72
21.54
12.15
7.90
3.05
1.70
Sources: www.epdc.org/Nigeria_coreusaid, www.ibe.unesco.org, www.nigerianstat.gov.ng/nada/index.php/catalog/27.
Following this deduction, the study sourced data and estimated the total number of households that
resided within 45 minutes of a health facility and the number of people who visited these facilities for
their health concerns. Thus, the total number of households were divided by the number of years under
consideration to get the average annual change of the number of households existing within 45 minutes
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Revista Galega de Economía 2021, 30 (2), 6862
of a health centre. Therefore, the study estimated the average annual cost of providing public health
services by the Federal Ministry of Health to be N68,006.00 ($188.91) (study estimate), and divided by
the number of households living within 45 minutes of a health facility. To get the marginal cost, the unit
cost of one household member within 15-45 minutes’ walk to a health facility to obtain quality health
services was then multiplied by the number of people that accessed these facilities (Table 8).
Similarly, estimating the marginal stock (i.e. farm access of km per sq. km in %) of people that have
access to modest farm-roads was estimated by calculating how much it would cost to build one
kilometre of rural road and the number of people that have access to these roads. To obtain the marginal
cost concerning access to farm roads, total length of feeder roads was multiplied by the number of
kilometres of road to farms and local markets and this outcome was used to obtain the marginal cost.
Hence, the estimated unit cost of N23, 602.00 ($65.56) (study estimate) was gotten (Table 8). These
marginal costs are then divided by their respective marginal effects to obtain estimated marginal
returns.
3.6. Public investments and marginal agricultural productivity returns in Nigeria
Table 9 presents marginal agricultural productivity returns on public spending while Tables 7 and 8
present estimates of marginal cost and the marginal effects that were used for the estimate.
Table 9. Marginal agricultural productivity returns to public investments in Nigeria
Agro-ecological zone
Explanatory variables
Total
sample
Agriculture
Education
Access to health care
Access farm roads
12.97***
3.03**
5.14**
5.03**
Marginal
savanna
Derived
savanna
8.05**
1.91
3.02***
3.06***
7.08***
1.34
2.61***
2.91***
Rainforest Mangrove/
zone
Swamp zone
5.92**
0.82***
1.63**
-2.35*
2.05**
0.42
1.71*
-0.82
Notes: See Table 1 for a detailed description of the variables. All continuous variables are transformed by natural
logarithm, which is indicated by Ln. *, ** and *** means that the coefficient is statistically significant at the 10
percent, 5 percent or 1 percent level, respectively. Source: own elaboration.
Marginal effects were derived using Tables 4 and 5 as a guide and were presented in Table 7. The
study computed the marginal cost by estimating the current population and then multiplied it with the
unit cost (Table 8). Hence, marginal cost and marginal effects results were then used to assess the
marginal agricultural productivity returns on the various types of public investment in the four agroecological zones (as displayed in equations 7 and 8). The results were presented in Table 9. The
study revealed that significant amounts were extended to these indicators but returns were not
commensurable (Table 9). For the years under consideration, the country had invested over N71.37
billion ($190M), while the marginal Nigerian naira (N) invested in the agricultural sector was N12.97
($0.036) as a margin of the total value of agricultural productivity returned. Surprisingly, drivers of
agricultural productivity recorded low marginal Naira invested, (education N3.03, access to health care
N5.14 and access to farm-roads N5.03). These results established a benefit-cost ratio of 4.4:1, meaning
that increasing public spending on education, farm feeder roads and health care by 4.4% would enhance
agricultural productivity by 1%. Although these indicators are significant and positive, according to
world indicators, they are very low. Results of the agricultural productivity returns from access to farm
roads indicated a negative in the rainforest and mangrove swamp zones. These outcomes thus suggest
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that vegetation in these zones did not create a smooth access for agricultural activities. The assessed
marginal returns on the various types of public investments vary among the four agro-ecological zones,
the highest being the marginal and derived savannah zones, followed by the rainforest area (Table 9).
Marginal returns on public spending on education are the highest, followed by the health sector and
finally, access to farm roads.
3.7. Policy implications of major findings
Public spending on agriculture in Nigeria remains low regardless of the signs used. Allocation to
agriculture from total public spending (annual budget) averaged 4.88 percent between 1981 and 2018.
The marginal/short grass savannah agro-ecological zone received the most (7.32%), while the
mangrove/swamp agro-ecological area saw only 2.39%. Budgetary allocation to agriculture compared
with other key sectors was also low despite the sector’s role in the fight against poverty, hunger, and
unemployment and in the pursuit of economic development. Agricultural contribution to GDP (%) from
1981-2018 averaged 32.70%, while total funding (shares) to the agricultural sector also indicated
32.52%, 47.16%, 37.80% and 17.82% across the zones respectively. In this regard, intervention of local
and foreign direct investments in public spending on agriculture showed 63.47%, 76.51%, 80.34 and
74.69 in marginal/short grass savannah, derived/woodland long grass savannah, rainforest and
mangrove/swamp agro-ecological areas respectively. This finding was supported by the studies of
Mongues et al. (2008) and Benin & Nin-Pratt (2015) which recognized the role local and foreign direct
investment intervention agencies played in agricultural development in Nigeria and in Africa.
Evidence from other African countries revealed that public expenditure on agriculture in Ghana
averaged 3.5-6.9% in 1995-2005, in Kenya (6.5-7.5%) and in Uganda (3-10%). In Uganda,
development/capital spending reliably accounts for around 15% of total sector spending, leaving
85% of the budget for recurrent costs. Hence international donor agencies have traditionally provided
the majority of funding for development/capital operations in Uganda (Makhtar, 2017; Ministry of
Foreign Affairs, 2005; Otsuka & Hayami, 1988). Recurrent expenditure (personnel stipends and general
administration) took 70-80% in Ghana, and in Kenya, 69%. Intervention funding (such as international
donors) i.e., non-government funding in agriculture (both local and direct investment) in Ghana is
between 59.1-73.5% and in Kenya 62-83% (Ministry of Foreign Affairs, 2005). Moreover, Asia, China,
India and Thailand allocated 10-15% of the state budget to agriculture with capital expenditure
accounting for 75% of spending; only 25% of the budget is for salaries, operations and maintenance. In
the European Union public spending and related spending to agricultural development is between
43.5% and 51.5% (European Commission, 2016a, 2017, 2018). These countries witnessed higher
returns on agricultural productivity. Proof from the regression results show the positive and significant
role public spending played in agricultural productivity. This suggests the significant role quality public
spending plays in agricultural growth.
The marginal effect of educational access in Nigeria was positive in the savannah zones, suggesting
that people with higher education also work on the farms. This finding was supported by Quiroga et al.
(2017) and Kostlivý et al. (2017). Access to quality health services holds universally true, hence,
inclusive access to health services enhanced productivity. The study’s result of the marginal effects
(0.028) for access to farm roads was significant and positive indicating that a 1% increase in agricultural
public spending on road development is related to an 0.028 increase in the value of agricultural
production per capita. Meanwhile improved spending on health and rural roads independently could
motivate better agricultural productivity. Hence, this study suggests that harmonizing along with quality
spending on access to health, education and rural roads for the enhancement of agricultural productivity
is paramount.
Annual budget allocation to education is the largest among all sectoral public spending in Asia: at $87
per person, while in Europe it reaches $115, and in South America $45-52 per person (European
Commission, 2017, 2018) whereas in sub-Saharan Africa (SSA) countries spent a meagre $11per capita
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Revista Galega de Economía 2021, 30 (2), 6862
for education and $8 for infrastructure from 2007 to 2013 (Ojiako et al., 2016). In Nigeria, the study
indicated $8 per person was spent to acquire a minimum education (at least primary education). Hence,
public spending on the educational sector in Nigeria is low compared to other countries. In Asia, Europe,
South America and other developed countries the educational sector received precedence in resource
allocation, with about 16% of the government budget dedicated to educational related activities
(Kostlivý et al., 2017; Quiroga et al., 2017), while in Nigeria less than 9% is allocated to education, and
less than 7% to health and roads during the years under review.
4. Conclusions
The study examined the effect of public spending on agricultural productivity in Nigeria (19812018). The results revealed that public spending on agriculture in Nigeria is poor by international
standards and therefore limited agricultural productivity. Also, public spending on drivers of
agricultural growth such as access to quality health care, education and farm feeder roads that can
enhance agricultural productivity was poor throughout the years under review, hence, meagre
agricultural productivity returns. Benin & Nin-Pratt (2015), Benin (2015), Alene & Coulibaly (2009),
Fan et al. (2008) and Thirtle, Lin & Piesse (2003) revealed evidence of agricultural productivity returns
on quality public spending on agricultural sectors as well as on drivers of agricultural growth (access
to health care facilities, education and farm feeder roads).
Consequently, quality public spending can be efficiently used to motivate agricultural growth and
improve agricultural productivity. Nigerian governments need to increase public spending on
agriculture and drivers of agricultural growth (farm feeder roads, education and healthcare.) The study
revealed that in Nigeria, these drivers were not effectively motivated and were poorly funded. In
addition, the low budget (less than 5%) appropriated to agriculture during the reviewed years
influenced poor agricultural productivity returns. Hence, the study recommends that governments
should improve on the existing public spending in the agricultural sector and drivers of agricultural
growth to improve agricultural productivity.
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