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https://doi.org/10.56556/gssr.v3i4.1069 

                                                                  

 
 

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RESEARCH ARTICLE  

The effects of public and private investments on food security in 

Cameroon 

Votsoma Philémon1*,  Vangvaidi Albert2, Pene Zongabiro Nina Pelagie3, Mohamadou Oumarou1, 

Ndouyang Balguessam Bruno1, Mohamadou Sani1, Bilkissou Hadjara1, Kaltoumi Raihanatou1, Fatal 

Esperance1, Abdoulaye Assana1 

   
1Faculty of Economics and Management, University of Garoua, P.O.Box : 346, FSEG, Garoua, Cameroon 
2Faculty of Economics and Management, University of Maroua, P.O.Box : 46, FSEG, Maroua, Cameroun 
3Faculty of Economics and Management, University of Ngaoundere, P.O.Box : 454, FSEG, Ngaoundère, 

Cameroon 

 

Corresponding Author: Votsoma Philémon. Email: philemonvotsoma@yahoo.fr 

Received: 06 November, 2024, Accepted: 14 December, 2024, Published: 20 December, 2024 

Abstract 

This paper aims to analyse the effects of public as well as private investments on food security in Cameroon. 

The study, used data from 1988 to 2020 and Generalized Least Squares method for estimation. The results 

revealed that (i) public investment reduce effect on food security while (ii) private investment has a positive 

effect on food security. Therefore, the study suggests to Cameroonian government to direct public policies 

towards investments in agricultural sector in order to boost food production, food availability and food 

accessibility in the country.  

Keywords: Investments; food security; public sector; private sector; Generalized Least Squares method 

Introduction

An increase in the rate of food insecurity in the world is still a hot topic. The rate of food insecurity in the world 

has evolved over time. As of 2019, nearly 750 million people, or one in ten people worldwide, have experienced 

food insecurity (Food and Agriculture Organization (FAO), 2020). According to the report entitled ‘The State 

of Food Security and Nutrition in the World 2020’, almost 690 million people, or 8.9% of the world’s population 

suffer from hunger. While the fight against hunger is stagnating, the COVID-19 pandemic is intensifying global 

vulnerabilities and food shortages. In addition, the world’s population has increased thanks to a rise in the birth 

rate and improvements in the quality of human health care. In fact, the increase in population growth rate is a 

factor that boost demand for food on global level. However, despite a favourable outlook for global food supply, 

food prices increase and high transportation costs increase the cost of imported food. Therefore, many countries 

experienced inflation in retail food price. Labour shortages and the rise in the price of fertilisers and agricultural 

inputs are also other cause of increase in food prices. In the United Nations Development Programme’s Human 

Development Report (UNDP, 2019), Cameroon’s Human Development Index (HDI) was 0.563, placing the 

county in the 153rd out of 189 poorest countries. This HDI value places Cameroon in the ‘medium human 



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development’ category. Let notice that the socio-political crises, has deteriorated food insecurity in Cameroon 

from 12.8 % in 2019 to 20.5% in 2020. The distribution of food insecurity among regions in Cameroon are as 

follows: 40% in the North-West, 30.7% in the West, 22.1% in Adamaoua, 24.8% in the Far North (Ministry of 

Agriculture and Rural Development, 2020, World Food Program (WFP), 2020, and FAO, 2020). Tough the 

country has enormous potential for sufficient food availability, it faces a number of humanitarian, security and 

health crises that compromise food security (FAO, 2021). Climate change, limited access to food, and poverty 

linked to economic shocks and inequality are factors of food insecurity.  In addition, mainly terrorism (Boko 

Haram in Far North), socio-political crisis in North-West and South -West regions, the increase of migrants 

from central Africa Country in the East region are other causes of food insecurity. Food security in Cameroon 

is also affect by speculation. In fact, the country is identified as the main producer of agricultural crop products 

in Central Africa, meanwhile, Daka, Wang and Hu (2021) identified Cameroon as the breadbasket of Central 

Africa.  Thus, producer and retailers choose to sale their products to neighbouring countries (Gabon, Equatorial 

Guinea, Chad, Central African Republic) where market price is higher than the one applies on local market, 

leading to shortage in local markets. Yet, the agricultural sector in Cameroon remains underdeveloped due to 

the inadequacies of investment in agricultural sector. The part of Cameroonian government budget allocate to 

agricultural sector is 4.5% of the government total of annual expenditure (National Statistics, 2019). Despite 

the engagement took in The Maputo Declaration in July 2003, where Heads of African States agreed to allocate 

at least 10% of their budgets to develop agricultural sector in order to achieve agricultural growth at 6% (New 

Partnership for Africa’s Development, Mbaku, 2004). The country has not respected this engagement, for 

instance in 2021 just 1.86% of budget was allocate to agricultural sector (National statistics, 2021). With respect 

to current debate on the link between investment and food security in the world, the authors propose to 

participate to this debate trough this paper entitle: the effect of public and private investment on food security 

in Cameroon. The study aims to assess the effects of both public and private investment on food security in 

Cameroon through this main question: what is the link between investment and food security? To answer this 

question, two assumptions was made: firstly, the effect of public investment on food security and secondly, the 

effect of private investment on food security. 

To answer this question, we will use two assumptions: firstly, the effect of public investment on food security 

and secondly, the effect of private investment on food security. This paper is structure as follows: section two 

presents literature review on the relationship that actually exist between different types of investments and food 

security, methodology of the study is in section three, while the main results and their interpretations are 

presented in section four, finally the study is concludes with main recommendations towards the publics 

authorities in Cameroon. 

Literature Review 

The agricultural policy of Cameroon was defined in the five -year plans from 1975 to 1986 and aims to increase 

agricultural production in order to achieve food security. However, the increase in agricultural productivity is 

linked to the increase of investment in agricultural sector. For instance, the joint statement on global food 

security issued by the G8 meeting held in Aquila (Italy) in June 2009 recognises that, the combination of chronic 

under-investment in agriculture and economic instability are two mains causes of persistent food insecurity. 

Thus, both public and private investment in the agricultural sector can play an important role in ensuring food 

security for the population. Investment in agriculture appear as a most important and effective strategy for 

reducing food insecurity in rural areas, where the majority of the world’s poorest people live (World Bank, 

2007 and FAO, 2012). Since the investment needed to develop an agricultural value chain plays an important 



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role in reducing food security, consequently investment in agricultural sector have had a positive impact in 

many countries.  

 

Public investment and food security 

 

Review of global literature on the link between public investment and food security 

 

Agricultural value chains have often increased the availability and quality of food, if they focus on a diversity 

of crops consumed locally, and have also contributed to increase food production (Bishwajit and Yaya (2024), 

Cleaver, 2013; Rutherford et al., 2016; Kaplan et al., 2016). In this perspective, public spending can be directed 

towards subsidising agricultural projects (Daka, Wang and Hu, 2021). The subsiding of agricultural input is 

generally intended to increase productivity through better access to fertilisers. Thereby contributing to a rise in 

producers’ income (lower production costs) and a fall in consumer prices (increased supply) (Kazukauskas, et 

al., 2014; Jayne et al., 2018; Solaymani et al., 2019). In this vein, Kanter et al, (2015) have shown that input 

subsidy policies are likely to lead an increase in agricultural production, which later provides additional income 

to farmers, enabling them to purchase foodstuffs that can improve the nutritional status of the household. 

Therefore, the financing of agricultural activities is also crucial to ensuring food security. The financing of 

agriculture makes a significant contribution to agricultural production. While strengthening and diversifying 

agricultural financing promotes the development of food markets (Ibrahima Thiam and Malick, 2020). 

Meanwhile, the financing of agricultural sector in Cameroon shows that there is a positive relationship over 

time between agricultural financing and food security (Biligil, 2017). Public investment in agriculture is 

necessary to reduce food insecurity (World Food Program, 2020 and World Bank, 2017). As food prices likely 

to rise, investment in the agricultural sector has become more essential for guaranteeing food security around 

the world. As a result, investment in research and development, physical infrastructure, and communications 

technologies contribute to improve the availability and access to food (Kristova et al.,2017; Heisey and Fuglie, 

2018). Thus, spending on research and development as well as on support services of agriculture contribute to 

food security by promoting food systems that guarantee basic diets (Shankar, Chunk and Frank, 2017). It has 

also noted that investment in education has a positive effect on the population’s food security. It promotes 

agricultural productivity, the higher the level of education of the farming population, the higher will be the 

agricultural productivity. In this vein, Mengoub (2018) in her paper entitled ‘Agricultural investment in Africa: 

a low level… many opportunities’, shows that investment in education is seen as an effective means of 

increasing agricultural productivity gains. This improvement in agricultural productivity increases the 

availability of foodstuffs.  

Despite many policies adopted by various government across the world, the agricultural sector still faces limited 

investment that is view as main factor of food insecurity. Meanwhile, food security has become main objective 

of agricultural policy for every country. Thus, investment is recognized as a key variable mainly in rural areas 

where it has been identified as an essential element of the fight against poverty and food insecurity (, World 

Bank, 2007; Barret et al., 2010; De Janvry and Sadoulet, 2010). In this line, Alston et al., (2009) show that 

agricultural investment from farmers or from the public sector to increase productivity at farm level can also 

improve the availability of food on the market and exert downward pressure on prices, making food affordable 

to consumers. Public investment in the agricultural sector should therefore have a positive impact on food 

security and poverty. Increase in public investment in agricultural sector expected to have a positive impact on 

food security, not only in rural areas but also in urban areas, as the fall in prices resulting from growth in 



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production will satisfy both rural and urban populations (Burchi, Scarlato and d’Agostino, 2018; Timmer, 2000; 

FAO,2012).  

 

Review of synthetic literature on the link between public investment and food security 

 

According to Douillet and Girard (2013), agricultural production depends not only on the soil and climate, but 

also on the technologies available, farming practices and public policies that directly or indirectly affect 

farmer’s activities, through their general or specific economic orientation. These authors highlight the 

importance of state intervention in the agricultural sector through public investment and the quality of the 

institutions that promote agricultural development. Though agriculture remains a main pillar of development in 

many developing countries, over time its contribution to Gross Domestic Product (GDP) has declined in many 

parts of the world, partly due to low investment and the neglect of this key sector in favour of manufacturing 

industries (Forum for Agricultural Research in Africa (FARA), 2006). Investment in agricultural sector is 

divided into economic and social infrastructure. Public investment in agricultural research, road, electrification 

and education generates high returns in terms of agricultural growth and poverty reduction, that are highly 

complementary to private investment. State funding of agricultural activities plays an important role in food 

security. In fact, agricultural financing makes significant contribution to production, strengthening and 

diversifying agricultural activities, thus promotes the development of food markets The funding of agricultural 

projects or granting subsidies to farmers increase the availability of foodstuffs trough the increasing of 

agricultural productivity (Ibrahima, Thiam et Malick, 2020).  

In this same vein, Biligil (2017) analysed the impact of public funding of the agricultural sector on the growth 

of agricultural products in Cameroon. The results show a positive relationship over time with public spending 

allocated to the agricultural sector. The growth of agricultural production has contributed to the reduction of 

food insecurity among local population. Thus, public investment in agricultural sector has a positive impact on 

food security. Public investment in education mainly in vocational training in agriculture, improve farmers 

skills who are more effective and efficient in practising modern technics of farming (Mengoub,2018). Public 

spending to support food prices mainly towards price support and aid for farmers has a positive effect on food 

security. For instance, Kaya and Erden (2008) in their paper concluded that development aid devoted to 

agricultural sector and growth have a positive relationship. Therefore, the level of food prices is a key 

determinant of food security. In fact, when prices are low, people are able to buy and vary their diet more. 

However, Timmer (2010) argued that, the volatility of food prices is evidence of the existence of food crises 

due to dynamic public policies to support the production or consumption of food and non-agricultural products. 

Investment in agricultural sector can also reduce the vulnerability of food supply to shocks, thereby improving 

the stability of consumption. On the supply side, a period of high prices leads the government to encourage 

research and investment in order to increase agricultural production. Public and private sectors can invest in the 

construction of food storage and other infrastructure to ensure supply. However, the period of low prices reduces 

the interests of governments, resulting in a decline in public support. 

 

Private investment and food security 

 

Review of global literature on the link between private investment and food security 

 

The World Bank (2007) recommends to governments to ease the pressure on national food security by attracting 

Foreign Direct Investment (FDI) in order to solve chronic problems of low investment in agricultural sector. 



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However, a negative effect of Foreign Direct Investment in major developing countries were noticed. FDI has 

led to an increase in the rate of food insecurity, due to foreign investors seeking to maximise their interests by 

exploiting the resources of developing countries (Bjornlund, Bjornlund and Rooyen, 2022). For instance, World 

Bank (2004) noted earlier that FDI flows into agriculture tend to be highly volatile.  Agriculture is land-based 

activities, and land is owned by major local elites who concentrated benefits from FDI. In this same line, Alfaro 

(2003) and Aykut and Sayet (2007) have shown in cross-country empirical analysis that FDI in primary 

agriculture hurt economic growth in developing countries.  FDI in agricultural sector has led to environmental 

problems. In this line, Clapp (2003) in his studies noted that governments in developing countries have followed 

foreign agricultural policies that favour the use of chemical-dependent technologies due to the involvement of 

multinational firms in agricultural projects. Multinational firms, use high-intensity pesticides and fertilisers that 

replaced natural crop rotation and organic fertilisers (Altieri, 2000 and Jorgenson, 2007). In some cases, foreign 

investors in developing countries use substances that are banned in developed countries for environmental 

norms (Magdof et al.,2000 and Shiva and Bedi, 2002). The pollution of water sources poisoned farmland and 

later force the migration or the abandonment of subsistence farms by local producers. In addition, the use of 

chemical fertilisers can be the cause of certain diseases in developing countries. Therefore, private investment 

can act as a brake on the development of poor countries (Mihalache and Li, 2011). 

Studies of the effects of FDI on food security in developing countries show that energy consumption was 

negatively related to FDI in agricultural sector in both short and long term (Djogoto, 2022). In fact, some social 

conflict arises mainly the large-scale of land acquisitions by foreign firms that lack transparency in land 

transfers and the absence of consultation with local stakeholders. In addition, land transfers involve the 

displacement of local small holders and the loss of grazing land for nomadic pastoralists that later lead to food 

insecurity in local population due to the export of crops produce by foreign firms. Moreover, there are concerns 

on the highly mechanised production methods from foreign firms, that limited job creation and increase the rate 

unemployment of local population (Hallam, 2011). Despite, the negative effects stresses in the preview 

paragraph, FDI in agricultural sector have positive effects on food security. For instance, subsidising high-

productivity agriculture increases the quantity of food available. In this line, Frimpong and Oteng (2008) have 

noted the diverse importance of FDI for host countries including: the influx of foreign capital increases the 

supply of funds for investments, thereby promoting capital formation, and the direct contribution to the food 

security of local population. Multinational firms have also helped to set up restaurants and large supermarket 

where consumers can find wide variety of foodstuff at competitive prices. As a result, the population is food-

secure. In this same vein, Aloui and Maktouf (2024) found out in the studying the impact of FDI and political 

stability of food security in SSA found out that FDI positively impact food security in the region. 

 

Review of synthetic literature on the link between private investment and food security 

 

Skoet, Kostas and Deuss (2004), argued that growth, poverty reduction and food security, particularly in the 

poorest countries, depend on investment and rural economic activities. The low level of investment in 

agricultural sector in major developing countries is reflected in low productivity and stagnant production. 

Despite the priority given to agriculture, many developed countries are facing limited financial capacities to 

invest in agriculture in developing countries. While, developing countries have limited access to bank loans 

from commercial bank and microfinance, meanwhile these countries turn to FDI. FDI in agricultural sector is 

an historical phenomenon since colonial period where large firms were established to import technical 

equipment and export raw materials and crops product to support European industries. Thus, private investment 

has important effect on food security, including the exploiting of fertile and irrigable land took from local 



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population. In the same line, Corporations (2009) stresses that the technological contributions of multinational 

firms have been limited due to difficult transfer and diffusion of technologies to small farmers. In fact, the 

advantages of technologies and productions from FDI is near zero, if the crops produce are entirely export to 

investor countries. In addition, multinational firms paid very low wages to local population unenabled them to 

meet their food requirements and encouraged the move of rural population to the cities, thereby the reduction 

of agricultural workforce and the increase in food prices in the cities (Reardon et al., 2003). A model will be an 

important tool for analysing the impact of national investment on food security in Cameroon. 

 

Empirical review of models on the effects of investment on food security 

 

Major analysis of the effect of investment on food security use different models. For instance, Zidouemba and 

Gerard (2015) in their paper entitle: ‘investment and food security in Burkina Faso’ used Computable General 

Equilibrium (CGE) model to analyse the effect of public investment in agriculture in Burkina Faso. While, 

Ibrahima Thiam and Malick (2020) in their paper entitle ‘empirical links between agricultural financing and 

food security in Senegal’ used multiple regression model inspired by Kpodar (2006) for analysis. While Aloui 

and Maktouf (2024) used General Moment Method to the impact of FDI and political stability of food security 

in SSA. Among various models, this study uses multiple linear regression model and the error correction model 

for analysis. Expliquez pourquoi? 

The above literature stresses the effect of both public and private investment on food security. All authors 

agreed on the fact that public and private investments have a certain effect on food security, however the effect 

can be negative or positive. Some studies highlight the factors that cause food insecurity, some factors are 

identify on individual (social) level while other are on national and/or regional level (political, economic, 

cultural). Literature also, stress variables that affect food security, including: climate change, natural resources, 

land, financing, education, health, political conflicts, government policies, physical infrastructures, ect. In 

addition, major studies on food security are done on regional level or in cross countries studies, few studies are 

done on a single country due to a difficulty to constitute a strong database over a long period of time. However, 

this study overcome this challenge by focusing on Cameroon context and shed light on how both public and 

private investments and various variables could significantly affect food security in the country. 

Methodology 

 

Multiple regression model 

Based on the assumption that financing of agricultural sector contributes significantly to food security. Ibrahim, 

Thiam, and Malick (2020), used the multiple regression model to analyse the linkages between agricultural 

financing and agricultural production in Senegal. These authors model is based on the models of Kpodar (2006) 

and Jeanneney and Kpodar (2011). Both models present the impact of financial development on poverty. 

The theoretical model can write as follow: 

 

𝑙𝑜𝑔(𝑃𝑆𝐴𝑡) = 𝑎0 + 𝑎1 log(𝐹𝐼𝐴𝑡) + 𝑎2 log(𝑉𝐴𝐴𝑡) + 𝐴𝑋                                                           (1) 

 

Where, PSA is the prevalence of undernutrition, FIA is agricultural financing consisting of bank credit, VAA is 

agricultural value added and X is a set of control variable (with elasticity A), food availability in kilocalories 



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(DISK), gross national income per capita (RNB_H) and outstanding loans from microfinance institutions 

(C_IMF), t is the period index. 

The theoretical model can be rewrite as follow: 

 

log (𝑃𝑆𝐴𝑡 )  = 𝑎1 log(𝐹𝐼𝐴𝑡) + 𝑎2 log(VA𝐴𝑡) + 𝑎3 log ( 𝑉𝐴𝑁𝐴𝑡) + 𝑎4 log(𝑃𝑂𝑃𝑡) + 𝑎5(𝐷𝐼𝑆𝐾)𝑡 +

𝑎6 log(𝑅𝑁𝐵_𝐻𝑡) + 𝑎7log (𝐶_𝐼𝑀𝐹𝑡) + 𝐶 + 𝐸𝑡.                                                        (2) 

 

The prevalence of undernutrition (PSA) represents the level of food security in a country that is capable of 

capturing the population with insufficient access to adequate, regular and nutritional food for an active and 

healthy life. Agricultural financing (FIA): is made up of short-term financing ( 𝐹𝐶𝑇𝑡 ),medium-term 

financing(𝐹𝑀𝑇𝑡 ), and long-term financing(𝐹𝐿𝑇𝑡 ), so, 𝐹𝐼𝐴𝑡 = 𝐹𝐶𝑇𝑡+𝐹𝑀𝑇𝑡 +𝐹𝐿𝑇𝑡 .Agricultural Value Added 

(VAA) is an indicator that informs the agricultural entrepreneur about his ability to pay labour and capital factors. 

Non-agricultural value added (VANA),  it is part of the agricultural value chain. Food availability in Kilo calorie 

(DISK): is the daily per capita food energy availability that corresponds to the available food for consumption 

during the reference period. Gross National Income per capita (RNB_H) is the sum of value added produced by 

all residents plus all tax revenues. Population (POP) is the number of people or inhabitants to be fed during a 

given period. The outstanding credit of MFIs (C_IMF)  is an indicator to measure the financing of MFIs 

intended for the client.  

 

 Error Correction model 

 

The model specification is based on the approach used by Nupuko (2007), adapted from the models of Ojo and 

Oshikoya (1995), Ghura and Hadjimicheal (1996) and Tenou (1999) on real GDP growth in African countries. 

The model considers the two channels of public expenditures’ effects on the growth of agricultural sector. The 

formulation links production and factors that may influence growth of agricultural sector. These are direct and 

indirect factors. The direct factors are labour, private capital, land and agricultural public expenditures. Indirect 

factors are general public goods such as electricity and education. 

The general specification of model can be written as follow: 

 

𝑃𝑟𝑜𝑑 = 𝑓(𝐷𝑃𝐴, 𝐿𝑎𝑏, 𝐿𝑎𝑛𝑑, 𝑃𝑟𝑖𝑣, 𝐸𝑙𝑒𝑐, 𝐸𝑑𝑢)            (3) 

 

Where, Prod is agricultural production, DPA: Public Expenditure on Agriculture, Lab: Labour, Land: Land, 

Priv: Private capital, Elec: Electricity, Educ: Education. 

 

The equation can be written as follow: 

 

△ 𝐼𝑛𝑃𝑟𝑜𝑑 = 𝑎1 △ 𝐷𝑃𝐴 + 𝑎2𝐿𝑎𝑏 + 𝑎3 △ 𝐼𝑛𝐿𝑎𝑛𝑑 + 𝑎4 △ 𝐼𝑛𝑃𝑟𝑖𝑣 + 𝑎5 △ 𝐼𝑛𝐸𝑑𝑢 + 𝑎6 △ 𝐼𝑛𝐸𝑙𝑒𝑐 + 𝑎7 △

𝐼𝑛𝑃𝑟𝑜𝑑 + 𝑎8 △ 𝐷𝑃𝐴−1 + 𝑎9𝐿𝑎𝑏−1 + 𝑎10 △ 𝐼𝑛𝐿𝑎𝑛𝑑−1 + 𝑎11 △ 𝐼𝑛𝑃𝑟𝑖𝑣−1 + 𝑎12𝐼𝑛𝐸𝑑𝑢𝑐 + 𝑎13𝐼𝑛𝐸𝑙𝑒𝑐−1                                                                         

(4) 

In this expression, △ is the derivative factor with respect to time. The coefficients 𝑎𝑖are the elasticities.  

The coefficients  𝑎1 𝑡𝑜 𝑎6   characterize short-term dynamic while the coefficients 𝑎8 to 𝑎13 allow the long-

term equilibrium behaviour of the growth rate of agricultural production to be derived and the coefficient 𝑎7 is 

the error correction coefficient. 

 



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Model specification 

 

The model of this study is based on the models of Kpodar (2006) and Jeanneney and Kpodar (2008). The 

originality of these models is a relationship between Cobb-Douglas function and agricultural production and its 

explanatory factors.  In fact, this type of function was used by Barro and Salai-Martin (2004) and Guillaumont 

(2004). Mundlak et al., (2002) also used this type of function for a determinant analysis of agricultural growth 

in Indonesia, the Philippines and Thailand. 

The model of equation is in the following form: 

 

𝑃𝐼𝐵𝑡 =𝑓(𝑋1𝑡 , 𝑋2𝑡, 𝑋3𝑡 , … . 𝑋𝑛𝑡).             (5)  

 

The equation can be rewrite as follow: 

 

𝑃𝐼𝐵𝑡 = 𝑋1𝑡 , 𝑋2𝑡 , 𝑋3𝑡, … 𝑋𝑛𝑡.                                    (6)  

 

𝑃𝐼𝐵𝑡 is the agricultural domestic product, is a function of agricultural production. 

𝑋𝑛𝑡 represent the variables explaining the rate of food insecurity at a given time t. 

The equation in logarithmic form is as follows: 

 

𝐿𝑜𝑔(𝑃𝐼𝐵𝑡) = 𝑎0 + 𝑎1𝑙𝑜𝑔𝑋1𝑡 + 𝑎2𝑙𝑜𝑔𝑋2𝑡 + ⋯ 𝑎𝑛𝑋𝑛𝑡 + 𝐸𝑡. (7) 

  

In the context of the effect of national investment on food security in Cameroon, several variables explain food 

insecurity measured by agricultural gross domestic product. The main variables explaining agricultural 

production are (i) public and private investments. The variables of the model are: public investment (𝐼𝑝𝑎𝑡), 

private investment measured by foreign direct investment (Ide), electricity (Elec), rural population 

(𝑃𝑜𝑝𝑡) ,gross domestic product (𝑃𝑖𝑏𝑡), inflation (𝐼𝑛𝑓𝑡), agricultural land (𝑇𝑒𝑟). 

The model for the analysis is as follows: 

 

𝐿𝑜𝑔(𝑇𝐼𝐴) = 𝑎0 + 𝑎1log(𝐼𝑝𝑎𝑡) + 𝑎2log(𝐼𝑑𝑒𝑡) +𝑎3𝑙𝑜𝑔𝐸𝑙𝑒𝑡 + 𝑎4 log 𝑃𝑜𝑝𝑡 + 𝑎5𝑙𝑜𝑔𝑃𝑖𝑏𝑡 + 𝑎6𝑙𝑜𝑔𝐼𝑛𝑓𝑡 +

𝑎7𝑙𝑜𝑔𝑇𝑒𝑟𝑡 + 𝐸𝑡                                                                          (8) 

  

where 𝑎0 is a model constant 𝑬𝒕 is the error term, 𝑎𝑖  represent the coefficients of the variables of the model. 

 

Description of variables of the model 

 

Seven variables were selected to explain the rate of food insecurity in Cameroon. 

 

Dependent or explained variables 

 

The rate of food insecurity at a time 𝒕 (𝑻𝑰𝑨𝒕): it represents a situation where all people have sustainable 

physical, social and economics access to sufficient and nutritional food that meets their need   and dietary 

preference. In this study, the value added of agricultural production proxy by gross domestic product it used as 

dependent variable. 

 



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Independent or explanatory variables 

 

Public investment expenditure (𝑰𝒑𝒂𝒕) expenditure by the state to promote a country’s socio-economic 

development. Public investment in agriculture is used to acquire durable goods and services for uses as means 

of production. Public investment is gross fixed capital formation (GFCF) by general government. The choice 

of this variable is justified by its effectiveness in increasing agricultural production. Figure 1 represent public 

investment in agricultural sector in Cameroon. 

   
 

Source: authors’ own elaboration from Stata 14.0 

Public investment in agricultural sector in Cameroon is periodic and not stationary. There is a succession of 

growth and decline over time. From 1988 to 1992, public investment fell to the lowest level. From 1992, public 

investment will grow and vary moderately until the 2020s. 

 

Private investment measured by foreign direct investment (Ide), 

 

These are net investment inflows to acquire a lasting stake in a sector operating in an economy other than that 

of the investor. The choice of this variable is justified by the fact that FDI uses much of the agricultural land in 

developing countries for its activities.  

 

 
Figure 2: private investment in agricultural sector 

Source: authors’ own elaboration from Stata 14.0 

FDI has increase year to year from 1990 up to 2020 period. 

Figure 1: public investment in agricultural sector in Cameroon 

 



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Electricity (Elec), is considered to be public infrastructure that helps to open up rural areas and improve 

conditions for farming. Electricity encourages the development and the establishment of agri-food industries in 

rural areas. The figure 3 represents the rural population has access to electricity service. 

 

 
Figure 3: Electricity distribution  

Source: Authors’ own elaboration from Stata 14.0 

 

 

 
Figure 4: agricultural population in Cameroon 

Source: Authors’ own elaboration from Stata 14.0  

The curve representing agricultural population is increasing and linear over the period from 1990 to 2020. This 

growth in the agricultural population may be due to an increase in the birth rate in rural areas. 

 

This curve representing access to electricity in Cameroon, from 1988 to 1991, the series is periodic and from 

1991 public investment in electricity increases more and more. show us that Cameroon is multiplying its efforts 

to offer a significant quantity of electricity to its population. From 2019 the curve declines sharply indicating a 



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drop in public investment in the electricity sector in Cameroon. Rural population (𝑃𝑜𝑝𝑡) represents all the 

people living in rural areas who are part of farming households. It refers to people living in rural areas as defined 

by the national statistics institution. The interest of choosing this variable is to see the impact of the increase in 

the rural population on the rate of food security. The evolution of agricultural population in Cameroon is 

represents in figure 4. 

Agricultural Gross Domestic Product (𝑃𝑖𝑏𝑡) is an indicator that quantifies the total value of annual wealth 

production by economic agents in a given territory, while the agricultural gross domestic product represents the 

aggregate of production value, it is annual wealth created by the agricultural sector in a given country. Figure 

5 present the evolution of the agricultural gross domestic product in Cameroon. 

 

 

 
 

Figure 5: agricultural population in Cameroon 

Source: Authors’ own elaboration from Stata 14.0  

 

The curve of agricultural gross domestic product in Cameroon decreasing over the period from 1988 to 2020. 

This decline can be explained from the decrease in marginal labour productivity of the agricultural sector. It 

may be also to the decline in public investment, draining of natural resources and the infertility of agricultural 

land. 

Inflation (𝐼𝑛𝑓𝑡): is a loss of purchasing power of money, resulting in a general and sustained increase in the 

prices of goods and services. Inflation as measured from the consumer price index reflects changes in the cost 

of a basket of goods and services purchased by the average consumer. The inflation was chosen as variable 

because the rise in prices influences consumption power. Figure 6 presents the variation in the prices of 

consumer goods in Cameroon. 

 

 

 

 



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Figure 6: Inflation in Cameroon 

Source: Authors’ own elaboration from Stata 14.0  

 

Figure 6 shows that inflation is not stable. A consumer prices evolves from a minimum and negative value to 

reach a maximum value in 1992. From 1992, the curve decreases and the stabilise between 0 and 5 until 2020. 

 

Agricultural land (𝑇𝑒𝑟), is a part of land that is arable and permanently cultivated or grazed. Arable land 

includes land defines by the FAO as land for temporary crops, temporary land for reaping or grazing, land 

where vegetable gardens are grown and temporary land set aside. Figure 7 presents agricultural land in hectares 

in Cameroon.  

 
Figure 7: Agricultural Land in Cameroon  

Source: Authors’ own elaboration from Stata 14.0 

 

Figure 7 highlights that, the surface of agricultural land in Cameroon has remained stable over time, though 

there was an exponential increase over a short period of one year before falling to in a minimum value by 2020.  



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Results and discussions 
 

Descriptive statistics test 

 

Table 1. Descriptive statistics of the variables 

Source: Authors’ own elaboration from Stata 14.0 

 

Overall comments that can be made from table 1 is that variables relating to gross domestic product (131907), 

public investment (2814199) and agricultural population (42943) have the largest standard deviations, this 

means a great variability or volatility of these variables. Conversely, variables such as agricultural land (1, 3366) 

and access to electricity (1.4793) have a higher standard deviation, that means they are less variable or volatile. 

 

Stationarity test for variables 

 

A time series is said to be stationary weak, if its statistical properties do not vary over time (expectations and 

variances). In this study, to test the stationarity of variables, the augmented Dickey Fuller test was used. This 

test allows to highlight the stationary or non-stationary character of a time series by determining a deterministic 

or stochastic trend (Bourbonnais,2006). The Dickey-fuller test allows to test the stationarity of a series with for 

hypothesis zero (the series is not stationary) against and alternative hypothesis (the series is stationary) at the 

error threshold of 5%.  The decision rule (accept or reject null hypothesis) consists of comparing the absolute 

value of the Augmented Dickey-Fuller statistic (ADF) with the absolute value of Mc Kinnon’s (1973) critical 

value (CV) read. 

 

Table 2. Results of stationary test on the variables 

Source: Authors’ own elaboration from Stata 14.0 

 

Variables  Obs. Average Standard 

Deviation 

Min. Max 

Agricultural gross domestic product 33 5,806245 1319075 5,525321 6,215273 

Inflation 30 16,76927 1.180574 12,98454 19,41165 

Agricultural land 27 11,1802 1.336653   7,972011   12,82858 

Direct agricultural investment 29 15,91068   1.43013 12,38236 17,75597 

Agricultural population 33 18,96968 42943 18,45742 21,07685 

Public investment 29 6,729237 2814199 6,143799 7,102718 

Electricity 27 3,632012 1.479305  1,479305  5,189085 

Variables Dickey-Fuller statistical 

values 

P-values at 5% 

critical threshold  

Integration 

order 

Public investment -6,303 -2,994 I (1) 

Foreign Direct Investment -7,268 -3 I (1) 

Electricity -7,053 -3,00 I (1) 

Agricultural population -10,727 -2,980 I (0) 

Inflation  -8,100 -2,983 I (1) 

Agricultural gross domestic product -3,998 -2,980 I (0) 

Agricultural land  -6,900 -3,00 I (1) 



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Table 2 presents the results of the stationarity tests for the variables of the model. It can be noticed that variables 

in the econometric model are not stationary at any level. These values are augmented Dickey-Fuller statistical 

values where I (0) represents the order of integration of the level test and I (1) the order of integration of the 

first difference test. Thus, the variables, agricultural gross domestic product and agricultural population are 

integrated at level and are therefore stationary that means that the means, variances and covariances do not 

depend on time. The other variables: agricultural land, inflation, foreign direct investment, public investment 

and electricity are stationary in first difference. The variables have different integration orders, meanwhile they 

are integrated at different levels so the co-integration test will be performed to see the co-integration between 

these variables. 

 

Johannsen co-integration test between variables 

 

The co-integration test is to examine how two or more non-stationary time series can be tested, to verify whether 

the combined value of these series is stationary. Table 3 presents the results of Johannsen co-integration test. 

Results from table 3 shows that the trace statistical values are greater that the critical values at the 5% threshold. 

Therefore, the null hypothesis of co-integration between variables is accepted. 

 

Correlation test on series 

 

In the analysis of our series, it is necessary to see the correlation that exists between the variables of the model. 

Table 4 presents the correlation test result. 

 

Model validation tests 

 

Jarque-Bera test is used to check the normality of statistical distribution. Normality exists when the Jarque-

statistical value is less than 5.99 or when its probability is greater than the 5% threshold. Table 5 present the 

results of Jarque-Bera test. 

The Jarque-Bera test of normality gives a probability of 0.00 less than 5%, it can be concluded that values are 

not well distributed or do not follow the normal law (normal distribution). 

 

Breush-Godfrey test, allows to check whether the errors are auto-correlated or not. There is autocorrelation if 

statistical value calculated in absolute value is less than the unit. Table 6 present the result of Breush-Godfrey 

test. 

 

Table 3.  Results of Johannsen co-integration test. 

 

 

 

 

Source: Authors’ own elaboration from Stata 14.0 

 

 

 

 

Rank Trace statistics  P-value at 5% 

0 33,824 0, 1 

1 6,618 0,634 



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Table 4. the correlation test result 

Source: Authors’ own elaboration from Stata 14.0 

The results from table 4 shows that the dependent variable (agricultural gross domestic product) is not correlated 

with its explanatory variables. 

 

Table 5.  Result of normality test of Jarque-Bera 

Equations Df Statistical values Prob 

agricultural gross domestic product 2 68,265 0000 

Set 2 68,265 0000 

Source: Authors’ own elaboration from Stata 14.0 

 

 

Table 6. Breush-Godfrey test 

Observation Statistical value Probability 

1 2,444 0,1323 

Source: Authors’ own elaboration from Stata 14.0 

 

The result of the Breush-Godfrey test gives us a statistical value in absolute terms that is greater than unity, so 

we can conclude that the errors are not correlated. 

 

Ramses test, allows to see whether the model suffers from the omission of one or more relevant variables. The 

test consists of testing the null hypothesis that the model is well specified (probability greater than 5%) against 

the alternative hypothesis that the model is not well specified (probability less than 5%). Table 7 present the 

result of Ramses test. 

 

 

 

 Inflation Agricul

tural 

land  

Foreign direct 

investment  

Agricult

ural 

populatio

n 

Agricultural 

gross 

domestic 

product   

Public 

investme

nt   

Electric

ity  

Inflation 1.0000       

Agricultural 

land 

-0.4978 1.0000      

Foreign 

direct 

investment 

-0.5082 0.9983 1.0000     

Agricultural 

population 

-0.4217 0.5421 0.5887 1.0000    

Agricultural 

gross 

domestic 

product   

-0.0390 -0.0005 -0.0218 -0.1266 1.0000     

Public 

investment   

-0.4337 0.5468 0.5937 0.9974 -0.1627 1.0000  

Electricity   -0.5152   0.9953 0.9992 0.6182 -0.0330 0.6237 1.0000 



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Table 7. Result of Ramses test 

Missing observation Chi-2 Df Prob ˃ Chi-2 

1 6,1 1 0,004 

Source: Authors’ own elaboration from Stata 14.0 

 

Table 7 present the probability of 0.04 which is less than the value of 0.05, this means the linear model is not 

correctly specified. 

 

Cusum and Cusum squares stability test, this test enables to check the stability of the estimated model. There is 

stability if the curves do not leave the corridor. Figure 8 present the result of Cusum test. 

 

 
Figure 8: Cusum test 

Source: Authors’ own elaboration from Stata 14.0 

 

Figure 8 shows that the curve associated with this test does not cut through the corridor (in red). The conclusion 

is that the model is therefore stable at 5% threshold. 

 

Final results, interpretations and discussions  

 

Public investment and food security 

 

The summary of the results of the estimate model is gives in table 8. 

The overall comments that can be made from table 8 are as follows: the estimate model gives a probability 

associated with Fisher statistic of 0.0077. As this gives a lower probability than the 5% significance level, we 

can conclude that the model is globally significant. A coefficient of determination of the model is equal to 0.39 

(R2 = 0.39) this shows that 39% of the variability of gross domestic product is explained by agricultural 

population and public investment. The results of individual significance tests by the probabilities associated 

with the Student statistic show that the variable public investment and the agricultural population are significant 

at 10% significance level. 

 



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Table 8. Results of the estimated model 1 

Variables Coefficient Standard 

Deviation 

Statistical values  P- value 

Agricultural population 1,187814 682194 1,74 0,095 

Public investment -0,9226278 4636124 -1,99 0,059 

Inflation 0,059 0111875 -1,17   -1,17   

Constant -10.26126 9.902853 -1,04 0,311 

Probabilité  > F R-carré R-carré ajusté 

0.0077 0.3977 0.3192 

Source: Authors’ own elaboration from Stata 14.0 

 

 

Public investment (IPA): appears with a negative sign, this means it is negatively correlated with the agricultural 

gross domestic product. It is also significant at 10% level, meaning that an increase of one unit in public 

investment results in a 0.9226 reduction in the value of agricultural domestic product. The reduction in 

agricultural value added (in gross domestic product), following an increase in public investment can be 

explained by the fact that not all investment is favourable for the development of the agricultural sector. Increase 

in public spending on long-term investments such as economic and social infrastructure do not have a direct 

impact on agricultural production.  The increase in government spending may be due to tax increases that have 

negative effects on farmers’ incomes.  These results are different from the results of Biligil (2017) when 

studying public spending and agricultural growth in Cameroon, find out that an increase in public spending in 

the agricultural sector is followed by an increase in agricultural production.Rural population (Pop): the 

coefficient associated with the agricultural population variable has a positive sign. This variable is significant 

at 10% level. However, an increase of one unit in the rural agricultural population will lead to an increase of 

1.187 in agricultural gross domestic product.  An increase in the highly active population is a labour force. The 

quality of care and training of farmers determines the level of agricultural production. The more educated and 

healthier the agricultural population, the more productive it will be and consequently an increase in agricultural 

value added (Mengoub,2018).Inflation on consumption goods, the coefficient associated with inflation variable 

is positive but not significant in the model.  

 

Foreign Direct Investment and food security 

 

The summary of the results of the estimation 2 is gives in table 9 

 

Table 9. Results of the estimation 2 

Variables Coefficient Standard 

Deviation 

Statistical values  P- value 

Agricultural Land -0,209 0,400 -0,23 0,000 

Foreign direct investment  0,202 0,348 5,82 0,000 

Inflation -0,003 0,24  0,24 0,815 

Constant 4,847 357 13,55 0,000 

Probability > F R2 R2 adjusted 

0,0001   0,650 0,6000     

Source: Authors’ own elaboration from Stata 14.0 



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The results of estimation 2 from table 9 show that a probability associated with the Fisher statistic is 0.0001, 

this probability is below the 1% significance level, therefore the model is globally significant.  The variables 

foreign direct investment, agricultural land and inflation are globally significant at 1% significance level. In 

this model the coefficient of determination R2 shows 64% of the variability of gross domestic product is 

explained by foreign direct investment and agricultural land. 

 

Agricultural land: the coefficient associated with the agricultural land variable has a negative sign, but 

significant at 1% significance level. Therefore, an increase of one unit of agricultural land will lead to a decrease 

of 0.20 in agricultural value added (agricultural domestic product). This reduction in added value may be due 

to poor use of the land by farmers. Many factors can explain this situation: the renting of arable and irrigable 

by industrial firms, the lack of financial means to practice extensive agriculture on a large area of agricultural 

land, the scarcity of arable land (farmers are not able to access arable land) therefore reduce agricultural value 

(Corporations, 2009). 

 

Foreign Direct Investment (FDI): is positively correlated with agricultural gross domestic product at the 1% 

significance level. The entry of one unit of FDI in Cameroon will increase gross domestic product by 0.20. this 

result is in line with Ibrahim, Thiam, and Malick (2020), who found out that FDI positively impact agricultural 

sector. The increase in agricultural value added is the outcome of the benefits of FDI inflows into agricultural 

sector through various channels including: technology transfer mainly new techniques in agricultural practices 

that enable local population to practice extensive and highly profitable agriculture, building infrastructure that 

directly or indirectly supports the development of agricultural activities and financing (Zidouemba and Gerard, 

2015). In fact, foreign investors are intervening in agricultural sector in Cameroon through the financing of 

different agricultural projects from various ministries. 

 

Inflation: this variable is negatively correlated with agricultural value added but is not significant in the model. 

 

Conclusion 

 

This paper aims to access the effects of both public and private investment on food security in Cameroon. 

Literature review highlights that food security for the population is an objective pursued by several institutions 

and government. The study used multiple regression model and error correction model to stress the relationship 

between both public and private investment on food security.  The different estimations show the significant 

effect of both investment on food security.  Public investment appears to have a negative effect on food security 

while private investment proxy by Foreign Direct Investment (FDI) have a positive effect on food security. The 

argument for these controversial results, is that an increase in government spending may be due to tax increases 

that have negative effects on farmers’ incomes and the fact that not all government investment is favourable to 

agricultural sector. The positive effect of Foreign Direct Investment is mainly explained by the involvement of 

foreign investors mainly Non-Governmental Organisations (NGOs) in the financing of various agricultural 

project from different ministries in Cameroon. Thus, to ensure food security in Cameroon, particular attention 

needs to be paid to rural areas, where the majority of poor live. The government therefore can invest in the 

construction of social and economic infrastructures mainly road and communications infrastructures in order to 

enable farmers to obtain accurate information on supply and demand and easily access to local and national 

markets in order to deliver their agricultural products. Government of Cameroon can ensure vocational training 



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of Cameroonian youth that represent the major of active population through the creation of training schools for 

agricultural practices and natural resources transformation that are important factors for food security of the 

Cameroonian population. Finally, Cameroonian government can ensure that policies, laws and regulations that 

govern agricultural investment mainly land management are basic conditions for modern agriculture and ensure 

social peace. 

 

Declaration  

 

Acknowledgment: N/A 

 

Funding: N/A 

 

Conflict of interest: N/A 

 

Ethics approval/declaration: N/A 

 

Consent to participate: N/A 

 

Consent for publication: N/A 

 

Data availability: From the authors 

 

Authors contribution: Authors contribution: Votsoma Philémon led the conceptualization, data analysis, and 

manuscript preparation; Vangvaidi Albert  contributed to methodology and validation; Pene Zongabiro Nina 

Pelagie handled statistical analysis; Mohamadou Oumarou assisted with literature review and editing; 

Ndouyang Balguessam Bruno managed data collection; Mohamadou Sani revised  the  manuscript;  Bilkissou 

Hadjara  worked  on  coding  and  model  implementation;  Kaltoumi Raihanatou provided  supervision;  Fatal 

Esperence aided  in  data  curation; Abdoulaye assana  reviewed  and  revised  the  manuscript 

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