Understanding What Informs the Choice of Agro-Processing Firm Siting: Evidence from a Developing Country

Abstract

This article investigates the complexity of factors that influence the location choice for agro-processing firms from a developing country and SME perspective. It further examines the underpinning role of government policies regarding the siting, operation and effectiveness of agro-processing firms. Using a quantitative methods research design, 124 randomly sampled agro-processing firms were surveyed. The collected data was analysed using exploratory factor analysis and logistic regression analysis. It emerged that business support, economic and socio-cultural factors had a direct impact on decisions regarding the siting of agro-processing firms. Also, the availability of raw materials and cheap labour equally contributes to such decisions. Government policies turned out to be ineffective in influencing decisions on the siting of agro-processing firms. In terms of policy, there is a need to refocus efforts to incentivise agro-entrepreneurs, open up access to sustainable funding options and build the innovative capacity of agro-processing firms. This is critical to attracting agro-processing investments to remote areas such as Northern Ghana to revamp the local economy. This article highlights the impact of policy on the location of agro businesses in developing countries and also demonstrates the nexus between the location of a business and its performance.

Keywords

Agro-processing developing country government policy socio-cultural factors siting

Background to the Study

Businesses rely on strategic decisions such as location to achieve competitive advantage. A number of studies have examined the factors that determine the choice of business location and the role of incentives devised to entice businesses to such locations (Cheng and Kwan 2010; Head and Ries 2009; Moriarty 2018). The multiplicty of factors influencing business location decisions may be personal, economic and socio-cultural. While others believe that tangible, non-transferable assets lead entrepreneurs to a particular locality, other investors regard social capital and fiscal considerations as significant location determinants (Cheng and Kwan 2010).

There is considerable interest among academics and policymakers regarding the analysis of decisions on the location of firms. The interest from academics is due to the great uncertainty surrounding the process of making location decisions; mainly in terms of the methodologies used and the spatial aggregation issues (Ketokivi et al. 2017; Najib, Kiminami and Yagi 2011). Policymakers are more concerned with the effects of business location on economic growth and identification of location determinants to implement effective entry-promotion policies (Arauzo-Carod 2013). It is thus imperative that businesses are sited in the right location for cost minimisation and other benefits to remain successful (Plaziak and Szymańska 2014).

Recent research efforts on business location appears to focus on the effect of economic governance and location choice on performance (Leiblein and Awate 2015; Seetanah, Sannassee and Neeliah 2020); the foreign location choice of businesses (Kim and Aguilera 2016) and the location choices of businesses in deprived regions (Orhan 2016). More importantly, it is increasingly becoming imperative that research efforts on business location are devoted to the attraction of agro-processing firms to rural communities of developing countries. This is critical towards the eradication of poverty in the rural areas of such countries where agricutlure is the main economic activity (Kumi-Boateng, Mireku-Gyimah and Stemn 2015; Kuwornu, Bashiru and Dumayiri 2014). Also, there is limited capcity among the rural folks to add value to the farm produce (Devaux et al. 2018; Nagai et al. 2009). The situation in Ghana is not different especially in the Savannah Ecological Zone (Northern region, Upper West region and Upper East region) of Ghana where households depend on rain-fed agriculture for production. Crop production and livestock keeping are largely rural comprising 92% of rural Savannah (Ghana Statistical Service 2015). Due to the high level of poverty in these three northern regions, households need to cope with increasing difficulties in agricultural production by diversifying into non-farm activities related to farm produce such as processing (Adams, Caesar and Asafu-Adjaye 2021; Akinola et al. 2014). Based on this background therefore, this current study seeks to understand the decisions which inform the location of agro-processing firms in the Northern part of Ghana.

Statement of the Problem

Location incentives is one of the key strategies utilised by governments in developing countries to ensure that investments are evenly distributed (Lang 2012; Rephann 2020). These incentives reduce the tax burden of enterprises to encourage investment in particular sectors or localities. With most investments in Ghana mainly concentrated in the Greater Accra, Ashanti, Central and Western regions, the use of incentives is critical to attracting agro-processing investors to regional/rural areas for growth and development of the local economy (Obeng 2016).

In as much as tax incentives remain a key strategic indicator for the business location of agro-processing firms in Ghana, previous studies have not paid attention to it as focus has been on the factors that influence the business location decisions of biotechnology firms (Koo et al. 2009; Pajunen and Javinen 2018;), automobile firms (Hanawalt 2020; Klier and Rubenstein 2010) and high-tech firms (Hackler 2004). Related studies in Ghana have focused their attention on issues such as determinants of firm growth (Al-Hassan 2015; Owoo, Oduro and Ackah 2019), factors that influence risk management decisions and the influence of macro-environmental factors on firm performance (Akrofi 2016). This article thus focuses on identifying the factors that influence decisions regarding the siting of agro-processing firms in Northern Ghana.

This study offers a developing country insight on factors that influence decisions for the siting of agro-processing firms. In their review of research on agro-processing business firms’ location between 1990 and 2014, Johnson, Nketia and Quaye (2015) noted that previous studies have primarily focused on multinational general agro-business location (Kimelberg and Williams 2013). Although aspects of these studies are useful, the authors encouraged more studies as well as a broader focus pertaining to studies from Africa. In addition, although considerable research has highlighted the significance of business location in business performance, the focus has remained on multinational firms (Carlton 2011).

Literature Review

Agro-business and Socio-economic Development

In many developing countries such as Ghana, agro-business plays a critical role in economic development. It positively impacts gross domestic product (GDP) and enhances employment opportunities. Bruinsma (2009) explains agro-process to include all operations that are involved in the manufacturing of farm supplies, production, operations on the farm as well as the transformation and distribution of farm commodities and items made from them. Also, Wilkinson and Rocha (2009) posited that agro-processing business involves all activities right after harvesting through the transformation, preservation and preparation of agricultural produce for intermediary or final consumption of food and non-food products. Earning of foreign exchange is, one of the key benefits of the agro-processing business and provides avenues for employment (FAO 2008; Ogunmodede, Ogunsanwo and Manyong 2020). Barrett et al. (2010) also opined that there is greater success when there is an integrated approach in the agriculture business in which industrialisation is adopted coupled with diversification of occupation and land reforms, structuring of credit systems and massive investment by government in the agriculture sector. According to Beintema and Gert-jan (2008), there is a strong relationship between agro-business and poverty alleviation due to the fact that agro-business encourages entrepreneurial skill and also raises the demands for agricultural products. Consequently, this enhances productivity and quality of agricultural production, farm returns and economic stability for rural households, food security and innovation throughout the value chain.

Business Location Decisions

Vlachou and Iakovidou (2015) explain ‘location’ to be a place where a factory, warehouse, office or any business enterprise is built. This article adopts the above definition. Given the strategic importance of business locations, decisions regarding it must be thorough and systematic. Business location decisions may involve searching for a new site for a new business or its branch or relocating the business or a unit of it to a different site (Mariotti, 2005; Perez-Benitez, Gemar and Hernández 2021). It is, therefore, obvious that the process is very complex, often encompassing several phases, persons, departments and objectives (Lee 2008). Once a company invests its assets in a specific location, the company accepts the prevailing conditions at the particular place where the facilities will be built and where future operations will be conducted. The effects of proper location of operations are enormous, and it affects the conditions under which the business will be conducted, including the size of operations depending on how attractive the location is, the level of costs incurred and consequently, the efficiency and effectiveness of the whole organisation.

In recent times, the location of businesses is influenced by factors such as globalisation, market changes driven by technology and socio-demographic, cultural and environmental changes (Małgorzata and Siemińska 2012). However, the factor covering the broadest array of issues is government policy (Adam 2012). These policies are expected to define priorities and main guidelines for economic and social activities; and create certain conditions and principles for the functioning of various business entities. These activities could stimulate or restrict entrepreneurs to locate at a particular region or state or be instrumental in governments’ regulatory efforts. Therefore, government policy, in combination with the global situation, basically determines whether or not business is good for a given entity, affecting the firms’ financial situation and the nature of the location decisions it makes (Donnelly and Manolova 2020; Mast 2020).

Tax rebates offer additional incentives for agro-processing businesses. In Ghana for instance, agro-processing firms in the Northern Savannah Ecological Zone enjoy the lowest income tax rate of 5%. Taxes are however a smaller part of a larger network of government interventions that could affect firms either negatively or positively (Wanjiru et al. 2013). These interventions certain share a nexus with economic factors which must be considered during firm location decisions.

Businesses consider product markets as one of the key factors when making decisions on location. Product markets are also the source of final demand (Henderson and McNamara 2000). Goetz (1997) found that access to product markets had a positive influence on food manufacturing site location. Closeness to product markets is more important for demand-oriented food processing firms because most of the total production costs of these firms are associated with distribution of final products (Donnelly and Manolova 2020; Henderson and McNamara 2000).

Manufacturing productivity depends on labour availability. A diversified work force increases the likelihood of acquiring workers with the necessary skillset to fill positions at all levels of production. It is hypothesised that a positive relationship exists between food processor location decisions and labour availability. The need for specialised expertise and skills has increased the pressure on firms to find the right type of talent and patterns have been identified showing firms moving to certain locations in order to find the right talent (Porter and Heppelmann 2015).

Theoretical Perspective

The conceptual model of this article is underpined by the New Economic Geography theory (Krugman 1979).

The New Economic Geography (NEG) or Krugman Theory

Also, known as the Krugman model, the New Economic Geography theory (Krugman 1979) effectively enables the investigation of location decisions among firms from both a theoretical and empirical perspective (Ellison et al. 2010). It suggests that locations link with similar activities and generate valuable agglomeration economies for firms namely better access to skilled labour (labour market pooling), specialised supplies (shared inputs) and knowledge spill overs from competing firms. As a result, firms’ location choices may create competitive advantage by improving access to key resources. Particularly, Shaver and Flyer (2000) argue that large firms may be less motivated to co-locate because their presence would dramatically increase local economic activity, thereby, reducing costs for neighbouring competitors. Addressing potential knowledge spill overs specifically, Alcácer and Wilbur (2007) argue that the cost of knowledge lost to competitiors depends on whether competitors can absorb and use that knowledge. When other competing firms cannot leverage the knowledge garnered from technically advanced firms, industry leaders are free to enjoy the benefits of agglomeration without the attendant risk. Further, the Krugman model demonstrates that the closer a firm is to the centre of economic activity, the better the access to a market in order to sell the firm’s goods.

Conceptual Model and Hypothesis

The following econometric model is specified as for the study: In (Pi/1–P1) = (Pi/1–P1) = β0 + B1X1 + B2X2 + B3X3 + B4X4 + B5X5 + B6X6 + B7X7 + ε

Where: Pi is the likelihood of a firm being located at the northern region

(1 – Pi) is the likelihood of a firm not being located at the northern part of Ghana.

The ratio Pi/ (1 – Pi), known as the odd ratio, is simply the odd in favour of a business being located at the northern part of Ghana.

β0 = Constant

X1 = Infrastructural factors

X2 = Business support factors

X3 = Economic factors

X4 = Government–business relations factors

X5 = Socio-economic factors

X6 = Demand factors

X7 = Quality of life factors

β1 = Co-efficient of infrastructural factors

β2 = Co-efficient of business support factors

β3 = Co-efficient of economic factors

β4 = Co-efficient of government–business relations factors

β5 = Co-efficient of socio-economic factors

β6 = Co-efficient of demand factors

β7 = Co-efficient of quality of life factors

ε = Error term

Overall, the logit model means that the log of the odd ratio is a linear function of the explanatory variables.

Figure 1.

Source: Field data (2018).

In this study, the likelihood of whether or not a firm will locate its agro-processing business in the Northern region takes the value of 1. On the other hand, if a firm will not locate its agro-processing business at the Northern region but will do so in the Upper East and West, takes on the value of zero (0). Consequently, the response variable is a limited dependent variable. The independent variables are measured as follows based on the literature include:

Infrastructure—Availability of road, high volume of water supply, consistent power supply, sufficient educational facilities, availability of telecom services, availability of IT infrastructure, availability of health facilities, availability of public waste systems, availability of rail transport and availability of natural gas supply (MacCarthy and Atthirawong 2003; Turhan, Ozbag and Cetin 2007).

Business support—Human resource business support, insurance services, adequate security services, availability of spare parts, availability of equipment maintenance services, availability of advertising agencies, availability of hospitality services, availability of IT support, availability of industrialised zoned land and distributors for company’s product.

Economic factors—availability of skilled labour, favourable tax rates, availability of short-term finance, availability of long-term finance, availability of land, low transport costs, low cost of labour, low cost of land and low cost of construction (MacCarthy and Atthirawong 2003; Tripathi and Kumar 2017).

Government–business relations—fairness of the judicial/court system, effective contract management, less management time spent in dealing with regulatory bodies, effective government ministries, agencies and departments in supporting businesses, opportunities for securing government contracts, low degree of bribery and corruption in securing government services, favourable tax policy (rate and incentives) and provision of a stable political environment by the local assembly (Indarti 2004; Tripathi and Kumar 2017).

Socio-economic factors- customs of the people, values of the people, language of the people, religion and beliefs of the people, gender roles of the society, population density, customer buying habits, attitude towards work, attitude towards local products and differences in social classes (MacCarthy and Atthirawong 2003; Tripathi and Kumar 2017).

Demand factors- High purchasing power of the people, high disposable income levels of the people, availability of marketing facilities, low product facilities, consumer tastes and preferences, low competition in the market, accessible distribution channels, availability of substitutes of the products, market potential of company’s products, and seasonal demand for products (Tripathi and Kumar 2017).

Quality of life- Low crime rate, fresh air and water, availability of outdoor recreational opportunities, availability of quality healthcare, availability of public safety services such as police and fire stations, low cost of living, good attitude of the people towards conflicts, availability of residential housing, low cost of residential housing and availability of municipal services (Elly 2014).

Based on the discussion of the literature, the following hypotheses were developed:

H₁: Firms are likely to either locate at Northern Region or Upper East and Upper West regions due to the presence of infrastructural factors.

H₂: Firms are likely to either locate at Northern Region or Upper East and Upper West regions due to the presence of business support factors.

H₃: Firms are likely to either locate at Northern Region or Upper East and Upper West regions due to the presence of economic factors.

H₄: Firms are likely to either locate at Northern Region or Upper East and Upper West due regions to the presence of government’s business relations factors.

H₅: Firms are likely to either locate at Northern Region or Upper East and Upper West regions due to the presence of socio-cultural factors.

H₆: Firms are likely to either locate at Northern Region or Upper East and Upper West regions due to the presence of demand factors.

H₇: Firms are likely to either locate at Northern Region or Upper East and Upper West regions due to the presence of quality-of-life factors.

Methodology, Sampling and Procedures

In total, 124 randomly sampled agro-processing firms were surveyed for the survey. Once the pilot survey was successful, the questionnaire was confirmed to be reliable and valid. The questionnaire was pre-tested using a sample of 12 respondents that were not part of the main study. This also helped to reduce error. It was also done to check its general and specific use of language, consistency and ambiguity, and completion time (Sekaran and Bougie 2016). The reliability of the data collection instrument was checked using the Cronbach’s alpha values. The general rule range of +.5 to +.9 was applied (Taber 2018). The lowest Cronbach Alpha value was .618 while the highest being .877. This gives the implication that the items used in measuring the variables were statistically reliable. The reliability results are presented in the data analysis section.

Relevant questions to test each of the identified variables were developed and grouped appropriately. A five-point Liker scale was adopted (Likert 1932), to measure variables that could not be observed as discrete values. The five-point Likert scale also contributes to improving the construct validity (Bandalos 2014). A structured questionnaire that had closed-ended questions were used to achieve a standardised response pattern and aid easy coding. Levy and Lemeshow’s (2013) guideline on the design of survey instruments was followed for this study.

In all, there are about one hundred and sixty (160) agro-processing firms located in the Savannah Ecological Zone in the Northern part of Ghana. The minimum sample based on Raosoft (2004) indices should be 114 as a result. However, following Saunders et al. (2009) the actual sample was calculated using the formula shown below:

n^{a} = \frac{n \times 100}{r e}

Where n^a is the actual sample size required;

n is the minimum sample size (calculated by Raosoft 2004) = 114

r^e is the estimated response rate expressed as a percentage = 92

The calculated actual sample was as follows when imputed into the formula:

\begin{matrix} n^{a} = \frac{114 \times 100}{92} \\ n^{a} = 123.91 \approx n^{a} = 124 \end{matrix}

Ethical steps were taken to control for error and biases. Using the trained research assistants, the study respondents were given the assurance of anonymity and confidentiality of their responses; meaning the researcher–participant relationship were not be exploited (Polit et al. 2001). Respondents were not coerced into taking part in this study. Respondents were given the right to decide whether to participate without incurring any penalty (Polit et al. 2001). Respondents were approached and the purpose of the study explained to them (to prevent inaccurate responses) and money was not offered to participants.

Results and Findings

Quantitative Results

Demographic Characteristics

The distribution of the demographic characteristics related to the respondents is shown in Table 1. More female students (62.1%) were involved in the study compared to males (37.9%); suggesting Ghana’s agro-processing sector in the North is dominated by women.

Table 1.

Demographic Data of Respondents.

Gender	Frequency	Percentage (%)
Male Female Total	47 77 124	37.9 62.1 100
Age	Frequency	Percentage (%)
26–30 31–35 36–40 41–45 46–50 51–55 56 and above Total	10 21 18 24 22 16 13 120	8.1 16.9 14.6 19.5 17.9 13.0 10.4 100
Educational level	Frequency	Percentage (%)
Secondary/basic Degree/diploma Masters/postgraduate No formal education Total	19 21 16 68 124	13.0 16.9 15.4 54.8 100
Location of business	Frequency	Percentage (%)
Northern region Upper-east and west Total	102 22 124	82.3 17.7 100
Agro-processing products	Frequency	Percentage (%)
Rice Shea butter Meat Others Total	41 50 13 20 124	33.6 41.0 10.7 16.1 100

Source: Field data (2018).

Exploratory Factor Analysis

An exploratory factor analysis (EFA)was performed on the 7 categories of business location factors to avoid data complications by ensuring that variables did not measure different aspects of the same underlying variable (Henson and Roberts 2006). The EFA allowed the reduction of the total number of variables to process and, most importantly, assess construct validity (Groth-Marnat 2009). The total KMO, Bartlett’s Test of Sphericity, variance explained and pattern matrix showing the factor loadings and eigen values for all the 7 factors can be found in the attached Appendix A. All KMO values for individual items were > .50, which is well above the acceptable limit of .5 (Field 2009).

Logistic Regression Results

The logistic regression analysis was useful in assessing the predictive relationship between business location factors (infrastructure, business support, economic factors, government business relations, socio-cultural, demand, quality of life) and the choice of business location (either Northern Region or Upper-East and West) by agro-processing firms. The results of the logistic regression analysis are presented as follows:

The Wald value in Table 2 assesses the significance and strength of the relationship between the independent variables (business location factors) and the dependent variable. A Wald value of less than .05 implies a significant relationship exists between the independent variable and the dependent variable. A value greater than .05 indicates an insignificant relationship. It is evident that business support (Wald = 4.465, Sig = .035) is a significant predictor of choice of business location. Moreover, economic factors also significantly predict the choice of business location for agro-processing firms in the Northern part of Ghana (Wald = 4.047, Sig = .04). The results also showed that a significant relationship exists between socio-cultural factors and choice of business location for agro-processing firms (Wald = 9.815, Sig = .02). Further, a significant relationship exists between quality of life and choice of business location for agro-processing firms in the Northern part of Ghana (Wald = .128, Sig = .02). On the contrary, there were insignificant relationships between the remaining factors (Infrastructure: Wald = .063, Sig = .80; Government–Business Relations: Wald = 2.547, Sig = .11; Demand Factors: Wald = .128, Sig = .721) and choice of business location for agro-processing firms.

Table 2.

Variables in the Equation.

		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1^a	Infrastructure	–.279	1.117	.063	1	.802	.756
	Business support	3.107	1.470	4.465	1	.03	22.35
	Economic factors	2.556	1.271	4.047	1	.04	12.881
	Government–business relations	1.792	1.123	2.547	1	.111	6.001
	Socio-cultural factors	–3.427	1.094	9.815	1	.02	.032
	Demand factors	–.377	1.055	.128	1	.721	.686
	Quality of life	–3.905	1.691	5.336	1	.021	.020
	Constant	1.380	5.580	.061	1	.805	3.976

Source: Field data (2018).

Note: a. Variable(s) entered on step 1: Infrastructure, Business support, Economic factors, Government–business relations, Socio-cultural factors, Demand factors, Quality of life: Level of significance = .05.

In terms of the strength of the relationship between the significant independent variables (business support, economic factor, socio-cultural factors and quality of life) and choice of business locations, the odds value which is expressed as Exp (B) was used. From the results, it was found that business support predicted the likelihood of business location choice for agro-processing firms by 22.35 times while economic factors predicted the likelihood of business location choice for agro-processing firms by 12.81 times. However, socio-cultural factors predicted the likelihood of business location choice for agro-processing firms by .032 times while quality of life predicted the likelihood of business location for agro-processing firms by .020 times. The implication is that whether agro-processing firms decide to locate in the Northern Region or Upper-East/West is highly dependent on business support and economic factors and lowly dependent on socio-economic factors and quality of life.

From the results, it could be inferred that four out of seven factors have positive relationships with the likelihood of agro-processing firms locating to the Northern Region or Upper East and Upper West Regions. These four factors comprise business support factors, economic factors, socio-cultural factors and quality of life factors. The positive relationships that they have with the likelihood of agro-processing firms locating to either the Northern Region or Upper East and West gives the implication that the more these four factors increase, the more agro-processing firms are likely to locate to either the Northern Region or Upper East and Upper West Regions and vice versa. Moreover, there were insignificant relationships between factors such as infrastructure, government business relations, demand factors and the dependent variable. The implication, therefore, is that infrastructure, government–business relations and demand factors cannot statistically prove the likelihood that agro-processing firms will locate to the Northern Region or Upper West and East Regions. Table 3 provides a summary of the hypothesis tested.

Table 3.

Summary of Hypotheses.

	Hypotheses	Outcome
H ₁	Firms are likely to either locate at northern region or upper east and upper west due to the presence of infrastructural factors.	Not supported
H ₂	Firms are likely to either locate at northern region or upper east and upper west due to the presence of business support factors.	Supported
H ₃	Firms are likely to either locate at northern region or upper east and upper west due to the presence of economic factors.	Supported
H ₄	Firms are likely to either locate at northern region or upper east and upper west due to the presence of government–business relations factors.	Not supported
H ₅	Firms are likely to either locate at northern region or upper east and upper west due to the presence of socio-cultural factors.	Supported
H ₆	Firms are likely to either locate at northern region or upper east and upper west due to the presence of demand factors.	Not supported
H ₇	Firms are likely to either locate at northern region or upper east and upper west due to the presence of quality-of-life factors.	Supported

Source: The authors.

Discussion, Implications and Recommendations

Discussion of Results

In discussing the findings of the study with empirical literature, it could be deduced that there were consistencies and inconsistencies between the study’s findings and empirical literature. This is because in some instances, there were aspects of the findings that was in congruence with that of literature and, in some cases, there were inconsistencies between the findings and empirical literature. For instance, in a study by MacCarthy and Atthirawong (2003), their findings identified infrastructural factors such as modes of transportation, quality and reliability of utilities such as water supply, power supply and telecommunication systems as the factors which influence the business location decisions of manufacturing firms. In another study conducted by Amimo (2013) to assess the factors that influenced the location decisions of manufacturing firms in Kenya, infrastructure was identified as one of the factors together with three other factors namely roads, stable social and political environment as well as ease of doing business. However, in this study, infrastructural factors were among the factors which influenced the likelihood of agro-processing firms locating to the Northern Region or Upper East and West.

This gives the indication that in terms of infrastructure as a factor which determines the likelihood of firms locating to either the Northern Region or Upper West and East Regions, the findings of the study were inconsistent with that of empirical literature. Moreover, empirical works from MacCarthy and Atthirawong (2003) identified legal factors such as compensation laws, insurance laws, industrial relations laws among others as factors that influenced the location decisions of manufacturing firms. This was also inconsistent with the study’s findings since legal factors were not identified as part of the factors which influenced the likelihood of firms locating to either the Northern Region or Upper East and West Regions. Moreover, government and political factors such as government structure, consistency of government policy and government stability were identified as factors which enhance business location factors of manufacturing firms. However, this was inconsistent with this study since government–business relations was not identified as a factor which influenced the likelihood of firms locating to either the Northern Region or Upper West and East Regions.

That notwithstanding, the study confirmed with empirical literature regarding economic factors. This is because in a study conducted by Tripathi and Kumar (2017) on economic determinants that influenced business location decisions firms in India, it was found that economic factors such as operating profits, fixed assets, material costs and working capital did have positive impact on firm location decisions to large cities in India. Moreover, the study by MacCarthy and Atthirawong (2003) also found that quality-of-life factors, community attitudes towards business and industry as well as low crime rate and good standards of living among others did contribute to the location decisions of manufacturing firms. In another study conducted by Kilvits (2012), the researcher found that quality of life factors such as housing, environment and infrastructure, low crime rate, educational systems, low cost of living, quality and cost of housing, quality of water and air as well as the existence of recreational facilities were important determinants for firm location decisions. Furthermore, in line with the findings of the current study, social and cultural factors such as norms and customs, language and culture were identified as factors which enhanced the business location decisions of manufacturing firms (MacCarthy and Atthirwoong 2003).

Implications of the Study

The findings of the study have significant implications from a policy and practical perspective. Despite the relevance of infrastructural indicators to the development and survival of agro-processing firms, it appears that they are not exploiting the opportunities associated with them. Also, agro-processing firms need to optimise and harness available indicators such raw materials, markets, labour among others to their fullest potential.

Capacity building for agro-processing firms is critical to their growth and success. Government interventions must be refocused to provide training incentives for firms willing to locate their businesses in the Northern parts of Ghana. The absence of government–business relations and support for agro-processing firms located in the Northern part of Ghana will negatively impact their output especially in terms of labour, since most of their labour are unskilled. The unskilled labour continues to exist because the training, education and capacity-building programs which are supposed to improve their skills is not forthcoming.

Still on policy interventions from government, it is critical that restrictions are placed on imported goods which are already produced by agro-processing firms to improve their access output markets. Such restrictions will protect both startup agro-processing firms and those whose strength may be diminishing. It is imperative that government provide protection for agro-processing firms to grow, enjoy stability and remain competitive. This also means that where necessary, government must provide funding opportunities to agro-processing investors who are willing to situate their operations in the Northern parts of Ghana as a way of revamping the local economy.

Conclusion of the Study

A key limitation that this study confronted had to do with difficulties in having access to agro-processing firms which met the criteria of the research. However, care was taken to ensure that only agro-processing firms which were active and registered with the Ghana Revenue Authority (GRA) were included in the study. To do this, it involved a lot of time and firm-sorting processes to ensure that agro-processing firms which were dormant in their operations were not part of the study. All these firm-sorting processes to get the firms which met the criteria for data collection contributed to the delays in the data-gathering process.

The outcome of this study presents interesting findings for practical and theoretical implications. The study concluded that business location decisions for agro-processing firms in Ghana is influenced by a multiplicity of factors that needs to be thoroughly analysed by the investor to optimise the benefits associated with siting their factories in the Northern parts of the country that abounds in economic potential. Further, policy interventions from government must focus on encouraging investors with tax incentives, protection from foreign competition, improved access to output markets, increased access to cheaper alternative funding, capacity building, and so on, to promote the siting of more factors in such regional areas to revamp the local economy as well as create jobs.

APPENDIX A

Infrastructure

Table A1.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy		.671
Bartlett’s Test of Sphericity	Approx. Chi-Square	405.992
	df	45
	Sig.	.000

Source: Field data (2018).

Table A2.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.203	32.030	32.030	2.763	27.628	27.628
2	1.789	17.892	49.922	1.450	14.497	42.125
3	1.365	13.646	63.568	.898	8.978	51.103
4	.889	8.893	72.461
5	.747	7.467	79.928
6	.643	6.428	86.357
7	.523	5.227	91.584
8	.402	4.017	95.601
9	.285	2.849	98.449
10	.155	1.551	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A3.

Factor Matrix.

	Factor
	1	2	3
Availability of road infrastructure	.258	.125	.319
Availability of high-volume water supply	.258	.265	.738
Consistent power supply	.557	.106	.215
Sufficient educational institutions	.558	.164	.048
Availability of telecom services	.775	.183	−.272
Availability of IT infrastructure	.430	.477	−.317
Availability of public waste systems	.236	.436	−.139
Availability of health facilities	.631	.162	−.045
Availability of rail transport	−.633	.732	−.051
Availability of natural gas supply	−.607	.560	.071
Extraction method: Principal axis factoring.

Source: Field data (2018).

Business Support

Table A4.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy		.713
Bartlett’s Test of Sphericity	Approx. Chi-Square	405.467
	df	45
	Sig.	.000

Source: Field data (2018).

Table A5.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.598	35.977	35.977	3.165	31.652	31.652
2	1.498	14.975	50.952	1.075	10.751	42.404
3	1.199	11.987	62.940	.793	7.931	50.335
4	.988	9.880	72.820
5	.722	7.219	80.039
6	.659	6.593	86.631
7	.493	4.926	91.558
8	.341	3.409	94.966
9	.316	3.161	98.128
10	.187	1.872	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A6.

Factor Matrix.

	Factor
	1	2	3
Human resource business support	.499	.327	–.039
Insurance services	.548	–.048	–.131
Adequate security services	.520	–.403	–.070
Availability of spare parts	.834	–.064	–.350
Availability of equipment maintenance services	.779	–.106	–.228
Availability of advertising agencies	.558	–.198	.235
Availability of hospitality services	.422	–.037	.667
Availability of IT support	.595	–.079	.277
Availability of industrialised zoned land	.434	.809	.071
Distributors for company’s product	.102	.294	–.112

Source: Field data (2018).

Economic Factors

Table A7.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.672
Bartlett’s Test of Sphericity	Approx. Chi-Square	646.500
	Df	45
	Sig.	.000

Source: Field data (2018).

Table A8.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.484	34.842	34.842	3.117	31.174	31.174
2	2.147	21.471	56.313	1.954	19.537	50.712
3	1.792	17.923	74.236	1.395	13.946	64.658
4	.741	7.405	81.641
5	.568	5.684	87.325
6	.409	4.088	91.412
7	.348	3.475	94.888
8	.221	2.212	97.099
9	.197	1.972	99.071
10	.093	.929	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A9.

Factor Matrix.

	Factor
	1	2	3
Availability of unskilled labour	.749	.170	–.122
Availability of skilled labour	.272	–.322	.554
Favorable tax rates	–.009	–.181	.709
Availability of short-term finance	.005	.887	.298
Availability of long-term finance	.000	.929	.193
Availability of land	.747	.057	.343
Low transport costs	.518	.191	–.296
Low cost of labour	.823	.111	–.251
Low cost of land	.739	–.270	.314
Low cost of construction	.659	–.119	–.277

Source: Field Data (2018).

Note: Extraction method: Principal axis factoring.

Government–Business Relations

Table A10.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.817
Bartlett’s Test of Sphericity	Approx. Chi-Square	763.886
	df	45
	Sig.	.000

Source: Field data (2018).

Table A11.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	5.212	52.124	52.124	4.914	49.142	49.142
2	1.233	12.326	64.450	.923	9.232	58.375
3	1.053	10.525	74.975	.799	7.992	66.367
4	.697	6.966	81.941
5	.524	5.244	87.185
6	.500	5.004	92.189
7	.284	2.840	95.029
8	.226	2.263	97.292
9	.146	1.463	98.755
10	.125	1.245	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A12.

Factor Matrix.

	Factor
	1	2	3
Fairness of the court system	.650	.469	.062
Effective contract enforcement	.744	.497	−.031
Less management time spent in dealing with regulatory bodies	.672	.349	−.071
Effective government ministries in supporting businesses	.781	−.037	−.373
Effective government agencies in supporting businesses	.791	−.281	−.467
Effective government departments in supporting businesses	.735	−.325	−.112
Opportunities for securing government contracts	.739	−.301	.204
Low degree of bribery and corruption in securing government services	.770	−.178	.550
Favorable tax policy (rate and incentives)	.551	−.154	.142
Provision of a stable political environment by the local assembly	.517	.046	.234

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Socio-cultural Factors

Table A13.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.818
Bartlett’s Test of Sphericity	Approx. Chi-Square	739.130
	df	45
	Sig.	.000

Source: Field data (2018).

Table A14.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.734	47.345	47.345	4.476	44.757	44.757
2	1.368	13.684	61.029	.983	9.831	54.589
3	1.352	13.520	74.549	.822	8.218	62.806
4	.714	7.138	81.687
5	.634	6.345	88.032
6	.370	3.698	91.729
7	.352	3.522	95.251
8	.208	2.083	97.334
9	.149	1.485	98.819
10	.118	1.181	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A15.

Factor Matrix.

	Factor
	1	2	3
Customs of the people	.818	–.070	–.425
Values of the people	.778	.135	–.176
Language of the people	.923	.071	–.184
Religion and beliefs of the people	.785	–.413	–.052
Gender roles of the society	.568	.614	.066
Population density	.052	–.103	.434
Customer buying habits	.387	.398	.262
Attitude towards work	.543	–.451	.329
Attitude towards local products	.852	–.126	.180
Differences in social classes	.487	.136	.414

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Demand Factors

Table A16.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.797
Bartlett’s Test of Sphericity	Approx. Chi-Square	476.611
	df	45
	Sig.	.000

Source: Field data (2018).

Table A17.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	4.190	41.897	41.897	3.772	37.724	37.724
2	1.515	15.153	57.049	1.106	11.057	48.780
3	1.056	10.560	67.609	.606	6.057	54.837
4	.738	7.381	74.990
5	.615	6.151	81.140
6	.534	5.337	86.477
7	.488	4.882	91.359
8	.372	3.715	95.075
9	.281	2.809	97.884
10	.212	2.116	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A18.

Factor Matrix.

	Factor
	1	2	3
High purchasing power of the people	.622	.530	–.264
High disposable income levels of the people	.616	.444	–.314
Availability of marketing facilities	.712	–.326	–.187
Low product facilities	.259	.331	.425
Consumer tastes and preferences	.769	–.159	.109
Low competition in the market	.626	.080	.113
Accessible distribution channels	.480	–.421	–.233
Availability of substitutes of the products	.569	.046	.026
Market potential of company’s products	.739	–.416	.167
Seasonal demand for products	.591	.167	.338

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Quality of Life

Table A19.

KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.742
Bartlett’s Test of Sphericity	Approx. Chi-Square	568.992
	df	45
	Sig.	.000

Source: Field data (2018).

Table A20.

Total Variance Explained.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.393	33.926	33.926	3.121	31.210	31.210
2	2.422	24.218	58.144	1.953	19.535	50.744
3	1.435	14.354	72.499	.974	9.740	60.484
4	.695	6.953	79.452
5	.586	5.861	85.313
6	.490	4.897	90.210
7	.355	3.549	93.758
8	.292	2.919	96.677
9	.200	1.996	98.673
10	.133	1.327	100.000

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Table A21.

Factor Matrix.

	Factor
	1	2	3
Low crime rate	.426	.273	–.406
Fresh air and water	.153	.610	.211
Availability of outdoor recreational opportunities	.260	.579	.300
Availability of quality healthcare	.209	.667	–.293
Availability of public safety services, e.g., police, fire station	–.077	.697	–.069
Low cost of living	.771	.135	–.243
Good attitude of the people towards conflicts	.854	–.193	–.032
Availability of residential housing	.811	–.328	.191
Low cost of residential housing	.886	–.179	.057
Availability of municipal services	.184	.217	.696

Source: Field data (2018).

Note: Extraction method: Principal axis factoring.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Livingstone Divine Caesar

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