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Abstract

Africa’s destiny hinges on accessing the lucrative markets in the northern hemisphere. However, non-tariff measures in particular sanitary and phytosanitary (SPS) measures have become a prominent tool in the regulation of international trade in agricultural and food products and have been increasingly recognized as one of the major determinants of market access, particularly to the European Union. However, there is very limited empirical evidence on the impact of sanitary and phytosanitary measures on Africa and Cameroon’s agricultural exports. This paper contributes to the literature by investigating the impact of changes in sanitary and phytosanitary measures in importing countries on coffee exports from Cameroon at the 6-digit HS level, using the gravity model and the modified Poisson pseudo-maximum likelihood estimators. The analysis is based on trade data between Cameroon and 10 major importing countries in the Organization for Economic Cooperation and Development (OECD) between 2001 and 2020. These results suggest that coffee export from Cameroon is not significantly affected or influenced by sanitary and phytosanitary measures in major importing markets; that is, the standards had weak trade effects on coffee exports. Other factors such as income, language, and labor size were significant in influencing trade flows in the export commodities. These results further point to the low productive capacity of the country’s coffee sub-sector. The supply-side constraints in the coffee sub-sector can be addressed by the government by improving access to high-yield coffee varieties by farmers, educating and training farmers on good agricultural practices through agricultural extension programs, and upgrading the market infrastructure.

Plain Language Summary

The study aims at examining the effect of changes in Sanitary and Phytosanitary measures imposed by importing countries on Cameroon’s coffee export using the gravity model. The findings provide a basis upon which domestic agricultural and food policies can be designed and implemented in line with international standards. It is expected that this publication shall contribute to the body of knowledge on agricultural and food policies in Africa, particularly for developing countries such as Cameroon, and assist policymakers and other stakeholders in the coffee sub-sector in enhancing trade policymaking and long-term perspective planning to promote agricultural trade.

Keywords

trade non-tariff barriers sanitary and phytosanitary measures coffee exports gravity model

Introduction

Trade remains an essential ingredient in Africa’s economic development and integration into the global economy. Development experts assert that such trade can play a pivotal role in the economic growth and development of a nation (Abendin & Duan, 2021; Bardi & Hfaiedh, 2021; Kumar, 2019). The virtues of Africa’s potential gains from trade hinge on historic paths and decisive roles trade played in fueling the industrial revolution in Britain, Europe, and the United States as well the industrialization of Japan. The rapid industrial development of the Asian “tigers” (China, South Korea, and Singapore) provides lessons for the developing countries in Africa. With the majority of the Least Developed Countries (LDCs) being African and their exports dominated by primary products including oil, cocoa, coffee and minerals, strategic positioning on global trade remains the Achilles heel to unleash their growth and development. However, recent trends in some LDCs show a diversification of exports and export revenue away from primary commodities. The changing dynamics and paradigm shift are in part due to measures taken by rich nations in the past years to broaden the market preferences available to LDCs through various bilateral trade initiatives such as the Everything But Arms (EBA), the Economic Partnership Agreements (EPAs), and the African Growth and Opportunity Act (AGOA) (Chigudu, 2021). International trade can, without doubt, be an important instrument through which some of the 2030 Sustainable Development Goals (SDGs) such as Goal 2 (end hunger), Goal 8 (decent work and economic growth), Goal 9 (industry, innovation, and infrastructure), and Goal 10 (reduce inequality) can be achieved (Hoekman, 2016; Sudsawasd et al., 2019). However, for this to happen, LDCs in Africa in particular must overcome the barriers impeding trade.

Trade in agricultural and food products has undergone a significant structural transformation during the last 60 years. The contribution of agricultural trade to world merchandise trade has faded since the 1950s. According to the World Trade Organization (WTO) in 2017 trade in agricultural products accounted for 10% of world merchandise trade, a decline from 38% in the 1950s and 27% in the 1960s. According to the WTO, the top 10 natural exporters of agricultural products in 2017 except for Argentina had positive growth rates ranging from 5% (China and the USA) to 24% (Indonesia). Together, these countries accounted for 73% of world exports of agricultural products. The European Union (EU) and the US are both leading exporters and importers of agricultural and food products. The EU recorded a staggering US$647 billion as exports value with a growth rate of 8% (WTO, 2018). Net exporters in the south include Brazil, China, Indonesia, Thailand, India, and Argentina.

As applied tariffs continue to decline, protectionists policies in the agricultural sector are increasingly been pursued by many countries for many years now notably in the form of non-tariff barriers (NTBs) (Hejazi et al., 2022; Ishaq et al., 2022; WTO, 2022). Hillman (1991) as cited in Beghin and Bureau (2001) defines NTBs as “any governmental device or practice other than a tariff which directly impedes the entry of imports into a country and which discriminates against imports, but does not apply with equal force on domestic production or distribution.” The terms non-tariff barriers and non-tariff measures (NTMs) are sometimes used interchangeably in the trade literature (Assoua et al., 2022). According to the OECD, NTMs “covers a diverse set of measures in terms of purpose, legal form and economic effect and comprise all policy measures other than tariffs and tariff-rate quotas that have a more or less direct impact on international trade” (OECD, 2022). NTBs measures are diverse and include import controls, state aid and subsidy measures, public procurement and localization policies and others such as Sanitary and Phytosanitary Standards (SPS), Technical Barriers to Trade (TBT), and capital controls (Kinzius et al., 2019). Both industrialized and developing countries are guilty of erecting various forms of agricultural trade barriers over the past three decades, despite their growing participation in multilateral trade negotiations. Agricultural protectionism is the use of specific tools or instruments to protect domestic producers from foreign competition, usually by preventing or discouraging imports.

Like in most Sub-Saharan Africa (SSA) countries, Cameroon’s agricultural export is limited to a few commodities such as cocoa, coffee, banana, and rubber which account for more than 40% of agricultural export revenue. Yet, despite its contribution to the national economy, agricultural production and productivity remains very low, and farmers’ incomes are low coupled with growing levels of food and nutrition insecurity. Coffee production and export is an important source of foreign exchange earnings for Cameroon’s economy with the share of coffee in exports averaging 1.5% for the period 2010 to 2014 (National Institute of Statistics [NIS], 2018). It contributes significantly to the agricultural value added for the national economy. According to the NIS (2018), the share of coffee in Gross Domestic Product (GDP) amounted to between 0.32% and 0.48% for the period 2010 to 2015.

According to the cocoa and coffee sector development strategy, around 423,000 households involving around 2,961,000 people are engaged directly or indirectly in coffee-related activities in Cameroon. Production activity accounts for 94.6% of the jobs created in the sector, compared to factory work at only 2.4%, marketing at 2.4%, processing and distribution at 0.2%, quality control and plant health treatment at 0.2%, and ancillary services (transport, loading and unloading, and transit) with 0.2% (Ministry of Commerce et al., 2009). Cameroon produces and exports both Robusta and Arabica coffee. According to the National Cocoa and Coffee Board (NCCB), Arabica and Robusta coffee account for 10% and 90% respectively of the marketed production (NCCB, 2019). The Littoral and the West region account for the highest share of output: 69.4% and 18.26% of the total respectively (NCCB, 2019). The Centre, East and South regions account for approximately 5%, compared to the North-West, with 3%, and the South-West with about 15% of both Arabica and Robusta (NCCB, 2019). According to the International Coffee Organization [ICO], the cultivation of coffee is dominated by smallholder farmers, composed mainly of 75% smallholdings and 25% estates, though Robusta holdings are smaller on average (0.5 ha compared to 1.9 ha for Arabica). Based on the coffee sector revitalization and development plan, in 2014, the total cultivated areas were 60,000 and 10,000 ha for Robusta and Arabica coffees respectively (ICO, 2018).

However, coffee like most food items must be of good physical, aromatic and health quality. According to the World Health Organization (2015), unsafe food is responsible for production losses amounting to $95 billion in Lower Middle-Income Countries (LMICs), and more than 600 million people fall ill and an additional 420,000 die every year as a result of eating contaminated food. An understanding of the role of food safety standards in agricultural and food product trade has been a matter of concern in the literature and is also of high policy relevance for governments and development agencies. Despite the large body of literature on the role of standards in agricultural and food product trade, the economic literature is still very limited both empirically and analytically for Cameroon and other African countries, that rely on income from trade to drive the sustainable development agenda. This study is timely and important for several reasons: (1) recently, private and public regulators in major importing countries have tightened the maximum residue level (MRL) on coffee beans; (2) the EU, which is Cameroon’s major export destination of coffee, has in recent past rejected some quantities of these commodities due to high levels of contaminants. To this effect, the main aim of this study is to analyze the effect of changes in sanitary and phytosanitary regulations on the flow of Cameroon’s agricultural exports. This study focuses on coffee which represents one of the most commonly rejected categories of Cameroon’s agricultural exports. In addition, together with cocoa, coffee represents a key agricultural export for Cameroon accounting for 60% of its total agricultural trade; thus, the focus is on coffee. The novelty of this paper is to exclusively focus on coffee, which represents a significant portion of Cameroon’s agricultural exports and has been susceptible to rejections in the past few years. Also, this study is the first to investigate the effect of sanitary and phytosanitary (SPS) measures in the top 10 importing countries on Cameroon’s coffee trade at the 6-digit HS level. These countries account for over 80% of all imports. The current study contributes to the African Union’s Agenda 2063 of achieving health and nutrition and the global discourse on achieving SDGs 2 and 3 to “end hunger, achieve food security and improved nutrition and promote sustainable agriculture; and ensure healthy lives and promote well-being,” respectively.

Our study, therefore, bridges the gap in the literature by engaging in the discourse of non-tariff measures, particularly SPS measures, as important tools in the regulation of international agricultural trade. We investigate the influence of changes in SPS measures in importing nations on Cameroon’s coffee exports, to show that SPS regulations in major importing markets have some minimal impact on coffee exports. This finding only emphasizes the country’s coffee sub-sector’s low production capacity, not necessarily the pervasive role of SPS measures. This current attempt is nonetheless limited by the unavailability of comprehensive maximum residue limit (MRL) information, particularly country-specific MRL on mycotoxins and aflatoxins in Cameroon’s agricultural exports. In addition, country firm-level data to document NTMs that exporters perceive as problematic on agricultural exports is not available officially. These would have been very useful in shedding light on the effect of these measures on exporting firms. However, while these features are likely to constrain the scale and scope of our investigation and the extent to which the data used can be seen as representative of the sector, our findings remain robust given that we employ a long-time period of aggregate data which subsumes the activities of individual firms. Again, we show that other factors impacting trade flows in export commodities included income, language, and labor size. To tidy up the key issues, the rest of the paper is organized as follows: Section “Cameroon’s Coffee Production and Export” provides an overview of Cameroon’s coffee production and exports and describes the two mycotoxins of interest in the global coffee trade. Section “Review of Literature” is the review of the literature. The research methods and data are described in Section “Methodology and Data Description” and the results and discussion are presented in Section “Results and Discussions.” Section “Policy Implications and Conclusion” draws the conclusion and policy implications.

Cameroon’s Coffee Production and Export

Cameroon’s coffee export market share in recent years has shrunk, declining steadily, as the country’s coffee exports face stiff competition from other major coffee-producing nations in Africa such as Ethiopia, Kenya, and Uganda as well as from other countries such as Brazil, Indonesia, and Vietnam, decreasing the market share of Cameroon’s coffee exports in the key markets. This shrinking market share could also be in part due to the decline in productivity, yield and area harvested, as well as stringent SPS measures in importing countries.

According to the International Trade Centre (ITC, 2022) for the period 2010 to 2017, Belgium was the main market destination of Cameroon’s coffee accounting for about 60% of total exports value, while Algeria remained an important destination on the African market and the United States accounted for the bulk of exports to North America. Notwithstanding, China, South Korea and Indonesia remain potential market destinations in the Asian continent in the coming years as Cameroon seeks to penetrate new markets. Coffee together with cocoa represents about 60% of Cameroon’s agricultural exports, thus contributing significantly to the country’s foreign exchange earnings; we consider the top 10 importing countries of Cameroon’s coffee bean given the fact that these countries make up above 80% of Cameroon’s coffee exports for the period under study. Other importing countries are not included in the analysis since they accounted for a smaller volume of Cameroon’s coffee exports and therefore play a minimal role in the country’s coffee trade.

Figure 1 depicts coffee production and export quantity in Cameroon for the period 1961 to 2021. The export of coffee has been very erratic in recent years. The world’s top three coffee-producing countries are Brazil, Vietnam, and Columbia. Cameroon once ranked the world’s 12th coffee producer, currently sits on the 31st rank. Coffee production in Cameroon has not performed fairly well compared to cocoa production. In 1961 Cameroon produced 44,700 tonnes of coffee. Coffee production witnessed a steady rise up till 1971, when 94,790 tonnes were produced. By 1980 production levels had reached 112,207 tonnes. The highest level of coffee production since independence was recorded in 1984, standing at 137,900 tonnes. The increased production was in part due to an increase in the price of coffee in the world market, the rapid expansion and growth of the area harvested, government support measures granted to farmers, an increase in demand in foreign markets, and macroeconomic stability. Production then plummeted to 76,200 and 68,417 tonnes in 1992 and 1993 respectively. This period was marked by the decline in the prices of its main commodities in the world market, the liberalization of the cocoa and coffee sub-sectors, and the withdrawal of government subventions. Production peaked at 112,532 tonnes in 1998 and declined to a record low of 26,322 tonnes in 2011. Overall, coffee production from Cameroon witnessed a steady decline from 1989 to 1997.

Figure 1.

Coffee production and export (tonnes), in Cameroon, 1961 to 2021.

In 1961 Cameroon exported 35,484 tonnes of coffee. By 1974 export volume had reached 100,722 tonnes; the increase in export volume was in part due to the increase in the price of coffee in the world market, government support measures through subsidies and price guarantee schemes, together with the rapid expansion and growth of the area harvested and government support measures. Between 1974 and 1988, export volume was characterized by high levels of fluctuations, dropping to 95,000 tonnes in 1988. The highest level of export was recorded in 1990 at 157,149 tonnes. The Export volume then plummeted to 104,200 and 54,395 tonnes in 1992 and 1994 respectively. This period was marked by the decline in the price of coffee in the world market which began after 1986, the liberalization of the coffee and cocoa sub-sector, the devaluation of the FCFA, and the withdrawal of government subventions. Production peaked to 85,654 tonnes in 1999 and declined to a record low of 28,246 in 2015. Overall, coffee export volume from Cameroon witnessed a steady decline from 1989 to 1997. Export quantities have plummeted since the 1970s and 1980s, declining sharply between 2005 and 2013 from 43,387 to 26,604 tonnes respectively.

Cameroon’s major coffee market destinations by continent are Europe, North America, and Africa. The European Union is the leading export destination amidst growing demand potential and new market opportunities from Asia particularly from China and South Korea. Italy is currently the largest importer accounting for approximately US$20 million yearly export or equal to 30.65% of total coffee exports from Cameroon. Belgium is the second export destination country accounting for about US$8 million of export each year. Algeria is the major buyer of Cameroon’s coffee in Africa with the average value of exports approximately US$3 million per year. The export value of coffee to Germany and France are around US$7 million and US$6 million respectively.

Despite the trade potentials for coffee, the liberalization of traditional trade barriers such as tariffs and quotas, non-tariff barriers are increasingly being used as a major tool in the regulation of international trade in agricultural and food products. The increased rate of notifications of SPS measures to the WTO since 1995, the stringency, non-harmonization of food safety standards and its consequent impact on trade in agricultural and food products has caught the attention of several scholars and the discussion to quantify their impact has gained prominence in recent trade and agricultural policy debates (Bose et al., 2019; Santeramo & Lamonaca, 2022). Figure 2 depicts increased notifications of SPS measures since the creation of the WTO in 1995.

Figure 2.

SPS notifications submitted per year, 1995 to 2018.

There is a considerable body of theory and empirical evidence that non-tariff barriers such as SPS measures can impede trade through non-compliance or propel trade through compliance. For instance, F. O. Kareem et al. (2015, 2022) opine that the poor simplification of trade procedures, especially inefficient borders, poor food logistics and supply chain procedures in African countries, increase the vulnerability of African food exports to be rejected at the EU border and further exacerbates Africa’s ability to access EU markets. Furthermore, Wongmonta (2021) reported that the restrictiveness of SPS measures has a positive and substantial effect on export volumes. His results suggest that non-arbitrary and informative SPS requirements imposed by China on Thai fruit exports would propel agricultural trade. Also, Wu et al. (2022) found that both technical barriers to trade (TBT) and SPS measures have no significant effect on gross exports, however, TBT measures have a significant inhibiting effect on domestic value-added exports into the United States. O. I. Kareem (2019) sought to investigate whether agri-food safety regulations are necessarily or sufficiently trade impeding for Africa using three products: bananas, grapes, and tomatoes. The study found that the food safety regulations are trade-impeding for grapes and tomatoes but not for bananas. However, the measures become sufficiently trade-enhancing due to upgrading of the quality standards, compliance and certification of exports.

Ochratoxins A (OTA) and Aflatoxins represent recent changes in food policy regulations which affect the global trade in agricultural commodities such as coffee and cocoa (Nugroho, 2014). Figure 3 depicts these two mycotoxins and the major categories that pose serious health risks to humans. OTA (is a mycotoxin produced by fungi which belongs to the genera Aspergillus and Penicillium) and Aflatoxins are mycotoxins originating from molds which grow on crops on the farm or during storage. Warm and humid conditions favor the growth of these mycotoxins. They are commonly found in tropical and sub-tropical areas such as Equatorial Africa, South America, and Southeast Asia. Some of the causes of mycotoxin growth include poor coffee harvest, poor post-harvest practices, poor drying, rewetting, and poor storage. The EU recently promulgated new guidelines on the maximum residue level (MRL) of OTA for roasted and soluble coffee in mid-2005 which may influence the global coffee trade significantly. These new standards and regulations could affect the global coffee trade and hurt Cameroon’s coffee exports. The major categories of Ochratoxins that pose serious health risks are designated A, B, and C; with A identified as the most toxic, being nephrotoxic and carcinogenic. Although the carcinogenicity of Ochratoxin A in humans lacks scientific merit according to the International Agency for Research on Cancer (IARC), its overall assessment is categorized in group 2B which is possibly carcinogenic to humans (Nugroho, 2014).

Figure 3.

Mycotoxins: Ochratoxins A (OTA) and aflatoxins.

The EU regulation on Ochratoxins A (OTA) has undergone some major changes in recent years. OTA in coffee trade had previously been regulated under European Commission (EC, 2005) No. 123/2005 of January 26, 2005 setting the MRL for the case of roasted and soluble coffee at five parts per billion (ppb) and 10 ppb respectively. This guideline was an amendment to Commission Regulation (EC, 2001) No. 466/2001 and came into force on April 1, 2005. The most recent regulation on OTA is European Commission (EC, 2006) No. 1881/2006 of December 19, 2006 which came into force on March 1, 2007 maintaining the MRL for OTA in roasted coffee (including grounded coffee) and soluble coffee and it still did not provide reference limit of OTA in green coffee (Nugroho, 2014). According to the FAO, the proposed 5 ppb limit of OTA presence in green coffee might result in about 17% rejection of coffee from African producers (FAO, 2006).

Review of Literature

Empirical Literature

The analysis of the role of NTMs has been a preoccupation of recent studies (Dou et al., 2015; Krishnan, 2016; Murina & Nicita, 2015; Sun et al., 2021; Thuong, 2018). For instance, Thuong (2018) investigated the effect of SPS measures on Vietnam’s rice exports using a Poisson pseudo maximum likelihood (PPML) gravity model approach. The results indicate that SPS measures imposed by importing countries have a significant impact on Vietnam’s rice exports as it resulted in considerably lower levels of trade with Vietnam, compared to importers with no food safety measures. Krishnan (2016) analyses the impact of NTBs on Indian exports using a panel data regression method and factor analysis. The results indicate that a 1% increase in the rejections of export consignment, will cause the value of Indian exports to plummet to 0.12%.

Similarly, Nugroho (2014) using a gravity model, empirically estimated the impact of food safety standards on Indonesia’s coffee exports and established that stringent regulations on ochratoxin A imposed by importing countries, and country-specific regulations such as carbaryl imposed by Japan, all have significant negative impacts on Indonesia’s coffee trade. Atici (2013) examines the linkages and interactions between food safety regulations and export performance using Turkey’s fig and hazelnut exports to the EU as a case study. The results suggest that the harmonization of EU food policy regulation in 2002 increased hazelnut exports, whereas the EU food policy regulation in 2007 reduced the volume of fig exports.

F. O. Kareem et al. (2017) sought to ascertain whether the EU SPS measures on tomatoes and citrus fruits exports (oranges, limes, and lemons) from Africa constitute a disguised restriction to trade, by employing the Helpman et al. (2008) model and a product-level protectionism index for pesticide standards. Results showed that EU tomato standards were deemed to be more stringent than the Codex standards and therefore trade restricting, while in the case of oranges, limes and lemons, the protectionists’ policies were absent. O. I. Kareem (2016) begins from the premise that the conspicuous use and rigor in the application of food safety measures has trade effects for Africa, and tends to affect economic operators differently. Using a two-step Helpman et al. (2008) model covering 52 African countries between 1995 to 2012, the study investigated the effects of EU standards regulations on African fish exports at extensive and intensive margins. Based on the findings, fish standards increased trade at the extensive margins, but impeded trade at the intensive margins.

Neeliah et al. (2013) assessed the effect of SPS measures in the EU on Mauritius’ fishery and horticultural product exports using firm-level survey data and in-depth qualitative key informant interviews. An interesting finding is that SPS measures were not a major determinant of export and as such not a barrier for exporting firms. Similarly, Neeliah et al. (2012) investigated the effect of SPS measures on fishery product exports from Mauritius using firm-level data of Mauritian fish exporters. The findings reveal that SPS requirements were not a major determinant of fish product exports to the EU market, but rather acted as a major catalyst in enhancing their competitiveness, with fishery exporters adopting a reactive compliance strategy (Table 1).

Table 1.

Summary of Empirical Reviews on Regulations and Trade.

Author(s), publication year	Country/continent/region	Method/data	Key findings
Sun et al. (2021)	China	Import refusal	Regulations are trade restrictive
Hejazi et al. (2022)	USA/EU	Structural gravity framework	Stricter MRL impede trade
Ishaq et al. (2022)	China	Gravity model	Regulations do not affect exports
F. O. Kareem et al. (2017)	Africa/EU	Helpman et al. (2008) model	EU tomato standards are trade restricting
O. I. Kareem (2016)	Africa/EU	Helpman et al. (2008) model	Standards impeded and propelled trade
Neeliah et al. (2012)	Mauritius/EU	Business survey	Standards enhanced fish exports
Neeliah et al. (2013)	Mauritius/EU	Business survey	Standards did not impede trade
Thuong (2018)	Vietnam	Gravity model	Standards were trade restrictive
Atici (2013)	Turkey	Gravity model	Standards impeded and enhanced trade
Nugroho (2014)	Indonesia	Gravity model	Standards had a negative impact on trade
Murina and Nicita (2015)	LICs	Gravity model	Compliance costs are higher in LICs
Arita et al. (2015)	USA/EU	Gravity model	Standards found to impede trade
Assoua et al. (2022)	Cameroon	Gravity model/business survey	Standards did not influence exports
Krishnan (2016)	India	Gravity model	Rejections higher in food products

Theoretical Considerations

The success of the implementation of trade policies depends on the success in changing the nature of the incentives and the relative power and ability of different interest groups to influence the key decision-makers. There are three sets of key drivers of change: the structures which represent the impact of values and ideas; the institutions which account for the role of formal political institutions and informal social, political and cultural norms; and agents which are interests and incentives-power relations. Trade policies are not always in the best interest of national welfare. Protectionist measures are often driven by influential interest groups with vested interest making it difficult to devise trade policies that increase social welfare. In this section, this study considers the political economy of protectionism which argues that protectionist measures are often driven by powerful interest groups lobbying the government for policy changes. This study examines the classic Grossman-Helpman protection-for-sale (PFS) model to shed light on the political economy of trade protection through standards.

The protection-for-sale model of trade was propounded by Grossman and Helpman (1994) in an attempt to provide a political economy explanation of trade protectionism. In essence, the model highlights the powerful role of various lobby or interest groups in influencing government policy and may as well help us understand the political economy behind the use of standards. Grossman and Helpman (1994) approach assumes a small economy with homothetic preferences, differences in factor endowments, and utility maximization; they assume constant returns to scale, constant technology, and the only set of trade policy instruments available to the government to implement are tariffs and subsidies. However, this study, we draw from the works of Jonelis and Suwanprasert (2022), Annicchiarico and Marvasi (2019), Malik (2022), Saha (2018), F. O. Kareem et al. (2017), and Swinnen and Vandemoortele (2011, 2012) who employed the protection for sale model to study the political economy of standards.

Jonelis and Suwanprasert (2022) sought to test the PFS model empirically. Their extended protection-for-sale model predicts that a government with greater political power generally levies higher tariffs. They submit that political strength can be proxied by the share of seats held by a ruling party or a government coalition in Congress or Parliament and test the predictive power of the model using panel data covering 95 product categories and 105 countries, from 1996 to 2014. The results seem to suggest support for the protection-for-sale theory. Annicchiarico and Marvasi (2019) extend the PFS model by introducing a general model of monopolistic competition including variable markups and incomplete pass-through. They reported that the structure of protection emerging in the political equilibrium depends on the weight attached by the government to consumer welfare in its policy decision, the extent of market power of producers and the terms-of-trade variations due to the degree of pass-through. The findings highlight the importance of preferences in shaping the structure of protection by different consumer and producer lobbyist groups.

F. O. Kareem et al. (2017) make the following key assumptions: There are two countries in the world, domestic and foreign engaged in agricultural trade; The domestic country is a large food-importing country and the foreign country is a small food-exporting country; The foreign country is a price and standards taker due to its lack of capacity to introduce standards while the domestic country is a standard setter and imposes its standards on the small food exporting country. Following Grossman and Helpman (1994) and F. O. Kareem et al. (2017) assume that food policy regulations are the only set of trade policy instruments available to the government to implement. It is therefore assumed that the government sets “politically optimal standards” with the objective to protect consumers’ health and maximize societal welfare; there is also a “social planner” such as the World Health Organization (WHO) and the FAO who set benchmark standards or “socially optimal standards” with the objective to maximize global societal welfare (F. O. Kareem et al., 2017). They submit that although the government initiates and implements food policy regulations to maximize societal welfare, this action is influenced by interest groups (both producers and consumers) who may either lobby for or against certain standards. Producer and consumer interest groups will seek to maximize producer and consumer welfare respectively. Consequently, producer interest groups will lobby politicians or political parties by sponsoring election campaigns of candidates with the aim that once in office, these candidates will implement food policy regulations that protect domestic producers from international competition. Also, due to increasing awareness of food safety issues and increasing consumer demand for quality products, consumer interest groups will equally lobby to influence government policy to achieve acceptable levels of food safety and quality.

It is assumed that though the objective of the government is to maximize societal welfare, its policies will also be influenced by these lobby groups whose welfare must also be maximized. According to F. O. Kareem et al. (2017), “Since government values both the weighted sum of the total level of political contributions from the interest group and also the social well-being of the people, the total government objective function is given by the summation of social welfare and contributions from each of these lobbyist groups.” They posit that the contributions received by politicians or political parties will increase proportionately to the perceived benefits that accrue to producers and consumers; that is, the higher the consumer or producer surpluses, the higher the contributions from the interest groups and vice versa. Saha (2018) estimated a standard PFS model for India using a measure of political organization, and panel data to estimate the new measure of relative lobbying effectiveness. He reports that a high output-to-import ratio translates into higher trade protection for the most effective sectors; while for the least effective sectors, higher output-to-import ratio translates into lower trade protection. Examining some of the political economy influences on lobbying effectiveness, the results suggest that producing homogenous goods reduces the positive effect of geographical proximity on effectiveness.

According to Swinnen and Vandemoortele (2012), if the compliance costs of standards for domestic producers are high relative to foreign producers, this may cause producer interest groups to lobby for less stringent standards and reduce their political contributions to the government since they perceive a decrease in producer and consumer surpluses. On the other hand, if the compliance cost for foreign producers is high relative to domestic producers or the standards are applied arbitrarily to constitute a disguised restriction to trade, producer lobby groups will be incentivized to increase their political contributions to the government to achieve import restrictions. Also, changing consumer tastes and preferences in favor of high food safety and quality measures can cause consumer lobby groups to influence government policies in favor of stringent food policy regulations to satisfy consumer demands and increase consumer welfare, and vice versa. Government standards are considered sub-optimal if there are discrepancies between the politically optimal standard and the socially optimal standards (F. O. Kareem et al., 2017).

Methodology and Data Description

Analytical Model

In line with empirical literature (O. I. Kareem, 2016; Murina & Nicita, 2015; Nugroho, 2014; Thuong, 2018; Wilson & Otsuki, 2004), this study employs the time-honored gravity model of international trade. A simplified version of the gravity equation applied in international trade can be expressed as

X_{ij} = α \frac{γ_{i}^{β_{1}} γ_{j}^{β_{2}}}{D_{ij}^{β_{3}}}

(1)

Where $X_{ij}$ is the value of exports from country i to j, $Y_{i} Y_{j}$ represent the GDP (income) of countries i and j, and $D_{ij}$ is the geographical distance between the two countries’ capital cities. The simple gravity equation of bilateral trade connotes that the volume of trade between two countries is proportional to their masses (GDP) but inversely related to the distance between them (Tinbergen, 1962). This leads to the following logarithmic specification of the augmented gravity equation:

\begin{matrix} Ln (EX P_{ijt}) = β_{0} + β_{1} \ln (GD P_{jt}) + β_{2} \ln (GD P_{it}) β_{3} \ln (PO P_{jt}) \\ + β_{4} \ln (DIS T_{ij}) + β_{5} \ln (PO P_{it}) + β_{6} \ln (PRO D_{i, t - 1}) \\ + β_{7} (SP S_{jit}) + β_{8} CO L_{ij} + β_{9} \ln (REX R_{ijt}) + β_{10} LAN G_{ij} \\ + β_{11} \ln IN F_{it} + β_{12} \ln AV A_{it} + β_{13} \ln LAN D_{it} + ε_{ijt} \end{matrix}

(2)

Where i, j, and t denote exporting country (Cameroon), importing countries and trade year respectively. The parameters β are coefficients of explanatory variables to be estimated and $ε_{ijt}$ is a log-normally distributed error term with $ε (\ln ε_{ij}^{n, t}) = 0 .$

${EXP}_{ijt}^{k}$ denotes the export value in product k from Cameroon to country j in year t. $EX P_{ijt}$ is measured in thousands of US dollars.

$GD P_{jt}$ is the real gross domestic product of the importing country j in year $t$ , measured in billions of US dollars. $GD P_{it}$ is the real gross domestic product of Cameroonian year $t$ , measured in billions of US dollars. The GDP captures the market size, the size of the economies and the demand and supply side effects of the coffee trade (Nugroho, 2014; Thuong, 2018).

$PO P_{jt}$ is the population of importing country j in year t, which captures the market size of importers. $PO P_{it}$ is the population of Cameroon in year t and captures domestic consumption.

$DIS T_{ij}$ denotes the geographical distance between the capital cities of Cameroon and importers, and is measured in kilometers;

$PRO D_{i, t - 1}$ denotes Cameroon’s total coffee production lagged 1 year, measured in thousands of tonnes. This variable captures the supply-side effect (productive capacity) of Cameroon’s coffee trade. The variable $PRO D_{i, t - 1}$ is lagged by 1 year to address the potential endogeneity.

$REX R_{ijt}$ is the real exchange rate between countries i and j at time t. It captures the effects of currency fluctuations.

$CO L_{ij}$ is the colonial tie dummy which captures the existence of a past colonial tie between Cameroon and trading partner j.

$LAN G_{ij}$ is the language dummy which captures the existence of a common official language between Cameroon and trading partner j.

$IN F_{it}$ is the rate of inflation of Cameroon in year t and captures the general price level at time t.

Given the data availability and limitations, in this research, $SP S_{jit}$ is a binary variable equal to one if importer j imposes SPS measures on coffee imported from Cameroon, and zero otherwise (Curzi et al., 2020; Nugroho, 2014; Thuong, 2018).

$AV A_{it}$ is used as a proxy for the total public expenditure on agriculture in Cameroon in year t.

$LAN D_{it}$ represents the total arable land of Cameroon in year t. It is hypothesized that when the exporting country expands its arable land, it could increase production, and productivity which may lead to an increase in coffee exports from Cameroon. $ε_{ij}^{n, t}$ is a log-normally distributed error term with $ε (\ln ε_{ij}) = 0 .$

Traditionally, various forms of structural gravity models have been estimated using log-normal methods. However, Yotov et al. (2016) note that one major setback of the traditional log-linear approach is that it does not account for zero trade flows values since observations with zero trade flows values are simply dropped from the estimation sample when we take the log-linear version of the trade values, resulting in sample selection bias. When the trade values are transformed into a logarithmic form, zero trade flow values are dropped from the sample, since the logarithm of zero is unspecified, giving rise to missing data points and sample selection bias (Heckman, 1979). In the context of this study, there are some reasons why frequent zero trade flows may occur in coffee exports for Cameroon. First, a bilateral distance may limit coffee trade between Cameroon and its trading partners in particular years. Second, coffee production or harvest deficit can take place due to climate variability and disease, which may result in a reduction in the ability to export for Cameroon.

Scholars have proposed several approaches that can be used to handle the presence of zero trade flows which include inter-alia: adding a very small arbitrary value to replace the zero trade flows before taking logarithms; the use of the Tobit estimator as an econometric solution to the presence of zero trade flows; the use of a two-step Helpman et al. (2008) model, estimated in two stages which include: a first-stage Probit estimation, which determines the probability to export, and a second-stage OLS estimation based on the positive sample of trade flows that also account for selection into exporting due to fixed costs of exporting (Yotov et al., 2016). Alternatively, Yotov et al. (2016) point out that a convenient method to handle the presence of zero trade flows is to estimate the gravity model in multiplicative form instead of logarithmic form, by applying the Poisson pseudo-maximum likelihood (PPML) estimator advocated by Santos-Silva and Tenreyro (2006) to estimate the gravity model.

The PPML estimator is widely acclaimed because of its intuitiveness and other inherently good qualities it possesses when applied to gravity trade modelling (Shepherd, 2013). Shepherd (2013) identifies some desirable attributes in the application of PPML on gravity modelling. He opines that the PPML is reliable in the presence of fixed effects (FE), which can be entered as dummy variables as in simple Ordinary Least Square (OLS) (Shepherd, 2013). This is particularly important for gravity modelling because most theory-consistent models require the inclusion of FE by the exporter and by the importer. Second, the PPML estimator intuitively includes observations with zero trade flows; Observations with zero trade values are dropped from the traditional OLS model because the logarithm of zero is unspecified (Shepherd, 2013). He further notes that dropping sample observations with zero trade flows as is the case with the OLS estimator leads to sample selection bias, which has become an important issue in recent empirical work.

The second challenge in estimating structural gravity models relates to heteroscedasticity which is very common when dealing with trade data. Santos-Silva and Tenreyro (2006) note that owing to Jensen’s Inequality, the parameters of the log-linearized gravity equation are biased and cannot be interpreted as the true elasticities when the gravity model is estimated with the OLS estimator. To address this problem, Santos-Silva and Tenreyro (2006) propose estimating the gravity equation model in its original multiplicative form using the PPML estimator.

Based on the major advantages of these methods, this study employs the Zero-truncated Poisson pseudo-maximum likelihood (ZTPPML), and the Zero-inflated Poisson pseudo-maximum likelihood (ZIPPML) estimators. With this in mind, the Poisson estimation framework for the gravity model can be written as:

\begin{matrix} X_{ijt} = \exp {α_{j} + α_{t} + β_{1} \ln (GD P_{jt}) + β_{2} \ln (GD P_{it}) β_{3} \ln (PO P_{jt}) \\ + β_{4} \ln (DIS T_{ij}) + β_{5} \ln (PO P_{it}) + β_{6} \ln (PRO D_{i, t - 1}) \\ + β_{7} (SP S_{jit}) + β_{8} CO L_{ij} + β_{9} \ln (REX R_{ijt}) + β_{10} LAN G_{ij} \\ + β_{11} \ln IN F_{it} + β_{12} \ln AV A_{it} + β_{13} \ln LAN D_{it} + ε_{ijt}} \end{matrix}

(3)

Where $α_{j}$ and $α_{t}$ are the time effect and country-specific effect respectively.

Justification of Variables

Exports

This is the dependent variable and it represents the export value of coffee from Cameroon to importing countries in year t and is expressed as a nominal value (Nugroho, 2014; Shepherd, 2013). It is measured in thousands of US dollars. It is expected that the introduction of good agricultural practices and the adoption of agricultural technologies will boost agricultural production and the export of coffee.

Gross Domestic Product (GDP)

GDP is likened to the mass of two bodies that determine the force of attraction between them as propounded in the law of gravity, and can influence trade flows between trading nations. The GDP of the trading partners represents both the demand and supply-side capacity that determines the trade flow between these countries. GDP is expected to have a positive influence on trade flows. Economic theory suggests that an importing country’s GDP will likely influence the trade flow originating from exporting countries. In our model, the GDP of the importing country captures the purchasing power (income of the consumer), the size of the importing countries’ economies and the demand-side effect; It is expected that importing countries’ GDP will play a significant role in determining the trade flow originating from the exporting country, Cameroon; while the GDP of the exporter captures the size of the exporting country’s economy and the supply-side capacity (productive capacity) of the products. In the gravity model, it is expected that an exporting country’s GDP will play a relatively less significant role than that of the importing countries in determining trade flows of goods originating from the exporting country. The coefficients on importer’s and exporting country’s GDP are expected to be positive. The GDP is expressed in nominal value (Nugroho, 2014; Shepherd, 2013) and is measured in billions of US dollars.

Population

The population variable constitutes the total population of the exporting country (Cameroon), and the importing countries, which captures the domestic consumption and market size respectively (Thuong, 2018). The population may influence trade flow between countries ceteris paribus; an increase in population may increase trade flow due to an increase in market size on the one hand, and can negatively influence trade flow, through low per capita income or the demographic distribution (e.g., working age) of the population. Hence, the impact of population on trade flow is inconclusive, it may be positive or negative.

Distance

Distance is an important determinant of trade flow not only in line with the gravity model but also empirically. In this study, it measures the geographical distance (in kilometers) between the capital cities of the exporter and importers. Here, it is introduced as a multilateral resistance term in the model (Jagdambe & Kannan, 2020; Thuong, 2018) and used to capture the proxy for the trade cost between countries. The effect of distance on trade flow may also depend on other transaction costs which may act as barriers to trade and are responsible for the significant high cost of trade in Sub-Saharan Africa (SSA) countries. Economic theory suggests that countries with short distances between them are more likely to experience an increase in trade volume than those who are wide apart due to a lower transaction cost, ceteris paribus. It is expected to be negative.

Production

This variable represents the total production of coffee in Cameroon for a given period, measured in tonnes and captures the supply side effect on the exports of Cameroon’s coffee. The literature suggests that production could be influenced by the on-going export opportunities and is therefore lagged by 1 year to avoid the problem of endogeneity (Thuong, 2018; Wei et al., 2012). Production coefficients are expected to be positive suggesting that an increase in output will lead to higher exports.

SPS

In the trade policy literature, SPS measures have been measured using several approaches and proxies. Some studies have captured the SPS variable as a dummy (dichotomous) variable which is equal to 1 if importing countries impose SPS measures, and 0 otherwise (Thuong, 2018); Others have employed frequency measures, coverage ratios, and prevalence score ratios for SPS measures, while others have used the Maximum Residue Limits (MRLs) (Otsuki et al., 2001a, 2001b) of the specific element used to regulate the trade flow of agricultural commodities. Given the data availability and limitations, in this research, $SP S_{jit}$ is a binary variable equal to one if importer j imposes SPS measures on coffee imported from Cameroon, and zero otherwise (Curzi et al., 2020; Nugroho, 2014; Thuong, 2018). This captures the effect of changes in food policy regulations on Cameroon’s coffee exports. It is generally expected that stringent regulations would impede trade flow and limit market access to importing countries, and less stringent regulations will propel trade flows.

Colonial Tie Dummy

This is a binary variable which is equal to 1 if there is a colonial tie between Cameroon and the importing countries, and is 0 otherwise. Countries with colonial ties are likely to trade more amongst themselves due to the trade relations they established in the past and the common language and values they share, which reduces the transaction cost of trading.

Exchange Rate

The exchange rate is a monetary policy variable. The exchange rate was included in the model to capture the effects of currency fluctuations (Krishnan, 2016). It captures the rate at which one currency exchanges for another; in other words, it is the price of one currency in terms of another. It is expressed in nominal values. The exchange rate may affect the trade flow between trading countries as a domestic currency depreciates or appreciates. Theoretically, the depreciation of a currency might lead to an increase in domestic exports as exports become cheaper and imports dearer.

Language

Language is a binary variable which captures the existence of a common official language between the trading partners, that is, between the importing country and the exporting country. It is likely that countries with colonial ties are expected to speak a common official language and are likely to trade more amongst themselves due to the trade relations they established in the past and the common language and values they share, which reduces the transaction cost of trading.

Inflation

Inflation is a monetary policy variable expressed in consumer prices. Economic theory defines inflation as the persistent rise in the general price level. It captures the general price level in the exporting country.

Agriculture, Value Added

This is used as a proxy for total public expenditure in agriculture meant to capture annual total government spending in the agricultural sector. It is measured as a percentage of GDP. It is generally expected that an increase in total government spending in the agricultural sector will boost agricultural yield and productivity, increase the export volume and boost the export value.

Land

Land is a vital factor in agricultural production. The variable captures the total arable land of Cameroon and is measured in hectares. It is expected that the expansion and growth of the total arable land would increase agricultural production, which may lead to an increase in coffee exports (Assoua et al., 2022; Thuong, 2018).

Nature and Source of Data

Data on GDP, and exchange rate, are sourced from the World Development Indicator (WDI) database of the World Bank and the PENN World Table version 9.1 respectively. Information on Cameroon’s coffee production is collected from the Food and Agricultural Organization statistical database (FAOSTAT). Data on coffee export is sourced from Trade Map of the International Trade Centre (ITC), which is based on the United Nations Commodity Trade Statistics Database (UN COMTRADE) of the United Nations Conference on Trade and Development (UNCTAD). Population data is obtained from the WDI database of the World Bank. Coffee bean, HS0901 is the main commodity for the study. The geographical distance between the capital cities of Cameroon and importer countries is sourced from the Institute for Research on the International Economy (CEPII). Data on agriculture, value-added, total arable land, and data on inflation are sourced from the World Development Indicator (WDI) database of the World Bank (2018). The SPS data is obtained from the WTO Integrated Trade Intelligence Portal (I-TIP), and other pesticide databases of importing countries (Table 2).

Results and Discussions

The results of the modified poison estimates are presented in Tables 3 and 4. Tables 3 and 4 report the ZTPPML and ZIPPML estimates for a series of specifications respectively. Specification (1) consists of the PPML model with no effects. Columns (2) and (3) include respectively the PPML model with exporter time-varying effects, importer time-varying effect, and country pair time-varying effects and the PPML model including the lag of coffee exports; Specification (5) introduces the year dummies.

Table 2.

Summary Statistics.

Variables	Mean	SD	Min	Max
Export	8.132	1.199	3.434	10.736
Production (log)	10.723	0.269	10.178	11.163
Log of importers’ GDP	27.480	1.659	23.817	30.601
Log of exporter’s GDP	23.869	0.354	23.062	24.277
Population exporter (log)	16.801	0.130	16.584	17.017
Population importer (log)	17.618	1.094	16.097	19.599
SPS dummy	0.407	0.493	0	1
Exchange rate (log)	6.280	0.127	6.104	6.597
Agriculture, value added (log)	14.390	1.137	13.096	17.429
Inflation (log)	0.563	0.831	−1.454	1.675
Arable land (log)	156.193	0.196	156.006	156.401
Common language	0.5	0.502	0	1
Colonial-tie	0.204	0.404	0	1
Distance (log)	8.525	0.271	8.209	9.172

Source. Authors’ computation.

Note. Coefficients estimates are interpreted as elasticities. The a priori expectations are not as expected and not all variables are significant for the various specifications. However, some of the gravity-type variables have the expected sign and are significant.

Table 3.

Zero Truncated PPML Models.

	(1)	(2)	(3)	(4)	(5)
Production (log)	−0.00775 (0.0337)	−0.0118 (0.0332)	−0.0219 (0.0323)	−0.0219 (0.0323)	−0.0212 (0.0684)
GDP importer (log)	0.142*** (0.0358)	0.133*** (0.0355)	0.0831** (0.0301)	0.0831** (0.0301)	0.0854* (0.0351)
GDP exporter (log)	−0.0419 (0.974)	−0.0663 (0.949)
Population exporter (log)	0.0585 (2.379)
Population importer (log)	−0.157*** (0.0235)	−0.148*** (0.0238)	−0.0945** (0.0293)	−0.0945** (0.0293)	−0.0979** (0.0352)
SPS dummy	−0.109** (0.0403)	−0.117** (0.0399)	−0.0746**** (0.0444)	−0.0746 (0.0444)	−0.0892 (0.0566)
Exchange rate (log)	−0.157 (1.036)	−0.199 (1.008)
Agriculture, value added (log)	−0.00640 (0.0189)	−0.00750 (0.0196)	−0.00189 (0.0186)	−0.00189 (0.0186)	0.0413 (0.0333)
Inflation (log)	−0.00786 (0.0124)	−0.00768 (0.0125)	−0.00939 (0.00783)	−0.00939 (0.00783)	0.0239 (0.0168)
Arable land (log)	0.0941 (0.0868)	0.0930 (0.0850)	0.0943 (0.0873)	0.0943 (0.0873)
Common language	0.0228 (0.0221)	−0.0142 (0.0195)	−0.00603 (0.0155)	−0.00603 (0.0155)	−0.00875 (0.0155)
Colonial-tie	0.0435 (0.0671)	0.00751 (0.0493)	0.000934 (0.0309)	0.000934 (0.0309)	0.00339 (0.0289)
Distance (log)	−0.368* (0.180)	−0.273 (0.180)
_cons	−9.390 (11.19)	−8.005 (35.63)	−5.744 (41.43)	−5.744 (41.43)	5.402** (1.809)
N	162	162	156	156	156
R ²	0.498	0.557	0.613	0.613	0.650
AIC	675.8	674.2	648.9	648.9	643.9
BIC	709.8	708.2	682.5	682.5	671.3
df	151	151	145	145	147

Source. Authors’ analysis based on data.

Note. (i) Dependent variable: value of exports. Estimator: zero truncated poison pseudo-maximum likelihood and (ii) Robust standard errors are in parentheses.

p < 0.05. **p < 0.01. ***p < 0.001. ****p < 0.1 (10%).

Table 4.

Zero Inflated PPML Models.

	(1)	(2)	(3)	(4)	(5)
Production (log)	−0.0637 (0.0633)	−0.0632 (0.0624)	−0.0539 (0.0555)	−0.0539 (0.0555)	−0.157 (0.195)
GDP importer (log)	0.172*** (0.0385)	0.150*** (0.0321)	0.102*** (0.0291)	0.102*** (0.0291)	0.106** (0.0353)
GDP exporter (log)	−0.606 (1.072)	−0.654 (1.086)
Population exporter (log)	1.031 (2.501)
Population importer (log)	−0.159*** (0.0470)	−0.136*** (0.0346)	−0.0922** (0.0323)	−0.0922** (0.0323)	−0.0969* (0.0387)
SPS dummy	−0.0525 (0.0612)	−0.0766 (0.0506)	−0.0574 (0.0443)	−0.0574 (0.0443)	−0.0727 (0.0544)
Exchange rate (log)	−0.631 (1.120)	−0.736 (1.148)
Agriculture, value added (log)	−0.0232 (0.0271)	−0.0205 (0.0266)	−0.00828 (0.0210)	−0.00828 (0.0210)	0.0464 (0.0331)
Arable land (log)	0.0765 (0.0882)	0.0736 (0.0863)	0.0920 (0.0890)	0.0920 (0.0890)
Common language	0.00934 (0.0487)	−0.0688* (0.0299)	−0.0558* (0.0268)	−0.0558* (0.0268)	−0.0543* (0.0265)
Colonial-tie	0.0139 (0.0661)	−0.0638 (0.0353)	−0.0568* (0.0278)	−0.0568* (0.0278)	−0.0515 (0.0280)
Distance (log)	−0.382* (0.195)	−0.177 (0.152)
Inflation (log)	−0.0120 (0.0133)	−0.0101 (0.0130)	−0.0104 (0.00803)	−0.0104 (0.00803)	0.0424 (0.0266)
_cons	−6.410 (11.76)	11.50 (39.71)	−2.296 (41.25)	−2.296 (41.25)	8.192 (4.950)
N	166	166	159	159	159
R ²	0.353	0.466	0.503	0.503	0.550
AIC	730.2	722.4	686.6	686.6	679.1
BIC	761.3	753.5	720.4	720.4	706.7
df	156	156	148	148	150

Source. Authors’ analysis based on data.

Note. (i) Dependent variable: value of exports. Estimator: zero truncated poison pseudo-maximum likelihood and (ii) Robust standard errors are in parentheses.

p < .05. **p < .01. ***p < .001.

The coefficient of the production variable (lag by 1 year) is negative across all five specifications and statistically insignificant, indicating that production has a negative influence on export flow. This is contrary to economic theory which suggests that an increase in output will lead to higher export flow. The results of the production variable suggest that production negatively influences trade flows, which is a percentage increase in coffee production, does not lead to higher coffee exports. The results indicate that a 1% increase in coffee production will lead to a decrease in the commodity export by say −0.008 (specification 1). The result is not in tandem with the findings of Nugroho (2014) and Thuong (2018). A possible explanation for this result may be due to the increase in domestic industrial and semi-industrial processors, up from 21 to 24 from 2016 to 2018, together with the increased campaign to promote domestic and local consumption to reduce the heavy reliance on the export market.

The coefficients estimate for the importer’s GDP is positive across all five specifications and statistically significant as expected. The sign is consistent with the a priori expectation. The coefficient estimate of the importer’s GDPs is positive and significant (columns 1,2,3,4, and 5) as expected. The result is highly significant in specifications 1, 2, 3, and 4. This variable accounts for the purchasing power and demand-side effect of importing countries, and hence, this result is consistent with prior expectations. The coefficient estimate of exporting country’s GDP is negative and insignificant (columns 1 and 2), indicating a negative influence on trade flow. The result of importing countries’ GDP shows that it is a significant factor affecting the trade flow of the export commodity. The result suggests that growth in importers’ consumption capacity would be followed by an increase in coffee export from Cameroon. What this means is that a 1% increase in the importer’s GDP will lead to an increase in the export commodity by say 0.142%. This is in tandem with F. O. Kareem and Martinez-Zarzoso (2020), Disdier et al. (2008), O. I. Kareem (2016), Krishnan (2016), Otsuki et al. (2001a, 2001b), Wilson and Otsuki (2004), and Gebrehiwet et al. (2007).

The effect of exporter’s GDP is negative and insignificant (columns 1 and 2), corroborating the findings of O. I. Kareem (2016), Otsuki et al. (2001a, 2001b), and Scheepers et al. (2007). This result suggests that Cameroon’s GDP growth has not translated into increased export of coffee. What this implies is that there are little efforts in the promotion and expansion of coffee export from the growth being experienced by Cameroon in recent years, despite the huge demand and absorptive capacity for this commodity locally, and in importing countries. The fact that the GDP of the exporting country is negative and is not significant supports the notion of a limited supply-side capacity (productive capacity) of the export product. De Vylder (2007) concludes that Africa’s inability to export is linked to domestic factors such as supply-side constraints rather than limited market access. Cameroon currently produces an estimated 30,984 tonnes of coffee, which could be considered far below the country’s productive capacity.

The effect of exporting country’s population is insignificant and positive (columns 1 and 2). The coefficient of importing country’s population which accounts for the demand side effect on Cameroon’s coffee export is negative across all five specifications and highly significant (columns 1, 2). The result of the exporter’s population corroborates the findings of Wilson and Otsuki (2004) and Gebrehiwet et al. (2007) suggesting that the exporting country’s population plays a less significant role in influencing the trade flows of coffee exports. The result of the importers’ population which determines the market size of importers, corroborates the findings of Wilson and Otsuki (2004) and Gebrehiwet et al. (2007) suggesting that an additional percentage increase in the population of importers will decrease the trade flow of coffee exports.

The estimated elasticity for the SPS variable is negative across all five specifications for both models that are not significant except for specifications 1, 2, and 3 (columns 1, 2, and 3) suggesting that changes in SPS measures in some markets were not restrictive to the fact that they will impede Cameroon’s coffee exports. This indicates that coffee exporters take measures to comply with standards requirements before to exporting to these foreign markets. This result tallies with Thuong (2018), Disdier et al. (2008), O. I. Kareem (2016), and F. O. Kareem and Martinez-Zarzoso (2020). This is indicative that changes in SPS regulations in major importing markets do not significantly reduce the trade flow of the export commodity and therefore are not trade impeding, but are justifiable to protect animal and plant life. Also, coffee exporters in Cameroon receive technical and financial assistance from government agencies such as the National Cocoa and Coffee Board (NCCB), the Cocoa and Coffee Inter-professional Council (CICC), the Ministry of Trade, the Ministry of Agriculture and Rural Development, and other international partners such as the International Coffee Organization to enable them to comply with SPS measures. Thus, the failure of coffee exporters to comply with standards and the resulting rejection of the export commodity reflects in part the weak domestic SPS regulatory and institutional framework, poor agricultural practices, and weak response mechanisms.

The exchange rate variable is negative in the first specification and positive in the second but was not shown to be significant, contrary to prior expectations. Results had shown a positive relationship between the value of exports and the exchange rate (column 2) in line with Krishnan (2016), and a negative relationship (column 1) but are not significant suggesting that variations in the exchange rate will not have a significant impact on export. For example, the devaluation of the FCFA by 50% in 1994 had a slightly positive but not significant impact on coffee export value. Agriculture, value added is negative (columns 1, 2, 3, and 4) but positive (column 5) and was shown to be statistically insignificant across all five specifications. The results show that an increase in value-added for agriculture would not attract higher exports. This is indicative that a 1% increase in value added in agriculture, on average, will lead to a decrease in export by −0.006%. The coefficient sign of the variable common language is negative (columns 2, 3, 4, and 5) but positive in the first specification (column 1) and was shown to be statistically insignificant. A common language (columns 2, 3, 4, and 5) between the trading partners did not significantly propel trade, meaning it does not significantly impact Cameroon’s coffee trade, negating our a priori expectation of a significant positive trade effect. This indicates that sharing a common official language with the importing country is not a factor that propels trade flows for Cameroon’s coffee exporters. This is evident of the fact that eight out of the top 10 coffee importing countries of Cameroon’s coffee do not share a common official language with Cameroon. This result tallies with F. O. Kareem and Martinez-Zarzoso (2020) who posit that common language does not significantly propel African exports in fish.

The effect of the colonial tie dummy is positive across all five specifications and they are not statistically significant across the specifications, suggesting that coffee exports are not affected by past colonial ties. In line with our a priori expectations, the colonial-tie dummy is positive, but insignificant, indicating that past colonial ties did not significantly boost trade flows. This is indicative that the bulk of Cameroon’s coffee export is not destined for countries (such as France, Great Britain or Germany) with past colonial ties. For example, amongst Cameroon’s top 10 coffee-importing countries during the period under study, only two countries (Germany and France) have had past colonial ties with Cameroon. The others do not have any previous colonial ties with Cameroon. The growing importance of South-South trade and the penetration into new markets in Asia could explain these changes in trade patterns. In sum, the results may suggest that colonial ties do not appear to significantly propel Cameroon’s coffee export trade. In line with economic theory, the trade costs proxied by distance is negative (columns 1 and 2), and statistically significant (column 1). It shows a negative effect on coffee exports (columns 1 and 2), and is statistically significant (column 1).

Policy Implications and Conclusion

The preponderance of the use of food safety standards in industrialized countries and emerging market economies has attracted both public and private interest in developing countries such as Cameroon. Non-tariff barriers contribute to impeding trade from poor developing countries such as Cameroon to rich countries in the OECD. While sanitary and phytosanitary measures are applied to protect human or animal life or health from risks that arise from food-borne diseases and plant-carried infections, their blanket application too means that commodities grown by peasant farmers are subject to regulations that may block trade. The significant gains that were achieved in the domain of tariff reduction and or elimination has been eroded by the increased use of non-tariff measures such as sanitary and phytosanitary measures as a disguised restriction to trade. This is evident by the data on the increased notifications of sanitary and phytosanitary measures and export rejections at the port of entry due to non-compliance since the founding of the World Trade Organization 26 years ago. This is further compounded by the stringency and non-harmonization of food safety standards across countries. To that effect, SPS measures are now viewed as a major tool in regulating trade in agricultural and food products.

This study examined the impact of changes in sanitary and phytosanitary measures in importing countries on Cameroon’s coffee exports between 2001 and 2020. Results show that SPS measures had a weak trade effect on coffee exports during the period under study. This study found negative effects of SPS measures on Cameroon’s coffee exports that are not significant. The GDP of importing countries, the population of importing countries, and the production of coffee were identified as other important variables which determined the export value of coffee. An increase in market size will translate to an increase in trade flows for the export commodity. Also, an increase in production did not translate to an increase in trade flows of coffee, contrary to theory; the production variable did not significantly influence the export value of coffee. The population of importing countries though negative, significantly influenced trade flow. The study found that an increase in importing country’s population did not translate to an increase in trade flow. Therefore, this study concludes that SPS measures in importing countries have a weak trade effect on the flow of coffee exports from Cameroon to importing countries. In other words, SPS measures have weak trade effects on Cameroon’s coffee exports and are therefore not trade-impeding. It may rather be the weak capacity and inability of Cameroon’s coffee exporters to comply with SPS measures and the resulting rejection of the export commodity that is hindering trade.

These results have important implications for farmers or producers, exporters and policy makers. For policymakers engaged in agricultural trade negotiations as well as those mandated in setting standards, this study enlightens policy on the role of SPS measures on agricultural and food product trade and contributes towards finding suitable policy measures to minimize the negative impacts of such measures. To the various actors along the coffee value chain, such as farmers, exporters, processors, and buyers, the study highlights the need for the adoption of good agricultural practices (GAP) that reduces the associated health risks with regard to post-harvest management practices. The government of Cameroon should carry out broad-based institutional reforms, especially the regulatory framework to improve the quality of its institutions, and the “Cameroon label” in the international market. This will enable these institutions to move from being standards takers to standards-setters and through learning by doing will be ready to deal with issues relating to conformity assessment, risk assessment, transparency, harmonization, equivalence, control, inspection, and approval procedures, as well as standards accreditation and certification. In light of the limited supply-side capacity (productive capacity) of coffee, the government needs address issues of supply-side constraints. This could be achieved by increasing spending on research to develop high-yield coffee varieties, ensure access to high-yield varieties by farmers, educate and train farmers on good agricultural practices through agricultural extension programs, improving access to information on recent SPS measures and notifications, and upgrading the market infrastructure. Cameroon’s policymakers must concentrate on acquiring negotiating skills for technical capacities that enable them to participate effectively in the Agreement on sanitary and phytosanitary measures, strengthen the standards-setting institutions, and must address the supply-side constraints such as the lack of production capacity in the coffee sub-sector.

Further research may investigate farmers’ perception and response to international standards and how these food safety regulations affect farm decision-making. Also, future studies could focus on generating country firm-level data for Cameroon to document NTMs that coffee exporters perceive as problematic. Furthermore, future research may be interested in using a cost-benefit approach to investigate the cost-benefit analysis of compliance with SPS measures for exporting firms. This will require analyzing the cost of compliance to exporting firms, and the perceived benefits as a result of complying with SPS measures in importing countries. Also, one may be interested to find out how the various standards affect firm’s decisions, the structure, and governance of the value chain. The current study has dwelled on coffee, which gives room to further investigate the trade impact of various NTMs on other agricultural and food products (e.g., fruits and vegetables).

Footnotes

Appendix

List of importing countries: Netherland, Denmark, Korea Republic of, Spain, Belgium, Italy, Portugal, United States, Germany, France.

Author Contribution Statement

All authors listed have contributed to the development and the writing of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Joe E. Assoua

Data Availability Statement

Data will be made available on request.

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Evidence of Sanitary and Phytosanitary Measures on Africa’s Agricultural Trade: The Case of Coffee Exports from Cameroon to the OECD

Abstract

Plain Language Summary

Keywords

Introduction

Cameroon’s Coffee Production and Export

Review of Literature

Empirical Literature

Theoretical Considerations

Methodology and Data Description

Analytical Model

Justification of Variables

Exports

Gross Domestic Product (GDP)

Population

Distance

Production

SPS

Colonial Tie Dummy

Exchange Rate

Language

Inflation

Agriculture, Value Added

Land

Nature and Source of Data

Results and Discussions

Policy Implications and Conclusion

Footnotes

Appendix

Author Contribution Statement

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References