1. Introduction

Around 8 million tons of coffee were exported from producing countries in 2019 (International Coffee Organization, 2020), which makes coffee one of the most traded food commodities worldwide (Girotto et al., Reference Girotto, Pivato, Cossu, Nkeng and Lavagnolo2018). Coffee production is also one of the most important activities in the rural regions of many developing countries as around 20–25 million families in 51 nations depend on coffee production for their livelihoods (Castro et al., Reference Castro, Montes and Raine2004; Lewin et al., Reference Lewin, Giovannucci and Varangis2004; Prasad, Reference Prasad2019). According to Castro et al. (Reference Castro, Montes and Raine2004), in Central America coffee production supports 291,000 farmers and provides around 1 million seasonal jobs.

In Costa Rica, coffee production is an important economic activity for many rural cantons (Pelupessy and Díaz, Reference Pelupessy and Díaz2008), where primary production is mostly carried out by small farms. In fact, 92 per cent of coffee farmers have plots that are less than 5 hectares in size and 6 per cent have plots that are between 5 and 20 hectares (ICAFE, 2017; Dragusanu et al., Reference Dragusanu, Montero and Nunn2021).

The coffee value chain is made up of coffee growers, coffee milling companies, exporting firms and roasting firms. According to the Costa Rican Coffee Institute (ICAFE, 2017), 43,035 coffee growers, 246 coffee milling companies, 76 exporting firms and 65 roasting firms were operating in Costa Rica in 2017. During the 2016–2017 harvest, 1.4 million 46-kg bags of green coffee were exported, representing US$288.3 million. In 2017 coffee production accounted for roughly 0.28 per cent of gross domestic product (GDP) and 8.17 per cent of agricultural GDP (ICAFE, 2017).

Coffee price is, on the one hand, one of the most volatile among primary commodities (Lukanima and Swaray, Reference Lukanima and Swaray2014), and on the other hand, one of the most important determinants of profitability for all actors in the coffee value chain, especially for growers. Price drops affect both short-term and long-term profitability for growers by discouraging investment in coffee plantations, making plots more vulnerable to pests and diseases (Avelino et al., Reference Avelino, Cristancho, Georgiou, Imbach, Aguilar, Bornemann, Läderach, Anzueto, Hruska and Morales2015). As a result, the productivity of plantations falls, generating a double crisis for producers, as the effects of the price reductions are aggravated by those of lower productivity (Renard, Reference Renard2010; Eakin et al., Reference Eakin, Tucker, Castellanos, Díaz-Porras, Barrera and Morales2013). Furthermore, Pelupessy and Díaz (Reference Pelupessy and Díaz2008), Sick (Reference Sick2008), Rettberg (Reference Rettberg2017) and Prasad (Reference Prasad2019) have reported that low international coffee prices also entail higher levels of unemployment, poverty, migration, violence and corruption in coffee-producing regions.

Within the value chain, coffee mills (agro-industrialists) are usually the ones that export the coffee and deal with international buyers. In this scenario, coffee growers face a problem of asymmetric information, as they usually lack knowledge about the expected evolution of international prices, which are highly uncertain and fluctuating. In order to mitigate this information asymmetry, Costa Rica's 2762 Law on the ‘Regime of relationships between coffee producers, manufacturers and exporters’ was enacted in 1961 to regulate coffee berry prices in the country. This law is intended to solve the market failures due to this information gap and to establish equitable relationships between mills and growers in such a way that the price that mills pay for coffee berries must be based on the international prices they get. Specifically, it creates a consignment mechanism in which prices paid to farmers by coffee mills must be a function of the seasonal average final price, production costs and processing yields (Asamblea Legislativa de Costa Rica, 1961; Adams and Ghaly, Reference Adams and Ghaly2007; Dragusanu et al., Reference Dragusanu, Montero and Nunn2021).

Despite this law, it has been observed that coffee mills report quite different annual average prices (see figure 1). Given this variability and the importance of prices for coffee growers, it seems relevant to ask what factors matter most in determining coffee prices and, ultimately, the profitability of this activity.

Figure 1.

Costa Rica. Annual average prices of coffee berries reported by mills in US$ per bushel from 2007–2008 to 2015–2016 coffee harvests.

Note: Each gray dot represents the annual average price reported by a buyer. The black dots represents the overall average across buyers.

Source: Authors' elaboration based on data from ICAFE (2020).

This article aims at assessing the main driving forces behind the prices paid to coffee farmers in Costa Rica. Among such forces, we identify three groups. First are some external factors, such as the international price or the impact of multinational companies. Second are some intrinsic characteristics that are related to coffee quality, but difficult or impossible to modify by coffee growers, such as altitude or the yield of coffee berries. The third group has to do with differentiation strategies that farmers can use to put them in a better position, mainly by adopting social and environmental certifications and programs. In the latter group, we focus on two cases that are particularly relevant in the Costa Rican coffee sector: Fairtrade (FT), which is a company certification, and organic coffee (OC), which is a product certification linked to coffee quality and the technology used to produce it. We address the following questions: (i) What are the main drivers of coffee prices in Costa Rica? And, more specifically, (ii) What differentiation strategies used by coffee growers are more successful in obtaining better prices?

Among the researchers who have studied the determinants of coffee prices in Costa Rica, Donnet et al. (Reference Donnet, Weatherspoon and Hoehn2008) estimated a hedonic pricing function using data from e-auctions in Central and South America and found that market clearing prices are influenced by sensory characteristics and reputation, including third-party quality rankings, country of origin, coffee variety, and quantity. They also found that e-auction coffee sales result in substantially higher prices than those obtained in conventional commodity markets.

Pelupessy and Díaz (Reference Pelupessy and Díaz2008) concluded that the highest-quality coffees harvested in the highlands of Central America obtain better prices in international markets than coffee harvested in lowland areas, which tend to be of lower quality. However, lowland producers can also increase their sale prices and survive in the coffee sector through market differentiation in terms of environmental and social attributes. Samper (Reference Samper2010) argued that quality and the use of organic production systems are positively related to better prices for coffee growers in Costa Rica. Likewise, Wollni and Zeller (Reference Wollni and Zeller2007) found that farmers participating in the specialty coffee segment in three coffee regions of Costa Rica received higher prices than those participating in conventional channels. Dragusanu et al. (Reference Dragusanu, Montero and Nunn2021) found that the FT certification is associated with a higher sale price and greater sales by coffee cooperatives.

Like some previous studies, we focus on the prices that coffee growers receive, i.e., the first link in the coffee value chain (see, e.g., Wollni and Zeller, Reference Wollni and Zeller2007; Pelupessy and Díaz, Reference Pelupessy and Díaz2008; Samper, Reference Samper2010). However, previous analyses focus on the influence of individual variables such as quality, environmental certifications, or regional differences in coffee prices. The present paper goes beyond these approaches by considering the effect of several groups of factors, including producers' strategic decisions, coffee characteristics and external elements such as the international price. We use a panel data analysis, which has the advantage of allowing us to control for the effect of omitted variables and to test more complicated behavioral hypotheses than is possible using data from a single cross-section or time series (see Hsiao, Reference Hsiao2007).

The remainder of the paper has the following structure. Section 2 presents the conceptual framework and the hypotheses to be tested, and section 3 explains the methodological aspects, including the econometric approach, the variables considered, and the data sources used. Section 4 presents and discusses our findings, and section 5 gathers the main policy implications. Finally, our conclusions are summarized in section 6, along with some suggestions for future research.

2. Theoretical framework and hypotheses

We are concerned about the effect on price of coffee attributes, and especially those that can be strategically decided by growers. To prevent estimation biases, we also control for other elements that may have an influence on the coffee price, such as the international price and the power of multinationals. In what follows, we detail each of the factors under consideration.

2.1 External factors

2.1.1 International coffee prices

It is well-established that international markets are crucial in determining the domestic prices of commodity products, especially in small countries with open economies (Igami, Reference Igami2015). Like other commodities in developing countries, it has been shown that domestic coffee berry prices are strongly influenced by international prices (Worako et al., Reference Worako, van Schalkwyk, Alemu and Ayele2008; Mofya-Mukuka and Abdulai, Reference Mofya-Mukuka and Abdulai2013). An important implication is that the periods during which prices for green coffee are low in international markets tend to generate economic crises in coffee growing regions (see Avelino et al., Reference Avelino, Cristancho, Georgiou, Imbach, Aguilar, Bornemann, Läderach, Anzueto, Hruska and Morales2015; Prasad, Reference Prasad2019).

Therefore, our study includes the international price as an important control variable. Currently, the main international references for coffee prices are the London International Financial Futures and Options Exchange, and especially the New York Stock Exchange (Jarvis, Reference Jarvis2012; Lukanima and Swaray, Reference Lukanima and Swaray2014). Moreover, the International Coffee Organization established a general price indicator system based on prices of the different types of green coffee that are marketed around the world. Within this system, we take a specific price indicator for ‘Other mild arabicas’, which is how Costa Rican coffee is classified (ICO, 2011).

Figure 2 shows that, in the period between 1990 and 2017, domestic coffee prices in Costa Rica followed a very similar trend to that of international prices. Table 1 shows basic statistical information about the domestic price and the international price.

Figure 2.

Nominal coffee prices. US Dollar Cents per pound.

Source: Authors' elaboration based on ICO statistics.

Table 1.

Descriptive statistics of national and international prices

Period: 2008–2016.

Source: Authors' elaboration based on ICO statistics.

2.1.2 Multinational coffee companies

Multinational companies (MCs) control value-added activities in several countries (Dunning and Lundan, Reference Dunning and Lundan2008). These companies are often vertically integrated, in the sense that different stages in the production process take place in a single firm, and/or are horizontally integrated, insofar as they establish the same or similar production processes in different locations, mainly to gain market access or because of tariffs and transportation costs (Borga and Zeile, Reference Borga and Zeile2004).

MCs typically extract a ‘fee’ to offset the risk of investing in specialized and real-estate assets in a host country. This fee might take the form of higher prices for goods they sell and/or lower prices for the commodities they purchase (Teece, Reference Teece1985: 237). Similarly, Dunning and Lundan (Reference Dunning and Lundan2008) consider that MCs can use their dominant position to gain competitive advantages in the form of cheaper inputs in different countries (see also Markusen, Reference Markusen1995).

It has been noted that these phenomena are clearly present in the coffee sector, as well as in other strategic activities dependent on absolute advantages linked to natural resources, such as soil productivity or climate. In fact, as noted by Talbot (Reference Talbot1997, Reference Talbot2002), multinational coffee companies (MCCs) tend to integrate most of the value-added processes along the coffee production chain (vertical integration), and also tend to carry out agro-industrial processes in different countries or different regions in the same country (horizontal integration). These strategies are prone to put MCCs in an advantageous position when it comes to negotiating prices. Therefore, we expect these companies to pay lower prices. Accordingly, we formulate the following hypothesis:

H₁: MCCs pay lower prices to coffee growers than other types of coffee mills.

3. Material and methods

3.3 Variables

We take the logarithm of annual average prices paid by a mill according to coffee type and production region (logDP_{i (rc)t}) as the dependent variable. Regarding the independent variables, some of them are qualitative and some quantitative; the former pertain to buyers and coffee characteristics, while the latter refer to coffee berries yields and international prices. Table 2 shows the definition and the main statistics of each variable.

Table 2.

Summary statistics (n = 2415)

Notes: Panel period 2008–2016.

3.4 Econometric models

Two alternative lineal models were considered. The first seeks to identify individual effects of certified organic coffee (ocof_{i (rc)}) and certified FT buyers (ftp_i(rc)t) separately, while the second model considers the cross effects of both variables.

(1)\begin{align} \textrm{Log}D{P_i}_{(rc)t} & = {\alpha _{it}} + {\beta _1}\log I{P_t} + {\beta _2}\log y{b_{i(rc)t}} + {\beta _3}bp{a_{i(rc)}} \notag \\ & \quad + {\beta _4}wvp{a_i}_{(rc)} + {\beta _5}guap{a_{i(rc)}} + {\beta _6}tuap{a_{i(rc)}} + {\beta _7}tp{a_{i(rc)}} \notag\\ & \quad + {\beta _8}mc{c_{i(rc)}} + {\beta _9}ft{p_{i(rc)t}} + {\beta _{10}}oco{f_{i(rc)}} + {u_{it}} \end{align}

(2)\begin{align} \textrm{Log}D{P_i}_{(rc)t} & = {\alpha _{it}} + {\beta _1}\log I{P_t} + {\beta _2}\log y{b_{i(rc)t}} + {\beta _3}bp{a_{i(rc)}} \notag\\ & \quad + {\beta _4}wvp{a_i}_{(rc)} + {\beta _5}guap{a_{i(rc)}} + {\beta _6}tuap{a_{i(rc)}} + {\beta _7}tp{a_{i(rc)}} \notag\\ & \quad + {\beta _8}mc{c_{i(rc)}} +{\beta _{11}}ftpoco{f_{i(rc)t}} + {\beta _{12}}ftpNoco{f_{i(rc)t}} + {\beta _{13}}Nftpoco{f_{i(rc)t}} + {u_{it}} \end{align}

where u_it is a random term and the rest of the variables are as previously defined in table 2.

To determine the most adequate approach, the pooled, fixed effects and random effects (RE) versions of the two specifications (equations (1) and (2)) were estimated. In all cases, the results of Hausman Tests and Breusch-Pagan Lagrange Multiplier Tests confirm that a General Least Square Estimator with an RE regression is the most appropriate model for estimating these equations, as shown in table 3.

Table 3.

Specification and diagnosis tests

^a Breusch-Pagan Lagrange Multiplier Tests.

Additionally, the RE model is the most suitable when the regressors include variables that are constant over the observed time interval but vary between units or groups (Heij et al., Reference Heij, de Boer, Hans Franses, Kloek and van Dijk2004). Since in our panel data set some variables, such as coffee type, multinational company, and production regions, are constant in the same group, this is an additional argument to prefer the RE model.

The Wooldridge test and the Wald test reveal the presence of autocorrelation and heteroscedasticity in our data. To address these problems, we use the Panel Corrected Standard Errors and the Prais-Winsten estimator, which allows us to correct the problems of heteroscedasticity and autocorrelation following a first-order autoregressive process, even when the panel data is not balanced (Baltagi and Wu, Reference Baltagi and Wu1999; Freybote, Reference Freybote2016), as is the case in this study. In addition, the standard errors of the Panel Corrected Standard Errors are more accurate than those of Feasible Generalized Least Squares in these circumstances (Beck and Katz, Reference Beck and Katz1995).

5. Policy implications

This study provides some insights that can help come up with some recommendations in terms of farmers' strategies and in terms of public policy.

Regarding coffee growers' strategies, it is important that they become fully aware of the expected responses of prices with respect to different factors and which of those factors are under their control or not. Our results reveal that coffee prices are clearly related to coffee attributes, some of which are intrinsic characteristics of the coffee itself and others are more easily managed by growers.

Within environmental certifications, organic production practices seem to have a significant positive effect on the price while, on the other hand, FT mills have not reported higher average annual prices than non-certified ones. It is important to bear in mind that our analyses are restricted to prices. A fully fledged assessment of the FT program, which is not the object of this study, should also consider additional elements, such as the fact that FT cooperatives use part of the premium to carry out community development activities or training and technical assistance to coffee growers.

To some extent, our findings inform about the success opportunities for coffee growers, who should generate value-added strategies such as the foundation of micro-mills and private family enterprises, producing quality coffee, and the search for more direct marketing channels in international markets (see, e.g., Snider et al., Reference Snider, Afonso-Gallegos, Gutiérrez and Sibelet2017a; Nuñez-Solis, Reference Nuñez-Solis2019).

We can also arrive at some conclusions regarding public policy. Since coffee price is sensitive to some variables that are beyond the control of small growers, such as the fluctuations in the international prices, the intervention of public institutions can be crucial. Such institutions should undertake and improve strategies of market research and adaptation to low coffee prices such as adequate price forecasting systems. A sound prediction strategy would involve continuous monitoring of international coffee prices, by studying the main stock exchanges and the use of statistical forecasting models. Making such information available to coffee producers would help them prepare for downward trends in prices. Currently, ICAFE has a web page where current coffee prices on the New York Stock Exchange are reported (see, e.g., ICAFE, 2015) but no price forecasts are offered. Financial risk reduction programs and business education would also help coffee growers to learn how to cope with fluctuations in coffee prices. In this regard, Naranjo et al. (Reference Naranjo, Pieters and Alpízar2019) point out insurance as a relevant channel to foster private investment, and also to reduce the vulnerability of coffee farmers to shocks.

Environmental international policies point towards countries' carbon neutrality (CN) as a key target. The European Union aims to be climate-neutral, which involves zero net greenhouse gas emissions, by 2050 (European Commission, 2021). Similar objectives were set in various countries around the world (see Zou et al., Reference Zou, Xiong, Xue, Zheng, Ge, Wang, Jiang, Pan and Wu2021), including Costa Rica. In this framework, low-carbon coffee production could be a mechanism to improve market access while rendering environmental benefits in coffee-growing regions (see, e.g., Valenciano-Salazar et al., Reference Valenciano-Salazar, André and Martín-de-Castro2021). Some steps have already been taken in this respect. Actually, the first company in the world to produce CN certified coffee is a cooperative in Costa Rica (Birkenberg and Birner, Reference Birkenberg and Birner2018; Birkenberg et al., Reference Birkenberg, Narjes, Weinmann and Birner2021). Also, the Costa Rican Coffee Institute and the Ministry of Agriculture have designed a strategy to reduce carbon emissions in the coffee sector. The Nationally Appropriate Mitigation Actions (NAMA) program has been adopted by the Costa Rican Government as a way to reach CN at the national level by 2050. In the agricultural sector, some special focus has been given to coffee and livestock (Ángel-Valdés and Pintor-Pirzkall, Reference Ángel-Valdés and Pintor-Pirzkall2017; Birkenberg and Birner, Reference Birkenberg and Birner2018).

Public policy can also contribute to fostering and guaranteeing the success of agricultural diversification. To this aim, public authorities should carry out agroecological studies to determine the products that best suit the climatic and soil conditions of each region. Hethcote et al. (Reference Hethcote, van Zonneveld, Solano, Méndez and Vásquez2016) studied a successful diversified system mixing coffee production with tomato and sweet pepper in a Turrialba coffee region of Costa Rica. Babin (Reference Babin2015: 99) concluded that ‘when addressing agricultural development crises, the promotion of agroecological practices that cut costs may be as good a strategy or better than one that focuses on enhancing yields or establishing price supports’. Alternative activities such as ecotourism and rural tourism could also be encouraged and incentivized, not only as a complement to coffee production, but also as a means to promote the care of nature and local culture (Heyne and Vargas-Camacho, Reference Heyne, Vargas-Camacho, Azara, Michopoulou, Niccolini, Taff and Clarke2018; Howitt and Mason, Reference Howitt and Mason2018).

Coffee growers of higher altitude production regions, such as Tarrazú, have a comparative national and international advantage that allows them to obtain a higher quality of coffee, which ensures higher prices. In order to guarantee the positioning of quality coffee from Costa Rica in general, and from the regions with the highest altitude in particular, the regulatory entity (ICAFE) may promote the Protected Designation of Origin that will allow the main brands of ‘Costa Rican coffee’ to maintain and widen their position in international markets. This strategy has been successfully carried out by producers of wine, meat, fruits, cheese, and other products in Europe (Hajdukiewicz, Reference Hajdukiewicz2014; Marcoz et al., Reference Marcoz, Melewar and Dennis2014; Sadílek, Reference Sadílek2019).