INTRODUCTION

When discussing agricultural exports of Sub-Saharan African countries, one is literally talking about coffee, cocoa, rubber and cotton; although sugar, tea, and tobacco are next in line. According to U.S Bureau of Economic Analysis, Sub-Saharan Africa agricultural export value increased from about 41 billion dollar in 1990 to 141 billion dollar in 2016. However, with the case of Cameroon, cocoa export has declined from 169,773 tons in 2004 to 123,785 tons in 2014 which has posed a significant problem to the local traders and farmers. Since the introduction of cocoa in the coastal zone of Cameroon in 1892, cocoa has played an important role to the livelihood of farmers, and to the economy of Cameroon. According to International Cocoa Organization (ICCO), Cameroon is the 5th largest producer of cocoa in the world. With respect to farmers’ welfare, this sector constitutes a source of stable income and employment for over 500,000 farm household. Furthermore, it is also an important source of foreign exchange earning to Cameroon whose growth and debt serving depends on. Compared to other cash crops (coffee, palm oil, cotton), cocoa alone contributes about one third of total agricultural exchange earnings (FAO).

Due to its importance, cocoa export can not only be geared towards exports earnings, but also towards the enormous contribution it makes to the livelihood of local farmers. In Cameroon, about 30% of cocoa farmers sell their cocoa in domestic markets (ICCO)

As discussed above, most farmers who trade locally are exempted from foreign competition or fluctuation in world prices since their prices are not regulated internationally. Over the years, the price of cocoa in Cameroon market has been relatively stable. However, due to the fluctuation of world prices, volatility of exchange rate, differences in management technique, climatic condition, and low cocoa quality (post harvesting handling conditions), export quantity of cocoa has been unstable (Figure I). This situation has affected local prices as excess volumes of exports are sold in local markets. The excessive supply of cocoa in the domestic market has made the domestic price to fall significantly.

The continuous decline in the export quantity of cocoa has important effect to the domestic price of cocoa. The purpose of this study is to assess the determinant of cocoa export in Cameroon. Previous studies on the subject (Nwachukwu et al. 2010; O.O Olubanjo et al. 2009; N. Emmanuel Tambi 1998) were based upon data for over 13 years ago, and since then, there has been a significant change in the quantity of export. Over this time period, there has been an increase and a decrease in the export quantity of Cocoa. ; N. Emmanuel Tambi (1998) incorporated other cash crops (cotton coffee and cocoa), while Nwachukwu et al. (2010) and O.O Olubanjo et al. (2009) carried out their studies in Nigeria. Therefore, it is certain that such studies will not represent the current state of affairs of cocoa export in Cameroon. This study focuses on the determinants of cocoa for a period of 33 years.(1980-2013).

LITERATURE REVIEW

Studies on the determinants of cocoa export are scarce and unavailable in the case of Cameroon. Knowing and understanding these determinants is a key aspect in drafting and implementing appropriate policy measure to foster exports and improving the domestic market of cocoa in Cameroon.

N. Emmanuel Tambi (1998) used the Engle-Granger, Johansen co-integration and Error-correction model to estimate the factors influencing Cameroon’s exports of Coffee, Cocoa, and cotton. The result reveals that, the ECM term for coffee display the appropriate sign but was statistically insignificant. GDP, export domestic price and foreign exchange rate were found to affect export of cocoa. Daniel and Khan (2002) used an OLS technique to analyze agricultural export in Cameroon. The result from the study showed that producer price, road network and export credit affects cocoa export.

In regards to studies in other African Countries: Boansi David (2013), W.A Yusuf (2007), Grafoute Amoro and Yao Shen (2013), and Nkang et al. (2006). The Boansi David (2013) study for Ghana used a co-integration method and found export price, competitiveness of Ghana in exports of cocoa beans, total cocoa products and processed cocoa export to influence cocoa export. Similarly, Nkang et al. (2006) used a Co-integration and Error correction model to examine cocoa export supply in Nigeria from 1970-2003. Their finding reveals that real producer price affect cocoa export. Okoruwa et al. (2003), cited, output and net value of world trade and GDP to affect cocoa export in Nigeria between 1960 to1997. In a study which set out to determine agricultural export of cocoa and rubber in Cote d’Ivoire, Grafoute Amoro and Yao Shen (2013) used an OLS regression. Result from the estimation reveals that, cocoa output, domestic consumption and rainfall have a significant effect on cocoa export.

None of the studies relating to Cameroon investigated the determinant of cocoa export taking into account structural break. In addition, most studies did not study cocoa as a single product. However, these studies; N. Emmanuel Tambi (1998) and Daniel and Khan (2002) incorporated products such as rubber, coffee, banana, and cotton in their studies. Therefore, this study seeks to investigate only the determinants of cocoa export in Cameroon.

METHODOLOGY

Conceptual Framework.

The analysis of the data for this study is based on cointegration and Error Correction Model (ECM). The model is developed to bypass the problems of spurious regression caused by non-stationary time series data. The ECM ascertains the stationarity of a time series data. A non-stationary data requires differencing it to become stationary. As such, it is important to assess the order of integration of the variables. An equilibrium relationship exists when variables in the model are co-integrated. For example, a stationary time series x_t has a mean, variance and autocorrelation that are constant over time. Implying that the error structure is time invariant (Adam, 1992; Tambi, 1999; Niemi, 2003). However most economic time series data tend to exhibit nonstationary stochastic processes of the form(1)

Where α is a constant drift β=1, and e_t is the error term. If x_t is non-stationary, the variance become infinite and any shock may cause it not to return to a proper mean level. As showed by Engle and Granger (1987), a non-stationary series has no error correction representation. The conventional test procedure of unit root test includes Kwiatkowski- Phillips-Schmidt-Shin test statistics (KPCC), Augmented Dickey-Fuller (ADF) statistics and the Philx lips-Perron (PP) test. while unit root test with structural break was also calculated. That is, where the t-statistic from the ADF test of unit root is at a minimum (Perman and Byrne, 2006). The ADF, PP, and KPCC are tests for the null hypothesis that the variables in the study are nonstationary.

The following equations indicate the various types of test procedures.

For Kwiatkowski-Phillips-Schmidt-Shin test statistics (KPCC)

For Augmented Dickey-Fuller ADF test(3)

Phillip-Perron test.(4)

Regressions involving non-stationary variables often display first order serial correlation and lead to spurious results. In other to generate an error correction model, there is need to examine the existence of a long-run relationship between the variables. If x_t and z_t are economic time series variables which are integrated in order Dx, they can be tested for co-integration as long as their stationarity properties have been established. A co-integration set is form when these series are integrated in the same order. The test was carried out by testing the residual from the bivariate co-integration regression for stationarity using Johassen and Juselius (1990).

In summary, co-integration assumes there is a common stochastic non-stationary I(1) process underlying two or more processes X and Y.(5)(6)(7)(8)

ε₁, τ₁ are stationary process I(0) with zero mean, but they can be correlated. Although X_t and Y_t are both nonstationary I(1), there exists a linear combination of them, which is stationary δ₁X₁ - γ₁Y_t. In other words, the regression of X and Y yields stationary residual (ε). According to Engle and Granger (1987), when variables are co-integrated, there exist a valid error correction model describing their relationship, with the implication that cointegration between variables involved is a precondition for the error correction model. The model is called an ECM model because it corrects the disequilibrium that happened in a short period by bringing the situation to a steady state in the future. The error correction model is estimated to incorporate ECM_t-1 as a regressor to capture the short-run dynamics.

Thus, the empirical model for estimating the Error Correction Model (ECM) is represented in a double log format as follows(9)

The dependent variable (Exp_t) represent the export quantity of cocoa in Cameroon from 1985 to 2013. Independent variables (l p r i c e t − 1 , l e r t − 1 , l o d a t − 1 , l c o c o a t − 1 , l c e p t − 1 , a n d l g d p t − 1), ECM term (ECM_t-1), and error term (u_t)

The value of price is derived as such:(10)

Where ev is the export value and eq is the export quantity.

Cameroon export is expected to be positively influenced by export prices as cocoa producers attempt to take advantage of higher prices. The export price of cocoa is measure in tone per US dollar. Since cocoa is been sold using the US dollar, the exchange rate (ER), measures the unit of foreign currency (US dollar) in term of the home currency (France CFA) is expected to be positive because a strong dollar makes Cameroonian cocoa less expensive in the world market.

Official development assistance (ODA/US Dollar) is expected to have a positive effect on cocoa export. It is expected that farmers will benefit from such assistance and increase their cocoa output. Cocoa output is expected to have a positive influence on cocoa export. Increase in cocoa output is expected to increase exports as well.

The comparative advantage is calculated as follows(11)

Where Xc is the export value of Cameroon cocoa; Xb is the total value of agricultural export from Cameroon; Xc is the export value of cocoa in the world and Xa is the total value of world agricultural export.

An increase in CEP reflects strengthen competiveness; which means a positive value signifies the country has a competitive advantage in the production of cocoa. GDP measures the monetary value of what a country produces. It is expected that as GDP increase export of cocoa will increase.

EMPIRICAL RESULTS

Data

In assessing the determinants of cocoa export in Cameroon, data on cocoa production, exchange rate, and cocoa export was collected from FAOSTAT, while data on export price and comparative advantage was calculated by the author using statistics from FAOSTAT. Also, data on gross domestic product and official development assistance was collected from WORLD BANK.

Table 1, presents the descriptive statistics of the variables use in the study. A total of 34 observations were used in the study. The average amount of cocoa exported was 118,238.6 tones, while the average amount of cocoa produce is 149,186.3 tones. The quantity exported is less than the quantity produce, this indicates that some of the products are sold in the domestic market. The maximum amount of development assistance receive in Cameroon was around 1.9 billion US dollar.

Results of unit root tests

The augmented Dickey Fuller (ADF), the Phillips-Perron (PP), and the KPSS are being applied to check under the hypothesis if the series has unit root or not. Schwarz info criterion was used for lag selection.

The result presented on Table 2 shows the result of ADF, PP and KPSS unit root test. The null hypothesis (non-stationary) which is the presence of a unit root was tested to see whether there is an absence of a unit root test (stationary). According to the ADF and PP test, the variables when tested at level were non-stationary, but after taking 1^st difference, the variables became stationary. On the other hand, with KPSS, GDP was stationary at level and 1st difference. Since the variables are stationary at first difference, the next step is to test for co-integration among the variables.

The unit root test for structural break is presented in Table 3. The Vogelsang (1993) one sided P-value was used to measure the significant of the variables. The break dates were presented alongside the critical values of the variables. Export of cocoa, exchange rate and official development assistance of unit root cannot be rejected at level and first differencing. Export price, cocoa output, gross domestic product and competitive advantage are stationary after first difference.

Table 4 and 5, represents results of the Johansen System co-integration test and Augmented Dickey-Fuller co-integration test. The results from both tables indicate that there is a long run relationship among the variables. From Table 4, the trace statistics indicate that there are three co-integrating equations at 0.05 level, while in Table 5, the absolute value of the test statistics is greater than the critical value at 1%, 2% and 3% respectively, also the P-value is significant meaning we accept the alternative hypothesis of co-integration.

Long run Co-integration and short run Error Correction Model (ECM) Results.

Table 6, presents the short run ECM results. The presence of a co-integration among the variables clearly necessitated the use of ECM estimation in the study. The ECM term is negative and significant at 1%, which supports the existence long run relationship between the various independent variables. The coefficient of -0.6037 indicates that the deviation of cocoa export from the equilibrium in the past period (t-1) is corrected by 60% in the next period. The value of R² implies that, the independent variables explain 92% of the variation that occurs in the dependent variable. The data used in the study is normally distributed, has no serial correlation. According to Breusch-Pagan- Godfrey test, heteroscedasticity does not pose any problem. The coefficient of exchange rate (lnER) is positive and significant. This finding is consistent with Boansi David (2013) and Adesina Yusuf and Edom (2007) who also reported a positive relationship between exchange rate and cocoa export. An increase in exchange rate implies a strong local currency. Regarding export price (lnPrice), the results of the estimations signifies that an increase in export price causes export quantity to fall. With regards to the break point, the financial crises of 1980 and the devaluation of Cameroon currency in 1992 discourage most farmers from producing. Although the devaluation was meant to boast production, most farmers face difficulty dealing with low prices. Also, majority of the cocoa plantation had aged and most of the old generation farmers were uneducated towards modern techniques used in farming, as such this lead to fall in export. As expected, Comparative advantage (lnCep) is positive and significant.

Similarly, with respect to Cotton in Chad, David Bonansi (2014) found a positive relationship between comparative advantage and export. This implies that cocoa production has been enhanced through incentive measures such as, reduction in farm tax, and improvement in quality of exports. Furthermore, the effect of gross domestic product (lnGDP) on cocoa export is significant and has a positive sign which is similar to the above prediction that an increase in GDP will result in an increase in export. This result is also in agreement with Korhan et al. (2015) and Emmanuel Tambi (1998) who found a positive relationship between GDP and export.

The long run results are presented in Tables 7. In the long run, export price, comparative advantage and exchange rate are found to be the key determinant of cocoa export from Cameroon. The coefficient of export price is positive and significant. This implies that, in the long run, there are chances that cocoa farmers in Cameroon will take advantage of the high export prices. Surprisingly, the coefficient of exchange rate is the same as in the short run. Also, the comparative advantage of Cameroon in producing and exporting cocoa expands in the long run.

CONCLUSION

Using a long run Co-integration and short run ECM model, the study examines the determinants of cocoa export and how it affects domestic price of cocoa in Cameroon. The coinergration result shows that there is a long run relationship. The significance of the error-correction terms for cocoa export concludes that any disequilibrium in the export of cocoa will be adjusted to an equilibrium state in the next period ((ECM(-1)). That is, the ECM corrects the error or the disequilibrium of the previous year by adjusting to an equilibrium state in the future. From the result, exchange rate, competitive advantage, gross domestic product, and export price explain changes in the export of cocoa in the short run. The study demonstrates that cocoa farmers have been reacting negatively toward export price in the short run. However, in the long run, the cocoa farmers had more time and resource to react positively to increase export price. Also, the long run results confirm the fact that Cameroon has a comparative advantage in cocoa production. In view of this finding, it is recommended that, old cocoa tress should be replace with high yielding ones. In addition, modernize farming equipment educational program on new methods of farming should be implemented to help farmers improve on their export quality and quantity in the future. The increase in cocoa exports in the long run signifies that if the government can protect farmers from international price volatility, cocoa farmers will be able to make great benefit from cocoa export. Also, since Cameroon has a Comparative advantage in producing cocoa, the Government should invest (farm loans, provide farm tolls) in this sector in other to boast cocoa output.

적 요

ECM 분석을 통해 카메룬 코코아 수출시 GDP와 비교우위 가 주요 결정요인임이 밝혀졌다. 수출시장에서의 환율변동에 따른 코코아 수출가격의 변동은 국내 코코아 가격을 결정하는 주요인이다. 가격변동에 따라 수출되지 못하는 코코아의 국내 시장 공급은 과잉공급으로 연결되고 이는 국내시장가격의 현 저한 하락을 초래한다.