1. Introduction and Problem Definition

As it gradually replaces cassava and yam in households’ food bundles, there is a transformation in the status of rice in Nigeria (Mafimisebi et al., 2014).  To most Nigerians, irrespective of socio-economic status, rice is now a staple rather than a luxury food item that it was in the 1970s and early 1980s (Akande and Akpokodje, 2003; Daramola, 2005 and Odusina, 2008, Agunbiade, 2013). According to Akanji (1995), Akpokodje et al., (2001) and Agunbiade (2013), a combination of factors, which include rapid urbanization and ease of preparing rice for food, seems to have triggered the structural increase in rice demand and consumption. Beside an increasing rice demand at the household level, (Odusina, 2008; Agunbiade 2013), there is an upward trending number of fast food vending points as a result of rapid urbanization which is causing rising consumption of “out-of-home” foods by peri-urban or urban dwellers engaged in the formal or informal sector jobs (Mafimisebi et al., 2014). Going by this scenario, it is reasonable to expect that the demand for rice will continue to rise at an increasing rate (FAO, 2005; Bamidele et al., 2010; Odularu, 2010; Agunbiade, 2013.) Furthermore, as more married women, who were hitherto full-time housewives, enter the workforce in both the formal and informal sectors, the opportunity cost of their time will increase and convenience food such as rice, which can be quickly prepared for meal will become more popular (Odusina, 2008 and Bamidele et al., 2010, Agunbiade, 2013) in household diets.

Nigeria’s annual rice demand is estimated at 5 million tonnes, out of which only about 2.2 million tonnes is produced locally (Federal Ministry of Agriculture and Rural Development, [FMARD], 2012). The annual rice demand supply gap of 2.7 million tonnes (or 56% of demand) is bridged by importation (FMARD, 2012). Research findings from several states in Nigeria have shown that rice availability and prices have become a major welfare determinant for the low-income segment of the country’s populace in which the highest incidence of food insecurity is recorded (Akande and Akpokodje, 2003; Rahji and Adewunmi, 2008; Kassali et al., 2010). Stable price for food in general and rice in particular is therefore vitally important to human welfare in Nigeria where the national poverty incidence figure is above 65% (National Bureau of Statistics, 2010). The overriding requirement for attaining the goal of stable rice price in Nigeria is to see that imported rice markets achieve and maintain an acceptable extent of market integration.

Market integration is a generally acceptable proxy for marketing efficiency (Timmer 2004, Mafimisebi, 2012). If markets are integrated, the price differential or spread between contiguous markets for the same commodity cannot be higher than transfer costs (Mafimisebi, 2012). Price is the central mechanism by which linkage or integration is established between markets as well as the key driver of the resource allocation process that takes place through markets (FAO, 2005; Mafimisebi, 2012). Furthermore, the understanding of price formation process is important for the formulation of effective policy decisions. In view of the above, efficient functioning of the imported rice market network becomes a sine-qua-non in agricultural and economic development of Nigeria.

Over the years, most research efforts have been geared toward increasing local rice production to meet self-sufficiency in its production, making locally produced rice compete favourably with imported rice, reversing the excessive outflow of foreign exchange for importing rice and indeed raising local rice consumption in Nigeria (Daramola, 2005; Bamidele et al., 2010). Sadly, little importance has been accorded research into the country’s rice marketing and distribution system at both zonal and national levels. In view of the important role imported rice plays in the nutrition of Nigerian households and the surge in the imports of the commodity into the Nigerian market in response to increasing demand in the last two decades, there is need to examine the efficiency and competitiveness of the market for imported rice.

The major objective of the study from which this paper was derived was to examine the pricing contacts in the market for imported rice in Southwest zone of Nigeria. The specific objectives were to (i) compute and explain the trend in price; (ii) compute and explain price variability; (iii) test the presence and degree of pricing contacts, if any, between spatial markets and (iv) identify market(s) that assume(s) the leadership position (s) in price formation and transmission in the zone.

2. Theoretical Framework

Co-integration is a statistical property possessed by some time-series data that is defined by the concept of stationarity and order of integration of the series. It deals with relationship between or among variables where (unconditionally) each has a unit root. It means that despite being individually non-stationary, a linear combination of two or more time series can be stationary. A stationary series is one with a mean value which will not vary with the sampling period. In contrast, a non-stationary series will exhibit a time varying mean. The order of integration of a series is given by the number of time the series must be differenced in order to produce a stationary series. A series generated by the first difference is integrated of order 1 denoted as I(1). Thus, if a time series is I(0), it is stationary; if it is I(1), then its change is stationary and its level is non-stationary.

The concept of co-integration and the method for estimating a co-integration relation or system (Engle and Granger, 1987; Johansen, 1988; Johansen and Juselius, 1990 and Juselius, 2006) provide a framework for estimating and testing for long run equilibrium relationships between non-stationary integrated variables. If two prices in spatially separated markets (or different levels of the supply chain), p1t and p2t, contain stochastic trends and are integrated of the same order, say 1(d), the prices are said to be co-integrated if p1t –βp2t ₌µ is 1(0).    

β is referred to as the co-integrating vector (in the case of two variables, a scalar), while the equation p1t –βp2t is said to be the co-integrating regression. Co-integration implies that these prices move closely together in the long run, although in the short run, they may wander in different directions, and this is consistent with the concept of market integration. Co-integration analysis thus provides a powerful discriminating test for spurious correlation: conducting co-integration analysis between apparently correlated I(1) series and finding co-integration confirms the regression.

Several methods have been used to measure market integration. Simple bivariate correlation coefficient, also called the Law of One Price (LOP) has long been the most popular model. The concept of constant market price and arbitrage are consistent with LOP. This method was strongly criticized most notably by Harriss (1979) and Ravallion (1986). Ardeni (1989) and Baffles (1991) found the LOP to be a short-run phenomenon. Advancements in time-series econometrics led to the development of models that address some of the perceived weaknesses in the correlation coefficient approach. In this respect, Ravallion (1986) proposed a dynamic model of spatial price differentials incorporating time lags.

In spite of this modification by Ravallion, one major drawback still remained. Both the LOP and Ravallion models according to Marks (2008) test whether price changes in one market will be translated on a one-for-one basis to the other market, either instantaneously (LOP) or with lags as in the Ravallion model. It should be understood however that prices in different markets will only move on a one-for-one basis if the inter-market price differential is equal to transfer costs. Thus, price movements inside the bandwith set by the transfer costs do no harm to the hypothesis of market integration, whereas these models will possibly reject this hypothesis.

Palakas and Harris-White (1993) and Alexander and Wyeth (1994) extended Ravallion’s model using Co-integration and Granger causality ordinary least squares (OLS) technique. This allowed testing for more general notions between markets and measuring whether prices in two markets wander within a fixed band (Baulch, 1997). According to Marks (2008), the limitation of these models however, is that all models are in fact “static” with the interpretation that markets are either integrated or not. This requires the assumption of a constant market structure throughout the period covered by the time-series data collected for analysis. It implies that when observations for different sub-periods are limited, then doing integration analysis is not feasible.

Presently, the most common approach for testing for market integration is the Johansen Co-integration technique and Vector Error Correction model applied among others by Rufino (2008), Mohammad and Wim (2010) and Mafimisebi (2012). This paper used this approach.

3. Methodology

3.1. Sources of Data Used in the Study

Secondary data were used for this study. The secondary data were sourced from National Bureau of Statistics (NBS). Data points were monthly retail prices of imported rice in urban areas of the six Southwest states of Nigeria which are Ekiti, Lagos, Ogun, Ondo, Osun and Oyo States. The data covered the period from January 2001 to December 2010, giving a total of 120 data points per state.

3.2 Analytical Procedure

The data analytical techniques that were used in this study comprised of descriptive statistics and co-integration technique (Johansen co-integration test). The descriptive statistics that were used included frequency counts, means and co-efficient of variation (CV). Augmented Dickey Fuller (ADF) test and Philip Perron (PP) test were used for the stationarity test. Johansen Co-integration model was conducted to test for long run market integration between spatial markets that were stationary of the same order.

3.2.1 Mean Spatial Prices and Variability Index 

Average monthly growth rate of prices for the whole period considered were computed as well as coefficient of variation (CV).

3.2.2. Test for Order of Econometric Integration (unit root test)

A stationary series is one with a mean value which will not vary with the sampling period. In contrast, a non-stationary series will exhibit a time varying mean (Juselius 2006, Mafimisebi, 2012). Before examining integration relationships between or among variables, it is essential to test for unit root, and identify the order of stationarity, denoted as I(0) or I(1). This is necessary to avoid spurious and misleading regression estimates (Adams, 1992).

The framework of ADF methods is based on an analysis of the following model:

                                                                            (1)

Here,  is the rice price series being investigated for stationarity,  is first difference operator,  is time trend variable,  represents zero-mean, serially uncorrelated, random disturbances, k is the lag length;    are the coefficient vectors. Unit root tests were conducted on the  parameters to determine whether or not each of the series is more closely identified as being I(1) or I(0) process. The test statistics is the t statistics for . The test of the null hypothesis of equation (1) shows the existence of a unit root when against alternative hypothesis of no unit root when  ≠ 1. The null hypothesis of non-stationarity is rejected when the absolute value of the test statistics is greater than the critical value. When  is non-stationary, it is then examined whether or not the first difference of  is stationary (i.e. to test   I(1) by repeating the above procedure until the data were transformed to induce stationarity.

The Philips-Perron (PP) test is similar to the ADF test. PP test was conducted because the ADF test loses its power for sufficiently large values of “k” which is the number of lags (Ghosh et al., 1999). It includes an automatic correction to the Dickey-Fuller process for auto-correlated residuals (Brooks, 2008, Mafimisebi and Thompson, 2012). The regression is as follows:

                                                                                                                             (2)

Where  is the rice price series being investigated for stationarity,  and    is serially correlated error term.

3.2.3. Testing for Johansen Co-integration (Trace and Eigenvalue tests)

If two series are individually stationary at same order, the Johansen and Juselius (1990) and Juselius (2006) approach can be used to estimate the long run co-integrating vector from a Vector Auto Regression (VAR) model of the form:

                                                                                                (3)

Where is a nx1vector containing the series of interest (rice price series) at time (t)  is the first difference operator,  and  nxn matrix of parameters on the ith and kth lag of    ₋    ₋  Ig is the identity matrix of dimension g,  is constant term,  is nx1 vector of white noise errors. Throughout, p is restricted to be (at most) integrated of order one, denoted I(1), where I(j) variable requires jth differencing to make it stationary. Equation (2) tests the co-integrating relationship between stationary series. Johansen and Juselius (1990) and Juselius (2006) derived two maximum likelihood statistics for testing the rank of , and for identifying possible co-integration as the following equations show:

                                                                                              (4)

                                                                                              (5)

Where r is the number of co-integration pair-wise vector, is ith eigenvalue’s value of matrix .  is the number of observations. The  is not a dependent test, but a series of tests corresponding to different  -value. The  tests each eigenvalue separately. The null hypothesis of the two statistical tests is that there is existence of r co-integration relations while the alternative hypothesis is that there is existence of more than r co-integration relations. In this study, this model was used to test for; (1) integration between pair-wise price series in any two contiguous markets in the zone and (2) integration among the six price series taken together.

3.2.4. Test for Granger-causality

After undertaking co-integration analysis of the long run linkages of the various market pairs, and having identified the market pair that were linked, an analysis of statistical causation was conducted. The causality test used an error correction model (ECM) of the following form;

 xxxxxxxx

Where m and n are number of lags as determined by Akaike information Criterion (AIC).

Rejection of the null hypothesis i.e. that prices in market j does not Granger cause prices in market i (by a suitable F-test) for  _h = 0 for h = 1, 2 ….n and =0 indicates that prices in market j Granger-cause prices in market i. If prices in i also Granger cause prices in j, then prices are determined by a simultaneous feedback mechanism (SFM). This is the case of bi-directional Granger causality. If the Granger-causality runs one way, it is called uni-directional Granger-causality and the market which Granger-causes the other is tagged the exogenous market.

4. Results and Discussion

4.1 Descriptive Analysis

4.1.1. Average Growth Rate in Retail Prices

The average growth rates of retail prices as depicted in Table 1 showed that growth was highest in Osun Market (33.4%), followed by Oyo Market (31.3%) and Ondo Market (29.8%). The highest average growth rate was recorded in year 2008. It can also be observed that there was a negative growth rate across the markets in the six states in the year 2010. This observation could be linked with the lifting of the ban on rice imports which engendered an increase in importation of the commodity in the periods immediately following lifting of the ban. The implication was the flooding of the Nigerian market with imported rice and the consequent depression in the prices.

4.1.2. Variability in Average Retail Prices

Variability is one of the major attributes that explain the characteristics of most price data. This attribute has important implications for food policy and the welfare of food consumers and a nation’s economy (Mafimisebi, 2012). The degree of variability in the prices of imported rice over the period is reflected in the coefficient of variation computed for the commodity in the region (Table 2). Retail prices were more volatile in Oyo Market (35.8%). The least price volatility was recorded in Ogun Market (31.1%). In general, the relatively low price variability implies that consumers can effectively plan their expenditure pattern on price with a fairly high degree of expectation that prices are not likely to substantially deviate from what they are (Mafimisebi, 2012). This is a positive signal for the welfare of rice consumers.

4.1.3. Order of Econometric Integration of Rice Price Series

The augmented Dickey-Fuller (ADF) test showed that all rice price series subjected to the model were non-stationary at their level. This meant that they all contained a unit root since the absolute values of their test statistics was less than their critical values at both 1% and 5% levels of significance. However, stationarity was reached after the first difference (Table 3). As discussed in the methodology section, this meant all price data were integrated of order one I(1), a requirement for Johansen’s co-integration analysis (Johansen and Juselius, 1990 and Juselius, 2006).

To bolster our findings concerning the I(1) and I(0) nature of the price series at their levels and their first differences, respectively, the Phillip-Perron (PP) test was also conducted. The PP test, like the ADF test, indicated significance for all variables, rejecting the null hypothesis of stationarity at the 1% and 5% levels of significance. The findings here were in accordance with earlier findings and conclusion that food commodity price series are mostly stationary of order one i.e. I(1) (Mafimisebi, 2001; Okoh and Egbon, 2003; Mafimisebi 2008, 2012). According to Mafimisebi (2012), the result is probably explained by the fact that most food price series have trends in them owing to inflation which culminates in such data exhibiting mean non-stationarity.

4.1.4. Results of Long-run Integration Test

The co-integration test results for market pairs were presented in Table (4). Generally, the results indicated price co-integration at 5% level of significance by rejecting the null hypothesis in favour of the alternative, in fourteen (14) out of fifteen (15) market pairs. Thus, co-integration was found in 93% of the market pairs tested. Both the maximal eigenvalue and trace tests were perfectly in agreement as to the number of co-integrating vectors, despite differences in the value of the tabulated test statistics. This confirms earlier results by Mafimisebi (2001, 2008, 2012) for the Nigerian dry and fresh fish markets. It can be observed that eleven (11) market pairs showed evidence of co-integration at 1% level while the remaining three (3) did so at 5% level. The implications of these findings according to Gonzalez-Rivera and Helfand (2001) is that, though all the markets belonged to the same economic market, but 11 market pairs  that were co-integrated at 1% were more intimate in pricing contacts compared to those 3 market pairs that exhibited co-integration at 5%. Thus, the results indicated that prices were strongly linked in the imported rice market in the long-run.

 What can be deduced from these results is that, prices were strongly linked and markets were generally integrated for imported rice in Southwest Nigeria. It could be said further that, there is high degree of marketing efficiency in the imported rice market in the region since market integration is a proxy for marketing efficiency (Hopcraft, 1987; Okon and Egbon, 2003 and Mafimisebi, 2012). This implies that, excluding transportation and risk factors, retail prices of imported rice in one market do not substantially differ from prices of the same commodity in other markets. This finding, according to Agunbiade (2013), may be attributed to free the flow of information on prices within and across the contiguous states.   

4.1.5 Multiple Co-integration in the Imported Rice Market

The Johansen’s multiple co-integration results for imported rice market are presented in Table 5. Both the Trace test and Maximal Eigenvalue tests were in agreement as to the number of co-integration vectors that existed in the market. The two tests indicated four (4) co-integration vectors /relationships at 95% confidence interval, meaning that, there were four co-integrating equations in our estimation. The interpretation of this is that imported rice markets were stationary in four directions and non-stationary in two directions (Mohammad and Wim, 2010). The economic implication of this finding is that imported rice markets in Southwest Nigeria, were only moderately linked together, during the period studied, and not perfectly linked as suggested by the result of the pair-wise model.

4.1.6. Price Exogeneity in Imported Rice Market

On Table 6 is shown the results of Granger-causality test for identifying leader market(s). From the result, 19 market pairs (out of 28), rejected the null hypothesis of no causality. While 10 market links of the 19 exhibited bi-directional causality, the remaining 9 showed uni-directional causality. Market pairs that exhibited bi-directional causality included Ogun-Osun; Ondo-Osun; Ondo-Ekiti, Osun-Ekiti and Oyo-Ekiti. While Ekiti and Ogun Markets were stronger as they Granger-caused Ondo and Osun Markets at 1% level, respectively, the latter Granger-caused the former at 5% level. The remaining six market pairs showed equal strength as they revealed bi-directional Granger-causality at 1%.

The results revealed that Lagos Market uni-directionally Granger-caused the remaining five markets. Oyo Market also showed uni-directional causality with Lagos, Ogun, Ondo and Osun Markets. The findings therefore provide a sufficient ground to conclude that imported rice deficit markets drive the market for imported rice in the region. This is in view of the fact that Lagos is the only port City which serves as the landing point for imported rice from where other markets get their supply in the region.

5. Summary, Recommendations and Conclusion

5.1. Summary and Recommendations

  This study examined pricing contacts of the markets for imported rice in Southwest Nigeria with the aim of showing the extent of pricing efficiency, if any. The price variability analysis showed that there were low fluctuations in the retail prices of the commodity over the period studied. The low values of the coefficient of variation provided the evidence that there was remarkable stability in imported rice prices during the period covered by the time-series data used in this study. The negative growth rate in the retail prices observed in some years could be a reflection of deliberate government policies toward securing cheap rice for its citizens. The economic implication of the negative growth rate recorded is that, if growth in price remains at this trend, then the welfare of rice consumers in the study area may be secured. This however, will be at the expense of local rice producers, who may be experiencing relatively small increases in the prices of their products.

The result of the stationarity tests indicated that the price series for local rice exhibited stationarity after first-differencing. The study discovered existence of a high level of spatial pair-wise integration in imported rice markets across the six states which suggest very strong pricing contacts in the market. All market pairs had strong long-run price linkages as seen from Johansen’s multiple co-integration test. This implied that short-run price deviations from the equilibrium will readily be corrected through the efficient transmission of price information.

The Granger causality tests conducted on all inter-state market pairs showed that Lagos Market takes a sole leadership position in price formation and transmission process in the imported rice market.

Based on the results of the study, some important policy implications and recommendations emerge for the various Governments in Southwest Nigeria in the rice industry. It is recommended that the problem of highly inefficient and fragmented distribution and transportation systems be addressed for the rice traders to take advantage of the high level of regional spatial market integration. Also inter-state rice markets, government price support and other market-oriented policies should be pursued as they will achieve intended goals. Such interventions should begin from the Lagos Market which has been shown to be the leading imported rice market in the zone.

5.2. Conclusion

The existence of strong pricing contacts in the imported rice market in the Southwest Nigeria has been established in this study. The results of this study contradicted findings by past researchers of a generally low agricultural commodity market integration in Nigeria which had been attributed to the fragmented distribution system and oftentimes inefficient transportation system (Alexander and Wyeth, 1994; Mafimisebi, 2012).Also, the strong to near perfect market integration may have arisen from the fact that rice is not as highly perishable as other agricultural produce and the fact that the markets examined are very close in terms of spatial distance. In spite of this commendable degree of market integration, there is the need for all the stakeholders in the Southwest rice market to continue to effectively perform their assigned roles so that the economic benefits derivable from these very high regional pricing contacts can be fully realized and sustained.

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