Wheat value chains and vertical price transmission in South Africa: A nonlinear autoregressive diagnostic lag bound approach

Swapi, Asemahle; Akande, Joseph Olorunfemi; Hosu, Yiseyon Sunday Article Wheat value chains and vertical price transmission in South Africa: A nonlinear autoregressive diagnostic lag bound approach Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Swapi, Asemahle; Akande, Joseph Olorunfemi; Hosu, Yiseyon Sunday (2024) : Wheat value chains and vertical price transmission in South Africa: A nonlinear autoregressive diagnostic lag bound approach, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 12, pp. 1-21, https://doi.org/10.3390/economies12120340 This Version is available at: https://hdl.handle.net/10419/329267 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Swapi, Asemahle, Joseph Akande, and Yiseyon Sunday Hosu. 2024. Wheat Value Chains and Vertical Price Transmission in South Africa: A Nonlinear Autoregressive Diagnostic Lag Bound Approach. Economies 12: 340. https://doi.org/ 10.3390/economies12120340 Academic Editors: Richard John Roberts, José-María Montero, María del Carmen Valls Martínez, Viviane Naimy and José Manuel Santos-Jaén Received: 4 September 2024 Revised: 22 November 2024 Accepted: 26 November 2024 Published: 12 December 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Article Wheat Value Chains and Vertical Price Transmission in South Africa: A Nonlinear Autoregressive Diagnostic Lag Bound Approach Asemahle Swapi 1, Joseph Akande 2and Yiseyon Sunday Hosu 1,3,* 1Department of Business Management and Economics, Faculty of Economics and Financial Sciences, Walter Sisulu University, P.B X1, Mthatha 5117, South Africa; [email protected] 2Department of Accounting Science, Faculty of Economics and Financial Sciences, Walter Sisulu University, P.B X1, Mthatha 5117, South Africa; [email protected] 3Small Scale Agri-Business Rural Nonfarm Enterprises Research Niche, Faculty of Economics and Financial Sciences, Walter Sisulu University, P.B X1, Mthatha 5117, South Africa *Correspondence: [email protected] Abstract: This study examined the vertical transmission of wheat price among the main value chain, looking at the pricing behaviour of certain role players in the wheat production/supply and the distribution of bread, which is a major staple food consumed in South Africa. A nonlinear autoregressive distributed lag (NARDL) model was used to analyse the yearly time series data for the period of 2000 to 2022. The results of the stationarity test showed that all variables were of order one, I (1). The study used two pairs, namely farmgate price and retail price, and farmgate price and wholesale price, to examine the existence of asymmetry between these prices, with rainfall and temperature as control variables. The results indicate the existence of a positive long-run asymmetry of 35.9% between the farmgate price of wheat and retail price of bread, and 3.49% asymmetry between the farmgate price and wholesale price of wheat. To develop informed policies on food security, this study suggests that the government should enhance regular access to data and sustain its monitoring and communication of food price information across the whole country. For farmers, a policy on price transparency can help them to establish a public platform to share market prices, ensuring that they receive fair prices. This paper also recommends the provision of subsidies for wheat farmers to help the wheat industry, reduce the cost of bread production, and make bread more affordable and accessible for consumers. Keywords: vertical price transmission; farmgate price; wholesale price; retail price; price asymmetry 1. Introduction The ability of markets to communicate price signals horizontally and vertically is an essential aspect of many current economic trends (Deb et al. 2020). Due to ongoing changes in the market, producers of agricultural commodities are becoming more interested in price transmission (Rose and Paparas 2023). The exchange of price shocks between manufacturers and retailers illustrates vertical price transmission, one way of assessing the upstream and downstream implications of the linkages in a supply chain (Rose and Paparas 2023). To better understand how prices, interact at different levels of a marketing chain, research on vertical price transmission processes must be done (Tifaoui and Von Cramon-Taubadel 2017). Wheat was introduced to South Africa in the middle of the 17th century, and by 1684, wheat production was well established in the Cape. According to Kriel (2023), 75% of wheat in South Africa is produced under dryland conditions and 25% under irrigation. The Free State (21%), Northern Cape (17%), and Western Cape (42%) produce most of South Africa’s wheat. The remaining provinces produce less. South Africa is still the second-largest Economies 2024,12, 340. https://doi.org/10.3390/economies12120340 https://www.mdpi.com/journal/economies Economies 2024,12, 340 2 of 21 sub-Saharan wheat supplier behind Ethiopia by exporting to nearby countries and acting as a passage for cereals brought in from beyond the region. It continues to import more wheat than it grows because it requires 40 to 50 percent of its own requirements in imports. The annual yields of wheat in South Africa range between 1.5 million and 3 million metric tons, with 2–2.5 tons/ha under dryland but not less than 5 tons/ha under irrigation. Research from a variety of fields, including plant breeding, agronomy, crop physiology, and crop protection, has contributed to the rise in wheat production’s efficiency, productivity, and quality over time. However, production has also been affected by climate change, such as excessive or lack of rainfall and high temperatures, which can lead to droughts over the years. According to Pachauri et al. (2014), rising temperatures, as well as rainfall, are projected to lower the production of crops such as wheat, which will have a significant negative impact on the country’s food security. It is estimated that the wheat business contributes around ZAR 5 billion annually to the gross value of agricultural produce (Mphateng 2022). Studies have been carried out to examine price relationships between farm, wholesale, and retail markets in South Africa. A study by Mosese (2020) aimed to determine the nature of price transmission in the South African potato market. A study by Mandizvidza (2013) attempted to fill the knowledge gap on the performance of Limpopo Province’s tomato markets by examining vertical price linkages amongst successive marketing levels. Mphateng (2022) analysed the transmission of world wheat prices to the domestic wheat market in South Africa in which they focused on world prices; they suggested that a study on the vertical price transmission of wheat in South Africa should be conducted. This study aims to fill the gap, as suggested by Mphateng (2022); by focusing on domestic prices to examine the price transmission in the wheat-to-bread value chain and it will use a nonlinear autoregressive diagnostic lag (NARDL) model. Statistics provided by the Department of Agriculture, Land Reform and Rural Development in 2020 revealed that the total annual production of wheat is generally less than the domestic consumption requirements. The observation provided a general indication that wheat production volumes are on the decline while consumption is continually increasing. During the marketing year 2018, the wheat production volume was about 1.80 million tons while the consumption amounted to about 3.23 million tons. This has left a deficit of about 1.43 million tons of wheat. During 2018, wheat production increased by 19% compared to the previous year, 2017. Over the same period, the local wheat utilization/consumption slightly increased by 1.3% from 3.19 million tons to 3.23 million tons. On average, South Africa produced only 56% of the country’s consumption requirements over the past ten-year period (2009–2018), and the balance came from imports. The purpose of this study is to examine the vertical price transition of wheat in South Africa, looking at the transmission of wheat farmgate prices to wheat wholesale prices to retail prices of bread from the period 2000 to 2022. The availability of data was taken into consideration when deciding the period that the study used. With a few studies performed by other authors regarding the price transmission of agricultural commodities, the vertical price transmission of wheat in South Africa was not yet performed; this led to a decision to perform this research. The objectives of this paper include analysing the long-run relationship between the wheat farm price and the wholesale wheat price and retail price, examining the asymmetry between the farmgate price and retail price, and examining the asymmetry between the farmgate price and wholesale price. The hypotheses of this paper are as follows: H1. There is no long-run relationship between wheat farmgate, wholesale, and retail prices. H2. There is no existence of price asymmetry between the farmgate price and retail price. H3. There is no existence of price asymmetry between the wholesale price and farmgate price. Economies 2024,12, 340 3 of 21 The objectives of this research were achieved under the section of estimation techniques. The first objective was achieved by performing a nonlinear bounds test to check the existence of a long-run relationship between the three prices (farmgate price, wholesale price, and retail price). The second objective of this paper can be found in the results for the estimates of the long-run coefficients for the farmgate price of wheat and the retail price of bread. The last objective was attained from the estimates of the long-run coefficients for the farmgate price and wholesale price of wheat. 2. Literature Review 2.1. Theoretical Literature 2.1.1. Market Power Theory According to the market power theory, companies merge to reduce output or work together to increase their capacity to determine product prices. Among the arguments that have been provided to account for asymmetric price movements, the most cited is the presence of market power in retail and processing industries (Bakucs et al. 2013). Generally, it is expected that downstream food enterprises, able to exert market power, transmit price movements that threaten their marketing margin faster than price movements that improve it (Bakucs et al. 2013). More specifically, market power in downstream sectors may affect price transmission by depressing purchasing prices in upstream sectors below the level of a perfectly functioning market and deterring entry or fostering exit. Firms with market power can set prices higher than in a market with greater competition (Vaidya 2023). Consumer prices may increase as a result, and this may spread across the supply chain. On the other hand, firms can also use their bargaining power to negotiate lower prices for inputs, resulting in lower prices for producers, and this can be passed on throughout the supply chain (Conforti 2004). 2.1.2. Theory of Asymmetric Information The economic theory of asymmetric information was developed by three economists, namely Akerlof, Spence, and Stiglitz, in the 1970s and 1980s as a plausible explanation for market failures (Ross 2022). The theory proposes that an imbalance of information between buyers and sellers can lead to market failure (Ross 2022). Asymmetry in information has a big impact on agricultural commodity prices. Businesses may have asymmetric knowledge about future period prices, harvest, or other pertinent variables in a lot of agricultural markets (Perloff and Rausser 1993). This informational inequality may result in market power and the capacity to set prices, which may have an impact on distribution and welfare. Businesses that own market power in processing or exporting industries, for instance, are probably more knowledgeable than other agents, and they can utilise this knowledge to boost their market power in subsequent transactions. When price information was given to farmers in the context of agricultural transactions, like the sale of crops from farmers to traders, average margins earned by traders did not change, suggesting that there was information asymmetry about prices present in these transactions (Mitra et al. 2012). 2.2. Empirical Literature This section presents the analysis of empirical literature by other scholars using different methodologies in different countries with the objective of identifying gaps that exist and other potential variables. A study by Rezitis (2019) investigated price transmission in the Finnish dairy sector and a nonlinear ARDL model was applied to monthly price data to analyse vertical price transmission among farm and retail markets for a variety of dairy products in Finland. Hillen (2021) used detailed price data on farm gate, wholesale, export, and retail levels, and they applied asymmetric vector autoregression and vector error correction models to study vertical price transmission in Swiss dairy and cheese chains. A study by Arida et al. (2023) empirically measured and analysed the long-run relationship and asymmetric price transmission of the rice market in Aceh Province, Indonesia. The study used a series of Economies 2024,12, 340 4 of 21 econometric techniques comprising cointegration, causality, and the Error Correction Model (ECM) to investigate the research objectives. The results of all of the studies mentioned present the existence of a positive long-run asymmetry between prices. A few studies regarding price transmission have been conducted in South Africa. In a study by Ramoshaba (2019), a price transmission mechanism is described with an agricultural product within the dairy industry, which is pasteurised liquid milk. The VECM results showed asymmetric price transmission, implying that retailers and processors react quicker to price increases than to price decrease. Price monitoring policy is suggested to protect the consumers from unfair prices passed on by the retailers. A study by Mosese (2020) aimed to determine the nature of price transmission in the South African potato market. The study made use of the Error Correction Model and the Granger Causality test. The Empirical results reveal the existence of price asymmetry in the South African potato value chain. Furthermore, the results show that retail prices are more responsive to producer price increases than they are to producer price declines. Mphateng (2022) used the average weekly wheat prices for the months of January 2010 through December 2019 to evaluate how changes in global wheat prices affected South Africa’s domestic wheat market. The goals of the study are to apply the Error Correction Model to determine how far the transmission of global wheat prices to domestic prices in South Africa takes place and to establish the extent of the cointegration—long-term relationship—between the two variables. The findings verified that, over time, South Africa’s domestic market eventually receives a transmission of global wheat prices. The results also show that domestic wheat prices were found to have a relatively low rate of corrections or modifications towards equilibrium conditions. More research is advised by the study, with a focus on the vertical price transmission from wheat to wheat-flour and other wheat-based items including cereals and bread. 2.3. Wheat-to-Bread Value Chain in South Africa South Africa’s wheat industry plays a vital role in the nation’s agricultural economy and contributes significantly to food security. Wheat is grown primarily in the western parts of South Africa, while farmers in the northern regions experience a competitive disadvantage due to pricing based on the Randfontein grain market in Gauteng (Midgley 2016). Even though wheat is one of South Africa’s top crops, the country remains a net importer, with imports fluctuating around 1.4 million to 1.8 million tonnes annually (Matohlang Mohlotsane et al. 2018). After wheat is harvested, activities that are done to it involve milling and producing wheat flour, bran, and meal. Bakery products account for a substantial share of the retail price of bread following the deregulation of the wheat industry (Abdelaziz et al. 2022). The wheat-to-bread value chain is illustrated in Figure 1. Figure 1above shows the process of how wheat is transferred from farmgate to retail/consumer. There are several types of storage facilities for wheat, all of which also store imported wheat. Subsequently, the wheat is taken to milling firms where it is converted to wheat flour. This flour is then used in baking, such as for bread, rolls, frozen dough, and other wheat-based goods like cereals, pasta, and biscuits. A tiny proportion of low-quality wheat is used to make animal feed (Midgley 2016). The milling, baking, and retail industries are the key players in the value chain. There are some challenges that are faced by the wheat-to-bread value chain in South Africa. The first one is climate risks where famers are increasingly exposed to climate variability and change, putting them at risk financially without adequate support or partnerships with institutions and government (Midgley 2016). The second one is the shift towards import dependence, as with local production unable to meet demand, there is a growing trend toward increased wheat imports, potentially leading to higher costs for both producers and consumers (Midgley 2016). Consumers have felt the effects in the form of higher pricing for basic wheat-based commodities like bread. The retailer may receive a significant portion of the miller-to-retail margin. Retailers appear to respond faster to Economies 2024,12, 340 5 of 21 shocks that stretch their market margins than to those that squeeze them, which is linked to the food market chain’s anti-competitive nature. Figure 1. Wheat-to-bread value chain. Source: Author’s own drawing. 2.4. Wheat and Bread Prices in South Africa Farmgate pricing is determined only after a crop has been harvested and the agricultural producer has sold their excess. The average transport difference and handling expenses are subtracted from the SAFEX spot price to arrive at the producer price of wheat in South Africa. Drought and a harsh winter can harm crops, which lowers output and drives up costs (Pettinger 2022). The wholesale price is the market price for the commodity that includes the demand and supply as well as the expected demand and supply of the commodity. Since it includes additional expenses like transportation, marketing, storage, and the profit margin of the participating companies, the market price is often greater than the farmgate price. For South Africa, wheat imports result in the fluctuation of local prices because of effects such as global wheat price movements and changes in the value of the South African currency, which is a rand, as well as the variations in transportation costs. This is one reason why there might be price changes for wheat products in the domestic economy. A sizable portion of South Africa’s population is living in poverty and rapidly urbanizing. The consumption of ready-to-eat food increases as cities grow. The majority (70–80%) of the wheat flour produced in South Africa is used to make bread, making it the most significant product in the baking industry (Mphateng 2022). According to estimates from the Department of Agriculture, Land Reform and Rural Development (2020), the nation consumes 2.8 billion loaves of bread annually, or 62 loaves per person. The cost of wheat, manufacturing costs, and transportation costs are only a few of the variables that might affect bread prices in South Africa. Figure 2provides the fluctuation of wheat prices (at farmgate and wholesale) and bread prices (at retail level) in South Africa, from the period of 2000 to 2022. Wheat prices at the farmgate level increased from 55.9% in 2000 to 78.2% in 2002. From 2003 to 2005, the producer price of wheat decreased from 74.6% to 55%, respectively. According to Theunissen (2005), in 2002 and 2003, the profit margin of wheat was high, after which it dropped in 2004 and 2005. Although the price per ton had dropped in 2005, the biggest reason for the loss in profitability was the fact that there was a lower yield due to the drought. In 2007, the prices then rose by 149.9% but fell by 93.1% to a paltry 93.1 percent two years later. It was 10% better based on the wheat production calculation against an increase of 10% relative to the five-year average of 1.91 million tons (2002/2003 to 2006/2007 seasons), which was indicative that this season’s final harvest was higher Economies 2024,12, 340 6 of 21 than what has been harvested recently—in the preceding season—which means there will be more cereal that year than the previous (Winter Cereal Trust 2007). Figure 2. Farmgate price of wheat, wholesale price of wheat, and retail price of bread in South Africa from 2000 to 2022. Source: Author’s own drawing. In 2009, farmgate wheat prices were 93.1%; then, they showed an increasing trend up until 2016, when they were the highest at 200.1%. In 2015–2016, there were droughts in South Africa, and high poverty levels could have impacted the increase in wheat prices. There was a drastic decrease in farmgate prices from being 199.2% in 2019 to 122.42% in 2020. This was caused by a decline in planted areas, and with less than 2 million tons annually, South Africa’s wheat production fell by about 50%. With 1.5 million tons harvested, the wheat crop for 2019–20 is the smallest since deregulation in 1997. Wheat prices then increased between 2020 and 2022, from 122.4% to 128.57%, respectively; this is because wheat production in South Africa increased by approximately 2.3 million metric tons compared to 2020. The market wheat prices in 2000 were 50.59% and they increased to 65.22% in 2002. In 2006, the percentage of wheat prices were 63.23% and they increased to 115.23% in 2008 and then decreased to 71.2% in 2010. The total production of wheat in 2008 was not sufficient to meet the domestic requirements; therefore, South Africa had to import the remainder to meet the domestic consumption, and this resulted in an increase in wheat prices. Except for 2010, when 2.9 million tons were used overall, wheat consumption has always been above 3 million tons annually (Mphateng 2022). The percentage of wheat prices was 84.39% in 2011 which further increased to 94.29% in 2016. From 2018 to 2022, the percentage wheat prices increased from 77.47% to 109.4%, respectively. The increase in wheat prices between 2019 and 2020 was caused by an increase in the local demand for wheat. Since then, the wheat price has shown an increasing trend; this is because of wheat prices in South Africa reaching a record high due to price trends in the international market and domestic exchange rate movements (Omarjee 2022). Bread price percentage decreased from 70.71% in 2000 to 67.78% in 2003. In 2006, the percentage of bread prices rose from 74.92% to 95.13% in 2009. According to an article by Reliefweb (2007), in importing poor nations, like South Africa, the combination of increased export prices and skyrocketing freight rates in 2007 drove up domestic prices of bread and other staple goods. The year-on-year consumer price index for food in 2008 increased. The percentage of the retail price of bread was 95.13% in 2009, and it decreased to 91.57% in 2010. South Africa has been consuming more than 3 million tons of wheat annually, but in 2010 it was recorded at 2.9 million tons; this decrease in the consumption of wheat means that there was a decrease in the production of bread. After this decrease, the percentage in Economies 2024,12, 340 7 of 21 bread prices started increasing up to 108.56% in 2016, then decreased to 99.17% in 2018 and then increased up to 103.58% in 2022. According to data, South Africa’s average annual increase in consumer food prices for the first 11 months of 2021 was 6.5%, up from 4.6% in 2020. The increase in the headline consumer food price inflation has been mostly supported by items from the category of food inflation, such as “bread and cereals”. When the price of wheat and wheat products (such as bread) increased, the South African government was unable to take the same action to mitigate the effects of rising costs on its population as they did in 2022 in response to rising oil prices. 3. Research Methodology 3.1. Methodological Design This paper applied a positivist research philosophy, which means that the study is limited to objective data collection and interpretation. The findings under this research philosophy are typically measured and observable. Put in another way, positivist philosophy maintains that problems can be solved through accurate measurement and data analysis, particularly regarding numerical data (Jansen 2023). The time series research design which the study followed was the explanatory analysis, which attempts to understand the data and the relationships within it, as well as cause and effect. This paper used a quantitative research approach, which involves gathering and analysing numerical data to explain, forecast, or regulate relevant occurrences. As this paper focused on the wheat-to-bread value chain in South Africa, it used yearly data from the period 2000 to 2022 and the reason for the choice of this period is that the data for one of the variables, which is the farm price of wheat, is only available from 2000 (available at South Africa: producer price index of wheat 2000–2019 (Statista, published by Cowling (2023)). Data for farmgate prices (wheat producer price) were collected from Statista. Data for wholesale prices (wheat market prices) and retail prices (white bread price) were sourced from the South African Grain Information Service (SAGIS); this is because SAGIS is a government-run platform, ensuring that data comes directly from sources, reducing the risk of bias and manipulation. Data for the national average rainfall and temperature (minimum and maximum) were sourced from South African Weather Services (SAWS). 3.2. The Estimated Model This paper uses nonlinear autoregressive distributed lag (NARDL) to estimate the model. NARDL proposes an expanded nonlinear version of the linear ARDL model. Ben Abdallah et al. (2020) indicates in their study the advantages of using NARDL such as that every variable does not need to have the same integration order. Even with tiny samples, the NARDL model allows for assessing asymmetries and cointegration dynamics. The NARDL model allows for the simultaneous determination of both shortand long-nonlinear interactions, which is necessary to achieve the goals of the study. This model was used by Ben Abdallah et al. (2020) in their study to find the effects of food price on macroeconomic variables and test the hypothesis of asymmetric price transmission between farmers and retailers, where their explicative variable was the farmgate price of raw milk and the independent variables were different prices of milk products. They first developed a long-run equation, given as follows: lnyt=α0+α+lnx+ t+α− 2lnx− t+εt(1) where lnyt refers to the natural logarithm of the dairy products to be analysed, and α = ( a0 , a+ 1 , a− 2 ) is a cointegrating vector or vector of long-run parameters to be estimated. Equation (1) was then adjusted to apply to this research and to achieve the first objective of the study. RPt=a0+a+FP+ t+a− 2FP− t+a+ 3WSP+ t+a− 4WSP− t+εt(2) Economies 2024,12, 340 8 of 21 Comparing Equations (1) and (2), lnyt is replaced with RPt because the dependent variable for this equation is retail price, and the dependent variables are farmgate price (FPt) and wholesale price (WSPt). In the following Equations (3) and (4), lnx+ t and lnx− t are partial sums of positive and negative changes in lnxt which designs the logarithm of the independent variable (x). lnx+ t= t ∑ i=1 ∆lnx+ t= t ∑ i=1 max(∆lnxt, 0)(3) lnx− t= t ∑ i=1 ∆lnx− t= t ∑ i=1 min(∆lnxt, 0)(4) Their NARDL model was expressed as follows: lnyt=β0+β1lnyt−1+β+ 2lnPPRM+ t−1+β− 3lnPPRM− t−1+∑p i=1ω1i∆yt−1 +∑q i=0ω2i∆lnPPRM+ t+∑m i=oω3i∆lnPPRM− t+εt(5) where β+ 2=−β1/a+ 1 and β− 2=−β1/α+ 2 .p,q, and mare lag orders of dependent and independent variables, εt is the error correction term of the NARDL model; ∑p i=1ω 1 i , ∑q i=0ω2i , and ∑m i=oω3i are the coefficients of short-run asymmetric cointegration models; ∑q i=0ω2i , and ∑m i=oω3i are the positive and negative coefficients, respectively, of the exogenous variable; and ∑p i=1ω1iare coefficients of lagged dependent variables. Farmgate-to-wholesale and farmgate-to-retail price transmission are the two levels at which this study examined price asymmetry. The section that follows presents the NARDL model that was used in this investigation. (i) Farmgate-to-retail price transmission Equation (5) is then adjusted to apply to this research, and the new equation for farm-to-wheat price transmission is given as follows: lnRPt=β0+β1lnRPt−1+β+ 2lnFP+ t−1+β− 3lnFP− t−1+∑p i=1ω1i∆RPt−1 +∑q i=0ω2i∆lnFP+ t+∑m i=oω3i∆lnFP− t+εt(6) Comparing Equations (5) and (6), yt is replaced with RPt because the dependent variable for this equation is retail price (RP); PPRM (the producer price of raw milk) is removed; and FP (farmgate price) is added as an independent variable. (ii) Farmgate-to-wholesale price transmission lnFPt=β0+β1lnFPt−1+β+ 2lnWSP+ t−1+β− 3lnFP− t−1+∑p i=1ω1i∆FPt−1 +∑q i=0ω2i∆lnWSP+ t+∑m i=oω3i∆lnWSP− t+εt(7) For this equation, FP (farmgate price) is the dependent variable and WSP (whole-sale price) is the independent variable. 3.3. Variable Description Each variable is described below, and the expectation of their relationships is mentioned for each equation/model. FPt refers to the farmgate price of wheat during period (t). The farmgate price of wheat refers to the price that farmers receive for their wheat crops. This price can vary depending on factors such as location, supply and demand, and government policies. WSPt refers to the wholesale price of wheat. The wholesale price of wheat refers to the price at which wheat is sold in bulk to retailers, processors, and other buyers. This price can vary depending on factors such as location, supply and demand, and the quality of the wheat. Economies 2024,12, 340 15 of 21 and long-run variables, and the return to equilibrium. The statistical significance of the adjustment speed, which is − 0.92, is established by the p-value being less than 5% of the significance level. This clarifies that any short-term imbalances will be fixed during the first period and will be adjusted back to equilibrium at 92% of these imbalances. Table 8b presents the short-run equation of the farmgate price (LFP), wholesale price (LWSP), rainfall (LRAINFALL), and temperature (LTEMP), where LFP is the dependent variable. From the table above, it is shown that a unit increase in wholesale prices is associated with a 1.79% increase in farmgate prices, and the results are significant at 1%, which means that there is a positive relationship between wholesale prices and farmgate prices. The response of farmgate prices to negative changes in wholesale prices is negative, meaning that a unit decrease in wholesale price is associated with a 3.47% increase in farmgate prices; these results are significant because the p-value is less than the 5% significance level, and there is an existence of asymmetry between the farmgate price of wheat and wholesale price of wheat. A unit increase in rainfall results in a 0.07% decrease in farmgate prices, these results are insignificant because the p-value is greater than the 5% significance level. A unit decrease in rainfall results in a 2.01% increase in farmgate prices, these results are significant because the p-value is less than the 5% significance level and the relationship is negative. A unit increase in temperature results in a 12.56% increase in farmgate prices and these results are insignificant because the p-value is greater than the 5% significance level and the relationship is negative. As the p-value is below the 5% significance level, the adjustment speed, which is − 0.34, is statistically significant. This clarifies that any short-term imbalances will be fixed during the first period and will be restored back to equilibrium at 34% of these imbalances. 4.7. Diagnostic Tests Diagnostic tests are used to help identify issues like dynamic, omitted variables, nonconstant errors, non-linearity, and long memory structures (Sekar 2010). The tests that are used in this section are tests for normality, serial correlation, and heteroskedasticity. The conclusion for each test is based on its null hypothesis, H0 , and whether it is accepted or rejected. Table 9a,b presents the results of the diagnostic tests for each pair. Table 9. (a) Diagnostic results: farmgate price and retail price. (b) Diagnostic results: farmgate price and wholesale price. (a) Test Null Hypothesis T-Statistics Probability Results Breusch–Godfrey LM test There is no correlation 1.23 0.38 There is no correlation Jarque–Bera test (JB) Residuals are normally distributed 0.35 0.83 Normally distributed Breusch–Pagan–Godfrey There is homoscedasticity 1.76 0.25 There is homoscedasticity White No heteroscedasticity 0.51 No heteroscedasticity (b) Test Null Hypothesis T-Statistics Probability Results Breusch–Godfrey LM test There is no correlation 0.0462 0.9567 There is no correlation Jarque–Bera test (JB) Residuals are normally distributed 3.4477 0.1783 Normally distributed Breusch–Pagan–Godfrey There is homoscedasticity 0.2553 0.9716 There is homoscedasticity White No heteroscedasticity 1.4586 0.4262 No heteroscedasticity Source: Author’s computation. The diagnostic test for the pair of farmgate price and retail price is given in Table 9a. The Breusch–Godfrey LM test, a correlation test, yields a p-value of 0.38, over the 5% significance level. In other words, the null hypothesis—that there is no correlation—is accepted. The null hypothesis is accepted, and the residuals are normally distributed if the p-value for the normality test is higher than the 5% significance level. Given that the Economies 2024,12, 340 16 of 21 Breusch–Pagan–Godfrey test’s p-value is higher than the 5% significance level, the null hypothesis—that homoscedasticity exists—is accepted. The null hypothesis that there is no heteroscedasticity is accepted when the White test, the final diagnostic test, yields a p-value larger than the 5% significance level. The diagnostic test for the pair of farmgate price and wholesale price is given in Table 9b. The Breusch–Godfrey LM test, which measures correlation, yields a p-value higher than the 5% significance level. In other words, the null hypothesis—that there is no correlation—is accepted. The null hypothesis is accepted, and the residuals are normally distributed if the p-value for the normality test is higher than the 5% significance level. Given that the Breusch–Pagan–Godfrey test’s p-value is higher than the 5% significance level, the null hypothesis—that homoscedasticity exists—is accepted. The null hypothesis that there is no heteroscedasticity is accepted when the White test, the final diagnostic test, yields a p-value larger than the 5% significance level. 4.8. Stability Test This paper employed two recursive tests which involve the CUSUM test and CUSUM of squares to check the model as to whether it is good or not. The stability test for both pairs is given in the figures below (Figures 3–6). Figure 3. CUSUM test—farmgate price and retail price. Source: Author’s own computation. The CUSUM line is in between the 5% significance level, meaning that the series is stable, which is good for the model. The CUSUM of square line is in between the 5% significance level, meaning that the series is stable, which is good for the model. The CUSUM line is in between the 5% significance level, meaning that the series is stable, which is good for the model. The CUSUM of square line is in between the 5% significance level, meaning that the series is stable, which is good for the model. Economies 2024,12, 340 17 of 21 Figure 4. CUSUM of Squares test—farmgate price and retail price. Source: Author’s own computation. Figure 5. CUSUM test—farmgate price and wholesale price. Source: Author’s own computation. Economies 2024,12, 340 18 of 21 Figure 6. CUSUM of Squares test—farmgate price and wholesale price. Source: Author’s own computation. 5. Conclusions and Recommendations This paper can be related to the price theory, which states that for food and agricultural items, supply shocks have a greater impact on price formation than demand shocks, this is because demand tends to be steady due to established consumption habits (Mgale and Yan 2020). The other theory that the paper can be related to is the cost pass-through theory, which states that each market adjusts its prices of the products or services provided to meet the adjustments of its own costs. The existence of price asymmetry between wheat farmgate price, the wholesale price of wheat, and the retail price of bread can be related to both theories. The farm prices of wheat, which is the main ingredient for making bread, depend on the production of wheat; if there is more supply, then the prices will be less, and if there is less supply, the prices will be more. These price changes can affect retailers because it will not be easy for them to just change the prices of bread at the same time that the price of wheat changes, and therefore this will affect consumers. Government subsidies can assist in this matter because if there is a lower supply of wheat, farmers will not quickly change their prices. However, this does not affect the wholesalers because the margin between the farmgate price and wholesale price of wheat is relatively small, and the farmers receive a fair share of the final price paid by consumers. Price theory also assumes that consumers and producers behave rationally to maximise their individual utility and profits. Market intervention can improve market efficiency and address asymmetric information through various measures. Asymmetric information can be addressed through information-related interventions. This can be by requiring firms to disclose relevant information, enabling investors and consumers to make informed decisions, and providing information, such as market data and research, also helps reduce information asymmetry. Regulatory interventions are another crucial tool to address asymmetric information, by certificating common standards, ensuring market participants meet minimum requirements, and by verifying market participants’ credibility through licencing and accreditation mechanisms and enforcing mechanisms such as penalties for non-compliance. Effective implementation requires collaboration among regulatory bodies, institutions, and market participants. By leveraging technology, education, and economic incentives, South Africa can promote a more transparent and efficient market. Economies 2024,12, 340 19 of 21 The findings of this study indicated that there is an existence of long-run and short-run asymmetry between the wheat farm price and the retail price of bread and between the wheat farmgate price and wholesale price of wheat. The relationship between wheat price and retail price is positive, and the relationship between farmgate price and wholesale price is negative. However, the results for the relationship between the farmgate price of wheat and wholesale price do not conform to the expectations of the study, which was a positive relationship between these prices. The existence of asymmetry between the farmgate price and retail price in South Africa can be caused by multiple factors such as marketing margins, value-added processes, taxes and levies, profit margins, and seasonality, to mention a few. The existence of asymmetry between farmgate price and wholesale price can be caused by market power, information asymmetry, transaction costs, and imperfect price transmission. The South African government policy that has a significant impact on the price of wheat and bread is the trade policy. Trade policy is a government’s strategy concerning international trade, including the regulation of imports and exports, and the protection of domestic industries. Many countries provide subsidies to their wheat farmers, while South Africa does not. The policy also states that the South African government has imposed tariffs on imported wheat to protect domestic producers, but this can increase the cost of wheat for consumers. The government’s decision on subsidies, tariffs, and trade agreements can impact the profitability of wheat farming, the cost of wheat for consumers, and the overall availability of bread. For the government to be able to make well-informed decisions regarding food security, this study suggests that it accelerates its efforts to track food prices across the nation. Another recommendation made by the study is the provision of subsidies for wheat farmers to help the wheat industry reduce the cost of bread production and make bread more affordable and accessible for consumers. The Sustainable Development Goals (SDGs) that the study aims to achieve are as follows: Zero hunger—ensuring access to safe, nutritious, and sufficient food such as bread. Decent work and economic growth—promoting fair prices for wheat farmers and supporting sustainable agriculture. Reduced inequalities—reducing the gap between the producer price of wheat, wholesale price of wheat, and the retail price of bread, ensuring fair returns for farmers and wholesalers, and affordable bread for consumers. Partnership for the goals—collaboration with stakeholders, including government, farmers, wholesalers, and consumers to address price asymmetry and ensure a sustainable food system. However, it will not be easy to implement these developments; there could be challenges faced. One of the biggest challenges would be the intervention of government by providing subsidies to the farmers, as the economy of South Africa is not performing well, but hopefully it will be included in the next budget speech. The findings of the study are the existence of asymmetry between the farmgate price of wheat, wholesale price of wheat, and retail price of bread. However, the study did not venture comprehensively into the underlying causes of the price asymmetry in South Africa; this is an area that requires further research. Since this paper is mainly focused on finding the asymmetry between prices, further research on the relationship between the prices (the farmgate price of wheat, wholesale price of wheat, and retail price of bread) can also be conducted, as it was not really specified in the analysis and findings. Further research on the relationship between prices can be done with or without the control variables mentioned in this study (rainfall and temperature). Author Contributions: Conceptualization—J.A. and Y.S.H.; Methodology, data curation and data analysis—A.S.; Validation—J.A. and Y.S.H.; Writing—original draft preparation; A.S.; Writing − review and editing—A.S. and Y.S.H.; Visualisation—A.S. and Y.S.H.; Supervision—J.A. and Y.S.H.; Project administration—A.S.; funding acquisition, Y.S.H. All authors have read and agreed to the published version of the manuscript. Economies 2024,12, 340 20 of 21 Funding: This research did not receive external funding, the institution, Walter Sisulu University, will provide the funding. Informed Consent Statement: Not Applicable. Data Availability Statement: Data for farm price of wheat in South Africa available at https://www. statista.com/statistics/1135803/producer-price-index-of-wheat-in-south-africa/ (5 March 2024). Data for wholesale price of wheat and retail price of bread in South Africa available at: https: //www.sagis.org.za/historical%20prices%20local.html (5 March 2024). Data for Temperature and Rainfall in South Africa was enquired from the South African Weather Service, of which they stated that it cannot be shared with other researchers. Conflicts of Interest: The authors decare no conflict of interest. References Abdelaziz, Fatma, Amy William, Kibrom A. Abay, and Khalid Siddig. 2022. An Assessment of Sudan’s Wheat Value Chains: Exploring Key Bottlenecks and Challenges. 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