The study focuses on the evaluation of credit risk in Structured Finance lending transactions in agriculture. The secondary motivation of the study is that Structured Finance lending techniques have the potential of increasing access to credit for farmers, especially smallholder farmers, in the agricultural sectors of developing and emerging countries. Recent studies, in agriculture finance, done by the World Bank, Food and Agriculture Organization (FAO) and the United Nations Conference on Trade and Development (UNCTAD), highlights that application of Structured Finance lending techniques such as warehouse receipts, agricultural value chain financing and securitization, inter alia, has the potential of deepening credit services in agricultural sectors, especially in developing countries. Access to credit services, among other things, has the ability to unlock the potential for agriculture in developing and emerging countries. The primary motivation of the study is the observation that most of the studies that have been done so far, with regard to the application of Structured Finance in agriculture, have primarily focused on the principles underlying Structured Finance lending techniques in agriculture and not on the fundamental question that is of importance to a lending institution, in any lending transaction, namely: how to evaluate or measure the credit risk associated with Structured Finance lending transactions in agriculture. Therefore, the study contributes to the body of literature on Structured Finance in agriculture finance by developing a model or tool that can be used to measure credit risk in agricultural based Structured Finance lending transactions. Therefore, the primary objective of the study is to develop a credit risk model for agricultural-based Structured Finance lending transactions. To develop the credit risk model, the study conceptualizes theoretical framework of modelling credit risk as proposed by Merton (1974) as well as the principles underlying Structured Finance lending techniques in agriculture. Time series econometric forecasting techniques and risk simulation techniques are used to achieve the primary objective of the study. The developed model measures credit risk as the Probability of Default (PD). To demonstrate the application of the developed credit risk model, the study uses a conceptualized example, where the production of white and yellow maize in the Free State province of South Africa, during the 2009/2010 production season, is financed by Structured Finance loans. Using the developed model, the study shows that the probability of a farmer in the Free State province, defaulting on a Structured Finance white maize production loan with a face value of R3783/ha (for instance) is 0.0347 or 3.47%. The output of the developed model, which is the probability of default (PD), can be used by agricultural financial institutions (or agricultural lenders in general) to appraise Structured Finance loans; appropriately price Structured Finance loans and determine the amount of capital to hold against credit risk, inter alia. In other words, the developed credit risk model is a tool that can help financial institutions to manage credit risk in agricultural based Structured Finance lending transactions.

The overall objective of the study was two-folded, firstly to improve the success of rural agricultural development in the North West Province (NWP) through the development of an institutional framework and secondly, to quantify the impact of the proposed institutional changes on the different agricultural sectors in the province. The development of an institutional framework contributes towards the existing mechanisms available to role-players and decision makers involved with rural agricultural development. The ability to quantify and simulate the impact of changes in the institutional framework addresses the concerns of researchers that theory is outstripping empirical research to an excessive extent in the field of institutional economics. Furthermore, by simulating the impact of the proposed institutional framework, indepth knowledge on the economic impact of rural agricultural development in the NWP was gained. In order to reach the first objective, a review/study was undertaken of the principles of the New Institutional Economics theory and how it relates to agricultural development in the NWP. This was followed by a SWOT-analysis to identify the main agricultural opportunities and factors inhibiting rural agricultural development in the province. From this, an institutional framework was developed to create an enabling environment for rural agricultural development in the NWP. The proposed institutional arrangements/improvements include amongst others: the establishment of public-private partnerships between government, private sector and communities, the introduction of rural finance systems, equity sharing schemes, integrated research-training programmes and market access solutions. A strategic framework for the implementation of the proposed institutions and institutional arrangements was also developed. The second objective was achieved through the application of two methodological approaches. In the first approach, the economic impact of the proposed institutional framework was estimated through a partial macro-economic equilibrium model, calibrated to a Social Accounting Matrix for the NWP. Different scenarios were simulated, with the land reform programme that served as a proxy for calculating the impact of the proposed institutional changes. From this, the baseline scenario assumed 30% of agricultural land being redistributed with a 20% success rate. This scenario closely mimics reality in the province. The second scenario assumed a success rate of 35%, with the success rate in the third scenario being 50%. The main results from this analysis include the quantitative impact of the land reform policy on the different agricultural sectors of the province as well as the impact of the proposed institutional framework thereon. The simulated results proved that development policies (i.e. land redistribution) yield different economy-wide impacts within the various agricultural sub-sectors of the province. Results from the baseline scenario show that the grain and oil-seed sectors of the province have the most significant impact on the economy, reducing provincial GDP by 6.19% compared to the 4.19% of the livestock sector. Moreover, under the assumptions of the baseline scenario, the grain and oilseeds sub-sector will reduce employment opportunities with 25 307, and government income with an estimated R 160 million. The rest of the scenarios confirm that the creation of an enabling environment for rural agricultural development through the introduction of the proposed institutional framework will significantly reduce the impacts of development policies. For example, in scenario 3 the grain and oilseed sector reported a 3.19% decline in the contribution to GDP compared to the 6.19% under the assumptions of the baseline scenario. The impact on employment opportunities is also likely to decrease by 3% for every 15% increase in the success rate. The second methodological approach entails the calculation of three sets of economic multipliers (production, value added and labour). The calculated multipliers were used to determine the economy-wide impact of the proposed institutional framework. Despite numerous shortcomings of economic multipliers, this analysis was performed to quantify the direct, indirect and induced economy-wide impacts resulting from the proposed institutional changes. Results from the multiplier analysis confirm the positive impact that the creation of an enabling environment might have on the proposed land reform policies. The main conclusion of the study is that the lack of proper and functional institutions could be seen as the main reason for the high rate of rural agricultural development failure in the NWP. Thus, should government fail to address the identified institutional shortcomings, the success rate of rural agricultural development will remain a mere 20%, which will have severe consequences for the economy and the rural people in the province. It therefore calls for the creation of an enabling environment that will support rural agricultural development. This could be achieved through the implementation of the proposed institutional framework; however, commitment from all role-players involved in rural agricultural development will be a prerequisite for success in this regard.

Over the last decade South Africa experienced two events during which food prices increased significantly. The periods of high food prices were also characterised by a high degree of volatility in prices. The result of the aforementioned events were that food security in South Africa was threatened, but at the same time evidence emerged that due to the current market structure in the agricultural industry certain role players used their market power to manipulate food prices. In an effort to better understand pricing behaviour in the food industry it is necessary to investigate the nature of price transmission in different agro-food chains. It is furthermore important to understand the nature of price volatility and the degree to which such volatility spillover from one level of a value chain to the next. The primary objective of this study is to measure asymmetric price and volatility spillover in the broiler value chain. The poultry (broiler) industry was chosen as a case study because there is an increasing demand for broiler meat in South Africa, culminating in increased per capita consumption compared to other meat categories such as the red meats. It is estimated that the per capita consumption of broiler meat increased steadily from 2001 to 2009. The sector is one of largest and fastest growing agricultural sectors in the country, contributing significantly to the total gross production value of agriculture. The specific issues addressed in measuring asymmetric price and volatility spillover in the broiler value chain includes: (i) the identification of the direction of flow of information (causality) between producers and retailers, (ii) examining the degree of asymmetric price transmission across the farm-retail value chain, (iii) quantifying volatility and volatility spillover across farm and retail prices, and (iv) investigating volatility spillover from feed materials to farm and retail market prices. The data used for this study include farm and retail poultry prices, as well as the daily nearmarket monthly spot prices for yellow maize, sunflower seed and soybeans. Two types of adjustment models, namely the threshold autoregressive (TAR) and momentum threshold autoregressive (M-TAR) models were used to investigate asymmetry in farm-retail market prices, whereas the exponential generalised autoregressive conditional heteroskedasticity (EGARCH) model was used to measure the price volatility and the volatility spillover effect between retail and farm prices and between these prices and poultry feed ingredients (yellow maize, soybean and sunflower oilseed). The result obtained with the M-TAR model shows that the relationship between farm and retail prices is asymmetric. The retail price was found to respond asymmetrically to both positive and negative shocks arising from changes in producer prices, but the response is greater when the shocks are negative, i.e. when the producer price rises to lower marketing margins in the value chain. The sizes of the adjustment parameters in the farm-retail combination reveal that retail prices do not respond to shocks completely and instantaneously, but respond within a distributed time lag. The results indicate that within one month, the retail prices adjust so as to eliminate approximate 2.8 % of a unit-negative change in the deviation from the equilibrium relationship caused by changes in producer prices. This implies that the retailers must increase their marketing margin by 2.8% in order to respond completely to a unit-negative change in farm prices. The results show that farm price granger cause retail price, implying that retailers depend on what happens at the farm level in order to form their market expectations. The results obtained with the M-TAR error correction model were to a great extent consistent with the results obtained with the EGARCH model. For instance, results from the volatility model show that the magnitude of volatility in the retail and farm prices for the periods 2000M1 to 2008M8 is 1.8% and 2.8%, respectively. The volatility in the farm price was found to approximate the volatility implied by the adjustment shocks in the farm-retail price relationship investigated with the M-TAR error correction model. The results of the asymmetric volatility measurement show that there is significant asymmetric volatility spillover from the farm to the retail market implying that the response to rising prices differs from the response to a price decline. This relationship was also observed with the asymmetric price transmission model. An investigation into the impact of the prices of the major broiler feed materials, namely yellow maize, sunflower and soybean, shows that there is a volatility spillover from the yellow maize price to farm and retail prices. This implies that any change in the price of yellow maize will have a significant impact on the retail and farm prices. Market influence also flows from the sunflower oilcake price to the retail market price. The presence of an asymmetric relationship between farm and retail prices signifies the existence of concentration and market power. In a situation like this, tighter anti-competition laws will discourage anti-competitive behaviours. It will be worthwhile to increase access to agricultural information systems amongst the role players in order to reduce information bottlenecks in the vertical market system.

Approximately 70 % of the Namibian population depends on agricultural activities for their livelihoods. Moreover, agriculture remains an important sector in Namibia, because its national economy is widely dependent on agricultural production. However, two distinct land tenure systems (communal and commercial farming sectors) separated by the Veterinary Cordon Fence (VCF) complicated the marketing of cattle from the Northern Communal Areas (NCA). Cattle producers in the NCA have the option to market their cattle via the formal or informal market. Although efforts have been made to encourage producers to market their cattle through the formal market, limited improvement has been observed. In this study a number of factors were analysed to determine their influence on the decisions made in respect of cattle marketing. Factors influencing the decision of whether or not to sell through the formal market were analysed using the Probit Model. Factors influencing the proportion of cattle sold through the formal market in cases where the producer has decided to use that market to sell her/his cattle were analysed using the Truncated Model. Testing the Tobit Model against the alternative of a two-part model was done by means of Craggâs Model. Factor analysis was used to study the underlying structure resulting in transaction costs. The empirical results revealed that problems related to transport to MeatCo, improved productivity, accessibility to market-related information and accessibility to information on new technology are some of the factors significantly affecting the decision of whether or not to sell through the formal market. Payment arrangements by MeatCo, animal handling, accessibility to new information technology, age of respondents and lack of access to marketing expertise are some of the factors influencing the proportion of cattle sold through the formal market. The results suggest that substantially more information is obtained by modelling cattle-marketing behaviour as a two-decision-making framework instead of a single-decision-making framework. Factor analysis identified discounting factors, delivery aspects and market features as the underlying structure resulting in transaction costs.

The main aim of this study is to examine the impact of trade liberalisation on agricultureâs ability to contribute to economic growth and poverty reduction in South Africa. Several secondary objectives were examined that address: (i) the impact of trade liberalisation on the South African agricultural international trade performance; (ii) the relationship between trade liberalisation and poverty alleviation; (iii) the impact of trade liberalisation on Total Factor Productivity (TFP) in agricultural industries, and (iv) the short-term source of agricultural adjustments. Different methodologies were applied to achieve the specified sub-objectives, including calculation of the Intra-Industrial Trade (IIT) coefficientsâ (with its key determinants) Gravity model, the Error Correction Vector Model and the Exact Maximum Likelihood method. The Gini coefficient of exports and imports was calculated as 0.55 and 0.62, respectively. The aggregate, with respect to the South African agricultural IIT, was higher than the average attributed to advanced countries. This shows that South Africa needs to reinforce the position of a bilateral agreement, which should be accompanied by regional or even multilateral liberalisation. The econometric analysis conducted on determinants of high IIT, gives a more magnified effect of the coefficients of export to import ratios and the TIMB (trade balance). If the South African industries implement and increase trade liberalisation on the diversified level of industrial specialisation, the IIT level would remain high, and significant economic gain might be achieved. The gravity model finding shows that all variables were significant at one percent, and carried the expected sign. Only the EU dummy variable had an inverse relationship, implying that the EU trade agreement creates a negative impact on export capacity for South African farmers. Essentially, South African farmers are not in a position to compete with the subsidised farmers of the development involved. These results have several important policy implications for South Africa. Firstly, trade agreements, whether implemented unilaterally or bilaterally, will enhance potential trade flows between South Africa and other countries or regions. Secondly, from an export promotion standpoint, the distance variable in the modelâs results shows that importing countriesâ per capita income is elastic and significant in determining export. Therefore, it is important for South Africa to maintain trade links and, in order to realise export potential, to extend these to high per capita income countries or regions. On the other hand, to avoid vulnerability and potential crises in EU regions or countries where the largest proportion of South Africaâs export is directed, it is important that South Africa continues to concentrate its export promotion efforts in other regions of the world. The study has also tested the impact of trade liberalisation using both the cross-sectional and time series approach, covering nine agricultural commodities; the cross-sectional approach covered the period of 1995-2007, and the time-series covered the period of 1970- 2007. Both approaches validate the above proposition with a high degree of statistical reliability. Finally, the study identified the main sources of agricultural economic growth by categorising the variables into five main areas: cyclical reversion, structural policies and institutions, stabilisation policies, cyclical volatility and external conditions. The components of the structural policies and institutions category were found to be statistically significant, and were positive at the specified significance level (only RDGDP was related negatively). This implies that the growth was achieved with improved education, financial depth and trade openness. However, the negative relationship of RDGDP shows that the sector is suffering from debt crisis. Subsequently, farmers need to follow an effective debt management system.

Potatoes are the most important vegetable product in South Africa and the third most important food crop in the world. Potatoes are planted in all the regions and sold on all of the Fresh Produce Markets of South Africa. The markets serve as the price setter for the industry and producers are not sure about the movement and the fairness of prices they receive. Marketing strategies are based on price information and producers cannot accurately determine their strategy if price information is incorrect or unavailable. The study therefore investigated the integration of potato markets in South Africa based on price data. The primary objective of the study is to analyse market integration within the potato industry of South Africa. The existence of price relationships and spatial linkages between markets are determined by the study. Market integration was determined by applying the Threshold Vector Error Correction Model (TVECM). The TVECM is used more often in recent literature and is methodologically stronger than some of its predecessors. The method allows for nonstationarity of variables and considers the possibility of non-linear and asymmetric type of variables. The pivotal role played by transaction costs are incorporated into the model. The study also tested whether a two or three regime model would best fit the data, instead of imposing a specific regime. The data used in the study is weekly data ranging from January 1999 to June 2009. The study was done on eight selected Fresh Produce Markets (FPM) namely Johannesburg (JHB), Pretoria (PTA), Bloemfontein (BFN), Kimberley (KBY), Durban (DBN), Cape Town (CTN), Pietermaritzburg (PMB) and Port Elizabeth (PE). The following results were obtained. First, on the statistical properties of the variables - all price variables are non stationary. Based on co-integration analyses long run relationships between all market pairs considered were found. The market pairs are thus co-integrated or integrated in the long run. Second, after results suggested non-linearity, decisions were made to test for the presence of market integration in the short run by fitting TVECM. A set of two and three regime TVECM were estimated. Overall, results indicated that in the short run, the markets are not integrated. In addition, results from regime switching showed no discernible pattern on the time of switches between regimes. In conclusion the results from the direction of causality test indicated a one directional flow with Johannesburg FPM being the main destination market. Overall, results attest to a prior expectation that Johannesburg is the leading FPM in South Africa. Markets are integrated in the long run but are not integrated in the short run.

The primary objective of the study was to develop a financial measurement-based benchmarking system for GWK on the financial status of their producers. Financial benchmarking systems provide the producers with opportunities to evaluate their past and current financial performance, not only to their own financial status but also to other producers that are included in the benchmarking data. To have a better understanding of the performance of the farmâs financial status and to have a possible explanation of why certain changes had occurred, the first secondary objective was to compare trends of the whole agriculture sector of South Africa with those that occurred to the GWK producers in the study. It was found that the GWK producers had followed more or less the same trends than those experienced by the South African agricultural sector. After the trends were compared between GWK producers and the South African agricultural sector, the limited financial statements obtained from GWK, were analyzed by calculating the financial measurements for each farm over the five years. This was done in order to determine the border values that can be used to divide each measurement into three performance groups. These groups will be used to determine the position for each measurement of a farm relevant to the other farms in the benchmarking system. When the producer has seen the indication that a certain measurement is in the midpoint of bottom performance groups, he knows there are other options available to improve that position, as is already being done by other producers. This leads to another secondary objective that was identified and analyzed.When a producer wants to improve one or even more than one financial measurement, certain changes have to be made that will influence the income statement and balance sheet. As these statements are interactive and a change in one area of the statement will have an influence on the overall results, it is necessary to have an idea or indication of what these influences can be. To provide some background on what the possible outcomes can be the correlation between the measurements and their determinants were determined. These correlations will provide important information on what the possible results of a certain change by a producer on a farm can be. As the financial market is ever-changing, the changes cannot always be hundred percent predictable, but one can at least provide an idea of what can be expected. The last secondary objective is to rank the farms according to their operating efficiency for each enterprise, using DEA. As results indicated, this method of benchmarking can be used in coordination with the border measurement benchmarking system. The difference that exists is that the DEA benchmarking system only divides the farms into two groups as being efficient and inefficient. These two groups can be compared to the results obtained from the border measurement benchmarking system; the farms identified as being the efficient ones are mostly the farms that had most of their financial measurements in the top performance and the top half of the midpoint performance groups. The opposite is also true for the farms identified as being inefficient. Conclusions and recommendations from the study include that a benchmarking system can provide very important information to producers on the performance of their farms, not only to past performance, but also with regard to their rivals. When certain adjustments have to be made to improve the performance of the farm, it is important to remember the possible correlation that exists between the financial measurements and what the possible outcomes can be. The correlation between the measurements is also a point that is available for future research. Lastly, it is recommended that when a farmâs financial position is benchmarked to other competitors, more than one benchmarking system is to be used. This will provide more accurate information to the actual performance of the farm, as a wider spectrum is covered by using for example the border measurement and DEA benchmarking systems.

Growing per capita income and changing consumption patterns have led commercial retailers to restructure their marketing techniques with the aim of obtaining a greater market share of the consumerâs pocket. Retailers have focussed more on bulk procurement and consistent supply of quality produce from a few large food producers. Consequently, small-scale farmers are either excluded from the commercial markets or the few that participate in commercial markets are struggling to meet the stringent requirements from retailers. However, some scholars advise that support is needed for small-scale farmers to participate in commercial markets. FairTrade (FT) is one of the organisations that have provided an opportunity to small-scale farmers in developing countries to participate in commercial markets. Eksteenskuil raisin producers are among the farmers that have been given the opportunity to participate in commercial markets. Despite the support, Eksteenskuil raisin producers are unable to meet market requirements such as stipulated raisin volumes of adequate quality. Hence, this study estimated the level of technical efficiencies and assessed factors affecting efficiencies of Eksteenskuil raisin producers. The farming operation of Eksteenskuil raisin farmers is divided into two production levels, production and quality. Consequently, a Two-stage Data Envelopment Analysis (DEA) Model was used to understand the level of technical efficiencies in each production level. Due to a small sample size and a large number of independent variables used, degrees of freedom were identified as a problem. A Tobit Principal Component Regression (PCR) was used to reduce the dimensionality of the variables without losing important variables that explain inefficiencies. Primary data was used to obtain technical efficiency estimates and factors hypothesised to influence efficiency. Primary data was obtained through a structured questionnaire and personal interviews. A sample of 28 raisin producers in Eksteenskuil was used. A similar sample of 28 large-scale farmers was also conducted to be used for benchmarking with small-scale farmers. The empirical results revealed that production efficiencies of small-scale farmers are relatively high although farmers are struggling to increase raisin volumes. When small-scale farmers are benchmarked against each other the mean production efficiency of 81% was estimated. This means that on average small-scale farmers have the potential to operate on the efficient frontier if the mean production efficiency increases by 19 percentage points. On the other hand, the results of a benchmark of both small-scale and large-scale farmers revealed a mean production efficient of 69% and 85% respectively. This implies that small-scale farmers are less efficient relative to large-scale farmers in producing maximum possible raisin volumes with available inputs. Variables that were identified to increase the level of production efficiency are: farmerâs age, formal education, farming experience, land tenure, formal credit, record keeping, timely pruning, entrepreneur index, and Middle Island (soil fertility). Thus farmers who are located on the efficient frontier display a number of the variables mentioned above in their characteristics. On the other hand family labour, social capital and area harvested were also hypothesised to either increase or decrease the level of production efficiency. Hence, a positive or negative sign was expected. Results on the second stage of the two-stage DEA model revealed a mean quality efficiency of 97% for small-scale farmers when benchmarked against each other. The results indicate that small-scale farmers have the potential to increase their mean efficiency by three percentage points to operate on the quality efficient frontier when benchmarked against each other. A benchmark of both small-scale and large-scale raisin producers revealed a mean quality efficiency of 79% and 88% respectively. The scope of variations between the quality efficiency scores of small-scale farmers was recognised to be limited. Due to limited variations, none of the hypothesised variables were found to be significant. Policy implication highlighted from this study is that education and training should be prioritised by policy makers in the study area. Existing support from various stakeholders involved with small-scale farmers in Eksteenskuil should be intensified in order to prevent poverty from becoming an epidemic in the community

One of the factors hindering development in Lesotho is the limited access to credit. The development of the rural economy in developing countries depends on growth and development in the agricultural sector and other small and medium enterprises. These enterprises constitute the engine of growth, employment and income for the rural community. In an effort to make the landscape of rural finance more attractive and to fulfil the national objectives of increased production, policy makers and donors adopted the conventional approach of advancing credit, where all practices and operational procedures were geared towards the interests of the borrower. The initiatives to advance credit include amongst others, an emphasis on project appraisals, relaxing collateral requirements and the charging of close to market interest rates. Despite the changes, the problem of limited access to financial services still exists. In fact, these approaches (policies) invariably resulted in distortions in the financial markets, and reduced the number of financial products and services to which farmers have access. The purpose of this study therefore, was to examine factors that influence small-scale farmersâ access to credit, thereby affecting their productivity and to make suggestions for government interventions and for the reduction of market failures in the rural financial markets of Lesotho. The study was conducted in two agro-ecological zones in Lesotho, namely; the Lowlands and the Highlands regions. A random sample of districts in the regions was done to select representative districts in each region. Leribe, Mafeteng, and Berea districts represented the Lowlands while Mohaleâs Hoek and Thaba-Tseka districts represented the Highlands region. Stratified random sampling was employed to select borrowers and non-borrowers for the study. The study employed the logistic regression model (logit) within the principal component regression (PCR) framework to assess factors affecting small-scale farmersâ access to credit. PCR was used to take care of the multicollinearity between the variables. Firstly, the variables included in the logit model were subjected to principal component analysis (PCA) in order to reduce the variables into a few uncorrelated principal components (PCs). After principal components (PCs) were calculated, PCs with the smallest eigenvalues were eliminated and then PCR was fitted using standardised variables to improve the estimation power of the logit model. The empirical evidence of the study indicates that non-farm income, savings and remittances and pensions confirmed that increasing the householdâs total income reduces the probability of a household being credit constrained. This shows that a better household situation affects the decision of the lender to ration the loan or that the household has less demand for loans because of its own equity capital accumulated through past income earnings. Farm income on the other hand, is positive, confirming that a higher farm income may improve the farmerâs creditworthiness and in some cases create a demand to expand production, thus increasing the demand for credit. The study revealed that farm income values of borrowers are higher than those of nonborrowers but lack of baseline data makes it difficult to associate the differences to the loans obtained by borrowers. However, the changes in income among borrowers are linked to the use of credit, confirming the hypothesis that credit has a positive effect on income and improvement of living conditions of credit users. Research into the behaviour of credit institutions in Lesotho will help to explain some of the actions taken by credit institutions, and at the same time assist policy-makers in formulating appropriate interventions.

The main objective of this study was to explore the relationship between economic literacy and allocative efficiency of small-scale producers in South Africa. The study was conducted in Eksteenskuil, where small-scale producers export raisins via the fairtrade initiative. Data regarding production inputs and their relative prices was gathered by means of a structured questionnaire survey. The allocative efficiency of farmers was calculated by means of cost efficiency, using a mathematical linear programming technique called Data Envelopment Analysis (DEA). The inputs that were used to calculate the respondentsâ cost efficiency were fertiliser in the form of nitrogen, phosphate and potassium, labour, and fuel. It was hypothesised that economic literacy of individuals will have an effect on the ability of the producers to allocate their resources efficiently. The economic literacy of respondents was measured by means of proxy variables presented in the questionnaire. The economic literacy variables were regressed on cost efficiency by making use of the Tobit Regression Model since the dependent variable is bounded from above. The results from the DEA showed substantial inefficiencies among the small-scale raisin producers of Eksteenskuil, indicating that a significant capacity for cost efficiency improvement exists. By improving cost efficiency of producers, profit of producers will also increase. Economic literacy of raisin producers was measured to be below average. The total economic literacy score of respondents was found not to have a significant effect on their cost efficiency. However, some of the individual proxies for economic literacy were found to influence cost efficiency. Economic literacy questions were divided into two groups. The applied economic concept group: where respondents needed to think about the question, exhibit knowledge and make a rational decision. And the comprehension economic concept group: where respondentsâ knowledge on economics surrounding their farms, was tested. Interestingly, only questions from the applied economic concept group were found to have a statistically significant effect on the cost efficiency of respondents. Socio-economic factors of respondents were further measured in order to understand the characteristics associated with higher economic literacy levels of respondents. The hypothesised socio-economic factors were regressed on the statistically significant economic literacy questions found in the Tobit Regression Model. A Probit Regression Model and an Ordinary Least Squares (OLS) Regression Model were used to determine the effect of socio-economic factors on specific economic literacy questions. Most of the factors that were statistically significant in influencing economic literacy, relate to activities undertaken by the farmers to increase human capital. Other factors that were found to contribute to economic literacy, relate to farm specific factors like farm size and specialisation. The results show that economic literacy does affect the decision-making ability of individuals when it comes to the allocation of production inputs. Cost inefficiencies can be improved by improving the economic literacy of respondents. One of the important ways to improve economic literacy of smallscale producers is by simplified, goal-oriented, practical training related to the individualsâ specific farming practices.