Este estudo analisa o efeito da informação e do capital social sobre o volume de negócios no mercado de crédito rural. Discute-se a conexão entre capital social e informação e como esta relação pode contribuir para a redução dos custos de transação da intermediação financeira, de modo a permitir aumento no volume de crédito rural. Para análise empírica, foi elaborado e testado um modelo econométrico (lógite) utilizando dados referentes ao Estado de São Paulo. Os dados foram obtidos de levantamentos estatísticos oficiais (censo agropecuário, LUPA e banco de dados do SEADE) referentes à safra 1995-1996. Os resultados indicam que o nível de capital social afeta o volume de crédito rural. Assim, incentivos, em especial com apoio do setor público, para formação e manutenção de capital social permitiriam aumento da eficiência da intermediação financeira e, em consequência, maior desenvolvimento do setor rural. / This study analyses the effect of information and social capital on the volume of contracts in the rural credit market. It discusses the connection between social capital and information and how this relation contributes to the reduction of financial intermediation’s transaction costs. A logit regression model was used to empirically test the effect of social capital on the volume of rural credit. The data, from the municipalities of the State of São Paulo, are from 1995 ~ 1996 official statistics (Farm Census, LUPA and SEADE’s data basis). The results indicate that the level of social capital affects the amount of rural credit. Thus, incentives to further increase and maintain social capital would increment the efficiency of financial intermediation and, as a consequence, help rural sector’s development.

Este estudo analisa o efeito da informação e do capital social sobre o volume de negócios no mercado de crédito rural. Discute-se a conexão entre capital social e informação e como esta relação pode contribuir para a redução dos custos de transação da intermediação financeira, de modo a permitir aumento no volume de crédito rural. Para análise empírica, foi elaborado e testado um modelo econométrico (lógite) utilizando dados referentes ao Estado de São Paulo. Os dados foram obtidos de levantamentos estatísticos oficiais (censo agropecuário, LUPA e banco de dados do SEADE) referentes à safra 1995-1996. Os resultados indicam que o nível de capital social afeta o volume de crédito rural. Assim, incentivos, em especial com apoio do setor público, para formação e manutenção de capital social permitiriam aumento da eficiência da intermediação financeira e, em consequência, maior desenvolvimento do setor rural. / This study analyses the effect of information and social capital on the volume of contracts in the rural credit market. It discusses the connection between social capital and information and how this relation contributes to the reduction of financial intermediation’s transaction costs. A logit regression model was used to empirically test the effect of social capital on the volume of rural credit. The data, from the municipalities of the State of São Paulo, are from 1995 ~ 1996 official statistics (Farm Census, LUPA and SEADE’s data basis). The results indicate that the level of social capital affects the amount of rural credit. Thus, incentives to further increase and maintain social capital would increment the efficiency of financial intermediation and, as a consequence, help rural sector’s development.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / Institutions of the Farm Credit System (FCS) focus on risk-based lending in accordance with regulatory direction. The rating of risk also assists retail staff in loan approval, risk-based pricing, and allowance decisions. FCS institutions have developed models to analyze financial and related customer information in determining qualitative and quantitative risk measures. The objective of this thesis is to examine empirical account data from 2006-2012 to review the probability of default (PD) rating methodology within the overall risk rating system implemented by a Farm Credit System association. This analysis provides insight into the effectiveness of this methodology in predicting the migration of accounts across the association’s currently-established PD ratings where negative migration may be an apparent precursor to actual loan default. The analysis indicates that average PD ratings hold relatively consistent over the years, though the distribution of the majority of PD ratings shifted to higher quality by two rating categories over the time period. Various regressions run in the analysis indicate that the debt to asset ratio is most consistently statistically significant in estimating future PD ratings. The current ratio appears to be superior to working capital to gross profit as a liquidity measure in predicting PD rating migration. Funded debt to EBITDA is more effective in predicting PD rating movement as a measure of earnings to debt than gross profit to total liabilities, although the change of these ratios over time appear to be weaker indicators of the change in PD rating potentially due to the variable nature of annual earnings of production agriculture operations due to commodity price volatility. The debt coverage ratio is important as it relates to future PD migration, though the same variability in commodity price volatility suggests the need implement multi-year averaging for calculation of earnings-based ratios. These ratios were important in predicting the PD rating of observations one year into the future for production agriculture operations. To further test the predictive ability of the PD ratings, similar regression analyses were completed comparing current year rating and ratios to future PD ratings beyond one year, specifically for three and five years. Results from these regression models indicate that current year PD rating and ratios are less effective in predicting future PD ratings beyond one year. Furthermore, because of the variation in regression results between the analyses completed for one, three and five years into the future, it is important to regularly capture ratio and rating information, at least annually.

The World Bank has estimated that an additional $80 billion in financing are needed annually to achieve the 70 % increase in food supply required to feed the world in 2050. One of the cornerstones in achieving this increase in production is expected to be improved agricultural technology, where one of the latest additions is precision agriculture. It is believed that the money for investing in this technology must come from the private sector, but financial institutions are hesitant in lending money to farmers. This, in part, comes down to a high perceived riskiness in agricultural lending stemming from the risk composition in agriculture compared to other industries as well as from weak collaterals provided by farmers. This thesis aims to find what factors are most prominent in banks´ risk assessment of agricultural firms during the lending process and look at how precision agriculture could help mitigate these risks. We have gathered aggregated quantitative data from FAOSTAT and the Swedish Board of Agriculture on farm income and hectare yield (productivity) at Swedish farms. These variables were found to be two of the most important factors in agricultural lending based on previous research. In addition to this data, information on e.g. weather, ecological farming and expenditure related to pesticides, fertilizer, and machinery were collected to further the analysis. Precision agriculture is made up from a myriad of different technologies. We have opted to not separate the technologies in this study as the adoption of each technology included in the term is currently not sufficiently well understood. This aggregation of technologies allowed for us to use the dynamic AAGE-model to estimate the adoption based on the minimum hectare size where precision agriculture should be profitable at each point in time. The study finds that precision agriculture does have a positive impact on farm productivity and income volatility. Hence, precision agriculture should reduce the risk of agricultural financing given to adopting farmer which would increase the access to credit and, in continuation, lead to an increase in aggregated food production. In addition, we conclude that financial institutions should gain a better knowledge of precision agriculture technologies and use this information to improve the credit evaluation process in agricultural lending. Lastly, banks should understand how the risks related to information asymmetry and moral hazard could be reduced by utilizing the data available through farmers use of precision agriculture technology.