Principal investigator: Viviane Pires Ribeiro
Paper Title: Does Access to Rural Credit Help Decrease Income Inequality in Brazil?
Authors: Mateus de Carvalho Reis Neves, Carlos Otávio Freitas, Felipe de Figueiredo Silva, Davi Rogério de Moura Costa and Marcelo José Braga
Location of the Intervention: Brazil
Sample Size: 15.402 individuals
Main theme: Finances
Main Variable of Interest: Income inequality
Type of Intervention: Analysis of the impact of rural credit on income inequality.
Methodology: Unconditional quantile regression method
The Brazilian agricultural sector has grown exponentially in recent decades and has expanded its participation in the international market. However, Brazil continues to face a high level of rural inequality. Neves et al. (2020) sought to identify whether access to credit increases or reduces inequality among rural families in Brazil. The results suggest that the National Program for Strengthening Family Farming (PRONAF) is not associated with increased inequality. However, access to rural credit from sources other than PRONAF has led to greater income inequality among families. The results also indicate that higher levels of education and access to rural extension services have enhanced the effect of credit on income.
Evaluation Context
Brazilian agricultural production has increased in recent decades. Despite this, the rural population continues to face income inequality. That is, even though the country has engaged in a stronger participation in the global market, commercial agricultural production remains concentrated in large farms.
Although income inequality has decreased over time, much remains to be done to achieve even lower levels of inequality. Several factors can contribute to a more equitable distribution of income in rural areas, including access to rural extension services and financial markets. The Brazilian government, for example, has implemented several public policies aimed at reducing such inequality.
In 1965, the National Rural Credit System (SNCR) was created to increase agricultural production and improve the living conditions of rural families. The National Agricultural Policy, created in 1991, also contributed to income generation in the country's rural areas. To overcome inequality in the distribution of benefits, the Brazilian government created the Family Farming Strengthening Program in 1995. However, Neves et al. (2020) emphasize that there is a consensus in the literature indicating that, although Brazil has improved rural families' access to financial markets, income inequality in these areas remains high, and the policies established to combat inequality have benefited large rural producers.
Intervention Details
Neves et al. (2020) identified two limitations in the literature addressing access to rural credit and income inequality. The first limitation is that the literature lacks research identifying additional factors that contribute to reducing rural inequality, such as rural extension, which enhance the effect of credit on household income. The second limitation is that the literature also lacks analyses that break down the effect of credit on income by income quantiles (for example, access to credit may have a stronger effect on the income of higher-income families compared to lower-income families). Therefore, the authors address these two limitations by estimating the effect of credit on household income in rural areas of Brazil.
To estimate how access to credit affects (not causally) family income, the authors used the 2014 National Household Sample Survey (PNAD) from the Brazilian Institute of Geography and Statistics (IBGE). This survey categorizes rural credit into (i) PRONAF and (ii) other sources (i.e., other public programs and/or bank loans for rural use).
The sample considers rural producers who are (i) economically active; (ii) employers or self-employed workers (these being the individuals interviewed in the questionnaire); and (iii) primarily engaged in agricultural activity. The sample also includes a small percentage of rural property managers who live in urban areas. After excluding missing and outliers, the final sample consisted of 15.402 individuals.
Methodology Details
The dataset was used to determine the effect of rural extension on household income. First, Neves et al. (2020) used the unconditional quantile regression method to identify the effect of rural credit on different income quantiles in the Brazilian rural area. Second, they identified household characteristics that may generate income disparity as a result of access to rural credit.
The dependent variable is monthly family income in R$ (reais), which is a proxy for the farmer's income. To control for other factors that also influence the level of family income, the following variables were included:
a) sex: a dummy variable that equals 1 if the individual is male;
b) race: a dummy variable that equals 1 if the individual is black;
c) Education: several dummy variables divided into the categories “cannot read and write”, “incomplete primary education”, “complete primary education”, “incomplete secondary education”, “complete secondary education”, “incomplete higher education” and “complete higher education”;
d) Age: several dummy individuals, distributed into “up to 25 years”, “26 to 35 years”, “36 to 45 years”, “46 to 55 years”, “56 to 65 years” and “age equal to or greater than 65 years”;
e) rural: a dummy variable that equals 1 if the individual resides in a rural area;
f) extension: a dummy variable that equals 1 if the individual received technical assistance and rural extension from a private or governmental source;
g) Land ownership: several dummy variables seek to identify the producer's status in relation to the land, such as whether the producer is a partner, tenant, occupant, owner, or other status;
h) Farm size: four dummy variables represent farm size, which are divided into very small (up to 10 hectares), small (10-100 hectares), medium (100-1.000 hectares) and large (> 1.000 hectares);
i) Regions: five dummy variables represent the Brazilian macro-regions – North, Northeast, Southeast, South, and Central-West.
Results
The results suggest that access to credit may be correlated with monthly family income and income inequality in rural Brazil. They also suggest that access to credit is not achieving one of its intended objectives. That is, in Brazil, public policies regarding the availability of rural credit also aim to increase rural income, providing rural families with the opportunity to acquire more inputs, access new technologies, and reduce the effects of market imperfections.
The variables that capture the effect of gender and race did not show different effects on the quantiles of family income. A difference is observed only at the base of the income distribution, where women have a higher income compared to men. The results suggest that households headed by Black people have lower incomes compared to other individuals. Experience, represented in the study by the individual's age, has a greater influence on the base of the income distribution.
Variables related to higher education levels (“complete primary education”, “secondary education”, and “higher education”) increased family income relative to the baseline variable (“people who cannot read or write”). Thus, Neves et al. (2020) find that education can reduce income inequality, i.e., large income returns at the “secondary education” level in the lower quantiles of the income distribution.
The results also suggest that the larger the property, the higher the income, and that households in the South, Midwest, and Southeast regions are in a better situation compared to those in the North and Northeast (base).
Therefore, the analysis indicates that credit has led to greater family income inequality in rural areas of Brazil. The study determines that households in the higher income quantiles experienced significant benefits from access to credit compared to those in the lower quantiles. Families that also had access to rural extension services experienced greater benefits in accessing credit contracts. This combined effect of credit granting is greater among families in the higher income quantiles. These results indicate that the articulation of public policies for access to credit and rural extension services simultaneously would result in greater benefits for rural families.
Lessons in Public Policy
The analysis conducted by Neves et al. (2020) suggests that Brazilian rural credit policy was able to increase the income of rural families across all income quantiles, but this also increased income inequality. However, PRONAF (National Program for Strengthening Family Farming) had a smaller influence on increasing inequality. Additionally, the authors found that rural credit from other sources has a greater effect on rural income in the higher income quantiles. A decomposition of the income differential demonstrated that differences in individual characteristics explain most of the income differential in the upper portion of the income distribution.
The results indicate that a higher level of education and access to rural extension services may be associated with a greater influence of rural credit on family income, implying that access to rural credit alone cannot raise the social well-being of low-income farmers. Furthermore, the analysis indicates that designing a joint public policy incorporating rural credit, rural extension, and human capital development would have a much stronger effect on reducing income inequality in rural areas. This suggests the existence of synergy between public policies and public services linked to rural credit. Additionally, it is important to note that the Northeast region of Brazil should receive greater focus in the context of receiving extension services and human capital development policies. This would allow its producers to perform similarly to producers in the South and Southeast regions, thus maximizing the impact of rural credit programs in that region.
References
NEVES, Mateus de Carvalho Reis et al. Does Access to Rural Credit Help Decrease Income Inequality in Brazil?. Journal of Agricultural and Applied Economics, v. 52, no. 3, p. 440-460, 2020.
