Does access to rural credit help reduce income inequality?

Responsible researcher: 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

Intervention Location: Brazil

Sample Size: 15,402 individuals

Big topic: Finance

Variable of Main Interest: Income inequality

Type of Intervention: Analysis of the impact of rural credit on income inequality

Methodology: Unconditional quantile regression method

The Brazilian agricultural segment has grown exponentially in recent decades and expanded its participation in the international market. However, Brazil continues to face a high level of rural inequality. Neves et al. (2020) seek 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 Agriculture (PRONAF) is not associated with increased inequality. However, access to rural credit from sources other than PRONAF led to greater family income inequality. The results also indicate that higher levels of education and access to rural extension have enhanced the effect of credit on income.

Assessment 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 stronger participation in the global market, commercial agricultural production continues to be concentrated on large farms.

Although income inequality has decreased over time, there is still much to be done to achieve lower levels of inequality. Several factors can contribute to a more equitable income distribution in rural areas, including access to rural extension and financial markets. The Brazilian government, for example, has implemented several public policies designed to reduce 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 Agriculture Policy, created in 1991, also contributed to income generation in rural areas of the country. To overcome inequality in the distribution of benefits, the Brazilian government created the Family Farming Strengthening Program in 1995. However, Neves et al. (2020) highlight 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 that deals with access to rural credit and income inequality. The first limitation is that the literature lacks research that identifies additional factors that contribute to the reduction of rural inequality, such as rural extension, which enhance the effect of credit on family income. The second limitation is that the literature also lacks analyzes 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). Thus, the authors address these two limitations by estimating the effect of credit on family income in rural areas of Brazil.

To estimate how access to credit affects (non-causally) family income, the authors used the 2014 National Household Sample Survey (PNAD) from the Brazilian Institute of Geography and Statistics (IBGE). This research 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) mainly occupied by agricultural activities. The sample also includes a small portion of rural property managers who live in urban areas. After excluding missing and outlier values, the final sample consisted of 15,402 individuals.

Methodology Details

The data set was used to determine the effect of rural extension on family income. Firstly, 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. Secondly, they identified characteristics of households that can generate income disparity in the outcome of access to rural credit.

The dependent variable is the monthly family income in R$ (reais), which is a proxy for the farmer's income. To control other factors, which also influence the level of family income, the following variables were included:

a) sex: a dummy variable that is equal to 1 if the individual is male;

b) race: a dummy variable that is equal to 1 if the individual is black;

c) education: several dummy variables divided into the categories “does not 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 dummies, distributed as “up to 25 years old”, “26 to 35 years old”, “36 to 45 years old”, “46 to 55 years old”, “56 to 65 years old” and “age equal to or greater than 65 years";

e) rural: dummy variable that is equal to 1 if the individual lives in a rural area;

f) extension: dummy variable that is equal to 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 condition in relation to the land, such as whether the producer is a partner, tenant, occupant, owner or other condition;

h) farm size: four dummy variables represent the size of the farm, 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 provide evidence 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. In other words, in Brazil, public policies for 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 imperfection.

The variables that capture the effect of gender and race did not present different effects on family income quantiles. There is only a difference at the base of 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 basis of income distribution.

The variables related to higher education (“complete primary education”, “high school” and “higher education”) increased family income in relation to the base variable (“people who cannot read or write”). Thus, Neves et al. (2020) find that education can reduce income inequality, that is, large income returns to the “high school” 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, Central-West and Southeast regions are in a better situation compared to those in the North and Northeast (base).

Therefore, the analysis indicates that credit led to greater family income inequality in rural areas of Brazil. The study determines that households in higher income quantiles saw high benefits in accessing credit compared to those in lower quantiles. Families that also had access to rural extension had more benefits in accessing credit contracts. This combined effect of granting credit is greater among families in higher income quantiles. These results indicate that the articulation of public policies for access to credit and rural extension would simultaneously result in greater benefits for families in rural areas.

Public Policy Lessons

The analysis carried out by Neves et al. (2020) suggest 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, there was a lesser influence of PRONAF on the increase in inequality. Additionally, the authors found that rural credit from other sources has a greater effect on rural income in higher income quantiles. A decomposition of the income differential demonstrated that the difference in individual characteristics explains 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 may be associated with a greater influence of rural credit on family income, which implies that access to rural credit alone cannot increase the social well-being of farmers. low income. Furthermore, the analysis indicates that the design of a joint public policy incorporating rural credit, rural extension and human capital promotion 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 benefit extension services and policies to increase human capital. This would allow its producers to have similar performance to producers in the South and Southeast regions, which would enhance the results of rural credit programs in this 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 , vol. 52, no. 3, p. 440-460, 2020.