Heterogeneous regional effects of rural credit on agricultural production in Brazil

Sobreira, Diogo Brito; Tabosa, Francisco José Silva; Costa, Edward Martins; Khan, Ahmad Saeed

doi:10.1590/1806-9479.2023.271082

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Revista de Economia e Sociologia Rural

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Article • Rev. Econ. Sociol. Rural 62 (4) • 2024 • https://doi.org/10.1590/1806-9479.2023.271082 copy

Heterogeneous regional effects of rural credit on agricultural production in Brazil

Efeitos regionais heterogêneos do crédito rural sobre a produção agropecuária no Brasil

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Access to rural credit has been persistently heterogeneous across Brazilian regions over time. This work aimed to estimate the regional effects of rural credit on agricultural production due to the heterogeneity observed in access to credit. Based on data from the 2017 Agricultural Census and climate variables, the effect of rural credit was estimated by standard regressions, combined with the entropy balancing technique. The results show positive and significant effects of rural credit on Brazilian agricultural production, even after controlling for observed covariates. This result was consistent even after balancing the covariates with regard to entropy. The effect of rural credit proved to be heterogeneous across Brazilian regions, being positive and significant for regions with greater access to rural credit and statistically null in less credit-intensive regions. Furthermore, estimates show that technical assistance is an important transmission mechanism of the rural credit effect.

Keywords:
rural credit; technical assistance; agricultural production; Brazil

Authors	Rural Credit	Methodology	Analysis Units	Period	Source	Variables of interest
Magalhães et al. (2006)Magalhães, A. M., Silveira Neto, R., Dias, F. M., & Barros, A. R. (2006). A experiência recente do PRONAF em Pernambuco: Uma análise por meio de propensity score. Economia Aplicada, 10(1), 57-74. http://dx.doi.org/10.1590/S1413-80502006000100004 http://dx.doi.org/10.1590/S1413-80502006...	Pronaf beneficiaries and non-beneficiaries	OLS and Propensity Score	Family farmers in the state of Pernambuco	2001	Primary	1 – Production value; 2 – Production value per hectare; 3 - Production value per person;
Garcias & Kassouf (2016)Garcias, M. O., & Kassouf, A. L. (2016). Assessment of rural credit impact on land and labor productivity for Brazilian family farmers. Nova Economia, 26(3), 721-746. http://dx.doi.org/10.1590/0103-6351/2761 http://dx.doi.org/10.1590/0103-6351/2761...	Greater and lesser restrictions to credit	Propensity score matching	Brazilian municipalities	2006	Agricultural Census /IBGE	1 – Land productivity; 2 – Labor productivity
Gasques et al. (2017)Gasques, J. G., Bacchi, M. R. P., & Bastos, E. T. (2017). Impactos do crédito rural sobre variáveis do agronegócio. Revista de Política Agrícola, 26(4), 132-140.	Total rural credit	Transfer functions in time series with AR and MA components	Brazil	1996 to 2015	Central Bank; IBGE; Cepea/USP e MAPA	1 - Gross value of production; 2 - Agribusiness GDP; 3 – Agricultural GDP; 4 - TFP
Araújo & Vieira Filho (2018)Araújo, J. A., & Vieira Filho, J. E. R. (2018). Análise dos impactos do Pronaf na agricultura do Brasil no período de 2007 a 2016 (Texto para Discussão, No. 2412). Rio de Janeiro: IPEA.	Quantity and total value of Pronaf agriculture and livestock contracts	Panel vector autoregressive	26 states and Federal District in Brazil	2007 to 2016	Central Bank and IBGE	1 - Planted area; 2 - Gross value of agricultural and livestock production; 3 - Land productivity
Costa & Freitas (2018)Costa, L. V., & Freitas, C. O. (2018). Crédito e extensão rural: impactos isolados e da sinergia sobre a eficiência técnica dos agricultores brasileiros. In Anais do 46º Encontro Nacional de Economia. Niterói: ANPEC.	Access to rural credit	stochastic frontier and sample selection model	Farmers in Brazil	2006	Microdata-Agricultural Census /IBGE	1 – Technical efficiency
Costa & Vieira Filho (2018)	Quantity and value of contracts by segment (agriculture or livestock)	Panel vector autoregressive	26 states and Federal District in Brazil	2007 to 2016	Central Bank and IBGE	1 - Planted area; 2 - Quantity harvested; 3 - Value of agricultural production; 4-Number of cattle;
Assunção & Souza (2019)Assunção, J., & Souza, P. (2019). Resumo para política pública: o impacto do crédito rural na agricultura brasileira e no meio ambiente. Rio de Janeiro: Climate Policy Initiative.	Total rural credit	Shift-share models in panel data	Brazilian municipalities	2002 to 2015	Central Bank, IBGE, MapBiomas and RAIS	1 – Municipal GDP; 2 Agricultural GDP; 3 –Land and labor productivity; 4 – Planted area
Araújo et al. (2020a)Araújo, J. A., Alencar, M. O., & Vieira Filho, J. E. R. (2020a). Crédito rural e agricultura familiar no Brasil: uma avaliação do Programa Nacional de Fortalecimento da Agricultura Familiar. Redes, 25(2), 2009-2034. http://dx.doi.org/10.17058/redes.v25i0.14470 http://dx.doi.org/10.17058/redes.v25i0.1...	Pronaf beneficiaries and non-beneficiaries	Propensity score matching	Farmers in Brazil	2014	PNAD/IBGE	1 – Agricultural income; 2 – Land productivity
Eusébio et al. (2020)Eusébio, G. S., Maia, A. G., & Silveira, R. L. F. (2020). Crédito rural e impacto sobre o valor da produção agropecuária: uma análise para agricultores não familiares. Gestão & Regionalidade, 36(108), 89-109. http://dx.doi.org/10.13037/gr.vol36n108.5622 http://dx.doi.org/10.13037/gr.vol36n108....	Access to rural credit	Two-Stage Estimation Method	Non-family farmers in Brazil	2006	Microdata - Agricultural Census /IBGE	1 - Total production value
Figueira (2020)Figueira, S. R. F. (2020). Impactos dos preços e do crédito rural sobre a produção de cana-de-açúcar no estado de São Paulo. Revista de Economia e Sociologia Rural, 58(4), e186266. http://dx.doi.org/10.1590/1806-9479.2020.186266 http://dx.doi.org/10.1590/1806-9479.2020...	total rural credit for agricultural activity	Panel data: pooled, fixed and random effect	Rural Development Offices in São Paulo (municipalities)	1995 to 2012	Central Bank	1 – Area planted with sugar cane; 2 – Sugarcane productivity
Freitas et al. (2020)Freitas, C. O., Silva, F. A., & Teixeira, E. C. (2020). Crédito rural e desempenho produtivo na agropecuária brasileira. In J. E. R. Vieira Filho & J. G. Gasques (Eds.), Uma jornada pelos contrastes do Brasil: cem anos do Censo Agropecuário. Brasília: IPEA. http://dx.doi.org/10.38116/978-65-5635-011-0/cap20. http://dx.doi.org/10.38116/978-65-5635-0...	Greater and lesser access to rural credit by resource source	OLS and Entropy Balancing; Stochastic frontier	Brazilian municipalities	2017	Agricultural Census /IBGE	1 – Value of agricultural production; 2 – Technical efficiency
Maia et al. (2019)Maia, A. G., Eusébio, G. S., & Silveira, R. L. F. (2019). Can credit help small family farming? Evidence from Brazil. Agricultural Finance Review, 80(2), 212-230. http://dx.doi.org/10.1108/AFR-10-2018-0087 http://dx.doi.org/10.1108/AFR-10-2018-00...	Pronaf beneficiaries and non-beneficiaries	Propensity score matching	Farmers in Brazil and Regions	2006	Microdata - Agricultural Census /IBGE	1 – Value of agricultural production;
Araújo et al. (2021)Araújo, B. S., Heck, C. R., & Carrara, A. F. (2021). Crédito rural e mecanização da agricultura: o impacto do Moderfrota. Revista de Política Agrícola, 30(4), 45-63.	Total value of MODERFROTA program	Structural vector error correction	Brazil	2002 to 2019	IPEA; BNDES; IBGE;	1 – Agricultural GDP; 2 – Harvested area
Souza et al. (2021)Souza, P., Mourão, J., & Assunção, J. (2021). Os Impactos do crédito rural na agropecuária e no uso da terra: uma análise dos biomas brasileiros. Rio de Janeiro: Climate Policy Initiative.	Total rural credit	Shift-share models in panel data	Brazilian municipalities–by biomes	2002 to 2018	Central Bank, IBGE, MapBiomas	1–Planted areas; 2–Agricultural production; 3–Land productivity.
Borges & Parré (2022)Borges, M. J., & Parré, J. L. (2022). O impacto do crédito rural no produto agropecuário brasileiro. Revista de Economia e Sociologia Rural, 60(2), e230521. http://dx.doi.org/10.1590/1806-9479.2021.230521 http://dx.doi.org/10.1590/1806-9479.2021...	Total rural credit and by purpose	Vector autoregressive		1999 to 2018	Central Bank, IBGE	1 - Agricultural GDP

Variables	Before entropy weighting (A)				After entropy weighting (B)
Variables	T	C	diff	p-value	T	C	diff	p-value
Rondônia	0.004	0.010	-0.006	0.018	0.004	0.004	0.000	0.999
Acre	0.000	0.004	-0.004	0.000	0.000	0.000	0.000	0.010
Amazonas	0.000	0.013	-0.013	0.000	0.000	0.000	0.000	0.000
Roráima	0.000	0.003	-0.003	0.000	0.000	0.000	0.000	0.006
Pará	0.000	0.029	-0.029	0.000	0.000	0.000	0.000	0.000
Amapá	0.000	0.003	-0.003	0.000	0.000	0.000	0.000	0.012
Tocantins	0.008	0.025	-0.017	0.000	0.008	0.008	0.000	0.999
Maranhão	0.000	0.045	-0.045	0.000	0.000	0.000	0.000	0.000
Piauí	0.013	0.045	-0.032	0.000	0.013	0.013	0.000	0.999
Ceará	0.004	0.038	-0.034	0.000	0.004	0.004	0.000	0.999
Rio Grande do Norte	0.024	0.031	-0.007	0.249	0.024	0.024	0.000	0.998
Paraíba	0.036	0.041	-0.005	0.486	0.036	0.036	0.000	0.998
Pernambuco	0.001	0.038	-0.037	0.000	0.001	0.001	0.000	0.994
Alagoas	0.001	0.020	-0.019	0.000	0.001	0.001	0.000	1.000
Sergipe	0.003	0.014	-0.011	0.000	0.003	0.003	0.000	0.999
Bahia	0.005	0.088	-0.082	0.000	0.005	0.005	0.000	1.000
Minas Gerais	0.078	0.166	-0.088	0.000	0.078	0.078	0.000	0.997
Espírito Santo	0.004	0.016	-0.012	0.000	0.004	0.004	0.000	0.999
Rio de Janeiro	0.000	0.018	-0.018	0.000	0.000	0.000	0.000	0.003
São Paulo	0.045	0.125	-0.080	0.000	0.045	0.045	0.000	0.998
Paraná	0.197	0.053	0.144	0.000	0.197	0.196	0.000	0.997
Santa Catarina	0.181	0.033	0.148	0.000	0.181	0.181	0.000	0.998
Rio Grande do Sul	0.337	0.051	0.286	0.000	0.337	0.337	0.000	0.996
Mato Grosso do Sul	0.011	0.015	-0.004	0.296	0.011	0.011	0.000	0.999
Mato Grosso	0.027	0.026	0.001	0.882	0.027	0.027	0.000	0.998
Goiás	0.021	0.048	-0.027	0.000	0.021	0.021	0.000	0.998
Distrito Federal	0.000	0.000	0.000	0.317	0.000	0.000	0.000	0.319
Male’	0.888	0.825	0.063	0.000	0.888	0.888	0.000	0.996
Age ≥ 65 years’	0.231	0.253	-0.022	0.000	0.231	0.231	0.000	0.994
Low education level’	0.438	0.487	-0.049	0.000	0.438	0.438	0.000	0.997
Ln (area)’	3.871	3.807	0.064	0.124	3.871	3.871	0.000	0.997
Establishment owner’	0.867	0.806	0.061	0.000	0.867	0.867	0.000	0.995
Resides on the establishment’	0.729	0.666	0.064	0.000	0.729	0.729	0.000	0.997
Ln (workers)’	1.125	1.109	0.017	0.310	1.125	1.125	0.000	0.999
Non-family farming’	0.236	0.279	-0.043	0.000	0.236	0.236	0.000	0.999
Association’	0.600	0.349	0.251	0.000	0.600	0.600	0.000	0.995

	Dependent variable:	After entropy weighting (B)
Regions	Gross Production Value	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]
Brazil	ATT	0.324***	0.328***	0.293***	0.202***	0.194***	0.111**	0.108**	0.083*
		[0.049]	[0.048]	[0.050]	[0.031]	[0.031]	[0.037]	[0.037]	[0.039]
	Irrigation	No	No	No	No	0.475**	No	0.339*	0.395*
						[0.166]		[0.159]	[0.156]
	Technical assistance	No	No	No	No	No	0.838***	0.818***	No
							[0.120]	[0.123]
	Public Technical assistance	No	No	No	No	No	No	No	0.084
									[0.118]
	Private Technical assistance	No	No	No	No	No	No	No	0.722***
									[0.112]
North	ATT	0.025	-0.03	-0.02	0.079	0.079	0.069	0.07	0.058
		[0.115]	[0.107]	[0.106]	[0.069]	[0.069]	[0.072]	[0.072]	[0.072]
	Irrigation	No	No	No	No	0.682	No	0.64	0.371
						[0.526]		[0.524]	[0.522]
	Technical assistance	No	No	No	No	No	0.507	0.48	No
							[0.484]	[0.482]
	Public Technical assistance	No	No	No	No	No	No	No	-0.104
									[0.496]
	Private Technical assistance	No	No	No	No	No	No	No	1.627
									[1.010]
Northeast	ATT	-0.02	0.018	0.01	0.006	-0.002	-0.007	-0.012	-0.014
		[0.055]	[0.053]	[0.051]	[0.042]	[0.041]	[0.041]	[0.041]	[0.040]
	Irrigation	No	No	No	No	1.447***	No	1.386***	1.293***
						[0.192]		[0.190]	[0.189]
	Technical assistance	No	No	No	No	No	0.612**	0.468*	No
							[0.201]	[0.189]
	Public Technical assistance	No	No	No	No	No	No	No	-0.201
									[0.216]
	Private Technical assistance	No	No	No	No	No	No	No	1.664***
									[0.436]
Southeast	ATT	-0.005	0.048	0.028	0.104*	0.105*	0.058	0.066	0.041
		[0.065]	[0.063]	[0.060]	[0.047]	[0.046]	[0.046]	[0.045]	[0.044]
	Irrigation	No	No	No	No	1.091***	No	0.983***	0.960***
						[0.222]		[0.212]	[0.211]
	Technical assistance	No	No	No	No	No	0.690***	0.586***	No
							[0.158]	[0.149]
	Public Technical assistance	No	No	No	No	No	No	No	0.095
									[0.176]
	Private Technical assistance	No	No	No	No	No	No	No	0.853***
									[0.118]
South	ATT	0.343***	0.296***	0.292***	0.209***	0.211***	0.160***	0.162***	0.145***
		[0.057]	[0.052]	[0.052]	[0.032]	[0.032]	[0.033]	[0.033]	[0.034]
	Irrigation	No	No	No	No	0.292*	No	0.274+	0.322*
						[0.137]		[0.162]	[0.163]
	Technical assistance	No	No	No	No	No	0.391***	0.386***	No
							[0.094]	[0.091]
	Public Technical assistance	No	No	No	No	No	No	No	-0.059
									[0.094]
	Private Technical assistance	No	No	No	No	No	No	No	0.339***
									[0.068]
Center-west	ATT	0.739***	0.696***	0.596***	0.390***	0.393***	0.256**	0.256**	0.228**
		[0.156]	[0.158]	[0.149]	[0.082]	[0.082]	[0.082]	[0.081]	[0.081]
	Irrigation	No	No	No	No	-0.214	No	-1.119	-0.568
						[1.015]		[0.998]	[0.847]
	Technical assistance	No	No	No	No	No	1.334***	1.474***	No
							[0.352]	[0.352]
	Public Technical assistance	No	No	No	No	No	No	No	-0.884
									[0.776]
	Private Technical assistance	No	No	No	No	No	No	No	1.513***
									[0.343]
X	Mesoregion Dummies	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Temperature and precipitation	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Producer characteristics	No	No	Yes	Yes	Yes	Yes	Yes	Yes
	Establishment characteristics	No	No	No	Yes	Yes	Yes	Yes	Yes

Dependent variable: Gross Production Value	*After entropy weighting (B) - T = mean(Z)+0.75SD(Z) - Panel A**
Dependent variable: Gross Production Value	BR	NO	NE	SE	S	CO
ATT	0.088**	0.054	-0.034	0.031	0.135***	0.343***
	[0.028]	[0.071]	[0.037]	[0.041]	[0.032]	[0.074]
Irrigation	0.665***	0.543	1.315***	0.879***	0.285+	-0.28
	[0.135]	[0.530]	[0.175]	[0.190]	[0.166]	[0.957]
Public Technical assistance	0.072	-0.082	-0.148	0.024	-0.041	-0.152
	[0.088]	[0.483]	[0.209]	[0.170]	[0.082]	[0.695]
Private Technical assistance	0.764***	2.516*	1.791***	0.869***	0.420***	1.069***
	[0.075]	[1.060]	[0.399]	[0.110]	[0.065]	[0.317]
Dependent variable: Gross Production Value	*After entropy weighting (B) - T = mean(Z)+1.25SD(Z) - Panel B**
Dependent variable: Gross Production Value	BR	NO	NE	SE	S	CO
ATT	0.073*	-0.005	-0.035	0.035	0.129***	0.324**
	[0.035]	[0.072]	[0.040]	[0.046]	[0.037]	[0.100]
Irrigation	0.288+	0.447	1.422***	0.986***	0.244	-0.423
	[0.161]	[0.561]	[0.214]	[0.238]	[0.148]	[1.007]
Public Technical assistance	0.011	-0.304	-0.275	0.315	-0.086	-0.412
	[0.102]	[0.540]	[0.218]	[0.215]	[0.089]	[0.866]
Private Technical assistance	0.673***	1.348	1.517***	0.925***	0.297***	1.141***
	[0.091]	[0.967]	[0.451]	[0.128]	[0.079]	[0.344]

Dependent variable	Treatment: Acess rural credit	Brazil	North	Northeast	Southeast	South	Center-west
Technical assistance (A)	ATT	0.111***	0.017	0.019*	0.058***	0.140***	0.109***
Technical assistance (A)	S.E.	[0.014]	[0.015]	[0.009]	[0.014]	[0.023]	[0.016]
Public technical assistance (B)	ATT	0.008	0.006	0.015+	0.001	0.001	-0.004
Public technical assistance (B)	S.E.	[0.011]	[0.015]	[0.008]	[0.012]	[0.025]	[0.009]
Private technical assistance (C)	ATT	0.159***	0.014+	0.009	0.073***	0.224***	0.125***
Private technical assistance (C)	S.E.	[0.016]	[0.008]	[0.006]	[0.012]	[0.026]	[0.022]
X	Mesoregion Dummies	Yes	Yes	Yes	Yes	Yes	Yes
	Producer characteristics	Yes	Yes	Yes	Yes	Yes	Yes
	Establishment characteristics	Yes	Yes	Yes	Yes	Yes	Yes
N	-	5436	422	1757	1619	1179	459

Sociedade Brasileira de Economia e Sociologia Rural Av. W/3 Norte, Quadra 702 Ed. Brasília Rádio Center Salas 1049-1050, 70719 900 Brasília DF Brasil, - Brasília - DF - Brazil
E-mail: sober@sober.org.br

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[1] Source: Prepared by the authors.