Acesso e impacto do Pronaf no Brasil: evidências sobre as tipologias e a concentração regional

Machado, Bruno de Souza; Neves, Mateus de Carvalho Reis; Braga, Marcelo José; Costa, Davi Rogério de Moura

Bruno de Souza Machado

Departamento de Economia Rural, Programa de Pós-Graduação em Economia Aplicada, Universidade Federal de Viçosa (UFV), Viçosa (MG), Brasil. E-mails: bruno.machado@ufv.br; mateus.neves@ufv.br; mjbraga@ufv.br

https://orcid.org/0000-0003-0901-5578

Mateus de Carvalho Reis Neves

Departamento de Economia Rural, Programa de Pós-Graduação em Economia Aplicada, Universidade Federal de Viçosa (UFV), Viçosa (MG), Brasil. E-mails: bruno.machado@ufv.br; mateus.neves@ufv.br; mjbraga@ufv.br

https://orcid.org/0000-0001-5523-8663

Marcelo José Braga

Departamento de Economia Rural, Programa de Pós-Graduação em Economia Aplicada, Universidade Federal de Viçosa (UFV), Viçosa (MG), Brasil. E-mails: bruno.machado@ufv.br; mateus.neves@ufv.br; mjbraga@ufv.br

https://orcid.org/0000-0002-8161-405X

Davi Rogério de Moura Costa

Faculdade de Administração, Economia e Contabilidade, Universidade de São Paulo (USP), Ribeirão Preto (SP), Brasil. E-mail: drmouracosta@usp.br

https://orcid.org/0000-0001-5653-478X

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Figure 1
Intensity levels, access to Pronaf, and classification of representative family establishments into intensive and non-intensive, Brazil, 2017. Source: Own preparation based on data from the 2017 Agricultural Census (Instituto Brasileiro de Geografia e Estatística, 2017)Instituto Brasileiro de Geografia e Estatística - IBGE. (2017). Sistema IBGE de Recuperação Automática de Dados (SIDRA) - Censo Agropecuário 2017. Rio de Janeiro: IBGE..

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Figure 2
Intensity levels, access to Pronaf, and classification of representative family establishments classified as Pronaf V into intensive and non-intensive, Brazil, 2017. Source: Own preparation based on data from the Agricultural Census 2017 (Instituto Brasileiro de Geografia e Estatística, 2017Instituto Brasileiro de Geografia e Estatística - IBGE. (2017). Sistema IBGE de Recuperação Automática de Dados (SIDRA) - Censo Agropecuário 2017. Rio de Janeiro: IBGE.).

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Figure 3
Intensity levels, access to Pronaf and classification of representative family establishments classified as Pronaf B in intensive and non-intensive, Brazil, 2017. Source: Own preparation based on data from the 2017 Agricultural Census (Instituto Brasileiro de Geografia e Estatística, 2017Instituto Brasileiro de Geografia e Estatística - IBGE. (2017). Sistema IBGE de Recuperação Automática de Dados (SIDRA) - Censo Agropecuário 2017. Rio de Janeiro: IBGE.).

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Figure 4
Number of family establishments that accessed Pronaf by Federative Unit of Brazil according to Pronaf B and Pronaf V typologies, 2017. Source: Own preparation based on data from the 2017 Agricultural Census (Instituto Brasileiro de Geografia e Estatística, 2017Instituto Brasileiro de Geografia e Estatística - IBGE. (2017). Sistema IBGE de Recuperação Automática de Dados (SIDRA) - Censo Agropecuário 2017. Rio de Janeiro: IBGE.).

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Table 1
Description and source of the variables at the municipality level1.

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Table 2
Number of establishments in relation to access and potential access to Pronaf for family farming (FF) and its Pronaf typologies for regions and Brazil, 2017.

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Table 3
Robustness tests for each level of intensity in the analysis of access to Pronaf according to family farming and its typologies of Pronaf from the Agricultural Census 2017 (Instituto Brasileiro de Geografia e Estatística, 2017Instituto Brasileiro de Geografia e Estatística - IBGE. (2017). Sistema IBGE de Recuperação Automática de Dados (SIDRA) - Censo Agropecuário 2017. Rio de Janeiro: IBGE.).

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Table 4
Impacts of Pronaf access on performance measures for each intensity level according to family farming, 2017, Brazil.

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Table 5
Impacts of Pronaf access on performance measures for each intensity level according to Pronaf V typology of family farming, 2017, Brazil.

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Table 6
Impacts of Pronaf access on performance measures for each intensity level according to Pronaf B typology of family farming, 2017, Brazil.

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Typology Variable Intensity 1 Intensity 2 Intensity 3 Intensity 4 I NI I NI I NI I NI Family Farming (FF) Gross Production Value (in thousand R$) 51.7347 31.5958 55.2411 32.1805 59.3844 32.7769 62.2740 33.3064 Partial Land Productivity (in thousand R$ per hectare) 2.8907 2.0587 3.1334 2.0577 3.3189 2.0870 3.4554 2.1111 Partial Total Labor Productivity (in thousand R$ per employed person) 23.6382 14.6801 25.2157 14.9357 27.1875 15.1787 28.4345 15.4258 Partial Family Labor Productivity (in thousand R$ per employed person) 26.7550 17.4178 28.2924 17.7116 30.2201 17.9803 31.4915 18.2321 Input Expenses (in thousand R$) 23.3200 13.5510 24.8299 13.8837 26.8916 14.1494 28.6558 14.3493 Profitability (in thousand R$) 28.4146 18.0448 30.4112 18.2968 32.4928 18.6275 33.6182 18.9572 Number of Representative Establishments 1,107 3,103 860 3,350 651 3,559 521 3,689 FF – Pronaf V Gross Production Value (in thousand R$) 77.5077 61.6140 79.9007 62.1128 81.9372 62.8209 83.6849 63.3182 Partial Land Productivity (in thousand R$ per hectare) 3.6780 3.0853 3.8010 3.0935 3.8502 3.1287 3.9089 3.1488 Partial Total Labor Productivity (in thousand R$ per employed person) 32.7889 25.1245 33.9889 25.3508 35.1036 25.6666 36.0900 25.8908 Partial Family Labor Productivity (in thousand R$ per employed person) 37.3362 31.2155 38.3180 31.3889 38.9987 31.6899 39.8217 31.8534 Input Expenses (in thousand R$) 34.4295 23.2007 36.3914 23.4693 38.2946 23.8890 40.2803 24.1485 Profitability (in thousand R$) 43,0782 38.4133 43.5094 38.6435 43.6426 38.9319 43.4046 39.1696 Number of Representative Establishments 730 1,740 583 1,887 451 2,019 363 2,107 FF – Pronaf B Gross Production Value (in thousand R$) 5.5969 5.7430 5.5686 5.7379 5.5095 5.7405 5.5315 5.7271 Partial Land Productivity (in thousand R$ per hectare) 0.5234 0.7001 0.5174 0.6869 0.5122 0.6793 0.4802 0.6761 Partial Total Labor Productivity (in thousand R$ per employed person) 2.7661 2.9440 2.7249 2.9396 2.6789 2.9374 2.6657 2.9275 Partial Family Labor Productivity (in thousand R$ per employed person) 3.0700 3.2601 3.0291 3.2546 2.9759 3.2532 2.9513 3.2441 Input Expenses (in thousand R$) 5.1880 5.2851 5.2324 5.2657 5.1886 5.2724 4.8506 5.3145 Profitability (in thousand R$) 0.4089 0.4579 0.3362 0.4722 0.3210 0.4682 0.6809 0.4127 Number of Representative Establishments 622 1,695 468 1,849 369 1,948 277 2,040 Source: Own preparation based on data from the 2017 Agricultural Census (Instituto Brasileiro de Geografia e Estatística, 2017). Note: I = Intensive; NI = Non-Intensive; The input expenses variable is not an outcome but was included because it is used in the calculation of profitability.

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(1)

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(2)

Variable	Description	Source
Man	Proportion of establishments whose manager is male.	CA
Owner	Proportion of land owners of the establishments.
Age	Proportion of establishments by manager age groups (From 25 to less than 35, from 35 to less than 45, from 45 to less than 55, from 55 to less than 65, from 65 to less than 75, and 75 or more).
Education	Proportion of establishments by education groups of the manager (Higher or Graduate, High School, Elementary School complete and Elementary School incomplete).
Literate	Proportion of owners who declared they could read and write.
Technical Assistance	Proportion of establishments that accessed technical assistance.
Member of the cooperative	Proportion of establishments associated with a cooperative.
Internet	Proportion of establishments with Internet access for technical information such as proxy for Internet access.
Soil	Proportion of establishments that do soil preparation.
Agricultural practice	Proportion of establishments that perform some agricultural practice in production.
GVP	Proportion of the Gross Production Value (GVP) (in R$ 1,000) of the sum of vegetal and animal production (except agribusiness).
Area	Proportion of the area (in ha), devoted to production.
Labor	Weighted proportion of men and women 14 years old and older and under 14 years old that make up the total occupied people and total family occupied people in production.
Capital	Proportion of tractors (in units) as a proxy for capital.
Expenses	Proportion of expenses (in R$ 1,000) salaries paid, fertilizers, correctives, seeds, seedlings, pesticides, animal medicines, salt, feed, other supplements, purchase of machinery and vehicles, fuel, lubricants, and electric power.
Profitability	Difference between GVP and input expenses as a proxy.
Large Regions	Dummies regionals (South, Southeast, Northeast, and Midwest, with the North as the base region).
Temperature	Average mean temperature anomaly (in ºC) for the year 2016.	THRG
Precipitation	Average mean precipitation anomaly (in mm) for the year 2016.	THRG

Typology	Pronaf Status	N	NE	MW	S	SE	Brazil
FF	Potential	384,594	1,133,715	215,544	591,412	635,629	2,960,894
(4,210)	Accessed	12,017	46,961	12,065	98,149	48,828	218,020
(4,210)	% of access	3.12%	4.14%	5.60%	16.60%	7.68%	7.36%
FF – Pronaf V	Potential	82,529	55,846	78,594	320,886	175,451	713,306
(2,470)	Accessed	4,522	3,913	6,517	87,203	20,260	122,415
(2,470)	% of access	5.48%	7.01%	8.29%	27.18%	11.55%	17.16%
FF – Pronaf B (2,317)	Potential	170,459	760,402	69,210	144,006	215,097	1,359,174
	Accessed	2,478	29,376	2,086	4,223	11,344	49,507
	% of access	1.45%	3.86%	3.01%	2.93%	5.27%	3.64%

Typology (Obs.)	Intensity level (Treated)	Sample1	$P s e u d o R^{2}$	$L R > c h i^{2}$	$p > c h i^{2}$	B	R
FF	1	UP	0.258	1,250.27	0.00	130.5*	2.57*
(4,210)	(1,107)	P	0.000	0.00	1.00	0.0	1.05
	2	UP	0.312	1,328.01	0.00	150.6*	2.41*
	(860)	P	0.000	0.00	1.00	0.0	1.06
	3	UP	0.356	1,291.45	0.00	170.0*	2.09*
	(651)	P	0.000	0.00	1.00	0.2	1.09
	4	UP	0.373	1,173.53	0.00	180.9*	1.84
	(521)	P	0.000	0.00	1.00	0.3	1.07
FF – Pronaf V	1	UP	0.324	972.21	0.00	158.9*	1.51
	(730)	P	0.000	0.02	1.00	0.7	1.31
	2	UP	0.362	976.67	0.00	175.2*	1.18
(2,470)	(583)	P	0.000	0.00	1.00	0.1	1.18
	3	UP	0.388	909.87	0.00	190.5*	0.98
	(451)	P	0.000	0.00	1.00	0.2	1.20
	4	UP	0.397	818.63	0.00	198.1*	0.83
	(363)	P	0.000	0.00	1.00	0.4	1.13
FF – Pronaf B	1	UP	0.102	275.60	0.00	81.1*	0.74
	(622)	P	0.000	0.04	1.00	1.1	1.07
	2	UP	0.117	273.68	0.00	89.7*	0.76
	(468)	P	0.000	0.00	1.00	0.0	1.02
(2,317)	3	UP	0.116	236.38	0.00	91.2*	0.79
	(369)	P	0.000	0.00	1.00	0.0	0.89
	4	UP	0.139	235.24	0.00	103.8*	0.67
	(277)	P	0.000	0.00	1.00	0.1	0.73

Performance Measure	Result	Intensity level
Performance Measure	Result	1	2	3	4
Gross Value of Production	ATT	4.33%	4.52%	5.27%	4.97%
	Coefficient	0.0433**	0.0452**	0.0527***	0.0497***
	Coefficient	(0.0212)	(0.0203)	(0.0191)	(0.0187)
Partial Land Productivity	ATT	-10.47%	-8.03%	-8.56%	-7.91%
	Coefficient	-0.1047***	-0.0803***	-0.0856***	-0.0791***
	Coefficient	(0.0271)	(0.0259)	(0.0246)	(0.0237)
Partial Total Labor Productivity	ATT	-	3.78%	5.22%	4.81%
	Coefficient	0.0325^NS	0.0378*	0.0522***	0.0481**
	Coefficient	(0.0220)	(0.0210)	(0.0198)	(0.0194)
Partial Family Labor Productivity	ATT	-	-	3.77%	3.24%
	Coefficient	0.0246^NS	0.0284^NS	0.0377*	0.0324*
	Coefficient	(0.0217)	(0.0206)	(0.0193)	(0.0190)
Profitability	ATT	-	-	-	-
	Coefficient	-0.0018^NS	0.0011^NS	0.0087^NS	0.0303^NS
	Coefficient	(0.0236)	(0.0225)	(0.0212)	(0.0213)

Performance Measure	Result	Intensity level
Performance Measure	Result	1	2	3	4
Gross Value of Production	ATT	5.12%	4.86%	4.56%	4.53%
	Coefficient	0.0512***	0.0486***	0.0456***	0.0453***
	Coefficient	(0.0104)	(0.0101)	(0.0097)	(0.0094)
Partial Land Productivity	ATT	-	-	-	-
	Coefficient	0.0086^NS	0.0133^NS	0.0118^NS	0.0078^NS
	Coefficient	(0.0246)	(0.0230)	(0.0210)	(0.0198)
Partial Total Labor Productivity	ATT	4.96%	4.66%	4.19%	5.02%
	Coefficient	0.0496***	0.0466***	0.0419***	0.0502***
	Coefficient	(0.0138)	(0.0132)	(0.0127)	(0.0124)
Partial Family Labor Productivity	ATT	4.06%	4.01%	3.23%	3.81%
	Coefficient	0.0406***	0.0401***	0.0323***	0.0381***
	Coefficient	(0.0116)	(0.0111)	(0.0106)	(0.0104)
Profitability	ATT	-	-	-	-
	Coefficient	0.0085^NS	0.0037^NS	-0.0105^NS	-0.0118^NS
	Coefficient	(0.0105)	(0.0101)	(0.0096)	(0.0095)

Performance Measure	Result	Intensity level
Performance Measure	Result	1	2	3	4
Gross Value of Production	ATT	-2.54%	-3.04%	-3.56%	-
	Coefficient	-0.0254*	-0.0304**	-0.0356***	-0,0202^NS
	Coefficient	(0.0139)	(0.0138)	(0.0138)	(0.0136)
Partial Land Productivity	ATT	-8.71%	-7.78%	-7.39%	-6.99%
	Coefficient	-0.0871**	-0.0778**	-0.0739**	-0.0699**
	Coefficient	(0.0348)	(0.0346)	(0.0348)	(0.0347)
Partial Total Labor Productivity	ATT	-4.25%	-4.90%	-5.34%	-3.08%
	Coefficient	-0.0425**	-0.0490***	-0.0534***	-0.0308*
	Coefficient	(0.0171)	(0.0170)	(0.0169)	(0.0164)
Partial Family Labor Productivy	ATT	-3.56%	-4.37%	-5.19%	-3.76%
	Coefficient	-0.0356**	-0.0437***	-0.0519***	-0.0376**
	Coefficient	(0.0162)	(0.0161)	(0.0162)	(0.0158)
Profitability	ATT	-15.77%	-18.31%	-20.02%	-14.87%
	Coefficient	-0.1577***	-0.1831***	-0.2002***	-0.1487***
	Coefficient	(0.0432)	(0.0430)	(0.0437)	(0.0423)

[1] Source: Own preparation based on data from the 2017 Agricultural Census (CA) (Instituto Brasileiro de Geografia e Estatística, 2017Instituto Brasileiro de Geografia e Estatística - IBGE. (2017). Sistema IBGE de Recuperação Automática de Dados (SIDRA) - Censo Agropecuário 2017. Rio de Janeiro: IBGE.) and Terrestrial Hydrology Research Group (THRG), Princeton University. Note: ¹ The municipality reflects the sum of the information from establishments that are in its territory.

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Acesso e impacto do Pronaf no Brasil: evidências sobre as tipologias e a concentração regional

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