Efeitos do crescimento do bem-estar multidimensional na pobreza e desigualdade do Brasil no período de 2004-2008 e 2016-2019

Por meio deste trabalho objetiva-se analisar os efeitos do crescimento do bem-estar multidimensional na pobreza e desigualdade do Brasil nos períodos de 2004-2008 e 2016-2019. Empiricamente, são utilizadas as metodologias de Kakwani e Pernia (2000KAKWANI, Nanak e PERNIA, Ernesto M (2000), What is Pro-poor Growth?. Asian Development Review, v. 18, n. 1, p. 1-16.), Kakwani, Khandker e Son (2004KAKWANI, Nanak; KHANDKER, Shahid e SON, Hyun H (2004), Pro-poor growth: concepts and measurement with country case studies. International Poverty Centre: Working Paper, n.1, p. 1-24.) e a decomposição de Shapley. Os resultados mostraram que houve diminuição da pobreza multidimensional entre 2004 e 2008 e um aumento entre 2016 e 2019. O crescimento do bem-estar multidimensional no segundo período foi antipobre. Com a decomposição constatou-se que, enquanto o crescimento do bem-estar multidimensional contribuiu para a redução da pobreza entre 2004 e 2008, entre 2016 e 2019 a concentração contribuiu para a elevação da pobreza.

PALAVRAS-CHAVE:
Bem-estar; pobreza multidimensional; desigualdade.

Autoria

OTAVIO JUNIO FARIA NEVES

Doutorando em Demografia na Universidade Estadual de Campinas (Unicamp), Campinas-SP, Brasil. Mestre em Economia pela Universidade Federal de Alfenas (UNIFAL), Varginha-MG, Brasil. E-mail: otavionevescg@hotmail.com.Universidade Estadual de Campinas (Unicamp)BrasilCampinas, SP, Brasil Doutorando em Demografia na Universidade Estadual de Campinas (Unicamp), Campinas-SP, Brasil. Mestre em Economia pela Universidade Federal de Alfenas (UNIFAL), Varginha-MG, Brasil. E-mail: otavionevescg@hotmail.com.

https://orcid.org/0000-0001-7248-5439

ANA MÁRCIA RODRIGUES DA SILVA

Professora do Programa de Pós-Graduação em Economia da Universidade Federal de Alfenas (UNIFAL), Varginha - MG, Brasil. E-mail: ana.rodrigues@unifal-mg.edu.br.Universidade Federal de Alfenas (UNIFAL)BrasilVarginha, MG, Brasil Professora do Programa de Pós-Graduação em Economia da Universidade Federal de Alfenas (UNIFAL), Varginha - MG, Brasil. E-mail: ana.rodrigues@unifal-mg.edu.br.

https://orcid.org/0000-0003-0892-4841

SCIMAGO INSTITUTIONS RANKINGS

Tabelas | Fórmulas

Tabelas (5)
Fórmulas (10)

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Table 1:
Selected dimensions, cut-off levels, and indicator weights^*

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Table 2:
Pro-poor growth indexes based on multidimensional well-being in Brazil in 2004-2008

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Table 3:
Pro-poor growth indexes based on multidimensional well-being in Brazil in 2016-2019

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Table 4:
Decomposition of the multidimensional poverty variation between well-being growth and redistribution in the period of 2004-2008

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Table 5:
Decomposition of the change in multidimensional poverty between well-being growth and redistribution over the period of 2016-2019

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Table 1: Selected dimensions, cut-off levels, and indicator weights^*

Dimension	Indicators with the cut-off level (z)	Weight
Economic Vulnerability	Income below the poverty line	1/3
	More dependents than non-dependents	1/3
	Lack of a paid occupation	1/3
Housing Conditions	Not your own house	1/5
	More than three people living in one room	1/5
	Lack of electricity	1/5
	Walls made of substandard material	1/5
	Roofing made of substandard material	1/5
Sanitary Conditions	Lack of a toilet	1/4
	Lack of sewage system or septic tank	1/4
	Inadequate waste disposal	1/4
	Inadequate water supply	1/4
Lack of Goods	Lack of a landline or cell phone	1/5
	Lack of a TV set	1/5
	Lack of a refrigerator	1/5
	Lack of a washing machine	1/5
	Lack of a computer	1/5
Educational Characteristics	Presence of illiterate adults	1/3
	Presence of adults lacking complete elementary education	1/3
	Presence of children aged 6 to 15 out of school	1/3
Occupational Conditions	Lack of women’s participation in employment	1/4
	Inadequate occupation	1/4
	Lack of someone employed with a signed contract	1/4
	Lack of someone employed earning an income higher than minimum wage	1/4

For the calculation of Income below the poverty line the World Bank poverty line of US$ 5.50 was used, converted into Brazilian Reais on the basis of purchasing power parity. It was considered as Inadequate water supply when water was not supplied from a general distribution network in urban areas, or not from a general distribution network, or from a well or spring in rural areas. As Walls made from substandard material materials other than brickwork were considered, and as Roofing made of substandard material different tile and concrete slab materials were considered. Since there is no direct waste collection in most rural areas, the possibility of waste being burned or buried on the property was also considered when individuals were located in rural locations, in the variable Inadequate waste disposal. By Inadequate occupation, underemployment was understood to be due to insufficient hours or insufficient wages.
Source: Authors’own compilation based on the PNAD and PNADC data covering the years 2004-2008, 2016-2019.

Table 2: Pro-poor growth indexes based on multidimensional well-being in Brazil in 2004-2008

Pro-poor growth indexes	Estimate	se*	lb*	ub*
Well-being growth rate (g)	0.1210	0.0016	0.1178	0.1242
Kakwani and Pernia’s Index (2000KAKWANI, Nanak e PERNIA, Ernesto M (2000), What is Pro-poor Growth?. Asian Development Review, v. 18, n. 1, p. 1-16.)	0.8570	0.0117	0.8341	0.8799
PEGR	0.1037	0.0020	0.0997	0.1077
PEGR - g	-0.0173	0.0014	-0.0020	-0.014

*se = standard error; lb= lower-bounded (95%); ub = upper-bounded (95%).
Source: Authors’own compilation based on the PNAD and PNADC data covering the years 2004-2008.

Table 3: Pro-poor growth indexes based on multidimensional well-being in Brazil in 2016-2019

Pro-poor growth indexes	Estimate	se*	lb*	ub*
Well-being growth rate (g)	0.0117	0.002	-0.055	-0.047
Kakwani and Pernia’s Index (2000KAKWANI, Nanak e PERNIA, Ernesto M (2000), What is Pro-poor Growth?. Asian Development Review, v. 18, n. 1, p. 1-16.)	-1.3092	0.069	1.079	1.348
PEGR	-0.0153	0.005	-0.072	-0.051
PEGR - g	-0.0270	0.004	-0.018	-0.003

^*
se = standard error; lb= lower-bounded (95%); ub = upper-bounded (95%).
Source: Authors’own compilation based on the PNAD and PNADC data covering the years 2016-2019.

Table 4: Decomposition of the multidimensional poverty variation between well-being growth and redistribution in the period of 2004-2008

Groups	Shapley’s decomposition
Groups	$C_{G}^{S}$	$C_{R}^{S}$	∆P
Region
North	-0.1286	0.0122	-0.1164
Northeast	-0.1204	0.0320	-0.0884
Southeast	-0.1124	0.0063	-0.1061
South	-0.1144	0.0100	-0.1044
Midwest	-0.1250	0.0053	-0.1197
Gender
Male	-0.1092	0.0082	-0.101
Female	-0.1204	0.0206	-0.0998
Ethnicity
Caucasian	-0.1130	0.0154	-0.0976
African	-0.1386	0.0177	-0.1209
Asian	-0.0150	-0.0239	-0.0389
Mixed	-0.1397	0.0278	-0.1119
Native	-0.0842	0.0026	-0.0816
Census tract
Urban	-0.1165	0.0091	-0.1074
Rural	-0.1049	0.0370	-0.0679
Economic activity sectors
Agriculture	-0.1494	0.0564	-0.093
Other industrial activities	-0.0462	-0.0016	-0.0478
Manufacturing	-0.0600	0.0029	-0.0571
Construction	-0.1254	0.0223	-0.1031
Trade and repair	-0.0766	-0.0070	-0.0836
Accommodation and food	-0.1011	0.0166	-0.0845
Transportation	-0.0614	-0.0047	-0.0661
Public administration	-0.0383	-0.0106	-0.0489
Education and health	-0.0287	-0.0115	-0.0402
Domestic services	-0.1257	0.0369	-0.0888
Other community services	-0.0807	-0.0109	-0.0916
Other activities	-0.0312	-0.0011	-0.0323
Poorly defined activities	-0.0738	-0.0388	-0.1126

Source: Authors’own compilation based on the PNAD and PNADC data covering the years 2004-2008.

Table 5: Decomposition of the change in multidimensional poverty between well-being growth and redistribution over the period of 2016-2019

Groups	Shapley’s decomposition
Groups	$C_{G}^{S}$	$C_{R}^{S}$	∆ P
Region
North	0.0499	0.0350	0.0849
Northeast	0.0308	0.0332	0.0640
Southeast	-0.0348	0.0531	0.0183
South	-0.0122	0.0391	0.0269
Midwest	-0.0083	0.0472	0.0389
Gender
Male	0.0000	0.0427	0.0427
Female	-0.0229	0.0583	0.0354
Ethnicity
Caucasian	-0.0507	0.0756	0.0249
African	0.0249	0.0207	0.0456
Asian	-0.0748	0.1251	0.0503
Mixed	0.0267	0.0204	0.0471
Native	0.0337	0.0289	0.0626
Census tract
Urban	-0.0154	0.0530	0.0376
Rural	0.0450	0.0052	0.0502
Economic activity sectors
Agriculture	0.0624	-0.0133	0.0491
Other industrial activities	0.0000	0.0267	0.0267
Manufacturing	0.0450	0.0060	0.0510
Construction	0.0136	0.0156	0.0292
Trade and repair	0.0281	-0.0034	0.0247
Accommodation and food	0.0466	0.0065	0.0531
Transportation	-0.0225	0.0355	0.0130
Public administration	-0.0359	0.0433	0.0074
Education and health	-0.0395	0.0432	0.0037
Domestic services	-0.0045	0.0532	0.0487
Other community services	0.0735	-0.0163	0.0572
Other activities	0.0009	0.0844	0.0853

Source: Authors’own compilation based on the PNAD and PNADC data covering the years 2016-2019.

\frac{\partial P (z, x)}{\partial x} < 0 \frac{\partial^{2} P (z, x)}{\partial x^{2}} > 0

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θ = \int_{0}^{z} P (z, x) f (x) d x

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\frac{d θ}{θ} = \frac{1}{θ} \int_{0}^{z} \frac{\partial P}{\partial x} d (x) f (x) d x

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d L_{n} (θ) = \frac{1}{θ} \int_{0}^{H} \frac{\partial P}{\partial x} x (p) g (p) d p

d L n (x (p)) = d L n (μ) + d L n (L' (p))

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d L n (θ) = y η + \frac{1}{θ} \int_{0}^{H} \frac{\partial P}{\partial x} x (p) d L n (L' (p)) d p

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η = \frac{1}{θ} \int_{0}^{H} \frac{\partial P}{\partial x} x (p) d p

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C_{G}^{S} = \frac{1}{2} [P (μ_{2}, L_{1}) - P (μ_{1} L_{1})] + \frac{1}{2} [P (μ_{2}, L_{2}) - P (μ_{1} L_{2})]

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C_{R}^{S} = \frac{1}{2} [P (μ_{1}, L_{2}) - P (μ_{1} L_{1})] + \frac{1}{2} [P (μ_{2}, L_{2}) - P (μ_{2} L_{1})]

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∆ P = C_{G}^{S} + C_{R}^{S}

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Efeitos do crescimento do bem-estar multidimensional na pobreza e desigualdade do Brasil no período de 2004-2008 e 2016-2019

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[TFN1] For the calculation of Income below the poverty line the World Bank poverty line of US$ 5.50 was used, converted into Brazilian Reais on the basis of purchasing power parity. It was considered as Inadequate water supply when water was not supplied from a general distribution network in urban areas, or not from a general distribution network, or from a well or spring in rural areas. As Walls made from substandard material materials other than brickwork were considered, and as Roofing made of substandard material different tile and concrete slab materials were considered. Since there is no direct waste collection in most rural areas, the possibility of waste being burned or buried on the property was also considered when individuals were located in rural locations, in the variable Inadequate waste disposal. By Inadequate occupation, underemployment was understood to be due to insufficient hours or insufficient wages.

[TFN2] Source: Authors’own compilation based on the PNAD and PNADC data covering the years 2004-2008, 2016-2019.