Productivity analysis in resource allocation of FUNDEB in the state of Amapa between 2010 and 2019

Grillo, Cecília Calcagno; Souza Júnior, Celso Vila Nova de; Moreira, Tito Belchior Silva; Galvão, César Augusto de Souza Pinto

doi:10.1590/S1413-24782024290023

Cecília Calcagno Grillo

Writing - original draft

Writing - review & editing

Methodology

Fundo Nacional de Desenvolvimento da Educação, Brasília, DF, Brazil. E-mail: cecilia.grillo@fnde.gov.br

http://orcid.org/0000-0002-9393-873X

Celso Vila Nova de Souza Júnior

Formal Analysis

Translation

Universidade de Brasília, Brasília, DF, Brazil. E-mail: celsovilanova@unb.br

http://orcid.org/0000-0003-3623-9251

Tito Belchior Silva Moreira

Data curation

Translation

Fundação Getúlio Vargas, Escola de Políticas Públicas e Governo, Brasília, DF, Brazil. E-mail: tito.moreira@fgv.br

http://orcid.org/0000-0002-2382-1480

César Augusto de Souza Pinto Galvão

Data Collection

Escola Nacional de Administração Pública, Brasília, DF, Brazil. E-mail: cesaraugusto.galvao@gmail.com

http://orcid.org/0000-0001-8522-2566

Conflicts of interest:

The authors declare they don’t have any commercial or associative interest that represents conflict of interests in relation to the manuscript.

CECÍLIA CALCAGNO GRILLO has a master’s degree in Public Management from Universidade de Brasília (UnB). She is a Specialist at Fundo Nacional de Desenvolvimento Educacional (FNDE).

CELSO VILA NOVA DE SOUZA JÚNIOR has a doctorate in Economics from Universidade de Brasília (UnB). He is a professor at Universidade de Brasília (UnB).

TITO BELCHIOR SILVA MOREIRA has a doctorate in Economics from Universidade de Brasília (UnB). He is a full professor at the Getúlio Vargas Foundation, School of Public Policies and Government (FGV EPPG).

CÉSAR AUGUSTO DE SOUZA PINTO GALVÃO is graduating in Statistics from (UnB). He is an Analyst at Nacional de Administração Pública (ENAP).

Municipality	Inputs	Output
Amapá	Fundeb proportional transfer deflated. AP LEF HAD	IDEB Outcome
Calçoene
Cutias do Araguari
Ferreira Gomes
Itaubal
Laranjal do Jari
Macapá
Mazagão
Oiapoque
Pedra Branca do Amapari
Porto Grande
Pracuuba
Santana
Serra do Navio
Tartarugalzinho
Vitória do Jari

Year	Statistic	Fundeb D (R$)	AP	HAD	LEF	IDEB
2010	Mean	105,923.4	50.18	4.14	0.86	3.2
2010	Standard deviation	371,311.1	19.24	0.15	0.16	0.4
2011	Mean	110,253	49.79	4.12	0.87	3.7
2011	Standard deviation	370,846.9	25.48	0.14	0.14	0.4
2012	Mean	124,007.3	49.66	4.13	0.85	3.7
2012	Standard deviation	415,413.4	29.51	0.15	0.12	0.4
2013	Mean	148,662.1	38.74	4.12	0.89	3.5
2013	Standard deviation	486,670.6	18.35	0.13	0.10	0.5
2014	Mean	166,583.4	34.63	4.20	0.86	3.5
2014	Standard deviation	552,875.2	18.19	0.27	0.14	0.5
2015	Mean	168,960.1	31.23	4.17	0.84	3.8
2015	Standard deviation	580,660	11.65	0.15	0.14	0.4
2016	Mean	204,093.9	34.73	4.13	0.86	3.8
2016	Standard deviation	687,360	20.01	0.12	0.14	0.4
2017	Mean	241,024.3	33.64	4.14	0.85	3.9
2017	Standard deviation	830,371.6	14.74	0.14	0.13	0.6
2018	Mean	228,291.6	31.56	4.11	0.89	3.9
2018	Standard deviation	757,224.1	9.34	0.13	0.08	0.5
2019	Mean	252,364.6	29.70	4.13	0.87	4.2
2019	Standard deviation	843,536.7	11.63	0.16	0.15	0.4

Year		Fundeb D (R$)		AP		HAD		LEF		IDEB
2010	Minimum	Pracuuba	425.35	Pracuuba	22.90	Six DMUs	4.00	Oiapoque	0.53	Amapá	2.6
2010	Maximum	Macapá	1,542,715.91	Mazagão	91.83	FourDMUs	4.30	Four DMUs	1.00	Macapá	4
2011	Minimum	Itaubal	390.73	Pracuuba	21.03	Eight DMUs	4.00	Laranjal do J.	0.57	Amapá	3
2011	Maximum	Macapá	1,543,250.22	Mazagão	126.25	Four DMUs	4.30	Four DMUs	1.00	Santana	4.8
2012	Minimum	Cutias	498.12	Pracuuba	16.43	Seven DMUs	4.00	Cutias	0.63	Amapá	3
2012	Maximum	Macapá	1,728,237.82	Mazagão	139.67	Two DMUs	4.40	Two DMUs	1.00	Santana	4.8
2013	Minimum	Cutias	424.82	Pracuuba	16.11	Seven DMUs	4.00	Mazagão	0.70	Tartarugalzinho	2.7
2013	Maximum	Macapá	2,024,336.56	Mazagão	89.50	Pracuuba	4.40	Five DMUs	1.00	Santana	4.6
2014	Minimum	Cutias	384.93	Pracuuba	15.42	Five DMUs	4.00	Santana	0.62	Tartarugalzinho	2.7
2014	Maximum	Macapá	2,301,080.85	Mazagão	91.33	Tartarugalzinho	5.10	Five DMUs	1.00	Santana	4.6
2015	Minimum	Serra do N.	204.09	Pracuuba	15.54	Five DMUs	4.00	Pedra B. do A.	0.57	Tartarugalzinho	3.1
2015	Maximum	Macapá	2,340,088.78	Macapá	58.05	Two DMUs	4.40	Two DMUs	1.00	Santana	4.6
2016	Minimum	Serra do N.	498.88	Pracuuba	16.41	Five DMUs	4.00	Macapá	0.49	Tartarugalzinho	3.1
2016	Maximum	Macapá	2,857,688.90	Mazagão	99.69	Three DMUs	4.30	Three DMUs	1.00	Santana	4.6
2017	Minimum	Cutias	446.32	Pracuuba	15.97	Seven DMUs	4.00	Ferreira G.	0.62	Pracuuba	2.7
2017	Maximum	Macapá	3,450,385.50	Mazagão	74.08	Five DMUs	4.30	Three DMUs	1.00	Serra do Navio	5.1
2018	Minimum	Cutias	707.47	Pracuuba	17.34	Nine DMUs	4.00	Amapá	0.69	Pracuuba	2.7
2018	Maximum	Macapá	3,147,762.41	Macapá	64.43	Four DMUs	4.30	Four DMUs	1.00	Serra do Navio	5.1
2019	Minimum	Serra do N.	1,336.36	Serra do N.	17.07	Eight DMUs	4.00	Vitória do J.	0.52	Calçoene	3.5
2019	Maximum	Macapá	3,507,313.29	Macapá	62.27	Serra do N.	4.50	Five DMUs	1.00	Macapá	5

Municipality	Fundeb D	AP	HAD	LEF	IDEB
Amapá	2,135.77	37.29	4.00	0.87	3.4
Calçoene	4,679.96	29.98	4.00	0.92	3.5
Cutias	614.47	39.46	4.04	0.85	3.2
Ferreira G.	4,655.97	24.51	4.27	0.87	3.5
Itaubal	1,252.29	31.69	4.16	0.89	3.7
Laranjal do J.	74,175.94	36.35	4.20	0.84	3.9
Macapá	2,444,286.02	64.61	4.19	0.84	4.3
Mazagão	12,429.60	86.78	4.05	0.87	3.4
Oiapoque	21,700.21	29.88	4.00	0.82	3.7
Pedra B. do A.	5,960.84	31.46	4.06	0.89	4.1
Porto G.	19,603.06	35.90	4.03	0.86	3.6
Pracuuba	1,307.86	17.46	4.32	0.85	3.3
Santana	186,004.66	46.50	4.28	0.88	4.6
Serra do N.	877.12	21.71	4.33	0.86	4.5
Tartarugalzinho	5,828.60	44.04	4.11	0.87	3.3
Vitória do J.	14,749.59	26.34	4.25	0.86	3.6

Year	EFF	TECH	PE	SE	FTP
2010-2011	1.045	1.121	1.014	1.030	1.171
2011-2012	1.035	0.955	1.023	1.011	0.988
2012-2013	0.946	0.985	0.957	0.989	0.932
2013-2014	1.013	1.038	1.028	0.985	1.051
2014-2015	1.004	1.093	1.019	0.985	1.097
2015-2016	1.013	0.974	0.989	1.025	0.987
2016-2017	0.989	1.045	0.982	1.007	1.033
2017-2018	0.940	1.071	1.001	0.939	1.007
2018-2019	1.048	1.046	0.996	1.053	1.096
Mean	1.003	1.035	1.001	1.002	1.038

\underset{M P I}{\underset{⏟}{m_{o} (y_{s}, x_{s}, y_{t}, x_{y})}} = \underset{E F F}{\underset{⏟}{\frac{d_{0}^{t} (y_{t}, x_{y})}{d_{0}^{s} (y_{s}, x_{s})}}} \underset{T E C H}{\underset{⏟}{{[\frac{d_{0}^{s} (y_{t}, x_{t})}{d_{0}^{t} (y_{t}, x_{t})} \times \frac{d_{0}^{s} (y_{s}, x_{s})}{d_{0}^{t} (y_{s}, x_{s})}]}^{\frac{1}{2}}}}

[1] Conflicts of interest:

The authors declare they don’t have any commercial or associative interest that represents conflict of interests in relation to the manuscript.

Brasil

Brasil

Productivity analysis in resource allocation of FUNDEB in the state of Amapa between 2010 and 2019

ABSTRACT