Avaliação de impacto do programa Cartão Mais Infância Ceará sobre a performance escolar

Marinho, Emerson; Bastos, Felipe; Araujo, Jair Andrade de; Oliveira, Francisco

Emerson Marinho

Universidade Federal do Ceará(CAEN/UFC), Fortaleza, CE, Brasil. emarinho@ufc.br; felipebastos@caen.ufc.br; jairandrade@ufc.br; alveschico@ymail.com

https://orcid.org/0000-0001-5828-6169

Felipe Bastos

Universidade Federal do Ceará(CAEN/UFC), Fortaleza, CE, Brasil. emarinho@ufc.br; felipebastos@caen.ufc.br; jairandrade@ufc.br; alveschico@ymail.com

https://orcid.org/0000-0001-5598-2935

Jair Andrade de Araujo

Universidade Federal do Ceará(CAEN/UFC), Fortaleza, CE, Brasil. emarinho@ufc.br; felipebastos@caen.ufc.br; jairandrade@ufc.br; alveschico@ymail.com

https://orcid.org/0000-0001-6297-9558

Francisco Oliveira

Universidade Federal do Ceará(CAEN/UFC), Fortaleza, CE, Brasil. emarinho@ufc.br; felipebastos@caen.ufc.br; jairandrade@ufc.br; alveschico@ymail.com

https://orcid.org/0009-0009-7972-4231

EDITOR-CHEFE

Dante Mendes Aldrighi https://orcid.org/0000-0003-2285-5694 Professor - Department of Economics University of São Paulo (USP)

EDITOR ASSOCIADO

Fábio Waltenberg https://orcid.org/0000-0003-3404-7424

Variable	Description/References	Source
Dependent
School Performance (Y)	Student grades in Portuguese (PL) and mathematics (MATH) exams within the scope of SPAECE for the 2nd, 5th, and 9th grades of Elementary School (ES) and the 3rd grade of High School (HS). These exams are administered annually by the Ceará State Department of Education (SEDUC).	SEDUC
Treatment
Mais Infância (T )	Binary variable that takes the value 1 if the student belongs to a beneficiary family of the Mais Infância Card, and 0 otherwise.	Developed by the authors using SPS data
Intervention Dummy $(D_{2019})$	Dichotomous variable that identifies the start of the intervention, i.e., the initiation of the Mais Infância Card. As CMIC became officially established after a state law passed in March 2019, this variable takes the value 1 if the period in the sample refers to the year 2019, and 0 otherwise.	Developed by the authors
$D_{2019} \times T$	Product of the variable Mais Infância and the dummy variable that identifies the start of the intervention. Therefore, it is also a binary variable that takes the value 1 if the student belongs to a beneficiary family of CMIC and the intervention has already started, and 0 otherwise. The coefficient of this variable will identify the impact of CMIC on school performance in Portuguese and mathematics exams.	Developed by the authors
Covariates (X)
Age	Age of the student.	School Census
Gender	Binary variable that takes the value 1 if the student is male, and 0 otherwise. Park et al (2020Park, R. J., Goodman, J., Hurwitz, M., and Smith, J. 2020. “Heat and Learning.” American Economic Journal: Economic Policy, 12(2), 306-339.) and Melo and Suzuki (2021Melo, A. P., and Suzuki, M. 2021. “Temperature, effort, and achievement: Evidence from a large-scale standardized exam in Brazil.” Working Paper.).	School Census
Race	Binary variable that takes the value 1 if the student self-identifies as white, and 0 otherwise.	School Census
Bolsa Família	Proxy for family income. It is a dichotomous variable that takes the value 1 if the student’s family is a beneficiary of the program, and 0 otherwise.	SEDUC
Computer lab	Binary variable that takes the value 1 if the school has a computer lab, and 0 otherwise.	School Census
Library	Binary variable that takes the value 1 if the school has a library, and 0 otherwise.	School Census
Internet access	Binary variable that takes the value 1 if the school has internet access, and 0 otherwise.	School Census
Sewage network	The binary variable takes the value 1 if the school has access to the public sewage network, and 0 otherwise.	School Census
Location	The dichotomous variable takes the value 1 if the school is located in an urban area, and 0 if it is in a rural area. It is a variable related to a higher incidence of child labor, poverty of educational resources, school infrastructure, and teacher quality.	School Census
State-owned	Dummy variable that takes the value 1 if the school belongs to the state education system, and 0 otherwise. In this case, the other considered systems are municipal and federal, as private schools were excluded from the sample.	School Census

Variables	Overall	2018		2019		Mean difference			$(a) - (b)$
Variables	Overall	T	C	T	C	$T_{18} - C_{18}$	$T_{19 - 18}^{(a)}$	$C_{19 - 18}^{(b)}$
$\ln (P L)$	5.452	5.280	5.437	5.525	5.467	-0.157**	0.246*	0.0301*	0.216
$\ln (P L)$	0.275	0.326	0.278	0.224	0.272
Gender	0.502	0.185	0.502	0.174	0.502	-0.316*	-0.011	0.001	-0.012
Gender	0.500	0.396	0.500	0.381	0.500
State-owned	0.248	0.333	0.249	0.783	0.247	0.084	0.449*	-0.002**	0.452
State-owned	0.432	0.480	0.432	0.414	0.431
Race	0.138	0.111	0.139	0.113	0.137	-0.028	0.002	-0.002**	0.004
Race	0.345	0.320	0.346	0.318	0.344
Location	0.782	0.667	0.777	0.896	0.786	-0.111	0.229**	0.008*	0.221
Location	0.413	0.480	0.416	0.307	0.410
Age	12.865	12.815	12.883	19.565	12.844	-0.069	6.750*	-0.039*	6.790
Age	4.137	5.167	4.156	6.209	4.114
Bolsa Família	0.880	0.815	0.881	0.983	0.880	-0.066	0.168**	-0.002**	0.169
Bolsa Família	0.325	0.396	0.324	0.131	0.326
Internet access	0.930	0.889	0.913	0.957	0.948	-0.024	0.068	0.035*	0.033
Internet access	0.254	0.320	0.282	0.205	0.222
Library	0.579	0.593	0.560	0.826	0.599	0.033	0.233**	0.039*	0.194
Library	0.494	0.501	0.496	0.381	0.490
Computer lab	0.615	0.667	0.655	0.861	0.574	0.012	0.194**	-0.081*	0.275
Computer lab	0.487	0.480	0.475	0.348	0.494
Sewage network	0.009	0.000	0.008	0.009	0.011	-0.008*	0.009	0.003*	0.005
Sewage network	0.096	0.000	0.087	0.093	0.104

Nº Obs	747,707	27	375,762	115	371,803

Variables	Overall	2018		2019		Mean difference			$(a) - (b)$
Variables	Overall	T	C	T	C	$T_{18} - C_{18}$	$T_{19 - 18}^{(a)}$	$C_{19 - 18}^{(b)}$
$\ln (M A T H)$	5.519	5.340	5.513	5.530	5.525	-0.173*	0.190*	0.013*	0.178
$\ln (M A T H)$	0.230	0.235	0.232	0.199	0.228
Gender	0.499	0.211	0.498	0.170	0.499	-0.288*	-0.041	0.001*	-0.041
Gender	0.500	0.419	0.500	0.377	0.500
State-owned	0.328	0.526	0.333	0.840	0.322	0.193***	0.313**	-0.011*	0.325
State-owned	0.469	0.513	0.471	0.369	0.467
Race	0.145	0.105	0.144	0.123	0.146	-0.039	0.017	0.001	0.016
Race	0.352	0.315	0.352	0.330	0.353
Location	0.805	0.737	0.802	0.906	0.808	-0.065	0.169	0.006*	0.163
Location	0.397	0.452	0.399	0.294	0.394
Age	14.504	15.368	14.556	20.557	14.448	0.813	5.188*	-0.108*	5.296
Age	3.432	4.633	3.498	5.399	3.358
Bolsa família	0.878	0.842	0.878	0.981	0.877	-0.036	0.139	-0.001	0.140
Bolsa família	0.328	0.375	0.327	0.137	0.328
Internet access	0.939	1.000	0.924	0.962	0.955	0.076*	-0.038**	0.031*	-0.069
Internet access	0.240	0.000	0.265	0.192	0.208
Library	0.618	0.684	0.589	0.868	0.648	0.095	0.184	0.059*	0.125
Library	0.486	0.478	0.492	0.340	0.478
Computer lab	0.662	0.790	0.698	0.906	0.624	0.091	0.116	-0.074*	0.190
Computer lab	0.473	0.419	0.459	0.294	0.484
Sewage network	0.010	0.000	0.009	0.009	0.010	-0.009*	0.009	0.001*	0.008
Sewage network	0.097	0.000	0.095	0.097	0.100

Nº Obs	582,653	19	299,203	106	283,325

Portuguese Exam (PL)
	ln(PL)	Standard deviation.	Value-p
2018 (baseline)
Control	5.8340
Treatment	5.7220
Diff (T-C)	-0.1120	0.0560	0.0450**
2019 (follow-up)
Control	5.8610
Treatment	5.8590
Diff (T-C)	-0.0020	0.0300	0.9560
DID	0.1100	0.0640	0.0840***
Matemathics Exam (MATH)
	ln (MATH)	Standard deviation	Value-p
2018 (baseline)
Control	5.8340
Treatment	5.7250
Diff	-0.1080	0.0530	0.0410**
2019 (follow-up)
Control	5.8450
Treatment	5.8470
Diff (T-C)	0.0020	0.0290	0.9480
DID	0.1100	0.0580	0.0550***

Variables	Exams
Variables	Portuguese	Value-p	Matemathics	Value-p
Gender	-0.0690*	0.0000	0.0210	0.2780
Gender	(0.0190)	0.0000	(0.0190)
Race	0.0390**	0.0320	0.0190	0.4950
Race	(0.0180)	0.0320	(0.0280)
Age	-0.0150*	0.0000	-0.0150*	0.0000
Age	(0.0030)	0.0000	(0.0030)
Bolsa família	0.0560***	0.0930	0.0340	0.2660
Bolsa família	(0.0030)	0.0930	(0.0310)
State-owned	-0.0400	0.7840	-0.0460	0.4110
State-owned	(0.1440)	0.7840	(0.0560)
Location	-0.0170	0.4870	0.0290	0.1220
Location	(0.0250)	0.4870	(0.0190)
Internet access	0.0310	0.4840	0.0150	0.5160
Internet access	(0.0450)	0.4840	(0.0230)
Library	-0.0060	0.8130	-0.0160	0.4160
Library	(0.0250)	0.8130	(0.0200)
Computer lab	0.0200	0.4980	0.0010	0.9440
Computer lab	(0.0290)	0.4980	(0.0210)
Sewage network	-0.0530*	0.0010	-0.0820**	0.0590
Sewage network	(0.0160)	0.0010	(0.0430)
$D_{2 º g r a d e}$	-0.5220*	0.0000
$D_{2 º g r a d e}$	(0.1480)	0.0000
$D_{5 º g r a d e}$	-0.3120*	0.0180	-0.3160*	0.0000
$D_{5 º g r a d e}$	(0.1320)	0.0180	(0.0640)
$D_{9 º g r a d e}$	-0.1010	0.4060	-0.1520*	0.0070
$D_{9 º g r a d e}$	(0.1210)	0.4060	(0.0570)

Weighted variables	Control	Treatment	Mean difference	p-Value
Gender	0.4660	0.1850	-0.2810	0.0002^***
State-owned	0.2630	0.3330	0.0700	0.4386
Race	0.1350	0.1110	-0.0240	0.6954
Location	0.7620	0.6670	-0.0950	0.2952
Age	12.7250	12.8000	0.0750	0.9384
Bolsa Família	0.8730	0.8150	-0.0580	0.4368
Internet access	0.9100	0.8890	-0.0210	0.7225
Library	0.5600	0.5920	0.0330	0.7301
Computer lab	0.6540	0.6660	0.0120	0.8919
Sewage network	0.0000	0.0000	0.0000	-
$D_{2 º g r a d e}$	0.2510	0.3340	0.0830	0.3600
$D_{5 º g r a d e}$	0.3060	0.3330	0.0270	0.7684
$D_{9 º g r a d e}$	0.2040	0.1850	-0.0180	0.8059

Weighted variables	Control	Treatment	Mean difference	p-Value
Gender	0.4300	0.1670	-0.2630	0.0029***
State-owned	0.3800	0.4980	0.1180	0.3168
Race	0.1380	0.1120	-0.0260	0.7259
Location	0.8110	0.7250	-0.0860	0.4121
Age	14.5020	15.1830	0.6810	0.5230
Bolsa Família	0.8690	0.8330	-0.0360	0.6798
Internet access	1.0000	1.0000	0.0000	-
Library	0.6150	0.6650	0.0500	0.6523
Computer lab	0.7290	0.7770	0.0480	0.6258
Sewage network	0.0000	0.0000	0.0000	-
$D_{5 º g r a d e}$	0.4070	0.4980	0.0910	0.4376
$D_{9 º g r a d e}$	0.2450	0.2790	0.0340	0.7469

Variables	Random Subsamples							Covariate Subsets		Full Sample
Variables	n = 40	n = 50	n = 60	n = 70	n = 80	n = 90	n = 100	Student	School
DID	0.198***	0.191***	0.172***	0.224**	0.214**	0.200**	0.177***	0.107**	0.157**	0.109***
DID	0.100	0.104	0.098	0.110	0.089	0.077	0.093	0.054	0.062	0.063
Gender	‐0.237*	‐0.113***	‐0.153**	‐0.044	-0.040	-0.040	-0.064	‐0.068*		‐0.074*
Gender	0.083	0.063	0.064	0.053	0.041	0.038	0.042	0.022		0.010
Race	0.093	0.075	0.106**	0.056	0.033	0.069**	0.050	0.043**		0.048**
Race	0.087	0.055	0.049	0.054	0.054	0.030	0.037	0.018		0.019
Age	‐0.053*	‐0.041**	‐0.055**	‐0.025*	-0.017	-0.014	-0.041*	‐0.015*		‐0.015*
Age	0.013	0.019	0.024	0.008	0.015	0.003	0.010	0.003		0.003
Bolsa Família	0.035	0.16***	‐0.087	0.235**	0.173**	0.217*	0.188**	0.074**		0.055***
Bolsa Família	0.086	0.094	0.124	0.113	0.083	0.066	0.092	0.034		0.032
Location	0.031	‐0.024	‐0.049	‐0.054	0.111**	-0.035	-0.063		‐0.018	‐0.018
Location	0.079	0.050	0.059	0.057	0.057	0.053	0.053		0.028	0.025
State-owned	‐0.223	0.106	0.187***	0.187	-0.182	-0.171	0.018		0.125*	‐0.042
State-owned	0.156	0.110	0.108	0.113	0.236	0.231	0.079		0.043	0.140
Internet access	0.055	0.097	0.015	0.100	0.001	-0.018	0.015		0.058	0.031
Internet access	0.112	0.124	0.103	0.124	0.119	0.062	0.065		0.053	0.045
Library	‐0.019	0.006	‐0.017	‐0.051	0.065	0.035	0.040		0.004	‐0.005
Library	0.086	0.075	0.078	0.057	0.059	0.041	0.048		0.036	0.025
Computer lab	‐0.032	‐0.021	0.049	‐0.026	-0.006	-0.020	-0.021		0.046	0.019
Computer lab	0.099	0.074	0.063	0.064	0.073	0.073	0.052		0.040	0.029
Sewage network	0.000	0.000	0.000	0.000	0.000	-0.06*	0.000		‐0.031**	‐0.053*
Sewage network	0.000	0.000	0.000	0.000	0.000	0.023	0.000		0.014	0.016
Race^T^ $D_{2019}$										‐0.025
Race^T^ $D_{2019}$										0.045
Gender^T^ $D_{2019}$										0.017
Gender^T^ $D_{2019}$										0.064
$D_{2 º g r a d e}$	‐1.240*	‐0.601**	‐0.685**	‐0.335***	-0.675***	-0.662*	-0.646*	‐0.489*		‐0.524*
$D_{2 º g r a d e}$	0.268	0.301	0.331	0.173	0.389	0.256	0.161	0.049		0.146
$D_{5 º g r a d e}$	‐0.926*	‐0.382	‐0.431	‐0.139	-0.421	-0.392***	-0.468*	‐0.281*		‐0.315*
$D_{5 º g r a d e}$	0.216	0.233	0.286	0.154	0.315	0.220	0.159	0.034		0.128
$D_{9 º g r a d e}$	‐0.287**	‐0.039	0.034	0.142	-0.107	-0.165	-0.055	‐0.074*		‐0.103
$D_{9 º g r a d e}$	0.146	0.124	0.148	0.105	0.168	0.140	0.090	0.022		0.118

Variables	Random Subsamples							Covariate Subsets		Full Sample
Variables	n = 40	n = 50	n = 60	n = 70	n = 80	n = 90	n = 100	Student	School
DID	0.171***	0.340*	0.147***	0.206**	0.201***	0.141***	0.166**	0.127**	0.161*	0.105***
DID	0.098	0.126	0.084	0.104	0.103	0.082	0.083	0.054	0.049	0.055
Gender	-0.068	0.104	0.093***	0.065	0.009	0.085***	0.076***	0.020		‐0.002
Gender	0.088	0.086	0.055	0.053	0.068	0.044	0.041	0.020		0.007
Race	0.156	0.156	0.028	0.066	0.070	-0.017	0.024	0.019		0.062**
Race	0.133	0.119	0.045	0.096	0.079	0.063	0.051	0.029		0.025
Age	-0.024	-0.034	-0.014*	-0.044**	-0.013	-0.060*	-0.016*	‐0.015*		‐0.015*
Age	0.018	0.027	0.003	0.019	0.022	0.019	0.003	0.003		0.003
Bolsa Família	0.028	0.167	0.068	0.139	0.217**	0.153**	0.113	0.024		0.031
Bolsa Família	0.084	0.109	0.106	0.095	0.103	0.070	0.068	0.028		0.028
Location	0.164**	0.110***	0.020	0.180*	0.111***	0.070***	0.051		0.036**	0.027
Location	0.080	0.062	0.052	0.046	0.061	0.040	0.048		0.018	0.018
State-owned	0.006	0.101	-0.044	-0.329*	-0.078	-0.283*	-0.050		0.081*	‐0.052
State-owned	0.122	0.121	0.121	0.100	0.152	0.075	0.098		0.027	0.058
Internet access	0.042	0.000	-0.094	0.000	-0.054	-0.046	-0.042		0.039	0.006
Internet access	0.137	0.000	0.079	0.000	0.098	0.098	0.060		0.03	0.029
Library	-0.011	-0.005	-0.047	-0.051	-0.047	-0.013	-0.075		‐0.017	‐0.014
Library	0.056	0.060	0.041	0.053	0.062	0.037	0.048		0.019	0.019
Computer lab	-0.034	-0.009	0.056	0.010	0.074	-0.002	-0.013		0.002	‐0.002
Computer lab	0.091	0.096	0.081	0.056	0.076	0.056	0.056		0.020	0.021
Sewage network	0.000	0.000	0.000	-0.154*	0.000	-0.064	-0.116**		‐0.062***	‐0.081***
Sewage network	0.000	0.000	0.000	0.041	0.000	0.107	0.055		0.036	0.043
Race^T^ $D_{2019}$										‐0.111***
Race^T^ $D_{2019}$										0.062
Gender^T^ $D_{2019}$										0.082
Gender^T^ $D_{2019}$										0.061
$D_{5 º g r a d e}$	-0.332	-0.481***	-0.299**	-0.805*	-0.276	-0.921*	-0.352*	‐0.489*		‐0.324*
$D_{5 º g r a d e}$	0.236	0.265	0.148	0.174	0.275	0.197	0.105	0.049		0.066
$D_{9 º g r a d e}$	-0.147	-0.124	-0.110	-0.462*	-0.110	-0.399*	-0.150***	‐0.281*		‐0.159*
$D_{9 º g r a d e}$	0.123	0.145	0.115	0.074	0.155	0.095	0.077	0.034		0.060

Variables	n = 40	n = 50	n = 60	n = 70	n = 80	n = 90	n = 100	Student	School	Full Sample
Gender	0.099	0.025	-0.103	0.009	-0.046	0.037	-0.020	-0.316***		-0.281***
Race	0.112	0.017	-0.005	0.090	-0.019	0.040	-0.115	-0.028		-0.024
Age	1.175	1.184	0.217	-1.220	0.770	2.005	0.205	-0.068		0.057
Bolsa Família	0.033	-0.098	-0.115	0.011	0.053	0.171	0.114	-0.066		-0.058
Location	-0.186	0.179	0.031	0.038	0.024	-0.120	-0.081		-0.122	-0.094
State-owned	0.112	0.162	0.043	0.108	0.087	0.124	-0.065		0.096	0.069
Internet access	-0.090	0.198	0.052	0.031	0.018	0.072	-0.021		-0.027	-0.022
Library	0.216	0.141	0.001	-0.123	-0.003	-0.015	0.086		0.029	0.032
Computer lab	-0.144	0.235	-0.024	-0.035	0.074	0.058	0.016		0.019	0.012
Sewage network	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000
$D_{2 º g r a d e}$	-0.016	-0.131	-0.093	-0.028	-0.065	-0.176	-0.007	0.100		0.084
$D_{5 º g r a d e}$	-0.160	-0.028	0.043	-0.024	-0.021	0.010	-0.034	0.049		0.026
$D_{9 º g r a d e}$	0.096	0.065	0.052	0.108	0.048	0.062	0.086	-0.070		-0.018

Variables	n = 40	n = 50	n = 60	n = 70	n = 80	n = 90	n = 100	Student	School	Full Sample
Gender	-0.253	0.196	-0.221	0.025	-0.033	-0.114	-0.005	‐0.329***		‐0.262***
Race	0.113	-0.076	0.128	0.015	-0.007	0.039	0.037	‐0.030		‐0.026
Age	-1.931	1.097	1.640	0.346	1.094	1.027	-0.177	0.777		0.631
Bolsa Família	0.333	-0.082	0.196	-0.143	-0.025	0.029	0.107	‐0.045		‐0.037
Location	-0.103	-0.018	0.084	0.109	0.120	-0.040	0.059		‐0.123	‐0.082
State-owned	-0.250	0.154	0.230	-0.026	0.228	-0.054	0.029		0.187	0.115
Internet access	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000
Library	-0.144	0.221	0.279	-0.054	0.158	0.051	0.128		0.051	0.048
Computer lab	0.077	-0.072	0.058	0.030	0.027	-0.017	0.109		‐0.001	0.047
Sewage network	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000
$D_{5 º g r a d e}$	0.280	-0.107	-0.292	0.000	-0.171	-0.056	-0.044	0.127		0.089
$D_{9 º g r a d e}$	-0.197	-0.018	0.177	0.026	0.110	-0.058	0.159	‐0.057		0.036

[1] EDITOR-CHEFE

Dante Mendes Aldrighi https://orcid.org/0000-0003-2285-5694 Professor - Department of Economics University of São Paulo (USP)

Brasil

Brasil

Avaliação de impacto do programa Cartão Mais Infância Ceará sobre a performance escolar

Resumo