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School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September 2017 Stata UGM 2017, Portugal Universidade do Porto 1 / 37

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Page 1: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School and teacher characteristics vs. student progress

Sandra Sousa, Miguel Portela and Carla Sá

September 2017

Stata UGM 2017, PortugalUniversidade do Porto

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Page 2: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsMotivation

• The decision to invest in education is taken jointly by the family and the student, whocompare the benefits and costs of such investment

• Educational externalities• Education is one of the main services provided by governments• School assessment has been an instrument of a guarantee of productivity and efficiency ofthe education systems, and used to improve the quality of education

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Page 3: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsMotivation

• School performance can be evaluated by its value-added• Using data at the student level, for the period 2010–2012, we analyse possible factors thatinfluence students’ achievement gains on Portuguese and Mathematics national exams

• One concludes that achievement gains, both in Portuguese and in Mathematics, are mainlydetermined by student’s characteristics

• While peers’ characteristics seem not to influence students’ performance, class size doesplay a role

Research question:

What are the factors that determine students’ performance?

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Page 4: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsLiterature

Internacional

• In the economics of education literature, one of the most common conceptual frameworksemployed takes the form of a production function, also referred to as “input–output”analysis:

I The output corresponds to the results achieved by the students at the end of a cycle of studiesI Education outcomes: test scores, in particular, the maths, reading and science scores, students’

success rates, attendance rates, repetition rates and dropout ratesI 3 groups of inputs: student, family e school characteristics

• Parametric estimators: OLS, multilevel, fixed-effects• Non-parametric: DEA

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Page 5: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsLiterature

Internacional

Leading results in the literature:• Lee e Barro (2001) – family background and socio–economic factors are the most importantdeterminants of student performance as compared to school resources

• Hanushek et al. (2003) and Kirjavainen (2012) – the higher the prior achievement scores,the higher the final achievement scores

• Hanushek (1986) and Lee e Barro (2001) – growing up in a low–income family has anegative impact on educational outcomes

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Page 6: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsLiterature

Internacional

• Hanushek (1986), Lee and Barro (2001), Woessman (2003) and Kirjavainen (2012) –parents’ education level influences positively student’s performance

• Hanushek (1997), Krueger (2003) and Lee and Barro (2001) – the results suggest onlyweak relationships between school expenditures and student performance, once one controlsfor family characteristics

• Lee e Barro (2001) and Akerhielm (1995), for example - smaller classes have a positiveeffect on student achievement

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Page 7: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsLiterature

Internacional

• Empirical evidence on peer effects is rather mixed• However, the average peer group achievement (Hanushek, 2003), the average education ofmothers of other students in the same class (McEwan, 2003), and a high proportion of girls,(Kirjavainen2012, have a highly significant effect on student performance

• Brunello and Rocco (2013) – the higher the share of immigrant pupils in schools, the lowerthe performance of native students, especially those with a disadvantaged parentalbackground

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Page 8: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsLiterature

Portugal

• Carneiro (2008) –I The observable factor that contributes the most to the inequality in student performance is the

family backgroundI The school resources have a limited role on student results

• Pereira (2010) –I Male students perform better in mathematics and female students have better reading

performance;I Socio–economic background has a strong effect on test scoresI Parents’ education (secondary and university education) has a positive impact on student

achievement;

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Page 9: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsLiterature

Portugal

• Ferrão (2012): the relationship between prior achievement and student performance isstronger than the relationship between socio–economic status and student performance

• Oliveira and Santos (2005): school environment characteristics (e.g., unemployment rate,access to health care services, adult education and living infrastructures) are determinantsof school efficiency

• Several authors, e.g., Oliveira and Santos (2005), Pereira e Reis (2012) and Portela et al.argue that coastline schools have better performance when compared too the inland ones

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Page 10: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsMethodology

Education production function

logAijk = λ logAi9 + βXijk + δCjk + θSk + εijk (1)

Aijk: student’s achievement in Portuguese or Mathematics, measured by the 12th grade nationalexam score, for student i in class j in school k;A9

i : student’s achievement in the 9th grade exam in the same subject;Xijk : observable student and family characteristics;Cjk: measurable class j, in school k, characteristics;Sk: measurable school characteristics;εijk: error term

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Page 11: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsMethodology

Estimation methods

• OLS• Fixed – effects (school level)• A multilevel model with 3 levels – student, class and school• Non–parametric approach: Data Envelopment Analysis (DEA)

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Page 12: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsData

The dataset was built from two distinct databases managed by the Portuguese Ministry ofEducation:

• MISI (Sistema de Informação do Ministério da Educação): it is a very detailedadministrative database that contains information on pre–school education, as well as basicand secondary education, in public schools, overseen by the Ministry of Education. Itcontains information at student–level, such as gender, nationality, academic outcome,grade, social support eligibility, type of student, type of education, residence, availability ofcomputer and internet at home, kinship of legal–guardians, legal–guardians’/parents’employment situation and legal–guardians’/parents’ education, class and school.Information at school–level includes location, school resources

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Page 13: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsData

• JNE (Statistics published by Júri Nacional de Exames – Direção Geral deEducação) : information on scores in national exams on all disciplines of basic andsecondary education subjected to examination.

• Time interval: 2010 – 2012

• Estimations are performed, separately, for national exams on Mathematics and Portuguese• About 36,000 students performed the exam of Mathematics type A and/or Portuguese atupper secondary education (“12o ano”)

• Only internal students and students who enrolled in the national exam but who attendedthe discipline throughout the school year are included

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Page 14: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsData

• ≈ 25% – benefits from social support• ≈ 26% – without internet at home• ≈ 71% – the mother is the legal–guardian• ≈ 53% – with parents/legal–guardians with at least the high school diploma• 351 public schools (Portugal mainland)

• 4,817 teachers in Mathematics and PortugueseI ≈ 35% of teachers work outside their countyI ≈ 92% have an undergraduate degreeI ≈ 75% are women

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Page 15: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsDescriptive statistics

Table 1: Descriptive statistics on studentsVariable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

2010 2011 2012

Mathematics 125.34 46.164 108.33 47.256 106.833 44.743Portuguese 116.95 29.499 105.93 31.347 112.418 30.261Maths 9th 95.00 43.401 124.28 47.051 133.712 38.589Portuguese 9th 138.37 24.046 138.60 25.401 129.029 26.927Female 0.596 0.588 0.579Age 18.11 0.401 18.280 0.551 18.27 0.528Portuguese student 0.991 0.995 0.995Internet 0.587 0.806 0.832Beneficiary s.s. 0.267 0.249 0.221

Parent/legal–guardianFather 0.219 0.203 0.195Mother 0.712 0.700 0.709

Own 0.045 0.076 0.073Other 0.025 0.021 0.023

Parent/legal–guardian educationTertiary 0.249 0.227 0.241

Secondary 0.221 0.237 0.2443rd cycle 0.209 0.239 0.2352nd cycle 0.174 0.165 0.157

1st cycle or less 0.147 0.133 0.122

Parent/guardian employment statusWorker for others 0.640 0.636 0.633

Self–employed 0.114 0.101 0.104Unemployed 0.049 0.053 0.061

Student 0.048 0.079 0.075Domestic/retired 0.140 0.125 0.119

Other 0.009 0.006 0.008Source: Computations of the author based on MISI and JNE Statistics, 2010–2012.Note: The number of observations in all variables, except Mathematics and Portuguese variables, is12,782, 9,984 and 13,131 in 2010, 2011 and 2012, respectively. The corresponding values for Mathe-matics variable are 7,800, 6,109 and 8,454, and for Portuguese variable are 12,773, 9,550 and 12,583,respectively.

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Page 16: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsDescriptive statistics

Table 2: Descriptive statistics on classes and schools

Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

2010 2011 2012

Class level variables

Class size 23.80 4.961 25.11 5.194 25.57 5.453% economically disadvan-taged

24.46 17.31 23.96 16.74 21.67 15.45

% of girls 57.05 17.91 56.55 17.55 55.98 17.74% of immigrants 3.608 5.876 3.667 5.666 3.335 5.166% more educated par-ent/guardian

83.06 14.88 82.94 14.50 82.49 14.25

School level variables

School size 969.7 315.9 1,056 325.3 1,030 342.0% economically disadvan-taged

31.87 14.67 29.67 14.24 28.90 14.03

% of girls 51.51 3.755 50.59 3.878 50.05 3.655% of immigrants 4.496 4.844 4.890 5.413 4.590 4.941% more educated par-ent/guardian

86.40 9.389 86.53 9.869 86.37 9.460

Expenditure–student ratio 567.1 229.1 518.1 204.5 477.6 165.2Source: Computations of the author based on MISI and JNE Statistics, 2010–2012.Note: The number of classes observed is 1,817, 1,928 and 2,115 in 2010, 2011 and 2012, respectively; thenumber of schools is 284, 283 and 322 in 2010, 2011 and 2012, respectively.

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Page 17: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsDescriptive statistics

05

10

15

20

boy girl

excludes outside values

Mathematics Portuguese

Source: Created by the author based on MISI and JNE Statistics, 2010–2012

Figure 1: Mean national exams scores by discipline and by gender

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Page 18: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsEmpirical analysis: Multilevel

Table 3: Multilevel model results (Year of examination: 2012)(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES Dependent variable: Log of Mathematics scores Dependent variable: Log of Portuguese scores

Fixed–effects Parameters

Log Maths 9th 1.0110*** 1.0000*** 1.0006***(0.0246) (0.0246) (0.0246)

Log Portuguese 9th 0.5869*** 0.5873*** 0.5874***(0.0098) (0.0099) (0.0099)

Female 0.0742*** 0.0773*** 0.0785*** 0.0488*** 0.0484*** 0.0483***(0.0106) (0.0106) (0.0106) (0.0043) (0.0044) (0.0044)

Age -0.1037*** -0.1039*** -0.1026*** -0.1025*** -0.1018*** -0.1019***(0.0128) (0.0128) (0.0128) (0.0045) (0.0045) (0.0045)

Beneficiary S.S. -0.0571*** -0.0533*** -0.0534*** -0.0062 -0.0080 -0.0080(0.0142) (0.0144) (0.0144) (0.0054) (0.0055) (0.0055)

Internet 0.0217 0.0166 0.0190 -0.0009 -0.0003 -0.0004(0.0158) (0.0158) (0.0158) (0.0063) (0.0063) (0.0064)

Portuguese student -0.0120 -0.0161 -0.0152 -0.0418 -0.0482 -0.0483(0.0726) (0.0727) (0.0726) (0.0283) (0.0284) (0.0284)

Class size 0.0281** 0.0303** 0.0123*** 0.0122***(0.0106) (0.0106) (0.0034) (0.0034)

Class size sq -0.0006** -0.0006** -0.0002*** -0.0002***(0.0002) (0.0002) (0.0001) (0.0001)

% of economically disadvantaged (class) -0.0004 -0.0007 0.0003 0.0003(0.0006) (0.0006) (0.0002) (0.0002)

% of female (class) -0.0009 -0.0008 -0.0015* -0.0015*(0.0017) (0.0017) (0.0006) (0.0006)

% more educated parents/guardians (class) -0.0029*** -0.0040*** 0.0004 0.0004(0.0006) (0.0007) (0.0002) (0.0003)

% more educated parents/guardians (school) 0.0031** -0.0002(0.0011) (0.0005)

Continued on next page

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Page 19: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsEmpirical analysis: Multilevel

Table 5.4.3 – continued from previous page

Random–effects Parameters

Level–three variance:σ2

r0 Intercept variance 0.0130*** 0.0067*** 0.0075*** 0.0073*** 0.0036*** 0.0021*** 0.0020*** 0.0020***(0.0028) (0.0017) (0.0018) (0.0017) (0.0006) (0.0004) (0.0003) (0.0003)

Level–two variance:σ2

u0 Intercept variance 0.0361*** 0.0174*** 0.0148*** 0.0147*** 0.0079*** 0.0034*** 0.0033*** 0.0033***(0.0040) (0.0025) (0.0023) (0.0023) (0.0007) (0.0004) (0.0004) (0.0004)

Level–one variance:σ2

e Residual variance 0.2714*** 0.2101*** 0.2101*** 0.2101*** 0.0727*** 0.0506*** 0.0507*** 0.0507***(0.0046) (0.0035) (0.0035) (0.0035) (0.0010) (0.0007) (0.0007) (0.0007)

Deviance 13877.12 11463.36 11414.94 11406.04 3910.46 -935.15 -959.05 -959.18Observations 8454 8454 8454 8454 12583 12583 12583 12583LR test (χ2) 430.80 217.47 196.72 192.84 598.56 384.73 351.22 349.85Prob > chi2 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000Source: Computations of the author based on MISI and JNE Statistics, 2010–2012.Note: Standard errors in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1. All regressions include a set of dummies for the parents’/legal–guardians’ education, except in the null model.

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Page 20: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsEmpirical analysis: Multilevel

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Mathematics: 2012

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Portuguese: 2012

Source: Created by the author based on MISI and JNE Statistics, 2010–2012.Note: Each number represents a district: 1 – Aveiro; 2 – Beja; 3 – Braga; 4 – Bragança; 5 – Castelo Branco; 6 –Coimbra; 7 – Évora; 8 – Faro; 9 – Guarda; 10 – Leiria; 11 – Lisboa; 12 – Portalegre; 13 – Porto; 14 – Santarém;15 – Setúbal; 16 – Viana do Castelo; 17 – Vila Real; 18 – Viseu.

Figure 2: District effects and approximate 95% confidence intervals versus ranking of districts inMathematics and Portuguese achievement gains (district identifiers are shown on the top of the error bar)

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Page 21: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsEmpirical analysis: Multilevel

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60606060606060606060606060606060606060606060606060606060606060606060606060606060606060606012

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263

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8

−.1

0.2

5P

redi

ctio

n of

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dom

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cts

50454035302520151051Rank

Mathematics: 2012

404040404040404040404040404040404040404040404040404040404040404020

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1139

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12121212121212121212121212121212121212121919191919191919191919191919191919191919191919192626262626262626262626262626262626262619

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225

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249494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494949494917

717

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319

319

319

319

319

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319

39393939393939393939393939393939393939393939393939393939393

255

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75757575757575757575757575757575757575757575757575757575757575757575757575757575757575752525252525252525252525252525252525252525

9696969696969696969696969696969696969696969696969696969696969696969613131313131313131313131313131313131313131313131313131313131313131313131326

126

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127

827

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4224

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303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303030303050505050505050505050505050505050505050505050505011

111

111

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5236

236

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236

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−.1

0.2

5P

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cts

50454035302520151051Rank

Portuguese: 2012

Source: Created by the author based on MISI and JNE Statistics, 2010–2012

Figure 3: School effects and approximate 95% confidence intervals versus ranking of 50 best schools inMathematics and Portuguese achievement gains (school identifiers are shown on the top of the error bar)

21 / 37

Page 22: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsEmpirical analysis: Multilevel

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266

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235

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235

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818

818

830

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

330

317

117

117

117

117

117

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117

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117

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117

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117

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123

123

123

123

123

123

123

123

123

123

123

123

123

123

123

123

7777777777777777777777777777777777777777777777777777777777777777777777329

329

329

329

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329

329

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329

329

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348

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348

348

230

230

230

230

230

230

230

230

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230

230

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230

230

230

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230

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230

211

211

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21121

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552525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252

200

200

200

200

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200

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200

200

200

142

142

142

142

142

142

142

142

142

142

142

142

142

142

142

142

142

142

142

142

179

179

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179

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179

179

179

282

282

282

282

282

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282

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282

282

282

282

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149

149

149

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149

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337

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8206

206

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154

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270 275 280 285 290 295 300 305 310 315 320Rank

Mathematics: 2012

226

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606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060606060182

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171

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17128

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710

710

710

710

710

710

710

710

710

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710

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710

710

710

710

710

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035

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035

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035

035

035

035

035

035

035

035

035

035

035

035

035

035

035

035

035

035

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035

035

035

035

035

035

035

035

035

035

035

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015

415

415

415

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415

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186

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186

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186

797979797979797979797979797979797979797979797979797979797979797979797979797979797979797979797929

329

329

329

329

329

329

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329

329

329

329

329

329

329

329

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329

329

329

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329

329

329

329

329

329

329

329

3214

214

214

214

214

214

214

214

214

214

214

214

214

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

103

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103

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103

103

103

103

103

103

103

248

248

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248

248

116

116

116

116

116

116

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116

116

116

116

116

116

116

116

116

116

116

116

116

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116

63636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363638282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282

222

222

222

222

222

222

222

222

222

222

222

222

222

222

205

205

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205

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292

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292323232323232323232323232323232323232323232323232323232323232323232323232323232323232323232323235

235

235

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212

012

012

012

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814

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831

631

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7196

196

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133

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13334

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414

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213

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224

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6113

113

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Portuguese: 2012

Source: Created by the author based on MISI and JNE Statistics, 2010–2012.

Figure 4: School effects and approximate 95% confidence intervals versus ranking of 50 worst schools inMathematics and Portuguese achievement gains (school identifiers are shown on the top of the error bar)

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Page 23: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsDiscussion

• Student prior achievement is the strongest predictor of current performance in bothsubjects, Mathematics and Portuguese (the effect is stronger for Math)

• Female students perform better than males students in both fields; results for 2012 showthat on average girls perform better than boys in about 8% for Mathematics and 5% forPortuguese

• Age has a negative and significant influence on student achievement gains, which probablyreflects a grade repetition effect

• Low income students perform worse than the higher income ones, mainly in Mathematics(-5% in 2012)

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Page 24: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

School characteristicsDiscussion

• Student background characteristics, in particular, the parent/guardian education has animportant, positive influence on student achievement gains

• School resources, measured by the teaching expenditure–student ratio, have no effect onstudent achievement gains

• At the school level, the class size gets the highest weight in students’ performance, mainlyin Mathematics: by adding 5 students to a class of size 20 increases student’s performanceby about 1,7%; an increase of 10% reduces the effect to 0,3% (estimates for 2012))

• In most cases the school–effect is null

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Page 25: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristics

How do teacher characteristics impact on student’s performance?

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Page 26: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsMethodology

Production function

logAijt = λ logAi9 + βXit + δTjt + γt + εijt (2)

Aijt: is the student outcome in the 12th grade national exams Portuguese or Mathematics A,measured by the national exam score;Ai

9: score of the ith student in the national exam of 9th grade;Xit : vector of student and family background characteristics;Tjt: measurable teacher characteristics and includes the class size;γt: time fixed-effects;εijt: error term.

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Page 27: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsMethodology

Estimation methods

• Ordinary Least Squares (OLS);• Fixed-effects (FE)

logAijt = λ logAi9 + βXit + δTjt + τj + εijt (3)

where τj is a teacher fixed–effect.

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Page 28: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsData

• A matched student–teacher dataset including the national test scores of students inMathematics and Portuguese, student background information, and teacher information;

• Information at the student–level: gender, date of birth, nationality, academic outcomes, yearof schooling, social support eligibility, residence, availability of computer and internet athome, parents’ employment situation and parents’ education, class and school, amongothers;

• Information at the teacher–level: gender, date of birth, education, teaching experience,disciplinary group, salary, county and district of residence, among others;

• Period: 2010 – 2012;

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Page 29: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsData

The sample contains:• 21,549 student observations:

I attended the scientific–humanistic courses of secondary education;I performed the 12th grade national exams of Mathematics and Portuguese;I aged between 17 and 20 years (91% of students are 18 years old);I 23% of the students benefit from social support;I 73% of the students have internet access at home.

• 4,817 unique teachers:I working in 446 Portuguese public secondary schools;I about 35.1% are working outside their county of residence;I 91.8% of the teachers have a bacharelato or bachelor degree as the highest level of education;I 75% are female;I 50% of the teachers have at least 25 years of experience;

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Page 30: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsEmpirical results: OLS

Table 4: OLS estimates

(1) (2) (3) (4) (5) (6) (7) (8) (9)All students Mathematics Portuguese

Log of 9th exam scores 0.7110*** 0.7101*** 0.7101*** 0.9534*** 0.9503*** 0.9505*** 0.6972*** 0.6959*** 0.6959***(0.0129) (0.0129) (0.0129) (0.0242) (0.0241) (0.0241) (0.0133) (0.0133) (0.0133)

Female student 0.0598*** 0.0597*** 0.0598*** 0.0594*** 0.0589*** 0.0589*** 0.0602*** 0.0604*** 0.0604***(0.0050) (0.0050) (0.0050) (0.0103) (0.0103) (0.0103) (0.0044) (0.0044) (0.0044)

Age -0.1211*** -0.1210*** -0.1210*** -0.1865*** -0.1845*** -0.1841*** -0.0974*** -0.0968*** -0.0968***(0.0077) (0.0077) (0.0077) (0.0234) (0.0232) (0.0232) (0.0066) (0.0066) (0.0066)

Beneficiary S.S. -0.0494*** -0.0489*** -0.0488*** -0.0853*** -0.0842*** -0.0844*** -0.0301*** -0.0295*** -0.0296***(0.0062) (0.0062) (0.0062) (0.0141) (0.0141) (0.0141) (0.0053) (0.0053) (0.0053)

Internet 0.0196*** 0.0195*** 0.0196*** 0.0175 0.0177 0.0179 0.0157*** 0.0157*** 0.0157***(0.0057) (0.0057) (0.0057) (0.0126) (0.0126) (0.0126) (0.0049) (0.0049) (0.0049)

Continued on next page

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Page 31: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsEmpirical results: OLS

Continued from previous page

Female teacher 0.0219*** 0.0219*** 0.0222*** 0.0288** 0.0278** 0.0281** 0.0120** 0.0120** 0.0120**(0.0060) (0.0060) (0.0060) (0.0117) (0.0117) (0.0117) (0.0053) (0.0053) (0.0053)

Advanced degree 0.0157* 0.0161* 0.0165* -0.0082 -0.0089 -0.0080 0.0254*** 0.0253*** 0.0252***(0.0089) (0.0089) (0.0089) (0.0198) (0.0198) (0.0197) (0.0071) (0.0071) (0.0071)

Experience 0.0013*** 0.0013*** -0.0010 0.0012 0.0012 -0.0061 0.0007** 0.0007** 0.0011(0.0004) (0.0004) (0.0022) (0.0008) (0.0008) (0.0046) (0.0003) (0.0003) (0.0018)

Experience sq 0.0000 0.0002 -0.0000(0.0000) (0.0001) (0.0000)

Commuting -0.0081 -0.0078 -0.0081 -0.0162 -0.0152 -0.0163 -0.0084* -0.0080* -0.0079*(0.0053) (0.0053) (0.0053) (0.0114) (0.0113) (0.0114) (0.0046) (0.0046) (0.0046)

Class size 0.0156*** 0.0156*** 0.0355*** 0.0356*** 0.0064** 0.0065**(0.0035) (0.0035) (0.0087) (0.0087) (0.0028) (0.0028)

Class size sq -0.0003*** -0.0003*** -0.0007*** -0.0007*** -0.0001** -0.0001**(0.0001) (0.0001) (0.0002) (0.0002) (0.0001) (0.0001)

Observations 21,549 21,549 21,549 8,100 8,100 8,100 13,449 13,449 13,449R-squared 0.251 0.252 0.252 0.298 0.300 0.300 0.329 0.329 0.329RMSE 0.245 0.245 0.245 0.245 0.245 0.245 0.245 0.245 0.245Source: Computations of the author based on MISI and JNE Statistics, 2010–2012.Note: Robust standard errors in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable is log 12th

grade national exam score. All regressions include a set of dummies to control for district/region and year.

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Page 32: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsEmpirical results: FE

Table 5: FE estimates

(1) (2) (3) (4) (5) (6)All students Mathematics Portuguese

Log of 9th exam scores 0.7067*** 0.7066*** 0.8347*** 0.8341*** 0.6466*** 0.6463***(0.0149) (0.0148) (0.0297) (0.0296) (0.0151) (0.0151)

Female student 0.0544*** 0.0544*** 0.0661*** 0.0661*** 0.0607*** 0.0607***(0.0055) (0.0055) (0.0126) (0.0126) (0.0054) (0.0054)

Age -0.1112*** -0.1105*** -0.1983*** -0.1963*** -0.0932*** -0.0925***(0.0087) (0.0088) (0.0289) (0.0289) (0.0078) (0.0078)

Beneficiary S.S. -0.0443*** -0.0442*** -0.0706*** -0.0714*** -0.0327*** -0.0325***(0.0072) (0.0072) (0.0188) (0.0187) (0.0064) (0.0064)

Internet 0.0141* 0.0142* 0.0017 0.0024 0.0226*** 0.0225***(0.0080) (0.0080) (0.0191) (0.0190) (0.0073) (0.0073)

Continued on next page

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Page 33: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsEmpirical results: FE

Continued from previous page

Advanced degree -0.0223 -0.0218 -0.0957 -0.0913 0.0505 0.0501(0.0507) (0.0505) (0.1136) (0.1107) (0.0411) (0.0411)

Experience 0.0565*** 0.0567*** 0.2238*** 0.2195*** 0.0076 0.0084(0.0211) (0.0209) (0.0492) (0.0486) (0.0179) (0.0179)

Experience sq -0.0018*** -0.0018*** -0.0065*** -0.0063*** -0.0003 -0.0003(0.0004) (0.0004) (0.0010) (0.0010) (0.0004) (0.0004)

Commuting -0.0216** -0.0215** -0.0136 -0.0136 -0.0214** -0.0212**(0.0100) (0.0100) (0.0255) (0.0254) (0.0090) (0.0090)

Class size 0.0142** 0.0315* 0.0103**(0.0060) (0.0169) (0.0051)

Class size sq -0.0003** -0.0007** -0.0002*(0.0001) (0.0003) (0.0001)

Observations 21,549 21,549 8,100 8,100 13,449 13,449No. of teachers 4,817 4,817 2,868 2,868 3,828 3,828Source: Computations of the author based on MISI and JNE Statistics.Note: Robust standard errors in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1. The dependent variable islog 12th grade national exam score. All regressions include a set of dummies to control for district/region.

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Page 34: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsNotes

• Results show that female teachers have a positive effect on students’ achievement gains• Teachers working away from home have negative and significant effects on student results• Teachers with more qualifications (postgraduate, masters or PhDs) do not show betterperformance than those with a bacharelato or licentiate diploma

• Teachers with more experience are more effective in increasing students’ performance thanthose with less experience

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Page 35: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsMultiple fixed-effects

Table 6: Teacher & School fixed-effects: mathematics

No FE Teacher FE School FE T. and S. FE

Math9 0.6551*** 0.6279*** 0.6324*** 0.6284***(0.0129) (0.0142) (0.0131) (0.0143)

Female 0.0868*** 0.0845*** 0.0853*** 0.0847***(0.0086) (0.0092) (0.0086) (0.0092)

R2-adjusted 0.30 0.33 0.32 0.33RMSE 0.42 0.41 0.42 0.41Source: Computations of the author based on MISI and JNE Statistics,2010–2012.Note: The number of observations 11,407. 2018 teachers & 286 schools.52% girls. Math9: mean = 135, median = 140, 10th percentile = 74, sd= 40.

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Page 36: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsMultiple fixed-effects

44.5

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Predictive Margins of eeid with 95% CIs

Source: Created by the author based on MISI and JNE Statistics, 2010–2012

Figure 5: Marginal effects by School

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Page 37: School and teacher characteristics vs. student progress€¦ · School and teacher characteristics vs. student progress Sandra Sousa, Miguel Portela and Carla Sá September2017 StataUGM2017,Portugal

Teacher characteristicsMultiple fixed-effects

3.5

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Source: Created by the author based on MISI and JNE Statistics, 2010–2012

Figure 6: Marginal effects by Teacher

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