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O330'8(2B#10#16+,+#'+,4*1,S#,03(0+30207(3#,1&14,#1+28,#10#)+#F0,(1(C+*<#30''+*&1+8#W(16# 1+,1#,30'+,$#E3600*,#W(16#'(36+'#,148+21,#02#&C+'&B+#,30'+#&)041#>H$"#F0(21,#6(B6+'#16&2# F00'#,3600*,$# M6+#,1&1(,1(3&**<#,(B2(5(3&21#2+B&1(C+#30+55(3(+21#05#742(3(F&*`,+,`&51+'#(28(3&1+,S# 60W+C+'S#16&1#16+#2+W#F0*(3(+,#&8C&21&B+8#,3600*,#W(16#F00'+'#,148+21,#(2#1+'7,#05#1+,1# ,30'+#(23'+&,+$#M6+#2+W#F0*(3(+,#,++7#10#B(C+#&2#&),0*41+#&8C&21&B+#10#F00'+'#,148+21,# (2#=16#B'&8+S#&28#10#F&'1(&**<#'+843+#16+#B&F#)+1W++2#16+7#&28#'(36+'#,148+21,#(2#:16# B'&8+$##

;Q =#96#((&/$(** @00#'2&P#$#(()* 8.%4*9%4*21./#* Random-effects GLS regression Group variable: id_escola R-sq: within = 0.7187 between = 0.4285 overall = 0.5620 corr(u_i, X)

= 0 (assumed)

Average math score 5th grade

P0+5$#

12009 12011 municipal Municipal*12009 Municipal*12011 Morning shift proportion Number of students by class Socioeconomic status Number of classes _cons

# #

=H# !

Number of obs Number of groups Obs per group: min avg max Wald chi2(9) Prob > chi2

G0)4,1# E18$# Q''$#

= = = = = = =

2231 811 1 2.8 3 3901.77 0.0000

## h:=K# IfgYg# P025$#

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:31%,2,#)#*9%4*21./#* Random-effects GLS regression Group variable: id_escola R-sq: within = 0.6294 between = 0.4852 overall = 0.5152 corr(u_i, X)

Number of obs Number of groups Obs per group: min avg max Wald chi2(9) Prob > chi2

= 0 (assumed)

Average portuguese score 5th grade

## P0+5$#

12009

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12011 municipal Municipal*12009 Municipal*12011 Morning shift proportion Number of students by class Socioeconomic status Number of classes _cons

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## Y#

= = = = = = =

2231 811 1 2.8 3 2979.75 0.0000

## ## IfgYg# h:=K#P025$#

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Number of obs Number of groups Obs per group: min avg max Wald chi2(9) Prob > chi2

= 0 (assumed)

Average math score 9th grade

## P0+5$#

12009

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* #

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1328 474 1 2.8 3 876.35 0.0000

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:31%,2,#)#*;%4*21./#* Random-effects GLS regression Group variable: id_escola R-sq: within = 0.3037 between = 0.6389 overall = 0.5497 corr(u_i, X)

Number of obs Number of groups Obs per group: min avg max Wald chi2(9) Prob > chi2

= 0 (assumed)

Average portuguese score 9th grade 12009 12011 municipal Municipal*12009 Municipal*12011 Morning shift proportion Number of students by class Socioeconomic status Number of classes _cons

## G0)4,1# P0+5$# E18$#Q''$# "$D@!# D$AD"# N>$@A># A$">"# :$:@:# @$=>.# >$@=A# @$"@.# A$D>"# A$H"=# "$"@># >$:>@# NA$>HD# A$A."# @A$H.@# >$H=.# A$=AA# A$D"!# @@A$=@:# D$AH=#

= = = = = = =

1328 474 1 2.8 3 1045.71 0.0000

## ## ## ## Y# IfgYg# h:=K#P025$# X21+'C&*i# @$!>A# A$A>H# >$D"@# >D$@"H# N>$H.A# A$A:!# N@$HAH# A$@AD# D$:!A# A$AAA# !$::!# >!$.HD# A$!HA# A$H!"# N!$A:"# H$=:"# A$!"A# A$HD:# N>$AA"# >$H!># !$A!A# A$AAA# D$:"!# >>$!H:# N>$."A# A$AH># NA$DDD# A$AA.# >@$!.A# A$AAA# >"$!D!# @D$:D># >$D!A# A$>.># NA$@D!# >$@D!# ">$:!A# A$AAA# @>!$=@># @@H$=D"#

@VA&2?*,$%*K6##*=&%&$9* 8.%4*9%4*21./#* Random-effects GLS regression Group variable: id_escola R-sq: within = 0.6532 between = 0.4465 overall = 0.5414 corr(u_i, X)

= 0 (assumed)

Average math score 5th grade After municipal Municipal*after Morning shift proportion Number of classes (size) Size*after Size*municipal Size*municipal*after Number of students by class Ses*after Socioeconomic status (ses) Ses*municipal Ses*municipal*after _cons

=.# !

Number of obs Number of groups Obs per group: min avg max Wald chi2(16) Prob > chi2

= = = = = = =

2231 811 1 2.8 3 4189.37 0.0000

## G0)4,1# ## ## ## ## P0+5$# E18$#Q''$# Y# IfgYg# h:=K#P025$# X21+'C&*i# N>=$>H# :$:"# N>$=@# A$>D# ND!$"A# !$D"# N>A$".# .$."# N>$@@# A$@@# N@.$>"# H$HA# !D$D"# >A$A># !$DD# A$AA# @D$"=# HD$AA# >$>># >$@:# A$.H# A$D:# N>$!># D$HD# @$HH# @$A:# >$@"# A$@A# N>$!!# H$"=# A$>:# @$DH# A$A.# A$:!# N!$!=# !$.@# N@$:"# @$>@# N>$!A# A$>H# N"$>D# >$>:# NA$A"# @$!A# NA$AD# A$:.# N!$".# !$H!# NA$>># A$A"# N>$=.# A$>># NA$@=# A$AD# @@$@!# !$>=# =$D=# A$AA# >!$>A# DA$D.# >!$=@# @$HD# =$=@# A$AA# :$DH# >:$H.# A$!@# @$H@# A$>H# A$."# N!$"@# =$=H# N>:$"A# !$D@# N!$=H# A$AA# N@.$>H# N>>$@D# @A:$.># .$.># @D$.@# A$AA# >:@$=!# @@"$A"#

:31%,2,#)#*9%4*21./#* Random-effects GLS regression Group variable: id_escola R-sq: within = 0.6121 between = 0.4924 overall = 0.5197 corr(u_i, X)

Number of obs Number of groups Obs per group: min avg max Wald chi2(16) Prob > chi2

= 0 (assumed)

Av. Portuguese score 5th After municipal Municipal*after Morning shift proportion Number of classes (size) Size*after Size*municipal Size*municipal*after Number of students by class Ses*after Socioeconomic status (ses) Ses*municipal Ses*municipal*after _cons

P0+5$# G0)4,1#EQ$# N>H$"@@# .$">@# N.$A=D# "$!="# D"$@!H# .$"=># A$A"A# >$A.D# @$=D@# >$=::# A$>!@# >$:=H# N@$:.!# >$HD@# A$>=@# >$:.=# NA$AA@# A$A=.# @D$==A# D$D.@# >>$H"@# @$@""# @$:>"# @$D>># N@@$A@"# D$=>@# >..$.H@# "$D:@#

= = = = = = =

2231 811 1 2.8 3 3749.25 0.0000

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8.%4*;%4*21./#* Random-effects GLS regression Group variable: id_escola R-sq: within = 0.1075 between = 0.6116 overall = 0.4857 corr(u_i, X)

= 0 (assumed)

Average math score 9th After municipal Municipal*after Morning shift proportion Number of classes (size) Size*after Size*municipal Size*municipal*after Number of students by class Ses*after Socioeconomic status (ses) Ses*municipal Ses*municipal*after _cons

Number of obs Number of groups Obs per group: min avg max Wald chi2(16) Prob > chi2

= = = = = = =

1328 474 1 2.8 3 833.74 0.0000

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* :31%,2,#)#*;%4*21./#* Random-effects GLS regression

Number of obs

1328

Group variable: id_escola

Number of groups

474

R-sq:

= 0.2824

Obs per group: min =

between = 0.6436

avg =

2.8

overall = 0.5531

max =

Wald chi2(16)

1180.46

Prob > chi2

0.0000

within

corr(u_i, X)

= 0 (assumed)

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;Q =#96#((&/$*/A"A2* ^&$\*-#2I##$*9#22&$9*2G#*-/$A(*,$%*G&9G*('G//.*#$6/..<#$2* Cross-sectional time-series FGLS regression Coefficients: generalized least squares ## Panels: heteroskedastic ## Correlation: no autocorrelation ## Estimated covariances = 306 Estimated autocorrelations = 0 Estimated coefficients = 5

Proportion of secondary education enrollment

P0+5$#

Bonus(=1 if obtained) Average score in 9th grade in 2007 Socioeconomic status School size _cons

## ## ##

E18$#Q''$#

## ## ## ## ## ## Number of obs = Number of groups = Obs per group: min = avg = max = Wald chi2(4) = Prob > chi2 =

IfgYg#

451 306 1 1.473856 2 2751.86 0.0000

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# Fixed-effects (within) regression Group variable: id_escola

Number of obs Number of groups

= =

465 315

R-sq:

Obs per group: min = avg = max =

1 1.5 2

within = 0.3054 between = 0.0745 overall = 0.1084

corr(u_i, Xb)

F(2,148) Prob > F

= -0.0159

Proportion of secondary education enrollment

P0+5$# A$AHA# A$A>"# ND!$=AA#

Bonus(=1 if obtained) year _cons

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^&\#.&G//%*/0*]#22&$9*2G#*C/$A(* C$$31/&(2*%3*=1#'&3,)*)$31#D* Logistic regression

Number of obs Wald chi2(4) Prob > chi2 Pseudo R2

Log pseudolikelihood = -138.21735

Bonus Previous average score in 5th grade Previous average score in 9th grade Number of classes (average 2009/2011) Socioeconomic status (average 2009/2011) _cons

## b88,#G&1(0#

G0)4,1# ## E18$#Q''$# Y#

## IfgYg#

= = = =

217 6.46 0.1671 0.0247

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# Linear regression

Bonus Previous average score in 5th grade Previous average score in 9th grade Number of classes (average 2009/2011) Socioeconomic status (average 2009/2011) _cons

Number of obs F( 4, 212) Prob > F R-squared

= = = =

217 1.76 0.1376 0.0320

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# C$$31/&(2*%3*)3$&3#$3(37&$*)%.%,)*.(/*)$4335*)&6#D* In 2009 Logistic regression# # # Log pseudolikelihood = -521.23419

Bonus Socioeconomic status Number of classes _cons

# # "H# !

Number of obs # # Wald chi2(2) Prob > chi2 Pseudo R2

= = = =

873 40.79 0.0000 0.0572

## G0)4,1# ## ## ## ## b88,#G&1(0# E18$#Q''$# Y# IfgYg# h:=K#P025$# X21+'C&*i# >$>@># A$@@"# A$="A# A$="># A$"==# >$HH"# A$H@=# A$A!"# NH$DAA# A$AAA# A$=!A# A$"@D# >$":@# A$D"@# @$.>A# A$AA=# >$>:D# @$H:D#

# Linear regression

Number of obs F( 2, 870) Prob > F R-squared

## Bonus

G0)4,1#

P0+5$#

Socioeconomic status Number of classes _cons

= = = =

873 26.87 0.0000 0.0538

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# In 2011# #

Logistic regression

Log pseudolikelihood = -590.49409

Bonus Socioeconomic status Number of classes _cons

# Number

of obs # Wald chi2(2) Prob > chi2 Pseudo R2

= = = =

878 16.18 0.0003 0.0141

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# Linear regression

Bonus Socioeconomic status Number of classes _cons

Number of obs F( 2, 875) Prob > F R-squared

## P0+5$#

G0)4,1# E18$#Q''$# A$>H># A$A!H# NA$A@.# A$A>D# A$H=A# A$AD:#

= = = =

878 9.04 0.0001 0.0190

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