Lampiran 1 Hasil Estimasi Model Infrastruktur Jalan dengan Program STATA
SE 10
. regress lnjln10 q61 q64r1 q64r3 q64r5 q71 q79r1 q79r2 q79r4 q79r5 q82 q114br1 lnpdrbkap09 lnbin lnbin_d79r3 dkota djawa
Source | SS df MS Number of obs = 245
---+--- F( 16, 228) = 14.63
Model | 401.871882 16 25.1169926 Prob > F = 0.0000
Residual | 391.331048 228 1.71636425 R-squared = 0.5066
---+--- Adj R-squared = 0.4720
Total | 793.20293 244 3.25083168 Root MSE = 1.3101
---
lnjln10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---+--- q61 | -.0032264 .0059065 -0.55 0.585 -.0148647 .0084119 q64r1 | -.0027805 .0111499 -0.25 0.803 -.0247505 .0191895 q64r3 | .0119546 .0073092 1.66 0.098 -.0024476 .0263569 q64r5 | -.0017167 .0079836 -0.22 0.830 -.0174477 .0140144 q71 | .00026 .0059197 0.04 0.965 -.0114043 .0119243 q79r1 | -.0069329 .0109197 -0.63 0.526 -.0284493 .0145836 q79r2 | -.0030451 .0067707 -0.45 0.653 -.0163863 .010296 q79r4 | .0023979 .0041946 0.57 0.568 -.0058672 .010663 q79r5 | -.0101051 .007988 -1.27 0.207 -.0258447 .0056346 q82 | .0107978 .0128054 0.84 0.400 -.0144342 .0360298 q114br1 | -.0038791 .0010433 -3.72 0.000 -.0059349 -.0018232 lnpdrbkap09 | .1099678 .1739669 0.63 0.528 -.2328206 .4527562 lnbin | -.1112153 .0587499 -1.89 0.060 -.2269776 .0045469 lnbin_q79r3 | .0014887 .000597 2.49 0.013 .0003123 .002665 dkota | 2.319122 .2494501 9.30 0.000 1.827599 2.810644 djawa | 1.502165 .2300491 6.53 0.000 1.04887 1.955459 _cons | 4.718952 1.246827 3.78 0.000 2.262175 7.175729 --- . estat vif
Variable | VIF 1/VIF
---+--- q64r1 | 7.94 0.125885 q79r1 | 7.30 0.137041 q64r5 | 4.38 0.228168 lnbin_q79r3 | 4.32 0.231291 q64r3 | 3.60 0.277784 q79r5 | 3.29 0.303692 q79r2 | 3.20 0.312247 lnbin | 2.89 0.346523 q71 | 1.80 0.557060 q82 | 1.79 0.559314 q61 | 1.58 0.633949 lnpdrbkap09 | 1.33 0.753195 q79r4 | 1.30 0.769065 dkota | 1.29 0.778016 q114br1 | 1.21 0.826171 djawa | 1.15 0.868007 ---+--- Mean VIF | 3.02 . estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of lnjln10
chi2(1) = 2.66
Lampiran 2 Hasil Estimasi Model Infrastruktur Air Bersih dengan Program
STATA SE 10
. regress lnair10 q61 q64r4 q71 q79r1 q79r2 q79r4 q79r5 q114br3 lnpdrbkap09 lnbin lnbin_d79r3 dkota djawa
Source | SS df MS Number of obs = 245
---+--- F( 13, 231) = 4.57
Model | 459.490573 13 35.3454287 Prob > F = 0.0000
Residual | 1785.36174 231 7.72883871 R-squared = 0.2047
---+--- Adj R-squared = 0.1599
Total | 2244.85232 244 9.20021441 Root MSE = 2.7801
---
lnair10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---+--- q61 | -.0053183 .011802 -0.45 0.653 -.0285717 .0179351 q64r4 | .0024567 .012163 0.20 0.840 -.0215079 .0264213 q71 | -.0151609 .0100225 -1.51 0.132 -.0349081 .0045863 q79r1 | -.0176535 .013785 -1.28 0.202 -.0448138 .0095068 q79r2 | -.0061704 .0138045 -0.45 0.655 -.0333692 .0210284 q79r4 | .0085748 .0087749 0.98 0.329 -.0087143 .0258638 q79r5 | .0200062 .0169056 1.18 0.238 -.0133026 .053315 q114br3 | -.0138075 .0044454 -3.11 0.002 -.0225664 -.0050487 lnpdrbkap09 | 1.032273 .3674977 2.81 0.005 .3081972 1.756349 lnbin | -.1247 .1236205 -1.01 0.314 -.3682677 .1188678 lnbin_q79r3 | .0016862 .0012616 1.34 0.183 -.0007994 .0041719 dkota | 1.634095 .5154508 3.17 0.002 .6185092 2.649681 djawa | .7341221 .4808715 1.53 0.128 -.2133327 1.681577 _cons | 6.346358 1.838035 3.45 0.001 2.724903 9.967813 --- . estat vif
Variable | VIF 1/VIF
---+--- lnbin_q79r3 | 4.29 0.233248 q79r5 | 3.28 0.305318 q79r2 | 2.96 0.338245 lnbin | 2.84 0.352429 q79r1 | 2.58 0.387228 q64r4 | 1.72 0.580735 q61 | 1.40 0.714997 lnpdrbkap09 | 1.32 0.760039 q79r4 | 1.26 0.791339 dkota | 1.22 0.820514 q71 | 1.14 0.875089 q114br3 | 1.13 0.884139 djawa | 1.12 0.894564 ---+--- Mean VIF | 2.02 . estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: fitted values of lnAIR
chi2(1) = 2.37
Lampiran 3 Hasil Estimasi Model Infrastruktur Listrik dengan Program STATA
SE 10
. regress lnlis10 q61 q64r5 q114br4 q108 lnpdrbkap09 lnbin lnbin_d79r3 dkota djawa
Source | SS df MS Number of obs = 245
---+--- F( 9, 235) = 11.79
Model | 145.754183 9 16.1949092 Prob > F = 0.0000
Residual | 322.889847 235 1.37399935 R-squared = 0.3110
---+--- Adj R-squared = 0.2846
Total | 468.644031 244 1.92067226 Root MSE = 1.1722
---
lnlis10 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---+--- q61 | -.0035808 .0047291 -0.76 0.450 -.0128977 .0057361 q64r5 | .0078101 .0043847 1.78 0.076 -.0008282 .0164484 q114br4 | .0001432 .0025529 0.06 0.955 -.0048863 .0051727 q108 | -.0811115 .0389781 -2.08 0.039 -.1579027 -.0043204 lnpdrbkap09 | .6621737 .1528661 4.33 0.000 .3610107 .9633367 lnbin | -.0126607 .0428284 -0.30 0.768 -.0970374 .071716 lnbin_q79r3 | -.0000227 .0003729 -0.06 0.952 -.0007574 .0007121 dkota | .9898073 .2179475 4.54 0.000 .5604266 1.419188 djawa | .5309507 .2247074 2.36 0.019 .0882523 .9736491 _cons | 3.489686 .5185848 6.73 0.000 2.468017 4.511355 --- . estat vif
Variable | VIF 1/VIF
---+--- lnbin_q79r3 | 2.11 0.474518 lnbin | 1.92 0.521987 q64r5 | 1.65 0.605554 q108 | 1.48 0.677091 djawa | 1.37 0.728294 lnpdrbkap09 | 1.28 0.780900 q61 | 1.26 0.791647 dkota | 1.23 0.815885 q114br4 | 1.06 0.942528 ---+--- Mean VIF | 1.48 . estat hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance
Variables: fitted values of lnlis10
chi2(1) = 0.44
Lampiran 4 Nilai korelasi Pearson infrastruktur Correlations Fit for lnjln10 Fit for lnjln10 Pearson Correlation 1 Sig. (2-tailed) N 245 Fit for lnair10 Fit for lnlis10 Fit for lnair10 Fit for lnlis10 Pearson Correlation .723** 1 Sig. (2-tailed) .000 N 245 245 Pearson Correlation .835** .817** 1 Sig. (2-tailed) .000 .000 N 245 245 245
Lampiran 5 Hasil Estimasi Metode 2SLS dengan Program SPSS 16 * 2-Stage Least Squares. TSET NEWVAR=NONE. 2SLS lnJLN10 WITH Q61 Q64R1 Q64R3 Q64R5 Q71 Q79R2 Q79R4 Q79R5 Q114bR1 lnPDRBKap09 lnBIN lnBIN_Q79R3 dKota dJawa /INSTRUMENTS Q61 Q64R1 Q64R3 Q64R5 Q71 Q79R2 Q79R4 Q79R5 Q82 Q114b R1 lnPDRBKap09 lnBIN lnBIN_Q79R3 dKota dJawa lnJLN09 /CONSTANT. Model Description Type of Variable Equation 1 lnJLN10 dependent Q61 predictor & instrumental Q64R1 predictor & instrumental Q64R3 predictor & instrumental Q64R5 predictor & instrumental Q71 predictor & instrumental Q79R2 predictor & instrumental Q79R4 predictor & instrumental Q79R5 predictor & instrumental Q114bR1 predictor & instrumental lnPDRBKap09 predictor & instrumental lnBIN predictor & instrumental lnBIN_Q79R3 predictor & instrumental dKota predictor & instrumental dJawa predictor & instrumental Q82 instrumental lnJLN09 instrumental MOD_3 Model Summary Equation 1 Multiple R .710 R Square .505 Adjusted R Square .474 Std. Error of the Estimate 1.307 ANOVA
Sum of Squares df Mean Square F Sig. Equation 1 Regression Residual Total 400.258 14 28.590 16.734 . 000 392.945 230 1.708
Coefficients Unstandardized Coefficients B Std. Error Beta t Sig. Equation 1 (Constant) 5.502 .823 6.687 .000 Q61 -.003 .006 -.033 -.567 .571 Q64R1 -.009 .008 -.103 -1.134 .258 Q64R3 .011 .007 .136 1.552 .122 Q64R5 -.001 .008 -.013 -.137 .891 Q71 .003 .005 .034 .672 .502 Q79R2 -.002 .007 -.030 -.372 .710 Q79R4 .002 .004 .029 .541 .589 Q79R5 -.012 .008 -.123 -1.542 .124 Q114bR1 -.004 .001 -.187 -3.680 .000 lnPDRBKap09 .108 .174 .033 .622 .534 lnBIN -.114 .059 -.154 -1.950 .052 lnBIN_Q79R3 .002 .001 .247 2.566 .011 dKota 2.347 .246 .496 9.526 .000 dJawa 1.537 .227 .333 6.776 .000 * 2-Stage Least Squares. TSET NEWVAR=NONE. 2SLS lnAIR10 WITH Q61 Q64R4 Q71 Q79R1 Q79R2 Q79R4 Q79R5 Q114bR3 lnPDR BKap09 lnBIN lnBIN_Q79R3 dKota dJawa /INSTRUMENTS Q61 Q64R4 Q71 Q79R1 Q79R2 Q79R4 Q79R5 Q114bR3 lnPDRBK ap09 lnBIN lnBIN_Q79R3 dKota dJawa lnAIR09 /CONSTANT.
Model Description
Type of Variable Equation 1 lnAIR10 dependent
Q61 predictor & instrumental Q64R4 predictor & instrumental Q71 predictor & instrumental Q79R1 predictor & instrumental Q79R2 predictor & instrumental Q79R4 predictor & instrumental Q79R5 predictor & instrumental Q114bR3 predictor & instrumental lnPDRBKap09 predictor & instrumental lnBIN predictor & instrumental lnBIN_Q79R3 predictor & instrumental dKota predictor & instrumental dJawa predictor & instrumental lnAIR09 instrumental MOD_5 Model Summary Equation 1 Multiple R .452 R Square .205 Adjusted R Square .160 Std. Error of the Estimate 2.780 ANOVA
Sum of Squares df Mean Square F Sig. Equation 1 Regression 459.495 13 35.346 4.573 .000
Residual 1785.357 231 7.729 Total 2244.852 244
Coefficients Unstandardized Coefficients B Std. Error Beta t Sig. Equation 1 (Constant) 6.346 1.838 3.453 .001 Q61 -.005 .012 -.031 -.451 .653 Q64R4 .002 .012 .016 .202 .840 Q71 -.015 .010 -.095 -1.513 .132 Q79R1 -.018 .014 -.121 -1.281 .202 Q79R2 -.006 .014 -.045 -.447 .656 Q79R4 .009 .009 .064 .977 .329 Q79R5 .020 .017 .126 1.184 .238 Q114bR3 -.014 .004 -.194 -3.106 .002 lnPDRBKap09 1.032 .367 .189 2.809 .005 lnBIN -.125 .124 -.100 -1.009 .314 lnBIN_Q79R3 .002 .001 .162 1.336 .183 dKota 1.634 .515 .205 3.170 .002 dJawa .734 .481 .095 1.527 .128 * 2-Stage Least Squares. TSET NEWVAR=NONE. 2SLS lnLIS10 WITH Q61 Q64R5 Q114bR4 Q108 lnPDRBKap09 lnBIN lnBIN_Q79R3 dKota dJawa /INSTRUMENTS Q61 Q64R5 Q114bR4 Q108 lnPDRBKap09 lnBIN lnBIN_Q79R3 d Kota dJawa lnLIS09 /CONSTANT. Model Description Type of Variable Equation 1 lnLIS10 Q61 Q64R5 Q114bR4 Q108 lnBIN dKota dJawa lnLIS09 dependent predictor & instrumental predictor & instrumental predictor & instrumental predictor & instrumental predictor & instrumental predictor & instrumental predictor & instrumental instrumental MOD_34
Model Summary Equation 1 Multiple R .558 R Square .311 Adjusted R Square .285 Std. Error of the Estimate 1.172 ANOVA Sum of Squares df Mean Square F Sig. Equation 1 Regression 145.746 9 16.194 11.786 .000 Residual 322.899 235 1.374 Total 468.644 244 Coefficients Unstandardized Coefficients B Std. Error Beta t Sig. Equation 1 (Constant) 3.489 .519 6.728 .000 Q61 -.004 .005 -.046 -.757 .450 Q64R5 .008 .004 .124 1.781 .076 Q114bR4 .000 .003 .003 .056 .956 Q108 -.081 .039 -.137 -2.079 .039 lnPDRBKap09 .662 .153 .265 4.332 .000 lnBIN -.013 .043 -.022 -.296 .767 lnBIN_Q79R3 -2.248E-5 .000 -.005 -.060 .952 dKota .990 .218 .272 4.541 .000 dJawa .531 .225 .150 2.363 .019 GET FILE='D:\SUTARSONO\Tesis\Data\Data Tesis.sav'.
DATASET NAME DataSet0 WINDOW=FRONT.
* 2-Stage Least Squares.
TSET NEWVAR=NONE.
2SLS gpdrbkap1011 WITH lnpdrbkap10 Q40 Q54R2 Q68R1 Q106 lnJLN_cap ln
AIR_cap lnLIS_cap lnmys lnbm
/INSTRUMENTS lnpdrbkap10 Q40 Q54R2 Q68R1 Q106 lnJLN_cap lnAIR_ca
p lnLIS_cap lnmys lnbm
Model Description
Type of Variable
Equation 1 gpdrbkap1011 dependent
lnpdrbkap10 predictor & instrumental
Q40 predictor & instrumental
Q54R2 predictor & instrumental
Q68R1 predictor & instrumental
Q106 predictor & instrumental
lnJLN_cap predictor & instrumental
lnAIR_cap predictor & instrumental
lnLIS_cap predictor & instrumental
lnmys predictor & instrumental
lnbm predictor & instrumental
MOD_1
Model Summary
Equation 1 Multiple R .382
R Square .146
Adjusted R Square .110
Std. Error of the Estimate 3.239
ANOVA
Sum of Squares df Mean Square F Sig.
Equation 1 Regression 420.628 10 42.063 4.010 .000
Residual 2454.831 234 10.491
Coefficients
Unstandardized Coefficients
B Std. Error Beta t Sig.
Equation 1 (Constant) 6.065 6.180 .981 .327 lnpdrbkap10 -1.955 .598 -.366 -3.270 .001 Q40 -.043 .021 -.133 -2.028 .044 Q54R2 -.032 .013 -.167 -2.389 .018 Q68R1 .027 .013 .148 2.011 .045 Q106 .030 .011 .169 2.629 .009 lnJLN_cap .171 .414 .064 .412 .681 lnAIR_cap .074 .272 .030 .274 .785 lnLIS_cap -.401 .907 .089 -.442 .659 lnmys 1.668 1.558 .083 1.070 .286 lnbm .199 .415 .032 .479 .632 * 2-Stage Least Squares. TSET NEWVAR=NONE. 2SLS gPDRBKap1011 WITH lnPDRBKap10 Q40 Q54R2 Q68R1 Q106 lnJLN_cap lnM YS lnBM /INSTRUMENTS lnPDRBKap10 Q40 Q54R2 Q68R1 Q106 lnJLN_cap lnMYS lnBM /CONSTANT. Model Description Type of Variable Equation 1 gPDRBKap1011 dependent
lnPDRBKap10 predictor & instrumental Q40 predictor & instrumental Q54R2 predictor & instrumental Q68R1 predictor & instrumental Q106 predictor & instrumental lnJLN_cap predictor & instrumental lnMYS predictor & instrumental lnBM predictor & instrumental MOD_31
Model Summary Equation 1 Multiple R .381 R Square .145 Adjusted R Square .116 Std. Error of the Estimate 3.228 ANOVA
Sum of Squares df Mean Square F Sig. Equation 1 Regression 416.845 8 52.106 5.002 .000 Residual 2458.614 236 10.418 Total 2875.459 244 Coefficients Unstandardized Coefficients B Std. Error Beta t Sig. Equation 1 (Constant) 6.447 6.110 1.055 .292 lnPDRBKap10 -1.684 .375 -.315 -4.489 .000 Q40 -.044 .021 -.135 -2.065 .040 Q54R2 -.031 .013 -.166 -2.397 .017 Q68R1 .028 .013 .154 2.117 .035 Q106 .029 .011 .165 2.592 .010 lnJLN_cap .382 .196 .142 1.944 .053 lnMYS 1.724 1.546 .086 1.115 .266 lnBM .220 .408 .036 .539 .590
* 2-Stage Least Squares. TSET NEWVAR=NONE. 2SLS gPDRBKap1011 WITH lnPDRBKap10 Q40 Q54R2 Q68R1 Q106 lnAIR_cap lnM YS lnBM /INSTRUMENTS lnPDRBKap10 Q40 Q54R2 Q68R1 Q106 lnAIR_cap lnMYS lnBM /CONSTANT. Model Description Type of Variable Equation 1 gPDRBKap1011 dependent
lnPDRBKap10 predictor & instrumental Q40 predictor & instrumental Q54R2 predictor & instrumental Q68R1 predictor & instrumental Q106 predictor & instrumental lnAIR_cap predictor & instrumental lnMYS predictor & instrumental lnBM predictor & instrumental MOD_32 Model Summary Equation 1 Multiple R .374 R Square .140 Adjusted R Square .111 Std. Error of the Estimate 3.237 ANOVA
Sum of Squares df Mean Square F Sig. Equation 1 Regression 403.102 8 50.388 4.810 .000
Residual 2472.356 236 10.476 Total 2875.459 244
Coefficients Unstandardized Coefficients B Std. Error Beta t Sig. Equation 1 (Constant) 5.637 6.160 .915 .361 lnPDRBKap10 -1.890 .426 -.354 -4.432 .000 Q40 -.042 .021 -.129 -1.983 .049 Q54R2 -.032 .013 -.169 -2.434 .016 Q68R1 .028 .013 .155 2.121 .035 Q106 .028 .011 .158 2.481 .014 lnAIR_cap .312 .199 .125 1.564 .119 lnMYS 2.385 1.455 .119 1.640 .102 lnBM .177 .408 .029 .433 .665 * 2-Stage Least Squares. TSET NEWVAR=NONE. 2SLS gPDRBKap1011 WITH lnPDRBKap10 Q40 Q54R2 Q68R1 Q106 lnLIS_cap lnM YS lnBM /INSTRUMENTS lnPDRBKap10 Q40 Q54R2 Q68R1 Q106 lnLIS_cap lnMYS lnBM /CONSTANT. Model Description Type of Variable Equation 1 gPDRBKap1011 dependent
lnPDRBKap10 predictor & instrumental Q40 predictor & instrumental Q54R2 predictor & instrumental Q68R1 predictor & instrumental Q106 predictor & instrumental lnLIS_cap predictor & instrumental lnMYS predictor & instrumental lnBM predictor & instrumental MOD_33 Model Summary Equation 1 Multiple R .381 R Square .145 Adjusted R Square .116 Std. Error of the Estimate 3.227
ANOVA
Sum of Squares df Mean Square F Sig. Equation 1 Regression 417.537 8 52.192 5.011 .000 Residual 2457.922 236 10.415 Total 2875.459 244 Coefficients Unstandardized Coefficients B Std. Error Beta t Sig. Equation 1 (Constant) 6.130 6.114 1.003 .317 lnPDRBKap10 -2.097 .460 -.392 -4.560 .000 Q40 -.042 .021 -.129 -1.987 .048 Q54R2 -.033 .013 -.173 -2.511 .013 Q68R1 .026 .013 .145 1.987 .048 Q106 .030 .011 .168 2.621 .009 lnLIS_cap .826 .421 .182 1.961 .051 lnMYS 1.750 1.537 .087 1.139 .256 lnBM .152 .404 .025 .376 .707
Infrastructure in Asia Probit, dan
GMM
pendapatan per kapita,
keterbukaan
perdagangan
infrastruktur
Kis-Katos &
Sjahrir (2011)
Does local governments’
responsiveness increase
with decentralization and
democratization?
Evidence from sub-
national budget allocation
in Indonesia
Data Panel Belanja pembangunan
(pendidikan, kesehatan,
infrastruktur),
pendapatan, rasio
puskesmas, rata2 lama
sekolah, partisipasi
sekolah, share desa
dengan jalan beraspal,
kepala daerah
Indonesia, 271
kab/kota, 1993-
2007
Desentralisasi fiskal dan
administrasi meningkatkan res pemerintah lokal terhadap
pengalokasian dana penyediaan fasilitas publik, tetapi pemiluk berdampak sebaliknya. Chowdhury et al. (2007) Governance Decentralization and Infrastructure Provision in Indonesia OLS, Fixed Effect, Ordered Probit
Jalan desa, sekolah,
puskesmas, karakteristik
kepala desa (umur, jenis
kelamin, pendidikan),
penduduk
Indonesia, 1996,
2000, 2006
(Podes)
Penyediaan pelayanan publik dipengaruhi oleh endowment
pemerintah lokal.
Elhiraika
(2007)
Fiscal Decentralization
and Public Service
Delivery in South Africa
Data Panel PAD, transfer, GNP per
kapita, share pengeluaran pendidikan & kesehatan 8 provinsi di Afrika Selatan, 1996-2005
Desentralisasi fiskal tidak
berdampak signifikan terhadap
share belanja untuk penyediaan pelayanan publik (pendidikan
kesehatan) Muriisa
(2008)
Decentralisation in
Uganda: Prospects for
Desentralisasi di Uganda
— vidence from State-
level Cross-section Data
of the United States
keterbukaan, penduduk,
luas wilayah
Mulloch &
Sjahrir (2008)
Endowments, Location or
Luck? : Evaluating the
Determinants of Sub-
National Growth in
Decentralized Indonesia
Data Panel PDRB, penduduk,
pendidikan,
infrastruktur,
kemiskinan
1993-2005 Kemiskinan cenderung
konvergen.
Sebelum krisis, secara spasia pertumbuhan ekonomi diverg
faktor endowment tidak
signifikan terhadap pertumbu ekonomi.
Lessmann
(2006)
Fiscal Decentralization
and Regional Disparity:
A Panel Data Approach
for OECD Countries
Data Panel OLS Desentralisasi (penerimaan, pengeluaran, pajak), ketimpangan GDP per
kapita, adgini, wcov
17 negara
OECD, 1980-
2001
Desentralisasi berpengaruh po terhadap disparitas wilayah tet tidak signifikan
Im dan Lee Time, Decentralization
and Development Data Panel Random Effect GDP growth, desentralisasi fiskal, desentralisasi politik, inflasi, pertumbuhan penduduk 130 negara, 1970-2007 Di NB: desentralisasi politik berorelasi negatif, dan
desentralisasi fiskal tdk
berpengaruh terhadap growth.
Di NSM: desentralisasi fiska berkorelasi negatif terhadap growth.
Di NM: desentralisasi politik fiskal tidak signifikan terhadap
industrialized countries pengangguran,
disparitas regional,
pajak, pengeluaran
pemerintah
Pengeluaran pemerintah
berkorelasi negatif dengan
disparitas wilayah
Ebel &
Yilmaz (2002)
On the Measurement and
Impact of Fiscal
Decentralization
OLS Output perkapita, PAD
(pajak, non pajak,
hibah), pengeluaran
pemerintah, pendapatan
OECD, 1997-
1999
PAD berpengaruh positif terhad pertumbuhan output per kapita Vazquez &
McNab
(1997)
Fiscal Decentralization,
Economic Growth, and
Democratic Governance
Kajian
literatur
Desentralisasi fiskal dan
pemerintahan yang
demokratis
Terdapat hubungan timbal-bali
antara desentralisasi fiskal dan pemerintahan yang demokratis
