Saran - KESIMPULAN DAN SARAN - Peran Pengembangan Infrastruktur dalam Pengentasan Kemiskinan di

VI. KESIMPULAN DAN SARAN

6.2. Saran

1. Hasil analisis menyimpulkan bahwa infrastruktur jalan yang sangat penting untuk meningkatkan pertumbuhan ekonomi, ternyata tidak berpengaruh nyata secara statistik. Panjang jalan di Pulau Jawa sudah dalam kondisi “jenuh”. Pertumbuhan jumlah kendaraan bermotor yang selalu naik per tahun jauh melampaui pertambahan ruas jalan membuat kapasitas jalan di Jawa semakin jenuh, sehingga penambahan panjang jalan tidak mampu secara signifikan meningkatkan pertumbuhan ekonomi. Hal serupa juga terjadi di Luar Jawa, yaitu ketika panjang jalan bertambah, pertumbuhan ekonomi tidak terakselerasi secara signifikan. Kondisi ini dapat dipahami dengan alasan bahwa jumlah penduduk dan jumlah kendaraan bermotor di Luar Jawa sedikit, sehingga akan sedikit juga orang yang mengakses jalan. Untuk Luar Jawa, diperlukan kebijakan yang sedikit berbeda yaitu kebijakan mengenai penambahan panjang jalan mengingat luasnya wilayah Luar Jawa sehingga masih sangat dimungkinkan untuk menambah jalan.

2. Berdasarkan hasil estimasi bahwa infrastruktur listrik memiliki elastisitas paling tinggi, untuk itu pemerintah perlu meningkatkan pembangunan infrastruktur listrik, yang kemudian akan mendorong aktivitas perekonomian di sektor industri. Dari data yang ada bahwa komposisi pelanggan industri di Luar Jawa (15,64 persen) lebih sedikit daripada di Jawa (40,47 persen). Namun, pembangunan infrastruktur listrik di Jawa lebih banyak kendalanya daripada di Luar Jawa. Kondisi tersebut dapat menjadi alasan untuk lebih mengembangkan infrastruktur listrik di Luar Jawa.

3. Fenomena yang cukup menarik untuk dicermati adalah bahwa pertumbuhan ekonomi di Luar Jawa meningkatkan kemiskinan, maka diperlukan kebijakan untuk menurunkan tingkat kemiskinan.

4. Model dan metode analisis yang digunakan dalam penelitian ini masih dapat terus dikembangkan lebih lanjut. Penyempurnaan model dapat dilakukan dengan menambah satu persamaan yaitu persamaan ketimpangan, karena ketimpangan merupakan salah satu faktor penting yang dapat menurunkan kemiskinan. Selain itu juga dapat menambah variabel infrastruktur lain seperti infrastruktur irigasi dan telekomunikasi.

5. Metode analisis yang digunakan dapat dikembangkan dengan memanfaatkan metode yang lebih tepat sehingga dapat menjawab permasalahan penelitian dengan lebih gamblang.

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Lampiran 1 Hasil Estimasi Pengaruh Infrastruktur terhadap Pertumbuhan untuk Model di Jawa dengan Program STATA SE 10

# Mendifinisikan data dalam format panel . xtset prop tahun, yearly

panel variable: prop (strongly balanced) time variable: tahun, 1993 to 2009 delta: 1 year

# Penghitungan dalam model FEM

. xtreg ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, fe

Fixed-effects (within) regression Number of obs = 85 Group variable: prop Number of groups = 5 R-sq: within = 0.8488 Obs per group: min = 17 between = 0.0088 avg = 17.0 overall = 0.0028 max = 17 F(6,74) = 69.23 corr(u_i, Xb) = -0.5441 Prob > F = 0.0000 --- ln_pdrb | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---+--- ln_listrik| .2973954 .0689863 4.31 0.000 .1599373 .4348536 ln_ab | .0181748 .0269611 0.67 0.502 -.0355463 .071896 ln_jln | -.1277735 .0246865 -5.18 0.000 -.1769625 -.0785845 ln_pskesms| .0035885 .0006412 5.60 0.000 .0023109 .004866 ln_tk | -.3602829 .2084909 -1.73 0.088 -.7757102 .0551443 dd | -.0000348 .0263029 -0.00 0.999 -.0524445 .052375 _cons | 24.53426 3.588197 6.84 0.000 17.38462 31.6839 ---+--- sigma_u | 1.3455315 sigma_e | .06810587

rho | .99744453 (fraction of variance due to u_i)

--- F test that all u_i=0: F(4, 74) = 36.47

Prob > F = 0.0000 . est sto fixed

# Penghitungan dalam model REM

. xtreg ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, re

Random-effects GLS regression Number of obs = 85 Group variable: prop Number of groups = 5 R-sq: within = 0.7112 Obs per group: min = 17 between = 0.9992 avg = 17.0 overall = 0.9883 max = 17 Random effects u_i ~ Gaussian Wald chi2(6) = 6594.51 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

rho | 0 (fraction of variance due to u_i)

. est sto random

# Penghitungan dalam Uji Hausman . hausman fixed random

---- Coefficients ----

| (b) (B) (b-B)

| fixed random Difference S.E. ---+--- ln_listrik | .2973954 .4550678 -.1576724 .047314 ln_ab | .0181748 .1679195 -.1497446 . ln_jln | -.1277735 -.0195267 -.1082468 . ln_puskesmas | .0035885 .0064039 -.0028154 . ln_tk | -.3602829 .8616967 -1.22198 .2074717 dd | -.0000348 -.1509627 .1509279 . --- b = consistent under Ho and Ha; obtained from B = inconsistent under Ha, efficient under Ho; obtained from Test: Ho: difference in coefficients not systematic

chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 104.97

Prob>chi2 = 0.0000

Uji Hausman menunjukkan tolak H0, berarti model FEM yang paling sesuai

# Penghitungan dalam Uji Woolridge untuk Asumsi Tidak Ada Autokorelasi Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation F( 1, 4) = 60.803 Prob > F = 0.0015

Uji Woolridge menunjukkan tolak H0, berarti terdapat autokorelasi dalam model

terpilih

# Penghitungan Uji Wald untuk Asumsi Homoskedastisitas . xttest3

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i chi2 (5) = 51.84

Prob>chi2 = 0.0000

Modified Wald Test menunjukkan tolak H0, berarti terdapat masalah

heteroskedastisitas dalam model terpilih.

# Model terpilih dengan koreksi terhadap permasalahan heteroskedastisitas contemporaneously correlated across panel, and first order autokorelasi (ar1)

. xtpcse ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, corr (ar1)

Prais-Winsten regression, correlated panels corrected standard errors (PCSEs)

Group variable: prop Number of obs = 85 Time variable: tahun Number of groups = 5 Panels: correlated (balanced) Obs per group: min = 17 Autocorrelation: common AR(1) avg = 17 max = 17 Estimated covariances = 15 R-squared = 0.9971 Estimated autocorrelations = 1 Wald chi2(6) = 7560.99 Estimated coefficients = 7 Prob > chi2 = 0.0000 --- | Panel-corrected

ln_pskesmas| .0045787 .0011317 4.05 0.000 .0023607 .0067967 ln_tk | .8807358 .0246133 35.78 0.000 .8324946 .928977 dd |-.1208316 .0530424 -2.28 0.023 -.2247927 -.0168705 cons | 7.235929 .5941238 12.18 0.000 6.071468 8.40039 ---+--- rho | .5400293 ---

Lampiran 2 Hasil Estimasi Pengaruh Pertumbuhan terhadap Kemiskinan untuk Model di Jawa dengan Program STATA SE 10

# Penghitungan dalam model FEM

xtreg ln_miskin ln_pdrb ln_pengangguran ln_rataratasklh, fe

Fixed-effects (within) regression Number of obs = 85 Group variable: prop Number of groups = 5 R-sq: within = 0.3199 Obs per group: min = 17 between = 0.0154 avg = 17.0 overall = 0.0238 max = 17 F(3,77) = 12.07 corr(u_i, Xb) = -0.7973 Prob > F = 0.0000 --- ln_miskin | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---+--- ln_pdrb | .5214539 .2396445 2.18 0.033 .0442608 .998647 ln_pengang~n| .2513296 .1116214 2.25 0.027 .0290629 .4735963 ln_ratarat~h|-.8307567 .6754577 -1.23 0.222 -2.175765 .5142515 _cons | 2.63936 3.189329 0.83 0.410 -3.711406 8.990126 ---+--- sigma_u | .98905095 sigma_e | .21901915

rho | .95325492 (fraction of variance due to u_i)

--- F test that all u_i=0: F(4, 77) = 82.21 Prob > F = 0.0000 . est sto fixed

# Penghitungan dalam model REM

. xtreg ln_miskin ln_pdrb ln_pengangguran ln_rataratasklh, re

Random-effects GLS regression Number of obs = 85 Group variable: prop Number of groups = 5 R-sq: within = 0.2710 Obs per group: min = 17 between = 0.0841 avg = 17.0 overall = 0.1157 max = 17 Random effects u_i ~ Gaussian Wald chi2(3) = 26.48 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 --- ln_miskin | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---+--- ln_pdrb |-.0167787 .1641919 -0.10 0.919 -.338589 .3050315 ln_pengang| .2261664 .1029963 2.20 0.028 .0242975 .4280354 ln_ratarat| .4017806 .4166726 0.96 0.335 -.4148827 1.218444 _cons | 9.889398 2.02754 4.88 0.000 5.915493 13.8633 ---+--- sigma_u | .29101195 sigma_e | .21901915

rho | .63839628 (fraction of variance due to u_i)

--- . est sto random

# Penghitungan dalam Uji Hausman . hausman fixed random

---- Coefficients ----

| (b) (B) (b-B)

| fixed random Difference S.E. ---+--- ln_pdrb | .5214539 -.0167787 .5382326 .174558 ln_pengang~n | .2513296 .2261664 .0251632 .0430246 ln_ratarat~h | -.8307567 .4017806 -1.232537 .5316268 --- b = consistent under Ho and Ha; obtained from B = inconsistent under Ha, efficient under Ho; obtained from Test: Ho: difference in coefficients not systematic

= 9.45 Prob>chi2 = 0.0239

Uji Hausman menunjukkan tolak H0, berarti model FEM yang paling sesuai

# Penghitungan dalam Uji Woolridge untuk Asumsi Tidak Ada Autokorelasi . xtserial ln_miskin ln_pdrb ln_pengangguran ln_rataratasklh, output

Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation

F( 1, 4) = 44.045 Prob > F = 0.0027

Uji Woolridge menunjukkan tolak H0, berarti terdapat autokorelasi dalam model

terpilih

# Penghitungan Uji Wald untuk Asumsi Homoskedastisitas . xttest3

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i

chi2 (5) = 1.98 Prob>chi2 = 0.8524

Modified Wald Test menunjukkan terima H0, berarti tidak terdapat masalah

heteroskedastisitas dalam model terpilih.

# Model terpilih dengan koreksi terhadap permasalahan autokorelasi . xtgls ln_miskin ln_pdrbpre ln_pengangguran ln_rataratasklh, corr (ar1) Cross-sectional time-series FGLS regression

Coefficients: generalized least squares Panels: homoskedastic

Correlation: common AR(1) coefficient for all panels (0.6545)

Estimated covariances = 1 Number of obs = 85 Estimated autocorrelations = 1 Number of groups = 5 Estimated coefficients = 4 Time periods = 17 Wald chi2(3) = 52.12 Prob > chi2 = 0.0000 --- ln_miskin | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---+--- ln_pdrb |-.7070452 .1417731 -4.99 0.000 -.9849153 -.4291751 ln_pengang| .7549185 .115229 6.55 0.000 .5290739 .9807631 ln_ratat~h| 1.421027 .3754204 3.79 0.000 .6852164 2.156837 _cons | 12.86544 1.425478 9.03 0.000 10.07156 15.65933 ---

Lampiran 3 Hasil Estimasi Pengaruh Infrastruktur terhadap Pertumbuhan untuk Model di Luar Jawa dengan Program STATA SE 10

# Penghitungan dalam model FEM

. xtreg ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, fe

Fixed-effects (within) regression Number of obs = 357 Group variable: prov Number of groups = 21 R-sq: within = 0.7008 Obs per group: min = 17 between = 0.6493 avg = 17.0 overall = 0.6428 max = 17 F(6,330) = 128.84 corr(u_i, Xb) = 0.3843 Prob > F = 0.0000 --- ln_pdrb | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---+--- ln_listrik | .235594 .0405048 5.82 0.000 .1559139 .3152742 ln_ab |.0129663 .0218751 0.59 0.554 -.030066 .0559986 ln_jln | .020831 .0147626 1.41 0.159 -.0082097 .0498717

rho | .95234428 (fraction of variance due to u_i)

--- F test that all u_i=0: F(20, 330) = 212.03 Prob > F = 0.0000 . est sto fixed

# Penghitungan dalam model REM

. xtreg ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, re

Random-effects GLS regression Number of obs = 357 Group variable: prov Number of groups = 21 R-sq: within = 0.7005 Obs per group: min = 17 between = 0.6522 avg = 17.0 overall = 0.6466 max = 17 Random effects u_i ~ Gaussian Wald chi2(6) = 791.17 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 --- ln_pdrb | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---+--- ln_listrik|.2500969 .0394618 6.34 0.000 .1727532 .3274405 ln_ab |.0142935 .0221207 0.65 0.518 -.0290623 .0576494 ln_jln |.0249174 .0148838 1.67 0.094 -.0042543 .0540891 ln_psksmas| .005243 .0016986 3.09 0.002 .0019139 .0085721 ln_tk |.6834638 .092101 7.42 0.000 .5029491 .8639785 dd |.0472341 .0210585 2.24 0.025 .0059601 .0885081 _cons |8.131539 1.5173 5.36 0.000 5.157686 11.10539 ---+--- sigma_u | .41165812 sigma_e | .11644974

rho | .92590797 (fraction of variance due to u_i)

--- . est sto random

# Penghitungan dalam Uji Hausman . hausman fixed random

---- Coefficients ----

| (b) (B) (b-B)

| fixed random Difference S.E. ---+--- --- ln_listrik | .235594 .2500969 -.0145028 .0091328 ln_ab | .0129663 .0142935 -.0013273 . ln_jln | .020831 .0249174 -.0040863 . ln_puskesmas | .0048843 .005243 -.0003587 . ln_tk | .6618725 .6834638 -.0215913 .0478803 dd | .0555075 .0472341 .0082734 . --- b = consistent under Ho and Ha; obtained from B = inconsistent under Ha, efficient under Ho; obtained from Test: Ho: difference in coefficients not systematic

chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 75.95

Prob>chi2 = 0.0000

Uji Hausman menunjukkan tolak H0, berarti model FEM yang paling sesuai

# Penghitungan dalam Uji Woolridge untuk Asumsi Tidak Ada Autokorelasi . xtserial ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, output

Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation

F( 1, 20) = 129.062 Prob > F = 0.0000

Uji Woolridge menunjukkan tolak H0, berarti terdapat autokorelasi dalam model

# Penghitungan Uji Wald untuk Asumsi Homoskedastisitas . xttest3

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i chi2 (21) = 1698.34

Prob>chi2 = 0.0000

Modified Wald Test menunjukkan tolak H0, berarti terdapat masalah

heteroskedastisitas dalam model terpilih.

# Model terpilih dengan koreksi terhadap permasalahan heteroskedastisitas contemporaneously correlated across panel, and first order autokorelasi (ar1)

. xtpcse ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd, corr (ar1)

(note: estimates of rho outside [-1,1] bounded to be in the range [-1,1]) Prais-Winsten regression, correlated panels corrected standard errors (PCSEs)

Group variable: prov Number of obs = 357 Time variable: tahun Number of groups = 21 Panels: correlated (balanced) Obs per group: min = 17 Autocorrelation: common AR(1) avg = 17 max = 17 Estimated covariances = 231 R-squared = 0.9922 Estimated autocorrelations = 1 Wald chi2(6) = 524.81 Estimated coefficients = 7 Prob > chi2 = 0.0000 --- | Panel-corrected

Lampiran 4 Hasil Estimasi Pengaruh Pertumbuhan terhadap Kemiskinan untuk Model di Luar Jawa dengan Program STATA SE 10

# Penghitungan dalam model FEM

. xtreg ln_miskin ln_pdrb ln_pengangguran ln_rataratasklh, fe

Fixed-effects (within) regression Number of obs = 356 Group variable: prov Number of groups = 21 R-sq: within = 0.2365 Obs per group: min = 16 between = 0.0209 avg = 17.0 overall = 0.0316 max = 17 F(3,332) = 34.27 corr(u_i, Xb) = -0.1349 Prob > F = 0.0000 --- ln_miskin | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---+--- ln_pdrb | .3397415 .1184836 2.87 0.004 .1066683 .5728147 ln_pengangn| .1635478 .0388328 4.21 0.000 .0871584 .2399373 ln_ratarata|-.4222983 .3155182 -1.34 0.182 -1.042965 .1983686 _cons | 6.328538 1.364 4.64 0.000 3.645365 9.011711

---+--- sigma_u | .93059962

sigma_e | .21877232

rho | .94762826 (fraction of variance due to u_i)

--- F test that all u_i=0: F(20, 332) = 295.52 Prob > F = 0.0000 . est sto fixed

# Penghitungan dalam model REM

. xtreg ln_miskin ln_pdrb ln_pengangguran ln_rataratasklh, re

Random-effects GLS regression Number of obs = 356 Group variable: prov Number of groups = 21 R-sq: within = 0.2365 Obs per group: min = 16 between = 0.0208 avg = 17.0 overall = 0.0315 max = 17 Random effects u_i ~ Gaussian Wald chi2(3) = 103.26 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 --- ln_miskin | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---+--- ln_pdrb |.3349692 .1144573 2.93 0.003 .110637 .5593015 ln_pengang~n|.1629681 .0386328 4.22 0.000 .0872491 .2386871 ln_ratarat~h|-.4179115 .3054819 -1.37 0.171 -1.016645 .180822 _cons |6.401366 1.336088 4.79 0.000 3.78268 9.020051 ---+--- sigma_u | .96635108 sigma_e | .21877232

rho | .95124627 (fraction of variance due to u_i)

--- . est sto random

# Penghitungan dalam Uji Hausman . hausman fixed random

---- Coefficients ----

| (b) (B) (b-B)

| fixe rando Difference S.E. ---+--- pdrbpre | .3397415 .3349692 .0047723 .0306248 ln_pengang~n | .1635478 .1629681 .0005797 .0039361 ln_ratarat~h | -.4222983 -.4179115 -.0043868 .0789465 --- b = consistent under Ho and Ha; obtained from B = inconsistent under Ha, efficient under Ho; obtained from Test: Ho: difference in coefficients not systematic

chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.60

Prob>chi2 = 0.8968

Uji Hausman menunjukkan tidak cukup bukti untuk menolak H0, berarti model

Lampiran 5 Hasil Estimasi Pengaruh Infrastruktur terhadap Pertumbuhan untuk Model Gabungan dengan Program STATA SE 10

# Penghitungan dalam model FEM

. xtreg ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd dw_ab, fe Fixed-effects (within) regression Number of obs = 442 Group variable: prop Number of groups = 26 R-sq: within = 0.6846 Obs per group: min = 17 between = 0.7982 avg = 17.0 overall = 0.7943 max = 17 F(7,409) = 126.83 corr(u_i, Xb) = 0.4407 Prob > F = 0.0000 --- ln_pdrb | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---+--- ln_listrik | .2367901 .035486 6.67 0.000 .1670324 .3065478 ln_ab |-.0174076 .0302395 -0.58 0.565 -.0768518 .0420366 ln_jln | .0055385 .0120538 0.46 0.646 -.0181567 .0292338 ln_pskesmas | .0016628 .0006531 2.55 0.011 .0003789 .0029468 ln_tk | .7014113 .0954085 7.35 0.000 .513859 .8889636 dd | .0305829 .0186421 1.64 0.102 -.0060635 .0672293 dw_ab | .0006064 .032123 0.02 0.985 -.0625404 .0637531 _cons | 7.916411 1.602358 4.94 0.000 4.766527 11.06629 ---+--- sigma_u | .68413574 sigma_e | .11456928

rho | .97272029 (fraction of variance due to u_i)

--- F test that all u_i=0: F(25, 409) = 202.18 Prob > F = 0.0000 . est sto fixed

# Penghitungan dalam model REM

. xtreg ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd dw_ab, re Random-effects GLS regression Number of obs = 442 Group variable: prop Number of groups = 26 R-sq: within = 0.6805 Obs per group: min = 17 between = 0.8137 avg = 17.0 overall = 0.8106 max = 17 Random effects u_i ~ Gaussian Wald chi2(7) = 1047.82 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 --- ln_pdrb | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---+--- ln_listrik | .190894 .032126 5.94 0.000 .1279282 .2538598 ln_ab | .02188 .028114 0.78 0.436 -.0332225 .0769824 ln_jln | .0048612 .0123382 0.39 0.694 -.0193212 .0290437 ln_pskesmas| .0021692 .000675 3.21 0.001 .0008462 .0034921 ln_tk | .8779248 .0579636 15.15 0.000 .7643183 .9915313 dd | .0313222 .0192732 1.63 0.104 -.0064525 .0690969 dw_ab | -.020199 .0286626 -0.70 0.481 -.0763766 .0359787 _cons | 4.833236 1.002299 4.82 0.000 2.868766 6.797706 ---+--- sigma_u | .36242941 sigma_e | .11456928

rho | .90914998 (fraction of variance due to u_i)

--- . est sto random

# Penghitungan dalam Uji Hausman . hausman fixed random

---- Coefficients ----

| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | random fixed Difference S.E.

---+--- ---

ln_listrik | .190894 .2367901 -.0458961 . ln_ab | .02188 -.0174076 .0392876 . ln_jln |.0048612 .0055385 -.0006773 .0026337 ln_puskesmas |.0021692 .0016628 .0005063 .0001702 ln_tk |.8779248 .7014113 .1765135 . dd |.0313222 .0305829 .0007393 .0048913 dw_ab |-.020199 .0006064 -.0208053 . --- b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic

chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 19.90

Prob>chi2 = 0.0058

Uji Hausman menunjukkan tolak H0, berarti model FEM yang paling sesuai

# Penghitungan dalam Uji Woolridge untuk Asumsi Tidak Ada Autokorelasi Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation F( 1, 25) = 89.195 Prob > F = 0.0000

Uji Woolridge menunjukkan tolak H0, berarti terdapat autokorelasi dalam model

terpilih

# Penghitungan Uji Wald untuk Asumsi Homoskedastisitas . xttest3

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i chi2 (26) = 2074.99

Prob>chi2 = 0.0000

# Model terpilih dengan koreksi terhadap permasalahan heteroskedastisitas contemporaneously correlated across panel, and first order autokorelasi (ar1)

. xtpcse ln_pdrb ln_listrik ln_ab ln_jln ln_puskesmas ln_tk dd dw_ab, corr (ar1)

(note: estimates of rho outside [-1,1] bounded to be in the range [-1,1]) Prais-Winsten regression, correlated panels corrected standard errors (PCSEs)

Group variable: prop Number of obs = 442

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