CHƯƠNG 6 : KẾT LUẬ N– KIẾN NGHỊ
6.4.Hướng nghiên cứu trong tương lai
Trong suốt quá trình viết nghiên cứu đề tài này tồn tại một số khía cạnh bị hạn chế do nguồn tài liệu, năng lực và thời gian. Một số hạn chế đã được đề cập trong nội dung nghiên cứu, một số vấn đề nên được tiếp tục thực hiện trong thời gian tiếp theo về đề tài nghiên cứu này:
Đầu tiên là cần thu thập dữ liệu đầy đủ hơn về các loại tham nhũng khác nhau (tham nhũng chính trị, tham nhũng hành chính, tham nhũng kinh tế,…). Điều này sẽ làm tăng đáng kể chất lượng của dữ liệu nghiên cứu, tăng độ tin cậy khi thực hiện phân tích định lượng. Từ đó có thể nghiên cứu tác động của từng loại hình tham nhũng đối với dịng vốn đầu tư nước ngồi.
Thứ hai, dựa trên các cơ sở lý thuyết về mối quan hệ giữa dòng vốn FDI và tham nhũng, thực hiện phân tích bằng các mơ hinh kiểm định, phương pháp nghiên cứu khác. Từ đó so sánh, đối chiếu kết quả đạt được để nhận định những tác động mới hoặc đề xuất giải pháp nghiên cứu phù hợp hơn.
Thứ ba, có thể mở rộng hướng nghiên cứu xem xét tác động ngược trở lại của đầu tư trực tiếp nước ngoài vào tham nhũng ở mỗi quốc gia.
Thứ tư, đánh giá tác động của tham nhũng vào từng ngành, lĩnh vực cụ thể. Từ đó đưa ra cái nhìn tổng quan nhất về tham nhũng đối với toàn bộ nền kinh tế. Đề xuất giải pháp phù hợp cho từng lĩnh vực, ngành nghề khác nhau.
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Variable Obs Mean Std. Dev. Min Max FDI 230 5.010047 5.222044 -2.75744 26.52121 FFC 230 36.00594 23.34916 10 94 GDP 230 5.530498 3.684158 -13.12673 15.24038 AGE 230 70.07008 6.229147 55.189 82.79512 EDU 230 68.65944 23.82539 16.60103 120.6316 PPP 230 8.21796 13.60637 .1389249 57.7143 PORT 230 4.027741 1.306326 2.178439 6.830574 RATE 230 12.18533 7.029999 4.33404 32.15417 TRADE 230 124.4661 93.84274 .1674176 441.6038
2 . reg FDI FFC GDP AGE EDU PPP PORT RATE TRADE
Source SS df MS Number of obs = 230 F( 8, 221) = 72.76 Model 4526.22937 8 565.778672 Prob > F = 0.0000 Residual 1718.54234 221 7.77620967 R-squared = 0.7248 Adj R-squared = 0.7148 Total 6244.77171 229 27.2697455 Root MSE = 2.7886 FDI Coef. Std. Err. t P>|t| [95% Conf. Interval]
FFC -.0408588 .0263024 -1.55 0.122 -.0926944 .0109767 GDP .2799161 .0586314 4.77 0.000 .1643679 .3954643 AGE .1309795 .0758014 1.73 0.085 -.0184066 .2803656 EDU -.0687374 .0174884 -3.93 0.000 -.1032029 -.0342719 PPP .170663 .0247375 6.90 0.000 .1219115 .2194145 PORT -.5602147 .3860689 -1.45 0.148 -1.321062 .200633 RATE .0434894 .0544361 0.80 0.425 -.0637909 .1507697 TRADE .0467906 .0048829 9.58 0.000 .0371676 .0564136 _cons -5.025013 5.492363 -0.91 0.361 -15.84912 5.799096 3 . vif
Variable VIF 1/VIF FFC 11.11 0.090033 PORT 7.49 0.133506 AGE 6.57 0.152307 TRADE 6.18 0.161724 EDU 5.11 0.195592 RATE 4.31 0.231872 PPP 3.34 0.299735 GDP 1.37 0.727772 Mean VIF 5.69
4 . xtreg LNFDI l.FFC l.LNGDP l.AGE l.EDU l.PPP l.PORT l.RATE l.TRADE, fe
Fixed-effects (within) regression Number of obs = 207 Group variable: Year Number of groups = 23 R-sq: within = 0.4402 Obs per group: min = 9 between = 0.0593 avg = 9.0 overall = 0.3866 max = 9 F(8,176) = 17.30 corr(u_i, Xb) = -0.0994 Prob > F = 0.0000
LNGDP L1. .0887362 .0887294 1.00 0.319 -.0863744 .2638467 AGE L1. -.0235044 .0338069 -0.70 0.488 -.0902234 .0432146 EDU L1. .016819 .0056607 2.97 0.003 .0056474 .0279906 PPP L1. -.0146727 .0080179 -1.83 0.069 -.0304963 .0011509 PORT L1. -.9019008 .1209119 -7.46 0.000 -1.140525 -.663277 RATE L1. -.0686409 .015297 -4.49 0.000 -.0988302 -.0384517 TRADE L1. .0030331 .001535 1.98 0.050 3.84e-06 .0060624 _cons 5.373825 2.054888 2.62 0.010 1.318432 9.429218 sigma_u .34348031 sigma_e .7548377
rho .17154088 (fraction of variance due to u_i)
F test that all u_i=0: F(22, 176) = 1.70 Prob > F = 0.0315 5 . xttest3
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
H0: sigma(i)^2 = sigma^2 for all i chi2 (23) = 156.55
Prob>chi2 = 0.0000
6 . xtreg LNFDI l.FFC l.LNGDP l.AGE l.EDU l.PPP l.PORT l.RATE l.TRADE, fe
Fixed-effects (within) regression Number of obs = 207 Group variable: Year Number of groups = 23 R-sq: within = 0.4402 Obs per group: min = 9 between = 0.0593 avg = 9.0 overall = 0.3866 max = 9 F(8,176) = 17.30 corr(u_i, Xb) = -0.0994 Prob > F = 0.0000
LNGDP L1. .0887362 .0887294 1.00 0.319 -.0863744 .2638467 AGE L1. -.0235044 .0338069 -0.70 0.488 -.0902234 .0432146 EDU L1. .016819 .0056607 2.97 0.003 .0056474 .0279906 PPP L1. -.0146727 .0080179 -1.83 0.069 -.0304963 .0011509 PORT L1. -.9019008 .1209119 -7.46 0.000 -1.140525 -.663277 RATE L1. -.0686409 .015297 -4.49 0.000 -.0988302 -.0384517 TRADE L1. .0030331 .001535 1.98 0.050 3.84e-06 .0060624 _cons 5.373825 2.054888 2.62 0.010 1.318432 9.429218 sigma_u .34348031 sigma_e .7548377
rho .17154088 (fraction of variance due to u_i)
F test that all u_i=0: F(22, 176) = 1.70 Prob > F = 0.0315 7 . xtserial LNFDI FFC LNGDP AGE EDU PPP PORT RATE TRADE
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation
F( 1, 22) = 0.800 Prob > F = 0.3809
8 . xtreg LNFDI l.FFC l.LNGDP l.AGE l.EDU l.PPP l.PORT l.RATE l.TRADE, fe robust Fixed-effects (within) regression Number of obs = 207 Group variable: Year Number of groups = 23 R-sq: within = 0.4402 Obs per group: min = 9 between = 0.0593 avg = 9.0 overall = 0.3866 max = 9
F(8,22) = 40.31
corr(u_i, Xb) = -0.0994 Prob > F = 0.0000 (Std. Err. adjusted for 23 clusters in Year) Robust
LNFDI Coef. Std. Err. t P>|t| [95% Conf. Interval] FFC L1. .0136919 .0066671 2.05 0.052 -.0001348 .0275186 LNGDP L1. .0887362 .0642313 1.38 0.181 -.0444715 .2219438 AGE -.0235044 .0263725 -0.89 0.382 -.0781976 .0311888
L1. -.0146727 .0050804 -2.89 0.009 -.0252088 -.0041366 PORT L1. -.9019008 .1160588 -7.77 0.000 -1.142592 -.6612096 RATE L1. -.0686409 .0138641 -4.95 0.000 -.0973934 -.0398885 TRADE L1. .0030331 .0013275 2.28 0.032 .00028 .0057863 _cons 5.373825 1.617073 3.32 0.003 2.02022 8.727429 sigma_u .34348031 sigma_e .7548377
rho .17154088 (fraction of variance due to u_i)
9 . xtreg LNFDI l.FFC l.LNGDP l.AGE l.EDU l.PPP l.PORT l.RATE l.TRADE, re robust Random-effects GLS regression Number of obs = 207 Group variable: Year Number of groups = 23 R-sq: within = 0.4324 Obs per group: min = 9 between = 0.1196 avg = 9.0 overall = 0.3954 max = 9
Wald chi2(8) = 230.41
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 (Std. Err. adjusted for 23 clusters in Year) Robust
LNFDI Coef. Std. Err. z P>|z| [95% Conf. Interval] FFC L1. .0221763 .0053318 4.16 0.000 .0117262 .0326264 LNGDP L1. .1223375 .0572247 2.14 0.033 .0101792 .2344958 AGE L1. -.0459463 .0209664 -2.19 0.028 -.0870398 -.0048529 EDU L1. .0186978 .0047942 3.90 0.000 .0093014 .0280943 PPP L1. -.0133987 .00481 -2.79 0.005 -.022826 -.0039713 PORT L1. -.8212891 .1267314 -6.48 0.000 -1.069678 -.5729002 RATE L1. -.0562635 .0162775 -3.46 0.001 -.0881669 -.0243601 TRADE L1. .0012408 .0012259 1.01 0.311 -.0011619 .0036434 _cons 6.163277 1.26534 4.87 0.000 3.683256 8.643298 sigma_u 0
Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: heteroskedastic
Correlation: no autocorrelation
Estimated covariances = 23 Number of obs = 207 Estimated autocorrelations = 0 Number of groups = 23 Estimated coefficients = 9 Time periods = 9 Wald chi2(8) = 203.96 Prob > chi2 = 0.0000 LNFDI Coef. Std. Err. z P>|z| [95% Conf. Interval] FFC L1. .0196675 .0058595 3.36 0.001 .008183 .0311519 LNGDP L1. .1055424 .07581 1.39 0.164 -.0430425 .2541273 AGE L1. -.0548129 .0248139 -2.21 0.027 -.1034473 -.0061785 EDU L1. .0137997 .0042893 3.22 0.001 .0053928 .0222065 PPP L1. -.0103559 .0059754 -1.73 0.083 -.0220675 .0013557 PORT L1. -.7841459 .0932987 -8.40 0.000 -.9670079 -.6012838 RATE L1. -.0695382 .0127421 -5.46 0.000 -.0945123 -.0445642 TRADE L1. .0014544 .0011758 1.24 0.216 -.0008502 .003759 _cons 7.259514 1.55135 4.68 0.000 4.218923 10.3001 11 .