SCHOOL OF ECONOMICS INSTITUTE OF SOCIAL STUDIES UNIVERSITY OF ECONOMICS ERASMUS UNIVERSITY ROTTERDAM HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLAND VIETNAM - THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE MUTUAL EFFECTS OF SHADOW ECONOMY AND FINANCIAL DEVELOPMENT IN ASEAN COUNTRIES by Nguyen Hoang Phu A thesis submitted in partial fulfilment of the requirements for the degree of Master of Art in Development Economics Ho Chi Minh city, January 2018 VIETNAM - THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE MUTUAL EFFECTS OF SHADOW ECONOMY AND FINANCIAL DEVELOPMENT IN ASEAN COUNTRIES by Nguyen Hoang Phu A thesis submitted in partial fulfilment of the requirements for the degree of Master of Art in Development Economics Academic Supervisor: Dr Pham Thi Thu Tra Ho Chi Minh city, January 2018 DECLARATION I hereby declare that my dissertation entitled “THE MUTUAL EFFECTS OF SHADOW ECONOMY AND FINANCIAL DEVELOPMENT IN ASEAN COUNTRIES” is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text I also confirm that:  This thesis was done wholly while in candidature for a research degree at VNP;  Where any part of this thesis has previously been submitted for a degree or any other qualification at VNP or any other institution, this has been clearly stated;  Where I have consulted the published work of others, this is always clearly attributed;  Where I have quoted from the work of others, the source is always given  With the exception of such quotations, this thesis is entirely my own work, and I have acknowledged all main sources of help Date: January 02, 2018 Signature Full name: Nguyen Hoang Phu ACKNOWLEDGEMENT This thesis cannot complete without the support of my supervisor, Dr Pham Thi Thu Tra, who has spent the value time, efforts, and energy to guide me on the thesis during the time of completing the thesis Her dedication made me motivated when I have a chance to discuss with her, her expertise is what makes me impressive when I ask her questions about my thesis’s topic, and she also kept me in the “can – do” attitude when I faced any difficulties in doing thesis All of these leave me with the most unforgettable memory and experience My purpose of this acknowledgement is to express my gratitude to my supervisor Without her supports, I may not have a chance to pursue my dream I would like to send my special thanks to Prof Nguyen Trong Hoai, Dr Pham Khanh Nam, Dr Truong Dang Thuy for their valuable command, guidance and support during the program Without your support and encouragement, I may not complete the thesis as expected Additionally, my thanks are given to all of the lectures who have been my knowledge guiders and the staff who have been my service supporters throughout the master program at University of Economics and Erasmus University Rotterdam Without their help, never can I have an opportunity to proceed and complete my master thesis Last but not least, I would like to thank Mr Nguyen Cong Thanh, Truong Thi Thu May and my family who have always been a pillar for me to rely on during the hardships of attempting to achieve the master thesis It is their unspoken sacrifice and untiring work that bring me more spare time to be able to reach the final destination of my progress ABSTRACT This study focuses on examining the mutual relationship between financial development and shadow economy by applying the theoretical and empirical framework Our research contributed to the way of calculating the size of shadow economy applied the currency demand approach with updated data from 1997 to 2015 for ASIAN countries In particular, to have a robust result, we used estimation methods including POLS, FEM, REM and SGMM to calculate the value of the size of shadow economy of each country Then, we took each received results to examine the mutual effect with the financial development using P – VAR approach We found that when the positive shock caused by the financial sector affects the shadow economy, the shadow economy will immediately respond negatively to the shock On the other hand, when a positive shock caused by credit for private sector will lead to the positive responses of the shadow economy Interestingly, in this case, the response tends to last longer with the estimated results from static model of shadow economy in comparison with dynamic model of shadow economy Keywords: Shadow economy, financial development JEL classifications: G32, H26 TABLE OF CONTENTS Chapter 1: Introduction 11 1.1 Problem statements 11 1.2 Research objectives 13 1.3 Scope of the study 14 1.4 Structure of the thesis 15 Chapter 2: Literature review 16 2.1 Review of theory 16 2.1.1 The theory of shadow economy 16 2.1.2 The review on financial development theories 21 2.2 Review of empirical studies on the relationship between financial development theory and shadow economy theory 27 2.3 Summary 31 Chapter 3: Research methodology 34 3.1 Analytical framework 34 3.2 Econometric models 36 3.3 Data 40 3.4 Variables and sampling 40 Chapter 4: Research results 42 4.1 Overview of the research topic 42 4.2 Descriptive statistics 44 4.3 Regression results and discussions 45 Chapter 5: Conclusions 55 5.1 Conclusions 55 5.2 Limits of the study 56 Reference 58 Appendices 65 LIST OF TABLES Table 1: Descriptive Statistics for the whole dataset 44 Table 2: Matrix of correlation coefficients 44 Table 3: Estimated results of currency demand model 46 Table 4: Optimal model selection tests 47 Table 5: Estimated value of Shadow economy over GDP 48 Table 6: The results of Unit Root Test 49 LIST OF CHARTS Figure 1: Conceptual framework of shadow economy and financial development 31 Figure 2: The technical structure to deal with data 34 Figure 3: The analyzed results of impulse response function when the size of shadow economic creates a shock with the estimated value of shadow economy from the static models (POLS, FEM, REM) 51 Figure 4: The analyzed results of impulse response function when the size of shadow economic creates a shock with the estimated value of shadow economy from the dynamic models (SGMM) 52 Figure 5: The analyzed results of impulse response function when the financial development creates a shock with the estimated value of shadow economy from the static models (PLOS, FEM, REM) 53 Figure 6: The analyzed results of impulse response function when the financial development creates a shock with the estimated value of shadow economy from the dynamic models (SGMM)) 54 LIST OF APPENDICES Appendix 1: Shadow economy estimation using POLS 65 Appendix 2: Shadow economy estimation using FEM 65 Appendix 3: Shadow economy estimation using SGMM 66 Appendix 4: Shadow economy estimation using REM 67 Appendix 5: Hausman Test 67 Appendix 6: Breusch & Pagan Lagrangian multiplier test for random effects 68 Appendix 7: Stationary test for shadow size estimated by POLS 68 Appendix 8: Stationary test for first difference of shadow size estimated by POLS 69 Appendix 9: Stationary test for shadow size estimated by FEM 69 Appendix 10: Stationary test for first difference of shadow size estimated by FEM 70 Appendix 11: Stationary test for shadow size estimated by REM 71 Appendix 12: Stationary test for first difference of shadow size estimated by REM 71 Appendix 13: Stationary test for shadow size estimated by SGMM 72 Appendix 14: Stationary test for first difference of shadow size estimated by SGMM 72 Appendix 15: Stationary test for financial development measured by Credit for private sectors 73 Appendix 16: Stationary test for first difference of financial development measured by Credit for private sectors 73 Appendix 17: Stationary test for financial development measured by Credit from financial sectors 74 Appendix 18: Stationary test for first difference of financial development measured by Credit from financial sectors 74 Appendix 19: Stationary test for money supply ratio 75 Appendix 20: Stationary test for first difference of money supply ratio 75 Appendix 21: Stationary test for natural logarithm of GDP per capita 76 Appendix 22: Stationary test for first difference of natural logarithm of GDP per capita 76 ABBREVIATIONS POLS: Pooled Ordinary Least Squared FEM: Fixed Effects Model REM: Random Effects Model Isham, J., Woolcock, M., Pritchett, L., & Busby, G (2005) The varieties of resource experience: natural resource export structures and the political economy of economic growth The World Bank Economic Review, 19(2), 141-174 Jaffee, D., & Levonian, M (2001) The structure of banking systems in developed and transition economies European Financial Management, 7(2), 161-181 Johnson, S., Kaufmann, D., & Shleifer, A (1997) Politics and entrepreneurship in transition economies Johnson, S., Kaufmann, D., & Zoido-Lobaton, P (1998) Regulatory discretion and the unofficial economy The American Economic Review, 88(2), 387-392 Khan, A (2001) 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Ngân hàng(109), Vũ Việt Quảng, & Lê Thị Phương Vy (2016) Mối quan hệ thể chế khả tiếp cận thị trường vốn doanh nghiệp Tạp chí Phát triển Kinh tế, 27(6), 80- 101 Wooldridge, J M (2015) Introductory econometrics: A modern approach Nelson Education 64 Appendices Appendix 1: Shadow economy estimation using POLS Source SS df MS Model Residual 9.70905237 10.3589491 88 2.42726309 117715331 Total 20.0680015 92 218130451 ln_mic_m2 Coef ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita _cons 2.921145 -.7560456 2572766 -.2064133 -.38018 Std Err .4707391 166444 0573786 0423472 355015 t 6.21 -4.54 4.48 -4.87 -1.07 Number of obs F(4, 88) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.000 0.000 0.000 0.287 = = = = = = 93 20.62 0.0000 0.4838 0.4603 3431 [95% Conf Interval] 1.98565 -1.086818 1432487 -.2905694 -1.085698 3.85664 -.4252731 3713045 -.1222571 3253378 Appendix 2: Shadow economy estimation using FEM Fixed-effects (within) regression Group variable: cross Number of obs Number of groups = = 93 R-sq: within = 0.3813 between = 0.1580 overall = 0.2025 Obs per group: = avg = max = 13.3 16 corr(u_i, Xb) F(4,82) Prob > F = -0.3946 Std Err t ln_mic_m2 Coef ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita _cons 2.064902 -.539919 -.1719121 -.3959178 1.663089 5217305 1703693 0333121 0970453 7574193 sigma_u sigma_e rho 46932269 13393875 92468794 (fraction of variance due to u_i) 3.96 -3.17 -5.16 -4.08 2.20 F test that all u_i=0: F(6, 82) = 82.57 P>|t| = = 0.000 0.002 0.000 0.000 0.031 12.64 0.0000 [95% Conf Interval] 1.027014 -.8788378 -.2381805 -.5889718 1563407 3.10279 -.2010002 -.1056437 -.2028638 3.169837 Prob > F = 0.0000 65 Appendix 3: Shadow economy estimation using SGMM Dynamic panel-data estimation, two-step system GMM Group variable: cross Time variable : years Number of instruments = 31 Wald chi2(5) = 44.13 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max Std Err z P>|z| = = = = = 88 12.57 15 ln_mic_m2 Coef [95% Conf Interval] ln_mic_m2 L1 -.3916444 9166027 -0.43 0.669 -2.188153 1.404864 ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita _cons 4.583396 -1.340635 -.4540552 -1.284231 8.304857 8.676479 1.699513 2524483 1.289465 10.27017 0.53 -0.79 -1.80 -1.00 0.81 0.597 0.430 0.072 0.319 0.419 -12.42219 -4.671618 -.9488448 -3.811536 -11.8243 21.58898 1.990349 0407345 1.243073 28.43401 Warning: Uncorrected two-step standard errors are unreliable Instruments for first differences equation Standard D.(ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita) GMM-type (missing=0, separate instruments for each period unless collapsed) L2.L.ln_mic_m2 Instruments for levels equation Standard ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL.L.ln_mic_m2 Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but -0.36 0.14 overid restrictions: chi2(25) = 24.68 but not weakened by many instruments.) overid restrictions: chi2(25) = 0.13 weakened by many instruments.) Pr > z = Pr > z = 0.716 0.886 Prob > chi2 = 0.480 Prob > chi2 = 1.000 Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(12) = 0.13 Prob > chi2 Difference (null H = exogenous): chi2(13) = -0.00 Prob > chi2 iv(ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita) Hansen test excluding group: chi2(21) = 0.02 Prob > chi2 Difference (null H = exogenous): chi2(4) = 0.11 Prob > chi2 66 = = 1.000 1.000 = = 1.000 0.999 Appendix 4: Shadow economy estimation using REM Random-effects GLS regression Group variable: cross Number of obs Number of groups = = 93 R-sq: within = 0.3764 between = 0.1902 overall = 0.2160 Obs per group: = avg = max = 13.3 16 corr(u_i, X) Wald chi2(4) Prob > chi2 = (assumed) Std Err z ln_mic_m2 Coef ln_gov_tax_gni_p1 ln_pri_exp_gni ln_dep_int ln_gdp_per_capita _cons 2.039711 -.4038426 -.1472631 -.2963167 8737868 4592554 1440318 0348676 0739597 5850719 sigma_u sigma_e rho 21887821 13393875 72755756 (fraction of variance due to u_i) 4.44 -2.80 -4.22 -4.01 1.49 P>|z| = = 0.000 0.005 0.000 0.000 0.135 42.58 0.0000 [95% Conf Interval] 1.139587 -.6861398 -.2156023 -.4412751 -.272933 2.939835 -.1215455 -.0789239 -.1513583 2.020507 Appendix 5: Hausman Test Coefficients (b) (B) fem rem ln_gov_tax~1 ln_pri_exp~i ln_dep_int ln_gdp_per~a 2.064902 -.539919 -.1719121 -.3959178 2.039711 -.4038426 -.1472631 -.2963167 (b-B) Difference sqrt(diag(V_b-V_B)) S.E .0251917 -.1360763 -.024649 -.0996011 2475626 0909974 0628311 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(4) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 0.80 Prob>chi2 = 0.9382 (V_b-V_B is not positive definite) 67 Appendix 6: Breusch & Pagan Lagrangian multiplier test for random effects Breusch and Pagan Lagrangian multiplier test for random effects ln_mic_m2[cross,t] = Xb + u[cross] + e[cross,t] Estimated results: Var ln_mic_m2 e u Test: sd = sqrt(Var) 2181305 0179396 0479077 4670444 1339387 2188782 Var(u) = chibar2(01) = Prob > chibar2 = 157.82 0.0000 Appendix 7: Stationary test for shadow size estimated by POLS xtunitroot fisher shadow_pols_gdp, dfuller lag(1) trend (40 missing values generated) Fisher-type unit-root test for shadow_pols_gdp Based on augmented Dickey-Fuller tests = Number of panels Avg number of periods = Ho: All panels contain unit roots Ha: At least one panel is stationary AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 13.29 ADF regressions: lag Statistic p-value 29.1074 -2.2865 -2.4578 2.8550 0.0101 0.0111 0.0093 0.0022 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 68 Appendix 8: Stationary test for first difference of shadow size estimated by POLS xtunitroot fisher d.shadow_pols_gdp, dfuller lag(1) trend (47 missing values generated) Fisher-type unit-root test for D.shadow_pols_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 12.29 ADF regressions: lag Statistic p-value 63.1031 -5.0914 -6.3735 9.2796 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 9: Stationary test for shadow size estimated by FEM xtunitroot fisher shadow_fe_gdp, dfuller lag(1) trend (40 missing values generated) Fisher-type unit-root test for shadow_fe_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 13.29 ADF regressions: lag Statistic p-value 13.3563 -0.6659 -0.6011 -0.1217 0.4987 0.2527 0.2756 0.5484 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 69 Appendix 10: Stationary test for first difference of shadow size estimated by FEM xtunitroot fisher d.shadow_fe_gdp, dfuller lag(1) trend (47 missing values generated) Fisher-type unit-root test for D.shadow_fe_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 12.29 ADF regressions: lag Statistic p-value 86.2281 -5.2346 -8.5720 13.6498 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 70 Appendix 11: Stationary test for shadow size estimated by REM xtunitroot fisher shadow_re_gdp, dfuller lag(1) trend (40 missing values generated) Fisher-type unit-root test for shadow_re_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 13.29 ADF regressions: lag Statistic p-value 13.6674 -0.6890 -0.6247 -0.0628 0.4748 0.2454 0.2679 0.5251 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 12: Stationary test for first difference of shadow size estimated by REM xtunitroot fisher d.shadow_re_gdp, dfuller lag(1) trend (47 missing values generated) Fisher-type unit-root test for D.shadow_re_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 12.29 ADF regressions: lag Statistic p-value 90.6858 -5.5869 -9.1158 14.4922 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 71 Appendix 13: Stationary test for shadow size estimated by SGMM xtunitroot fisher shadow_gmm_gdp, dfuller lag(1) trend (40 missing values generated) Fisher-type unit-root test for shadow_gmm_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 13.29 ADF regressions: lag Statistic p-value 79.4185 -3.5499 -7.6736 12.3629 0.0000 0.0002 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 14: Stationary test for first difference of shadow size estimated by SGMM Fisher-type unit-root test for D.shadow_gmm_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 12.29 ADF regressions: lag Statistic p-value 55.3747 -3.1128 -5.1191 7.8191 0.0000 0.0009 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 72 Appendix 15: Stationary test for financial development measured by Credit for private sectors xtunitroot fisher credit_private, dfuller lag(1) trend (2 missing values generated) Fisher-type unit-root test for credit_private Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 18.71 ADF regressions: lag Statistic p-value 88.4868 -1.3597 -5.8852 14.0767 0.0000 0.0870 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 16: Stationary test for first difference of financial development measured by Credit for private sectors xtunitroot fisher d.credit_private, dfuller lag(1) trend (9 missing values generated) Fisher-type unit-root test for D.credit_private Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 17.71 ADF regressions: lag Statistic p-value 113.9170 -6.4883 -11.7024 18.8825 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 73 Appendix 17: Stationary test for financial development measured by Credit from financial sectors xtunitroot fisher credit_financial, dfuller lag(1) trend (2 missing values generated) Fisher-type unit-root test for credit_financial Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(34) Modified inv chi-squared P Z L* Pm 18.71 ADF regressions: lag Statistic p-value 6.4830 2.3695 2.5651 -1.4206 0.9528 0.9911 0.9926 0.9223 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 18: Stationary test for first difference of financial development measured by Credit from financial sectors xtunitroot fisher d.credit_financial, dfuller lag(1) trend (9 missing values generated) Fisher-type unit-root test for D.credit_financial Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 17.71 ADF regressions: lag Statistic p-value 47.2937 -4.3308 -4.8123 6.2919 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 74 Appendix 19: Stationary test for money supply ratio xtunitroot fisher m2_gdp , dfuller lag(1) trend (40 missing values generated) Fisher-type unit-root test for m2_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 18.71 ADF regressions: lag Statistic p-value 4.8585 2.7548 2.8894 -1.7276 0.9877 0.9971 0.9969 0.9580 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 20: Stationary test for first difference of money supply ratio xtunitroot fisher d.m2_gdp , dfuller lag(1) trend (47 missing values generated) Fisher-type unit-root test for D.m2_gdp Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Avg number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 17.71 ADF regressions: lag Statistic p-value 78.5803 -6.4935 -8.2119 12.2045 0.0000 0.0000 0.0000 0.0000 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 75 Appendix 21: Stationary test for natural logarithm of GDP per capita xtunitroot fisher ln_gdp_per_capita , dfuller lag(1) trend (38 missing values generated) Fisher-type unit-root test for ln_gdp_per_capita Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 19 ADF regressions: lag Statistic p-value 3.0405 2.8816 2.8933 -2.0712 0.9990 0.9980 0.9969 0.9808 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels Appendix 22: Stationary test for first difference of natural logarithm of GDP per capita xtunitroot fisher d.ln_gdp_per_capita , dfuller lag(1) trend (45 missing values generated) Fisher-type unit-root test for D.ln_gdp_per_capita Based on augmented Dickey-Fuller tests Ho: All panels contain unit roots Ha: At least one panel is stationary Number of panels = Number of periods = AR parameter: Panel means: Time trend: Drift term: Asymptotics: T -> Infinity Panel-specific Included Included Not included Inverse chi-squared(14) Inverse normal Inverse logit t(39) Modified inv chi-squared P Z L* Pm 18 ADF regressions: lag Statistic p-value 23.1102 -1.3151 -1.4753 1.7217 0.0585 0.0942 0.0741 0.0426 P statistic requires number of panels to be finite Other statistics are suitable for finite or infinite number of panels 76 ... that financial development and shadow economy have mutual effect In fact, the financial development will be constrained if the shadow economy gains the advantages On the other hand, the shadow economy. .. understanding of the origin of financial development concept Then, the determinants of financial development will be discussed 2.1.1 The theory of shadow economy The shadow economy is the general... if the level of financial development improves This findings is obviously the evidence of the mutual relationship of shadow economy and the financial development The formal economy vs shadow economy