J Knowl Econ DOI 10.1007/s13132-017-0453-5
Financial Stability, Monetary Policy, and Economic Growth: Panel Data Evidence from Developed and Developing Countries Moheddine Younsi 1 & Amine Nafla 2
Received: 3 April 2015 / Accepted: 1 February 2017 # Springer Science+Business Media New York 2017
Abstract This paper examines the relationship between financial stability, monetary policy, and economic growth in 40 developed and developing countries by using the annual panel data over the period of 1993–2015. Fixed and random effects panel data regression models were fitted to determine the impact of financial stability and monetary policy on economic growth. Our results indicate that trade openness, capital account openness, and foreign direct investment have positive impacts on economic growth with a high degree in developed countries. Our empirical results also indicate a positive and significant impact of research and development on financial development as well as economic growth in developed countries. Financial crises, bank liquid reserves, and bank nonperforming loans affect negatively financial stability, financial development, as well as economic growth. This impact essentially amounts to the sensitivity and fragility of the banking system. In addition, inflation continues to affect economic growth in a negative way. The main findings confirm the complementarity and the importance of real, financial, monetary variables and bank solidity as well their significant impacts on financial stability and economic development. Keywords Financial stability . Monetary policy . Economic growth
* Moheddine Younsi
[email protected] Amine Nafla
[email protected]
1
Faculty of Economics and Management, Unit of Research in Development Economics, University of Sfax, Street of airport, km 4.5, LP 1088, 3018 Sfax, Tunisia
2
Higher Institute of Management, Unit of Research in Management Science, University of Sousse, Sousse, Tunisia
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Introduction The economic and financial stability is a concern at both national and multilateral. As evidenced in the recent financial crises, countries are becoming more and more interdependent (Tezcan 2009; Belka 2009a; Dhameja 2010; Staehr 2010a; Stephen and Enisse 2012, 2015). The problems which arise in a sector may affect other areas and have an impact beyond the borders. In the light of economic and financial stability, there is no country Bisland.^ As well known, the global financial crisis exposed fundamental weaknesses in financial systems worldwide. This kind of crisis exposed a number of structural problems and had a significant impact on the developed and developing economies such as unsustainable levels of public or private debt or declining competitiveness (Cali et al. 2008; Marco 2008 ; Belka 2009a, Belka 2009b; Tezcan 2009; Dietz and Protsky 2009; Dhameja 2010; Staehr 2010a; Staehr 2010b; Terazi and Senel 2011). The global challenge is to limit the consequences of this financial turmoil already evident, on the real economy, to preserve the long-term financing and investment necessary to initiate a global economic governance to rebuild a stable and socially acceptable. Over the past few decades, several studies have been done to establish how the financial markets stability might be affected by the economic and financial crises, but little previous research has assessed what might happen to global financial systems (e.g., King and Levine 1993; Devarajan, Swaroop, and Zou 1996; Levine and Zervos 1996, 1998; Borenztein et al. 1998; Beck, Levine, and Loayza 2000; Beck and Levine 2001, 2004; Rioja and Valen 2002). Financial crises and their countermeasures have pronounced and unintended effects in financial regulation and supervision proved devastating to the stability of financial institutions. However, King and Levine (1992, 1993) have focused on the relationship between financial development and economic growth. They concluded, from a cross-sectional study, that beyond the positive relationship between the two variables that financial development can well predict the economic growth in the 10–30 years to come. They also found that a high level of financial development was associated with a further improvement the rate of accumulation and efficiency in terms of capital allocation. Devarajan, Swaroop, and Zou (1996) examined the relationship between the level of public expenditure and economic growth, using panel data from 43 developing countries over 20 years. The results indicate that there is evidence of a negative relationship between per capita growth and the capital component of public expenditure. Furthermore, the study makes evident that an increase in the share of current expenditure has a positive and significant impact on economic growth. In the same line of ideas, Borenztein et al. (1998) examined the impact of foreign direct investment (FDI) on economic growth, using data on FDI flows from industrial countries to 69 developing countries over the last 20 years. The study suggests that FDI contributes to economic growth only when a sufficient absorptive capability of the advanced technologies is available in the host economy. In addition, these results prove that the higher productivity of FDI holds only when the host country has a minimum threshold stock of human capital. In the same thought, Beck, Levine, and Loayza (2000) found that the short-run relationship between financial development indicators is positive with the rate of
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economic growth, the rate of capital accumulation, and total factor productivity, while it is still ambiguous with the private savings rate. The study of Rioja and Valen (2002) suggests that financial development may affect economic growth in low-income countries through capital accumulation, while in high-income countries is the increased productivity which serves belt transmission. In fact, most of the previous studies which have focused on the relationship between financial development and economic growth often used ratios measuring the state of the banking system. These measures conceal a part of the recorded financial development over these recent years in many developing countries have resulted in a rise of the financial markets in the case of stock exchanges especially in the emerging countries. As well to better identify the financial development, various existing studies incorporate indicators of measuring the size and liquidity of the stock market. The works of Levine and Zervos (1996, 1998) and Beck and Levine (2001) provide evidence that the development of stock markets is an indicator that predicts good prospects for economic growth. At the same current literature, Bekaert, Harvey, and Lundblad (2005) show that the economies that have liberalized their stock markets have high economic growth rates. The work of Henry (2000) concluded from the methodology of event studies that the liberalization of stock markets positively affects the level of private investment. Although the cleavage between Bfinancial system oriented market^ and Bfinancial system oriented bank^ seems to be exceeded (Jacquet and Pollin 2007), since the banks contribute actively to the development of stock markets, and the two systems will dread more easily in a logic of complementarities. Tadesse (2002) suggests that financial systems dominated by banks are more favorable to growth in underdeveloped countries financially, whereas in the developed countries financially, the oriented systems market would be most promising growth. By against, Atje and Jovanovic (1993) found that the indicators of the banking sector are less correlated to investment performance than those of the stock market. Many arguments making reference to the sources and nature of financial instability have been developed to show the weak link or the negative impact of financial development on economic growth. In fact, several existing studies have recognized the incontestable association between financial development and financial instability (Demirgüç-Kunt and Detragiache 1998, Kaminsky and Reinhart 1999, Hung 2001, Guillaumont and Kpodar 2004; Loayza and Ranciere 2004). If it is known that financial instability is detrimental to economic growth, one can conclude that financial development and financial instability may penalize economic growth. However, Beck and Levine (2004) examined the financial system stability effect on economic growth over the period of 1976–1998, by verifying the incidence of the banking and stock exchange sector on the economy growth for 40 countries. The results reveal that the stock exchange and the banking system affect positively and significantly the economic growth. The results of Guillaumont and Kpodar (2004) study suggest that financial liberalization is a good determinant that can promote financial stability and financial development to achieve economic development, taking into account attention to changes in the political and economic environment. In the same field, Wan-Chun and ChenMin (2006), Mario (2006), Fu-Sheng Hung (2009), and Eggoh (2010) have highlighted the interaction between financial developments and economic growth and recognizing the essential role that the real sector plays in economic growth.
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In a similar vein, Eggoh (2010) examined the link between financial development, financial stability, and economic growth using simultaneous equation models with both panel and time series econometric techniques for 71 countries. The findings suggest that, in long term, financial instability does not have predictive power on economic growth and the relationship between the latter and financial development. But in short term, financial instability has a negative impact on the relationship between financial development and economic growth. However, despite their evidences reveal the negative impact of financial instability on the relationship between financial development and growth, their conclusions are no longer limited in the short term. In addition, the study of Wahyoe, Fouad, and Amine (2011) observed that the big power of the market on the banking sector results in bigger instability. While banks are better capitalized in less competitive markets, their risk of defect remains high. A profound investigation shows, however, that such a behavior depends on the economic environment. Economic growth contributes to neutralization risk-taking and annihilates the instability in the less competitive markets. We propose in this study a deepening of the work by analyzing the three-way linkages between financial stability, monetary policy, and economic growth in both developed and developing countries over the period of 1993–2015. The paper is structured as follows. The BData and Methods^section presents the data description and econometric model. The BResults^ section discusses the estimation results. The BConclusions^ section summarizes the main results.
Data and Methods Data and Variables In the present study, we use the annual panel data over the period of 1993–2015 for 40 countries. The panel data on real, financial, and monetary indicators are taken from the World Development Indicators (http://data.worldbank.org/indicator). These countries were classified into two groups according to their level of development: 22 developed countries, namely, Australia, Austria, Belgium, Canada, Denmark, France, Finland, Germany, Greece, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Taiwan, UK, and US and 18 developing countries, namely, Algeria, Argentina, Brazil, Chile, Colombia, Egypt, India, Indonesia, Malaysia, Mexico, Morocco, Philippines, Pakistan, Peru, South Africa, Turkey, Tunisia, and Venezuela. In this study, we have introduced some important variables in the economic development of each country. These variables can be classified into three categories: (1) real variables (i.e., public expenditure level as a share of GDP, foreign direct investment, human capital, gross domestic product, research and development, population growth rate, gross fixed capital formation, trade openness of economy growth, growth rate of GDP), (2) financial variables (i.e., market capitalization to GDP, index measuring a country’s degree of capital account openness, ratio of bank nonperforming loans to total gross loans, bank liquid reserves to bank assets ratio, rate spread (debit/credit)), (3) monetary variables (i.e., inflation rate, liquidity indicator).
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The main contribution is to examine the three-way linkages between financial development, financial stability, and economic growth by introducing five new variables of control, which appear to us very important, even undeniable to assess the financial development and financial stability effects on economic growth. The first variable is the foreign direct investment. This variable helps the integration of financial markets in different countries in the international financial system and brings significant benefits, such as efficiency, stability, and soundness of the financial system. The second is the gross fixed capital formation, the later plays a crucial role in preserving the economic and financial system. If investors continue to implement these investments and roll on domestic debt, reserves will decline, which cause the decrease in interest rates and the birth of a pressure change, touching the portfolios of banks and creating a repayment difficulties. The third is the research and development which seems very important to economic development. Given the crucial role of the latter in the creation of new theories to improve both financial and economic situation of a country, we have tried to introduce it into our models to test their effects on economic growth. In addition, two other indicators (i.e., bank liquid reserves to bank assets ratio and Bank Nonperforming Loans to total gross loans) are introduced into the model to better explain the incidence of financial stability on economic development rooted in the banking sector. These variables provide a clear and transparent idea especially on the vulnerability and the performance of the financial system in developed countries and even their counterparts. Moreover, productive or performing loans help more than the other types of loans to strengthen the financial system and improve the monetary policy and the real economy of a country. Moreover, we are taken into account the impact of the financial crisis by introducing Bcrisis^ as a dummy variable, if a country is affected by a financial crisis. We also injected another variable of control and stability which is the Bdegree of integration^ to clarify the specificity of the place and the value that each country holds in the estimation. All variables were log-transformed except crisis and degree of integration which are dummy variables. The Model To examine the relationship between financial stability, monetary policy, and economic growth, we use the annual panel data model that takes the following form: Y it ¼ α0 þ α1i lnGDPit þ α2i lnPEXPit þ α3i lnTOit þ α4i lnHKit þ α5i lnGFCFit þα6i lnFDIit þ α7i lnTOEGit þ α8i lnPGRit þ α9i lnRDit þ α10i lnINFit þα11i lnKAOPit þ α12i lnLIQit þ α13i lnRSPit þ α14i lnMCAPit þ α15i lnBLRit
ð1Þ
þα16i lnBNPLit þ α17i DIit þ α18i CRISISit þ εit ;
where the subscript i = 1,…, N denotes the country (in our study, we have 40 countries) and t = 1,.., T denotes the time period (our time frame is 1993–2015), Y is the average annual growth of per capita GDP, lnGDP is log GDP per capita,
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lnPEXP is log public expenditure level as a share of GDP, lnTO is log trade openness, lnHK is log human capital, lnGFCF is log gross fixed capital formation, lnFDI is log foreign direct investment, lnTOEG is log trade openness of economy growth, lnPGR is log population growth rate, lnRD is log research and development, lnINF is log (1 + inflation rate), lnKAOP is log index measuring a country’s degree of capital account openness, lnLIQ is log liquidity indicator, lnRSP is log rate spread (debit/credit), lnMCAP is log market capitalization to GDP, lnBLR is log bank liquid reserves to bank assets ratio, lnBNPL is log ratio of bank nonperforming loans to total gross loans, DI is dummy variable of degree of integration, CRISIS is dummy variable of crisis, and ε is the error term. To correct the error specification in our model,1 we reestimate Eq. (1) by using fixed and random effects panel data regression models by successively adding variables of development and financial stability or monetary policy action in Eqs. (2)–(5) as following:
Y it ¼ α0 þ α1i lnGDPit þ α2i lnPEXPit þ α3i lnTOit þ α4i lnHKit þ α5i lnGFCFit þα6i lnFDIit þ α7i lnTOEGit þ α8i lnPGRit þ α9i lnRDit þ α10i DIit þ εit
ð2Þ
Y it ¼ α0 þ α1i lnGDPit þ α2i lnPEXPit þ α3i lnTOit þ α4i lnHKit þα5i lnGFCFit þ α6i lnFDIit þ α7i lnTOEGit þ α8i lnPGRit
ð3Þ
þα9i lnRDit þ α10i lnINFit þ α11i lnLIQit þ α12i DIit þ εit
Y it ¼ α0 þ α1i lnGDPit þ α2i lnPEXPit þ α3i lnTOit þ α4i lnHKit þ α5i lnGFCFit þα6i lnFDIit þ α7i lnTOEGit þ α8i lnPGRit þ α9i lnRDit þ α10i lnBLRit
ð4Þ
þα11i lnBNPLit þ α12i DIit þ εit
Y it ¼ α0 þ α1i lnGDPit þ α2i lnPEXPit þ α3i lnTOit þ α4i lnHKit þα5i lnGFCFit þ α6i lnFDIit þ α7i lnTOEGit þ α8i lnPGRit þα9i lnRDit þ α10i lnMCAPit þ α11i lnKAOPit þ α12i lnRSPit
ð5Þ
þα13i lnBLRit þ α14i lnBNPLit þ α15i DIit þ α16i CRISISit þ εit
1 We used the Blundell and Bond methods (1998) to correct the problems of autocorrelation and homoscedasticity.
−0.186
−0.662 0.399
−0.488
−0.240 −0.818
Mean
PGR
LIQ
Variables
0.567 −0.917
0.091
RD
6.416 2.827
0.290 2.126
−0.832 5.143
Developing c. All countries
5.132 5.364
−0.522 −1.791
−0.462 −0.325
All countries Developed c.
Developed c. Developing c.
0.527 0.700
7.480 0.837
−10.886 3.365
Developing c. All countries
2.822 3.805
7.480 8.128
Developed c. Developing c.
10.801
18.298
−10.886 −8.530
10.908 10.908
All countries Developed c.
18.298 11.491
All countries Developed c.
10.136 8.972
8.877 9.801
9.513 10.14
GDP
Developing c.
2.722 4.495
3.999 3.398
Developing c. All countries
Developed c. Developing c.
3.238 2.623
All countries Developed c.
Y
Kurtosis
Skewness
Std.dev.
Min
Max
Median
Mean
Variables
Table 1 Descriptive statistics, 1993–2015
3.632 3.173
−0.502 4.092
−0.475 0.075
0.210 0.343
1.635 0.279
1.635 2.100
3.304
3.304 3.268
2.612 2.634
2.605 2.618
2.620 2.631
PEXP
1.058 1.814
1.429
RSP
6.514 6.605
0.830 6.529
0.516 −1.165
0.848 1.156
−1.262 1.162
−3.080 −3.080
7.731
0.731 2.762
0.899 1.969
2.010 1.266
1.365 0.827
INF
3.062 0.704
2.001
DI
3.143 5.007
1.043 4.135
0.543 −0.019
0.544 0.649
2.715 0.596
2.715 2.785
6.094
6.094 5.406
4.180 4.036
4.084 4.091
4.153 4.209
TO
0.539 0.548
0.541
CRISIS
20.021 3.515
−1.336 12.410
3.090 −3.495
0.222 0.966
−1.919 0.635
−1.919 −0.864
0.944
0.944 0.944
0.944 0.388
0.167 0.944
0.648 0.845
KAOP
13.226 5.592
−0.776 7.847
−0.521 −1.330
0.077 0.140
4.238 0.110
4.236 4.236
5.052
5.052 4.830
4.635 4.700
4.681 4.646
4.655 4.637
HK
4.004 3.339
3.703
MCAP
4.097 3.883
0.580 4.130
0.720 0.936
0.191 0.234
2.493 0.211
2.493 2.695
3.786
3.786 3.674
3.043 3.052
3.061 3.048
3.065 3.068
GFCF
0.607 2.799
1.558
BLR
7.541 9.453
−1.834 8.511
1.387 −1.181
1.427 1.059
−4.997 1.274
−7.187 −7.187
2.497
4.539 4.539
0.728 0.813
0.617 0.785
0.650 0.678
FDI
0.519 1.903
1.222
BNPL
2.166 2.166
−0.418 2.166
−0.418 −0.418
0.165 0.165
1.621 0.165
1.621 1.621
2.091
2.091 2.091
1.957 1.947
1.912 1.957
1.912 1.912
TOEG
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2.928 12.285 −0.800 10.602
1.683 1.683
0.988 −6.432
−6.432 −0.515
0.931 0.996
0.305 1.802
−1.382 −0.194
8.814 7.852
2.662
0.896 0.896
0.270 −1.795
−1.005 −1.795
0.525 0.375
0.513 −0.142
0.718 0.251
2.937 3.896
2.373
Source: authors’ estimation
Kurtosis
Skewness
Std.dev.
Min
Max
0.426
−0.785
119.72
215.94 3.240
1.843 0.487
−0.855 −3.033
31.832 −3.033
31.832 1.439
−0.962
0.352 0.652
0.071 −0.570
−0.486 −0.269
Median
RD
PGR
LIQ
Variables
Table 1 (continued)
4.330
242.021 191.311
−12.908 1.1615
0.760 12.871
1.384 1.702
−24.122 0.0422
4.378 −24.122
4.378 2.234
1.663
1.482 1.257
RSP
1.849
1.562 1.533
−0.501 0.617
0.807 0.465
1.967 1.995
0.000 0.000
2.012 0.000
2.000 5.012
0.012
1.012 4.022
DI
1.025
1.025 1.025
−0.105 −0.105
0.510 −0.105
0.510 0.510
0.000 0.000
1.012 0.000
0.541 1.012
1.012
1.012 1.012
CRISIS
2.871
2.832 2.626
−0.320 0.092
0.910 −0.337
0.860 0.682
2.123 1.001
5.807 1.001
3.705 5.604
3.293
3.749 4.037
MCAP
2.978
2.026 2.990
−0.268 −0.290
0.712 −0.025
1.365 0.858
−1.459 1.116
4.763 −1.597
1.558 2.439
2.857
1.601 0.643
BLR
2.263
2.685 2.876
0.045 −0.132
0.887 0.055
1.049 0.786
−1.597 −0.211
3.550 −1.459
1.222 2.521
1.928
1.131 0.482
BNPL
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Results Descriptive Statistics Table 1 presents descriptive statistics of the real, financial, and monetary variables included in our model for 22 developed countries, namely, Australia, Austria, Belgium, Canada, Denmark, France, Finland, Germany, Greece, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Taiwan, UK, and US and 18 developing countries, namely, Algeria, Argentina, Brazil, Chile, Colombia, Egypt, India, Indonesia, Malaysia, Mexico, Morocco, Philippines, Pakistan, Peru, South Africa, Turkey, Tunisia, and Venezuela. The results show that the average economic growth rate (Y) of the total sample is 3.226% (SD = 3.353) for the study period (1993–2015). About developed and developing countries, the average economic growth rate is 2.611% (SD = 2.810) and 3.987% (SD = 3.793), respectively. The average economic growth rate is ranged from 0.62% (Japan) to 6.725% (India). For the financial development indicators, developed countries have recorded the minimum values for the fragility of the banking system and the level of liquid assets. On the other hand, developing countries have recorded maximum values with a large gap among them for certain indicators. Bank nonperforming loans to total gross loans (BNPL) ranged from 0.507 (SD = 0.774) for the developed countries to 1.891(SD = 0.875) for the developing countries even for bank liquid reserves (BLR), the SDs are certainly related to differences in development between the countries of our sample. To correct this problem, we make a logarithmic transformation. The correlation matrix is provided in Appendices Table 4 and 5. The results indicate a positive and significant correlation between financial development variables and economic growth in the most cases. On the other hand, a negative and insignificant impact in other cases is found, particularly between the indicators of fragility or banking and financial instability on economic development. In addition, we observed a strongly correlation between real variables. For the monetary and financial variables, the correlation differs depending on the nature of the variables. Regression Results and Discussions The results presented in column 1 (Tables 2 and 3) show that degree of capital account openness (KAOP) has a positive and significant impact on economic growth in all developed countries. For developing countries, no significant relationship was found. This phenomenon is essentially due to the low degree of integration (DI) and trade openness on the outside and the protectionist barriers implanted by countries especially in the countries with low competitiveness against products and foreign capital. The results show that FDI has a positive and significant impact on economic growth. In fact, this indicator may help to reduce the unemployment and provide capital to countries that really needed it. This economic indicator probably involved the evolution of competition and industry especially in host countries. In addition, FDI can improve productivity by implementing new communication technologies. The introduction of bank liquid reserves to bank assets ratio (BLR) and bank nonperforming loans to total gross loans (BNPL), and the crisis as a dummy variable is designed to test the impact of bank strength and stability of financial system on financial development
−1.820
1.742
−2.220
−1.810
−2.131
−1.110
−2.105
0.410
−1.652
−0.086
0.008
−0.050
−0.075
−0.025
−0.014
−0.018
0.006
0.004
−0.007
70.081 (0.000)
0.516
2.610 (0.005)
lnRD
lnINF
lnKAOP
lnLIQ
lnRSP
lnMCAP
lnBLR
lnBNPL
DI
CRISIS
Hausman stats. (prob.)
R-squared
F-stats. (prob.)
Source: authors’ estimations
−1.952
−0.007
lnPGR
0.571
0.241
−1.470
0.001
−0.079
lnFDI
lnTOEG
0.350
−0.822
0.033
−0.044
lnGFCF
lnHK
1.631
−3.895
0.085
−0.223
lnPEXP
lnTO
1.272
0.056
lnGDP
0.106
0.686
0.573
0.042
0.271
0.038
0.076
0.032
0.090
0.075
0.058
0.148
0.811
0.418
0.729
0.110
0.000
0.211
−2.611
−0.672
−1.344
7.640 (0.000)
0.246
20.360 (0.026)
−0.013
−0.010
−0.003
1.805
−0.051
−0.001 0.028
−2.034
2.242
1.791
−3.342
1.00
−1.382
t-stats.
−0.036
0.093
0.040
−0.103
0.020
−0.380
−1.170
−0.028
Constant
0.249
Coeff.
t-stats.
Coeff.
Prob.
Model 2
Model 1
Variables
0.010
0.500
0.181
0.281
0.963
0.043
0.026
0.075
0.001
0.317
0.170
Prob.
−2.480
−1.217
1.360
−0.733
−2.130
0.562
0.034
−2.031
1.910
2.130
−2.942
1.700
−1.812
t-stats.
6.750 (0.000)
0.285
16.461 (0.171)
−0.015
−0.051
0.004
−0.011
−0.006
0.017
0.001
−0.039
0.084
0.052
−0.097
0.036
−0.540
Coeff.
Model 3
0.014
0.045
0.175
0.468
0.034
0.579
0.973
0.044
0.057
0.034
0.004
0.091
0.072
Prob.
6.391 (0.000)
0.322
18.611 (0.098)
0.382
−1.352
−0.007 0.003
−0.940
−1.235
−1.382
0.671
0.720
−4.845
4.592
0.115
−6.540
0.161
−1.573
t-stats.
−0.004
−0.031
−0.004
0.020
0.001
−0.143
0.250
0.003
−0.279
0.004
−0.028
Coeff.
Model 4
Table 2 Results of fixed-effect models of financial stability, monetary policy, and economic growth of developed countries
0.701
0.177
0.348
0.220
0.169
0.507
0.474
0.000
0.000
0.912
0.000
0.875
0.118
Prob.
−1.370
1.111
−0.405
−0.562
−0.780
−2.781
−1.081
−3.310
−0.385
1.040
−0.422
1.443
1.570
0.772
−5.080
4.841 (0.000)
0.479
43.810 (0.000)
−0.005
0.009
−0.002
−0.003
−0.005
−0.016
−0.012
−0.108
−0.001
0.034
−0.001
0.051
0.129
0.026
−0.246
0.231 −0.902
0.107
t-stats.
−0.025
Coeff.
Model 5
0.174
0.271
0.689
0.574
0.438
0.007
0.283
0.001
0.706
0.303
0.676
0.152
0.119
0.446
0.000
0.369
0.820
Prob.
J Knowl Econ
−0.042
0.521
−1.524
−0.016
0.191
0.341
lnPEXP
lnTO
lnHK
0.337
−0.031
−2.120
−1.870
−1.870
−0.530
−1.031
−0.572
1.421
−0.970
2.440
−0.860
−0.008
−0.007
−0 .028
−0.029
−0.073
−0.071
−0.047
−0.090
−0.043
0.051
−0.021
28.590 (0.051)
0.962
4.250 (0.129)
lnPGR
lnRD
lnINF
lnKAOP
lnLIQ
lnRSP
lnMCAP
lnBLR
lnBNPL
DI
CRISIS
Hausman stats. (prob.)
R-squared
F-stats. (prob.)
Source: authors’ estimation
26.851 (0.003)
−1.024
−0.178
0.451
0.093
0.403
0.250
0.606
0.381
0.632
0.160
0.159
0.124
0.981
0.385
−0.551
0.220
0.405
−0.511
4.470 (0.000)
−0.007
0.001
0.018
−0.047
0.062 −1.010
0.002 −0.007
lnTOEG
0.226
0.135
2.031
−0.278
−1.321
−0.059
0.077
0.192
−1.380
3.511
−1.290
0.007
−0.102
0.203
lnGFCF
0.637
0.319
0.974
0.978
t-stats.
lnFDI
1.192
−0.031
−0.005
lnGDP
−1.143
−0.340
−1.036
Constant
0.756
Coeff.
t-stats.
Coeff.
Prob.
Model 2
Model 1
Variables
0.584
0.823
0.688
0.613
0.315
0.954
0.752
0.849
0.173
0.001
0.201
Prob.
−0.880
−1.531
−1.982
0.296
0.782
−1.210
−0.205
−
−0.242
0.533
−1.490
3.582
−1.461
t-stats.
4.180 (0.000)
0.388
56.341 (0.000)
−0.011
−0.069
−0.016
0.001
0.035
−0.122
−0.001
−
−0.045
0.027
−0.116
0.228
−1.268
Coeff.
Model 3
0.381
0.042
0.051
0.771
0.440
0.231
0.839
−
0.811
0.595
0.140
0.001
0.149
Prob.
0.193
−0.260
0.292
0.130
1.411
−0.840
−0.772
−1.490
−1.991
0.450
−1.110
3.651
0.870
t-stats.
3.531 (0.000)
0.391
15.601 (0.210)
0.002
−0.005
0.009
0.001
0.077
−0.094
−0.006
−
−0.646
0.020
−0.097
0.268
1.281
Coeff.
Model 4
Table 3 Results of fixed-effect models of financial stability, monetary policy, and economic growth of developing countries
0.854
0.796
0.770
0.896
0.164
0.402
0.446
0.142
0.050
0.652
0.271
0.001
0.386
Prob.
1.244
0.721
−2.833
0.055
3.321
−0.088
−0.470
0.792
−1.901
−1.142
−0.705
−2.210
−0.903
1.962
1.561
−0.820
1.182
t-stats.
1.932 (0.158)
0.774
13.210 (0.657)
0.030
0.018
−0.141
0.003
0.157
−0.004
−0.008
0.001
−0.572
−0.245
−0.016
−0.403
−0.694
0.307
0.467
−0.123
3.631
Coeff.
Model 5
0.247
0.493
0.020
0.964
0.009
0.941
0.647
0.452
0.090
0.283
0.503
0.054
0.392
0.082
0.153
0.435
0.267
Prob.
J Knowl Econ
J Knowl Econ
and economic growth, particularly in the crisis period (see Table 3). The empirical results show that these variables have a significant negative impact on the strength and stability of banking system. This impact particularly amounts to the sensitivity and fragility of banking system. In fact, increase in BNPL affects negatively the financial stability and financial development as well as long-term economic growth. In order to correct the problem of the significance of financial and monetary variables, we have tried to eliminate the effect of autocorrelation and homoscedasticity by applying the generalized least squares method with a cross-sectional time series. The effect of most variables remained significant and correct. The negative impact of two variables banking stability on economic growth appears clear, as for inflation. On the other hand, capital account openness rate (KAOP) has a significant positive impact on economic growth with a high degree in developed countries. Financial crises have affected negatively the financial stability as well as financial development and economic growth. In accordance with several existing literatures (Demirgüç-Kunt and Detragiache 1998; Beck, Levine, and Loayza 2000; Detragiache and Spilimbergo 2001; Aglietta 2003; Loayza and Ranciere 2004; Cali et al. 2008; Tezcan 2009; Dhameja 2010), our findings confirm that crisis has a detrimental effect on the fragility and instability of the financial system in the first place and the economic development in the second. This impact varies according to the country group. In line with other studies (Wan-Chun and Chen-Min 2006; Jacquet and Pollin 2007; Marco 2008; Eggoh 2010), our results suggest that development of banking system is the key factor in economic development. To overcome the poor economic conditions that lived several countries in the world, it is interesting to consider the indicator of research and development (R&D) in our model. However, our results show a positive and significant impact of R&D on financial development and economic growth. This relationship appears clearly in developed countries given the importance of this variable, particularly in the field of invention which facilitates the smooth functioning of multiple systems and ensures economic development. By linking the variables of development and financial stability, which are interested in the preservation of the banking sector as well stock exchange with those of the real economy, we can see well the negative impact of financial crisis with the other two variables banking strength development. However, the results show that the proper functioning of the stock market, particularly in developed countries, promotes economic growth. In addition, the results confirm the positive and increasing effect of financial openness on financial development as well as economic growth, especially in developed countries group. The estimation of development variable and financial adjustment Brate spread^ (RSP) by using the generalized least squares method with a cross-sectional time series reveals that this variable has a positive and significant impact on financial stability as well as economic growth. Indeed, the variation of the interest rate may affect economic growth if the countries do not put preservation tools. The fault lies in the inability of developing countries to solve urgent cases as soon as possible. In addition, our empirical results indicate that market capitalization (MCAP) has positive and significant impact on the financial system in developed countries
J Knowl Econ
given the dominance of the banking sector. The relationship was found insignificant among developing countries, and this may be returned to the country decomposition method. It is likely that if we use a different classification, complete, which include for example the OECD countries and LDCs, the results become more correct. But that does not prevent the MCAP contributes to the financial development, especially in developed countries through the creation of an animation on the financial markets. The existence of a positive and significant relationship between the stock market development and economic growth does not cause reciprocity viewed that the direction of causality between the two variables is absent. This is why testing the causal link between the granger market capitalization and growth remains important. Furthermore, inflation (INF) continues to affect economic growth in a negative way. However, our empirical findings support other existing literatures that suggest the significant negative impact of INF on economic growth. This phenomenon appears clear in developing countries than developed countries, given the rapid and appropriate reactions of these and their ability to overcome the poor financial situation, practicing methods of adjustment, and mitigation of this phenomenon. Regarding model 2, which included only the real economy variables, the results shown in Tables 2 and 3 indicate that public expenditure (PEXP) and gross fixed capital formation (GFCF) affect negatively the financial development. In contrast, human capital (HK) and trade openness of economy growth (TOEG) have positive and significant impacts on financial development as well as economic growth. These results appear logical to us, because PEXP can affect the stability of the balance of payments, even for fast and unexpected movements in investment. In addition, literacy contributes to the evolution of human intellectual capacity which improves the productivity in particular and economic growth in general. For model 3 which relates monetary variables (i.e., LIQ and INF) to real variables (i.e., PEXP, TO, GFCF, FDI, TOEG), the results indicate a negative and significant effect of LIQ and INF on economic growth. However, the significant negative effect of INF in developing countries is linked mainly to several circumstances such as the financial crisis and their traces, political turmoil (e.g., Tunisia, Egypt), and the evolution of public debt, which can also affect growth by strengthening expected inflation, volatility, and macroeconomic uncertainty (e.g., Turkey). In developed countries, the impact of INF is found negligible and insignificant. This is mainly due to the ability of these countries to overcome the crisis, by an effective settlement system and an acceptable level of public debt, while the impact of LIQ seems clear negative and significant in developed countries as well as in developing countries. Regarding the real variables, the results shown in Table 2 indicate that PEXP and GFCF have negative and significant effects on economic growth in developed countries. In contrast, TO, FDI, and TOEG have significant positive impacts on financial development as well as economic growth. For developing countries, the results shown in Table 3 indicate that PEXP, GFCF, FDI, and TOEG affect negatively the financial development as well as economic growth.
J Knowl Econ
Model 4 binds the two variables of the banking and financial stability (gross national income (GNI), gross national product (GNP)) with the indicators of the real economy. The results show the harmful effects of these indicators on economic growth particularly in developed countries where their systems are very complex. Finally, model 5 highlighted the link between all financial variables (banking and trading) and those of the real economy, taking into account the impact of INF. The results show the existence of a significant impact of several financial variables on economic growth in both developed and developing countries. Generally, the effect is unfavorable to economic growth, such as the cases of GNI, GNP, LIQ, and RSP. In addition, financial openness promotes financial and economic stability. We know that in the last few decades, the subprime crisis has negatively affected banking sector in particular and financial stability in general. The main cause of this deterioration bank returns to the increase in nonperforming loans without the existence of an adjustment and regulation system. In order to have a competitive advantage, leaders lend, even if the bank is in the failure process. In addition, the lack of an adequate level of provision to cover the default risk can damage the active party by threatening the sustainability of the financial institution. Indeed, banking instability contributes to the instability of the financial system initially and the economic growth in the second place. Similarly, the poor adoption of financial liberalization in some countries, especially developing countries, has generated an increase in appropriations. The latter may increase the risk of failure and in some cases constitutes a brake to economic development. Our empirical findings confirm the importance of FDI in the financial systems stability and economic growth. By its positive impact, we notice well that the capital movement towards host countries can help them to have other sources of funding. The latter allow implementing more investments and increasing employment levels, which in turn increases the purchasing power. Moreover, FDI allows encourage domestic companies to increase their comparative advantage if not they risk going bankrupt. In fact, the positive impact of FDI contributes to economic growth. This impact appears insignificant on economic growth in developed countries. This returns essentially to the outputs of funds to developing countries, since the investments are less expensive. For this reason, the investors who want to reach maximum profits are heading to developing countries. In addition, the subprime crisis, for its spread, affected especially the developed countries, what led the agents to financing capacity to head emerging. Therefore, we notice that in developing countries, FDI has a positive and significant impact on economic growth for developed countries and a negative impact for developing countries. This result is logical proving, as regional economic integration mechanisms are more developed in developed countries. This amounts to several reasons such as the signatures of many regional agreements and continuous adoption of new technologies. Indeed, the regional space is a world economic regulation conducive. Moreover, the simplicity of the procedures in place for the signing of international conventions helps developed countries to have an adequate degree of integration.
J Knowl Econ
It should be noted that the opening-up allows to receive funds that help finance investments and also to increase sources of liquidity for the financial institutions. Our results indicate that developed countries have a negative and significant score, this returns particular to the negative impact of the financial crisis, especially in the USA and the countries that have close relations with this. However, the impact of this variable is low or nonexistent in developing countries, because the latter are places of receptions in the majority of cases. Moreover, emerging countries have protectionist barriers that limit competition from foreign products and national institutions will always promote. Our findings confirm the importance of financial, real, monetary variables and bank solidity as well as their significant impacts on financial stability as well as economic development. We can conclude that the degree of financial development differs according to the group of countries. Obviously, developed countries possess an important level of liquidity rate and upper to the developing countries. The financial stability remains very close to rich countries than their counterparts.
Conclusions The current study confirms the findings of several existing studies which affirm the detrimental role of INF in the sustainability of financial development and economic growth. However, our contribution is shown by the introduction of six variables which two are considered as indicators of controls. The variables used are divided into two real indicators such as (FDI) and (R&D), given the importance of the latter in economic development. The empirical evidence confirms the importance of FDI in economic growth in developed countries as well emerging countries. For R&D, its impact remains unclear, despite their importance today in overcoming our economic difficulties, especially about the negative effects of the climate on the viability of investments. We have also introduced two other banking and financial stability variables (i.e., GNI and GNP). These are related to the banking sector. The results show that these two indicators have adverse effects on banking stability, what contributes to worsen financial instability and slow economic development. But the impact of these variables might bank health in poor countries than rich countries. These results are obvious, since developing countries rely on banking stability in the first place and lack the preservation techniques of financial stability in the second place. In addition, we have added two variables dummies (i.e., crisis and DI), to take account of the evolution of financial instability and financial development of each country. The results show the negative impact of the crisis on financial stability as well as economic growth. On the other hand, the impact of DI remains insignificant. Market capitalization, meanwhile, remains favorable development, and the results are clear for developed countries, but a little ambiguous to their counterparts, since most of the developing countries focus on banking activities and neglect the stock exchange.
RD
PGR
LIQ
TOEG
FDI
GFCF
HK
KAOP
TO
INF
PEXP
(0.0629)
GDP
(0.1663)
0.0092
(0.0190)
−0.0621
(0.0000)
(0.0024)
−0.0727
0.1652**
−0.1047*
(0.0552)
(0.0000)
(0.2458)
−0.1229**
−0.2591***
0.3865***
−0.0602
0.1215**
(0.0121)
−0.1314**
(0.0017)
−0.1620***
(0.0066)
0.1428***
(0.0000)
−0.2531***
0.0522
(0.0000)
(0.2138)
(0.3230)
−0.5331***
−0.0646
0.1465***
(0.0052)
(0.0565)
(0.0000)
(0.1269)
−0.3475***
(0.0000)
0.3042***
(0.7094)
0.0211
(0.2911)
0.0570
(0.0017)
0.1707***
(0.0000)
0.2578***
−0.0090
(0.2969)
0.0548
(0.0000)
−0.2604***
(0.0015)
0.1639***
(0.0000)
0.3871***
(0.0440)
0.1045**
(0.3756)
−0.0467
−0.0963* (0.0790)
(0.1830)
0.0727
1.000
TO
(0.0001)
−0.2166***
−0.0419
(0.4436)
(0.4721)
0.0388
1.000
INF
(0.0000)
0.2776***
(0.0011)
−0.1746***
1.000
PEXP
0.1005*
0.2862***
0.0804
(0.5556)
(0.0000)
0.0322
−0.2866***
−0.0634
(0.2214)
(0.0483)
(0.0000)
0.1077**
−0.2626***
(0.7278)
(0.4583)
0.0188
−0.0385
−0.0487
1.000
GDP
(0.3472)
0.2251
1.000
Y
Y
Table 4 Correlation matrix for developed countries
Appendix
0.2226***
(0.1676)
−0.0764
(0.2970)
0.0603
(0.5059)
0.0364
(0.9362)
0.0045
(0.2074)
−0.0690
(0.0067)
0.1494***
1.000
KAOP
−0.2935***
(0.0001)
−0.2079***
(0.0000)
0.2227***
(0.1919)
0.0687
(0.0265)
0.1188**
(0.0000)
−0.2355***
1.000
HK
−0.1209**
(0.0000)
0.2815***
(0.0000)
0.2707***
(0.4434)
0.0399
(0.0836)
−0.0914*
1.000
GFCF
−0.0293
(0.0000)
0.2995***
(0.0118)
−0.1398**
(0.0010)
0.1724***
1.000
FDI
J Knowl Econ
HK
KAOP
TO
INF
PEXP
GDP
Y
BNPL
BLR
MCAP
CRISIS
DI
RSP
(0.0000)
−0.1671***
(0.0027)
−0.2010***
(0.0002)
LIQ
(0.0007)
−0.0609
(0.2783)
−0.0518
(0.3483)
TOEG
PGR
(0.0001)
−0.2084***
(0.0039)
−0.1611***
(0.5962)
0.0276
(0.6641)
(0.1068)
(0.7475)
0.3215***
0.0225
−0.0835
0.1753***
(0.0001)
(0.0000)
(0.9476)
0.1993***
0.5547
0.0372
(0.0404)
PEXP
0.0167
0.0009
0.2210***
(0.0080)
−0.0034
0.2068***
(0.8775)
(0.2963)
0.1660***
GDP
Y
Table 4 (continued)
RD
(0.9385)
−0.0045
(0.0002)
0.2148***
RSP
(0.0301)
−0.1194**
(0.0000)
−
(0.0423)
−0.1053**
(0.0434)
(0.5817)
−0.1091**
−0.0286
(0.0000)
0.3178***
(0.8080)
0.0131
(0.0005)
−0.1853***
0.0114
−0.1585**
−0.0301 0.6495
(0.8803)
TO
(0.0000)
INF
DI
(0.0095)
0.1505**
(0.0001)
−
(0.0041)
0.1564***
(0.6431)
−0.0253
(0.1822)
0.0728
0.0003
−0.2419***
(0.0003)
KAOP
CRISIS
(0.9767)
−0.0016
(0.1989)
−0.0732
(0.1036)
0.0859
(0.8773)
−0.0081
(0.0000)
MCAP
0.4045***
0.0000
0.3768***
(0.0000)
HK
(0.5315)
0.0347
(0.0000)
BLR
0.2676***
(0.0000)
−0.2701***
(0.5874)
−0.0282
(0.0000)
−0.2902***
0.0000
−0.2752***
(0.0415)
GFCF
BNPL
(0.0000)
−0.2404***
(0.5237)
0.0365
(0.0000)
0.2487***
(0.7352)
0.0179
(0.0001)
0.2029***
0.0314
0.1370**
(0.6268)
FDI
J Knowl Econ
(0.1027)
−0.1009*
(0.0648)
(0.0159)
−0.2334***
(0.0000)
0.1849***
(0.0017)
0.2523***
(0.0000)
0.0012
(0.9833)
−0.0657
(0.2338)
0.1454***
−0.0892
0.1246**
(0.0078)
0.7490
0.6989
0.2915***
−0.3477***
0.0212
−0.0244
(0.0000)
0.2767
(0.9459)
(0.0057)
−0.1757***
(0.8682)
0.0115
(0.4643)
0.0520
−0.1777*** (0.0061)
(0.5676)
0.0362
(0.9080)
0.0073
(0.0037)
0.1818***
1.000
RSP
(0.0000)
0.3435***
(0.7615)
−0.0181
(0.0001)
−0.2353***
0.0009
−0.2314***
1.000
RD
P values are in parentheses. *, **, and *** indicate significance at the 10, 5, and 1% levels, respectively
(0.0000)
−0.2934***
(0.0001)
0.2236***
(0.3332)
0.0510
(0.6631)
−0.0229
(0.0000)
−0.0691
(0.5793)
−0.0041
−0.0333
1.000
0.1550***
(0.7318)
PGR
(0.0088)
0.0190
0.1496***
1.000
LIQ
(0.0042)
Source: authors’ estimations
BNPL
BLR
MCAP
CRISIS
DI
RSP
RD
PGR
0.3065***
LIQ
(0.0000)
1.000
TOEG
FDI
GFCF
TOEG
Table 4 (continued)
(0.0002)
0.2035***
(0.0000)
−0.2859***
(0.1336)
0.0779
(0.5296)
−0.0326
1.000
DI
(0.0585)
0.1043*
(0.2428)
0.0656
(0.1987)
−0.0668
1.000
CRISIS
(0.0104)
−0.1409**
(0.0002)
−0.2084***
1.000
MCAP
(0.6610)
0.0248
1.000
BLR
1.000
BNPL
J Knowl Econ
RSP
RD
PGR
LIQ
TOEG
FDI
GFCF
HK
KAOP
TO
INF
PEXP
GDP
(0.5097)
0.2621***
(0.0000)
−0.0207
(0.8095)
−0.1155**
(0.0436)
−0.0405
(0.4933)
−0.0570
(0.4372)
−0.0446
(0.1052)
0.0945
(0.9975)
(0.2056)
−0.1626**
(0.9705)
−0.1200*
0.1558**
(0.3530)
0.0838
0.2908**
(0.0630)
−0.0003
0.0032
(0.0013)
0.1099*
(0.3043)
−0.0626
(0.0000)
−0.2703***
(0.0013)
−0.1923***
(0.2541)
0.0675
(0.0083)
0.1710***
0.1096
(0.0000)
0.0385
−0.0141
(0.7683)
−0.0169
(0.1865)
(0.0504)
(0.3667)
−0.1828***
−0.1120*
−0.0518
(0.0001)
0.2425* **
(0.3347)
(0.0027)
(0.5339)
−0.3719***
0.1895***
−0.0397
(0.3447)
−0.0849
(0.0080)
−0.2249***
(0.7268)
(0.0000)
(0.6076)
(0.0000)
−0.3705***
1.000
INF
(0.0000)
−0.5824***
(0.0000)
0.4453***
1.000
PEXP
0.0757
0.3775***
0.0441
(0.0000)
(0.0000)
0.4196***
−0.2576***
−0.0519
(0.3808)
(0.4037)
(0.0000)
0.0479
−0.3159***
−0.0993*
1.000
GDP
(0.0829)
(0.1729)
1.000
0.0781
Y
Y
Table 5 Correlation matrix for developing countries
−0.1913**
(0.5051)
−0.0579
(0.7875)
0.0155
(0.0072)
0.1581***
(0.0064)
0.1555***
(0.7190)
−0.0210
(0.4724)
0.0412
(0.0012)
−0.2050* **
(0.8390)
0.0175
1.000
TO
−0.6746***
(0.1236)
−0.2010
(0.0055)
0.2351***
(0.0000)
0.3582***
(0.5651)
0.0494
(0.0880)
−0.1458***
(0.1479)
−0.1238
(0.9187)
−0.0103
1.000
KAOP
0.5113***
(0.9746)
−0.0030
(0.1048)
−0.1032
(0.0000)
−0.3278***
(0.6680)
0.0274
(0.0075)
−0.1734***
(0.0000)
−0.5425***
1.000
HK
−0.3220***
(0.2395)
0.1019
(0.0015)
−0.1809***
(0.0000)
0.3080***
(0.0307)
−0.1235**
(0.0019)
0.1798***
1.000
GFCF
0.1077*
(0.4151)
0.0715
(0.0091)
−0.1515***
(0.5533)
0.0357
(0.0001)
0.2215***
1.000
FDI
J Knowl Econ
HK
KAOP
TO
INF
PEXP
GDP
Y
BNPL
BLR
MCAP
CRISIS
DI
(0.2198)
0.1188*
(0.5993)
−0.0084
(0.8946)
LIQ
(0.6190)
0.0454
(0.4719)
TOEG
PGR
(0.0593)
−0.0759
(0.4278)
RD
(0.0105)
0.1659***
(0.0032)
0.1874***
(0.0435)
(0.8313) −0.1197**
(0.7375)
0.0126
(0.0024)
−0.1782***
(0.0188)
INF
−0.0456
0.0325
(0.2596)
(0.3828)
0.0192
(0.8544)
(0.0002)
−0.0646
−0.0501
−0.0308
(0.0000)
(0.6538)
−0.0105
(0.0000)
(0.0841)
0.3312***
−0.0257
0.2144***
(0.0108)
(0.0608)
PEXP
0.0992*
GDP
Y
Table 5 (continued)
RSP
(0.2582)
−0.0713
(0.0000)
−0.3078***
(0.0334)
0.1221**
(0.3223)
−0.0568
(0.0783)
−0.1008*
(0.0026)
TO
DI
(0.5640)
0.0551
(0.0001)
0.3294***
(0.3736)
−0.0763
(0.8573)
0.0154
(0.6736)
−0.0362
(0.0000)
KAOP
CRISIS
MCAP
(0.6771)
0.0288
(0.1005)
−0.1137
(0.0000)
−0.3186**
(0.9053)
0.0076
(0.3903)
0.0548
(0.0000)
HK
BLR
(0.6057)
−0.0326
(0.0022)
0.1877***
(0.0000)
0.3000**
(0.2495)
0.0660
(0.5163)
−0.0372
(0.0000)
GFCF
BNPL
(0.0113)
−0.1626**
(0.7086)
0.0236
(0.0000)
0.3220***
(0.2470)
−0.0676
(0.0000)
0.2852***
(0.0988)
FDI
J Knowl Econ
(0.2023)
−0.0226
(0.7020)
(0.0012)
−0.2334*
(0.0000)
0.1321**
(0.0379)
0.3169* **
(0.0000)
0.0449
(0.4682)
−0.1965***
(0.0017)
(0.0000)
−0.0753
0.1845***
0.4539***
−0.3422***
(0.0000)
(0.6228)
(0.0007)
(0.6349)
−0.3422***
0.0316
0.1932***
−0.0305
(0.2084)
(0.6858)
0.0381
(0.5904)
−0.0371
(0.1825)
−0.0902
(0.0389)
−0.1326
(0.8619)
0.0112
(0.1749)
0.0869
1.000
RSP
(0.0000)
−0.2611***
(0.0055)
0.1709**
(0.0000)
0.4127**
(0.3703)
−0.0514
1.000
DI
P values are in parentheses. *, **, and *** indicate significance at the 10, 5, and 1% levels, respectively
(0.1011)
0.1033
(0.9858)
−0.0017
−0.1526* *
(0.0132)
(0.7735)
0.0250
−0.1210**
(0.0349)
(0.2050)
−0.1098
(0.5869)
−0.0472
(0.9095)
0.0112
1.000
RD
(0.0744)
0.1022*
(0.0000)
(0.0583)
0.1094
−0.1634*
1.000
(0.6265)
(0.8509)
PGR
0.0423
−0.0111
−0.4378***
1.000
LIQ
(0.0000)
Source: authors’ estimations
BNPL
BLR
MCAP
CRISIS
DI
RSP
RD
PGR
0.1309**
LIQ
(0.0263)
1.000
TOEG
FDI
GFCF
TOEG
Table 5 (continued)
(0.0593)
0.1187*
(0.9168)
−0.0065
(0.1049)
−0.0932
1.000
CRISIS
(0.0396)
−0.1300***
(0.8116)
0.0148
1.000
MCAP
(0.0013)
0.2070***
1.000
BLR
1.000
BNPL
J Knowl Econ
J Knowl Econ
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