This article empirically explores the finance-growth relationship and the performance of the financial system measured by financial depth, accessibility, and efficiency of both financial sectors, that is, institutions and stock markets. It also examines the role of fiscal policy in conjunction with the performance of financial development during both normal and stress times. The data consists of a panel of 26 European Union countries over the period 1990–2020. The results show that during normal times, the finance-led growth relationship and the stock market are greatly important, while during stress times the relationship becomes insignificant. Interestingly, financial institutions are found to be more effective at promoting growth and there is clear evidence that a potentially dynamic positive effect of institutions to growth is absorbed by macroeconomic shocks. In addition, there is evidence for a threshold at a lower level compared to those previously identified in the literature. This latter finding can be attributed to different measures of financial institutions used and the impact of macroeconomic shocks. The inability of both financial sectors to enhance economic activity seems to exhibit persistence from the occurrence of the global financial crisis until the onset of the recent Covid-19 pandemic.

1 INTRODUCTION

A large amount of the literature has examined the finance-growth nexus and most of the evidence suggests the existence of a positive relationship. In the aftermath of the global financial crisis of 2008–2009 (henceforth, GFC), the impact of the financial sector on growth has been decreased, while the crisis transformed into a debt crisis and the subsequent austerity measures undertaken amplified further the fiscal impact on the crisis (Lane, 2012). In addition, the recent Covid-19 pandemic has negatively affected economies worldwide and governments committed to protect their economies by purchasing sovereign debt, among other measures. These two crises have different roots but have had severe detrimental effects on economies.

Although research about the finance-growth relationship for EU economies has grown rapidly, empirical evidence about the relationship comparing normal times, stress times including the GFC, and the recent pandemic, has been rather scarce. In addition, the effectiveness of fiscal policy during the above crises has important implications for EU countries, substantiating the argument of high financial integration and the complexity of the financial system (Ramey, 2019). The existing evidence demonstrates that the finance-growth relationship depends on the particular variables used as proxies for financial development (e.g., see Alexiou et al., 2018 and Asteriou & Spanos, 2019), while the multidimensional nature of finance is not widely considered (Purewal & Haini, 2022) leading to inconclusive findings. The question that emerges regards the nature of the relationship between financial development and economic growth, in both the short- and the long-run horizon, within and outside the two recent global crises, with a special interest for the effectiveness of fiscal policy. Furthermore, there is a need to re-evaluate the relationship using a superior measure of financial development, thus capturing more effectively the performance of the overall financial system.

In this article we explore the finance-growth relationship with a special focus on the role of fiscal policy using data for 26 European Union countries over the period 1990–2020. We contribute to the existing literature in four aspects. First, we use the new financial indices proposed by Svirydzenka (2016) which consider the multidimensional nature and complexity of the financial system and measure the financial depth, accessibility, and efficiency for both major financial sectors, institutions and stock markets. Second, we adopt a dynamic panel model to capture the performance of the two financial sectors across several sub-periods: normal times, GFC, and the start of the Covid-19 pandemic. Third, we estimate the impact on economic growth that arises from the response of financial development when fiscal policy shocks are employed as additional variables, thus capturing more effectively the sovereign debt crisis and the quality of fiscal policy. Finally, we use the new financial indicators to investigate threshold effects for both financial sectors on economic growth.

Our results shed light on the performance of the finance-growth relationship in relation to the quality of fiscal policy at normal times and periods of stress. Our findings indicate that stock markets contributed positively to economic growth at normal times but had adverse effects at times of stress also depending on the magnitude of fiscal shocks. We additionally find that financial institutions are more effective at promoting economic growth and provide evidence that a potentially dynamic positive effect from institutions to growth is absorbed by macroeconomic shocks from government expenditures and taxes. Nevertheless, the overall estimated results, indicate that after more than a decade from the GFC, both financial sectors show high persistence in favour of an insignificant effect on economic growth, and this insignificant effect may potentially exist for a long time until the return to levels observed before the crisis. Our article also provides new evidence regarding the existence of threshold effects of financial institutions and stock markets on economic growth. In particular, we find a lower level of threshold point compared to those in the literature, possibly due to the use of different measures of financial institutions and high levels of debt. Irrespective of the group of countries examined and the degree of financial integration, our results indicate that fiscal shocks from sovereign debt and the size of the government play a critical role in the performance of the financial system both at normal times and at severe times of stress.

The rest of the article is organized as follows: Section 2 reviews the literature, Section 3 describes the data and the methodology, Section 4 contains the empirical results and discussion, and Section 5 concludes.

2 LITERATURE REVIEW

After the GFC many central banks and other financial institutions raised the holdings of their governments' debts in order to enhance their credibility and avoid a default, thus following to some degree the practice of the financial repression. Studies have illustrated that the repression policy has been most successful in liquidating debts (Reinhart & Rogoff, 2011).

From a theoretical perspective, the research on the role of financial development in economic growth can be traced back to Bagehot (1873) who argued that well-organised capital markets in England enhanced resource allocation towards more productive investments. During the 1960s, studies emphasised the critical role of the banking system for economic growth in mobilising savings and encouraging investments (Goldsmith, 1969; Hicks, 1969). McKinnon (1973) produced a theoretical framework focusing on the liberalisation of the interest rate which leads to increases in savings thus spurring investments and eventually driving economic growth. He argued that the outcome of repression would be low savings, high consumption, low investments, thus preventing the bank sector of an economy to function at its full capability and lead to depressed economic growth. Generally, the literature suggests that financial liberalisation is a strong prerequisite to promote economic growth while one of the most critical factors that provided an impetus for moving to financial liberalisation was the pressure from globalisation.

Following financial liberalisation most cross-sectional studies used measures of financial development such as liquid liabilities to GDP, credit to private sector to GDP, and stock market capitalisation to GDP, and confirmed the existence of a positive finance-growth relationship, finding in line with King and Levine (1993) and Levine and Zervos (1998). These studies recognised that economic growth without a well-developed financial sector would be detrimental to the long-run sector prospects in developing countries and that suitable reform programs can lead to higher financial development which in turn can contribute to higher economic growth (Shleifer & Vishny, 1997). Another strand of the literature provided substantial evidence that the primary channel through which financial development is positively related to economic growth is the efficiency of investments (Rajan & Zingales, 1998). However, there is also cross-country evidence suggesting negligible or weak negative association in the finance-growth relationship (Bumann et al., 2013). Furthermore, significant discrepancies are found because of the heterogeneities of developed and developing countries and structural or institutional issues (Ahmed, 1998).

In studies employing time-series data, the finance-growth relationship has not reached a consensus with regards to the direction of causality. Studies that investigated individual countries concluded in favour of unidirectional causality (Asteriou & Price, 2000; Yang & Yi, 2008), while others that used more than one country found bi-directional causality (Demetriades and Hussein, 1996). When VAR, VECM or ARDL models were employed, the conclusions were in favour of a positive effect of financial development on growth (Ang & McKibbin, 2007; Arestis et al., 2001), while considering the period of the GFC findings indicated that financial development enhanced and impaired economic performance, in the period before and after the crisis, respectively (Akan et al., 2021). In a recent study, Barradas (2020) examined the relative importance of banks and stock markets in contributing to economic growth and found that stock markets are more powerful in promoting growth than money markets.

Significant contributions to knowledge were made by studies that employed panel data which considered the dynamic properties of country heterogeneity effects and longer time-periods. In particular, Hassan et al. (2011) and Anwar and Cooray (2012) found a significant impact on economic development over the periods 1980–2007 and 1970–2009 while the interaction of the quality of governance proxied by government expenditures was found to be significantly positive as well.

In general, the literature indicates absence of consensus concerning the extent to which financial development plays an important role on economic growth. It seems that it is not only the performance of economies during the GFC that has shed doubt on the finance-growth nexus relationship but there are other fundamental issues that need to be addressed. Ayadi et al. (2015) argued that the finance-growth relationship is negative for the bank sector and confirmed deficiencies in credit allocation in different regions and pointed towards the weaknesses of financial regulations, supervision and quality of institutions. Samargandi et al. (2015) revisited the relationship between financial development and economic growth using data for 52 countries and found evidence in favour of an insignificant impact of financial development on economic growth in the short-run and an inverted U-shaped relationship in the long-run. Further, the impact varies across countries due to the heterogeneous nature of economic structures, institutional quality and financial markets. In this direction, Arcand et al. (2015) showed that the relationship turns negative at very high levels of financial development while Cecchetti and Kharroubi (2012) examined effects at both country and industry level and concluded that the level of financial development is a good predictor for growth but only up to a point. These results in favour of an inverse U-shaped financial development effect are consistent with the diminishing effects reported by Rousseau and Wachtel (2011).

Using data from the Groningen Growth and Development Centre database for 41 economies Aizenman et al. (2015) examined the interaction between quality and quantity of finance and output growth for ten sectors. They reported a negative impact of financial depth on growth in several sectors and provided results which point towards a nonlinear impact of financial development on growth. Alexiou et al. (2018) found that over the period 1998–2014, increased credit flows negatively affected 34 European and Commonwealth of Independent States economies while the significant positive effect of money supply on growth before the GFC is not confirmed for advanced economies in contrast to developing countries. Makrychoriti et al. (2022) used a sample of EU countries over the period 2002–2020 and suggested that trust can influence the behaviour of economic agents and improve access to financing for both households and corporations. Therefore, trust could mitigate the negative impact of financial stress on economic growth. Purewal and Haini (2022) found that financial markets and institutions promote growth, with institutions dominating with regards to this positive effect, while they also confirmed the existence of an inverse U-shaped finance-growth relationship.

Another strand of the literature provided evidence that fiscal reaction functions changed after the outbreak of the GFC. The ease with which countries could roll over debt and finance new debt increased risk and also the lack of liquidity in government debt markets are likely among the factors which have enhanced fiscal prudence across Europe (Lane, 2012; Shambaugh et al., 2012). In addition, following the GFC several EU governments experienced serious fiscal problems with some estimations revealing a divergent fiscal performance in the countries before and after the crisis (Baldi & Staehr, 2016).

In conclusion, there is not an agreement about the finance-growth relationship behaviour which can be attributed to different characteristics of countries, periods examined, and types of data. Making use of advancements in econometric methodologies and particularly in panel data, the focus of research has shifted to the determinants or sources of financial development and the ingredients of growth rather than the finance-growth link itself (Law & Singh, 2014).

3 DATA AND METHODOLOGY

This study employs annual data for a panel of 26 EU countries over the period 1990–2020 with a total of 806 observations. The sample countries are Austria, Belgium, Bulgaria, Croatia, Czech Republic, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden and the United Kingdom.

Equation (1) below presents our baseline panel model:

ggd p_{it} = a + β^{'} F D_{it} + γ^{'} X_{it} + u_{it} .

(1)

The dependent variable is economic growth expressed as the annual percentage rate of GDP (ggdp) at constant 2010 US dollars. $FD$ is a matrix of financial development variables which includes three financial development indices from the IMF Financial Development Index database proposed by Svirydzenka (2016) that consider the depth, access, and efficiency of the respective financial sector: financial institutions (fininst) which includes banks, insurance companies, mutual and pension funds; financial markets (finmar) which includes stock and bond markets; and a financial development index (findev) which measures the broadly multi-dimensional financial system and is a combination of both fininst and finmar. Matrix $X$ includes control macroeconomic variables that are considered significant for economic growth, namely inflation (inf), foreign direct investment (fdi) and trade openness (open). Inflation is a measure of the degree of uncertainty and is expected to negatively affect the economy (Alexiou et al., 2018; Barro, 2003), while foreign direct investment is expected to be positively associated with economic growth because of the endogenous relationship between them (Li & Liu, 2005). Trade openness is the sum of exports and imports to GDP and constitutes an index for the degree of international trade. Openness is expected to contribute to economic growth through competition and technological progress (Hye & Lau, 2015).

We additionally attempt to examine the response of financial development measures to the quality of fiscal policy which plays an essential role in the current and future directions in economic growth. In this direction, we attempted to identify unexpected fiscal policy changes in government debt (debt) and government size proxied by tax revenues (tax) and public spending (exp), as well as budget deficits (bd). To isolate these unexpected changes we fit AR (1) models¹ that employ as independent variable observations from previous time steps in order to predict the value at the next time step. The AR (1) equations for each country are estimated in first differences to guarantee stationarity and are given below.

Δdeb t_{t} = {\hat{β}}_{1} Δdeb t_{t - 1} + e_{debt},

(2)

Δta x_{t} = {\hat{β}}_{2} Δta x_{t - 1} + e_{tax},

(3)

Δex p_{t} = {\hat{β}}_{3} Δex p_{t - 1} + e_{\exp},

(4)

Δb d_{t} = {\hat{β}}_{4} Δb d_{t - 1} + e_{bd},

(5)

where debt, tax, exp and bd, stand for the predicted values for time t from its own previous value in time t − 1. The obtained residuals

e_{debt}

e_{tax}

e_{\exp}

and

e_{bd}

are the unexpected changes in fiscal policy and indicators of the quality of fiscal policy which are used as additional explanatory variables in our regressions.

For comparison purposes, besides the full sample period (1990–2020), we estimate our models for various sub-periods, namely the pre-GFC period (1990–2007), the GFC period (2008–2009), the post-GFC period (2008–2020), and the Covid-19 pandemic period (2019–2020). All the data for the macroeconomic variables are obtained from the World Bank, the International Monetary Fund and the European Data Warehouse. Table 1 presents the description and source of the variables. Summary statistics and the correlation matrix are provided in the Appendix (Tables A1 and A2) along with panel unit root tests to establish the order of integration of the variables (Table A3).

TABLE 1. List of variables

Variables	Description	Source
$ggdp$	Growth rate of GDP (%)	World Bank
$findev$	Financial development index	International Monetary Fund
$fininst$	Financial institutions index	International Monetary Fund
$finmar$	Financial markets index	International Monetary Fund
$\inf$	Inflation rate (%)	World Bank
$fdi$	Foreign direct investment (% of GDP)	World Bank
$open$	Trade openness (exports + imports, % of GDP)	European Data Warehouse
$debt$	Government debt (% of GDP)	International Monetary Fund, Global Debt Database
$tax$	Tax revenue (% of GDP)	European Data Warehouse
$\exp$	Government expenses (% of GDP)	European Data Warehouse
$bd$	Budget deficit (% of GDP)	European Data Warehouse

Panel methodologies such as OLS and fixed effects estimators are consistent when N is large but also T is large (Baltagi et al., 2016). However, the existence of bi-directional causality between variables, omitted variable bias, time invariant country characteristics (fixed effects) which may be correlated with the explanatory variables that can lead to endogeneity, as well as the presence of autocorrelation, are well known shortcomings of traditional panel methods (Alexiou et al., 2018; Bond, 2002; Caselli et al., 1996).

The generalised method of moments (GMM) estimator was developed to overcome the above shortcomings as it controls for possible specification bias (Blundell & Bond, 1998) and it is well suited for datasets with small T and larger N. Following Holtz-Eakin et al. (1988), Arellano and Bond (1991) developed a GMM estimator that instruments the differenced variables with all their available lags in levels. A problem with this estimator is that lagged levels are poor instruments for first differences if the variables are close to a random walk. System GMM is an augmented version developed by Blundell and Bond (1998) that overcomes this issue by employing both levels and differences as instruments while the assumption is that these differences are uncorrelated with the country-specific effects. Difference and System GMM are applied in one and two step variants. The two-step variants use a weighting matrix that makes two-step GMM asymptotically efficient. In this article we employ the system GMM estimator proposed by Roodman (2009) using a two-step approach and obtain robust standard errors with Windmeijer's (2005) finite sample correction. We provide results for the robustness and sensitivity of the instruments and coefficients and report Hansen's test of instrument validity and overidentifying restrictions, as well as the Arellano and Bond test of serial correlation.

Finally, we additionally attempt to examine the existence of nonlinearity in the finance-growth relationship and apply the fixed-effect panel threshold model proposed by Hansen (1999). The empirical model is provided below:

ggd p_{it} = μ_{i} + β_{1}^{'} F D_{it} (I) (q_{it} \leq γ) + β_{2}^{'} F D_{it} (I) (q_{it} > γ) + β_{3}^{'} Φ_{it} + u_{it},

(6)

where

μ_{i}

is the vector of the country-specific fixed effect,

FD

is the vector of the regime-dependent variable of institutions or stock market sectors (fininst or finmar),

Φ

is a matrix that includes the financial development index which is the not regime-dependent variable as well as the control variables (inflation, foreign direct investment and trade openness), the variable

q

includes the threshold variables fininst or finmar used to split the sample into regimes,

γ

is the unknown threshold parameter, and

(I)

is the indication function which takes the value 1 if the argument in parenthesis is valid, and 0 otherwise. The model examines a single-threshold and divides the equation into two regimes with coefficients

β_{1}^{'}

and

β_{2}^{'}

4 EMPIRICAL RESULTS

4.1 System GMM panel estimation results

Table 2 presents the results for the finance-growth relationship during different sub-periods. The results for the full sample time period (Models I and II, 1990–2020) suggest that the positive impact of financial development (findev) on economic growth can be attributed to the significant positive effect of the stock market (finmar). Interestingly, the findings show that during the full sample period financial institutions (fininst) have not enhanced economic activity. These results are in line with the findings of studies who suggested that financial development promotes economic growth (Beck & Levine, 2004; Demirgüç-Kunt & Maksimovic, 1998; King & Levine, 1993; Levine et al., 2000; Levine & Zervos, 1998; Rajan & Zingales, 1998). In the pre-GFC period (Models III and IV, 1990–2007) the results are similar to those in the full sample period, while in the GFC period (Models V and VI, 2008–2009) stock markets exhibit a statistically significant negative effect. In the post-GFC period (Models VII and VII, 2008–2020) and the recent Covid-19 pandemic period (Models IX and VIII, 2019–2020) both sectors do not appear to have an significant effect. Our results confirm to some degree previous findings in favour of an insignificant relationship at higher levels of financial development (Aghion et al., 2005; Alexiou et al., 2018; Allen et al., 2012; Arcand et al., 2015; Cecchetti & Kharroubi, 2012). The results for the control variables show a statistically significant negative effect of inflation on economic growth in the full sample and pre-GFC periods which is nonsignificant during the GFC and then turns to positive in the post-GFC period and the pandemic. Trade openness has been a positive driving force for economic growth during and after the GFC, while foreign direct investment is statistically insignificant across all examined periods.

TABLE 2. Panel estimation results

Period	Full (1990–2020)		Pre-GFC (1990–2007)		GFC (2008–2009)		Post-GFC (2008–2020)		Covid-19 pandemic (2019–2020)
Period	Model I	Model II	Model III	Model IV	Model V	Model VI	Model VII	Model VIII	Model IX	Model X
${ggdp}_{t - 1}$	0.597***	0.584***	0.426***	0.410***	0.043	0.214	0.450***	0.445***	−0.718	−0.753
	(10.46)	(10.34)	(6.40)	(6.01)	(0.11)	(0.66)	(4.29)	(4.22)	(−1.62)	(−1.38)
$Δ findev$	0.088**		0.134*		−0.130**		0.029		−0.506
	(2.01)		(1.79)		(−2.09)		(0.42)		(−1.34)
$Δ fininst$		0.025		0.068		0.223		0.099		−0.453
		(0.33)		(0.64)		(1.36)		(1.09)		(−0.91)
$Δ finmar$		0.052***		0.084***		−0.148***		−0.002		−0.209
		(3.10)		(3.15)		(−3.46)		(−0.06)		(−0.93)
$\inf$	−0.011**	−0.011**	−0.013***	−0.013***	0.493	0.190	0.347***	0.325**	1.497***	1.595***
	(−2.36)	(−2.13)	(−3.23)	(−2.61)	(1.58)	(0.52)	(2.61)	(2.02)	(4.95)	(3.44)
$fdi$	−0.0007	−0.001	−0.040	−0.048	0.009	0.008	−0.0008	−0.001	0.036	0.040
	(−0.10)	(−0.27)	(−1.10)	(−1.27)	(1.44)	(1.35)	(−0.16)	(−0.21)	(1.24)	(1.05)
$Δ open$	0.057	0.054	−0.037	−0.041	0.270***	0.345***	0.261***	0.266***	0.265**	0.248**
	(1.53)	(1.39)	(−0.91)	(−0.95)	(5.16)	(5.52)	(5.81)	(5.71)	(2.45)	(2.50)
Constant	0.691***	0.722***	1.210***	1.211***	−0.768**	−0.387	−0.599***	−0.527**	−0.873	−1.028
	(2.97)	(3.12)	(5.05)	(4.43)	(−2.54)	(−0.91)	(−3.06)	(−2.33)	(−0.62)	(−0.79)
Observations	779	779	442	442	52	52	337	337	51	51
AR (1) p-value	0.008	0.009	0.001	0.002	0.180	0.502	0.112	0.144	0.076	0.093
AR (2) p-value	0.859	0.825	0.329	0.316	0.355	0.615	0.290	0.325	0.253	0.266
Hansen p-value	0.182	0.270	0.125	0.184	0.361	0.657	0.122	0.212	0.143	0.249
Number of instruments	22	26	23	20	17	20	22	26	17	20

Note: The dependent variable is ggdp. Δ denotes the first difference operator used for the variables that were transformed to become stationary. Models (I) includes the financial development index and control variables; Model (II) includes both financial development indices (financial institutions and stock markets). Models (III)–(X) are defined similarly to models (I)–(II) but with different sample periods examined. All estimations are based on a two-step estimation procedure and Windmeijer's corrected standard error. Robust z-statistics are given in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.

The findings suggest that the financial development indicators exhibit quite different behaviour in crisis periods compared to normal periods. There is strong evidence to suggest that stock markets positively affected the economy in the pre-GFC period while both financial sectors have not contributed to economic growth across all the periods after the GFC. A possible reason for the detected insignificant effects of financial institutions might be the type of credit available to the private sector. In particular, household credit and enterprise credit have a much different impact on economic growth, where the former has no effect or even reduces economic growth (Beck et al., 2009; Jappelli & Pagano, 1994) while the later enhances economic activity (Levine, 2005). In addition, over-lending to households created a credit boom that led to banking crises, which in turn reduced economic growth (Demirguc & Detragiache, 1998; Kaminsky & Schmukler, 2002).

Regarding the insignificant effect of financial stock markets on economic growth more than a decade after the GFC, this might be related to the liquidity trap (Krugman, 1988) with investors preferring to keep their assets in the form of cash or savings. This explanation is also consistent with the arguments provided by Asteriou and Spanos (2021) who show that savings are responsible for the insignificant impact of financial stock markets on economic growth. However, after the GFC many EU countries experienced high sovereign debts exerting upward pressure on interest rates and expecting higher taxes in the future which in turn led stock prices to fall (Wisniewski & Jackson, 2021). In addition, strong linkages and stock market co-movements due to the integration of the EU countries' financial markets might be a possible explanation for the weakness of stock markets to promote economic growth after the crisis.

At the next step, we estimate our baseline model this time adding the indicators of the quality of fiscal policy. Table 3 presents the results for the full sample period (1990–2020) and the findings in Models I to IV verify the significant and consistent effect of financial development (findev) in line with the results in Table 2, Model I. In Models V to VIII, we notice an insignificant effect of financial institutions (fininst) that becomes significant after adding taxes and government expenditures, an indication of the importance of fiscal policy shocks relative to the government size. The results also show a possible trade-off between financial institutions and the proxies for government size with the negative effects of fiscal shocks relevant to higher expenditures through taxes being absorbed by financial institutions. Therefore, the negative effects of government spending and taxes may hinder financial efficiency and accessibility to promote economic growth. Also, increasing government spending can lead to a more inefficient allocation of resources since governments tend to be less effective in spending money, while higher public spending can crowd-down domestic credit. Our results are in line with Purewal and Haini (2022) and Haini and Loon (2021), who argued that financial development is a positive contributor to growth while government spending has a negative impact. The estimated coefficients for the stock market index (finmar) are significant and positive with the exception of the model with government debt (Model V). Finally, across all models we notice that the macroeconomic factors for the quality of fiscal policy have not enhanced economic activity, thus indicating that changes of fiscal policy may not always live up to expectations for more economic growth.

TABLE 3. Panel estimation results, full sample period (1990–2020)

	Model I	Model II	Model III	Model IV	Model V	Model VI	Model VII	Model VIII
${ggdp}_{t - 1}$	0.457***	0.552***	0.570***	0.589***	0.421***	0.533***	0.569***	0.580***
	(7.48)	(13.30)	(14.19)	(12.35)	(9.30)	(13.14)	(14.63)	(12.52)
$Δ findev$	0.105**	0.113***	0.100***	0.098***
	(2.36)	(2.74)	(3.17)	(2.70)
$Δ fininst$					0.053	0.127*	0.104**	0.084
					(1.03)	(1.84)	(2.00)	(1.45)
$Δ finmar$					0.019	0.031**	0.028**	0.037***
					(0.98)	(2.17)	(2.13)	(2.86)
$\inf$	−0.014	−0.012**	−0.010***	−0.008*	−0.011*	−0.012**	−0.010***	−0.009*
	(−1.48)	(−2.56)	(−3.37)	(−1.69)	(−1.85)	(−2.49)	(−3.10)	(−1.83)
$fdi$	0.0004	0.001	−0.005	−0.005	0.005	−0.002	−0.006	−0.007
	(0.04)	(0.11)	(−0.70)	(−0.71)	(0.68)	(−0.26)	(−1.10)	(−1.32)
$Δ open$	0.062	0.054	0.037	0.058	0.037	0.040	0.039	0.050
	(1.04)	(1.12)	(0.78)	(1.06)	(0.86)	(0.89)	(0.89)	(1.06)
$edebt$	−0.289***				−0.344***
	(−5.03)				(−6.88)
$etax$		−0.340***				−0.329***
		(−2.93)				(−2.62)
$eexp$			−0.397***				−0.405***
			(−3.20)				(−3.41)
$ebd$				−0.269**				0.281**
				(−2.11)				(−2.40)
Constant	0.968***	0.806***	0.814***	0.716***	1.137***	0.912***	0.831***	0.765***
	(3.34)	(3.63)	(3.44)	(2.91)	(4.37)	(4.00)	(3.60)	(3.18)
Observations	750	753	753	753	750	753	753	753
AR (1) p-value	0.001	0.001	0.001	0.001	0.001	0.004	0.001	0.001
AR (2) p-value	0.904	0.501	0.549	0.495	0.734	0.488	0.544	0.480
Hansen p-value	0.121	0.713	0.519	0.529	0.681	0.628	0.606	0.657
Number of instruments	14	14	14	14	16	16	15	16

Note: The dependent variable is ggdp. Δ denotes the first difference operator used for the variables that were transformed to become stationary. Models (I)–(IV) include the financial development index, control variables, public debt ( $edebt$ ), tax revenues ( $etax$ ), government expenditures ( $eexp$ ) and the budget deficit ( $ebd$ ) as proxies for fiscal policy. Models (V)–(VIII) are similar to models (I)–(IV) but with both financial institutions and stock markets instead of the financial development index. All estimations are based on a two-step estimation procedure and Windmeijer's corrected standard error. Robust z-statistics are in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.

Table 4 presents the results for the pre-GFC period (1990–2007). The findings suggest that the signs and statistical significance for all the financial development indices do not change after adding the fiscal policy variables, thus confirming the robustness of the results in Table 2, Models III–IV. In addition, there is substantial evidence that public debt is one of the most detrimental factors that adversely affected the economy during this period. The insignificant effect of financial institutions to promote growth in the pre-GFC period might be an indication of the financial system fragility and the lack of supervision. Loans not being properly supervised led to non-performance loans which in turn harmed real economic activity and exposed financial institutions to extreme leverage and risk. This explanation is in line with the findings of Singh (1997), Duffie (2019) and Wu et al. (2010), who found that improving risk diversification and information services of commercial banks results in more stable economic development and a reasonably supervised financial system can be more resilient to severe shocks.

TABLE 4. Panel estimation results, pre-GFC (1990–2007)

	Model I	Model II	Model III	Model IV	Model V	Model VI	Model VII	Model VIII
${ggdp}_{t - 1}$	0.433***	0.467***	0.459***	0.465***	0.465***	0.380***	0.683***	0.618***
	(8.22)	(9.14)	(9.19)	(10.35)	(7.39)	(5.22)	(12.49)	(10.19)
$Δ findev$	0.109**	0.121**	0.099**	0.100**
	(2.00)	(1.99)	(1.97)	(1.97)
$Δ fininst$					0.066	0.079	0.002	0.007
					(0.66)	(0.76)	(0.03)	(0.09)
$Δ finmar$					0.039**	0.071***	0.033***	0.034**
					(1.97)	(3.03)	(2.86)	(2.34)
$\inf$	−0.013***	−0.013***	−0.013***	−0.012***	−0.013**	−0.014**	−0.009**	−0.009**
	(−2.81)	(−2.94)	(−3.31)	(−2.89)	(−2.15)	(−2.54)	(−2.48)	(−2.53)
$fdi$	−0.019	−0.018	−0.019	−0.022	−0.017	−0.070	−0.012	−0.003
	(−0.83)	(−0.97)	(−1.15)	(−1.26)	(−0.69)	(−1.26)	(−0.87)	(−0.21)
$Δ open$	−0.024	−0.044	−0.046	−0.046	−0.015	−0.039	−0.031	−0.041
	(−0.50)	(−0.92)	(−1.00)	(−0.99)	(−0.27)	(−0.76)	(−0.81)	(−0.95)
$edebt$	−0.252***				−0.240***
	(−4.61)				(−3.56)
$etax$		−0.166				−0.151
		(−1.55)				(−1.16)
$eexp$			−0.288***				−0.189
			(−2.93)				(−1.55)
$ebd$				−0.191*				−0.178
				(−1.66)				(−1.56)
Constant	1.534***	1.590***	1.621***	1.605***	1.435***	1.703***	1.434***	1.538***
	(5.88)	(7.00)	(7.21)	(6.92)	(4.53)	(4.44)	(4.81)	(5.47)
Observations	413	416	416	416	413	416	416	416
AR (1) p-value	0.001	0.001	0.001	0.001	0.001	0.001	0.001	0.001
AR (2) p-value	0.789	0.243	0.287	0.251	0.800	0.170	0.586	0.479
Hansen p-value	0.199	0.160	0.189	0.195	0.165	0.126	0.362	0.448
Number of instruments	26	26	26	26	23	23	23	23

Note: See Table 3.

Table 5 presents the results for the GFC period (2008–2009). The financial development index is not statistically significant at any reasonable level while the stock market exhibits a negative effect thus confirming the robustness of the main results in Table 2, Models V–VI. Focusing on the fiscal policy variables, we note that taxes are insignificant, while all other macroeconomic factors for the quality of fiscal policy have negatively affected economic activity.

TABLE 5. Panel estimation results, GFC (2008–2009)

	Model I	Model II	Model III	Model IV	Model V	Model VI	Model VII	Model VIII
${ggdp}_{t - 1}$	−0.019	−0.137	0.322	0.026	0.088	0.013	0.300	0.359
	(−0.06)	(−0.34)	(0.75)	(0.08)	(0.21)	(0.04)	(1.40)	(0.97)
$Δ findev$	−0.057	−0.085	−0.040	−0.096*
	(−0.52)	(−0.97)	(−0.62)	(−1.66)
$Δ fininst$					0.148	0.165	0.180	0.230
					(0.83)	(0.94)	(1.56)	(1.53)
$Δ finmar$					−0.101**	−0.102*	−0.112*	−0.139***
					(−1.99)	(−1.78)	(−1.75)	(−2.58)
$\inf$	0.416	0.625*	0.204	0.525*	0.241	0.427	0.140	0.182
	(1.57)	(1.84)	(0.50)	(1.81)	(0.71)	(1.33)	(0.61)	(0.58)
$fdi$	0.019*	0.008	0.005	0.016***	0.017	0.005	0.007**	0.014**
	(1.74)	(0.63)	(0.85)	(2.68)	(1.26)	(0.69)	(1.96)	(2.31)
$Δ open$	0.218***	0.294***	0.114**	0.154***	0.235***	0.329***	0.131***	0.185***
	(3.93)	(3.20)	(2.07)	(4.19)	(3.57)	(3.93)	(5.72)	(4.02)
$edebt$	−0.405***				−0.385***
	(−7.41)				(−3.71)
$etax$		−0.202				−0.157
		(−0.25)				(−0.28)
$eexp$			−0.945***				−0.954***
			(−4.57)				(−4.47)
$ebd$				−0.546***				−0.647***
				(−4.38)				(−4.02)
Constant	0.391	−0.709	0.101	−0.488*	0.439	−0.531	0.189	−0.274
	(1.21)	(−0.90)	(0.33)	(−1.67)	(0.92)	(−0.72)	(0.67)	(−0.74)
Observations	52	52	52	52	52	52	52	52
AR (1) p-value	0.313	0.313	0.559	0.323	0.485	0.345	0.426	0.475
AR (2) p-value	0.629	0.653	0.379	0.610	0.575	0.627	0.420	0.566
Hansen p-value	0.403	0.195	0.630	0.593	0.392	0.328	0.342	0.607
Number of instruments	19	19	19	19	22	22	22	22

Note: See Table 3.

Table 6 presents the results for the post-GFC period (2008–2020) which confirm the existence of an adverse effect of fiscal policy on growth. Financial institutions have mostly failed to promote growth in the years after the crisis and this might be due to the ineffectiveness of unconventional monetary policy implemented by the European Central Bank through quantitative easing which aimed to help the Eurozone countries to refinance their sovereign debts. Also, the inability of stock markets to contribute to economic activity might be an indication that EU stock markets are subject to significant effects indirectly through the transmission of fiscal shocks. In this context, Hanousek et al. (2009) argued that EU stocks markets are significantly affected by macroeconomic announcements.

TABLE 6. Panel estimation results, post-GFC (2008–2020)

	Model I	Model II	Model III	Model IV	Model V	Model VI	Model VII	Model VIII
${ggdp}_{t - 1}$	0.280**	0.386***	0.426***	0.445***	0.254	0.424***	0.431***	0.433***
	(2.49)	(3.62)	(3.20)	(3.38)	(1.59)	(3.40)	(3.04)	(2.69)
$Δ findev$	0.021	0.028	0.021	0.029
	(0.24)	(0.44)	(0.31)	(0.47)
$Δ fininst$					0.016	0.097	0.094	0.089
					(0.14)	(1.16)	(1.13)	(0.92)
$Δ finmar$					0.002	−0.015	−0.01	0.001
					(0.05)	(−0.40)	(−0.27)	(0.04)
$\inf$	0.256***	0.335***	0.295***	0.329***	0.266***	0.266*	0.272*	0.319**
	(3.76)	(3.10)	(2.59)	(2.85)	(2.59)	(1.81)	(1.94)	(2.04)
$fdi$	0.001	−0.003	−0.002	−0.001	0.002	−0.004	−0.003	−0.002
	(0.32)	(−0.81)	(−0.54)	(−0.22)	(0.31)	(−0.81)	(−0.54)	(−0.34)
$Δ open$	0.177***	0.259***	0.233***	0.242***	0.167***	0.273***	0.239***	0.249***
	(4.50)	(6.20)	(5.45)	(5.16)	(2.95)	(5.33)	(4.73)	(4.23)
$edebt$	−0.310***				−0.322***
	(−4.29)				(−4.44)
$etax$		−0.573**				−0.577**
		(−2.52)				(−2.32)
$eexp$			−0.345***				−0.346***
			(−3.38)				(−3.27)
$ebd$				−0.232**				−0.241**
				(−2.57)				(−2.48)
Constant	0.317	−0.455**	−0.363	−0.473**	0.338	−0.356	−0.348	−0.469*
	(0.86)	(−2.02)	(−1.42)	(−2.08)	(0.90)	(−1.60)	(−1.33)	(−1.89)
Observations	337	337	337	337	337	337	337	337
AR (1) p-value	0.238	0.250	0.268	0.265	0.271	0.284	0.286	0.297
AR (2) p-value	0.503	0.418	0.447	0.481	0.505	0.401	0.451	0.493
Hansen p-value	0.186	0.184	0.133	0.129	0.126	0.142	0.178	0.120
Number of instruments	20	20	20	20	23	23	23	23

Note: See Table 3.

Finally, Table 7 presents the results for the onset of the Covid-19 pandemic (2019–2020) which show that the estimated parameters of the financial development indices are statistically insignificant, a finding in line with Table 2, Models IX–X. Interestingly, from the group of the fiscal policy variables only public debt presents a significantly negative effect.

TABLE 7. Panel estimation results, Covid-19 pandemic (2019–2020)

	Model I	Model II	Model III	Model IV	Model V	Model VI	Model VII	Model VIII
${ggdp}_{t - 1}$	0.004	−1.754***	−1.785***	−2.466***	0.459	−1.820***	−2.110***	−2.624***
	(0.01)	(−3.16)	(−2.96)	(−3.30)	(1.27)	(−3.58)	(−3.20)	(−3.89)
$Δ findev$	−0.155	−0.483	−0.391	−0.346
	(−1.38)	(−1.59)	(−1.07)	(−0.84)
$Δ fininst$					−0.050	−0.641	−0.910	−0.834
					(−0.38)	(−1.00)	(−1.31)	(−1.41)
$Δ finmar$					−0.067	−0.031	−0.064	−0.068
					(−0.90)	(−0.20)	(−0.30)	(−0.30)
$\inf$	0.580***	1.924***	1.917***	2.148***	0.439**	2.071***	2.116***	2.316***
	(2.73)	(6.30)	(6.15)	(4.72)	(2.11)	(4.80)	(5.48)	(4.86)
$fdi$	0.010	0.069*	0.077***	0.0823***	−0.001	0.081**	0.087***	0.089***
	(1.40)	(1.79)	(3.06)	(6.75)	(−0.15)	(2.45)	(4.13)	(6.38)
$Δ open$	0.021	0.345***	0.291***	0.327***	0.015	0.305***	0.291***	0.318***
	(0.39)	(3.39)	(3.52)	(4.36)	(0.35)	(3.02)	(3.42)	(4.50)
$edebt$	−0.414***				−0.417***
	(−7.60)				(−9.15)
$etax$		−0.084				−0.061
		(−0.14)				(−0.10)
$eexp$			−0.074				−0.033
			(−0.68)				(−0.26)
$ebd$				−0.040				−0.050
				(−0.30)				(−0.39)
Constant	−0.047	1.654	1.708	3.179	−1.054	1.355	1.820	2.846
	(−0.07)	(0.90)	(0.83)	(1.32)	(−1.30)	(0.93)	(1.08)	(1.60)
Observations	51	51	51	51	51	51	51	51
AR (1) p-value	0.285	0.249	0.258	0.257	0.299	0.254	0.253	0.245
AR (2) p-value	0.593	0.858	0.855	0.906	0.502	0.850	0.863	0.899
Hansen p-value	0.433	0.195	0.190	0.521	0.410	0.194	0.224	0.584
Number of instruments	14	13	14	14	16	16	16	16

Note: See Table 3.

The overall results are consistent with the initial findings in Table 2. There is substantial evidence that the different performance of the financial system during normal versus stress times can be partly attributed to the quality of fiscal policy. Focusing on financial institutions, the results indicate that during the full sample period they respond to government size and are positive, implying that apart from unproductive loans and lack of supervision financing government expenditures might be an additional reason for the difficulty of financial institutions to promote growth. However, the findings during the sub-periods (pre-GFC, GFC, post-GFC, pandemic) suggest that financial institutions are insignificant and failed to promote growth. Focusing on the stock market, the initial positive effect on growth at normal times reversed during the GFC. The failure of the stock market to enhance economic activity, taking also in mind the role of fiscal policy, indicates that deviations from expectations play an important role in this effect. The findings show that stock market performance is sensitive to fiscal shocks and reflect the higher degree of financial spillover effects of fiscal shock at times of economic recessions.

A general conclusion is that fiscal policy has negatively affected economic growth in EU economies and this can be mostly attributed to government debt, public expenditures, and budget deficits, with the estimated coefficients having much larger magnitudes after the GFC. The significant and negative effect of debt on economic growth at normal times might indicate the lack of incentives for some Eurozone countries to control and reduce public debt thus making them less able to service their liabilities hence leading to higher interest rates and a fall in stock market prices. This explanation is in line with the findings of Conway and Orr (2000) and Ardagna et al. (2007), who argued that accumulation of higher public debt and spending leads to higher inflationary pressures and as a consequence higher short- and long-term interest rates. However, the findings that government expenditures negatively affected the economy is in contrast to previous results by Alexiou et al. (2018) who suggested that this effect is positive.

The significant negative effects of debts and budget deficits continued for many years after the GFC indicating that the financial crisis was converted to a debt crisis (Reinhart & Rogoff, 2011; Romer, 2012). A possible explanation is that the sharp recession in 2008 and the sudden fall in output, increased the debt-to-GDP ratio and the active spending policies or austerity measures undertaken to reduce it led to an even deeper recession. Moreover, governments were under pressure to offer a sovereign risk premium for their debt to be sold and when this happens the cost of debt rises even further, making default more likely. Thus, in order to avoid default it is necessary for governments to impose fiscal limits, namely, limits to tax revenues and to public spending. Nevertheless, after the GFC our results imply that the effectiveness of the undertaken austerity measures by policymakers did not have the expected outcomes. The results are consistent with existing findings which argue that surprising changes may not constitute good policy (Candelon & Lieb, 2013; Mountford & Uhlig, 2009).

4.2 Fixed effects panel threshold results

Table 8 reports the results of estimating Equation (6) using financial institutions as threshold as well as regime-dependent variable. Model I does not include the fiscal policy variables and the estimated threshold value ( $\hat{γ}$ ) is around 68% to GDP, Model II includes the debt variable and produces a threshold value of 62%, Model III involves taxes and results in a threshold value of 68%, while Models IV and V with spending and the budget deficit produce a threshold value of 64%. Regarding the estimated coefficients $β_{1}$ and $β_{2}$ , below the threshold there is evidence that economic growth is endorsed while above it the effect becomes negative and insignificant. The empirical findings suggest a nonlinear relationship between finance and growth and are to some degree in line with other studies that found finance contributing to economic growth up to a certain point after which it turns negative (Arcand et al., 2015; Cecchetti & Kharroubi, 2012; Samargandi et al., 2015; Shen & Lee, 2006). However, the thresholds are around 62% to 68% and are remarkably lower than those reported in the literature (80%–100%).² This last finding might be due to the different financial indicators we employ since we use a new index that includes indicators of financial depth, access and efficiency, while the main variables used in the literature are indicators of financial depth, namely liquid liabilities and credit to private sector.

TABLE 8. Panel threshold estimation results for financial institutions

	Model I	Model II	Model III	Model IV	Model V
$Δ fininst$	0.062***	0.047***	0.061***	0.055***	0.058***
	(2.85)	(2.81)	(2.88)	(2.70)	(2.75)
$\inf$	−0.017***	−0.024***	−0.023***	−0.017***	−0.015***
	(−4.94)	(−8.95)	(−6.52)	(−5.21)	(−4.35)
$fdi$	−0.012*	−0.009*	−0.014**	−0.015**	−0.013**
	(−1.81)	(−1.95)	(−2.31)	(−2.42)	(−2.09)
$Δ open$	0.164***	0.103***	0.165***	0.126***	0.137***
	(8.81)	(7.06)	(9.16)	(7.36)	(7.79)
$edebt$		−0.376***
		(−20.72)
$etax$			−0.586***
			(−5.75)
$eexp$				−0.347***
				(−7.52)
$ebd$					−0.206***
					(−4.39)
Constant	2.032***	2.172***	2.083***	2.193***	2.167***
	(14.31)	(19.80)	(15.09)	(16.58)	(15.95)

Threshold estimates, financial institutions index $fininst$
$\hat{γ}$	68.93	62.17	68.93	64.80	64.80
F-statistic	14.41***	40.40***	14.87***	12.06***	10.36**
Bootstrap p-value	0.006	0.000	0.010	0.010	0.016
95% confidence interval	62.9–68.9	61.04–62.59	62.9–68.9	59.4–65.02	59.05–65.02

Regime variable, financial institutions index $Δ fininst$
${\hat{β}}_{1}$	0.369***	0.340***	0.385***	0.403***	0.379***
	(6.49)	(7.96)	(6.97)	(3.14)	(2.87)
${\hat{β}}_{2}$	−0.007	−0.011	−0.044	−0.025	−0.057
	(−0.10)	(−0.10)	(−0.53)	(−0.23)	(−0.49)
Observations	650	650	650	676	676

Note: See Table 3.

Table 9 reports the results of estimating Equation (6) using financial stock market as threshold and regime-dependent variable. Interestingly, across all models, we find high F-statistic p-values thus we do not reject the null hypothesis of a linearity. With the exception of Model II, the results for the coefficients $β_{1}$ and $β_{2}$ show that below the thresholds the effect on economic growth is insignificant but turns to significant and positive after the threshold values (around 33%).

TABLE 9. Panel threshold estimation results for financial stock markets

	Model I	Model II	Model III	Model IV	Model V
$Δ finmar$	0.253***	0.216***	0.270***	0.240***	0.238***
	(5.37)	(5.83)	(5.88)	(5.33)	(5.13)
$\inf$	−0.017***	−0.024***	−0.023***	−0.018***	−0.015***
	(−4.86)	(−8.65)	(−6.31)	(−5.29)	(−4.37)
$fdi$	−0.011*	−0.008*	−0.013**	−0.013**	−0.011*
	(−1.69)	(−1.67)	(−2.12)	(−2.14)	(−1.78)
$Δ open$	0.174***	0.113***	0.175***	0.143***	0.154***
	(9.26)	(7.51)	(9.59)	(7.76)	(8.13)
$edebt$		−0.364***
		(−19.46)
$etax$			−0.539***
			(−5.21)
$eexp$				−0.377***
				(−7.70)
$ebd$					−0.247***
					(−4.94)
Constant	2.080***	2.232***	2.144***	2.114***	2.082***
	(14.62)	(19.92)	(15.43)	(15.54)	(14.91)

Threshold estimates, financial markets index $finmar$
$\hat{γ}$	33.18	33.18	28.42	33.18	33.18
F-statistic	1.30	0.96	0.89	0.75	1.13
Bootstrap p-value	0.670	0.700	0.840	0.870	0.690
95% confidence interval	33–33.20	33–33.18	28.16–28.52	33–33.21	33–33.19

Threshold estimates, financial markets index $finmar$
$\hat{γ}$	33.18	33.18	28.42	33.18	33.18
F-statistic	1.30	0.96	0.89	0.75	1.13
Bootstrap p-value	0.670	0.700	0.840	0.870	0.690
95% confidence interval	33–33.20	33–33.18	28.16–28.52	33–33.21	33–33.19

Note: See Table 3.

To sum up, we find a non-linear effect of financial institutions on growth, while for stock markets the evidence indicates linearity. Financial institutions significantly affect the economy up to a certain point with the positive effect below the threshold reflecting their efficiency in providing funds to the private sector. The effect deteriorates above the threshold implying that financial institutions possibly channel credit to unproductive investment projects. This explanation is in line with Huang and Lin (2009) who argue that financial development facilitates investment loans that tend to promote growth, while consumption loans which are not productive tend to impede growth. Turning to the stock market, the results reveal that the direction of the effect on growth does not change before and after the threshold while the effect is stronger above a value of around 33%. Based on our results and the findings in the available literature, it is suggested that the prevalence of excess bank credit can dampen economic growth (Arcand et al., 2015; Rousseau & Wachtel, 2011) while stock markets can have a comparative advantage over banks and other institutions in raising funds for innovative and high-tech investments.

5 CONCLUSIONS

Following the GFC many studies re-examined the relationship between finance and economic growth. Contrary to earlier studies which supported the finance-led-growth hypothesis, more recent studies argued that the finance-growth relationship is nonlinear. Although the literature has highlighted the adverse effects of a financial turmoil, the transformation of the GFC into a debt crisis in EU countries and the role of fiscal shocks in the finance-growth nexus presented risks for the achievement of long-term sustainable growth.

This study used data on 26 EU countries over the period 1990–2020 to examine the finance-growth relationship in conjunction with the role of fiscal policy. We adopt a dynamic estimation model and consider the performance of financial systems measured by financial depth, accessibility, and efficiency of both financial sectors, institutions and stock markets, across different sub-periods of normal and stress times. We provide new evidence regarding the impact on economic growth that arises from the behaviour of financial development when fiscal policy shocks are used as additional variables thus understanding better the sovereign debt crisis in some EU countries in the aftermath of the GFC.

The results indicate a different performance of the financial system and its interaction with the quality of fiscal policy at normal versus stress times. When the full time period is examined, the results provide support that financial development promotes economic growth and both sectors contribute to this positive effect. In contrast, when the sample period is divided to the pre-GFC, the GFC, the post-GFC, and the pandemic periods, the results reveal a much different behaviour of the financial system. The significant and positive effect of the stock market at normal times becomes insignificant or changes to negative, while on the other hand financial institutions are insignificant in the sub-periods examined. Furthermore, during the recent pandemic the effect of both sectors is statistically insignificant. Overall, our findings show that more than a decade since the GFC both sectors exhibit insignificant effects on economic growth.

We additionally provide evidence in favour of a nonlinear relationship between financial development and economic growth where after a threshold point more finance from financial institutions has a negative effect on economic growth. It should be noted that our findings suggest the existence of a threshold at lower level compared to those previously identified in the literature possibly due to the different financial indicators we employ which regard financial depth, access and efficiency. On the other hand, our results show that the effect of the stock market on economic growth is linear.

The results of the current study can have important implications for the financial sector and its sensitivity to fiscal policy shocks. It is clear that financial development somewhat failed to play a safeguarding role for economic growth in a mostly homogeneous group of countries such that of the EU. Regulators should formulate policies to improve prudential regulations and supervisory framework to advance the financial system, allowing institutions to have sufficient capital and adequate risk controls in place. Also, further revisions to the regulatory treatment of government bonds should be advanced, since sovereign exposures that are relatively high before a stress event could drive the bank-sovereign nexus to aggravate a crisis. By achieving the proper balance between prudential risk and government bond holdings, financial institutions can be more effective in promoting growth, while macroprudential policy can mitigate the risk of stock markets which in turn can play a significant role for the overall economy. Further research is needed to find the best strategy for refinancing public debt and finding the optimal threshold for the debt-growth relationship. Finally, policymakers can strengthen the macroeconomic environment by encouraging savings and discouraging speculation, thus preventing under-investments and misallocation of resources in periods of stress events.

Endnotes

¹ The assumption behind an AR (1) model is that the behaviour of a time series is largely determined by its own value in the preceding period. So, what will happen in time t is largely dependent on what happened in t-1. Alternatively, what will happen in t + 1 will be determined by the behaviour of the series in the current time t.

² For example, see some interesting findings from the literature in the Appendix, Table A4.

APPENDIX A

TABLE A1. Summary statistics

Variables	N	Mean	SD	Min	Max
$ggdp$	805	1.79	4.47	−34.90	25.18
$findev$	795	51.20	21.01	0.82	94.71
$fininst$	793	61.09	18.79	10.35	96.93
$finmar$	795	39.63	25.84	1.618	94.94
$fdi$	806	6.580	20.80	−40.08	280.1
$\inf$	806	17.02	93.82	−9.66	1500
$open$	806	94.46	39.45	17.49	252.3
$tax$	806	42.09	7.18	23.43	61.35
$\exp$	806	44.58	7.62	23.70	67.75
$debt$	803	58.56	34.98	0.300	211.2
$bd$	806	2.49	3.81	−13.98	32.02

TABLE A2. Correlation matrix

Variables	Findev	Fininst	Finmar	Inf	Fdi	Open	Debt	Tax	Exp
$findev$	1.000
$fininst$	0.878	1.000
$finmar$	0.942	0.666	1.000
$\inf$	−0.174	−0.201	−0.131	1.000
$fdi$	0.063	0.056	0.058	−0.035	1.000
$open$	−0.145	−0.036	−0.201	−0.050	0.209	1.000
$debt$	0.427	0.382	0.397	−0.075	0.059	−0.139	1.000
$tax$	0.343	0.255	0.355	−0.107	−0.096	−0.182	0.303	1.000
$\exp$	0.322	0.246	0.330	−0.154	−0.103	−0.270	0.402	0.882	1.000

TABLE A3. Panel unit root tests

Variables	Specification	LLC	Breitung	IPS	ADF	PP	I (·)	I (·)
$ggdp$	Intercept	−2.78***		−8.30***	165.16***	163.17***	I (0)
$ggdp$	Intercept and trend	−4.88***	4.56	−6.59***	135.08***	110.44***	I (0)	I (0)
$ggdp$	None	−12.35***			225***	233***	I (0)
$findev$	Intercept	−3.66***		−2.99***	65.72	108.04***	I (0)
$findev$	Intercept and trend	4.68	−0.34	3.427	16.8	104.95***	I (1)	I (1)
$findev$	None	2.32			16.1	10.35	I (1)
$Δ findev$	Intercept	18.01		−3.39***	72.81**	475***	I (0)
$Δ findev$	Intercept and trend	29.92	−4.25***	−4.94***	99.90***	760***	I (0)	I (0)
$Δ findev$	None	−9.60***			157.4***	867***	I (0)
$fininst$	Intercept	−1.69**		−0.529	50.5	85.98	I (1)
$fininst$	Intercept and trend	3.844	4.7	3.245	28.74	106.5***	I (1)	I (1)
$fininst$	None	1.162			24.55	23.41	I (1)
$Δ fininst$	Intercept	6.871		−4.172***	98.1***	498***	I (0)
$Δ fininst$	Intercept and trend	10.51***	−2.88***	−4.31***	100.3***	1129***	I (0)	I (0)
$Δ fininst$	None	−9.57***			177.88***	847***	I (0)
$finmar$	Intercept	−4.36***		−3.77***	81.63***	92.03***	I (0)
$finmar$	Intercept and trend	2.364	−0.591	0.47	33.53	62.25	I (1)	I (1)
$finmar$	None	0.575			23.01	23.37	I (1)
$Δ finmar$	Intercept	13.70***		−5.149***	103.93***	552.01***	I (0)
$Δ finmar$	Intercept and trend	23.73	−3.628***	−4.418***	91.11***	552.52***	I (0)	I (0)
$Δ finmar$	None	−10.54***			195.93***	745.81***	I (0)
$\inf$	Intercept	−21.47***		−22.10***	238.49***	273.5***	I (0)
$\inf$	Intercept and trend	−40.57***	−4.20**	−26.79***	609***	861.1***	I (0)	I (0)
$\inf$	None	−158***			940.8***	703.9***	I (0)
$fdi$	Intercept	−8.73***		−9.40***	190.43***	192.07***	I (0)
$fdi$	Intercept and trend	−7.71***	−5.39***	−7.00***	140.7***	136.2***	I (0)	I (0)
$fdi$	None	−6.76***			120.99***	161.64***	I (0)
$open$	Intercept	4.6		−0.78	51.08	23.27	I (1)
$open$	Intercept and trend	−6.28***	−0.09	−4.69***	115.15***	88.24***	I (0)	I (1)
$open$	None	4.19			9.85	2.73	I (1)
$Δ open$	Intercept	−16.50***		−21.73***	457***	475***	I (0)
$Δ open$	Intercept and trend	−10.57***	−5.40***	−18.64***	364***	409***	I (0)	I (0)
$Δ open$	None	−25.94***			635***	645***	I (0)

Note: Δ denotes the first difference operator. ***, ** and * indicate rejection of the null hypothesis at the 1%, 5% and 10% levels, respectively.

TABLE A4. Summary of studies who investigated the nonlinear properties and threshold effects in the relationship between finance and growth

Authors	Sample size	Data	Method	Threshold and findings
Cecchetti and Kharroubi (2012)	50	Panel data, 1980–2009	Pooled OLS	$\hat{γ}$ = 90% (PRIVY)
Arcand et al. (2015)	>100	Cross-sectional and panel data, 1960–2010	Semi-parametric GMM	$\hat{γ}$ = 100% (PRIVY)
Law and Singh (2014)	87	Panel data, 1980–2010	Kremer (2013) GMM	$\hat{γ}$ = 80% (PRIVY) $\hat{γ}$ = 91% (LLY) $\hat{γ}$ = 99% (PRIVY)
Samargandi et al. (2015)	52	Panel data, 1980–2008	Caner and Hansen (2004)	FD index Bank sector index $\hat{γ}$ = 91.5% (FD) $\hat{γ}$ = 91.8% (FD) $\hat{γ}$ = 43.3% (FD)

Note: PRIVY stands for credit to private sector, and LLY stands for liquid liabilities.

Open Research

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

Aghion, P., Howitt, P., & Mayer-Foulkes, D. (2005). The effect of financial development on convergence: Theory and evidence. The Quarterly Journal of Economics, 120(1), 173–222.
Web of Science® Google Scholar
Ahmed, S. (1998). Comment on the legal environment, banks, and long-run economic growth. Journal of Money, Credit and Banking, 30(3), 614–620.
10.2307/2601260
Web of Science® Google Scholar
Aizenman, J., Jinjarak, Y., & Park, D. (2015). Financial development and output growth in developing Asia and Latin America: A comparative sectoral analysis (No. w20917). National Bureau of Economic Research.
Google Scholar
Akan, T., Hepsag, A., Bozoklu, S., & Mollaahmetoğlu, E. (2021). Finance as a friend, enemy and stranger in the US economy, 1952-2019. Socio-Economic Review. mwab040. https://doi.org/10.1093/ser/mwab040
10.1093/ser/mwab040
Web of Science® Google Scholar
Alexiou, C., Vogiazas, S., & Nellis, J. G. (2018). Reassessing the relationship between the financial sector and economic growth: Dynamic panel evidence. International Journal of Finance & Economics, 23(2), 155–173.
10.1002/ijfe.1609
Web of Science® Google Scholar
Allen, F., Gu, X., & Kowalewski, O. (2012). Financial crisis, structure and reform. Journal of Banking & Finance, 11(36), 2960–2973.
10.1016/j.jbankfin.2012.06.002
Google Scholar
Ang, J. B., & McKibbin, W. J. (2007). Financial liberalization, financial sector development and growth: Evidence from Malaysia. Journal of Development Economics, 84(1), 215–233.
10.1016/j.jdeveco.2006.11.006
Web of Science® Google Scholar
Anwar, S., & Cooray, A. (2012). Financial development, political rights, civil liberties and economic growth. Evidence from South Asia. Economic Modelling, 29(3), 974–981.
10.1016/j.econmod.2012.02.009
Web of Science® Google Scholar
Arcand, J. L., Berkes, E., & Panizza, U. (2015). Too much finance? Journal of Economic Growth, 20(2), 105–148.
10.1007/s10887-015-9115-2
Web of Science® Google Scholar
Ardagna, S., Caselli, F., & Lane, T. (2007). Fiscal discipline and the cost of public debt service: Some estimates for OECD countries. Topics in Macroeconomics, 7(1), 1417.
Google Scholar
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.
10.2307/2297968
Web of Science® Google Scholar
Arestis, P., Demetriades, P., & Luintel, K. (2001). Financial development and economic growth: The role of stock markets. Journal of Money, Credit and Banking, 33(1), 16–41.
10.2307/2673870
Web of Science® Google Scholar
Asteriou, D., & Price, S. (2000). Financial development and economic growth: Time series evidence for the case of UK. Ekonomia, 4(2), 122–141.
Google Scholar
Asteriou, D., & Spanos, K. (2019). The relationship between financial development and economic growth during the recent crisis: Evidence from the EU. Finance Research Letters, 28, 238–245.
10.1016/j.frl.2018.05.011
Web of Science® Google Scholar
Asteriou, D., & Spanos, K. (2021). The mechanisms linking the finance-growth relationship in view of the financial crisis: An empirical investigation of the EU countries. Journal of Economic Studies, 238–245.
Web of Science® Google Scholar
Ayadi, R., Arbak, E., Naceur, S. B., & Groen, W. P. D. (2015). Financial development, bank efficiency, and economic growth across the Mediterranean. In Economic and social development of the southern and eastern Mediterranean countries (pp. 219–233). Springer.
10.1007/978-3-319-11122-3_14
Google Scholar
Bagehot, W. (1873). Lombard street: A description of the money market. Kegan, Paul & Trench.
Google Scholar
Baldi, G., & Staehr, K. (2016). The European debt crisis and fiscal reactions in Europe 2000-2014. International Economics and Economic Policy, 13(2), 297–317.
10.1007/s10368-014-0309-4
Google Scholar
Baltagi, B. H., Feng, Q., & Kao, C. (2016). Estimation of heterogeneous panels with structural breaks. Journal of Econometrics, 191(1), 176–195.
10.1016/j.jeconom.2015.03.048
Web of Science® Google Scholar
Barradas, R. (2020). Does the financial system support economic growth in times of financialisaton? Evidence for Portugal. International Review of Applied Economics, 34(6), 785–806.
10.1080/02692171.2020.1782854
Web of Science® Google Scholar
Barro, R. J. (2003). Determinants of economic growth in a panel of countries. Annals of Economics and Finance, 4, 231–274.
Google Scholar
Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking & Finance, 28(3), 423–442.
10.1016/S0378-4266(02)00408-9
Web of Science® Google Scholar
Beck, T., Demirgüç-Kunt, A., & Levine, R. (2009). Financial institutions and markets across countries and over time-data and analysis. World Bank Policy Research Working Paper, (4943).
Google Scholar
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
10.1016/S0304-4076(98)00009-8
Web of Science® Google Scholar
Bond, S. R. (2002). Dynamic panel data models: A guide to micro data methods and practice. Portuguese Economic Journal, 1(2), 141–162.
10.1007/s10258-002-0009-9
Google Scholar
Bumann, S., Hermes, N., & Lensink, R. (2013). Financial liberalization and economic growth: A meta-analysis. Journal of International Money and Finance, 33, 255–281.
10.1016/j.jimonfin.2012.11.013
Web of Science® Google Scholar
Candelon, B., & Lieb, L. (2013). Fiscal policy in good and bad times. Journal of Economic Dynamics and Control, 37(12), 2679–2694.
10.1016/j.jedc.2013.09.001
Web of Science® Google Scholar
Caner, M. and B.E. Hansen (2004) “Instrumental Variable Estimation of a Threshold ModeL”. Econometric Theory, 20, 2004, 813–843. https://doi.org/10.1017/S0266466604205011
10.1017/S0266466604205011
Google Scholar
Caselli, F., Esquivel, G., & Lefort, F. (1996). Reopening the convergence debate: A new look at cross-country growth empirics. Journal of Economic Growth, 1(3), 363–389.
10.1007/BF00141044
Google Scholar
Cecchetti, S. G., & Kharroubi, E. (2012). Reassessing the impact of finance on growth. BIS, Working Paper No 381.
Google Scholar
Conway, P., & Orr, A. (2000). The process of economic growth in New Zealand. Reserve Bank of new Zealand Bulletin, 63(1), 4–20.
Google Scholar
Demetriades, P., & Hussein, K. A. (1996). Does financial development cause economic growth? Time-series evidence from 16 countries. Journal of Development Economics, 51(2), 387–411.
10.1016/S0304-3878(96)00421-X
Web of Science® Google Scholar
Demirguc, A., & Detragiache, E. (1998). The determinants of banking crises; evidence from developing and developed countries. International Monetary Fund, 45(1), 81–109.
10.2307/3867330
Google Scholar
Demirgüç-Kunt, A., & Maksimovic, V. (1998). Law, finance, and firm growth. The Journal of Finance, 53(6), 2107–2137.
10.1111/0022-1082.00084
Web of Science® Google Scholar
Duffie, D. (2019). Prone to fail: The pre-crisis financial system. Journal of Economic Perspectives, 33(1), 81–106.
10.1257/jep.33.1.81
Web of Science® Google Scholar
Goldsmith, R. W. (1969). Financial structure and development. Yale University Press.
Google Scholar
Haini, H., & Loon, P. W. (2021). Does government ideology affect the relationship between government spending and economic growth? Economic Papers: A Journal of Applied Economics and Policy, 40(3), 209–216.
10.1111/1759-3441.12319
Web of Science® Google Scholar
Hanousek, J., Kočenda, E., & Kutan, A. M. (2009). The reaction of asset prices to macroeconomic announcements in new EU markets: Evidence from intraday data. Journal of Financial Stability, 5(2), 199–219.
10.1016/j.jfs.2008.01.003
Web of Science® Google Scholar
Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368.
10.1016/S0304-4076(99)00025-1
Web of Science® Google Scholar
Hassan, M. K., Sanchez, B., & Yu, J.-S. (2011). Financial development and economic growth: New evidence from panel data. The Quarterly Review of Economics and Finance, 51(1), 88–104.
10.1016/j.qref.2010.09.001
Google Scholar
Hicks, J. (1969). A theory of economic history. Economic Journal, 80, 350–352.
Web of Science® Google Scholar
Holtz-Eakin, D., W. Newey and H. S. Rosen (1988) “Estimating Vector Autoregressions with Panel Data”. Econometrica, 56(6), 1371–1395.
10.2307/1913103
Web of Science® Google Scholar
Huang, H.-C., & Lin, S.-C. (2009). Non-linear finance-growth nexus: A threshold with instrumental variable approach 1. The Economics of Transition, 17(3), 439–466.
10.1111/j.1468-0351.2009.00360.x
Web of Science® Google Scholar
Hye, Q. M. A., & Lau, W. Y. (2015). Trade openness and economic growth: Empirical evidence from India. Journal of Business Economics and Management, 16(1), 188–205.
10.3846/16111699.2012.720587
Web of Science® Google Scholar
Jappelli, T., & Pagano, M. (1994). Saving, growth, and liquidity constraints. The Quarterly Journal of Economics, 109(1), 83–109.
10.2307/2118429
Web of Science® Google Scholar
Kaminsky, G., & Schmukler, S. (2002). Emerging markets instability: Do sovereign ratings affect country risk and stock returns? World Bank Economic Review, 16(2), 171–195.
10.1093/wber/16.2.171
Web of Science® Google Scholar
King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics, 108(3), 717–737.
10.2307/2118406
Web of Science® Google Scholar
Kremer, S. (2013). “Inflation and Growth: New evidence from a dynamic panel threshold analysis”. Empirical Economics, 44(2), 878.
10.1007/s00181-012-0553-9
Web of Science® Google Scholar
Krugman, P. (1988). Financing vs. forgiving a debt overhang. Journal of Development Economics, 29(3), 253–268.
10.1016/0304-3878(88)90044-2
Web of Science® Google Scholar
Lane, P. R. (2012). The European sovereign debt crisis. Journal of Economic Perspectives, 26(3), 49–68.
10.1257/jep.26.3.49
Web of Science® Google Scholar
Law, S. H., & Singh, N. (2014). Does too much finance harm economic growth? Journal of Banking & Finance, 41, 36–44.
10.1016/j.jbankfin.2013.12.020
Web of Science® Google Scholar
Levine, R. (2005). Finance and Growth: Theory and Evidence. Handbook of Economic Growth, 1, 865–934. https://doi.org/10.1016/S1574-0684(05)01012-9
10.1016/S1574-0684(05)01012-9
Google Scholar
Levine, R., & Zervos, S. (1998). Stock markets, banks, and economic growth. American Economic Review, 88(3), 537–558.
Web of Science® Google Scholar
Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46(1), 31–37.
10.1016/S0304-3932(00)00017-9
Web of Science® Google Scholar
Li, X., & Liu, X. (2005). Foreign direct investment and economic growth: An increasingly endogenous relationship. World Development, 33(3), 393–407.
10.1016/j.worlddev.2004.11.001
Web of Science® Google Scholar
Makrychoriti, P., Pasiouras, F., & Tasiou, M. (2022). Financial stress and economic growth: The moderating role of trust. Kyklos, 75(1), 48–74.
10.1111/kykl.12285
Web of Science® Google Scholar
McKinnon, R. I. (1973). The value-added tax and the liberalization of foreign trade in developing economies: A comment. Journal of Economic Literature, 11(2), 520–524.
Web of Science® Google Scholar
Mountford, A., & Uhlig, H. (2009). What are the effects of fiscal policy shocks? Journal of Applied Econometrics, 24(6), 960–992.
10.1002/jae.1079
Web of Science® Google Scholar
Purewal, K., & Haini, H. (2022). Re-examining the effect of financial markets and institutions on economic growth: Evidence from the OECD countries. Economic Change and Restructuring, 55(1), 311–333.
10.1007/s10644-020-09316-2
Web of Science® Google Scholar
Rajan, R. G., & Zingales, L. (1998). Power in a theory of the firm. The Quarterly Journal of Economics, 113(2), 387–432.
10.1162/003355398555630
Web of Science® Google Scholar
Ramey, V. A. (2019). Ten years after the financial crisis: What have we learned from the renaissance in fiscal research? Journal of Economic Perspectives, 33(2), 89–114.
10.1257/jep.33.2.89
Web of Science® Google Scholar
Reinhart, C. M., & Rogoff, K. S. (2011). From financial crash to debt crisis. American Economic Review, 101(5), 1676–1706.
10.1257/aer.101.5.1676
Web of Science® Google Scholar
Romer, D. (2012). Advanced macroeconomics ( 4th ed.). McGraw-Hill.
Google Scholar
Roodman, D. (2009). How to do xtabond 2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136.
10.1177/1536867X0900900106
Web of Science® Google Scholar
Rousseau, P. L., & Wachtel, P. (2011). What is happening to the impact of financial deepening on economic growth? Economic Inquiry, 49(1), 276–288.
10.1111/j.1465-7295.2009.00197.x
Web of Science® Google Scholar
Samargandi, N., Fidrmuc, J., & Ghosh, S. (2015). Is the relationship between financial development and economic growth monotonic? Evidence from a sample of middle-income countries. World Development, 68, 66–81.
10.1016/j.worlddev.2014.11.010
Web of Science® Google Scholar
Shambaugh, J. C., Reis, R., & Rey, H. (2012). The euro's three crises [with comments and discussion]. Brookings Papers on Economic Activity, 2012, 157–231.
10.1353/eca.2012.0006
Google Scholar
Shen, C.-H., & Lee, C.-C. (2006). Same financial development yet different economic growth-why? Journal of Money, Credit, and Banking, 38(7), 1907–1944.
10.1353/mcb.2006.0095
Web of Science® Google Scholar
Shleifer, A., & Vishny, R. W. (1997). A survey of corporate governance. The Journal of Finance, 52(2), 737–783.
10.1111/j.1540-6261.1997.tb04820.x
Web of Science® Google Scholar
Singh, A. (1997). Financial liberalisation, stock markets and economic development. The Economic Journal, 107(442), 771–782.
10.1111/j.1468-0297.1997.tb00042.x
Web of Science® Google Scholar
Svirydzenka, K. (2016). Introducing a new broad-based index of financial development. International Monetary Fund.
Google Scholar
Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.
10.1016/j.jeconom.2004.02.005
Web of Science® Google Scholar
Wisniewski, T. P., & Jackson, P. M. (2021). Government debt expansion and stock returns. International Journal of Finance & Economics, 26(4), 5017–5030.
10.1002/ijfe.2052
Web of Science® Google Scholar
Wu, J. L., Hou, H., & Cheng, S. Y. (2010). The dynamic impacts of financial institutions on economic growth: Evidence from the European Union. Journal of Macroeconomics, 32(3), 879–891.
10.1016/j.jmacro.2009.09.003
PubMed Web of Science® Google Scholar
Yang, Y., & Yi, M. H. (2008). Does financial development cause economic growth? Implication for policy in Korea. Journal of Policy Modelling, 30(5), 827–840.
10.1016/j.jpolmod.2007.09.006
Web of Science® Google Scholar

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Volume29, Issue2

April 2024

Pages 2495-2514

Financial development, economic growth and the role of fiscal policy during normal and stress times: Evidence for 26 EU countries

Abstract

1 INTRODUCTION

2 LITERATURE REVIEW

3 DATA AND METHODOLOGY

4 EMPIRICAL RESULTS

4.1 System GMM panel estimation results

4.2 Fixed effects panel threshold results

5 CONCLUSIONS

Endnotes

APPENDIX A

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

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Financial development, economic growth and the role of fiscal policy during normal and stress times: Evidence for 26 EU countries

Abstract

1 INTRODUCTION

2 LITERATURE REVIEW

3 DATA AND METHODOLOGY

4 EMPIRICAL RESULTS

4.1 System GMM panel estimation results

4.2 Fixed effects panel threshold results

5 CONCLUSIONS

Endnotes

APPENDIX A

Open Research

DATA AVAILABILITY STATEMENT

REFERENCES

Citing Literature

References

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