Institutional quality and financial development: The United States perspective

J. of Multi. Fin. Manag. 49 (2019) 67–80

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Institutional quality and financial development: The United States perspective Muhammad Asif Khan a,b,∗ , Muhammad Atif Khan b,c , Mohamued Elyas Abdulahi a,d , Idrees Liaqat b , Sayyed Sadaqat Hussain Shah a a b c d

Huazhong University of Science and Technology, Wuhan, PR China Department of Commerce, University of Kotli, AJK, Pakistan Department of Finance, Zhongnan University of Economics and Law, Wuhan, PR China Jigjiga University, Somali Region, Jigjiga, Ethiopia

a r t i c l e

i n f o

Article history: Received 31 October 2018 Accepted 16 January 2019 Available online 26 January 2019 JEL classification: G34 G18 G28 P48 O13 P28 Keywords: Institutional quality Financial development Natural resource rent

a b s t r a c t This paper revisits the ambiguous natural resource rent and finance nexus in the context of the United States, incorporating the vital role of institutional quality in this paradigm. The literature on the subject matter lacks consensus and overlooks the institutional framework. The study utilizes a unique data set by the International Monetary Fund (IMF), considered the most comprehensive measure of financial development. Our findings based on a robust cointegration approach conclusively confirm that institutional quality is a significant prerequisite to financial development in the United States. The natural resource rent affects financial development negatively when we include proper controls in the estimation. However, we find that institutional quality moderates the natural resource rent and finance nexus. We recommend that policymakers and researchers consider the importance of institutions to come up with realistic estimations and policy inputs. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The recent work by Shahbaz et al. (2018) utilizes the time series data of United States from 1960 to 2016 to establish the cointegration and causal association between resource-abundance and financial development of the United States. They measure financial development by a traditional proxy of “real domestic credit to private sector per capita,” and resourceabundance by “resource rent of all natural resources.” Their empirical findings document that natural resource rent positively stimulates the financial development of the United States. The estimation in Shahbaz et al. (2018) seems to be overestimated due to omitted variable bias, and traditional measurement of financial development. This misspecifications motivates us to explore the matter-therefore to come up with relatively realistic and reliable estimation. We transform the shortcoming in Shahbaz et al. (2018) into following questions and attempt to answer at the later stages of this study empirically;

∗ Corresponding author. E-mail address: [email protected] (M.A. Khan). https://doi.org/10.1016/j.mulfin.2019.01.001 1042-444X/© 2019 Elsevier B.V. All rights reserved.

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1 Does natural resource rent is directly proportionate with the financial development of the United States in long-run, or there is the moderating role of institutional quality? As the institutional quality of resource-abundant countries plays a critical role in economic progress. The resource-rich countries are winner or loser; the final results depend on the quality of institutions, the resource curse appears in countries with inferior institutions (Mehlum et al., 2006). They further argue, “institutions mediate the effect of resources on growth.” 2 Is it the right time to replace an obsolete measure of financial development, with a relatively comprehensive index by IMF in (Svirydzenka, 2016)? and 3 What are essential controls overlooked in the recent study that may have a significant impact on the financial development of the United States? Undeniably, the resource-abundance is a blessing for a country, yet its high rent potentially lacks the prediction of financial developments explicitly. The natural resource rent requires a sound institutional framework, which may prevent misuse (Boschini et al., 2013). Bhattacharyya and Hodler (2014), document the role of institutions in the interaction of natural resource revenues with financial development; conclude that if the institutions are weak in a country, these revenues may worsen the contract enforcement. While, the weak contract enforcement leads towards substantial harm to financial development, all the same, the scenario is different in the presence of better institutions; the contract enforcement is useful in stimulating the financial development. Kolstad and Soreide (2009), identify the corruption as the source of meager economic performance for resource-rich countries, probably due to the rent-seeking behavior and patronage. They suggest policies should be specific to enhance the role of institutions to restraint the rent-seeking behavior and patronage, instead solely focusing on the macroeconomic management in resource-rich countries. Costa and Santos (2013), using the rule of law as a measure of institutions, analyze how institutions are responsible for allocating the hydrocarbon royalties to minimize the resource curse, find a violation on the part of institutions. The author suggests to strengthen the institutions with close monitoring of the oil revenue allocation, and involvement of public participation may be helpful in alleviating the resource curse. Rathinam and Raja (2010), utilize the index of procedural law, regulation and institutional development to investigate the long-run causal relations with the financial sector, the results witness the causal flow from legal and institutional developments towards financial sector growth that ultimately triggers the economic growth in the context of India. Yuxiang and Chen (2011) empirically examine the panel data of China to examine the relationship between resource-abundance and financial development and find a negative association. The authors argue that regions with natural resource-abundance tend to have a slower pace of financial development relative to resource-poor regions, and financial development acts as an important channel through which resource abundance affect economic growth. Huang (2010), observes whether political, institutional improvement promotes financial development for the panel of ninety developed and developing countries. The empirical results support the decisive role of institutions in boosting financial development at least in short-run for developing nations, in particular, the democratic transformation is typically followed by an increase in financial development. Bulte et al. (2005) notice that there is an ambiguous direct effect of mineral resources on development, however, an indirect adverse effect is seen when institutional quality is introduced. The brief literature review on natural resource rent; firstly, lacks the consensus to quantify the role of institutional quality in this connection. The efforts so far attempted to attribute the financial development explicitly with resource rent, which is misleading subject to biased–overestimation resulting due to the absence of institutional quality and relevant controls. The quality of institutions determines whether resource rent poses a resource curse or blessing (Badeeb et al., 2017). Secondly, measuring the multidimensional financial development with few conventional measures seem incapable of representing the entire financial system. These traditional measures often face critics among research scholars due to incomprehensive nature that makes them conflicting in the arena of financial economics literature. Huang (2010), Anwar and Cooray (2012), Yilmazkuday (2011), J.-S. Yu et al. (2012), Hassan et al. (2011), Shahbaz et al. (2018), and many others used an individual proxy of financial development (either bank-based or stock market-based). The financial development measurement from a single proxy may not produce relatively perfect estimation (Dorrucci et al., 2009). Rudra P. Pradhan et al. (2014) developed the aggregate index, combining several conventional measures as a composite index. These measures do not take into account the complex multidimensional nature of financial development (Svirydzenka, 2016). Therefore, we use the comprehensive index of financial development proposed by IMF (Svirydzenka, 2016) to overcome the shortcomings with the traditional approach. 1.1. Brief overview of the United States institutional quality pattern Before proceeding further, let us have a look at the United States historical institutional quality patterns. Fig. 1 illustrates the average institutional quality index of the United States over the study period from 1984 to 2016. The study period covers the maximum available data with respect to institutional quality from International Country Risk Guide (ICRG) dataset. The pattern in the above figure shows an unstable state of institutional quality for the United States, which is not in line with the argument that the institutions among developed nations are stable. The United States scored 2.04 reaching an all-time highest score in 1984, followed by 2.03 and 2.00 during 2000 and 1999 respectively. The ever-lowest score is observed in 1992–1993 (1.72) and 1995 (1.76). From 1984 to 1988 and 1989 to 1992, the downward trend is reported, while the institutional quality has greatly improved after 1995 until 2000. From 1984 to 1993 the institutional quality score sharply dropped by 0.32 points. It improved greatly with 0.30 points until 2000, yet experienced a declining trend till 2017

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Fig. 1. Institutional quality index of the United States from 1984 to 2016.

and dropped by 0.12 points. The changing pattern of institutional quality index reveals that over the time policies of the United States are not the same. The dynamic pattern is expected to influence the financial development along with other sectors. This study employs robust Autoregressive Distributed Lag (ARDL) bounds test to cointegration and Error Correction Model (ECM) to identify the cointegration between financial development, institutional quality, natural resource rent, economic growth, economic policy uncertainty, openness, and capital. These approaches only estimate the presence of cointegration; however, policy implications often require the direction of causality. We deploy the Vector Error Correction Granger causality mechanism (VECM), utilizes maximum available longitudinal data of the United States to examine the short and long-run causal flow between each pair. This study contributes to the literature by answering questions identified in the above section. 1 Institutional quality plays a significant moderating role in the natural resource rent and finance nexus over the period from 1984 to 2016 in the context of the United States. 2 Representation of multi-dimensional financial development with an obsolete traditional measure “domestic credit to the private sector” undermines the weight of the entire financial system. This does not account for the multidimensional financial system (Svirydzenka, 2016). This traditional measure often fails to grasp the nuances and subtleties of the development of financial markets. The bank-based measure overlooks the other types of financial markets, such as equity, bonds and insurance markets (Ito and Kawai, 2018). At the same time, it cannot include the qualitative aspects of financial development, such as the diversity (scale) of financial markets, efficiency, liquidity and the surrounding institutional environment (Ito and Kawai, 2018). Therefore, such ambiguous and conflicting measure of diverse financial development needs to be replaced with a comprehensive financial development index by IMF in Svirydzenka (2016). 3 Including additional relevant controls (openness and economic policy uncertainty) mitigates the omitted variable bias and pronounces relatively reliable estimation as compared to previous studies. Rest of the paper is organized as: Section 2 illustrates the construction of model and data collection, Section 3 outlines the methodological strategy, Section 4 accounts for empirical results while Section 5 concludes the study. 2. Construction of model and data collection This study empirically investigates how the institutional quality of United States stimulates the financial development incorporating the role of natural resource rent, economic growth, economic policy uncertainty, capital, and openness. Financial development is an integral part of economic growth, an efficient and fully functioning financial system leads to economic prosperity. Financial sector plays a pivotal role in the efficient allocation of scarce economic resources, and its rapid pace leads towards total factor productivity growth (Han and Shen, 2015). Financial market efficiency and competitiveness promote economic growth (Ro et al., 2017), while financial access and financial efficiency as key determinants of financial development with spill-over effect through economic development (Rashid and Intartaglia, 2017; X. Yu et al., 2017). A stable institutional framework upheaves economic growth while weak institutional activities cause economic growth to a stagnant state (Jain et al., 2017). A high-quality institutional environment is important in explaining financial development (Law and Azman-Saini, 2012). Institutional factors play a crucial role in the economic and financial development and pose pressure on policymaker to establish stabilized reforms to cater the uncertainty (Cherif and Gazdar, 2010). Bhattacharyya and Hodler (2014) highlight the vital role of institutions in explaining the effect of natural resource revenues on financial

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development, they contend that poor institutions fail to enforce the contract, the natural resource revenues ultimately influence the financial development negatively. However, sound institutions moderate this effect through contract enforcement. Backed up by these arguments, we intend to examine the role of institution quality in explaining the financial development incorporating the natural resource rent due to its conflicting association with financial development (Bhattacharyya and Hodler, 2014; Hoshmand et al., 2013; Law and Moradbeigi, 2017; Shahbaz et al., 2018; Yuxiang and Chen, 2011). The institutional quality is expected to be directly proportionate to financial development while in the presence of quality institutions the natural resources rent is also expected to have a positive association with financial development. The economic growth such as arguments of expanding economic actions gives rise to the demand for financial products and services, resulting in enlarging the banking sector and capital markets to match with stimulated demand (Chow and Fung, 2013; Mukhopadhyay et al., 2011; Shahbaz et al., 2018). Therefore, we include economic growth as a control for financial development. Openness is likely to affect financial development through an increase in the demand for financial services stimulates by exports and an increase in market size (Menyah et al., 2014). An increase in trade openness generates the demand for new financial instruments to finance the emerging demand and to hedge the associated risk with international exchange rate fluctuations. The study of Sub-Saharan Africans by David et al. (2014) shows that openness is very vital determinant for financial development if there exists a healthier institutional quality. Capital account and trade openness are important determinants of financial development (banking sector development), and they foster financial development, either both are open simultaneously or one without other (Baltagi et al., 2009). The capital account liberalization has a sound connection with financial depth in case of advanced and developing countries (Klein and Olivei, 2008). The openness seems to be positively associated with financial development and consider as a necessary control for financial development (Le et al., 2016). Adjei and Adjei (2017)explain economic policy as a blend of regulatory, fiscal and monetary policies potentially determined by the central bank of a country through government actions. Economic policy plays a vital role in shaping financial markets and needs frequent adjustment whenever any change occurs in the economy (Adjei and Adjei, 2017), and any uncertainty in these policies eventually decelerates the development process (Raza et al., 2018). We incorporate economic policy uncertainty in Baker et al. (2016) to test its effect on financial development. Finally, following Shahbaz et al. (2018) we also include capital as a determinant of financial development in the developed economy. The theoretical relationship between the underlying variables is expressed in a general functional form as; Ft = f (It , Rt , Gt , Pt , Kt , Tt , It × Rt )

(1)

where Ft is financial development, It is institutional quality, Rt shows natural resource rent, Gt represents the economic growth, Pt denotes the economic policy uncertainty, Kt is capital, Tt shows openness, and It × Rt captures the moderation effect of institutional quality and natural resource rent. All the variables are transformed into per capita and natural log form to obtain potentially reliable and efficient estimation. Such transformation assures normal distribution of data and reliable empirical results (Ahmed et al., 2016; Farooq et al., 2013; Shahbaz et al., 2018). The empirical model is expressed in following functional form; ln Ft = 0 + I ln It + R ln Rt + G ln Gt + P ln Pt + 1 ln Kt + T ln Tt + IR ln It ∗ Rt + i

(2)

where ln is natural log of the respective variable,  0 measures the intercept,  1 – 1R are sequential coefficient of institutional quality, natural resource rent, economic growth, economic policy uncertainty, capital, openness, and moderation effect of institutional quality and natural resource rent ␮i accounts for error term that is normally distributed. Financial development is measured by the comprehensive index of financial development establish by IMF in Svirydzenka (2016).1 This index considers nine indices that summarize how development financial institutions and financial markets are in terms of their depth, access, and efficiency. The institutional quality is predicted value of six indicators of political risk service,2 aggregated using principal component analysis (PCA). The PCA allows the orthogonal linear transformation of high frequency indicators to aggregate and construct single index that nearly holds most of the features of the original dataset (Batuo et al., 2017; Le et al., 2016; Tang and Tan, 2014). The institutional quality indicators include “government stability, corruption, democratic accountability, bureaucratic quality, law and order, and investment profile” (Daude and Stein, 2007; Law et al., 2013; Law and Habibullah, 2006). The ICRG rating system assigns a score to these components based on 100 points criteria, the lowest score shows high risk (low quality), and the highest score is evidence of low risk (high quality). We prefer Political Risk Service (PRS) indicators to the World Governance Indicators (WGI) due to longitudinal coverage.3 Natural resources rent is the sum of oil rent, natural gas rent, coal rent, mineral and forest rents (the World Bank-world development indicators, 2017). Economic growth is measured by real GDP per capital; economic policy uncertainty is captured by Economic Policy Uncertainty index; openness by the ratio of imports and export to GDP; and capital by fixed gross capital formation. We source IMF dataset for financial development index; ICRG for institutional quality indicators; World Bank for economic growth, openness and capital; while we compile economic policy uncertainty data from Economic Policy Uncertainty database by Baker et al. (2016).

1 2 3

http://www.imf.org/external/pubs/ft/wp/2016/wp1605.pdf. Further details are available at ICRG-PRS official website (https://www.prsgroup.com/). WGI by world bank covers relatively shorter time span from 1996 to date, while ICRG-PRS have relatively longer time span from 1984 to date.

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3. Methodological strategy The study employed inspirational autoregressive distributed lag approach to cointegration, symbolized as ARDL in Pesaran and Shin (1998) with the extended version as Bounds testing (Pesaran et al., 2001) to examine the long-run association between financial development, institutional quality, natural resource rent, economic growth, economic policy uncertainty and capital. Unlike other conventional methods4 such as, Engle and Granger (1987) and Johansen (1991), this approach is flexible to different integration orders [I(1), I(0) or mixed] and provides best estimates, in particular, the results are robust for small sample size (Haug, 2002; Pesaran and Shin, 1998). The ARDL bounds testing approach to cointegration is modeled as under;  ln Ft = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 +

p 

q 

ϕI  ln It−1 +

t−1

u 

+

r 

ϕR  ln Rt−1 +

t−0

ϕG  ln Gt−1 +

t−0

s 

t 

ϕP  ln Pt−1 +

t−0

u 

ϕK  ln Kt−1 +

t−0

ϕT  ln Tt−1

(3)

t−0

ϕT  ln It−1 ∗ ln Rt−1 + ϕD D1 + i

t−0

 ln It = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 +

p 

q 

ϕI  ln It−1 +

r 

ϕR  ln Rt−1 +

t−1

t−0

+

ϕT  ln Tt−1 + ϕD D2 + i

u 

ϕG  ln Gt−1 +

t−0

s 

t 

ϕP  ln Pt−1 +

t−0

ϕK  ln Kt−1

(4)

t−0

t−0

 ln Rt = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 +

p 

q 

ϕI  ln It−1 +

t−1

u 

+

r 

ϕR  ln Rt−1 +

t−0

ϕG  ln Gt−1 +

t−0

s 

t 

ϕP  ln Pt−1 +

t−0

u 

ϕK  ln Kt−1 +

t−0

ϕT  ln Tt−1

(5)

t−0

ϕT  ln It−1 ∗ ln Rt−1 + ϕD D3 + i

t−0

 ln Gt = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 +

p 

q 

ϕI  ln It−1 +

t−1

u 

+

r 

ϕR  ln Rt−1 +

t−0

ϕG  ln Gt−1 +

t−0

s 

t 

ϕP  ln Pt−1 +

t−0

u 

ϕK  ln Kt−1 +

t−0

ϕT  ln Tt−1

(6)

t−0

ϕT  ln It−1 ∗ ln Rt−1 + ϕD D4 + i

t−0

 ln Pt = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 +

p 

q 

ϕI  ln It−1 +

t−1

u 

+

t−0

r 

ϕR  ln Rt−1 +

ϕG  ln Gt−1 +

t−0

ϕT  ln It−1 ∗ ln Rt−1 + ϕD D5 + i

t−0

4

These methods require unique order of integration respectively.

s  t−0

t 

ϕP  ln Pt−1 +

t−0

u 

ϕK  ln Kt−1 +

t−0

ϕT  ln Tt−1

(7)

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M.A. Khan et al. / J. of Multi. Fin. Manag. 49 (2019) 67–80

 ln Kt = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 p 

+

q 

ϕI  ln It−1 +

t−1

u 

+

r 

ϕR  ln Rt−1 +

t−0

s 

ϕG  ln Gt−1 +

t−0

ϕP  ln Pt−1 +

t 

t−0

u 

ϕK  ln Kt−1 +

t−0

ϕT  ln Tt−1

(8)

t−0

ϕT  ln It−1 ∗ ln Rt−1 + ϕD D6 + i

t−0

 ln Tt = 0 + F ln Ft−1 + I ln It−1 + R ln Rt−1 + G ln Gt−1 + P ln Pt−1 + 1 ln Kt−1 + T ln Tt−1 +IR ln It−1 ∗ ln Rt−1 p 

+

q 

ϕI  ln It−1 +

t−1

u 

+

r 

ϕR  ln Rt−1 +

t−0

s 

ϕG  ln Gt−1 +

t−0

ϕP  ln Pt−1 +

t 

t−0

u 

ϕK  ln Kt−1 +

t−0

ϕT  ln Tt−1

(9)

t−0

ϕT  ln It−1 ∗ ln Rt−1 + ϕD D7 + i

t−0

Here,  is the first difference operator, p-u are the optimal number of lags,5  and ϕ are respective coefficients, while, D represents the dummy variable in Kim and Perron (2009) for structural breaks. The null hypothesis of no cointegration (H0 :  F =  I =  R =  G =  P =  K =  IR = 0) assumes absence of the long-run relationship, tested against the alternative hypothesis (H0 :  F = / I = / R = / G = / P = / K = /  IR = / 0) that implies long-run relationship holds. The estimation indicates existence of cointegration when computed ARDL F-statistics exceeds the upper bound at 5% level of significance. Absence of cointegration appears when F-statistics remains below the lower bound, while we remain indifference in a case of laying between two bounds. We applied diagnostics and stability analysis to test the reliability and stability of the model. 3.1. The VECM Granger causality The ARDL Bounds test holds that there exists a long-run relationship among the variable under consideration. Thus, we need to test the direction of causality between financial development, institutional quality, natural resource rent, economic growth, economic policy uncertainty, capital and moderating term. Engle and Granger (1987) identify that there should be at least unidirectional causality between those variables which are cointegrated with the unique integration order. Therefore, we employ VECM Granger causality to identify the short-run and long-run causal flow between the variables, which substantiates the comprehensive policy formulation process accordingly with known directions. The VECM Granger causality is modeled using following equation;

⎡

ln Ft

⎤

⎡

ω1

⎤ ⎡

b13i b14i b15i b16i b17i b18i

⎤ ⎡

ln Ft−1

⎤

⎢ ln I ⎥ ⎢ ω ⎥ ⎢ b b b b b b ⎥ ⎢ ln I ⎥ ⎢ t ⎥ ⎢ 2 ⎥ ⎢ 25i 26i 27i 28i 29i 30i ⎥ ⎢ t−1 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ln Rt ⎥ ⎢ ω3 ⎥ ⎢ b37i b38i b39i b40i b41i b42i ⎥ ⎢ ln Pt−1 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ (1 − L) ⎢ ⎥=⎢ ⎥+⎢ ⎥×⎢ ⎥ + ... ⎢ ln Gt ⎥ ⎢ ω4 ⎥ ⎢ b49i b50i b51i b52i b53i b54i ⎥ ⎢ ln Gt−1 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ln Pt ⎦ ⎣ ω5 ⎦ ⎣ b61i b62i b63i b64i b65i b66i ⎦ ⎣ ln Pt−1 ⎦ ⎡

ln Kt

ω6

b13i b14i b15i b16i b17i b18i

b73i b74i b75i b76i b77i b78i

⎤ ⎡

ln Ft−1

⎤

⎡ ⎤

ln Kt−1

⎡

ε1t

⎤

(10)

˛ ⎢ b b b b b b ⎥ ⎢ ln I ⎥ ⎢ε ⎥ ⎢ 25i 26i 27i 28i 29i 30i ⎥ ⎢ t−1 ⎥ ⎢ ˇ ⎥ ⎢ 2t ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ b37i b38i b39i b40i b41i b42i ⎥ ⎢ ln Rt−1 ⎥ ⎢  ⎥ ⎢ ε3t ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ +⎢ ⎥×⎢ ⎥ + ⎢ ⎥ ECMt−1 + ⎢ ⎥ ⎢ b49i b50i b51i b52i b53i b54i ⎥ ⎢ ln Gt−1 ⎥ ⎢ ı ⎥ ⎢ ε4t ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎢ ⎥ ϕ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ b61i b62i b63i b64i b65i b66i ⎦ ⎣ ln Pt−1 ⎦ ⎣ ε5t ⎦ b73i b74i b75i b76i b77i b78i

ln Kt−1

ω

ε6t

where (1 − L) is the difference operator, ECMt−1 is the lagged error correction term attained from long-run equation. The statistically significant with negative coefficient ECMt−1 confirms the existence of long-run causality. The short-run causal

5 Under VAR lag order selection criteria, the AIC (Akaike information criterion), SC (Schwarz information criterion), and HQ (Hannan-Quinn information criterion) suggest 2 optimal lags for estimation. The table is not reported to preserve the space, the authors can be contacted via email where requires.

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Table 1 Descriptive statistics and correlation matrix.

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera P-value ln Ft ln It ln Rt ln Gt ln Pt ln Kt ln Tt ln It *Rt

ln Ft

ln It

ln Rt

ln Gt

ln Pt

ln Kt

ln Tt

ln It *Rt

4.3241 4.4668 4.4893 3.7787 0.2176 −1.0229 2.6255 5.9472 0.0511 1.0000 0.2307 −0.5089 0.9167 0.1343 0.9268 0.9223 −0.5001

1.9004 1.9159 2.0369 1.7155 0.0797 −0.8650 3.2875 4.2291 0.1207

−0.0027 0.0417 0.9395 −1.2677 0.4570 −0.6613 4.6988 6.3736 0.0413

10.6560 10.7155 10.8660 10.3504 0.1587 −0.3845 1.7434 2.9843 0.2249

4.6815 4.6907 5.1551 4.1367 0.2687 0.0454 2.1016 1.1211 0.5709

28.4771 28.6099 28.8356 27.9671 0.2945 −0.3622 1.5162 3.7487 0.1535

28.5695 28.7364 29.2551 27.5702 0.5393 −0.4144 1.8079 2.8985 0.2348

−0.0198 0.1715 4.5926 −6.0224 2.1376 −0.6849 4.9472 7.7930 0.0203

1.0000 0.1017 0.2705 0.1000 0.3702 0.2410 −0.0828

1.0000 −0.4572 −0.1537 −0.4223 −0.4727 0.9991

1.0000 0.2576 0.4848 0.4948 −0.4556

1.0000 0.1891 0.2757 −0.1650

1.0000 0.3729 −0.4190

1.0000 −0.4687

1.0000

relationship is guided by first differences of the variables, if corresponding test statistics is significant. For example b14i = / 0∀i indicates that causal flows from institutional quality to financial development and causal flows from financial development to institutional quality if b25i = / 0 ∀ i. 4. Empirical results and discussion Table 1 shows the descriptive statistics (mean, median, maximum, minimum, standard deviation and Jarque-Bera) and correlation matrix respectively. The mean values for openness, capital, and economic growth are relatively high as to that of financial development, economic policy uncertainty, and others. Standard deviation explains relatively high volatility in openness, natural resource rent, capital, and economic policy uncertainty respectively. The Jarque-Bera satisfies the normality assumption for further estimation. The correlation results reveal the high and positive correlation of capital, openness and economic growth with financial development. Similarly, the institutional quality and economic policy uncertainty document positive association with financial development, however, only natural resource rent is negatively related to financial development. Economic growth, economic policy instability, capital, and openness are inversely correlated with natural resource rent, while rest of all pairs have a positive association. The study determines the stationarity properties of variables through Augmented Dickey-Fuller (ADF) (Dickey and Fuller, 1979, 1981) and Phillip-Perron (PP) (Phillips and Perron, 1988) tests. The result is incorporated in Table 2 that reveals the financial development with its sub-indices (financial markets and institutions) are stationary at level and the first difference with ADF and PP tests. As per ADF, the institutional quality, natural resource rent, economic growth, capital, openness, economic policy uncertainty, and moderating term6 (institutional quality*natural resource rent) are found non-stationary at I(0); become stationary after converted to I(1), except openness which is stationary at both orders. The PP test affirms the ADF results in all regards except openness. Both these test (ADF and PP) do not account for structural breaks within data set probably resulted either from, a policy reform or due to the happening of any natural disaster. Such shocks often produce unexpected results. Clemente et al. (1998) developed a mechanism that has the potential to detect two unknown structure breaks in the given data. In case of more than two unknown structural breaks, the application of this method is questionable. To overcome these issues, we apply a structural break unit root test suggested by Kim and Perron (2009). This allows identifying more than two structural breaks at the same time. The right portion of Table 2 shows the results of Kim and Perron (2009) multiple break unit root test. We find financial development, institutional quality, economic policy uncertainty, and capital having stationary at level although there are breaks. Natural resource rent, growth, openness, and moderating term are non-stationary at the level I(0), however, become stationary after converted to the first difference I(1). The structural break unit-root results conform with ADF and PP with the mixed order of integration. The mixed order of integration suggests (i.e., ADF, PP, and KP) the application of ARDL bounds test to cointegration in Pesaran et al. (2001). This attribute to handle a mixed order of integration distinguishes ARDL bounds testing approach from other methods restricted to unique integration order. The ARDL bounds test results are provided in Table 3. The ARDL F-test exceeds the upper bound (critical bound values are based on Pesaran et al., 2001) at all confidence level when financial development, institutional quality, economic policy uncertainty, economic growth, openness, capital, and moderating term are a swap as the dependent variable, except the natural resource rent which stands between upper and lower bounds,

6

Moderating term we mean the interaction of institutional quality with natural resource rent.

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Table 2 Unit root analysis. Without break

ln Ft ln It ln Rt ln Gt ln Pt ln Kt ln It *Rt ln Tt ln FIt ln FMt ln Ft ln It ln Rt ln Gt ln Pt ln Kt ln Tt ln It *Rt ln FIt ln FMt

With break

Augmented Dickey-Fuller

Phillips-Perron

T-statistics

Critical value

T-statistics

Critical value

Kim and Perron T-statistics

P-value

Break point

−3.6032* −2.5633 −2.5519 −1.6054 −3.0981 −1.9820 −2.1467 −2.9571 −4.6684** −3.5687* −3.5875* −4.5524** −5.7266*** −3.6446* −4.4555** −3.0333* −4.7798** −5.7241*** −2.9980* −3.6032*

−3.2380 −3.5577 −3.5577 −3.5628 −3.5577 −3.5628 −2.9571 −2.3166 −2.9918 −2.9810 −3.4576 −3.5628 −3.5683 −3.5628 −3.6220 −2.9604 −2.9604 −2.9640 −2.6387 −3.2157

−4.8781** −2.6395 −2.4626 −1.1958 −2.9370 −1.4559 −1.9831 −3.3431 −7.3679*** −1.9074* −5.3554*** −4.5524** −6.6951*** −3.3443* −9.3930*** −2.9604* −4.7798* −6.6898*** −4.4374* −5.1300**

−2.9604 −3.5577 −3.5577 −3.5577 −3.5577 −3.5577 −2.9571 −2.9571 −2.9571 −2.9571 −3.5628 −3.5628 −3.5628 −2.9604 −3.5628 −2.9047 −2.9604 −2.9604 −2.9604 −2.9604

−11.4128*** −8.0388*** −4.3309 −4.2459 −4.8808** −4.9374** −4.5910 −2.7581 −8.1663*** −17.4674*** −7.0712*** −6.1127*** −7.2699*** −6.6654*** −5.1478** −4.5443* −7.6576*** −7.4421*** −7.9551*** −4.289

0.0100 0.0100 0.1960 0.2328 0.0471 0.0395 0.1338 0.9601 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0207 0.1173 0.0100 0.0100 0.0100 0.2118

2003 2002 2014 2008 2006 2008 2014 2008 2003 2008 2000 2006 2014 2008 2003 2009 2009 2014 1998 1999

Note: (***)(**)(*) denotes the significance of t-stat at 1%, 5% and 10% level. The significance is observed when computed test statistic exceeds the critical value for both ADF and PP respectively.

Table 3 ARDL bounds test to cointegration. ARDL bounds test

Diagnostic and stability tests

Estimation method

Optimal lag length

F-statistics

Breakpoint

JB

LM

BPG

RR

Ft = f(It , Rt , Gt , Pt , Tt , Kt , It *Rt ) It = f(Ft , Rt , Gt , Pt , Tt , Kt , It *Rt ) Rt = f(It , Ft , Gt , Pt , Tt , Kt , It *Rt ) Gt = f(It , Rt , Ft , Pt , Tt , Kt , It *Rt ) Pt = f(It , Rt , Gt , Ft , Tt , Kt , It *Rt ) Tt = f(It , Rt , Gt , Pt , Ft , Kt , It *Rt ) Kt = f(It , Rt , Gt , Pt , Tt , Kt , It *Rt ) It *Rt = f(Kt , It , Rt , Gt , Pt , Tt , Ft ) Critical value bounds Significance 10% 5% 1%

(1, 1, 2, 2, 2, 1, 2,1) (1, 0, 0, 2, 2, 2, 0, 2) (2, 1, 2, 1, 2, 1, 1, 1) (1, 2, 1, 2, 2, 1, 1, 1) (1, 2, 0, 2, 2, 2, 2, 1) (2, 2, 1, 0, 0, 1, 2, 1) (2, 2, 0, 2, 1, 2, 2, 1) (1, 2, 2, 2, 1, 2, 0, 2)

5.7251*** 6.8428*** 3.1524* 11.2893*** 8.6847*** 5.3806*** 14.5844*** 6.5805***

2003 2002 2014 2008 2006 2008 2008 2014

0.6414 0.2159 0.6688 0.9246 0.0782 0.6207 0.4968 0.3097

0.1751 0.092 0.0832 0.0809 0.0001 0.0567 0.0073 0.0190

0.9068 0.7482 0.6562 0.4692 0.7881 0.3595 0.9943 0.7724

0.0836 0.4044 0.4082 0.3718 0.6119 0.6395 0.4867 0.5723

Lower bound (I0) 1.92 2.17 2.73

Upper bound (I1) 2.89 3.21 3.90

Note: (***) (**) and (*) indicate significance at 1%, 5% and 10% level respectively. JB: Jarque-Bera; LM: Breusch-Godfrey Serial Correlation LM Test; Heteroskedasticity Test: Breusch-Pagan-Godfrey test; RR: Ramsey RESET for stability condition.

indicating inconclusive status. Moreover, the diagnostics and stability analysis supplement the robustness of the ARDL bounds testing approach (Pesaran et al., 2001). The results confirm the presence of cointegration of institutional quality, natural resource rent, economic growth, economic policy uncertainty, capital, moderating term to financial development from 1984 to 2016 for the United States. We find robust and reliable estimation with ARDL bounds test to cointegration that highlights the usefulness of this approach for policy implication. After confirmation of cointegration relationship, we proceed to capture the short and long-run dynamics. The upper and bottom portions of Table 4 exhibit the short run and long-run parameters respectively. We find that the institutional quality, natural resource rent, economic growth, openness, and capital positively and significantly drive the financial development in the United States in short-run, whereas, policy instability does not influence financial development in short run. We observe the institutional quality, economic growth, capital and trade positively and significantly integrated with financial development. While the moderating term, and natural resource rent influence it adversely. The pivotal role financial sector inefficient allocation of scarce economic resources and total factor productivity growth (Han and Shen, 2015), depends upon the stable institutional framework (Jain et al., 2017). A high-quality institutional environment is vital in explaining financial development (Law and Azman-Saini, 2012). Therefore, the institutional factors play a crucial role in the economic

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Table 4 Short-run and long-run analysis. Dependent variable ln FDt Variable Short-run ln It ln Rt ln It *Rt ln Gt ln Pt ln Kt ln Tt Dt ECMt−1 C R-squared F-statistic P-value Diagnostics and stability testing Test JB LM BPG RR Long-run Variable ln It ln Rt ln It *Rt ln Gt ln Pt ln Kt ln Tt Dt C

Coefficient

Std. Error

t-Statistic

P-value

0.1777 −0.0238 −0.4250 0.4549 −0.1100 0.4963 0.8914 −0.0864 −0.5428 −10.9742 0.8532 64.0079 0.0000

0.0694 0.0083 0.0651 0.1035 0.0373 0.1003 0.1790 0.0455 0.0603 2.4179

2.5581 −2.8767 −6.5310 4.3943 −2.9512 4.9482 4.9810 −1.9006 −8.9983 −4.5386

0.0228 0.0122 0.0000 0.0006 0.7902 0.0001 0.0001 0.0781 0.0000 0.0002

F-stat 1.4933 2.5883 2.0139 2.2689

P-value 0.4739 0.0718 0.0992 0.1365

Coefficient 0.3550 −0.0057 0.5352 0.5227 −0.0687 0.4586 0.5880 0.1592 −14.6765

Std. Error 0.1369 0.0323 0.2576 0.1393 0.0733 0.1560 0.2266 0.1209 4.6677

t-Statistic 2.5935 −0.1752 2.0778 3.7530 −0.9380 2.9396 2.5949 1.3165 −3.1443

P-value 0.0246 0.8634 0.0419 0.0178 0.3642 0.0106 0.0238 0.2091 0.0072

and financial development and pose pressure on policymaker to establish stabilized reforms to cater the uncertainty (Cherif and Gazdar, 2010). The error correction term suggests that the system corrects previous period shocks and disequilibrium with an annual speed of adjustment of around 54%. The empirical findings document the critical role of the institutional framework as an engine for financial sector development and economic growth through efficient mobilization of capital flows and effective trade policies. The findings persuade policymakers to stem focus on institutional framework and its multi-layered impact on various economic indicators. This, in turn, serves as a booster to the financial sector development in long-run. Shahbaz et al. (2018) claim a positive and long-run association of natural resource rent with financial development in the United States, while our findings do not confirm this argument. That is a possibility because they disregard to account for the moderating role of institutional quality in the optimal cultivation of natural resource rent and most importantly the check on miss-utilization of national natural resources in the absence of sound institutional framework (Kolstad and Soreide, 2009). Also, we find a negative coefficient of natural resource rent in both short and long-run, which is consistent with the view that natural resource-dependence diminishes the financial development (Hoshmand et al., 2013; Law and Moradbeigi, 2017). To logically estimating the real effect of natural resource rent on financial development, the vital role of interaction of institutional quality with natural resource rent management should be taken into account (Kolstad and Soreide, 2009). Without incorporating institutional quality into the analysis, the effect of natural resource rent seems overestimated as it can be seen in Shahbaz et al. (2018). Bhattacharyya and Hodler (2014) note that in countries with weak institutional quality, natural resource rent can hinder financial development. Nevertheless, policy reforms concerning natural resources utilization and economic policy measures need careful consideration as any negligence may distort the financial sector instantly. Financial sector consists of two main pillars; financial institutions and financial markets. We bifurcate financial development to come up with robust analysis as to the role of institutional quality in devising the right policies culminating the development of these vital segments. We present these robustness results in Table 5. In similar lines, we identify comovement of institutional, financial institutions and financial market development. This cointegration relationship signals policymakers to equally weight both financial institutions and market when potential policy reforms are to be initiated. Stemming into only one segment may lead towards mass destruction because both segments are indispensable for each other. Financial institutions channelize the funds through financial markets, and efficient financial markets play a useful role to reduce information asymmetry. The United States is an advanced country about technology; therefore, interminable

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Table 5 Robustness with FI and FM (ARDL bounds test to cointegration). ARDL bounds test

Diagnostic and stability tests

Estimation method

Optimal lag length

F-statistics

JB

LM

BPG

RR

FIt = f(It , Rt , Gt , Pt , Tt , Kt , It *Rt ) It = f(FIt , Rt , Gt , Pt , Tt , Kt , It *Rt ) Rt = f(It , FIt , Gt , Pt , Tt , Kt , It *Rt ) Gt = f(It , Rt , FIt , Pt , Tt , Kt , It *Rt ) Pt = f(It , Rt , Gt , FIt , Tt , Kt , It *Rt ) Tt = f(It , Rt , Gt , Pt , FIt , Kt , It *Rt ) Kt = f(It , Rt , Gt , Pt , Tt , FIt , It *Rt ) It *Rt = f(Kt , It , Rt , Gt , Pt , Tt , FIt ) FMt = f(It , Rt , Gt , Pt , Tt , Kt , It *Rt ) It = f(FMt , Rt , Gt , Pt , Tt , Kt , It *Rt ) Rt = f(It , FMt , Gt , Pt , Tt , Kt , It *Rt ) Gt = f(It , Rt , FMt , Pt , Tt , Kt , It *Rt ) Pt = f(It , Rt , Gt , FMt , Tt , Kt , It *Rt ) Tt = f(It , Rt , Gt , Pt , FMt , Kt , It *Rt ) Kt = f(It , Rt , Gt , Pt , Tt , FMt , It *Rt ) It *Rt = f(Kt , It , Rt , Gt , Pt , Tt , FMt )

(2, 0, 0, 2, 0, 2, 1, 2) (2, 2, 2, 2, 2, 2, 2, 1) (1, 2, 1, 1, 0, 2, 2, 2) (2, 1, 2, 2, 2, 2, 1, 2) (2, 1, 0, 2, 0, 0, 2, 2) (2, 1, 1, 1, 2, 1, 2, 1) (1, 2, 1, 2, 2, 2, 2, 1) (2, 2, 2, 2, 0, 1, 2, 2) (1, 1, 1, 2, 0, 2, 2, 2) (2, 1, 2, 2, 0, 0, 0, 2) (3, 2, 2, 1, 1, 2, 2, 2) (2, 1, 2, 1, 1, 2, 2, 2) (1, 2, 0, 2, 2, 0, 2, 2) (2, 2, 0, 0, 1, 1, 2, 2) (2, 2, 1, 2, 1, 2, 1, 2) (2, 2, 2, 2, 1, 2, 0, 2)

16.54*** 4.46** 5.23** 14.36*** 6.60*** 5.41*** 21.55*** 6.94*** 5.41*** 8.33*** 4.18** 8.31*** 5.64*** 13.93*** 9.04*** 4.34***

0.4187 0.7776 0.8691 0.3075 0.3823 0.5138 0.9164 0.3635 0.9730 0.2076 0.3591 0.8968 0.3917 0.6545 0.9395 0.5994

0.0906 0.1717 0.1685 0.2934 0.0099 0.0040 0.1021 0.3039 0.0766 0.5760 0.2801 0.0806 0.0005 0.0046 0.0613 0.0332

0.3473 0.1030 0.9680 0.7000 0.9142 0.2573 0.2175 0.5029 0.4632 0.5760 0.8571 0.8581 0.6299 0.2363 0.7856 0.3324

0.2746 0.0259 0.0148 0.6127 0.3849 0.9034 0.5464 0.0927 0.3600 0.2900 0.2301 0.9892 0.5055 0.7702 0.8533 0.1432

Note: Critical bounds values for lower and upper bound are 2.254–3.388 for 10%; 2.685–3.96 for 5%; and 3.713–5.326 for 1% respectively. While (***) (**) and (*) indicate significance at 1%, 5% and 10% correspondingly. JB: Jarque-Bera; LM: Breusch-Godfrey Serial Correlation LM Test; Heteroskedasticity Test: Breusch-Pagan-Godfrey test; RR: Ramsey RESET for stability condition.

Table 6 Robustness with FI and FM (long-run and short-run). Dependent variable FI Variable

Coefficient

Long run 0.1957 ln It −0.0445 ln Rt ln It *Rt 0.0443 2.1535 ln Gt ln Pt −0.0411 0.9606 ln Kt 0.2373 ln Tt −6.7637 C −0.7027 ECMt−1 Short-run ln It 0.1639 −0.0057 ln Rt −0.1442 ln It *Rt −1.6444 ln Gt −0.0043 ln Pt 0.4842 ln Kt 0.4186 ln Tt −3.5363 C 0.8264 R-squared F-statistic 39.5815 0.0000 P-value Diagnostics and stability testing F-stat Test 0.4772 JB 4.6087 LM 1.0251 BPG 2.6458 RR

Dependent variable FM Std. Error

t-Statistic

P-value

Coefficient

Std. Error

t-Statistic

P-value

0.0814 0.0137 0.0578 0.5093 0.0186 0.1215 0.1181 1.1108 0.0509

2.4058 −3.2574 0.7668 4.2284 −2.2111 7.9056 2.0098 −6.0890 −13.7935

0.0257 0.0049 0.4609 0.0006 0.0419 0.0000 0.0442 0.0000 0.0000

0.7196 0.0192 −0.0815 11.8916 0.2073 4.0590 1.8410 −36.8397 −1.1678

0.1625 0.0477 0.3952 2.0327 0.1153 0.5863 0.4107 4.9496 0.2810

4.4281 0.4031 −0.2062 5.8502 1.7982 6.9233 4.4822 −7.4430 −4.1565

0.0014 0.6929 0.8407 0.0000 0.0937 0.0000 0.0005 0.0000 0.0010

0.1137 0.0180 0.0338 0.5529 0.0253 0.1630 0.1213 1.6394

1.4418 −0.3166 −4.2670 −2.9740 −0.1706 2.9717 3.4502 −2.1570

0.1634 0.7545 0.0016 0.0070 0.8661 0.0070 0.0023 0.0422

0.3924 −0.0438 −0.7752 −7.3229 −0.0223 2.0809 1.4826 −19.9290 0.8320 33.0921 0.0000

0.3228 0.0510 0.1423 1.5697 0.0718 0.4626 0.3445 4.6543

1.2157 −0.8584 −5.4462 −4.6651 −0.3106 4.4981 4.3037 −4.2819

0.2370 0.3999 0.0003 0.0001 0.7590 0.0002 0.0003 0.0003

F-stat 0.7334 3.7220 1.4338 1.0127

P-value 0.6930 0.0165 0.2421 0.4862

P-value 0.7877 0.0226 0.4417 0.0926

and complex financial innovation need parallel regulation to safeguard the interest of stakeholders, to prevent a mass destruction alike sub-prime crises of 2008. We examine the long and short-run dynamics of financial development to have a deeper insight into the impact of institutional quality of financial institutions and financial markets combined with the natural resource rent. The empirical results (Table 6) portray that institutional quality is a significant determinant of financial institutions and markets development in case of the United States over long-run. The natural resource rent has negative and significant long-run influences on financial institutions and short-run on financial markets by not statistically significant. The results of the controlling variables are consistent with the findings of the primary model.

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Table 7 Robustness with sub-components of I (Ft is dependent variable). ARDL bounds test

Diagnostic and stability tests

Estimation method

Optimal lag length

F-statistics

JB

LM

BPG

RR

Ft = f(I Ft = f(I Ft = f(I Ft = f(I Ft = f(I Ft = f(I

(2, 0, 1, 2, 0, 2, 2) (1, 0, 2, 2, 0, 2, 2) (1, 0, 0, 2, 0, 2, 2) (2, 0, 1, 2, 0, 2, 2) (1, 1, 1, 2, 1, 2, 0) (1, 1, 0, 1, 0, 2, 0)

5.5548*** 3.4178* 4.8089*** 2.6232 5.0693*** 5.6152***

0.0563 0.9837 0.4733 0.2661 0.9431 0.5470

0.2129 0.0882 0.3232 0.1713 0.1614 0.7767

0.1302 0.8795 0.6180 0.8976 0.9650 0.2674

0.1926 0.0894 0.1911 0.2503 0.0782 0.1117

GSt , Rt , Gt , Pt , Tt , Kt ) CRt , Rt , Gt , Pt , Tt , Kt ) LWt , Rt , Gt , Pt , Tt , Kt ) DAt , Rt , Gt , Pt , Tt , Kt ) BQt , Rt , Gt , Pt , Tt , Kt ) IPt , Rt , Gt , Pt , Tt , Kt )

Note: (***) (**) (*) indicate significance at 1%, 5% and 10% respectively. Critical lower-bound values at 10%, 5% and 1% = 2.12, 2.45, 3.15; for upper bound = 3.23, 3.61 and 4.43 respectively. JB: Jarque-Bera; LM: Breusch-Godfrey Serial Correlation LM Test; Heteroskedasticity Test: Breusch-Pagan-Godfrey test; RR: Ramsey RESET for stability condition.

Table 8 VECM Granger causality results. Sources of causation Dependent variable

ln Ft ln It ln Rt ln Gt ln Pt ln Kt ln Tt ln It *Rt

Short-run

Long-run

ln Ft

ln It

ln Rt

ln Gt

ln Pt

ln Kt

ln Tt

ln It *Rt

ECMt−1

− − 5.9870* (0.0501) 1.3764 (0.5025) 1.0438 (0.5934) 0.5230 (0.7699) 0.5422 (0.7625) 1.8124 (0.4041) 0.0085* (0.0926)

4.8405* (0.0889) − − 0.7254 (0.6958) 4.0317 (0.1332) 0.1068 (0.9480) 1.4189 (0.4919) 5.0945* (0.0783) 0.7667 (0.3812)

0.2817 (0.8686) 1.1599 (0.5599) − − 1.2673 (0.5306) 0.3996 (0.8189) 1.5956 (0.4503) 0.5203 (0.7709) 1.0970** (0.0294)

3.7375 (0.1543) 4.4237* (0.0951) 5.3875* (0.0676) − − 13.7088*** (0.0011) 2.7441 (0.2536) 0.7756 (0.6786) 0.1203 (0.7287)

7.4917** (0.0236) 3.9894 (0.1361) 0.1214 (0.9411) 4.3731 (0.1123) − − 7.1593** (0.0279) 8.5676** (0.0138) 2.8609* (0.0908)

0.5330 (0.7661) 1.4685 (0.4799) 5.4373* (0.0660) 0.2191 (0.8962) 8.1263** (0.0172) − − 0.2715 (0.8731) 0.0368* (0.0848)

4.1671 (0.1245) 1.6331 (0.4420) 2.6036 (0.2720) 3.1377 (0.2083) 13.4375*** (0.0012) 7.8499** (0.0197) − − 1.1414** (0.0285)

0.4470* (0.0738) 0.8362** (0.0360) 0.9939* (0. 0618) 0.1159 (0.7335) 0.0484** (0.0325) 0.0387** (0.0444) 0.6557 (0.4181) − −

−18.9074*** (0.0043) −7.3845 (0.2868) −7.4331 (0.2826) −19.3754*** (0.0036) −10.4461* (0.0907) −18.4132*** (0.0006) −9.5855*** (0.0143) −5.4609*** (0.0005)

Note: (***) (**) (*) indicates the level of significance at 1%, 5%, and 10% respectively, at which null hypothesis for no causation is rejected, while numbers in parenthesis are corresponding p-values.

As mentioned in the methodology section, the institutional quality is the aggregate of six indicators “government stability, corruption, democratic accountability, bureaucratic quality, law and order, and investment profile” using the PCA approach. We performed cointegration analysis with each component of institutional quality for robustness; the results are shown in Table 7. Except for democratic accountability, all components of institutional quality are cointegrated with the financial development of the United States in the sample period. This implies the importance of these indicators in explaining financial development. This also infers the interconnectivity of institutional quality components in underpinning the smooth functioning of the comprehensive institutional framework. The ARDL Bounds test and ECM approach confirm the existence of cointegration between financial development, institutional quality, natural resource rent, economic growth, economic policy uncertainty, and capital. The policymakers need to adhere to the direction of causal flows to reconcile the policy framework effectively. We overcome this issue; by apply VECM Granger causality to identify the short and long-run causal flow between the variables. Engle and Granger (1987), argues that there should be at least one-way causality if the variables are cointegrated. The VECM Granger causality results are summarized in Table 8. For the United States, we uncover a short-run bidirectional causal flow between institutional quality and financial development; economic policy uncertainty and openness; economic policy uncertainty and capital. The moderating term and finance have bidirectional short and long-run flows to each other. These results shed light on the immediate effect of policy reforms relevant to each pair. Interestingly the institutional quality and economic policy uncertainty ignored in recent literature (Shahbaz et al., 2018) has appeared as the deterministic parameters to optimize the financial development, and economic growth channeled through efficient mobilization of capital in trading activities. Also, the short-run unidirectional causality flows from financial development to economic policy uncertainty; institutional quality to economic growth; natural resource rent to economic growth; natural resource rent to capital; economic policy uncertainty to economic growth; and capital to openness. This implies that the parameters estimated in this study are worthwhile for immediate policy actions. About the long-run VECM Granger Causality affirms the findings of ARDL bounds

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test and ECM as to the cointegration between financial development, institutional quality, natural resource rent, economic growth, economic policy uncertainty, capital, and openness. In addition to these results, we have noted the long-run association of the parameters with capital and openness respectively. For the United States, we have empirically confirmed that in natural resource rent and finance nexus estimation in Shahbaz et al. (2018) have neglected the promising role of institutions and economic policy uncertainty. We believe future research should be aligned with efforts to optimize the regulatory framework that may substantiate the channels following which the natural resource rent can contribute to economic and financial development. 5. Conclusion This study is important to offer multidimensional policy implications in both financial economics and natural resource management under the institutional quality framework. To formulate effective and result oriented policies; the nexus among institutional quality, natural resource rent, economic growth, economic policy uncertainty, openness, capital, and financial development is essential to understand. We infer that there needs to learn much more about the various connections among these variables with a particular focus on the role of institutional quality in understanding the realistic perspective of natural resource rent and pass-through impact on financial development at one end and effective resource management at other in the context of developed economies. The literature in this paradigm lacks consensus and often fails to identify the dynamic moderating role of institutional quality, which potentially stimulates the financial development if diligently regulated institutional framework prevents the misuse of natural resource rent. This study is robust to incorporate the cointegration moderating role of institutional quality with financial development and to capture reliable estimation in the natural resource rent nexus directly and indirectly including essential controls overlooked in recent studies (Shahbaz et al., 2018). Likewise, this study replaces the conventional measure of financial development with the multidimensional index by IMF (Svirydzenka, 2016), due to rising critics attributable to conventional measure in ignoring the broader perspective of financial system and measurement limitations (Ito and Kawai, 2018), hence, tosses-up this index for further validation across developed and developing economies. In brief, our results carry three policy implications; Firstly, we recommend policy attention to promote financial development through the efficient allocation of scarce financial resources among various sectors of the economy by fully understanding and incorporating the dynamic moderating role of institutional quality. Rudra P. Pradhan et al. (2017) call to surpass the banking sector development with the efficient allocation of financial resources combined with sound banking system regulation. We attribute a vital institutional role in the entire financial system rather only banking system regulations. Moreover, the impact of finance on economic development is generally stronger in high-income countries as to low-income, for example, financial development enhances the economic growth when government size is low, and it weakens the process in increasing size (Herwartz and Walle, 2014). A high-quality institutional environment is vital in explaining financial development (Cherif and Gazdar, 2010; Law and Azman-Saini, 2012) and economic growth (Jain et al., 2017), at the contrary, weak institutions impede such development (Bhattacharyya and Hodler, 2014). Secondly, about the natural resource rent and finance nexus, the recommendation is to carefully identify the channel through which the natural resource rent stimulates the financial development. This process is not straightforward, because natural resource rent can indirectly foster the financial development, possibly through sound regulations that prevent the misuse of natural resource rent for non-state priorities (Kolstad and Soreide, 2009). The argument that natural resource rent diminishes financial development (Hoshmand et al., 2013; Law and Moradbeigi, 2017) signifies the misuse of resource rent. 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