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Do Financial Development and Economic Openness Matter for Economic Progress in an Emerging Country? Seeking a Sustainable Development Path

College of Economics and Trade, Hunan University, Changsha 410082, China
Faculty of Information Engineering, Shaoyang University, Shaoyang 422000, China
School of Economics, Zhejiang University, Hangzhou 310058, China
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
Authors to whom correspondence should be addressed.
J. Risk Financial Manag. 2021, 14(6), 237;
Submission received: 18 April 2021 / Revised: 17 May 2021 / Accepted: 18 May 2021 / Published: 26 May 2021


While emerging economies face the challenge of competing with developed nations, they are capable of catching up to the developed world. In this context, financial development and the degree of economic openness may provide better living conditions for the current generation without giving up future generations’ prosperity. Therefore, this research’s prime intention is to investigate the impact of economic openness and financial development on economic progress, employing Pakistan’s time-series data from 1975–2018. To examine the long-term association between economic openness, financial development, and economic progress, Autoregressive Distributed Lag (ARDL) cointegration tests were performed and the results present a long-term association between these variables. Findings from ARDL estimates indicate that the relationship between financial development and economic progress is significantly positive in the long term. Contrastingly, the relationship between economic openness and economic progress is significantly positive in the short term. A fully modified ordinary least square technique was applied to check the robustness of the long-term links. The Granger causality test revealed that economic progress is motivated by both economic openness and financial development in an emerging economy such as Pakistan. Thus, policies boosting financial development and economic openness are proposed to put the emerging economies on a path of sustainable economic development.

1. Introduction

The significance of financial development and its function in the intermediation of finance has been contentious about, as it has played a major part in economic progress over the last few decades and has assumed a prominent place in financial development. Many scholars have proposed that financial development importantly stimulates economic progress by promoting industries, investments, the distribution of loanable funds, and the accumulation of capital (Ahmad et al. 2020b). In fact, they claimed that emerging countries require it to obtain well-developed capital markets. On the other hand, Khalikov (2017) used economic analysis to claim that financial development and economic progress obtain immense traction from each other. Yet, the nature of their interaction has remained inconclusive depending on the type of models, data, and empirical methodologies used to assess it.
There are several drivers of non-savings and credit markets in developed countries. The allocation of sufficient capital depends primarily on a country’s economic progress and success in the production cycle, as well as the fair distribution of income among the persons in that specific society (Ahmad et al. 2021e). As a further argument, Phong et al. (2018) claimed that real national gross domestic product (GDP), overall country population, and per capita output are significant in the long-term economic progress cycle. Additionally, Khairutdinov et al. (2018) stated that economic progress is a trend that is continuously affected by a country’s competitiveness and that increases over a sustained period. Many researchers have addressed the unpredictability of increasing concentrations within diverse states. Capital and labor are the main factors that trigger differences between growth rates, and their impacts are attributable to differences in opportunities (Alvarez–Cuadrado et al. 2017). In the Pakistani context, several contributing factors have caused a low level of economic progress. These factors involve inflation levels, export reductions, foreign loans, service volatility, weak social asset management, regulatory scenarios, and the instability prevalent in the country (Komal and Abbas 2015). Likewise, the role of commercial liberalization in macroeconomic performances in Pakistan was reported by Chaudhry et al. (2012). Along these lines, Evan et al. (2002) analyzed 82 countries and demonstrated that development relied on income distribution. They also stressed the complementarities of financial and capital development.
A widely discussed topic in the academic literature is the correlation between financial stability and economic progress. A substantial amount of analytical literature argues that economic progress is driven by financial stability (Ibrahim and Alagidede 2018). Those studies considered that an appropriately planned financial framework should be a requirement for a high level of economic progress. The pioneering research (Schumpeter 1911) in this area primarily proposed the linkage between financial development and economic progress. Schumpeter claimed that economic prosperity relies on a healthy financial system, whereas the supporters of economic progress argued that financial structures are essentially required for urbanization, a view which Turok (2016) and Wu et al. (2019) also endorsed. It was concluded that financial markets that are more liberal accelerated economic progress, whereas conservative financial markets slowed economic progress. Consequently, Carroll and Jarvis (2015) and Spolander et al. (2016) advocated that to sustain economic progress, implementing liberal policies is important.
In the late 1980s and early 1990s, it was often suggested by the contemporary growth theories that financial progress is a key determining factor of economic progress (Nelson 1998). The theorists of economic progress concluded that financial progress tends to increase the productivity of capital allotment, increase equity risk management, efficiently diversify investments of creditors, and boost the efficacy of investment ventures (Ahmad et al. 2021c). Such factors can enhance the competitiveness of capital, which positively affects economic progress (Soekapdjo et al. 2020). On the other side, Pece et al. (2015) argued that theoretical debates overemphasize capital markets’ role in economic progress. Additionally, Pearson and Elson (2015) argued that in the absence of adequate laws and guidelines, finance would have negative consequences on social security and economic progress.
From an empirical perspective, finance and development analysis generally shows that countries with improved banking and financial markets are growing rapidly. An improved financial sector makes it easier for businesses to avoid funding constraints, allowing for a smoother investment flow and faster expansion (Ahmad et al. 2021b). However, overall, the main criticism of econometric research on financial development and economic progress is that it is unable to endorse or disprove theoretical models. This is because they are unable to quantify the principles that can be derived from hypothetical modeling (Alvarado et al. 2021). Similarly, Dellink et al. (2017) attempted to alleviate this issue by employing various financial stability indicators and concluded that financial stability predicts long-term growth.
The literature has introduced the bilateral correlation between financial development and growth. In Greenwood and Jovanovic (1990)’s model, on the one hand, financial institutions encourage financial development by efficient capital distribution, even though it involves a cost for accessing them. On the other hand, urbanization makes it comparatively cheaper to enter financial intermediation. This scenario would allow more agents to participate, thus, having positive second-order effects on the expansion (Irfan et al. 2020). Both the sum of savings available for investment and its returns do matter for economic progress. Additionally, the advancement of financial intermediation helps to gain a better return on invested capital, which consequently feeds on economic progress (Ahmad et al. 2020a). There is broad and rather diverse section of the analytical literature on the connections between financial and economic expansion. Despite the enormous amount of the literature that focused on this connection (using a diverse range of estimation techniques), no unique consensus has been developed on this connection, which leaves ambiguity.
Researchers have tried several alternatives to rectify estimation issues. For instance, Boldeanu and Tache (2016) have designed two measures to assess the financial sector component, i.e., the financial system liabilities as a part of GDP and the percentage of funds granted to corporations. Claessens et al. (2018) have employed two indicators of the financial system’s performance. They first assessed the share of overall credit, directly issued by banks in the private sector rather than the central bank. In contrast, the second estimated the component of overall funds available to private corporations. These two indicators assume that a market with more corporate credit and investment in the private sector should provide a more effective allocation of foreign funding. A bank in the corporate sector, aiming to maximize earnings, would be more inclined to finance profitable infrastructure ventures than a government bank. The latter aims to meet specific guidelines for determining loans. It has been further argued that the level of financial depth predicts long-term economic progress. Feldstein (2017) affirmed an economically robust and statistically noteworthy association of financial growth with real GDP growth per capita and overall productivity. However, the positive moderating influence of financial growth on the physical accumulation of capital, and the rate of personal saving, remained uncertain in their analysis.
Regarding causality, the top of the line is the “allow to disagree”, as variations in a long-term correlation, and short-term association or in the non-linearity of the correlation itself, is often taken up in various analyses, which makes it difficult to draw a conclusive decision regarding which course of causality to take. Nevertheless, with a 17-country regression, Loayza and Ranciere (2006) found an optimistic long-term correlation among financial intermediation and output development interacts with mainly a short-term negative interaction. Additionally, with a threshold regression, Deidda and Fattouh (2002) found a significant positive association between the degree of financial depth and economic progress for wealthier countries and no meaningful association for poorer countries, which is compatible with the non-monotonic correlation, as suggested in the study.
This study aims to assess the impact of economic openness and financial development on economic progress in Pakistan during 1975–2018. Past research mostly used panel data analysis, overlooking the country-specific characteristics of the stated impacts. For policymaking, a pooled panel data analysis is not very helpful, since it gives a general idea regarding the relationship among variables, disregarding the individual country’s particular situation of variables under investigation. While resolving a specific problem, studies that have focused on a particular country could provide more helpful insights. This study is an excellent addition to the literature, as it focuses on country-specific features of an emerging economy that would assist in practical policymaking. This research included the variable of financial development to address the impact of the fast-developing financial sector on economic progress. The inclusion of economic openness benefits the study, as Pakistan has open economy-oriented policies, providing the opportunity to earn on exports to boost economic progress. The Autoregressive Distributed Lag (ARDL) cointegration of Pesaran et al. (2001) is used in this study for data analysis purposes. Granger causality is applied to the causal direction among those variables, since it is a practical approach for series with mixed integration order.
This study is divided into the following sections: The literature review regarding the finance–trade–growth nexus is described in Section 2. Description of data, model, and econometric methodology is in Section 3. Next, Section 4 exhibits the econometric findings of this study. Finally, Section 5 contains the conclusion and recommendations for policymakers.

2. Review of the Literature

The varied economic literature has been drawn up on the avenues through which the development of the financial sector influences the real economic field’s development cycle. Lane and Milesi-Ferretti (2018) put forward their arguments that financial progress is a critical component of a nation’s rapid economic progress. The exploration of the financial growth nexus owes the concrete foundation of Schumpeterian Growth Theory. The literature advocates two essential arguments while linking the progress of the financial sector and economic progress. Haiss and Sümegi (2008) posed the first argument in their study that the intensity of the financial sector’s relationship with the real economic industry goes beyond a shift in the savings rate. There is no indication of a persistent long-term correlation between the increase in physical capital accumulation and real economic progress in the financial growth literature. Hence, while evaluating the technical help of financial–economic nexus, one can look for specific theories that explain resource allocation decision-making that encourages productive growth.
The second argument focuses on two obscurities about the partnership between the emergence of financial sector structures for productive resource distribution and the rise in the real economy (Irfan et al. 2019b; Ibrahim and Alagidede 2018). If higher returns are anticipated, the savings rate may react uncertainly and increase or decrease due to higher income and replacement impact returns. In the case of a lower risk and productive distribution of resources, the reverse can occur regardless of the increase in the savings rate. Saving rates will decline due to effective resource management and the reduction in risk resulting from financial reforms (DeAngelo and Stulz 2015).
The spectrum of financial intermediaries’ services includes savings and resource allocation, investment strategy assessment, risk control by providing diversified investment choices, corporate governance by controlling companies and executives, and promoting the transaction phase. Consequently, these facilities contribute to innovation and technical improvements in industrial processes (Ahmad et al. 2021d). Throughout the sense of Schumpeter’s statements of positive relations, debates were arising about the financial-growth correlation (Rohde and Breuer 2016). Nevertheless, some empirical evidence has been given to prove the positive correlation between financial innovations and progress.
In the pioneering work of Schumpeter (in the context of a developed economy), it was claimed that in situations where a revolutionary invention triggers the substitution of old business with a new industry, a cycle called “Creative Destruction”, in which all downturns and bubbles cannot be avoided or corrected (Kılınç et al. 2017). In light of these findings, it can be concluded that higher-level financial progress contributes to productivity growth. In the view of a cross-country evaluation of the difference between the poorest and wealthiest countries, it is assumed that if financial progress is pursued above the threshold point, the divergent dynamics between the countries’ growth rates do not continue over the long term (Aghion et al. 2005).
The empirical background on the nexus between trade openness and economic progress has its roots in Neoclassical Growth Theory. It is assumed that the determination of emerging economies to associate their domestic economies with different countries is influenced by economic openness. Considering this assumption, Shahbaz et al. (2013a, 2013b; Shahbaz 2012) established a hypothesis that there is a correlation between economic openness and economic progress, which results in growth and profitability. A bilateral correlation between international trade and economic progress was reported by (Omri et al. 2015), whereas (Ahmad et al. 2020c) demonstrated a bilateral causality between financial prosperity, and economic openness. The discussion regarding the correlation between economic openness and economic progress ranges from variance in economic openness indexing to utilization of cross-sectional research and the correlational directions (Keho 2017). Additionally, Truby (2018) argued that other scholars have analyzed that the interactions between the two do not comprehensively explain economic structures and trade policy choices. In comparison, Zahonogo (2017) claimed that trade and economic progress have a beneficial relationship.
Country-specific studies show contradictory facts. For instance, Uddin et al. (2013) employed Cobb-Douglas functional form, matrix-based ARDL cointegration, and structural break cointegration to recognize the long-term association between the variables of interest in Kenya’s context. A positive association was uncovered between financial performance and economic progress. Likewise, the study by Odhiambo (2010) applied a Granger causality test between economic output and money supply. The authors recognized a bilateral relationship among financial development indices through the money supply ratio to economic progress and GDP. Odeniran et al. (2010) found economic progress boost in response to the stock market turnover. However, the correlation was not strong in the long-term, with causality from economic progress to financial performance. Bojanic (2012) employed cointegrated multiple linear regression models, the Granger regression model, and the error correction models to analyze Bolivian data from 1940–2010. The association among economic progress, financial stability, and exchange accessibility was evaluated, and revealed long-term stability among the study variables. Furthermore, Granger’s unilateral causality was identified, varying from financial stability metrics and economic openness to economic progress.
During the last three decades, the association between economic openness and economic progress has gained substantial consideration in both (i.e., conceptual and empirical) areas of the literature; though, there is no evidence regarding whether economic progress is stimulated by economic openness. According to the theory of comparative advantage, when a nation chooses to compete with another country, the latter must manufacture products on which it has a competitive advantage. It concentrates on the field where it has more substantial component capabilities and manufactures commodities on a greater level (Irfan et al. 2019a). Consequently, this sector’s productivity and exports will increase, which would improve overall economic progress (Iqbal et al. 2021). Many economists have further expanded the theory. Xuefeng and Yaşar (2016) claim that globalization promotes consolidation in industries with economies of scale, optimizing long-term output and productivity. As a consequence of the international proliferation of emerging technology, new endogenous development mechanisms describe a favorable association between economic openness and economic progress.
An economy with such greater openness has a more substantial potential to leverage advanced economies’ technologies. This ability helps it develop more quickly than a country with less economic openness. The importance of imitation factors in the relationship of trade and growth is also indicated by (Musila and Yiheyis 2015). If the emerging economies have fewer innovation costs than the developed world, they are expected to grow higher than the developed world. Thus, the process towards convergence will take effect. It is suggested by both arguments that countries that are in the process of development gain a lot from the foreign exchange with mature technological countries. While several other statements have been made, there may be some situations when access to markets may be unfavorable for economic progress, such as when the country does not find it necessary to use research and development for its operation (Almeida and Fernandes 2008). The structure of trade also has an impact on product development (Hausmann 2016). Quick learning of global technologies and adapting to the local climate also depends on whether a country profits from the international exchange (Elavarasan et al. 2021).
The literature (Sun et al. 2019) has indicated a correlation between economic openness and economic progress. Rassekh (2007) examined the link of trade and development for 150 countries. He stated that international trade is more efficient than those with higher wages for countries with lower revenues. Chang et al. (2009) focused on 82 countries and recorded that economic progress was motivated by economic openness. Afzal and Hussain (2010) found no connection between exports/imports and economic progress in Pakistan. On the contrary, Can et al. (2021) found a significant relationship between the two variables. Additionally, Klasra (2011) and Shahbaz (2012) challenged their claims, confirming that Pakistan’s development hypothesis is dependent on trade (economic openness).

3. Methodology and Data

3.1. Model

The Cobb-Douglas production function has been opted to provide the following estimation model:
Y = F (Financial development, economic openness, and foreign direct investment)
The functional form for estimation of growth equation is:
G r o w t h t   = α 0 + α 1 D M A t   + α 2 D C P t   + α 3 T O P t   + α 4 F D I t   + ε t  
All variables have undergone logarithmic formation to estimate the parameters, except the DMA and FDI. Therefore:
ln G D P t   = α 0 + α 1 D M A t   + α 2 l n D C P t   + α 3 l n T O P t   + α 4 F D I t   + ε t  
where α 0 and ε t represent constant and the stochastic error term, correspondingly.

3.2. Data

The primary purpose of this paper is to analyze the links of financial development and economic openness with economic progress in Pakistan. This research employed the annual time series data from 1975 to 2018 gathered from the world bank database (World Bank 2019). The economic progress is determined using the GDP (current USD) year prices. Financial development is measured from two perspectives: the first one is related to the financial depth, measured through the deposit money banks’ asset to GDP (%). The second one is financial efficiency. The financial system is said to be efficient if it can perform its primary function of transforming deposits to the credits efficiently (Asongu 2015). The study uses credit given to the private sector domestically as the indicator of the efficiency of the financial system. Following Ahmad et al. (2021a), foreign direct investment is used as the control variable to capture the macro-economic environment. The description of the study variables is provided in Table 1.

3.3. Estimation Methodology

The traditional cointegration approaches such as Johansen’s cointegration have constraints due to systemic breaks in macroeconomics dynamics (Uddin et al. 2013). Another econometric methodology called an Autoregressive Distributed Lag boundary testing method was established by (Pesaran and Shin 1999). ARDL cointegration has various advantages over traditional econometric strategies.
Firstly, this methodology could be extended to a limited sample size of the analysis (Pesaran et al. 2001), and thus performing bounds testing for this study is justified. Secondly, both the variables I(0) and I(1) could also be included in the case of the mixed order of integration. Thirdly, the short-term dynamics and long-term equilibrium for an unregulated error correction model are concurrently calculated by a simple transformation matrix of the variables. Fourthly, it calculates the short- and long-term components, which can together eliminate autocorrelation and omitted variables issues. This technique often offers projections without any bias of a long-term model and appropriate t statistics when the model is subject to the endogenous problem (Sloboda 2004). This analysis uses a unit root test to verify that variable I(2) is not integrated. We used the ARDL method and prediction equation to classify long-term relationships:
Δ l n G D P = β 0   + i = 1 p   β 1   i k   Δ l n G D P   t i + i = 0 p   β 2   Δ D M A t i + i = 0 p   β 3   Δ l n D C P t i + i = 0 p   β 4   Δ l n T O P t i + i = 0 p   β 5   Δ F D I t i + λ 1   l n G D P   t i + λ 2 D M A t i + λ 3   l n D C P t i + λ 4   l n T O P t i + λ 5   F D I t i + ε t
The ε t and β 0 in Equation (3) above are the error term and the drift element, respectively. The terms having summation signs symbolize the dynamics of error correction in the short term, though half of the equation above shows the relationship in the long term, too. By considering the null hypothesis of no co-integration, the existence of long-term correlation is assessed using Wald statistics/F-statistics, Ho = λ1 = λ2 = λ3 = λ4 = λ5 = λ6 = λ7 = 0, whereas the alternate hypothesis states that all variables under consideration are not equal to 0. In contrast, Pesaran et al. (2001) calculated F-statistics by assuming that either variable value will be stationary I(0) and I(1). The optimal lag for variables might be nominated by the Akaike Information Criteria (AIC), considering the lag length. The number of regressions was estimated as (p + 1) K by the ARDL method, where lags are shown by P and K, symbolizing the variables in equation. The AIC nominates the maximum lag length interrelated to the variables. The model of error correction is symbolized in the following was if a long term relationship exists after considering the lag length:
Δ l n G D P = β 0   + i = 1 p   β 1   i k   Δ l n G D P t i + i = 0 p   β 2   Δ D M A t i + i = 0 p   β 3   Δ l n D C P t i + i = 0 p   β 4   Δ l n T O P t i + i = 0 p   β 5   Δ F D I t i + λ 1   l n G D P   t i + λ 2 D M A t i + λ 3   l n D C P t i + λ 4   l n T O P t i + λ 5   F D I t i + θ   E C T t i + ε t
whereas the term θ   signifies the equilibrium speed of adjustment in the long term after the shock in the short term. Various tests such as heteroscedasticity, functional form, and serial correlation are applied in this study to analyze the model’s goodness of fit. For testing stability and reliability, tests such as CUSUM and CUSUMSQ were also performed. The regression model is said to be stable if the results of these are within a 5% level of significance.

4. Empirical Results and Discussions

To ensure that the time series data are stationary, the integration order of those series is important. For series integration at both I(0) and I(1), the ARDL cointegration method application is justified. The long-term relationship may also be investigated by Johansen and Juselius’ (Johansen and Juselius 1990) technique. This study included unit root tests, Augmented Dicky-Fuller, and the Phillips-Perron (PP), in determining the integration order of concerned variables (see Table A1 in Appendix A). The outline of applied methods is shown in Figure 1.
It, thus, defines the integration order as a mixture of the level and the first difference, rendering the usage of the ARDL cointegration valid. The AIC and SBC determine the optimal lag length. The F-statistics appeared significant, concluding the long-term association among the variables of interest (see Table A2 in Appendix A). This infers that cointegration among the variables exists. The values estimated by F-statistics are greater than the values for the upper bound, which are the critical values with constant and no trend (Shahbaz 2012).
Table A3 provides the long-term forecasts for the ARDL method with specific diagnostic test statistics (see Appendix A). The findings indicate that the long-term association with DMA is significantly positive, showing the vanishing effect of financial development (Osei and Kim 2020). More specifically, an increase of 1 unit in deposit money reserves corresponds to an improvement of 0.0140 units in economic progress. In the long-term, deposits will therefore be seen as the leading source for a long-term investment with significant economic progress implications. The findings further show a negative association between lending to the private sector domestically and long-term economic progress. This is not aligned with the growth effect of financial development (Nasreen et al. 2020). Additionally, this finding did not lend credence to (Ahmad et al. 2019). Yet, in the short term, this impact is significantly positive. It entails that the effective distribution of private credit to productive domestic ventures can boost business operations and economic progress. In the same vein, Wang and Tan (2021) found economic development promotion effect of financial development across Chinese provinces. The literature (Ernst 2019) found similar findings by revealing that financial market development leads to economic development. Along these lines, Vo et al. (2019) presented interesting empirical outcomes. In the short term, financial derivatives were revealed to boost economic progress in India, the United States, and Japan. This effect vanished in the long-term process for these countries. However, for China, those derivatives provided a short-term negative and long-term positive impact on economic progress. In 20 Indian states, Pradhan et al. (2021) revealed economic growth boosting the impact of financial inclusion, which is consistent with our results in the short term, and vice versa. In contrast, Cheng et al. (2021) spun out the negative influence of financial development on economic development across selected 72 global economies.
Moreover, the connection between economic progress and economic openness is negative in the long term but positive in the short-term. This result is consistent with (Huang and Sun 2019) in the case of Korea, China, and the United States. At the same time, it diverges from (Rajan and Zingales 2003), since they showed a positive link between economic progress and economic openness in the long term. Similarly, Majumder et al. (2020) found a statistically significant and positive impact of trade openness on economic growth in 95 economies worldwide. In addition, Arvin et al. (2021) revealed a positive influence of trade openness on the economic growth of G-20 countries, which is aligned with our results in the short term. Li and Wei (2021) found a positive impact of openness and financial development on economic growth with a differentiated degree of influence across 30 Chinese provinces.
In the same way, Atil et al. (2020) disclosed a positive linkage between economic growth and financial development in Pakistan. Furthermore, Ghazouani et al. (2020) found that trade openness accelerated the economic progress of Asia-Pacific nations. Finally, Kong et al. (2021) found an interesting finding, demonstrating that trade openness promoted economic growth quality in both the long and short-term for the Chinese economy. There is a statistically significant negative association between GDP and ECOP in the long term, while a positive relationship in the short term. This finding calls for adaptations in dealing with international trade policy to enhance economic openness. It is essential to say that, provided this condition, the policymakers should be very cautious in manipulating their actions in the opening of an emerging economy in the light of long-term negative effects.
For short-term links, the error correction method (ECM) is applied. A short-term correlation among finance–trade–growth exists when the error correction term’s coefficient is significantly negative (see Table A4 in Appendix A). The findings reveal that the error correction term has a negative and statistically significant value of 23.8%, which tells that concerned variables are connected in the long term. This demonstrates that any instability in economic progress would be resolved in the long term with a pace of 23.8%. The long-term and short-term relationships are graphically presented in Figure 2.
This study employed sensitivity analysis to analyze the model’s reliability. In the lower panel of Table A3, findings for normality of the model, serial correlation, heteroscedasticity, and Ramsey reset test’s functional form are shown (see Appendix A).
The research model’s stability is examined through the cumulative sum (CUSUM) and the cumulative sum of the squares (CUSUMSQ) tests. The estimated graphs of the CUSUM and the CUSUM square are shown in Figure 3 and Figure 4. The ARDL cointegration is applied to observe the coefficient’s stability. All the figures illustrate that the assessed line is reasonable and within the critical boundaries at a 5% significance level. Thus, models are found to be reliable and stable after estimation.

4.1. Granger Causality and Variance Decomposition Analysis

The details of the Granger-based causality tests are reported in Table 2. The findings indicate that there is a clear unilateral causality between financial development and economic openness. The results also reveal that a bilateral causality exists between economic openness and economic progress. The Innovation Accounting method involving the analysis of variance decomposition was also used to observe the anticipated influence of economic openness and financial development on economic progress, as the ARDL estimates do not give any conclusion regarding the correlation outside the sample period. It has been posited that the generalized impulse response function only tells the nature of the shock but does not demonstrate the degree of that particular shock (Shahbaz et al. 2013a). So, the variance decomposition is used to find the magnitude of this shock (see Table 3). It determines the input of every innovation to h-step ahead dependent variable’s forecast error variance. It gives a mean value for influential shocks’ comparative significance in illuminating the deviation in the dependent variable (Pesaran et al. 1999). Hashimzade et al. (2013) put forward that the outcomes revealed by the variance decomposition approach were more reliable than other econometric methods in the same domain.
Hence, this study expects the dynamic causal links of financial development and economic openness with the economic progress of Pakistan for the next ten years. The results explain that about 85.10% of economic progress is affected by its standard innovation shock. GDP responds by 6.80%, 2.84%, 3.39%, and 1.85% when the one standard deviation variation is attributed to the financial development, economic openness, and foreign direct investment, respectively. The result is confirmed by the outcome of the impulse response function, which is also anticipated for the next ten years. Financial development would be the main contributor to economic progress. Moreover, the impulse response function plots also confirmed these impacts.

4.2. Fully Modified Least Squares

The estimations accomplished for the long term from the ARDL approach are then examined for robustness by applying another single Equation estimator approach that is known as FMOLS (Stock and Watson 1993) (see Table 4). The most important benefit of the FMOLS approach is that it reflects the combination of integration orders of variables cointegrated in the time series data. The valuation of this approach elaborates the regression of the I(1) variables in response to other I(1) variables having leads (p), the first difference lags (_p), and the other variables having the integration order I(0) having the constant term (Kao et al. 1999). FMOLS approach is advantageous as it solves the problem of possible endogeneity and bias in a small sample.

5. Conclusions and Policy Proposals

The research explored the linkage of financial development and economic openness in Pakistan’s economic progress during 1975–2018. For assessing the existence of cointegration in the concerned variables, the ARDL cointegration was utilized. We used the deposit money bank asset (DMA) as a percentage of GDP and domestic credit to the private sector (DCP) as a percentage of GDP to reflect the calculation of financial development. At the same time, economic openness was represented by the contribution of export and import to GDP. The long-term findings indicated the long-term stability among the variables of interest. The Granger causality method is applied to assess the direction of causality among all the variables under consideration. A robustness analysis was performed using FMOLS to validate the stability and accuracy of the ARDL findings.
Unit root test revealed the mixed integration order of series under analysis. The findings of the ARDL suggested that there was a long-term association between financial development and economic progress, whereas, in the short term, the association existed between economic openness and economic progress. In comparison, the ECTt−1 coefficient (−0.2388) had the predicted sign with significant outcomes. This indicates the speed of correction from short-term imbalance to long-term balance by roughly 23.88%. In addition, the findings of the CUSUM and CUSUMSQ stability tests declared the stability of the estimated models.
To achieve the goal of economic progress, policymakers and the government should take steps to develop a solid financial system and ensure that financial institutions can offer adequate funds to the productive areas of the Pakistani economy. Policymakers should make sure that the funds given to the private sector are being utilized for innovative programs to set up a sustainable development path for the economy. In addition to that, the capital sector also needs to be consolidated to enable the utilization and effective transfer of resources into economically active areas. The financial sector should be improved to help the introduction of government privatization. Consequently, policymakers may use shifts in the economic progress or real sector to control the course of development and growth of commercial and financial regions of the economy as the economic progress is linked to economic openness and financial development. Finally, in light of our findings, the short-term macroeconomic policies should promote economic openness to support open economy macroeconomic activity in the short run to promote economic prosperity and sustainable economic progress in the long run.

Author Contributions

Conceptualization, A.H. and M.I.; methodology, M.A.; software, M.A.; validation, M.A. and M.I.; formal analysis, M.A.; investigation, M.I.; resources, A.O.; data curation, A.H.; writing—original draft preparation, A.H., M.I.; writing—review and editing, M.I., M.A., F.S.; visualization, M.A.; project administration, A.O.; funding acquisition, A.O. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Results of Unit root test.
Table A1. Results of Unit root test.
VariablesInterceptIntercept &Trend
Level1st DiffLevel1st Diff
Ln GDP−1.0914−5.5010 **−2.2875−5.4528 **
DMA−0.6846−6.0107 **−2.6637−5.9696 **
Ln DCP−1.6708−5.3959 **−3.0575−5.3159 **
Ln ECOP−2.3015−6.9202 **−2.5545−6.9659 **
FDI−2.9479 **−4.3951 **−3.1589−4.0991 **
Ln GDP−1.0434−5.5010 **−2.4490−5.4528 **
DMA−0.2895−7.4951 **−2.8454−9.5753 **
Ln DCP−1.8055−5.3959 **−2.5848−5.3159 **
Ln ECOP−2.3604−7.2970 **−2.5450−9.2747 **
FDI−2.2911−4.3951 **−2.4570−4.3686 **
Note: ** indicates 5% significance level.
Table A2. Bound testing for ARDL model.
Table A2. Bound testing for ARDL model.
SignificanceLower I(0)Upper I(1)
Table A3. Long-term findings based on the chosen ARDL model.
Table A3. Long-term findings based on the chosen ARDL model.
VariablesCoefficientsStd. Errorp-Value
Ln DCP1.29480.26170.0001
Ln ECOP−2.57600.89630.009
Diagnostic Tests
LM Serial correlation tests1.3929 (0.2809)
Harvey Heteroscedasticity0.9045 (0.5849)
Functional form Ramsey reset test1.0860 (0.3104)
Normality0.0772 (0.9621)
Table A4. Short-term findings with error correction representation.
Table A4. Short-term findings with error correction representation.
VariablesCoefficientsStd. Errorp-Value
ΔLn DCP−0.30650.07720.0008 **
ΔLn DCP(-1)−0.01270.10610.9059
ΔLn DCP(-2)0.14070.06740.0499 **
ΔLn ECOP0.06000.03160.0330 **
ΔLn ECOP(-1)−0.05900.17570.7402
ΔLn ECOP(-2)0.02390.16680.8873
ΔLn ECOP(-3)0.71900.14170.0001
ΔLn FDI0.04620.01200.0011
ΔLn FDI(-1)0.00680.01760.7012
ΔLn FDI(-2)−0.00050.01630.9748
ΔLn FDI(-3)−0.03010.01110.0135
ECM(-1)−0.23880.05420.0003 ***
Diagnostic Tests
R20.8066Durbin-Watson Stat.2.0666
F-Statistics 4.3925
Prob. F-statistics0.0009
Note: *** and ** indicate 1% and 5% significance levels, respectively.


  1. Afzal, Muhammad, and Ijaz Hussain. 2010. Export-Led Growth Hypothesis: Evidence from Pakistan. Journal of Quantitative Economics 8: 130–47. [Google Scholar]
  2. Aghion, Philippe, Peter Howitt, and David Mayer-Foulkes. 2005. The Effect of Financial Development on Convergence: Theory and Evidence. Quarterly Journal of Economics 120: 173–222. [Google Scholar] [CrossRef]
  3. Ahmad, Munir, Abbas Ali Chandio, Yasir Ahmed Solangi, Syed Ahsan Ali Shah, Farrukh Shahzad, Abdul Rehman, and Gul Jabeen. 2020a. Dynamic Interactive Links among Sustainable Energy Investment, Air Pollution, and Sustainable Development in Regional China. Environmental Science and Pollution Research. [Google Scholar] [CrossRef] [PubMed]
  4. Ahmad, Munir, Gul Jabeen, and Yiyun Wu. 2021a. Heterogeneity of Pollution Haven/Halo Hypothesis and Environmental Kuznets Curve Hypothesis across Development Levels of Chinese Provinces. Journal of Cleaner Production 285: 124898. [Google Scholar] [CrossRef]
  5. Ahmad, Munir, Gul Jabeen, Muhammad Irfan, Cem Işık, and Abdul Rehman. 2021b. Do Inward Foreign Direct Investment and Economic Development Improve Local Environmental Quality: Aggregation Bias Puzzle. Environmental Science and Pollution Research. [Google Scholar] [CrossRef]
  6. Ahmad, Munir, Gul Jabeen, Muhammad Khizar Hayat, Rana Ejaz Ali Khan, and Shoaib Qamar. 2020b. Revealing Heterogeneous Causal Links among Financial Development, Construction Industry, Energy Use, and Environmental Quality across Development Levels. Environmental Science and Pollution Research. [Google Scholar] [CrossRef] [PubMed]
  7. Ahmad, Munir, Inayatullah Jan, Gul Jabeen, and Rafael Alvarado. 2021c. Does Energy-Industry Investment Drive Economic Performance in Regional China: Implications for Sustainable Development. Sustainable Production and Consumption 27: 176–92. [Google Scholar] [CrossRef]
  8. Ahmad, Munir, Zeeshan Khan, Muhammad Khalid, and Gul Jabeen. 2021d. Do Rural-Urban Migration and Industrial Agglomeration Mitigate the Environmental Degradation across China’s Regional Development Levels ? Sustainable Production and Consumption 27: 679–97. [Google Scholar] [CrossRef]
  9. Ahmad, Munir, Zhen Yu Zhao, Muhammad Irfan, and Marie Claire Mukeshimana. 2019. Empirics on Influencing Mechanisms among Energy, Finance, Trade, Environment, and Economic Growth: A Heterogeneous Dynamic Panel Data Analysis of China. Environmental Science and Pollution Research, 14148–70. [Google Scholar] [CrossRef]
  10. Ahmad, Munir, Zhen Yu Zhao, Muhammad Irfan, Marie Claire Mukeshimana, Abdul Rehman, Gul Jabeen, and Heng Li. 2020c. Modeling Heterogeneous Dynamic Interactions among Energy Investment, SO2 Emissions and Economic Performance in Regional China. Environmental Science and Pollution Research 27: 2730–44. [Google Scholar] [CrossRef] [PubMed]
  11. Ahmad, Munir, Zhen-Yu Zhao, Marie Claire Mukeshimana, and Muhammad Irfan. 2021e. Dynamic Causal Linkages Among Urbanization, Energy Consumption, Pollutant Emissions and Economic Growth in China. In CRIOCM 2018: Proceedings of the 23rd International Symposium on Advancement of Construction Management and Real Estate. Singapore: Springer, pp. 90–105. [Google Scholar] [CrossRef]
  12. Almeida, Rita, and Ana Margarida Fernandes. 2008. Openness and Technological Innovations in Developing Countries: Evidence from Firm-Level Surveys. Journal of Development Studies 44: 701–27. [Google Scholar] [CrossRef]
  13. Alvarado, Rafael, Qiushi Deng, Brayan Tillaguango, Munir Ahmad, and Diana Bravo. 2021. Do Economic Development and Human Capital Decrease Non- Renewable Energy Consumption? Evidence for OECD Countries. Energy 215: 119147. [Google Scholar] [CrossRef]
  14. Alvarez-Cuadrado, Francisco, Ngo Van Long, and Markus Poschke. 2017. Capital-Labor Substitution, Structural Change, and Growth. Theoretical Economics 12: 1229–66. [Google Scholar] [CrossRef]
  15. Arvin, Mak B., Rudra P. Pradhan, and Mahendhiran Nair. 2021. Uncovering Interlinks Among ICT Connectivity and Penetration, Trade Openness, Foreign Direct Investment, and Economic Growth: The Case of the G-20 Countries. Telematics and Informatics 60: 101567. [Google Scholar] [CrossRef]
  16. Asongu, Simplice A. 2015. Financial Sector Competition and Knowledge Economy: Evidence from SSA and MENA Countries. Journal of the Knowledge Economy 6: 717–48. [Google Scholar] [CrossRef] [Green Version]
  17. Atil, Ahmed, Kishwar Nawaz, Amine Lahiani, and David Roubaud. 2020. Are Natural Resources a Blessing or a Curse for Financial Development in Pakistan? The Importance of Oil Prices, Economic Growth and Economic Globalization. Resources Policy 67: 101683. [Google Scholar] [CrossRef]
  18. Bojanic, Antonio N. 2012. The Impact of Financial Development and Trade on the Economic Growth of Bolivia. Journal of Applied Economics 15: 51–70. [Google Scholar] [CrossRef]
  19. Boldeanu, Florin Teodor, and Ileana Tache. 2016. The Financial System of the EU and the Capital Markets Union. European Research Studies Journal 19: 59–70. [Google Scholar] [CrossRef]
  20. Can, Muhlis, Munir Ahmad, and Zeeshan Khan. 2021. The Impact of Export Composition on Environment and Energy Demand: Evidence from Newly Industrialized Countries. Environmental Science and Pollution Research. [Google Scholar] [CrossRef]
  21. Carroll, Toby, and Darryl S. L. Jarvis. 2015. The New Politics of Development: Citizens, Civil Society, and the Evolution of Neoliberal Development Policy. Globalizations 12: 281–304. [Google Scholar] [CrossRef]
  22. Chang, Roberto, Linda Kaltani, and Norman V. Loayza. 2009. Openness Can Be Good for Growth: The Role of Policy Complementarities. Journal of Development Economics 90: 33–49. [Google Scholar] [CrossRef] [Green Version]
  23. Chaudhry, Sharif Imran, Yasmeen Qamber, and Fatima Farooq. 2012. Monetary Policy, Inflation and Economic Growth in Pakistan: Exploring the Co-Integration and Causality Relationships. Pakistan Journal of Commerce and Social Sciences (PJCSS) 6: 332–47. [Google Scholar]
  24. Cheng, Chih Yang, Mei Se Chien, and Chien Chiang Lee. 2021. ICT Diffusion, Financial Development, and Economic Growth: An International Cross-Country Analysis. Economic Modelling 94: 662–71. [Google Scholar] [CrossRef]
  25. Claessens, Stijn, Jon Frost, Grant Turner, and Feng Zhu. 2018. Fintech Credit Markets around the World: Size, Drivers and Policy Issues. BIS Quarterly Review. Basel: BIS, pp. 29–49. Available online: (accessed on 26 May 2021).
  26. DeAngelo, Harry, and René M. Stulz. 2015. Liquid-Claim Production, Risk Management, and Bank Capital Structure: Why High Leverage Is Optimal for Banks. Journal of Financial Economics 116: 219–36. [Google Scholar] [CrossRef]
  27. Deidda, Luca, and Bassam Fattouh. 2002. Non-Linearity between Finance and Growth. Economics Letters 74: 339–45. [Google Scholar] [CrossRef] [Green Version]
  28. Dellink, Rob, Jean Chateau, Elisa Lanzi, and Bertrand Magné. 2017. Long-Term Economic Growth Projections in the Shared Socioeconomic Pathways. Global Environmental Change 42: 200–14. [Google Scholar] [CrossRef]
  29. Elavarasan, Rajvikram Madurai, S. Leoponraj, A. Dheeraj, Muhammad Irfan, G. Gangaram Sundar, and G. K. Mahesh. 2021. PV-Diesel-Hydrogen Fuel Cell Based Grid Connected Configurations for an Institutional Building Using BWM Framework and Cost Optimization Algorithm. Sustainable Energy Technologies and Assessments 43: 100934. [Google Scholar] [CrossRef]
  30. Ernst, Ekkehard. 2019. Finance and Jobs: How Financial Markets and Prudential Regulation Shape Unemployment Dynamics. Journal of Risk and Financial Management 12: 20. [Google Scholar] [CrossRef] [Green Version]
  31. Evan, Alun Dwyfor, Christopher J. Green, and Victor Murinde. 2002. Human Capital and Financial Development in Economic Growth: New Evidence Using the Translog Production Function. International Journal of Finance And Economics 140: 123–40. [Google Scholar] [CrossRef]
  32. Feldstein, Martin. 2017. Underestimating the Real Growth of GDP, Personal Income, and Productivity. Journal of Economic Perspectives 31: 145–64. [Google Scholar] [CrossRef] [Green Version]
  33. Ghazouani, Tarek, Jamel Boukhatem, and Chung Yan Sam. 2020. Causal Interactions between Trade Openness, Renewable Electricity Consumption, and Economic Growth in Asia-Pacific Countries: Fresh Evidence from a Bootstrap ARDL Approach. Renewable and Sustainable Energy Reviews 133: 110094. [Google Scholar] [CrossRef]
  34. Greenwood, Jeremy, and Boyan Jovanovic. 1990. Financial Development, Growth, and the Distribution of Income. Journal of Political Economy 98: 1076–107. [Google Scholar] [CrossRef] [Green Version]
  35. Haiss, Peter, and Kjell Sümegi. 2008. The Relationship between Insurance and Economic Growth in Europe: A Theoretical and Empirical Analysis. Empirica 35: 405–31. [Google Scholar] [CrossRef]
  36. Hashimzade, Nigar, Michael Thornton, and James Davidson. 2013. Cointegration and Error Correction. In Handbook of Research Methods and Applications in Empirical Macroeconomics. Cheltenham: Edward Elgar Publishing. [Google Scholar] [CrossRef] [Green Version]
  37. Hausmann, Ricardo. 2016. Economic Development and the Accumulation of Know-How. Welsh Economic Review 24: 13–16. [Google Scholar] [CrossRef] [Green Version]
  38. Huang, Alan Guoming, and Kevin Jialin Sun. 2019. Equity Financing Restrictions and the Asset Growth Effect: International vs. Asian Evidence. Pacific Basin Finance Journal 57: 101053. [Google Scholar] [CrossRef]
  39. Ibrahim, Muazu, and Paul Alagidede. 2018. Effect of Financial Development on Economic Growth in Sub-Saharan Africa. Journal of Policy Modeling 40: 1104–25. [Google Scholar] [CrossRef]
  40. Iqbal, Najaf, Kashif Raza, Riazullah Shinwari, Wan Guangcai, Munir Ahmad, and Kai Tang. 2021. Does Exports Diversification and Environmental Innovation Achieve Carbon Neutrality Target of OECD Economies? Journal of Environmental Management 291: 112648. [Google Scholar] [CrossRef]
  41. Irfan, Muhammad, Zhen-Yu Zhao, Manoj Kumar, Fareed Hussain, Heng Li, Ali Jan, Munir Ahmad, and Abdul Rehman. 2020. Assessing the Energy Dynamics of Pakistan: Prospects of Biomass Energy. Energy Reports 6: 80–93. [Google Scholar] [CrossRef]
  42. Irfan, Muhammad, Zhen-Yu Zhao, Munir Ahmad, and Marie Claire Mukeshimana. 2019a. Critical Factors Influencing Wind Power Industry: A Diamond Model Based Study of India. Energy Reports 5: 1222–35. [Google Scholar] [CrossRef]
  43. Irfan, Muhammad, Zhen Yu Zhao, Munir Ahmad, Kiran Batool, Ali Jan, and Marie Claire Mukeshimana. 2019b. Competitive Assessment of Indian Wind Power Industry: A Five Forces Model. Journal of Renewable and Sustainable Energy 11: 063301. [Google Scholar] [CrossRef]
  44. Johansen, Søren, and Katarina Juselius. 1990. Maximum Likelihood Estimation and Inference on Cointegration—With Applications To the Demand for Money. Oxford Bulletin of Economics and Statistics 52: 169–210. [Google Scholar] [CrossRef]
  45. Kao, Chihwa, Min Hsien Chiang, and Bangtian Chen. 1999. International R&D Spillovers: An Application of Estimation and Inference in Panel Cointegration. Oxford Bulletin of Economics and Statistics 61: 691–709. [Google Scholar] [CrossRef] [Green Version]
  46. Keho, Yaya. 2017. The Impact of Trade Openness on Economic Growth: The Case of Cote d’Ivoire. Cogent Economics and Finance 5: 1–14. [Google Scholar] [CrossRef]
  47. Khairutdinov, Ramil R., Tatjana G. Antropova, Oleg N. Golland, Flera G. Mukhametzyanova, Alfiya Sh Yarullina, and Luiza K. Karimova. 2018. Comparative Perspectives on Innovative Development of Russian Economy: Influence of Sustainable Factors? Journal of Entrepreneurship Education 21: 2651. [Google Scholar]
  48. Khalikov, Ulugbek. 2017. Role of Stock Exchanges in Economic Development of Uzbekistan. International Business Research 10: 172. [Google Scholar] [CrossRef] [Green Version]
  49. Kılınç, Dilara, Ünal Seven, and Hakan Yetkiner. 2017. Financial Development Convergence: New Evidence for the EU. Central Bank Review 17: 47–54. [Google Scholar] [CrossRef]
  50. Klasra, Mushtaq Ahmad. 2011. Foreign Direct Investment, Trade Openness and Economic Growth in Pakistan and Turkey: An Investigation Using Bounds Test. Quality and Quantity 45: 223–31. [Google Scholar] [CrossRef]
  51. Komal, Rabia, and Faisal Abbas. 2015. Linking Financial Development, Economic Growth and Energy Consumption in Pakistan. Renewable and Sustainable Energy Reviews 44: 211–20. [Google Scholar] [CrossRef]
  52. Kong, Qunxi, Dan Peng, Yehui Ni, Xinyue Jiang, and Ziqi Wang. 2021. Trade Openness and Economic Growth Quality of China: Empirical Analysis Using ARDL Model. Finance Research Letters 38: 101488. [Google Scholar] [CrossRef]
  53. Lane, Philip R., and Gian Maria Milesi-Ferretti. 2018. The External Wealth of Nations Revisited: International Financial Integration in the Aftermath of the Global Financial Crisis. IMF Economic Review 66: 189–222. [Google Scholar] [CrossRef]
  54. Li, Guangchen, and Weixian Wei. 2021. Financial Development, Openness, Innovation, Carbon Emissions, and Economic Growth in China. Energy Economics 97: 105194. [Google Scholar] [CrossRef]
  55. Loayza, Norman, and Romain Ranciere. 2006. Financial Development, Financial Fragility, and Growth. Journal of Money, Credit, and Banking 38: 1051–76. [Google Scholar] [CrossRef] [Green Version]
  56. Majumder, Monoj Kumar, Mala Raghavan, and Joaquin Vespignani. 2020. Oil Curse, Economic Growth and Trade Openness. Energy Economics 91: 104896. [Google Scholar] [CrossRef]
  57. Musila, Jacob W., and Zelealem Yiheyis. 2015. The Impact of Trade Openness on Growth: The Case of Kenya. Journal of Policy Modeling 37: 342–54. [Google Scholar] [CrossRef]
  58. Nasreen, Samia, Mantu Kumar Mahalik, Muhammad Shahbaz, and Qaisar Abbas. 2020. How Do Financial Globalization, Institutions and Economic Growth Impact Financial Sector Development in European Countries? Research in International Business and Finance 54: 101247. [Google Scholar] [CrossRef]
  59. Nelson, Richard R. 1998. The Agenda for Growth Theory: A Different Point of View. Cambridge Journal of Economics 22: 497–520. [Google Scholar] [CrossRef]
  60. Odeniran, Samson O., Elias A. Udeaja, and Economic Growth. 2010. Financial Sector Development and Economic Growth: Empirical Evidence from Nigeria. Economic and Financial Review 48: 91–124. [Google Scholar]
  61. Odhiambo, Nicholas M. 2010. Finance-Investment-Growth Nexus in South Africa: An ARDL-Bounds Testing Procedure. Economic Change and Restructuring 43: 205–19. [Google Scholar] [CrossRef]
  62. Omri, Anis, Saida Daly, Christophe Rault, and Anissa Chaibi. 2015. Financial Development, Environmental Quality, Trade and Economic Growth: What Causes What in MENA Countries. Energy Economics 48: 242–52. [Google Scholar] [CrossRef] [Green Version]
  63. Osei, Michael J., and Jaebeom Kim. 2020. Foreign Direct Investment and Economic Growth: Is More Financial Development Better? Economic Modelling 93: 154–61. [Google Scholar] [CrossRef]
  64. Pearson, Ruth, and Diane Elson. 2015. Transcending the Impact of the Financial Crisis in the United Kingdom: Towards Plan F-a Feminist Economic Strategy. Feminist Review 109: 8–30. [Google Scholar] [CrossRef]
  65. Pece, Andreea Maria, Olivera Ecaterina Oros Simona, and Florina Salisteanu. 2015. Innovation and Economic Growth: An Empirical Analysis for CEE Countries. Procedia Economics and Finance 26: 461–67. [Google Scholar] [CrossRef] [Green Version]
  66. Pesaran, M. Hashem, and Yongcheol Shin. 1999. An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. In Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium. Cambridge: Cambridge University Press. [Google Scholar] [CrossRef]
  67. Pesaran, M. Hashem, M. Hashem Pesaran, Yongcheol Shin, and Ron P. Smith. 1999. Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association 94: 621–34. [Google Scholar] [CrossRef]
  68. Pesaran, M. Hashem, Yongcheol Shin, and Richard J. Smith. 2001. Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics 16: 289–326. [Google Scholar] [CrossRef]
  69. Phong, Le Hoang, Dang Thi Bach Van, and Ho Hoang Gia Bao. 2018. The Role of Globalization on Carbon Dioxide Emission in Vietnam Incorporating Industrialization, Urbanization, Gross Domestic Product per Capita and Energy Use. International Journal of Energy Economics and Policy 8: 275–83. [Google Scholar] [CrossRef]
  70. Pradhan, Rudra P., Mak B. Arvin, Mahendhiran S. Nair, John H. Hall, and Sara E. Bennett. 2021. Sustainable Economic Development in India: The Dynamics between Financial Inclusion, ICT Development, and Economic Growth. Technological Forecasting & Social Change 169: 120758. [Google Scholar] [CrossRef]
  71. Rajan, Raghuram G., and Luigi Zingales. 2003. The Great Reversals: The Politics of Financial Development in the Twentieth Century. Journal of Financial Economics 69: 5–50. [Google Scholar] [CrossRef]
  72. Rassekh, Farhad. 2007. Is International Trade More Beneficial to Lower Income Economies? An Empirical Inquiry. Review of Development Economics 11: 159–69. [Google Scholar] [CrossRef]
  73. Rohde, Marc, and Christoph Breuer. 2016. Europe’s Elite Football: Financial Growth, Sporting Success, Transfer Investment, and Private Majority Investors. International Journal of Financial Studies 4: 12. [Google Scholar] [CrossRef] [Green Version]
  74. Schumpeter, Joseph A. 1911. The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Harvard: Harvard Economic Studies. [Google Scholar]
  75. Shahbaz, Muhammad, Muhammad Shahbaz Shabbir, and Muhammad Sabihuddin Butt. 2013a. Effect of Financial Development on Agricultural Growth in Pakistan: New Extensions from Bounds Test to Level Relationships and Granger Causality Tests. International Journal of Social Economics 40: 707–28. [Google Scholar] [CrossRef] [Green Version]
  76. Shahbaz, Muhammad, Qazi Muhammad Adnan Hye, Aviral Kumar Tiwari, and Nuno Carlos Leitão. 2013b. Economic Growth, Energy Consumption, Financial Development, International Trade and CO2 Emissions in Indonesia. Renewable and Sustainable Energy Reviews 25: 109–21. [Google Scholar] [CrossRef] [Green Version]
  77. Shahbaz, Muhammad. 2012. Does Trade Openness Affect Long Run Growth? Cointegration, Causality and Forecast Error Variance Decomposition Tests for Pakistan. Economic Modelling 29: 2325–39. [Google Scholar] [CrossRef] [Green Version]
  78. Sloboda, Brian. 2004. Applied Time Series Modelling and Forecasting. International Journal of Forecasting 20: 137–50. [Google Scholar] [CrossRef]
  79. Soekapdjo, Soeharjoto, Debbie Aryani Tribudhi, Dini Hariyanti, and Lucky Nugroho. 2020. Factors Affecting Economic Growth in Central Java. International Journal of Commerce and Finance 6: 155–65. [Google Scholar]
  80. Spolander, Gary, Lambert Engelbrecht, and Annie Pullen Sansfaçon. 2016. Social Work and Macro-Economic Neoliberalism: Beyond the Social Justice Rhetoric. European Journal of Social Work 19: 634–49. [Google Scholar] [CrossRef]
  81. Stock, James H., and Mark W. Watson. 1993. A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica. [Google Scholar] [CrossRef]
  82. Sun, Hua ping, Gulzara Tariq, Muhammad Haris, and Muhammad Mohsin. 2019. Evaluating the Environmental Effects of Economic Openness: Evidence from SAARC Countries. Environmental Science and Pollution Research 26: 24542–51. [Google Scholar] [CrossRef]
  83. Truby, Jon. 2018. Decarbonizing Bitcoin: Law and Policy Choices for Reducing the Energy Consumption of Blockchain Technologies and Digital Currencies. Energy Research and Social Science 44: 399–410. [Google Scholar] [CrossRef]
  84. Turok, Ivan. 2016. Getting Urbanization to Work in Africa: The Role of the Urban Land-Infrastructure-Finance Nexus. Area Development and Policy 1: 30–47. [Google Scholar] [CrossRef]
  85. Uddin, Gazi Salah, Bo Sjö, and Muhammad Shahbaz. 2013. The Causal Nexus between Financial Development and Economic Growth in Kenya. Economic Modelling 35: 701–7. [Google Scholar] [CrossRef] [Green Version]
  86. Vo, Duc, Son Huynh, Anh Vo, and Dao Ha. 2019. The Importance of the Financial Derivatives Markets to Economic Development in the World’s Four Major Economies. Journal of Risk and Financial Management 12: 35. [Google Scholar] [CrossRef] [Green Version]
  87. Wang, Rong, and Junlan Tan. 2021. Exploring the Coupling and Forecasting of Financial Development, Technological Innovation, and Economic Growth. Technological Forecasting and Social Change 163: 120466. [Google Scholar] [CrossRef]
  88. World Bank. 2019. World Development Indicators (WDI)|Data Catalog. Data Catalog, United Nations World Data Bank. Washington, DC: World Bank. [Google Scholar]
  89. Wu, Haitao, Yu Hao, and Jia-hsi Weng. 2019. How Does Energy Consumption Affect China’s Urbanization? New Evidence from Dynamic Threshold Panel Models. Energy Policy 127: 24–38. [Google Scholar] [CrossRef]
  90. Xuefeng, Qian, and Mahmut Yaşar. 2016. Export Market Diversification and Firm Productivity: Evidence from a Large Developing Country. World Development 82: 28–47. [Google Scholar] [CrossRef]
  91. Zahonogo, Pam. 2017. Trade and Economic Growth in Developing Countries: Evidence from Sub-Saharan Africa. Journal of African Trade 3: 41. [Google Scholar] [CrossRef]
Figure 1. Outline of methods applied in the current study. Source: Authors’ drawing.
Figure 1. Outline of methods applied in the current study. Source: Authors’ drawing.
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Figure 2. Long-term and short-term impacts of explanatory variables on economic progress in Pakistan. Source: Authors’ own drawing based on ARDL estimations.
Figure 2. Long-term and short-term impacts of explanatory variables on economic progress in Pakistan. Source: Authors’ own drawing based on ARDL estimations.
Jrfm 14 00237 g002
Figure 3. The plot of the cumulative sum of recursive residuals.
Figure 3. The plot of the cumulative sum of recursive residuals.
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Figure 4. The plot of the cumulative sum of squares of the recursive residuals.
Figure 4. The plot of the cumulative sum of squares of the recursive residuals.
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Table 1. Description of study variables.
Table 1. Description of study variables.
GDPEconomic progressCurrent USDWorld Bank (2019) Indicators
ECOPEconomic opennessSum of total imports and exports (% of GDP)World Bank (2019) Indicators
DMAFinancial developmentDeposit money bank assets (% of GDP)World Bank (2019) Indicators
DCPFinancial developmentDomestic credit to private sector (% of GDP)World Bank (2019) Indicators
FDIForeign direct investmentForeign direct investment net inflows (% of GDP)World Bank (2019) Indicators
Table 2. Results of Granger causality test.
Table 2. Results of Granger causality test.
Dependent VariableExplanatory Variables
Table 3. Variance decomposition.
Table 3. Variance decomposition.
Variance decomposition of GDP
Variance decomposition of DMA
Variance decomposition of DCP
Variance decomposition of ECOP
Variance decomposition of FDI
Table 4. Results of the fully modified least squares (FMOLS).
Table 4. Results of the fully modified least squares (FMOLS).
VariablesCoefficientsStd. Errorp-value
Ln DCP−0.37480.16820.0318
Ln TOP1.11920.51180.0350
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Hussain, A.; Oad, A.; Ahmad, M.; Irfan, M.; Saqib, F. Do Financial Development and Economic Openness Matter for Economic Progress in an Emerging Country? Seeking a Sustainable Development Path. J. Risk Financial Manag. 2021, 14, 237.

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Hussain A, Oad A, Ahmad M, Irfan M, Saqib F. Do Financial Development and Economic Openness Matter for Economic Progress in an Emerging Country? Seeking a Sustainable Development Path. Journal of Risk and Financial Management. 2021; 14(6):237.

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Hussain, Ammara, Ammar Oad, Munir Ahmad, Muhammad Irfan, and Farhan Saqib. 2021. "Do Financial Development and Economic Openness Matter for Economic Progress in an Emerging Country? Seeking a Sustainable Development Path" Journal of Risk and Financial Management 14, no. 6: 237.

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