economic growth in pakistan research paper

The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

• Open access
• Published: 20 February 2019

Innovation, total factor productivity and economic growth in Pakistan: a policy perspective

• Hummera Saleem 1 ,
• Malik Shahzad 2 ,
• Muhammad Bilal Khan 3 &
• Bashir Ahmad Khilji 4  

Journal of Economic Structures volume  8 , Article number:  7 ( 2019 ) Cite this article

19k Accesses

61 Citations

3 Altmetric

Metrics details

The objective of this study is to endorse the driving factors behind total factor productivity (TFP) and economic growth in Pakistan. Pakistan’s average growth rate is 5% for last few decades, and although this growth level is satisfactory, Pakistan faced several formidable challenges yet. The economic growth has been determined mainly through labor-intensive technology and export-oriented manufacturing activities. However, TFP is assessed from the aggregate production function using the Cobb–Douglas production function that permits for the simultaneous expansion of outputs and contraction of inputs. The annual timer series data have been extracted from 1972–2016 World development Indicator (WDI) for this study. The overall results reveal that almost all variables are statistical significant. Moreover, innovation significantly contributes to economic growth and production level in Pakistan. This analysis may have significant suggestions to policy makers in Pakistan and other emerging economies when framing sustainable growth policy.

1 Introduction

Recent economic growth theories draw devotion toward endogenous technological change, which describes the growth patterns of world economies. Romer ( 1986 ) established an endogenous growth model in which technological innovation was formed in the research and development (R&D) areas including human capital and the existing knowledge stock. Then, it was used in the production of all final goods and led to permanent rises in the output growth rate.

Innovation is a significant factor of economic growth in the mind of various experts especially the policy makers. Moreover, innovation is not directly related to the amount of productive resources; therefore, it affects growth of the economy mostly through TFP. Technological innovation and non-technological factors are two main divisions of innovation, where new production and services are related to the technological innovation and non-technological innovations in the form of organizational or marketing modifications. However, growth level in itself can be attained by putting more inputs for process of production and through attaining higher levels of output with the same quantity of resources. There is no clear indication determining whether there is a casual association between innovations and economic growth through productivity or whether these both procedures occur at a time in developing countries such as Pakistan. Answering this query has critical relevance for Pakistan since unconventional answers lead toward different policy recommendations regarding innovation and technology policies.

The objective of this study is to investigate how innovation and economic growth are interrelated to each other in Pakistan. How has the enrollment of TFP to economic growth changed over time in the expectation that shedding some light on the significance of innovation and showing a clearer picture of Pakistan’s economic growth? Using patent data (residential and non-residential) as a proxy for innovation, this paper gives support in the view that a growth in patents leads to rise in economic growth for long run. Moreover, at what extent, enrollment of TFP to economic growth changed over the time period and potential determinants of TFP?

This study is employing annual time series data to fill the gap by providing up-to-date estimates of TFP and exploring the determinants of TFP (this study follows the traditional approach of estimating TFP growth using a production function) and contribution of innovation, in this manner detecting future growth engines for the long-run sustainable development in Pakistan. A significant conclusion points out the relationship between innovative capabilities, TFPG and economic development. This study has recommended some of important findings for policy makers such as, the combination of innovational activities, TFPG moving toward sustainable economic growth are essential and simulated policies are the best practice for significant contributions in economic development.

The study is organized as follows: Sect.  2 introduces the research methodology and data. The crucial point of this analysis is the decisive prediction that total factor productivity (TFP) growth has contributed significantly to economic growth. Finding from the prior literature, it can be found that these analyses give only indirect evidence of the role played by innovation on economic growth. Section  3 describes the empirical results and discussion. Section  4 finally draws conclusions and discussion with their implication.

2 Literature review

Many studies have revealed the presence of positive relationship between innovation and productivity. The theoretical argument has converged to realize that the growth of productivity is infused by the innovation based on enterprises. However, several economists have been concerned in the contribution of economic growth from traditional neoclassical model (Solow 1957 ). Furthermore, evidence of productivity growth has been discussed by pioneer studies that capital and labor inputs illuminate less than half of the variation in productivity.

The unexplained portion, which is called “residual,” is usually reflected by the influence of the technological change on the level of productivity. For this purpose, these empirical analyses try to find different measures for technological change (R&D activities, quality of work and improvement in capital) in order to describe the residual productivity growth (Cassiman and Golovko 2011 ; Griliches 1979 , 2000 ; Huergo and Jaumandreu 2004 ; Ortega-Argilés et al. 2005 ; Tsai and Wang 2004 ; Wakelin 2001 ).

According to Christensen ( 1997 ) that sustaining technological change than it’s reinforce the technological model and business routines; they do not lead to the creation of new products, but rather the development of the existing ones. In order for Pakistan’s to catch up and reach up to the levels of per capita similar to advanced countries, productivity is essential. The most important challenge for Pakistan is improving the level of productivity and growth. As supported in the studies (IDB 2010a, b), low productivity growth was the main cause of the poor economic performance of region in the last few decades, whereas innovation is playing an important role for development of growing productivity.

Meanwhile, several studies determine a virtuous circle in which innovation, productivity and per capita income jointly reinforce each other and lead countries to long-term sustained growth rates (Hall and Jones 1999 ; Rouvinen 2002 ). At the firm level, there was resounding evidence for advanced countries showing the positive links between innovation, R&D and productivity (Griffth et al. 2006 ; Griffth et al. 2004 ; Mairesse and Mohnen 2010 ; Mairesse et al. 2006; Shabbir 2016 ).

The innovation and economic development based on small and medium enterprises in Pakistan is studied by Subhan et al. ( 2014 ). This study adds new contribution in the existing literature to develop an efficient relationship among innovation, TFPG and economic growth in Pakistan. Moreover, the results of ARDL model and the Toda–Yamamoto–Dolado–Lutkepohl (TYDL) approach showed that there is the casual relationship found among innovation, TFPG and economic growth.

3 Research methodology and data

This study gauges the potential drivers of TFP in two-stage process. First, TFP is calculated using a neoclassical production function which describes the relationship between inputs and output of production function. In second stage, the significant potential drivers of TFP are tested applying the fixed effect estimator.

3.1 Macroeconomic model: theoretical framework

However, growth accounting methods traditionally depend upon a decomposition on output rely on an aggregate production function (with constant returns to scale) that explains accumulated factors of production (physical capital ( K ) and human capital, denoted by ( H ) into output ( Y is real GDP)). The traditional theory is also deliberated in detail and depends on prior work by Diewert and Morrison ( 1986 ).

In precise, study assumes a Cobb–Douglas production function approach that takes the following form:

Following the literature of Hall and Jones ( 1999 ) it is described that the stock of human capital ( H ) can be estimated through the labor force and the product of the quality from labor force ( h ). The TFP is denoted by the parameter A , which shows the efficiency and factors of production are jointly used in the economy.

Based on the existing literature (Klenow and Rodri´guez-Claire 2005 ), this study assumes that the capital share ( a  = 1/3) is almost the same across the countries and also constant over the time. Literature shows this standard assumption which is mainly based on the evidence for the USA. While there is a significant variation across different economies in this parameter described by Gollin ( 2002 ), this deviation does not follow any specific pattern. In precise, once informality and entrepreneurship are taken into account, and it is not associated with the level of growth (GDP per capita).

This study uses time series data of physical and human capital to estimate measure of TFP by:

The study from a development perspective is concerned in decomposing GDP per capita ( y ). Moreover, Eq. ( 1 ) can be written in following form with reference to growth level perspective

where the term ( f ) is the contribution of the labor force in the total population, whereas the population ages consist of 15–64 years. However, physical capital (kt) investment has a visibly based on the level of TFP. The indirect effect of TFP following the literature of Klenow and Rodriguez-Claire ( 1997 ) and production function mentioned in ( 3 ) can be rewritten in intensive form as:

where “κ” is defines as the capital-output ratio ( K / Y ).

The growth decomposition input and the contribution of TFP do not detect policy suggestions because it only explains the significant factors behind the projected TFP growth rates. A complementary query at that time is the consequence of a policy outcome like fiscal deficit and the inflation or on the capital accumulation (TFP growth). In finding for a stable relationship between the actual growth rates of output and numerous variables recommended by the ancient and new economic concepts, various studies have complemented exercises of growth accounting with growth regressions for an economy or different group of countries. The traditional (neoclassical) model indicates that steady-state growth and hence the probability of improving living standards over time are due to the growth of TFP. The Solow–Swan model assumes that the key parameter capital-input ratio is stable over time.

3.2 The total factor production and its potential drivers

This paper describes the direct sources of TFP by modeling TFPG as a function ( f ) along with a set of potential variables. The possible endogeneity between some variables and TFP can be controlled by applying the 2SLS model with 1-year lagged explanatory variables as instruments. The dynamic data model is also used for the same purpose by (Arellano and Bover 1995 ; Blundell and Bond 1998 ).

All the variables are converted into logarithm form.

where TFP is a total factor productivity, GDP is real gross domestic product, LPT is number of patents, TRD is trade openness, INF is inflation, PRL is private credit, EDU is education, IMM is imported machinery, FDI is foreign direct investment and \( e_{\text{t}} \) is error term. Moreover, TFPG is related to total factor productivity growth; X is vector of all determinants; and \( \mu \) is related to error term.

3.3 Variables description

Klenow and Rodri´guez-Claire ( 2005 ) developed a model to examine the relationship between TFP and human capital augmented through Cobb–Douglas production function. This paper develops TFP measure using information from the Penn World Tables, version 9.0 (followed by Heston et al. 2009 who used version 6.3).

This study uses a perpetual inventory method to construct capital stock, following the methodology explained in Easterly and Levine ( 2002 ). Precisely, the total capital formation equation states that

where K t is the capital stock l in period t , I is investment and \( \varvec{\delta} \) is the rate of depreciation. The data have been taken from the World Development Indicators (WDI) databases and Penn World Tables 9.0 version. Whereas capital stock estimation is computed using the perpetual inventory method, data of a depreciation rate (delta) are also taken from Penn world table (version 9.0). The delta is based on average depreciation rate of the capital stock. The data of capital stock are used at constant 2011 national prices (in million 2011US$). Finally, gross fixed capital formation in real terms is taken from WDI statistics.

This study examines the total factor productivity and economic growth in Pakistan with its potential drivers using time series data for the period 1972–2016. This paper follows the Hall and Jones ( 1999 ) study for estimation of human capital efficiency, which established the index ( h ) as a function of the average years of schooling. Moreover, to find the contribution of labor in output with its efficiency, this study used the data on years of schooling and returns to education. Furthermore, human capital indexes are used as a proxy for human capital accumulation from Penn world 9.0 tables, and labor input is estimated by number of total persons engaged (in millions) from Penn world 9.0 tables. Footnote 1 The data of total working age population (15–64 years’ ages) are taken from WDI statistics, and the rate of its contribution in the labor force. The output (Y) is measured as real GDP at constant 2011 national prices (in mil. 2011US$) from the Penn world 9.0 tables. The explanatory variables (potential drivers) of TFP are taken from the WDI data bases. The innovative capability is used as proxy to measure the number of certified patent per thousand head. The patent applications data work as an appreciable resource for estimating innovative activity and have been comprehensively used in the literature of patent as measures of technological change (Kortum 1997 ). Also, Griliches ( 1989 ) and Joutz and Gardner ( 1996 ) discussed that patent applications are a significant measure of technological output. Followed the information of Bravo-Ortega and Marin ( 2011 ), this study constructs an unbalanced data with observations averaged of 2 years. There are two important causes for using data averaged over relatively long periods. First, patent data are missing for many years, and thus, averaging over longer periods provides more successive observations. This is predominantly helpful for estimating dynamic specifications. Similarly, applying long time periods, we evade cyclical factors that may have influenced innovations.

However, foreign direct investment (FDI) and import of machinery capture the influence of knowledge transmission. The data of FDI and import of machinery are taken from WDI databases. However, FDI has a significant effect on TFPG via new efficient production processes, the knowledge spillovers from transfer of technology and superior managerial skills (Borensztein et al. 1998 ). Moreover (imports may bring machinery/equipment embodied advanced technology from a small number of innovative countries into domestic production), economies have high chance of getting an advantage from technology diffusion (Grossman and Helpman 1991 ), and Miller and Upadhyay ( 2000 ), Dollar and Kraay ( 2002 ) and Loko and Diouf ( 2009 ) also consider it as an important determinant of TFP.

The data on inflation (inflation rate) have also been taken from WDI. The purpose of inflation rate is to check the regulatory quality, macro-instability and uncertainty (Daude and Fernández-Arias 2010 ). A set of human capital variables is used to measure the impact of education and its indirect impact via improving the knowledge absorptive capacity. According to Loko and Diouf ( 2009 ), inflation also has effects on TFPG, whereas human capital is significant determinant of TFPG and proxy by years of schooling in the population. Moreover, human capital index, based on years of schooling and returns to education, is used as a proxy for the human capital. The share of number of graduates from primary, secondary, high and higher education in the total population (Pakistan) is not considered due to non-availability of data. The basic level of education shows labor effectiveness in the process of production, and higher education is essential for technological innovation. Furthermore, human capital is a significant factor of the research and development projects, for instance (Romer 1990 ; Daude and Fernández-Arias 2010 ; Zhang et al. 2014 ) and play an important role in facilitating TFP catch-up and driving innovation (Benhabib and Spiegel 2005 ).

This study depicts the effect of structural changes in the country with reference to two variables: manufacturing output industry in GDP (secondary sector), and services sector (tertiary industry) output in GDP taken from WDI statistics. However, higher value-added contribution of countries with high productivity growth sectors is related to greater aggregate productivity growth (Jaumotte and Spatafora 2007 ; Loko and Diouf 2009 ; Shabbir 2015 ). The domestic credit to financial sector as a percentage of GDP is used as proxy of financial development, reflecting the depth of financial markets (WDI statistics). Moreover, TFP growth through financial development is positively affected by efficiency of banks loan; Mastromarco and Zago ( 2012 ) and King and Levine ( 1993 ) found a positive connection between financial development and physical capital accumulation, successive rates of economic and productivity growth (Nigeria). The trade openness is measured as the ratio of exports to GDP. Prior studies reveal that institutions and geography, along with integration (openness), have strong effects on TPFG (Isaksson 2007 ).

The study uses annual time series to observe the granger causality between variables, and data are taken from World Development Indicators (WDI) for Pakistan (1972–2016). This paper also uses different indicators for number of patents application by nonresidents (per thousand population) and number of patents by residents (per thousand population) as the proxies of innovation. These two proxies for innovation have been applied previously by Galindo and Mendez ( 2014 ), Pradhan et al. ( 2016 ) in their analysis.

4 Results and discussion

It is important to discuss here that the evolutionary highlights are important phase of TFP: Whereas literature of standard growth is assumed to estimate technological progress, absolute deteriorations are not easy to interpret in this way. Consequently, a more common interpretation of TFP is required. In particular, the accurate interpretation measured the degree of proficiency for TFP and institutions and market work together for allocation of productive factor in the economy. Remarkably, under this wider interpretation, efficiency can deteriorate in absolute terms for a long period of time, as we detect for the case of Pakistan. This study analyzes Pakistan’s sources of growth (in Table  1 ) for different periods between 1970–1974, 1975–1979, 1980–1984, 1985–1989, 1990–1994, 1995–2000, 2001–2005, 2006–2010 and 2011–2014 showing the different economic growth in TFP growth. The higher Pakistan coincided with the rate of growth due to TFPG in different time periods and gradually increased.

The traditional neoclassical model indicates that steady-state growth and therefore the possibility of improving living standards over time are due to TFP growth. Indeed, suppose that the important parameter ( a ) of the model of Solow–Swan is stable over the time period. Table  2 shows the results of growth accounting approach by alternative method, where results showed that average TFPG is increasing gradually. Since the beginning, the average (per worker) labor productivity growth shows improvement, but in column three, it is shown that in 2008–2012 the average%age TFPG was found to be negative.

4.1 Results of potential drivers of TFP

Table  3 reports the estimation for two-stage least squared method (2SLS). The results of diagnostic test show that data have no problem of heteroskedasticity (applied ARCH test), and no serial correlation (Breusch–Godfrey LM test is used). The \( H_{0} \) of Ramsey RESET test designates that model is correctly specified; further, we also accept \( H_{0} \) in case of Jarque–Bera ( JB test) which shows that data are normally distributed. Null hypothesis indicates that the values are greater than 5% level of significance. The TFP is used as a wide range of potential drivers along with main three dimensions, for instance, innovation and its spillover effects, supply of factors and efficient allocation and integration factors.

Innovation and its spillover effects

Patent has a significant and positive effect on TFP growth. However, innovation and knowledge creation tend to be more relevant to advanced countries. The results of (Benhabib and Spiegel 2005 ; Zhang et al. 2014 ) describe a positive significant impact of TFP, but the magnitude of the influence is rather small. The results of import of machinery (IMM) are positive and statistically significant at 10% and 1% levels, signifying that imported machinery as carriers of knowledge induces TFP growth, consistent with literature. However, those countries with more imported machinery have more chance to get advantage from technology diffusion (Grossman and Helpman 1991 ), as imports may bring machinery/equipment embodied advanced technology from a small number of innovative countries into domestic production. The result of foreign direct investment (FDI) found negative and significant relationship with TFP in models (2, 3 and 4). Moreover, FDI usually brings key technology superior managerial skills and proficient organizational forms from advanced countries to developing ones, but we find no evidence for the spillover effect of FDI on productivity. The results have also revealed consistent with prior literature that the positive impact of FDI on TFP is hardly detected in developing countries (Isaksson 2007 ). But FDI became positive in our study, when we add variable of import machinery. Furthermore, same results are found in the study of Zhang et al. ( 2014 ), and in some models, the coefficient of FDI was found to be positive as well as negative in case of China.

Supply of factor and efficient allocation

The influence of human capital, in terms of education, is found to be positive and significant. The degree of impact rises with the level of education, endorsing the significance of higher education in stimulating productivity. The findings of this study give evidence that human capital (education) plays a key and positive role in determining technological innovation (Romer 1990 ; Black and Lynch 1995 ; Loko and Diouf 2009 ).

The results of financial development are found to have a positive effect on TFP, signifying that Pakistan’s has less-developed financial markets, and for private credit, in our study, we do not find a significant effect. The manifestation of market imperfection and distortion in the Pakistani banking system leads to the unproductive allocation of capital, which in turn adversely affects productivity. Finally, results are consistent with (Daude and Fernández-Arias 2010 ; Mastromarco and Zago 2012 ).

Integration and other variables

The coefficients on trade openness are positive and significant in our study model. Moreover, several prior empirical analyses that commonly find an economically significant and positive effect of trade on productivity (Alcalá and Ciccone 2004 ) revealed that the causation runs from trade to productivity. The case of the macro-instability, regulatory quality and uncertainty (proxy by the inflation). The result indicates that inflation is negative with significantly related to productivity, and some studies are supported by (Daude and Fernández-Arias 2010 ). A stable monetary condition is the substance for the efficient operation of a market economy. Barro (1995) recommended for those economies, where inflation exceeds from 15% or a 10% rise in inflation leads to a decrease in GDP growth per year of 0.2–0.3% and a drop in the investment-to-GDP ratio of about 0.4–0.6%. The real GDP growth is also positive and significantly related to TFP.

This study incorporates the following empirical model to test possible directions of causality among all these variables. The data set of time series requires special care before the empirical analysis, because data are non-stationary in nature. So it is crucial to find the potential unit root problem in the first instance and to detect the order of integration of each factor. Moreover, if ignoring non-stationary issue, it would lead to cause of spurious regression. Numerous econometric methods like method of Johansen multivariate co-integration, Engle Granger and the recently developed ARDL method (Pesaran et al. ( 2001 )) for evaluating the time series data, can be used.

The long-run as well as the short-run correlation between endogenous and exogenous variables can be analyzed by several econometric models, which are available in the several published literatures. The auto-regressive distributed lags (ARDL) are designed by Pesaran et al. ( 2001 ) to observe the long-run and short-run analysis, and similarly, Pesaran and Shin ( 1999 ); Laurenceson and Chai ( 2003 ) and Shabbir ( 2018 ) also preferred ARDL model because of its several advantages. The study of Monte Carlo demonstrates that ARDL approach is significantly important and generates consistent results even for small sample (Pesaran and Shin 1999 ). The technique of ARDL is used to observe the relationship between innovation, total factor productivity and GDP growth for the following reasons. This method solves the problem of most restrictive assumptions, for instance, specific model with its variables must have the same order of integration, if order of integration is not different{( I (0) or I (1)}, and still this technique can be used (Pesaran and Pesaran 1997 ). The ARDL approach diminishes the problem of endogeneity because it is free of residual relationship and it takes proper lags which are adjusted for the problem of serial correlation and endogeneity.

5 Co-integration analysis (ARDL)

The ARDL technique (bound testing) approach is lately developed technique. The method of ARDL co-integration is a stepwise procedure. The framework of ARDL method can be written as follows:

The null hypotheses are: \( H_{0} = \tau_{\text{LPR}} = \tau_{\text{GDP}} = \tau_{\text{LPN}} = \alpha_{\text{TFP}} = 0 \) , \( H_{0} : = \beta_{\text{LPN}} = \beta_{\text{GDP}} = \beta_{\text{LPR}} = \beta_{\text{TFP}} = 0 \) , \( H_{0} : \delta_{\text{INNO}} = \delta_{\text{LPN}} = \delta_{\text{GDP}} = \delta_{\text{LPR}} = \delta_{\text{TFP}} = 0 \) , while alternative hypotheses are: \( H_{2} : \ne \tau_{\text{LPN}} \ne \tau_{\text{GDP}} \ne \tau_{\text{LPR}} \ne \tau_{\text{TFP}} \ne 0 \) , \( H_{2} : = \beta_{\text{LPN}} \ne \beta_{\text{GDP}} \ne \beta_{\text{LPR}} \ne \beta_{\text{TFP}} \ne 0 \) , \( H_{2} : \ne \delta_{\text{LPR}} \ne \delta_{\text{GDP}} \ne \delta_{\text{LPN}} \ne \delta_{\text{TFP}} \ne 0 \) .

\( {\text{The}}\;\beta_{1} ,\delta_{1} \;{\text{and}} \;\tau_{1} \) (intercepts) are drift component, and \( \mu_{1} \) is error term and supposed to be white noise. Moreover, to detect the absence of serial correlation problem, Akaike information criterion (AIC) is chosen for optimal lag length criteria.

5.1 The Toda–Yamamoto–Dolado–Lutkepohl (TYDL) approach

The Granger causality approach in levels or in difference systems of VAR model or even in the method of ECMs is found to be risky (Toda and Yamamoto 1995 ; Rambaldi and Doran ( 1996) Zapata and Rambaldi 1997 ). Non-standard distributions and Nuisance parameters enter the theory of limit, when either the essential rank condition does not fulfill the requirement of VECM and also for method of the Johansen–Juselius route (for more detail see Toda and Phillips 1993 , 1994). Following all studies mentioned here, testing causality with the multi-step procedure conditional on the calculating of a unit root problem, a co-integration rank and as well as co-integration vectors as frequently applied by prior studies in the context of previous literature. So, this study uses TYDL Granger causality statistics test which is a simple technique demanding the estimation of “over-fitted “or an “augmented” VAR that is valid irrespective of the co-integration or degree of integration present in the system. It applies a Wald test with some modifications called modified Wald (MWALD) test to check for constraints on the parameters of the VAR ( p ) model. This technique has an asymptotic Chi-squared ( \( \chi^{2} \) ) distribution with degrees ( k ) of freedom in the limit, when a value of VAR [ k  +  d maxi ] is calculated (where d maxi refers to the maximal order of integration for the selected series in the system). The following main steps are included in instigating this procedure. The first phase contains determination of maximal order of integration (symbolized as d maxi in the method) and the properties of non-stationarity. In this respect, the ADF root test is conducted at 5% level of significance.

The second phase is to define the co-integration association among the variables based on time series analysis having same order of integration. The Johansen and Juselius approach for co-integration correlation with statistics of maximum eigenvalue is concluded at 5% level of significance, which investigates the null hypothesis ( \( H_{0} \) ) of ‘ r ’ co-integrating associations against the alternative ( \( H_{1} \) ) of ‘ r  + 1’ relations of co-integrating. The test is computed for ‘ N ’ number of observations as:

where r  = 0, 1, 2, 3, 4…… k  − 1.

The next procedure is to detect the proper lag length ( k ) of the system of VAR applying some appropriate information criteria. This study also implemented the standard vector auto-regression (VAR) approach, which is given as follows:

where \( \varepsilon_{t } \) is the residual term, \( {\text{ZE}}_{t} \) is a vector of selected endogenous variables (INNO, LPN, LPR, GDP and TFP) and \( \delta_{1} ,\delta_{2} ,\delta_{3} \ldots \delta_{j} \) are the matrices of unknown parameters. Moreover, \( {\text{DE}}_{t } \) is related to deterministic vector (with constant and as well as exogenous variables), while the term \( \gamma \) is the parameters of matrix of the deterministic vector.

6 Estimation and analysis

6.1 unit root analysis.

Although the ARDL methodology does not require the pre-testing of non-stationary (unit root) problem, it is still very important to find out the above-mentioned test to check that none of them are integrated of order more than one. The result for the ADF test is stated in Table  4 .

The ADF is used to intercept as well as intercept and trend (simultaneously). The results of unit roots have confirmed that \( {\text{LPN}}_{\text{t}} \) and \( {\text{GDP }} \) of the incorporated variables are non-stationary at all levels and all of them become stationary at I (1) first difference.

6.2 Lags selection

However, to find out the co-integration among variables; this study continues the model of the unrestricted error correction model (UECM). Before applying the technique of UECM, the main concern is the selection of maximum number of lags by using Schwarz (SC) and Akaike information criterion (AIC). Then, the Wald test is used to check the existence of co-integration.

The next step is to evaluate the F statistic calculated with critical bounds value by Turner (2006) to investigate the long-run (co-integration) relationship between variables existing or not. If calculated F-statistic value is greater than upper critical bound values, then it shows that long-run (co-integration) relationship exists among variables. If computed F-statistics value is lower than lower critical bound value, then there is no co-integration. The decision of co-integration is inconclusive when the value of F statistic lies between lower and upper critical bounds.

6.3 ARDL estimation

Previous section showed that all the selected variables are co-integrated. The next stage is related to the model of ARDL and to check the long-run association existing between the entire variables. The ARDL co-integration model is estimated in the following table, where the estimation of the long-run coefficients of the independent variables is given.

The null hypothesis ( \( H_{0} ) \) explained that there is no problem of heteroskedasticity (applied ARCH test), and no serial correlation (Breusch–Godfrey LM test is used). The overall results indicate that we accepted null hypothesis (which means long-run relationship exists). The \( H_{0} \) Ramsey RESET test designates that model is correctly specified. Further, this study also accept null hypothesis in case of Jarque–Bera ( JB test), which shows that data are normally distributed. Moreover, null hypothesis specifies that the values are greater than 5% level of significance. Hence, the results of estimated ARDL model are consistent.

7 Empirical findings

In order to check the integration of all variable, this study applied the ADF root tests and results are revealed in Table  4 . The results based on ADF tests from Table  4 show that two variables have unit root problem at level but found stationary at first difference level. The next procedure is to detect whether or not there is any long-run connection among all these variables. The next (before arranged to testing of co-integration analysis) main step is to take the optimal lag length of these variables, and the results of Table  5 indicate that AIC and SC values are taken at lag 2. Table  6 shows the projected value of Wald test ( F Value) statistics is 6.87, greater than the lower and upper bound values (Narayan 2005 ). The results show that no longer correlation between the selected variables of ( \( H_{0} ) \) is rejected at different levels of significance, when all factors are treated as response variables. Therefore, results specify that GDP, LPN, LPR and TFP are co-integrated, and there is a significant long-run relationship found between them. Furthermore, \( {\text{R}}^{2} \) and adjusted \( R^{2} \) statistics describes that ARDL technique (Table  8 ) for bounds test is best fitted.

7.1 Discussion on the results of long-run analysis and error correction model

The results are reported in Table  8 which exhibits that economic growth is positively related to LPN, LPR and TFP and also significant in the context of Pakistan economy. Table  7 shows the long-run relationship between economic growth and LPR, and the results are statistically significant indicating 1% change in LPR will raise GDP by 0.33% at 1% level of significance. The results of this study indicate the impact of LPR on GDP is consistent with the research of Shearmur and Bonnet ( 2011 ) and Pradhan et al. ( 2016 ). Moreover, LPN has also significant and positive impact on GDP in the long-run relationship between GDP and LPN, whereas the coefficient of LPN is estimated to be 0.18 with a positive sign, significantly indicating that 1% change in LPN will raise GDP by 0.18%. The findings of these results are supported by the studies of Pradhan et al. ( 2016 ). Finally, TFP is an important in case of GDP growth and estimated to be statistically significant at 1% level of significance as it describes 0.52% variation in GDP due to 1% change in TFP. These results are supported by Zhang et al. ( 2014 ).

This study also identifies that the one period lagged error correction terms (ECM (− 1)) are statistically significant at the 1% level or better when GDP, LPN, LPR and TFP are the variables for equation of ECM. Whereas, Table  8 shows coefficient of the ECM (− 1) identifies the speed of adjustment of all given variables to reach their long-run equilibrium position after a short-run shock. While the significance of coefficient of ECM (− 1) based on t statistics test describes the presence of a long-run causal association between variables. In the next stage, this study found the direction of causality among all these variables of interest via the Toda–Yamamoto–Dolado–Lutkepohl (TYDL) test. After the confirmation of the result of co-integration existing among variables, this test is used. This study uses the co-integration test to the model of UECM with 2 lags.

7.2 The findings of modified WALD test with the unrestricted level VAR ( k  +  d maxi ) system

The method of unrestricted level VAR ( k  +  d maxi ) is assessed in this phase, where ‘ d maxi ’ shows the maximum order of integration in the model. In this paper, VAR test is estimated where the maximum order of integration is 4. The results in Table  9 indicate that there is long-run relationship between the variables. Then, this study applies standard “Wald statistical test” to the find ‘ k ’ coefficient matrix of VAR only in order to apply implication on Granger causality.

Table  10 explains the main findings of Granger causality by applying MWALD statistical test. The existence of the co-integrating association among GDP, LPN, LPR and TFP specifies the long-run relationship among these variables. Results show that Granger causality running between GDP to LPN, LPN to GDP, LPR to GDP and GDP to LPR which found significant represents bidirectional causal correlation between these variables. GDP is significantly affected by TFP, LPN and LPR. The variables LPN and LPR also have bidirectional relationship. Moreover, TFP and GDP have bidirectional relationship; TFP is significantly affected by LPN having unidirectional relationship. Finally, results show that GDP is significantly affected by LPN, LPR and TFP.

8 Conclusions and policy implications

Pakistan is essentially an agrarian economy, employing more than 42.3% of the economically dynamic population and generating more than 19.5% of GDP (Pakistan Economic Survey, 2016–2017). However, economic growth has consistently weakened, deteriorating far short of what is required to substantially increase living standards. The study tries to observe causal relationships between innovation, total factor productivity and economic growth in Pakistan simultaneously. The results reveal that variables are co-integrated. The study investigates the total factor productivity by first estimating a Cobb–Douglass production function over 1972–2016. Furthermore, contributing to the unsatisfactory TFPG were inappropriate macroeconomic policies, political disturbances and deterioration in the terms of trade (TOT), openness to trade, financial sector development, import of machinery, GDP growth, education, terms of trade improvements, innovation (residential plus non-residential) and financial sector development are all associated with higher TFP growth. Moreover, inflation is negative and significantly related to productivity growth.

The results of this empirical analysis suggest that to stimulate sustained economic growth in the Pakistan, policy makers may focus importance to improve educational system, control inflation and increased GDP growth. However, financial sector reforms certify the efficient allocation of financial resources to improve both productive and allocate efficiencies in the economy. The results indicate that long-term economic growth is highly dependent on the potential ability of country to move up on the innovation scale to remain globally competitive. This needs the allocation of appropriate resources for research and development (R&D) activities to push key economic sectors in the country.

For more details see Human capital PWT9.

Alcalá F, Ciccone A (2004) Trade and productivity. Q J Econ 119:613–646

Article   Google Scholar  

Arellano M, Bover O (1995) Another look at the instrumental variable estimation oferror-components models. J Econ 68:29–51

Barro RJ, Sala-i-Martin X (2004) Economic growth. MIT Press, Cambridge

Google Scholar  

Benhabib J, Spiegel MM (2005) Human capital and technology diffusion. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier, Amsterdam

Black SE, Lynch LM (1995) Beyond the incidence of training: evidence from a national employers survey. NBER Working Paper No. 5231. NBER, Cambridge

Blundell R, Bond S (1998) Conditions and moments restrictions in dynamic panel data models. J Econ 87:115–143

Borensztein E, De Gregorio J, Lee JW (1998) How does foreign direct investment affect economic growth? J Int Econ 45:115–135

Bravo-Ortega C, García Marín A (2011) R&D and productivity: a two way avenue? World Dev 39(7):1090–1107. https://doi.org/10.1016/j.worlddev.2010.11.006

Cassiman B, Golovko E (2011) Innovation and internationalization through exports. J Int Bus Stud 42(1):56–75

Christensen CMCM (1997) The innovator’s Dilemma: when new technologies cause great firms to fail. Harvard Business School Press, Boston

Daude C (2010) Innovation, productivity and economic development in Latin America and the Caribbean. Development centre working papers. http://www.oecd.org/dev/wp

Daude C, Fernández-Arias E (2010) On the role of aggregate productivity and factor accumulation for economic development in Latin America and the Caribbean, IDB-WP-155. Inter American Development Bank, Washington DC

Diewert WE, Morrison CJ (1986) Adjusting output and productivity indexes for changes in the terms of trade. Econ J 96:659–679

Dollar D, Kraay A (2002) Institutions, trade, and growth. J Monet Econ 50:133–162

Easterly W, Levine R (2002) It’s not factor accumulation: stylized facts and growth models. Working Papers Central Bank of Chile 164, Central Bank of Chile

Galindo M, Mendez MT (2014) Entrepreneurship, economic growth, and innovation: are feedback effects at work? J Bus Res 67(5):825–829. https://doi.org/10.1016/j.jbusres.2013.11.052

Gollin D (2002) Getting income shares right’. J Polit Econ 110(2):458–474

Griffth R, Redding SJ, Van Reenen J (2004) Mapping the two faces of R&D: productivity growth in a panel of OECD industries. Rev Econ Stat 86(4):883–895

Griffth R, Huergo E, Mairesse J, Peters B (2006) Innovation and productivity across four European Countries. Oxf Rev Econ Policy 22(4):483–498

Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116

Griliches Z (1989) Patents: recent trends and puzzles. In: Brookings papers on economic activity, microeconomics, pp 291–330

Griliches Z (2000) R&D, education and productivity, vol 214. Harvard University Press, Cambridge

Grossman G, Helpman E (1991) Innovation and growth in the global economy. MIT Press, Cambridge

Hall R, Jones C (1999) Why do some countries produce so much more output per worker than others?”. Q J Econ 114:83–116

Heston A, Summers R, Aten B (2009) Penn world table Version 6.3. In: Center for international comparisons of production, income and prices at the University of Pennsylvania (CICUP)

Huergo E, Jaumandreu J (2004) Firms’ age, process innovation and productivity growth. Int J Ind Organ 22(4):541–559

Isaksson A (2007) Determinants of total factor productivity: a literature review. In: United Nations industrial development organization working paper

Jaumotte F, Spatafora N (2007) Asia rising: a sectorial perspective. In: IMF Working Paper No. 07/130. International Monetary Fund, Washington

Joutz FL, Gardner TA (1996) Economic growth, energy prices and technological innovation. South Econ J 62(3):653–666

King R, Levine R (1993) Finance and growth: schumpeter might be right. Q J Econ 108:717–738

Klenow P, Rodri´guez-Claire A (2005) Externalities and Growth. In: Aghion P, Durlauf S (eds) Handbook of economic growth, vol 1, 1st edn. Elsevier, Amsterdam

Klenow P, Rodriguez-Claire A (1997) The neoclassical revival in growth economics: Has it gone too far? NBER Macroecon Annual 12:73–103

Kortum SS (1997) Research, patenting, and technological change. Econometrica 65(6):1389–1419

Laurenceson J, Chai JCH (2003) Financial reform and economic development in China. Advances in Chinese economic studies series. Edward Elgar, Cheltenham

Book   Google Scholar  

Loko B, Diouf MA (2009) Revisiting the determinants of productivity growth: What’s new? In: IMP working paper No. 225

Mairesse J, Mohnen P (2010) Using innovation surveys for econometric analysis. In: NBER working paper 15857. National Bureau of Economic Research, Washington

Mastromarco C, Zago A (2012) On modelling the determinants of TFP growth. Struct Change Econ Dyn 23:373–382

Miller SM, Upadhyay MP (2000) The effects of openness, trade orientation, and human capital on total factor productivity. J Dev Econ 63:399–423

Narayan PN (2005) The saving and investment nexus for China: evidence from co integration tests. Appl Econ 37(17):1979–1990

Ortega-Argilés R, Potters L, Vivarelli M (2005) R&D and productivity: testing sectoral peculiarities using micro data. Empir Econ 41(3):817–839

Pesaran MH, Pesaran B (1997) Working with Microfit 4.0: interactive econometric analysis. Oxford University Press, Oxford

Pesaran MH, Shin Y (1999) An autoregressive distributed lag modeling approach to co integration analysis. Chapter 11. In: Strom S (ed) Econometrics and economic theory in the 20th century: the ragnarfrisch centennial symposium. Cambridge University Press, Cambridge

Pesaran MH, Shin Y, Smith R (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16(3):289–326

Pradhan RP, Arvin MB, Hall JH, Nair M (2016) Innovation, financial development and economic growth in Eurozone countries. Appl Econ Lett 23(16):1141–1144. https://doi.org/10.1080/13504851.2016.1139668

Rambaldi AN, Doran HE (1996) Testing for granger non-causality in co integrated systems made easy. In: Working paper in econometrics and applied statistics, vol 88, University of New England

Romer PL (1986) Increasing returns and long-run growth? J Polit Econ 94:1002–1037

Romer P (1990) Endogenous technological change. J Polit Econ 96:S71–S102

Rouvinen P (2002) R&D–productivity dynamics: causality, lags and dry holes. J Appl Econ 5:123–156

Shabbir MS (2015) Innovation and competitiveness lead to industrial trade. Bus Econ J 6(4):181

Shabbir MS (2016) The impact of financial development on economic growth of Pakistan economy. Am Based Res J 5(3):35–43

Shabbir MS (2018) The impact of foreign portfolio investment on domestic stock prices of Pakistan. In: MPRA

Shearmur R, Bonnet N (2011) Does local technological innovation lead to local development? A policy perspective. Reg Sci Policy Pract 3(3):249–270

Solow R (1956) A contribution to the theory of economic growth. Q J Econ 70:65–94

Solow RM (1957) Technical change and the aggregate production function. Rev Econ Stat 39:312–320

Subhan QA, Mehmood T, Sattar A (2014) Innovation and economic development: a case of small and medium enterprise in Pakistan. Pak Econ Soc Rev 52(2):159–174

Toda HY, Phillips PCB (1993) Vector auto-regressions and causality. Econometrica 61:1367–1393

Toda HY, Yamamoto T (1995) Statistical inference in vector auto-regression with possibly integrated processes. J Econ 66:225–250

Tsai K, Wang J (2004) The R&D performance in taiwan’s electronics industry: a longitudinal examination. R&D Manag 34(2):179–189

Wakelin K (2001) Productivity growth and R&D expenditure in UK manufacturing firms. Res Policy 30(7):1079–1090

Zapata HO, Rambaldi AN (1997) Monte Carlo evidence on co-integration and causation. Oxf Bull Econ Stat 59(2):285–298

Zhang J, Jiang C, Wang P (2014) Total factor productivity and china’s growth miracle: An Empirical Analysis. SSRN. https://doi.org/10.2139/ssrn.2456009

Download references

Authors’ contributions

The HS is the main author of the research, other co-authors namely MS wrote the literature review, MB collected the data and BAK reviewed the paper and improved the quality of paper by qualitative and quantitative analyses. All authors read and approved the final manuscript.

Acknowledgements

Authors are thankful to their colleagues who provided expertise that greatly assisted the research.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and affiliations.

Department of Economics, Wuhan University, Wuhan, People’s Republic of China

Hummera Saleem

University of Lahore, Lahore, Pakistan

Malik Shahzad

Department of Accounting, Wuhan University, Wuhan, People’s Republic of China

Muhammad Bilal Khan

Department of Business Administration and Commerce, G.C University Faisalabad Layyah Campus, Layyah, Pakistan

Bashir Ahmad Khilji

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hummera Saleem .

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Saleem, H., Shahzad, M., Khan, M.B. et al. Innovation, total factor productivity and economic growth in Pakistan: a policy perspective. Economic Structures 8 , 7 (2019). https://doi.org/10.1186/s40008-019-0134-6

Download citation

Received : 22 September 2018

Accepted : 10 January 2019

Published : 20 February 2019

DOI : https://doi.org/10.1186/s40008-019-0134-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Economic growth
• Total factor production

JEL Classification

• Training & Development

Pakistan Institute of Development Economics

• Introduction
• PIDE Profile
• Contribution
• Vision & Mission
• Vice Chancellor
• Organization Chart
• Advisory Board / Fellows / Senior Fellows
• Academic Council
• Rules & Regulations
• Executive Development Centre
• ICT Division
• Publication Division
• Staff Directory

Our Portals

• Academic Portal
• Alumni Portal
• Collaborative Portal
• Jobs Portal
• Professional Dept
• Thesis Portal

• Annual Report
• Barometer Series
• Basics Notes
• CEECC Working Paper
• Costonomics
• Discourse (P & R)
• Economy Watch
• Inflation Report
• Infographics
• Initiatives
• Knowledge Brief
• Key Messages
• Lecture Series
• Macroeconomic Brief
• Monograph Series
• Our Researchers
• PIDE Analytics
• PIDE In Focus
• PIDE in Press
• PIDE Reprints
• Policy and Research
• Policy Viewpoint
• Population & Health Working Paper Series
• Poverty & Social Dynamics Paper Series
• Press Release
• Prominent Economists
• Research by Author & Title
• Research Brief
• Research Report
• Research Showcase
• Seminar Archives
• Sludge Audits
• Sludge Series
• Urban Monograph Series
• Webinar Series
• Webinars Brief
• Working Paper
• Degree Verification

THE PAKISTAN DEVELOPMENT REVIEW  

Total factor productivity and economic growth in pakistan: a five-decade overview (article).

Download PDF

This paper traces Pakistan’s TFP and GDP growth from 1972 to 2021. The analysis shows that Pakistan’s TFP and economic growth have declined over time. The sectoral—agriculture, industry, and services—trends are also not different. The TFP and GDP growth rates of the total economy and the three sectors were the highest in the 1980s. In general, whenever TFP growth has increased, Pakistan’s economic growth has also increased. The analysis further shows that whenever attempts were made to deregulate and liberalise the economy, it resulted in higher TFP growth and  consequently higher GDP growth. Similarly, macroeconomic and political stability also seems to be important factors in higher TFP and GDP growth. The comparison with other countries shows that Pakistan’s TFP growth performance has been reasonable, especially when compared with India. At the same time, however, the experience of other countries shows that to achieve GDP growth above 8 percent, Pakistan needs to enhance its productivity growth to 3 percent or above.

OMER SIDDIQUE

Please download the PDF to view it:

Recent Articles​

Vol. 63 no.2-2024, political economy of tax and digital transformations in pakistan - special invited lecture (article), static and dynamic comparison of monetary and non-monetary multidimensional poverty: evidence from morocco (article), microcredit versus child schooling nexus: exploring child schooling decisions in rural bangladesh (article).

Subscribe Now

Get in touch.

• +92-51-9248051

Useful Links

• Application Forms
• Subscribe Publications
• Employees Welfare
• Prime Minister Youth Laptop Scheme

Quick Links

• PIDE Repec Archive
• Econ Papers
• Digital Thesis
• Microsoft for All
• Research Ethics Review Committee
• PIDE Purchase Committee
• Right of Access to Information (Focal Person)
• Anti Harassment Committee
• Student Affair / Grievances Committee
• Employees Grievance Committee
• Disability Support

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Response of Pakistan’s economic growth to macroeconomic variables: an asymmetric analysis

Hafiz m. sohail.

1 School of Economics & Management, South China Normal University, Guangzhou, 510631 China

Mirzat Ullah

2 Graduate School of Economics and Management, Ural Federal University, Mira 19, 620002 Yekaterinburg, Russia

Faheem Ur Rehman

3 Business School, NingboTech University, Ningbo, 315100 Zhejiang China

Associated Data

The data sets used during the current study are available from the corresponding author on reasonable request.

Ethical approval.

Not applicable.

This study examines the impact of several important macroeconomic variables such as quality of education, infrastructure development, foreign direct investment inflow, and green energy transitions on economic growth. We analyzed annual time series data sample for estimation of the above macroeconomic indicators during 1990 to 2020. We use nonlinear auto-regressive distributive lag model (NARDL) approach to detect the short-term and long-term effects of undermentioned macroeconomic variables on economic growth of Pakistan. The results primarily reveal that the quality education, foreign direct investment inflow, and infrastructure development are playing a significant positive role in the economic growth of Pakistan. Similarly, in short term the foreign direct investment inflow, infrastructure, and green energy transition coefficients are significantly positive related to sustainable development goals. However, the education found as unsubstantial as contributive as other variables. Moreover, the Granger causality and structural break estimations are employed to estimate the causal association between the selected parameters and unexpected change over the economy. The estimated outcomes find the unidirectional causality from education and green energy transition towards economic growth, where education is found within relation to infrastructure. Additionally, bidirectional causal relationship is found between FDI and infrastructure towards economic growth which shows that the increase in foreign investment has the potential to boost the economic growth. Finally, all the estimated indexes are considered as important sources towards the economic growth.

Introduction

The current transition towards green economy is affecting the world economies and dynamics. Given the considerable hydrocarbon dependency, we scrutinize the key aspects among sustainable development goals (SDGs) that have the potential to make remarkable change in the developing economies especially the case of Pakistan. As other developing economies: Pakistan is adopting the United Nations (UN) endorsed campaign of green transition of 17 SDGs. The UN Agenda-2030 for SDGs intends to solve the current environmental challenges of the twenty-first century in the interests of citizens. The sustainable development efforts are considered to contribute to economic growth without affecting the green environment of the society. It is indispensable to address interconnected pillars of economic growth through supportive policies to attain sustainable economic growth. Sustainable development objectives include providing quality education, building resilient infrastructure, promoting of clean energy transition especially the use of natural gases, and inviting foreign investments to boost the domestic economic growth.

The education at both levels industry and society are very important in the way to build green environment and make economic growth. The demand for edification is one of the most critical human assets since it can increase productivity in allocation with resources. In contrast, it has been observed by Seetanah ( 2009 ) that education fosters economic development and increases human livelihoods by boosting the quality of the workforce, promoting democratization, better life, decreasing fertility, and boosting equality. According to the United Nations Research Institute for Social Development, economic sustainability comprises healthy living, education, access to products, and socio-economic achievement (Armeanu et al. 2018 ; Beets 2005 ; Johnston 1998 ; Schwab 2018 ). The current study reveals that improving education in society can contribute to economic growth in long run. Moreover, there is an association between transportation infrastructures and energy consumption by examining the economic growth. To create sustainable environment the study reveals that the economic growth is veiled in creating less CO 2 emission and energy consumption. In adopting the green energy, the study uses the adaptation of natural gas, which is considered as crucial clean energy source as comparison with other petrochemicals such as petroleum and coal. The increase in natural gas consumption is predicted to enhance Pakistan’s environmental quality and allow the country to maintain its economic performance. Apergis and Payne ( 2010a ) examined that natural gas can minimize CO 2 emissions. IEA ( 2016 ) reported the adaptation of natural gas consumption from 2012 to 2030 that use of natural gas in the power sector will rise by 2.2% and in industry can boost by an average of 1.7% per year. It is expected that converting power and industrial sector form petroleum into natural gas the Pakistan can save 17% CO 2 emission and economy can grow with the same ratio (Sadiqa et al. 2022 ).

Achieving targeted economic growth is one of the critical challenges of government policymakers in case of developing countries like Pakistan. The prevailing concern for all economies is to sustain the economy (Shabbir 2013 ). According to UN Agenda-2030 the SDG-8 support the sustained, inclusive, and sustainable economic growth; productive employment; and clean environment for all. According to the World Business Council for Sustainable Development, companies may meet human desires by inventing the modern technologies, improving efficiency, creating jobs, and ensuring that solutions are accessible to a broad range of people (Carree et al. 2007 ). For measuring economic growth, current study has adopted the same proxy used by (I. Khan et al. 2021 ; Omri 2013 ). Yusuf et al. ( 2020 ) documented that most economies aspire to attract foreign direct investment ( FDI ) because of its recognized benefits as a catalyst for economic growth. Likewise, this research looks at the empirical association between FDI and economic growth in Pakistan, as well as the factors that influence FDI into the Pakistani economy.

As a result, this study focused to develop a theoretical and statistical solution to the question: do the goals of the UN Agenda-2030 of SDGs impact the economy of Pakistan? This study aims to develop and test the models to determine the association between education (SDG-4), FDI (SDG-17.3), infrastructure and technology (SDG-9), clean energy (SDG-7), and sustainable economic growth (SDG-8) in Pakistan (Fig.  1 ). This research study examines the impact of sustainable development goal indexes (SDGs) include the levels of quality education, resilient infrastructure, clean energy transition proxied by use of natural gases, and foreign direct investments on domestic economic growth of Pakistan (Fig.  2 ). These SDGs are selected form current stream of United Nations Agenda-2030 of green energy transitions. This paper contributes to existing efforts for economic growth narrative in several ways like introducing new statistical measurement of nonlinear auto-regressive distributed lag (NARDL) model which is considered as most superior to the conventionally used auto-regressive distributed lag (ARDL) model. This model has the potential to predict the effects of the explanatory variable in domain of both positive and negative shocks on the outcome variable (Neog and Yadava 2020 ). Additionally, the existing literature studied toured the impacts of energy consumption on economic growth with the conventional ARDL linear modeling methods which only assessed the impacts of external shocks related to consumption of energy on the nation’s GDP (Sohail et al. 2022 ).

Conceptual framework of the study.

Source : Author’s calculations

a Trend in the economic growth of Pakistan with respect to Bangladesh and Iran economies.

Source : Author’s calculations. b Historical trending in the economic growth of Pakistan. Source : Author’s calculations

In line with the above discussion, this research study aimed several specific goals. First , we assess the economic impact of inclusive and equitable quality education to encourage possibilities for lifelong learning. Second , we assess the impact and enactment of the global partnership to invite more foreign direct investment to home country on economic growth. Third , we analyze the effect of resilient infrastructure, sustainable industrialization, and foster innovation on economic growth. Finally , we determine the effect of affordable, reliable, sustainable, and modern energy on economic growth. This study is different from previous studies in several ways, where we focused on each category of SDG impact on national economy. We mainly focused on the SDGs which are included in UN agenda, where the member developing countries are UN suggestions. Most important, the impact of such SDGs was used in our study, for example, the long-run effect of education on national economy. The government subsidizes and spends more on higher education to produce skill full labors where the impact of such spending is negative on economic growth, which is against the previous research studies and opposing the general theoretical support. This impact is very true to current economic situation of Pakistan. There are several reasons like citizens are getting the stipend and scholarships for higher studies and leave the country. On the other hand, government failed to provide jobs to graduates so skillful labors are contributing negative to economic growth in the long term. Additionally, the FDI inflow into the developing economy has vast substantial impact of economic growth. In this study we used several statistical estimations like VECM and NARDL to find out the enactment of the global partnership on economic growth proxied by FDI inflow.

The remaining manuscript is set as follows: a historical overview of Pakistan’s economy is presented in the “ Overview of Pakistan economy ” section, review of literature is summarized in the “ Literature review ” section, whereas the methodology and empirical results are covered in the “ Methodology and data source ” and “ Empirical analysis ” sections, respectively. Finally, the “ Conclusion and policy implications ” section presents the conclusion and policy implications.

Overview of Pakistan economy

Pakistan stands in developing economy with GDP of $264 billion. In comparison with other geographic economies the Pakistan economy grew up with many challenges and experience serious issues. Pakistan has recurrently experienced economic and financial crises, including rampant inflation, trade deficits, depleted foreign reserves, and currency depreciation. The economy confronted with a mixture of overpopulation, terrorism, bad governance, and low literacy level.

The two most serious threats to the country’s low GDP are the accumulation of rising inflation and a payment crisis triggered by a mixture of global and regional elements (Tehsin et al. 2017 ). Similarly, the pandemic (COVID-19) has exacerbated, and Pakistan’s foreign exchange reserves plummeted to multiyear low (Pakistan 2021 ).

Over a given time period, this secures the difference between a country’s exchange of goods and services, as well as net transactions such as overseas aid. A persistently high deficit may result in a surplus of a country’s currency in the foreign exchange market, reducing the currency’s value. This is one of the reasons why the Pakistani rupee (PKR) has plummeted (Khan et al. 2022 ). To overcome all the issues Pakistan requests to IMF bailout package and, additionally, borrow from other countries like China, Saudi Arabia, and USA. The Pakistan’s economy experienced significant regime changes, during every regime the level of uncertainty changed for the trade effected, and this is the main issue faced to the economy. The volatility behavior of Pakistan economy is divided in four major phases. First was the phase where it is generally agreed that Pakistan had economic progress during the 1960s (Looney 2004 ). However, at the same time the defense spending throughout the late 1960s slowed the country’s economic growth and caused it to stagnate (Looney 1991 ). During 1958–1973, increased defense spending harmed economic growth, particularly during the conflicts with neighbor country in 1965 and 1971 (Looney 1994 ).

The second phase of economic expansion emerged in 1980s, with the most significant annual GDP growth of 10.2% reported in 1980 and a yearly growth rate of 6.1% over 10 years of 6.1% on average (Tehsin et al. 2017 ). Despite this progress in economic development, Pakistan may perhaps be not maintained it until miliarial administration. Similarly, following the previous regime the military acquired the country administration and several structural changes were enabled, accepted globalization, and welcomed international trade and investment; Pakistan’s economy progressed toward sustainable economic growth. This was the era from 2000 to 2007 during which the economy grew at a rapid pace (Amjad and Awais 2016 ). Based on the lessons learned during the miliarial administration, it can be predicted that the social and economic consequences of another effort at economic development will manifest themselves in the structure of extreme right-wing violence in society. Ultimately, due to the worldwide coronavirus outbreak, Pakistan’s GDP growth fell to 1.9% in 2019, down from a decade-high of 5.8% the previous year when the new elected administration took the charge. The economy grew, ranging from 7.0 to 7.5% in the final years of the miliarial administration, primarily due to advances in the recital of the service sector (Looney 2009 ). The final phase could be considered in economy favor to reduce the country’s large budget deficits and excessive public debt caused by the prior newly elected democratic administration have act as poor economic management (Looney 2004 ). According to Looney, the miliarial administration did not adhere to the effective governance indicators set forth by the World Bank (Looney 2004 ). These include political freedoms, the efficiency of government, and anti-corruption (Kaufmann et al. 2011 ).

Literature review

This part includes a detailed assessment of previous work that has been done in context of economic growth, development in education, green energy transition, foreign direct investment, and infrastructure development. This part will assist the readers in fully understanding the bridge and connection between SDGs and the economic growth of Pakistan. The following is a review of the literature on each domain subject.

Economic growth

In the perspective of economic growth of Pakistan, it has been facing number of challenges like high fiscal deficit and low investment, rising rate of poverty and unemployment, and heavy external and domestic debts. For success of any economy, it depends upon stable, efficient, and active government financial structure. To support the ongoing economic projects, Sohail et al. ( 2022 ) examined that the economy has been facing the lowest growth rate among South Asian countries since 1990. They concluded that the exogenous factors could enhance the current low growth and award a development in economy. Additionally, the economy of Pakistan has been facing a large spillover effect of war on terror, internal instability that is promoted by frenemies and hostile neighbors (Sadiqa et al. 2022 ). Endogenous mishandling of energy crises started from 1990 and which is the result of poor economic management, governance, and institutional framework. Yusuf et al. ( 2020 ) concluded that the economy needs to improve the political stability for promotion of investment form of domestic and foreign investors and suggested the openness to international trade and private foreign investment. It created untenably large lags in policy formulation and implementation (Sohail et al. 2022 ). Nazir et al. ( 2021 ) documented that the manufacturing sector contributed its share in economic growth and played a key role. They examined that Pakistan due to this sector progressed from its status as a low-income to a lower middle-income country. It helps the nation to achieve the objective of poverty reduction. In the current scenario, Pakistan needs to significantly increase foreign direct investments, as well as national saving, to overcome the budgetary issue and to address the domestic and external debt burden.

Education and economic growth

Pakistan’s literacy rate is substantially lower as comparison with other developing countries. The literacy rate is being significantly higher for males with high difference than for females, and for the females, the educational levels are much lower. Sustainable development goal comprises the ensuring of inclusive and quality education and promoting lifelong learning opportunities for all (Seetanah 2009 ). Similarly, Kingdon ( 2007 ) stated that the education quality in Pakistan is different from other developing countries and needs a serious attention; it is concluded that certain economies with a high educational level (for example, Taiwan) also have a thriving economy. Self and Grabowski ( 2004 ) examined the determinants of education on economic development, that the educational attainment is accountable for fluctuations in a financial product. Their study also demonstrated educational levels which are related to each other. Chowdhury ( 2022 ) investigated the internationalization of education on economic growth; the main findings revealed significant disparities between education levels in terms of their effect on economic growth. The tertiary education does not appear to have a causal effect on development. They examined the association between level of education and economic growth and conclude that when the population is separated into groups based on the gender of the individuals then education has a significant causal effect on the nation’s economic growth in the long term. Many other experts have concentrated their efforts on investigating the relationship between education and the economic prosperity of a country, but in developing economies, this is considered as first time to examine the effect of education on economic growth.

Lin ( 2003 ) studied the association of economic growth, education, and technical progress and confirmed the long-run association of parameters. Hassan and Rafaz ( 2017 ) study the association between gender’s education and economic growth in Pakistan. Using OLS regression and data spanning the years 1990 to 2016, the results reveal that increasing female education, female labor force participation, education expenditure, and fertility rate by 1% resulted in a 9.6% rise in Pakistan’s gross domestic product (GDP). Hanif and Arshed ( 2016 ) employed three indices for educational level in the SAARC countries to determine that higher education enrollment has the most significant impact on growth. Saeed and Awan ( 2020 ) attempted that the technical improvement has a significant impact on Pakistan economy and suggested the positive association between research and development and innovations and those discoveries can help to increase the pace of GDP growth. Yasmin et al. ( 2021 ) employed a generalized method of moments (GMM) to investigate the link between economic growth and several factors such as education, poverty, and unemployment in Pakistan. Educational attainment and trade expansion both have a favorable impact on the economy, whereas joblessness has an adverse influence on the determination of economic expansion, according to the conclusions of the research. Despite the fact that each researcher took a distinct strategy to their investigation, the outcomes appear to be comparable. The question is if the connection between educational factors and economic growth in Pakistan has altered over time, and whether there are any other factors that are associated with these variables. As a result, explanation and specificity of the association between education and economic growth may be regarded valuable information. A trend of education is shown by Fig.  3 .

Trend in education including primary, secondary, and higher education (1990–2019).

Foreign direct investment and economic growth

The theoretic basics of the foreign direct investment ( FDI ) to economic growth is founded on the neoclassical and endogenous growth models. Chanegriha et al. ( 2020 ) deliberate that the FDI has an optimistic effect on economic growth by growing investment level. However, in the endogenous growth models the FDI increases total economic growth in host countries by familiarizing new inputs, technologies, and products; augmenting managers and labor skills; and increasing local competition. Ciftci and Durusu-Ciftci ( 2021 ) studied that the FDI inflows for the host countries have several advantages, such as creating new business areas and human capital enhancement, lessening the market power of present firms, being a catalyst for domestic capital stock, and tax revenues in respect to other types of financial capital.

Ahmad et al. ( 2022 ) investigated the Chinese FDI flow into Pakistan. They documented a massive increase after the groundbreaking of Belt and Road Initiative (BRI) and China-Pakistan Economic Corridor (CPEC). According to the Chinese government, investments in Pakistan increased from $695.9 million in 2014 to $1002.9 million in 2020. Similarly, Abdouli and Omri ( 2021 ) conducted an empirical study to investigate the association between FDI and GDP. They suggested that they would have a significant impact on Pakistan’s economic growth. Thereby, FDI provides a number of benefits to the host country, including the formation of new jobs, technical progress, resource optimization, and competitive merchandise. According to these figures, Chinese investment accounted for an average of 43.8 percent of total FDI . Murshed et al. ( 2022 ) claimed that FDI puts stress on local firms to innovate and develop technologically, which may explain why developing countries welcome FDI . With FDI assistance, Pakistan’s economy is poised to close the savings-investment gap. This FDI creates new job opportunities, technology transfer, increased productivity, and contest. Benefits like these encourage developing economies like Pakistan to adopt FDI -friendly policies.

Hamid et al. ( 2022 ) scrutinized the impact of FDI and imports on economic growth; they discovered a dual-directional relationship between output and FDI and imports. The findings support the assertion of output growth caused by FDI and imports. Ulucak and Erdogan ( 2022 ) investigated the ambiguous environmental effects of FDI inflows. They demonstrated the negative environmental consequences of hosting FDI , which indicates that as FDI flows into host countries, environment protection tends to worsen. Similarly, according to Wang et al. ( 2022 ), pollution halo effect highpoints the optimistic environmental significances of FDI . In this respect, these FDI are supposed to be immersed in tidy industries within the host nations, thereby plummeting the possibility of FDI making contributions to augmented CO 2 emissions. Khan et al. ( 2020 ) studied the FDI inflows and CO 2 emission nexus, applied data of BRICS countries from 1986 to 2016, highlighted the harmful environmental effects of FDI inflows, and documented that higher FDI inflows contribute to higher emissions of CO 2 . Furthermore, the country-specific results revealed that, with the exception of Russia, the pollution haven hypothesis holds true for the other BRICS nations. Moreover, Fig.  4 presents the trend of FDI in Pakistan.

Trend in foreign direct index inflow to Pakistan.

Infrastructure and economic growth

The sustainable development goal in UN Agenda-2030 is to create resilient infrastructure, promote inclusive and sustainable industrialization, and inspire innovation. Reliable infrastructure is required to connect supply chains and efficiently move products and services across the borders (Sohail et al., 2021 ). Construction of infrastructure links households throughout urban regions, helps in flow of trading commodities, provide quick access to quality healthcare facilities, and award access to educational opportunities. Infrastructure plays a significant role in connecting markets by transporting products and consumers of an economy (Brons et al. 2014 ). As a result, efficient transportation methods allow contractors to deliver their goods and services to the market on time while also facilitating the movement of people to the most relevant occupations in demand. A good telecommunication infrastructure allows for a rapid flow of information, which improves the country’s overall economic efficiency (Rehman et al. 2022 ; Schwab 2018 ). Because of their connection, air and road transportation stimulate a broader range of activities. The authors also provided proof for a significant optimistic reaction of passenger-kilometer due to favorable change in earning (Rehman and Sohag 2022 ; Marazzo et al. 2010 ). It has been estimated that an increase of 1% in air passenger traffic results in a rise of 0.943% in the gross domestic product (Hu et al. 2015 ). Figure  5 shows the trends of infrastructure in Pakistan.

Trend in infrastructure including rail lines, container, air, and rail passengers (1985–2019).

Natural gas consumption and economic growth

The purpose to ensure access to affordable, reliable, sustainable, and modern energy for all depicts that sustainable goal is included in UN Agenda-2030. Natural gas is considered a reliable source of green energy transition. In this research study we are considering that the natural gas is a significant energy supply for almost every economy of the country, more specifically the developing country like Pakistan. Natural gas is used to generate the country’s total national output. Additionally, it is a cleaner and environmentally friendly resource of energy in comparison to other petrochemical or coal. Increasing the natural gas consumption level in Pakistan is expected to advance the country’s environmental quality, and will allow the government to maintain its economic performance (Sohail et al. 2022 ). One effective option for achieving energy structure optimization is transitioning from coal to low-carbon energy sources (Li et al. 2019b ). Compared to coal and oil, natural gas usage produces much fewer CO 2 emissions per unit of energy (Solarin and Shahbaz 2015 ).

Natural gas has been considered as a low-carbon and environmentally friendly among all other energy sources; it can significantly reduce air pollution (Xiao et al. 2016 ). Increased NGC can contribute to the achievement of the twin dividend, i.e., economic growth and emission reduction (Feng et al. 2015 ). Because natural gas is one of the energy input variables at the micro-level, the price of natural gas will directly influence the use and production of all industries, particularly the secondary industry, which is one of the most energy-intensive industries. Promotion and exploitation of natural gas as an industrial material in factories and as a household commodity could significantly impact the way people live and produce, especially when compared to other alternative energy sources, remarkably price, and accessibility. Much academic research has been undertaken on the causality relationship between NGC and economic growth, most of which have been conducted at the national level and compared the results of other countries. For example, the research from 67 nations over the period 1992–2005 revealed that NGC and economic growth were linked in a two-way causal manner both in the short and long terms (Apergis and Payne 2010b ). Among G7 member countries, there were three types of causality between NGC and economic growth (unidirectional causality, reverse causality, and bidirectional causality) (Apergis and Payne 2010b ), the first being the most common (Ozturk and Al-Mulali 2015 ). The evidence from the Gulf Cooperation Countries throughout the period 1980–2012 demonstrated that NGC was beneficial to long-term economic growth. Depending on the outcomes of their research, different experts have come to different conclusions about the connection between natural gas use and economic development.

As a result, while numerous studies have established that NGC has a favorable effect on economic growth (Ozturk and Al-Mulali 2015 ), others have claimed that economic growth has a detrimental impact on NGC (Sari et al. 2008 ). Furthermore, there was a mismatch among regions regarding the association between NGC and economic growth (Fatai et al. 2004 ). In this aspect, the association between NGC and economic growth was statistically insignificant in Australia and New Zealand. According to the findings from different OPEC member countries, there were growth, conservation, and neutrality correlations between NGC and economic development, in other OPEC member countries. It was shown that the association between NGC and economic growth differed significantly between China and Japan. In China, there was no evidence of indirect causality, whereas in Japan, there was evidence of two-way causality (Sari et al. 2008 ). Finally, trend in natural gas consumption in Pakistan is shown in Fig.  6 .

Trend in natural gas consumption.

Source: Author’s calculations

Methodology and data source

To estimate the growing stream of Pakistan economy, in the consideration of UN Agenda-2030 of green transition SDGs, we analyze that a long period contains three-decade annual time series data for the SDG’s indexes from period 1990 to 2020. Further, we apply the principal component analysis and vector error correction model vector auto-regression method advanced by Antonakakis and Gabauer ( 2017 ). VECM econometric techniques are used to investigate the statistical significance of considered SDG’s indexes to study the effect both in long and short terms. From the UN Agenda-2030 of green transition SDGs, we are considering the five most vulnerable variables along with indexes to examine the affect in most effective way. These indexes are education index ( EDI ), foreign direct investment ( FDI ), infrastructure index ( INFRI ), and natural gas consumption ( NGC ). For batter examination we are considering the GDP per capita as the proxy of economic growth ( EG ).

Estimation process

We scrutinized the influence of four SDGs on economic growth along with the indexes. Hence, a statistical tool called principal component analysis (PCA) can minimize the number of variables in a multivariate data set. The foremost benefit of this approach is that it permits the variance to keep the input data’s maximum informative value intact while simultaneously minimizing the dimensions (Tripathi and Singal 2019 ). PCA is a well-accepted approach for selecting independent variables and removing duplicate or strongly correlated parameters commonly used in economic estimations. Using the PCA estimations this method to identify the variation within an extensive collection of associated variables are proposed by (Jolliffe 1972 ).

However, the empirical investigation starts with determining the sequence of the variables’ integration (Fig.  7 ). This sequence is critical because ARDL methods can accept variables incorporated at level or at first difference, but not at second difference. This model has a single disadvantage: it cannot be used if the parameters are integrated at the I (2) (Ibrahim 2015 ).

Estimation strategy.

Vector error correction model

Here is the long-run association among the series I (1); it is, therefore, the VECM approach that estimates the long-run and short-run association among the selected variables (Tran 2018 ). Additionally, for better representation of causal association between the time series variables this study used Granger causality techniques (Rossi and Wang 2019 ). This current study analyzes the causal association between selected indexes of SDGs such as education (EDI), foreign direct investment (FDI), infrastructure (INFRI), and green energy transition (NGC) and check the impact on economic growth (EG).

Nonlinear cointegration results

PESARAN et al. ( 2001 ) examined the cointegration analysis to deliver the proof of a linear relation between dependent variables. The NARDL is a nonlinear stretched form of usual ARDL model to study the effect of both short run and long run. For the conceivable asymmetric effects, this model is practical for investigation in the short and the long run. Similarly, the present study employs the estimation of nonlinear auto-regressive distributive lag (NARDL) model developed for the first time by Shin et al. ( 2014 ). By using this modern statistical model the presence of an association between levels of education, infrastructure, FDI inflow, and natural gas consumptions in Pakistan is investigated. Using NARDL in this study have several advantages over other techniques like it reduces the characteristic supposition in the cointegration analysis that all selected variables necessarily be integrated of the same order with excluding the second difference I (2) variables. Additionally, as studied by Li et al. ( 2019a ), it examines the potential cointegration, so that it evades neglecting any association which is not evident in an existing established linear setting. Similarly, Economou ( 2019 ) documented that the NARDL estimator allows to discriminate the existence of linear cointegration, nonlinear cointegration, and lack of cointegration. The present study is using the NARDL model that covers the optimistic and pessimistic partial sums and also the short- and long-run effects on economic growth. Furthermore, as Pakistan have witnessed sever crisis in 1998, 2008, and 2019, structural-break test is very vital for the current study. The study utilized unit root test against the alternative of trend stationary process with a structural breakpoint both in intercept and slope is based on the analysis. Thus, using an error correction model the NARDL model studies the resultant equations for

where EG t is the economic growth in Pakistan in year t ; EDI t shows the education index in year t ; FDI t presents the foreign direct investment inflow in year t ; INFRI t stands for the infrastructure index in year t ; NGC t shows the natural gas consumption in year t ; e ^ t - 1 shows the error correction term; α 1 , α ij i , and β 1 are the parameters; and ε log e g i t , ε log e d i i t , ε log f d i i t , ε log i n f r i i t , and ε log n g c i t are the white noise disturbance terms that may be correlated with each other. The rank of cointegration in VECM designates the total cointegrating vector. For instance, the number of rank (2) specifies that the linear combination of two nonstationary repressor variables will become stationary. A significant negative sign of the e t  − 1, also called the error correction model (ECM) parameter, displays that any variation in the short-run association between the independent variable and dependent variable will set up a significant long-run association between them.

Empirical analysis

This study used the augmented Dickey Fuller (ADF) estimation proposed by Dickey and Fuller ( 1979 ) and PP (Phillips and Perron 1988 ) tests for conformation of integration level among the selected variables; the results are presented in Table ​ Table1. 1 . The outcome shows that all the other variables are integrated at level I (1). The exact order of integration level guides to apply the VECM estimation technique (Pradhan and Bagchi 2013 ). In above econometric equation we estimate the structural break to estimate an unexpected economic change over the study time frame in the parameters of regression model; such estimation enables us to measure the unreliability and forecasting errors of the estimation model used in our study. The empirical results of structural break-ZA are shown in Table ​ Table2. 2 . The results show that the structural breaks, i.e., 2003, 2005, 2007, 2008, 2009, 2010, 2012, and 2013, are founded in the selected variables such as EG , EDI , FDI , INFRI , and NGC . Most of the breaks are started from 2003 to 2012. This break may happen due to financial crisis shocks of Pakistan.

Stationary results

*** shows the significance level at 1%

Zivot-Andrews structural break

The year wise structural breaks, shows the financial crisis shocks of Pakistan’s Economy

Prior to the application of Johansen cointegration test, it is imperative to confirm the long-run association among the selected parameters; we have to select the lag order by utilizing the VAR approach for I (1) variables (Nielsen 2001 ). The number of lag selection criteria has been applied in previous studies such as Akaike Information Criterion, Hannan-Quinn Information Criterion, Modified Ranking LR test statistics, Schwartz Information Criterion, and Final Prediction Error. In Table ​ Table3, 3 , many tests confirm a (1) lag length.

Lag order selection

Standard errors are showed by () and the *, **, and *** indicate the significance levels at 1%, 5%, and 10%, respectively

At the moment, the cointegration test is used to determine whether there are any long-run equilibrium relationships between the four variables EDI , FDI , INFRI and NGC and economic growth. This employs the maximum likelihood ratio test and examines two test statistics trace statistics and leading eigenvalue statistics. The result of the Johansen cointegration rank test is shown in Table ​ Table4, 4 , which indicates that there are two cointegrating vectors at 5% levels of significance (i.e., the null hypothesis of no cointegration is forbidden for a rank of 0 and less than or equal to 2). This indicates that there is a long-run association between the four variables. The favorable results in Table ​ Table4 4 require the modeling of VECM and not a vector auto-regressive (VAR) model as stated in the model selection process.

Unrestricted cointegration rank test (trace)

The tracking test refers to two equations involved in integration at 0.05

* Rejection of hypothesis at level 0.05

In Table ​ Table5, 5 , both long-term and short-term coefficients were obtained through the VECM. The long-term coefficients of EDI , FDI , INFRI , and NGC are statically significant. Additionally, the study established that the education index has a favorable effect on economic growth in nine remittance-receiving countries. At a 1% significance level, this optimistic effect of education on economic development is highly noteworthy. The coefficient of EDI implies that increasing the EDI by 1% increases economic growth by 0.08 percent. Our results are the same as the recent study on nine selected remittance-receiving countries exposed (Zaman et al. 2021 ). In comparison, other studies showed that increasing educational levels boosts economic growth (Habibi and Zabardast 2020 ; Hanushek and Woessmann 2020 ; Kousar et al. 2020 ). Likewise, numerous studies have also demonstrated a beneficial correlation between education spending and economic growth (Glewwe et al. 2014 ; Jalil and Idrees 2013 ). These research findings corroborate our findings, indicating the existence of a noteworthy long-term optimistic link between economic expansion and education.

Unrestricted cointegration rank test (maximum eigenvalue)

High eigenvalue test indicates 2 cointegrations at 0.05 level

* Rejection of assumptions at 0.05

The FDI coefficient affirms that a 1% augmentation in FDI will boost economic growth by 0.504 percent. A 1-percent drop in FDI will conversely influence economic growth with the same quantity. Modern studies examining the relation between FDI influx and economic growth indicated that FDI inflow increases economic growth (e Ali et al. 2021 ). Similarly, other studies reported that FDI increased economic progress in the long run (Saleem and Shabbir 2020 ; Tiwari and Mutascu 2011 ).

In addition, the VECM model’s long-run fallout shows that INFRI (infrastructure index) also leads to an essential positive connection in the direction of economic growth at a 1% significance level. The coefficient of INFRI increases by 1%; economic growth will also augment by 0.757, respectively. The outcomes logically aligned with Mohanty and Bhanumurthy ( 2019 ) and C. Wang et al. ( 2020 ) showed a significant positive association between economic growth and infrastructure. Another study conducted in India also authenticated our results that increasing infrastructure enhances economic growth (Mohanty and Bhanumurthy 2019 ; Chakamera and Alagidede 2018 ).

Finally, the selected VECM model shows that NGC also helpfully manipulates the economic growth in the chosen country. As NGC is raised by 1%, it boosts economic enlargement by 0.460%, while declining NGC by 1% will move down economic growth with the identical percent in the case of Pakistan. This positive bond between NGC and economic expansion is highly noteworthy at a 1% significance level. Our consequences are associated with the recent study by Sohail et al. ( 2022 ) and previous studies (Apergis and Payne 2010b ; Shahbaz et al. 2013 ; Solarin and Ozturk 2016 ) showing that economic growth can be improved with the increase of NGC .

In the second part of Table ​ Table6, 6 , the error correction term ( CointEq1 , CointEq2 ) is significant. It has a negative sign, which means that the series are cointegrated and go together toward long-term equilibrium (Mahadevan and Asafu-Adjaye 2007 ). The negative response is required for balancing the EG series in the long term. As the error correction term is adverse and significant, we have causality in at least one direction. The short-run consequences of the nominated VECM model display that FDI inflow has an essential positive connection with economic growth, indicating that a 1% increase in FDI inflow will enhance economic growth by 0.315 percent in the short run.

During the short-run term, EDI has a significant pessimistic association with economic growth, which means that increasing education by 1% reduces economic expansion by 0.460 percent. INFRI has a considerable positive link with economic increase in the short run. In the near run, a 1% increase in INFRI boosts economic growth by 0.149 percent and vice versa. NGC demonstrated a substantial negative link with economic intensification in the short run, indicating that a 1% increase in NGC causes economic growth to decelerate by 0.321 percent. The R 2 value is close to 1 ( R 2  = 0.835). It can be said that the interpretation is consistent. This result supports the result obtained from the cointegration test. Therefore, the comments made for this equation are consistent as well.

The advance methodology was introduced by Shin et al. ( 2014 ) to examine the time series data for nonlinear and significant relationship. The results from NARDL are estimated in Table ​ Table7, 7 , where we follow the estimation from Shin et al. ( 2014 ). We examined that there is cointegrated and significant association between economic growth and education, natural gas, and infrastructure. Additionally, we extend the model to check the short-run and long-run asymmetries. The level of significance is examined at every model from 1 to 3. In this study we employed the structural break to estimate an unexpected economic change throughout in our study timeline. In line with this, we have proposed our estimation as basic model for economic growth ( EG ), model 2 represents the secondary economic growth ( EG-S ), model 3 the primary growth ( EG-P ), and model 4 indicates the tertiary economic growth ( EG-T ) for making such supposition we follow (Shin et al. 2014 ). Additionally, the economic growth is based on cointegration consequences and VECM is organized to recognize the path of causality. The results from short-run NARDL represent that education and infrastructure have no effect on secondary and tertiary economic growth ( EG-S ; EG-T ). However, in the long run the education has significant and positive impact over primary economic growth ( EG-P ). Similarly, in all models the FDI has vital role in determination of economic growth at every level. Moreover, the consumption of natural gas impacts in the long term and in the long run.

Nonlinear cointegration results (NARDL)

Note : *, **, and *** indicate the significance levels at 1%, 5%, and 10%, respectively

The consequences of Granger causality are presented in Table ​ Table8. 8 . The outcome shows the unidirectional causality from EDI to economic growth ( EDI  ≥  EG ), natural gas to economic growth ( NGC  ≥  GCF ), and EDI to infrastructure ( EDI  ≥  INFRI ).

Granger causality result

Note : → indicates no Granger cause

Bidirectional causality is found between economic growth and FDI ( EG  < = >  FDI ) and economic growth and infrastructure ( EG  < = >  INFRI ). Bidirectional causality implies that when one parameter is increased, the other parameter also increases. When the parameters are exchanged, the parameters reinforce one another and become self-reinforcing. As a result, the policymaker would have an easier time dealing with this circumstance (Kónya 2006 ). However, if this is not the case, the study must incorporate more variables, and policy formation becomes more complicated (Riman and Akpan 2010 ). In summary, EDI has a noteworthy consequence on economic growth in Pakistan, owing to its bidirectional causation.

To check the result health of this study, we also report some problem-solving tests (Sohail et al. 2022 ) such as LM test, White test, and Jarque–Bera test. The results are provided in Table ​ Table9 9 and confirmed the model’s strength on the relationship among EDI , FDI , INFRI , NGC , and Pakistan economy.

Diagnostic tests

Conclusion and policy implications

In adaptation of green energy transition and responding to the United Nation Agenda-2030 of sustainable development goal initiation, this research study focused with the impact of sustainable development goals (SDGs) on economic growth of Pakistan. More specifically, we examined the association of several important SDGs on economic growth with regards to previously missing links of sustainable economic development. Interestingly, this study is considering several new dimensions through the influence of SDGs: firstly the education (SDG-4) which is found in opposing to the theoretical approaches, secondly the foreign direct investment (SDG-17) which is found to more contributive to national economy, thirdly infrastructure and technological development (SDG-9) which is found as an important factor to boost the economy, and lastly the clean and green energy of increasing usage of natural gas (SDG-7) on the economy of Pakistan. Specifically, this research study provides help to the policymakers to overcome several permanent issues faced to the economy like the authorities should review the current policies regarding equitable quality education and lifelong learning opportunities to make it more contributive to the economic growth. Additionally , the economy needs more friendly policies to attract more FID inflow. Moreover , the policymakers should provide attention over the existing infrastructure to make it more sustainable business environment for industrialization and foster innovation. Finally , the usage of natural gas is considered more affordable and sustainable environmentally friendly in account of national economy.

We examined the impact several substantial SDGs that has potential to influence the economic for long time period historical data starting from 1990 to 2020. The structural break estimation indicates that there is presence of unexpected growth in several years. In line with this, we used the VECM and NARDL estimation techniques, where we found that the long-term coefficients of education, FDI inflow, and usage of natural gas impact the economy. We determined that FDI inflow and infrastructure are the most potent goals for Pakistan. Similarly, usage of natural gas and education are found contributions to economic growth. The consequences of asymmetric probing are stimulating, which confirmed the mi x outcomes regarding short-term and long-term asymmetries. The short-run and long-run results obtained from NARDL are significant and interesting in the case of developing economy. The government needed to motivate more FDI inflow to improve the infrastructure, and shell also improves education system at different level. Similarly, the adaptation of natural gas is creating a favorable environment. Collectively with the finding of four SDGs, all indicators are essential.

Recommendation and policy implications

The authorities need to imply more appealing policies to attract more foreign investment inflow. The existing policies related to quality education especially in the long term need to be revised to obtain the projected goals. The infrastructure that builds with aim of sustainable development can provide gateway to expansion for business operations. While the usage of natural gas is deemed as more clean energy source in comparison to oil and coal, the production level is to be increased, which could result in increasing of economic growth. This study can be extended to examine the relationship among sustainable development goals by analyzing the influence of other SDGs towards economic growth for other developing economies with fresh date data set.

Author contribution

H. M. S. conceptualized, conducted the econometric analysis, and write of chapter introduction. M. Z. U. conceptualized, conducted the econometric analysis, and conduct overall study, with writing of conclusions and recommendations. K. S. reviewed the literature, discussed the findings, and edited the entire draft. F. U. R. supervised the entire study and drafting.

This paper was supported by the National Social Science Foundation of China (21&ZD184).

Availability of data and materials

Declarations.

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Hafiz M. Sohail, Email: nc.ude.uncs.m@smh .

Mirzat Ullah, Email: [email protected] .

Kazi Sohag, Email: ur.ufru@gahkosk .

Faheem Ur Rehman, Email: ur.ufru@namher .

• Abdouli M, Omri A. Exploring the nexus among FDI inflows, environmental quality, human capital, and economic growth in the Mediterranean region. J Knowl Econ. 2021; 12 (2):788–810. [ Google Scholar ]
• Ahmad MS, Szczepankiewicz EI, Yonghong D, Ullah F, Ullah I, Loopesco WE. Does Chinese foreign direct investment (FDI) stimulate economic growth in Pakistan? An application of the autoregressive distributed lag (ARDL bounds) testing approach. Energies. 2022; 15 (6):2050. [ Google Scholar ]
• Amjad R, Awais N (2016) Pakistan’s productivity performance and TFP trends 1980–2015: cause for real concern
• Antonakakis, N., & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR.
• Apergis N, Payne J. Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Appl Energy. 2010; 38 (1):656–660. [ Google Scholar ]
• Apergis N, Payne JE. Natural gas consumption and economic growth: a panel investigation of 67 countries. Appl Energy. 2010; 87 (8):2759–2763. [ Google Scholar ]
• Armeanu DŞ, Vintilă G, Gherghina ŞC. Empirical study towards the drivers of sustainable economic growth in EU-28 countries. Sustainability. 2018; 10 (1):4. [ Google Scholar ]
• Beets SD. Understanding the demand-side issues of international corruption. J Bus Ethics. 2005; 57 (1):65–81. [ Google Scholar ]
• Brons M, Kalantzis F, Maincent E, Arnoldus P (2014) Infrastructure in the EU: developments and impact on growth. DG ECFIN, Occasional Paper, 203
• Carree M, Van Stel A, Thurik R, Wennekers S. The relationship between economic development and business ownership revisited. Entrep Reg Dev. 2007; 19 (3):281–291. [ Google Scholar ]
• Chakamera C, Alagidede P. The nexus between infrastructure (quantity and quality) and economic growth in Sub Saharan Africa. Int Rev Appl Econ. 2018; 32 (5):641–672. [ Google Scholar ]
• Chanegriha M, Stewart C, Tsoukis C. Testing for causality between FDI and economic growth using heterogeneous panel data. J Int Trade Econ Dev. 2020; 29 (5):546–565. [ Google Scholar ]
• Chowdhury MB. Internationalisation of education and its effect on economic growth and development. The World Economy. 2022; 45 (1):200–219. [ Google Scholar ]
• Ciftci C, Durusu-Ciftci D (2021) Economic freedom, foreign direct investment, and economic growth: the role of sub-components of freedom. J Int Trade Econ Dev 1–22
• Dickey DA, Fuller WA. Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc. 1979; 74 (366a):427–431. [ Google Scholar ]
• Ali MS, Khan UU, Parveen S. The relationship between financial development and foreign direct investment and its impact on economic growth of Pakistan. iRASD J Econ. 2021; 3 (1):27–37. [ Google Scholar ]
• Economou F. Economic freedom and asymmetric crisis effects on FDI inflows: the case of four South European economies. Res Int Bus Financ. 2019; 49 :114–126. [ Google Scholar ]
• Fatai K, Oxley L, Scrimgeour FG. Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, The Philippines and Thailand. Math Comput Simul. 2004; 64 (3):431–445. doi: 10.1016/S0378-4754(03)00109-5. [ CrossRef ] [ Google Scholar ]
• Feng K, Davis SJ, Sun L, Hubacek K. Drivers of the US CO 2 emissions 1997–2013. Nat Commun. 2015; 6 (1):1–8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Glewwe P, Maiga E, Zheng H. The contribution of education to economic growth: a review of the evidence, with special attention and an application to Sub-Saharan Africa. World Dev. 2014; 59 :379–393. [ Google Scholar ]
• Habibi F, Zabardast MA. Digitalization, education and economic growth: a comparative analysis of Middle East and OECD countries. Technol Soc. 2020; 63 :101370. [ Google Scholar ]
• Hamid I, Alam MS, Kanwal A, Jena PK, Murshed M, Alam R (2022) Decarbonization pathways: the roles of foreign direct investments, governance, democracy, economic growth, and renewable energy transition. Environ Sci Pollut Res 1–16. [ PubMed ]
• Hanif N, Arshed N. Relationship between school education and economic growth: SAARC countries. Int J Econ Financ Issues. 2016; 6 (1):294–300. [ Google Scholar ]
• Hanushek EA, Woessmann L (2020) Education, knowledge capital, and economic growth. Econ Educ 171–182
• Hassan SA, Rafaz N. The role of female education in economic growth of Pakistan: a time series analysis from 1990–2016. International Journal of Innovation and Economic Development. 2017; 3 (5):83–93. [ Google Scholar ]
• Hu Y, Xiao J, Deng Y, Xiao Y, Wang S. Domestic air passenger traffic and economic growth in China: evidence from heterogeneous panel models. J Air Transp Manag. 2015; 42 :95–100. [ Google Scholar ]
• Ibrahim MH. Oil and food prices in Malaysia: a nonlinear ARDL analysis. Agricultural and Food Economics. 2015; 3 (1):1–14. [ Google Scholar ]
• IEA (2016) International Energy Agency. In: OECd/IEa
• Jalil A, Idrees M. Modeling the impact of education on the economic growth: evidence from aggregated and disaggregated time series data of Pakistan. Econ Model. 2013; 31 :383–388. [ Google Scholar ]
• Johnston M. Fighting systemic corruption: social foundations for institutional reform. The European Journal of Development Research. 1998; 10 (1):85–104. [ Google Scholar ]
• Jolliffe IT. Discarding variables in a principal component analysis. I: Artificial data. Journal of the Royal Statistical Society: Series C (Applied Statistics) 1972; 21 (2):160–173. [ Google Scholar ]
• Kaufmann D, Kraay A, Mastruzzi M (2011) The Worldwide Governance Indicators (WGI) project. Retrieved April, 21, 2012
• Khan I, Hou F, Zakari A, Tawiah VK. The dynamic links among energy transitions, energy consumption, and sustainable economic growth: a novel framework for IEA countries. Energy. 2021; 222 :119935. [ Google Scholar ]
• Khan NM, Ahmad M, Cao K, Ali I, Liu W, Rehman H, ..., Ahmed T (2022) Developing a new bursting liability index based on energy evolution for coal under different loading rates. Sustainability 14(3):1572
• Khan ZU, Ahmad M, Khan A. On the remittances-environment led hypothesis: empirical evidence from BRICS economies. Environ Sci Pollut Res. 2020; 27 (14):16460–16471. [ PubMed ] [ Google Scholar ]
• Kingdon GG. The progress of school education in India. Oxf Rev Econ Policy. 2007; 23 (2):168–195. [ Google Scholar ]
• Kónya L. Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Econ Model. 2006; 23 (6):978–992. [ Google Scholar ]
• Kousar S, Batool SA, Batool SS, Zafar M (2020) Do government expenditures on education and health lead toward economic growth? Evidence from Pakistan. Journal of Research & Reflections in Education (JRRE) 14(1)
• Li K, Cursio JD, Sun Y, Zhu Z. Determinants of price fluctuations in the electricity market: a study with PCA and NARDL models. Economic research-Ekonomska istraživanja. 2019; 32 (1):2404–2421. [ Google Scholar ]
• Li Z-G, Cheng H, Gu T-Y. Research on dynamic relationship between natural gas consumption and economic growth in China. Struct Chang Econ Dyn. 2019; 49 :334–339. [ Google Scholar ]
• Lin T-C. Education, technical progress, and economic growth: the case of Taiwan. Econ Educ Rev. 2003; 22 (2):213–220. [ Google Scholar ]
• Looney R. Failed economic take-offs and terrorism in Pakistan: conceptualizing a proper role for US assistance. Asian Surv. 2004; 44 (6):771–793. [ Google Scholar ]
• Looney R (2009) Failed take-off: an assessment of Pakistan’s October 2008 economic crisis
• Looney RE. Defense expenditures and economic performance in South Asia: tests of causality and interdependence. Confl Manag Peace Sci. 1991; 11 (2):37–67. [ Google Scholar ]
• Looney RE. Budgetary dilemmas in Pakistan: costs and benefits of sustained defense expenditures. Asian Surv. 1994; 34 (5):417–429. [ Google Scholar ]
• Mahadevan R, Asafu-Adjaye JJEP (2007) Energy consumption, economic growth and prices: a reassessment using panel VECM for developed and developing countries. 35(4):2481–2490
• Marazzo M, Scherre R, Fernandes E. Air transport demand and economic growth in Brazil: a time series analysis. Transportation Research Part e: Logistics and Transportation Review. 2010; 46 (2):261–269. [ Google Scholar ]
• Mohanty RK, Bhanumurthy NR. Analyzing the dynamic relationships between physical infrastructure, financial development and economic growth in India. Asian Economic Journal. 2019; 33 (4):381–403. [ Google Scholar ]
• Murshed M, Nurmakhanova M, Al-Tal R, Mahmood H, Elheddad M, Ahmed R (2022) Can intra-regional trade, renewable energy use, foreign direct investments, and economic growth mitigate ecological footprints in South Asia? Energy Sources, Part B: Economics, Planning, and Policy 1–26
• Nazir MS, Abdalla AN, Sohail H, Tang Y, Rashed GI, Chen W (2021) Optimal planning and investment of multi-renewable power generation and energy storage system capacity. J Electr Syst 17(2)
• Neog Y, Yadava AK. Nexus among CO 2 emissions, remittances, and financial development: a NARDL approach for India. Environ Sci Pollut Res. 2020; 27 (35):44470–44481. [ PubMed ] [ Google Scholar ]
• Nielsen B (2001) Order determination in general vector autoregressions
• Omri A. CO 2 emissions, energy consumption and economic growth nexus in MENA countries: evidence from simultaneous equations models. Energy Economics. 2013; 40 :657–664. [ Google Scholar ]
• Ozturk I, Al-Mulali U. Natural gas consumption and economic growth nexus: panel data analysis for GCC countries. Renew Sustain Energy Rev. 2015; 51 :998–1003. [ Google Scholar ]
• Pakistan SBO (2021) https://www.daynews.tv/2021/11/24/sbp-releases-annual-report-on-the-state-of-pakistans-economy/ . Retrieved from
• Pesaran MH, Shin Y, Smith RJ. Bounds testing approaches to the analysis of level relationships. J Appl Economet. 2001; 16 (3):289–326. [ Google Scholar ]
• Phillips PC, Perron P. Testing for a unit root in time series regression. Biometrika. 1988; 75 (2):335–346. [ Google Scholar ]
• Pradhan RP, Bagchi TP. Effect of transportation infrastructure on economic growth in India: the VECM approach. Res Transp Econ. 2013; 38 (1):139–148. [ Google Scholar ]
• Rehman FU, Sohag K (2022) Does transport infrastructure spur export diversification and sophistication in G-20 economies? An application of CS-ARDL. Appl Econ Lett.10.1080/13504851.2022.2083554
• Rehman FU, Islam MM, Sohag K (2022) Does infrastructural development allure foreign direct investment? The role of Belt and Road Initiatives. International Journal of Emerging Markets, (ahead-of-print). 10.1108/IJOEM-03-2022-0395
• Riman BH, Akpan ES. Causality between poverty, health expenditure and health status: evidence from Nigeria using VECM. Eur J Econ Finance Adm Sci. 2010; 27 :120–128. [ Google Scholar ]
• Rossi B, Wang Y. Vector autoregressive-based Granger causality test in the presence of instabilities. Stand Genomic Sci. 2019; 19 (4):883–899. [ Google Scholar ]
• Sadiqa BA, Zaman K, Rehman FU, Nassani AA, Haffar M, Abro MMQ (2022) Evaluating race-to-the-top/bottom hypothesis in high-income countries: controlling emissions cap trading, inbound FDI, renewable energy demand, and trade openness. Environ Sci Pollut Res 1–14 [ PubMed ]
• Saeed F, Awan AG (2020) Does technological advancement really affects economic growth of Pakistan. Global Journal of Management, Social Sciences and Humanities 6(2)
• Saleem, H., & Shabbir, M. S. (2020). The short-run and long-run dynamics among FDI, trade openness and economic growth: using a bootstrap ARDL test for co-integration in selected South Asian countries. South Asian Journal of Business Studies .
• Sari R, Ewing BT, Soytas U. The relationship between disaggregate energy consumption and industrial production in the United States: an ARDL approach. Energy Economics. 2008; 30 (5):2302–2313. [ Google Scholar ]
• Schwab K (2018) The global competitiveness report 2018. Paper presented at the World Economic Forum
• Seetanah B. The economic importance of education: evidence from Africa using dynamic panel data analysis. J Appl Econ. 2009; 12 (1):137–157. [ Google Scholar ]
• Self S, Grabowski R. Does education at all levels cause growth? India, a case study. Econ Educ Rev. 2004; 23 (1):47–55. [ Google Scholar ]
• Shabbir S (2013) Does external debt affect economic growth: evidence from developing countries. Retrieved from
• Shahbaz M, Lean HH, Farooq A. Natural gas consumption and economic growth in Pakistan. Renew Sustain Energy Rev. 2013; 18 :87–94. [ Google Scholar ]
• Shin Y, Yu B, Greenwood-Nimmo M. Festschrift in honor of Peter Schmidt. Berlin: Springer; 2014. Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework; pp. 281–314. [ Google Scholar ]
• Sohail HM, Li Z, Murshed M, Alvarado R, Mahmood H. An analysis of the asymmetric effects of natural gas consumption on economic growth in Pakistan: a non-linear autoregressive distributed lag approach. Environ Sci Pollut Res. 2022; 29 (4):5687–5702. [ PubMed ] [ Google Scholar ]
• Sohail HM, Zatullah M, Li Z. Effect of foreign direct investment on bilateral trade: experience from asian emerging economies. SAGE Open. 2021; 11 (4):21582440211054487. [ Google Scholar ]
• Solarin SA, Ozturk I. The relationship between natural gas consumption and economic growth in OPEC members. Renew Sustain Energy Rev. 2016; 58 :1348–1356. [ Google Scholar ]
• Solarin SA, Shahbaz M. Natural gas consumption and economic growth: the role of foreign direct investment, capital formation and trade openness in Malaysia. Renew Sustain Energy Rev. 2015; 42 :835–845. [ Google Scholar ]
• Tehsin M, Khan AA, Sargana T-U-H. CPEC and sustainable economic growth for Pakistan. Pakistan Vision. 2017; 18 (2):102–118. [ Google Scholar ]
• Tiwari AK, Mutascu M. Economic growth and FDI in Asia: a panel-data approach. Economic Analysis and Policy. 2011; 41 (2):173–187. [ Google Scholar ]
• Tran N. The long-run analysis of monetary policy transmission channels on inflation: a VECM approach. Journal of the Asia Pacific Economy. 2018; 23 (1):17–30. [ Google Scholar ]
• Tripathi M, Singal SK. Use of principal component analysis for parameter selection for development of a novel water quality index: a case study of river Ganga India. Ecol Ind. 2019; 96 :430–436. [ Google Scholar ]
• Ulucak R, Erdogan S. The effect of nuclear energy on the environment in the context of globalization: consumption vs production-based CO 2 emissions. Nucl Eng Technol. 2022; 54 (4):1312–1320. [ Google Scholar ]
• Wang C, Lim MK, Zhang X, Zhao L, Lee PT-W. Railway and road infrastructure in the Belt and Road Initiative countries: estimating the impact of transport infrastructure on economic growth. Transportation Research Part a: Policy and Practice. 2020; 134 :288–307. [ Google Scholar ]
• Wang Q, Wang X, Li R (2022) Does urbanization redefine the environmental Kuznets curve. An empirical analysis of 134
• Xiao B, Niu D, Guo X. Can natural gas-fired power generation break through the dilemma in China? A system dynamics analysis. J Clean Prod. 2016; 137 :1191–1204. [ Google Scholar ]
• Yasmin N, Safdar N, Yasmin F, Khatoon S (2021) Education, poverty, and unemployment: a way forward to promote sustainable economic growth in Pakistan. Journal of Contemporary Issues in Business Government 27(06)
• Yusuf AM, Abubakar AB, Mamman SO. Relationship between greenhouse gas emission, energy consumption, and economic growth: evidence from some selected oil-producing African countries. Environ Sci Pollut Res. 2020; 27 (13):15815–15823. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zaman S, Wang Z, Zaman Q, u. Exploring the relationship between remittances received, education expenditures, energy use, income, poverty, and economic growth: fresh empirical evidence in the context of selected remittances receiving countries. Environ Sci Pollut Res. 2021; 28 (14):17865–17877. doi: 10.1007/s11356-020-11943-1. [ PubMed ] [ CrossRef ] [ Google Scholar ]

• DOI: 10.46224/ECOC.2021.2.4
• Corpus ID: 235273713

Impact of inflation on economic growth in Pakistan

• Published in Economic Consultant 1 June 2021

Tables from this paper

5 Citations

Driving the pulse of the economy or the dilution effect: inflation impacting economic growth, impact of corruption, unemployment and inflation on economic growth evidence from developing countries.

• Highly Influenced

Inflation and Bank Credit

• 15 Excerpts

Examining Determinants of Regional Inflation Heterogeneity — A Robust Panel Data Analysis

Enhancing institutional quality to boost economic development in developing nations: new insights from cs-ardl approach, 19 references, effect of inflation on the growth and development of the nigerian economy (an empirical analysis), does inflation affect economic growth the case of pakistan, impact of inflation on economic growth: a case study of tanzania, impact of inflation rate on the economic growth in nigeria, the relationship between inflation and economic growth of bangladesh: an empirical analysis from 1961 to 2013, inflation and economic growth in nigeria, inflation and economic growth in bangladesh, inflation and economic growth: a dynamic panel threshold analysis for asian economies, the relationship between gdp growth rate and inflationary rate in ghana: an elementary statistical approach, inflation and economic growth in bangladesh: 1981-2005, related papers.

Showing 1 through 3 of 0 Related Papers

This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here .

ORIGINAL RESEARCH article

Surge in economic growth of pakistan: a case study of china pakistan economic corridor.

• 1 Institute of Business and Management, University of Engineering and Technology, Lahore, Pakistan
• 2 Hailey College of Commerce, University of the Punjab, Lahore, Pakistan
• 3 Department of Computer Sciences, University of Engineering and Technology, Lahore, Pakistan

China Pakistan Economic Corridor (CPEC) is considered a massive investment that can change the economic scenario of Pakistan. The purpose of the study is to examine the contribution to the economic growth of the sectors where CPEC is investing. This research uses time-series data for 31 years to investigate the impact of macro-economic variables like foreign direct investment (FDI), human capital investment (HCI), transport investment, and information communication technology (ICT) on the economic growth of Pakistan. The results of Fully Modified Ordinary Least Square Regression Specification (FMOLS) show a positive nexus between FDI, HCI, and economic growth while economic growth and ICT show a negative relationship. The results for the impact of transportation infrastructure on economic growth are statistically insignificant. This research suggests that an increased focus on building knowledge, expertise, and skillset of human resources will help in reaping the benefits of CPEC’s investment. Future researchers can increase the period of the study to ascertain the implicit or explicit impact of CPEC on economic growth. The results also suggest that policymakers and researchers should focus on developing human capital to reap the investment benefits of CPEC.

Introduction

The main idea of the economic corridor was initiated from the concept of the transport corridor. When these corridors extend to cover regions, benefits from increased investment and multilateral trade can be witnessed. However, a great deal of effort and infrastructure is required for retaining and improving such transportation networks. Therefore, a transport corridor mainly concentrating on upgraded infrastructure was established as an Economic Corridor ( Arif, 2015 ). One Belt One Road (OBOR) is viewed as China’s strategic initiative to play power on the global level. As a part of China’s OBOR initiative, the China-Pakistan Economic Corridor (CPEC) is expected to provide financial and economic incentives to Pakistan. The importance of CPEC cannot be denied by both countries as Pakistan needs it to eradicate its economic, social, and energy problems, and it is needed by China because it requires safe and improved trade routes that can be used for oil supply from the Middle East countries to China ( Ramay, 2016 ).

Amidst the crippling economic condition of Pakistan, CPEC is considered an investment that is expected to improve Pakistan’s economic growth. CPEC is China’s biggest foreign direct investment deal to invest in Pakistan ( Shah, 2015 ). A huge amount is going to be spent on transportation, infrastructure, energy, and industrial zones. FDI is now commonly recognized to bring economic benefits along with technology, foreign exchange, competition, new market access, and capital ( Crespo and Fontoura, 2007 ). This investment through CPEC makes up almost 20% of Pakistan’s Gross domestic product (GDP) ( Stevens, 2015 ). In recent years, particularly from 2013 to 2014, Pakistan has faced a decline in FDI from the US in comparison with 2008; in such a demise, the economic corridor is expected to be an opportunity to increase economic growth ( Board of Investment, Government of Pakistan, 2015 ). Full benefits of CPEC will be achieved by 2030 yet some rail, road, and energy projects are expected to be completed in the next few years ( Ali and Faisal, 2017 ).

According to a report ( Andrés et al., 2013 ) despite huge amounts being invested by the private sector, Pakistan’s infrastructure services are always insufficient to fulfill domestic needs. The report suggested increasing spending of GDP on telecommunication, electricity, and transport sectors by.71, 5.5, and 1.23%, respectively, until 2020. Pakistan’s economic growth and exports are suffering from a lack of necessary infrastructure. Recently, FDI increased up to 56% in Pakistan mainly due to increased investment in infrastructure through CPEC. The transport and electricity sectors have attracted huge amounts in form of FDI for the projects that are still under construction ( Ahmed, 2017 ). Information and communication technology (ICT) infrastructure plays a substantial role in catalyzing economic growth, especially in today’s era of internet and mobile telecommunication ( Lee et al., 2014 ; Rohman and Bohlin, 2014 ; Pradhan et al., 2016 ). Technology transfer from developed countries to developing countries is one of the mechanisms, by which FDI contributes to the economic growth of a country ( Balasubramanyam et al., 1996 ). Likewise, Romer (1990) considered FDI as a medium of technological and economic growth. To benefit from the latest technology and ICTs, Van Reenen et al. (2010) recommended that developing countries like Pakistan should invest more in the training and education sector and CPEC is going to invest in this sector to provide Pakistan with the fifth route for its telecommunication traffic.

According to past studies, the researchers recommend investigating the impact of investment in transportation projects at the corridor level ( Berechman et al., 2006 ). However, only increasing seaports and airports can result in traffic congestion on roads if roads are not properly constructed to cater to increased needs ( Diaz et al., 2016 ), therefore, there is a need to investigate the impact of different modes of transportation infrastructure on economic growth. Economic Growth (EG) can be instigated by having a good transportation infrastructure, although it is not mandatory to find a positive impact on EG by all transportation modes ( Ding, 2013 ; Diaz et al., 2016 ). Similarly, contrasting results between theoretical and empirical studies, showing a positive and negative nexus between FDI and economic growth are found ( Ray, 2012 ; Akalpler and Adil, 2017 ). This investment is expected to improve the economic condition of Pakistan, as a researcher ( Bucci, 2014 ) claimed that all types of capital investments, namely, human, physical, and innovational capital, lead to economic benefits.

Although CPEC is considered to play a vital role in the economic development of Pakistan, less attention is being paid to its sustainability and the spillover effect of these projects on human capital sustainability. CPEC is a project of massive investment, and such projects lead toward sustainable development ( Zhuang et al., 2021 ) because infrastructural development affects all economic, social, human capital, and environmental policies and activities. Sustainability attempts to satisfy three major performance indicators, i.e., human, environment, and economic ( Di Fabio and Peiroì, 2018 ). However, the human dimension of sustainability is often ignored ( Pfeffer, 2010 ; Abid et al., 2020 ).

Since CPEC is a mega-investment project in Pakistan, it has the potential to change the economic scenario of not only the country but also the whole region. It is the need of the hour to investigate the sectors where investment and correct policy measures should be applied to cultivate the real economic benefits behind the CPEC. This research, therefore, is needed to identify which investment among human capital, information technology, transportation, and foreign direct investment be given prime importance. To recommend policies to make Pakistan a hub for foreign investment and to make it reap the benefits of infrastructure, information technology, enhanced human capital investments, and other economic benefits associated with CPEC; this research is much needed. To reap the benefits of this mega project, it needs time to analyze the impact of this investment on factors that will help Pakistan’s economy to benefit from it to its fullest potential. Therefore, this research is aimed at having a comprehensive study that can integrate economic changes occurring due to CPEC and provide relevant answers to the role of human capital investment, transport infrastructure, information communication technology, and foreign direct investment on economic growth.

This research attempts to estimate the impact of foreign direct investment, human capital, ICT, and transport infrastructure on Pakistan’s economic growth in connection with CPEC by taking data from FY-1985 to FY-2018 using fully modified ordinary least squared (FMOLS). Although CPEC regional integration analysis calls for including other countries in the analysis, the present investigation is limited to the approximation of the implicit impact of CPEC on Pakistan’s economy only. The results show a positive significant impact of FDI and human capital development on the economic growth of Pakistan. The benefits of all investments coming through CPEC can be realized if Pakistan develops and train its labor with the required knowledge and skills, whereas a negative relation between ICT and the economic growth of Pakistan is found. The study shows an insignificant relationship between the density of roads and economic growth during the selected time frame enhancing Pakistan’s concern to consider the route very carefully if it wants this project to provide expected benefits. This paper comprises six sections: introduction, literature review, methodology, discussions, conclusion and implications, and limitations and future recommendations.

Literature Review

A transport corridor is considered a network within one country or between any two countries to connect economic centers ( Khan et al., 2015 ). OBOR is viewed as China’s strategic initiative to play as power on a global level. This endeavor was started by China’s president XI Jinping in 2013 as a part of its economic integration of Eurasia through networks of roads ( Aneja, 2016 ). OBOR is divided into two parts: the maritime Silk Road and Silk Road economic belt. As a part of China’s OBOR initiative ( McCartney, 2021 ), CPEC is going to be extremely important for China and Pakistan in competition with territorial and global nations ( Ali, 2016 ).

According to an announcement by a government official, the initial investment in the project was almost $46 billion, which increased up to $55 billion at first and later up to $62 billion ( The Express Tribune, 2017 ). It would take almost $34 billion to invest in energy projects that are expected to contribute almost 17,000 MW of electricity, whereas the remaining $12 billion investment would be focused on infrastructure development out of the initial $46 ( Shah, 2015 ). CPEC is composed of three types of projects based on the duration including short-term projects expected to be completed by 2017, medium-term projects that would be completed by 2025, and long-term projects that are expected to be completed by 2030 ( Jawad, 2013 ).

The CPEC is China’s biggest foreign direct investment deal to invest in Pakistan ( Shah, 2015 ). A huge amount is going to be spent on transportation, infrastructure, energy, and industrial zones ( Abid and Ashfaq, 2015 ). The authors ( Ali and Asghar, 2016 ) examined the sectoral impact of FDI on EG by taking the manufacturing, services, and agricultural sectors of Pakistan. Using the standard error model, they concluded that only two sectors, agricultural and manufacturing, have a significant positive impact on the outcome variables, stressing more to use the manufacturing sector as an option to enhance economic growth. Younus et al. (2014) used Two-Stage Least Squares estimation techniques and concluded a positive relation between FDI and the economic growth of Pakistan. Falki (2009) found a negative relation betwixt FDI inflows and the GDP of Pakistan from 1980 to 2006 based on endogenous growth theory. Atique et al. (2004) concluded that FDI plays a significant role in the economic growth of Pakistan particularly under the export regime between 1970 and 2001. The said phenomenon is explained by various studies; Nabi et al. (2022) investigated the impact of ICT, trade, and financial development on economic growth using ARDL, and Majeed et al. (2021) concluded that low- to middle-income countries involved in belt and road project tend to attract larger amounts of FDI, Bahrini and Qaffas (2019) compared the impact of ICT on economic growth between Middle Eastern and Sub Sharan African countries, Ibrahim and Alagidede (2018) concluded that a larger amount of finance, as well as a high level of per capital income, and human capital is needed for long-run economic growth. Latif et al. (2018) used OLS, FMOLS, and DOLS to examine the contribution of ICT, FDI, and globalization on economic growth, Niebel (2018) compared the impact of ICT on economic growth among developed, developing, and emerging economies. McCartney (2022) stated that owing to a lack of sound industrial policy CPEC may not have a revolutionary impact on Pakistan’s economy. McCartney (2021) stressed linking the economic outcome of CPEC with increased economic activity and prosperity in Western China. Javid (2019) used FMOLS to conclude that the impact of public infrastructure investment on Pakistan’s economy is better than the private sector’s investment. Khan and Liu (2019) investigated various challenges associated with CPEC including geopolitical environment, political instability, debt, terrorism, and issues with neighboring countries.

The authors ( Padilla-Perez and Nogueira, 2016 ) stated that developing countries have witnessed an increasing trend of FDI in recent years. Both large and small economies have seen a trend of investing abroad by their domestic businesses. The researchers ( Akalpler and Adil, 2017 ) concluded a negative relationship between GDP, gross savings, FDI, and international trade. A financial development index was developed for India in this research, and an investigation of FDI and EG showed a negative relationship both in a long and short period ( Hye, 2011 ), while another study supports a positive relationship between FDI and EG in Pakistan ( Chaudhry et al., 2013 ). In contrast, some studies found no link between FDI and EG, particularly in developing economies ( Bende-Nabende et al., 2003 ); whereas a systemic link was investigated ( Azman-Saini et al., 2010 ) between FDI and EG and concluded that there is no direct effect of FDI on EG.

Both developed and developing countries experienced a boost in their economic growth due to urbanization ( Armeanu et al., 2021 ). Cities developed with investment in energy, transport, and infrastructure sectors attract a large amount of talent because of more economic opportunities. CPEC is expected to provide cooperation in infrastructure, telecommunication, energy, transportation sectors, and socio-economic development ( Ahmed and Azam, 2016 ; Tasneem, 2018 ), some researchers ( Ahmed et al., 2017 ) are focused on human resource development in the wake of CPEC. Pakistan neglected human resource development for decades ( Abbassi and Burdey, 2008 ; Asrar-ul-Haq, 2015 ). The researchers ( Ahmed et al., 2017 ) claimed that a proper human resource development policy will help sustain Pakistan’s economy. A study ( Zia et al., 2018 ) claimed that CPEC-related project is not only providing employment opportunities but also helps in enhancing the capacity of domestic human resources as well.

The literature supported the relationship between HCI and EG ( Bryant and Javalgi, 2016 ). Glaeser et al. (2004) found that HC is more effective than institutions in generating EG. O’Mahony (2012) found that continuous learning and formal education contributed toward building HC that, in turn, contributed to EG. Olimpia (2013) developed a way to compute the value of HCI in OECD countries and resulted that HCI contributed toward EG despite the different levels of competencies and efficiencies among different countries. CPEC is not only focused on transport infrastructure, telecommunication, and energy infrastructure but also focuses on the necessary physical infrastructure of the whole region ( Iqbal et al., 2019a ). Out of 46 billion approximately 13.58 billion USD was expected to be spent on infrastructure. As per ( Zia et al., 2018 ) almost 52, 000 direct jobs were created under 6 CPEC-related road infrastructure projects. They also negated the myth of the Chinese getting more employment opportunities in CPEC projects. It is argued by authors ( Xia et al., 2022 ) that FDI and gross capital formation lead to human capital development in both the short and long run, subsequently leading to the economic progression of the country. Jakhon (2021) claimed that there is a close association between the workforce and the economic progress of a country. Abid et al. (2020) claimed that organizations should work on their employee’s skillsets to achieve sustainability. It is also stated ( Hosan et al., 2022 ) that any country can benefit from investments in IT or the economy if its employees are more skilled. A decrease in the skilled, motivated, and knowledgeable workforce will result in a slowdown in economic activities ( Naeruz et al., 2022 ).

Roads and transportation infrastructure plays an important role in the economic growth of a country Zhang and Levinson (2007) . The researchers ( Ali et al., 2018a ) used a questionnaire to determine the attitude of local people toward roads and transport infrastructure built under CPEC.

The results indicated a strong socio-economic impact of road infrastructure that in turn affected local people’s attitude toward CPEC projects. Pakistan ranks at 105th position in overall infrastructure in 2019 as compared to 93rd in 2018 and 100th in 2017, its ICT adoption has also faced a decline from 127 to 131st position from 2018 to 2019 Haider (2019 , Oct 10). The decline in the overall quality of infrastructure has affected adversely its economic competitiveness. Despite serious concerns for the development of infrastructure, this sector was always neglected historically ( Mehar, 2017 ). Under CPEC, 3,000 km of networks of roads, railway roads, fiber optics, and oil pipelines are expected to be built ( Ali et al., 2018b ). Under CPEC, a 100-km road is planned to be built between Karachi and Lahore and an approximately 2,700-km highway between Kashgar to Gwadar. Moreover, many highways will be upgraded to improve Pakistan’s connectivity with China and some neighboring countries ( Ranjan, 2015 ). Gwadar port will be used as headquarter for China and a hub for Pakistan to facilitate its trading throughout the world ( Mahmood et al., 2020 ).

This widespread network of infrastructure is going to help the local trader in exports by providing ease of connectivity and saving their costs ( Abid and Ashfaq, 2015 ). Extensive literature is available on the contribution of infrastructure toward economic growth ( Estache and Iimi, 2008 ; Sahoo and Dash, 2009 ). The USA observed an increased volume of bilateral trade by enhancing its port efficiency ( Clark et al., 2004 ). Improved infrastructure attracts FDI and increases the volume of trade ( Edmonds and Fujimura, 2008 ). The researchers ( Jebran et al., 2018 ) claimed that terms of trade have a positive impact on Pakistan’s economy using the ARDL regression model. The reason behind China’s prosperity and growth in the last decades is largely attributed to the huge development of its physical infrastructure ( Straub, 2008 ). This supports the argument that the development of infrastructure in Pakistan under CPEC is going to open a new horizon of prosperity, growth, and fortune for its people ( Qureshi, 2015 ).

In this modern era of technology, no government is ignorant of the fact of how ICTs can play a prime role in the dissemination of information that will subsequently create awareness regarding socio-political and ecological issues ( Ali, 2018 ). The use of ICTs has witnessed a steady increase globally, from 400 million users in 2003 to 3.2 billion users in 2015 ( ITU, 2015 ; Iqbal et al., 2019b ).

A lot of research has focused on the contribution of IT investment to the economic growth of the countries. The authors ( Dedrick et al., 2003 ) investigated the economic performance of a country in terms of profitability, labor welfare, and economic growth in response to the investment in the information technology sector. Some researchers ( Piatkowski, 2004 , 2006 ; Jalava and Pohjola, 2008 ; Vu, 2011 ; Ahmed and Ridzuan, 2013 ) argued that economic growth can be stimulated through ICTs. Besides investing in roads and railways, CPEC is also aimed at strengthening information connectivity in Pakistan. Construction on the Pakistan-China fiber optic project has started in May 2016 to expand the telecommunication structure in Gilgit Baltistan, while focusing on facilitating Pakistan with the 5th route for telecommunication traffic ( Economic Times, 2016 ).

Methodology

This article attempts to estimate the impact of foreign direct investment, human capital, information and communication technology, and transport infrastructure on Pakistan’s economic growth, in connection with CPEC, by taking data from FY-1985 to FY-2018. CPEC regional integration analysis calls for including other countries in the analysis, but the approximation of the implicit impact of CPEC is limited to Pakistan’s economy only.

To analyze the relationship between exogenous variables and economic growth we attempt to approximate the determinants of economic growth. GDP per capita in US$ is used as an endogenous variable. The exogenous variables used for this purpose are foreign direct investment net inflow in US$ (FDI), human capital (HC), fixed telephone subscription (FTS), and density of roads (DRO). The annual time series data is used covering the period from 1985-to 2018. All relevant variables data is taken from World Development Indicators (WDI) and Penn World Table (PWT). After that, a natural logarithmic transformation was used to normalize the data for further estimation. Sensitivity analysis is done by incorporating other macroeconomic variables, such as central government debt, real interest rate, real effective exchange rate, and the unemployment rate in the model, but the results are robust even without the inclusion of these variables. The variable’s description and their specification for the empirical analysis are as follows given in Table 1 .

Table 1. Definition of variables.

Data Analysis

The time-series data shows the trendy behavior, i.e., deterministic or stochastic. There may be a problem of non-stationarity in time series data and approximated parameters are spurious, and it is essential to eliminate this problem for best linear unbiased estimator (BLUE) parameters. In the first step, the unit root is checked and the stationarity of the variables at I (0), or I (1) is identified. There are enormous unit root tests in econometrics literature for time series data and this analysis checks the stationarity of data through Augmented Dickey and Fuller (1979) and Phillips and Perron (1988) unit root tests. The results of these tests will help to select the appropriate econometric regression specification.

Unit Root Test

The Augmented Dickey-Fuller test diagnosed the stationarity or non-stationarity of the variable. The general equation with time and trend applied for the ADF test is as follows:

Whereas △ Y i −1 is lag difference term, β 0 is a constant term and t is time trend. To incorporate lag difference in the model, no serial correlation was found among errors ε t terms. The ADF test used additional lags of the first differenced variable. The Phillips and Perron (1988) test estimates the same equation used for checking the stationary, but they contain the first lag and difference in the unit root regression. The large negative values support accepting the null hypothesis, i.e., the series has a unit root, and the alternative is vice-versa. To check the stationarity variables, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests are used, and estimated results are presented in the following Table 2 .

Table 2. Unit root test.

Cointegration

A further step is to investigate common stochastic trends or cointegration among variables because growth is a long-running phenomenon rather than the short run. This research uses Johansen (1988) and Johansen and Juselius (1990) , and Engle and Granger (1987) cointegration test. These tests indicate a long-run relationship in time series data. These tests estimate multiple heterogeneous cointegration vectors, i.e., preferred over traditional time series cointegration technique with the null hypothesis that “no cointegration” exists among variables.

Johansen Cointegration Test

As proposed by Johansen (1988) and Johansen and Juselius (1990) , there are two test statistics for checking the cointegration vectors in the model. The Johansen test can be seen as a multivariate generalization of the Augmented Dickey-Fuller test. This generalization estimates the linear combinations of variables for unit roots. The estimation procedure of this test is to examine the cointegrating vectors through the Trace test and Eigenvalue. The trace test supports the H 0 , which implies the number of cointegrating vectors equal to (0, 1, 2, 3…). and the Eigen value checks the presence of cointegrated vectors against H A . Therefore, H 0 for testing numbers of cointegrated vectors can be set as H 0 =0 against the H 1 =1 and H 0 =1 against the H A =1, and so on ( Khalafalla and Webb, 2001 ; Dritsakis, 2004 ). In other words, among five variables there are three variables with unit roots and, at most, two cointegrating vectors exist. This relationship is depicted as follows:

Engle and Granger Cointegration Test

The cointegration vector is unknown and another way to test the existence of cointegration is the residual-based static regression method, i.e., purposed by Engle and Granger (1987). The obtained residuals are tested for the presence of unit-roots. When the estimated residuals are stationary, cointegration exists among variables. In this case, an appropriate model for long run estimation is an error correction mechanism shown below:

The outcome of the unit root is very important to check before estimating the long-run equation.

Two prominent cointegration tests check the long-run relationship among variables or not at I (1), i.e., Johansen Cointegration and Engle-Granger.

In the Johansen Cointegration test, interpretation is made based on trace statistics and maximum eigenvalue statistics. These values verified the cointegrating vector among variables. In the case of this study, there exists an exclusive long-running association between gross domestic product, foreign direct investment, human capital, fixed telephone subscription, and density of road at “none as well as at most one”. The Johansen Cointegration test estimates multiple equations for checking the long-run relationship, but the Engle-Granger test estimates a single equation. The reported results in Table 3 confirmed that exogenous, as well endogenous variables, have a long-running relationship at a 5% significance level. In other words, gross domestic product, foreign direct investment, human capital, fixed telephone subscription, and density of road are cointegrated and have a long-running equilibrium. In the literature, multiple regression specifications exist for empirical estimation such as ARDL, VAR, and VECM. However, this analysis uses FMOLS in the empirical estimation because it can be used to estimate long-run coefficients.

Table 3. Johansen cointegration test.

Regression Specification

In the real world, every economy tries to grow at a faster rate than earlier, and scholars have been interested in the rate at which the economy is amplified. In this regard, investment in human capital, transport, telecommunication infrastructure, and FDI are being considered a determinant of economic growth for analysis as these are the major areas in which China is investing in Pakistan through CPEC.

The study adopts the Cobb-Douglas production function as follows:

Whereas X is a vector of explanatory variables and β is a vector of parameters for period t = 1, 2,., T . Taking the log of the above equation and transforming the function as follows we get:

double log-linear regression specifications are as follows:

Whereas lnGDP is the natural log real GDP per capita in US$ ( Hong et al., 2011 ; Dhrifi, 2015 ) and lnFDI is a net inflow of foreign direct investment in US$ ( Mun et al., 2008 ; Dhrifi, 2015 ). The variable HC is the human capital index to have smoothly stimulated economic growth ( Ederer et al., 2007 ); whereas lnFTS fixed telephone subscriptions as a proxy of information and communication technology in the natural log ( Sridhar and Sridhar, 2007 ) and lnDRO density of roads used as a proxy of transport infrastructure in the natural log ( Hong et al., 2011 ).

Fully Modified Ordinary Least Squared Regression

In the empirical estimation, the presence of cointegration is usually measured through using two regression technique that has been based on the OLS method, i.e., fully modified ordinary least squared (FMOLS) or dynamic ordinary least squared (DOLS). These regression specifications, FMOLS and DOLS, were developed by Phillips and Hansen (1990) and Stock and Watson (1993) , respectively. FMOLS adopts the semi-parametric method for the approximation of long-run parameters. This technique gives asymptotically efficient and reliable coefficients even when the sample size is small. Similarly, this technique solves the problem of endogeneity, serial correlation, and heterogeneity in the long-run parameters ( Agbola, 2013 ; Al-Mulali et al., 2014 ; Bashier and Siam, 2014 ; Fereidouni and Al-Mulali, 2014 ). FMOLS estimates a single cointegration equation for all exogenous variables cointegrated with a time trend. Amarawickrama and Hunt (2008) reported that the FMOLS technique makes an appropriate correction to the inference problem in Engel and Granger’s cointegration methodology and, hence, the long-run approximated parameters are valid. The FMOLS allows consistent and efficient estimator in the presence of cointegration, and, at the same time, it indicates the problem of nonstationary, as well as simultaneity biases in the heterogeneous cointegrated variables. The estimated parameters from FMOLS are unbiased due to endogeneity determined in the I (1) and the model can be written as follows:

where μ ^ 1 ⁢ t is the residual of the cointegration equation estimated by OLS and μ ^ 2 ⁢ t are the differenced residual regressors equations or the residual of the differenced regressors equations. The FMOLS estimators and their covariance are given by:

where λ ^ 12 + = λ ^ 12 - ω ^ 12 ⁢ Ω ^ 22 - 1 ⁢ Λ ^ 21 are called bias correction terms and z t = ( x t ′ , d 1 ⁢ t ′ ) ′ ⁢ ω ^ 12 is the estimation of long-run covariance of μ 1 t conditional on μ 2 t .

For analysis FMOLS developed by Phillips and Hansen (1990) is used. The advantage of this technique allows for greater flexibility in the presence of heterogeneity in the cointegration vectors. Another advantage lies with the interpretation of the point estimates if cointegrated vectors are not homogenous. This study uses FMOLS as it allows consistent and efficient estimator in the presence of cointegration ( Olofin et al., 2019 ; Peng and Wu, 2020 ; Srivastava and Talwar, 2020 ), and, at the same time, it indicates the problem of nonstationary and simultaneity biases in the heterogeneous cointegrated variables.

The following Table 4 shows the long-run parameters from FMOLS. The overall performance of the regression specification is seemingly good because 80% of the variation can be explained and most of the coefficient signs are consistent with the theory, as well as prior empirical studies. Consequently, the t -stat of the explanatory variables shows that all variables are relevant to the model, except the independent variable of the density of road, which has often been found insignificant in some of the studies. The variables are transformed into natural logarithm for normalizing them. The expected sign of FDI is related to prior literature and estimated parameters have low elasticity due to low coefficient value, where one percentage change in FDI can bring a change in gross domestic product up to sixteen percent.

Table 4. Fully modified least squares (FMOLS).

The second predictor variable is very important in the point of explicit economic growth, i.e., investment in human capital. The sign of the human capital coefficient also supports the prior literature as well as the theory. The calculated coefficient has a large value and positive relationship with GDP; whereas one percentage change in HCI can raise the gross domestic product by twenty-seven percent and the slope coefficient is also highly significant. Correspondingly, enormous literature is available on estimating the impact of human capital on economic growth with the help of other proxy variables, such as Gross or Net enrollment ratio, but complete data on the human capital index is easily available, and HCI is used in this analysis ( Ederer et al., 2007 ).

There are numerous proxies available for the approximation of the relationship between economic growth and technology. Prior literature has used different proxies, such as household internet connection, mobile cellular subscription, and individual internet usage ( Iqbal et al., 2019b ; Asongu and Odhiambo, 2020 ). Most of the studies empirically estimate technology as a control variable because they indicated that this indicator has an implicit impact on economic growth. At present, there is no ambiguity that the role of technology rapidly boosts economic growth and this study used fixed telephone subscriptions as a proxy for technology. The estimated coefficient sign is negative and not inimical for economic growth, whereas percentage change in fixed telephone subscriptions significantly deteriorates gross domestic product by forty-eight percent and the slope coefficient is statistically highly significant. The proxy of transport infrastructure is the density of road and researchers used different proxies for this, such as the length of the road, railway carriages, etc. In the prior literature, the researcher verified that role of transport infrastructure plays a vital role in sustainable economic development. So, in other words, transport help to boost implicit economic growth, and the estimated coefficient is positively affiliated in the case of this research. The estimated value seems good but insignificant ( Diaz et al., 2016 ). Economic growth has a multidimensional aspect and a lot of fundamental variables possible in the empirical estimation, but the estimated slope coefficient gives an appropriate outcome due to data constraints in this study being limited on these variables.

The residual diagnostic tests, such as LM, Q-Statistics, Normality Test, Breusch-Pagan-Godfrey, and White Heteroscedasticity tests, describe the intensity of regression specification. The calculated statistics of these tests are favorable for the estimated regression specification. Therefore, the causality among variables is also checked and found that most of the variables have a unidirectional relationship with each other. In the sensitivity analysis, the long-run parameters were also approximated with the help of an error correction mechanism and no significant change was observed, the reason why estimated coefficients from FMOLS are more reliable.

The key objective of this research is to empirically approximate determinants of the economic growth of Pakistan from 1985 to 2018. Enormous literature is available on determining economic growth using different exogenous variables with different proxies, but this research attempts to integrate all major variables in which CPEC is going to invest. It is aimed at estimating whether the sectors in which CPEC is going to invest will empirically help in boosting Pakistan’s economy.

Several empirical investigations and theoretical studies have been conducted on FDI and EG ( Dhrifi, 2015 ). The positive impact of FDI on EG is widely recognized in the theoretical literature, but mixed results are prevalent in the empirical investigation over the past 20 years using a simultaneous equation model. Similarly, Hansen and Rand (2006) argued that, theoretically, FDI plays a substantial role in the growth of developing countries. However, literature regarding the negative impact of FDI is also available. Falki (2009) argued that the negative impact of FDI can redress the positive effects of FDI. Another researcher ( Lipsey, 2004 ) argued that the positive effects of FDI are more in just country. Research shows mixed results for each variable’s contribution to economic growth. A negative relationship between FDI and economic growth ( Hye, 2011 ; Ray, 2012 ; Akalpler and Adil, 2017 ) and a positive nexus between FDI and economic growth ( Chaudhry et al., 2013 ; Kalai and Zghidi, 2019 ). The results of this study are in line with previous studies ( Azman-Saini et al., 2010 ; Chaudhry et al., 2013 ; Dhrifi, 2015 ; Kalai and Zghidi, 2019 ), showing a positive significant impact of FDI on the economic growth of Pakistan. This result also conforms to the study by Baiashvili and Gattini (2020) , claiming an increased effect of FDI on low- to middle-income level countries than on high-income countries.

The result showed a significantly positive impact of human capital on the economic growth of Pakistan. The estimated coefficient of human capital contributes to economic growth by 27%. This is also in accordance with previous research ( Bryant and Javalgi, 2016 ; Maitra, 2016 ; Musibau et al., 2019 ; Amna Intisar et al., 2020 ; Matousek and Tzeremes, 2021 ). The human capital of any country is one of the strategic factors for enhancing its economic growth. It is claimed ( Affandi et al., 2019 ) that medium- to long-run economic growth can be achieved by investing in human capital as human capital can help in building cognitive skills, which, in turn, enhances the quality of the labor force participating in developing economy in various regions. Among all other variables, human capital seems to contribute more to the economic growth of Pakistan. The benefits of all investments coming through CPEC can be realized if Pakistan develops and trains its labor with the required knowledge and skills. Thus, it can be said that human capital directly affects economic growth by expanding knowledge and skills.

The infrastructure also plays a vital role in the economic development of a country. To approximate the impact of transport and telecommunication infrastructure, this study uses a proxy of the density of roads and ICT, respectively. The appropriate proxy of ICT used in empirical estimation is a fixed telephone subscription. In this era of globalization, ICT is considered one of the prime factors that can contribute to the economic development of countries. However, few studies support this theory. A recent study ( Soomro et al., 2022 ) has shown a positive impact of ICT on economic growth in few countries, whereas negative in others. Previous studies have shown a bidirectional relationship between telecommunication infrastructure and economic growth in high-income countries ( Shiu and Lam, 2008 ; Farhadi et al., 2012 ), and a unidirectional relationship between these two was found ( Datta and Agarwal, 2004 ; Yousefi, 2011 ). However, as Pakistan is one of the developing countries that is still striving to enhance its information and communication technology infrastructure, the results of the analysis are in line with the previous literature ( Yousefi, 2011 ), showing negative relation between ICT and the economic growth of Pakistan.

Transportation infrastructure helps in boosting economic activity. There is a time gap between which transportation infrastructure is being built and benefits start to be realized. Therefore, the literature regarding the mixed results of transportation infrastructure on economic growth is also available; an insignificant relationship between transport infrastructure and economic growth ( Baum-Snow, 2013 ), whereas research on the positive impact of transportation infrastructure on economic growth is also available ( Zhang et al., 2012 ; Chatman, 2014 ; Irshad and Ghafoor, 2022 ). It is also evident from the literature that transportation infrastructure contributes positively to the economy, but not all modes of transportation contribute equally to the economy ( Hong et al., 2011 ; Diaz et al., 2016 ). The study shows an insignificant relationship between the density of roads and economic growth during the selected time frame. Although this study is in line with previous studies ( Diaz et al., 2016 ), Pakistan needs to consider the route very carefully if it wants this project to provide the expected benefits.

Conclusion and Implications

It is a widely held view that economic corridors bring several benefits to the region where they are being built. Whether be an increased economic growth or an enhanced living standard, the prosperity of a country or region is related to economic corridors. There is extensive literature available suggesting that new corridors being built in African countries have the potential to change the economic position of these countries. Similarly, as Asia is becoming a trade hub, the development of the economic corridor is necessary to fulfill the increased demands of trade ( Hussain, 2017 ). The CPEC, a sub-project of OBOR, is also serving the same purpose. This research primarily focuses on identifying the factors where China is investing to investigate whether this investment will bring the desired benefits. The results glorify the importance of developing human capital to reap the benefits of investment coming under CPEC.

The research concludes that among the factors chosen human capital investment contributes the most to the development of the economy of Pakistan. The researchers ( Musibau et al., 2019 ) claim that human capital development may lead countries toward sustainable economic growth. In countries with poorly developed education and capital markets, many qualified citizens may also be unable to find the proper skills to commit to their full potential for economic growth. The development of human capital assures foreign investment that will subsequently help in reducing poverty in developing countries and is prioritized by many researchers. CPEC is going to revolutionize the business sector of Pakistan as bilateral trade between Pakistan and China will increase. As indicated by our results, if Pakistan’s local business community wants to reap the benefits of CPEC, they must work on developing the human capital. It is claimed ( Abid et al., 2020 ) that organizations can achieve sustainability by focusing on factors affecting long-term growth and by developing skillset and knowledge base of their human capital to deal with and adapting changes. These skillset enhances their make them optimistic which in turn increases their capabilities to thrive at work leading toward good human capital ( Abid et al., 2021 ) and hence, economic growth of the country. One important aspect of the skillset of human capital, i.e., leadership, can also work wonders to utilize the benefits of CPEC to its fullest potential because ethical leadership builds the trust of employees resulting in improved work engagement and better productivity at workplace ( Ilyas et al., 2020 ). Given the importance of human capital in reaping the benefits of CPEC investment, it is recommended to pay attention to developing a knowledge base, skillset, and fair perception of employees as it can result in subjective well-being of people ( Abid et al., 2019 , 2020 ).

One of the prime areas of investment is IT infrastructure. The role of IT investment in the economic growth of the countries cannot be denied. Although these results of the analysis show a negative relationship between ICT and Economic growth, which is in line with Farhadi et al. (2012) who states that countries with low-income levels have a weaker relationship between ICT and economic growth. One of the reasons for this relationship would be the use of the proxy as data is not available on the latest proxies.

A positive nexus between FDI and the economic growth of Pakistan during the chosen period is found, as FDI is attracted more to open economies with fewer rules and regulations and having a skilled workforce and opportunities to grow. The inflow of FDI is beneficial for the host country in terms of creating employment opportunities, creating a competitive market, raising exports, and advances in technology. This is going to be a positive contributor to the economy of Pakistan as CPEC is expected to bring massive investments to Pakistan and Pakistan would be ready to benefit from its spillover effects in many more years to come.

Quite contrary to many studies focusing more on the development of transport infrastructure for better economic growth, these results show an insignificant relationship between transport infrastructure and economic growth. This is largely due to the spatial effect of such investments and time lags between when these investments were made and when one country starts realizing the benefits associated with the investment. These results are also in line with Berechman et al. (2006) who claimed that without consideration of the time lag between when transport investments were made and the realization of benefits, the results obtained can have an element of bias in it. Therefore, Pakistan must wait for some time in order to gauge the empirical effects of transportation infrastructure on economic growth.

As CPEC is expected to enhance imports and exports of the country and specific products of regions, particularly in route of CPEC, private investors would benefit from the study in identifying those sectors whose imports and exports would benefit them the most and have an impact on the overall economy. This research will be useful for researchers to conduct a similar type of research at corridors level projects all around the world like OBOR. Human capital investment can transform a developing country into a productive population, therefore, policy makers are required to emphasize investment in education.

Limitations and Future Recommendations

One of the limitations is the limited time span for study. This study can be replicated with an increased time span. Future studies can use these proxies with more reliable and complete data for selected variables for the increased time span. This research can be replicated with other proxies, or the use of indexes already developed. The study can include more variables like gross capital formation, trade openness, and exchange rates. It is yet to decide whether CPEC is going to have an implicit effect or an explicit one. To get a clear answer, the research can be done by increasing the period to gauge real times effects of CPEC-related investment. The recent economic scenario of the world has changed owing to the COVID-19 pandemic. Future studies may attempt to empirically investigate the impact of COVID-19 on CPEC investments and economic growth. The research can be extended to include the sustainability of the workforce, environment, and economic progression in the wake of CPEC before, during, and after COVID-19.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

MF conceived the idea, collected the data, and prepared writeup of the study. Z-u-R performed analysis and written the methodology part. MS refined the writeup of the study and re-checked flow of the study. All authors contributed significantly for this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbassi, Z., and Burdey, M. B. (2008). The changing paradigms of human resource in the economic development of Pakistan. IBT J. Bus. Stud. 4, 1–11.

Google Scholar

Abid, G., Ahmed, S., Elahi, N. S., and Ilyas, S. (2020). Antecedents and mechanism of employee well-being for social sustainability: a sequential mediation. Sustain. Prod. Consumpt. 24, 79–89. doi: 10.1016/j.spc.2020.06.011

CrossRef Full Text | Google Scholar

Abid, G., Arya, B., Arshad, A., Ahmed, S., and Farooqi, S. (2021). Positive personality traits and self-leadership in sustainable organizations: mediating influence of thriving and moderating role of proactive personality. Sustain. Prod. Consumpt. 25, 299–311. doi: 10.1016/j.spc.2020.09.005

Abid, G., Contreras, F., Ahmed, S., and Qazi, T. (2019). Contextual factors and organizational commitment: examining the mediating role of thriving at work. Sustainability 11:4686. doi: 10.3390/su11174686

Abid, M., and Ashfaq, A. (2015). CPEC: challenges and opportunities for Pakistan. J. Pak. Vis. 16, 142–169.

Affandi, Y., Anugrah, D. F., and Bary, P. (2019). Human capital and economic growth across regions: a case study in Indonesia. Eurasian Econ. Rev. 9, 331–347. doi: 10.1007/s40822-018-0114-4

Agbola, F. W. (2013). Does human capital constrain the impact of foreign direct investment and remittances on economic growth in Ghana? Appl. Econ. 45, 2853–2862. doi: 10.1016/j.vhri.2017.08.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Ahmed, A. (2017). FDI Jumps on Rising Chinese Investment. Dawn News. Available online at: https://www.dawn.com/news/1338062/fdi-jumps-on-rising-chinese-investment (accessed July 9, 2017).

Ahmed, A., Arshad, M. A., Mahmood, A., and Akhtar, S. (2017). Neglecting human resource development in OBOR, a case of the China–Pakistan economic corridor (CPEC). J. Chin. Econ. Foreign Trade Stud. 10, 130–142. doi: 10.1108/JCEFTS-08-2016-0023

Ahmed, M., and Azam, M. (2016). Causal nexus between energy consumption and economic growth for high, middle and low income countries using frequency domain analysis. Renew. Sustain. Energ. Rev. 60, 653–678. doi: 10.1016/j.rser.2015.12.174

Ahmed, E. M., and Ridzuan, R. (2013). The impact of ICT on East Asian economic growth: panel estimation approach. J. Knowl. Econ. 4, 540–555.

Akalpler, E., and Adil, H. (2017). The impact of foreign direct investment on economic growth in Singapore between 1980 and 2014. Eurasian Econ. Rev. 7, 435–450. doi: 10.1007/s40822-017-0071-3

Ali, A. (2016). China Pakistan economic corridor: prospects and challenges for regional integration. Arts Soc. Sci. J. 7, 1–5. doi: 10.4172/2151-6200.1000204

Ali, M. (2018). Pakistan’s quest for coal-based energy under the China-Pakistan economic corridor (CPEC): implications for the environment. Environ. Sci. Pollut. Res. 25, 31935–31937. doi: 10.1007/s11356-018-3326-y

Ali, M. H., and Asghar, M. M. T. (2016). “The role of the sectoral composition of foreign direct investment on economic growth: a policy proposal for CPEC and regional partners,” in Proceedings of the 32 AGM and Conference, Pakistan Society of Development Economists , (Islamabad: Pakistan Institute of Development Economics).

Ali, M. M., and Faisal, F. (2017). CPEC, SEZ (Special Economic Zones) and Entrepreneurial Development Prospects in Pakistan. Pak. Dev. Rev . 56, 143–155.

Ali, T., Ali, W., Ali, M., Raza, B., and Niazi, A. A. K. (2018b). China-Pak economic corridor (CPEC): economic transformation-challenges and opportunities for the local residents. J. S. Asian Stud. 4, 17–30.

Ali, L., Mi, J., Shah, M., Shah, S. J., Khan, S., Ullah, R., et al. (2018a). Local residents’ attitude towards road and transport infrastructure (a case of China Pakistan economic corridor). J. Chin. Econ. Foreign Trade Stud. 11, 104–120. doi: 10.1108/JCEFTS-08-2017-0024

Al-Mulali, U., Fereidouni, H. G., and Lee, J. Y. (2014). Electricity consumption from renewable and non-renewable sources and economic growth: evidence from Latin American countries. Renew. Sustain. Energy Rev. 30, 290–298.

Amarawickrama, H. A., and Hunt, L. C. (2008). Electricity demand for Sri Lanka: a time series analysis. Energy 33, 724–739. doi: 10.1016/j.energy.2007.12.008

Amna Intisar, R., Yaseen, M. R., Kousar, R., Usman, M., and Makhdum, M. S. A. (2020). Impact of trade openness and human capital on economic growth: a comparative investigation of Asian countries. Sustainability 12:2930. doi: 10.3390/su12072930

Andrés, L., Biller, D., and Dappe, H. (2013). Reducing Poverty by Closing South Asia’s Infrastructure Gap. Washington, DC: The World Bank.

Aneja, A. (2016). Milestones on Beijing’s OBOR Plan. Available online at: http://www.thehindu.com/opinion/columns/milestones-on-beijings-obor-plan/article8394515.ece (accessed July 3, 2017)

Arif, M. (2015). CPEC to be Guarantor of Future Progress, Unity of Federation of Pakistan: Senator Mushahid. Available online at: http://www.pakistanchina.com/newsdetail.php?id=NTE2&pageid=news (accessed November 21, 2017).

Armeanu, D. S., Joldes, C. C., Gherghina, S. C., and Andrei, J. V. (2021). Understanding the multidimensional linkages among renewable energy, pollution, economic growth and urbanization in contemporary economies: quantitative assessments across different income countries’ groups. Renew. Sustain. Energy Rev. 142:110818.

Asongu, S. A., and Odhiambo, N. M. (2020). Foreign direct investment, information technology and economic growth dynamics in Sub-Saharan Africa. Telecommun. Policy 44:101838. doi: 10.1016/j.telpol.2019.101838

Asrar-ul-Haq, M. (2015). Human resource development in Pakistan: evolution, trends and challenges. Hum. Resour. Dev. Int. 18, 97–104.

Atique, Z., Ahmad, M. H., Azhar, U., and Khan, A. H. (2004). The impact of FDI on economic growth under foreign trade regimes: a case study of Pakistan [with comments]. Pak. Dev. Rev. 43, 707–718.

Azman-Saini, W. N. W., Baharumshah, A. Z., and Law, S. H. (2010). Foreign direct investment, economic freedom and economic growth: international evidence. Econ. Model. 27, 1079–1089.

Bahrini, R., and Qaffas, A. A. (2019). Impact of information and communication technology on economic growth: evidence from developing countries. Economies 7:21. doi: 10.3390/economies7010021

Baiashvili, T., and Gattini, L. (2020). EIB Working Papers-Impact of FDI on Economic Growth: The Role of Country Income Levels and Institutional Strength (No. 2020/02). Luxembourg: European Investment Bank.

Balasubramanyam, V. N., Salisu, M., and Sapsford, D. (1996). Foreign direct investment and growth in EP and IS countries. Econ. J. 106, 92–105.

Bashier, A. A., and Siam, A. J. (2014). Immigration and economic growth in Jordan: FMOLS approach. Int. J. Humanit. Soc. Sci. Educ. 1, 85–92.

Baum-Snow, N. (2013). Urban Transport Expansions, Employment Decentralization, and the Spatial Scope of Agglomeration Economies (Working Paper). Providence, RI: Brown University.

Bende-Nabende, A., Ford, J. L., Santoso, B., and Sen, S. (2003). The interaction between FDI, output and the spillover variables: co-integration and VAR analyses for APEC, 1965-1999. Appl. Econ. Lett. 10, 165–172. doi: 10.1080/1350485022000044057

Berechman, J., Ozmen, D., and Ozbay, K. (2006). Empirical analysis of transportation investment and economic development at state, county and municipality levels. Transportation 33, 537–551. doi: 10.1007/s11116-006-7472-6

Board of Investment, Government of Pakistan (2015). Country Wise FDI Inflows in Pakistan. Available online at: http://boi.gov.pk/ForeignInvestmentinPakistan.aspx (accessed April 12, 2016).

Bryant, C. E., and Javalgi, R. G. (2016). Global economic integration in developing countries: the role of corruption and human capital investment. J. Bus. Ethics 136, 437–450. doi: 10.1007/s10551-014-2490-3

Bucci, A. (2014). Population, competition, innovation, and economic growth with and without human capital investment. Int. Rev. Econ. 61, 61–84. doi: 10.1007/s12232-013-0192-2

Chatman, D. G. (2014). Estimating the effect of land use and transportation planning on travel patterns: three problems in controlling for residential self-selection. J. Transp. Land Use 7, 47–56.

Chaudhry, N. I., Mehmood, A., and Mehmood, M. S. (2013). Empirical relationship between foreign direct investment and economic growth: an ARDL co-integration approach for China. China Finance Rev. Int. 3, 26–41. doi: 10.1108/20441391311290767

Clark, X., Dollar, D., and Micco, A. (2004). Port efficiency, maritime transport costs, and bilateral trade. J. Dev. Econ. 75, 417–450.

Crespo, N., and Fontoura, M. P. (2007). Determinant factors of FDI spillovers–what do we really know? World Dev. 35, 410–425. doi: 10.1111/idj.12160

Datta, A., and Agarwal, S. (2004). Telecommunications and economic growth: a panel data approach. Appl. Econ. 36, 1649–1654. doi: 10.1007/s11356-021-17755-1

Dedrick, J., Gurbaxani, V., and Kraemer, K. L. (2003). Information technology and economic performance: a critical review of the empirical evidence. ACM Comput. Surveys 35, 1–28. doi: 10.1145/641865.641866

Dhrifi, A. (2015). Foreign direct investment, technological innovation and economic growth: empirical evidence using simultaneous equations model. Int. Rev. Econ. 62, 381–400. doi: 10.1007/s12232-015-0230-3

Di Fabio, A., and Peiroì, J. M. (2018). Human capital sustainability leadership to promote sustainable development and healthy organizations: a new scale. Sustainability 10:2413. doi: 10.1186/s13012-016-0452-0

Diaz, R., Behr, J. G., and Ng, M. (2016). Quantifying the economic and demographic impact of transportation infrastructure investments: a simulation study. Simulation 92, 377–393.

Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74, 427–431.

Ding, C. (2013). Transport development, regional concentration and economic growth. Urban Stud. 50, 312–328.

Dritsakis, N. (2004). Tourism as a long-run economic growth factor: an empirical investigation for Greece using causality analysis. Tour. Econ. 10, 305–316.

Economic Times (2016). Pakistan-China 820-Kilometre-Long Optical-Fibre Cable Project Launched. Available online at: http://telecom.economictimes.indiatimes.com/news/pakistan-china-820-kilometrelong-optical-fibre-cable-project-launched/52349319 (accessed May 19, 2016)

Ederer, P., Schuler, P., and Willms, S. (2007). The European Human Capital Index: The Challenge of Central and Eastern Europe , Vol. 2. Belgium: Lisbon Council Policy Brief.

Edmonds, C., and Fujimura, M. (2008). “Road infrastructure and regional economic integration: evidence from the Mekong,” in Infrastructure and Trade in Asia , Vol. 143, eds D. H. Brooks and J. Menon (Cheltenham: Edward Elgar), 172.

Engle, R. F., and Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica 55, 251–276.

Estache, A., and Iimi, A. (2008). Procurement Efficiency for Infrastructure Development and Financial Needs Reassessed. World Bank Policy Research Working Paper No. 4662. Washington, DC: World Bank.

Falki, N. (2009). Impact of foreign direct investment on economic growth in Pakistan. Int. Rev. Bus. Res. Pap. 5, 110–120.

Farhadi, M., Ismail, R., and Fooladi, M. (2012). Information and communication technology use and economic growth. PLoS One 7:e48903. doi: 10.1371/journal.pone.0048903

Fereidouni, H. G., and Al-Mulali, U. (2014). The interaction between tourism and FDI in real estate in OECD countries. Curr. Issues Tour . 17, 105–113. doi: 10.1080/13683500.2012.733359

Glaeser, E. L., La Porta, R., Lopez-de-Silanes, F., and Shleifer, A. (2004). Do institutions cause growth? J. Econ. Growth 9, 271–303.

Haider, M. (2019). Global Competitiveness Index: Pakistan slips to 110th from 107th Position Among 141 Countries. Karachi: The News International.

Hansen, H., and Rand, J. (2006). On the causal links between FDI and growth in developing countries. World Econ. 29, 21–41.

Hong, J., Chu, Z., and Wang, Q. (2011). Transport infrastructure and regional economic growth: evidence from China. Transportation 38, 737–752. doi: 10.1007/s11116-011-9349-6

Hosan, S., Karmaker, S. C., Rahman, M. M., Chapman, A. J., and Saha, B. B. (2022). Dynamic links among the demographic dividend, digitalization, energy intensity and sustainable economic growth: empirical evidence from emerging economies. J. Clean. Prod. 330:129858. doi: 10.1016/j.jclepro.2021.129858

Hussain, E. (2017). China–Pakistan economic corridor: will it sustain itself? Fudan J. Humanit. Soc. Sci. 10, 145–159. doi: 10.3390/ijerph182312832

Hye, Q. M. (2011). Financial development index and economic growth: empirical evidence from India. J. Risk Finance 12, 98–111. doi: 10.1108/15265941111112820

Ibrahim, M., and Alagidede, P. (2018). Nonlinearities in financial development–economic growth nexus: evidence from sub-Saharan Africa. Res. Int. Bus. Finance 46, 95–104. doi: 10.1016/j.ribaf.2017.11.001

Ilyas, S., Abid, G., and Ashfaq, F. (2020). Ethical leadership in sustainable organizations: the moderating role of general self-efficacy and the mediating role of organizational trust. Sustain. Prod. Consumpt. 22, 195–204. doi: 10.1016/j.spc.2020.03.003

Iqbal, S., Chu, J., and Hali, S. M. (2019a). Projecting impact of CPEC on Pakistan’s electric power crisis. Chin. J. Popul. Resour. Environ. 17, 310–321.

Iqbal, K., Hassan, S. T., and Peng, H. (2019b). Analyzing the role of information and telecommunication technology in human development: panel data analysis. Environ. Sci. Pollut. Res. 26, 15153–15161. doi: 10.1007/s11356-019-04918-4

Irshad, R., and Ghafoor, N. (2022). Infrastructure and economic growth: evidence from lower middle-income countries. J. Knowl. Econ. 1–19. doi: 10.1007/s13132-021-00855-1

ITU (2015). Global Cybersecurity & Cyberwelness profiles. Available online at: http://handle.itu.int/11.1002/pub/80c63097-en

Jakhon, K. S. (2021). Analysis of factors of intensive economic growth in Uzbekistan. JournalNX 7, 310–315. doi: 10.1001/jamaoncol.2021.6987

Jalava, J., and Pohjola, M. (2008). The roles of electricity and ICT in economic growth: case Finland. Explor. Econ. Hist. 45, 270–287.

Javid, M. (2019). Public and private infrastructure investment and economic growth in Pakistan: an aggregate and disaggregate analysis. Sustainability 11:3359. doi: 10.3390/su11123359

Jawad, R. (2013). Chinese Firms Ready to Invest Billions of Dollars in Pakistan. Available online at: https://www.thenews.com.pk/archive/print/631508-chinese-firms-ready-to-invest-billions-of-dollars-in-pakistan accessed (June 15, 2017)

Jebran, K., Iqbal, A., Rao, Z. U. R., and Ali, A. (2018). Effects of terms of trade on economic growth of Pakistan. Foreign Trade Rev. 53, 1–11. doi: 10.1177/0015732516663315

Johansen, S. (1988). Statistical analysis of cointegration vectors. J. Econ. Dyn. Control 12, 231–254.

Johansen, S., and Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with appucations to the demand for money. Oxf. Bull. Econ. Stat. 52, 169–210.

Kalai, M., and Zghidi, N. (2019). Foreign direct investment, trade, and economic growth in MENA countries: empirical analysis using ARDL bounds testing approach. J. Knowl. Econ. 10, 397–421. doi: 10.1007/s13132-017-0460-6

Khalafalla, K. Y., and Webb, A. J. (2001). Export–led growth and structural change: evidence from Malaysia. Appl. Econ. 33, 1703–1715.

Khan, M. G. M., Reddy, K. G., and Rao, D. K. (2015). Designing stratified sampling in economic and business surveys. J. Appl. Stat. 42, 2080–2099. doi: 10.1080/02664763.2015.1018674

Khan, S., and Liu, G. (2019). The China–Pakistan economic corridor (CPEC): challenges and prospects. Area Dev. Policy 4, 466–473. doi: 10.1016/j.marpolbul.2020.111422

Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., and Jianqiu, Z. (2018). The dynamics of ICT, foreign direct investment, globalization and economic growth: panel estimation robust to heterogeneity and cross-sectional dependence. Telemat. Inf. 35, 318–328. doi: 10.1016/j.tele.2017.12.006

Lee, H., Kwak, N., Campbell, S. W., and Ling, R. (2014). Mobile communication and political participation in South Korea: examining the intersections between informational and relational uses. Comput. Hum. Behav. 38, 85–92.

Lipsey, R. E. (2004). “Home-and host-country effects of foreign direct investment,” in Challenges to Globalization: Analyzing the Economics , ed. L. A. Winters (Chicago, IL: University of Chicago Press), 333–382.

Mahmood, S., Sabir, M., and Ali, G. (2020). Infrastructure projects and sustainable development: discovering the stakeholders’ perception in the case of the China–Pakistan economic corridor. PLos One 15:e0237385. doi: 10.1371/journal.pone.0237385

Maitra, B. (2016). Investment in human capital and economic growth in Singapore. Glob. Bus. Rev. 17, 425–437.

Majeed, A., Jiang, P., Ahmad, M., Khan, M. A., and Olah, J. (2021). The impact of foreign direct investment on financial development: new evidence from panel cointegration and causality analysis. J. Competitiveness 13, 95–112. doi: 10.1007/s11356-016-7413-7

Matousek, R., and Tzeremes, N. G. (2021). The asymmetric impact of human capital on economic growth. Empir. Econ. 60, 1309–1334. doi: 10.1007/s00181-019-01789-z

McCartney, M. (2021). The prospects of the China–Pakistan Economic Corridor (CPEC): the importance of understanding western China. Contemp. South Asia 29, 358–375. doi: 10.1080/09584935.2020.1855112

McCartney, M. (2022). The China-Pakistan economic corridor (CPEC): infrastructure, social savings, spillovers, and economic growth in Pakistan. Eurasian Geogr. Econ. 63, 180–211. doi: 10.1080/15387216.2020.1836986

Mehar, A. (2017). Infrastructure development, CPEC and FDI in Pakistan: is there any connection? Transnatl. Corp. Rev. 9, 232–241.

Mun, H. W., Lin, T. K., and Man, Y. K. (2008). FDI and economic growth relationship: an empirical study on Malaysia. Int. Bus. Res. 1, 11–18. doi: 10.5539/ibr.v1n2p11

Musibau, H. O., Yusuf, A. H., and Gold, K. L. (2019). Endogenous specification of foreign capital inflows, human capital development and economic growth. Int. J. Soc. Econ. 46, 454–472. doi: 10.1108/IJSE-04-2018-0168

Nabi, A. A., Tunio, F. H., Azhar, M., Syed, M. S., and Ullah, Z. (2022). Impact of information and communication technology, financial development, and trade on economic growth: empirical analysis on N11 countries. J. Knowl. Econ. 1–18. doi: 10.1007/s13132-022-00890-6

Naeruz, M., Afiffudin, S., Ruslan, D., and Syafii, M. (2022). The impact of economic growth on technological developments, emoneys and fluctuations interest rates and exchange rates in Indonesia. E3S Web Conf. 339:05008. doi: 10.1051/e3sconf/202233905008

Niebel, T. (2018). ICT and economic growth–Comparing developing, emerging and developed countries. World Dev. 104, 197–211. doi: 10.1016/j.worlddev.2017.11.024

Olimpia, N. (2013). Human capital: cause and effect of the economic growth. An empirical analysis. Ann. Fac. Econ. 1, 726–735.

O’Mahony, M. (2012). Human capital formation and continuous training: evidence for EU countries. Rev. Income Wealth 58, 531–549. doi: 10.1111/j.1475-4991.2011.00476.x

Olofin, O. P., Aiyegbusi, O. O., and Adebayo, A. A. (2019). Analysis of foreign direct investment and economic growth in Nigeria: application of spatial econometrics and fully modified ordinary least square (FMOLS). Foreign Trade Rev. 54, 159–176. doi: 10.1177/0015732519851631

Padilla-Perez, R., and Nogueira, C. G. (2016). Outward FDI from small developing economiesFirm level strategies and home-country effects. Int. J. Emerg. Markets 11, 693–714. doi: 10.1108/IJoEM-11-2015-0236

Peng, Z., and Wu, Q. (2020). Evaluation of the relationship between energy consumption, economic growth, and CO2 emissions in China’transport sector: the FMOLS and VECM approaches. Environ. Dev. Sustain. 22, 6537–6561. doi: 10.1007/s10668-019-00498-y

Pfeffer, J. (2010). Building sustainable organizations: the human factor. Acad. Manage. Perspect. 24, 34–45.

Phillips, P. C., and Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I (1) processes. Rev. Econ. Stud. 57, 99–125.

Phillips, P. C., and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika 75, 335–346.

Piatkowski, M. (2004). The Impact of ICT on Growth in Transition Economies. MPRA Paper No. 29399. Available online at https://mpra.ub.uni-muenchen.de/29399/ (accessed March 14, 2011).

Piatkowski, M. (2006). Can information and communication technologies make a difference in the development of transition economies? Inf. Technol. Int. Dev. 3, 39–53.

Pradhan, R. P., Arvin, M. B., Hall, J. H., and Nair, M. (2016). Innovation, financial development and economic growth in Eurozone countries. Appl. Econ. Lett. 23, 1141–1144.

Qureshi, A. H. (2015). China/Pakistan economic corridor: a critical national and international law policy based perspective. Chin. J. Int. Law 14, 777–799.

Ramay, S. A. (2016). China Pakistan Economic Corridor: A Chinese Dream Being Materialized Through Pakistan. Islamabad: Sustainable Development Policy Institute.

Ranjan, A. (2015). The China–Pakistan Economic Corridor: Options Before India. Delhi: Institute of Chinese Studies.

Ray, S. (2012). Impact of foreign direct investment on economic growth in India: a co integration analysis. Adv. Inf. Technol. Manage. 2, 187–201.

Rohman, I. K., and Bohlin, E. (2014). Decomposition analysis of the telecommunications sector in Indonesia: what does the cellular era shed light on? Telecommun. Policy 38, 248–263.

Sahoo, P., and Dash, R. K. (2009). Infrastructure development and economic growth in India. J. Asia Pac. Econ. 14, 351–365.

Shah, S. (2015). China’s xi Jinping Launches Investment Deal in Pakistan. New York, NY:The Wall Street Journal.

Shiu, A., and Lam, P. L. (2008). Causal relationship between telecommunications and economic growth in China and its regions. Reg. Stud. 42, 705–718.

Soomro, A. N., Kumar, J., and Kumari, J. (2022). The dynamic relationship between FDI, ICT, trade openness, and economic growth: evidence from BRICS countries. J. Asian Finance Econ. Bus. 9, 295–303.

Sridhar, K. S., and Sridhar, V. (2007). Telecommunications infrastructure and economic growth: evidence from developing countries. Appl. Econ. Int. Dev. 7, 37–56.

Srivastava, S., and Talwar, S. (2020). Decrypting the dependency relationship between the triad of foreign direct investment, economic growth and human development. J. Dev. Areas 54, 1–14.

Stevens, A. (2015). Pakistan Lands $46 Billion Investment from China. New York, NY: CNN Money.

Stock, J. H., and Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica 61, 783–820.

Straub, S. (2008). Infrastructure and Growth in Developing Countries: Recent Advances and Research Challenges. World Bank Policy Research Working Paper No. 4460). Washington, DC: World Bank, doi: 10.1596/1813-9450-4460

Tasneem, N. (2018). “Assessment of labor market dynamics in the cities of Punjab: implications for CPEC,” in Proceedings of the 34th Annual Conference of the Pakistan Society of Development Economics , Islamabad.

The Express Tribune (2017). CPEC Investment Pushed From $55b to $62b. Karachi: The Express Tribune.

Van Reenen, J., Bloom, N., Draca, M., Kretschmer, T., Sadun, R., Overman, H., et al. (2010). The Economic Impact of ICT. Final Report . London: Centre for Economic Performance, London School of Economics.

Vu, K. M. (2011). ICT as a source of economic growth in the information age: empirical evidence from the 1996–2005 period. Telecommun. Policy 35, 357–372.

Xia, C., Qamruzzaman, M., and Adow, A. H. (2022). An asymmetric nexus: remittance-led human capital development in the top 10 remittance-receiving countries: are FDI and gross capital formation critical for a road to sustainability? Sustainability 14:3703.

Younus, H. S., Sohail, A., and Azeem, M. (2014). Impact of foreign direct investment on economic growth in Pakistan. World J. Econ. Finance 1, 2–5.

Yousefi, A. (2011). The impact of information and communication technology on economic growth: evidence from developed and developing countries. Econ. Innov. New Technol. 20, 581–596.

Zhang, L., Hong, J., Nasri, A., and Shen, Q. (2012). How built environment affects travel behavior: a comparative analysis of the connections between land use and vehicle miles traveled in US cities. J. Transp. Land Use 5, 40–52.

Zhang, L., and Levinson, D. (2007). “The economics of transportation network growth,” in Essays on Transport Economics , eds P. Coto-Millán and V. Inglada (Heidelberg: Physica-Verlag HD), 317–339.

Zhuang, Y., Yang, S., Razzaq, A., and Khan, Z. (2021). Environmental impact of infrastructure-led Chinese outward FDI, tourism development and technology innovation: a regional country analysis. J. Environ. Plan. Manage. 1–33. doi: 10.1007/978-3-7908-1765-2_18

Zia, M. M., Malik, B. A., and Waqar, S. (2018). Special Economic Zones (SEZs): a comparative analysis for CPEC SEZs in Pakistan. Pak. J. Soc. Sci. 9, 37–60.

Keywords : IT infrastructure, FDI, human capital, transport infrastructure, economic growth

Citation: Farooq M, Rao Z-u-R and Shoaib M (2022) Surge in Economic Growth of Pakistan: A Case Study of China Pakistan Economic Corridor. Front. Psychol. 13:900926. doi: 10.3389/fpsyg.2022.900926

Received: 21 March 2022; Accepted: 01 June 2022; Published: 08 August 2022.

Reviewed by:

Copyright © 2022 Farooq, Rao and Shoaib. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zia-ur-Rehman Rao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.