 Research
 Open Access
Innovation, total factor productivity and economic growth in Pakistan: a policy perspective
 Hummera Saleem^{1}Email author,
 Malik Shahzad^{2},
 Muhammad Bilal Khan^{3} and
 Bashir Ahmad Khilji^{4}
https://doi.org/10.1186/s4000801901346
© The Author(s) 2019
 Received: 22 September 2018
 Accepted: 10 January 2019
 Published: 20 February 2019
Abstract
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 laborintensive technology and exportoriented 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.
Keywords
 Innovation
 Economic growth
 Total factor production
 Pakistan
JEL Classification
 D24
 D33
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 nontechnological factors are two main divisions of innovation, where new production and services are related to the technological innovation and nontechnological 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 nonresidential) 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 uptodate 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 longrun 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; OrtegaArgilés 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 longterm 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 twostage 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).
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´guezClaire 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).
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 steadystate 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 capitalinput ratio is stable over time.
3.2 The total factor production and its potential drivers
3.3 Variables description
Klenow and Rodri´guezClaire (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 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.^{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 (Kortum1997). Also, Griliches (1989) and Joutz and Gardner (1996) discussed that patent applications are a significant measure of technological output. Followed the information of BravoOrtega 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, macroinstability and uncertainty (Daude and FernándezArias 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 nonavailability 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 FernándezArias 2010; Zhang et al. 2014) and play an important role in facilitating TFP catchup 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 valueadded 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
Results of growth accounting
Years  % TFPG (average)  % ∆ in TFP (average)  % Contribution of TFP in GDPG (per capita) (average)  % ∆ in GDPG 

1972–1976  0.983  0.628  7.208  0.258 
1977–1981  1.012  0.350  7.386  0.628 
1982–1986  1.027  0.615  7.630  0.637 
1987–1991  1.073  0.879  7.857  0.499 
1992–1996  1.112  0.585  8.042  0.405 
1997–2001  1.116  0.179  8.197  0.313 
2002–2006  1.118  − 0.076  8.337  0.513 
2007–2011  1.113  − 0.037  8.572  0.344 
2012–2016  1.146  0.774  8.674  0.291 
Results of growth accounting (alternative approach)
Years  %TFPG (average)  % ∆ in TFP (average)  Labor productivity growth (per worker in average) 

1972–1976  1.041  0.775  3.872 
1977–1981  1.057  0.476  3.889 
1982–1986  1.103  0.812  3.962 
1987–1991  1.159  0.943  4.064 
1992–1996  1.213  0.852  4.109 
1997–2001  1.236  0.475  4.136 
2002–2006  1.273  0.611  4.161 
2007–2011  1.283  − 0.057  4.192 
2012–2016  1.314  0.649  4.186 
4.1 Results of potential drivers of TFP
The determinants of TFP (2SLS method)
Description  Model 1  Model 2  Model 3  Model 4  Model 5  Model 6 

TFP(− 1)  0.91* (− 3.38)  0.93* (3.90)  
INF  − 0.02* (− 2.62)  0.04*** (− 1.90)  − 0.02* (− 2.50)  − 0.02*** (− 1.86)  − 0.02*** (− 1.70)  − 0.02 (− 0.325) 
FDI  0.13* (− 3.22)  − 0.04 (− 0.83)  − 0.03 (− 1.22)  − 0.04 (− 0.63)  0.10* (− 4.68)  
TRD  0.04* (5.85)  0.05** (− 2.36)  
LGDP  1.17* (− 2.88)  0.80* (− 5.78)  1.06* (− 9.63)  
PRL  0.01 (− 0.9)  
EDU  0.19*** (− 1.97)  0.12** (− 1.97)  0.30* (− 2.54)  
IMM  0.03** (− 1.98)  0.03*** (− 1.97)  0.03* (− 9.63)  0.06 (− 0.24)  
LPT  0.05*** (− 1.97)  
CONSTANT  0.49 (− 1.59)  − 3.53 (− 1.42)  0.48 (− 1.59)  − 1.716 (− 2.15)  0.03 (− 0.04)  − 0.96 (− 1.19) 
Diagnostic tests  
Rsquared  0.23  0.85  0.3  0.65  0.86  0.3 
Adjusted Rsquared  0.22  0.83  0.28  0.6  0.77  0.2 
JB normality test  2.53 (0.28)  
Breusch–Godfrey LM test  1.68(0.23)  
ARCH test  1.38 (0.82)  
Ramsey reset test  t = 0.74 (0.31)  
Durbin–Watson stat  1.78 

Innovation and its spillover effects

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).

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 macroinstability, 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 FernándezArias 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 investmenttoGDP 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 nonstationary 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 nonstationary issue, it would lead to cause of spurious regression. Numerous econometric methods like method of Johansen multivariate cointegration, Engle Granger and the recently developed ARDL method (Pesaran et al. (2001)) for evaluating the time series data, can be used.
The longrun as well as the shortrun correlation between endogenous and exogenous variables can be analyzed by several econometric models, which are available in the several published literatures. The autoregressive distributed lags (ARDL) are designed by Pesaran et al. (2001) to observe the longrun and shortrun 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 Cointegration analysis (ARDL)
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). Nonstandard 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 multistep procedure conditional on the calculating of a unit root problem, a cointegration rank and as well as cointegration 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 “overfitted “or an “augmented” VAR that is valid irrespective of the cointegration 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 Chisquared (\( \chi^{2} \)) 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 nonstationarity. In this respect, the ADF root test is conducted at 5% level of significance.
6 Estimation and analysis
6.1 Unit root analysis
Unit root testing
Variables  Level  First difference  

Constant  Constant plus trend  Constant  Constant plus trend  Conclusion  
\( {\text{LPN}}_{\text{t}} \)  − 2.44  − 2.68  − 8.51*  − 8.45*  I(1) 
\( {\text{LPR}}_{\text{t}} \)  − 3.84**  − 3.46**  − 9.88*  − 9.79*  I(0) 
\( {\text{GDP}}_{\text{t}} \)  − 0.38  − 1.69  − 5.30*  − 5.30*  I(1) 
\( {\text{TFP}}_{\text{t}} \)  − 2.69**  − 1.12  − 5.24*  − 6.69*  I(0) 
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 nonstationary at all levels and all of them become stationary at I(1) first difference.
6.2 Lags selection
However, to find out the cointegration 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 cointegration.
The next step is to evaluate the F statistic calculated with critical bounds value by Turner (2006) to investigate the longrun (cointegration) relationship between variables existing or not. If calculated Fstatistic value is greater than upper critical bound values, then it shows that longrun (cointegration) relationship exists among variables. If computed Fstatistics value is lower than lower critical bound value, then there is no cointegration. The decision of cointegration 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 cointegrated. The next stage is related to the model of ARDL and to check the longrun association existing between the entire variables. The ARDL cointegration model is estimated in the following table, where the estimation of the longrun 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 longrun 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
Lags selection
No of lags  AIC  SC 

1  − 5.113  − 3.476 
2  − 9.100  − 9.012 
3  − 5.126  − 3.978 
4  − 4.921  − 2.796 
Wald test
Level of significance  Critical values  WALD test (F value)  

Lower limit  Upper limit  
1%  4.29  5.61  
5%  3.23  4.45  6.87 
10%  2.27  3.77 
7.1 Discussion on the results of longrun analysis and error correction model
Results of normalized longrun coefficients
Variables  Coefficients  T scores  Probability (P values) 

\( {\text{LPR}}_{\text{t}} \)  0.366  11.274  0.000* 
\( {\text{TFP}}_{\text{t}} \)  0.523  12.984  0.000* 
\( {\text{LPN}}_{\text{t}} \)  0.181  9.546  0.001* 
Results of error correction model
Variables  Coefficient  T scores  Probability (P values) 

\( {\text{D}}({\text{GDP}}_{\text{t}} \)(− 1))  − 0.157  − 1.277  0.211 
\( {\text{D}}({\text{LPR}}_{\text{t}} \))  0.018  2.509  0.017** 
\( {\text{D}}({\text{LPR}}_{\text{t}} \)(− 1))  0.018  2.094  0.044** 
\( {\text{D}}({\text{TFP}}_{\text{t}} \))  0.016  0.554  0.583 
\( {\text{D}}({\text{TFP}}_{\text{t}} \)(− 1))  0.016  2.836  0.008* 
\( {\text{D}}({\text{LPN}}_{\text{t}} \))  − 0.010  − 2.234  0.033** 
\( {\text{D}}({\text{LPN}}_{\text{t}} \)(− 1))  0.010  1.963  0.058*** 
ECM(− 1)  − 0.154  − 4.432  0.000* 
Constant  0.360  4.400  0.000* 
Diagnostic tests  
Rsquared  0.982  
Adjusted Rsquared  0.97  
JB normality test  1.09 (0.77)  
Breusch–Godfrey LM test  5.17(0.33)  
ARCH test  1.48 (0.82)  
Ramsey reset test  T = 0.84 (0.31)  
Durbin–Watson stat  1.91 
7.2 The findings of modified WALD test with the unrestricted level VAR (k + d _{maxi}) system
Statistical results of unrestricted cointegration rank test (maximum eigenvalue)
Hypothesized  Eigen value  Max–eigenvalue  0.05  

No. of CE (s)  Statistic  Critical value  Prob.** (values)  
None*  0.523  57.144  47.856  0.005 
At most 1*  0.314  28.227  29.797  0.075 
At most 2*  0.264  13.481  15.494  0.098 
At most 3  0.037  1.508  3.841  0.219 
Statistical results of Granger causality (MWALD test)
Variables  Description  Chi square  df  Prob 

LPN  Does not Granger cause GDP  12.960  2  0.002* 
LPR  Does not Granger cause GDP  14.225  2  0.001* 
TFP  Does not Granger cause GDP  25.358  2  0.000* 
GDP  Does not Granger cause LPN  6.988  2  0.030** 
LPR  Does not Granger cause LPN  7.853  2  0.021** 
TFP  Does not Granger cause LPN  2.693  2  0.261 
GDP  Does not Granger cause LPR  11.588  2  0.003* 
LPN  Does not Granger cause LPR  8.974  2  0.011* 
TFP  Does not Granger cause LPR  5.823  2  0.054** 
GDP  Does not Granger cause TFP  10.056  2  0.072*** 
LPN  Does not Granger cause TFP  8.020  2  0.020** 
LPR  Does not Granger cause TFP  0.509  2  0.775 
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 cointegrated. 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 nonresidential) 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 longterm 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.
Declarations
Authors’ contributions
The HS is the main author of the research, other coauthors 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.
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References
 Alcalá F, Ciccone A (2004) Trade and productivity. Q J Econ 119:613–646View ArticleGoogle Scholar
 Arellano M, Bover O (1995) Another look at the instrumental variable estimation oferrorcomponents models. J Econ 68:29–51View ArticleGoogle Scholar
 Barro RJ, SalaiMartin X (2004) Economic growth. MIT Press, CambridgeGoogle Scholar
 Benhabib J, Spiegel MM (2005) Human capital and technology diffusion. In: Aghion P, Durlauf S (eds) Handbook of economic growth. Elsevier, AmsterdamGoogle Scholar
 Black SE, Lynch LM (1995) Beyond the incidence of training: evidence from a national employers survey. NBER Working Paper No. 5231. NBER, CambridgeGoogle Scholar
 Blundell R, Bond S (1998) Conditions and moments restrictions in dynamic panel data models. J Econ 87:115–143View ArticleGoogle Scholar
 Borensztein E, De Gregorio J, Lee JW (1998) How does foreign direct investment affect economic growth? J Int Econ 45:115–135View ArticleGoogle Scholar
 BravoOrtega 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 View ArticleGoogle Scholar
 Cassiman B, Golovko E (2011) Innovation and internationalization through exports. J Int Bus Stud 42(1):56–75View ArticleGoogle Scholar
 Christensen CMCM (1997) The innovator’s Dilemma: when new technologies cause great firms to fail. Harvard Business School Press, BostonGoogle Scholar
 Daude C (2010) Innovation, productivity and economic development in Latin America and the Caribbean. Development centre working papers. http://www.oecd.org/dev/wpGoogle Scholar
 Daude C, FernándezArias E (2010) On the role of aggregate productivity and factor accumulation for economic development in Latin America and the Caribbean, IDBWP155. Inter American Development Bank, Washington DCGoogle Scholar
 Diewert WE, Morrison CJ (1986) Adjusting output and productivity indexes for changes in the terms of trade. Econ J 96:659–679View ArticleGoogle Scholar
 Dollar D, Kraay A (2002) Institutions, trade, and growth. J Monet Econ 50:133–162View ArticleGoogle Scholar
 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 ChileGoogle Scholar
 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 View ArticleGoogle Scholar
 Gollin D (2002) Getting income shares right’. J Polit Econ 110(2):458–474View ArticleGoogle Scholar
 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):883–895View ArticleGoogle Scholar
 Griffth R, Huergo E, Mairesse J, Peters B (2006) Innovation and productivity across four European Countries. Oxf Rev Econ Policy 22(4):483–498View ArticleGoogle Scholar
 Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116View ArticleGoogle Scholar
 Griliches Z (1989) Patents: recent trends and puzzles. In: Brookings papers on economic activity, microeconomics, pp 291–330Google Scholar
 Griliches Z (2000) R&D, education and productivity, vol 214. Harvard University Press, CambridgeGoogle Scholar
 Grossman G, Helpman E (1991) Innovation and growth in the global economy. MIT Press, CambridgeGoogle Scholar
 Hall R, Jones C (1999) Why do some countries produce so much more output per worker than others?”. Q J Econ 114:83–116View ArticleGoogle Scholar
 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)Google Scholar
 Huergo E, Jaumandreu J (2004) Firms’ age, process innovation and productivity growth. Int J Ind Organ 22(4):541–559View ArticleGoogle Scholar
 Isaksson A (2007) Determinants of total factor productivity: a literature review. In: United Nations industrial development organization working paperGoogle Scholar
 Jaumotte F, Spatafora N (2007) Asia rising: a sectorial perspective. In: IMF Working Paper No. 07/130. International Monetary Fund, WashingtonGoogle Scholar
 Joutz FL, Gardner TA (1996) Economic growth, energy prices and technological innovation. South Econ J 62(3):653–666View ArticleGoogle Scholar
 King R, Levine R (1993) Finance and growth: schumpeter might be right. Q J Econ 108:717–738View ArticleGoogle Scholar
 Klenow P, Rodri´guezClaire A (2005) Externalities and Growth. In: Aghion P, Durlauf S (eds) Handbook of economic growth, vol 1, 1st edn. Elsevier, AmsterdamGoogle Scholar
 Klenow P, RodriguezClaire A (1997) The neoclassical revival in growth economics: Has it gone too far? NBER Macroecon Annual 12:73–103View ArticleGoogle Scholar
 Kortum SS (1997) Research, patenting, and technological change. Econometrica 65(6):1389–1419View ArticleGoogle Scholar
 Laurenceson J, Chai JCH (2003) Financial reform and economic development in China. Advances in Chinese economic studies series. Edward Elgar, CheltenhamView ArticleGoogle Scholar
 Loko B, Diouf MA (2009) Revisiting the determinants of productivity growth: What’s new? In: IMP working paper No. 225Google Scholar
 Mairesse J, Mohnen P (2010) Using innovation surveys for econometric analysis. In: NBER working paper 15857. National Bureau of Economic Research, WashingtonGoogle Scholar
 Mastromarco C, Zago A (2012) On modelling the determinants of TFP growth. Struct Change Econ Dyn 23:373–382View ArticleGoogle Scholar
 Miller SM, Upadhyay MP (2000) The effects of openness, trade orientation, and human capital on total factor productivity. J Dev Econ 63:399–423View ArticleGoogle Scholar
 Narayan PN (2005) The saving and investment nexus for China: evidence from co integration tests. Appl Econ 37(17):1979–1990View ArticleGoogle Scholar
 OrtegaArgilés R, Potters L, Vivarelli M (2005) R&D and productivity: testing sectoral peculiarities using micro data. Empir Econ 41(3):817–839View ArticleGoogle Scholar
 Pesaran MH, Pesaran B (1997) Working with Microfit 4.0: interactive econometric analysis. Oxford University Press, OxfordGoogle Scholar
 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, CambridgeGoogle Scholar
 Pesaran MH, Shin Y, Smith R (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16(3):289–326View ArticleGoogle Scholar
 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 View ArticleGoogle Scholar
 Rambaldi AN, Doran HE (1996) Testing for granger noncausality in co integrated systems made easy. In: Working paper in econometrics and applied statistics, vol 88, University of New EnglandGoogle Scholar
 Romer PL (1986) Increasing returns and longrun growth? J Polit Econ 94:1002–1037View ArticleGoogle Scholar
 Romer P (1990) Endogenous technological change. J Polit Econ 96:S71–S102View ArticleGoogle Scholar
 Rouvinen P (2002) R&D–productivity dynamics: causality, lags and dry holes. J Appl Econ 5:123–156View ArticleGoogle Scholar
 Shabbir MS (2015) Innovation and competitiveness lead to industrial trade. Bus Econ J 6(4):181Google Scholar
 Shabbir MS (2016) The impact of financial development on economic growth of Pakistan economy. Am Based Res J 5(3):35–43Google Scholar
 Shabbir MS (2018) The impact of foreign portfolio investment on domestic stock prices of Pakistan. In: MPRAGoogle Scholar
 Shearmur R, Bonnet N (2011) Does local technological innovation lead to local development? A policy perspective. Reg Sci Policy Pract 3(3):249–270View ArticleGoogle Scholar
 Solow R (1956) A contribution to the theory of economic growth. Q J Econ 70:65–94View ArticleGoogle Scholar
 Solow RM (1957) Technical change and the aggregate production function. Rev Econ Stat 39:312–320View ArticleGoogle Scholar
 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):159–174Google Scholar
 Toda HY, Phillips PCB (1993) Vector autoregressions and causality. Econometrica 61:1367–1393View ArticleGoogle Scholar
 Toda HY, Yamamoto T (1995) Statistical inference in vector autoregression with possibly integrated processes. J Econ 66:225–250View ArticleGoogle Scholar
 Tsai K, Wang J (2004) The R&D performance in taiwan’s electronics industry: a longitudinal examination. R&D Manag 34(2):179–189View ArticleGoogle Scholar
 Wakelin K (2001) Productivity growth and R&D expenditure in UK manufacturing firms. Res Policy 30(7):1079–1090View ArticleGoogle Scholar
 Zapata HO, Rambaldi AN (1997) Monte Carlo evidence on cointegration and causation. Oxf Bull Econ Stat 59(2):285–298View ArticleGoogle Scholar
 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 View ArticleGoogle Scholar