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 ; Rouvinen The innovation and economic development based on small and medium enterprises in Pakistan is studied by Subhan et al. This study adds new contribution in the existing literature to develop an efficient relationship among innovation, TFPG and economic growth in Pakistan. 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.
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 In precise, study assumes a Cobb—Douglas production function approach that takes the following form:.
Following the literature of Hall and Jones 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.
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 , 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 study from a development perspective is concerned in decomposing GDP per capita y. Moreover, Eq. 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 and production function mentioned in 3 can be rewritten in intensive form as:.
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.
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 ; Blundell and Bond This study uses a perpetual inventory method to construct capital stock, following the methodology explained in Easterly and Levine Precisely, the total capital formation equation states that.
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. The delta is based on average depreciation rate of the capital stock. 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 — This paper follows the Hall and Jones 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. The output Y is measured as real GDP at constant national prices in mil. 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 Also, Griliches and Joutz and Gardner discussed that patent applications are a significant measure of technological output. Followed the information of Bravo-Ortega and Marin , 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. 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.
The data on inflation inflation rate have also been taken from WDI. A set of human capital variables is used to measure the impact of education and its indirect impact via improving the knowledge absorptive capacity. 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. 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 ; Loko and Diouf ; Shabbir 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 and King and Levine 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 The study uses annual time series to observe the granger causality between variables, and data are taken from World Development Indicators WDI for Pakistan — 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 , Pradhan et al. 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. 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. 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 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.
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 ; Zhang et al. 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 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. 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 ; Black and Lynch ; Loko and Diouf 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.
The coefficients on trade openness are positive and significant in our study model. The case of the macro-instability, regulatory quality and uncertainty proxy by the inflation.
A stable monetary condition is the substance for the efficient operation of a market economy. 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. 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 study of Monte Carlo demonstrates that ARDL approach is significantly important and generates consistent results even for small sample Pesaran and Shin The technique of ARDL is used to observe the relationship between innovation, total factor productivity and GDP growth for the following reasons.
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. 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:.
Moreover, to detect the absence of serial correlation problem, Akaike information criterion AIC is chosen for optimal lag length criteria. 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 , 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.
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. The second phase is to define the co-integration association among the variables based on time series analysis having same order of integration. 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:. 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. However, to find out the co-integration among variables; this study continues the model of the unrestricted error correction model UECM. 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 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.
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 overall results indicate that we accepted null hypothesis which means long-run relationship exists.
Further, this study also accept null hypothesis in case of Jarque—Bera JB test , which shows that data are normally distributed. Hence, the results of estimated ARDL model are consistent. 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. The findings of these results are supported by the studies of Pradhan et al. These results are supported by Zhang et al. 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.
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. Pakistan is essentially an agrarian economy, employing more than 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 — 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.
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Compared to imports, results are similar because both are driven by the international economic situation and thus correlated. However, whereas exports as well as imports have no significant stand-alone impact on TFPCH, for the case of exports a weakly significant negative interaction term essentially denies a positive general role of exports in particular for laggards.
Our last test of robustness involves instrumental variable IV regressions using higher order lags as instruments , see Tables 15 , 16 and This eliminates potential biases including the Nickell bias and confirms by and large the results in Tables 5 , 6 and 7 in terms of the estimated coefficients and their significance; in some cases, significance even increases.
Almost all estimates of the catchup term TFPGAP increase and thus strengthen the importance of catching up on the growth of total factor productivity, of efficiency, and of technological progress.
This investigation offers insights into productivity dynamics using a panel of 12 manufacturing industries in 12 industrialized countries for to The distance to the frontier affects TFP growth in two ways.
This supports the previous findings of catching-up patterns for the extended observation period. By contrast, for TECHCH which refers to innovation we find the opposite relation: the bigger the TFP gaps, the smaller will be productivity growth through frontier-shifts. This suggests some asymmetry of development, which Acemoglu et al. We find no indication for a direct impact of import shares on TFP growth.
Nevertheless, the decomposed measures show evidence of a at least minor role for technology transfer. Effects on TECHCH are ambiguous negative for imports directly but positive if interacting with respect to the distance from the frontier. Our findings support the view that trade helps importers by accelerating technology transfer. Increasing openness to trade could therefore present a policy option for making manufacturing industries more competitive in terms of productivity—recall the observation in Melitz and Trefler for Canada benefitting from NAFTA.
Backward industries benefit more from investments in ICT than the average industry but with a substantial delay four-year time lag due to the time it takes to take advantage of ICT technologies Table 8. Whereas one of our objectives was to update and to clarify issues open in Griffith et al. First and presumably, the most important unsolved issue is to explain the large differences in TFP levels and their components. Second, one would like to understand better the role of convergence.
One way to address both questions is to broaden the set of explanatory variables, e. Other directions of future research are to account for the large differences between sectors that were somewhat averaged out by the use of panel regressions and for geography, i.
Aldieri and Cincera The inclusion of more recent data should also allow addressing how far the Great Recession affected international differences in TFP and its drivers including catching-up. However, this extension and its focus on potential breaks due to the events in and thereafter makes sense for an analysis in a separate paper in the future.
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Published : 20 March Issue Date : May 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. Skip to main content. Search SpringerLink Search. Download PDF. Introduction Economic growth is one of the great miracles of human development and of rather recent origin.
Table 3 Variables and data sources Full size table. Full size image. Table 4 Selected variables—means and standard deviations Full size table.
United Kingdom. United States of America. Estimation We use a three-dimensional panel regression design with country, industry and time fixed effects in order to explain productivity growth rates.
Conclusions This investigation offers insights into productivity dynamics using a panel of 12 manufacturing industries in 12 industrialized countries for to Notes 1. Acknowledgements Open access funding provided by University of Vienna.
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Appendices Appendix 1: Complementary Statistical Analyses See Tables 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , Table 12 Additional tests I —more lags Full size table. Table 13 Additional tests II —more lags Full size table. Table 18 Industry heterogeneity in terms of Catching-Up Full size table. About this article. Cite this article Haider, F.
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