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A Time Series Analysis on the Relation between Foreign Direct Investment & Total Factor Productivity Growth of the Indian Organised Manufacturing Sector: 198081 to 201011 By, Narasingha Das, Research Scholar, Dept. of Economics, Vidyasagar University, West Bengal, India. Email narasinghadas@gmail.com A Time Series Analysis on the Relation between Foreign Direct Investment & Total Factor Productivity Growth of the Indian Organised Manufacturing Sector: 198081 to 201011
Abstract: This paper attempts to measure productivity performance of the Indian Manufacturing Industry during the period 198081 to 201011. To measure total factor productivity growth& various other related performance indicators a nonparametric approach, namely, Malmquist Data Envelopment Analysis has been used.A comparative analysis between the pre (198081 to 19901991)& post liberalisation (199192 to 201011) era has also been taken up in this study. The paper also seeks to examine the impact of foreign direct investment on the overall total factor productivity growth of the Indian Manufacturing Industry. The present study shows a positive TFP growth for the entire period. But, again, there is a fall in the TFPG from pre to postliberalisation period.Interestingly, we observed a positive impact of foreign direct investment on total factor productivity growth for the Indian Manufacturing Industries.
Key Words: Indian Manufacturing Industry, Total Factor Productivity, Malmquist index, Data Envelopment Analysis, FDI.
JEL Classification: C14, C32, D24.
The impact on the host economy of investment by multinational enterprises remains an unresolved and debated issue (Aghion et al. 2006, Bitzer and Görg 2005). Some countries and industries benefit significantly from foreign entry in terms of productivity and knowledge spillovers. Others benefit less or are even hurt. While academics tend to treat FDI as a homogenous capital flow, policy makers, on the other hand, seem to believe that some FDI projects are better than others. National policies toward foreign directinvestment (FDI) seek to attract some types of FDI and regulate other types in a pattern which seems to reflect a belief among policymakers that FDI projects differ greatly in terms of the national benefits to be derived from them. UNCTAD’s World Investment Report 2006 for instance describes “quality FDI” as “the kind that would significantly increase employment, enhance skills and boost the competitiveness of local enterprises.” While in theory the nexus between FDI and growth (in terms of output and productivity) is in general positive, the empirical literature is far less conclusive. Some studies find positive effects from outward FDI for the investing country (Van Pottelsberghe and Lichtenberg, 2001; Nachum et al., 2000), but suggest a potential negative impact from inward FDI on the host country. This results from a possible decrease in indigenous innovative capacity or crowding out of domestic firms or domestic investment. Thus, in their view and in line with the standard literature on the determinants of FDI (i.e. Dunning’s OLI paradigm, see Dunning 1988) inward FDI is intended to take advantage of host country (locational) characteristics instead of disseminating new technologies originating in the sending country. Other studies report more positive findings: Nadiri (1993) finds positive and significant effects from US sourced capital on productivity growth of manufacturing industries in France, Germany, Japan and the UK. Also Borensztein et al. (1998) find a positive influence of FDI flows from industrial countries on developing countries’ growth. However, they report also a minimum threshold level of human capital for the productivity enhancing impact of FDI, emphasizing the role of absorptive capacity. Absorptive capacity or minimum threshold levels in a country’s ability to profit from inward FDI is often mentioned in the literature (see also Blomström et al. 1996). Consequently the effect of FDI depends among other things to a large extent on the characteristics of the country that receives FDI. However, the resulting issue of crosscountry heterogeneity has largely been neglected in the literature so far with few exceptions. Blonigen and Wang (2005) stress explicitly crosscountry heterogeneity as the crucial factor which determines the effect of FDI on growth. Further, NairReichert and Weinhold (2001) and MayerFoulkes and Nunnenkamp (2005) explicitly take up this aspect in their analysis. Our paper will follow their direction and introduce two forms of heterogeneity, differences between countries and differences between receiving industries. We argue that since host country heterogeneity plays a role, it is equally likely that the impact of FDI on the host economy differs greatly according to the receiving industry. FDI in constant returns to scale industries will have different effects than FDI in increasing returns to scale industries. Likewise, the effect of FDI may be related to the technology and human capital intensity of the industry and other factors. As a very intuitive example, heavy FDI in the extractive sector in Nigeria has not improved the country’s growth performance (Akinlo, 2004). Consequently, the potential for positive spillovers does not solely depend on a country’s overall absorptive capacity, but also on which sectors or industries in the economy receive FDI. Thus, the impact of FDI differs depending on country specific absorptive capacity or stage of development as well as on the sectoral and industrial structure and allocation of FDI. Since the two are in general related, this implies a relationship between the industrial pattern of inward FDI and its effect on the host country. The economy wide effect of industry specific FDI inflows will then further depend on the extent of intraindustry versus interindustry spillovers.
The concept of technical efficiency indicates the degree of success in the utilization of productive resources. Technical efficiency is considered to be an important determinant of productivity growth and international competitiveness in any economy (Taymaz and Saatci, 1997). There are different schools of thought in estimating the technical efficiency. Technical efficiency consists of maximizing the level of production that can be obtained from a given combination of factors. In the Indian context, number of studies examined the technical efficiency of the manufacturing industry, e.g., Page (1984), Little et al. (1987), Patibandla (1998), Mitra (1999), Agarwal (2001), and Mitra et al. (2002), Bhandari et al. (2007a, 2007b) and many others. Krishna and Mitra (1998) investigated the effects on competition and productivity of the dramatic 1991 trade liberalization in case of Indian manufacturing industries. Using firmlevel data from a variety of industries, they find some evidence of an increase in the growth rate of productivity. Driffield and Kambhampati (2003) estimate frontier production functions for six manufacturing industries. Their findings suggest an increase in overall efficiency in five out of the six manufacturing industries in the postreform period. Mukherjee and Ray (2005) examined the efficiency dynamics of a ‘typical’ firm in individual states during the pre and postreform period. Their findings establish no major change in the efficiency ranking for different states after the reforms was initiated. Using a panel dataset of 121 Indian manufacturing industries from 1981 to 1998, Pattnayak and Thangavelu (2005) find evidence of total factor productivity improvements for most of the industries after the reform period. While the 1991 economic reform was radical, India adopted a gradualist approach to reform, meaning a frustratingly slow pace of implementation (Ahluwalia, 2002). It suggests that it is more appropriate to examine the effect of liberalization on manufacturing sectors’ efficiency using a longer time span for both pre and postreform. How did this economic reform program shifted Indian manufacturing into global stage and influencing technical and scale economies of major industries? In answering this question, we employ a nonparametric approach in explaining productivity changes, technical progress and scale efficiencies of industries within the sector. In this paper, we examine the impact of liberalization on the technical efficiency of Indian Manufacturing industry by comparing pre and post economic reform periods. Analysis of technical efficiency of manufacturing industries in developing countries has received considerable attention in the economic literature in recent years. Recent literature includes Onderet al. (2003) for Turkey, Pham et al. (2009) for Vietnam, Margonoet al. (2010) for Indonesia, and Mastromarco (2008) for developing countries among others. Technical efficiency is concerned with how closely the production unit operates to the frontier for the production possibility set. The historical roots of a rigorous approach to efficiency measurement can be traced to the works of Debreu (1951) and Farrell (1957). Over the past three decades, a variety of approaches, parametric and nonparametric, have been developed to investigate the failure of producers to achieve the same level of efficiency. For a detailed survey on such methodologies, see Kalirajan and Shand (1999). In parametric models, one specifies an explicit functional form for the frontier and econometrically estimates the parameters using sample data for inputs and output, and hence the accuracy of the derived technical efficiency estimates is sensitive to the nature of the functional form specified. In contrast, the method of Data Envelopment Analysis (DEA) introduced by
In each of the formulas i.e., equation (6) & (7), a value greater than one indicates a positive growth of TFP (an improvement) from a period ‘t’ to ‘t+1’ and a value smaller than one represents deteriorations in performance over time. We can decompose the total factor productivity growth in following way as well: MTFPI = Technical Efficiency change X Technical Change (Catching up effect) (Frontier effect) MTFPI is the product of measure of efficiency change (catching up effect) at current period ‘t’ and previous period ‘s’ (average geometrically) and a technical change (frontier effect) as measured by shift in a frontier over the same period. The catching up effect measures that a firm is how much close to the frontier by capturing extent of diffusion of technology or knowledge of technology use. On the other side frontier effect measures the movement of frontier between two periods with regards to rate of technology adoption. In DEAMalmquist TFP Index does not assume all the firms or sectors are efficient so therefore any firm or sector can be performing less than the efficient frontier. In this methodology we will use the output oriented analysis because most of the firms and sectors have their objective to maximize output in the form of revenue or profit. It is also assumed that there is constant return to scale (CRS) technology to estimate distance function for calculating Malmquist TFP index and if technology exhibits constant return to scale (CRS), the input based and output based Malmquist TFP Index will provide the same measure of productivity change. 2.2. Time Series Econometric tests to assess the nature of the relationship between TFP and FDI: Step – I: The Stationarity Test (Unit Root Test): It is suggested that when dealing with time series data, a number of econometric issues can influence the estimation of parameters using OLS. By regressing a time series variable on other time series variables using OLS estimation can result in a very high R^{2}, although there is no meaningful relationship between the variables. This situation reflects the problem of spurious regression between totally unrelated variables generated by a nonstationary process. Therefore, prior to testing Cointegration and implementing the Granger Causality test, econometric methodology needs to examine the stationarity; for each individual time series. Most macroeconomic data are nonstationary, i.e., they tend to exhibit a deterministic and/or stochastic trend. Therefore, it is recommended that a stationarity (unit root) test be carried out to test for the order of integration. A series (X_{t}) is said to be stationary if the mean and variance are timeinvariant. A nonstationary time series will have a time dependent mean or make sure that the variables are stationary, because if they are not, the standard assumptions for asymptotic analysis in the Granger test will not be valid. Therefore, a stochastic process that is said to be stationary simply implies that the mean [E(X_{t})] and the variance [var (X_{t})] of X remain constant over time for all t, and the covariance [cov(X_{t}, X_{s})] i.e., the correlation between any two values of X taken from different time periods depends on the difference apart in time between the two values for all t≠s. Since standard regression analysis requires that data series be stationary, it is obviously important that we first test for this requirement to determine whether the series used in the regression process is a difference stationary or trend stationary. To test the stationary of the variables, we use the Augmented Dickey Fuller (ADF)test, PhillipsPerron Unit Root Test&Dickey Fuller test with GLS detrending. Now, once the number of unit roots in the series is decided, the next step before applying Johansen’s (1988) cointegration test is to determine an appropriate number of lags to be used in estimation since the choice of lag length is crucial in the Johansen procedure. In selection of appropriate lag length, standard literature follows either Akaike Information Criteria (AIC) or Bayesian Information Criteria (BIC) which is also known as Schwarz Information Criteria (SIC) or both. In our study, the appropriate lag length is selected on the basis of Schwarz Information Criteria (SIC) as we are more interested to identify the true model rather than to find out the best approximating model to the unknown data generating process which AIC actually gives (Henry deGraft Acquah, 2009). Another reason is that, AIC does not depend directly on sample size. Bozdogan (1987) noted that because of this, AIC lacks certain properties of asymptotic consistency. Although BIC takes a similar form like AIC, it is derived within a Bayesian framework, reflects sample size and have properties of asymptotic consistency. For reasonable sample sizes, BIC apply a larger penalty than AIC, thus other factors being equal it tend to select simple models than does AIC. From a Bayesian view point this motivates the adoption of the Bayesian information criteria. Bickel & Zhang (1992) & Zhang (1993) demonstrate that BIC is consistent whilst in contrast AIC is not. Step – II:The Cointegration Test Regression on non stationary variables is permitted if their linear combination is stationary. It has been recognized in recent literature that if a linear combination of integrated variables is stationary then such variables are said to be cointegrated. Although Engle and Granger (1987) was the first to introduce the cointegration test, the tests propounded by Stock and Watson (1988), Johanson (1991) and Johansen and Juselius (1990) are more useful in testing the long run equilibrium relationships in multivariate setting. Step III: The Granger Causality Test: Causality is a kind of statistical feedback concept which is widely used in the building of forecasting models. Historically, Granger (1969) and Sim (1972) were the ones who formalized the application of causality in economics. Granger causality test is a technique for determining whether one time series is significant in forecasting another (Granger, 1969). The standard Granger Causality Test (Granger, 1988) seeks to determine whether past values of a variable helps to predict changes in another variable. The definition states that in the conditional distribution, lagged values of Y_{t} add no information to explanation of movements of X_{t} beyond that provided by lagged values of X_{t} itself (Green, 2003). We should take note of the fact that the Granger causality technique measures the information given by one variable in explaining the latest value of another variable. In addition, it also says that variable Y is Granger caused by variable X if variable X assists in predicting the values of variable Y. if this is the case, it means that the lagged values of variable X are statistically significant in explaining variable Y. The null hypothesis (H_{0}) that we test in this case that X variable does not Granger cause variable Y and variable Y does not Granger cause variable X.
In this section, we have calculated total factor productivity growth and its component using Malmquist Productivity Index under two inputs labour & capital and one output framework. Estimation of annual TFP growth rate of Indian manufacturing industries for the pre as well as postreform period at aggregate level are presented in Table: 1, Table: 2 & Table: 3 respectively. Table: 1 – Malmquist Index Summary of Annual Means for the Entire Period
Source: Authors own estimates by using DEAP software, version 2.1 From Table1, we find that the annual average TFPG for the Indian manufacturing sector for the entire period under study (198081 to 201011) is positive and it is 2.0. From Table2, for the prereform period (198081 to 199091) the annual average TFPG is also positive and it is 3.70. Table: 2 – Malmquist Index Summary of Annual Means for the Prereform Period
Source: Authors own estimates by using DEAP software, version 2.1
Table: 3 – Malmquist Index Summary of Annual Means for the Postreform Period
Source: Authors own estimates by using DEAP software, version 2.1 Table3 represents the annual average TFPG of the Indian Manufacturing industries and it is 1.2. Therefore we can say that there is a fall in the TFPG from pre to post reform period. This results reveals that decline in the industry’s TFPG is due to its productivity based frontier capability. 3.2 Empirical results from the causal relationship between change in foreign direct investment and change in total factor productivity growth for the Indian Manufacturing Industry: From unit root testing, we have the following results as presented in Table 3.2.1, 3.2.3& 3.2.4.
Table 3.2.1: Results from ADF Unit Root Test
Source: Authors own estimate. (*, **, *** represents the significance level at 1%, 5% & 10% respectively) The result of ADF unit root tests is presented in Table3.2.1. Each variable is tested in their level & first difference with intercept only and trend & intercept. It is found that the null hypothesis of unit roots cannot be rejected at conventional significance levels for FDI. Therefore it can be concluded that all series are stationary at level i.e., all the series are I(1). For the ADF test, the optimum lag selection is based on Schwartz Information Criterion. Table 3.2.2 suggest that the appropriate lag length is 2 for the total factor productivity and for FDI the appropriate lag length is 1. Table 3.2.2: Selection of Appropriate Lag Length by SIC
Source: Authors own estimate The asterisks (*) in the table 3.2.2 indicates the best (that is, minimized) values of the SIC. Table 3.2.3: Results from PhillipsPerron Unit Root Test
Source: Authors own estimate. (*, **, *** represents the significance level at 1%, 5% & 10% respectively) The result of PhillipsPerron unit root tests is presented in Table3.2.3. Each variable is tested in their level & first difference with intercept only and trend & intercept. It is found that the null hypothesis of unit roots cannot be rejected at conventional significance levels for FDIs. Thus it can be concluded that all series are stationary at level i.e., all the series are I(1).
Table 3.2.4: Results from DF GLS detrending Unit Root Test
Source: Authors own estimate. (*, **, *** represents the significance level at 1%, 5% & 10% respectively) The result of DFGLS detrending unit root tests is presented in Table3.2.4. Each variable is tested in their level, first difference and second difference with intercept only and trend & intercept. It is found that the null hypothesis of unit roots also cannot be rejected at conventional significance levels for the ΔFDI. Therefore it can be concluded that all series are stationary at their level i.e., all the series are I(1). Results from Cointegration Test: Having established the time series properties of the data, the test for presence of longrun relationship between the variables using the Johansen Cointegration test is conducted. The Johansen approach can determine the number of cointigration vectors for any given number of nonstationary variables of the same order. The results reported in Table3.2.5.suggests that the null hypothesis of no cointegrating vectors can be rejected at 1% level of significance. It can be seen from the trace statistics that we have one cointegration equation at both 1% and 5% level.
Table 3.3.5: Johansen Cointegration Test Results
From Johansen Co integration test result the normalized co integration equation can be written as: ΔTFP= 2.768683 + 0.619896 ΔFDI (3.12*) (2.91*) From the above normalized cointegration equation we can say that one unit change in FDI leads to 0.62 unit change in TFP for the Indian Manufacturing industry. tstatistics are given in the parenthesis which are also significant at l% (*) level of significance. Thus we can say that there is a longrun relationship between total factor productivity growth and foreign direct investment for the Indian manufacturing industries. Findings from Granger Causality Test: The results of Pair wise Granger Causality between ΔTFP and ΔFDI for the Indian Manufacturing industry are presented in Table3.2.6. The results reveal that there is a unidirectional causal relationship between change in TFP and change in FDI. Our result confirms that change in FDI is the Granger cause of change in TFP at lag 2, 3 & 4. Table 3.2.6: Granger Causality test Results
Source: Authors own estimate.
The following are the major findings of our study: First, the result on the overall productivity displays a declining trend of MTFPG in post reform period as compared to pre reform period for the Indian Manufacturing industry. Therefore, it may be concluded that the liberalization policies has its adverse impact on the overall Indian manufacturing industry’s TFP growth. Second, our result also confirms a significant causal and longrun relationship between foreign direct investment and total factor productivity for the Indian Manufacturing industry. The direction of causality is from FDI to real TFP growth. Therefore, FDI act as an ingredient of total factor productivity growth for the Indian Manufacturing Industries. References: Acquah H. deGraft. 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