Chapter 2
Before describing the theoretical and empirical models of this paper in detail, it is useful to review the literature pertaining to my study of effect of foreign direct investment in India. These include literatures pertaining to foreign direct investment and its effect on gross domestic product, consumption, exports, imports, and also literature on foreign policies.
(Loree, D. and Guisinger, S., 1995) using US department of Commerce Benchmark Surveys, examined the effects of policy and non-policy variables on location. They found significant positive effects for investment incentives, and negative effects for performance requirements and host country effective tax rates.
Using above theory (Manuela Tvaronaviciene, Virginija Grybaite, Renata Korsakiene, 2008) examined factors that have biggest impact on attracting FDI between India and new EU members- Lithuania, Latvia & Estonia. They used study period from 1991-2008. Variables considered for their study were set of indicators, reflecting economic stability, institutional hindrance, costs and socio-geographical characteristics. They considered above variables as FDI driving force for their study purpose. They used Simple Additive Weighting (SAW) and multifunctional complex evaluation (MCE) method.
where - weight of i-element; m-number of elements (i=1….n)
For evaluation of FDI attractiveness they used annual data of indicators (i=1…..m; j=1,….n), m-number of indicators, n-number of countries. In this case m=16, n=6. Their study answers following hypothesis: (1) Investor treats all indicators equally. (2) more importance is given to macroeconomic and institutional indicators and minimum weights to other indicators included in the set. (3) more importance is assign to cost indicators and less weight to others indicators.
Their study result shows that, for first assumption, India obtains higher values of aggregated indicator. For second assumption, Baltic States obtain higher attractiveness compared to India. Also, attention should be paid, as fluctuations in FDI cannot be explained by macroeconomic and institutional situation alone. Third assumptions, India is relatively attractive when cost is the major factor.
Another study conducted by (Chandana Chakraborty and Parantap Basu, 2002) tried to find the linkage between FDI and growth for India using structural cointegration model with vector error correction model. They try to answer the following hypothesis in their study. (1) Has FDI in India been significantly influenced by the liberalization policy of the government? (2) What comes first for India: FDI or growth? (3) How does FDI impact the share of labour in the total factor cost?
By using VECM model they found out that hypothesis (1) have some positive short run impact. With regard to hypothesis number 2, their test results shows that GDP in India is not a Granger caused by FDI. The causality runs from GDP to FDI. Finally, third result suggests that FDI tends to lower the unit labour cost that is indicative of the fact that FDI in India is labour displacing. With regard to long-run equilibrium relationships between GDP, FDI, ULC (unit labour cost) and IMPD (the share of import duty in total tax revenue), they found that FDI is related positive to GDP and inversely to IMPDT.
(Nidhiya Menon and Paroma Sanyal , 2007) investigate how labour conflicts, credit constraints, and indicators of a state's economic health influence location decision of foreign firms. They performed their study in two steps: Step 1 - to study the effect of labour disputes on shares of FDI that state receives.
Two equations are underlined:
(1)
where = share of foreign projects in location (state) j at time t. = matrix of exogenous variables where i denotes a particular variable, j denotes a location, and t denotes time, is a matrix of labor dispute variable where denotes a particular labor conflict for j. Eq. 1 relates to the share of FDI projects in state j at time t to labor conflict and other variable in , under the assumption that the variables on right hand side in eq.1 are exogenous. They modified eq.1 to account for state-specific heterogeneity.
(2)
Eq.2 is a fixed effects regression that controls for state-specific unobservable
(). Another variable influence the location of FDI projects across the various states of India include loans from ICICI Bank, loans from EXIM Bank, real growth state domestic product, state support for R&D, and other input cost variables such as wage levels and power rates. In order to show labor conflicts variables, they used proxies such as number of lockouts, number of strikes, and the number of man-days lost in the state due to work stoppages. State economic health is characterized by gross state domestic product (GSDP), measure of credit availability such as EXIM and ICICI bank loans disbursements, planned outlay by the state on the manufacturing sector, and the measures of research and development expenditures by the state government.
They found out that lagged man-days lost normalized by size of the workforce and the number of lockouts normalized by size of workforce has significant negative impact on share of FDI projects that a state receives whereas, loan disbursements from ICICI and EXIM banks, and the income level of a state have strong positive effects on FDI. Planned outlays and R&D expenditure by states have no significant effects on FDI, although input cost variables such as average wage of unskilled labor has significant negative effect on the share of FDI projects that a state receives. A state with high labour conflicts receives less share of FDI. They also tried to find the bias, if any, by differentiating state economies by its size. Dummy variable with value 1 for "Big State" was introduced. This dummy took value 1 if a state's industrial gross state product exceeds the median value. Their estimates do not show any presence of bias resulting from the differing size of state economies.
(D Sethi, SE Guisinger, SE Phelan and DM Berg, 2003) provided a rationale for changing trends in flow and determinants of FDI as a result of macro-economic and firm strategy considerations. They analyze US FDI into Western Europe and Asia over 20 years period 1981-2000. They tested following assumptions (1) Is there a statistically significant regional pattern in the flows of US FDI to Western Europe and Asia? (2) What traditionally have been the determinants of US FDI into Western Europe? (3) Is the mix of determinants of US FDI into Asia any different from them? (4) What is the difference in the US FDI stocks and flows into two regions over time? (5) How have the difference in political and economic stability and wage levels between the two regions affected US FDI? (6) Is cultural proximity to the USA still a significant determinant? They used variables like FDI stock, FDI flow, Dummy Europe (taking value 1 when country is from Europe, and 0 when it is from Asia), Wages, Wage differential, Population, GNP, Political and economic stability, cultural differences and Time. Model 1 had the aggregate US FDI stock in respective countries, as a dependant variable. Model 2 had annual US FDI flows into respective countries as the dependent variable, but now FDI stocks is also included as a control variable. By comparing the coefficient of above two models, changes in determinants of US FDI flow are revealed.
Their finding reveals that US FDI has historically gone to Western Europe, with high political and economic stability, and also with high GNP's and low populations. FDI stock is included in the other model; results reveal that GNP is significant (0.001 level) and negative. This signifies the change in the trend in US FDI flows. This is because of the declining profit and cost pressure forced US MNEs to start making much more efficient-seeking FDI into Asia. This finding therefore also supports Proposition 6, that cultural proximity to USA is no longer an important consideration for US MNEs. The coefficient of the market attractiveness factor is negative and significant (0.001 level), implies that trend of FDI flows now is to regions with low market attractiveness. Thus, liberalization of Asian economies and to take advantage of low wages, US MNEs are considering efficient-seeking market FDI.
(Dukhabandhu Sahoo and Maathai K. mathiyazhagan, 2003) uses export promotion to identify the role of FDI in the economic growth of India. They used data from 1979-89 to 2000-01. They present theoretical model, which shows that growth rate of an economy as a function of FDI along with other variables. They argued that dimension of FDI flows into India can be explained in terms of its growth and size, and sectoral compositions. They highlight the reasons for the decline in trend of FDI inflows from 1998, which they attribute to factors including the US sanctions imposed in the aftermath of the nuclear tests, the East Asian melt-down and the perceived Swedish image of government, and other important point to consider is that the number of countries investing in India has increased from 29 countries in 1991 to 86 countries in 2000. Out of which 39% of FDI inflow came from Mauritius alone, 24% was from US and the rest shared 28% (UK, Japan, Germany, South Korea and Australia). i.e., only seven countries accounted for over 90% of FDI during the period of study out of 86 countries. The other reason being Mauritius, largest contributor of FDI in India is because Mauritius and India have signed double taxation treaty. Between these two countries, stipulates a dividend tax of 5% while treaty between US and India stipulates dividend tax of 15% (World Investment Report, 1999). It is regarded as a tax heaven for MNEs.
Also, they try to find relation between IIP and FDI, by replacing GDP in above equation with IIP. According to them, the reason for adding the export variable to the above equation is that higher the export higher will be the growth. They found out using OLS equation that there is positive relation between LFDI and LGDI and it is significant at 1%. Same relation is also found out between LFDI and LIIP. It is remarkably to mark that coefficient of LEX is greater than coefficient of LFDI. Coefficient of LFDI=0.05 and 0.17, while coefficient of LEX=0.41 and 0.28 respectively. Thus, they prove that export implies greater promotion in growth than FDI alone although FDI, GDP and Export are positively related. Finding of this study is contrast with the finding of study by (Dua,P. and A.I Rashid, 1998), (Sharma) and (Chakraborty, C. and Basu P., 2002). [2]
Another study by (Keshava) mention that FDI in India was just 3.4% of FDI flows as a percentage of Gross Fixed Capital Formation in India by 2004 and 5.9% of FDI stocks as a percentage of GDP by 2004, whereas in China it was 8.2% of FDI flows as a percentage of Gross Fixed Capital Formation and 34.9% of FDI stocks as a percentage of GDP during the same year. So, certainly, India lags way behind in attracting FDI. Hence, he tries to answer the following hypothesis; (1) the impact of FDI on Indian economic development is moderate (2) Chinese are successful in utilizing the FDI for developement of their economy (3) hard factors in India are more severe than China in making it as a less attractive FDI destination. He considered in his study period from 1981-2004. He tested the following equation:
where Y= GDP in year t, X1 =GDI in year t-1, X2=FDI in year t-1, X3=Human Capital in year t-1, X4=Labour force in year t-1. This equation, he then transformed into log form to facilitate use of OLS method. 't' ratio implies that constant A, GDI (x1), HC (x3), LF (x4) all are greater than two implying the strong significance of these variables on the GDP, but FDI is showing positive, but not relatively significant effect on GDP. R2 value for the whole model is 0.93; F value is high signifying the fitness of the model. Thus, model shows that 1% increase in GDI leads to increase in GDP by almost 0.5% whereas 1% increase in FDI brings about increase in GDP by 0.12%. Coefficient for HC and labour force are 0.34% and 0.7% respectively. This implies that GDI and HC have significant affect the GDP. We should not forget that coefficient of FDI is not significant but it is still positive. He further classifies the economic effect of FDI into micro and macro effects. He uses two simple analysis channels, namely, Product Market Competition (PMC) and Linkage Effect, put forward by Markusen and Venables (1997) to find the micro effect of FDI. By micro effect, he refers to structural changes in the economic and industrial organization. He further says that through PMC, the MNCs will be substituting the products of domestic firms in the host country. By linkage effects he suggests that MNCs may work as complimentary firms to domestics firms in host country where it is possible for FDI to act as a catalyst leading to the development of local industry. In order to access the macro-effect he used macro variables namely exports, expenditure, private final consumption, Forex, GDI, gross domestic savings, trade balance, balance of payments. He uses 23 years data from 1980-81 to 2003-04. He used independent variable as FDI that is lagged (t-1) to assess the impact on macro variables. He found out that R2 is high for all variables except BOP (balance of payments). He concludes by saying that India is still far behind China in attracting FDI for reasons such as power shortage, poor infrastructure, security consideration, absence of an exit policy, etc.,
In a report sited by UNCTAD, (Assesing the impact of current financial crisis on global FDI flows, 2009), year 2007 saw a record of $1.9 trillion of FDI inflows. Due to crisis, FDI declined by 15% in year 2008. The report also sites that all major types of FDI namely, market-seeking, efficiency-seeking and resource-seeking were affected. For eg: China showed a 26% decline in FDI inflows during the first two months of 2009 over the same period of 2008. The growth rate of FDI inflows to developing countries in 2008, while lower than in 2007, has reached estimated 7%. This is mainly due to positive and even relatively high economic growth rates that still prevailed in several developing economies(BRICs) in 2008. The report also mentioned about the possible reasons for the slow down. Further it mentioned that crisis affected the capacity of TNC's to invest abroad because of tighter credit and lower profits. Other reason was credit had become less abundant and more expensive. For eg: losses at S&P 500 amounted to $182 billion dollars for 4th quarter of 2008. FDI slowed down same year because of the risk aversion factors of TNC's. In India, although is no exception, effect of crisis was not that deep enough. U.S is second largest recipient country for FDI inflow in India, was worst affectted and therefore, a conclusion can be made that 2008-2009 saw less FDI flow from US to India. Report also sites three forms of recovert and its affect on FDI. In case of V shaped recovery (optimistic)-quick turn in FDI flows which would begin by end of 2009. U shape recovery-FDI would begin to pick up in 2011. L shape recovery-FDI flows does not pick up until 2012. Report also mentions that during crisis government of many countries increased their stake in domestic banks and insurance firms. As long as state ownership continues, FDI or private investment will reduce. Also, protectionism would reduce FDI. The main concern that is sited in the report is how can developing countries retain existing investment and FDI in times of global recession. Economic Stimulus is one of the incentives but many developing countries do not have much needed financial resource. India had thrice announced $4 billion stimulus package. Despite the slowdown, BRICs still remain the favourable locations for market-seeking FDI.
(Chandana Chakraborty and Peter Nunnemkamp, 2006) investigates whether India's reforms in 1991 have induced changes in the structure and type of FDI for growth purpose. Second, whether the growth impact of FDI differs between primary, secondary and tertiary sectors. Data used by the author is from period of 1987-2000. For this purpose they categorize three sectors: Primary, Secondary and Tertiary. [3]
Regarding first hypothesis, they show that food industry experience stable and relatively low output growth, while FDI stocks were on upward trend with fluctuations. After the reform period, 1991, FDI surge in electrical and machinery sector. Thus, according to author, FDI is most likely to be associated with higher output growth in the transport and equipment industry. They apply a panel cointegration framework that allows for heterogeneity across 15 industries in the primary, secondary and tertiary sectors. They highlight two important purpose: (1) is there a long run steady state relationship between FDI and output for all of the 15 industries included in their panel? (2) Given the existence of a cointegrated relationship, can we accurately identify the chronology of casual effects between FDI and output by unraveling the short-run dynamics of the long-run relationship? They found out that correlation coefficient between output and FDI is 0.89. They used following equation to know the relationship between output and FDI:
where (i=1,2,…15) refers to industry-specific effects, =time effects, and =estimated residual indicating deviations from long-run steady state relationship. They concluded that FDI and economic growth in India are positively related. They also run for Granger causality to find long-run relationship using an error correction model and they found out that, for primary sector, the null of no causality from output to FDI stocks and that of no causality from FDI stocks to output cannot be rejected for either the short-run, or long run. This by contrast, for manufacturing industry displays robust bi-directional causal links in the long run relationship between the two variables of FDI stocks and output. Tertiary sector, which attracted bulk of FDI flow after post reforms shows no strong evidence of long-run causal links between the two variables of interest. They support the view that quality of FDI matters at last as much as the volume of FDI for the growth implications in host economies. They make interesting conclusion that current euphoria about FDI in India is unlikely to work wonders because of weak empirical foundations.
But in another study by (Fuat Erdal and Ekrem Tatoglu, 2002) discuss the location-related determinants of FDI. They perform the study using time-series analysis of major locational factors that impact the level of FDI inflow in Turkey from period 1980-1998. They used the following model to test the location determinant on FDI in Turkey.
where, FDI is influenced by the size of domestic market (Y), openness of the economy to foreign trade (X/M), infrastructure of the host country (I), attractiveness of the domestic market (), exchange rate stability (), and economic instability (). To measure (Y) they used GDP, openness of the economy to foreign trade (X/M) is calculated by ratio of exports to imports. (I), infrastructure of host country is measured by share of transportation, energy and communication expenditure in GDP. () is measured by real growth in GDP. () represents fluctuations in exchange rate of Turkish currency and measured by percentage change in a foreign exchange basket based on a trade-weighted average of the major currencies ($, £, DM, Fr, Lt) of five countries. (R) signifies real interest rate on commercial sight deposits to measure approximate overall economic stability. They found that size of domestic market is positively related to FDI. Also, they conclude that since FDI is in physical form, investor would prefer host country with better infrastructure. Attractiveness of the host country is positive and significant. Exchange rate instability causes negative impact on FDI. To conclude, they found out that host country market size, openness of the economy to foreign trade, physical infrastructure of the host country, attractiveness of host country market had a positive effect, but exchange rate instability had negative effect on FDI in turkey. Although effect of economic stability was negative but it was not that significant. Model used by the author is convincing as it covers the macro variables, although more study has to be carried out using both macro and micro variables. Drawback is that the sample size is relatively small which cannot give glance of full picture.
Major study done by (Franklin R. Root & Ahmed A. Ahmed, 1978) examined how does the policies of host country influence on their capacity to attract FDI in manufacturing. They have categorized developing countries into 3 groups, namely, "unattractive", "moderately attractive", and "highly attractive" and tested 44 economic, social, political, and policy variables for their significance in discriminating between above mentioned three groups of countries. Following Hypothesis were tested: (1) Higher the GDP, more attractive the country to foreign investors. (2) Comparatively high tax levels deter FDI. (3) As its per capita GDP rises, a developing country first experiences an improvement in its export/import ratio but later, deterioration. (4) Developing countries most attractive to foreign manufactures are far more urbanized than others developing countries. (5) Greater the volume of its commerce, transport, and communication, more attractive a country is to foreign investors. (6) A comparatively high level of regular executive transfers defers foreign investments in manufacturing. Discriminant analysis was applied to sample of 44 developing countries. Unattractive group were classified as those that received an average annual per capita inflow of one U.S dollar or less. Moderately attracting countries where classified as those receiving more than One US dollar or under $4.10. $4.10 is the median per capita. Highly attractive country had more than $4.10. Study was carried for the period of 1966-1970.
They again classified variables into Economic, Social, Political and Policy group. Economic once included: (1) GDP, (2) GDP per capita, (3) GDP growth rate, (4) per capita growth rate, (5) Manufactured imports/GDP, (6) Ratio of exports to imports, (7) International Liquidity (8,9) purchasing power of currency (change in external value relative to internal value), (10,11) Local credit (ratio of banking system claims on economy to GDP and on private sector to GDP), (12) Ratio of commerce, transport and communication to GDP, (13) Energy Production (equivalent tons of coal per 1000 population). (14) Degree of economic integration, (15) Ratio of manufacturing to GDP, (16) Ratio of raw material export to GDP. Social included: (17,18) Ratio of literacy & Social enrollment, (19) availability of technical and professional workers (size of middle class). (20) Modernization of outlook, (21) strength of labor movement (22) Extent of Urbanization. Political included: (23-28) Frequency of government changed by type and period, (29,30) number of internal armed attacks by period, (31) Degree of administrative efficiency, (32) degree of nationalism (33-35) Per capita foreign aid from US, non US sources, and sum of both, (36) Colonial affiliation, (37,38) Role of government in economy. Policy category included: (39) Corporate taxation, (40) tax incentive laws, (41) tax incentive: liberality, (42) Attitude toward joint ventures, (43) Local content requirement (44) Limitations on foreign personnel.
They carried out test using multiple discriminant analysis. As the number of variables was large, stepwise procedure was used in the discriminant analysis. Variables were selected one by one that contributes to the variance among groups at the 5% level of significance. 6 variables were selected as essential discriminators at 5% level of significance are as follows: (1) Per Capita GDP (2) Corporate Tax level (3) Ratio of exports to imports (4) extent of urbanization (5) Commerce, Transport and communication (6) Regular executive transfers.
This study is interesting and it is different from other researchers, as they examine influence of policy instrument on manufacturing related FDI in developing countries. It also makes it different because of use of quantitative and qualitative variables. Their study result reveals that Corporate Taxation is major significant determinant of FDI in manufacturing. Tax incentives are necessary but not the major determinant of FDI. As far as other variables are considered, attitudes towards JV, local content requirements and limitations on foreign personnel failed to distinguish these groups.
Study dates back to 1978, and thus it can be questioned as government policies have changed since then. Also classification of a country is done on basis of per capita inflows. Therefore, countries like India and China would get less advantage, and are placed in unattractive group. But now these countries are major for attracting FDI. I reviewed this study for the fact that as wide range of variables were covered, particularly qualitative factor.
(S Rameshkumar & V Alagappan, 2008) examined trend and pattern in the foreign direct investment inflows in India during post-liberalization period. This study is limited to top ten investing countries and top ten level sectors that attract large inflows of FDI. Their study covers period of 14 years from 1991-2004. They used linear trend model and semi-log model as well as percentage analysis. They found out by using trend analysis that actual flow of FDI had been fluctuating and unsteady during the study period. They assign reasons for decline FDI inflows to several factors including several restrictions imposed on India by the USA on account of the nuclear test carried out by India at Pokhran, the political instability, the slow down of the Indian economy, the restrictions imposed on FDI inflows regarding trade related investment measures, the poor domestic industrial environment and unfavorable external economic factors such as the financial crisis of South-East Asia. They also mentioned about huge gap between the approvals and actual inflows as this was caused by inefficiency of the government including projects delays, lack of facilities, limited market opportunities, political uncertainty and like. There are four routes through which FDI can be allowed, namely: (1) Government approvals (2) Reserve bank of India (RBI) automatic approval (3) Non Resident Indian (NRI) investments, and (4) through acquisitions of shares. They found out that government approvals top the list with (66.26%) followed by acquisition of share route (16.18%), RBI approvals (13.68%) and NRI route (9.88%). Regarding countries wise investments, Mauritius toped list with (34.51%) followed by USA (17.07%), Japan (7.25%), Netherlands (7.16%), UK (6.49%), Germany (4.81%), France (2.85%) and like. Thus, five countries that dominated FDI scene in India were Mauritius, USA, Japan, Netherlands and UK. 50% of FDI during 1991-2004 came from Mauritius and USA alone. Sector wise, they found out that the electrical equipment sector topped list with (14.81%), followed by transportation industry (11.21%), telecommunication(10.25%), fuel sector (9.43%), service sector (8.65%), chemical sector (6.46%), food processing (4.34%), drugs and pharmaceuticals sector (3.19%), metallurgical sector (1.86%) and consultancy services (1.56%). This top ten sector collectively accounted for 71.76% for total FDI inflow into India during 1991-2004. It can be seen clear that with rising India's midldle class, there is more potential for GDP growth, which can be seen positivily related with FDI. Thus, more FDI inflows can be expected in near foreseen future along with policies lossing.
(Srivastava, 2006) examined the casual relationship between FDI inflows and services exports in the Indian Economy during the post-liberalization period since 1991 till 2002. She performed multivariate VAR (vector Auto regression) framework for the analysis. In addition to variables, services exports and FDI inflows, another variable, index of industrial production (IIP), is included. Data are converted to logarithmic form, and test confirms that all three series under construction (SEREXP, FDIIN and IIP) are non-stationary in levels but stationary in first difference. Cointegration test showed no evidence of a long run relationship, therefore, Granger approach with first-difference VARs are conducted next. Result of granger causality shows that univariate causality were from FDI to service exports. Thus, evidence of strong positive unidirectional ganger causality from FDI to services exports indicates that FDI has positively influenced the growth of service exports in Indian economy, particularly in short run, after economics reforms in 1991.
Another study carried out by (Kamath, 2008) analyzed the impact of foreign direct investment (FDI) on GDP and exports in India for the period of 1991-2005. She tried to find the relationship between FDI and exports and GDP in Indian economy. Also, she examined trend in FDI in India. She used technique like percentage and growth rates to analyze the data. However, to capture the impact of FDI on export, imports and GDP, techniques like simple regression and standard deviation are used. In this study, she used FDI as an independent variable whereas GDP, exports and imports are used as dependant variables. The coefficient of determination (R square) between FDI and export is very high and also significant. Interpretation can be that on an average, US $1 mn inflow of FDI in the economy results in around US $7.8 mn exports in the economy. In case of imports, the coefficient has a positive value and is statistically significant showing that there is an increase in imports along with an increase in the exports with every flow in FDI in the economy. The result of regression shows that with every million $US increase in FDI, there is an increases of imports to the extent of $US 10mn. Also, results show a positive and statistically significant relationship between GDP and FDI in Indian economy during period of study,
It can be concluded that, there is no doubt researchers have put in effort to work on Foreign Direct Investment related study in India. Even though India started its economic reforms in 1991, still India has not opened much of its sectors to foreign investment. Also, due to political uncertainty and delays in execution of projects has led to diversion of foreign investments to other countries.
