A Panel Data Analysis of Sectoral Allocation of FDI in India: Where Does Construction (including Infrastructure Activities) Stand?

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1 83 A Panel Data Analysis of Sectoral Allocation of FDI in India: Where Does Construction (including Infrastructure Activities) Stand? Seshanwita Das, Assistant Professor, Galgotia s Institute of Management & Technology, Greater Noida, Uttarpradesh, India Tapas Das, Assistant Professor, Department of Management Studies, JSS Academy of Technical Education, Noida, Uttarpradesh, India Dr. Rajiv Upadhyaya, Professor, Department of Economic Administration & Financial Management, University of Rajasthan, Jaipur, Rajasthan, India ABSTRACT Although it may seem natural to argue that foreign direct investment (FDI) can convey great advantages to host countries, it is generally seen that the benefits of FDI vary greatly across sectors. Allocation of FDI into different sectors, enjoying differential knowledge spillovers or technology spillovers, is an important aspect to be considered due to its variable impact on the overall growth of the economy. This study empirically examines the allocation of FDI into select sectors during in the post-reforms era. With the help of log-lin panel regression model, we have seen that allocation of FDI into different sectors over the years has experienced heterogeneity effect. That is to say, growth rate of FDI into different sectors over the years has been different and this heterogeneity is the cause of such fixed factors across all sectors as, low level of development in the economy encompassing all the sectors, bureaucratic delays and widespread corruption and such random variables, which vary over cross-section but are fixed over time as government policy regarding FDI across different sectors. Keywords: Sectoral allocation of FDI, Panel Data Analysis, FDI in infrastructure 1. Introduction: Inward FDI is conjectured to bring about increases in productivity levels. More importantly, it acts as a conduit for the transfer of technology, leading to technological spillovers or knowledge spillovers and also increases economic growth in host countries by increasing the level of investment. Blomstrom and Kokko (1997) have identified a number of ways through which technological diffusion can occur, such as, licensing, joint ventures and goods-trade. But, needless to say, FDI has always remained a dominating channel for spreading technology. Though it seems natural to argue that FDI can augur gains to host countries, such gains might differ across primary, manufacturing and services sectors. UNCTAD World Investment Report (2001, pp 138), for instance, argues that in the primary sector, often the scope for linkages between foreign affiliates and local suppliers is limited. The manufacturing sector has a broad variation of linkageintensive activities. In the tertiary sector, the scope for dividing production into discrete stages and subcontracting out large parts to independent domestic firms is also limited. The capacity for FDI to serve as a channel of technological spillover might differ by sectors. For instance, secondary sector could rely more upon imports than FDI as an avenue of technological transfer, as the importance of FDI as a channel of technological transfer might not be so important in the secondary sector compared to the tertiary sector due to the fact that services are less trade-able than manufactured goods. Moreover, knowledge is created by R&D, which itself is classified under the tertiary sector. Nevertheless, according to UNCTAD (2005), the largest R&D expenditure is concentrated in the IT hardware, Automotive, Pharmaceuticals and Biotech industries, which are mostly in the manufacturing sector. Crowding-out effect of domestic suppliers as a result of competition from the entry of MNCs could also vary by sector. By and large, higher tendency has been observed in tertiary sector UNCTAD (2004). Now the question is if sectoral allocation of FDI matters for growth, then the shift of FDI from the primary and manufacturing sectors towards the tertiary sector could have important growth effects. Relatively few scholars (for instance Wheeler and Mody, 1992; Loree & Guisinger, 1995; Richaud et al, 1999; Asiedu, 2002; Sekkat, 2004) have acknowledged the crucial role of infrastructure in stimulating FDI by arguing that good infrastructure is a necessary pre-requisite for foreign investors to operate successfully. Poor infrastructure or unavailability of public inputs increase costs for firms. A freeway is faster than a washed out dirt road, is faster than the post-office and obviously

2 84 time is money. Public input is non-excludable and noncongestible and it will lower the costs of doing business for multinational and indigenous firms alike. Multinationals are in fact profit-seeking entities that seek to minimize the costs of doing business. If locating in a developing economy to take advantage of lower labor costs means, on the other hand, losing patent protection to imitators, higher transport costs due to inadequate transportation or missed supply shipments due to communication and transport problems, then they will refrain from doing business there. Infrastructure and public inputs, or the lack thereof, contribute to firms cost structures and should be included in a model that explains the multinational s as well as the host government s decisions for investment. Infrastructure should thus improve the investment climate for FDI by subsidizing the cost of total investment by foreign investors and thus raising the rate of return. Availability of crucial infrastructure, such as roads, highways, ports, communication networks and electricity should increase productivity and thereby attract higher levels of FDI. As Wei and al. (2000) said that a location with good infrastructure is more attractive than the others. 2. LITERATURE REVIEW: Theoretical work of Findlay (1978) and Wang & Bloomstrom (1992) that models the importance of FDI as a conduit for transferring technology relates to the foreign investment inflows to manufacturing or service sectors rather than to the primary sector. In addition, FDI s potential to create linkages to domestic firms might also vary across sectors, as Albert Hirschman (1958) described in his seminal book on economic development. Hirschman (1958) emphasized that not all sectors have the same potential to absorb foreign technology or to create linkages with the rest of the economy. He noted, for example, that linkages are weak in agriculture and mining. He warned that in the absence of linkages, foreign investments could have limited effect in spurring growth in an economy. The grudge against what has become known as the enclave type of development, he wrote, is due to this ability of primary products from mines, wells, and plantations to slip out of a country without leaving much of a trace in the rest of the economy. About the consequences in potential linkages effects differences in manufacturing and agriculture, Hirschman (1958) wrote, the absence of direct linkage effects of primary production lends these views (enclaves) a plausibility that they do not have in the case of foreign investment in manufacturing. More recently, the theoretical work on linkages, by Rodiguez-Clare (1996), shows that multinationals intensive use of intermediate goods enhances production efficiency in host economies. In this framework, increased demand for inputs leads to a positive externality to other producers owing to an increase in variety. Greater varieties of inputs, however, seem to be more relevant to the manufacturing than to the agricultural sector. Likewise, Markusen and Venables (1999) analyze the effect of foreign firms on the development of domestic firms in the industrial sector. In their model, foreign companies compete with domestic producers while creating additional demand for domestically produced intermediate goods through linkages with local suppliers. This can lead to domestic firms entering into the intermediate goods sector, which can result in lower costs that, reflected in lower final prices that increase demand, can benefit domestic firms producing final goods. Most of the micro studies on FDI spillovers, as Lipsey (2002) points out, tend to use manufacturing data and have regressed local firm productivity on within sector FDI. Although such studies find no horizontal technology transfer, the empirical work at the intra-industry level might not be suitable for capturing wider spillover effects on the host economy, such as those created by backward and forward linkages with domestic firms. One of the main reasons to examine productivity spillovers from foreign-owned to domestically owned firms, as Lipsey (2002) mentions, is to understand the contribution of inward FDI to host country economic growth. If foreign firms at the expense of lower productivity in domestic firms achieve higher productivity, there might be no implications for aggregate output or growth. However, there might be growth effects without spillovers just from the operation of foreign firms, which can be analyzed in terms of the impact of FDI on a country s output or growth. Moreover, because multinationals seek to minimize technology leakages to competitors while improving the productivity of suppliers by transferring knowledge, were FDI to generate spillovers they would more likely be vertical than horizontal. Park Jongsoo (2004) conducted a study on Korean Perspective on FDI in India: Hyundai Motors Industrial Cluster indicates that industrial clusters are playing an important role in economic activity. The key to promoting FDI inflows into India may lie in industries and products that are technology intensive and have economies of scale and significant domestic content. Sharma EAS (2005) in his paper Need for Caution in Retail FDI examines the constraints faced by traditional retailers in the supply chain and give an emphasis on establishment of a package of safety-nets as Thailand has done. India should also draw lessons from restrictions placed on the expansion of organized retailing, in terms of sourcing, capital requirement, zoning etc, in other Asian countries. The article comments on the retail FDI report that as commissioned by the Department of Consumer Affairs and suggests the need for a more comprehensive study.

3 85 Gonzalez J.G (1988) in his study Effect of Foreign Direct Investment in the presence of sector specific unemployment extends the work done by Srinivasan (1983) International factor movements, commodity trade and commercial policy in a specific factor model, by making an analysis of the welfare effects of foreign investment. The study shows that if there are no distortions, foreign investment enhances the social uplift of the people. The study strongly favours import substitution policies since such a strategy provides greater job opportunities to the people and consequently improves their standards of living. But the study finds that welfare effects of foreign Investment do not explain the pattern of trade in the economy. Thus, both Srinivasan (1983) and Gonzalez (1998) concluded that foreign direct investment and distortions of the labour market results in social uplift of the people. Sharma Rajesh Kumar (2006) in his article FDI in Higher Education: Official Vision Needs Corrections, raises four issues which need critical attention: the objectives of higher education, its contextual relevance, the prevailing financial situation and the viability of alternatives to FDI. The conclusion of the article is that higher education needs long-term objectives and a broad vision in tune with the projected future of the country and the world. Higher education will require an investment of Rs. 20,000 to 25,000 crore over the next five or more years to expand capacity and improve access. For such a huge amount, the paper argues, we can look to FDI. 3. MOTIVATION: Empirical researches suggest that capacity for FDI to serve as a channel of technological spillover might differ by sectors. But, relatively few scholars like Wheeler and Mody (1992), Loree & Guisinger (1995), Richaud et al. (1999), Asiedu (2002) and Sekkat (2004) have acknowledged the important role of infrastructure in stimulating FDI by arguing that availability of crucial infrastructure, such as roads, highways, ports, communication networks and electricity increases productivity and thereby attracts higher levels of FDI. This sheer fact of positive of impact of roads, bridges & highways development on economic growth motivated us to evince special interest to look into the sectoral allocation of FDI in construction (including infrastructure), which invariably encompasses FDI in roads & bridges development during our study period OBJECTIVE: To see, in terms of growth rate, the relative growth of construction (including infrastructure activities) in sectoral allocation of FDI into select sectors of the economy over the period METHODOLOGY: We have collected sectoral FDI data for select nine sectors from the factsheet of foreign direct investment (FDI), August 1991-Feb 2011, DIPP (Department of Industrial Policy & Promotion) (dipp.nic.in/english/publication/fdi_statistics/fdi_statitistic s.aspx). We have taken data for seven years ( ), as data for construction (including infrastructure) before that period is not available. Sectoral Allocation of FDI (Rs. Crore) Sectors Services Sector (Financial & Nonfinancial) 1,986 2,399 21,047 22,054 28,516 20,776 14,958 Computer Software & Hardware 2,441 6,172 11,786 5,476 7,329 4,351 3,490 Telecommunication 570 2,776 2,155 4,846 11,727 12,338 6,398 Housing & Real Estate ,121 7,186 12,621 13,586 5,070 Construction(including Infrastructure Activities) ,424 6,119 8,729 13,516 4,839 Automobile Industry ,254 2,204 5,212 5,754 5,962 Power ,003 4,382 6,908 5,639 Metallurgical Industries 836 6,540 7,866 3,856 4,157 1,935 4,783 Chemicals (Other than fertilizers) 909 1, ,427 1,707 1,746 Source: Factsheet on Foreign Direct Investment (FDI), August 1991-Feb 2011, DIPP Here the data is of panel nature, where if log of FDI is regressed on year/time period will give growth rate of FDI over the period ( ). In order to see the allocation of FDI into different sectors over the period, first we run a pooled regression of log of FDI on year, which is equivalent to estimation with neither fixed nor random effects, as given in the following equation. The Eviews 6 output is also shown below;

4 86 LFDI it = ß 1 + ß 2* YEAR i + u it LFDIit = * YEAR i t = ( )*** ( )*** SE = ( ) ( ) (F-Statistics = ) *** (R 2 = ), Here we see that both, the intercept and the regressor, are statistically significant at below 5% (***). Since FDI is in logarithmic form (i.e., the model being a log-lin panel regression model), so the slope estimate of corresponds to an approximately 38.71% growth-rate in FDI over the years. But this pooled regression assumes that the intercepts are the same for each sector, which could be an inappropriate assumption. Instead, we can estimate a model with cross-section fixed, which is also known as Least Square Dummy Variable Model (LSDV), which will allow us to capture the latent sector-specific heterogeneity, as given in the following equation. The Eviews 6 output with effects specification cross-section fixed (dummy variables) is also shown below; LFDI it = ß 1i + ß 2* YEAR i + u it LFDIit = * YEAR i t = ( )*** ( )*** SE = ( ) ( ) (F-Statistics = ***) (R 2 = ), Here also we see that allocation or growth rate of FDI into different sectors over the years is positive and statistically significant. We can also see the sector-specific heterogeneity from the table below; Cross-section Specific Heterogeneity Sectors Effect Services Sector-Financial & Non-financial Computer Software & Hardware Telecommunication Housing & Real Estate Construction ( including infrastructure activities) Automobile Industry Power Metallurgical Industries Chemicals (other than fertilizers) Actual Differential Intercept Values of Nine Sectors Sectors Intercepts Services Sector-Financial & Non-financial Computer Software & Hardware Telecommunication Housing & Real Estate Construction ( including infrastructure activities) Automobile Industry Power Metallurgical Industries Chemicals (other than fertilizers) Form the above tables, we see that heterogeneity in terms of allocation of FDI across different sectors over the years has been captured and this contribution has been obtained to be the highest in case of Services Sector-Financial & Non-financial, followed by Computer Software & Hardware, Telecommunication, Construction Activities and Metallurgical Industries whereas the same has been obtained to be negative in case of Housing & Real Estate, Automobile Industry, Power and Chemicals (other than fertilizers). Next we go in for random effects (cross-section) model, which is sometimes also known as error component model. Under random effects model, the intercept for each cross-sectional unit are assumed to arise from a common intercept, which is same for all cross-sectional units and

5 87 over time, plus a random variable that varies over crosssection but is constant over time, as given in the following equation. The Eviews 6 output with effects specification cross-section random and idiosyncratic random is shown below; LFDI it = ß 1 + ß 2* YEAR i + ε i + u it Or, LFDIit = ß 1 + ß 2* YEAR i + W it LFDIit = ß 1 + ß 2* YEAR i + W it t = ( )*** ( )*** SE = ( ) ( ) (F-Statistics = ***) (Weighted R 2 = ), (Un-weighted R 2 = ) Simultaneously, it is also worth determining that whether fixed effects are necessary or not, as shown in Eviews 6 output below; Redundant Fixed Effects Tests Effects Test Statistic d.f. Cross-section F *** (8,53) Cross-section Chi-square *** 8 Redundant fixed effects test has been employed to see whether fixed effects are necessary or not, each in both χ 2 and F-test versions, restricting cross-section fixed effects to zero. From the above output, we see that cross-section fixed effects restrictions are not supported by data, meaning thereby that a pooled sample could not be employed. Next we see whether fixed effect model is preferred over random effect model or not, using Hausman test, as shown below in Eviews 6 output; Correlated Random Effects - Hausman Test Test Summary Chi-Sq. Statistic Chi-Sq. d.f Cross-section random *** (1) Cross-section random effects test comparisons Variable Fixed Random Var(Diff.) Prob. LFDI NA From the above output, we see that Hausman test is not significant. Therefore, it can be concluded that though random effect model is preferred over fixed effect model but there is no significant difference between them, which is shown in the cross-section random effects test comparisons of the Correlated Random Effects - Hausman Test. This means that heterogeneity in terms of allocation or growth-rate of FDI into different sectors over the years could be arising from two legs. One is the common intercept, which is constant over cross-section and over time periods. This invariably emerges out of low level of development in the economy encompassing all the sectors, bureaucratic delays and widespread corruption, which could not attract considerable amount of FDI, resulting in an initial negative growth rate. The other is the random variable, which is constant over time but varies over cross-section, which is invariably government policy regarding FDI across different sectors. Now, we can compare three models, such as, Pooled OLS Model, Fixed Effect (LSDV) model and Random Effect Model (Error Component Model) in a tabular form, as given below; Dependent Variable: LFDI Coefficient (t-ratio) [Standard Error] Independent Variable OLS FEM REM YEAR ( )*** [ ] ( )*** [ ] ( )*** [ ] Observations 63 R ***Significant at 1%

6 88 6. CONCLUSION: Form the above results, we see that during the study period , the growth-rate of FDI has found to be positive and statistically significant. But allocation of FDI, during these years, into different sectors has experienced heterogeneity, which arises from two legs. One is the common intercept, which is negative and constant over cross-section and over time periods. This invariably emerges out of extant low level of development in the economy encompassing all the sectors, bureaucratic delays and widespread corruption, which could not attract considerable amount of FDI, resulting in an initial negative growth rate. The other leg is the random variable, which varies over cross-section but constant over time, which is invariably government policy regarding FDI across different sectors. Moreover, the contribution to this heterogeneity in sectoral allocation of FDI has been obtained to be the highest in case of Services Sector- Financial & Non-financial, followed by Computer Software & Hardware, Telecommunication, Construction Activities (including Infrastructure) and Metallurgical Industries whereas the same has been obtained to be negative in case of Housing & Real Estate, Automobile Industry, Power and Chemicals (other than fertilizers). Thus, we see that though construction activities (including infrastructure) had a positive contribution in the heterogeneity in sectoral allocation of FDI, but is poorly placed in the cross-section specific heterogeneity table, which should have been other way round for attracting more FDI and subsequent economic growth. REFERENCES: [1] Asiedu, E. (2002). On the Determinants of Foreign Direct Investment to Developing Countries: Is Africa Different?. World Development, Vol. 30(1), pp [2] Alfaro, L. (2003). Foreign Direct Investment and Growth: Does the Sector Matter? Harvard Business School. Boston. [3] Billington, Nicholas, (1999). The location of foreign direct investment: an empirical analysis. Applied Economics, Vol. 31, pp [4] Blomstrom, M. and A. Kokko (1997). "The Impact of Foreign Invest- ment on Host Countries: A Review of the Empirical Evidence." World Bank Policy Research Working Paper(No. 1745). [5] Blomstrom, M., S. Globerman, et al. (1999). The Determinants of Host Country Spillovers from Foreign Direct Investment: Review and Syn- thesis of the Literature. Working Paper No. 76, The European Institute of Japanese Studies. [6] Borensztein, J., J. De Gregorio and J-W. Lee (1998). How does foreign direct investment affect economic growth?. Journal of International Economics, Vol. 45, pp [7] Dunning, J. D. (1981). Explaining the international direct investment position of countries toward a dynamic or development approach. Weltwirtschaftliches Archiv, vol. 117, pp [8] Goodspeed T, Martinez-Vazquez J and Zhang L (2006). Are Government Policies More Important Than Taxation in Attracting FDI. ISP Working Paper Number 06-14, International Studies Program working paper series. [9] Haughwout, AF., (2001). Infrastructure and Social Welfare in Metropolitian America. Economic Policy Review, Vol. 7, No.3 [10] Holtz-Eakin, D. and Schwartz, A. E., (1995). Spatial productivity spillovers from public infrastructure: Evidence from state highways. NBER Working Paper Series, No 5004, NBER. [11] Kokko, A. (1994). "Technology, market characteristics and spillovers." Journal of Development Economics vol. 43 pp [12] Loree D. W. and S. E. Guisinger (1995). Policy and non-policy determinants of U.S. equity foreign direct investment. Journal of International Business Studies, Vol. 26 (2), pp [13] Limao N, and A, J, Venables (2001). Infrastructure, geographical disadvantage, transport costs and trade. World Development Economic Review, Vol. 15, pp [14] Mencinger, J. (2003). "Does Foreign Direct Investment Always Enhance Economic Growth?" Kyklos 56(4): [15] Morrison, C J. and A E Schwartz., (1996). State Infrastructure and Productive Performance. American Economic Review, Vol 86, Issue 5. [16] Richaud, C., K. Sekkat and A. Varoudakis (1999). Infrastructure and Growth Spillovers: A Case for a Regional Infrastructure Policy in Africa. Mimeo, University of Brussels. [17] Schneider, F. and Frey, B. (1985). Economic and Political Determinants of Foreign Direct

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