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Regional Disparity in FDI Inflows: Evidence from India

Siddhartha Sanghi1, Vaibhav Patni1

Abstract Since liberalization, Indian economy has been receiving huge FDI inflows and emerged as third most preferred investment destination for foreign investors. However, analysis of FDI statistics displays huge disparity in FDI distribution among Indian states. This paper tries to quantify the reasons behind the skewed distribution of FDI among states. An understanding of such factors will assist Indian government to formulate policies for equitable growth of FDI inflows. The results obtained confirm the presence of agglomeration effect, a strong impact of policy environment in the region; positive impact of market size, labour condition, infrastructure and output variables. JEL Classification F21, R12, H70, F23, O14 Keywords Foreign Direct Investment, Regional Inequality, Agglomeration Effect, Policy

Environment

1. Introduction This is an era of globalization and financial integration. Having a closed economy has proved to be disastrous and biggest impediment in the socio-economic development of a country. Foreign Trade and Foreign Direct Investment (FDI) are two important pillars supporting the bilateral relations between economies. One of the major issues of debate on globalization is the effect of economic integration on income inequality. Generally developed, capital rich countries invest in developing countries to get benefits of a new potential market, cheap labour and unexploited natural resources, which increase their profits and cost efficiency of production. Not only the investing country but the host country also benefits in the form of technology and knowledge spill overs. FDI is generally preferred by policy makers because of its dual benefit one it is more stable source of funding and two, it is not considered as a part of country’s external debt.

Indian economy got liberalized in 1992 under the leadership of the government of P. V. Narasimha Rao and Finance Minister, Dr Manmohan Singh (currently Prime Minister of India) the entire foreign policy framework was structured to attract foreign investors. As per IMF’s Global Stability Report, October 2012, India emerged as one of the major recipients of FDI among the emerging market economies and has consistently ranked among the top three global investment destinations. India’s Foreign Direct Investment (FDI) policy has gradually been improving since 1992 to make market more investor friendly and to attract more investments.

Traditionally sectors, which received major share of foreign investments, were mining, manufacturing and plantations but gradually with development of technology and proliferating service sector the sectorial distribution of FDI has undergone significant changes. Sectors like banking and insurance are also started receiving significant foreign investment share. Today 34% of the total share of FDI that we receive is in service sector, hotel and tourism, telecommunication and computer hardware and software with service sector having highest 19% share among all the sectors.

                                                                                                                         1  Department  of  Economics,  Indian  Institute  of  Technology  Kanpur,  UP,  India-­‐  208016  Disclaimer:  The    

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Today India is the third-largest economy in the world in PPP terms, According to IMF October 2013 report India is the third most preferred destination for foreign investors among emerging economies. India has received foreign capital inflow of USD 306.88 billion since 2000 with 94% of the total share during this decade. For the period 1999–2004, India received only USD 19.52 billion as foreign investment. While in the period 2010-2013, foreign investment inflows further increased to USD 172.82 billion. Only during the financial year 2012–13, India attracted foreign investment worth USD 22.42 billion. India’s strengths are telecommunication, information technology, power, metallurgical industries, hotel and tourism, auto components, pharmaceuticals, and jewellery. India has large pool of engineers, doctors and management professionals to satisfy the workforce requirement of foreign companies. The size of the middle-class population stands at 300 million, which represents a growing consumer market. Our major investors are Mauritius, Singapore, UK, Japan and USA, with Mauritius accounting for 37% of net FDI inflow. High capital flows from Mauritius are due to routing of international funds through the country given significant tax advantages; because of a tax treaty between India and Mauritius double taxation can be avoided and thus creating an efficient transmission mechanism for FDI..

The major issue which India is facing today is widening regional inequality arising due to uneven distribution of FDI. The distribution is so skewed that the top five regions Maharashtra, New Delhi, Tamil Nadu, Karnataka and Gujarat account for 67% of the total share of FDI with Maharashtra individually receiving 31% of the total FDI inflows (from April 2000- December 2013). The paper targets to analyse the factors behind this unequal distribution of FDI inflows. To ensure that the entire country benefits from the policy of liberalization and globalization, it is essential to examine the major determinants affecting regional distribution of FDI flows to India.

The paper has been further divided into five sections, where section two provides the overview of the FDI inflows, section three discusses theoretical background behind the dependence of FDI inflows on several factors following a literature review. Section four discusses the methodology used and data. The last section depicts the results and conclusion. Table 1: State-wise FDI Inflows (in Million USD) to Indian States (Apr 2000- Dec 2013); Source: Department Of Industrial Policy & Promotion, Ministry of Commerce and Industry, Govt. of India

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2. Overview of FDI Inflows

2.1 Global Trend of FDI Inflows

Figure 1: Global FDI Inflow in million USD: 1970-2012

FDI can arguably be attributed as one of the key drivers of growth. It certainly is an important element of the rapid globalization process. The foreign capital inflow via FDI as a medium has been following a cycle of huge crust and troughs. Global FDI inflows

0  

500000  

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1970   1976   1982   1988   1994   2000   2006   2012  

FDI  Infl

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illion  USD

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World  FDI  Inflow  

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increased from USD 14 billion in the year 1970 to USD 1413 billion in year 2000. The period of increase followed a steep decline with global FDI inflows curbing to USD 626 billion in the year 2003. It again rose to USD 2002 billion, in year 2007 with before falling again in the years 2008-09 due to the Global Financial Crisis, popularly known as GFC.

Figure 2: Distribution of Global FDI Inflow in million USD: 1970-2012

With the emergence of Asia as one of the global frontiers, its stake in the global FDI inflows has been increasing since. At one point in the year 2000 the difference between Developed Economies and Developing Economies and Asia was USD 910 billion and USD 985 billion respectively while the data from the year 2012 shows that developing countries have surpassed the total inflows in the developed countries by approximately USD 140 billion. Among other developing continents, Asia has showed remarkable growth in its FDI inflows over the past two decades. By 1990, Asia’s total share was 10.9 percentage compared to 1.37 percentage to Africa which changed to 5.27 percentage to Africa as compared to a whopping 27 percentage to Asia by the year 2009.

2.2 FDI Flows to India: Some Stylised Facts

Figure 3: FDI Inflows to India in million USD: 1970-2012

Indian economy liberalized in 1992, the foreign policy of the country got revamped. Since then, the FDI inflows to the country has increased many folds from USD 0.5 billion to USD 47 billion in the year 2008. This strong persistent growth ended as an outcome of the financial crisis in the year 2008-09. As per IMF’s Global Stability Report, October 2012, India emerged as one of the major emerging recipients of FDI among the emerging market

-­‐200000  

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1970   1978   1986   1994   2002   2010  

FDI  Infl

ow  (in  Million  USD

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   Developing  economies  

   Developed  economies  

       Developing  economies:  Africa  

       Developing  economies:  America  

       Developing  economies:  Asia  

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1970   1976   1982   1988   1994   2000   2006   2012  FDI  Infl

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India  

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economies and is consistently ranked among the top three most preferred global investment destinations. India’s Foreign Direct Investment (FDI) policy has gradually been improving to make market more investor friendly. The share of India to world FDI inflows increased from a mere 0.3 percent to 3 percent by the year 2009. India has received foreign capital inflow of USD 306.88 billion since 2000 with 94% of the total share during this decade. For the period 1999–2004, India had received only USD 19.52 billion as foreign investment. While in the period 2010-2013, foreign investment inflows further increased to USD 172.82 billion. Only during the financial year 2012–13, India attracted foreign investment worth USD 22.42 billion.

Figure 4: Share of India in Global FDI Inflows: 1970-2012

According to a survey conducted by tax advisory firm Grant Thornton, for the period January–November, 2013 India witnessed mergers and acquisitions deals worth USD 26.76 billion. Policy framework in India “A foreign company planning to set up business operations in India may:

• Incorporate a company under the Companies Act, 1956, as a Joint Venture or a Wholly Owned Subsidiary.

• Set up a Liaison Office / Representative Office or a Project Office or a Branch Office of the foreign company which can undertake activities permitted under the Foreign Exchange Management (Establishment in India of Branch Office or Other Place of Business) Regulations, 2000.”

-Reserve Bank of India: Foreign Investments in India (Updated: 28th Jan, 2014) There are two routes through which an Indian company may receive Foreign Direct Investment:

i. Automatic Route: Investment through this procedure can be without prior approval of either the Government or the Reserve Bank of India in all specified activities/sectors in the consolidated FDI Policy, issued by the Government of India from time to time.

ii. Government Route: Investment through this route is not covered under the automatic route and requires prior approval of the Government under the Foreign Investment Promotion Board (FIPB), Department of Economic Affairs, and Ministry of Finance.

-­‐0.5  0  

0.5  1  

1.5  2  

2.5  3  

3.5  

1970   1976   1982   1988   1994   2000   2006   2012  

%  of  World  FDI  Inflows  

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The company receiving FDI either through the Automatic route or the Government route is required to comply with provisions of the FDI policy which includes reporting the FDI to the Reserve Bank of India. Major capital inflow through FDI in the country is through automatic route, followed by reinvested earnings and acquisition of shares. The share of FDI through government approval route has declined over time. Total investments from Mauritius, Singapore, U.K. and USA amount to 64% of the net FDI inflows with Mauritius being our major investor investing 37% of the total share of FDI.

Figure 5: Regional distribution of FDI to India: 2004-2012

Though after liberation India emerged as one of the prime investment locations for foreign investors but the major concern lies in the distribution of FDI. The spatial inequality of FDI is one of the major reasons of rising regional differences. The aggregate data for region-wise FDI inflows to India shows that the inflows very focused and concentrated with two regions, Maharashtra and NCR-Haryana accounting for about 3/4th of the total FDI inflows. States with largest land area, Rajasthan and Uttar Pradesh account for very minimal share of FDI (totalling less than 1 percent). This study is dedicated to analyse the strategies of locational preference adopted by Multinational Enterprises (MNEs) and policy changes that can be achieved in order to deal with this issue.

Figure 6: India’s Top Five Recipient states of FDI in million USD: 2004-2012

44%  

25%  

7%   7%  6%  

6%  

2%  

1%  

1%  1%  

1%  

1%  

0%  0%  0%  0%  2%  

Regional  distribu:on  of  FDI  (2004-­‐2012)  

Maharashtra  

NCR-­‐Haryana  

Karnataka  

Tamil  Nadu  

Gujarat  

Andhra  Pradesh  

West  Bengal  

Chandigarh  

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Top  5  States  

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3. Spatial Inequality in FDI inflows: A Theoretical Framework 3.1 Literature Review Dunning (1998) discussed that the locational preferences and strategies of MNEs towards FDI have significantly changed between 1970 and 1990s. He identified certain instrumental developments in the world economy in relation with the locational preference decisions of MNEs during this period. The increasing knowledge component in manufacturing goods, increase in share and demand for professional and skilled workers in labour force, increase in share of expenditure in IT sector reflect a major development in the growth of intellectual capital. Increasing significance of the non-material knowledge intensive assets was the result of growth in service sector, particularly knowledge and information oriented services. The location of creation and use of these knowledge intensive assets have been increasingly influenced by the presence of immobile clusters of complementary value-added activities. Clustering benefitted firms in similar and related activities in many forms like localized support facilities, specialized factor inputs and distribution networks. This gave rise to “alliance capitalism” in which the key shareholders in wealth sharing process need to collaborate more actively and purposefully with each other. There were also many evidences where MNEs were increasingly seeking locations which offer the best economic and institutional facilities for core competence to be efficiently utilized. Fourth, the renaissance of market economy and the consequent changes in the macroeconomic policies and macro-organizational strategies of many national governments have also contributed significantly to the economic and political risk assessment of FDI by MNEs. Since the last two decades the agglomeration factor has proved to be a vital factor in formulating the locational preferences for FDI. Agglomeration economies emerge when there are some positive externalities in collocating near other economic units due to presence of knowledge spill overs, specialized labour markets and supplier network (Krugman, 1991). Several studies focusing on emerging economies statistically proved that foreign investors favour locations that could minimize information costs and offer agglomeration economies (He Canfei, 2002).

Incentives offered by state government like tax abatements, job credits, grants, sales tax abatements, job training, infrastructure subsidies and loan guarantees play a significant role in attracting the investment inflows (Fisher and Peters, 1998). Santis, Mercuri and Vicarelli (2001) did a study on European Union and states that FDI inflows were influenced more by the total fiscal wedge than corporate tax. In the Chinese context, Luo et al (2008) did a study on 98 hinterland cities of China for the period 1999 to 2005 using panel data analysis and found abundance of unexploited natural resources and low labour costs were not important in determining FDI flows to China’s Hinterland. Instead, policy incentives and industrial agglomerates were the most important determining factors in deciding FDI sites. Xu et al (2008) used panel data analysis for the at provincial level data in China for period 1998 to 2007, found agglomeration effect playing an important role in determining locational preferences for FDI. Cumulative FDI in a region had strong demonstration effect in the decision making of new FDI entrants. This study also indicated that the quality of labour along with the labour costs is a crucial component in setting up the decisions regarding foreign investments from US and Europe. Two major cities Shanghai and Hong Kong being the core of agglomeration had significant impact on the locational choices of FDI in China.

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In the Indian context, Siddharthan (2006) found that the determinants of regional distribution of FDI flows are quite similar for India and China. In both the countries a large share of FDI flows to relatively developed regions, while other regions poor in physical, social and institutional infrastructure received a very small share from the whole pie. For China, Eastern Zone provinces with high per capita income, better socio economic indicators, better infrastructure facilities like availability of electricity, rail and road network attracted higher FDI inflows. Similarly, in India states which are relatively developed and situated at strategic coastal location receive high share of FDI inflows. Moreover, the regions receiving high FDI flows were the regions with high domestic investment. Goldar (2007) found that the factors affecting the locational choices of local company plants by and large also affect the location decision of foreign multinational enterprises. His analysis of plants located in 100 major cities in 17 different states revealed that city-size is a very important factor in setting up locational preferences. States with metropolitan cities had a favourable influence capturing the advantage in ‘headquartering’ the country operations of MNEs. Availability of skilled educated workers, the existing investment climate and availability of civic amenities in cities had a strong influence.

Morris (2007) argued that in a country like India, the regions with the metropolitan cities have an advantage in headquartering the country operation of MNEs and so attracted a huge share of FDI inflows. Nunnenkamp and Stracke (2007) found significant positive correlation of FDI with per capita income, per capita bank deposits, telephone density, standard of education, population density and per capita value added in manufacturing in India. On the other hand FDI was negatively correlated with state population and had insignificant effect of availability of electricity and unemployment rate. Aggarwal (2005) found rigid labour laws to be one of the major impediments in FDI inflows. The effect of labour cost and market rigidities are more pronounced for export oriented market rather than domestic market. He also pointed out the importance of export processing zone in deciding location for investment. Statistical evidences from the study suggested that infrastructure, human development and regional development were also important in attracting higher FDI both in export and domestic market sectors. Lall and Mengistae (2005) in their study on business environment, clustering and plant location in the Indian cities using firm level data from 2003 round of Investment Climate Survey, India, found local business environment to have a strong effect on locational preferences. Predatory enforcement of business regulation is a big setback for investment plans in a region and prevalence of such activities decreases interest of foreign investors in the region. Better access to finance, better infrastructure facilities and availability of land attracted firms to invest in the region. Firms were also attracted by the agglomeration economies from clustering of firms for dependent industries therefore new firms will prefer to locate their activities in a region where their line of business is already established.

Ramachandran and Goebel (2002) assessed why Tamil Nadu has emerged as one of the most favoured investment destination in India. He cited several advantages like political stability with proactive government policy framework, investor friendly and transparent decision making process, sufficient availability of power, abundance of skilled and professional labour, sound diversified industrial infrastructure, harmonious industrial relations, high quality of work culture, highly cosmopolitan composition and high proportion of English speaking population which makes Tamil Nadu as one of the best

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locations to invest in India. FDI inflow in Tamil Nadu is heavily dominated by the IT sector. Atri Mukherjee (2011) has analysed major determinants causing regional disparity in FDI inflows to Indian states. Her analysis shows that variables like market size, size of industrial and service base and agglomeration effects have positive impact on FDI inflows. On the other hand the impact of taxation and cost of labour is negative. While in her analysis impact of quality of labour had ambiguous effects whereas infrastructure, however, has. She emphasized on the need of a conscious and coordinated effort at the national and state level to minimize this problem and to attract more FDI in the country. We have borrowed the variables of consideration from her work and have incorporated the policy environment variables, which were left out from her analysis. 3.2 Selection of Variables

Market Size One of the major attractions for the foreign firms to invest in a region is the potential returns on the investment. The return on investment depends a lot on the local demand for the goods and services produced by the investor. Bigger the market for a firm, higher the returns on the investment and thus higher would be the investments made. The size of local market influences the location choice of FDI as it determines a potential demand of the investing firm’s output. One can argue that in case of exports this argument would not hold, but critically analysing the investment patterns in large countries like India we found that local sales are generally found to be more profitable and is one of the prime incentives for the foreign investor. In larger countries, the benefits of the economies of scale may be eventually reaped. Despite changing choice patterns in Multi National Enterprises, large and growing domestic market continues to remain a major determinant of market-seeking FDI inflows.

Literature review showed that several empirical studies conducted in the context of China, India and the US have taken into account variables such as GDP, GDP growth, Disposable income, population size, population density, consumption levels etc. Thus for our study, we have used per capita net state domestic product (NSDP) and population density of each state. Table 2: Characteristics of Indian States (early 2000s); Note: The second column is from Sachs, Bajpai, and Ramiah (2002) and the first, third and fourth columns are from Fan, Hazell, and Thorat (1999) and the rest are from author’s calculation from the RBI database (2012 data)

Region GDP per capita (1997/8)

Major port city

Road density Literacy

FDI Inflows (aggregate)

Population Density

Andhra Pradesh 2,521 7,072 33.26 341387.70 285.83 Bihar 1261 14700 27.77 1686.89 663.29 Gujarat 4505 Kandla 3604 50.07 374042.70 270.09 Haryana 4516 Delhi 7624 35.6 814.64 Himachal Pradesh 3844 58.76 1531207.00 Jammu and Kashmir 3013 30.89 Karnataka 3109 7236 37.84 442525.00 284.97

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Kerala 2823 5437 81.73 40454.16 838.61 Maharashtra 5690 Mumbai 2235 33.41 43874.43 192.41 Madhya Pradesh 2286 5498 40.52 2706785.00 327.49 Orissa 1871 11153 38.51 16170.63 244.00 Punjab 5079 Delhi 8623 49.32 44641.18 301.23 Rajasthan 2621 1816 28.4 31115.16 170.49 Tamil Nadu 3454 Chennai 14747 49.8 435329.00 497.22 Uttar Pradesh 2023 2560 36.55 15721.92 624.45 West Bengal 3308 Kolkata 6369 52.5 93195.00 805.82 Infrastructure It is a notion that better infrastructure is crucial for the growth of industries. Since medieval times, places with better connectivity with other parts of the world in presence of major ports, coast locations etc. have been the hub of all trade and economic activities. With the developments of road rain and airports, they are the new infrastructure conditions that enterprises seek while deciding locations for their businesses. Dunning (1998) has argued that much of the FDI is governed by the macroeconomic environment and institutional framework of the host country. Availability of transportation facilities to reach the nearest port or output markets have historically been considered as an important determinant of setting a business in a particular place. Most commonly used variables include the presence of major ports, close to the coast location, availability and quality of road and rail network. Thus here we test the hypothesis that regions with better infrastructure attract higher FDI inflows comparatively. Out of numerous infrastructure parameters, two indicators, viz., road route density and railway route density have been considered in this study.

Industrial Linkages Past researches such as Dunning (1993) suggested that natural resource seeking FDI looks for foreign locations that have natural resources and related infrastructure. Siddharthan (2006) found that the states with higher industrial output have attracted high levels of FDI. Sectorial Distribution of FDI has significantly changed since 1990s. Initially the major focus of the Multinational Enterprises was on the primary sector to satisfy their raw material needs. Slowly with the advent of technology and knowledge, with the boom in IT and service sector and availability of a learned and talented Indian work force the focus has shifted towards the service and IT sector. Traditionally the FDI from developed to developing countries was in sectors like mining, infrastructure, oil extraction and refining, plantation crops and gradually due to the advancement in technology and knowledge their operations have now also include services such as banking, insurance, IT etc. Looking at the sectorial distribution of FDI in India the service sector, hotel and tourism, telecommunication and computer hardware and software together constitute 34% of total capital inflow with service sector having highest 19% share among all the sectors. Thus we test the following hypotheses:

• Indian states with strong industrial base tend to attract more FDI flows; • Indian states with higher services sector activity attract more FDI flows.

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The explanatory variables considered are the per capita manufacturing, services, industrial and mining output of each state. Higher the output, more prosperous the state and higher would be the attractiveness of that state among the investors. Table 3: Sectors Attracting Highest FDI Inflows; Source: Department Of Industrial Policy & Promotion, Ministry of Commerce and Industry, Govt. of India, Dec 2013

Effect of Agglomeration on FDI Inflows: The industries prefer to set up their businesses in the areas where physical infrastructure is sufficiently available and subsequently, the related industries would gravitate forming clusters in that region. These industry clusters result in geographical areas or regions specializing in certain activities. This clustering of firms, which is also known as the “agglomeration” effect has emerged as an important determinant in the regional distribution of FDI flows within a country during the last two decades. Similar trend was followed in India, industry clusters were formed from the very beginning of industrialization. Places such as Bangalore have come up as a result of this clustering in the service and IT sector. The presence of agglomeration economies is reflected in terms of prior presence of foreign investors, prior concentration of manufacturing plants and number of enterprises in a region. We will try to see this effect in the lagged FDI term. In the Indian context, the Agglomeration effect was first studied by Atri Mukherjee (2011). She argues that as more similar firms in the related fields of business cluster, to reduce their production costs significantly with increased competition at the supplier end as well as specialization and division of labour and benefiting from the industrial linkages. Firms in related fields of business cluster together, their costs of production may decline significantly (firms have competing multiple suppliers, greater specialization and division of labour result Hakan Yilmazkuday 2010). Further, as Krugman (1991) has discussed,

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agglomeration economies emerge when there are some positive externalities in collocating near other economic units due to the presence of knowledge spill overs, specialized labour markets and supplier network.

We will test this hypothesis that relatively higher existing stocks of foreign investment attract future investments, in the region which will confirm the effect of agglomeration externalities. As evident from the Figure 7, the distribution of FDI has not changed significantly during the period 2004-2012. The eastern parts of the country have been deprived of FDI inflows and the Southern states account for the maximum share in FDI inflows.

Figure 7: Recent Trend in FDI Inflows (in million USD) to India's Zones: 2004-2012

Policy Environment The policy environment of a region is characterized by the policy framework adopted by the government. As we have discussed in the previous sections, the policy framework of India was very stringent prior to 1991, after which the government liberalized its’ policy to reap the benefits of globalization. Since then, various states have been adopting different policies for promoting there states as an attractive destination for foreign investments. Arti Mukherjee (2011) discussed the host of measures adopted by the governments such as libral regulations, gutantee provisions, settlement of investment disputes and various tax related incentives to attract FDI inflows. For our study on Indian economy, we used entry time and exit time as proxy for the entry and exit barriers and test the hypothesis that ease of doing business predominantly form the entry and exit conditions affects the FDI inflows to that region.

Further, as a proxy for the efficiency of governance and reliability of the system, we use the number of registered cases per capita in each region and ease of doing business ranking relaeased by World Bank. Labour Conditions With the boom in IT and service sector and there is a huge demand for highly skilled workforce and as higher wage levels reflect higher labour productivity or higher quality of human capital, therefore an investing firm may be attracted by the higher wage rate. In this paper, wages per worker in Indian states have been used as an indicator of labour cost. The

0  100000  200000  300000  400000  500000  600000  700000  800000  900000  

2004   2005   2006   2007   2008   2009   2010   2011   2012  

FDI  Infl

ows  (in  m

illion  USD

)  

South   North  and  West   East  

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proxy for the quality of labour we have used literacy rate and per capita number of educational institutions for higher studies (degree and above) in each state. Table 4: List of Explanatory Variables and Tentative Conclusions Type of Factor Variables Sign A. Market size Per capita NSDP

Population Density + +

B. Infrastructure Road route density Railway route density

+ +

C. Industrial Orientation Per capita manufacturing output Per capita services output Per capita industrial output Per capita mining output

+ + + +

D. Agglomeration Effects Per capita FDI stock + E. Policy Environment Entry Time

Exit Time Per capita registered corruption cases Ease of doing business Rank

- - - -

F. Labor Conditions Wages per worker Literacy rate Per capita number of higher educational institutes

+ + +

4. Methodology and Data The empirical analysis in this study is based on regional level panel dataset of India over the period 2003-04 to 2012-13 covering 16 regions including all 31 states and Union territories, viz., Maharashtra, Delhi, Karnataka, Gujarat, Tamil Nadu, Andhra Pradesh, West Bengal, Chandigarh, Goa, Madhya Pradesh, Kerala, Rajasthan, Uttar Pradesh, Orissa, Assam and Bihar. The dependent variable in the study is region wise annual per capita FDI inflow. The data for the region wise FDI inflow is released by Department of Industrial Policy and Promotion (DIPP), Ministry of Commerce and Industry, Government of India. The regional inequality across Indian states can be observed by analysing socio-economic conditions, per capita FDI inflows and geographical features of the states. The land area varies from around 350,000 square km in Rajasthan to only 300 square km in Punducherry, a union territory. The population density in NCR- Haryana region was 933 and was only 174 in Assam region for year 2012. NSDP per capita varies from INR 167,838 in Goa to INR 26,634 in Bihar for year 2012. Kerala has the highest literacy rate of approximately 100 percent while states like Bihar are lagging far behind with literacy rate of only 64%. NCR- Haryana region enjoys the best rail connectivity in the country followed by West Bengal whereas Assam region lacks any significant rail network. Wage rates also vary across states with per day wage rate of INR 217 in Chandigarh to INR 808 in Maharashtra. There is a huge difference in the policy framework of every state with state’s own tax revenue as per cent of NSDP is highest for Karnataka at 10.55 per cent and the lowest for Nagaland at 2.03 per cent.

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The following model examines the effect of host of explanatory variables on FDI inflow in a region

𝐹𝐷𝐼!"  =  𝛼!"𝐹𝐷𝐼!(!!!) +   𝛽!"#𝑋!"#!!!! + γ!"𝑍!"  !

!!! +  𝜀!" (1)

In equation (1) above, FDIit is the monthly inflow of FDI in region i and FDIi(t-1) is the lagged value. Xijt represents the characteristics of the region i varying with time. Zij represents the time invariant characteristics (fixed effects) of region i.

There are several econometric problems that may arise in the estimation of equation .The time invariant variable Xijt are assumed to be exogenous but there may arise a problem of causality – these regressors may be correlated with the error term. There may arise a problem of endogenity. The presence of the lagged dependent variable FDIi(t-1) gives rise to autocorrelation. The panel dataset has a short time dimension (T = 9) and a larger region dimension (N = 16)

The solution of these problems is to use GMM. There are two very famous GMM techniques that are generally used:

1. Arellano-Bond GMM procedure 2. Arellano–Bover/Blundell–Bond linear dynamic panel-data estimation

We have used Arellano-Bover Procedure to model the impacts of host of explanatory variables on FDI inflows. This model is used because it is better for modelling small sample and at modelling non-stationary data. There is one more problem cited during the econometric analysis of equation (1), the problem of multicolinearity among dependent variables. Related variables like literacy rate, number of educational institutions and wages earned per worker are source of this problem. The solution of this problem, is to form principal components of related variables and use an index of these variables in the analysis. It may be worthwhile to note that because of the diverse characteristics of states Principal component of each state is being calculated separately. Table 5: List of Data Sources

Variable Data Sources

FDI Ministry of Commerce and Industry Net state gross domestic product Ministry of finance, Economics Division Population, Literacy rate, Educational Institutions

Census India, ASER Report

Sectorial Output Central Statistical Organization Rail and Road route density Ministry of Statistics and Program

Implementation Time Costs, Cost to start a business, Rank World Bank “Ease of Doing Business” 2009

Report State’s own tax revenue as percent of NSDP (TAX)

Ministry of Finance, Planning Commission India

No. of corruption cases Registered PRS Legislative research incubated by Center for Policy research, GOI

Data Bases: CEIC database, CMIE database, RBI data base

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5. Results and Conclusion 5.1 Results Table 6: Regression Results Explanatory  variables     Model  1   Model  2   Model  3  

Cons 104299.5*

(55064.4) 147243***

(41297.55) -1783.8

(4034.164) FDI per capita (lag) 0.0922

(0.0842) 0.0873

(0.0835) 0.0784

(0.0818) Population Density 3.244

(7.356) 9.046***

(2.309) NSDP per capita 0.0393

(0.0280) 0.0281*

(0.0166)

Road density -0.0043 (0.0048)

Rail density 1.423 (1.10)

2.114**

(0.9502) 0.622

(0.3994) Entry time -1082.52

(878.56) -1859.58***

(463.17)

Exit Time -21.178** (10.542)

-20.638**

(10.541)

Corruption cases -6.465**

(3.319) -9.178**

(3.306)

Rank: Ease of Doing Business -757.15** (375.387)

Output (PC of Manufacturing, mining, industrial and service sector output)

-1.096 (1.613)

-0.0524 (0.9623)

3.194*** (0.3622)

Labour (PC of wages per worker, literacy rate and per capita number of higher educational institutions)

0.7738 (0.4915)

0.9448**

(0.4812)

Total pool (balanced)observations 128 128 Note: Figures in the parenthesis represent the t values. ***,** and * denote significance at

1%, 5% and 10% level, respectively. In model 1, we observe a positive impact of Population density, NSDP, Railway density, FDI lags, Rank of ease of doing business and Labour conditions on FDI inflow although the results obtained are not significant. Time costs and Corruption have a significant negative impact on FDI flows. There is insignificant negative impact of road density on FDI inflow, this contradiction with the theory is arising because of the shortcomings of the data as the data available for road density is only for the national highways, which does not include the road data within the states. Therefore in model 2 we considered to analyse data without including variable road.

In model 2, the significance of variables increased. Now variables NSDP per capita, Rail, labour have a significant positive effect on FDI inflows. Time costs and Corruption have a

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significant negative impact. Output has a negative impact (although insignificant) on capital inflows.

In model 3, we use the rank of ease of doing business region wise issued by the World Bank to proxy for the policy environment and the bureaucracies involved in a particular state. Results show that the rank has a very high negative impact on the FDI inflows which implies that the states where doing business is relatively smooth and at `ease’, the FDI inflows is very high compared to the other states. Also, the output coefficient is significantly positive showing that higher the industrial output in a region, more attractive is the state from the FDI investments. Positive coefficient of FDI lag confirms the presence of agglomeration effect whereas significantly positive coefficient of population density shows a positive impact of the local market size. 5.2 Conclusion We found a positive impact of market size variables, population density and NSDP per capita on FDI inflows. MNEs invest in developing economies like India to capture the potential market where their business can flourish. A high population density implies a large demand of goods and services. A high NSDP per capita is a sign of economic prosperity of the state and higher purchasing power of the individuals, along with substantiating the market potential it also demonstrates the performance and returns of existing businesses. More the existing local businesses flourish better are the chances of a new company to grow in that region. Availability of better infrastructure has a positive impact on FDI inflows. Railway density has a positive impact, which implies that states with better transportation facilities are more attractive for investors.

Time costs have a significant negative impact on FDI inflows because a delay in starting a project can have severe ramifications on the returns obtained from the project. High time costs make the market infeasible for investment. With increase in potential, scope and size of Service, IT and Telecom sector in India, companies require more professionals to be a part of their workforce and so better the labour condition variables, literacy rate and per capita availability of professional educational institutes better is the local populace and would attract more investors. Output index has a negative impact on FDI inflows, this implies that as businesses expand, they become independent they do not require foreign aid and become self-sustainable. Though the coefficient of the lag term of FDI is insignificant but the positive sign indicates an existence of agglomeration effect in the economy.

The results obtained from this study imply that government must focus on development of human resource, primary infrastructure, transportation facilities and policy framework of the regions receiving small share of foreign capital investments. Government should provide incentives and better infrastructure for investors to invest in the disadvantaged regions which are landlocked or presently less developed. Considering the huge potential of India some structural changes can attract huge foreign investments and can lead to a stable growth.

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