The Impact of Telecommunications Infrastructure on FDI in India

A Regional-Level Empirical Analysis

By Alice I. Rossignol Professor Will Olney, Advisor

A thesis submitted in partial fulfilment Of the requirements for the

Degree of Bachelor of Arts with Honours In Economics

WILLIAMS COLLEGE Williamstown, Massachusetts

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ABSTRACT This thesis examines the impact of changes in infrastructure on foreign direct investment (FDI) inflows in India. We use panel data from 2000 and 2014, which covers the 16 geographic regions used by the Reserve Bank of India to collect data. The first part of the paper analyses the impact of different telecommunications infrastructures on FDI; we find that variables measuring telephone penetration, but not internet or energy, are positively and significantly correlated to FDI. The second part of the paper includes additional measures of infrastructure in our regression to check the robustness of our results; the correlation between telephones and FDI remains, and no additional variables stand out as significant. The final part of our paper attempts to address reverse causality and endogeneity concerns by reversing our main specification and exploring avenues for an instrumental variable. While it is difficult to establish a causal relationship, our model indicates that increased telephonic infrastructure is positively correlated to FDI inflows one year later. This suggests that state investment into larger and more reliable telephone networks (and maybe IT and services more generally) could potentially encourage future foreign investment, at least in the service sector. ACKNOWLEDGEMENTS I would like to thank Professor Olney for all his help throughout the thesis process, starting from the rather gargantuan and loopy emails I sent him from when I was abroad Junior Year to the just as incessant questions I subjected him to this past year. His advice proved truly invaluable in moving forward with this project every step of the way. Many thanks also to Professors Rai and LaLumia for the thoughtful comments they gave me, allowing me to step back and take another, better look at my work. And finally, merci papa et maman for not telling me ‘I told you so’ once I finally realized the scope of the project I’d embarked on, and helping me check that I wasn’t writing in French. I’ll try to listen next time.

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TABLE OF CONTENTS ABSTRACT .................................................................................................................... ii 1 INTRODUCTION ........................................................................................... 1

2 INDIA AND FDI ........................................................................................... 7 2.1 Liberalisation of the Economy ....................................................... 7

2.2 FDI Incentives ............................................................................... 10 2.3 Case Study: Kerala ............................................................................... 12

3 LITERATURE REVIEW ............................................................................... 15 4 DATA ....................................................................................................... 19

4.1 Foreign Direct Investment ................................................................... 19 4.2 Telecommunications ............................................................................... 22 4.3 Control Variables ............................................................................... 25 4.4 Additional Infrastructure Measures ....................................................... 26 4.5 Summary Statistics ............................................................................... 28

5 EMPIRICAL STRATEGY AND RESULTS ........................................... 30 5.1 Base Telecommunications Specification ........................................... 30 5.2 Including Additional Infrastructure ....................................................... 35 5.3 Causality and Endogeneity ................................................................... 38 5.4 Model Limitations ............................................................................... 44

6 CONCLUSION ...................................................................................... 45 REFERENCES ....................................................................................................... 51

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“Constituting one-sixth of humanity, India has both a special claim on the world and a particular responsibility to it. Consequently, it must be strong economically, cohesive socially, robust politically and engaged internationally. […] At home, we have launched initiatives to generate faster and more inclusive growth, aimed at realising tangibly better lives for all Indians by 2022, the 75th anniversary of India’s independence. This entails eliminating poverty within a democratic polity on a scale unparalleled in human history.”

Narendra Modi, Prime Minister of India (2015)

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1 INTRODUCTION

India’s Prime Minister, Narendra Modi, has tried to build a growth narrative that

emphasises India’s power and influence, projecting the country in the future to emphasise

the impacts that the new government’s initiatives could have. Amongst the changes he

expects to implement, are “major increases in capital expenditure on infrastructure,”

which would lead to a “large multiplier effects on private investment” (Modi 2015).

Private investment, which is defined as the sum of domestic and foreign investments, is

in turn expected to lead to an increase in the gross domestic product of the country,

contributing towards the government’s goals of eliminating poverty. In an attempt to

understand the causal chain between the inputs (more capital expenditure in

infrastructure), the outcome (more investments) as well as its impact (growth), this

empirical paper seeks to analyse the relationship between infrastructure and investments.

In an effort to narrow down the field of research and focus on specific determinants of

growth, we specifically research the relationship between telecommunications

infrastructure and foreign direct investment (FDI) inflows in India at the regional-level,

using a panel data set spanning 14 years (2001-2014) and 16 geographic regions.

The OECD (2008) defines FDI as an investment, which “reflects the objective of

establishing a lasting interest by a resident enterprise in one economy (direct investor) in

an enterprise (direct investment enterprise) that is resident in an economy other than that

of the direct investor.” ‘Lasting interest’ is qualified by shares that amount to at least 10%

of the voting power of the direct investment enterprise. FDI flows have grown

exponentially over the past few decades as the world has become more globalised and

many trade barriers have been lowered. This situation is visible at a global level, and

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even more so when narrowing our focus on developing countries as a whole, and India

specifically. Data from the United Nations Conference on Trade and Development

(UNCTAD) reveals an increase from $13.3 billion global FDI flows in 1970 to $ 1,200

billion1 in 2013. Over the same time period, FDI flows to developing countries grew from

$3.9 billion to $680 billion; these flows have constituted a growing proportion of this

market, rising from 29 to about 55 percent of global flows (UNCTAD 2016). The

proportion of FDI flows to overseas development assistance (ODA) has also increased

significantly over that time period, suggesting that attracting FDI is more important than

ever when building the capital of a developing country. As to India, The BRICS Post

(2014) reported that its FDI inflows were $42 billion in 2014, second only to China.

While the literature is contested, it is widely believed that FDI can have positive

effects on a country’s economy. “With the right policy framework, FDI can provide

financial stability, promote economic development and enhance the well being of

societies” (OECD 2008, 3). Given the right conditions2 and assuming an accompanying

increase in trade, increases in a country’s FDI have been linked to economic growth

through several impact channels including capital accumulation, the transfer of skills

through employment, and greater tax revenues (Moran, Graham, and Blomström 2005;

Moosa 2005). As implied by this, understanding the factors that drive FDI is of particular

relevance. Assuming we understood the main determinants in FDI decisions,

governments could develop relevant instruments and policies designed to increase

investment.

1 Both measures are in real 2014 dollars. 2 Including lower barriers and fewer restrictions to trade.

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Policies used to attract FDI range from liberalising the economy and promoting

investment overall, to providing specific incentives to investors, which may include tax

breaks or specific infrastructure and utilities access. The way that these measures are

packaged varies from country to country –or even from state to state in federal countries

like the United State, Brazil, and India– depending on the political and economic

environment, and depending on the specific objectives of policy makers. The economic

literature on FDI appears to suggest that the determinants of FDI themselves are not

uniform across different countries or different sub-national regions within the same

country.3

Given the growth of FDI, the positive impact that it may have on an economy,

and lack of homogeneity in attracting FDI worldwide, it would be important for

policymakers to understand better what factors lead to higher FDI in their own

economies. This question is especially crucial for emerging economies where public

financial resources are limited and private investments are critically needed to achieve

overall growth and poverty reduction objectives. This is the case for a country like India.

The example of India is all the more relevant as its size and political structure as a federal

state allows to understand the impact of policy measures or infrastructure development,

using comparators from state to state. Besides, given its sheer size, understanding some

of the determinants of FDI in India could have important repercussions on how to

influence infrastructure and private sector growth in other countries.

3 Despite this, most FDI analyses seem to have used inter-country analyses examining data collected at the national level, rather than research FDI within individual countries using data collected at a sub-national level.

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India initiated reforms to liberalise its economy in 1991, dismantling its so-called

‘License Raj,’ opening up to FDI, and gradually changing the economic environment

within which business operated.4 The introduction of the Foreign Exchange Management

Act (1999) and the Prevention of Money Laundering Act (2005), which both met WTO

standards, are further credited with helping India emerge as part of the global economy.

Today, it is hard to deny the importance of India as an economic actor, and the role it

may come to have in regional and global politics. India was listed as the third largest

economy in the World Bank’s GDP ranking, PPP based (2016), while the IMF (2016)

mentioned that “the gradual increase in the global weight of fast-growing countries such

as China and India [. . .] plays a role in boosting global growth” (18). India’s GDP

growth has been estimated at 7.4 percent for 2015, compared to 6.2 percent in China, and

2.9 percent globally (OECD 2015). This makes it the fastest growing economy in the

world, two years ahead of the schedule and predictions that were published by the World

Bank Group (2015). This growth seems unlikely to slow down in the near future: fifty

percent of the population is under the age of 24, and India “will soon have 20% of the

world’s working-age population” (Lingenheld 2015). By 2020, India’s population is

forecasted to “account for 12% of higher education graduates globally” (Lingenheld

2015).

In the context of the overall promise that India represents for the growth of the

global economy, this paper is interested in evaluating the specific impact that changes in

infrastructure can have on FDI in India, narrowing down on telecommunications

infrastructure. Infrastructure development was one of the seventeen Sustainable

4 We discuss this further in Section 2.1

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Development Goals adopted by world leaders in September 2015. According to the

United Nations (2015), “constraints regarding infrastructure affect firm productivity by

around 40 per cent” in Africa. The case of infrastructure in India is special, however.

Where other developing economies may perhaps have investors entering a greater mix of

sectors, or be the focus of more primary and secondary economic activity, India has been

the focus (and acquired a reputation) for being an important location for the outsourcing

of tertiary sector activities.5 The service sector often comes to mind when speaking of

outsourcing economic activities to India, and India’s call centres feature prominently in

academic literature and pop culture references6 alike. To some extent, this is an accurate

reflection of the Indian economy as regards FDI.

The services sector, individually, attracts the most FDI of any economic sector in

India today. In addition, when we combine sectors that rely on telecommunications

infrastructure,7 and compare these to other sectors, like manufacturing-related sectors8 or

food processing industries, telecommunications-dependent sectors far surpass any other

economic activity in terms of magnitude relative to total FDI. The visual representation

of this can be seen in the graph below (Figure 1). Given that sectors dependent on

telecommunications seem to attract the most FDI in India, we have focused our analysis

5 Primary sector activities are based on the direct extraction of natural resources, such as agriculture, fishing, and mining. Secondary sector activities are focused on the transformation of these resources through manufacturing, while the tertiary sector of the economy includes activities surrounding the provision of a service. 6 The protagonist in the film Slumdog Millionaire, for example, works in call centre. The film won eight Academy Awards, and was a huge success in box offices worldwide. 7 For the data/chart presented below, we have included the following sectors: Consultancy, Electricals Equipment (Including Computer Software), Hotels and Tourism, Service Sector, Telecommunications, Trading. 8 We have included the following sectors in this measure: Cement and Gypsum Products, Chemicals, Fuels (Power & Oil Refinery), Metallurgical Industries, Textiles.

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on teasing out the effects that changes in telecommunications infrastructure may have on

FDI.

Our analysis uses panel data from 2001 to 2014, aggregating sub-national data

into the sixteen geographic units that correspond to the regions used by the Reserve Bank

of India (RBI) for data collection. We use a series of regressions with lagged independent

variables, first examining the impact of telecommunications on FDI, then including

additional measures of infrastructure to check the robustness of our variables, and

concluding with specifications examining issues of reverse causality and endogeneity.

Given the emphasis placed on infrastructure investments in India and the routing of much

FDI towards the service sector, we expect to see a positive correlation between

telecommunications and FDI. Since other infrastructure measures include roads and

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railways, which may change more slowly over time9 and do not impact the tertiary sector

activities as much as telecom might, we do not expect the inclusion of these measures to

change our (predicted) results concerning the relationship between telecommunications

and FDI.

The paper is structured as follows. Section 2 covers additional background on the

liberalisation of the Indian economy and changes in FDI policy, as well as examining a

case study of telecommunications and FDI in Kerala, one of India’s states. Section 3

consists of a literature review of existing studies that link FDI and infrastructure more

generally, as considering papers that have focused on Indian FDI specifically. Section 4

discusses our data. The fifth section presents our econometric model and results,

employing OLS regressions with lagged variables, as well as additional robustness

checks that seek to address concerns of omitted variable bias, reverse causality, and

endogeneity. Increases in telecommunications infrastructure appear to be positively and

significantly correlated to FDI in the subsequent period, while other forms of

infrastructure produce no significant results. Finally, Section 6 summarises our findings,

and discusses potential policy implications and avenues for future research.

2 INDIA AND FDI

2.1 Liberalisation of the Economy The liberalisation of the Indian economy has attracted a lot of attention. In 1951, the

Industries (Development and Regulation) Act was passed, bringing a number of

9 Building such infrastructure takes a long time. In addition, India has had a large network of roads and railways in place for many decades, so we would expect fewer changes to unfold.

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industries under central government planning. This ‘License Raj’ heavily regulated

businesses activities; it was dismantled in two waves, in 1985 and 1991, gradually

abolishing many licensing requirements for firms. The effects of this liberalisation on

private investment have been studied by a number of economists (Emran et al. 2007;

Aghion et al. 2008), as have the introduction of the Foreign Exchange Management Act

(1999) and the Prevention of Money Laundering Act (2005) (Bajpai and Sachs 2000;

Bedi and Kharbanda 2014; Singh 2005; Kumar 1998; Nagaraj 2003). Gradual changes to

the central FDI policy have also allowed more foreign investment to enter the country

over time, as well as facilitated the procedures by which it happens.

India places a ‘cap’ on FDI in certain economic sectors, limiting both the level of

FDI relative to private investment overall, and the percentage of foreign ownership in

certain firms. By this rule, FDI can only constitute up to a specific percentage of certain

sectors, which may vary from zero to one hundred percent of the activity and companies.

Assuming the cap has not been surpassed, one of two routes is available to a company for

investment, depending on the economic sector in which it is investing. The ‘automatic

route’ requires neither approval from the government or from the Reserve Bank; this

generally applies to less ‘sensitive’ sectors,10 or to sectors of the economy with higher

FDI caps. The ‘government route’ requires that the Indian Government approve the

investment project. Applications are “considered by the Foreign Investment Promotion

10 We might see a ‘sensitive sector’ as something like the Defense Manufacturing industry, which is capped to 49% of FDI under government approval. Defense is essentially to the country’s security and stability.

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Board, Department of Economic Affairs, Ministry of Finance,” and occasionally by the

Reserve Bank of India.11

In previous decades, actual FDI inflows seemed to be lower than approved FDI

values; “as much as 60% of FDI approved [was] not materialised” between 1990 and

2001 (Singh and Saluja 2000). In the twentieth century then, we may expect approved

FDI to have been more closely link to business environment changes, while FDI inflows

occurred in a more lagged fashion.12 While it is unclear if this is the case, much of the

literature surrounding this phenomenon (in the 1990s) seems to run under the assumption

that the ‘dropout’ of FDI project was random.

However, if we look at the graph below (Figure 2), the gap between FDI inflows

and approvals in the past fifteen years has reversed, appearing to reflect India’s

liberalisation measures. The caps on FDI within each economic sector continue to rise,

with the most recent reforms occurring in 2014. In addition (and perhaps more

importantly), many more FDI projects are approved automatically and counted as part of

FDI Inflows immediately. Since only projects that pass through the government approval

route are recorded in ‘FDI Approvals’ data, the Approvals data no longer reflects investor

interest, or actual investment. Recorded inflows are now greater than recorded approved

projects.

This graph suggests that using FDI Approvals data is not a viable measurement

for actual FDI, at least when examining investment trends in the past fifteen years. While

11 “Frequently Asked Questions: Foreign Investments in India.” 2015. Reserve Bank of India: India’s Central Bank. Modified February 10. https://www.rbi.org.in/scripts/FAQView.aspx?Id=26 12 The most direct effect of changes (assuming there is one) would be probably seen on proposed FDI projects and inflows, but unfortunately that data is not available.

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most papers on India focus on earlier time periods and rely on FDI Approvals data, it is

no longer appropriate to do when examining when examining post-2000 trends. This

paper thus relies on FDI Inflows as a measure for investment and investors’ responses to

changes in the business climate.

2.2 FDI Incentives The qualification of an investment as an FDI is often associated with certain incentives,

from the central government, as well as from state governments whose “broad categories

of […] incentives include: stamp duty exemption for land acquisition, refund or

exemption of value added tax, exemption from payment of electricity duty etc.”13

13 “Foreign Direct Investment: Incentives.” 2015. Make in India. Accessed December 7. http://www.makeinindia.com/policy/foreign-direct-investment

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While incentives may be important in certain studies of FDI, we do not consider

this a primary concern for this paper. A report published by UNCTAD (2000) posited

that,

As a factor in attracting FDI, incentives are secondary to more fundamental determinants, such as market size, access to raw materials and availability of skilled labour. Investors generally tend to adopt a two-stage process when evaluating countries as investment locations. In the first stage, they screen countries based on their fundamental determinants. Only those countries that pass these criteria go on to the next stage of evaluation.

Bellak & et al. (2008) also determine that this is the case countries with less economic

development since, again, other factors matter more than lowered taxes. Morisset (2003)

further suggests that, “Tax incentives have a more apparent effect on the composition of

foreign direct investment than on its level.” Other empirical studies and papers

conducting overviews of the FDI and tax incentive literature on a global and India-

specific scale have supported these arguments (Kandpal and Kavidayal 2014; Chakrabarti

2001; Moosa 2005; Biglaiser and DeRouen 2006; Montero 2008).

Though states control smaller sections of FDI policy, the primary decision and

policy are decided and published at the national level. As a result, we can assume three

things. First, large changes in FDI policy will be captured by time fixed effects, as they

will be instituted by the central government. Second, time invariant differences between

FDI incentives offered by states should be captured by state fixed effects. Third, time

variant differences in state incentives relating to taxes14 should not have a driving impact

on the total FDI flowing into the state. While it is possible that a state’s choice of

incentives is correlated to their infrastructure and may slightly bias those results upwards,

14 From looking at state-specific policies, these appear to be the predominant type of incentive used by states.

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they could be considered a more comprehensive indication of the impact of government

policy regarding infrastructure as a whole. Finally, it is also important to consider the fact

that papers focusing on FDI and infrastructure in India have not flagged state incentives

as a concern to their empirical strategies; it seems possible to use this as an indicator that

it is acceptable in the literature to not incorporate the state incentives. Incorporating these

policies in our empirical analysis falls outside the scope of this paper.

2.3 Case Study: Kerala FDI trends in India increased overall during the past decade and a half. However, it is

easier to look at the reasoning of an individual state (or Minister) relative to its policy

changes and foreign investment trends, rather than to try to look at India as a whole.

Kerala is a state in southwest India, formed in 1956. With a population of 33.3 million

people in 2011, it was the thirteenth largest state in India population-wise, out of 29 total

states. While Kerala “had initial (1960-1961) levels of per capita income lower than the

all-India average,” it has since converged towards the national average (Gosh 2013).

FDI in the state follows much the same pattern, initially lagging behind the

national average but increasing over the past decade following efforts made by the state

government. As reported by a 2006 article in the Hindu,

[The] Union Minister of State for External Affairs E. Ahamed on Saturday called for development of world-class infrastructure to attract private investment. He was inaugurating a seminar on `Facilitation of FDI in Kerala,' organised by the Kerala Chamber of Commerce and Industry here. Kerala needed to create a conducive environment to attract FDI. With most States wooing investors, the latter had a wide choice. Compared to some States, Kerala was not able to attract significant private investment. 15

15 The Hindu. 2006. “Ensure better infrastructure to attract FDI, says Ahamed.” September 24. http://www.thehindu.com/todays-paper/tp-business/ensure-better-infrastructure-to-attract-fdi-says-ahamed/article3080203.ece/

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Kerala, then, made the specific choice of investing in infrastructure with the purpose of

‘wooing’ foreign investors.

More specifically, Kerala seems to have targeted telecoms development. Kerala’s

‘Technopark,’ or technology park, was set up in 1991 but expanded over the course of

two decades, becoming “[India]’s largest and most sophisticated IT park” in 2007.16 The

concept of an IT park is to fully develop and concentrate telecommunications

infrastructure for the explicit purpose of attracting investors that need such technology for

their operations. An article by the Economic Times in 2010 seems to support the

hypothesis that telecommunications infrastructure is important to investors (or at least

considered important by states for attracting foreign investment). “Technopark

continue[d] to attract international IT brands on the strength of its infrastructure offering”

(emphasis added) despite the financial crisis affecting foreign investment in most Indian

states.17 In short, it is reasonable to expect that increases in the telecommunications

measures that we use (telephones and internet) would be partially linked to the creation

and growth of Kerala’s Technopark and technology industry.

It remains, then, to examine whether or not the data backs the expectations set by

Kerala’s focus on the development of technology parks, as well as the hypothesis that

greater telecommunication infrastructure is followed by greater foreign investment. The

graph below (Figure 3) examines the trends between FDI and Telephones (one of our

infrastructure measures). A relatively sharp increase in Telephones is followed by an

16 Rajeev. 2007. “God’s own country to house largest IT park.” The Indian Express. Last Modified March 3. http://archive.indianexpress.com/news/gods-own-country-to-house-largest-it-park/24662/. 17 The Economic Times. 2010. “Technopark aims to be among top 5 IT investment locations.” July 27. http://articles.economictimes.indiatimes.com/2010-07-27/news/27611626_1_technopark-phase-iii-kundara/.

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increase in FDI over the subsequent years. While FDI inflows appear somewhat volatile,

sometimes dipping down before going back up, this is a common factor of FDI in all

Indian states, and even in aggregate Indian data. What is more important, then, is to

observe that the average level of FDI, over 2-3 years, seems to be rising.

As with the hypotheses that we have previously drawn, this graph seems to suggest that,

with important portions of FDI going into economic sectors dependent on

telecommunications like telephones (call centres) and internet, greater state investment in

telecom infrastructure could attract greater foreign investment over time. While the rest

of our paper focuses on India as a whole, anecdotal evidence like Kerala allows us to

better understand the patterns of our overall data and potentially find reasons for sources

of observed change.

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3 LITERATURE REVIEW

While some FDI analyses have been conducted on a large-scale level, comparing

many types of economies and countries, it has been argued by a few authors that

developing countries are, quite simply, different. Shah (2014) refers to other literature

and suggests that there are fundamental differences between developed and developing

countries, in part due to the “type and pattern of inward FDI [which] is expected to be

reflective of a country’s level of development (Loungani et al. 2002) and causes it to

become more horizontal as development proceeds (Maskus 1998)” (2). Reducing his

analysis to 90 developing countries from 1980 to 2007, Shah determines that

infrastructure, proxied by teledensity as we have (in part) done in this paper, has a

positive and significant impact on FDI, and should be included in a “coherent strategy to

increase the attractiveness of a developing country for the overseas investors” (11).

Pushing Shah’s reasoning one step further, Asiedu (2002) argues that the

determinants of FDI in Sub-Saharan Africa are not only structurally different from those

in developed countries but also from other developing countries; Asiedu suggests that

regional analysis might be more appropriate when examining which types of policies are

‘successful’ at attracting FDI. Even if a paper examines countries that fall within a

broader categories like ‘developing countries’ (Francois and Manchin 2013) may

fundamentally differ, such that comparison of their government policies and FDI may not

always be appropriate. Tembe and Xu (2012) suggest this is the case after conducting a

comparative study of FDI in Mozambique and China. It is hard to pull any policy

recommendations from an analysis examining multiple countries with different types of

governments, histories, cultures, and economies; the likelihood of endogenous

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differences driving the results, regardless of strategies employing fixed effects and

controls, is very high.

Though analyses comparing different economies can be useful for other purposes,

it may be more important to examine an individual country’s specific trends in order to

make more conclusive or simply more specific determinations regarding its economy and

policies. Shah et al. (2003) seek to identify the important determinants of FDI in Pakistan

using a country-level analysis that compiles data from 1960 to 1999. In particular, they

use expenditure on transport and communications infrastructure as a proxy for actual

infrastructure in the country, coming to the conclusion that “the governmental role in

Pakistan, in infrastructural provision has positive effects18 on inward FDI” despite

“lagging by two years, which suggests that the investor's response may come after a

longer period as these projects are taking time to be completed” (706). Other papers that

limit their analysis to a single developing country find similar results. Mollick et al.

(2006), for example, find in a seven-year state-level analysis of Mexico that both

transport and telecommunications infrastructures have a positive and significant effect on

FDI. Jordaan (2008) and Escobar (2012), also studying Mexico, come to the same

conclusion in their papers, despite using different empirical methods and time periods.

Paralleling these papers in the broader economic literature, infrastructure has been

earmarked as important for India by academics and by government officials like the

Prime Minister. Bedi and Kharbanda (2014) analyse the inflows of FDI in India,

presenting their conclusions regarding what they consider four “major impediments” to

18 Unfortunately, the authors do not provide a very good idea of the scope of the impact of infrastructure expenditure beyond mentioning that the coefficient is positive and significant at the one percent level.

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further investment. The first of these is “weak infrastructure [. . . which] continues to be a

major cause of concern in India [. . .] In the World Competitiveness Index for 2013-14,

India ranked 85 out of 148 countries for its infrastructure, much behind China which

ranked 48.” (681) It seems important to see if businesses actually mirror these concerns,

and respond to differences in infrastructure in India, not just comparatively to other

countries, but also within the country itself.

Unfortunately, the literature has a relatively large gap as concerns empirical

analyses of Indian FDI in general, especially at the state-level. Archana et al. (2014)

conduct a state-level analysis of FDI of eight states between 1991 and 2004, but examine

the impact of FDI on the economy rather than its determinants. Other unpublished papers

by the same authors that examine the determinants of FDI employ an industry-level

analysis but do not examine infrastructure. The purpose of our paper, then, is to fill a gap

in the literature. We analyse data that, to our knowledge, has never been used, both

covering a more recent time frame than any other paper, and using a regional-level

analysis to examine the specific impact of infrastructure on FDI.

One recent empirical paper by Chakrabarti et al. (2011) studies a similar topic and

finds a threshold level below which the variation in infrastructure of districts in India is

unimportant; beyond that threshold,19 infrastructure has positive and significant impacts

on FDI. The paper uses the most specific analysis level in the existing literature on Indian

FDI (district-level data) and, in that regard, is perhaps the most important empirically

convincing and relevant work currently available. Infrastructure in the dataset is

19 The threshold is defined as the median infrastructure value of Indian states, but the actual value of that threshold does not have broader significance given that the authors create their own index of infrastructure for their regressions.

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calculated by creating an index from four measures of roads, electricity, telephones, and

bank branches rather than the individual variables (12). This presents several issues.

First, while the authors examine FDI flows into almost 600 districts over a span of

five years (2002-2007), they only examine this as a factor of infrastructure in a single

year, 2001. The authors do this under the assumption that “creating new infrastructure is

a relatively time-consuming process; therefore, it is unlikely that the infrastructure in a

given district changes substantially during the time period 2002 to 2007” (15). However,

the authors’ infrastructure index includes a measure for telephone connections. Since our

own data indicates that telephones and telephone connections have increased dramatically

over the past decade and a half, it is likely wrong to assume that infrastructure did not

significantly change over the six years spanning 2002 to 2007.

Second, calculating infrastructure as an index prevents us from making

conclusions as to the type of development that may be important in a region. While the

authors’ results may tell us that greater infrastructure seems to lead to greater FDI, we

cannot pull out the individual effects of roads, telephones, etc. This makes policy

conclusions difficult, and is something we have tried to avoid in this paper by examining

the effects of different types of infrastructure on FDI.

Finally, the authors use the “amount of [Foreign Capital] investments that are

approved” as their primary proxy for FDI. As we discussed in Section 2.1, however, the

difference between FDI approvals and inflows is such that we do not believe approvals

accurately measure FDI (or investor interest more generally) in the twenty-first century.

While examining FDI approvals may have been appropriate in papers covering data prior

to the 2000s, we do not believe this is the case for the past fifteen years. Given their

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project, it is unclear that Chakrabarti et al. have an entirely appropriate measure of FDI.

In contrast, our paper employs actual FDI inflows data, rather than approvals data, which

(to our knowledge) has never been done in the literature surrounding Indian FDI.

Examining the determinants of FDI in India at the regional-level, and beginning to

disentangle the effects of different types of infrastructure in India at the state-level,

allows us to begin to fill an important gap in the economic literature surrounding India.

4 DATA

Several factors had to be taken into account and shaped the manner in which we created

our dataset, and which variables were ultimately included. One of the primary reasons

FDI research on India at the state or regional-level has remained limited appears to be the

general lack of information available at that level of specificity, and the ease with which

it can be obtained. Data is largely collected by the central government or the regional

branches of the Reserve Bank of India, but there are almost no state-based data collection

agencies. These constraints on data apply to both the dependent variable (FDI Inflows),

and the independent variables, for which the types of measures can also vary year to year.

Consequently, this empirical analysis is based on a panel dataset spanning 14 years

(2001-2014) and 16 geographic regions determined by the Reserve Bank of India, which

is the Indian central bank (referred to as RBI or RBIs). Altogether, these regions cover

the entirety of India’s Union Territories and States.

4.1 Foreign Direct Investment There are three measures of foreign direct investment in India: the FDI inflows received,

the number of FDI project proposals approved by the government, and the monetary

20

quantity of FDI proposal inflows that was approved. As discussed earlier in Section 2,

FDI approvals data only captures projects that have gone through the government

approval route, rather than being automatically approved; this creates a discrepancy

between FDI approvals and FDI inflows such that we do not believe FDI approvals are a

reliable measure for actual FDI. This paper consequently relies on FDI Inflows.

FDI data is collected by regional branches of the Reserve Bank of India.

Consequently, the only FDI inflows data that is available at the strict state-level20 is that

which was collected by a regional office covering only one state; the majority of regional

offices cover several states today, even if they did not when they were originally created

(see Table 1 below).21

20 I use the word ‘strict’ in this context to indicate data that covers only one state or union territory. 21 Several new states have been formed in the past two decades, often through a part of an existing state sectioning itself off. Certain Union Territories have also been added to regional offices’ portfolios though it is unclear why they were not included previously.

2000-2006 2006-2010 2013-2015Andhra Pradesh - - Assam, Arunachal, Manipur, Mizoram, Nagaland, Tripura + Meghalaya - Bihar + Jharkand - Gujarat - - Karnataka - - Kerala, Lakshadweep - - Madhya Pradesh + Chhattisgarh - Maharashtra + Dadra & Nagar, Daman & Diu - Odisha - - Rajasthan - - Pondicherry, Tamil Nadu - - Uttarakhand, Uttar Pradesh - - Andaman & Nicobar, Sikkim, West Bengal - - Chandigarh, Haryana, Himachal Pradesh, Punjab - - Delhi, Part of Uttar Pradesh, Part of Haryana - - Goa - -

Jammu & KashmirDashes indicate no change from the previous period. Source: Reserve Bank of India

Table 1. Composition of and Addition of States/UTs to Regional RBI Offices: 2000-2015

21

There are two situations for which the ‘state-level’ classification could potentially

be bent: the first for RBIs covering two states, one of which was carved out of the

second, such as Bihar and Jharkhand;22 a second exception could be made for RBIs

covering one state along with a Union Territory that is joint or even in the state, as is the

case of Tamil Nadu and Pondicherry. 23 Unfortunately, while these avenues were

explored, restricting the dataset to only strict state-level data limits the number of

observations such that no results can be drawn from the data. We consequently use a

regional-level analysis, that covers the sixteen regions/distinct geographic units originally

used by the Reserve Bank.

The data is further limited by the number of number of years for which data is

(digitally) publicly available. While the Indian government has collected FDI statistics

for several decades, 24 a significant number of years are only available in print

government publications (and only available in a select number of locations.25 As such,

the dataset consists of 14 years of FDI inflows data, from 2001 to 2014.

22 Jharkhand was carved out of Bihar in 2000. Chhattisgarh was carved out of Madhya Pradesh in 2000. Telangana, the newest Indian state, split off of Andhra Pradesh in 2014. 23 Under the condition discussed, an exception could probably be made for Pondicherry and Lakshadweep, and potentially extend to Daman & Diu, and Darda & Nagar. Pondicherry, as mentioned above, is geographically place in Tamil Nadu. Lakshadweep is composed of islands off the coast of Kerala, which are under the jurisdiction of the High Court of Kerala. Dadra & Nagar are in Maharashtra, and Daman & Diu are on the tips of Gujarat, right next to Maharashtra. 24 From parts of the literature, it appears that FDI may have been collected starting in the 1960s or 1970s. 25 While certain papers do use those values, Professors Archana and Nayak (co-authors of some of the papers I mentioned in my literature review) seemed to suggest that the FDI data I was unable to access was sourced from either connections or a private database that Williams does not have access to called CMIE. (This information is sourced from email communications I had with both authors dating from the 26th of October, and the 3rd of November.)

22

4.2 Telecommunications As discussed earlier, this paper focuses on the impact of telecommunications

infrastructure on foreign direct investment. Our most basic specifications in this paper

thus focus on measures of telephone and internet connectivity. We expect both of these

variables to have positive, significant correlations with FDI; since FDI seems largely

apportioned to economic sectors that depend on telecommunications (see Section 1), it

seems logical to believe that greater telecommunications infrastructure would lead to

higher investment.

Telephone connectivity is proxied via a measure of ‘teledensity,’ or the number of

telephones per 100 individuals in a state. We have estimated the number of telephones

per state (based on population data and the teledensity measure) so as to be able to

aggregate this data for the regional FDI offices. Because teledensity is equal to

phones/100 people and population is recorded in the thousands, we use the following

formula:

Telephones = Teledensity x Population x 10

The final dataset thus uses the number of telephones in a given state or region as a proxy

for telephone connectivity. As seen below, a basic scatterplot of FDI and lagged

telephone residuals,26 controlling for GDP with state and time fixed effects, seems to

indicate an initial positive correlation between the two variables.

26 Residuals are the variations exploited by our regressions.

23

Internet connectivity is estimated using data on the number of internet

subscribers. Internet subscriptions are recorded in the Indian government’s administrative

telecommunication network groupings, telecomm circles. Because these occasionally

combine states covered by different regional offices (which, as the reader will recall,

calculate FDI inflows), internet subscriptions have been estimated for certain states,

calculating them from population proportions between states. For example, data

stemming from a telecomm circle that combines two states would be disaggregated as

follows:

State 1 Internet Subscriptions = !"#!" ! !"#$%&'(")!"#$%& !"#$%&'(")

x Region Internet Subscriptions

The original data in this estimation is “Region Internet Subscriptions,” with ‘Region’

comprising of States 1 and 2, and the “State 1/2 Internet Subscriptions” being the final

-10

-50

5FD

I Res

idua

ls

-1 -.5 0 .5 1Telephones Residuals (Lagged by 1 Year)

Controlling for GDP with State and Year Fixed EffectsFDI v. Telephone Residuals

24

data points used in the regressions. As with telephones, the initial scatterplot of FDI and

lagged internet residuals seems to show a positive correlation between the two variables.

Energy has also been included in the base specifications of this paper, following

the thought that internet and telephone networks could not be built or used without

electricity. Since measures of interconnection (like electricity lines) were not available,

we have used gross energy generation, which sums all energy generating activities

including hydro and thermal power plants. Though initial scatterplots indicate a slight

positive relationship between energy and FDI, there are a number of outliers, and initial

regressions seem to suggest that the relationship is instead insignificant.

-10

-50

5FD

I Res

idua

ls

-8 -6 -4 -2 0 2Internet Residuals (Lagged by 1 Year)

Controlling for GDP with State and Year Fixed EffectsFDI v. Internet Residuals

25

4.3 Control Variables This analysis controls for state and year fixed effects. In addition, we have controlled for

other factors that may vary over time within states and effect a business’ decision to

invest in India. Contrary to inter-country analyses, our strategy has the advantage of

examining the same country. While the level of infrastructure in a given sub-national

region could still be partially be driven by FDI, restricting the analysis to a single country

reduces certain risks of endogenous variability driving our results. Factors specific to

Indian history and governmental organisation are expected to be common among all

states. In addition, India’s FDI policy is largely determined at the national level, with the

-10

-50

5FD

I Res

idua

ls

-4 -2 0 2 4Gross Energy Generation Residuals, Lagged by 1 year

Controlling for GDP with State and Year Fixed EffectsFDI v. Gross Energy Generation Residuals

26

exception of a few incentives depending on the state.27 We have thus controlled for three

factors: GDP, population, and skill level.

State GDP is controlled using data from India’s Central Statistics Office.28

Population employs data from the three most recent censuses carried out by the Indian

government (1991, 2001, 2011). The government does not release population estimates

between those census years; while it used to release population projections following a

census, these were often wrong and there were large jumps (up or down depending on the

state) between the projected population of the year before the census (e.g. 2000), and the

actual population of the census year (e.g. 2001). As a result, we have linearly interpolated

the population between census years.

Finally, a skill level index, or educational attainment data, does not exist at the

state-level in India, so we have used literacy rate as a proxy for the skill level of states’

workforces. Since the data also comes from the census carried out every ten years, we

have again linearly interpolated the literacy rate for the years in between.

4.4 Additional Infrastructure Measures Secondary independent variables seek to improve the robustness of the model, and ensure

that any variation indicated by telecommunications measures aren’t caused by other types

of infrastructure. We examine other factors of interconnection, which can have an effect

on a business from both ends of its process (both for materials and consumers), as well as

facilitate the spread of technology. In keeping with the idea that FDI in India is largely

directed towards the service industry, rather than the manufacturing one (and 27 Studies of regulatory incentives (e.g. tax reductions) at the country-level seem to show that incentives have largely ambiguous, or no effect on FDI. Further legal analysis of the business environment falls outside the scope and focus of this paper. (OECD 2016). 28 Previously the Central Statistics Organisation.

27

consequently does not rely on transportation as much as it does on telecommunication

infrastructure), we do not expect the coefficients of additional infrastructure measures to

be significant.

Transport routes, including roads and waterways, and new means of

transportation have been credited with increased trade historically – and can in part

explain the newest wave of globalisation and interconnected economies in the world.

While natural features of states should be captured by the fixed-effects in our

regression, 29 infrastructural features like roads and railways need to be built and

maintained. Our dataset consequently makes use of available highway and railway

lengths, which are gathered at the state-level. Though some road construction is entirely

new, certain changes in the data are caused by highway maintenance. This could, for

example, change an “unsurfaced” highway to one covered in blacktop, cement concrete,

or water bound macadam. Other changes in the length of national and state highways

appear to be due to a reclassification of road stretches as one type of highway or another.

Rather than keeping the original variables separate, we have consequently combined the

data as overall highway values that keep track of total length of highways, as well as total

un-surfaced and total surfaced length of highways for each state. Both highway and

railway measures show slight positive correlation to FDI in initial scatterplots, though

there are fairly important outliers in both scatterplots.

We considered introducing measures for ports and airports, in keeping with the

idea of interconnection of markets facilitating trade. However, the variance was too low

to include in the dataset, usually not changing at all, or only increasing by one or two

29 We have not included waterways in our specifications as a result.

28

units over our entire timeframe. In addition, the combination of ports and airports may

largely be a reflection of the state’s geography. We can consequently expect any impact

on FDI to be captured by the state fixed effects included in the regression.

Finally, we considered including measures of water availability since it is one of

the primary utilities needed by many businesses and households. Unfortunately, the data

was unavailable – we assume that this would be the case for most potential investors as

well, but this does serve as a potential weakness in our robustness section.

4.5 Summary Statistics The various measures covered in our dataset span a broad range of values and years. FDI

Inflows run from 2001 to 2014; the full range of FDI in India is included during that time,

though we examine it at a regional-level (16 geographic regions). Our other variables

have overlapping ranges, but do not always cover the full range of the time, which

explains why certain specifications in our model use fewer observations. Lagging our

infrastructure measures by one year is not only logical due to the nature of our analysis,

but allows us to increase the number of observations available for us to use. For example,

measures on highways are only available until 2012 – lagging FDI by one year means

regressions including highways can use FDI data that goes up to 2013, instead of losing

an additional two years’ worth of data. The full range of years covered by our data can

be seen in Table 2 below, as can the level of analysis at which it was collected.30

30 All values have been aggregated to the regional-level for the regressions themselves.

29

Moving beyond coverage issues, there is substantial variation within each of our

measures. Table 3 reports the summary statistics of the variables, before logarithmic

transformations, restricting the data to the observations that we used in our base

specification. FDI Inflows, for example, may vary anywhere from 0 INR in Bihar in 2001

to around 462,000 INR in Delhi in 2009. The same ‘pattern’ repeats for other variables in

our dataset, with standard variations of sometimes up to several million units. As such,

with the exception of literacy, which was reported as a rate out of 100, we have logged all

of our variables.

Measure Years SpecificityForeign Direct Investment Inflows (Rs.) 2001-2014 Regional-level

Teledensity (phones/100 people) 1997-2014 State-levelInternet 1999-2014 State-level

Gross Energy Generation 1995-2013 State-level

National Highways 1994-1995, 1997-1999, 2001-2012 State-levelState Highways 1991-2003, 2005-2012 State-levelRailway Length 2000-2014 State-level

ControlsGross State Domestic Product 1993-2014 State-levelPopulation (Overall, Male/Female, Urban/Rural)

Government Data 1991, 1995-2011 State-levelInterpolated/Projected Data 1991-2015 State-level

Literacy RateGovernment Data State-levelInterpolated/Projected Data 1991-2015 State-level

Sources: Department of Industrial Policy and Promotion, Central Statistical Office, Planning Commission

Table 2. Availability and Analysis-level of Variables Used

Transport

Telecommunications

Variable Observations Mean Std. Dev Min MaxFDI Inflows 158 30203.45 66303.17 0 461965.2Telephones 158 24800000 33100000 82612.34 158000000Internet 158 5199313 16100000 862 125000000Gross Energy Generation 158 20579.18 14978.46 206.99 67078.27Highways 98 15228.41 7219.93 72 37331Railway 158 4221.18 2159.16 69 9264.85Literacy Rate Projections 158 69.27 8.47 46.15 86.66Population Projections 158 80974.73 62333.39 1329.88 281617.8GDP 158 252373.3 194633.5 6757 985643

Table 3. Summary Statistics, Original Variables

30

While the variation observed in the summary statistics may partially be the result of

inherent differences between states (which will consequently be captured by the fixed

effects in our model), this is nonetheless consistent with the idea that there are large

differences in India that our model may be able to explore.

5 EMPRIRICAL STRATEGY AND RESULTS

5.1 Base Telecommunications Specification The objective of this paper is to examine the effect of telecommunications infrastructure

on FDI. While we run additional regressions to check the robustness of our results as well

as endogeneity and reverse causality concerns later in the paper, this section focuses on

our base specification and results.

We use OLS regressions to estimate the impact of infrastructure on foreign direct

investment using a combination of telecommunications measures, at the regional level.

The effect of each telecommunication measure is measured independently, culminating in

our main specification which includes all of our measures and controls:

(1) ln FDIit = β0 + β1 ln Telephonesi,t-1 + β2 ln Interneti,t-1 + β3 ln Energyi,t-1 + β4 ln GDPi,t-1 + β5 ln Populationi,t-1 + β6 ln LiteracyRatei,t-1 + λi + λt + εit

FDIit refers to the foreign direct investment in state i in year t of Indian states

expressed in millions of Indian rupees. The variable Telephones refers to the number of

telephones in an Indian state in year t-1, while Internet refers to the number of internet

subscribers in a state; both measure telecommunications infrastructure. Energy is the

gross energy production in a state, serving as a proxy for available electricity and

electrification, which could facilitate the spread of telecommunications technology. We

31

run the model on measures of Telephones, Internet, and Energy individually, before using

examining the effects of all three on FDI at the same time. Since the data on Telephones

has the greatest number of observations, we also restrict the number of observations used

by those regressions to Internet and Energy observations to ensure that any results are

due to actual effects on FDI, rather than data restrictions.

The variables GDP, Population, and LiteracyRate control for a state’s GDP

(expressed in ten millions of Indian rupees), population (in thousands), and literacy rate

(proxying for the state workforce’s skill level) respectively. The model also includes

fixed effects for time, λt, and for states, λi. Fixed effects should capture times invariant

differences between states (such as geography), and changes that occurred across all

states at the same time. The latter could include changes in national policy, for example.

Overall, incorporating fixed effects should increase the accuracy of coefficient

estimations of independent variables. Finally, εit is the standard error term; we cluster

these errors by state to account for any correlation of states’ errors.

Independent variables are lagged by one year to allow businesses to adapt their

investment strategy, and to account for the time it takes the government to release new

data.31 This also mirrors the difference in FDI inflows and approvals trends we observed

in Section 2.1, where inflows followed the same general trend as approvals (despite being

significantly larger), but were lagged by one year off the approvals. Lagging the variables

also addresses certain endogeneity concerns within the regression, making it easier to

trace the direction of the relationship; by lagging the independent variables, it is more

31 New investors that are not already getting feedback from an on-the-ground business they run may not capable of observing these changes themselves, and consequently have to rely on government releases.

32

likely that these are causing change in the dependent variable (FDI) rather than the

opposite.

In addition, all variables undergo a logarithmic transformation for the regression

since, as can be observed in Table 3, the range of most variables is very broad. This takes

the form of ln(Variable + 1) as a number of variables contain the value ‘0’ which holds

actual meaning rather than being a placeholder for incomplete data; running logged

regressions without adjusting the log would drop a number of important values.

The results of our base specification are reported in Table 4. From columns 1, 3,

4, and 6, we can see that Telephones are associated to a significant increase in foreign

direct investment at the five percent level; this occurs both when considered individually,

and when our Telephones measure is regressed with other variables (additional

telecommunications measures and controls). In contrast, the number of internet

connections and the amount of energy produced in a state do not have a statistically

significant impact on FDI, once controls are included in the regression. While Internet

initially has a positive correlation with FDI, significant at the five percent level, it

disappears with the inclusion of control variables; this suggests that the initial effect we

perceive is likely due to Internet picking up on the variation caused by another variable.

Given the logged nature of our variables, our most complete specification

(Column 8) indicates that a 1 percent change in the number of telephones in a state32 is

correlated to a 1.8 percent increase in foreign direct investment in the same state the

following year. This follows the logic explained earlier, whereby foreign investment

seems to largely be directed at the service sector; anecdotes of Indian call centres match

32 This is proxying for telecommunication/telephone network infrastructure

33

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Tele

phon

es2.

064*

*2.

090*

*2.

164*

*1.

837*

*(0

.760

)(0

.843

)(0

.906

)(0

.861

)In

tern

et0.

414*

*0.

0782

0.32

20.

115

(0.1

96)

(0.2

26)

(0.2

76)

(0.2

68)

Gro

ss E

nerg

y G

ener

atio

n0.

258*

-0.1

720.

280

0.18

6

(0.1

25)

(0.3

51)

(0.2

42)

(0.7

07)

Lite

racy

Rat

e0.

183

0.28

50.

384

0.19

3(0

.195

)(0

.217

)(0

.228

)(0

.323

)Po

pula

tion

7.93

24.

861

7.57

71.

461

(10.

47)

(19.

21)

(16.

82)

(14.

68)

GD

P1.

327

2.17

91.

885

1.77

8(2

.162

)(3

.186

)(2

.734

)(2

.537

)C

onst

ant

-25.

17*

3.68

6**

8.81

5***

-25.

02-1

44.8

-99.

86-1

02.2

-58.

33(1

3.94

)(1

.528

)(0

.552

)(1

7.65

)(1

06.9

)(1

88.3

)(9

3.01

)(9

5.85

)

Obs

erva

tions

232

209

209

176

190

172

188

158

R-S

quar

ed0.

852

0.83

20.

842

0.87

70.

852

0.83

30.

853

0.88

1

Stat

e FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year

FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Tabl

e 4.

Impa

ct o

f Tel

ecom

mun

icat

ions

Infr

astr

uctu

re o

n Fo

reig

n D

irec

t Inv

estm

ent (

FDI)

Inflo

ws

(1 Y

ear

Lag

)

Clu

ster

ed s

tand

ard

erro

rs (b

y st

ate)

in p

aren

thes

es**

* p<

0.01

, **

p<0.

05, *

p<0

.1

With

out C

ontr

ols

With

Con

trol

s

34

the idea that telecommunications (and especially telephone networks/connections) are

important to investors. In addition, none of the control variables appear to affect FDI

significantly, including GDP. It is possible that what matters to investors is not

necessarily how rich the economy is, but rather whether the factors that affect their

business (such as the presence of telecommunications networks) are present, regardless of

the rest of their environment. This has important policy considerations, and suggests that

governments – at least at the sub-national level in India – may be able to attract further

foreign interest by focusing state investment on specific types of infrastructure building

and growth strategies that maximise business’ capacities to operate within the country.

One potential concern for the results in Table 4 is whether our results are driven

by the smaller amount of data available for Internet and Gross Energy Generation, as

opposed to Telephones. Specifications (with controls) including only Telephones employ

190 data points, as opposed to the 172 observations and 188 observations that are

available for the base Internet and Gross Energy Generation regressions respectively, and

the 158 observations available when we include all three variables. To that effect, we

have also run the base Telephones specification, restricting the observations based on the

Internet and Energy data ranges.

As can be seen in Table 5, the results remain much the same, with a 1 percent

increase in Telephones correlated to approximately a 2 percent increase in FDI. This

indicates that the significant results we see in the previous table are not a function of the

observations but of the actual impact and correlation of our variables. While this

specification does not establish a causational relationship, lagging the independent

variables relative to FDI does suggest a possibility that increased telecommunications

35

infrastructure – and more specifically, increased telephone network infrastructure – could

have a positive effect on FDI.

5.2 Including Additional Infrastructure As discussed earlier, the regression is run with several variations. The next group of

specifications was intended to ensure the results obtained during our baseline regression

were not capturing the impact of other forms of infrastructure instead. With the inclusion

of new variables, the specification takes the form,

(2) ln FDIit = β0 + β1 ln Telephonesi,t-1 + β2 ln Interneti,t-1 + β3 ln Energyi,t-1 + β4 ln Highwayi,t-1 + β5 ln Railwayi,t-1 + β6 ln GDPi,t-1 + β8 ln Populationi,t-1 + β9 ln LiteracyRatei,t-1 + λi + λt + εit

Internet Restricted Energy Restricted Internet & Energy Restricted(1) (2) (3)

Telephones 2.143** 1.995** 1.942**(0.929) (0.826) (0.839)

Literacy Rate 0.181 0.177 0.171(0.200) (0.196) (0.201)

Population 7.871 0.989 0.671(10.91) (14.90) (15.18)

GDP 1.151 1.987 1.801(2.022) (2.737) (2.534)

Constant -99.42 -71.09 -50.08(68.18) (160.3) (91.47)

Observations 182 166 158R-Squared 0.878 0.852 0.881

State FE Yes Yes YesYear FE Yes Yes Yes

Table 5. Impact of Telecommunications Infrastructure on Foreign Direct Investment (FDI) Inflows, Restricted Observations (1 Year Lag)

*** p<0.01, ** p<0.05, * p<0.1Clustered standard errors (by state) in parentheses

36

The variable Highway denotes the length of national and state highways in a given state,

measured in kilometres, while Railway refers to length of railway lines in kilometres in a

state. The results of specification (2) are reported in Table 6.

As discussed earlier, the length of highways and railways were used as additional

measures of interconnection. These could potentially have an effect on FDI centred on

production or manufacturing of some sort; though our figures indicate that the service

sector is the most important type of FDI, it is nonetheless not the only one in our sample.

In addition, additional highways and railways could facilitate the spread of

telecommunications infrastructure, and thus be more important. As it turns out, and as

can be seen in Columns 1-3, our results for those measures are insignificant, both when

regressed independently and together.

There are a few explanations for this, and for the results on Telephones that we

consequently see. These results seem to confirm businesses’ attention to the factors that

truly impact their activity; in the case of FDI that appears to be dominated by the service

sector, transportation is less important than telephonic interconnection. It is also possible

that both highways and railways vary too slowly over time for the change to cause a

dramatic impact on businesses. Finally, it is important to examine the observations.

While railways do not change our number of observations from specification (1), at 158,

highway measurements reduce the sample size to 78 observations. This almost halves our

sample size, and could have a potentially crucial effect on the regressions.

This last point, on sample size, also likely explains the results we see on

Telephones. When adding in railways, in Column 2, the regression still indicates that a 1

percent increase in Telephones is correlated to a 1.8 percent increase in FDI, significant

37

at the five percent level. With the reduced number of observations due to including

Highway values however, the coefficient on Telephones increases, indicating a 3.3

(Column 1) or 3.5 (Column 3) percent increase in FDI, but only at the ten percent level.

Cutting away 60 observations on the sample is a likely reason for our results on

Telephones no longer being significant at the five percent level when including highways.

Highways Railways Both(1) (2) (3)

Telephones 3.314* 1.858** 3.513*(1.619) (0.865) (1.726)

Internet -0.364 0.118 -0.353(0.25) (0.27) (0.237)

Gross Energy Generation 1.52 0.194 1.569(1.16) (0.737) (1.258)

Highways -2.413 -2.364(1.812) (1.777)

Railways -0.825 -6.787(3.354) (6.46)

Literacy Rate 0.679*** 0.206 0.763***(0.23) (0.362) (0.228)

Population -5.251 1.456 -8.12(17.49) (14.81) (18.64)

GDP 0.388 1.807 0.29(1.396) (2.596) (1.366)

Constant -26.45 -56.4 62.6(202.8) (96.27) (205.5)

Observations 98 158 98R-Squared 0.917 0.881 0.918

State FE Yes Yes YesYear FE Yes Yes Yes

Table 6. Impact of Infrastructure on Foreign Direct Investment (FDI) Inflows, Robustness Check (1 Year Lag)

Clustered standard errors (by state) in parentheses*** p<0.01, ** p<0.05, * p<0.1

38

In conjunction with these changes, we observe that Literacy Rate becomes

significant at the one percent level with the introduction of Highways. Column 1 indicates

that a 1 percent increase in literacy is correlated to a 0.68 percent increase in FDI, while it

is correlated to a 0.76 percent increase in FDI in Column 2, a 0.82 percent increase in

FDI in Column 6. This, in theory, makes sense. If literacy is an accurate measure of the

workforce’s skill level, then a workforce with greater skill should attract more foreign

investment that needs skilled workers (e.g. a call centre, or another form of service or

tertiary sector). On the other hand, it is also possible that the restriction on observations

brought by the Highways data, which cuts two full years of FDI data and additional

observations, has biased the regressions and changed the perceived effect of data trends.

Though the coefficients on our Telephones variable change depending on the

specification, they remain positive throughout and are significant at at least at the ten

percent level. Where the significance of the coefficients reduces, the size of the

coefficients themselves increases. The steady and results associated to our Telephone

suggests that this correlation is persistent and relatively strong.

5.3 Causality and Endogeneity In addition to our base specifications, we also attempt to discern whether the correlation

we perceive between telecommunications and FDI has a particular direction.

Specification (1) lagged telecommunications measures by one year, then regressed them

on FDI. In contrast, specification (3), which we explore here, lags FDI by one year and

regresses it on telephones and internet respectively. This takes the form of:

(3) ln Telecommunicationit = β0 + β1 ln FDIi,t-1 + β2 ln GDPi,t-1 + β3 ln Populationi,t-1 + β4 ln LiteracyRatei,t-1 + λi + λt + εit

39

The variable Telecommunication is a stand-in for Telephones and Internet, which we

each examine in turn. The remaining details of the specification, including fixed effects,

controls, and lags remain the same as those of our base model. The results are displayed

in Table 7.

We have regressed FDI on Telephones and Internet both with and without

controls. Looking at columns 2 and 4 (which have controls), both coefficients are very

small, and neither is significant, even at the ten percent level. This, in addition to the lags

we have included in our regression, supports the idea that our analysis is not subject to

issues of reverse causality. The correlation, even if it ends up not being causal, has only

(1) (2) (3) (4)

FDI (Rupees) 0.0263* 0.00954 0.0591 0.0332(0.0133) (0.0126) (0.0424) (0.0383)

Literacy Rate 0.0846 0.128(0.0542) (0.0741)

Population 3.299 -3.2(3.303) (5.962)

GDP -0.246 0.561(0.272) (0.561)

Constant 17.37*** -15.18 11.01*** 17.12(0.213) (19.91) (0.437) (38.9)

Observations 218 179 208 166R-Squared 0.983 0.99 0.956 0.958

State FE Yes Yes Yes YesYear FE Yes Yes Yes Yes

Table 7. Impact of Foreign Direct Investment (FDI) Inflows on Telecommunications Infrastructure (1 Year Lag)

Dep Var = Telephones Dep Var = Internet

Clustered standard errors (by state) in parentheses*** p<0.01, ** p<0.05, * p<0.1

40

one direction of impact, from Telephones to FDI and not the reverse. This supports our

hypothesis that Telephones are correlated with FDI, potentially with a causal relationship.

In an attempt to draw an actual causal relationship out, we also looked into

potential instrumental variables (IV) that could explain variations in telephonic

interconnection, or the spread of telephone networks (telephone lines, cell phone towers,

etc.). Instrumental approaches have been used in a few instances in FDI literature that

also focuses on telecommunications, although their use remains rather limited; finding a

variable that is correlated to infrastructure but not investment is even more difficult than

finding a variable that is not directly correlated to GDP. Lydon and Williams (2005) used

a measure of investment in telecommunications to instrument for mobile phone

penetration. A few authors (Um et al. 2009, Straub and Terada-Hagiwara 2010) have

used an alternate IV method, and have used lagged versions of their independent

variables (beginning of the period indicators) as a type of instrument. However, this

approach constitutes an important part of our base specification, and seems more difficult

to justify as an IV in the first place.

As such, and to address concerns of endogeneity, we attempted to identify an

independent approach to find a viable IV for Telephones. We considered a few options:

railways and highways could potentially facilitate the spread of telephone lines; terrain

ruggedness could affect the number of cell phone towers needed to cover an area, and the

speed at which it was done; urbanisation of the state, or the number of large metropolis

cities could affect the actual need for more telephone networks, or the speed at which

access to telecommunications technology could spread. Unfortunately, all of these

variables are poor instruments for various reasons, starting from having little or no

41

variation over time (ruggedness), to potentially having a direct effect on FDI, or being

directly affected by FDI (highways, urbanisation). We also tried replicating Lydon and

Williams, but data on private investment in telecommunications is not available at the

state level in India. We further looked into using state expenditure on telecomm as an

instrument but this did not work either, likely due to the restrictions it placed on our

(already limited) data.

In our base specification, Energy appeared to have no significant correlation with

FDI. This may be logical if we consider that a large portion of FDI remains in the service

industry, rather than the manufacturing sector. The inherent logic behind using Energy as

an instrument for Telephones, then, is that electrification through electricity lines may

facilitate the spread of telephone lines, or facilitate the installation of cell phone towers

that require energy to operate. The two variables, Energy and Telephones, appear to be

correlated, while Energy does not appear to have an independent effect on FDI; these two

factors fulfil the conditions of a valid IV. The first stage regression takes the form:

(4) 𝑙𝑛 𝑇𝑒𝑙𝑒𝑝ℎ𝑜𝑛𝑒𝑠 = 𝜋! + 𝜋!𝐸𝑛𝑒𝑟𝑔𝑦 + 𝜐 And the second stage regression takes the form:

(5) ln FDIit = β0 + β1 𝑙𝑛 𝑇𝑒�𝑒𝑝ℎ𝑜𝑛𝑒𝑠i,t-1 + β2 ln GDPi,t-1 + β3 ln Populationi,t-1 + β4 ln LiteracyRatei,t-1 + λi + λt + εit

The results of the instrumental approach are in Table 8. We have included a mix of

specifications with and without controls; our final specification in column 3 also includes

our secondary measure for telecommunications, internet. All standard errors, as with the

other specifications in this paper, are clustered.

42

As can be seen in all three columns, the F-statistics for 2SLS specification are all

below 10, which indicates that Energy is not a strong instrument for Telephones.33 A

basic regression without controls results has an F-statistic of 2.83. The inclusion of

controls makes it drop to 0.93. While we have included all of our IV specifications in the

for perusal by the reader, it is fairly clear that Energy is not a good instrument for

Telephones, and we unfortunately cannot draw conclusive evidence from this method.

This remains a fairly important gap in the FDI and infrastructure literature that could

potentially be resolved with more accurate and detailed data in the field about

infrastructure and investor interest, although finding a variable that is correlated to

infrastructure but not to investment would remain a difficult task.

33 While the F-statistics for a specification with robust standard errors and without control variable was 11.83, and thus above the marker for a weak instrument, this did not hold beyond this very basic specification, once standard errors were clustered and control variables were included.

43

(1) (2) (3)

Energy 0.0558* 0.0313 0.124(0.0332) (0.0325) (0.0777)

Internet 0.0532(0.0373)

Literacy Rate 0.0801* 0.0878*(0.0470) (0.0505)

Population 3.422 3.628(3.127) (3.429)

GDP -0.114 -0.115(0.254) (0.313)

Constant 17.00*** -27.66 -32.57(0.336) (33.06) (35.75)

F-Statistic 2.83 0.93 2.56

Telephones 4.431*** 7.576** 3.331(1.213) (3.023) (5.072)

Internet 0.0355(0.400)

Literacy -0.222 0.0623(0.246) (0.214)

Population -15.92 -3.959(19.77) (19.28)

GDP 2.321 1.950(1.850) (1.991)

Constant -66.27*** 44.72 -38.23(20.91) (194.5) (163.0)

Observations 203 182 158R-Squared 0.862 0.815 0.877

State FE Yes Yes YesYear FE Yes Yes Yes

Table 8. Impact of Telecommunications Infrastructure on FDI Inflows (1 Year Lag), using Energy as an Instrumental Variable

Clustered standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

FIRST STAGE LEAST SQUARES

SECOND STAGE LEAST SQUARES

44

5.4 Model Limitations There are two primary limitations to these estimations and models. First, while we do

have FDI Inflow measures, this does not account for initial investor interest that did not

come to fruition because the proposal was rejected, because the FDI cap in a sector was

already exhausted, or because of a delay between the proposal and the time when the

investment could have legally gone through. Businesses could opt out of investments

because of changing factors in the business environment, or for entirely different (and

random) reasons. The delay and selection bias involved in the measurement of FDI

inflows makes it more difficult to predict the impact of changes on the infrastructure on

the interest of investors, and their perception of the business environment.

In addition, the model is subject to an omitted variables problem, which may bias

our results. The model as a whole has to contend with certain policy values and business

environment factors not being included since they fall outside the scope of this paper.

Those other policies and factors may be interacting with our variables, or prove to be

more accurate determinants of FDI. In addition, the model uses state- and region-wise

comparisons. These are valuable for understanding the differences between those regions

and the impact it has on their respective FDIs. However, it does not account for the fact

that India, for lack of a better phrase, does not operate in a vacuum. Decisions by

investors to invest in a given Indian state must thus be weighed against decisions to

invest in other Indian states, as well as decisions to invest in entirely different countries.

A change in infrastructure in India may be positive, but it may be a smaller change, or a

smaller absolute value than that which is present in an unaccounted-for economy. This

45

could potentially give insignificant coefficients34 to variables initially expected to have a

positive impact. In that case, the interpretation of that coefficient as having a negative

impact on FDI would be inaccurate.

Finally, despite specifications examining the direction of the relationship between

FDI and telecommunication and an attempt to determine whether a causal relationship

exists between the two variables through an instrumental variable (Section 5.3), it

remains hard to establish definitive causality. The issue of omitted variable bias discussed

earlier could be part of a ‘coincidental’ growth story, rather than a causal one. This may

especially be an issue because our numbers measure both mobile phones and landlines

over a period that has seen the growth of this technology worldwide. The level of analysis

could also be problematic if FDI is directed at specific parts of states that are more

developed zones, rather than being disseminated throughout the state. This is likely the

case, and could have implications for the relevance of our project relative to the level of

the analysis (country-wise as opposed to district- or county-wise).

6 CONCLUSION

Though its direct impact is sometimes contested, it is generally accepted that FDI helps

increase technology access, drive the workforce’s skills levels up, and ultimately increase

productivity levels, competitiveness and growth. This question is even more critical in a

country like India where overall macro economic growth needs to translate into jobs

creation for a fast growing population. Can FDI help modernise the country and aid the

34 And maybe even a negative coefficient, if a country outside of India had such a high relative growth in infrastructure and ‘business appeal’ that these overshadowed Indian economies and redirected FDI elsewhere.

46

transition of the economy towards secondary and tertiary sector activities, accelerating a

growth process that may have taken longer without additional capital and knowledge? If

we assume it can, what factors determine the direction of FDI? Our empirical paper

attempts to identify whether the levels of infrastructure development in different Indian

states have differing effects on FDI.

Focusing on telecommunications infrastructure (with additional infrastructure

measures as robustness checks), we specifically find that an increase in telephones (a

proxy for telephonic infrastructure) is positively correlated to FDI inflows one year later.

Unfortunately, without a rigorous instrumental variable, it is difficult to determine

whether or not the observed relationship is causational. While we cannot make definitive

conclusions from our data and models, it still has a few implications for our

understanding of FDI Policies in India at central and state levels. Finally it has some

implications for future research possibilities.

As relates to our findings, the correlation between telecommunications and FDI

suggests that investment in greater (or more reliable) telephone networks could

encourage foreign direct investment in India, at least by firms in the service sector.

Assuming this is correct, a first policy recommendation that could be implemented at the

state level would be to encourage states to develop and implement economic strategies

that focus on developing telephone networks in the geographic areas where policy makers

want to attract FDI. In proposing this, we are specifically referring to the new growth

strategy that focuses on the creation of 100 new ‘Smart Cities’ in India,35 but also to the

numerous Special Economic Zones that have been developed to attract FDIs, without

35 “Construction: Smart Cities.” 2016. Make in India. Accessed May 11. http://www.makeinindia.com/article/-/v/internet-of-things/.

47

much success (Jordan et al. 2012). The potential benefits in terms of jobs creation and

broader economic growth could be important. In addition, while this does not specifically

fall within the subject we have studied, this policy recommendation also suggests that

planning infrastructure needs for investors could be an important decision tool for

government authorities in planning and sequencing the use of their scarce public

resources.

Investors take risks in the investment process. However, the economic literature

usually agrees that higher uncertainty increases the potential costs and losses of an

investment, pushing businesses to postpone investments until more information can be

obtained. Dani Rodrik (1991), in discussing the stability of policy reforms, suggests that

any uncertainty regarding policy (and therefore the aforementioned contracts and action

frameworks) could serve as a ‘tax’ on investment, reducing investment in general, or

causing businesses to delay their investment decisions. Since investment choices are

essentially hedged bets on future outcomes of the economy, a firm with more information

will have higher likelihood of accurately predicting the profits they could make from a

project, and consequently of making the right investments. If a government wishes to

attract more investors and make investment decisions easier, then it may consider making

more relevant data publicly available (in this case on infrastructure). For India, the most

specific data is available through private databases like IndiaStat and CMIE,36 making it

more difficult for policy makers, advisors, academics and investors alike to examine the

economy’s trends. Data on infrastructure is especially lacking, which is fairly surprising

36 IndiaStat gathers all government data in one location (this was the database we used). CMIE is a private (and expensive) database that collects much of its own data on top of government reports.

48

given the multiple commitments made by the Indian government in this regard. This

would be our second policy recommendation.

Despite some of the problems mentioned above (and the suggested extensions on

data collection), there is still space for academics to step in and use the available data to

fill the gap in the literature examining FDI in India. First, we believe that examining FDI

inflows may be a more accurate reflection of the economic situation than looking at

approvals data, especially over the past fifteen years. In addition to the changes that the

economy has undergone in the past fifteen years, it is possible that the conclusions drawn

by studies examining FDI approvals data in the twentieth century are no longer

applicable.

Finally, this work could be completed by further research to incorporate more

measures of the economic and political environment of each state, both of which may

interact with FDI and infrastructure variables. For example, India has yet to have a

published skill level index or of the urbanisation of each state, but the incorporation of

these factors could provide better indicators regarding the workforce available to

businesses and their impact on FDI. Finding a way to index and understand the impact of

changes in state policies affecting FDI (like incentives or investment in given sectors of

the economy) might also be an important contribution to the literature on Indian and

global FDI alike; the advantage of doing this at the sub-national level is that the analysis

would likely not suffer from the same issues (inherent differences that cannot be

accounted for) that cross-country comparative law analyses can have. In suggesting this,

we also want to refer to Hallward-Driemeier and Pritchett (2015) who demonstrate that

past indexing and analysis of regulation, like the World Bank’s “Doing Business”

49

indicators, may not be effective in part due to differences in de jure and de facto

compliance burdens (123). Moving beyond regulatory indices, it may be interesting to

start directly surveying businesses and foreign investors about their decision process –

while worldwide surveys exist, conducting more targeted and frequent surveys within

India specifically, may allow the government to target its policies more appropriately.

Understanding the determinants of FDI in India continues to be a subject little covered by

the economic literature, and one that could have important ramifications for the

development of the country.

50

51

REFERENCES

Aghion, Phillipe, Robin Burgess, Stephen J. Redding, and Fabrizio Zilibotti. 2008. “The Unequal Effects of Liberalization: Evidence from Dismantling the License Raj in India.” American Economic Review 98(4): 1397-1412. https://www.aeaweb.org/articles?id=10.1257/aer.98.4.1397/.

Archana, Vani, N.C. Nayak, and P. Basu. 2014. “Impact of FDI in India: State-Wise Analysis in an Econometric Framework.” Global Journal of Human-Social Science: Economics 14(2)

Asiedu, Elizabeth. 2002. “On the Determinants of Foreign Direct Investment to Developing Countries: Is Africa Different?.” World Development 30(1): 107-119. http://ssrn.com/abstract=294662

Bajpai, Nirupam, and Jeffrey D. Sachs. 2001 “Foreign Direct Investment in India: Issues and Problems.” HIID Development Discussion Paper No. 759.

Bedi, Priyanka, and Ekta Kharbanda. 2014. “Analysis of Inflows of Foreign Direct Investment in India – Problems and Challenges.” Global Journal of Finance and Management. Volumne 6, Number 7: pp. 675-684.

Bellak, C., & et al. 2008. “Policies to attract foreign direct investment: An industry-level analysis.” OECD Global forum on international investment. http://www.oecd.org/investment/globalforum/40301081.pdf.

Biglaiser, Glen, DeRouen, Karl. 2006. “Economic reforms and inflows of foreign direct investment in Latin America.” Latin American Research Review 41 (1): 51–75.

Budhwar, P. 2001. “Doing Business in India.” Thunderbird Int'l Business Review, 43: 549–568. http://dx.doi.org/10.1002/tie.1013

Chakrabarti, A. 2001. “The Determinants of Foreign Direct Investment: Sensitivity Analyses of Cross-Country Regressions.” KYKLOS 54: 89-114.

Chakrabarti, Rajesh, Krishnamurthy Subramanian, Sesha Sai Ram Meka, and Kuntluru Sudershan. 2011. “Infrastructure and FDI: Evidence from district-level data in India.” http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1712185

“Construction: Smart Cities.” 2016. Make in India. Accessed May 11. http://www.makeinindia.com/article/-/v/internet-of-things/.

Deloitte. 2015. “Taxation and Investment in India 2015: Reach, relevance, and reliability.” UK: Deloitte Touche Tohmatsu Limited. https://www2.deloitte.com/content/dam/Deloitte/in/Documents/tax/in-tax-india-guide-2015-noexp.pdf

52

Emran, M. Shahe, Forhad Shilpi and M. Imam Alam. 2007. “Economic Liberalization and Price Response of Aggregate Private Investment: Time Series Evidence from India.” The Canadian Journal of Economics 40(3): 914-934. http://www.jstor.org/stable/4620637/.

Escobar, Octavio R. 2013. “Foreign Direct Investment (FDI) Determinants and Spatial Spillovers Across Mexico's States.” Journal of International Trade and Economic Development 22(7): 993-1012. http://dx.doi.org/10.2139/ssrn.2291426

“Foreign Direct Investment: Incentives.” 2015. Make in India. Accessed December 7. http://www.makeinindia.com/policy/foreign-direct-investment

“Frequently Asked Questions: Foreign Investments in India.” 2015. Reserve Bank of India: India’s Central Bank. Modified February 10. https://www.rbi.org.in/scripts/FAQView.aspx?Id=26

Francois, Joseph, and Miriam Manchin. 2013. “Institutions, Infrastructure, and Trade.” World Development 46: 165-175. http://dx.doi.org/10.1016/j.worlddev.2013.02.009

Hallward-Driemeier, Mary and Lant Pritchet. 2015. “How Business is Done in the Developing World: Deals versus Rules.” Journal of Economic Perspectives 29(3): 121-140. http://dx.doi.org/10.1257/jep.29.3.121/.

IMF. 2016. World Economic Outlook: Too Slow for Too Long. Washington: International Monetary Fund. http://www.imf.org/external/pubs/ft/weo/2016/01/pdf/text.pdf/.

Jordaan, J. A. 2008. “State Characteristics and the Locational Choice of Foreign Direct Investment: Evidence from Regional FDI in Mexico 1989–2006.” Growth and Change 39: 389–413. http://dx.doi.org/10.1111/j.1468-2257.2008.00431.x

Jordan, Luke, Ivan Rossignol, and Anand Venkatesan. 2012. “Learning from or repeating the past? Industrial Zones in India (Pt 1).” The World Bank: Private Sector Development. May 22. http://blogs.worldbank.org/psd/learning-from-or-repeating-the-past-industrial-zones-in-india-pt-1/.

Kandpal, Vinay, and P.C. Kavidayal. 2014. “An Overview of the Impact of Tax Incentives on Foreign Direct Investment in India.” Journal of Commerce & Management Thought 5(4): 557-573. http://dx.doi.org/10.5958/0976-478X.2014.00004.4

Kenny, Charles. “Can the Private Sector Deliver on the Infrastructure SDGs?” Center for Global Development. September 14, 2015. http://www.cgdev.org/blog/can-private-sector-deliver-infrastructure-sdgs

Kumar, Nagesh. 1998. “Liberalisation and Changing Patterns of Foreign Direct Investments: Has India’s Relative Attractiveness as a Host of FDI Improved?” Economic and Political Weekly 33(22): 1321-1329. http://jstor.org/stable/4406834

Lingenheld, Michael. 2015. “Demographics Will Power The World's New Growth Leader: India.” Forbes. February 25.

53

http://www.forbes.com/sites/michaellingenheld/2015/02/25/demographics-will-power-the-worlds-new-growth-leader-india

Maurya, Harikesh. 2015. “Trends and patterns of foreign direct investment in India.” South Asia Journal of Multidisciplinary Studies 1(3).

Modi, Narendra. 2015. “Gathering Steam.” The Economist: The World in 2016. November 2. http://www.theworldin.com/article/10489/gathering-steam

Mollick, A., R. Ramos-Duran, and E. Silva-Ochoa. 2006.“Infrastructure and FDI into Mexico: A Panel Data Approach.” Global Economy Journal 6: 1-25.

Montero, Alfred P. 2008. “Macroeconomic deeds, not reform words: the determinants of foreign direct investment in Latin America.” Latin American Research Review 43(1): 55-83.

Moosa, I. A. 2005. “The determinants of foreign direct investment: an extreme bounds analysis.” Journal of multi.fin.manag 16: 199-211. https://www.researchgate.net/publication/4909712_The_Determinants_of_Foreign_Direct_Investment

Moran, Graham, and Blomström. 2005. Does Foreign Direct Investment Promote Development? Washington D.C.: Institute for International Economics.

Morisset, Jacques. 2003. “Tax Incentives: Using Tax Incentives to Attract Foreign Direct Investment.” Viewpoint 253. The World Bank Group, Public Policy for the Private Sector. https://www.wbginvestmentclimate.org/uploads/taxincentivesforeigninvestment.pdf

Nagaraj, R. 2003. “Foreign Direct Investment in India in the 1990s: Trends and Issues.” Economic and Political Weekly 38(17): 1701-1712. http://jstor.org/stable/4413497

OECD. 2008 OECD Benchmark Definition of Foreign Direct Investment, Fourth Edition. Paris: OECD Publishing, 48. http://www.oecd.org/daf/inv/investmentstatisticsandanalysis/40193734.pdf

OECD. 2015. OECD Economic Outlook, Volume 2015(2). Paris: OECD Publishing. http://dx.doi.org/10.1787/eco_outlook-v2015-2-en

OECD. 2016. “Incentives for attracting foreign direct investment: An overview of OECD work.” OECD: Better Policies for Better Lives. Last Accessed April 8. http://www.oecd.org/daf/inv/investmentstatisticsandanalysis/incentivesforattractingforeigndirectinvestmentanoverviewofoecdwork.htm

Pradhan, J.P. 2006. “Rise of Service Sector and Outward Foreign Direct Investment from Indian Economy: Trends, Patterns, and Determinants.” GITAM Journal of Management 4(1): 70-97. https://mpra.ub.uni-muenchen.de/17078/2/MPRA_paper_17078.pdf

54

Rajeev. 2007. “God’s own country to house largest IT park.” The Indian Express. Last Modified March 3. http://archive.indianexpress.com/news/gods-own-country-to-house-largest-it-park/24662/.

Rodrik, Dani. 1991. “Policy uncertainty and private investment in developing countries.” Journal of Development Economics 36.

Sahoo, Pravakar. 2014. “Making India an Attractive Investment Destination: Analyzing FDI Policy and Challenges.” Seattle: The National Bureau of Asian Research.” http://www.nbr.org/downloads/pdfs/ETA/IIP_Sahoo_paper_121014.pdf

Shah, Mumtaz Hussain. 2014. "The Significance of Infrastructure for FDI Inflow in Developing Countries." Journal of Life Economics 2(2): 1-16.

Shah, Zahir, Qazi Masood Ahmed, and Rehana Siddiqui. 2003. “The Determinants of Foreign Direct Investment in Pakistan: An Empirical Investigation [with Comments].” The Pakistan Development Review 42 (4). Pakistan Institute of Development Economics, Islamabad: 697–714. http://www.jstor.org/stable/41260431.

Singh, Kulwindar. 2005. “Foreign Direct Investment in India: A Critical Analysis of FDI from 1991-2005” New Delhi: Center for Civil Society. http://dx.doi.org/10.2139/ssrn.822584

Singh, Manminder, and Sandeep Singh Saluja. 2000. “New Economic Reforms and Economic Growth” in Liberalisation And Globalisation Of Indian Economy, edited by K. R. Gupta, 19.

Straub, Stéphane, and Akiko Terada-Hagiwara. 2010. “Infrastructure and Growth in Developing Asia.” Asian Development Bank Economics Working Paper Series 231. http://www.adb.org/sites/default/files/publication/28275/economics-wp231.pdf/.

The BRICS Post. 2014. “FDI inflows into India rise to $42bn in 2014.” BRICS Media Limited. December 28. http://thebricspost.com/fdi-inflows-into-india-rise-to-42-bn-in-2014/#.VmZTZN-rRE4

The Economic Times. 2010. “Technopark aims to be among top 5 IT investment locations.” July 27. http://articles.economictimes.indiatimes.com/2010-07-27/news/27611626_1_technopark-phase-iii-kundara/.

The Hindu. 2006. “Ensure better infrastructure to attract FDI, says Ahamed.” September 24. http://www.thehindu.com/todays-paper/tp-business/ensure-better-infrastructure-to-attract-fdi-says-ahamed/article3080203.ece/

Um, Paul N., Stéphane Straub, and Charles Vellutini. 2009. “Infrastructure and Economic Growth in the Middle East and North Africa.” World Bank Policy Research Working Paper Series 5105. http://www-wds.worldbank.org/servlet/WDSContentServer/WDSP/IB/2009/10/31/000158349_20091031151739/Rendered/PDF/WPS5105.pdf/.

55

UNCTAD. 2000. “Tax Incentives and Foreign Direct Investment: A Global Survey.” United Nations Publication, Sales No. E.01.II.D.5. http://unctad.org/en/Docs/iteipcmisc3_en.pdf

UNCTAD. 2013. “Investment Country Profiles: India.” UNCTAD. http://unctad.org/en/PublicationsLibrary/webdiaeia2013d4_en.pdf

UNCTAD. 2016. “Foreign direct investment: Inward and outward flows and stock, annual, 1980-2014.” UNCTAD: STAT. Last accessed May 8. http://unctadstat.unctad.org/wds/TableViewer/tableView.aspx?ReportId=96740/.

United Nations. 2015. “Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation.” United Nations: Sustainable Development. Last Accessed December 7. http://www.un.org/sustainabledevelopment/infrastructure-industrialization/

World Bank Group. 2016. “GDP ranking, PPP based.” The World Bank: Data. Accessed May 10. http://data.worldbank.org/data-catalog/GDP-PPP-based-table/.

World Bank Group. 2015. “Global Economic Prospects: Having Fiscal Space and Using It.” Flagship Reports. Washington DC: The World Bank Group. http://www.worldbank.org/content/dam/Worldbank/GEP/GEP2015a/pdfs/GEP15a_web_full.pdf