the impact of telecommunications infrastructure on fdi in
TRANSCRIPT
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.
51
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