Does Environmental Governance Matter
for Foreign Direct Investment? Testing the
Pollution Haven Hypothesis for Indian States
Vinish Kathuria∗

This paper attempts to examine the role of environmental governance on foreign
direct investment by testing the pollution haven hypothesis for 21 Indian states
for the period 2002–2010. To test for the hypothesis, this study computes an
abatement expenditure index adjusted for industrial composition at the state
level using Annual Survey of Industries plant-level data. The methodology
used is based on that proposed by Levinson (2001). The index compares
actual pollution abatement expenditures in a particular state, unadjusted for
industrial composition, to predicted abatement expenditures in the same state.
(The predictions are based on nationwide abatement expenditures by industry
and each state’s industrial composition.) If the adjusted index is low for a
state, it implies that the state has poor environmental governance, which would
be expected to induce foreign firms to invest. Tuttavia, the results do not
find any evidence of the pollution haven hypothesis in the Indian context.
Other infrastructure and market-access-related variables are more important in
influencing a foreign firm’s investment decisions than environmental stringency.

Keywords: abatement expenditure, environmental governance, India, pollution
haven hypothesis
JEL codes: F18, F23

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IO. introduzione

Over the past 3 decades, developing economies have witnessed a significant
inflow of foreign direct investment (FDI). Total FDI flows to developing economies
as a share of the world total increased from 17% in the early 1990s to 52% In
2013 (UNCTAD 2013). FDI inflows to developing economies were buttressed by
the liberalization process embarked on by many economies in the early 1990s and

∗Vinish Kathuria: Professor, Shailesh J. Mehta School of Management, Indian Institute of Technology, Mumbai,
India. E-mail: vinish.kathuria@gmail.com. An earlier version of this paper was presented at the Conference on
Leveraging FDI for Sustainable Economic Development in South Asia organized by the Copenhagen Business School
in Denmark on 2–3 October 2015. I would like to thank the conference participants, the managing editor, and the
anonymous referee for helpful comments and suggestions. This work was supported by a grant from the National
Research Foundation of Korea (NRF-2014S1A2A2027622), which is funded by the Government of the Republic of
Korea. The usual disclaimer applies. The Asian Development Bank recognizes “Bombay” as Mumbai, “China” as
the People’s Republic of China, and “Korea” as the Republic of Korea.

Asian Development Review, vol. 35, NO. 1, pag. 81–107
https://doi.org/10.1162/adev_a_00106

© 2018 Asian Development Bank
and Asian Development Bank Institute

82 Asian Development Review

the high growth rates that resulted from such reforms. Many host economies also
devised suitable incentives to attract FDI. Another reason often cited in the literature
is that relatively lenient environmental regulations in an economy can attract FDI.
This is a process that has been described as a “race to the bottom” (Grossman and
Krueger 1991, Xing and Kolstad 1998). Keller and Levinson (2002) posited that a
key factor influencing a foreign firm’s choice of location could be the compliance
costs of local environmental regulations.

One of the ways in which compliance costs can be measured is to look
at how much firms are spending on pollution abatement. If these costs are
aggregated across firms in a particular location, they reflect the environmental
governance aspects in that location. All other things being equal, a firm in one
Indian state having higher pollution abatement expenditures in comparison with
another firm in the same sector in a different state indicates more stringent
environmental governance in the first state. This paper seeks to identify the
impact of actual abatement expenditures on the location choices of foreign firms
in India by computing an index of abatement expenditure for firms in each
state using plant-level data from the Annual Survey of Industries for the period
FY2002–2003 to FY2009–2010.1

Earlier studies attempting to measure environmental regulations have used
either pollution intensity (Vedere, Per esempio, Mani, Pargal, and Huq 1997; Jha and
Gamper-Rabindran 2004; Dietzenbacher and Mukhopadhyay 2007) or pollution
abatement costs divided by one of the following: total employment, gross state
domestic product (GSDP), or a state’s manufacturing output without controlling
for industry characteristics (Vedere, Per esempio, Friedman, Gerlowski, and Silberman
1992; Duffy-Deno 1992; Crandall 1993). A key problem with such measures
is that they fail to adjust for industrial composition. States that are home to
pollution-intensive industries such as steel, fertilizers, and chemicals will incur
they have stringent
relatively high pollution abatement costs whether or not
regulations. Così, pollution abatement costs that account for industrial composition
are needed to assess a state’s regulatory stringency.

in questo documento, I compute industrial-composition-adjusted abatement costs
using unit-level data from the Annual Survey of Industries for the period
FY2001–2002 to FY2009–2010. The data are aggregated at the National Industrial
Classification (NIC) 3-digit and 2-digit levels, and then computed as an index.
Subsequently, I use panel data techniques to test for the pollution haven hypothesis
for 21 major states in India. The results do not validate the pollution haven
hypothesis in the Indian context.

The remaining paper is organized as follows. Section II explores how
FDI and the environment are linked. Section III discusses measurements of

1In India, a fiscal year (FY) is the period between 1 April and 31 Marzo. FY2002–2003 implies the fiscal

year running from 1 April 2002 A 31 Marzo 2003.

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Does Environmental Governance Matter for Foreign Direct Investment? 83

environmental governance in the literature. This is followed by an explanation of
the methodology used to assess the role of environmental governance on FDI in
different Indian states in section IV, which also explains the methodology used
to construct the industrial-composition-adjusted environmental governance index.
Descriptive statistics and other control variables are given in section V. Section VI
reports the estimation results. The paper concludes with a discussion of the policy
implications in section VII.

II. The Relationship between Foreign Direct Investment and the Environment

The relationship between FDI and the environment in the literature can
be grouped into three main strands: (io) environmental effects of FDI flows,
(ii) competition for FDI and its effects on environmental standards, E (iii)
cross-border environmental performance (Pazienza 2015). Despite extensive
empirical work and case study evidence, there is still not a clear understanding
of the associated phenomena (Erdogan 2014, Pazienza 2015).

With respect to the environmental effects of FDI flows, Pazienza (2015)
argues that greater integration of the world economy through increased investment
flows (and trade) and mobility of factors will impact the environment through the
(io) scale effect (moving from a small to global scale), (ii) technique effect (adoption
of cleaner technology), E (iii) composition effect (a shift in preferences to cleaner
products and greater environmental protections with increases in income) (Kathuria
2008, Pazienza 2015). The net of these three effects is reflected in the ultimate
impact on the environment.

The literature exploring the relationship between FDI and environmental
regulations discusses two distinct phenomena: (io) the pollution haven hypothesis,
E (ii) the “race to the bottom” or “regulatory chill hypothesis.” In the context
of FDI, the pollution haven hypothesis emphasizes the possibility that investors
seek economies in which to locate with fewer regulatory requirements and therefore
cheaper costs of operation for industries. È interessante notare, most authors who focus on
the pollution haven hypothesis have adopted an empirical approach (see Dean 1992
for a survey taken before 1990 and Erdogan 2014 for a recent survey).

This strand in the literature has often been used to oppose globalization
given the impacts of foreign investment on local environmental standards. Generally
known as the “race to the bottom” or “regulatory chill effect,” the argument states
that foreign firms may induce governments to reduce local environmental standards
or freeze them at suboptimal levels (Erdogan 2014). Evidence shows that in the
People’s Republic of China, provinces compete intensely for foreign capital by
offering promises of preferential treatment to potential foreign investors, Quale
can include a tacit (or explicit) commitment to lax enforcement of environmental
standards (Esty and Gentry 1997). In resource-seeking industries, where products
are homogeneous, minor cost differences can translate into large gains in market

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84 Asian Development Review

condividere. Consequently, foreign investors occasionally exert considerable pressure on
recipient economies (Erdogan 2014). These competitive pressures can also operate
in the opposite direction as investors insist on higher environmental standards. For
esempio, foreign investors in Costa Rican banana production have insisted upon the
application of high environmental standards as their European customers demand
an environmentally sound product (Gentry 1999, Erdogan 2014).

The focus of the present study is on testing for the pollution haven hypothesis
in Indian states rather than on the responsiveness of environmental standards to FDI.

III. Pollution Abatement Costs as a Measure of Environmental Governance

Three broad methods have been used in the literature to characterize
environmental stringency (Keller and Levinson 2002): (io) qualitative indexes of
regulatory stringency, (ii) quantitative measures of enforcement on the part of
states and economies, E (iii) compliance costs incurred by plants. Crandall
(1993) and Friedman, Gerlowski, and Silberman (1992) were among the first to use
industrial-composition-unadjusted pollution abatement costs (as a share of either
GSDP or employment) as a measure of environmental regulation. Later studies
by Levinson (2001) and Keller and Levinson (2002) used industrial-composition-
adjusted pollution abatement costs to measure the level of environmental regulation.
Though variation in state-level environmental stringency is less than
variation across economies, state-level variation provides three benefits. Primo,
there are much better data on a state’s environmental costs than on costs at the
international level. Secondo, states are more comparable with one another than
different economies on nonenvironmental parameters (Keller and Levinson 2002).
In cross-economy studies, costs are different due to prevailing market conditions in
different economies rather than purely the result of abatement-related costs. Questo
bias is less if an analysis is conducted across states within the same economy.
Third, most studies on decision-making processes with regard to location show that
environmental regulations have a very small role in these decisions (OECD 1997).
Factors like political stability, size and growth potential of markets, access to other
markets, labor costs, ease of repatriation of profits, transparency and predictability
of administrative and legal frameworks, cultural affinity, infrastructure, and quality
of life are more important (Erdogan 2014). Many of these factors are the same
across states within an economy; così, the major key variable influencing the
locational choices of foreign firms would be environmental costs.

The use of pollution abatement (operating) expenses as a measure of
abatement costs is preferred for two reasons (Keller and Levinson 2002). Primo,
operating expenses for pollution abatement equipment are easier to identify
separately. Abatement capital expenses may be difficult to disentangle from other
investments in the production process that have little to do with pollution abatement.
Secondo, abatement capital expenditures are highest when new investment takes

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Does Environmental Governance Matter for Foreign Direct Investment? 85

place. This implies that Indian states with thriving economies such as Gujarat and
Tamil Nadu that have sufficient manufacturing investment also tend to have high
abatement capital expenses regardless of the stringency of their environmental laws.
Inoltre, operating costs show a more consistent year-to-year pattern (Levinson
2001), while capital expenses can vary in line with industry business cycles. Questo
implies that pollution abatement expenditure can be used as a proxy variable for
environmental regulation. Incidentally, the Annual Survey of Industries includes
the following three variables: (io) expenses incurred in the repair and maintenance
of pollution equipment (which was discontinued in 2008), (ii) gross addition
of pollution control equipment expenses during the year, E (iii) gross closing
expenses of pollution control equipment at the end of the year.2 In this paper, IO
use the latter two measures (gross addition expenses and gross closing expenses on
pollution control equipment) to compute an index of environmental governance.

IV. Methodology

UN. Measuring Environmental Governance

Friedman, Gerlowski, and Silberman (1992); Crandall (1993); and List
and Co (2000) used measures like pollution abatement costs divided by either
total employment or GSDP. A key problem with such measures is that they fail
to adjust for industrial composition. Based on Levinson (2001), I compute an
industry-adjusted abatement expenditure index for 25 Indian states for different
time periods to see if FDI inflows are affected by any variation in abatement
expenditure (reflecting the degree of environmental governance). The index
compares actual pollution abatement expenditure in a particular state, unadjusted
for industrial composition, to the predicted abatement expenditure in the same state.
These predictions are based solely on economywide abatement expenditures by
industry and each state’s industrial composition. This paper improves on Levinson
(2001) and Keller and Levinson (2002) by computing industry-adjusted abatement
expenditure at the NIC 3-digit level instead of the NIC 2-digit level.

Let the actual abatement expenditure per unit of output be denoted as

follows:3
Sst = Pst
Yst

(1)

2Neelakanta (2015) is the only study in the Indian context that used repair and maintenance expenses to
compute an abatement cost index for 2 years, 2002 E 2005. There seems to be a problem with the computations
as the industry-adjusted abatement cost index is well below 1 for all Indian states. Since it is a relative measure, IL
states with higher abatement costs should have an index value greater than 1.

3This paper uses the same notations as used by Levinson (2001) and Keller and Levinson (2002).

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86 Asian Development Review

where Pst is pollution abatement expenditure in state s in year t, and Yst is the
manufacturing sector’s contribution to the GSDP of state s in year t. Sst is the
unadjusted measure of compliance costs. By failing to adjust for the industrial
composition of each state, it probably overstates the compliance costs of states with
more pollution-intensive industries and understates the costs in states with relatively
clean industries. To adjust for industrial composition, compare equation (1) to the
predicted pollution abatement expenditure per unit of GSDP in state s:

ˆSst = 1
Yst

N(cid:2)

i=1

YistPit
Yit

(2)

where N is the total number of industries. In India’s case, industries are indexed
from 15 through 36 (covering 22 industries) following the 2-digit manufacturing
NIC codes. Yist is the contribution of industry i to the GSDP of state s at time t,
Yit is the economywide contribution of industry i to national GDP, and Pit is the
economywide pollution abatement expenditure of industry i. In other words, Sst is
the weighted average pollution abatement expenditure (per unit of GSDP), Dove
the weights are the relative shares of each industry in state s at time t. To construct
the industry-adjusted index of a state’s stringency, S∗
st, I compute the ratio of actual
expenditures in equation (1) to the predicted expenditures in equation (2):

S∗
st

= Sst
ˆSst
when S∗
st exceeds 1, industries in state s at time t spend more on pollution abatement
than similar industries in other states. When S∗
st is less than 1, industries in state s
at time t spend less on pollution abatement. By implication, states with large values
of S∗
st have relatively more stringent regulations than states with small values of S∗
(Levinson 2001).

(3)

st

B.

Hypothesis

A low adjusted index score for a state implies that the state has poor
environmental governance, which would induce foreign firms to invest. In other
parole, this study tests for the pollution haven hypothesis; questo è, a negative
relationship between FDI and environmental governance. To test for this hypothesis,
I have used the following equation that relates FDI to environmental governance
after controlling for several state-specific effects such as net state domestic product
(NSDP) per capita, share of manufacturing in NSDP, quality of infrastructure, E
geographic dummy (proximity to coast):
FDIs,t = α + βS∗

γ + εs,T

S,t−1 + X (cid:3)
S,T

(4)

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Does Environmental Governance Matter for Foreign Direct Investment? 87

β is the estimated parameter of a state’s abatement expenditure index and is
predicted to have a negative influence on FDI inflows; questo è, the more stringent
a state’s environmental governance, the smaller its FDI inflows. The index also
uses a lag given that a firm’s decision to invest, especially with regard to FDI,
is not instantaneous. Piuttosto, an established pattern of governance may induce
a firm to invest in the subsequent period. γ ’s are the coefficients of control
variables. The control variables included are per capita net income of the state
(NSDPc); share of manufacturing in NSDP; quality of infrastructure, particolarmente
the availability of electricity as measured by installed capacity (Instlcap) E
transmission and distribution (T&D) losses; investment received by the state that
has been implemented through an industrial entrepreneurs memorandum (IEM);
availability of human capital (Literacy); and proximity to the coast. The likely
effects of these control variables are summarized below.

C.

Control Variables

Market size and demand

A bigger market attracts FDI (Kathuria, Ray, and Bhangaonkar 2015), due to
significant potential demand and economies of scale (Walsh and Yu 2010). Market
size is measured by NSDPc. A larger market size is hypothesized to have a positive
sign (List and Co 2000; Keller and Levinson 2002; Fredriksson, List, and Millimet
2003; Drukker and Millimet 2007). The variable is used in log form.

Manufacturing share

NSDP accrues from the primary (agriculture), secondary (manufacturing),
and tertiary (services) sectors. The manufacturing sector is relatively more capital
and energy intensive in comparison with the agriculture and service sectors. UN
large manufacturing share in a state’s NSDP reflects its status as an industrial state,
which is likely to attract more FDI. Therefore, the current study uses the share of
manufacturing in NSDP (Manushr) as a control variable.

Availability of power

Due to the significant capital investment required, a potential foreign investor
often assesses whether a state has sufficiently available power before making
an initial investment. Relatively high installed capacity implies the likelihood
of available power, which is also likely to attract more FDI (Mukherjee 2011).
Although installed capacity is often a good measure of power availability, this may
not be the case in the Indian context where many states have T&D losses as high as
50% (Srivastava and Kathuria 2014). Actual power availability is more important

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88 Asian Development Review

for an investor than installed capacity. The level of T&D losses also indicates
the effectiveness of industrial regulations in the state. Così, I take both installed
capacity (Instlcap) and T&D losses as control variables impacting the likelihood of
foreign firms investing in a state. A state with low installed capacity and high T&D
losses is expected to have low levels of FDI.

Proximity to a coast

Many foreign firms invest in developing economies due to cheap labor and
to establish a manufacturing hub for exporting and participation in worldwide
supply chains (Zhang and Song 2000). From a foreign investor’s point of view, UN
manufacturing hub requires international connectivity in the form of a seaport so
that components and final goods can be imported and exported easily. Proximity to
a port reduces the transaction costs of the producer. Therefore, a state that is home
to a seaport will attract more FDI (Neelakanta, Gundimeda, and Kathuria 2013). UN
dummy variable that is equal to 1 if a state has a seaport and 0 otherwise is used.

Clustering effect

An existing stock of investment in a state can generate positive spillovers
through linkages (Kathuria 2016). It is also indicative of conducive conditions for
investment. The IEM implemented in each state may capture this clustering effect as
it reflects the readiness of a state to attract investment.4 The IEM is also a reflection
of better institutional characteristics like good governance, political stability, low
levels of corruption, and ease of doing business. We hypothesize that the more the
IEM is implemented in a state, the more FDI it will attract unless the congestion
costs exceed the cost of relocating (Adsera and Ray 1998).

Human capital effect

Dunning (1998) has argued that though FDI in developing economies is
often prodded by traditional factors—such as market size, lower input (labor) costs,
and the cheap availability of natural resources—physical and human infrastructure,
along with the host economy’s macroeconomic environment and institutional
framework, play a crucial role. At the state level, physical infrastructure is reflected
by the availability of power and pucca (permanent) roads, while the literacy rate
indicates the availability of human capital. The present study controls for the human
capital effect through the state-specific literacy rate (Literacy).

4IEM is an application for acknowledgment of a unit not requiring any kind of license. The more IEMs

implemented in a state, the more units in the state.

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Does Environmental Governance Matter for Foreign Direct Investment? 89

Time dummy

As my data are for 9 years, a time dummy (TIME) is employed that accounts
for any macroeconomic changes occurring during the period that would affect all
Indian states.

D.

Data

One key problem to undertake the empirical analysis is the nonavailability of
appropriate FDI data. I need state-wise FDI in the manufacturing sector. Tuttavia,
data on FDI inflows available from the Reserve Bank of India (RBI) and the
Department of Industrial Policy & Promotion under the Ministry of Commerce and
Industry are either by sector or by RBI region. RBI regions correspond to regional
offices, and cover several states.5 To solve this problem, I use responses to questions
raised by members of Parliament on state-wise FDI. Data are summarized in Table
A1. The data for all other variables were collected from different government
agencies. The data for NSDP per capita and manufacturing share were obtained
from the Central Statistical Organisation, power availability and T&D losses from
the Ministry of Power and various reports of the Planning Commission, and IEM
data from the Ministry of Industry and the Handbook of Statistics on Indian
Economy. State-wise literacy rates were taken from 2001 E 2011 census data.

E.

Econometric Specification

For the given objective, several estimation models exist. Tuttavia, a simple
pooled ordinary least squares (OLS) model would yield biased and inconsistent
parameters if time-invariant covariates are omitted. If omitted time-invariant
variables are correlated with the environmental governance variable, a fixed effects
(FE) model will provide a consistent and unbiased estimate of the parameters while
simultaneously controlling for unobserved unit heterogeneity. D'altra parte,
if these omitted time-invariant variables are uncorrelated with the environmental
governance variable, a random effects (RE) model would provide a more efficient
estimate than an FE model. The validity of these assumptions is examined by a
Hausman test. In case of the presence of autocorrelation and heteroskedasticity,
I will be using the generalized least squares method that corrects for these two
problems. For the estimation purpose, I limit the sample to only 21 states and
union territories for which data are available for all the variables for the period
FY2002–2003 to FY2009–2010.6 This is because many northeastern states and

5Per esempio, the region Bhopal covers the states of Madhya Pradesh and Chhattisgarh.
6A union territory is an area under the direct administration of the Government of India. A union territory in
India is similar in legal status to the District of Columbia in the United States. Though analysis in this paper includes
both states and union territories, they are generally addressed collectively as “states.”

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90 Asian Development Review

union territories have neither received FDI nor are any consistent data available for
their T&D losses or power consumption, thereby restricting the number of states
and union territories for analysis to 21.7

The final econometric model estimated is

ln FDIst = α + βS∗

S,t−1

+ γ1 ln NSDPcs,T + γ2InstlCaps,T + γ3T &DLosss,T

+ γ4Manushrs,T + γ5 ln IEMs,T + γ6Coastals + γ7Literacys
+ γ8−15T imet + εs,T

(5)

The estimations were carried out in STATA 12.

V. Descriptive Statistics

Tavolo 1 presents state-wise summary statistics for abatement costs after
controlling for industrial composition (S*) at the 3-digit and 2-digit NIC levels
(equation 3) and without controlling for industrial composition (S) (equation 1).
The correlation between adjusted (3-digit NIC data) and unadjusted abatement
expenditure index is 0.9.

From Table 1, it can be inferred that several states which appear to have
higher abatement expenditures as per the unadjusted index have a much lower
ranking once industrial composition is accounted for. States like West Bengal
and Meghalaya, which are among the top five in terms of unadjusted pollution
abatement expenditure, get a much lower ranking once industrial composition
is accounted for. Allo stesso modo, states like Uttarakhand and Jharkhand have a higher
ranking after controlling for industrial composition. This implies that using the
unadjusted measure of compliance would give a misleading picture of some states’
relative stringency. Column 2 of the table gives adjusted abatement expenditure
using a 2-digit NIC code. The rankings and values hardly change. The correlation
between the two is 0.99. Tavolo 1 also indicates that there are nine states for which
industry-adjusted abatement expenditure is greater than 1, implying that they are
spending much more than their industrial composition suggests.

Tavolo 2 gives the trend of environmental stringency measures over three
periods: period 1 (2002–2004), period 2 (2005–2007), and period 3 (2008–2010).
From Table 2, it can be seen that there are six states—Andhra Pradesh, Punjab,
Rajasthan, Odisha, Goa, and Haryana—which show an increasing environmental
stringency trend during the entire 9-year period under review. On the other
hand, there are eight states—Assam, Chhattisgarh, Gujarat, Delhi, Uttar Pradesh,

7To reflect popular sentiments, the official names of some states have recently been changed. Per esempio,
Pondicherry was renamed Puducherry in 2006, Uttaranchal was renamed Uttarakhand in 2007, and Orissa was
renamed Odisha in 2011. This study refers to all states using their current names only.

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Does Environmental Governance Matter for Foreign Direct Investment? 91

Tavolo 1. Adjusted versus Unadjusted Abatement Cost Index Averages,
2001–2009

State
Code

State
Nome

Abatement Abatement
Cost Index
Cost Index
S* (2 digit)
S* (3 digit)

Unadjusted
Index, S

2
3
5
6
8
9
10
20
21
19
11
13
14
16
17
18
22
23
24
27
30
28
29
32
33
35
4
26
25
7
34

Himachal Pradesh (HP)
Punjab (Pb)
Uttrakhand (Uk)
Haryana (Hr)
Rajasthan (Rj)
Uttar Pradesh (UP)
Bihar (Bi)
Jharkhand (Jh)
Odisha (Or)
West Bengal (WB)
Sikkim (Si)
Nagaland (Na)
Manipur (Mamma)
Tripura (Tr)
Meghalaya (Mg)
Assam (As)
Chhattisgarh (Ch)
Madhya Pradesh (MP)
Gujarat (Gj)
Maharashtra (Mh)
Goa (Go)
Andhra Pradesh (AP)
Karnataka (Ka)
Kerala (Kl)
Tamil Nadu (TN)
Andaman and N. Island (ANN)
Chandigarh (Cg)
Dadra and Nagar Haveli (DNH)
Daman and Diu (DD)
Delhi (Dl)
Puducherry (Po)
Average for lowest 5 stati
Average for highest 5 stati

0.309
0.640
1.568 (4)
0.570
1.077
1.267
0.098
1.642 (3)
2.165 (1)
1.447
0.269
0.002
0.002
0.000
0.829
0.062
1.287
0.966
0.994
0.875
0.388
1.467 (5)
2.150 (2)
0.911
0.663
0.000
3.223
0.091
0.168
0.128
0.097
0.035
1.772

0.00127
0.001934
0.005045
0.001081
0.005496
0.004616
0.000483
0.004923
0.01251 (1)
0.00646 (4)
0.001755
0.000004
0.000013
0.000000
0.00647 (3)
0.000424
0.005559
0.003674
0.004878
0.00291
0.001821
0.00643 (5)
0.00715 (2)
0.003778
0.002045
0.000000
0.007468
0.000349
0.000431
0.000206
0.000317

0.284
0.605
1.469
0.528
1.099
1.282
0.104
1.401
2.263
1.476
0.252
0.001
0.002
0.000
0.789
0.069
1.242
0.914
0.993
0.851
0.390
1.474
2.176
1.023
0.691
0.000
2.910
0.086
0.144
0.118
0.082
0.033
1.760

Notes: State codes 2, 3, 5, 6, E 8 are in the North; 9, 10, and 19–21 are in the East; 11, 13, 14,
and 16–18 are in the Northeast; 22 E 23 are in the Central part; 24, 27, E 28 are in the West;
28, 29, 32, E 33 are in the South; E 4, 7, 25, 26, 34, E 35 are union territories of India.
Fonte: Author’s calculations.

Uttarakhand, and Dadra and Nagar Haveli—which started with a high level of
environmental stringency but became more lenient during the review period. Of
the remaining states, eight experienced a decline in the value of the index with an
increase in the middle period (2005–2007), while five showed increased stringency
over the entire 9-year period with a decline in the value of the index in the middle
period. The last row of Table 2 gives the average value of the abatement index for
all three periods, which indicates that there is hardly any change in environmental
stringency across all states over the entire review period.

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92 Asian Development Review

Tavolo 2. Adjusted Abatement Cost Index, Period-Wise Analysis

Period 1
(2002–2004)

Period 2
(2005–2007)

Period 3
(2007–2010)

% Change
from
Period 1
to Period 3

Environmental
Stringency
Pattern

1.315
0.071
0.149
3.932
1.379
0.152
0.228
0.179
0.236
1.115
0.479
0.218
1.982
2.286
0.886
0.925
1.049
0.000
0.201
1.555
0.118
0.615
0.688
0.727
0.000
1.500
2.342
1.520
0.923

1.320
0.067
0.070
3.959
1.298
0.062
0.133
0.135
0.315
1.016
0.571
0.193
1.188
2.410
0.932
0.927
0.883
0.000
1.452
2.273
0.086
0.649
1.227
0.597
0.000
1.153
1.651
1.390
0.838

1.767
0.049
0.077
1.779
1.184
0.058
0.142
0.069
0.612
0.850
0.659
0.516
1.757
1.754
0.916
1.047
0.693
0.006
0.834
2.669
0.087
0.658
1.317
0.664
0.000
1.149
0.712
1.431
0.927

34.4
−30.9
−48.5
−54.8
−14.1
−62.0
−37.6
−61.3
159.1
−23.7
37.6
136.2
−11.3
−23.3
3.4
13.2
−33.9

315.4
71.7
−26.4
7.0
91.4
−8.7

−23.4
−69.6
−5.9
0.4

Increasing
Decreasing
Declined
Declined
Decreasing
Decreasing
Declined
Decreasing
Increasing
Decreasing
Increasing
Increased
Declined
Declined
Increased
Increased
Decreasing
Increased
Increased
Increasing
Declined
Increasing
Increasing
Declined
No change
Decreasing
Decreasing
Declined

State

Andhra Pradesh
Assam
Bihar
Chandigarh
Chhattisgarh
Dadra and Nagar Haveli
Daman and Diu
Delhi
Goa
Gujarat
Haryana
Himachal Pradesh
Jharkhand
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Manipur
Meghalaya
Odisha
Puducherry
Punjab
Rajasthan
Tamil Nadu
Tripura
Uttar Pradesh
Uttarakhand
West Bengal
Average

Fonte: Author’s calculations.

Figura 1 gives the plot for environmental stringency measure between period
1 and period 3. States lying above the 45-degree line showed increased stringency
between the two periods, while states falling below the line experienced a decline
in environmental stringency. With the exception of Andhra Pradesh and Odisha, IL
stringency of environmental governance declined in all states between period 1 E
period 3.

Figura 2, which gives a scatter plot between ln(FDI) and the lagged value
of the industry-composition-adjusted abatement cost index, does not indicate any
perceptible relation between the two.

Tavolo 3 reports the mean values of different variables used in the analysis.
It shows huge variation in the values for all variables. There are states like
Assam, Bihar, and Jharkhand, which hardly received any FDI. D'altra parte,

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Does Environmental Governance Matter for Foreign Direct Investment? 93

Figura 1. Change in Industry-Adjusted Abatement Expenditure Index (S*), 2002–2004
versus 2007–2010

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Note: For actual names of states, please refer to Table 1.
Fonte: Author’s calculations.

Figura 2. Relation between S∗

t−1 and ln(FDI)

Fonte: Author’s calculations.

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94 Asian Development Review

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Does Environmental Governance Matter for Foreign Direct Investment? 95

Maharashtra tops the list with an average of INR44.62 billion over this 9-year
period.8 Similarly, the share of manufacturing in NSDP is less than 5% in Bihar,
compared with more than 25% in Gujarat, Goa, and Puducherry. Regarding
installed capacity, Maharashtra, Andhra Pradesh, Karnataka, Gujarat, and Tamil
Nadu each have more than 5,000 megawatts of power generation capacity, while
states like Goa do not produce any electricity. The northern states, which do
not receive much FDI and have fewer electricity installations, are also plagued
with high T&D losses. Four states—Bihar, Madhya Pradesh, Rajasthan, and Uttar
Pradesh—account for 40% of all T&D losses during the review period, which may
discourage FDI from coming to these states. Of the five states with the highest
values for human capital, as measured by the literacy rate, only Delhi has received
substantial FDI, while the other four states are not even among the top 10 recipients
of FDI.

VI. Results and Discussion

Before estimating the model, correlations are noted between the different
control variables. Tavolo 4 gives the Spearman correlation matrix and reports the
significance of the correlation coefficient at the 5% level. A state with higher
NSDP per capita is able to attract more FDI (correlation = 0.33) and have a
high manufacturing share (positive correlation) with very high literacy (correlation
= 0.86) and low T&D losses (negatively correlated). A state with high installed
capacity is not only able to attract more FDI (correlation = 0.57), but also more
domestic investment (IEM) (correlation = 0.69), and does not have any correlation
with T&D losses. Allo stesso modo, a coastal state has high FDI (correlation = 0.35) and a
high manufacturing share (correlation = 0.3). As expected, a literate state has a high
manufacturing share and low T&D losses. Consequently, with partial correlation
being statistically significant for several of the variables, I could not use all the
controlled variables together.

UN.

Econometric Analysis

Tavolo 5 reports the results for the econometric estimations. Equazione (5)
was estimated first by pooling the data for all states (column 1). As discussed,
due to omitted variables, the OLS results were expected to be biased. Therefore,
panel data techniques were also required and both FE and RE models were
subsequently run. An F-test was carried out to see whether individual FEs exist
or not. Since the F-value (6.2) is greater than the tabulated value, it implies that
the null hypothesis (pooled OLS) is rejected and that FE and RE models need
to be estimated separately. Columns 2 E 3 give the results of the FE and RE

8In July 2009, $1 = INR48.7.

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96 Asian Development Review

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Does Environmental Governance Matter for Foreign Direct Investment? 97

Tavolo 5. Testing for the Pollution Haven Hypothesis Dependent Variable = ln(FDI)
Heteroskedastic
Panels
Corrected
Standard Errors
(4)

Random
Effects
(3)

Fixed
Effects
(2)

Variables

Pooled
Ordinary
Least
Squares
(1)
−0.329
(0.289)
2.964***
(0.45)
0.213*
(0.085)
0.729***
(0.116)
1.395***
(0.405)
−31.85***
(4.62)
Yes

168

0.51
13.38 (0.00)

S∗
t−1

ln(NSDPc)

ln(IEM)

ln(Installed capacity)

Coastal

Constant

Time dummies
Observations
R2
F-test/Wald Chi2
Number of states
Hausman test

0.203
(0.423)
0.798
(2.89)
0.086
(0.106)
−0.215
(0.577)

−3.377
(28.95)
Yes

168

0.0712
(0.355)
2.92***
(0.805)
0.131
(0.093)
0.662***
(0.183)
1.323*
(0.767)
−31.06***
(8.39)
Yes

168

0.264
(0.358)
3.205***
(0.838)
0.256***
(0.090)
0.527***
(0.128)
1.16**
(0.55)
−33.73***
(8.8)

Yes

168

0.112
1.55 (0.12)

21
4.96 (0.29)

0.50
49.9 (0.00)
21

0.40
81.83 (0.00)
21

Notes: Figures in parentheses below the coefficients are standard errors. The numbers in parentheses in the
F-test/Wald Chi2 and Hausman test are p-values. ***, **, E * denote significance at the 1%, 5%, E 10%
level, rispettivamente.
Fonte: Author’s calculations.

estimations. Whether these omitted variables (state-specific differences) are fixed
or random is tested using a Hausman test (ultima riga). This is a test for the correlation
between the error and the regressors. Under the null hypothesis of no correlation
between both, the RE model is applicable and its estimated generalized least squares
estimator is consistent and efficient. Under the alternative, it is inconsistent. Since
the test’s statistic (chi-square value = 4.96) is significant only at the 29% confidence
level, one cannot reject the null hypothesis. To see whether RE are needed, UN
Breusch–Pagan Lagrange–Multiplier (LM) test is carried out. Results lead to the
rejection of the null hypothesis, in favor of the alternative, cioè., the RE model.

Row 1 shows that the industry-composition-adjusted pollution abatement
expenditure index (S*) is negative, though statistically insignificant, and thus has
no impact on FDI investment. This implies that states’ environmental norms do not
figure in the investment decision of foreign firms. With respect to control variables,
a state with high per capita income (ln[NSDPc]), which reflects a bigger internal
market, can attract more FDI. A state with more domestic investment (ln[IEM]) È
not able to attract more foreign investment in statistical terms. D'altra parte,
proximity to the coast and the availability of infrastructure, as proxied by installed
capacity, has a direct bearing on foreign firms’ location decisions. High installed

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98 Asian Development Review

capacity implies that power is more readily available in a state. Così, foreign firms
are expected to prefer these states. Allo stesso modo, coastal states attract more FDI due to
their increased opportunities to export.

Given that panel data is used where values of different variables change over
time, the possibility of autocorrelation exists. A Wooldridge test for autocorrelation
(where the null is no first-order correlation) (F-value = 0.98, p = 0.33) negates this
possibility. A Pesaran cross-sectional dependence test is then employed to check
whether the residuals are correlated across panels, as cross-sectional dependence
(contemporaneous correlation) can lead to biased results. The test value of 2.7 È
significant at less than 1%, suggesting that there is cross-sectional dependence. UN
modified Wald test is also carried out to test for group-wise heteroskedasticity. UN
very high value of chi-square ((cid:2)175) indicates that the null of homoscedasticity
(constant variance) is rejected. Given the problem of heteroskedasticity, a panel
corrected standard errors model was subsequently employed and the results are
reported in column 4. S* retains the same sign and significance level even
after the correction. All other control variables also retain the same sign and
significance level except for domestic investment (ln[IEM]), which becomes highly
significant. The results suggest that FDI flows to states that are coastal and have
high installed capacity, high per capita income, and more domestic investment.
Environmental stringency does not influence a foreign firm’s location decision
when other infrastructure and market-access-related factors are considered. In other
parole, the results do not validate the pollution haven hypothesis in the Indian
context.

B.

Robustness Test

To see whether results are robust or not, I estimated several variants of the
modello. Tavolo 6 reports the results where some of the control variables are either
dropped or alternate control variables are used. Column 2 (modello 2) uses T&D loss
instead of installed capacity. The impact of the environmental governance index
(S*) variable on FDI remains the same. The coefficient of the T&D loss variable has
the expected sign, though it is not statistically significant. In model 3, the coastal
variable used in model 2 is dropped. In model 4, literacy is substituted for per
capita income (ln[NSDPc]), which was used in the base model. In model 5, IL
manufacturing share is used instead of investment in the state (ln[IEM]). In model
6, only the environmental governance index variable (S*) and year dummies with no
control variables are used. Lastly, modello 7 uses state dummies and time dummies
while all of the control variables continue to be excluded.

As can be seen from Table 6, the environmental management index (S*)
variable remains statistically insignificant in all variants of the model. The results
are thus robust to alternate control variables and to the noninclusion of control
variables. Most of the control variables retain the same sign and significance as

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Does Environmental Governance Matter for Foreign Direct Investment? 99

Tavolo 6. Testing for Robustness of Results–Pollution Haven Hypothesis Dependent
Variable = ln(FDI)

Variable
S∗
t−1

ln(NSDPc)

ln(Installed
capacity)

ln(IEM)

Coastal

T&D loss

Literacy

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
−0.06
(0.32)

0.22
(0.33)

0.18
(0.313)

0.264
(0.358)
3.205***
(0.838)
0.256***

0.52
(0.364)
1.655***
(0.756)

0.47
(0.34)
1.50**
(0.69)

(0.090)
0.527***
(0.128)
1.16**
(0.55)

0.314***
(0.085)
0.448
(0.62)
−0.031
(0.202)

0.343***
(0.081)

−0.048#
(0.031)

0.13
(0.30)
3.25***
(0.52)
0.616***

(0.103)

2.03***
(0.465)

0.385***

(0.12)
0.286***
(0.089)
1.35**
(0.63)

Manufacturing

condividere
Constant

State dummies
Time dummies
Observations
R2
F-test/Wald Chi2

Number of states

−33.7*** −13.84*
(9.61)
No
Yes

(8.8)
No
Yes

168

168

0.40
81.83
(0.00)
21

0.34
46.61
(0.00)
21

−11.55#
(7.58)
No
Yes

168

0.42
47.9
(0.00)
21

0.089**
(0.038)

−0.06***
(0.02)
−7.26*** −33.1***
(5.27)
(2.8)
No
No
Yes
Yes

168

0.40
114.41
(0.00)
21

168

0.47
176.38
(0.00)
21

2.79***
(0.65)
Yes
Yes

168

0.41
21.96
(0.00)
21

Yes
Yes

168

0.74
2,301.80
(0.00)
21

Notes: ***, **, E * denote significance at the 1%, 5%, E 10% level, rispettivamente. # denotes significance at the 15%
level.
Fonte: Author’s calculations.

predicted. When state dummies are included in all the variants (models 2 A 5),
the main variable remains statistically insignificant. When NSDP is used instead
of NSDP per capita, the main variable retains its sign and significance. Lastly, IL
results did not change when all models were reestimated by computing S* at the NIC
2-digit level. The results also remain the same irrespective of how I compute S*.
The use of both gross closing expenditure and gross addition expenses on pollution
abatement yield the same outcome. Based on the results, this study does not validate
the pollution haven hypothesis in the Indian context.

VII. Conclusione

This paper examines the impact of environmental governance on FDI
by testing the pollution haven hypothesis for 21 Indian states for the period

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100 Asian Development Review

2002–2010. An abatement expenditure index was computed and adjusted for
industrial composition at
the state level using the methodology provided by
Levinson (2001). The industry-adjusted abatement expenditure index was greater
di 1 for nine states, which implies that these states spend more on abatement
measures than their industrial composition suggests. The index also shows that
over the 9-year review period, six states showed an increasing trend of abatement
expenditure, while eight states showed a decreasing trend.

The paper then uses this industry-composition-adjusted pollution abatement
expenditure index to test the pollution haven hypothesis in a panel data framework.
The study finds that environmental stringency does not influence FDI decisions
once panel-specific heteroskedasticity is accounted for. The paper concludes that a
coastal state with high levels of per capita income and available power will attract
more FDI. Environmental stringency does not influence foreign firms’ decisions
when other infrastructure and market-access-related factors are considered. IL
results were subsequently tested for robustness by using alternate control variables
in a panel corrected heteroskedastic model. The results were found to be robust
for the inclusion of control variables. To conclude, the study does not validate the
pollution haven hypothesis in the case of Indian states.

There are several possible reasons why the study was not able to either
validate or refute the pollution haven hypothesis in the Indian context. Primo, Anche se
foreign firms establish operations abroad due to low operational costs, the relevance
of pollution abatement costs in comparison to total operating costs may be limited
(Erdogan 2014). Secondo, even if these costs are high, they may still be lower than
in other economies from where FDI is originating or in alternate destinations.
Therefore, it may not matter where to invest within a particular economy. Finalmente,
studies have suggested that foreign firms generally seek consistent environmental
enforcement over lax enforcement (Vedere, Per esempio, OECD 1997), which may also
hold true in the case of Indian states.

While the paper’s important findings have some limitations, it can be
extended to address these limitations. As mentioned, parliamentary questions were
relied on to get state-wise FDI data, which showed an extremely high value of FDI
for one state in a particular year. Inoltre, the paper considers all FDI inflows in
IL 21 states under review. Instead of total FDI, only manufacturing FDI could be
considered to assess the effects of environmental governance. Another extension
of the present study would be testing the pollution haven hypothesis only for FDI
that is associated with pollution such as investment in the chemicals and fertilizer
industries. Lastly,
if a race-to-the-bottom dynamic, rather than the pollution
haven hypothesis, is applied in the Indian context, then FDI and environmental
governance would be endogenous;
the testing of which requires the use of
instrumental variable estimations, which would be a further extension of the present
lavoro.

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Does Environmental Governance Matter for Foreign Direct Investment? 101

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Appendix. List of Questions and Responses by Members of the Parliament

on State-Wise Foreign Direct Investment

1. UNSTARRED QUESTION NO: 182

ANSWERED ON: 01.03.2005
FOREIGN DIRECT INVESTMENTS
ADHIR RANJAN CHOWDHURY

Will the Minister COMMERCE AND INDUSTRY be pleased to state:

(UN)

the details of proposals for foreign direct investments submitted during
2001–2002, 2002–2003, 2003–2004 and till date statewise with particular
reference to West Bengal;

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