The introduction of Special Economic Zones (SEZs) in India has injected hope for augmented economic growth in recent future. The motive behind establishment of SEZs was mainly to fuel rapid economic growth, provide world class infrastructure and employment, promote exports, increase foreign exchange reserves and attract more Foreign Direct Investment (FDI). The main objective of the paper is to investigate whether the enactment of SEZ policies had any impact on inflow of FDI among Indian states. This is tested using panel data techniques on 16 groups of states over 14 years period from 2001 to 2014. The results indicate that enactment of SEZ policy (as well as operational SEZs) in a state has induced more FDI inflow. Based on the results, it can be concluded that the states, which want to benefit from FDI inflow, they need to enact the policies sooner.
Recognising the importance of export promotion in triggering the economic growth, the Government of India established the first Export Processing Zone (EPZ) at Kandla in 1965 [
The shift from EPZs to SEZs happened in two phases―conversion of existing EPZs into SEZs during 2000-2003 and fresh approvals of new SEZs after the year 2003. To realise the objectives, the entities were accorded special privileges so as to specially attract investment into the SEZs including the foreign investment. The key privileges included―1) duty free import/domestic procurement of goods for development, operation and maintenance of SEZ units; 2) single window clearance from Central and State Approval; 3) 100% Income Tax exemption on export income for SEZ units under Section 10AA of the Income Tax (IT) Act for first 5 years, 50% for next 5 years thereafter and 50% of the ploughed back export profit for next 5 years; 4) exemption from minimum alternate tax under section 115 JB of the IT Act; 5) external commercial borrowing by SEZ units upto US $ 500 million in a year without any maturity restriction through recognised banking channels; and 6) exemption from Central Sales Tax, Service Tax, States sales tax, dividend distribution tax and other levies of the respective State Governments [
At present (as on 2nd September 2016), 204 SEZs are operating in India and 18 of these were notified prior to the enactment of SEZ Act (2005). Though SEZs were intended to act as a catalyst for regional development, they are concentrated only in few states (
One of the aims of setting up the SEZs is to attract FDI, given its significant role in economic development [
Elsewhere, Wang [
Under this backdrop, the objective of the present study is to examine whether SEZs have helped in attracting FDI in Indian States or not. Such an investigation of the role of SEZs on FDI inflow (having implication for regional growth) context is particularly meaningful in the case of a huge, developing country like India where regional diversity in growth and differential economic performances of states are evident. This is tested using panel data of 16 major Indian states over 14 years period from 2001 to 2014. The results indicate that formulating of SEZ policy in the state has resulted in increased FDI inflow when other factors influencing FDI is accounted for.
The remaining paper is structured as follows. Section 2 reviews the literature looking into the determinants of FDI flows into particular state with specific reference to SEZs. Section 3 discusses the methodology used. The section also gives the data used for the study. Section 4 gives the descriptive statistics of different variables used. This is followed by reporting and discussion of results in Section 5. The paper concludes in Section 6 with policy implications of the study.
FDI plays a major role in promoting development through transfer of financial resources, transferring technology, promoting innovation and improved management ( [
Researchers have noted that FDI inflow is concentrated in few states of India only (see for example, [
The disparities in a host of structural and institutional factors across states has been largely responsible for skewed inflow of FDI [
Could SEZs attract additional FDI since their establishment? Due to the benefits generated by industrial clusters in attracting FDI and corresponding domestic investment, many developing countries have established these zones [
Wang [
Thus, establishment of SEZs is one of the major factors behind attracting FDI. However, the role of other factors cannot be ignored. Which are the other significant factors in attracting FDI? Since the beginning of year 2000, several researchers have examined the determinants of FDI but not with special reference to SEZs. A very positive and significant factor in attracting FDI in SAARC countries (except Maldives, Nepal and Pakistan) is the size of the economy, measured by gross domestic product (GDP) [
Studies have also noted the significant role played by infrastructure in attracting the FDI ( [
However, the interstate variation in FDI inflows does not seem to be influenced by infrastructure as shown in a study by Chatterjee [
A State with good fiscal prudence or decentralization seems to be another significant factor in attracting FDI. Based on the panel data analysis, Canfei [
The factors determining the FDI flows can also vary across countries. Sinha, Kent and Shomali [
Based on above literature review, we can see that there exist several studies which have attempted to find the factors attracting FDI. However, only a handful of studies exist looking into the role of SEZs in attracting FDI. Incidentally, most of these studies are for China. The present study plugs this important gap in the literature by investigating the role of SEZs in attracting FDI in the Indian context.
In this section we describe the methodology looking into the role of SEZs in influencing FDI inflow after accounting for other factors having a significant role. Based on the discussion in the previous section and similar to [
Thus, in the model we considered the above groups of variables as potential determinants of FDI. The model looking into FDI inflow in a particular state is specified as follows:
FDI = f ( SEZ policy , Market factors , infrastructure , labourmeasure )
We estimated the equation of the form
Y i t = α i + β x i t + ε i t (1)
where i represent the state and t represents the time for the dependent variable (i.e., FDI inflow), y and the explanatory variables (x); α is the parameter specific to each state and does not vary over time. As explained in the earlier section, the following variables in linear form are considered
LFDI i , t = α i + β 1 LPGSDP i , t + β 2 PELEC i , t + β 3 HDENSITY i , t + β 4 URBANDENSITY i , t + β 5 NEARPORT i + β 6 SEZpolicy i , t + ε it (2)
where, β1 to β6 are the parameters to be estimated. Our key variable is SEZpolicy which is captured as a dummy which takes the value one from the year when a state implements SEZ policy and zero before that. If coefficient of β6 is positive, this would imply that SEZpolicy has worked and has induced FDI in the state. Alternatively, to see the robustness of the results, we also use number of Operational SEZs in the state and hypothesize that a state having more number of operational SEZ would be able to attract more FDI.
With respect to other variables, LPGSDP is the log of Per-capita GSDP (at constant Prices), which is used as a measure of size of the market. Higher the GSDP per capita, higher is the market potential. As SEZs are established primarily to attract investment and are export oriented, the size of the domestic market may not be very relevant. However, as we are looking factor influencing total FDI in a state, the market size captured by GSDP per capita becomes relevant. We thus include this variable in the model.
For FDI (be it export-oriented or for domestic market), nearness to Port is very important, as goods can be imported and exported freely. We have included this variable nearness to port (NEARPORT) as a dummy, which takes the value one if the state has a seaport and zero otherwise. As a measure of labour availability we have used level of Urbanisation as indicator. A state having higher urbanisation per unit area (URBANDENSITY) in turn means that it would have more skilled people available. We have proxied infrastructure with two variables ? electricity availability per GSDP (PELEC) and highway density (HDENSITY).
The required data set has been compiled from different sources apart from Indiastat which collates the data published in different secondary sources. The dependent variable in our study is the FDI inflow in million US dollars (USD). The source of FDI inflow data is the Reserve Bank of India (RBI), which publish the data based on their regional offices rather than for the individual state. For example, Maharashtra RBI office consists of FDI data from Maharashtra, Daman and Diu (DD), and Dadar and Nagar Haveli (DNH). Thus, the restriction in the data availability forced us to group the states into 16 groups based on RBI classification. The following groups of states were considered for analysis: (1) Maharashtra, DD, DNH; (2) Delhi; (3) Karnataka; (4) Gujarat; (5) TN, Pondicherry; (6) AP; (7) WB, Sikkim, Andaman and Nicobar Islands (AN Islands); (8) Haryana, Chandigarh, Punjab, Himachal Pradesh (HP); (9) Goa; (10) MP, Chhattisgarh; (11) Rajasthan; (12) Kerala, Lakshadweep; (13) Odisha; (14) Uttar Pradesh (UP), Uttaranchal; (15) Assam, Arunachal Pradesh, Manipur, Mizoram, Nagaland, Tripura, Meghalaya; (16) Bihar, Jharkhand. As Telangana state was formed in 2014, for the purpose of this study, it is clubbed together with AP. The variable as used in the estimation is in log form. Since FDI to several of the states may come intermittently, this implies they may not receive FDI for certain years. In order to not to exclude these observations, a value of 1 is added to FDI value for all the observations and then log is taken.
Market size data as measured by the PCGSDP in constant prices is compiled from the Planning Commission website. The data for all the explanatory varia- bles has been regrouped under the same state groups as that of FDI. The data on Electricity Capacity is compiled from CMIE website, Energy statistics of India and Indiastat. The data on highway length has been compiled by the Indiastat from Ministry of Road Transport and Highways and Lok Sabha starred questions. The population data and the urbanisation for the interim period i.e. between 2002 to 2010 and after 2012 are based on projections based on population growth rate and the growth in urbanisation. The states having seaports are identified from the data given in Centre for Coastal Zone Management. The number of Operational SEZ is collected from www.sezindia.nic.in. The data on SEZpolicy is taken from www.sezindia.nic.in. The variables used in the model with their expected signs and source are described in
The study uses panel data technique for 16 major states for the period 2001 to 2014 to estimate the model to avoid the potential biasedness that may arise due to state-level heterogeneity, which may not be well captured using cross-section or time-series data.
Variables | Description | Expected sign | Data Source |
---|---|---|---|
Dependent Variables | |||
LFDI | Logarithm of Foreign Direct Investment in USD million | Indiastat (originally taken from Lok Sabha Starred Question# and Ministry of Commerce and Industry and RBI) | |
Independent Variables | |||
LPGSDP | Logarithm of Per capita GSDP in USD (GSDP in USD million/population in million) | + | Planning Commission |
PELEC | Installed Electricity capacity (MW) per GSDP | + | CMIE website, Energy statistics of India, indiastat |
HDENSITY | Highway Density (Length of national highways in km/ Area) | - | Indiastat (originally data has been taken from Ministry of Road Transport and Highways and Rajya Sabha and Lok Sabha questions$) |
URBANDENSITY | Urban Density [=(Urban people/Total populations) × 100/Area] | + | Census 2001 and 2011 |
NEARPORT | Dummy variable with value ‘1’ for coastal States and ‘0’ otherwise. | + | Centre for Coastal Zone Management and Coastal Shelter Belt |
SEZpolicy | Dummy variable with value ‘1’ when state formulates SEZ policy and ‘0’ otherwise. | + | www.sezindia.nic.in |
OPSEZ | Number of Operational SEZs | + | www.sezindia.nic.in |
Source: Authors’ own description. Notes: #―Lok Sabha Starred Question No. 589, dated 02.05.2003, Unstarred Question No. 1038, dated on 28.11.2006; $― Lok Sabha Unstarred Question No. 793, dated on 19.03.2012, Rajya Sabha Unstarred Question No. 1836, dated on 28.08.2012, Lok Sabha Starred Question No. 56, dated on 23.07.2015 and Lok Sabha Starred Question No. 178, dated on 10.12.2015.
States | Average FDI Inflow (USD million) | % of FDI | Total FDI Inflow (USD million) |
---|---|---|---|
Maharashtra (DNH, DD) | 4854 | 40.60 | 67,954 |
Delhi | 2801 | 23.43 | 39,215 |
TN (Pondicherry) | 1128 | 9.44 | 15,792 |
Karnataka | 1072 | 8.97 | 15,014 |
Gujarat | 829 | 6.94 | 11,607 |
AP | 634 | 5.31 | 8881 |
WB (AN Islands, Sikkim) | 207 | 1.73 | 2901 |
Rajasthan | 89 | 0.74 | 1241 |
MP (Chhattisgarh) | 81 | 0.67 | 1130 |
Kerala (Lakshadweep) | 77 | 0.64 | 1074 |
Haryana (Chandigarh, Punjab, HP) | 76 | 0.64 | 1063 |
Goa | 49 | 0.41 | 684 |
UP (Uttaranchal) | 31 | 0.26 | 434 |
Odisha | 18 | 0.15 | 250 |
North Eastern states | 5 | 0.04 | 73 |
Bihar (Jharkhand) | 4 | 0.03 | 55 |
Source: Compiled from FDI inflow data from indiastat.
ceived 93% of FDI. Incidentally, these States are more reform-oriented in comparison to other less reform oriented states like Bihar, UP, Haryana, Kerala, Odisha, MP, Punjab, Rajasthan and WB ( [
The FDI flows to different states over different time period is given in
Regarding SEZ policy formulation,
The descriptive statistics of all variables used in the model are given in
States | Year of passing of SEZ policy | Year of enactment of SEZ Act |
---|---|---|
Maharashtra | 2001 | No |
WB | 2001 | 2003 |
MP | 2001 | 2003 |
TN | 2003 | 2005 |
Jharkhand | 2003 | No |
Punjab | 2005 | 2009 |
Chandigarh | 2005 | No |
Haryana | 2006 | 2005 |
Goa | 2006 | No |
UP | 2007 | 2002 |
Kerala | 2008 | No |
Karnataka | 2009 | No |
Chhattisgarh | 2011 | No |
Source: Compiled from http://www.sezindia.nic.in/ and http://www.investindia.gov.in/state-policies/; Note: Here ‘No’ means that the state has not enacted a separate SEZ Act yet.
SEZ (column 7), Maharashtra, Karnataka, Gujarat, TN and AP could establish more than 10 number of Operational SEZs over the period, whereas, MP, Rajasthan, Kerala, Odisha, UP, have less than five Operational SEZs. It can be seen from the
State/Region | FDI (USD million) (1) | Per capita GSDP (USD) (2) | Electricity capacity (MW) (3) | Highway (km) (4) | Urbanisation (%) (5) | Nearport (6) | OpSEZ (in no.)# (7) |
---|---|---|---|---|---|---|---|
Maharashtra, DD, DNH | 4853.9 (4590.7) | 1149.2 (357.8) | 21,817.7 (7746.6) | 4608.6 (837.2) | 44.3 (1.2) | 1 | 22 |
Delhi | 2801.5 (2530.6) | 1897.7 (601.2) | 4626.8 (2081.8) | 87.1 (18.8) | 96.0 (1.9) | 0 | 0 |
Karnataka | 1072.5 (997.9) | 803.4 (229.7) | 9649.5 (3329.6) | 4281.1 (687.04) | 37.2 (2.1) | 1 | 25 |
Gujarat | 829.1 (1077.6) | 1064.5 (351.4) | 15,239.7 (7541.7) | 3373.9 (731.3) | 40.9 (2.4) | 1 | 18 |
TN, Pondicherry | 1128.0 (1133.4) | 979.1 (320.6) | 14,601.9 (4511.1) | 5156.2 (1221.7) | 47.3 (1.9) | 1 | 34 |
AP, Telangana | 634.3 (545.3) | 795.1 (243.2) | 12,111.5 (3725.6) | 4992.3 (878.9) | 31.6 (2.9) | 1 | 42 |
WB, Sikkim, AN Islands | 207.2 (192.5) | 626.2 (131.4) | 7759.6 (1347.8) | 2874.0 (606.5) | 30.6 (1.8) | 1 | 6 |
Haryana, HP, Chandigarh, Punjab | 75.9 (111.3) | 1087.8 (286.5) | 14,553.1 (4273.2) | 4934.3 (875.6) | 33.0 (1.9) | 0 | 9 |
Goa | 48.9 (75.8) | 2506.1 (932.8) | 380.3 (49.2) | 269 (0) | 58.7 (6.04) | 1 | 0 |
MP, Chhattisgarh | 80.7 (124.9) | 480.0 (123.9) | 12,761.1 (6505.3) | 7875.4 (1315.1) | 25.9 (0.7) | 0 | 3 |
Rajasthan | 88.7 (160.1) | 549.6 (136.5) | 7902.4 (3739.6) | 6088.2 (1161.2) | 24.4 (0.6) | 0 | 5 |
Kerala, Lakshadweep | 76.6 (128.1) | 1017.6 (311.9) | 3474.5 (433.3) | 1490.8 (58.3) | 46.1 (15.3) | 1 | 11 |
Odisha | 17.9 (21.1) | 522.9 (154.4) | 5133.9 (1736.2) | 4079.1 (709.2) | 16.1 (0.7) | 1 | 1 |
UP, Uttaranchal | 30.9 (50.03) | 395.9 (94.7) | 12,766.9 (2883.9) | 8472.2 (1872.8) | 22.1 (0.7) | 0 | 9 |
North Eastern States | 5.2 (10.7) | 515.8 (113.1) | 2308.7 (319.7) | 7565.4 (1163.6) | 17.6 (1.3) | 0 | 0 |
Bihar, Jharkhand | 3.9 (6.8) | 291.3 (78.3) | 4391.1 (525.9) | 5989.7 (1054.8) | 14.0 (0.4) | 0 | 0 |
Overall Mean for all states | 747.2 | 917.6 | 9342.4 | 4508.6 | 36.6 | 185 |
Source: Own computation; Notes: D & D―Daman and Diu; D & N Haveli―Dadra and Nagar Haveli; TN―Tamil Nadu; A & N Island―Andaman and Nicobar Island; WB―West Bengal; HP―Himachal Pradesh; MP―Madhya Pradesh; UP -Uttar Pradesh and NE states―North Eastern states which include Assam, Tripura, Meghalaya, Mizoram, Manipur, Arunachal Pradesh and Nagaland. The values in parenthesis are standard deviation; #―operational SEZs are total number of Operational SEZs during 2014.
Variable | All (N = 224) | Before SEZ Policy (N = 106) | After SEZ Policy (N = 118) |
---|---|---|---|
LFDI | 4.14 (2.64) | 3.81 (2.62) | 4.44* (2.63) |
LPCGSDP | 6.62 (0.63) | 6.49 (0.64) | 6.73* (0.59) |
ELEC GEN (MW) | 8.79 (0.066) | 8.67 (0.082) | 8.91* (0.102) |
HDENSITY | 0.03 (0.02) | 0.029 (0.0017) | 0.033* (0.0014) |
URBANISATION (%) | 36.63 (20.09) | 37.62 (24.91) | 35.74 (14.52) |
OPSEZ (No.) | 5.16 (8.55) | 2.04 (3.99) | 7.96* (10.41) |
Source: Own Computation; Note:
Variable | VIF | 1/VIF |
---|---|---|
LPCGSDP | 2.41 | 0.41 |
PELEC | 1.40 | 0.72 |
HDENSITY | 2.09 | 0.48 |
URBANDENSITY | 2.51 | 0.39 |
NEARPORT | 1.53 | 0.65 |
SEZPOLICY | 1.56 | 0.64 |
Source: Own computation.
Before carrying out analysis, we tested for multicollinearity (correlation matrix is given in the appendix,
The Equation (2) has been estimated in three different ways5―1) pooled model (keeping α constant i.e. ignoring the state specific or temporal effects); 2) fixed effects; and 3) random effects. As the states are heterogeneous, random effects and fixed effects models control for the state specific effects, and the suitability of these models is tested using the Hausman specification test. We tested for the
Variables | OLS (1) | Fixed Effect (2) | Random Effect (3) | Regression with panel-corrected standard errors(4) | Regression with panel-corrected standard errors (SEZPOLICY replaced with OPSEZ variable) (5) |
---|---|---|---|---|---|
(Dependent Variable = LFDI) | |||||
LPCGSDP | 2.48*** (0.27) | 1.95*** (0.29) | 2.15*** (0.28) | 2.47*** (0.46) | 2.11*** (0.42) |
ELEC | 4.69** (1.82) | 3.05 (1.83) | 3.28 (1.75) | 3.70 (2.57) | 0.78 (2.1) |
HWDNSTY | −89.99*** (9.8) | −97.27*** (9.38) | −96.08*** (9.27) | −88.49*** (16.1) | −72.18*** (14.95) |
URBANDN | 83.40*** (9.63) | 96.02*** (10.13) | 91.99*** (10.11) | 78.84*** (18.14) | 70.98*** (15.7) |
NEARPRT | 1.56*** (0.3) | 1.91*** (0.26) | 1.79*** (0.26) | 1.53** (0.46) | 1.20*** (0.43) |
SEZPOLICY | 1.18*** (0.24) | 0.85** (0.26) | 0.97*** (0.26) | 1.17** (0.39) | |
OpSEZ | 0.09*** (0.02) | ||||
Constant | −12.33*** (1.59) | −8.35*** (1.77) | −9.68*** (1.69) | −11.87*** (2.95) | −8.98** (2.71) |
Observations | 224 | 224 | 224 | 224 | 224 |
R squared | 0.62 | 0.66 | 0.66 | 0.71 | 0.81 |
F test | 59.25 | 65.77 | - | - | - |
Wald Chi 2 | - | - | 396.36 | 104.80 | 152.01 |
Hausman test | chi2(6) = 10.47 | ||||
Breusch-Pagan /Cook-Weisberg test for heteroskedasticity | chi2(1) = 0.01 | ||||
Wooldridge test for autocorrelation | F( 1, 13) = 17.586 |
Source: Authors’ own computations using OLS and panel data techniques. For the sources of data, kindly refer
presence of time effects in the fixed effects model and found them to be not significant and hence the results are not reported here. The estimated results are given in
To check the suitability of fixed effects vis-à-vis the random effects, a Hausman test is carried out. As the test statistic (10.47) is lesser than the critical value, the null of Random effect being more efficient is accepted. We also carry out and additional test, Breush Pagan Lagrangian Multiplier test for random effect. The test statistics of 59.84 (prob. = 0.00) validates that random effect model is efficient in the present case. The Wooldridge test with value 17.58 (prob. = 0.00) indicates the presence of autocorrelation in the sample. Column 5 reports the results of the model corrected for panel specific autocorrelation. Since model given in Column 5 is our preferred model, we discuss these results only.
The results validate that SEZ policy has a direct influence on the FDI inflows in a state. The results indicate that a state which has formulated SEZ policy will be able to attract additional 3.21 million USD (=exp(β6)) FDI; vis-à-vis a state, which has not formulated the policy. Besides the policy formulation, other factors influencing FDI inflows are the market size and Urbanisation. A state having a seaport is also able to attract more FDI. Surprisingly, electricity generation in a state has no bearing on FDI inflow. One possibility could be that it is not the electricity generation as such, rather it is electricity availability that would influence FDI inflow. We did not have data to account for electricity availability; as a result we could include only energy generation variable only. Surprisingly, we find that highway density has a negative influence on FDI inflow. One probable reason is that extent of road infrastructure is not merely reflected by highways, even a simple tar road may add up to the infrastructure. For lack of data on all kinds of roads in a particular state for all the years, we could not include the variable.
To check the robustness of the results, we use number of operational SEZs instead of SEZ policy. The results are reported in column 6 of the table. All the variables retain same sign and significance. The key variable, i.e., no. of operational SEZ is not only positive but also statistically significant. This suggests that a state having more number of operational SEZ is able to attract more FDI. As a further robustness test (results not reported), we also looked into the effect of operational SEZ after removing all the variables except market size. Our key variable retains the same sign and significance. Thus to conclude, having an SEZ policy or more number of operational SEZ has resulted in higher FDI in a particular state, even when we account for other location specific variables like market size, nearness to coast or highway density.
The present study aimed to understand whether establishment of Special Economic Zones (SEZs) have succeeded in attracting more FDI in state. Towards this end, a panel data analysis of 16 states for the period 2001 to 2014 has been carried out to establish the relation between FDI inflows and SEZ policy after accounting for other state-specific variables (such as market size, infrastructure, location, and labour availability) having impact on FDI inflow. The results obtained were in tandem with the literature and showed that FDI inflows are significant in the states with higher Per-capita income (market size), Urbanisation and the Coastal infrastructure (nearness to the ports). The states which attracted more FDI―Maharashtra, Karnataka, Gujarat, TN and AP are all coastal states. However, the North Eastern states, Bihar and Jharkhand have a minimum number of SEZs and also very low FDI. It is also seen that the operational SEZs are mainly concentrated in those states with higher FDI, and thus may further exacerbate regional inequality.
The results do indicate a positive and significant relationship between FDI inflow and the formulation of SEZ policy. The results are robust to when we include the number of operational SEZs instead of SEZ policy as a variable explaining FDI in these states. The results thus indicate positive role of SEZ policy and operational SEZ for attracting FDI in a state. The results are in line with the findings of Aggarwal [
The earlier 18 SEZs which are established before enactment of SEZ Act 2005, are situated in following states, viz. Gujarat (3), Maharashtra (1), UP (2), TN (5), Kerala (1), WB (3), AP (1), Rajasthan (1), MP (1)6. The irony is that not all of these states have been able to attract FDI inflow. Among the above mentioned states, only Gujarat, Maharashtra and TN are successful in getting FDI inflow, thus corroborating the view of Kathuria and Rajesh Raj [
From policy perspective, if the objective of setting SEZ is to bring in more equitable growth among the states, this does not seem to concur with the results. FDI is still concentrated in those states, which has some locational advantages. FDI inflows have potential to develop the poorer states but on the contrary these states cannot attract FDI as the investors always look for states which offer them infrastructural, market advantages along with a risk free environment.
In terms of contribution, this study has attempted to find the relationship between SEZ and FDI in Indian context. The study has shown that the enactment of SEZ policy as well as operational SEZs in a state has increased FDI inflow. This can be relevant from the policy perspective for the states which want to get benefit from FDI inflow, they will require to enact SEZ policy sooner.
There are some avenues for further research. First of all, whether or not the magnitude of inflows is sufficient enough to offset the losses that the Government is incurring in foregoing the tax revenues, the impact on environment SEZs have etc. is a subject of further analysis.
The present study can be further improved by constructing an infrastructure index. The variables like electricity availability per GSDP and highway density have been used as proxy for infrastructure index. Another area of further research is looking into the nature of SEZ that can attract maximum FDI. In the present study, there was no distinction between whether SEZ is electronics or food processing or biotechnology or textile or mixed, it is possible that more mixed SEZ may attract more FDI.
An earlier version of the paper was presented in at 51st Annual conference held by The Indian Econometric Society (TIES) during 12th-14th December 2014 at Punjabi University Patiala. We are extremely thankful to conference participants for the comments. The usual disclaimers apply. We express our sincere gratitude for anonymous reviewer(s) for their comments on the earlier version of this paper.
Chakraborty, T., Gundimeda, H. and Kathuria, V. (2017) Have the Special Economic Zones Succeeded in Attracting FDI?―Analysis for India. Theoretical Economics Letters, 7, 623- 642. https://doi.org/10.4236/tel.2017.73047
Sectors | Formal Approvals (No.) | Formal Approval (%) | In-Principal Approvals (No.) | In-Principal Approvals (%) | Notified (No.) | Notified (%) |
---|---|---|---|---|---|---|
Agro | 4 | 1 | 2 | 6 | 4 | 1 |
Biotechnology | 23 | 6 | 0 | 0 | 16 | 5 |
Engineering | 15 | 4 | 1 | 3 | 15 | 5 |
Footwear/Leather | 6 | 1 | 0 | 0 | 5 | 2 |
Food Processing | 4 | 1 | 0 | 0 | 3 | 1 |
FTWZ | 10 | 2 | 4 | 13 | 7 | 2 |
IT/ITES/Electronic Hardware/Semiconductor/Services | 262 | 63 | 0 | 0 | 204 | 62 |
Multi-product | 19 | 5 | 11 | 34 | 17 | 5 |
Multi-Services | 7 | 2 | 1 | 3 | 7 | 2 |
Non-Conventional Energy | 2 | 0 | 0 | 0 | 2 | 1 |
Petrochemicals and petro./oil and gas | 2 | 0 | 1 | 3 | 0 | 0 |
Pharmaceuticals/chemicals | 16 | 4 | 2 | 6 | 16 | 5 |
Port-based multi-product | 5 | 1 | 1 | 3 | 3 | 1 |
Others | 42 | 10 | 9 | 28 | 31 | 9 |
Total | 417 | 100 | 32 | 100 | 330 | 100 |
Source: Own compilation from http://www.sezindia.nic.in/.
LPCGSDP (1) | ELEC (2) | HWDNSTY (3) | URBANDN (4) | SEZPLCY (5) | NEARPRT (6) | OPSEZ (7) | |
---|---|---|---|---|---|---|---|
LPCGSDP | 1.00 | ||||||
ELEC | −0.24* | 1.00 | |||||
HWDNSTY | 0.40* | −0.44* | 1.00 | ||||
URBANDN | 0.73* | −0.21* | 0.60* | 1.00 | |||
NEARPRT | 0.36* | 0.04 | −0.09 | 0.49* | 1.00 | ||
SEZPLCY | 0.21* | −0.41* | 0.22* | 0.04 | 0.07 | 1.00 | |
OPSEZ | 0.32* | 0.11 | −0.20* | 0.09 | 0.39* | 0.35* | 1.00 |
Source: Own computation; Note: * shows significance of correlation coefficient at minimum 5% level.