Mainstreaming International Trade into
National Development Strategy
Regional Trade Openness Index, Income
Disparity and Poverty
- An Experiment with Indian Data
Sugata Marjit and Saibal Kar
Centre for Studies in Social Sciences, Calcutta
July 2008
1
Introduction
• Trade affects regional income of a geographically
large developing country
• Egger, Huber and Pfaffermayr (2005) deals with
trade openness of EUs and regional disparity (based
on available regional trade data)
• Absence of regional/provincial trade data
• Lack of proper indicator of regional trade openness,
and relation between openness and poverty, regional
income differences, etc.
2
Approach, Questions, Observations
• How could one deal with the issue of trade openness
and poverty?
• Two ways to approach the issue: Macro and Micro
•This study is a Macro exercise -- devise a holistic
measure of trade openness (TOI) across regions – use
that openness index to relate with regional disparity in
income, regional indices of poverty and industrial
employment
•Most Important observations include positive impact
of TOI on urban HCR and rural inequality
3
Relevant Studies and Main Outcomes of this Study
• The relevant literature discusses within country openness and
the regional trade openness index created here is novel.
Previous attempts at convergence tests via openness includes
Maiti (2004) and Marjit and Maiti (2006), Purfield (2006),
Topalova (2005), etc.
•States with traditional emphasis on production of
commodities that are intrinsically import competing in
nature have suffered an income loss over these years.
•provinces that retained larger share of production in the
export category faced improvement in their PCNSDP
•Industrial employment showed increasing trends till the
immediate pre-reform period after which it falls at an
4
increasing rate
ROI – Initial Methodology and Improvements
• Unavailability of trade data by regions
• Devise a proxy for ‘trade’ by using production (export
and import competing commodities) data at the state
level.
•DGCIS is the source of trade data according to HS
classification
• ASI is the source of State industrial data according to
NIC classification
•Since ASI and DGCIS use different definitions, we
reclassify and merge comparable data at the 2-digit level
5
Methodology --continued
•For a specific state, the level of output (i.e. sum of
industrial and agricultural output) has been linked to allIndia trade figures to get an approximate indicator of how
much ‘open’ a particular state is.
• We exclude service sector due to lack of production or
trade data
•Instead of the arbitrary 0.5 as the share of both exports and
imports used previously –export goods share ( xit  X it )
M it
( mit  M )
t
Xt
And
as the import goods share of each industry
in total export or import --- used as weights to obtain the
weighted TOI.
6
The new TOI is then written as
~k
ROI 1  s R  s Rmt
k
t
R
s
k (the
mt
k
xt
k
xt
k
mt
export performance rank) and the inverse
~ k (the import competing performance rank)
Rmt
k
is share of exportable production of k-th state at t-th
xt
period
s
k
mt is share of importable production of k-th state at t-th
period
7
Econometric Model
The model follows GMM (Generalized Method
of Moments) specifications to get rid of statespecific factors (equation below)
 ln Yit  (1   ) ln Yit   X it  Z it  Di   t   it   it 
 ln Yit 
Table 1
The above term is used as the instrument and
Then substituted by
 ln PC _ GFCFit 2 Table 2
As a better Instrument
8
GMM RESULTS (TABLE 1)
Dependent variable:
Regressors
 ln PCNSDPi (t  2 )
(1)
0.517***
XCIit
-0.008
 ln PCNSDPit  ln PCNSDPit  ln PCNSDPit 1
MCI it
(2)
0.5172***
(3)
0.516***
(4)
0.515***
-0.0021
ROI1it
-0.0084
ROI 2 it
-0.0091
PC _ GEit
1.302
1.296
1.302
1.3
Di
0.004
0.0049
0.0055
0.0056
RDit
79.4*
81.03
81.5*
81.2*
ELCit
0.00014
0.00014
0.0001
0.00013
LITit
Instrumental
variables
0.005***
 ln PCNSDPi ( t  4 )
0.005***
 ln PCNSDPi (t  4)
0.005***
 ln PCNSDPi (t  4 )
0.0051***
 ln PCNSDPi (t  4 )
and further lags
and further lags
and further lags
and further lags
Wald chi2 (7)
AR(1)
1133.8
-1.62
358.34
-1.62
314.97
-1.63
307.85
-1.64
9
GMM RESULTS (TABLE 2)
Dependent variable:  ln PCNSDPit  ln PCNSDPit  ln PCNSDPit 1
Regressors
(1)
 ln PCNSDPi ( t  2 )
0.542***
XCI it
-0.0145**
MCI it
(2)
(3)
0.5554***
0.5466***
(4)
0.5487***
-0.0015
ROI 1it
-0.012**
ROI 2 it
-0.0093**
PC _ GEit
1.3***
1.28***
1.307***
1.288***
Di
0.0024
0.0028
0.0029
0.00299
RDit
113.43***
119.56***
119.038***
116.06***
ELCit
0.00009
0.00008
0.000077
0.000078
LITit
Instrumental
variables
0.0037**
0.0037**
0.0037**
0.0038**
 ln PC _ GFCFit  2  ln PC _ GFCFit  2  ln PC _ GFCFit  2
and further lags
and further lags
and further lags
 ln PC _ GFCFit  2
and further lags
Wald chi2 (7)
708.64
438.52
527.06
409.81
AR(1)
-1.71
-1.62
-1.67
-1.65
10
Relation between TOI and Industrial Employment across SIC
Fig. 7 Correlation between Regional TOI and Growth of Workers across
Industries (SIC)
1
Correlation Coefficient
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
1981- 1982- 1983- 1984- 1985- 1986- 1987- 1988- 1989- 1990- 1991- 1992- 1993- 1994- 1995- 1996- 199782
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
SIC20-21
SIC22
SIC23
Years
SIC25
SIC26
SIC27
SIC28
SIC29
SIC30
SIC33
SIC35-36
SIC37
Poly. (SIC20-21)
Poly. (SIC37)
11
Relationship between TOI and Urban-Rural HCR
Fig. 11 Correlation Coefficient between Urban and Rural HCR and TOI
0.8
Correlation Coefficient
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
1983 - 1986 - 1987 - 1988 - 1989 - 1990 - 1991 - 1992 - 1993 - 1994 - 1995 - 1996 - 1997 - 1999 84
87
88
89
90
91
92
93
94
95
96
97
98
00
Years
Urban HCR
Rural HCR
Linear (Rural HCR)
Linear (Urban HCR)
12
Relationship
between
TOI
and Urban-Rural
Poverty Gap
Openness
Index:
Methodology
(contd.)
Fig. 12 Correlation between TOI and Urban and Rural Poverty Gap
0.8
Correlation Coefficient
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
1983 - 1986 - 1987 - 1988 - 1989 - 1990 - 1991 - 1992 - 1993 - 1994 - 1995 - 1996 - 1997 - 1999 84
87
88
89
90
91
92
93
94
95
96
97
98
00
Urban PG
Rural PG
Years
Linear (Urban PG)
Linear (Rural PG)
13
Relationship between TOI and Urban-Rural SQ Poverty Gap
Fig. 13 Correlation between TOI and Urban and Rural Squared Poverty Gap
0.8
0.7
Correlation Coefficient
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
1983 - 1986 - 1987 - 1988 - 1989 - 1990 - 1991 - 1992 - 1993 - 1994 - 1995 - 1996 - 1997 - 1999 84
87
88
89
90
91
92
93
94
95
96
97
98
00
Years
Urban SPG
Rural SPG
Linear (Urban SPG)
Linear (Rural SPG)
14
Relationship Between Openness and Interregional
Relationship
between TOI and Urban-Rural Gini
Fig. 14 Correlation between TOI and Urban and Rural Gini
0.8
Correlation Coefficients
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
1983 - 1986 - 1987 - 1988 - 1989 - 1990 - 1991 - 1992 - 1993 - 1994 - 1995 - 1996 - 1997 - 1999 84
87
88
89
90
91
92
93
94
95
96
97
98
00
Years
Urban GINI
Rural GINI
Linear (Urban GINI)
Linear (Rural GINI)
15
Primary Surveys and Case Studies
 This is the first disaggregated (state-level) measure of
TOI
 Within state dis-aggregation is unobservable due to lack
of data (Topalova, 2005, looks at import competition at
the districts only, NOT TOI)
 Thus, identified certain areas with high trade related
activities for micro implications of trade
 Case studies from West Bengal based on primary survey
 Subsequently, two specific case studies from
Maharashtra and Gujarat – more akin to our previous
and continuing work on Trade in Informal sector
products and poverty (Kar and Marjit, IREF, 2008,
forthcoming; Marjit and Kar, 2007, PEP Working
Paper).
16
Trade, Development and Social Change
Case Studies from West Bengal
 The effect of international trade on low wage workers in
West Bengal
 subtle changes at the grass root level within a country is
often not captured
 Weavers of Santipur-Phulia (Nadia), Import Competing
production in Durgapur-Asansol (Burdwan), industrial
belts of Kolkata-Hoogly, Labor migration from
Sagardighi (Murshidabad)
 Five small scale exporting firms selected from all three
areas (except Sagardighi)
 150 employees were randomly selected and interviewed
with the help of structured questionnaires
 In Sagardighi 50 labour households were selected
17






SANTIPUR-PHULIA
Before 1991, Textile firms were many in number
few cooperatives but major business was
controlled by a few traders
Major demand from local and Kolkata markets
Firm Infrastructure was poor, low prices to cater
widely
Weavers were paid low wages
Limited formal credit facilities
18
Santipur..contd..
• Since 1991, some hurdles removed mainly via
access to information about markets in other
metros and overseas.
• Producers’ dependence on middlemen
substantially reduced, able to market directly, take
part in trade fairs etc.
• Tables show changes in conditions of employment
and level of living within last decade
• Textile producers maintain two different scales
and technologies of operation and expanding on
both
19
Santipur..contd..
Table1.Changes in employment conditions
Conditions of Employment
Nature of current
employment
Change in wage rate
Change in other benefits
Change in nature of job
Uncertainty
Employer employee relation
Categories
No. of Respondents
Contractual
Casual
Others
Increased
Decreased
Unchanged
Increased
Decreased
Unchanged
Need More Skill
Need less skill
Unchanged
Increased
Decreased
Unchanged
Better
Worse
Unchanged
48
02
49
01
45
05
46
04
45
05
46
04
20
Santipur..contd..
Level of Living
Table2.Changes in living conditions
Categories
No. of Respondents
Food Consumption
Housing
Children’s Education
Indebtedness
Increased
Decreased
Unchanged
Improved
Deteriorated
Unchanged
More Affordable
Less Affordable
Unchanged
Increased
Decreased
Unchanged
48
02
45
02
03
40
10
45
05
21
Case Study from Durgapur
• Durgapur was a booming industrial town till the late
eighties
• In the nineties, large PSU’s and millions of ancillary
industries based on them went out of business
• Industrial Resurgence is very recent – in the span of
last 3-5 years, mainly driven by demand for steel in
China
• The ailing ancillary industries have come back to
life
• 5 such companies surveyed with response from 50
employees -- conditions in the following tables
22
Durgapur…contd….
Table 3.
Conditions of Employment
Nature of current
employment
Change in wage rate
Change in other benefits
Change in nature of job
Uncertainty
Employer employee relation
Employment conditions (Durgapur)
Categories
No. of Respondents
Contractual
Casual
Others
Increased
Decreased
Unchanged
Increased
Decreased
Unchanged
Need More Skill
Need less skill
Unchanged
Increased
Decreased
Unchanged
Better
Worse
Unchanged
45
05
48
02
40
10
40
10
40
10
45
05
23
Durgapur…contd….
Table 4.
Level of Living
Food Consumption
Housing
Children’s Education
Indebtedness
LIving conditions (Durgapur)
Categories
No. of Respondents
Increased
Decreased
Unchanged
Improved
Deteriorated
Unchanged
More Affordable
Less Affordable
Unchanged
Increased
Decreased
Unchanged
48
02
45
05
45
05
42
08
24
Sagardighi (Murshidabad)
• Murshidabad is one of the poorest districts in West Bengal
and recently categorized under A category (severe) in terms
of concentration of minorities and the gaps that exist in per
capita basic amenities compared to the national averages.
• Only 38% of people live in Pucca house, general work
participation is 39%, 24% houses with electricity, 23%
houses with in-house toilet facilities, 92% students drop out
before 8th Standard
• High degree of migration for work from all the villages,
including Sagardighi (Table 5)
• Essentially, (not formally) linking labor mobility with high
activities in real estate, an outcome of capital inflow – a
possible future research agenda across religious communities,
gender and income classes
25
Sagardighi…contd..
Table 5: Migration for Work:Community wise District Averages (%) (HH Survey)
Muslim
Duration
Place of
work
Short Term
79.09
62.07
Long Term
20.91
37.93
Within District (Village)
3.60
3.45
Within District (Town)
5.41
27.59
Within State (Village)
4.50
6.90
32.43
31.03
2.70
0.0
49.55
27.59
Abroad
1.80
3.45
Professional Work
4.50
25.0
Administrative Work
0.90
7.14
0.0
3.57
Sales Work
7.21
10.71
Farmer
7.21
0.0
61.26
28.57
Student
1.80
10.71
Others
17.12
14.29
Household
84.40
88.46
Within State (Town)
Outside State (Village)
Outside State (Town)
Clerical Work
Reasons for
migration
Transport and labourers
Repatriation
Non-Muslim
26
Primary Surveys and Case Studies




Leather Products (Handbags) Industry of Dharavi
Since the Dharavi’s re-development plan most leather
exporting associations in the area are shifting the Rs
300-crore industry to Bhiwandi
International buyers sometimes reject Dharavi’s
products as they have a tendency of not being consistent
in quality.
International leather agents demand to work with only
those exporters who can offer quality products on a
large scale through mechanized production
Mumbai has lost its prowess in the leather business to
cities such as Kolkata, Chennai and Kanpur
27
Dharavi…contd..
• Shift in prosperity to other locations was the proximity
of abattoirs and tanneries to production centres
• Notably, this leather industry by itself may still be
profitable, but yielded to high land prices in the region
-- another possible outcome of high intensity of
openness, capital inflow in retail sectors and real estate,
growth of urban service sector – once again, not
formally tested, but relevant evidences for research in
openness, growth and displacement
• much of the work has little official status and lacks
professionalism
• A final factor pushing most entrepreneurs is the access
to credit
28
Paper Product Industry of Surat
• Demand Driven --• Local demand increased, as the literacy rate picked up.
• Export market opened up for Indian made notebooks and
all types of writing books etc.
• Indian manufacturers were accepting small orders,
whereas Chinese manufacturers wanted huge orders to
feed their big capacities
• The new Linomatic Ruling machine’s one-day production
equalled to 10 hand-ruling machines.
• One Linomatic machine operated by 2 people displaced 10
hand ruling machine operators
29
Several other issues for future research, some already
identified -- aggregate evidence for the general trends
and individual but linked case studies for more micro
level formalization for which secondary data is not
available.
1. Transition from regional trade openness
to growth to poverty reduction – an ambitious
project given the paucity of Indian data
2. Trade, firm structures and labor mobility
– specializations and vanishing occupations –
Theory and application with Indian data
30