Volume 2, Issue 1, June 2013
Transcript of Volume 2, Issue 1, June 2013
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013
ISSN: 2277-9108
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING
Volume 2 Issue 1 June 2013
CONTENTS
Pages
Articles
Regions and Crises: European and Asian Economic Governance
Petr Blizkovsky 1
Poverty and Calorie Deprivation across Socio-Economic Groups in Rural India: A Disaggregated Analysis
Abha Gupta &
Deepak K. Mishra
15
Technical Efficiency and its Determinants in Backward Agriculture: The Case of Paddy Farmers of Hailakandi District of Assam
Ritwik Mazumder &
Manik Gupta
35
Women Home based Workers across Indian States: Recent Evidences
Tulika Tripathi &
Nripendra K Mishra
55
Health Situation in India: An Overview Shabir Ahmad Padder 65 Book Review Unfolding Crisis in Assam's Tea Plantations: Employment and Occupational Mobility; Deepak K. Mishra, Vandana Upadhyay, Atul Sarma
Jhilam Ray 81
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING Editorial Team Chief Editor
Kalyanbrata Bhattacharya formerly of Department of Economics, University of Burdwan Editor
Rajarshi Majumder Department of Economics, University of Burdwan Managing Editor
Jhilam Ray Department of Economics, University of Burdwan Editorial Advisory Board
Aditya Chattopadhyay, Calcutta University
Ajit K Singh, Giri Institute of Development Studies (formerly),
Amitabh Kundu, Jawaharlal Nehru University
Alakh N Sharma, Director, Institute for Human Development
Biswajit Chatterjee, Jadavpur University
Dinesh C Sah, MPISSR
Kausik Gupta, Rabindra Bharati University
Rabindranath Bhattacharya, Kalyani University (formerly)
Rajendra P Mamgain, Giri Institute of Development Studies
Shankar K Bhaumik, Calcutta University
Sibranjan Misra, Viswa Bharati
Tarun Kabiraj, Indian Statistical Institute, Kolkata
If you take care of the parts, the whole will take care of itself
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013
Editorial Note
As we complete one year of publication of Journal of Regional Development and Planning, we
are both happy and sad. We are happy to observe that our journal has created interest among
researchers and administrators across the country and outside. We now receive a continuous
stream of good papers from serious authors who want to address the issue of regional
development and planning from diverse angles. However, being biennial, we cannot accommodate
more than ten papers in a year and so have a waiting list that keeps the editorial team happy. On
the contrary, we are yet to see the response that was expected from policy-makers. Addressing
issues of regional disparity at the policy level and taking concrete steps to bring down spatial
inequality is still not visible on the ground. Our earlier apprehension that persisting inequality
will only foment unrest and unlawful activities has been proved true by the recent spates of acts of
terror in central India, which is by far the least developed region of the country, and elsewhere.
And we are saddened by such developments.
We reiterate that Journal of Regional Development and Planning will continue to publish original
work that brings to light not only instances of regional development but also examples where lack
of homogeneous development of the constituent parts are leading to unfolding crisis.
RM
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING
Quarterly Journal of The Indian Society of Labour Economics
Indian Journal of Labour Economics (IJLE), being published since 1957, is a prestigious organ of the Indian Society of Labour Economics (ISLE). Now in its 55th year, the Journal aims at promoting scientific studies in labour economics, industrial relations and related fields. Salient Features It is one of the few prestigious Journals of its kind in South Asia. It provides eminent economists and academicians an exclusive forum
for an analysis and understanding of issues related to labour economics. It Includes peer reviewed articles, research notes, book reviews, documentation and statistical information, particularly in the context of India and other developing countries.
Contributors Eminent and well known national and international academicians, social experts, researchers contribute and write for the Journal. Some of the prominent ones among them are Bina Agarwal, Amit Bhaduri, Sheila Bhalla, L. K. Deshpande, Jean Dreze, Gary.S. Fields, Indira Hirway, Ravi Kanbur, K. P. Kannan, J Krishnamurty, Amitabh Kundu, G. K. Lieten, Dipak Mazumdar, Jesim Pais, Rajarshi Majumder, T. S. Papola, D. Narasimha Reddy, Gerry Rodgers, Ashwani Saith, Arjun Sengupta, Ajit Singh, Ravi S. Srivastava, Guy Standing, Sukhadeo Thorat, Jeemol Unni, A. Vaidyanathan, etc. Special Issues IJLE also brings out one Special Issue in a year occasionally. Some of the recent ones among them are on “The Informal Sector in South Asia”, “Labour Migration and Development Dynamics in India “and “Wages and Earnings in India”. Indexed and Abstracted The Journal is indexed and abstracted in COREJ, LABORDOC, EconLit, e-JEL and JEL of the American Economic Association (produced by the Journal of Economic Literature), GEOBASE: Human Geography and International Development Abstracts.
We welcome your subscriptions Annual Subscription Rates: India – Rs. 1000; SAARC Countries –US$ 120; Overseas—US$ 200. For subscription, payment should be made in favour of The Indian Journal of Labour Economics through DD or local cheque payable at Delhi/New Delhi
Write to us All editorial and business correspondence should be made to: The Editor/Managing Editor; The Indian Journal of Labour Economics; NIDM Building, IIPA Campus, IP Estate; M.G. Marg, New Delhi-110002 (India); Phones: 011-23358166, 23321610; Fax:011-23765410; Website : isleijle.org; E-mail: [email protected]
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 1
REGIONS AND CRISES: EUROPEAN AND ASIAN ECONOMIC
GOVERNANCE
Petr Blizkovsky 1
Macro-regions are experiencing regional crises. Interestingly, macro-regions are also able to
react in terms of policy responses. This article addresses economic governance against the
backdrop of two macro-regional cooperation models, in the European Union and in ASEAN.
Economic governance is arguably the critical missing element in the current economic
globalisation. The gap between market-driven sources - which contributed massively in the last
decades to welfare gains globally, and to Asia in particular –and suboptimal regulatory
coordination is a risky element. There is an absence of efficient economic governance or
cooperation at the macro-regional level and globally. The European Union and ASEAN are
examples of two different models of economic governance. Both of them represent an unfinished
project. Nevertheless, they both offer experience from which global economic governance or
economic governance in other macro-regions can benefit. In the end, the question is not whether
there is a need for global economic governance but rather: governance of what, between whom
and how far it should go.
INTRODUCTION
The world has changed considerably in last two decades. Politically, the period was marked in
Europe by the end of the cold war and of central governance, and in Asia by more market-friendly
reforms in several important countries. So, one can argue that the world has become a safer and
better place in recent times, notwithstanding sporadic incidences in specific hotspots. However,
from an economic perspective, we can see a dichotomy in this. On the one hand, economic growth
experienced exponential development over the last twenty years. GDP has grown considerably in
the developed economies and even more in the emerging countries. International trade has soared
to new heights. Welfare gains of the economic progress have been considerable and it seems that
globalisation has worked for most. However, on the other hand, there is a risk-related issue present
now. Various regions of the world have recently been attacked by the financial crisis. This
financial and debt crisis has negative implications for social policies and macroeconomic stability.
The movement of goods, services and capital has increased and become globalised but the
regulatory and institutional framework in which the market forces are operating is lagging behind
the globalisation of markets. In this article we argue that the new economic challenge of the
current stage of globalisation lies in governance. How to improve economic governance in the
multi-polar world while preserving the sovereignty of states? What model of cooperation is
appropriate and manageable at the current juncture? In this article we bring together the key
lessons from two macro-regional cooperation models – the cooperation between the nations
belonging to the European Union and those within the ASEAN. Both came from different starting
points and have different levels of ambitions, yet are examples of economic governance and
1 Petr Blizkovsky is Director at the General Secretariat of the Council of the European Union. The opinions
expressed in this article are personal of the author alone and does not represent the views of the organisation he works for.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 2
regional cooperation models. In this, they represent useful inspiration for the global model of
governance and lessons learnt may be replicated on a larger scale.
THE CASE OF THE EUROPEAN UNION
Economic governance represents a complex set of rules and procedures. Its aim is to assist
economic growth and provide stability in the EU as a whole. The rationale for economic
governance in the EU is to avoid or minimise negative slipovers among Member States. The most
visible cornerstone of economic coordination in the EU is the European Economic and Monetary
Union (EMU), agreed in the Treaty of Maastricht in 1992. The scope of economic governance in
the EU covers macroeconomic policy coordination, the single monetary policy for the euro area
Member States, coordination on microeconomic policies, the regulation of financial services, the
regulation and coordination of tax policies, support schemes and coordination of policies for
international fora. Let us explore the performance of the EU in each of these areas.
Macroeconomic policy coordination
Macroeconomic policy coordination remains the responsibility of Member States, which are
legally obliged to coordinate it within the EU. It is important to note that all Member States
subscribed to the Stability and Growth Pact (SGP) which stipulates that they should aim for a
budgetary position close to balance, or in surplus, over the medium-term in a period of normal
economic growth. They are also obliged to avoid an excessive deficit above 3% of GDP. In the
case of the UK, such an obligation strictly speaking does not exist (one may see Protocol 15 of the
Treaties). Member States should also have maximum 60% of government debt of GDP according
to the SGP. However, this criterion has not been made operational. The Pact contains a preventive
and a corrective arm, under which sanctions are foreseen.
The recent financial, economic and debt crisis of the EU triggered the creation of further
instruments and changed the European Union economic governance (Begg et al, 2011). The key
instruments that came into existence have been mentioned in a later section.
Monetary Policy
The EU Member States agreed to create a single monetary policy. This policy was confirmed by
the European Central Bank and European System of Central Banks. The creation of this single
currency was conceived without obliging all Member States to join at the same time. Two
countries were granted an "opt-out" clause from the common policy (the United Kingdom and
Denmark).
As conditions for adopting the euro, the Treaty established five convergence criteria for countries:
• maximum 3% of budget deficit of GDP
• maximum 60% of government debt of GDP
• inflation no higher than 1.5% above the average inflation of the three best performing countries
• long-term interest rates no more than 2% above the three best performing countries
• a two-year period without devaluation
• independence of the national central bank.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 3
Coordination of Microeconomic Policies
In order to ensure the coordination of the macro- and microeconomic policies of Member States,
the EU established a system of multilateral surveillance of economic policies conducted by
Member States. The economic policies of the individual Member States are regarded as a matter of
common concern and are coordinated within the Council. To this end, the Council formulates
Broad Economic Policy Guidelines (Articles 120–26 of the Treaty on the Functioning of the EU
(TFEU)). From a legal standpoint, therefore, the EU already has in place a legal base from which
to carry out macroeconomic surveillance. It covers a broad range of issues including
macroeconomic imbalances. However, this is not a mechanism which foresees sanctions and its
implementation record is mixed.
Table 1 Overview of economic governance in the European Union
Area of governance Nature Instrument Implementation Monetary policy Legislative Delegated power, single currency Strong
Economic policy
coordination
Political Stability and Growth Pact Mixed
Legislative Secondary legislation, Excessive
Deficit Procedure
Mixed so far,
new measures
Recommendatory
Lisbon strategy, EU 2020, Broad
Economic Policy Guidelines,
Integrated Guidelines
Mixed so far,
new measures
to be taken
Legislative or
administrative National budgetary frameworks To come
Regulatory framework
for financial services Legislative Secondary law
Strong
Regulatory framework
in tax policy
Legislative and
political Secondary law, agreements Mixed
Stability measures Legislative Budgetary support for the non-euro
area members Strong
International
coordination Political
Terms of Reference for international
fora (G20, IMF) Soft
In order to strengthen economic policy coordination, the EU agreed to the political peer pressure
exercise of the Lisbon strategy (in 2000) and of the EU 2020 (in 2020). The objectives of these are
structural reforms, a move towards research and development, sustainable growth in terms of the
environment, and social parameters. This instrument has no legal enforceability but regular
reforms screening and evaluation push Member States towards a more competitive economy.
However, the track record so far has been mixed.
Legislation for financial services and taxation
On top of this, the EU has legally binding rules and procedures for adopting legislation. These
cover the single market and include legislation in financial services (banking, insurance, capital
markets). In this area, the Council is a co-legislator together with the European Parliament. The
law, once adopted, is enforceable and, in the case of complaints, the European Court of Justice
provides its judgement. Similarly, the European Union adopts secondary law in the area of
taxation. It also coordinates direct tax policy in a soft way. The Council decides by unanimity and
the European Parliament only provides its opinion.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 4
International coordination
Apart from the measures described above, the EU has at its disposal the stability instrument for
Member States confronted with balance of payment difficulties.
The economic governance of the EU thus can be seen as bold and strong, taking into account the
fact that economic policy still remains the responsibility of Member States (Table 1).
RECENT MEASURES IN EU GOVERNANCE
In the aftermath of the economic crisis, several new measures have been taken to strengthen the
EU economic governance (Bishop, G. 2011, COUNCIL OF THE EUROPEAN UNION 2010).
These measures include the following:
• The creation of short-term crisis resolution mechanisms within the euro area. These take
various forms, such as ad hoc facility to support Greece, as a company under private law
and owned by the euro area countries
• The creation of a permanent crisis resolution mechanism within the euro area - the
European Stability Mechanism. This should be operational as of mid-2013. The
resolution mechanisms are compatible with Article 125 of the Treaty, the so-called "bail
out-clause" which specifically forbids that either the Union or Member States should be
liable for or assume obligations of euro area Member States.
• A limited Treaty change to allow the creation of the European Stability Mechanism
(EURO AREA, 2011). The new provision concerns Article 136 of the TFEU which states
that Member States whose currency is the euro may establish amongst themselves a
stability mechanism. The amended Treaty should enter in force on 1 January 2013.
• Strengthening of the Stability and Growth Pact through stricter monitoring of the
adjustment path of Member States towards their medium-term budgetary objectives. In
this context, alongside an analysis of the structural balance, the assessment of expenditure
developments will play an important role.
• Introducing the possibility to impose sanctions already in the preventive part of the
Stability and Growth Pact expecting that the adjustment towards the medium-term
objective (MTO) will be faster for high-debt countries.
• Adopting national budgetary frameworks which establish minimum requirements for the
budgetary frameworks of Member States including reporting requirements which will
allow the Commission to better monitor the evolution of the budget in Member States at
the various government levels.
• Implementing a surveillance framework for the monitoring and correction of excessive
imbalances. This Excessive Imbalances Procedure (EIP) consists in principle of three
parts, namely surveillance, alert and sanctions. It addresses potentially harmful
imbalances via a combination of indicators and alert thresholds included in a scoreboard,
and simultaneous a Commission analysis.
• The European Semester as a tool for stronger community involvement in the preparation
of national budgets. It brings together the Broad Economic Policy Guidelines (including
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 5
the employment guidelines) and the review of the Stability or Convergence programmes
in a single text for each Member State.
• The Euro Plus Pact - 23 Member States committed to increasing competitiveness,
fostering employment, reinforcing financial stability, and improving the state of public
finances, especially in the pension sector.
• Creating Euro Summit meetings of Heads of State or Government of the euro area. This
body will provide better guidance for the euro area.
These additional governance measures are presented in Table 2.
Table 2 Overview of the Additional Economic Governance Measures in the Euro Area
Area of governance Nature Instrument Implementation
Economic policy
coordination
Legislative Excessive Imbalances Procedure To come
Political Euro Plus Pact In the process
Stability measures
Legislative European Financial Stability
Mechanism
Strong
Ad hoc agreement Assistance to Greece Strong
Intergovernmental
agreement
European Financial Stability
Facility Strong
International Treaty European Stability Mechanism Strong (to come)
As a result of these recently added measures and conditions, the actors involved in economic
governance are becoming multiple. There is however a flexible geometry approach to the whole
governance pattern. According to the shared sovereignty and legal framework, the different actors
decide on different measures. Table 3 presents an overview of this approach.
Table 3 Overview of the Economic Governance Geometry in the European Union
Number of Member
States Body Role, Instrument Presidency
27
European Council Policy guidance Fixed term
Council Legislator/Co-Legislator Rotating
European Parliament Co-Legislator Fixed term
17
Euro Summit Policy guidance, decisions Election
Eurogroup Informal meeting, guidance of the euro area Fixed term
17
European Financial Stability Facility board meeting
European Financial Stability Facility decisions
Economic and Financial Committee of the EU (EFC)
16 Ad hoc facility Assistance to Greece Eurogroup Chair
23 plus Euro Plus Pact meeting
Euro Plus Pact, open to observers
Election (Euro Summit Chair)
17 plus European Stability Mechanism board
European Stability Mechanism decisions, open to other Members
Eurogroup Chair or election
Finally, in the complex situation of the economic crisis, the EU and its Member States have
developed a series of instruments (see also Featherstone, K., 2011, Verhelst, S., 2011). These vary
in scope and nature. An overview of the instruments is presented in Table 4.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 6
Table 4 Overview of Aid Schemes under EU
Name Nature Legal basis Financing Beneficiaries Financial
means (EUR bn)
Activity timeframe
BoP
Balance of
payments
assistance
European Union
instrument
(Council
Regulation
332/2002)
Treaty on the
Functioning
of the EU,
Article 143
Loans from the
European
Commission
guaranteed by
the EU budget
Non-euro area
Member States
(currently
Hungary, Latvia,
Romania)
50 No limit
EFSM
European
Financial
Stabilisation
Mechanism
European Union
instrument
(Council
Regulation
407/10)
Treaty on the
Functioning
of the EU,
Article 122
(2)
Loans from the
European
Commission
guaranteed by
the EU budget
EU Member
States (currently
Ireland)
60
No limit.
Expected to
expire with
the
activation of
the ESM
Stability
support to
Greece
Intergovernmental
instrument
International
agreement
Loans from 14
Member States Greece 80
One-off
facility
EFSF
European
Financial
Stability
Facility1
Intergovernmental
instrument under
private law
International
agreement
EFSF bonds,
guaranteed by
the
shareholders
Euro area
Member States
(currently
Ireland,
Portugal)
780
(maximum
guaranties)
440
(effective
lending
capacity)
Until mid-
2013
ESM
European
Stability
Mechanism2
International
financial
organisation
International
Treaty on
ESM
Loans and
direct public
bond
purchasing by
euro area
Member States
(non-euro area
Member States
may join on a
case-by-case
basis)
Euro area
Member States
700
(authorised
capital
stock)
500
(maximum
lending
capacity)
As of mid-
2013
ECONOMIC GOVERNANCE IN THE EUROPEAN UNION – A SUMMARY
We have so far explained the complexity of economic governance in the European Union. The
European Union represents the most developed and deep form of macro-regional cooperation. The
unprecedented voluntary sharing of sovereignty among Member States results in the robust
economic governance of the 27 Member States in terms of economic law-making and its effective
implementation. On top of this, 17 Member States, at this stage, share the single currency and
agreed to share fully their sovereignty in the monetary policy. The track record of this exercise is
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 7
very positive: the euro achieved its global position, it has a strong and stable exchange rate and it
is a low-inflation currency.
The track record on the economic governance of the economic policies remains however mixed.
The rules of public policy coordination, and the Stability and Growth Pact and the legislation
linked to it, have a sound economic logic. However their implementation has been far from
optimal. Also, the peer pressure to harmonise structural reforms and enhance economic
competitiveness has not been efficient.
The current economic crisis triggered the creation of new elements of economic governance. New
players and instruments have been put in place. Their objective is to strengthen the coexisting
legislative framework and coordinate economic policies more closely, namely among the euro
Area countries.
For economic governance, this means that there are now two types of governance: hard
(regulatory) and soft (incentive). The first happens under a legal framework and is of a
compulsory nature under the sanctions and ruling of the Court of Justice regime. The second is
based on political agreement. There are no sanctions related to it. Another new characteristic of
economic governance is its variable geometry, meaning that different Member States subscribed to
different instruments. The result is that, on top of the community method, there is now a sphere of
inter-governmental approach or alternatively an approach based on the new international Treaty
(Euro Area 2011) in parallel to the Treaty on the European Union.
THE CASE OF ASEAN
The Association of Southeast Asian Nations (ASEAN) is another example of macro-regional
cooperation. It has its own economic governance model. This model was subject to adjustment
after the economic crisis in the region in the last decade. The ASEAN is also reacting to the
current global economic crisis by stepping up its economic governance framework (Institute of
South Asian Studies, ISEAS, 2010; Koh, T., Manalo, R.G. and Woon, W., 2009; Severino, R.C.,
Thomson, E. and Hong, M. 2010; The Association of Southeast Asian Nations Secretariat 2010).
The ASEAN was established in 1967 with the signing of the ASEAN Declaration (Bangkok
Declaration) by the Founding Fathers of ASEAN, namely Indonesia, Malaysia, the Philippines,
Singapore and Thailand. Brunei Darussalam then joined on 7 January 1984, Viet Nam on 28 July
1995, Lao PDR and Myanmar on 23 July 1997, and Cambodia on 30 April 1999, making up what
are today the ten Member States of ASEAN.
The mission of the ASEAN economic cooperation (as set out in the ASEAN Declaration) is to
accelerate economic growth, social progress and cultural development in the region through joint
endeavours, in the spirit of equality and partnership, in order to strengthen the foundation for a
prosperous and peaceful community of Southeast Asian Nations.
At the initial stage, economic coordination had a soft character based on political agreement.
Subsequently, as part of the Vientiane Action Programme, adopted by ASEAN at the Tenth
ASEAN Summit in Laos in November 2004, ASEAN agreed to work towards the development of
an ASEAN Charter. The Kuala Lumpur Declaration on the Establishment of the ASEAN Charter
in December 2005, and the Cebu Declaration on the Blueprint of the ASEAN Charter in January
2007 further developed the process of drafting the ASEAN Charter. The ASEAN Charter serves as
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 8
a firm foundation in achieving the ASEAN Community by providing a legal status and
institutional framework for ASEAN. It also codifies ASEAN norms, rules and values, sets clear
targets for ASEAN, and presents accountability and compliance.
The ASEAN Charter entered into force on 15 December 2008 and has become a legally binding
agreement among the 10 ASEAN Member States. The ASEAN Charter is essentially the
Constitution of ASEAN. Amongst other things, the Charter: sets out the guiding principles
governing how ASEAN will conduct its affairs; confers a legal personality on ASEAN as a legal
entity in its own right; establishes the organs through which ASEAN will act; and institutes a
formal structure for decision-making.
The importance of the ASEAN Charter can be seen in the context of the political commitment at
the top level, the new legal framework, including the legal personality, and the creation of new
ASEAN bodies. The Charter creates a framework within which ASEAN Member States can enter
into substantive agreements on specific areas, such as economic integration, environmental
protection and climate change, equitable development, transnational crime and security. An
example of this is the ASEAN Economic Community Blueprint, which sets out detailed timelines
for a greater integration of ASEAN’s economies. The structure and organs set up by the ASEAN
Charter will play a critical role in ensuring the success of the Blueprint’s ambitious target of
establishing the ASEAN Economic Community by 2015.
At the 9th ASEAN Summit in 2003, the ASEAN Leaders decided to create the ASEAN
Community. At the 12th ASEAN Summit in January 2007, the Leaders affirmed their
commitment, and signed the Declaration, on the Acceleration of the Establishment of an ASEAN
Community by 2015. Each pillar has its own Blueprint and, together with the Initiative for
ASEAN Integration (IAI), the Strategic Framework and IAI Work Plan Phase II (2009-2015), they
form the Roadmap for an ASEAN Community 2009-2015 (ASEAN Studies Centre, 2009).
The ASEAN Community is comprised of three pillars:
• ASEAN Political-Security Community
• ASEAN Economic Community
• ASEAN Socio-Cultural Community
Let us briefly explore each of them.
ASEAN Economic Community (AEC)
Looking at economic governance, the mission of the AEC is to create a stable, prosperous and
competitive ASEAN economic region in which there is a free flow of capital, economic
development and a reduction in socio-economic disparities (Sim, E.W. 2009;The Association of
Southeast Asian Nations 2008). The AEC plans to establish ASEAN as a single market production
base, turning the diversity that characterises the region into opportunities for business
complementation making ASEAN a more dynamic and stronger segment of the global supply
chain.
The AEC areas of cooperation would include: human resource development and capacity building;
recognition of professional qualifications; closer consultation on macroeconomic and financial
policies; trade financing measures; enhanced infrastructure and communications connectivity;
development of electronic transactions through e-ASEAN; integrating industries across the region
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 9
to promote regional sourcing; and enhancing private sector involvement to build the AEC. In
short, the AEC will transform ASEAN into a region with free movement of goods, services,
investment and skilled labour, and a freer flow of capital.
The ASEAN Leaders adopted the ASEAN Economic Blueprint at the 13th ASEAN Summit on 20
November 2007 in Singapore to serve as a plan guiding the establishment of the ASEAN
Economic Community 2015. The realisation of the AEC in 2015 should open up opportunities for
socio-economic growth.
The implementation of regional commitments has been generally positive. However, several areas
need to be addressed by ASEAN Member States for a timely implementation to avoid a backlog of
unimplemented commitments with the onset of more new commitments and measures, including
those based on the AEC Blueprint, in the years to come. The completion of measures within the
targeted deadlines is critical to ensure ASEAN Member States comply with the AEC Blueprint.
Economic and financial governance in the ASEAN and ASEAN Plus Three (APT)
Over the last few years the institutionalisation of the APT process has started to take shape.
Government leaders, ministers and senior officials from the 10 members of the ASEAN and the
three Northeast Asian states — China, Japan, and South Korea — that together comprise the
participants in the process are consulting on an increasing range of issues (Majid, S., 2009;
Pradumna, B. R. 2002; Welfens, P.J.J., Ryan, C., Chirathivat, S. and Knipping, F. 2009). The
APT’s emergence raises questions about relations between it and other regional groupings such as
the Asia-Pacific Economic Cooperation (APEC) forum and ASEAN itself, as well as about the
overall prospects for its future development. Since the process began in 1997, the APT
cooperation has broadened and deepened. Cooperation is now being pursued in a number of areas.
Financial governance
Under the ASEAN Economic Community Blueprint, ASEAN envisages to achieve integrated
financial and capital markets by 2015. The objective is to create a more integrated and smoothly
functioning regional financial system, with more liberalised capital account regimes and
interlinked capital markets, which will facilitate greater trade and investment flows in the region.
As indicated in the Roadmap for Monetary and Financial Integration of ASEAN (RIA-Fin), as
evident from the Joint Media Statement of the 13th AEM+3 Consultations on 26 August 2010 in
Da Nang, Viet Nam, financial integration in ASEAN is to be facilitated through the following
initiatives:
• Financial Services Liberalisation: progressive liberalisation of financial services by 2015,
except for those sub-sectors and modes where pre-agreed flexibilities will be determined.
Five rounds of negotiations have been completed with binding commitments from each
ASEAN Member State to liberalise their financial services regime.
• Capital Account Liberalisation: removal of capital controls and restrictions to facilitate
freer flow of capital, including elimination of restrictions on current account transactions
and FDI and portfolio flows (inflows and outflows).
• Capital Market Development: build capacity and lay the long-term infrastructure for the
development of ASEAN capital markets, with the long-term goal of achieving cross-
border collaboration between the various capital markets in ASEAN. An
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 10
“Implementation Plan for an Integrated Capital Market” has been developed to enhance
market access, linkages and liquidity.
In terms of economic governance there are several initiatives to support financial stability in East
Asia. They comprise of the following measures.
Chiang Mai Initiative Multilateralisation
The Chiang Mai Initiative Multilateralisation (CMIM) is a USD 120 billion currency swap facility
involving the central banks and finance ministries of ASEAN, China, Japan and the Republic of
Korea (ASEAN+3), and the Monetary Authority of Hong Kong, China. It came into effect on 24
March 2010. The CMIM evolved from a network of bilateral currency swaps that first began in
2002. The decision to transform them into a multilateral currency swap contract was made in 2006
when the ASEAN+3 Finance Ministers recognised the need to facilitate prompt and simultaneous
currency swap transactions by establishing a common-decision making mechanism under a single
contract.
The initiative began as a series of bilateral swap arrangements after the ASEAN+3 countries met
on 6 May 2000 at the 33rd Annual Meeting of the Board of Governors of the Asian Development
Bank in Chiang Mai, Thailand. After the 1997 Asian Financial Crisis, member countries started
this initiative to manage regional short-term liquidity problems and facilitate the work of other
international financial arrangements and organisations such as the International Monetary Fund.
As of 16 October 2009, the network consisted of 16 bilateral arrangements among the ASEAN+3
countries worth approximately USD 90 billion. Additionally, the ASEAN Swap Arrangement had
a reserve pool of approximately USD 2 billion. In February 2009, ASEAN+3 agreed to expand the
fund to USD 120 billion up from the original level of USD 78 billion proposed in 2008. During
the April 2009 meeting of ASEAN finance ministers in Pattaya, Thailand, the individual
contributions to be made by each member state toward the reserve pool were announced. The
Chiang Mai Initiative Multilateralisation (CMIM) Agreement was signed on 28 December 2009
and later came into effect on 24 March 2010.
Each CMIM participant is entitled to swap its local currency with US dollars up to a multiple of its
contribution. Under the CMIM, China, Japan and the Republic of Korea contributed USD 96
billion, while ASEAN countries as a group contributed USD 24 billion (Indonesia, Malaysia,
Singapore and Thailand contributed USD 4.77 billion each, the Philippines USD 3.68 billion,
Brunei Darussalam USD 30 million, Cambodia USD 120 million, Lao PDR USD 30 million,
Myanmar USD 60 million, and Viet Nam USD 1 billion). As a reserve pooling arrangement,
CMIM members contribute to the facility in the form of a commitment letter. Each of the
contributing parties transfers its contribution on a pro rata basis according to its respective
commitments to the requesting party after the swap request has been approved. In effect, when
there is no request for funds, the parties continue to manage their reserves.
ASEAN Surveillance Process
This process has been in place since 1999 in order to strengthen regional economic surveillance
and monitoring and, since then, has been supporting regional policy dialogues, economic reviews,
and economic and financial integration. A high-level Macroeconomic and Finance Surveillance
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 11
Office (MFSO) is being set up at the ASEAN Secretariat to strengthen regional surveillance
capacity in the region.
Asian Bond Markets Initiative (ABMI)
This initiative was taken by the Finance Ministries and the Central Banks in ASEAN+3 (China,
Japan and Korea), which have, since December 2002, undertaken a comprehensive approach to
develop their bond markets in Asia (Chung, W.C. 2006, Japan Bank for International Cooperation,
2011, Stubbs, R. (2002).
Learning from the lessons of the Asian financial crisis in 1997, Asian countries have been trying
to reduce the risk of the financial crisis by alleviating the double-mismatch in financing (i.e.
currency and maturity) as well as deepening and developing the financial intermediary function to
facilitate the region's high savings to be used for investment in the region (Kanamori, T. 2011).
Therefore the development of bond markets is becoming an important policy agenda in Asia.
ABMI was originally proposed by Japan under the framework of ASEAN+3 in 2002 and since
then significant progress has been made. The basic thrust of ABMI is to develop efficient and
liquid bond markets in Asia in order to meet the needs for indigenous medium and long-term
financial resources and enable further economic development in the region. There are several
points worth noting about this initiative.
ASEAN+3 intends to progress further in the bond management area by facilitating access to the
market through a wider variety of issuers, and enhancing market infrastructure. This should:
address issues such as sovereign bond issuance by Asian governments to establish a benchmark;
ensure Asian government financial institutions issue bonds in Asia to meet their financing
requirements; create asset-backed securities markets, including collateralised debt obligations
(CDOs); allow bond issuance in the region by MDBs and government agencies; or link bond
issuance in the region for funding foreign direct investment in Asian countries.
ASEAN+3 Macroeconomic Research Office (AMRO)
The AMRO is a regional macroeconomic surveillance and crisis management unit, launched by
the ministries of finance, central banks and monetary authorities of China, Japan, South Korea and
the 10 countries of the ASEAN. AMRO (based in Singapore) performs a regional surveillance
function as part of the USD 120 billion Chiang Mai Initiative Multilateralisation (CMIM) currency
swap facility that was established by the ASEAN+3 Finance Ministers and Central Bank
Governors .
AMRO monitors macroeconomic trends, assesses financial vulnerability and provides assistance
on policy recommendations from the ASEAN+3 countries to safeguard regional financial stability.
It also acts to prevent financial crises through the supervision and execution of funds from
AMRO's reserve pool. AMRO provides contracting parties with financial loan support that is
below 20% of the lending pool with no extra conditions attached at a time without a crisis.
However, 80% of any swaps approved will be subjected to IMF conditions (Jialu, C. 2011).
Conclusions on economic governance in the ASEAN
In conclusion, we can observe a dynamic development in the area of economic governance in the
ASEAN and ASEAN+3 formations. This development is driven by both the internal needs of
economic cooperation and external shocks coming from the regional and global crisis.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 12
The potential of closer economic integration is high in the regions. However, there are also huge
economic disparities. That is why the level of integration in ASEAN does not touch on the area of
sharing national sovereignty. There is no legislation-making process, no sanction mechanism nor
any court involved in economic governance.
Instead, the ASEAN and ASEAN+3 use the soft economic model of cooperation. The future plans,
including the creation of the single market in ASEAN, should be measured according to their
results and implementation.
Finally, the economic governance of the financial area has been dynamic. The progress made in
debt swap and macroeconomic monitoring has been a good example of a governance reaction to
the economic challenges.
CONCLUSIONS
In this article, we argued, that economic governance is a dynamic concept. We demonstrated the
example of two macro-regional economic governance models, the European Union and ASEAN.
Both models are different in terms of the situation in which they operate, ambition, and the level
of integration.
In the case of the EU, economic governance is strong and deep. It implies sharing sovereignty.
There is a legislation-making process in place and the law made by this process prevails on
national legislation. However, there is the challenge of implementing economic policy
coordination. Public policy coordination has not been implemented properly and macroeconomic
surveillance was too weak in the past. That is why the EU recently decided to strengthen both
elements. Another new development in the EU is the closer coordination among the euro area
members. This part of economic governance also implies soft and incentive governance, based on
political agreement and an inter-governmental approach.
In the case of ASEAN, economic governance has existed for more than 40 years. It does not
engage in sharing sovereignty. Instead, it is based on a model of cooperation and political process.
Also in the ASEAN case, the crisis changed economic governance. Several initiatives in the
financial and bond area have been introduced. This is followed by macroeconomic research and
monitoring.
There is one difference between the EU and ASEAN economic model. While the EU has opted for
a deep model, the ASEAN has chosen a shallower one. However, ASEAN economic governance
tends to me more geographically open. Initiatives in the ASEAN+3 address the broad economic
region with the potential benefit of economic stability. Both macro-regional models of economic
governance represent an input for global economic governance. This is currently a lively topic at
fora such as the G20. The global economic crisis calls for a global response in terms of economic
governance. The EU and ESEAN can serve as food for thought.
_____________________________________
Notes 1 The existing Agreement is subject to update in 2011 2 The Treaty is to be ratified before 2013
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 13
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Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 15
POVERTY AND CALORIE DEPRIVATION ACROSS SOCIO-ECONOMIC
GROUPS IN RURAL INDIA: A DISAGGREGATED ANALYSIS
Abha Gupta1 & Deepak K. Mishra2
This paper examines the linkages between calorie deprivation and poverty in rural India at a
disaggregated level. It aims to explore the trends and pattern in levels of nutrient intake across
social and economic groups. A spatial analysis at the state and NSS-region level unravels the
spatial distribution of calorie deprivation in rural India. The gap between incidence of poverty
and calorie deprivation has also been investigated. The paper also estimates the factors
influencing calorie deprivation in rural India. The study point out that nutritional deprivation is
high among marginalized social groups and regions. It is the poor, scheduled castes, scheduled
tribes, illiterate people, agricultural labourers and Muslims who are more likely to be calorie
deprived.
INTRODUCTION
Notwithstanding India’s relatively robust economic performance since the economic reforms in
early 1990’s, significant deficits in human development parameters, most notably in health and
nutrition standards, remain a cause of concern. India has the largest number of under-nourished
children in the world. Not only that prevalence of child under-nutrition in India (43 percent) much
higher than the world average (25 percent), its performance is worse than some of the poorest
economies of the world (World Food Programme 2009).This prevalence is even higher among
some socio-economic groups and regions. One of the WHO’s millennium development goal is to
reduce the number of stunted, wasted and underweight children by 2015. Only few years are left to
achieve this goal but in India still 38.4 percent children under the age of 3 are stunted, 19.1 percent
are wasted and 46 percent children are underweight (National Family Health Survey 2005-06).
There has been a sluggish decline in this percentage over a decade but this decline is unimpressive
when compared across states and different socio economic groups. Besides poor performance in
terms of some anthropometric measures, average per capita per day calorie and protein intake is
also showing a declining trend in the post economic reforms period. Consumption and expenditure
on cereal food items, which are a good source of energy has recorded a decline whereas other food
items (vegetables, fruits, meat/egg/fish, oil, milk) have shown a slightly increasing share in the
diet of the population. However, decline in calories is not seen as deterioration of health by some
researchers rather it is viewed as a sign of improvement resulted by an increase in income,
development of rural infrastructure, mechanization, urbanization, improvement in health and
change in taste and preferences (Deaton and Dreze 2009, 2010; Verma et al. 2008; Rao 2000).
Another group of scholars, however, links this with the increasing deterioration in health and
1 Research Scholar, Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal
Nehru University, New Delhi-110067. E-mail: [email protected]
2 Associate Professor, Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal
Nehru University, New Delhi-110067. E-mail: [email protected].
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 16
nutrition standards of the population (Patnaik 2004, 2007, 2010; Nasurudeen et al. 2006; Ray
2005:10; Mehta and Venkatraman 2000; Shariff and Mallick 1999; Mehta 1982).
India’s growth ‘turn around’ has not resulted in remarkable improvements in health and nutrition
outcomes, and it has raised questions on the inclusiveness of the growth process (Radhakrishna et
al. 2004). The high level of undernourishment among children (46 percent, National Family
Health survey 2005), the relatively high infant mortality rate (47/000 live births, Sample
Registration System 2010) and signs of distress among marginalized sections of the society in a
country which has witnessed remarkable growth in recent decades has been a widely discussed
issue (Dubey and Thorat 2012; Reddy and Mishra 2010). However, India’s poverty measured in
terms of head count ratio, which is a measure based on minimum calorie norm, has seen consistent
decline during this period of growth. This evidence of declining poverty is not accepted by all and
it remains a contested question (Deaton and Dreze 2009, 2010; Patnaik 2007, 2010)1. The rising
gap between official head-count ratio and share of population having less than minimum calorie
intake that formed the basis of official poverty line has been a matter of wide public concern and
debate (Dev 2005; Sen 2005; Jones and Sen 2001). This debate surrounds over the method of
poverty measurement and the focus has been on whether the official poverty line is adequate to
account for rising expenditure on health and education, which, until recently, were being provided
by the state. Most of the studies on poverty deal with the level of rural and urban poverty at the all
India and state level. This paper attempts to unravel these issues at a more disaggregated level- at
the level of NSS (National Sample Survey) regions and also in terms of various socio-economic
groups.
The broad objectives of this paper are outlined as follows:
1) To examine changes in consumption of different food items in order to explain changes in
nutrition level.
2) To estimate changes in level of nutrients and deficiency of different nutrients from the
recommended dietary allowances (RDA) at disaggregated level and to show the gaps
between levels of poverty and levels of nutrition deficiency.
3) To estimate probability of being calorie deprived at disaggregated level using binary
logistic regression analysis.
From the policy perspective, the results of this paper have important implications for both the
methodology of poverty measurement and also for providing nutrition security to the vulnerable
sections of the population.
DATA AND METHODS
Data for this paper are obtained from National Sample Survey (NSS), 50th (1993-94), 61st (2004-
05) and 66th (2009-10) Consumer Expenditure Schedules. These rounds of the survey, by the NSS
are large scale sample surveys and provide information on consumer expenditure quinquennially
as part of its “rounds”. Consumer expenditure survey gives information on quantity and value of
different goods in a household with a reference period of last 30 days for each state/UT, all India
and separately for rural and urban areas. Among these goods, information on 142 items of food are
collected which can be converted into nutrition values2.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 17
In this paper, average per capita per day 2400 kcal has been used to show calorie deprivation
which is also used by Planning Commission to indirectly estimate head-count ratio for rural areas3.
For converting monthly household food consumption into per capita monthly consumption,
monthly household consumption is divided by household size. To get the per capita per day
consumption, per capita monthly consumption is divided by number 30. In order to show
probability of being calorie deprived across socio-economic and demographic groups, a logit
model has been fitted which is
� =1
1 + ���
P = 1/1+e-z..……………. (1)
Where P is the estimated probability, z is the predictor variable and e is the base of natural
logarithm with a value of 2.7183. After simplification, we get
Log z = P/1-P…………… (2)
Where (P/1-P) is called odds and log (P/1-P) is called log odds or the logit of P. Thus, equation
(2) becomes
logit P = Z…………….. (3)
The multivariate logistic function involves ‘n’ predictor variables which is represented by
P = (1/1+e-b0 + b1
x1+
b2
x2 +……… bn
xn) ………… (4)
Or, logit P = (bo + b1x1 + b2x2 +…… bnxn)…………. (5)
The coefficients b1 represents the additive effect of one unit change in the predictor variable x1 on
the log odds of the response variable. Whereas one unit increase in the x1, holding other predictor
variable constant, multiplies the odd by the factor eb1. For this reason the quantity eb
1 called the
odd ratio.
RESULTS AND DISCUSSION
Trends in Food Consumption in Rural India
Food is one of the basic needs for human survival. The variety of food that we consume
determines our nutrition behaviour in terms of calorie, protein, fat and other micronutrients. In
rural India, cereals have been the main constituents in people’s diet. Among cereals, rice recorded
an important share in total cereal consumption followed by wheat, coarse cereals, vegetables, milk
and fruits (Table 1).
During 1994-2005 the biggest decline was experienced by cereal consumption. This decline was
caused by fall particularly in coarse cereal consumption followed by rice and wheat consumption.
Pulse and milk consumption declined slightly. As far as change in consumption of ‘other food
items’ (vegetables, fruits, meat and edible oil) were concerned, highest increase was found in
vegetable consumption. Other food items recorded a slight increase in their consumption. A recent
round of NSS (66th Consumer Expenditure Survey, 2009-10) shows that cereals still hold the
highest place among all food items mainly because of higher rice consumption. However, cereal
consumption still continues to decline but the decline has been lesser during 2005-10 compared to
a decline during 1994-05. The consumption of wheat, rice and coarse cereals shows a marginal
decline. As far as consumption of ‘other food items’ (Vegetables, fruits, meat and edible oil) is
concerned, a marginal increase is seen in the consumption of these food items. From the analysis
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 18
above, it can be argued that last 15 years, often referred to as the ‘post economic reform period’,
rural India experienced a sharp decline in cereal consumption particularly coarse cereals, although
the precise linkages between economic reforms and calories deprivation needs to be examined
further. However, in recent five years (2005-2010) this decline has been minimal. The
consumption of other food items has been slightly increasing over the years but this increase is not
compensated by decline in cereals, as a result of which calorie and protein intakes are falling.
Table 1 Food Consumption Pattern and its Change in Rural India: 1994-2010
(Monthly Per Capita in kg*)
Food Items Year
Kg Change (1994-2005)
Kg Change (2005-2010) 1993-94 2004-05 2009-10
Cereal 13.40 12.11 11.35 -1.29 -0.76
Wheat 4.32 4.19 4.34 -0.13 0.15
Rice 6.79 6.38 6.13 -0.41 -0.25
Coarse cereal 1.97 1.27 0.87 -0.70 -0.40
Pulses 0.76 0.71 0.66 -0.05 -0.05
Milk Liquid (litres) 3.94 3.87 4.08 -0.07 0.21
Vegetable 4.75 5.25 4.58 0.50 -0.67
Fruits 0.22 0.30 0.21 0.08 -0.09
Fruits (nos.) 2.71 2.84 2.66 0.13 -0.18
Meat 0.12 0.14 0.14 0.01 0.00
Egg (nos.) 0.64 1.01 0.95 0.37 -0.06
Fish 0.18 0.20 0.21 0.02 0.01
Edible Oil (litres) 0.37 0.48 0.56 0.11 0.08
Source: Authors' calculation from NSS 50th, 61st and 66th Consumer Expenditure Schedule.
Note: unit in kg unless otherwise specified in brackets after the food-item.
Change in Nutrient share of various Food Items and level of Poverty in Rural India
It is believed that food consumption in India has changed much which has caused overall decline
in calories. There are various factors which affect consumption of food items such as production,
availability and prices, lower level of unemployment, rise in per capita expenditure, change in
taste, climate, decline in physical activity, improvement in health status, urbanization, increased
awareness among consumers about food nutrients, access to safe drinking water, health care and
environmental hygiene for effective conversion of food into energy (Kumar et al. 2007; WHO
2003; Bansil 2003; Viswanathan 2001; Martorell and Ho 1984). A group of scholars considers this
decline in calories as a positive and anticipated development and for them this decline is not a
matter of serious concern (Radhakrishna 2005; Radhakrishna and Reddy 2004; Rao 2000). On the
other hand, Patnaik (2007) has argued that decline in calories leads to deterioration in health and
poverty and blames Planning Commission for using faulty prices to adjust poverty in India as the
reason for artificially lowering the estimates of poverty. The average per capita per day (PCPD)
calorie consumption declined from 2148 kcal to 2044 kcal between 1993/94 to 2004/05 in rural
India. On an average PCPD intake of protein also recorded a fall from 59.9 gm to 55.1 gm during
the same period (Table 2).
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 19
Table 2 Change in share of nutrients from different food items between 1993/94-2004/05
in rural India
Food Groups Average Per capita per day intake of
Calorie (kcal) Average Per capita per day intake of
Protein (gm)
1993-94 2004-05 Calorie Change
1993-94 2004-05 Protein Change
Rice 809 755 -55 17.5 16.3 -1.2
Wheat 500 487 -13 17.7 17.2 -0.4
Coarse cereals 220 140 -80 6.6 4.3 -2.3
Cereals and cereal
substitutes 1530 1382 -147 41.8 37.9 -4.0
Root and Tubers 57 60 3 1.0 1.1 0.1 Sugar and honey 103 98 -5 0.0 0.0 0.0
Pulses, nuts and
oilseeds 106 92 -14 6.5 5.2 -1.3
Vegetables and
fruits 44 53 10 1.9 1.7 -0.2
Meat, eggs and fish 15 16 1 2.2 2.3 0.1
Milk and milk
products 132 131 -1 5.3 5.3 0.0
Oils and fats 115 151 36
Misc. food, food
products and
beverages
47 61 14 1.1 1.5 0.4
Total 2148 2044 -104 59.9 55.1 -4.9
Source: Authors' calculation from NSS 50th and 61st Consumer Expenditure schedule.
As it has already been pointed out a sharp decline in cereal consumption and a slow rise in
consumption of other food items is observed from the analysis of secondary data. Table 2 clearly
shows that calorie decline has been accompanied by a decline in protein intake. The main reason
for this decline is fall in cereal calories particularly coarse cereals and pulse intake. Consumption
of oil and fat contributed in total calories but these food items are lacking in protein and are rich in
fat. As a result, all-India average fat intake has increased (Nutrition Intake, NSS 61st round
report). Besides oil & fat, miscellaneous food and beverages also contributed much in calorie and
protein consumption. Before discussing calorie deprivation and poverty at disaggregated level, it
would be appropriate first to talk about the trends at rural all-India level, which helps in
understanding the general situation of the poverty.
The levels of calorie deprivation and poverty in rural India, as presented in Table 3, shows that
around 72 percent rural population was not getting required calories (per capita per day intake of
2400 Kcal) during 1993-94 and this percent has risen to 80, an increase of 8.4 percentage points in
2004-05, whereas level of poverty has declined if we consider Planning Commission’s estimate
accurate. In 1993-94, the level of poverty was 37 percent which has declined to 28.3 percent in
2004-05. The gap between calorie poverty level and planning commission’s poverty level has
increased from 35 percentage points in 1993-94 to 52 percentage points in 2004-05, a 17.1 points
increase. This mismatch between poverty and calorie intake continues to remain a contested issue
among researchers.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 20
Table 3 Change in Calorie Deprivation and Poverty Level in Rural India
between 1993/94 and 2004/05 Method of estimating poverty
1993-94 2004-05 Change between 1993/94 &
2004/05
Percent Below 2400
Kcal 71.60 80.0 8.40
Percent Below Official
Poverty Line 37 28.3 -8.7
Gap between Calorie
Poverty and Official
Poverty Line
34.6 51.7 17.1
Source: Same as Table 2.
Change in level of Nutrients at disaggregated level
The Planning Commission of India has officially taken recommended calories4 of 2400 Kcal
PCPD for rural and 2100 Kcal PCPD for urban areas in order to estimate poverty5. Besides, 60
gms PCPD protein intake has also been recommended by ICMR for nutrition measurement4. Table
4 presents average PCPD intake of calories and protein and their change over a decade (1993/94-
2004/05) with emphasis on deficit from RDA across various sections of the society. From a
demographic point of view it is found that never married persons consume lower level of calories
and protein than the married persons. In fact, this demographic group also shows highest decline
in nutrition parameters whereas widow/divorced/separated group enjoys relatively better access to
nutrition. Deficiency of calories is highest among never married persons showing 305 kcal
deficiency in 1993/94 which increased to 400 kcal during 2004/05. On the other hand are
widow/divorced/separated group whose calorie deficiency is much lower than other marital
groups. As far as deficiency of protein among marital groups is concerned, it has been higher
among never married persons than married. In rural India, different social classes show distinct
nutrition level from one another.
If we analyze family size, it is found that it is the bigger households who are suffering from lower
level of nutrition. In fact as size of a family increases, deficiency of calories and protein from
recommended tends to rise. Family consisting of 7-8 members showed a higher increase in
deficiency of calories than smaller families. In fact protein intake is quite low in these families.
Small families (1-4 members) tended to show much lower fall of calories and protein than other
family sizes. Similarly lower consumption of nutrients is found among less educated persons and
as education level rises, average calorie and protein intake also increases. Less educated persons
show a major decline in their nutrition level. Protein deficiency was much high in this group. On
the other hand are higher educated people who recorded an addition of 117 kcal in 1993/94 and
lower deficit of 17 kcal during 2004/05. This group added more protein in their diet in both
periods.
As far as religious groups are concerned, deficiency of nutrients is high among Muslims and
Christians. Least deficiency of calorie and protein was shown by ‘other’ religious people as only
223 kcal were lesser than recommendation.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 21
Table 4 Change in level of nutrients and deficiency from recommendation between 1993/94-2004/05
among socio-economic and demographic groups in rural India
Calorie Intake Deficit from
RDA, 2400 Kcal Protein Intake
Deficit from RDA, 60 gm
1993-94
2004-05
1993-94
2004-05
1993-94
2004-05
1993-94
2004-05
Marital Status
Never married 2095 2000 305 400 59 54 1 6 Married 2194 2081 206 319 61 56 +1 4 Widowed/divorced/
separated 2236 2129 164 271 61 56 +1 4
Household Size 1-4 2312 2199 88 201 63 57 +3 3 5-6 2088 2005 312 395 58 54 2 6
7-8 2070 1954 330 446 58 54 2 6 Above 8 2091 1955 309 445 60 55 0 5
Education Group Not Literate 2089 1974 311 427 59 54 1 6 Primary or below 2162 2031 238 369 60 55 0 5 Secondary 2332 2184 68 217 64 58 +4 2 Higher 2517 2383 +117 17 70 65 +10 +5
Religious Group Hindu 2159 2048 241 352 60 55 0 5 Muslim 2041 1979 359 421 57 53 3 7 Christian 1989 2075 411 325 52 53 8 7 Others 2307 2177 93 223 69 62 +9 +2
Social Group Scheduled Tribe 1993 1895 407 505 54 49 6 11 Scheduled Caste 2023 1948 377 452 57 53 3 7
Others 2212 2097 188 304 62 57 +2 3
MPCE Groups (Percentile) Lowest 5 1324 1369 1076 1031 38 36 22 24 10 1581 1571 819 829 44 42 16 18 20 1717 1676 683 724 48 45 12 15 30 1846 1796 554 604 51 49 9 11 40 1964 1881 436 519 54 51 6 9
50 2043 1958 357 442 56 52 4 8 60 2150 2038 250 362 60 55 0 5 70 2264 2154 136 246 63 58 +3 2 80 2405 2287 +5 113 67 61 +7 +1 90 2586 2378 +186 22 73 65 +13 +5 95 2798 2570 +398 +170 80 71 +20 +11 Highest 3253 3034 +853 +634 92 82 +32 +22
Poverty Line
Below poverty Line 1737 1639 663 762 48 44 12 16 Above Poverty Line 2388 2202 12 198 67 59 +7 1
Occupation Type Self empl in non agr 2076 2042 324 358 57 55 3 5 Agricultural Labour 1923 1849 477 551 52 48 8 12 Other Labour 1958 1892 442 508 54 51 6 9 Self empl in agri 2347 2181 53 220 67 60 +7 0
Others 2233 2169 167 231 62 58 +2 2
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 22
Source: Same as Table 2 Notes: Intakes are Average per capita per day in Kcal and metric Gram
respectively.
This religious group, on an average, added average 2 gm protein in their diet. In rural India,
Scheduled tribes (ST) and Scheduled castes (SC) are worst affected as both social groups show
lower intake of calorie as well as protein and also higher decline in nutrient intake compared to
other class people. The worst affected are the ST people who recorded highest level of calorie and
protein deficiency followed by SC in 2004-05. Calorie and protein deficiency had been lower
among 'other' social groups.
In terms of expenditure classes, it is found that it is the higher consumption expenditure groups
who are consuming sufficient calories and protein. The bottom classes suffer badly from lower
nutrient intake as well as its sharp decline. As consumption expenditure level rises, there is more
probability of consuming sufficient calories and protein. The top 20 percent showed higher intake
of calorie and protein and bottom 30 percent experienced as much as more than 500 kcal and 11
gm calorie and protein deficiency respectively during 2004/05. In terms of occupation groups in
rural areas, it is found that it is the agricultural labourers and ‘other’ labourers among which
calorie and protein intake is quite low and in fact these occupation groups also show a sharp
decline in nutrient intake over a decade. Agricultural labour and ‘other’ labourers are worst
affected occupation groups as both these groups had been unable to consume recommended intake
of calories and protein. The deficiency in the level of nutrients is much higher among agricultural
labour followed by ‘other’ labourers during 2004/05. Self employed in agriculture enjoyed better
level of nutrient intake as deficiency of calorie and protein was quite low in the same period.
Thus, from the above discussion it is found that there is significant relation between lower nutrient
consumption and socio-economic marginalization and deprivation. Never married persons, less
educated, lower Monthly per capita expenditure (MPCE) classes, SC, ST, Muslims, Agriculture
and ‘other’ labourers, big households are those sections of the society where nutrient intake is
quite low and at the same time decline in nutrient intake is considerably high among these groups.
Thus, the disaggregated picture of nutrition deficiency does not fit well with the argument that the
observed decline in calorie intake could be attributed to the diversity in the food basket of the
people as result of broader changes associated with economic development.
Level of Calorie Deprivation and Poverty
For reasons discussed above, methods of poverty estimation have been a widely discussed issue.
Even though the poverty line ensured the consumption of the normative calorie intake in 1973-74,
the rupee value of the poverty line at current prices is not sufficient for meeting the normative
requirements after other essential expenditures are taken into account (Sen 2005). As against this,
some scholars most notably Patnaik, have argued in favour of a ‘nutrition-invariant’ or ‘direct’
poverty estimate, by calculating the number of people not consuming the recommended daily
calorie intake. Some studies criticize direct method of poverty measurement through calorie and
deprivation (Deaton and Dreze, 2009; Verma et al. 2008; Dev 2005; Sen 2005; Rao, 2000). They
have highlighted the absurd results that it throws up when state level poverty estimates are carried
out. While the calorie-based approach has been termed as 'calorie fundamentalism' and has been
criticized for its narrow focus, the official poverty line based approach has been criticized for
being inconsistent with figures of calorie deprivation and malnutrition. One way of moving ahead
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 23
is to carry forward this comparison between percentage of population not having minimum
calories (on which the poverty line was based) and the official poverty estimates to a more
disaggregated level. This is what we have attempted here.
Table 5 Level of Calorie Deprivation and Poverty (Percentage) among Socio-Economic &
Demographic groups during 2004/05 Socio-Economic Demographic Groups Calorie deprivation Population Below Poverty line
Marital Status Never Married 82.30 31.3 Currently Married 78.10 25.4 Widow/Divorced/Separated 75.40 25.2
Household Size 1-4 70.60 17.0
5-6 82.30 29.1 7-8 85.30 37.4 Above 8 85.80 36.4
Social Group Scheduled Tribe 88.50 47.6 Scheduled Caste 85.10 36.8 Others 77.10 22.7
Religious Group
Hindu 79.70 28.9 Muslim 84.40 29.3 Christian 80.90 16.2 Others 69.70 15.2
Education Group Not Literate 83.50 36.5 Primary or below 81.10 27.1 Secondary 72.60 14.7 Graduate or above 59.70 5.0
MPCE Groups (Rs.) 0-235 99.70 100.0 235-270 99.00 100.0 270-320 98.40 100.0 320-365(poverty line Rs.356.30) 95.90 80.9 365-410 92.70 Nil 410-455 89.30 Nil 455-510 83.80 Nil
510-580 77.20 Nil 580-690 67.60 Nil 690-890 57.40 Nil 890-1155 43.00 Nil 1155 & more 32.80 Nil
Occupation Type Self employed in non agriculture 81.60 23.5 Agricultural Labour 88.90 46.4
Other Labour 87.40 30.4 Self employed in agriculture 73.10 21.5 Others 73.80 14.0
Source: Authors' calculation from NSS 61st Consumer Expenditure Schedule
Table 5 clearly shows that during 2004-05 among all groups where calorie deprivation level is
high, poverty level has also been higher. This analysis is based on gross effects and hence no
causalities are implied. It is found that never married persons report both relatively higher levels
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 24
of poverty and calorie deprivation compared to their group categories. In case of family size,
bigger the household higher is the level of calorie deprivation and poverty. Small households
covering 1-4 members experience lowest poverty and calorie deprivation level. Bigger households
(more than 7 members) perform worse on both counts. As far as social groups are concerned, it is
found that lower social groups such as ST and SC tend to have higher concentration of poverty
and calorie deprivation level, whereas the reverse is true for the 'other social group'. STs are worst
affected as poverty and calorie deprivation level is highest among them, followed by the SCs. If
we see deprivation and poverty level among the religious groups, we find that particularly
Muslims are in a worse condition as both calorie deprivation (84.4 percent) and poverty level (33
percent) are much higher among them in comparison to others. Education wise analysis shows that
it is the lower educated persons who are living in poverty and consuming lower calories than
standard norm. Higher is the education level lower is the levels of poverty and hunger. Illiterate
persons experience a highest level of poverty (36.5 percent) and calorie deprivation (83.5 percent)
level while educated people (with graduation and above) recorded lowest level of poverty (5
percent) and calorie deprivation (59.7 percent) level.
Similarly, lower the MPCE class, higher is the level of poverty and calorie deprivation. Thus,
bottom MPCE classes are unable to feed themselves even the standard calories and are living in
poverty. In terms of occupation groups, agricultural labourers perform worst on both counts
followed by ‘other’ labourer. Thus, while the official poverty measures and calorie deprivation
might show different levels of deprivation, there is a close correspondence among the two so far as
the pattern of deprivation across different groups are concerned.
INTERSTATE AND REGIONAL ANALYSIS
Inter-state variations in levels of deprivation has been one of the persistent themes in the poverty
debate in India (Deaton and Dreze 2010; Patnaik, 2007; Dev 2005). Specific to the divergence
between poverty estimates and calorie deprivation is the wide difference between the two
estimates in India's southern states. Many of the southern states have better human development,
demographic and social development indicators, and the records of state interventions in the areas
of food security, primary education and affirmative action in favour of the weaker sections are
generally considered to be better in most, if not all states of south India, particularly in comparison
with the densely populated north Indian states. In this backdrop, the fact that southern states
generally have a lower incidence of consumption poverty but a relatively higher degree of calorie-
deprivation has been an important issue in the discussion. Patnaik (2007) views poverty as being
underestimated in southern states, whereas Dev (2005) argues that poverty using calorie norm in
southern states give absurd results.
Deaton and Dreze (2009) criticizes calorie norm as poverty method as this norm places all
southern states at higher deprivation level despite a fact that these states perform better in some
anthropometric measures. The incompatibility of the poverty estimates and levels of calorie
deprivation is brought out sharply in Table 6.
The discussion here has been widened by incorporating two additional indicators of deprivation
and it is important to note that southern states particularly Karnataka, Tamil Nadu, Andhra
Pradesh rank high on more than two deprivation indicators which confirm their poor performance
on selected deprivation indicators. For example, Karnataka ranks 10th in poverty level, 21st in
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 25
calorie deprivation, 12th in children underweight and 13th in BMI of women. Similarly, Tamil
Nadu ranks 12th in poverty level, 19th in calorie deprivation, and 12th in BMI of women.
Performance of Andhra Pradesh in terms of deprivation indicators is 13th in calorie deprivation,
7th in children underweight and 11th in BMI. Kerala is the only state in southern region which
perform better in all deprivation indicators. Maharashtra however record better performance in
terms of anthropometric measures but poverty (14th) and calorie deprivation (18th) level is high in
this state. On the other hand, Punjab, Himachal Pradesh, Jammu and Kashmir and Haryana are
best performing states in all deprivation measures whereas worst performance is shown by
Jharkhand, Madhya Pradesh, West Bengal, Orissa, Chhattisgarh and Bihar (Fig. 1). At the state
level, a correlation among the different indicators of deprivation is low6.
Table 6 Performance of States on selected Deprivation Indicators and their ranking during 2004-05
States Below poverty Line*
Below 2400 Kcal*
Children (< 3) Under weight#
BMI below normal (Women)#
Jammu &
Kashmir 4.3 (1) 65.5 (1) 31.6 (2) 26.1 (6) Punjab 9 ( 2) 68.4 (4) 29.9 (1) 14.5 (3) Andhra Pradesh 10.5 (3) 83.8 (13) 40.4 (7) 37.5 (11) Himachal Pradesh 10.5 (4) 66.3 (2) 36.4 (5) 25.8 (5) Arunachal
Pradesh 10.9 (5) 70.9 (5) 42.1 (11) 14.3 (1) Haryana 13.2 (6) 67.6 (3) 41.8 (10) 32.5 (8) Kerala 13.2 (7) 75.4 (9) 31.9 (3) 14.3 (2) Rajasthan 18.3 (8) 74.5 (7) 45.9 (14) 36.5 (9) Gujarat 18.9 (9) 84.8 (15) 50 (17) 41.9 (15) Karnataka 20.7 (10) 89 (21) 45.1 (12) 38.2 (13) Assam 22.1 (11) 85.4 (16) 41.1 (9) 39.5 (14) Tamil Nadu 23 (12) 87.3 (19) 34.8 (4) 37.5 (12)
West Bengal 28.4 (13) 78.1 (10) 46.7 (15) 44.9 (18) Maharashtra 29.6 (14) 86.9 (18) 40.1 (6) 15.4 (4) Uttar Pradesh 33.3 (15) 73.3 (6) 49.4 (16) 37.2 (10) Madhya Pradesh 36.8 (16) 87.5 (20) 62.6 (20) 44.2 (17) Uttaranchal 40.6 (17) 74.5 (8) 40.8 (8) 30.8 (7) Chhattisgarh 40.8 (18) 84 (14) 54.6 (18) 45.7 (19) Bihar 42.6 (19) 78.8 (12) 59.3 (19) 45.9 (20) Jharkhand 46.2 (20) 85.7 (17) 63.1 (21) 47.8 (21)
Orissa 46.9 (21) 78.5 (11) 45.7 (13) 43.7 (16)
Source: * Same as Table 5, # Computed from National Family Health Survey, Fact Sheets, 2005-06
The level of nutrition (Table 7) in terms of calorie and protein intake across all major states show
that Karnataka, Tamil Nadu, Andhra Pradesh, Gujarat and Maharashtra are the states where calorie
and protein intake is quite low and in fact these states also show maximum decline in both the
nutrients between 1993-94 and 2004-5. The deficiency of calorie and protein from
recommendation is quite high in all southern states.
During 2004-05 deficiency of calorie was high in Andhra Pradesh (409 kcal), Gujarat (501 kcal),
Karnataka (538 kcal), Madhya Pradesh (472 Kcal), Maharashtra (476 kcal) and Tamil Nadu (536
kcal). In fact deficiency of protein was also larger in these states such as Andhra Pradesh (13 gm),
Assam (10 gm), Gujarat (9 gm), Karnataka (13 gm) Kerala (7 gm), Maharashtra (8 gm), Tamil
Nadu (16 gm) and West Bengal (10 gm).
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 26
This above analysis shows that calories and protein deprivations are consistently high in all
southern states except Kerala, whereas there are some states like Punjab, Himachal Pradesh,
Jammu and Kashmir, Haryana, Uttar Pradesh and Rajasthan where calorie intake recorded a slight
decline and consumption of protein is increasing during the period under consideration. In fact
states showing lower level of calorie deficiency (such as Haryana, Himachal Pradesh, Jammu and
Kashmir and Punjab) have performed better during 2004/05 and they have also recorded a larger
increase of calorie and protein in diet during the period under consideration.
Table 7 Change in level of nutrients and deficiency from recommendation between 1993/94-2004/05
across all major states in rural India
States Calorie Intake
Deficit from RDA, 2400 Kcal
Protein Intake Deficit from RDA,
60 gm
1993-94
2004-05
1993-94
2004-05
1993-94
2004-05
1993-94
2004-05
Andhra Pradesh 2044 1991 356 409 50.3 47.4 10 13 Arunachal Pradesh 2126 2316 274 84 61.3 59.4 +1 1 Assam 1983 2055 417 345 49.5 50.4 10 10 Bihar 2113 2021 287 379 60.1 54.9 0 5 Gujarat 1989 1899 411 501 55.3 50.5 5 9 Haryana 2486 2212 +86 188 78.2 67.8 +18 +8 Himachal Pradesh 2322 2314 78 86 70.4 67.0 +10 +7
Jammu & Kashmir 2504 2358 +104 42 75.3 62.4 +15 +2 Karnataka 2067 1862 333 538 54.7 47.0 5 13 Kerala 1956 2113 444 288 50.2 53.4 10 7 Madhya Pradesh 2158 1928 242 472 62.6 53.9 +3 6 Maharashtra 1933 1924 467 476 54.7 51.8 5 8 Orissa 2197 2008 203 392 52.6 46.2 7 14 Punjab 2414 2219 +14 181 74.6 64.5 +15 +4 Rajasthan 2461 2157 +61 243 78.9 67.1 +19 +7 Tamil Nadu 1872 1865 528 536 46.1 43.9 14 16
Uttar Pradesh 2303 2195 97 205 70.3 64.2 +10 +4 West Bengal 2210 2065 190 335 54.7 50.5 5 10 Total 2148 2044 252 356 59.9 55.1 0 5
Source: Same as Table 2. Notes: Same as Table 4
A state level analysis may hide the micro level variations in calorie deprivation. There is some
heterogeneity within the states so far as nutrition deficiency is concerned. Hence, an analysis has
also been performed at NSS region level (Fig. 2) which tries to identify the regions experiencing
calorie deprivation. Out of selected 72 NSS regions, 48 regions experience higher level of
nutrition deficiency (more than 80 percent). The worst performance is shown by regions of
Madhya Pradesh which include Vindhyan and south western parts. Dry areas of Gujarat also
exhibit higher nutrition deficiency. Coastal parts of Maharashtra and southern parts of Orissa show
more than 92 percent population to be calorie deprived. The performance of regions of southern
states also does not pose a better picture.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 27
Fig. 1
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 28
Fig. 2
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 29
Inland northern parts of Karnataka, coastal northern Tamil Nadu and south-western Andhra
Pradesh experiencing much higher level of nutrition deficiency which may be one of the reasons
of poor performance of southern states on deprivation indicators. It is clear from the figure (Fig.
2) that the regions in south India where level of calorie deprivation is relatively high form a
contiguous belt. The regions which pose a picture of relatively better nutrition sufficiency include
northern and southern parts of Punjab, Himachal Pradesh, western plains of West Bengal, Jhelum
Valley and mountainous parts of Jammu and Kashmir, central and western Uttar Pradesh.
Table 8 Logistic Regression Analysis for Showing Probability of Getting Required Calories
Variables Variable Categories Beta Sig.@ Exponential Beta
Social Group
Others (Ref)^ 0.000 1 Scheduled Tribe 0.421 0.000 1.524 Scheduled Caste 0.318 0.000 1.375
Religious group
Hindu (Ref)^ 0.000 1 Muslim 0.296 0.000 1.344 Christian -0.159 0.000 0.853 Others -0.4 0.000 0.671
Education Level
Primary or below (Ref)^ 0.000 1 Not Literate 0.108 0.000 1.114 Secondary -0.32 0.000 0.726 Graduate or above -0.631 0.000 0.532
Marital Status
Currently Married (Ref)^ 0.000 1 Never Married 0.104 0.000 1.109 Widow/Divorced/Separated -0.28 0.000 0.756
Household Size
1-4 (Ref)^ 0.000 1 5-6 0.682 0.000 1.978 7-8 0.924 0.000 2.52 Above 8 1.147 0.000 3.15
Occupation
Type
Self employed in non agriculture (Ref)^
0.000 1
Agricultural Labour 0.154 0.000 1.166 Other Labour 0.282 0.000 1.326 Self employed in agriculture
-0.524 0.000 0.592
Others -0.186 0.000 0.83
Poverty Line
Group
Above Poverty Line (Ref)^ Below poverty Line 2.649 0.000 14.146
Regions
Central (Ref)^ 0.000 1 North 0.147 0.000 1.159 East 0.107 0.000 1.113 North East 1.094 0.000 2.987 West 1.033 0.000 2.809 South 0.969 0.000 2.636
Constant 0.204 0.000 1.226
Source: Same as Table 5. Note:@Significance level, ≥ 0.01= 1 percent, 0.02-0.05= 5 percent, 0.06-0.1= 10 percent; ^Reference Category Dependent Variable: Calorie Intake, 1 shows below 2400 Kcal and 0 shows 2400 & above Kcal
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 30
PROBABILITY OF CONSUMING RECOMMENDED CALORIES: A
DISAGGREGATED ANALYSIS
In this section the factors affecting probability of consuming recommended calories have been
probed through a logistic regression (Table 8). Our results clearly show that probability of getting
recommended calories is quite low among all weaker socio-economic groups. For example, as the
family size increases, the likelihood of consuming recommended calories declines which exhibits
poor nutritional conditions of bigger households. The households covering more than 8 members
in the family exhibit higher probability (odd ratio 3.15) of being calorie deprived than the small
families having 1-4 members. Among social groups, ST are worst affected as the probability of
consuming recommended calories is very low compared to other social groups. SC people
however have lower likelihood (1.35 odd ratios) of being calorie deprived than ST (1.52 odd
ratios). Regarding religious group, Muslims suffer badly as they have higher probability of being
calorie deprived than the Hindus whereas Christians (0.853 odd ratio) and other religion people
(0.671 odd ratio) enjoy better calorie intake than the Hindus. Education level plays an important
role to determine calorie intake. It has been analysed that as the level of education increases, the
likelihood of consuming calories from the norm also rises. Highly educated people show more
chances of taking recommended calories than the other lower education group people.
Considering the probability of calorie intake among occupation groups, agricultural labourers and
other labourers have lesser probability of consuming recommended calories than the employed in
non-agriculture. Self employed in agriculture and other occupation groups have more chances of
becoming energy sufficient than those who are not self employed in agriculture. As far as poverty
level is concerned, people below the poverty line have a much higher likelihood of being calorie
deprived (14.146 odd ratios) than the Above Poverty Line category people.
The probability of consuming recommended calories across different geographical regions of rural
India show that compared to central region (covering states of Uttar Pradesh, Madhya Pradesh and
Chhattisgarh), all regions show lower likelihood to consume recommended calories. Among them
north eastern, western and southern region covering states of Gujarat, Maharashtra, Karnataka,
Tamil Nadu, Andhra Pradesh and Kerala exhibit more chances of being calorie deprived from
recommended calories. Northern and eastern states such as Punjab, Himachal Pradesh, Jammu and
Kashmir, Haryana, Rajasthan, Orissa and Bihar show lower probability of calorie deprived than
the other regions. However, these regions are prone to calorie deprivation when compared with
central region. A relatively lower likelihood of being calorie deprived is resulted by higher
consumption of cereals.
CONCLUSION
From this analysis it is found that over a decade (1994-2005) the consumption pattern of Indians
has changed significantly. Consumption of cereals, particularly coarse cereals, has declined
whereas consumption of other food items such as vegetables, fruits, milk and milk products, meat
increased slightly which have a direct bearing on nutrient intake. Due to decline in cereal
consumption and lower increase in consumption of other food items nutrient pattern in rural India
has also changed substantially. Share of cereals particularly coarse cereals to total calories has
declined whereas calories from oil and fat have increased. Since cereals are also a good source of
protein but its decline has also led to lowering down of protein. In rural India on an average per
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 31
capita per day calorie and protein intake is falling and consumption of oil and fat is increasing.
This, to some extent, is as per the expenditure of dietary transition models. However, given the
relative underperformance of India in the nutrition front, this decline in cereal consumption has
often been viewed as deterioration in the living standard of the poor. The disaggregated analysis of
calorie and nutrition deficiency in rural India carried out in this study clearly points out that
deprivation is higher among marginalized social and economic groups. It is the poor, SC and ST
groups, agricultural labourers who suffer most in terms of calorie deprivation.
There is much gap in official poverty and calorie deprivation level. We have estimated both
poverty and calorie deprivation across social groups. Those having bigger families, less education,
lower MPCE and those belonging to ST, SC, agricultural labour and other labour class, Muslims
are found to have higher levels of poverty as well as calorie deprivation. Thus, in terms of
distribution of deprivation across social and economic groups, there is a consistency between
poverty and calorie deprivation although the levels are quite different in many cases. The interstate
variations, however, does not show much consistency. The southern states particularly Karnataka,
Tamil Nadu, Andhra Pradesh perform poor on more than two deprivation indicators. Gujarat and
Maharashtra, considered as relatively developed states perform worse on both methods of poverty
measurement. On the other hand, Punjab, Himachal Pradesh, Jammu and Kashmir and Haryana
are best performing states in all deprivation measures. From a regional point of view, it is found
that most of the NSS regions having majority of population being calorie deprived than
recommendation fall in the southern, western and central parts of India. All the southern states
except Kerala and including Gujarat and Maharashtra presents maximum decline in calorie and
protein intake from the recommendation whereas Punjab, Himachal Pradesh, Jammu and Kashmir
and Haryana, Uttar Pradesh and Rajasthan show lower decline in calories and in fact increase in
protein intake. These states also show lower level of calorie deprivation and poverty.
The exercise undertaken to show probability of being calorie deprived concludes that never
married, big families, less educated, lower MPCE class, ST, SC, agricultural labour and other
labour class, Muslims, people living below poverty line and southern, north-eastern and western
states are some weaker sections and regions which are comparatively more prone to be poor and
undernourished than their respective reference categories. The debate so far has concentrated on
the observed divergence between poverty estimates and calorie deprivation. Our analysis,
however, points out that it is the relatively marginalized social and economic groups who face
greater calorie deprivation. Thus, there is an urgent need to focus on such high levels of
deprivation among the marginalized groups and regions.
_________________________________
Notes 1. Calorie norm has officially been taken to measure poverty level in India. Per capita per day intake of
2400 kcal for rural and 2100 kcal for urban areas are the norms to estimate poverty. Planning Commission makes adjustment in Consumer Price Index for Agricultural Labourers (CPIAL) and Consumer Price Index for Industrial Workers (CPIIW) to the base year poverty line (1973-74) for estimating rural and urban poverty respectively. Planning Commission’s estimation of poverty using indirect method shows lower level of poverty whereas directly using calorie norm to measure poverty gives a much higher level of deprivation.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 32
2. Food items have been converted into nutritive values using the standard units given in report no. 513(61/1.0/6) Nutritional Intake In India (2004-2005), NSS 61st round National Sample Survey Organisation, Ministry Of Statistics & Programme Implementation Government of India.
3. For further details on measurement of official poverty line in India and changes in it, see Utsa Patnaik, 2007.
4. Standard Calories are given in the Report of the Export Group on Estimation of Proportion and Number of Poor. Perspective Planning Division. Planning Commission, 1993 - 2400 kcal per capita for rural area and 2100 kcal for urban area and standard protein intake is recommended in report on ‘Nutritional Status of Rural Population’ by National Institute of Nutrition (1996) Indian Council of Medical Research, Nutritional Status of rural population, Report of the NNMB surveys, National Nutritional Monitoring Bureau, Hyderabad.
5. Official poverty has been calculated using the report of ‘Poverty Estimates For 2004-05’ Government of India Press Information Bureau [Online at] planningcommission.nic.in/news/prmar07.pdf , Accessed on 12/03/2010 at Jawaharlal Nehru University.
6. The correlation between Below poverty line (BPL) and Below 2400 kcal is 0.472 (significant at 0.05 level) which is low as compared to correlation between BPL and Children underweight below 3 (0.733, significant at 0.01 level) and between BPL and Body Mass Index of Women (0.622, significant at 0.01 level).
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JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 34
The Koshal Development Forum (KDF) is an informal research forum initiated
by the students of Jawaharlal Nehru University belonging to the Koshal region
of Orissa (Anugul, Bargarh, Bolangir, Boudh, Debagarh, Jharsuguda,
Kalahandi, Kandhamal, Koraput, Malkangiri, Nabarangapur, Nuapada,
Rayagada, Sambalpur, Sonepur, and Sundargarh district) with the support from
the students from other parts of India in 2003.Later the KDF got expanded with
general people supporting its cause across the country.
The basic objective of forming KDF is to create awareness about the problem of
underdevelopment and deprivation in the Koshal Region. The region, which is
infamous for illiteracy, poverty, starvation, and child-selling, has been the most
neglected part of modern India and since Independence it has seen the rarest
forms of deprivation. In this context, the struggle of Koshali people for the
creation of an independent state is significant for decentralization process in
India, as well as for ensuring that development percolates to the neglected
region. The Forum, being apolitical, non-violent, and democratic, is meant to
promote the general understanding on the problems of development of the
Koshal region through discussions, public meetings, seminars and research
publications.
For details visit:
https://sites.google.com/site/koshaldevelopmentforum/home
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 35
TECHNICAL EFFICIENCY AND ITS DETERMINANTS IN BACKWARD
AGRICULTURE: THE CASE OF PADDY FARMERS OF HAILAKANDI
DISTRICT OF ASSAM
Ritwik Mazumder1and Manik Gupta2
The study measures farm level technical efficiency among paddy farmers of Hailakandi district of
Assam on the basis of farm level primary data of 265 cultivators for the peak cropping season of
2009-10. A translog stochastic production frontier is estimated and selected non-input factors are
modelled to explain variations in technical inefficiency across cultivators. Age, education levels
and proportion of land leased in and HYV cultivation have positive influences on farm level
technical efficiency. However indebtedness and percentage of self consumption of farm produce
have negative influences. Government support through agricultural extension services is found
insignificant. Mean technical efficiency is found to be around 69 percent. Finally the study
observes decreasing returns to scale and a negative association between farm size and technical
efficiency.
INTRODUCTION
Northeastern India has a predominantly sub-tropical climate with hot and humid summers, severe
monsoons and mild winters. Along with the west coast of India, this region has some of the Indian
sub-continent's last remaining rain forests. Apart from tea industry, which is plantation based,
there is not much of a significant industrial contribution to national industrial output of all the
seven Northeastern states taken together. The regional economy is thus, primarily based on
agriculture and plantations. A noteworthy feature of the cropping patterns of the region is the pre-
dominance of paddy, which accounts for more than 90 percent of total cropped area (Source:
Comprehensive District Agricultural Plan (CDAP) 2009-10 to 2011-12, District Agriculture
Office, Hailakandi, Assam). Distinctions are drawn between three different paddy crops, namely,
autumn paddy, winter paddy and summer paddy depending on the harvesting season. The present
study focuses on farm efficiency in paddy cultivation in the district of Hailakandi, a remote district
in southern Assam. Southern Assam comprises of three districts – Cachar, Karimganj and
Hailakandi. Being a large flood plain of the river Barak (original Bengali name being Barabakra),
which flows mainly through Cachar and parts of Hailakandi, this region is also known as Barak
Valley. Soil fertility in the region is satisfactory, due to the presence of an interlinked network of
meandering rivers and small canals. Since the study is entirely confined to Hailakandi district of
south Assam, it is pertinent to provide a brief but detailed sketch of the study region – especially
its geographical location and agro-climatic features.
1 Corresponding author: Assistant Professor, Department of Economics, Assam University, Silchar-11,
Assam. Email: [email protected]
2 Assistant Professor, S.K. Roy College, Katlicherra, Hailakandi, Assam.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 36
ABOUT THE STUDY REGION
The district head quarters of Hailakandi, i.e., Hailakandi town is located at 24.68°N 92.57°E. The
area of Hailakandi town is 4.55 square kilometres according to the 2001 census. It has an average
elevation of 21 meters (68 feet). The district has an area of 1326.10 square kilometres. Out of this,
more than 50 percent is under reserve forest cover. The district has got inter-state borders with
Mizoram on its south having a length of 76 km besides having inter-district borders on other sides
with the two other districts of Barak Valley, namely Karimganj and Cachar. This is evident from
both the maps provided.
Source: www.mapsofIndia.com
The district consists of both plain and hilly areas. As per 2001 census, it is estimated to have a
population of 5,47,003. There are two reserved forests in Hailakandi district viz. the Inner line
reserved forest and the Katakhal reserved forest. The total rural area in the district is 1316.47
square kilometres and urban area covers 10.53 square kilometres. During the British Raj
Hailakandi was a civil subdivision till the 1st of June 1869. It was upgraded to a district as late as
in 1989. Hailakandi comprises of three notified towns viz. Hailakandi (district headquarters), Lala
and Algapur and one planned industrial township at Panchgram. A Municipal Board governs
Hailakandi town and a Town Committee governs Lala. As shown in the sampling chart of table 1,
the district has five development blocks viz. Algapur, Hailakandi, Lala, Katlicherra and South
Hailakandi. The district has 62 Gram Panchyats, 331 revenue villages, one municipality board and
two town committees. The small industrial township of ‘Hindustan Paper Corporation’ (a
government of India enterprise), located at Panchgram is under Algapur block.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 37
The connectivity of the district is via two vital national highways - NH-53 and NH-154 and
railway (Meter Gauge). Only 22 percent of the roads are surfaced and motorable, and 78 percent
of roads in the district are un-surfaced which is indicative of pitiable roadway infrastructure. The
nearest airport is Silchar Airport located at Kumbhigram in Cachar district about 83 km away from
the district headquarter town of Hailakandi. The population density of the district is 409 persons
per square kilometre and average literacy rate is 59.64 percent as per the 2001 census. The district
is primarily inhabited by Bengali, Manipuri (Bishnupriya and Meitei) and Rajbongshi community.
The climate of the district is characterized by hot and humid conditions where summer begins in
March-April and continues till June-July. The monsoon months are June to early October.
Average annual rainfall of the district is 2441.94 millimetres with 123 rainy days on an average
computed on the basis of last ten years records. Peak rainfall generally concentrates during the
month of July to September although floods are also experienced during March to April due to
occasional heavy rainfall in Mizoram. The overflowing waters of the river Dhaleshwari, which
flows through the district during times of excessive rainfall, is the precise cause of floods. The
annual mean maximum temperature ranges between 32.8° - 34.4° Celsius and mean annual
minimum temperature ranges between 10.0° - 12.2° Celsius. Average maximum temperature is
recorded at 33.9° Celsius and minimum at 11.5° Celsius on the basis of last ten years data. Being
a catchment area, monsoon is characterized by flooding of low land areas (which are actually
fertile and cultivable) during the peak cropping season (shali) resulting in loss of crops. Like the
rest of Eastern India, winter starts at the end of November and continues till late February. Winter
months generally remain dry with scanty rainfall.
As regards area distribution for agriculture and allied activities, 42.22 percent area of the district is
cultivable, 4.29 percent is cultivable waste, 3.09 percent is fallow, about 48 percent under forest
cover, 0.70 percent is pasture, 6.47 percent is land under non-agricultural use, 1.42 percent is
under different types of plantation that of course includes tea plantation and finally, 1.20 percent is
barren and waste land. As per the Comprehensive District Agricultural Plan (CDAP) 2009-10 to
2011-12, out of total cultivable area, about 82.49 percent is under cultivation. Lack of complete
utilization of cultivable area is due to inadequate irrigation facilities in the district.
Agriculture in the district is simply at the mercy of rainfall as only 2.59 percent of the cultivable
area is irrigated – an indication of extreme backwardness of irrigation infrastructure. The main
source of irrigation in the district is low lift pumps, installed by the farmers themselves.
Incidentally, for the present sample, only 18.4 percent of cultivators have access to such irrigation.
Rice is the principal food crop in the district and approximately 95 percent of the cultivators are
either marginal or small practicing ‘subsistence farming’ basically meant for self consumption.
Paddy occupies 82 percent of the gross cropped area of the district. Other important crops are
kharif and rabi vegetables, colocasia, potato, rajmah, sweet potato, pea, rapeseed and mustard. The
available irrigation facilities are mainly confined to summer paddy and rabi vegetables and that
too in few selected pockets. In the absence of planned canal irrigation, there is a very little scope
for intensive farming. About 33.2 percent of the district area is presently under cultivation
(although 42.22 percent is cultivable) out of which paddy, the principal crop covers an area of
36500 hectares. The agro-climatic conditions however make it better suited for plantation and
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 38
horticultural crops. About 3260 hectares are under fruit crops like banana, pineapple, lemon, etc.
About 5069 hectares are under vegetables, medicinal and aromatic plants. Plantation crops like
areca nut, coconut and cashew nuts cover about 3415 hectares.
The present study measures farm level technical efficiency among paddy cultivators in Hailakandi
district of southern Assam on the basis of farm level primary data by estimating a transcendental
logarithmic stochastic production frontier with inefficiency effects. The paddy output of the peak
agricultural season, i.e., winter paddy or Shali is chosen for this purpose. A host of non-input and
socioeconomic factors which might affect farm level technical efficiency are assumed to explain
inter-farm variations in the level of technical efficiency.
This paper is written in the following four sections. A brief introduction to the study is written in
section 1, followed by methodology and data sources in section 2. Section 3 deals with the
presentation of empirical results and its descriptive analysis and finally a short summary of the
study and conclusions are presented in section 4.
METHODOLOGY AND DATA
Econometric Approach
The present study uses the technical inefficiency effects model [originally due to Kumbhakar,
Ghosh and McGuckin (1991)], and estimates the stochastic frontier and the inefficiency effects
model parameters simultaneously, given appropriate distributional assumptions on the inefficiency
random variable (Battese and Coelli, 1995). The simultaneous estimation of the stochastic
production frontiers and models of technical inefficiency using maximum likelihood techniques
have also been further developed by Reifschneider and Stevenson (1991), Huang and Liu (1994),
and Battese and Coelli (1995). This approach has been applied empirically by Coelli and Battese
(1996) and Battese and Broca (1997).
Several studies have used a two step approach to determine the sources of inefficiency or factors
that affect farm level technical inefficiency. In the first step a stochastic frontier model is
estimated by maximum likelihood method and farm specific technical inefficiencies are calculated
under the assumption that the technical inefficiency effects are identically distributed. In this step
it is ignored that technical inefficiency is a function of farm specific and exogenous variables.
Once farm level technical inefficiencies are estimated it is regressed in the second stage on a set of
farm specific factors (or characters) and/or exogenous factors beyond the farm’s direct control but
which may explain inter-farm variation in technical inefficiency. These factors typically are not
inputs but may affect the way inputs are organised in production. In this step either logit or probit
models are used. The application of the logit or probit in the second step contradict the
assumption of identically distributed inefficiency effects in the stochastic frontier model since
predicted efficiencies are assumed to have a functional relationship with farm specific variables
and exogenous variables. In the second stage the estimated technical inefficiency effects are
modelled as a function of some farm specific and exogenous factors. This implies that
inefficiency effects are not identically distributed unless the coefficients of the farm specific
factors are simultaneously equal to zero (Kumbhakar and Lovell, 2000).
The problems of this two stage method can be addressed using a one stage formulation of
Kumbhakar, Ghosh and McGuckin (1991). They specified the technical inefficiency effects and
estimated the stochastic frontier and inefficiency effects simultaneously by using maximum
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 39
likelihood method, given appropriate distributional assumptions on the inefficiency component.
Kumbhakar et al (1991) model was developed for cross-sectional data. Reifschneider and
Stevenson (1991) and later, Huang and Liu (1994) also developed the one stage formulation based
on cross-sectional data. Battese and Coelli (1995) developed a model similar to Kumbhakar et al
(1991) but for panel data. The present study follows a Kumbhakar et al (1991) approach to
simultaneously measure farm level technical efficiency and to test the impact of a few (selected)
farm specific and non-input factors on the level of technical inefficiency among paddy cultivators
of Hailakandi district of Assam on the basis of primary data (covering 265 cultivators) collected in
November – December, 2009. Exact description of all relevant variables used in the study is
imperative. The list of variables with their units of measurement for the translog production
frontier model is listed below.
Output (Y) of the peak cropping season of 2009 in quintals, cultivated area (CA) in bigha, cost of
human labour (HL) including women workers in rupees, cost of traditional equipments (E in
rupees), expenditure in rupees on irrigation (I) facilities (including personalized micro-irrigation
system – i.e. pump sets etc.), value of fertilizers (F) in rupees, and value of pesticides (P) in
rupees. Bigha (and not hectare) has been kept as the unit of cultivated area as because there is a
predominance of small and medium sized plots across the sample under consideration.
The list of variables with their units of measurement for the inefficiency effects model are, age of
the cultivator as a proxy for experience, education as measured by total years of schooling in the
cultivator’s household, existing loans in rupees, area under HYV, self consumption as a
percentage of output, government support dummy (recipient of any technical help and support
from the State Dept. of Agriculture = 1, and 0 otherwise) and HYV Dummy (1 for cultivators of
HYV seeds, and 0 for others). The econometric model is presented in the Appendix (see appendix
2).
Data
Data for the present study is completely primary in nature based on paddy production for the shali
cropping season of 2009. The timing of sowing for winter paddy or shali paddy is around July-
August and the timing of harvest is around October-November. Shali is the largest crop both in
terms of area sown and total output. Moreover it is entirely rainfall dependent or monsoon
dependent as because privately arranged artificial irrigation facilities (like electric or diesel driven
pump sets to draw ground water) are rare in South Assam and especially so in Hailakandi (about
19 percent in the present sample).
The sampling strategy is illustrated in table 1 given in the appendix. All the five blocks of
Hailakandi district were selected for the study. In stage one, 30 percent (or roughly one-third) of
the Gram Panchayats under each block were randomly selected. In other words 4 Gram
Panchayats (G.P.) were randomly chosen from a block consisting of 12 G.P.s. Rounding-off was
obviously done for fractional results. In stage 2, one best performing village out of the three best
performing village in terms of paddy output of the last cropping season (as per secondary
information) was randomly chosen from each selected G.P. Thus only one village was randomly
selected from a G.P. This randomness is confined to three best agricultural villages under a G.P.
A different strategy was adopted for the purpose of cultivator selection. A’priori information on
size-class shows that sub-marginal, marginal and small farmers dominate the region. In fact such
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 40
is the domination of small farms that initially we found it too difficult to locate medium and large
farmers in each block during the course our survey. As per statistical records, sub-marginal,
marginal and small farmers add up to 79.12 percent of the population of cultivators in the district.
From each selected village the complete list of farmers according to size class was first obtained
from the Agricultural Development Circle Officer’s (ADO Circle) records of each selected village.
Finally, 1 percent of sub-marginal farmers, 1 percent of marginal farmers, 2 percent of small
farmers, 10 percent of medium size class farmers and 50 percent of large farmers were chosen
randomly from each selected village. The village wise samples obtained are shown in the
sampling chart in table 1 in the appendix. The total sample size for the present study is 265.
For the present study this sampling strategy was found to be operationally convenient. The first
point is that choosing 25 percent of G.P.s give us a fewer number of samples from a block which
make the block level sub-samples too small. We checked that, other things unchanged, if 25
percent of G.P.s are selected from each block the sample size shrinks drastically to about 160 from
265. On the other hand if 40 percent G.P.s are selected from each block, the sample size rises
substantially to about 420.
Table 1 Block and Village wise sampling chart
Block G.P. Village Sample Size
Algapur (a) Uttar Kanchanpur Dolidar grant 16
(b) Chandipur Chandipur -2 19
(c) Mohanpur Mohanpur-2 25
(d) Nitai Nagar Nitai Nagar 13
Hailakandi (a) Bahadurpur Bahadurpur -i 11
(b) Narainpur - Tupkhana Narainpur -iii 9
(c) Sudarsanpur Sudarsanpur -i 13
South Hailakandi (a) Jamira G.P. Jamira -i 6
(b) Karicherra Dariarghat Dariarghat - 19
(c) Paloicherra Paloicherra -i 7
(d) Baruncherra -Kukicherra Kukicherra grant 17
Katlicherra (a) Dinonathpur Dinonathpur -i 14
(b) Rangabak Rangabak -iii 12
(c) Sahabad Sahabad-i 11
Lala (a) Sudarsanpur Kalacherra Sudarsanpur -ii 20
(b) Purbakittarbond- Rajyeswerpur -ii 11
(c) Nimaichandpur Nimaichandpur -ii 14
(d) Chandrapur Chandrapur-i 12
(e) Dholcherra -Bilaipur Lalpani F.V. 16
Total sample size 265
The second point is that arguably, a larger number of villages could have been chosen from each
Gram Panchayat to get a more representative sample from a block. We do have a point to add in
this regard. From the ADO Circle Panchayat level secondary records we found that best
agricultural practices under a Gram Panchayat are more or less concentrated within three, or at
best four villages. Selection of one-third or roughly 30 percent of these three best practice villages
implies a random selection of a single village from a Gram Panchayat. We have checked that
inclusion of an additional village at random from a Gram Panchayat takes our samples size to
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 41
about 480, which is a bit beyond the scope of coverage for the present study. Admittedly
however, one additional village from a Gram Panchayat would have made the block level sample
more scattered or dispersed besides raising the statistical robustness of the estimates to a certain
extent.
ANALYSIS OF EMPIRICAL RESULTS
The size-class wise distribution of operational holdings of the sample of 265 cultivators
considered in the present study is presented in Table 2. Approximately 17 percent of the total
sample of cultivators belong to the sub-marginal size class (0 - 5 bigha). On the other hand 27
percent of sample cultivators belong to the marginal size class, which is basically below 1 hectare
size class. Around 17 percent of sample cultivators belong to the ‘small’ size class (that is 1- 2
hectare size class). Thus sub-marginal, marginal and small farmers comprise 51 percent of sample
cultivators. Interestingly these 51 percent sample cultivators enjoy around 22 percent of total
operational holding in the sample. Around 16 percent of sample cultivators belong to the semi-
medium size class of farmers. However, their operational holding is around 33 percent of total
operational holding in the sample. The medium size class of cultivators (4-10 hectare size-class)
constitutes approximately 19 percent of the sample of cultivators. However, this size class enjoys
around 40 percent of total operational holding in the sample. Finally only around 3.4 percent of
total sample cultivators belonging to the large size class (above 10 hectare) they enjoy around 4
percent of total operational holding in the sample.
Table 2 Frequency Distribution of Operational Holdings in the Sample
Size Class No. of
Cultivators Share in numbers
Share in operational
holding Sub-marginal (0 - 5 bigha) 45 16.98 5.09 Marginal (< 1hectare) 72 27.16 5.11 Small (1-2 hectare) 45 16.98 12.33 Semi medium (2- 4 hectare) 43 16.22 33.41 Medium (4 - 10 hectare) 51 19.24 39.95 Large (>10 hectare) 9 3.42 4.11 Total 265 100 100
Source: Author’s estimates based on sample observations.
In sum, the distribution in the Table 2 reveals that the semi-medium and medium size classes of
cultivators dominate the sample in terms of land holding. However, semi-medium and medium
sized cultivators comprise only 35 percent of sample cultivators but these two size classes together
hold roughly 73 percent of operational holdings in the sample. Standard measures of inequality
are not computed.
Table 3 presents the data summary or descriptive statistics of all variables used to estimate the
parameters of the translog stochastic production frontier. This is followed by Table 4, which
presents the summary statistics of the inefficiency effects variables. Table 5 presents the
maximum likelihood estimates of parameters of the translog production function. Evidently the
parameters of the translog production function do not have any direct interpretation. The partial
elasticities of output with respect to inputs are rather more important. Even then estimated values
of certain coefficients and t -ratios are worth noting.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 42
Table 3 Summary Statistics of Variables of the Translog Production Function
Variables Sample mean Min Max S.D. C.V. Y 163.48 10 800 147.21 0.900
CA 17.71 2 65 11.18 0.63
HL 73.91 30 241 35.098 0.47
E 5645.36 900 127598 11757.62 2.082
I 1744.68 0 4500 1431.08 0.820
F 2364.68 0 24920 3351.19 1.4171 P
1002.16
0
18550
1965.65
1.9614
Source: Author’s estimates based on sample observations.
Notes: Units of measurement of each variable are mentioned in Data Section
The constant term is found to be insignificant. So are the coefficients of irrigation, fertilizers and
pesticides. Cultivated area is statistically the most significant factor that determines output. This is
followed by human labour and traditional firm equipments. Moreover, it is revealed from table 5,
that all the interaction terms are not only statistically insignificant but have extremely small values
and hence play a very negligible role in determining the elasticities of output with respect to
factors.
Turning to the variance parameters of the stochastic frontier model it is found that both and are
statistically significant (following the Aigner et al (1977) parameterization).
Table 4 Summary Statistics of Inefficiency Effects Variables
Variables Sample mean Minimum Maximum S.D C.V
Outstanding Loans 2835.9 2100 95000 13002.9 4.5
Land leased 1.9 3 24 9.6 4.8
Self consumption 50.5 0 25 40.1 0.7
Education 4.2 1 11 2.8 0.6
Age 43.7 37 69 11.1 0.2 Source: Author’s estimates based on sample observations.
This is an indication of the presence of inefficiency. This implies that OLS would be an
inappropriate method to estimate parameters of the translog production function. Testing the null
hypothesis no technical inefficiency is important. The null hypothesis of no technical inefficiency
can be tested by applying the usual Likelihood Ratio Test. The likelihood ratio test is based on the
likelihood ratio statistic (LR) defined as, LR = - 2 ln[L(H0) / L(HA)], where L(H0) and L(HA) are
the values of the likelihood function (optimum) under the null and alternative hypotheses
respectively. But since the hypothesized value of lies on the boundary of the parameter space it is
difficult to interpret the test statistic. It can be shown that the LR statistic follows a mixed χ2
distribution that asymptotically approaches χ2 distribution with degrees of freedom equal to the
number of restrictions imposed in the model (Coelli, 1995).
Similar is the test of the hypothesis that inefficiency effects are absent in the model. All
estimations were done using the software package FRONTIER 4.1 (Coelli, 1995).
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 43
Table 5 Maximum Likelihood Estimates of the translog Stochastic Production Frontier
Coefficients of Estimated Value t- Value Constant 1.096 1.008 CA 0.060 2.890 HL 0.001 2.45 E 0.377 1.98 I 0.365 0.078 F 0.216 0.189 P 0.0001 0.911 CA×CA -0.0003 -0.921 HL×HL -0.005 -0.455 E×E 0.016 0.011 I×I 0.0007 -1.342 F×F 0.1178 0.546 P×P 0.0876 0.722 CA×HL 0.0002 0.215 CA×E 0.00001 0.004
CA×I 0.01×10-5
0.09×10-4
CA×F 0.03×10-5
0.02×10-3
CA×P 0.07×10-6
0.051
F×P -0.11×10-5
-0.008
HL×E -0.012×10-5
-0.045
HL×I 0.086×10-4
0.115
HL×F -0.058×10-3
-0.015
HL×P 0.077×10-4
0.04×10-3
E×I 0.001×10-5
0.001
E×F 0.025×10-6
0.113
E×P 0.017×10-4
0.17×10-3
I×F -0.015×10-7
-0.087
I×P -0.013×10-8
-0.903×10-4
Variance parameters
2 2 2
v uσ σ σ= + 0.035 8.005*
/u vλ σ σ= (Aigner et al 1977) 0.359 2.500*
2
vσ 0.031
2
uσ 0.004
Log Likelihood Value -1.110 3rd Central Moment of OLS Residuals -903.011
Source: Authors’ estimates based on primary data using econometric package FRONTIER 4.1.
The Battese and Coelli (1995) inefficiency effects model was adopted. The third central moment
of OLS residuals is found to be -903.011which is a fundamental requirement. Obtaining the
maximum likelihood estimates of all parameters of the traditional (OLS) model and inserting it
back in the log likelihood function gives the optimum value of the log likelihood function under
the null hypothesis of no technical inefficiency. For the null hypothesis no technical inefficiency
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 44
the LR statistic is computed at 50.48 which greater than tabulated χ2 with 8 degrees of freedom at
1 per cent level. Thus the hypothesis of no technical inefficiency is rejected at 1 per cent level. In
other words traditional average production function (OLS regression) model would be an
improper specification of the production function.
Table 6 Estimated Coefficients of the Inefficiency Effects
Variables of the Trans-log Production Frontier
Coefficients of Estimated Value t- Ratios
Constant 0.006 0.991 Age -0.256 -1.819* Outstanding Loans 8.956 3.688* Land Leased-in -0.222 -1.556 Education -0.007 -1.008 Self Consumption 3.476 1.333 Govt. Support HYV Dummy
0.90 -0.071
0.001 -1.631*
Source: Authors’ estimates based on primary data using FRONTIER 4.1.
Estimated coefficients of the inefficiency effects variables of the translog production frontier are
presented in Table 6. The constant term is found to be insignificant along with the coefficients of
education and government support dummy. The coefficient of ‘age’ of the cultivator is negative
and statistically significant at 4 percent level. This implies that higher the age of the cultivator, the
lesser the technical inefficiency, or alternatively, higher the technical efficiency. Similarly
percentage of land leased in by the cultivator negatively influences technical inefficiency, that is,
positively influences technical efficiency. Thus, higher the percentage of land leased in by the
cultivator, higher the technical efficiency. However, the coefficient of land leased is significant at
6 percent level and not at 5 percent level. Education as measured by number of years of formal
schooling in the cultivator’s household has positive influences on firm level technical efficiency
but the coefficient is insignificant even at 10 percent level.
Outstanding loans from all sources which is taken as an indicator of indebtedness in the present
study, has a strong and positive influence on technical inefficiency and the coefficient is found to
be significant at 1 percent level. In other words, the more indebted the farmer the lower is the
technical inefficiency. As mentioned before government support dummy is found to be
insignificant. It may be inferred that government support or agricultural extension programmes did
not have any significant influence on farm level technical efficiency for the selected sample of
cultivators. Incidentally only about 18 percent of the sample cultivators received any government
technical support in recent times. Thus an alternative interpretation could be that in view of the
poor extension service by the Assam State Agricultural Department, government support is not at
all important from the perspective of farmers’ day to day agricultural activities.
Self consumption as a percentage of farm output negatively influences technical efficiency, the
coefficient being significant at 9.3 percent. It appears that cultivators who have higher self
consumption (as a percentage of farm produce) are relatively more inefficient compared to those
having a smaller percentage of self consumption. In view of the fact that the district is dominated
by subsistence cultivators, this seems to be a significant finding. As noted earlier, majority of
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 45
cultivators in Hailakandi district are subsistence cultivators where primary motive of farming is
that of ‘self consumption’. However, higher percentage of self consumption lowers technical
efficiency. Although this observation needs a deeper investigation, it apparently seems that being
content with an output which is just sufficient for self-consumption throughout the year, plays a
dampening effect on the motivation to best utilize the resources to get maximum output or use
minimum amount of resources to achieve a target level of output. It is possible that the motive of
self consumption is not conducive to best utilization of available resources and this motive is
contrary to the motive of production for higher quantities of marketable surplus, or else, for sale of
farm produce.
The simple correlation coefficient between area cultivated and percent of self consumption turns
out to be -0.67 implying that smaller the farm size larger the percentage of self consumption, the
converse being also true. The simple correlation coefficient between output size and percentage of
self consumption turns out to be – 0.54, which is consistent with the farm size – self consumption
relation. Finally the correlation coefficient between self-consumption and indebtedness is 0.33
which is positive. That is, indebtedness and proportion of self consumption go hand in hand.
However these are based on sample observation and these correlations may well vary across
samples in the same study region depending on the degree of sampling fluctuations.
Unfortunately similar studies are reported in literature in Hailakandi and as such we do not have
an opportunity to cross-verify these findings.
Table 7 Estimated Output Elasticities of Inputs based on
Estimated Trans-log Production Function Parameters
Inputs Elasticity CA 0.62 HL 0.11 E 0.07 I 0.005 F 0.06 P
Returns to Scale 0.004 0.869
Source: Authors’ estimates based on primary data using FRONTIER 4.1.
The coefficient of HYV dummy turns out to be negative and significant at 5 percent level
implying that HYV cultivation has a positive influence on technical efficiency. For the chosen
sample only about 33.5 percent of paddy cultivators grow high yielding variety seeds and the rest
grow traditional seeds. But here the size class wise distribution is more important. As per
secondary data (Comprehensive District Agricultural Plan (CDAP) 2009-10 to 2011-12) the
fraction of HYV cultivators out of the combined size classes of sub-marginal, marginal and small
is only about 32 percent. Naturally 68 percent of cultivators in these three size class taken
together practice traditional varieties. Thus it is not incorrect to remark that HYV seeds are
unpopular among small and marginal cultivators in the district. On the other hand, among the
medium and large size classes taken together, about 69 percent of cultivators practice the HYV
varieties. Thus HYV is more popular among medium and large sized cultivators. The average
technical efficiency of HYV cultivators turns out to be about 74 percent while that of traditional
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 46
seeds is computed at around 62 percent thereby showing that HYV cultivators are more efficient
in the present sample.
Table 8 Frequency Distribution of Firm Level Technical Efficiency
Percentage Technical Efficiency (TE)
Frequency Percentage of Sample Farms
0-35 8 3.02 35-40 11 4.15 40-45 10 3.77 45-50 15 5.66 50-55 35 13.21 55-60 49 18.49 60-65 34 12.83 65-70 23 8.68 70-75 16 6.08 75-80 19 7.17 80-85 14 5.28 85-90 15 5.66 90-95 9 3.40 95-100 7 2.64
Mean TE (%) 63.24 Minimum TE (%) 32.70 Maximum TE (%) 97.10 Standard Deviation of Firm Specific TE 13.25
Source: Authors’ estimates based on primary data using FRONTIER 4.1.
Finally, the partial elasticities of output with respect to inputs computed on the basis of the
estimated parameters of the translog production function are presented in Table 7. Cultivated area
has the highest elasticity value (0.62) among all inputs. This is followed by human labour. The
remaining inputs considered in the study such as equipments, irrigation, fertilizers and pesticides
have negligible elasticity values. The returns to scale or the scale elasticity of output (which is the
sum of the partial input elasticities) turns out to be 0.869 which is less than unity. This is
indicative of decreasing returns to scale. This is a consequence of poor output elasticities with
respect to equipments, irrigation, fertilizers and pesticides. Arguably this is a consistent finding
in the context of the present study where personal or privately organised irrigation is relatively
expensive (if not beyond the financial capability of most cultivators) and its use is infrequent.
Furthermore controlled use of fertilizers and pesticides are rare in the sense that their application
per bigha, on most occasions, fall far below standard agricultural prescriptions yielding
suboptimal results. It is to be noted in this context that only 7.8 percent of the sample cultivators
have access to mechanized equipments like tractors and hand tillers. The district level picture is
even grimmer. Holders of mechanized equipments are actually confined to the 35 bighas and
above size classes. Consequently the smaller size classes of cultivators cannot reap the advantages
of mechanized devices and hence consume more labour time and cost (including animal and
human labour effort) in land tilling and combing during the beginning of the cropping season.
We separately computed the size-class wise mean technical efficiencies. The sub-marginal,
marginal and small sized cultivators have mean technical efficiency (within the class) of 54.2, 66.1
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 47
and 64.8 percent respectively. On the other hand semi-medium, medium and large sized
cultivators have mean technical efficiency of 69.3, 78.5 and 78.7 percent respectively. The
simple correlation coefficient between area cultivated and percentage technical efficiency turns
out to be -0.57 for the entire sample. Clearly there is an inverse association between farm size and
technical efficiency. The correlation coefficient between yield per bigha and area cultivated is -
0.02 which is statistically insignificant. Thus, for the present sample there is no pronounced
association between farm size and yield per bigha.
SUMMARY AND CONCLUSIONS
The present study measures farm level technical efficiency of paddy cultivators in Hailakandi
district of Assam by adopting a stochastic production frontier with inefficiency effects for cross-
sectional data. The stochastic frontier and the inefficiency effects parameters are simultaneously
estimated using maximum likelihood method (Battese and Coelli, 1995). A transcendental
logarithmic production frontier is adopted due to its flexibility. With due reservations, the study is
based on paddy output of the peak agricultural season of 2009-10 (i.e., winter paddy or Shali)
only. Area sown, labour, irrigation, pesticides and fertilizers are the key inputs assumed to explain
farm output. As per district level agricultural records, farm mechanization and automation are too
rare to be considered as inputs for the sample of cultivators chosen in the present study. A host of
non-input factors which usually affect farm level technical efficiency are assumed to explain inter-
farm variations in the level of technical efficiency.
All five blocks of Hailakandi district were selected for the study. Approximately 30 percent of the
Gram Panchayats (G.P.s) under each block were randomly selected. One best village in terms of
agricultural performance of the last cropping season (as per secondary information) was randomly
chosen out of the three best villages from each of the selected G.P.s. Considering the
overwhelming population of small and marginal farmers in the district all size classes of
cultivators were appropriately included from selected villages in order to draw a representative
sample of 265 cultivators covering all blocks.
The principal findings of the study are more or less obvious in the context of traditional and
backward agriculture where small plots are predominant, mechanized farming is rare and
irrigation infrastructure is poor and thus mono-cropping is the most common practice. A
noteworthy finding is that semi-medium and medium size classes of cultivators dominate the
sample in terms of land holding. Semi-medium and medium sized cultivators comprise only 35
percent of sample cultivators but together enjoy around 73 percent of operational holdings in the
sample.
Turning to the estimates of trans-log production function parameters, the constant term is found to
be insignificant. So are the coefficients of irrigation, fertilizers and pesticides. Cultivated area is
statistically the most significant factor that determines output. This is followed by human labour
and traditional firm equipments. Moreover, all the interaction terms are not only statistically
insignificant but have extremely small values and hence play a very negligible role in determining
the elasticities of output with respect to factors. The elasticities of output with respect to inputs
are more important and are computed from the estimated parameters of the translog production
function. Cultivated area has the highest elasticity value (0.62) among all other inputs. This is
followed by human labour. The remaining inputs consider in the study such as equipments,
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 48
irrigation, fertilizers and pesticides have negligible elasticities. The scale elasticity of output is less
than unity which clearly indicates decreasing returns to variable inputs.
Poor scale elasticity is primarily due to the poor output elasticities with respect to almost all
inputs. Arguably this is consistent in the context of backward agriculture where personally
organized irrigation is beyond the financial capability of most cultivators and thus its use is rare.
Moreover controlled use of fertilizers and pesticides are irregular and their application per bigha,
are on most occasions, far below standard agricultural prescriptions leading to suboptimal results.
These factors might explain the poor sensitivity of output with respect to inputs with the exception
of cultivated area.
The null hypothesis of no technical inefficiency in the data was statistically tested using
Likelihood Ratio Test. The results strongly indicate that technical inefficiency is present in the
data set. This further implies that traditional least squares method would be inappropriate to
estimate parameters of the production function. This is also apparent from the statistically
significant values of the variance parameters of the stochastic frontier model.
The influences of non-input factors on farm level technical inefficiency are crucial in the present
study. Among the non-input factors, years of formal education in the cultivator’s household
positively influences technical efficiency but the coefficient is found to be statistically
insignificant. The coefficient of government support dummy is also found to be statistically
insignificant thereby indicating that government support is rather unimportant. However both
indebtedness (as measured by outstanding loans) and percentage of self consumption of farm
produce have negative influences on technical efficiency. However, percentage of land leased-in
by the cultivator has a positive impact on technical efficiency and the coefficient is found to be
significant. Age (a proxy for experience in this study) of the cultivator has a positive impact on
technical efficiency. That is the older and more experienced farmers are technically more
efficient. This is in line with the findings of Wadud and White (2002) for Bangladeshi paddy
farmers. The coefficient of HYV dummy turns out to be negative and significant at 5 percent level
implying that HYV cultivation has a positive influence on technical efficiency. The average
technical efficiency of HYV cultivators is 74 percent while that of cultivators of traditional seeds
is around 62 percent showing thereby that HYV cultivators are more efficient.
The size-class wise mean technical efficiencies were separately computed. The sub-marginal,
marginal and small sized cultivators have mean technical efficiency (within the class) of 54.2, 66.1
and 64.8 percent respectively. But semi-medium, medium and large sized cultivators have mean
technical efficiency of 69.3, 78.5 and 78.7 percent respectively which is clearly higher than the
lower size classes. Mean technical efficiency for the whole sample is approximately 69 percent.
The simple correlation coefficient between cultivated area and percentage technical efficiency is -
0.57 thereby indicating an inverse association between farm size and technical efficiency. The
correlation coefficient between yield per bigha and area cultivated is -0.02 which shows the
absence of any significant association between farm size and yield per bigha.
On the basis of the key findings of the study we arrive at the following policy suggestions some of
which are rather obvious given the backwardness of agriculture as well as infrastructure in the
region. First, mono-cropping is prevalent in the region and one of the key reasons clearly being
absence of planned canal irrigation. This is all the more surprising as because there already exists
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 49
a network of small rivers and canals which overflow during monsoons due to their meandering
nature and heavy siltation. Needless to say, these small rivers and canals need urgent dredging. If
area under planned canal irrigation is expanded, yield may be increased besides raising cropping
intensity which is vital for overall annual production at the district level. Second, educational
attainments at the household level are important. Lack of literacy and education may be major
obstacles for learning new farming methods, accessing bank credits, bargaining and negotiating
during marketing of produce, etc. An educated cultivator is at a relative advantage on various
accounts. It is thus important to educate farmers and raise their awareness levels. In view of the
poor district level overall literacy rate, there is an enormous scope of improvement in this regard.
Third, the level of indebtedness seems to have a negative influence on farm level technical
efficiency. For the present sample only about 21 percent of cultivators have received credits from
Regional Rural Banks (RRBs). Informal money lenders still dominate the agricultural credit
market in backward states and Hailakandi district of Assam is no exception. These money lenders
often govern the decision making of small farmers as because they lend small sums of money at
exorbitantly high interest rates. The timing of sowing is largely dependent upon credit availability
during the beginning of the cropping season. These money lenders also play an active role in
ensuring that farmers actually do not get access to agricultural credit through the NABARD
controlled district level RRBs namely the Gramin Vikas Banks. In addition these money lenders
are on most occasions, influential political representatives at the Panchayat level. The more
indebted farmers have limited chance of receiving additional credits from money lenders and
consequently their capacity to purchase inputs during the beginning of the cropping season
become limited. Thus farmer’s distress and indebtedness are responsible to a large extent for
mistimed purchase and application of inputs. It is hence not surprising that more indebted
cultivators are technically less efficient. Here in lies the scope and importance of direct
government intervention in providing farm credit which is totally lacking in the district at present.
Timely credit may raise both yield and efficiency of the cultivators. Finally it is found that
cultivators of HYV seeds are technically more efficient. We found out during the course of our
field survey that most of the smaller size classes of cultivators do not get access to the HYV seeds.
These seeds have to be freshly purchased in the beginning of every cropping season and storage of
a part of previous season’s output does not have the same high yielding property and therefore
does not serve the purpose of the HYV cultivators. Thus the state agricultural department needs to
provide a steady supply of HYV seeds at the beginning of every cropping season. Also, the
delivery mechanism has to be efficient such that the disregarding their financial capability,
majority of the cultivators have access to HYV seeds. Finally, if these policy suggestions are
implemented at the district level, then yield per bigha may be expected to grow many folds,
thereby raising regional agricultural surpluses.
_______________________________
References
Aigner, D. Lovell, C.A.K. and Schmidt, P. (1977), ‘Formulation and Estimation of Stochastic Production Models,’ Journal of Econometrics, 6, pp. 21-37.
Ali, M. and J. C. Flinn (1989), ‘Profit Efficiency among Basmati rice producers in Pakistan Punjab’, American Journal of Agriculture Economics, 71, pp. 303-30.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 50
Battese, G.E. and S.S. Broca (1997), ‘Functional forms of Stochastic Frontier Production Functions and Models for Technical Inefficiency Effects: A Comparative Study for Wheat Farmers in Pakistan’, Journal of Productivity Analysis, 8, pp. 395-414.
Battese, G.E. and T. Coelli (1995), ‘A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data’, Empirical Economics, 20, No. 2, pp. 325-32.
Coelli, T. (1995), ‘Estimators and Hypothesis Tests for a Stochastic Frontier Function: A Monte Carlo Analysis,’ Journal of Productivity Analysis, 6, No.4, pp.247-68.
Jondrow, J., C.A.K. Lovell, I.S. Materov, and P. Schmidt (1982), ‘On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model,’ Journal of Econometrics, 19:2/3 (August), pp.233-38.
Heshmati, A and S. C. Kumbhakar (1997), ‘Estimation of Technical efficiency in Swedish crop Farms’, A Pseudo Panel Data Approach, Journal of Agriculture Economics, 48, pp. 22-37.
Huang, C. J., and J. T. Liu (1994), ‘Estimation of a Non-Neutral Stochastic Frontier Production Function,’ Journal of Productivity Analysis, 5:2 (June), pp.171-80.
Kumbhakar, Subal C. and C A Knox Lovell (2000), Stochastic Frontier Analysis, Cambridge University Press, NY.
Kalirajan, K. and J. C. Flinn (1983), ‘The measurement of Farm Specific Efficiency’, Pakistan Journal of
Applied Economics, 2, pp. 167-80. Kalirajan, K. and R. T. Shand (1989), ‘A Generalized Measure of Technical Efficiency,’ Applied Economics,
21, pp. 25-34. Kumbhakar, S.C. and A. Bhattacharya (1992), ‘Price distortion and Resource Use Efficiency in Indian
Agricultural: A Restricted Profit Function Approach’, Review of Economics and Statistics, 74, pp. 221-39.
Kumbhakar, Subal C., Ghosh, S. and T. J McGuckin (1991), ‘A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms’, Journal of Business and
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Functions with Composed Error,’ International Economic Review, 18, pp. 435-44 Pitt, M.M and. L.F. Lee (1981), ‘The Measurement and Sources of Technical Inefficiency in the Indonesian
Weaving Industry’, Journal of Development Economics, 9, pp.43-64. Reifschneider, D. and R. Stevenson (1991), ‘Systematic Departures from the Frontier: A Framework for
Analysis of Farm Inefficiency,’ International Economic Review, 18, pp. 435-44. Schmidt, P. and T.F Lin (1984), ‘Simple Tests of Alternative Specifications in Stochastic Frontier Models’,
Journal of Econometrics, 24, pp. 349-61. Stevenson, R.E. (1980), ‘Likelihood Functions for Generalized Stochastic Frontier Estimation,’ Journal of
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Bangladesh,’ Indian Economic Review, 2, pp. 183-97.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 51
Appendix 1
The Trans-log Stochastic Production Frontier
The stochastic production frontier developed separately by Aigner, Lovell and Schmidt (1977) and
Meeusen and van den Broeck (1977) decomposes the error term of the usual econometric
production function model into a white random noise component and a one sided inefficiency
random component. For the present, we assume a cross-sectional stochastic production frontier
model (specified in Kumbhakar et al, 1991) as
ln ln ( ; )i i i
y f x v uβ= + − (1.1)
i i iu zγ ε′= + (1.2)
The random noise component in the production process is introduced through the error component
iv which is ),0( 2
vNiid σ in equation (1.1). The second error component which captures the
effects of technical inefficiency has a systematic component izγ ′ associated with the firm specific
variables and exogenous variables along with a random component iε . Inserting equation (1.2) in
(1.1) gives the single stage production frontier model
ln ln ( ; ) ( )i i i i i
y f x v zβ γ ε= + − +. (1.3)
The condition that ui ≥ 0 requires that i izε γ ′≥ − which does not require
0izγ ′ ≥ for each
producer. It is now necessary to impose distributional assumptions on vi and εi and to impose the
restriction i izε γ ′≥ − in order to derive the likelihood function.
Kumbhakar et al (1991) imposed distributional assumptions on vi and ui and ignored εi. They
assumed that iu ̴
),( 2
uizN σγ ′+
i.e., the one-sided technical inefficiency error component has
truncated normal structure with variable mode depending on zi. It is still not necessary that
0izγ ′ ≥. If z1i = 1 and 2 3 0,
Qγ γ γ= = =LL
this model collapses to Stevenson’s (1980)
truncated normal stochastic frontier model with constant mode 1γ, which further collapses to the
Aigner, Lovell and Schmidt (1977) half normal stochastic frontier model with zero mode if
1 0.γ = Each of these restrictions can be statistically tested. Finally if ui and vi are independently
distributed, all parameters of equation (1.1) can be estimated by using maximum likelihood
estimation method. The log likelihood function is a simple generalization of that of Stevenson’s
(1980) truncated normal model having constant modeµ
, with only one change. Constant modeµ
is now replaced by the variable mode ,i izµ γ ′= so that the log likelihood function is
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 52
(1.4)
where
2 2*
2 2
v i u ii
v u
z eσ γ σµ
σ σ
′ −=
+,
2 2*2
2 2
v u
v u
σ σσ
σ σ=
+
and the ln ln ( ; )
i i ie y f x β= −
are the residuals obtained from estimating equation (1.1) simply
by OLS. The log likelihood function of (1.4) can be maximized to obtain ML estimates of 2 2( , , , ).v u
β γ σ σ These estimates can then be used to obtain producer specific estimates of
technical efficiency, employing the Jondrow, Lovell, Materov and Schmidt (1982) approach to
find the best point estimates of technical efficiency. These estimates are either
* ** *
* *
( / )( / )
( / )i
i i i
i
E u eφ µ σ
µ σµ σ
= +Φ
(1.5)
or
* * 0( / )
0 .
i i
i i
ifM u e
otherwise
µ µ ≥= (1.6)
Once technical efficiency has been estimated, the effect of each exogenous or environmental
variable on technical efficiency can be calculated from either
[ ( / ) / ] [ ( / ) / ]i i ik i i ikE u e z or M u e z∂ ∂ ∂ ∂. Battese and Coelli (1995) model is an
improvement over the Kumbhakar et al (1991) model as, (i) it is based on panel data and (ii) the
non-negativity requirement ( ) 0i i iu zγ ε′= + ≥
is modelled as iε̴
),0( 2
εσN with the
distribution of iε bounded below by the variable truncation point izγ ′−
. Battese and Coelli
(1995) have verified that this new distributional assumption on iε is consistent with the
distributional assumption on ui that iu ̴
),( 2
uizN σγ ′+
. We assume a translog production
function with 6 inputs to specify the underlying technology. All the six inputs are already
mentioned in the text.
∑ ∑∑= = =
++=6
1
6
1
6
1
0 lnlnln);(lnj j k
kjjkjj xxxxf ββββ (1.7)
Here (1.7) is the translog technological specification assuming six inputs. Here yi represents paddy
output of the ith cultivator over the studied cropping season.
Further iiiiiii zzzzzzz 7766554433221 γγγγγγγγ ++++++=′ (1.8)
22 2
2 21 1 1
( )1ln tan ln ( ) ln ln
2 2
N N Ni i i i
v u
i i iu u v
z e zNL cons t
γ µ γσ σ
σ σ σ σ
∗
∗= = =
′ ′ += − + − Φ + Φ −
+ ∑ ∑ ∑
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 53
where, the zi’s are firm specific non-input variables which may influence the technical efficiency
of cultivators. Specifically,
2iz = Age of the cultivator, as a proxy for experience.
3iz= Outstanding Loans of the cultivator as a measure of the degree of indebtedness.
4iz= land leased in by the cultivator expressed as a percentage of total cultivated area.
iz5 = Education of the cultivator as measured by number of years of formal schooling.
iz6 = Self consumption of farm produce as a percentage of farm output, and finally,
=iz7 Government support dummy (assuming 1 for farmers receiving agricultural extension
services, and 0 for farmers who have not received any such support). From the translog production
function given by (1.7) we calculated the elasticities of output with respect to each input by using
the relation
∑=
+=∂∂=6
1
lnln/lnk
kjkjjijxxY ββη
(1.7a)
All the factor elasticities are computed from estimated parameters and sample mean value of
inputs.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 54
ANVESAK
A bi-annual Journal of SPIESR
Vol. 42, No.1 & 2 January-December 2012
Key Note Address: Problems and Prospects Yoginder K. Alagh
Economic Viability and Sustainability of Small Scale Farming: A Study in the Irrigated Gangetic Plains of UP
Ajit Kumar Singh
Food Security Aspects and Diversification of Demand in the Context of Gujarat
Niti Mehta
Rationalisation of Agricultural Subsidies: Study of Electricity and Fertiliser Subsidies in Karnataka and Tamil Nadu
Elumalai Kannan
Institutional Reform for Water Use Efficiency in Agriculture Jharna Pathak
Political Economy of the Energy-Groundwater Nexus in India: Exploring Issues and Assessing Policy Options
Tushaar Shah, Mark Giordano
and Aditi Mukherji
Positive and Normative Aspects of Price and the Market in Indian Agriculture-A Look at Government Policy Interventions in Food Management in an Unchanging Narrative of Traditional Agriculture
Munish Alagh
Land, Livelihoods, and State in India: Issues and Challenges Sukhpal Singh
Sustainability of Rice Cultivation in the Kole Land of Kerala Jeena T. Srinivasan
Growth of Paddy Production in India’s North Eastern Region: A Case of Assam
Komol Singha
Determinants of Non-Farm Employment in Rural Uttar Pradesh Vachaspati Shukla
How Sustainability Can be Ensured in Uncomfortable Nexus of Water, Agriculture and Institutions?
Dalbir Singh
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 55
WOMEN HOME BASED WORKERS ACROSS INDIAN STATES: RECENT
EVIDENCES
Tulika Tripathi1 and Nripendra Kishore Mishra2
Globalisation has introduced a system of production where flexible work contract or sub-
contracting and ‘putting-out’ system of production is becoming a general practice. Either as cost
reduction strategy or as businesses strategy production is being out sourced and consequently a
set of workers have emerged which is known as ‘home based workers (HBW)’ or ‘home workers’
or ‘outworkers’. NCEUS (2007) highlighted the importance of estimating and knowing the
conditions of work of home workers. Information about these home based workers has been
limited till recently due to definitional issues and data limitations. Delhi Group has clarified many
definitional issues and NSS 66th round, (2009-10) has provided relevant data to know details of
these home based workers. This paper focuses on women home based workers as they constitute
the largest part and are characterised by worst employment conditions. NSS has launched another
round (68th) of unemployment-employment survey for year 2011-12, still 66th round is much more
important in regard to home based workers due to the fact that 68th round has dropped many of
question on sub-contracting. Therefore, this paper relies on 66th round data only. This paper is
basically an attempt to look at state wise pattern in women home based workers. It shows that
there are considerable state level variations. There is state like Assam with close to half and
Punjab with slightly above 10 per cent of their women home based workers working under any
form of subcontracting. This paper looks at industrial distribution of these women home based
workers and it is shown that there are certain industrial groups where share of home based
women workers is higher than non home based workers. Thus we have fair reason to agree with
NCEUS (2007) that these workers are a special category and they need specific policies to redress
their situation.
INTRODUCTION
Global production networks and rising competition in domestic market accompanied by failure of
formal sector to absorb additional labour force or loss of formal sector job has created a situation
in many countries, including India, where a substantial section of work force is not working in a
central work site. Flexible work contract or sub-contracting and ‘putting-out’ system of production
is becoming a general practice. Businesses are finding it much easier and profitable to procure
production from a network of workers/suppliers through intermediaries and middle man. The
general mode of payment is piece rate. In a way production process is being decentralised. This
decentralisation is of a different kind where production is not carried out by the business itself but
is out sourced. This phenomenon has expanded in recent times and thus a set of workers have
emerged which is known as ‘home based workers (HBW)’ or ‘home workers’ or ‘outworkers’.
However, conceptually there are differences in these terms, though many a times they are used
interchangeably. A clear definition of home-based work was lacking till recently. Only recently
the Ministry of Statistics and Programme Implementation has constituted an Independent Group
1 Assistant Professor, Centre for Studies in Economics and Planning, School of Social Sciences, Central
University of Gujarat, Gandhinagar- 382030, [email protected]
2 Professor, Department of Economics, Faculty of Social Sciences, Banaras Hindu University, Varanasi –
India, [email protected]
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 56
on Home Based Workers in India in 2007 to examine existing data sources and suggest means to
capture home based workers.
The Group recommended the following definitions for “home” and “home-based workers.” Home
is defined as (i) dwelling unit and/or (ii) structure attached to dwelling unit and/or (iii) open area
adjacent to the dwelling unit. With the above definition of home, home-based workers are defined
as:
a) own-account workers and contributing family workers helping the own-account workers,
involved in the production of goods and services in their homes for the market; and
b) workers carrying out work in their homes for remuneration, resulting in a product or service as
specified by the employer(s), irrespective of who provides the equipment, materials or other inputs
used and those contributing family workers helping such workers.
Workers referred to in (b), above, are home workers working in their homes according to ILO
Convention 177 on Home Work, 1996. For data collection purposes, such home workers may be
classified as own account workers. It may be noted that, unlike in the definition of “home
workers” given in the ILO Convention 177 on Home Work, 1996, the Group felt that “Own-
account workers and contributing family workers helping such own account workers, having their
workplaces in their own homes, qualify for inclusion in the group of home based workers” (GOI
2008). It can be noted that the 66th round of NSS (July 2009-June 2010) collected data on “home
based workers” using the definitions of “home” and “home-based workers” recommended above
by the Independent Group on Home-Based Workers.
There is a long tradition of home based work in India, like in handloom, dairy and carpet industry.
However, this issue has attracted attention of late not because of this tradition but because of new
tendency of corporate to outsource their production or disintegration of organized large units into
smaller ones. A study by UNIFEM finds a large number of home-based women assembling
bicycle parts in Ludhiana (UNIFEM, 2000). This has been possible because there is still, in many
places, control over women’s mobility and a strong social resistance to women working outside
the home, if work is offered to be done within the home, thus “invisible” and does not on the face
of it disrupt other established roles such as family care, women and their households are willing to
take it on. An example of disintegration of organized large units into smaller ones, including
home-based workers, is the hosiery industry in Ludhiana (UNIFEM, 2000). Such changes in the
organization of production reflect entrepreneurial responses to market demand, and a balancing of
costs of production including labour law compliance, and economies of scale, against flexibility
and looser regulatory requirements. Many big companies, including multinational corporations,
have evolved a vendor system of subcontracting for their production. Depending on the nature of
work, some of these vendors either employ women workers in large numbers or give out work to
HBWs mostly through contractors (Jhabvala and Sinha, 2002). The expansion of markets and
heightening of economic activity in production systems as a result of economic reforms in India
meant that “overall expanding markets led to an increase in homebased work, but not necessarily a
movement towards this form of production system” (Unni & Rani, 2004). Rani and Unni (2009)
argue that a rise in unit cost of labour is associated with an increase in female home based work.
Neetha (2010) finds that a large proportion of women within the category of self-employed are
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 57
unpaid and the increase in self employment during the reform period is on account of the swelling
number of unpaid workers.
However, the focus of this paper is on ‘home based’ women workers. The primary source of data
for this paper is unit data of NSS Employment-Unemployment Survey (2009-10). Though this
round of survey of NSS has been questioned on the ground that this was a drought year and
therefore results obtained may not reflect actual state of affairs of economy. That is why a fresh
Employment-Unemployment Survey for 68th round (2011-12) has been carried out by NSS.
Results from this round are likely to be known not before mid of next 2013. NSS experimented
with some questions on location of workers in 55th round, but somehow these questions were
dropped from 61st round. These questions were re-introduced in 66th round. However, some of
these questions, especially on sub-contracting, have again been dropped from 68th round. Thus,
even when data of 68th round is released, one is not likely to have a better picture of home based
worker than what we have in 66th round. Of course, if it is accepted that 2009-10 was a drought
year then definitely estimates of 66th round would be an underestimate. Still, 66th round provides
a much more detailed analysis of home based workers. Schedule 10 of 66th round of NSS enquires
about locations of workers in principal as well as subsidiary status. Many codes have been
provided for location of work force in 66th round. As per the definition of Independent Group, we
pick up following four categories of location of work place as signifying home based workers.
These categories are:
a. own dwelling unit
b. structure attached to own dwelling
c. open area adjacent to own dwelling unit
d. detached structure adjacent to own dwelling unit
The following discussion is based on these concepts and on the basis of unit data from 66th round
of Employment-Unemployment Survey (2009-10) of National Sample Survey. So far NSS has not
released findings in regard to home based workers. It does plan to release a detailed report on
home based workers. In absence of this expected report, this paper attempts to fill this gap.
MAGNITUDE AND STRUCTURE OF WOMEN HOME BASED WORK FORCE
The estimated number of women home based workers on the basis of usual principal status from
NSS 66th round is more than eight crores. Uttar Pradesh is home to the largest number of women
home based workers. Almost half of all home based workers are women and again out of all
women workers almost half of them are women. The percentage of women home based workers in
total women workers is generally high in northern and eastern states, except W.B. However, it is
interesting to note that these are also states where percentage of women home based workers in
total home based workers is low (Table 1).
NSS defines status of workers in various categories. Home based workers broadly correspond to
workers with statuses of own account workers in household enterprise, self employed as employer
and unpaid family workers. These statuses have varied implications denoting a vertical hierarchy
of workers within home based workers. Almost one third of home based women workers are
working without being paid anything for this. In fact, this ‘unpaid work’ is a black box about
which we know little. Rest of women home based workers are working as own account workers;
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 58
self employed as employer being almost negligible. However, there is considerable variation
across states. Unpaid work is the highest in M.P and the lowest in Kerala. The latter records an
abnormally high percentage for employer status. Northern and eastern states record higher
percentage of women home based workers working as own account workers. However, Kerala
again stands out as an exception (Table 2).
Table 1 State Wise Women Home Based Workers (UPS)
State Percent of women home based Workers in total (male+ female) Home based Workers
Estimated Number of Women Home Based Workers
Percent of Women Home based Workers in Total Women Workers
Punjab 46.56 1536101 46.44 Haryana 43.99 1763310 52.43
Rajasthan 47.24 6806879 66.55 Uttar Pr 45.72 14810507 64.59 Bihar 47.51 5411860 51.98 Assam 44.65 2909919 65.74 Bengal 46.70 5847879 45.04 Orissa 50.00 3626763 54.47 Madhya Pr 46.05 6326011 48.80 Gujarat 47.52 5237193 50.88
Maharashtra 46.62 8207836 42.16 Andhra Pr 49.80 7130102 38.51 Karnataka 46.28 4789575 40.06 Kerala 50.14 2075389 33.73 Tamil Nadu 50.10 4948031 31.04
India 47.26 81427355 48.02
Source: Extracted by authors from unit data set of NSS 66th
round.
Table 2 Status Distribution of Women Workers across States (UPS)
State Worked in hh. enterprise (self-
employed) as own account worker
Worked in hh. enterprise (self-
employed) as employer
Worked as helper in hh. enterprises (unpaid
family worker)
Punjab 70.08 4.56 25.36 Haryana 73.77 1.47 24.76 Rajasthan 61.33 0.20 38.47
Uttar Pr 68.80 0.83 30.36 Bihar 75.51 0.36 24.13 Assam 62.46 0.47 37.07 Bengal 77.57 3.19 19.23 Orissa 67.82 0.92 31.26 Madhya Pr 51.48 0.15 48.38 Gujarat 57.70 2.30 40.00 Maharashtra 54.55 4.11 41.34
Andhra Pr 63.25 2.24 34.52 Karnataka 58.77 2.04 39.19 Kerala 70.13 17.28 12.58 Tamil Nadu 62.72 6.51 30.77
India 64.21 2.32 33.47
Source: Extracted by authors from unit data set of NSS 66th
round.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 59
EARNINGS OF WOMEN HOME BASED WORKERS
Home based work by women is generally a coping strategy to supplement family income. The
employment status of the home-based workers can be seen along a continuum of dependence,
from being completely independent to being fully dependent on the contractor/ middleman for
design, raw material and equipment and unable to negotiate price of the product. They constitute a
separate production system forming a different layer or segment both in the product and labour
markets (Unni and Rani 2004). This category of work is something like a residual work.
Therefore, average wage of home based workers is bound to be lower than non home based
workers. Table 3 proves that this average wage is always lower in former (home based) across
major states of India, except Punjab. It is also noteworthy that average weekly wage in non home
based women workers is fairly stable across state, but there are wide variations in case of home
based women workers. This is so because home based work is much more heterogeneous and
prone to local variations. To some extent, home based work is informal and non home based work
is formal. Naturally, this brings in certain volatility in average wage of home based work.
Table 3 Average weekly earnings of women workers across major states of India (UPS)
State Average Weekly Earnings of Home Based Women Worker
(in Rs)
Average Weekly earnings of Non- Home Based Women
Worker (in Rs)
Andhra Pr 801.03 1235.73 Assam 626.67 1548.78 Bihar 493.50 905.70 Gujarat 617.78 1046.12 Haryana 547.07 1307.01 Karnataka 571.81 1254.44 Kerala 700.31 1335.07 Madhya Pr 1672.45 1059.93
Maharashtra 565.01 1224.71 Orissa 539.65 1040.41 Punjab 1327.19 1307.79 Rajasthan 956.77 1053.37 Tamil Nadu 910.87 1177.91 Uttar Pr 434.16 996.77 Bengal 680.24 980.92
Source: Extracted by authors from unit data set of NSS 66th round.
Mode of payment of wages is an important determinant of quality of employment. This is also
important in deciding labour processes. In most cases, the payment received for the work carried
out on order/contract is on the basis of piece rate. Time rate of wage payment belongs to bygone
times. It is generally accepted that informal sector is characterised by many innovative forms of
wage payment. Women home based workers belong primarily to informal sector and therefore
experience many innovative forms of wage payment. However, these varied modes of wage
payment are clubbed into two categories in 66th round of NSS, namely piece rate and contract
rate. Piece rate is a clearly much more manageable and cost reducing method of wage payment. As
said earlier, in modern times when there is stress on profit margins for producers and labour laws
have been suitably modified, piece rate is one which suits producers most. Of course, it further
exposes workers to uncertainty and economic shocks. In absence of any social security
mechanism, this mode of wage payment exposes workers to market fluctuations and volatility.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 60
Table 4 shows prevalence of different modes of wage payment to women home based workers
across states. Piece rate is widely prevalent basis of payment. As can be seen from this table, states
vary drastically. (Table 4).
Table 4 Mode of payment of women home based workers across major states (UPS)
State Piece Rate Contract Rate
Andhra Pr 91.01 9.00 Assam 45.41 54.59 Bihar 92.61 7.39 Gujarat 81.25 18.75 Haryana 81.25 18.75
Karnataka 77.10 22.90 Kerala 83.45 16.55 Madhya Pr 80.21 19.79 Maharashtra 69.69 30.31 Orissa 84.95 15.05 Punjab 89.04 10.97 Rajasthan 86.22 13.78 Tamil Nadu 89.13 10.87 Uttar Pr 76.70 23.30
Bengal 78.88 21.12
Source: Extracted by authors from unit data set of NSS 66th round.
SUB-CONTRACTING AND PUTTING OUT
NCEUS (2007) highlighted the importance of estimating and knowing the conditions of work of
homeworkers. It anticipated that in future outsourcing from large firms to small firms and further
subcontracting from smaller firms to homeworkers in manufacturing is going to increase. NSS
55th round did introduce for the first time some special questions to enable direct estimation of the
number and proportion of home based workers. These were questions on place of work and nature
of contract. These questions were withdrawn in 61st round and re-introduced in 66th round. These
questions are; whether worked under given specifications, which provided credit/raw
material/equipments, number of outlets of disposal, basis of payment and type of specifications.
These are specific questions addressed to home based workers and to understand their working
conditions and to know the degree of subcontracting. Specification is one indicator of
subcontracting which shows its coverage. NSS defines specification as “whether the person
carried out the production (i.e., goods and services) on the basis of given or laid product-
specifications of the ‘employer’. The term ‘employer’ means a person, natural or legal, who, either
directly or through an intermediary, whether or not intermediaries are provided for in national
legislation, gives out home work in pursuance of his or her business activity. When a person
procures the order/contract from the ‘employer’ for his or her household enterprise to supply
goods, normally an implicit or explicit specification of the product, written or oral, is laid by the
‘employer’. Sometimes, the whole activity is carried out under the specifications of the
‘employer’, or a part under the specifications of the ‘employer’ and rest of his own specification”.
Thus table 5 shows the extent of subcontracting in women home based workers. State wise we do
not have a clear trend. There is state like Assam with close to half and Punjab with slightly above
10 per cent of their women home based workers working under any form of subcontracting.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 61
Table 5 Percentage share of women home based workers as per specification across states
State Working under any form
of specification Not working under any
specification or not known
Andhra Pr 27.40 72.60 Assam 43.91 56.09 Bihar 23.24 76.76
Gujarat 33.83 66.17 Haryana 16.98 83.02 Karnataka 22.79 77.21 Kerala 23.23 76.77 Madhya Pr 39.94 60.06 Maharashtra 31.94 68.06 Orissa 27.59 72.41 Punjab 12.08 87.92
Rajasthan 21.13 78.87 Tamil Nadu 26.62 73.39 Uttar Pr 29.09 70.91 Bengal 34.32 65.68
Source: Extracted by authors from unit data set of NSS 66th round.
There are many forms of subcontracting prevalent in women home based workers. NSS 66th
round asks an integrated set of questions to self employed. It is ascertained from the self-employed
persons whether the ‘employer’ who gives product-specifications (in terms of the order/contract)
also provides credit/raw material/equipment to them. Here, ‘credit’ means cash advance for a
particular order or a group of orders and for working capital only (i.e., for purchase of raw
material and meeting other running expenses). However, credit provided for purchase of
equipment is not considered as ‘credit’ and instead is considered as ‘provided for equipments’. It
can be seen from table 6 that this subcontracting is largely of a type where the employer who gives
product specification does not provide credit or raw material or equipment. This own arrangement
means that the worker uses his own resources and is exposed to all sorts of risks and uncertainty.
Table 6 Extent of Support in Subcontracting
State own
arrangement credit only
raw material
only
equipments only
credit and raw material
only
raw material and equip
only
credit, raw material and equipment
Andhra Pr 77.68 0.46 16.40 1.33 2.70 1.26 0.17 Assam 87.54 0.00 7.07 1.97 3.11 0.00 0.31 Bihar 87.22 0.00 5.78 0.00 6.78 0.00 0.22 Gujarat 77.05 0.41 21.32 0.18 0.10 0.94 0.00 Haryana 41.83 0.00 55.45 0.00 2.72 0.00 0.00 Karnataka 53.69 13.23 31.32 0.00 0.00 0.75 1.01 Kerala 42.39 0.00 52.27 0.00 0.41 4.94 0.00 Madhya Pr 79.17 0.97 10.49 1.86 7.51 0.00 0.00
Maharashtra 65.04 0.51 20.15 5.06 0.00 0.98 2.11 Orissa 63.03 0.00 36.47 0.13 0.00 0.37 0.00 Punjab 66.79 3.13 28.73 0.00 1.35 0.00 0.00 Rajasthan 69.47 9.89 7.82 2.02 0.00 6.93 0.89 Tamil Nadu 49.68 13.93 32.34 0.24 2.22 1.11 0.02 Uttar Pr 37.54 14.26 33.20 0.00 6.28 2.94 0.87 Bengal 31.88 0.61 59.63 0.00 4.74 0.41 2.32
Source: Extracted by authors from unit data set of NSS 66th round.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 62
Table 6 shows that there are considerable variations across states. Close to 90 per cent women
home based workers are working with their own arrangement in states like Assam and Bihar and it
is as low as one third in W.B. All forms of support (credit, raw material and equipment) are almost
non-existent. The most common form of support is in terms of raw materials only. Credit support,
which is much more important than any other form of support, too is completely absent except for
Karnataka, Rajasthan, T.N and U.P.
Table 7 Number of Disposal Outlets
State One Outlet
Two Outlets
Three or more Outlets
Not Known
Andhra Pr 60.67 10.00 27.02 2.30 Assam 88.16 0.00 0.00 11.84
Bihar 57.90 12.70 3.92 25.48 Gujarat 55.41 3.35 23.58 17.66 Haryana 59.43 0.00 23.16 17.41 Karnataka 84.90 0.00 14.86 0.25 Kerala 73.99 0.00 24.92 1.09 Madhya Pr 73.46 0.00 21.07 5.47 Maharashtra 46.65 9.19 13.17 30.99 Orissa 80.57 1.29 5.50 12.64
Punjab 63.15 3.13 14.88 18.85 Rajasthan 53.42 9.78 20.40 16.41 Tamil Nadu 68.25 15.20 7.80 8.75 Uttar Pr 32.51 2.60 36.57 28.32 Bengal 61.61 11.42 14.16 12.80
Source: Extracted by authors from unit data set of NSS 66th round.
The women home based worker may be working for one or more than one employer. In other
words she may be disposing off her output to one or more than one outlets. Here, the outlet means
the ‘employer’ for whom the self-employed woman is working. There may be cases where the
self-employed may be working under the specifications of more than one ‘employers’. If she is
working for one employer she is more prone to exploitation. Ideally, the larger the number of
outlets, the better it is for the worker. Table 7 shows this across states of India. It is observed that
most of the women home based workers are working for only one outlet/employer. It appears that
the women are working either for one outlet or more than three outlets. Assam. Orissa and T.N
present an interesting example where one outlet is the most prevalent one. However, it is also seen
that there is state like U.P where one outlet and more than three outlets are equally prevalent.
Most of times, this specification agreement is oral only, resulting into flexibility of the employer
to terminate it as and when he deem fit. These women workers are not in a position to dictate their
terms and therefore are unable to secure written agreement. However, in some states these home
based workers are organising as in case of SEVA. Written agreement is possible only in case of
organisation of these workers. In some of the southern states, these organisations have come up.
Therefore, it is puzzling to note that a substantial percentage of these workers are reporting written
agreement in states like Assam, Bihar and Rajasthan (table 8), where we do not have any
organisation of these workers.
Table 8 Type of Specification
State One Outlet Two Three or Not
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 63
Outlets more Outlets Known
Andhra Pr 60.67 10.00 27.02 2.30 Assam 88.16 0.00 0.00 11.84
Bihar 57.90 12.70 3.92 25.48 Gujarat 55.41 3.35 23.58 17.66 Haryana 59.43 0.00 23.16 17.41 Karnataka 84.90 0.00 14.86 0.25 Kerala 73.99 0.00 24.92 1.09 Madhya Pr 73.46 0.00 21.07 5.47 Maharashtra 46.65 9.19 13.17 30.99 Orissa 80.57 1.29 5.50 12.64
Punjab 63.15 3.13 14.88 18.85 Rajasthan 53.42 9.78 20.40 16.41 Tamil Nadu 68.25 15.20 7.80 8.75 Uttar Pr 32.51 2.60 36.57 28.32 Bengal 61.61 11.42 14.16 12.80
Source: Extracted by authors from unit data set of NSS 66th round.
Table 9 Share of different industrial groups in home and non home based workers (UPS)
Industry
Home Based
Worker
Non- Home Based
Worker Total Agriculture, hunting and forestry 22.46 2.63 5.48 Manufacture of food products and beverages 3.49 2.19 2.63 Manufacture of tobacco products 4.06 0.09 1.81 Manufacture of textiles 6.37 1.94 3.64 Manufacture of wearing apparel 6.25 1.38 3.22 manufacturing of leather products 0.24 0.15 0.28 Manufacture of wood and wood products 3.10 0.97 1.47 Manufacture of other non-metallic mineral products 2.51 1.36 1.90 Manufacture of fabricated metal products 1.00 0.63 1.00 Manufacture of furniture 2.60 1.29 1.85 Manufacturing 31.71 12.80 22.19 Construction 3.11 20.67 18.35 Wholesale and retail trade and repairing 34.91 33.89 41.12 Hotels and restaurants 3.36 2.24 2.49 Transport, storage and communications 1.60 14.37 8.69 Financial intermediation 0.41 1.32 1.68 Real estate, renting and business activities 1.47 2.40 2.54 Education 1.81 3.97 5.48 Health and social work 1.19 1.13 1.74 Other community, social & personal services 4.47 6.50 4.01 Total 100 100 100
Source: Extracted by authors from unit data set of NSS 66th round.
INDUSTRIAL DISTRIBUTION OF WOMEN HOME BASED WORKERS
Apart from discussion made above it is also important to know in which principal industrial
groups these women home based workers are concentrated. It is also important to know the
difference between home based and non home based women workers. On the basis of principal
status we divide total women workers into home based and non home based workers. There are
certain industrial groups where share of home based women workers is higher than non home
based workers, e.g. manufacturing of tobacco products and manufacturing of wearing apparels, the
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 64
share is almost same in case of wood products. However, manufacturing of tobacco products
seems to be completely a home based work. Leather and textile are other important groups where
share of women home based workers is very high. However, manufacturing and wholesale and
retail trade absorbs almost one third each of total women home based workers. Within
manufacturing food products, tobacco products, textile, wood products and apparel industry are
important concentration sectors of women home based workers (Table 9).
SUMMING UP
Now with fresh set of questions re-introduced in NSS 66th round we can easily estimate the
number, proportion and quality of employment of home based workers. This exercise can be done
on the basis of gender also. Since women are mainly in home based work, this paper focuses on
that section of home based workers only. This paper shows its prevalence across states and across
different statuses on the basis of usual principal status. U.P. is the home of the largest number of
women home based workers. The percentage of women home based workers in total women
workers is generally high in northern and eastern states, except W.B. Almost one third of home
based women workers are working without being paid anything for this. Rest of women home
based workers are working as own account workers; self employed as employer being almost
negligible. Average wage is always lower in home based work across major states of India, except
Punjab. It is also noteworthy that average weekly wage in non home based women workers is
fairly stable across state, but there are wide variations in case of home based women workers.
Basis of payment to workers is generally piece rate. Subcontracting is very common and this is
reflected in percentage of workers working under any specification. This subcontracting may have
been beneficial had this been based on larger number of output disposals. But we observe that
majority of women home based workers are working in one disposal only. Thus we have fair
reason to agree with NCEUS (2007) that these workers are a special category and they need
specific policies to redress their situation. This becomes all the more important when they happen
to be women.
_______________________________
Reference Jhabvala, Renana and Shalini Sinha (2002) - “Liberalisation and the Woman Worker.” Economic and
Political Weekly, May 25, 2002, pp 2037 – 2044. NCEUS (2007): Report on Conditions of Work and Promotion of Livelihood in the Unorganised Sector, GOI Neetha N. (2010) - “Self Employment of Women: Preference or Compulsion?” Social Change, Vol. 40, No.
2, June 2010, pp 139-156. UNIFEM (2000) - “A Preliminary Study on the Productive Linkages of Indian Industry with Home-based
Women Workers through Subcontracting Systems in Manufacturing Sector”, New Delhi: United Nations Development Fund for Women.
Unni, Jeemol and Uma Rani (2005) – “Impact of Recent Policies on Home Based Work in India”, Human
Development Resource Centre, Discussion Paper Series - 10, UNDP, New Delhi.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 65
HEALTH SITUATION IN INDIA: AN OVERVIEW
Shabir Ahmad Padder1 Health is an essential input for development of human resources and quality of life and in turn the
social and economic development of nation .A positive health status is defined as a state of
complete physical, mental and social well-being and not only the absence of disease or infirmity
(WHO, 1946).Health is a regardless priority for sustained development interventions both at the
individual community and national levels. Improved health is a part of total socio-economic
development and is regarded as an index of social development. A provision of basic health care
services to rural community is the primary objective of the government as well non-government
organizations in the context of rural development .Rural health services, safe drinking water
,sanitation ,nutrition, etc., have therefore, been brought together in the form of an integral
package to improve the social, economic and health conditions of the people. Therefore, the
primary goal of any health care delivery system is to organize the health services in such a
manner as to optimally utilize the available resources, knowledge and technology, with a view to
preventing and alleviating diseases, disability and sufferings of the people.
INTRODUCTION
We stand at the threshold of a new era in striving for the goal of “Health for All” as a public health
professions looking at the 20thcentury picture of our health, or lack of it, the view is primarily
filled with the images of our struggles against the “old” diseases that have plagued our ancestors
for centuries. But as we look towards the 21stcentury, The view is markedly different. We see
ourselves confronting “new” diseases in a world where borders and geographic distances are
increasingly irrelevant to the pattern of disease in our “globe village” yet we also perceive
ourselves continuing to fight many of those “old” diseases that are learning “new tricks” to foil
our attempts to combat them.
Our challenge, at the threshold of the new millennium is not only to address the assault of the
disease – producing microbes around us , but also to recognize that many of the causes of our ill
health are increasingly related to our life – styles and man made changes in our environment.
Health, life styles and environments will ultimately return greater increments in longevity than any
additions scientific advantages. More importantly, improvement in life styles and the environment
will help to ensure that these extra years of life are of high quality. We need to add life to year
rather than simple adding years to life developing countries such as India, face three major
problems – ignorance, poverty and disease. The link between these is so strong that it is difficult
to identify which leads to what! Consequently, setting priorities for any of the above three has
become a problem. However, efforts are underway in the developing countries through various
development programmes to eradicate ignorance, poverty and ill – health prevalent among the
major portion of the population.
India is essentially a rural country with 80 per cent of the population living in about 600600
villages with unsatisfactory sanitary conditions, poor economics and educations standards. The
1 Assistant Professor, Department of Economics, Govt. Degree College Shopian, Kashmir (J&K) – 192303,
email: shabirpadder_college@rediff mail.com
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 66
central and state Governments have realized the importance of health, family planning and
welfare, nutrition and sanitation in the whole process of development.
For attaining the goal of Health for All, India requires not only thorough over hauling of existing
Strategies in education and training of medical services and health personal, but also a radical
restructuring of health services infrastructure. Methods have to be evolved within a fully
integrated planning frame which should seek to provide universal comprehensive primary health –
care services , relevant to actual needs and priorities of the community at a cost which the people
can afford ensuring that planning and implementation of the various health programmes is through
organized involvement and participation of the community, adequately utilizing the services
rendered by private voluntary organizations active in health the sector.
Presently, despite the constraints of resources, there is disproportionate emphasis on the
establishment of curative centres, hospitals and institutions for special treatment. The large
majority is concentrated in the urban areas in unplanned fashion which result s in the under –
utilization of some services and over – utilization of the other services. The vast majority of those
seeking medical relief have to travel long distance to nearest curative centre, seeking relief ailment
which should have been readily and effectively handled at the community level. Also for want of
well established referral systems those seeking curative care have a tendency to visit specialist
centres, thus further contributing to congestion, duplication of efforts and constantly waste of
resources. The objective of the paper is to assess the Health status of India in Global and National
perspective. Further issues of inequity in health services and financing of health care will also be
addressed. Suitable suggestions/ policy implications for making health care services / programmes
more relevant to the people will be given in this paper.
SOME COMPONENTS OF HEALTH IN GLOBAL PERSPECTIVE
The WHO highlights three specific dimensions of health – the physical, the mental and the social.
Health is multifactor as well. There are numerous factors influencing health like hereditary factors,
environmental factors, life style, adequate housing, basic sanitation and socio economic conditions
including income, education, availability and quality of health infrastructure and per capita health
expenditure.
Safe drinking water and proper sanitation has a significant role in health sector. water borne
diseases like diarrhoea, malaria , cholera and hepatitis are basically targeted to infants, children
and old people. Every year there are four billion cases of diarrhoea in the world causing two
billion deaths among children under – five and 15 per cent of deaths in developing courtiers
(WHO and 2000). Contaminated water is one of the most important causes of diarrhoea among
children. There is other water pollutant such as long term exposure to arsenic in drinking water,
which can causes cancer of skin, lungs, urinary bladder and kidney (Haq, 2004). The beginning of
the new millennium one sixth of the world population was without improved water source and two
fifths were without improved sanitation facilities (UNICEF 2000). Sanitation facilities still fail to
meet the requirements of all population groups, especially in India where access to sanitation
needs much progress.
Nutrition is an aspect of health where income matters – hungry people who have more money are
likely to spend it on food and as famously illustrated by Amartya Sen.’s ground breaking work on
famines, hungry often reflects the lack of means to acquire food rather than general food scarcity
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 67
(OECD, 2010)..However, more income does not always guarantee proper nutrition, and people
who are not poor can still go hungry.
Inadequate nutrition also affects the people – particularly children – acquire knowledge and
participate in society. It hampers the ability to work and be productive and thus limits the ability to
earn the income needed to lead a decent life. And the irreversibility of some health consequences
of malnutrition –blindness from Vitamin A deficiency, physical stunting from protein shortages –
reinforces the urgency of eradicating hunger (Neumayer, 2010).
Jean Dreze and Amartya Sen wrote that “Hunger is a many- headed monster” highlighting the
many ways a lack of food can affect people's freedoms (Southgate, 1990).Hunger is also a
behemoth and a stubborn one. Hunger persists despite the remarkable boost in food production
brought about by the green revolution between the early 1960s and the early 1980s. by 2000
further gain in food production had contributed to lower prices for most staples. The share of
undernourished people in developing countries fell from 25 percent in 1980 to 16 percent in
2005(HDR, 2010). While many millions of people have too little to eat, millions eat too much.
The recent rise in obesity, especially in children, jeopardizes advances in the care of
cardiovascular disease, stroke and diabetes. Severe obesity can reduce life by 5 – 20 years, leading
some specialists to conclude that life expectancy in United States is likely to level off and may
even fall by 2050 (Barro, 1991). These risks are the result not just of higher income but also of
cultural influences that can be transmitted across borders. Mexico were peoples incomes average
only a fifth those of the United States, has shares of obeyed and overweight people similar to those
in the United States(Ibrahim and Alkire, 2007).
HEALTH STATUS OF INDIA – INTERNATIONAL COMPARISON
Health is a vital indicator of human development. Health stands in India have improved
considerably since independence. The concerted efforts to the government and other agencies
engaged in expanding the health infrastructure have paid off, as evidenced by the improvement in
some of our health indicators. Longevity has more than doubled since independence, infant
Mortality Rate has fallen, malaria has been contained, small pox and guinea worm have been
completely eradicated and leprosy and polio are nearing elimination. We have made deeper
inroads into rural areas with focused schemes like the National Rural Health Mission and have
even started a scheme for health insurance for the poor population.
Despite these achievements, the health services that India provides to her people continues to be
far from adequate and compares rather poorly with even Asia n neighbours like Sri Lanka and
China. One fifth of the world’s share of diseases is in India, there are huge regional disparities in
health standards in the country and huge gaps in health care infrastructure, in rural areas. The
reasons for this can be many, with centralize planning and low government spending on health
being some of the major among them. India spends only 1.1% of GDP on health against the 7.5 %
by United States, 7.1% by Norway as is shown in Table. 1. It is evident from the table that still
12% of the population in India do not have access to safe drinking water and 69% do not have
access to proper sanitation facility. India has lowest sanitation coverage among the neighbouring
countries. In developed courtiers 100% of the people have access to safe drinking water and
proper sanitation facility.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 68
Table 1 Economic and Health Indicators of India and Few Selected Countries
Country GDP per capita US$
(2008)
Expenditure on
health per capita (PPP$) (2007)
Public expenditure
on health as a % of GDP (2000-07)
% of people without
access to Safe
drinking water (2008)
% of people without
access to Proper
sanitation (2008)
Prevalence of under
nourishment (% of
total population
) (2004 – 06)
Intensity of food
deprivation (average % short fall in minimum
dietary energy requirements (2004 – 06)
Norway 94,759 47,63 7.5 0 0 <5 -
US 46,350 7,285 7.1 1 0 <5 -
Japan 38,455 2696 6.5 0 0 <5 -
UK 43,541 2,992 6.9 0 0 <5 -
China 3,267 2,23 1.9 11 45 10 13
Sri Lanka 2,013 179 2.0 10 9 21 14
India 1,017 109 1.1 12 69 22 15
Pakistan 9,91 64 0.8 10 55 23 16
Bangladesh 497 42 1.1 20 47 26 17 Source: UNDP, Human Development Report 2010
Percentage of malnourished population is quite high in all developing countries China has lower
percentage of malnourished population than that of India 10% population suffer from malnutrition
and 13% face food deprivation in India. Figure is little bit satisfactory when compared with
Pakistan and Bangladesh. Table 2.shows health indicators / outcomes of India vis – a – vis other
developed and developing countries. Table reveals that Number of physicians available per ten
thousand of population is more than 20 in case of developed countries while as it is lower than 15
in developing countries. Similarly number of Hospital beds available per 10 thousand of the
population varies from 39 to 139 in developed countries while as it varies from 4 to 31 in
developing countries. Life Expectancy in developed countries is more than 80 years while as it is
comparatively low in developing countries. India has lowest life Expectancy 64 years when
compared other neighbouring countries like Bangladesh, Pakistan , Sri Lanka and China. Infant
Mortality rate per thousand live births is 3 to 7 in DC’s, it is much higher in south Asian Countries
ranging from 18 to 72. Maternal Mortality Rate is less than ten in developed countries while as it
is 450 in India and 570 in Bangladesh. It is only 45 China and 58 in Sri Lanka. Further Total
Fertility Rate is quite high in developing countries India (2.5), Pakistan (3.6) compared to
developed countries which are less than 2. Similarly 34% infants lack immunization facility in
India against DTP and 2% against measles.
Health outcomes are influenced more by the share public expenditure in health expenditure rather
than the share of health expenditure in GDP. Per capita income of developed countries vary from
more than 50 to 80 times that of India among the neighbouring countries China and Sri Lanka as a
higher per capita income. Health expenditure as a percentage of GDP is significantly higher in
developed countries as compared to India and the neighbouring developing countries.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 69
Table 2 Health Indicators of India and Few Selected Countries
Country Physicians (per 10 thousand
people 2000-09)
Hospital beds (per 10
thousand people 2000-
09)
Life Expectancy (2010 )
Infant Mortality Rate (per thousand live
births 2008)
Norway 39 39 81.0 3
US 27 31 79.6 7
Japan 21 139 83.2 3
UK 21 39 79.8 5
China 14 30 73.5 18
Sri Lanka 6 31 74.4 13
India 6 9 64.4 52
Pakistan 8 6 67.2 72
Bangladesh 3 4 66.9 43
Maternal Mortality Rate
(2003 – 08)
Total Fertility Rate
(2010-15)
Infants lacking Immunization
against DTP (2008)
Infants lacking Immunization against (% of one year’s olds)
Measles 2008
Norway 7 1.9 6 7
US 11 2.0 4 8
Japan 6 1.3 2 3
UK 8 1.9 8 14
China 45 1.8 3 6
Sri Lanka 58 2.2 2 2
India 450 2.5 34 30
Pakistan 320 3.6 27 15
Bangladesh 570 2.2 5 11
Source: UNDP , Human Development Report 2010
HEALTH EXPENDITURES AND FINANCING AGENTS
In India over 80% of the health expenditure is private. As against this, in most developed
countries, more than 80 per cent of health expenditure is borne by the public exchequer. The NHS
(National Health Service) of the UK is an especially stark example of a state run and publicity
funded health care system. Along with the Scandinavian countries, the UK uses tax finances to
pay for 80 per cent of the health care spending. Elsewhere in Europe, social insurance schemes
shoulder most of the financial burden for health care. The United States (US) has its own system
of financing health care relying on private insurance paid, mostly, by the employers, almost half
of the super – sized health spending of the US (16 per cent of the GDP) is still financed by tax
money for the care of the old and the very poor (Kurain , 2010).
Due to very minor rule of insurance in Indian Health Sector, almost three – fourth of the total
health expenditure is borne by the households as out of pocket expenditure and it is estimated that
one quarter of all Indians slip below the poverty line in the event of hospitalization and more than
40% of the individuals who are hospitalized in India in a year barrow money or sell assets to cover
the cost of health care. Rising health care costs are major cause of indebtedness and
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 70
impoverishment especially in the context of the poor and marginalized as is evident from the
Table 3 NSSO surveys have established that the proportion of households which are unable to
seek any health care in the event of illness on account of cost considerations is on the increase
(GoI 2007, NCAER 2001). These are matters of serious concern for a nation which is emerging a
major force in the world arena.
Table 3 Measured levels of Expenditure on Health in India 2003 – 2007 (latest)
Selected national health accounts indicators
2003 2004 2005 2006 2007
Total expenditure on health as % GDP
2 3 4 5 6
General government expenditure on health as %
of total expenditure on health
4.2 4.0 3.8 3.6 4.1
Private expenditure on health as % of total
expenditure on health 20.4 20.9 22.4 25.0 26.2
General government expenditure on health as %
of total government expenditure
3.0 3.0 3.2 3.4 3.7
External resources for health as % of total expenditure on
health 0.6 0.7 0.5 1.0 1.4
Social security expenditure on health as % of general
govt. expenditure on health 5.8 5.8 5.2 4.9 17.2
Out of pocket expenditure as % of private expenditure on
health 92.4 92.3 91.9 91.4 89.9
Private prepaid plans as % of private expenditure on health
1.0 1.0 1.1 1.1 2.1
Source: - World Health Statistics 2010 (latest)
Note: Data are harmonized by WHO for international comparability. They are not
necessarily t he official of member states, which may use alternative methods.
Several mechanisms of financing have been considered such as user charges of government
services community financing and insurance. Health insurance to meet the cost of hospitalization
for major illness may ensure that health care costs do not come as a major financial, burden to the
patients or their families, particularly of the low and middle income group of population. Thus,
there is a great scope for extending health services of private sector hospitals and nursing homes.
Further, if the health services are to be delivered at affordable cost, it is imperative that the pattern
of the public health expenditure s be charged and private health sector needs regulated and a
constructive public – private sector partnership nurtured.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 71
HEALTH STATUS IN INDIA – INTERSTATE COMPARISON
Table 4 presents state wise data on major economic indicators and achievements of important
health outcomes per capita income of Major States for 2004 – 05 at constant prices (1999 -2000) is
given in column 2. The figure varies from lowest level of Rs 6610 in Bihar to highest figure of
29887 in Haryana, Bihar, Orissa, Uttar Pradesh have low level of perfect income while Haryana
and Maharashtra have highest level of income. Health expenditure as a percentage of total state
expenditure is represented in column third the figure varies from 4.65% in Kerala to 2.88% in
Maharashtra.
Maternal Mortality Ratio varies from low figure of 95 in Kerala to high of 480 in Assam. Light
Expectancy at Birth during 2002 to 2006 for males , females and total is given in column 4,5,and 6
male expectancy varies from 71.4 in Kerala to 58.1 in Madhya Pradesh While Female life
expectancy varies from 76.3 in Kerala to 57.9 in MP. Total life expectancy varies from 74 in
Kerala to 58 in Madhya Pradesh. Male infant mortality rate for 2007 varies from 12 in Kerala to
72 in MP. Female infant mortality rate varies from 13 in Kerala to 72 in Orissa and MP. Total
infant mortality rate varies from 13 in Kerala to 15 in MP. The ranking of major states on the basis
of economic and health indicators is presented in Table 5. It appears that there is not strong
relationship between the level of health indicators and income across Indian States.
Given the size, diversity, and stratified nature of Indian society, the health outcomes can be
described as mirroring the multiple axes of socio-economic inequalities, such as rural-urban; inter
and intra state; caste; income; and gender. Several studies have tried to capture these inequalities
by using the association between variables like level of education, type of housing, income, and
social groups with health outcomes like Infant Mortality Rate and Under-5Mortality Rate. The
1998-99 National Family Health Survey (NFHS)-2 reveals sharp regional and socio-economic
divides in health outcomes with the lower caste, the poor, and less developed states bearing a
disproportionate burden of mortality. The scheduled castes and scheduled tribes are clearly at
disadvantage and studies show that improvement has been slow in case of these groups as
compared to others. It is well known that IMR is a sensitive indicator for socio-economic and
health services development. This can be discerned when the IMR is disaggregated across socio-
economic groups and the association between the two is obvious. As Deogankar’s (2009) analysis
shows:’ The Infant Mortality Rate in the poorest 20% of the population is 2.5 times higher than
that in the richest 20% of the population. In other words, an infant born in a poor family is two and
half times more likely to die in infancy, than an infant in abettor off family. A child in the ‘Low
standard of living’ economic group is almost four times more likely to die in childhood than a
child in the ‘High standard of living’ group. A child born in the tribal belt is one and half times
more likely to die before the fifth birthday than children of other groups. A female child is 1.5
times more likely to die before reaching her fifth birthday as compared to a male child’ Based on
the analysis of two rounds of NFHS, Subramanian et al.(2006) show the existence of gender and
caste differentials. The gender differentials are not marked for IMR but the divide becomes
apparent for the Under-5 Mortality Rates, indicating that social discrimination against girl children
begins early and contributes to their progressive neglect throughout their life. The risk of mortality
before the age of 5 is higher for girls than for boys on one hand, and for schedule caste, schedule
tribe, other backward classes, and the rural areas of one of the poorest states than for all India on
the other. While the all-India average for U-5MR came down from 95 to 74 between 1998 and
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 72
2006, it shows an increase in inequality in U-5MR for the scheduled caste and scheduled tribe
communities when compared to the all India average. The socio-economic inequalities get further
compounded by inter-state and intra-state inequalities in IMR and the Under-5 Mortality Rates.
Table 4 Economic Growth and Health Status in Major States
States PCI# 2006
Health Exp (%)
MMR LEB IMR
Male Female Total Male Female Total
Andhra Pradesh 21277 3.22 154 62.9 65.5 64.4 54 55 54
Assam 14950 3.08 480 58.6 59.3 58.9 64 67 66
Bihar 6610 4.12 312 62.2 60.4 61.6 57 58 58
Gujarat 26543 3.06 160 62.9 65.2 64.1 50 54 52
Haryana 29887 3.19 186 65.9 66.3 66.2 55 56 59
Karnataka 21829 3.77 213 63.6 67.1 65.3 46 47 47
Kerala 25657 4.65 95 71.4 76.3 74 12 13 13
Madhya Pradesh 12566 3.19 335 58.1 57.9 58 72 72 72
Maharashtra 29085 2.88 130 66.0 68.4 67.2 33 35 34
Orissa 13329* 4.41 303 59.5 59.6 59.6 70 72 71
Punjab 28605 3.01 192 68.4 70.4 69.4 42 45 43
Rajasthan 15219 3.9 388 61.5 62.3 62 63 67 65
Tamil Nadu 24308 3.43 111 65.0 67.4 66.2 34 36 35
Uttar Pradesh 10637 3.86 440 60.3 59.5 60 67 70 69
West Bengal 20485 4.32 141 64.1 65.8 64.9 36 37 37
Source: - Statistical Digest 2007 -08 Directorate of Economics and Statistics, Planning and
Development Department Government of J&K ; Economic Survey 2009 – 10 , GOI. National
Health Accounts Report 2004 – 05 of MOHFW, GOI; www.mp.gov.in/health/mmr-bult-
april2009pdf.
Note: # PCNSDP at constant 1999-2000 prices; Health Expenditure as percentage of Gross
State Expenditure
The sharp inter-state inequality in health outcomes can be illustrated by contrasting Kerala and
Tamil Nadu, which represent the better developed states, with Uttar Pradesh and Bihar, which are
ranked as less developed. While socio-economic factors are important determinants of health
outcomes, health services play an important role in averting deaths by providing both preventive
and curative services. Therefore, it can be argued that differences in availability, accessibility, and
quality of health services are an important determinant of variations in health outcomes. Available
evidence from India shows that there are variations in the financing and provisioning of public and
private health services (Baru, 1999; Krishnan, 1999). The better developed states have a functional
public sector as well as a large private sector, while less developed ones like Bihar, UP, MP, and
Rajasthan have a weak public and private sector. NSS data on utilization shows that there is high
reliance across states on the private sector for outpatient treatment, which is dominated by
informal practitioners. Given the federal nature of the State, the major responsibility for financing,
provisioning, and administration of health rests with the respective states that influence
availability, accessibility, and acceptability of services. Rao (2007) in his analysis of financial
variations shows that while per capita spending on heal this Rs 35.05 for Kerala and Rs42 for
Tamil Nadu, it is abysmally low for UP at Rs18.10p during 1998-99. This is just to illustrate the
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 73
extent of variation in health spending while fully acknowledging that per capita figures are mere
averages which, in themselves, mask inequities. The pattern of health spending influences the
structure of provisioning of health services.
Table 5 Major States Ranked on the basis of Economic Growth and Health Status
States PCI# 2006
Health Exp (%)
MMR LEB IMR
Male Female Total Male Female Total
Andhra Pradesh 8 9 5 8 8 8 8 8 8
Assam 11 12 15 14 14 14 12 10 12
Bihar 15 4 11 10 11 11 10 12 10
Gujarat 4 13 6 8 9 9 7 7 7
Haryana 1 10 7 4 6 4.5 9 9 9
Karnataka 7 7 9 7 5 6 6 6 6
Kerala 5 1 1 1 1 1 1 1 1
Madhya Pradesh 13 10 12 15 15 15 15 14 15
Maharashtra 13 15 3 3 3 3 2 2 2
Orissa 12 2 10 13 12 13 14 14 14
Punjab 3 14 8 2 2 2 5 5 5
Rajasthan 16 5 13 11 10 10 11 10 11
Tamil Nadu 6 8 2 5 4 4.5 3 3 3
Uttar Pradesh 14 6 14 12 13 12 13 13 13
West Bengal 10 3 4 6 7 7 4 4 4
Source: Same as Table 4
Note: Same as Table 4
Further there is no discernable relation between per capita income and share of government
expenditure on health care. Haryana, Maharashtra, Punjab rank high in per capita income while
Kerala, Orissa, West Bengal are top raking states in State expenditure on health care. Maharashtra
spend low share on health care Bihar, UP having low per capita income spend high share on health
care. In terms of health outcomes Kerala ranks 1 in all health indicators it ranks high in health
expenditure while it ranks fit in per capita income. Maharashtra which ranks two in per capita
income and health expenditure share rank of 15, has been second best ranking State in terms of
health outcomes followed by Punjab, Tamil Nadu.
Public and private expenditure on Health Care in Major States for 2004 – 05 on the basis of
National Health Accounts Statistics are presented in Table6. Per capita public expenditure on
health care varies from high figure of Rs. 630 in HP to a very low figure of Rs. 128 in UP. Private
expenditure across major Indian States is presented in column 3 where figure varies from Rs. 2663
in Kerala to a lower figure of Rs. 420 in Bihar. Assam ranks one in per capita public expenditure
while as Kerala stands on the lowest ebb in per capita public spending. High per capita income
states like Haryana, Maharashtra, and Punjab have relatively low level of per capita public
spending. Kerala , HP, Assam for top ranking states in terms of total expenditure while as Bihar
Rajasthan MP are bottom ranking states in total expenditure on health care.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 74
Table 6 Public and Private Expenditure on Health in Major States
Per capita Health Expense
per annum (Rs) Public Exp as % to total
Ranks
States Public Pvt Total Pub %
Pub Exp
Pvt Exp
Tot Exp
Andhra Pradesh 191 870 1061 18.00 12 11 8 9
Assam 841 613 1454 57.8 1 1 13 3
Bihar 93 420 513 18.1 10 17 17 17
Gujarat 198 755 953 20.7 6 10 10 12
Haryana 203 875 1078 18.8 8 9 7 8
Karnataka 233 597 830 28.0 4 6 14 14
Kerala 287 2663 2980 9.7 17 4 1 1
Madhya Pradesh 145 644 789 18.3 11 15 12 15
Maharashtra 204 1008 1212 16.8 14 8 5 7
Orissa 183 719 902 20.3 7 13 11 13
Punjab 247 1112 1359 18.17 9 5 2 4
Rajasthan 186 575 761 24.4 5 12 14 16
Tamil Nadu 223 1033 1256 17.7 13 7 4 6
Uttar Pradesh 128 845 974 13.1 16 16 9 11
West Bengal 173 1086 1259 13.7 15 14 3 5
Jammu & Kashmir 512 489 1001 51.14 2 3 16 10
Himachal Pr 630 881 1511 41.7 3 2 6 2
Source: National Health Accounts; (Ranks computed)
However, there is close relation between the income and expense indicators and the health status
indicators as evident from Table 7. Higher income and expense leads to lower infant and maternal
mortality and higher life expectancy.
Table 7 Association between Health Indicators and Select Variables
Per Capita Income
Per Capita Health Expense
MMR -0.77 -0.39
IMR -0.64 -0.72
LEB 0.77 0.70 Source: Author’s calculation
RURAL URBAN INEQUITY IN HEALTH SERVICES
Although health indicators have continued to improve over time, villagers are far behind the towns
and cities in case of healthcare facilities and their outcomes. This difference can be observed both
qualitatively and quantitatively. The major part of the healthcare facilities in rural areas are
provided by the unqualified and untrained medical professionals. Most of the public hospitals
and dispensaries are located in urban areas and almost all private clinics and nursing homes are in
the urban areas. If we look at the rural – urban difference in the level of healthcare infrastructure,
we find that villages are far behind the cities. For instance in 2001, there were 0.54 hospitals, 1.49
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 75
dispensaries and 15.05 hospital beds per lakh population in rural India. While the corresponding
figures in Urban India were 0.80,102.90 and 3.60 respectively.
Table 8 Rural Urban Inequity in Health Status
Total fertility rate 3.07 2.85
Births assisted by health professions % 33.5 73.3
Birth delivered in medical institution 24.6 65.1
Two does or of TT vaccination during pregnancy (%) 62.5 81.9
Mothers who had at least 3 antenatal care visits for their last birth (%)
22.4 54.7
Total unmet need for family planning (%) 16.7 13.4
Women whose body mass index is below normal (%) 40.6 22.6
Ever – married age 15 – 49 who are anaemic (%) 53.9 45.7
Children 12-23 months fully immunized (BCG , measles and 3 doses each of polio / DPT) %
29.3 51.9
Children 12 – 23 months who have received BCG % 67.1 86.8
Children 12 – 23 months who have received 3 doses of polio vaccine (%)
58.3 78.2
Children 12-23 months who have received 3 doses of DPT Vaccine (%)
49.8 73.4
Children with Diarrhoea in the last 2 weeks who received ORS (%).
25 32.7
Children with Diarrhoea in the last 2 weeks taken to a health facility (%)
59.9 75.2
Children with acute respiratory infect ion or f ever in the last 2 weeks taken to a health facility (%)
61.8 75.1
Children age 6 – 35 months who are anaemic (%) 75.3 70.8
underweight children below 3 years age (%) 49.6 38.4
infant death (per 1000 of live children) 73 68
under five mortality (per 1000) 103.5 63.1
CDR (pr 1000) 10.4 7.4
Source: National Family Health Survey III 2005-06.
Table 8 provides information on some of the health indicators for the last survey of National
Family Health Survey. Table reveals that Urban India has better outcome in case of almost all
health indicators. Fertility rate. Infant death rate, under – 5 mortality rate, and CDR are much
higher in rural areas than the urban areas. Percentage of total unmet need for family planning was
also higher in the rural area when compared to the urban areas.
It is evident from Table 8 that overall health status of women and children in the rural India is
much poorer than their urban counterparts. For example, the results of NFHS – III (2005 – 06)
reveal that percentage of rural women with body mass index (BMI) below normal was 38.8 while
the corresponding percentage in urban women was only 19.8. There were more anaemic women
and children in rural areas than in urban areas, as is evident from the data given in the Table.
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 76
Percentage of fully immunized children age 12.23 months were only 38.6 percent in rural areas,
while the corresponding percentage in urban areas was 57.5 percentages of children with acute
respiratory infection (ARI) in the last 2 weeks, who were taken to a health facility, were 59.9 in
rural areas and 78.1 in urban areas. These figures clearly indicate that there exists wide inequality
in the distribution of healthcare infrastructure among rural and urban locations. Due to the
deficiency of proper medical aid, the death rate, infant mortality rate and fertility rate all are
higher in the villages than the cities. Although these rates have been declining over the years,
these are still high especially in the rural India. Provision of public health services, such as, access
to basic and preventive healthcare sanitation , clean water and raising awareness about the causes
of illness and their treatment are necessary for improving human development in rural India.
POLICY IMPLICATIONS
The problems of health care are enormous. Access to primary health care is inadequate to the
majority of the people because of low availability of basic preventive and promotive health care
packages, clinics, doctors, drugs and paramedical persons in rural areas. Greater stress on
preventive health care medicine and health education should be laid. Health literacy efforts should
be made integral to preventive, promotive’ curative and rehabilitative health care .A meaningful
involvement of private sector and NGOs is critical in all these endeavours for promoting a
people oriented and sustain able health care system.
A vast network of health institutions has been developed .Rapid expansion has however, resulted
in a considerable drop in the quality of functioning of health institutions .For several reasons the
quality of services and work done by various health institutions and by different categories of
health personal are poor, resulting I n low credibility among rural community .Moreover, for want
of quality, the efficiency and effectiveness of the programmes and services has been limited and
the objectives not fully realized. This is one of the causes of non utilization or underutilization of
health services and facilities by the people especially the rural communities.
Organisation of health services has become complex, centralized and insensitive to the varying
health felt-needs of the rural community. It is suggested that organizational setup of health
services needs organization .While the health organization has grown tremendously, functionally
the structure has changed with the dynamic and divergent demands of effective health
management. The middle level management is weak because of low status accorded to training in
public health, inadequate decentralization of authority and resources allocation. The most
important problem is the mal-distribution of health manpower, both geographically and category
wise. Both technical knowledge and motivation to serve rural people fall short of requirement and
expectations.
Communicable diseases such as malaria, tuberculosis, leprosy are likely to continue to pose
challenges to the country in the coming years. Non-communicable diseases will become a major
health problem in the country due to the changing lifestyles, increasing stress and tensions and
cultural systems in the society. With increase in the number of aged people, there will be higher
incidence and prevalence of diseases like hypertension, diabetes, cancer in the whole range of
genetic problems.
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 77
Equitable distribution of rural health care should be ensured by the government. Location of health
services and facilities should be such that these are easily accessible and available to rural
community.
The most pervasive inadequacy and critical deficiency of our primary health care system is the
non – availability of medical staff and other supporting personnel in primary Health care system
sis the non – availability of medical staff and other supporting personal in rural areas. An
ineffective supervisory and monitoring system compounded by corrupt practices, helps to sustain
this situation. This virtually renders the public health units in rural areas non – functional. As a
result of the non – availability of doctors the implementation of many public health programmes
has been adversely affected.
The lack of effective initiative in regulating the private health sector is another area where the
“soft” character of the state is evident. The incidents of unethical practices reported from private
sector health providers are mounting, and these, are also reported by the media from time to time.
These practices range from exorbitant charges , unnecessary and superfluous investigations, lack
of quality care, negligence and total lack of accountability (Nandraj 1994) private sector hospitals
suffer from inadequate and unqualified medical and Para Medical Personnel, unclean
environment, improper location of facility, negligence and unethical behaviour. State
Governments have failed to take adequate steps by enacting tough laws and introducing strict
regulations followed by rigorous inspections to check unethical practices and to protect consumer
interests as well as health standards.
The location of facility and allocation of resources to specific health units / schemes/ programmed
that are, quite often , governed by political considerations ( Jeffery , 1988). Ideally, the available
funds should be distributed to units, areas and programmes as per norms based on objective
consideration. In respect of health units, for example, these considerations should include the
population and the physical areas to be served, the level of disease burden, status of existing
facilities, inefficiencies in the infrastructure and the priorities in gaps to be bridged, etc. however,
these objective norms are rarely followed in taking such decisions. The skewness in the
distribution of resources based on these influences leads to several distortions. One of them is the
vide gap in the quality of Health Care between Rural and urban areas. The latter, in any case,
consume larger resources because of the location of hospitals. Thus the rural – urban inequity gets
accentuated. The other consequence is the increase in the regional imbalance between backward
areas and more developed/ prosperous areas.
In view of experiences and difficulties faced in the provision of rural health services, it has been
realized that acceleration of the pace of implementation of rural health programmes is urgent and
concerted efforts need to be made for rapidly improving the health profile of the country. For
making rural health care services more meaningful to the rural community, it needed to bring
about fundamental changes in the approach to the entire health care delivery system in general and
rural health care in particular.
________________________________
Reference
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Kurian, N.J. 2010- “Issues of Health and Equity in India” India Social Development Report, Council for Social Development, Oxford University Press,
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OECD ( Organization for Economic Co – operation and Development) 2008-. Survey on Monitoring the
Paris Declaration Making Aid More Effective by 2010 Paris. Park (1994) , Preventive and Social Medicine, Banarasi Das Publishers , Jabalpur. Rao, M.(2007),‘Health in India in the Age of Globalised Governance’ in Kameshwar Choudhary (ed.)
Globalisation, Governance Reforms and Development in India, Sage, New Delhi, pp.491-521 Saxena, K.B( 2006)- “ Governance and Health Sector” , Securing Health for All Dimensions and Challenges
(eds). Sujata Prasad and C. Sathyamala, Institute of Human Development, New Delhi .
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Singh, S . P (2007)-, “Growing Rural – Urban Disparities in India” , Kurukshetra Vol. 56, No. 1. , November.2007.
Subramanian, S.V., S. Nandy, M. Irving, D. Gordon, H. Lambert, and G.D. Smith (2006)-, ‘The Mortality Divide in India: The Differential Contributions of Gender, Caste and Standard of Living Across the Life Course’, American Journal of Public Health, Vol.96,No 5, p. 818-25
UNDP(2010)- Human Development Report 2010, Palgrave Macmillan, New York. UNICEF (1998)- “ The State of the worlds Children” 2001 New York Oxford University Press. UNICEF (2000)- The State of the Worlds Children”. 1999, New York, Oxford University Press. UNDP (2004) -“Human Development Report 2004” Cultural Liberty in Todays Diverse World , New York:
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JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 80
INDIAN JOURNAL OF HUMAN DEVELOPMENT The Indian Journal of Human Development (IJHD) is a peer-reviewed multi disciplinary Journal, published bi-annually by the Institute for Human Development, New Delhi. It provides an open platform for promoting debate and discussions from a human development perspective and also promotes critical engagement with human development discourse. IJHD publishes articles, reviews, perspectives, research notes/commentaries, statistics relating to human development and book reviews on India and developing world. The Journal welcomes expressions of all shades and opinions.
CURRENT ISSUE
The current issue brings together works of internationally renowned scholars and Indian researchers on issues such as human development indicators and social exclusion, social sector expenditures and impacts on human development, the primacy of politics in poverty reduction and development, citizenship and displacement, social investments and interpretation of care needs, interdependence between growth and inequality and poverty and inequality in high growth periods in the Indian context.
Some of the articles published recently in IJHD include:
Amartya Sen: Children and Human Rights Arjun Sengupta: A Rights-Based Approach to Removing Poverty Amitabh Kundu: Achieving Diversity in Socio-economic Space: An Alternate Strategy of Intervention through the Diversity Index Ashwani Saith: Downsizing and Distortion of Poverty in India: The Perverse Power of Official Definitions Guy Standing: Reviving Egalitarianism in the Global Transformation: Building Occupational Security Jan Breman: The New Poverty Line: A Poor Deal Jean Drèze, Reetika Khera and Sudha Narayanan: Early Childhood in India: Facing the Facts Ravi Kanbur: What's Social Policy Got to Do with Economic Growth? Sabina Alkire and Suman Seth: Determining BPL Status: Some Methodological Improvements Sukhadeo Thorat: Social Exclusion in Indian Context Zoya Hasan: Equal Opportunity Commission and the Possibilities of Equality
SYMPOSIUM VOLUMES
IJHD publishes scientific papers and articles from symposiums and seminars on key aspects of human development. Some of the issues covered in recent volumes of IJHD includes Reports of the Expert Groups on Equal Opportunity Commission and Diversity Index (July-December 2009), Estimation of Poverty and Identifying the Poor (January-June 2010), and The Idea of Justice (January-June 2011). Details of papers in these volumes can be found at the Journal website. All correspondence should be addressed to :
The Editor
Indian Journal for Human Development
Institute for Human Development
NIDM Building, IIPA Campus,
IP Estate, New Delhi-110002
Email:[email protected]; Website : http://www.ihdindia.org/ihdjournal
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 81
Book Review
Unfolding Crisis in Assam's Tea Plantations: Employment and Occupational Mobility; Deepak K. Mishra, Vandana Upadhyay, Atul Sarma; (Vol. 3 of the Transition in
Northeastern India Series, series editor: Sumi Krishna); 246 pages; Price: ` 695; ISBN
0415523087
The book under review is the third under the series Transition in Northeastern India brought out
by Routledge. The series aims to broaden the focus to the processes and practices that have
shaped, and are shaping, the people’s identities, outlook, institutions and economy in the seven-
sister and a brother states of India’s northeast. Eschewing the homogenising term ‘North East’,
which was imposed on the region in a particular political context half a century ago, the series title
refers to the ‘north eastern region’ to more accurately reflect its heterogeneity and the varied
issues confronting its diverse people. In this book, the authors have drawn attention to the one of
the most important industry of the region and the crisis that is slowly engulfing this oft talked
about sector.
Of the six chapters, the introductory chapter contextualizes the historical perspective of the tea
industry, its importance and place in Indian economy vis-a-vis the present scenario of the industry,
nature of recent crisis and the probable reasons behind it. The tea industry presently is under threat
and the people dependent and involved with this sector are facing severe difficulties in terms of
maintaining their livelihood. The major crises being faced by the Indian tea industry include
production stagnation, declining export and shut down of number of tea gardens. Though a large
section of the book discusses these crises, the primary focus of the study is to understand the
working condition of tea industry in Assam, labour relations therein, issues related to livelihood
diversification and intergenerational changes in the sector. Though existing research points at high
labour cost arising out of security provided through state regulation as the major reason behind the
current crisis in the sector, the authors argue that the post-liberalization policies have destroyed the
foundation of Indian agricultural sector due to state negligence and lack of capital investment, and
the situation of tea industry is no exception. Though characteristically it is a sector which includes
both agricultural and industrial processes, it has lost due to the dwindling fortune of agricultural
sector, while not gaining from the industrial growth story. The importance of the study lies in this
new outlook.
The authors discussed in brief the historical perspective of tea sector and initial nature of worker
relation with the management in colonial time and argued that the root of the problem deeply lies
in that era. Though in Assam the tea plantation initially got started with workers from China for a
short period of time and then successively being carried over by the locals, quickly the situation
changed with migrant labour, mainly from tribal belt of central India, taking the predominant
place. The system of bonded labour, indenture and unfreedom became synonymous with
plantation labour market which in turn made the labourers vulnerable in spite of state regulation.
The authors’ argued that in Assam, the tea workers, mainly the tribal migrants, remained close to
the garden enclave and as a result their social integration with the locals happened to be weak with
low mobility which in turn excluded them socially and economically. The authors’ discussed the
implication of globalization in a labour market with rigid formal sector regulations and how the
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 82
labour market experienced inter sectoral wage gap which also saw informalisation of formal
sector. Though the tea sector is the ‘oldest globally oriented sector’, the labourers are
continuously facing new challenges of liberalized regime with fall in auction price after
liberalization, lower demand due to break down of Soviet Union (happened to be the highest
importer of Indian tea) and low productivity resulting in closing of tea gardens, eviction of
workers, higher malnourishment, dropout rates and higher female migration.
In the second chapter the authors have discussed the growth performance of Assam tea industry at
the district level in terms of area, production and productivity to capture the trend over time along
with a national comparison, linking it with world capitalism to capture the role of capitalism in
plantation sector throughout the world particularly in India under British rule. They argue that the
development of tea sector in Assam was associated with colonial interest as the capital and the
management were being provided by them whereas the land and labour were being procured from
local area. In Assam the process of shifting from subsistence farming to export oriented
production in colonial era occurred suddenly and hurriedly through means like preferential and
promotional treatment for planters neglecting interest of local farming and tribal communities
resulting in socio-cultural and economic discrimination and the process continued over time and
increased further under liberalised regime. They observe that the steady rise in area, production
and productivity of tea in India were suddenly reversed in the last decade or so, more so in Assam
because of the inadequate replanting of bushes and less investment in large and medium size
gardens along with proliferation of small gardens.
The third chapter of the book deals with demand and supply side of labour market, its
characteristics, composition and labour-use pattern in tea sector based on secondary data. Authors
point out that employment growth is slower in Assam compared to national average and
employment elasticity also declined in Assam
In the fourth chapter the authors have discussed the intergenerational occupational mobility among
tea garden labourers following the mobility matrix approach and also spatial mobility from tea
garden to outside. The study is crucial and highly appreciable from the point of view of paucity of
intergenerational mobility studies in Indian context. They used the primary data from their
rigorous field survey in three districts of Assam – Sivasagar, Dibrugarh and Lakhimpur. They
have divided the data into two sets – one set for households residing within tea gardens with main
sources of income coming from tea garden, and another set for households living outside tea
gardens but who were tea garden workers earlier. This chapter discussed elaborately the
occupational pattern and distribution among different categories for present and earlier generations
as well as presented outflow matrix to capture the intergenerational mobility scenario. It is found
that among the tea garden workers earlier generation has little occupational diversification while it
is higher for ex-tea garden households where the predominant category is farming. The immobility
is highest among categories of permanent and casual labourers of tea gardens for households
within tea garden while for the ex tea garden households quite expectedly immobility is highest
among the cultivators. The authors included unemployment as one of the occupational status
which has a significant implication for the present generations with the figure standing at 15 per
cent for the first set of household and 25 per cent for the later. Discussing this aspect in greater
detail would have added value to the publication. It is concluded that there is insignificant
presence of members from labour household in upper strata of jobs, vertical mobility is low and
Journal of Regional Development and Planning, Vol. 2, No. 1, 2013 83
dependence on tea gardens have been higher in the earlier generations, which is understandable.
But this process is true for the present generation also, and the authors have rightly pointed out
that their division of data between the two sets has less analytical significance in view of the
results obtained. The crux of the analysis lies in the fact that the household of present generation
inside or outside the garden in the study are finding severe difficulties to moving out of tea garden
in spite of its present deteriorating condition. To determine the factors influencing the spatial
mobility and diversification among the present generation a binary logistic regression is used. It is
concluded that among the tea garden household the crucial factors are age, gender, education of
the household member and nearness to urban location. It would have further improved the
intergenerational mobility analysis if a set of households were taken who are from non tea sector
of the same locality under study as a control set.
The final chapter provides policy implications based on observations from previous chapters as
well as from perception of workers regarding occupational conditions and factors behind
diversification strategies. Given the limitation of such perception survey the authors concluded
that along with various factors influencing the decision, the permanent workers are less inclined to
change the occupation compared to casual workers. The lack of education and skill is considered
as one of the barriers to move out which is resulting out of poor access to education. Relatively
better social security in gardens, low asset condition and fear of exclusion due to ethnic
background also worked as bottleneck to move out of garden for better jobs.
Notwithstanding the couple of limitations as pointed out above, this book is an excellent collection
for researchers, especially those who are unaware of the global political economy of tea industry
and the process through which the entire region came to be dependent on this sector, only to fall
prey to the whirlwinds of globalisation in recent years. This historical perspective which has been
shown to be equally relevant in the genesis of current crisis faced by the sector and the region is
the most important contribution of this book. We wish more such work from the authors and hope
that the series will next take up other geo-spatial regions of the country for extensive study.
.
Jhilam Ray
JOURNAL OF REGIONAL DEVELOPMENT AND PLANNING 84
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