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 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


Quarterly Journal of The Indian Society of Labour Economics

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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.

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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.


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.


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


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.


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


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


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


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


“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


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.


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


References

ASEAN Studies Centre (2009):''ASEAN economic community blueprint''. Singapore: Institute of Southeast

Asian Studies. Asian Survey, 42:3, pp. 440–455. ISSN: 0004–4687.

Begg, I., Belke, A., Dullien, S., Schwarter, D. and Vilpisauskas, R. (2011): European Economic Governance.

Impulses for Crisis Prevention and New Institutions. Europe in Dialogue 2011 / 12 Bertelsmann

Stiftung. ISSN: 1868-5048

Bishop, G. (2011): The EU Fiscal Crisis: Forcing Eurozone Political Union in 2011? Searching Finance Ltd,

London, ISBN 978-1-907720-14-7, p. 94

Chung, W.C. (2006): ''Asian Bond Markets Initiative''. Japanese Representative Office. Asian Development

Bank: August.

COUNCIL OF THE EUROPEAN UNION (2010): Final Report by the Task Force, 21 October 2011, doc

15302/10, Brussels

Dong, L. and Heiduk, G. (2007): ''The EU's Experience in Integration. A Model for ASEAN+3?'' Bern: Peter

Lang.

EURO AREA (2011): Treaty Establishing the European Stability Mechanism, website of the Council of the

European Union, http://www.consilium.europa.eu/policies/council-configurations/economic-and-

financial-affairs/the-eurogroup.aspx?lang=en

Featherstone, K. (2011): The Greek Sovereign Debt Crisis and EMU: A Failing State in a Skewed Regime.

Journal of Common Market Studies, vol. 49, No 2, pp. 193-217, Oxford

Heinen, N. (2011): Constitutional complaints. German rejection of rescue packages unlikely. Deutsche Bank

Research, Frankfurt am Main, Germany, March 17, 2011

Institute of South Asian Studies (ISEAS) (2010): ''The global economic crisis: implications for ASEAN''.

Singapore.

Japan Bank for International Cooperation (2011): ''Asian Bond Markets Initiative (ABMI)''.

Jialu, C. (2011): ''ASEAN Plus Three Countries deepen Cooperation''. China Daily, 20 May 2011.

Kanamori, T. (2011): ''Emerging Issues for Regional Cooperation in Asia-Pacific''. ADBI Research Policy

Brief NO. 14, Regional Cooperation, 19.05.2011.

Koh, T., Manalo, R.G. and Woon, W. (2009): ''The making of the ASEAN Charter''. Singapore: World

Scientific Publishing.

Majid, S. (2009): ''Global financial interdependence: ASEAN emerging markets versus US and Japan''.

Köln: Lambert Academic Publishing.

Pradumna, B. R. (2002): ''Monetary and Financial Cooperation in East Asia: The Chiang Mai Initiative and

Beyond''. (ERD Working paper No.6, Economics and Research Department). Asian Development

Bank.

Severino, R.C., Thomson, E. and Hong, M. (2010): ''Southeast Asia in a New Era. Ten countries, one region

in ASEAN''. Singapore: Institute of Southeast Asian Studies.

Sim, E.W. (2009): ''Improving Governance in the ASEAN economic Community''. Asian International

Economic Law Network (AIELN) Inaugural Conference, 9 June 2009.

Stubbs, R. (2002): ''ASEAN PLUS THREE. Emerging East Asian Regionalism?'' University of California,

Press, Journals Division.

The Association of Southeast Asian Nations (2008): ''ASEAN Economic Community Blueprint''. Jakarta,

Public Affairs Office of the ASEAN Secretariat.


The Association of Southeast Asian Nations Secretariat (2010): ''ASEAN Economic Community Scorecard:

Charting Progress towards Regional Economic Integration''. Public Outreach and Civil Society

Division: Jakarta, March 2010.

Verhelst, S. (2011): The Reform of European Economic Governance: towards a Sustainable Monetary

Union? Egmont - The Royal Institute for International Relations, Brussels, ISBN: 978-90-382-

1802-1, p. 62

Welfens, P.J.J., Ryan, C., Chirathivat, S. and Knipping, F. (2009): ''EU-ASEAN: Facing economic

globalisation''. Berlin: Springer, 2009.


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].


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.


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


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).


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.


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.


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


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


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


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


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).


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.


Fig. 1


Fig. 2


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


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


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.


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).

References Bansil, P.C. (2003) – “Demand For Food Grains By 2020 Ad”, in S. Mahendra Dev et al. (eds.)

Towards A Food Secure India: Issues And Policies, Institute For Human Development, New Delhi.

Deaton, A. & Dreze, Jean (2009) – “Food and Nutrition in India: Facts and Interpretations”, Economic & Political Weekly, Vol. 44, No. 7, pp. 42-65.

Deaton, A. & Dreze, Jean (2010) – “Nutrition, Poverty and Calorie Fundamentalism: Response to Utsa”, Economic & Political Weekly, Vol. 45, No. 14, pp. 78-80.

Dev, S. M. (2005) – “Calorie Norms and Poverty”, Economic & Political Weekly, Vol. 40, No. 8, pp. 789-792.

Dubey, A. & Thorat, S. K. (2012) – “Has growth been socially inclusive during 1993/94-2009/10?” Economic & Political Weekly, Vol. 47, No. 10, pp. 43-54.

International Institute for Population Studies (2005-06) – National Family Health Survey 2005-06,

Fact Sheet, International Institute for Population Studies, Mumbai. Jones, R. Palmer & Sen, K. (2001) – “On India’s Poverty Puzzles and Statistics of Poverty”,

Economic & Political Weekly, Vol. 36, No. 3, pp. 211-217. Kumar, P., Mruthyunjaya & Dev, Madan M. (2007) – “Long Term Changes in Indian Food Basket

and Nutrition”, Economic & Political Weekly, Vol. 42, No. 35, pp. 3567-3572. Martorell, Reynaldo & Ho, Teresa J. (1984) – “Malnutrition, Morbidity, and Mortality”,

Population and Development Review, Vol. 10, pp. 49-68. Mehta, Jaya & Venkatraman, S. (2000) – “Poverty Statistics, Bermicide’s Feast”, Economic &

Political Weekly, Vol. 35, No. 27, pp. 2377-2382. Mehta, Jaya (1982) – “Nutritional Norms and Measurement of Malnourishment and Poverty”,

Economic & Political Weekly, Vol. 17, No. 33, pp. 1332-1340. Ministry of Home Affairs (2010) – Sample Registration System, Report No. 1 of 2012, Statistical

Report 2010, New Delhi. Nasurudeen, P., Kuruvila, A., Sendhil, R. & Chandresekar, V. (2006) – “The Dynamics and

Inequality of Nutrient Consumption in India”, Indian Journal of Agriculture Economics, Vol. 61, No. 3, pp. 362-373.

Patnaik, Utsa (2004) – “Republic of Hunger”, Social Scientist, Vol. 32, No. 9/10, pp. 9-35. Patnaik, Utsa (2007) – “Neoliberalism and Rural Poverty in India”, Economic & Political Weekly,

Vol. 42, No. 30, pp. 3132-3150.


Patnaik, Utsa (2010) – “A Critical Look at Some Propositions on Consumption and Poverty”, Economic & Political Weekly, Vol. 45, No. 6, pp. 74-80.

Radhakrishna, R. & Reddy V. (2004) – ‘Food Security and Nutrition: Vision 2020’, [planningcommission.nic.in/reports/.../bkpap2020/16_Bg2020.pdf; accessed on 9 August 2010].

Radhakrishna, R. (2005) – “Food And Nutrition Security of the Poor, Emerging Perspectives and Policy Issues”, Economic & Political Weekly, Vol. 40, No. 18, pp. 1817-1821.

Radhakrishna, R., Rao, K. Hanumantha, Ravi, C. & Reddy, B. Sambi (2004) – “Chronic Poverty and Malnutrition in 1990s”, Economic &Political Weekly, Vol. 39, No. 28, pp. 3121-3130.

Rao, H. C. H (2000) – “Declining Demand for Food-Grains in Rural India: Causes and Implications”, Economic & Political Weekly, Vol. 35, No. 4, pp. 201-206.

Ray, Ranjan (2005-10) – ‘Analysis of Changes in Food Consumption and their Implications for Food Security and Undernourishment: The Indian Experience in the 1990s’, Discussion

Paper, University of Tasmania. Reddy, D. N. & Mishra, Srijit (2010) – Agrarian Crisis in India, Delhi: Oxford University Press. Sen, Pronab (2005) – “Of Calories and Things Reflections on Nutritional Norms, Poverty Lines

and Consumption behaviour in India”, Economic & Political Weekly, Vol. 40, No. 43, pp. 4611-4618.

Shariff, Abusaleh & Mallick, A. C. (1999) – “Dynamics of Food Intake and Nutrition by Expenditure Class in India”, Economic & Political Weekly, Vol. 34, No. 27, pp. 1790-1800.

Verma, Med Ram, Datta, K. K., Mandal, Subhasis & Tripathi, A. K. (2008) – “Diversification of Food Production and Consumption Patterns in India”, Journal of Agricultural & Food

Information, Vol. 8, No. 3, pp. 87-100. Viswanathan, Brinda (2001) – “Structural Breaks in Consumption Patterns: India 1952-1991”,

Applied Economics, Vol. 33, No. 9, pp. 1187-1200. World Food Programme (2009) – ‘India Tops World Hunger Chart’,

[http://www.wfp.org/Countries/India/News/Hunger-In-The-News?Page=4; accessed on 23 July 2011]

World Health Organisation (2003) – ‘Diet, Nutrition and the Prevention of Chronic Diseases’, Technical Report Series 916, Geneva.


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


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.


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.


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


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


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


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


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.


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).


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


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


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


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


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


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


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,


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


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.


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

Economic Statistics, 9, No. 3, July, pp. 287-96. Meeusen, W. and van den Broeck, J. (1977), ‘Efficiency Estimation from Cobb-Douglas Production

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

Econometrics, 13:1 (May), pp. 57-66. Wadud, A. Md. and Ben White (2002), ‘The Determinants of Technical Inefficiency of Farms in

Bangladesh,’ Indian Economic Review, 2, pp. 183-97.


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


(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 −

+ ∑ ∑ ∑


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.


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


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]


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


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;


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.


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.


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


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.


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


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



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


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


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


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


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


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.


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


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


(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.


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.


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


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.


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


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


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.


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


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.


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.


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


Baru, Rama V. (1889)- “ Private Sector in Medical Care”, Radical Journal of Health, March – (1995) , “Mixed Economy in Health Care: Some Issues”, IASSI Quarterly, Vol.14 No’s 1 and 2.

Baru, Rama V. (1999)- “The Structure and Utilization of Health Services: An Inter-state Analysis” in M. Rao (ed.) Disinvesting in health: the World Bank's prescriptions for health, Sage, New Delhi

Barro, R.J 1991- “Economic Growth in a Cross Section of Countries” Quarterly Journal of Economics 106 (2) : 407 – 43.

Comin, D, B Hobijn, and E. Rovito (2008)- “ Technology Us age Lags” Journal of Economic Growth 13 (4)

: 237 – 56 Deodhar,N.S (1999) Rural India-Policy and Management Perspective in Basic Rural Infrastructure and

Services for Improved Quality of life (ed.)R.C Choudhury and P. D. Durgaprasad, NIRD, Hyderabad

Deogaonkar, Milind (2010)- ‘Socio-economic Inequality and its Effect on Healthcare Delivery in India’, available athttp://www.sociology.org/content/vol8.1/test.html, site accessed on 4th March, 2010.

Government of India 2006- HEALTH, BULLETIN Ministry of Health and Family welfare, New Delhi Government of India, Economic Survey 2009-10, Ministry of Finance, Department Economic Affairs, Government of India 2000- National Health Policy, Ministry of Health and Family welfare, New Delhi.

Gupta , M.C (2002) Health and Law. Government of Jammu & Kashmir -Statistical Digest 2007 -08, Directorate of Economics and Statistics,

Planning and Development Department J&K. Government of India 2007- Selected Health Parameters : a Comparative Analysis across National Sample

Survey organization (NSSO) 422nd , 52nd and 60th Rounds, Ministry of Health and Family Welfare in collaboration with WHO Country Office for India.

ICSSR and ICMR (1981)- Health For All: An Alternative Strategy, Indian Council for Social Science Research and Indian Council for Medical Research.

Ibrahim, S and S. Alkire, (2007)- “Agency and Empowerment” A Proposal for internationally comparable

Indicators” Oxford Development Studies 35 (4) : 379 – 403. ICRIER (1999)-, Report on trade Potential in Health Sector Indian Council for Research on International

Economic Relations. Indian Express (2003)- “Apollo defends Itself on free Treatment for poor” May 20. Indian Economic Association (2006) “89

th Annual Conference Volume”. December 2006.

Jeffery, Roger (1988)- the Politics of Health in India

----------------------- (1996)- “Towards Political Economy of Health Care: Comparison of India and Pakistan” in Dasgupta M, Chen, Lincoin C and Krishna, T.N (1996) Health, Poverty and Development in India.

Kurian, N.J. 2010- “Issues of Health and Equity in India” India Social Development Report, Council for Social Development, Oxford University Press,

NCAER(National Council of Applied Economic Research) 2001-. Concurrent National Evaluation of Integrated Child Development Services.

National Family Health Survey 1998 – 99 .National Family Health Survey – II Mumbai, International Institute of Population Sciences.

--------- 2005 – 06 .National Family Health Survey – III Mumbai. International Institute of Population Sciences.

National Commission on Macroeconomics and Health (2005)- “Background Papers”, Ministry of Health and Family Welfare, Government of India, New Delhi. 2005.

Neumayer, E( 2010)-. “Human Development and Sustainability” Human Development Research paper 5. UNDP – HDR New York.

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 .


Southgate, D (1990)- “The Causes of Land Degradation along Spontaneously Expanding Agricultural Frontiers in the Third World” land Economics 66 (1) : 93 – 101.

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:

Oxford University Press. Voluntary Health Association of India,1995-;Health Status of India, VHA, New Delhi. WHO (2000) World Health Organization Global and Sanitation Report 2000. World Bank (1996)- World Development report 1996, “from Plan to market “ Oxford University Press, New

York. World Bank (2003)- World Development Report 2004: Making Services Work for Poor People, New York:

Oxford University Press.


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


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


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


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


INSTRUCTIONS TO AUTHORS

Manuscripts should be between 5000 and 8000 words accompanied by an abstract/summary of NOT MORE THAN 250 words and a short biographical note, submitted electronically to [email protected]. Contributors should note that they are addressing a diverse audience of academicians, policy makers, administrators and development practitioners. The manuscript should conform to the template and house style given here, including page size and margins (page size 10 inches long and 7.5 inches wide, with 1 inch wide margin all around). For detailed template see the journal website www.jrdp.in 1. Apart from the article/paper, the contributions should include: (i) the name(s) of the author(s); (ii)

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Reference list at the end containing details of all the References quoted in the paper. 2. Spellings: Use British spellings throughout, not American spellings. 3. Notes: These should be numbered serially in the text and expanded in the same chronological order at the

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7. Capitals: Capital letters should be used sparingly throughout the article as that affects readability. Numbers: Generally, numbers from one to ten should be spelt out. Numbers above ten should be given in figures. However, if several numbers occur in a sentence or paragraph, all of them should be in figures, for easy readability. In case of units or percentages, all numbers should be in figures. For instance, 3 km., 5 kg., 8 per cent, etc. In case of percentages, the word ‘per cent’ should be spelt out in the text, but the symbol ‘%’ can be used in tables, graphs, figures and equations. In case of large numbers, use only ‘millions’ and ‘thousands’, not ‘lakhs’ and ‘crores’.

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volume 2, issue 1, june 2013

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