DEVELOPMENT OF COMPOSITE INDICATORS TOMEASUREBACKWARDNESS OF DISTRICTS IN UTTARAKHAND

Submitted to

Directorate of Economics and StatisticsDepartment of Planning

Government of Uttarakhand

Charan Singh VermaShivakar Tiwari

GIRI INSTITUTE OF DEVELOPMENT STUDIES(An Autonomous Institute Funded by ICSSR and Govt. Of Uttar Pradesh)

Sector - O, Aliganj Housing SchemeLUCKNOW - 226024, (U.P.) INDIA

Phones: (0522) 2321860, 2325021E-mails: directorgids@gmail.com; gids@sancharnet.in

DECEMBER 2017

Preface

Increasing income inequalities and faster economic growth have co-existed the world over forquite some time, but has become important characteristics of the twenty first century marketeconomies. The recently released World Inequality Report 2018 has specifically highlightedthis duelist growth character of the so called ‘robust growth’ attainment. Here the case inpoint is the development trajectory followed by China and India: much appreciatedeconomies of Asian capitalist development model. India is among leaders of wideninginequalities in income, only next to West Asian economies.

Faster growth gives rise to the aspirations of the people irrespective of the inequalities indistribution. Development is a process of improving the quality of life of people. However,the benefits of ‘robust economic growth’ have not reached all regions resulting in pockets ofrelatively underdeveloped regions. The co-existence of fast urbanized neo- rich middle classand backward regions along with dwindling agriculture related incomes, led to flaring up ofregional sentiments and resentment among people. Such disparities in development gave riseto demands, campaigns and movements for separate states. After years of prolongedmovements, three states were carved out of larger states in 2000 based on the hypothesis thatsmaller states would lead to better governance, efficient monitoring and thus a balanceddevelopment. And thus Uttarakhand was born along with dreams of a better developed and aprosperous state.

It is only logical that after one and a half decade of the formation of the state, a review of itsdevelopment and disparity is undertaken. Since, like India and China, Uttarakhand has alsograbbed its share of limelight as one of the fastest growing states with growth rates in doubledigit, but with a fair share of stark disparities in some regions. A number of studiesundertaken on the economy of the state had highlighted uneven development of the state asseveral hill districts were still backward and continued to be source of outmigration of thepopulation. These studies also pointed out that the benefits of robust economic growth wereconcentrated in the plain districts only.

In this context this study was planned with a broader perspective. The study envisaged toundertake not only the assessment of the regional imbalances, but also developing acomposite index of development for measuring the relative backwardness of the districts ofthe state. The need and rationale of the study lies in the formation of the Uttarakhand itselfwhich was to address the uneven development of the hill areas lagging behind the plain areasof the undivided Uttar Pradesh. Since the state was formed on the lines of the demands of thenative people, it was expected to provide better governance and balanced economicdevelopment.

Uttarakhand is a unique state, having very distinct features of hills, valleys, plains, highlydeveloped urban centres, agriculture dependent backward rural areas of plains withconcentration of industrial belts, backward rural hill areas with very less diversifiedagriculture and almost non-existent industrial units. Rural urban disparity is so high thataverage standard of living in urban population is more than two times higher than the ruralpopulation. It also has a respectably developed tourism sector with adventure tourismdeveloping at a faster pace. Also the state is rich in natural resources with a potential of beinga power surplus state, forest produce and herbs and horticulture products. A concerted focuson these potential sectors can provide employment opportunities that may eventually checkoutmigration from hills. A detailed account of Uttarakhand economy is given in Chapter 3 onEconomic Profile of Uttarakhand.

However, the review of literature on Uttarakhand development reveals a conservative patternof confining their investigations to defining the state’s economy in terms of hills and plainsonly. Such studies have come up with obvious conclusions stating that benefits of economicgrowth are concentrated in the plain districts. Any policy formation based on suchconclusions will have the risk of mis-measuring development. Present study has attempted toinvestigate further into the issue of rural-urban income pattern of plain districts based on theconsumption data. It finds high level differentials in consumption expenditure of rural andurban population that reflects income differential which is always higher than consumptiondifferential. A significant rural population in the plain districts along with rising urbanisationrate also creates a ‘dual structure economy’ in these districts which requires furtherinvestigation. Also significant gap in urbanisation rate and population density makescomparable analysis of the districts unviable. It would better to analyse the rural area of theplain district with the districts where rural population share is more than 90 percent. In factgap in economic attainment among districts is clearly visible which is also due to the gap indemographic structure the causation of which needs further investigation.

In the study, level of development of a district is measured in five dimensions that includedemography, economic attainment and distribution, educational development, health facilitiesand basic household amenities. Recognising the limitation of using composite index whichcan be misused if poorly designed, we have tried to derive with sound methodologyelaborated in chapter 2. On the basis of district level index relative backwardness of districtsis comprehensively discussed for the period 2013-14. Further an exercise on measurement ofchange in development disparities is undertaken in Chapter 5 titled ‘Measuring the Change inDevelopment Disparities between 2004-05 and 2011-12. The exercise of measuring thechange for the period 2004-05 and 2011-12 shows the potential of composite index formonitoring progress by using comparable indicators.

The study highlights on the basis of composite index that development gap among thedistricts is clearly evident which support the hypothesis of rising inter district disparity along

with catching up of aggregate economic development with relatively developed states ofIndia. However, it needs to mention that the comparison of average per capita income (PCY)of districts, which is dominant in explaining the gap, is misleading as in plain districts PCY isbiased in favour of urban population and economic activity. In absolute terms the ruralpopulation in these districts is higher than total population in hill area of some of the districts.

We hope the study would be useful to researchers and policy makers in understanding themethodology, uses and limitations of Composite index in assessment of the inter-district andintra-district disparities of development in the state.

Charan Singh VermaShivakar Tiwari

Acknowledgements

We are grateful to the Ministry of Planning, Government of Uttarakhand, for supporting thisstudy from its inception and take this opportunity to express our gratitude to Shri Amit Negi,Secretary Planning and Shri Ranjit Kumar Sinha , Additional Secretary Plannning,government of Uttarakhand. We also express our sincere gratitude to Shri Sushil Kumar,Director, Department of Economics and Statistics (DES), UK, Dehradun and for theirconstant support and cooperation during this study.

We received inspiring support and guidance from Professor Surinder Kumar, Director, GiriInstitute of Development Studies, Lucknow. His inputs in discussions and review meetings ofthe project, hold immense importance toward final outcomes of the study. We are extremelygrateful to him.

In preparing this study, we sought insights from Professor Amitabha Kundu, JawaharlalNehru University, we are grateful to him for sharing his perspective with us. We also wouldlike to record our appreciation for the suggestions we received from Shri Pankaj Naithani,Additional Director, DES, Shri G.S. Pandey Deputy Director, DES, other officers of DESDehradun along with the District Statistical Officers from districts of the state during theCapacity Building Workshop for the DSOs and DES officers, at Dehradun. We alsoacknowledge with thanks, the cooperation from various departments of UttarakhandGovernment, particularly, Department of Education, Department of Health, DES Dehradunoffice, for making the data available in time.

Our special thanks go to Dr Manoj Kumar Pant, Joint Director DES, and Chief Co-ordinatingOfficer, State Planning Commission, Government of Uttarakhand, for his inspiring supportwho took keen interest in the progress of study at different stages and provided his usefulinputs from time to time. Dr Pant was responsible for conceptualizing the comparativeanalysis of the development at two points of time, which led to addition of a very usefulchapter in the report. We are deeply indebted to him for so generously sharing his ideas andtime throughout the study period.

We got excellent research support throughout the project period from Senior ResearchAssociate of the project, Dr Shivani Singh, who contributed significantly in the data analysis,literature review and discussions on different dimensions of the study. We gratefullyacknowledge her contribution to this report at every stage.

We also express our gratitude to Dr Manoj Kumar Sharma, GIDS, for his support in draftingthe proposal. Research support from Mr Anil Kumar and Ms Ruchi Shukla during the studyare duly acknowledged.

Our sincere thanks are due to our GIDS office particularly Mr R.S. Bisht, OfficeSuperintendent, Mr Ranjay Singh, Accounts Officer and Mr. Sunil Srivastava, Accountant fortheir cooperation. We also thank Mr. K.K. Verma for typesetting and formatting of the reportof the study.

Charan Singh VermaProject Director

TABLE OF CONTENTS

CHAPTER TITLE PAGE NO

Preface

Acknowledgments

Abbreviations

List of Tables

List of Figures

Chapter 1 Introduction to the Study 1-13

Chapter 2 Methodology and Data Description 14-24

Chapter 3 Socio-Economic Profile of Uttarakhand 25-55

Chapter 4 Analysis of Composite Index of

Development and its Dimensions

56-77

Chapter 5 Measuring the Change in Development

Disparities between 2004-05 and 2011-12

78-92

Chapter 6 Discussion and Comments 93-98

References 99-101

ABBREVIATIONS

ACRONYM EXPANSION

CAGR Compound Annual Growth Rate

CI Composite Index

DES Department of Economics and Statistics

GNP Gross National Product

GOI Government of India

HCR Head Count Ratio

HDI Human Development Index

HDR Human Development Report

IHDS India Human Development Survey

LFPR Labour Force Participation Rate

MPCE Monthly Per Capita Expenditure

NER Net Enrollment Ratio

NFHS National Family and Health Survey

NSSO National Sample Survey Organisation

OECD Organisation of Economic Cooperation and Development

PCA Principal Component Analysis

PCY Per Capita Income

SCs Scheduled Castes

SDP State Domestic Product

STs Scheduled Tribes

UNDP United Nations Development Program

WDR World Development Report

WPR Workforce Participation Rate

List of Tables

Table No. Title of the TablesTable 2.1 List of Indicators: Source and Availability

Table 3.1 Demographic Profile of Uttarakhand and All India

Table 3.2 District wise Population in Uttarakhand

Table 3.3 Annual Growth Rate of District wise GDP (at Constant prices 2004-05)

Table 3.4 Change in Poverty Incidence (HCR) between 2004-05 and 2011-12(Tendulkar Methodology)

Table 3.5 District wise Consumer Expenditure Distribution in Uttarakhand 2011-12

Table 3.6 Status of Labour force and Workforce Participation

Table 3.7 Worker-Population Ratio, Census 2011

Table 3.8 Status of Health, Nutrition, Drinking Water and Sanitation

Table 3.9 District wise Health Status (NFHS -4)

Table 3.10 Status of Education

Table 5.1 Composite Index Value and Ranking of Different Districts

Table 5.2 District-wise Value of Demographic Index and Its Ranking

Table 5.3 District-wise Value of Economic Index and Its Ranking

Table 5.4 District-wise Value of Education Index and Its Ranking

Table 5.5 District-wise Value of Health Index and Its Ranking

Table 5.6 District-wise Value of Access to Amenities Index and their Ranking

List of Figures

Figure No. Title of FigureFigure 3.1 District wise share of Urban Population 2001-2011Figure 3.2 District wise Population Composition (Age group wise)Figure 3.3 District wise Sex ratio in Uttarakhand, 2001-2011Figure 3.4 Trends in Annual growth rate of GSDP of Uttarakhand since 2001-02Figure 3.5 Trend in Annual GSDP growth rate of GSDP in Uttarakhand since 2001-02Figure 3.6 Trends of Sector wise percentage share in GSDP of Uttarakhand since 2001-

02Figure 3.7 District wise Sectoral share in GSDP for 2004-2005Figure 3.8 District wise Sectoral share in GSDP for 2013-2014Figure 3.9 Trend in share of manufacturing sector in GSDP and its Annual growth rate in

Uttarakhand since 2001-02Figure 3.10 District wise Female Literacy rate (Census 2011)Figure 3.11 District wise Pupil-Teacher Ratio in UttarakhandFigure 4.1 Relative ranking of districts Based on Composite Index ValueFigure 4.2 Decomposition of Composite index ValueFigure 4.3 Ranking of Districts Based on Demographic Index ValueFigure 4.4 Decomposition of Demographic Index ValueFigure 4.5 Ranking of Districts based on Economic index valueFigure 4.6 Decomposition of Economic Index ValueFigure 4.7 Ranking of Districts based on Education index valueFigure 4.8 Decomposition of Education Index ValueFigure 4.9 Ranking of Districts based on Health index valueFigure 4.10 Decomposition of Health Index ValueFigure 4.11 Ranking of Districts inverse to the Amenities index value, 2013-14Figure 4.12 Decomposition of Amenities Index in Different IndicatorsFigure 5.1 Ranking of Districts on The Basis of District Index of DevelopmentFigure 5.2 Contribution of Different Components in Aggregate Index, 2011-12Figure 5.3 Ranking of Districts on the Basis of Demographic IndexFigure 5.4 Ranking of Districts as per the Economic IndexFigure 5.5 Ranking of Districts as per the Education IndexFigure 5.6 Ranking of Districts as per the Health IndexFigure 5.7 Ranking of Districts as per the Access to Amenities IndexFigure 5.8 Population Distribution in Different Districts of Uttarakhand, Census-2011

Appendix Tables

Table No. Title of TableTable A3.1 District wise demographic features in Uttarakhand, (Census 2011)

Table A3.2 District wise Distribution of Rural Population in Uttarakhand

Table A3.3 Population Distribution in Five year age-group by Residence and sex in

Uttarakhand

Table A3.4 District wise Sex ratio in rural areas of Uttarakhand, 1901-2011

Table A3.5 Annual estimates of Birth rate, Death rate and Natural growth rate by

residence in Uttarakhand, 1999-2014

Table A3.6 District wise Gross Domestic Product in Uttarakhand since 2001-02

Table A3.7 Trend in Share of Different Sector in GSDP since 2001-02

Table A3.8 Annual Growth Rate of District wise GDP (at Constant prices 2004-05)

Table A3.9 GSDP Growth Rate and Share of Manufacturing Sector in Uttarakhand since

2001-02

Table A4.1 Index Value of different dimensions and Composite Index, 2013-14

Table A4.2 Correlation Coefficient among Demographic Indicators

Table A4.3 Correlation Coefficient among Economic Indicators

Table A4.4 Correlation Coefficient among Education Indicators

Table A4.5 Correlation Coefficient among Health Indicators

Table A4.6 Correlation Coefficient among Amenities Indicators

Table A5.1 Indicators for Index Construction: 2004-05 and 2011-12

1

Chapter-1

INTRODUCTION

1.1 NEED OF THE STUDY

In the post-independence India, formation of smaller states from large states or re-

organisation of states has been done on the basis of either linguistic, culture or topographical

structure but economic underdevelopment. States like Punjab, Haryana, Himachal Pradesh,

Maharashtra, Gujarat etc have been formed on these bases. However, in the year 2000, on the

basis of unbalanced economic development, three states namely Chhattisgarh, Jharkhand and

Uttarakhand were formed from Madhya Pradesh, Bihar and Uttar Pradesh respectively. These

states along with special packages from the Union Government were expected to fulfill the

aspirations of the people. Interestingly, there has been improvement in the economic

performance measured through average state domestic product (SDP) in Uttarakhand and

Chhattisgarh but Jharkhand lagged behind. After more than a decade and a half, it is

opportune time to further discuss the evenness of this achieved higher economic growth as it

does not make sense if it is again creating a divide at disaggregated level. Precisely, this is the

need of the present study.

Uttarakhand was formed to address the challenges of uneven development of the hill areas

argued to be lagging behind the plain areas of the undivided Uttar Pradesh. In fact, formation

of Uttarakhand was a result of a long-standing demand of people of Kumaon and Garhwal

region with the aspiration of acceleration in the pace of socio-economic and human

development of the region (Kar, 2007). It was carved out from Uttar Pradesh as 28th state of

India with 10 districts of undivided Uttar Pradesh which also include two plain districts of

Haridwar and Udham Singh Nagar. Presently the state has 13 districts divided into 45 sub-

divisions and 95 development blocks to ensure rapid economic development through

effective administration (Kar, 2007).

In terms of economic progress, at aggregate level, Uttarakhand is doing better than its

neighbouring states and all India average. During the period 2000-01 and 2011-12, SDP of

Uttarakhand has increased at the compound annual growth rate (CAGR) of around 11.6

2

percent which is higher than the all India growth rate of around 7 percent (DES, Government

of Uttarakhand and CSO, MoSPI). At this higher rate of average economic growth it was

expected to be reflected in improved standard of living of the people of state. However, even

after achieving significant improvement in aggregate economic growth of the state, there has

been voice of dissent from different regions of the state due to concentration of growth in few

regions. It has been argued that achieved growth has been highly biased in favour of plain

region leading to widening of already existing divide between the hill and plain districts of

the state. It also seems, to a large extent, correct if we just observe at the per capita income

level of Dehradun and Uttarkashi, the latter being primarily in hill region.

In the context of hill and plain region it is to be noted that Uttarakhand is primarily a

mountainous state as only less than 20 percent of its geographical area lies in plains, broadly

spread in four districts of Haridwar, Udham Singh Nagar and parts of Dehradun and Nainital.

However, it may not be right to say Uttarakhand a mountain economy as more than 70

percent of its SDP comes from four districts. So the concentration of growth in plain districts

is really a cause of concern as the gap in per capita income of plain area to that of hill districts

is more than two times in the year 2013-14 (DES, Govt of Uttarakhand). Such disparity is

seems to be more severe when one take into account the differential cost of living which is

relatively higher in hill region and thus affecting adversely the people living in these low

income districts (Papola, 1996). It could be argued that income is just one dimension of

disparity and hill districts may be doing not much worse in health and education however, it

is the one component which affects and is affected by other development indicators like

education, health, political rights etc (Stiglitz et. al. 2009).

In line with general aggregate all India pattern, the development path of the state needs to be

observed and understood. At the aggregate level, there is twin challenge of achieving higher

rate of economic growth for development of state directly through generation of economic

activity and indirectly through redistribution of resources generated to cater to the minimum

needs of the population. At the same time there is challenge of sustaining this higher rate of

growth conditioned upon balanced regional as well as inclusive development that shall also

be environmentally sustainable in the long-term. It is beyond doubt, as evident in empirical

literature, that in the post-reform period growth rate of all India level has moved onto higher

trajectory and disparities among states are narrowing in certain indicators, however, within

state gap is widening which is reflected in increasing inequality at household level. Analysis

of representative large micro level datasets like National Sample Survey (NSS), National

3

Family Health Survey (NFHS) and India Human Development Survey (IHDS) etc shows

rising inequality in consumption, income and assets for which interventions are warranted at

the disaggregated level. Thus intra-state rising disparity in Uttarakhand is understood as

achieving higher growth requires certain pre-condition which may benefit certain region as

compared to other and thus it need to be recognised and intervened.

To achieve inclusive and balanced development of the state, district is an important unit of

administration, planning and resource allocation. So to tackle the challenge of widening gap

between hill and plain area concerted intervention at districts level is required. But for this it

is important to measure the relative development/backwardness status of districts at a point of

time, since economic dimension is just one component and is means to ends of development.

Defining backwardness and designing intervention strategies for its reduction itself is a

complex phenomenon which is attempted in section 2 below.

So recognising its crucial significance the present study aims to measure relative

backwardness of different districts of Uttarakhand state by constructing composite index for

all the districts. In this context the study is targeted to address the multi-dimensionality of

development by designing composite index of backwardness for districts of Uttarakhand by

including different dimensions. Furthermore the study aims not only to capture existing

variations in the development outcome but also the deficiencies in capacity which hinders the

economic development and regenerates disparities among the districts of Uttarakhand. The

composite index thus designed will be useful for monitoring of progress over the period.

The chapter proceeds in the following fashion: In following section 2 it is attempted to

conceptualise the notion of backwardness as distinct from underdevelopment as discussed in

the literature. Section 3 reviews the empirical literature related to the study of disparities in

hill economy. The objectives of the study are outlined in section 4 followed by tentative

framework of the study.

1.2 CONCEPTUALISING BACKWARDNESS

Defining the terminology is important in measuring backwardness of districts through

composite indicator. Backwardness particularly economic backwardness is a relative concept

and thus backwardness of a particular region (here districts) is analysed in comparison to

other regions or with some benchmark such as state average. The gap in development level of

a district than the other on the similar indicator or group of indicators defined through

4

composite index may be termed as level of backwardness. So backwardness here for study is

conceptualised as antonym of development.

Generally economic backwardness and underdevelopment are interpreted as synonyms, but

there is conceptual distinction between the two terms (Baruah, 2010). Myint (1954)

distinguishes the two terms by associating backwardness with underdeveloped resources and

backward people while underdevelopment with underutilization of resources. Similarly,

economic development is a process of resource utilisation of region to achieve economic

growth for welfare of community/people in a region (Bandopadhyay and Datta, 1989). In

similar manner, the distinction between economic development and economic growth is

emphasised in development economics literature against notion of measuring development

through narrow definition of GDP growth1. Economic development distinct from economic

growth is a broader concept and the two do not have direct causation as the region with better

economic growth may not have performed better on other indicators of human development.

The efficacy of economic growth in measuring well-being itself is challenged time and again.

In World Development Report (WDR) economic growth is described just as an end to meet

goals of development that is described by Sen (1997) in terms of human capabilities or

freedom and development as a process of ‘expanding the real freedoms that people enjoy’.

However, economic growth is considered as a necessary condition and so in post-world war

period there has been emphasis on achieving higher economic growth to achieve

development of developing countries. But achievement in aggregate economic development

in terms of GNP is criticised as it can capture only the market production and to get holistic

view of development at point of time many other indicators needs to be incorporated.

Recently, in one such attempt, in post financial crisis, a committee2 appointed by French

president Nicholas Sarkozy recommended 12 indicators to measure well-being against using

only GDP. The commission has identified eight dimensions of well-being which are: Material

living standards (income, consumption and wealth); Health; Education; Personal activities

including work; Political Violence and governance; Social connections and relationships;

Environment (present and future conditions); Insecurity, of an economic as well as physical

nature. Thus, the broader concept of well-being to measure development led to consensus of

its multidimensional nature.

1 Stiglitz et al (2011): “Mis-Measuring Our Lives: Why GDP Doesn’t Add Up”, Bookwell Publications, New Delhi.2 The commission “The commission on the Measurement of Economic Performance and Social Progress” washeaded by Prof. Joseph E. Stiglitz with Prof. Amartya Sen as the Chair advisor and Jean-Paul Fitoussi ascoordinator.

5

Development policy prescribed for developing countries to achieve higher economic growth

has been based on trickle-down theory. However, the world over, the concern about

increasing divide among and within regions in the level of development indicators has

challenged the notion of trickle down approach. Now, since the last decade achieving

convergence by reducing relative gap among the regions is a cornerstone of economic policy.

Thus growth-equity trade off and regional disparity has also become central to the

consideration in acknowledgement of backwardness.

Along with multidimensionality of developing and rising inter-personal inequality, balanced

regional development is crucial in reducing backwardness of a region. Theoretically, given

the resource constraint as argued by Hirschman (1958), unbalanced strategy for growth may

be recommended, however, in terms of economic development, disparities should be

addressed. Over the period people’s tolerance against rising inequality may lead to instability

of the system as well as against the political and social rights of individual which is important

component of human freedom. Even Hirschman and Rotschild (1973) argued through tunnel

effect, the situation of intolerance against rising inequality. In India, for example, support for

left movement which engulfed many states is attributed to people’s reaction against rising

development disparity resulted from the economic policies adopted over the years. The

second Administrative Reform Commission (ARC) in its seventh report also recommended

addressing the issue of intra-state disparity in development.

In the post reform period, rising disparities among the regions has been emerging as a grave

challenge. Although inter-state disparities in terms of broad indicators like per capita GSDP

are reported to have declined to some extent but intra-state disparities has worsened

(Bhattacharya and Sakthivel, 2004). In this context, regional balance, as also argued in

twelfth five year plan signifies not only inter-state disparity in the level of development but

also aimed to reduce inter-districts disparities in the state. So, despite growth in the GDP

appearing to be converging between the states, the development in terms of other indicators

has not been very encouraging (Dreze and Sen, 2012).

Further, to understand backwardness it is not sufficient to look at it as a mere outcome

problem as it depends on the availability of resources and its utilisation. For example there

are region(s) with sufficient natural resources but are rated low in level of development

whereas at the same time there are regions with less resource base are at relatively higher

position in development hierarchy. The link between existing resource base and final

6

development outcome is through infrastructural facilities (Baruah, 2010). For example health

outcome in terms of higher IMR could be an indicator of backwardness but this is highly

dependent on the health infrastructure like number of health centre at a given size of

population, educational outcome in terms of enrollment which is again dependent on

infrastructure like school, number of teachers etc. Similarly physical infrastructure like roads

may have significant impact on rural transformation and so on aggregate productivity and

income.

Baruah (2010) pointed out three aims of program designed to address problem of backward

region that are: first, bridging critical gaps in infrastructure and other development

requirements, second strengthening the grassroots level institutions and third providing

professional support to local bodies for planning, implementation and monitoring of their

plans at different stages and times. So addressing the problems of backwardness requires

more effective approach at further disaggregated level in Uttarakhand.

Thus, backwardness like development is multi-dimensional in nature that requires to be

measured through various indicators. It includes direct outcome variables like per capita

income, poverty incidence, educational attainment, health status etc and input as well as

process indicator like quality of house, availability of electricity, drinking water, connectivity

etc. The requirement of measuring multi-dimensional nature of backwardness can be fulfilled

by designing a composite index comprises of various indicators which is attempted in the

study.

1.3 REVIEW OF EMPIRICAL LITERATURE

Despite a long period of ‘planned’ development strategy adopted in India, regional

imbalances have increased and have further worsened with adoption of new economic policy

in early 1990s.Even in policy sphere, it is accepted as growing challenge as reducing regional

balance has been adopted as one of the crucial goals in achieving inclusive development in

the Twelfth Five Year Plan (2012-2017). Empirical studies have tried to analyze causes of

rising regional disparities. As a policy response, at different points of time several

committees constituted by government suggested measures for identifying backward areas. In

this section, main findings of empirical literature on backwardness of region particularly

those related with hill economy is reviewed.

Development and Disparities

7

Majumder (2005) discusses regional disparities in development in the country and examines

its causes. It states that infrastructural disparities are a major cause of these regional

imbalances in the country. The study attempts to understand the interlinkages between

development and infrastructure by developing indices and analyzing the trends and patterns

of those indices. Development and infrastructure are further divided into various components

in order to analyze the relation between them. Development constitutes of agricultural,

industrial and social development while infrastructure is classified under financial, physical

and social sector. Separate indices for each component have been constructed. Further, two

composite indices of development and infrastructure are derived by simply adding up (linear

summation) of the sectoral indices. Convergence-divergence has been used to analyze the

trends in infrastructural and development indices. Further, the nature and direction of

association between development and infrastructural indices has been analyzed using

correlation and lagged correlation. Its findings are suggestive to validate the ‘Hansen theory’

in India, which states that effects of infrastructural advancements are different for different

regions. For this, various regions of the country are classified as advanced, intermediate and

lagging on the basis of their development levels and the association between composite

development index and sectoral infrastructural indices are observed.

The study found that agriculture sector has developed the most while human development has

lagged behind. Further, in case of infrastructure, financial infrastructure has advanced, but

educational sector is still the least developed. It suggests that improvement in infrastructure

sector facilitates development of other sectors viz., power sector and financial infrastructure

which are important for agriculture and industrial sector. It also emphasises the importance of

transport infrastructure since an increase in structural adjustment programmes over the years.

Improvement in transport and communication facilities leads to development in business

sector. However, the association between development and infrastructure is strong in initial

years for all the regions, with time the association remains strong for backward regions but

for other regions, it becomes insignificant. It further suggests that exclusive infrastructural

development programmes for lagging regions should be facilitated in order to curb various

regional inequalities in the country. Also, the economic activities in the advanced regions of

the country should be scrutinized and properly disseminated such that the infrastructural

facilities become less congested and other underdeveloped regions also get some privilege.

Focusing Hill economy

8

The main findings of three studies respectively by Kar (2007), Mittal et al (2008) and Kutwal

(2015) that examined situation of development and disparities in hill economy is reviewed

here. Kar (2007) analyzes the reasons for stagnation in the hilly regions of Uttarakhand and

the policies that can further inclusive development. Majority of population is primarily

dependent on mountain agriculture, but the scope for modern input-intensive agriculture is

constrained due to various physical, geographical and environmental peculiarities. This has

not only resulted to high outmigration, but the remaining rural population is forced to survive

on subsistence agriculture. The study further analyzes the social inequalities in the state for

scheduled castes (SCs), scheduled tribes (STs), which constitute to almost 21 percent of the

population and women. The social isolation along with physical isolation of vulnerable

groups further worsens the situation. Rural women are yet another vulnerable group in the

state and they constitute a major share in the agricultural workforce due to out-migration of

males.

It states that the geographical inequalities are noticeable among hilly and plain districts in the

form of various development indicators, especially infrastructure and suggest that these

disparities lead to poorer quality of life in hilly areas as compared to plains. Lack of irrigation

facilities, low population density, poor infrastructure in these areas leave little scope for

development of large scale, mechanized and input-intensive modern agriculture as well as for

market-based institutions. The study emphasizes that policymakers should focus on the

development of human capital in the hilly areas. Along with that promotion of tourism

(leisure, religious, adventure, nature and wildlife) can help in expansion of tourism industry

in the state. The linkages between the tourism sector and the local economy can further

generate other livelihood opportunities and for this it recommends focused approach and

micro planning on the government’s behalf.

Mittal et al., (2008) discusses the inter-district developmental disparities in Uttarakhand

along with its reasons and proposes a development strategy for the state. The paper

emphasizes the geographical, economic and infrastructural disparities between the hills and

plain areas. It argues that growth in manufacturing sector in the state has held a major share

in the overall growth of SDP over the years but it has been limited to plain areas only. For

this it attributes to the poor status of infrastructure in hill districts further widens the gap

between income levels and livelihood pattern. The study as similar to Kar (2007) emphasizes

on the growth of tourism, agriculture and forest based industries which it suggests can pave

the way for inclusive development in the state. It further suggests that organic farming, agro-

9

industry, small and medium scale enterprises and vocational training to youth can be

encouraged for sustainable development, livelihood security and to curb outmigration from

the state.

Kutwal (2015) has highlighted existing disparities in developmental level, along with

Uttarakhand among various hill regions in the country which includes Arunachal Pradesh,

Himachal Pradesh, Jammu & Kashmir, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim

and Tripura as well as Uttarakhand. The paper argues that such disparities occurred because

of diversified resource base and endowments. It has used secondary data pertaining to post-

reforms period on social, rural and economic development for these states. Female literacy

has been taken as a proxy for social development and non- agricultural rural workforce has

been taken into account to analyse rural development. Degree of urbanization in the state has

been taken as a proxy for economic development. Further, development index3 on the basis of

deprivation score4, (used by United Nations Institute for Social Research, 1991), has been

calculated to measure various dimensions of development. The values of development index

indicate towards the uneven development of Indian hill states. In case of social and economic

development, Mizoram holds the first place while in case of rural development it comes

across as the least developed state. However, the least developed state in terms of social and

economic development is Jammu and Kashmir. Also, Sikkim appears to be the first in case of

rural development.

Committee on Constructing Backward Index

Various studies have suggested that devolution of funds from Centre to various States on the

basis of certain criteria can be used to reduce these regional imbalances. There has been many

committee constituted to study the issue of regional imbalance. It includes Committee on

Dispersal of Industries (1960), Planning Commission Study Group for Draft of Fourth FYP

(1966-71), Pande Committee (1968), Wanchoo Committee (1968), Committee on Backward

Area headed by Prof. Sukhamoy Chakravarty (1972), National Committee on Development

of Backward Areas (1978), Committee to Identify 100 Most Backward and Poorest District in

the Country (1997), Inter-Ministry Task Group on Redressing Growing Regional Imbalances

(2005) and Committee on Evolving a Composite Development Index of States (2013) headed

3Development Index = 1- Deprivation Score4Deprivation Score = (Maximum Value –Actual Value)/ (Maximum Value –Minimum Value)

10

by Prof. Raghuram Rajan. The major summary finding of the report of the all the committee

is provided in Rajan Committee report and here we have reviewed the methodology of Rajan

committee.

Rajan Committee for evolving a Composite Development Index of States, (2013) proposes

that allocation of funds from the Centre to the states should be based on both the state’s

development needs and its development performance. In order to judge development needs of

the states, a composite index of underdevelopment has been suggested. For this ten criteria

with equal weightage have been included- (i) monthly per capita consumption expenditure, (ii)

education, (iii) health, (iv)household amenities, (v) poverty rate, (vi) female literacy, (vii) per

cent of SC-ST population, (viii) urbanization rate, (viii) financial inclusion, and (x)

connectivity. Less developed states rank higher on the index, based on their development

criterion. The value of the underdevelopment index for a state represents the need of an

average individual in a state.

For constructing under development index, per capita consumption expenditure has been

taken as a proxy for income and the data from the Consumption Expenditure Surveys of the

National Sample Survey Organisation (NSSO) has been used. Further, attendance ratio and

number of institutions for primary and secondary education have been taken as a proxy for

education. A weighted average of attendance ratio and number of institutions for primary and

secondary education per 1000 of state population in the age groups of 5-14 years has been

computed. Attendance ratio has been calculated for age categories 5-14, 15-19, and 20-24

years using NSSO’s data from the Employment and Unemployment Surveys, and is

measured as the fraction of a particular cohort that reports attending an educational institute.

Infant mortality rate has been taken as a proxy for health. It has been taken as the only health

indicator because of availability and reliability of the data and its sensitivity to change in

economic conditions. Source of power at home, access to drinking water and sanitation

facilities within premises, mobile phone and other specific assets have been taken as a proxy

for household amenities. Poverty ratios used in the Report have been taken from Planning

Commission and were based on the definition being then used by the Planning Commission.

Female literacy, percent of SC-ST population, and urbanization rate are other variables taken

into account. In addition to economic and social outcomes, an indicator of financial

development has also been included, which is the number of households availing banking

services. Another sub-index of connectivity has also been included, which is a weighted

11

average of length of surfaced national and state highways and other surface road and rail

route per 100 sq. km.

The ten sub-components have been further aggregated to create an overall index for

underdevelopment. For the baseline analysis, a simple arithmetic mean of the sub-

components has been taken. For those sub-components that are an aggregate over various

indicators (education, household amenities, and connectivity), weights were assigned on the

basis of Principal Component Analysis to the indicators that go into the sub-component. Each

sub-component has been normalized between 0 and 1, and rescaled such that 1 indicates a

higher degree of underdevelopment. States that score above 0.6 in the index have been

categorized as “least developed”, below 0.6 and above 0.4 score means a state is “less

developed” and a score below 0.4 means a state is “relatively developed”.

Pandey and Dasgupta (2014), discusses the use of ‘Disability Index’ for devolution of funds

from Centre to states. This study holds the view that the hilly states of India have several

developmental disadvantages as compared to other states which should be considered in the

devolution of funds from centre to states. Hilly states with large share of forest land with

enriched bio-diversity have a higher opportunity cost than any plain area. The uncompensated

positive externalities of the forest ecosystem are also fairly substantial. The unit cost of

providing public services in these states is comparatively high in these regions due to

geographic location, low population density and adverse climatic conditions. Thus the major

objectives of this study are to construct a cost disability index in provision of development

infrastructure for these states and to analyze and identify the legislation, procedures and

measures to speed up the process of environmental and forest clearances for development and

infrastructural projects. The study further identifies cost escalation in terms of time and

institutional costs due to legal requirements and federal restrictions as factors responsible for

higher cost disabilities.

The cost disability index is computed as a sum of two components viz., endowment effect

(geographical factor) and transaction costs which include topographical factors and federal

regulations. Component 2 includes proportion of land under hilly terrain and infrastructure

deficit in form of power index, road index and tele-density index. Then development

disability is calculated by the summation of the two components by giving equal weightage to

both or by giving higher weightage to any one component on the basis of the objective. On

the basis of the index, this study further concludes that states of Manipur, Arunachal,

12

Meghalaya, Nagaland, and Mizoram dominate in terms of disability index as they have more

than 60 per cent of the geographical area under forests, alongside substantial hilly terrain and

less industrialization. Also, states like Sikkim and Uttarakhand have a higher infrastructure

deficit component.

1.4 OBJECTIVES OF THE STUDY

As mentioned earlier development is multi-dimensional phenomenon and needs

multiple indicators to capture the variation among the regions in different dimensions. Here

in the context of Uttarakhand a composite index derived from multiple indicators

representing different dimension for districts will be analysed. Thus, the broad objectives of

the study can be outlined in following points:

1. To construct the district level composite index of backwardness for state of

Uttarakhand. Here ‘backwardness’ will be measured as relative gap in the level of

development among the districts.

2. Compilation of various indicators for measuring backwardness from relevant data

source and analysis of its suitability as well as comparability.

3. To examine the level of development and disparity among the various districts of the

Uttarakhand. It will be confined to the latest period of 2013-14 for which per capita

district domestic product (DDP) is available to see inter-district variations.

4. To assess the broad development trajectory of the Uttarakhand state at two points of

time 2004-05 and 2011-12 to see the role of composite index in examining the

improvement through policy intervention.

1.5 FRAMEWORK OF THE STUDY

The document has been organised in six distinct chapters, the tentative outline for

which is as follows:

1. Introduction: The chapter discusses the need of the study for Uttarakhand and

conceptualisation of notion of backwardness. The review of empirical

literature with respect to disparities in hill economy as well as brief outline of

committees constitutes to study backwardness will also be part of the chapter.

It has also elaborated the objectives attempted to be examined in the study.

13

2. Methodology and Data Description: It discusses the rationale of composite

index, methodology of index construction particularly choice of indicators,

normalisation technique and aggregation. The source of data and its

description is also discussed here.

3. Socio-Economic Profile of Uttarakhand: While studying the socio-economic

profile the focus is on examining the trend in performance at the district level

of per capita income, structural transformation, employment, education and

health.

4. Analysis of Composite Index of Development and its Dimensions: It will

examine the inter-district variation as per the index value of development for

the period 2013-14. For policy purpose the index value is decomposed among

different dimensions and this again is studied in terms of role of different

indicators.

5. Measuring the Change in Disparities between 2004-05 and 2011-12: The

purpose of this chapter is to examine the change in index value between two

points of time viz. 2004-05 and 2011-12. For these two periods the indicators

are comparable and most the indicators have gap of at least seven years or

more.

6. Discussions and Comments: Broad conclusion, discussion and comments are

in the chapter. It also outlines limitations of the study.

14

14

Chapter-2

METHODOLOGY AND DATA DESCRIPTION

2.1 INTRODUCTION

Composite index (CI) in the last three decades is increasingly recognised as a tool to

evaluate performance of certain phenomenon for policy analysis as well as for larger public

communications. However, it is not free from criticism and along with proponents there are

strong arguments against aggregation of different indicators5 in generating a single index

which is used for assessing or monitoring performance of some phenomenon (OECD, 2008).

But its potentiality cannot be undermined as CIs are useful in statistics based narratives for

informed debate. Mabub ul Haq who is considered as the father of Human Development

Index (HDI) acknowledging the limitations and potentialities of composite index stated that

“for any useful policy index, some compromise must be made”. So, even though there have

been certain limitations, but scientifically constructed index is an informative tool for policy

analysis.

In comparison to monitoring different indicators to examine development outcome

simultaneously, there are certain advantages of composite index discussed in OECD (2008).

Firstly, it summarises complex or multi-dimensional issues and facilitate decision-making by

policy makers. Secondly, composite index is easier to interpret than using trends of many

separate indicators for policy making. Thirdly, it also facilitates the task of ranking

regions/district over time on complex issues. Fourth, it reduces the size of a set of indicators

or includes more information within existing size limit. Lastly and more importantly it

facilitates communication with common citizens and promotes accountability. Furthermore,

although composite indicators have several advantages in examining and monitoring, but it is

not free from weakness particularly when constructed poorly. First, it may send misleading

policy messages if it is poorly constructed. Second, it may invite simplistic policy

conclusions which may not be possible for adoption. Third, it may be misused. Lastly, the

often point of criticism is the selection of indicators and weights.

5Broadly composite index is be defined as mathematical aggregation (linear, geometrical or multi-criteria) ofthe individual or groups of indicators representing different dimensions of the phenomenon whose descriptionis the objective of analysis.

15

Given the advantage and disadvantage of composite indicators for policy analysis it is desired

to construct the index by minimizing the disadvantages based on certain scientific procedure.

For this, in order to construct meaningful composite indicator, OECD (2008) has listed ten

important steps which can be listed for constructing sound index. They are (1) Theoretical

Framework (2) Data Selection (3) Imputation of missing data (4) Multivariate Analysis (5)

Normalisation of indicators (6) Weighting and Aggregation (7) Uncertainty and sensitivity

analysis (8) Back to the data (9) Links to other indicators (10) Visualisation of the results. All

these procedure has been discussed in detail in OECD (2008).

Thus given the limitations, the objective of present study is to produce a meaningful

composite index of development/backwardness for comparison of districts of the state. It is

tried to follow scientific procedure mentioned above at every step. The present chapter

discusses the methodology adopted in detail that includes along with other steps the relevance

of choice of indicator and appropriate technique for normalisation, weighting and aggregation.

2.2 RATIONALE FOR CONSTRUCTING COMPOSITE INDEX OF

BACKWARDNESS

Rationality for composite index of backwardness is derived from the conceptual

framework discussed in earlier chapter. It has been mentioned that concept of backwardness

is multi dimensional and each dimension has crucial place which needs to be measured

through several representative indicators. So in order to see the level of backwardness,

multiple indicators of different dimensions have to be looked into separately which make the

prescription sometimes very clumsy and thus need to be scientifically constructed in the form

of index.

Historically, over the last four decades concept of economic development has evolved from

mere measuring Gross Domestic Product (GDP) as barometer of development to monitoring

simultaneously various indicators of different dimensions. In overall development constraint

of human capabilities is strongly recognised as important factor. Further, Human

Development Report (HDR) since 1990 constructs and publishes ‘Human Development

Index’ (HDI) in its report based on several indicators. It upholds the advantage of composite

index in measuring development. Human Development Index (HDI) is a summary measure

of achievements in key dimensions of human development which include a long and healthy

life, access to knowledge and a decent standard of living. Furthermore, Multidimensional

Poverty Index (MPI) proposed by Alkire and Santos (2010) also identifies deprivations at the

16

household level in education, health and standard of living which together utilised to generate

a composite index of poverty.

In post independence India, several committees have been constituted by Government of

India (GOI) time and again to measure regional imbalance, underdevelopment or

backwardness. Almost all of these committees have identified several dimensions and

measured through multiple indicators for identifying regional imbalance or backwardness.

Latest being the committee constituted by Ministry of Finance, GOI for identification and

measurement of backwardness for different states headed by then economic advisor Prof.

Raghuram G Rajan (2013). The committee identified total ten variables to measure

backwardness of states of India that represent different dimensions6.

Since the focus of the present study is to construct district level composite index of

backwardness for the state of Uttarakhand, it has also considered different dimensions of

human development and infrastructural constrained to examine backwardness. At the outset it

is to be mentioned that indicators is selected on the premise of existing literature and

recommendations of different committees on measurement of regional balance and

backwardness. In order to considered unique diversity in mountain region, special attention

has been paid regarding the issue of measurement of variables with respect to mountain

economy.

2.3 EMPIRICAL STRATEGY FOR CONSTRUCTING COMPOSITE

INDICATOR

Based on the rationality for constructing composite index of backwardness here in this

section, empirical strategy adopted is discussed in detail. Since one of the points of

resentment between aggregators and non-aggregators is the lack of transparency in index

construction in selection of indicator, method of aggregation and weighting etc, it is

important to explain in detail the logic behind indicator selection and aggregation framework.

Primarily, construction of composite index involves two important steps viz. choice of

indicators and aggregation. Indicators chosen needs to representative, reliable, analytical

sound, timely available, accessible etc. Aggregation requires variable to be normalised or

scale free and assigning weights is a challenging task. These steps are discussed under the

empirical framework.

6It includes income, education, health, household amenities, poverty ratio, female literacy, percentage of SC-ST population, urbanisation rate, financial inclusion and connectivity index.

17

Choice of Indicators

As composite index is above all sum of its part, the quality of composite indicator depends

largely on the quality of underlying variables used to construct the index. Ideally as

mentioned above, variables should be based on its relevance, analytical soundness, timely

availability and accessibility. Indicators are also grouped in input, output and process

indicator which needs to be scientifically chosen. In the study depending upon our motive we

took both input and output indicators but not the process indicator except in the case of

maternal health. In order to represent different dimensions of development the first choice is

better output indicator. However, at the district level, in order to include the relative gap in

available infrastructure than the required particularly in the case of education and health,

input indicator has also been included.

Based on the objective to identify the intra-state disparity in the level of development

altogether 49 indicators are selected for five main components (latent variables) or

dimensions of backwardness viz. demographic, economic attainment, educational

development, health and basic amenities. The various indicators/variables selected for each

dimensions is given in appendix table. The indicators chosen are mostly for the latest

available period. Since most of the indicators pertains to the period 2013-14 so the index

constructed has been referred for the period 2013-14. Rational for choosing indicator has

been discussed for different dimension separately here below.

Demographic structure of districts is an important component in the index of development. Its

importance can be understood as planning commission formulae (Gadgil-Mukherjee) for

resource allocation and in formulae of different finance commissions while deciding

distribution of taxes population always remained an important criterion. In demographic

dimension, four indicators is used which include population density, urbanisation rate, non

SC/ST population share and child sex-ratio (0-6). The data source for all these indicators is

Census of India and the latest period for which data is used is census 2011. Population

distribution relative to area (population density) among districts indicates the resource need

of the districts. However, population density is generally positively correlated with level of

development. Urbanisation rate or share of urban population in total population is also

positively correlated with level of economic development and thus taken as indicator of

development. Further, concentration of disadvantage social groups like SC/STs is taken as

indicator of backwardness index by Rajan committee (2012). The logic behind it is that

18

SC/ST has been historically deprived and thus their concentration in a district reflects the

larger need of resource for bridging the gap. Lastly sex ratio is a normative indicator of

development which reflects the social development of districts. We have used child sex ratio

which is important if one want to account for child preference leading to female feticide and

other social problem.

Economic dimension of development is intended to capture the gap in level of economic

progress among districts which also resembles the relative resource base of the districts. To

measure economic attainment total 10 indicators has been selected which is given in

appendix table. The indicator chosen in this component reflects not only the level but also

structure of development in a district which is crucial for accounting of distributional aspect

of aggregate size of economy. Indicators includes along with district per capita income, the

indicators of economic diversification in occupation and income, agricultural output share

and productivity, financial development, poverty incidence etc. Per capita income here is

used as aggregate indicator of standard of living, however, there are two choices for

measuring standard of living in a district: per capita district domestic product provided by

department of economics and statistics (DES) of the state and monthly per capita

consumption expenditure (MPCE) collected and provided by national sample survey

organisation (NSSO), Ministry of Statistics and Program Implementation (MoSPI). Even

though consumption expenditure is argued as better indicator of standard of living as it avoids

fluctuations but representative per capita expenditure data is not available at the district level7

and thus PCI has been taken as indicator to measure gap in income level. Further, in note of

dissent, Dr. Shaibal Gupta, a member in Rajan committee on backwardness has pointed out

certain merit of using PCI over MPCE as an indicator since it also include saving and

government expenditure which is significant in assessing relative backwardness among

district. So here the choice of PCI seems to be more comprehensive in measuring

backwardness. Also there are few indicators mentioned in the list like share of non-

agriculture output, percentage of non-farm workers, employment rate and livestock

population per 1000 population have been dropped in sensitivity analysis which is discussed

in Chapter 4.

7 There has been attempt to measure district average monthly per capita consumption expenditure (MPCE)through pooled sample of state and central sample but still we feel that the sample size is not representativeparticularly if we considered rural and urban area separately.

19

Education and Health are two important dimensions of human capital base of the district. In

human development index (HDI), education and health are two important indicators of

development. As mentioned above, these two dimensions not only reflects relative

backwardness in the current period which is effect of past investment but they also lead to

increase in relative gap among districts over the period in future. In the study we have tried to

capture the gap in level of development in these dimensions through representative indicators.

Education component is weighted average of five indicators in which two are input indicators

and three are output indicators. The six indicators of education dimension include: (i) Female

literacy rate (ii) Net Enrollment Ratio (NER) in elementary education (iii) Retention Rate (iv)

Adult population (above 24 years of age) with secondary and above level of education (v)

Primary and Junior school per 100 sq km (vi) Inverse of pupil-teacher ratio in elementary

education. Districts with low female literacy rate may be considered as indicator of

backwardness. Further instead of Gross enrollment ratio (GER) it is decided to take NER

which also takes into the enrollment in specific age group. To account for drop out ratio we

have taken retention rate as indicator of education development. Although NER has impact

on economic and social development of districts in future, level of adult education represents

current human capital base of districts. Thus along with female literacy and NER, adult

population with secondary level of education is considered as one of the indicator in this

component. Although we have taken both input and output indicator to capture educational

development of the districts and gap in infrastructure for better educational facility, however,

for capturing educational differences among districts indicator of learning outcome is better

choice that requires to be collected at the districts level.

Health component will be weighted average of eleven indicators which include five input,

four output and two process indicators. Input indicators include doctors per 10000 population,

paramedical staff per 10000 population, PHC & sub-centre per 10000 population and Beds

per 10000 population. Output indicators include dot stunted, not wasted, not underweight and

female infant mortality rate. Lastly, two process indicators which are taken as proxy of

maternal health are anti natal checkup and institutional delivery. Along with children

nutritional status, IMR is considered as primary indicator of health primarily of three reasons.

First data on IMR is readily available and are reliable. Second IMR is more sensitive to

changes in economic conditions and is flash indicator of the health condition of poor (Boone,

1996). Third, reduction in IMR and CMR explains the substantial improvement in life

expectancy (Cutler, et al, 2006).

20

For sustainable livelihood employment oppurtunity, education facility and minimum health

facility is crucial however, apart from this there are certain amenities are essential which is

captured in amenities index. Increasing quality of individual life at the household level is

important objective of economic development and thus household amenities in a district is

considered as crucial in explaining relative backwardness of district. Also in poverty

literature, Multidimensional poverty index (MPI) uses six indicators of standard of living at

the household level which includes: access to electricity, drinking water, sanitation, cooking

fuel, floor and household assets for measuring poverty incidence. Basic amenities dimension

here include drinking water, sanitation facility, source of lightening and per capita electricity

consumption. The source of data is Census of India that collects data on household amenities

is available for year 2011 and per capita electric consumption as well as surface road is

provided by DES Uttarakhand.

Since percentage of household with electricity as primary source of lighting is taken as

indicator under household amenities which reflects the availability of electricity, connectivity

index measuring surface road has been taken as weighted average of total surface road in a

district per square km and per lakh population is taken as dimension of backwardness. In this

context it needs to be mentioned that Rajan committee on backwardness has considered both

surface road and railways under connectivity index, however, as the current study is

exclusively for Uttarakhand in which rail penetration has not been in every district it gives a

bias estimate. So we have considered only surface roads. Second the committee takes surface

road only in relative to area (per 100 sq km) which itself is criticised in dissent note. So we

have taken surface road both in relative of area and population. It is logically as in hilly place

concentration of population may be less but distance of road to reach the place is higher.

So far, the above discussed indicators seem to be exhaustive and have been used to construct

composite with sensitivity analysis that is discussed at the respective place in chapter 4 and 5

at the respective place. After data collection, selection criteria of the indicators have been

threefold as also mentioned in Bakshi et. al. (2015) that includes: data source criteria,

sensitivity criteria and correlation criteria. As per the first criteria, only indicators which have

been collected by authentic source with robust methodology as discussed above. Second,

sensitivity criterion implies that any variable selected should be able to differentiate between

backward and less backward regions. In this context it may be mentioned that indicators may

be tried to be used in a way that is negatively correlated with level of backwardness of district.

Third, the correlation criteria is a significant one which implies variable selected should

21

ideally be having correlation coefficients between 0.30 and 0.90 that is neither uncorrelated

nor so tightly correlated that they virtually lose their independence as explanatory factors.

Technically any indicators that are not adding to the variance of index has been dropped.

Normalisation of Indicators

Since the indicators often have different units of measurement and thus aggregating them

without making scale free will lead to weird index. Also combing two indicators with

different units may not capture the variation in information between two regions. For

example per capita income is measure in rupees and has relatively higher value as compared

to literacy rate which is expressed in percentage and thus combining the two without

normalizing or scale transformation will not hide the variation in literacy rate. Thus

normalisation of indicator is essential prior to any aggregation. There are many methods of

normalisation like ranking, standardisation (z-scores)8, min-max method9, distance to

reference, categorical scale etc. Choice of normalisation method is crucial as different

methods leads to different composite indicator. One important criterion is that normalisation

procedure should be invariant to changes in measurement unit. However, in this study we

have normalised the indicator with the objective of minimum loss in information. It is done

by dividing the observations of indicator with their mean value. Thus for indicator Xi the

normalised series Ii is calculated as

�� �����

Normalizing the indicator through this method preserves the information on standard

deviation, variance and range. It is crucial information for comparing the progress in index

value over the period as well as in understanding the gap in the index value among districts of

the state.

8Standardisation or Z-score for each indicator is calculated by subtracting the mean from the individual valueand dividing the result with the standard deviations. Symbolically, it can be written as:

� �� 㘠 ���

Where x is individual observation, �� is variable mean and σ is standard deviation. Furthermore, z score isnormally distributed with mean zero and standard deviation of one.

�� ��′〷 �t

9 Min-max method converts indicator into the common scale between 0 and 1. It is one of the commonly usedmethod of normalisation as for example HDI uses min-max method for normalisation which is done as:

�� �� 㘠 ���䁛

��㜠� 㘠 ���䁛

22

Weighting and Aggregation

As discussed earlier, apart from choice of indicators the other important step for composite

index construction is the weights to be assigned to each component of the index. In fact it is

one of the most contentious issues between supporter and opponent of composite indicator

for studying performance. Weights in composite indicators are having significant effect on

overall index and ranking on the basis of composite index values. There are various methods

of weighting exist as some are derived through statistical method like principal component

analysis (PCA), data envelopment analysis (DEA) etc. It has been argued that regardless of

which method is being used, weights are essentially value judgments (OECD, 2012). Here in

our study,for deriving index of different dimensions we have used the weights corresponding

to first Eigen value of the PCA that captures the maximum variation except in education

dimension which is discussed in chapter 4 at respective place. Further for generating

composite index of development from these five dimensions, equal weight is assigned to each

dimension. The rationality behind this strategy is consideration of taking all the dimensions

as equally important for overall development of the district.

Now for aggregation, in most of the studies (GOI, 2013; Bakshi et al, 2015 etc) linear

summation method based on equal weight index has been utilised to compute composite

index. In GoI (2013), backwardness index has also been computed through arithmetic

mean/linear summation for different components. In order to cross validation, in fact GoI

(2013) have also calculated weight through standard method of principal component analysis

(PCA) and found the index to be highly correlated with one that is computed with equal

weights (EW) and correlation coefficient of 0.99. Similarly Bakshi et al (2015) have

calculated index using three weighting diagram which includes: equal weight to each

component; weight generated from principal component analysis and Ordinal ranking. Rank

correlation coefficient from all the three models has been found to greater than 0.99.

So with this weighting strategy, index for each component will be calculated with method of

linear aggregation10. Regarding different dimensions which have more than one sub-

component, separate component index will be calculated through weighted mean which will

then be used further to calculated the final index of backwardness with equal weights.

��� ��� � t� � tt� � �݀� � ��

䁛

10 The two other methods of aggregation discussed in literature include geometric and multi-criteria method.

23

Ibi is the index of backwardness for ith districts; N is the population component; E is economic

dimensions; Ed is education dimension; He is health dimension; Ais household amenities.

Here n is the number of components in defining backwardness which is five. Since the

composite index is computed as linear summation with equal weight, so for each component

the weight will be 1/5 (0.2).

Now since each component itself consists of certain indicators, weights assigned to these sub-

components on the basis of principal component analysis (PCA). Although each components

thus obtained can normalized between 0 and 1 scale where 1 indicates for high degree of

backwardness. It is necessary, as discussed in GOI (2013) to ensure that no component has

disproportionate weight in overall index but here as discussed above in order to preserve the

variation in each component each component index value will be further aggregated to

measure aggregate composite index of development.

24

Table 2.1: List of Indicators: Source and Availability

S.No. Dimensions Indicators1

Demographic

Density of population (population per sq. km)

2 Urbanisation Share (%)

3 % SC Population

4 sex-ratio (0-6)

5

EconomicIndex

Per Capita Income at Current Price (2013-14 (A)6 Percentage Forest Land7 Percentage of Irrigated to Cultivable Area (2014-15)8 Per Worker Agriculture Output9 Agriculture Productivity (Quintal/Hectare)

12Number of workers in small scale units (including Khadi Udyog) per 1000population

13Number of small scale units (including Khadi Gramodyog) per 1000population

14 Banking per 100 sq km.15 Credit -deposit ratio (2015-16)17 Percentage Non-poor18

EducationIndex

Female Literacy Rate19 Net Enrollment Ratio (Elementary Education)20 Retention Rate21 Population with secondary & above education

1/Pupil-Teacher Ratio22 Number of Primary and Upper Primary per 100 sq km30

Health Index

Doctors (per 10000 pop.)31 Para Staff (per 10000 pop.)32 Hospital (per 10000 pop.)33 PHC & sub-centre (per 10000 pop.)34 Total Beds (per 10000 pop.)35 Not Stunted36 Not Wasted38 Not Underweight39 1/Female Infant Mortality Rate40 Ante Natal Checkup41 Institutional Delivery42

AmenitiesIndex

Percentage of household with drinking water within premises

43 Percentage of household with latrine facilities

44Percentage of household with electricity as primary source of lighting, census-2011

47 Surface Road

49 Per Capita Electricity Consumption

25

26

25

Chapter-3

SOCIO-ECONOMIC PROFILE OF UTTARAKHAND

3.1 INTRODUCTION

The state of Uttarakhand was formed with the objective of achieving balanced

development of all its regions. It has achieved robust economic growth higher than its

neighbouring states and even the national average, however, the growth is highly skewed

with rising inter-district and intra-district disparities. This chapter is an attempt to examine

these disparities in crucial indicators viz. demography, income and social indicators

(education and health).

In terms of demography, the population distribution in the state is much skewed with almost

62 percent of the population concentrated in the four districts of Dehradun, Haridwar, Udham

Singh Nagar and parts of Nainital while remaining 40 percent is scattered in rest of the hill

districts. Even the plain districts have witnessed a much varied urbanization rate in the last

decade and a half. Further, similar to all India level, economic growth in the state has also

witnessed robust economic growth with a continuous decline in the share of agriculture share

in income and gradual increase in share of manufacturing and services. In the year 2015-2016,

the share of agriculture, industry and services sector is 9 percent, 39 percent and 52 percent

respectively (DES, Government of Uttarakhand, 2016). However, workforce structure shows

that a large share of the population is still dependent on agriculture and forestry which has its

own distributional consequences.

The high economic growth rates reveal the one-sided story of development completely

forgetting the social goals of better education and health. On one hand there is lack of

economic opportunities in hill districts reflected in lower per capita income and on the other

hand, high population density in the plains increases pressure on the public utilities and

resources which needs to be investigated. There has been huge disparity in health outcome

and educational development that has negative consequence on human capital and further

development of districts.

26

Educational development in Uttarakhand has been better as compared to Uttar Pradesh and

All India level, as is reflected in literacy rate and net enrollment ratio. The pupil-teacher ratio

in Uttarakhand is still much better than Uttar Pradesh. The status of health in Uttarakhand, as

depicted by Total fertility rate, Infant mortality rates and Under Five mortality rates is much

better than All India level and Uttar Pradesh. However, the status of maternal and child health

and utilization of public healthcare in Uttarakhand is relatively poor as compared to other hill

states and All India level, as reflected by the share of institutional deliveries, especially in

public facilities, immunization, ante-natal care and nutrition. However, the facilities for

drinking water and sanitation in the state are better than other states and All India Level

(NFHS-4).

This chapter is an attempt to understand the socio-economic profile of the state at the

disaggregated level. Following section 2 discusses the demographic characteristics of districts

followed by performance of income of participation in labour market in section 3. Health

status and educational development of the districts is examined in section 4 and section 5

respectively, followed by discussion and comments in last section.

3.2 DEMOGRAPHIC PROFILE OF STATE

Uttarakhand is distinct in demographic profile with relatively high rate of migration

from hill to plain and other states of the nation. It has also created an imbalance in population

size of hill and plain area of the states, rural-urban, male-female composition etc. Here we

have tried to look into this distinct demographic feature of the states. Table below shows

certain aggregate indicators of the state.

The population size of Uttarakhand, according to census, 2011, with 1.01crore population and

Uttarakhand stands at 20th position with 0.83 percent of the population share of country on

1.63 percent of the land. During 2001-2011, the decadal population growth rate has been

19.17 percent which is higher than the national growth rate of 17.64 percent. The population

density of the state is 189 persons per square kilometer, much lower than the national average

of 382 persons per square kilometer (Table 3.1). Although the share of urban population has

increased from 20.8 percent in 2001 to 30.23 percent in 2011, still two-third of the total

population resides in rural areas, which is almost similar to all India level. Urbanization rate

for the state has increased from 25.7 percent in 2001 to 30.2 percent in 2011 which is

marginally less than the All India urbanization rate of 31 percent in 2011.Male and female

literacy rates in the state are 87 percent and 70 percent respectively which are higher than All

27

India literacy rates of 82 percent and 65 percent for male and female respectively. The

improvement of almost 10 percent in female literacy rate in Uttarakhand is also higher than

the improvement in national female literacy rate during 2001-2011. The sex ratio in the state

is 963 females per thousand males, which is higher than the ratio of 943 females per thousand

males in the country. However, in certain districts like Chamoli and Rudraprayag, the number

of females per 1000 males in urban areas is as low as 767 and 697 respectively as against 884

of the state average in urban areas. On the contrary, child sex ratio for the state has become

more adverse with 890 females per 1000 males in 2011 as compared to 908 females per 1000

males in 2001. It is also lower than the All India child sex ratio of 919 females per 1000

males in 2011. Almost 18.8 percent (one-fifth) of the total population belong to Scheduled

Castes while only 3 percent belongs to Scheduled Tribes in Uttarakhand as against 16.6

percent Scheduled Castes and 8.6 percent Scheduled Tribes in India. This reflects that the

share of Scheduled Caste population is slightly higher in Uttarakhand than the national

averages while the share of ST population is much lower.

Table 3.1: Demographic Profile of Uttarakhand and All India

IndicatorUttarakhand All India

2001 2011 2001 2011Area (thousand sq. km) 53.5 53.5 3287.5 3287.5Population (Crores) 0.84 1.01 102.7 121.1Population Density (per sq. km) 189 382 159 324Urban (%) 20.78 30.23 27.78 31.14Literacy (%) 72.28 79.00 65.38 74Male Literacy (%) 84.01 87.00 75.85 82Female Literacy (%) 60.26 70.00 54.16 65Sex Ratio 962 963 933 943Child Sex Ratio (0-6 Years) 908 890 914 919SC Population(%) 17.9 18.8 16.2 16.6ST population(%) 3.0 2.9 8.1 8.6Source: Census 2001 and 2011

Above discussion shows demographic features of the state in comparison to all India level,

however, there has been significant variation at disaggregated level. In order to closely

understand the demographic profile of the state, district wise population characteristics is

discussed here below. The population distribution among the hill and plain districts in the

state is much skewed. Almost 62 percent of the total population is concentrated in four plain

districts of Nainital, Haridwar, Dehradun and Udham Singh Nagar while remaining 38

28

percent is scattered in the other nine hill districts. The decadal growth rate, between 2001 and

2011, of population for these four districts is more than 25 percent while for other districts in

hilly areas it is less than 7 percent (Table 3.2) while Pauri Garhwal and Almora have

registered negative growth rate.

Table 3.2: District wise Population in Uttarakhand

District

DecadalGrowth(Percent)

SexRatio Literacy

Districtwise % ofPopulatio

n

Districtwise % of

SCPopulation(2001)

Districtwise % of

SCPopulation(2011)

Districtwise UrbanPopulation(2011)

Uttarakashi 12 959 75.81 3 22.9 24.4 7.4

Chamoli 6 1021 82.65 4 18.2 20.3 15.2

Rudraprayag 7 1120 81.3 2 17.7 20.1 4.1

Tehri Garhwal 2 1078 76.36 6 14.4 16.6 11.3

Dehradun 32 902 84.25 17 13.5 13.5 55.5

Pauri Garhwal -1 1103 82.02 7 15.3 17.8 16.4

Pithoragarh 5 1021 82.25 5 23 24.8 14.4

Bagheshwar 4 1093 80.01 6 25.9 27.7 3.5

Almora -1 1142 80.47 6 22.3 24.3 10

Champawat 16 981 79.83 3 17 18.3 14.8

Nainital 25 933 83.88 9 19.4 20 38.9Udham SinghNagar 33 919 73.1 16 13.2 14.5 35.6

Haridwar 31 879 73.43 19 21.7 21.3 36.7

Uttarakhand - 963 78.82 - 17.9 18.7 30.2Source: Census 2001 and 2011

The sex ratio in many hill districts is highly favourable as compared to the state and national

average. On the contrary, the sex ratio in plain districts is much lower than the state and

national average. One of the biggest reasons for this uneven distribution of population may be

lack of basic amenities and remoteness of hill districts. Likewise, lack of livelihood

opportunities in hill districts leads to severe outmigration of male members. Although, the

female members and children are mostly left behind which might be one of the major reasons

for high sex ratio in hill districts. More than 75 percent of the villages in Uttarakhand are left

with population less than 500 persons (Appendix Table A3.2). In districts like Pauri Garhwal,

this share is more than 90 percent. Also, the percentage of villages with population more than

2000 is very low. In most of the hilly districts, it is less than 1 percent. However, in districts

from plain areas, the percentage of villages with population more than 2000 is comparatively

high in Dehradun, Udham Singh Nagar and Haridwar, which are primarily plain areas.

29

Figure 3.1: District wise share of Urban Population 2001-2011

Source: Census 2001 and 2011

However, the growth rate of urbanization for the period 2001-2011 for the state is 40 percent,

higher than the All India growth rate of 31.8 percent for the same period. This is also

depicted through the increase in share of urban population in all the districts except

Pithoragarh (Figure 3.1). The highest growth is registered by Nainital, followed by

Champawat, Tehri Garhwal, Pauri and Almora, which are hill districts.

The composition of population by various age groups depict that almost 30 percent of the

total population belongs to 0-15 year age group (Appendix Table A3.3) and more than 50

percent of the total population belongs to 15-59 year (working) age group, while only about

10 percent belongs to 60 years and above age group. However, it is to be noted that the share

of older population is higher in rural areas as compared to urban areas and the share of

population in age group 15-59 is higher in urban areas as compared to rural areas. Likewise,

the share of females is slightly higher than males in all age groups except from 0-14 years.

30

Figure 3.2: District wise Population Composition (Age group wise)

Source: Census 2011

Figure 3.2 depicts that almost 40 percent of the population in the state is above 30 years of

age (adult) while 28.8 percent population is in the age group 15-29 years. It is to be noted that

plain districts like Udham Singh Nagar, Haridwar and Dehradun have the highest share of

youth with almost one third population in 15-29 years age group. Garhwal has the lowest

share of youth population and highest share of adult population (44.47%) among all districts.

While the share of youth population is higher in plain districts, the share of adult population

is higher in hilly districts. This is also depicted in Fig. 3.2, where the gap between youth and

adult population is higher in hilly districts as compared to plain districts.

The district wise gender ratio over the years as given in Figure 3.3 depicts that the number of

females per 1000 males in hilly districts has remained more or less the same since 1991. On

the other hand, the sex ratio in plain districts of Nainital, Haridwar, Dehradun and Udham

Singh Nagar has always remained much lower than the state average ever since 1901. In rural

areas also, the sex ratio has always remained better than the state average and has gradually

improved (Appendix Table A3.4). Likewise, in rural areas, the difference between the sex

31

ratios in hilly and plain districts is more significant as compared to state averages. The birth

rate, death rate and natural growth rate of Uttarakhand have remained lower than the national

average. Also, both the birth rate and death rate have reduced over the years(Appendix Table

A3.5).

Figure 3.3: District wise Sex ratio in Uttarakhand, 2001-2011

Source: District Census Handbook, Directorate of Census Operations, Uttarakhand, 2011.

3.3 ECONOMIC PROFILE OF UTTARAKHAND

The economic profile of the state which includes income, employment, poverty

incidence, and land pattern has been extensively discussed in this section. It has been

attempted here to see the level of variations at the district level along with aggregate state

level performance over the period.

3.3.1 STATUS OF INCOME

Gross State Domestic product (GSDP) and per capita GSDP of the state has been

taken as a proxy for economic development in the state. The compound annual growth rate

(CAGR) of GSDP at constant price for the period 2000-01 to 2015-16 was11.34 percent per

annum. Likewise, the CAGR of per capita GSDP for the period 2000-01 to 2015-16 was 9.68

percent. This depicts the fast pace of economic growth in the state as compared to all India

32

level. The average annual growth rate of GSDP and per capita GSDP depict an upward trend

after the formation of the new state, for the period 2001-02 to 2009-10 (Figure 3.4). However

there are several fluctuations in the same duration as after 2009-10, the growth rates for both

GSDP and per capita GSDP have declined continuously. This decline in growth rate has been

parallel to all India level trends.

Although, the trends in the GSDP and per capita GSDP depict the high levels of economic

growth in the state, the district GDPs provide the picture of skewed economic growth and

disparities among the hilly and plain districts (Appendix Table A3.6). The GDP of Nainital,

Haridwar, Dehradun and Udham Singh Nagar is very high as compared to that of hill districts

viz. Bageshwar, Champawat, Chamoli and Pauri Garhwal etc. This may be because of

concentration of working population as well as economic activities in the plain areas which is

discussed below. The annual growth rate of GDP has also remained higher for these plain

districts as compared to other districts from hilly areas (Table 3.3).The average growth rate

for the period 2005-2006 to 2011-2012 have remained higher for plain districts and a selected

few hill districts of Rudraprayag, Tehri and Pauri Garhwal.

Figure 3.4: Trends in Annual growth rate of GSDP of Uttarakhand since 2001-02

Source: Authors’ Calculation from CSO Data on state GSDP

33

Table 3.3: Annual Growth Rate of District wise GDP (at Constant prices 2004-05)

DistrictAnnual growth rate (Percent) (at Constant prices 2004-05)

2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 AverageUttarkashi 11.9 12.9 3.4 7.5 6.9 0.8 5.1 6.9Chamoli 4.9 16 13.2 7.2 10.1 -0.5 6.1 8.0Rudraprayag 7.5 14.7 6 8.8 10.4 19.5 10.5 10.6Tehri Garhwal 11.9 7.4 5.5 9.7 12 16.9 10.3 10.1Dehradun 14 13.9 16.8 15.4 11.3 16.4 9.2 13.0Pauri Garhwal 11.4 14.9 11.2 12.4 11.6 12.6 8.9 11.3Pithoragarh 7.2 10.1 10.7 8.5 12.2 16.9 1.3 9.2Bagheshwar 5.7 21.3 -2.1 6.8 9.6 12.8 9.5 8.8Almora 2.2 14.5 7.9 7.5 13.8 9.5 7 8.7Champawat 12.9 3 8.6 20.2 10.5 -6.6 11.2 8.4Nainital 20.7 8.8 18.9 12.2 13.8 10.9 10.5 12.9Udham Singh Nagar 26.2 20.7 25.2 15.1 11.3 23.2 11 17.5Haridwar 12.3 14.5 28.6 13 11.5 22.1 9.8 14.9Uttarakhand 14 14.1 17.8 12.7 11.6 16.4 9.4 12.9Source: Directorate of Economics & Statistics, Planning Department, Government of Uttarakhand.

Table 3.4: Change in Poverty Incidence (HCR) between 2004-05 and 2011-12(Tendulkar Methodology)

StatesHead Count Ratio (percent)

2004-05 2011-12

Rural Urban Rural Urban

Uttarakhand 35.10 26.20 11.62 10.48

Himachal Pradesh 25.00 4.60 8.48 4.33

Jammu & Kashmir 14.10 10.04 11.54 7.20

Uttar Pradesh 42.70 34.10 30.40 26.06

All India 41.80 25.70 25.70 13.70

Source: Government of India (2011 and 2013)

The impact of this economic growth is evident in the declining poverty incidence in the state

(Table 3.4). The poverty headcount in Uttarakhand has declined from 35.10 percent and 26.2

percent in rural and urban areas respectively during 2004-05 to 11.62 percent and 10.48

percent in rural and urban areas respectively during 2011-12. The decline in poverty

headcount is higher in rural areas with 23.48 percent while in urban areas it is 15.72 percent.

Although, the incidence of poverty in Uttarakhand is higher than that in Himachal Pradesh

and Jammu & Kashmir, it is much lower than that at All India Level as well as Uttar Pradesh,

which can be attributed to the robust economic growth in the state since its formation

34

Although the state has had robust economic growth since its formation, it is only

concentrated to urban areas of the state. Majority of the population still resides in rural areas

where the impact of economic growth is almost negligible. Though a number of studies state

that the impact of economic growth in Uttarakhand is limited to the plain districts, it is to be

noted that the rural areas of plain districts are equally deprived of the benefits of the

economic growth experienced by the state in last one and a half decade. Table 3.5 illustrates

the District wise distribution of the consumption expenditure in Rural and Urban areas for all

districts in the state along with the percentage gap between them for the year 2011-1211. This

explains the extent of disparities in the consumption expenditure pattern of the households in

rural and urban areas. Haridwar registers the highest percentage gap of 112 percent between

rural and urban consumption expenditure, which means that the average household

consumption expenditure in urban areas is more than two times higher than that in rural areas.

On the other hand, Nainital plains have least disparity (8 percent) with almost equal level of

consumption in rural and urban areas, which may be due to the income from Tourism

industry in the district. However, the disparity between rural and urban consumption

expenditure in Nainital hills is 107.5 percent, which is second highest in the state. In

Dehradun (plains) as well as in Dehradun Hills, the consumption expenditure in urban areas

is almost 57 percent higher than that in rural areas. For Udham Singh Nagar, the percentage

gap between rural and urban consumption expenditure level accounts to be 20 percent. One

of the reasons for less disparity in Udham Singh Nagar may be the growth of secondary

sector in the district between 2004-2005 and 2013-2014, despite the decline in primary and

tertiary sector (Figure 3.7 and 3.8).

More than half of the state’s population lives in these three districts (Table 3.2), out of which

nearly two-third population lives in rural areas in case of Haridwar and Udham Singh Nagar,

which amounts to a fairly large share of population of the state. Only in case of Dehradun,

share of urban population is 55 percent. The percentage gap in consumption expenditure of

rural and urban areas in other hill districts ranges between 60-85 percent except for Tehri

Garhwal, where it is 101.23 percent. This portrays the huge difference between the rural and

urban household consumption expenditure in all the districts and deprivation of the rural

households, especially in case of plain districts. This also contradicts to the general belief that

11 This data on District Wise Consumption Expenditure has been taken from a Study by Giri Institute ofDevelopment Studies on “Estimation of District wise Poverty in Uttarakhand”. However it has certainlimitations like small sample size at District level for rural and urban areas, especially in hill districts. However,this study helps in demystifying the general perception held by number of studies on Uttarakhand regardingprosperity of plain districts, overlooking the intra-district disparities among rural and urban sections.

35

the households in plain areas are better off. Although it is true that the economic

opportunities are more in plain areas, it can’t be overlooked that more than 50 percent

population lives in plains with almost 70 percent of them living in rural areas and are equally

deprived of resources. The difference between rural and urban consumption expenditure is

rather higher in plain districts like Haridwar as compared to hill districts of the state. Thus it

can be said that the robust economic growth in the state has only benefitted the urban

households in plain areas, while the distribution of income between rural and urban areas,

especially in plains has become more skewed.

Table 3.5: District wise Consumer Expenditure Distribution in Uttarakhand 2011-12

Region Rural (at Average Statelevel Rural prices)

Urban (at Average Statelevel Urban prices) Percentage Gap

Uttarkashi 1392.23 2396.08 72.1Chamoli 1339.43 2374.3 77.26Rudraprayag 1324.14 2442.17 84.43Tehri Garhwal 1352.02 2720.65 101.23Dehradun 1560.13 2456.98 57.48Pauri Garhwal 1294.87 2145.62 65.70Pithoragarh 1292.03 2379.78 84.18Bageshwar 1372.3 2524.46 83.96Almora 1509.75 2528.75 67.49Champawat 1519.73 1951.26 28.39Nainital 1927.07 2089.85 8.45Udham Singh Nagar 1665.15 1999.16 20.06Haridwar 1296.45 2751.4 112.23Nainital Hills 1345.21 2791.77 107.5Dehradun Hills 1314.99 2063.77 56.94Uttarakhand 1460.1 2403.53 64.61Source: Calculations by Authors based on the data from Unpublished Report on ‘Estimation of District-WisePoverty in Uttarakhand” conducted by Giri Institute of Development Studies, commissioned by the Governmentof Uttarakhand, 2017.

The demand of a separate state of Uttarakhand was made citing the economic disparities

between the hills and the plain districts. However, it is evident that even after one and a half

decade since its formation, there is no change, rather the rural-urban disparities have further

aggravated, both in hills as well as plains. Hence, there is a need to consider these economic

differentials between rural and urban areas in hills as well as plains and over-burden of

population on urban areas in the plains by the state policy makers for an inclusive

development policy.

36

3.3.2 STRUCTURAL TRANSFORMATION IN UTTARAKHAND

Over the period with the level of economic development, sectoral composition of

income also changes. For Uttarakhand also, the structural transformation of economy and the

sectoral distribution of income clearly depict the pattern of economic growth in the state as

similar to all India level with the lowest share of agriculture sector followed by Industry and

Services. The services sector share is almost 50 percent of GDP, and shows constant increase

over the years(Appendix Table A3.7).The average annual growth rate of agriculture sector has

always remained very low as compared to the other two sectors (Figure 3.5). The compound

annual growth rate of agriculture, industry and service sector for the period 2001-02 to 2015-

16 has been 3, 15.64 and 12.31 percent respectively. The CAGR for agriculture sector is the

lowest among all.

Figure 3.5: Trend in Annual GSDP growth rate of GSDP in Uttarakhand since 2001-02

Source: Authors’ Calculation from CSO Data on state GSDP

The share of industrial sector in state GDP also shows an increase over the time. However,

the percentage share of agriculture sector has consistently declined over the years (Figure 3.6).

While the share of industrial and service sector in the GSDP has increased at an increasing

rate, for agriculture sector, it has declined at an increasing rate.

37

Figure 3.6: Trends of Sector wise percentage share in GSDP of Uttarakhand since 2001-02

Source: Authors’ Calculation from CSO Data on state GSDP

The increase in industrial and service sector depicts the high growth trends in the state.The

impact of this sectoral transformation is also reflected in the per capita GSDP. The annual

growth rate of per capita GSDP in hill districts has declined over the years, which may be due

to decrease in the share of agriculture in GSDP. However,in recent two-three years, annual

growth rate of per capita GSDP in hill districts is slightly higher than that in the plain districts

(Appendix Table A3.8). Main reasons for this can be increasing employment opportunities

and resultant rapid increase in labour force in the plain areas, which may have led to lower

remunerations for the people. Even in plain areas, two-third of the population is rural, except

in Dehradun. Hence, it can be said that the benefits from this growth is concentrated in the

urban parts of these plain areas. The expansion of industrial and service sector is majorly

restricted to urban parts of the plain areas because of the lack of infrastructure in rural areas

and remoteness in the hilly districts. This further widens the gaps between rural and urban

areas in both hill and plain districts of the state.

38

Figure 3.7: District wise Sectoral share in GSDP for 2004-2005

Source: Authors’ Calculation from CSO Data on state GSDP

Figure 3.8: District wise Sectoral share in GSDP for 2013-2014

Source: Authors’ Calculation from CSO Data on state GSDP

The share of primary, secondary and tertiary sector in GSDP as given in Figure 3.7 and

Figure 3.8 further depicts that the share of primary sector has considerably declined from

23.48 percent in 2004-05 to 15.61 percent in 2013-14. District wise share further indicates the

sharp decline in share of primary sector from 2004-05 to 2013-14 in all the districts, with

39

highest decline in Champawat. On the other hand, secondary sector depicts an increase in its

share in GSDP in almost all districts, with highest increase of 14.09 percent in Udham Singh

Nagar from 2004-05 to 2013-14. The tertiary sector depicts the highest increase in GSDP

share in almost all the districts, especially in Champawat. However, the share of tertiary

sector in GSDP has declined in Udham Singh Nagar and Haridwar from 2004-05 to 2013-14.

Among all the districts, the share of tertiary sector is highest in Dehradun with 66.16 percent

while it is lowest in Udham Singh Nagar with 36.39 percent.

Figure 3.9: Trend in share of manufacturing sector in GSDP and its Annual growth ratein Uttarakhand since 2001-02

Source: Authors’ Calculation from CSO Data on state GSDP

Industrialization has always been considered as a key factor for economic growth. Hence, the

contribution of manufacturing sector in Uttarakhand over the years has also been studied

separately. It is to be noted that after the formation of Uttarakhand, the annual growth rate of

manufacturing sector shows an increase till 2007-08(Appendix Table A3.9). However, share

of this sector in state GDP has registered continuous increase since 2001-02). Conversely,

since 2008-09 annual growth rate has continuously declined (Figure 3.9). The compound

annual growth rate (CAGR) for 2001-02 to 2015-16 was18.33 percent per annum. However,

for the period 2001-02 to 2007-08 when annual growth rates have continuously increased,

CAGR was23.14 percent while for the period 2008-09 to 2015-16, it was12.26 percent.

However, the average growth rates of manufacturing sector reflect a fluctuating trend over

the years up to 2008-09. After that, the average annual growth rate of manufacturing sector

has declined (Figure 3.9).

40

3.4 WORKFORCE STRUCTURE

The labour force and workforce structure is an essential indicator of economic status

of any state. Labour force participation rate (LFPR)12 and workforce participation rate

(WPR)13 have been utilised as key indicator to understand the labour force and workforce

structure in the state with respect to national level. In India the information for labour market

comes exclusively from household survey by National sample survey organisation (NSSO).

The labour force participation rate in Uttarakhand is 42.68 percent which is slightly less than

All India LFPR of 44.81 percent (Table 3.6). This reflects that the share of children and old

age population is almost 57 percent in the state. The workforce participation rate for

Uttarakhand is 38.4 percent in 2011 (Census, 2011). It is still less than the national WPR of

39.8, however the increase in WPR is higher for Uttarakhand as compared to All India Level.

Nonetheless, the unemployment rate in Uttarakhand is 4.29 percent which is slightly less than

the All India rate of 5 percent. However, when compared to its adjacent hill states, LFPR and

WPR of Himachal Pradesh is highest among all and the unemployment rate is also less than

the All India level. As per NSS report, the unemployment rate in Himachal Pradesh is even

less than one percent. The LFPR and WPR of Jammu & Kashmir is also higher than that in

Uttarakhand, U.P. as well as the national averages. However, the unemployment rate is

almost equal to 10 percent (Census, 2011). On the contrary, LFPR and WPR of Uttarakhand

are higher than that for Uttar Pradesh. One of the major reasons can be higher share of female

workforce in Uttarakhand.

Table 3.6: Status of Labour force and Workforce Participation

State Labour force participation rate(2011)

Workforce participation rate(2011)

NSS Census NSS CensusUttarakhand 37.3 42.68 36.1 38.39Himachal Pradesh 52.6 56.35 52 51.85Jammu & Kashmir 40.3 43.54 38.9 34.47Uttar Pradesh 33.9 37.23 33.3 32.94ALL INDIA 39.5 44.81 38.6 39.8Source: NSS report (2012) and Census (2011).

12The labor force participation rate is the percentage of the population that is either employed or unemployed(that is, either working or actively seeking work).13The work force participation rate is the percentage of the employed population to the total population.

41

This is further explained in Table 3.7 which reflects that the worker-population ratio14 in the

districts of Uttarakhand. The female worker-population is much lower than the male worker

population ratio in plain districts. However, in case of hill districts like Chamoli, Pithoragarh,

Tehri Garhwal and Uttarkashi, female worker-population ratio is almost equal to that of male.

In case of districts like Rudraprayag and Bageshwar, the female worker-population ratio is

even higher than male. This may be due to outmigration of male population from hill districts

for better employment opportunities.

Table 3.7: Worker-Population Ratio, Census 2011

Districts Total Male FemaleAlmora 47.90 48.94 46.99Bageshwar 47.57 47.22 47.89Chamoli 46.20 48.37 44.08Champawat 38.35 46.08 30.45Dehradun 34.35 51.43 15.41Garhwal 39.89 45.09 35.17Hardwar 30.58 49.52 9.07Nainital 39.41 52.05 25.87Pithoragarh 44.78 47.45 42.17Rudraprayag 46.65 45.68 47.53Tehri Garhwal 45.31 47.26 43.50Udham Singh Nagar 35.87 51.77 18.59Uttarkashi 47.65 49.98 45.21Uttarakhand 38.39 49.67 26.68

Source: Census (2011).

3.5 HEALTH STATUS

Health is one of the most vital components of human development along with

education and standard of living. Better health status and improved nutrition not only

increases human productivity but also enhances cognitive development of children, leading to

formation of better human capital in a country. Thus, health is a means as well as an end to

economic development. Here, various health input and outcome indicators have been taken to

assess the status of health and nutrition along with health infrastructure in the state as

compared to selected hill states and All India level.

14 Worker population ratio is the proportion of an economy’s working age population that is employed.WPR= Number of employed persons *1000/ Total Population

42

It is to be noted that the total fertility rate (TFR) in the state is 2.1 which is almost equal to

All India level as well as replacement rate15 (Table 3.8). The TFR in other hill states of

Himachal Pradesh and Jammu and Kashmir is even lower than the replacement rate. However,

TFR in Uttar Pradesh is among the highest in the country. The infant mortality rate (IMR) is

40, which is marginally lower than the all India level of 41. The IMR in other hill states is

much lower than the national average as well as that in Uttarakhand. However, IMR in Uttar

Pradesh is 64 and is among the highest in the country. Further, under five mortality rate

(U5MR) is an important indicator of child health and nutrition. As per NFHS 2015-16,U5MR

in Uttarakhand is 47, which is less than the All India average of 50. However, it is lower in in

the other two hill states.

For better society, status of maternal health is also an important indicator. The share of

females seeing full ante natal care (ANC) was 11.5 percent, which is almost half of the level

of all India. However, the share of females seeking full ANC16 in Himachal Pradesh and J&K

is 36.9 percent and 26.8 percent, which is much higher than the national average. The share

of institutional deliveries which is important process indicator for maternal health, it was 68.6

percent in Uttarakhand, lower than the All India level of 78.9. However, the share of

institutional deliveries in J&K is 85.7, higher than the all India level, while in other U.P., it is

it is almost equal to Uttarakhand. The share of institutional deliveries at public facilities is

43.8 percent in the state which is lower than all India share of52.1 percent. The share of

institutional deliveries at public facilities is even higher for J&K and Himachal Pradesh,

while that in U.P. is almost similar to Uttarakhand. Share of fully immunized children is also

highest in J&K and Himachal Pradesh, while in remaining two states is even lower than the

national level.

The nutritional status of the children (0-6 year age) as measured by all the three indicators of

height for age, weight for height and weight for age is almost equivalent to the national

averages. It is to be noted that the status of health, nutritional status and utilization of health

facilities in Uttarakhand is better than Uttar Pradesh, of which it was a part before its

formation. However, other hill states like Himachal Pradesh and J&K have better health

status and infrastructure. On the contrary, Uttarakhand has better drinking water and

15Replacement level fertility” is the total fertility rate at which the population exactly replaces itself from onegeneration to the next, without migration. This rate is roughly 2.1 children per woman for most countries.16Full antenatal care is at least four antenatal visits, at least one tetanus toxoid (TT) injection and iron folic acidtablets or syrup taken for 100 or more days. (NFHS -4)

43

sanitation facilities as compared other states and All India Level apart from Himachal

Pradesh which fares even better than Uttarakhand.

Table 3.8: Status of Health, Nutrition, Drinking Water and Sanitation

Indicators Uttarakhand HimachalPradesh

Jammu &Kashmir

UttarPradesh India

Total fertility rate (children per woman) 2.1 1.9 2 2.7 2.2Infant mortality rate (IMR) 40 34 32 64 41Under-five mortality rate (U5MR) 47 38 38 78 50Mothers who had full antenatal care (%) 11.5 36.9 26.8 5.9 21Institutional births (%) 68.6 76.4 85.7 67.8 78.9Institutional births in public facility (%) 43.8 61.6 78.1 44.5 52.1Children age 12-23 months fullyimmunized (BCG, measles, and 3 doseseach of polio and DPT) (%)

57.7 69.5 75.1 51.1 62

Children under 5 years who are stunted(height-for-age) (%) 33.5 26.3 27.4 46.3 38.4

Children under 5 years who are wasted(weight-for-height) (%) 19.5 13.7 12.1 17.9 21

Children under 5 years who are severelywasted (weight-for-height) (%) 9 3.9 5.6 6 7.5

Children under 5 years who areunderweight (weight-for-age) (%) 26.6 21.2 16.6 39.5 35.7

Households with an improved drinking-water source (%) 92.9 94.9 89.2 96.4 89.9

Households using improved sanitationfacility (%) 64.5 70.7 52.5 35 48.4

Source: NFHS-4 (2015-16)

Although, the health indicators depict that the status of health in Uttarakhand is almost at par

with the All India level and far better than states like Uttar Pradesh, there are huge inter-

district disparities in the health status of the people as depicted in Table 3.9. The share of

institutional deliveries is as low as 53.3 percent in Chamoli, while it is 83.7 percent in

Dehradun. Similarly, huge inter-district disparities are visible in the nutrition status of

children up to 5 years. On one hand, in Pauri Garhwal, the share of stunted children under

five years is 22.9 percent, much lower than the state and all India average. On the other hand,

share of stunted children is as high as 39.1 percent in Haridwar. Similarly, share of severely

wasted and underweight children is highest in Tehri Garhwal. Udham Singh Nagar has the

lowest share of fully immunized children with 47.4 percent while hilly district of Pithoragarh

has highest share of fully immunized children with 74.2 percent. Thus, in case of some of the

health indices like Institutional births, hilly areas like Chamoli have performed poorly while

in case of child nutrition and immunization, some of the plain districts like Haridwar depict

lowest share. Though there are intra-district disparities in the health status of the people, the

44

reasons for poor performance of hilly districts can be difficult geographical terrain and less

availability of resources while in case of plain districts like Haridwar, it can be the over

burden of population on public health services. However, this needs to be further investigated.

Table 3.9: District wise Health Status (NFHS -4)

DistrictInstitutionalbirths

Childrenunder 5years

stunted (%)

Childrenunder 5yearswasted(%)

Childrenunder 5 years

severelywasted (%)

Childrenunder 5 yearsunderweight

(%)

Children age 12-23 months fullyimmunized (%)

Almora 66.3 32.9 14.4 7.7 22.5 60.6Bagheshwar 55.9 25.1 26.3 13.5 27.2 60.2Champawat 73.3 30.5 17.4 6.1 21.2 68.4Nainital 64.7 32.1 9 3.7 17 59Pithoragarh 73 30.6 20.6 9.2 16.6 74.2Udham SinghNagar 67.5 37.8 12 3.5 27.1 47.4

Uttarakashi 65.1 35.2 39.4 23.6 40.3 72TehriGarhwal 71.1 30.1 46.9 28.1 44.2 51.1

Rudraprayag 66.5 29.9 18.4 7.5 25.9 70.3PauriGarhwal 74.5 22.9 27.4 18.1 27.9 61.2

Haridwar 62.8 39.1 12.3 5.3 24.7 55.3Dehradun 83.7 28.5 30.1 12 30.7 60.7Chamoli 53.3 33.7 18 7.2 22.3 62.2Uttarakhand 68.6 33.5 19.5 9 26.6 57.7Source: NFHS-4 (2015-16)Note: Full immunization includes BCG, measles, and 3 doses each of polio and DPT

3.6 EDUCATION STATUS

Educational development of Uttarakhand has been analysed here in this section as

compared to all India level and other hilly states of India. Broad indicators like literacy rate,

NER and pupil-teacher ratio is produced in Table 3.10 below. Here, literacy rate, net

enrollment ratio and pupil-teacher ratio at upper primary level is taken as a proxy for

educational status.

Interestingly, literacy rate is higher in Uttarakhand and Himachal Pradesh, as compared to

J&K and U.P. Between census 2001 and 2011 literacy rate in Uttarakhand has increased and

the gap with Himachal Pradesh has declined from around five to four percentages point. Net

enrollment ratio (NER) at upper primary level for Uttarakhand is 70.4 in 2011 and depicts a

significant improvement over 47.7 in 2001. In other states, the highest improvement has been

registered by J&K, while Himachal Pradesh has had the best NER among all states.Pupil-

45

teacher ratio depicts the number of pupils per teacher in the state. Thus, lower the pupil-

teacher ratio, better is the educational status, however, it should be interpreted with caution as

in hilly states the lower ratio could result of certain dragging factors like migration of family

with children. In 2011, the ratio was 12 for Himachal Pradesh, which is lowest, while J&K

also follows closely. For Uttarakhand, the pupil-teacher ratio has remained almost similar,

while in Uttar Pradesh, it is still the highest. All India teacher-pupil ratio is 33, which is much

higher as compared to that in Uttarakhand, J&K and Himachal Pradesh.

Table 3.10: Status of Education

StateLiteracy Rate Net Enrollment Ratio

(Upper Primary) Pupil-Teacher Ratio (Primary)

2001 2011 2004-05 2011-12 2004-05 2011-12Uttarakhand 71.62 79.63 47.69 70.4 26 27

Himachal Pradesh 76.48 83.78 74.09 82.5 15 12

Jammu & Kashmir 55.52 68.74 47.07 80.8 20 14

Uttar Pradesh 56.27 69.72 27.73 47.1 65 62

ALL INDIA 64.84 74.04 61.8 33

Source: Census (2001& 2011) and DISE (2004 & 2011).

Taking female literacy rate as the proxy of education status in the state, Figure 3.10 depicts

that the female literacy rate is highest in Dehradun, followed by Nainital while in other two

plain districts, the female literacy rate is comparatively low. Uttarkashi has the lowest female

literacy among all districts. It is to be noted that the female literacy rate in most of the hill

districts is also higher than the All India Female literacy rate of 65.46 percent, which reflects

the better status of education in the state.

46

Figure 3.10: District wise Female Literacy rate (Census 2011)

Source: Census of India, 2011

Pupil-Teacher ratio has been taken as another proxy for education status in the state. Figure

3.11depicts that in most of the hill districts, the Pupil-teacher ratio is even lower than the state

average of 31 while in the plain districts like Udham Singh Nagar the Pupil-Teacher ratio is

49, which is much higher than the state average. Low Pupil-teacher in the hill districts reflect

the better state of education infrastructure as compared to plain districts. On the other hand,

high pupil-teacher ratio in the plain districts may be partially due to shortage of teachers in

primary schools and over-burden of school-going population in these districts.

47

Figure 3.11: District wise Pupil-Teacher Ratio (Elementary Education) in Uttarakhand

3.7 SUMMARY AND DISCUSSION

Since its formation, Uttarakhand has witnessed robust economic growth with most of

the economic opportunities arising in urban parts of the plain districts. However, this only

tells one-sided story of development, while the social sector tells a slightly different story.

Lack of economic opportunities in hill districts and high population density in the plains,

increased pressure on the public utilities and resources has altogether is leading to

diminishing opportunities for reasonable employment and livelihood in the plain districts.

This has further aggravated the developmental disparities in the state, highlighting the rural-

urban and hill-plain dichotomies.

The demographic profile of the state also reflects the repercussions due to developmental

disparities. The distribution of population is highly skewed with more than half of the state’s

population concentrated in three plain districts. However, nearly two-third of this population

lives in rural areas. With rapidly and unevenly increasing urbanization in these districts, the

48

concentration of adult population has increased in urban areas, resulting to higher

concentration of old age group population in rural areas. Similarly, the child sex ratio is less

favourable in all districts as compared to the sex ratio for all age groups, the reasons for

which need to be investigated further. The share of SC population in all the districts has

slightly increased, which also gives a possibility that the out-migration from the state is not

due to distress, rather in search of better educational and employment opportunities.

As reflected in the statistical analysis, the pace of economic growth in Uttarakhand has

undoubtedly remained high in the last one and a half decade which can be attributed to the

growth of industrial and services sector. This is also evident in the sectoral transformation

and increase in share of industrial and service sector in the state GDP. Following the growth

in GSDP, the per capita income also reflects an increasing trend. However, the annual

increase in per capita GSDP in hill districts is higher than that in the plain districts in recent

two-three years (Appendix Table A3.8). Main reasons for this can be increasing employment

opportunities and resultant rapid increase in labour force in the plain areas, which may have

led to lower remunerations and informalisation of jobs. The rapid and unplanned urbanization

and industrialization of the plain districts has further over-burdened the infrastructural

facilities and over exploitation of resources. Districts like Haridwar and Udham Singh Nagar

have better economic status but the status of health and education in these two plain districts

is way poor as compared to the hill districts. Dehradun being the state capital, is an exception.

Otherwise, majority of the state population is concentrated in these two districts, two-third of

which is living in rural areas. Declining share of agriculture in state GDP (Figure 3.6) and it

being a primary source of livelihood further worsens the situation. This economic deprivation

of rural households in the plain districts is clearly evident from the gap between rural and

urban household consumption expenditure. Thus, despite robust economic growth and ample

employment opportunities in the plain districts, huge rural urban divide is evident, which

effects majority of the population of the state, residing in plain districts. These districts also

lag behind in education and health, which further deprives more than half of the state’s

population from availing the benefits and economic opportunities provided to them. As

compared to them, the education and health status in the hill region is better. The socio-

economic analysis of the state further explains how the robust economic growth of the state

has only favoured a selected few living in the urban areas, either hills or plains, while

49

majority of the rural population, more so in plains, is not only economically deprived, but

also bears the burden of poor health and educational status.

Thus it becomes essential that the state should rather look into these intra-district and rural-

urban differentials for ensuring inclusive development of the state. It is high time that the

policy makers consider the stark intra-district disparities in the plain regions rather than only

concentrating on the disparities between hills and plains, for reducing the poverty gaps and

ensuring equitable and inclusive development of the state.

50

APPENDIXTable A3.1: District wise demographic features in Uttarakhand, (Census 2011)

District Place Percentage ofMale

Percentage ofFemale Sex Ratio Child sex ratio (0-

6)years

UttarkashiTotal 51 49 958 916Rural 51 49 968 924Urban 54 46 838 794

ChamoliTotal 50 50 1019 889Rural 48 52 1072 899Urban 57 43 767 829

RudraprayagTotal 47 53 1114 905Rural 47 53 1137 909Urban 59 41 697 803

TehriGarhwalTotal 48 52 1077 897Rural 47 53 1116 902Urban 55 45 817 846

DehradunTotal 53 47 902 889Rural 52 48 921 915Urban 53 47 886 864

GarhwalTotal 48 52 1103 904Rural 47 53 1144 913Urban 52 48 917 860

PithoragarhTotal 50 50 1020 816Rural 49 51 1039 831Urban 52 48 913 724

BagheshwarTotal 48 52 1090 904Rural 48 52 1097 906Urban 52 48 927 845

AlmoraTotal 47 53 1139 922Rural 46 54 1177 927Urban 54 46 848 861

ChampawatTotal 51 49 980 873Rural 50 50 997 881Urban 53 47 890 825

NainitalTotal 52 48 934 902Rural 51 49 948 908Urban 52 48 912 892

Udham Singh NagarTotal 52 48 920 899Rural 52 48 930 902Urban 53 47 903 894

HaridwarTotal 53 47 880 877Rural 53 47 889 883Urban 54 46 866 865

UTTARAKHANDTotal 51 49 963 890Rural 50 50 1000 899Urban 53 47 884 868

Source: Estimated from Primary Census Abstract, Uttarakhand, Census 2011

51

Appendix Table A3.2: District wise Distribution of Rural Population in Uttarakhand

District

Number and percentage of villages

Populationless than 200

Population200 - 499

Population500 - 999

Population1000 - 1999

Population2000 - 4999

Population5000 - 9999

Population10000 andabove

Uttarkashi186(27)

309(45)

142(20)

48(7)

8(1)

1(0)

0(0)

Chamoli591(51)

399(34)

138(12)

40(3)

2(0)

0(0)

0(0)

Rudraprayag270(41)

234(36)

105(16)

42(6)

2(0)

0(0)

0(0)

TehriGarhwal812(46)

652(37)

247(14)

56(3)

7(0)

0(0)

0(0)

Dehradun208(28)

251(34)

107(15)

75(10)

64(9)

19(3)

7(1)

PauriGarhwal2303(73)

683(22)

109(3)

28(1)

17(1)

2(0)

0(0)

Pithoragarh949(60)

433(28)

131(8)

45(3)

11(1)

3(0)

0(0)

Bagheshwar469(54)

278(32)

99(11)

20(2)

7(1)

1(0)

0(0)

Almora1177(54)

724(33)

235(11)

43(2)

5(0)

0(0)

0(0)

Champawat344(52)

180(27)

106(16)

22(3)

10(2)

0(0)

0(0)

Nainital384(35)

388(35)

193(18)

96(9)

34(3)

1(0)

1(0)

Udham SinghNagar

74(11)

104(15)

134(20)

191(28)

143(21)

25(4)

3(0)

Haridwar56(11)

49(9)

80(15)

118(23)

161(31)

44(8)

10(2)

Note: Figures in parenthesis are percentage of the above.Source: District Census Handbook of Uttarakhand, Directorate of Census Operations, Uttarakhand, 2011.

52

Appendix Table A3.3: Population Distribution in Five year age-group by Residence andsex in Uttarakhand

Age-group

Total Rural Urban

Persons Males Females Persons Males Females Persons Males Females

1 2 3 4 5 6 7 8 9 10All ages 100.0 50.9 49.1 69.8 34.9 34.9 30.2 16.0 14.20-4 9.2 4.8 4.3 6.7 3.5 3.2 2.5 1.3 1.15-9 10.5 5.6 4.9 7.6 4.0 3.6 2.9 1.5 1.310-14 11.4 6.0 5.4 8.3 4.3 4.0 3.1 1.7 1.415-19 11.1 5.8 5.3 7.9 4.0 3.8 3.3 1.8 1.520-24 9.6 4.8 4.8 6.4 3.1 3.3 3.2 1.7 1.525-29 8.0 3.9 4.1 5.3 2.5 2.7 2.8 1.4 1.430-34 6.9 3.4 3.5 4.6 2.2 2.4 2.4 1.2 1.135-39 6.6 3.3 3.3 4.4 2.1 2.2 2.2 1.1 1.140-44 5.6 2.8 2.8 3.8 1.9 1.9 1.9 1.0 0.945-49 4.9 2.5 2.4 3.2 1.6 1.6 1.6 0.9 0.850-54 3.9 2.0 1.9 2.7 1.3 1.4 1.3 0.7 0.655-59 3.2 1.5 1.7 2.2 1.0 1.2 1.0 0.5 0.560-64 3.3 1.6 1.6 2.4 1.2 1.2 0.8 0.4 0.465-69 2.2 1.1 1.1 1.6 0.8 0.8 0.6 0.3 0.370-74 1.6 0.8 0.8 1.2 0.6 0.6 0.4 0.2 0.275-79 0.8 0.4 0.4 0.6 0.3 0.3 0.2 0.1 0.180+ 1.0 0.5 0.6 0.8 0.4 0.4 0.3 0.1 0.1Age not stated 5.0 0.1 0.1 0.1 0.1 0.0 0.1 0.0 0.0Source: Census of India, 2011.

Appendix Table A3.4: District wise Sex ratio in rural areas of Uttarakhand, 1901-2011

District Census Year1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001 2011

Uttarkashi 1015 1026 1035 1017 976 996 977 915 909 939 961 968

Chamoli 1032 1036 1084 1069 1077 1092 1089 1069 1089 1040 1073 1072

Rudraprayag 1027 1033 1070 1054 1047 1146 1176 1173 1120 1104 1127 1137

Tehri Garhwal 1015 1026 1034 1017 976 1141 1225 1204 1115 1090 1109 1116

Dehradun 785 787 780 752 736 767 810 811 839 861 914 921

Garhwal 1058 1070 1114 1087 1108 1179 1221 1163 1154 1118 1155 1144

Pithoragarh 976 970 999 998 1013 1024 1051 1048 1052 1013 1066 1039

Bagheshwar 976 970 999 998 1010 1012 1025 1063 1048 1064 1117 1097

Almora 998 1015 1021 1021 1036 1102 1162 1153 1145 1137 1189 1177

Champawat 944 939 960 959 973 955 937 975 969 969 1055 997

Nainital 843 811 776 758 740 773 794 883 867 908 922 948

Udham Singh Nagar 779 749 697 682 658 774 664 763 838 868 916 930

Haridwar 880 855 881 849 863 830 829 832 836 849 874 889

Uttarakhand 943 944 963 948 953 998 995 990 984 978 1007 1000Source: District Census Handbook of Uttarakhand, Directorate of Census Operations, Uttarakhand,2011.

53

Appendix Table A3.5: Annual estimates of Birth rate, Death rate and Natural growthrate by residence in Uttarakhand, 1999-2014

Year Birth rate Death rate Natural growth rateTotal Rural Urban Total Rural Urban Total Rural Urban

1999 19.6 24.5 16.1 6.5 10.5 3.5 13.1 14.0 12.62000 20.2 24.6 17.1 6.9 10.3 4.5 13.3 14.3 12.6

2001 18.5 21.1 16.6 7.8 10.0 6.1 10.7 11.1 10.52002 17.0 18.1 16.2 6.4 9.0 4.4 10.6 9.1 11.82003 17.2 18.9 16.0 6.5 8.6 4.8 10.8 10.3 11.12004 20.5 21.6 16.2 7.2 8.0 4.4 13.2 13.6 11.72005 20.9 22.1 16.6 7.4 7.9 5.3 13.6 14.2 11.22006 21.0 22.0 17.3 6.7 7.0 5.5 14.2 14.9 11.72007 20.4 21.3 17.0 6.8 7.1 5.3 13.6 14.2 11.72008 20.1 21.0 16.5 6.4 6.7 5.6 13.6 14.4 10.92009 19.7 20.6 16.3 6.5 6.9 5.2 13.2 13.7 11.02010 19.3 20.2 16.2 6.3 6.7 5.1 13.0 13.5 11.12011 18.9 19.7 16.0 6.2 6.5 4.9 12.8 13.2 11.22012 18.5 19.1 15.9 6.1 6.5 4.8 12.4 12.7 11.1

2013 18.2 18.9 15.7 6.1 6.4 4.8 12.1 12.5 10.92014 18.2 18.5 17.3 6.0 6.3 5.2 12.2 12.2 12.0Source: SRS, Census of India.

Appendix Table A3.6: District wise Gross Domestic Product in Uttarakhand since 2001-02

DistrictGross State Domestic Product (Rs. Lakh) (at Constant prices 2004-05)

2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13Q

2013-14A

Uttarkashi 66086 73924 83439 86268 92736 99094 99906 105024 109852 117503

Chamoli 104775 109904 127543 144402 154849 170459 169630 179987 188048 201593

Rudraprayag 47104 50623 58075 61545 66972 73964 88367 97631 102631 109688

TehriGarhwal 153996 172321 185065 195168 214033 239639 280183 309116 324539 348318

Dehradun 454099 517661 589585 688835 794634 884699 1030027

1124403 1189291 1263305

PauriGarhwal 161172 179515 206330 229354 257788 287693 323822 352663 371710 394634

Pithoragarh 109860 117754 129622 143443 155670 174645 204089 206693 216236 230889

Bageshwar 50958 53886 65379 63986 68342 74915 84476 92489 97046 103539

Almora 155814 159268 182356 196846 211673 240782 263541 282013 295468 314961

Champawat 54640 61670 63538 69012 82953 91690 85609 95216 100241 106768

Nainital 251385 303385 330069 392388 440068 500579 554990 613358 649436 686671Udham SinghNagar 369866 466808 563629 705393 811993 904055 111415

2123723

1 1312778 1372021

Haridwar 498812 560047 641373 824813 931830 1038600

1267896

1392217 1472031 1542826

Uttarakhand 2478567

2826766

3226003

3801453

4283541

4780814

5566689

6088041 6429308 6792716

Source: Directorate of Economics & Statistics, Planning Department, Government of Uttarakhand.

54

Appendix Table A3.7: Trend in Share of Different Sector in GSDP since 2001-02

YearPercentage Share

Agriculture Industry Services2001-2002 27 23 462002-2003 25 25 472003-2004 25 27 502004-2005 22 28 502005-2006 19 31 502006-2007 17 33 492007-2008 15 35 502008-2009 13 35 532009-2010 12 35 532010-2011 11 36 522011-2012 11 37 522012-2013 11 38 512013-2014 10 39 512014-2015 9 40 502015-2016 9 39 52Source: Computed from the data from Directorate of Economics and Statistics, Government of Uttarakhand

Appendix Table A3.8: Annual Growth Rate of District wise GDP

(at Constant prices 2004-05)

DistrictAnnual growth rate (Percent) (at Constant prices 2004-05)

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13Q

2013-14A

Uttarkashi 11.9% 12.9% 3.4% 7.5% 6.9% 0.8% 5.1% 4.6% 7.0%Chamoli 4.9% 16.0% 13.2% 7.2% 10.1% -0.5% 6.1% 4.5% 7.2%Rudraprayag 7.5% 14.7% 6.0% 8.8% 10.4% 19.5% 10.5% 5.1% 6.9%TehriGarhwal 11.9% 7.4% 5.5% 9.7% 12.0% 16.9% 10.3% 5.0% 7.3%Dehradun 14.0% 13.9% 16.8% 15.4% 11.3% 16.4% 9.2% 5.8% 6.2%PauriGarhwal 11.4% 14.9% 11.2% 12.4% 11.6% 12.6% 8.9% 5.4% 6.2%Pithoragarh 7.2% 10.1% 10.7% 8.5% 12.2% 16.9% 1.3% 4.6% 6.8%Bageshwar 5.7% 21.3% -2.1% 6.8% 9.6% 12.8% 9.5% 4.9% 6.7%Almora 2.2% 14.5% 7.9% 7.5% 13.8% 9.5% 7.0% 4.8% 6.6%Champawat 12.9% 3.0% 8.6% 20.2% 10.5% -6.6% 11.2% 5.3% 6.5%Nainital 20.7% 8.8% 18.9% 12.2% 13.8% 10.9% 10.5% 5.9% 5.7%Udham SinghNagar 26.2% 20.7% 25.2% 15.1% 11.3% 23.2% 11.0% 6.1% 4.5%

Haridwar 12.3% 14.5% 28.6% 13.0% 11.5% 22.1% 9.8% 5.7% 4.8%Uttarakhand 14.0% 14.1% 17.8% 12.7% 11.6% 16.4% 9.4% 5.6% 5.7%Source: Directorate of Economics & Statistics, Planning Department, Government of Uttarakhand.

55

Appendix Table A3.9:GSDP Growth Rate and Share of Manufacturing SectorinUttarakhand since 2001-02

Year Annual growth rate Percentage Share in GSDP2001-2002 -9 11.92002-2003 11 12.02003-2004 10 12.32004-2005 17 12.72005-2006 47 16.32006-2007 27 18.22007-2008 46 22.52008-2009 21 24.22009-2010 24 25.42010-2011 14 26.32011-2012 11 26.62012-2013 12 27.82013-2014 12 28.62014-2015 12 29.32015-2016 2 27.8

Source: Computed from the data from Directorate of Economics and Statistics, Government of Uttarakhand

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Chapter-4

Analysis of Composite Index of Development and itsDimensions

4.1 INTRODUCTION

Index of development is constructed as per methodology discussed earlier. In this

chapter, the relative position of the districts based on the value of index is analysed. Districts

have been ranked based on the inverse of derived index value and thus for the reference year

2013-1417 the district with highest index value is ranked at top followed by the lower value

district. The composite index of development is summary index of five sup-components

namely demographic, economic, education, health and amenities. These sup-components

itself is summary index of different representative indicators linearly aggregated by assigning

weights generated through principal components analysis (PCA). So the index value of a

district is not free from the effect of different dimensions and the index value of different

dimension is also effect of all the indicators used in derived index. In this context, the

analysis in the chapter is to see the effect of different dimension on composite index as well

as the effect of indicators on dimension index value. Further since some of the indicator

mentioned in chapter 2 has been dropped while constructing final index, the rationality of

choosing indicator and those although initially included but dropped has been discussed here

for every sub-component.

The chapter proceeds as follows. Section 2 discusses the position of development of districts

through their ranking as per composite index of development. Decomposition of composite

index in different dimensions viz. demographic, economic, education, health and amenities is

also part of the section. Further in section 3 disaggregated analysis of each dimension for

every district and the decomposition of the dimension index in different indicators are done.

Last section concludes the pattern with comments.

17 The reference year for the index of development is 2013-14, however for all indicator the data for year2013-14 is not available and thus it is the index with most of the indicator is for the year 2013-14 or earliestavailable to this period.

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4.2 COMPOSITE INDEX OF DEVELOPMENT

Composite index of development for a district is suitable summary measure to capture

the multidimensional nature of development. Based on the index value of composite index,

the relative position of different districts has been reported in the Figure 4.1 below.

As evident from the Figure 4.1, Udham Singh Nagar is at the top of the rank order while

Uttarkashi is at the bottom of all the districts of the state. In the top order Udham Singh

Nagar is followed by Haridwar whose index value is almost similar to that of the Udham

Singh Nagar. So it can assert that both districts are at the same level of development.

Dehradun index value is slightly lower than Haridwar which is followed by Nainital. So the

top four districts in development index are those which are either in plain or in semi plain

region like Nainital. Further next to Pauri Garhwal are six districts upto Rudraprayag whose

index values are almost similar with a very slight decline in the value and thus are at the same

level of development. So it could be assert that the development divide becomes significant

between these two categories of districts. The first include Udham Singh Nagar, Haridwar,

Dehradun and Nainital while the latter category includes the remaining districts. Uttarkashi

including Bageshwar are lagging much behind if we compare them with Udham Singh Nagar

and Haridwar.

Figure 4.1: Relative ranking of districts Based on Composite index Value

Source: Authors’ Calculation

As composite index is constitutes of five sub-components and its value is the net effect of all

these sub-components. So, to understand thoroughly the gap in development, composite

index is decomposed in five different sub-components as shown in Figure 4.2 below.

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The decomposed value of composite index with their relative ranking shows the share of each

sub-component in the particular district. Overall it can be seen that demographic, economic

and amenities are the three sub-components whose distribution is much varied among the

districts. Also the share of these three sub-components has increased with the increase in the

ranking of district or index value. Its net effect is more visible when different sub-component

has been analysed in the section 3 below. Further in the remaining two sub-components,

education has little variation and its value seems to be similar in all the districts irrespective

of the ranking. But health index value has declined with the increase in the composite index

value. It is visible from the Figure 4.3 that health index share has been lower in Udham Singh

Nagar, Haridwar and Dehradun while it remained relatively stable in the remaining districts.

Figure 4.2: Decomposition of Composite index Value

Source: Authors’ Calculation

Thus we can see that as far as rising disparities among districts is concerned demographic,

economic and amenities are the dimensions where the gap is wide and needs to be addressed

to achieve balanced development of the state. Further education dimension has lowest

variation, but lowest share of health index at the top ranked districts needs to be understood

properly. It may not be the case that lower health value in the top rank districts is comfortable

sign but it shows the deterioration in health condition as elaborated in detail in sub-

component analysis below. Now, since these dimension itself are summary measure of

various indicators whose analysis are important for policy intervention. Recognising its

cruciality, sub-component index (rank order and decomposition) are discussed thoroughly in

the following section.

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4.3 SUB-COMPONENT OF DEVELOPMENT INDEX

The objective of sub-component analysis is thorough examination of districts’ relative

position on different dimensions as well as to understand the relative importance of different

indicator in explaining the variation among the districts sub-component index value is also

decomposed among different indicators. It provides insights for policy suggestion which is

target at the level of indicator variable. Also for all the five sub-component, the rationality of

indicator chosen when some of the indicator listed earlier is dropped is discussed in this

section. It should be mention outset that along with indicators, index by assigning weights

through PCA are compared with index value generated through assigning equal weights to

each indicator. The index value with equal weights is produced in Appendix Table A4.1.

4.3.1 Demographic Index

Demography of a region is an important for understanding development from both

supply and demand side. Rising demographic base of region will create demand for goods

and services that is required to be supplied and also the rise in labour force would contribute

in economic growth of a region. Further, from the policy perspective change in demographic

composition has strong impact on policy which requires to be adjusted accordingly18. The

demand structure for an economy with higher youth population would be different from

ageing society. Recognising the crucial role of demographic factors, here demographic index

is calculated by taking four important indicators. The indicators used include population

density of district, share of urban population in total population, percentage share of non-sc

population and child sex ratio (0-6). Since the objective of the study is to make a composite

index of development thus the indicators chosen are those with positive association with the

level of development. Population density of a district is supposed to be positive indicator of

development and similarly is share of urban population. Here we have included share of non-

SC population as it is found that poverty incidence is highly correlated with the share of SC

population. Further sex ratio is an important demographic indicator for social stability and

thus development of a district. In all the selected indicators the correlation coefficient is

positive and less than 0.8 for three indicators except for sex ratio. Sex ratio exhibits negative

correlation with all the three indicators which implies deterioration of sex ratio with rise in

18 It is more relevant in the case of Uttarakhand where outmigration from hill districts is bigger problem whichhas its impact on their economy and demography as well. For example one of the reason for higher femaleparticipation in labour market from hill district is the outmigration of male population in search of work atdifferent places.

60

development indicator. However, since sex-ratio here is a normative indicator it is decided to

be included in the index.

From all these four indicators which are normalized through ‘division by mean’ and linearly

aggregated by assigning weights generated through PCA, an index value is derived for every

districts of the state. The index derived by assigning equal weights is given in Appendix

Table A4.1 below. The rank order of the districts on the basis of demographic index value is

produced in Figure 4.3 below.

Figure 4.3: Ranking of Districts based on demographic index value

Source: Authors’ CalculationNote: Here although index value is mentioned for year 2013-14 but all the indicators are taken fromcensus 2011. So the ranking of the district for 2013-14 and 2011-12 will be similar.

It is evident here that rank order of districts based on demographic index value is similar to

that of composite index value but with greater variation among districts. The top ranked

districts are those which have better composite index value and similarly the lowest rank

district is the one with lowest composite index value. In the Figure 4.1 above, districts in

plain which include Dehradun, Haridwar and Udham Singh Nagar have also top three

positions in demographic index while hill district Uttarkashi, Bageshwar and Rudraprayag are

at the bottom of the order. This pattern is expected as also discussed above that the share of

demographic sub-component has been increasing with the increase in the value of composite

index.

In order to understand the role of different indicators in demographic index value the

decomposition of the same is done and is shown in Figure 4.4 below. It is evident here that

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among four indicators of demographic index, population density and urbanisation rate has

major contribution in inter-district disparity. The district with higher population density and

high share of urban population are those with higher composite index of development. The

districts in the plain region of the state are having much higher population density and

urbanisation rate while in hill area the value of the two indicators are much lower. Here as is

clear from decomposition exercise that despite higher population density, when compared

with ranking of composite index, Dehradun has replaced Udham Singh Nagar as it has

relatively higher share of urban population. As per census 2011, urbanisation rate of

Dehradun was 56 percent as compared to 36 percent of Udham Singh Nagar and 37 percent

and 39 percent respectively of Haridwar and Nainital.

Figure 4.4: Decomposition of Demographic Index Value

Source: Authors’ Calculation

It is important to understand the responsible factors for urbanisation rate and population

density in the plain districts. One point is clear that population density is higher in the places

with higher urbanisation rate which implies the role of migration from hill to plain districts.

Between census 2001 and 2011, the net increase in population in most of the hill districts

were negative with substantial rise in urban population of the plain districts. Most of the

migration from hill district is to urban area of the plain district. Thus understanding

urbanisation process in plain and hill district is crucial for balanced demographic

development of all the districts of the state.

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4.3.2 Economic Index

Economic index is constructed to capture disparity in those indicators which represent

both economic size of the district as well as those are affecting the livelihood of the

population and income distribution in the district. The indicators spread from per capita

income (DDP), agriculture productivity, employment structure, poverty level etc. Here for

constructing index we have taken together ten indicators as is given in list of indicators table

in appendix Table A4.3. However, there are certain indicators which we have dropped in

correlation analysis.

Among the indicators employment rate, livestock population per 1000 population, non-

agricultural output share and share of non-farm worker has been dropped. Employment rate

and livestock population are the two indicators which is dropped as both were having either

weak or negative correlation with many of the indicators that include per capita income.

Employment rate with per capita is showing negative correlation which seems surprising.

Here if we take unemployment rate instead of employment we may get positive correlation

with per capita income. It has got some better interpretation at the micro level as the

individual from well off household can afford to be unemployed until they get better

oppurtunities. Further we have also dropped percentage of non-agricultural output and

percentage of non-farm workers as both are having correlation coefficient of 0.90 with per

capita income. Also the other economic indicators are having correlation coefficient with the

per capita income slightly better than these two indicators. So it found suitable to keep only

per capita income instead of having three indicators with correlation coefficient higher than

0.9.

Based on the economic index value the districts are ranked which is shown in Figure 4.5

below. The decline in economic index value from top to bottom rank has been steeper than

the composite index value. As is visible in the Figure 4.5 below, Udham Singh is at the top of

the rank order followed by Haridwar, Dehradun and Nainital. But in the top order the decline

in the economic index value between Udham Singh Nagar and Nainital has been steeper than

composite index value. Similarly at the bottom of rank order is Chamoli preceded by

Rudraprayag, Uttarkashi and Almora. In the bottom order the gap in the index value has been

very less which is almost unchanged between Champawat and Chamoli. Here it seems that

higher economic growth achieved in the state in the last one and half decade is concentrated

in four districts. There has been deep divide in economic attainment between hill and plain

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districts of the states which is required to be understood. For this, the economic index value is

decomposed among ten indicators and is produced in Figure 4.6 below.

Figure 4.5: Ranking of Districts based on Economic index value

Source: Authors’ Calculation

Here economic index value is summary measure of ten indicators. The respective share of

each indicator across districts with their role in overall index (Figure 4.6) shows increasing

value in top ranked district for all indicators. Among the indicator, per capita income is

higher in top ranked districts as compared to bottom ranked districts but variation in indicator

value is not much higher. However, indicators like irrigated to cultivable area, agricultural

productivity, banking facility and credit-deposit ratio has been much varied across the district.

The value of these indicators has increased with increase in economic index value. The

disparity in these indicators has further exaggerated the disparity since they are highly

correlated with per capita income. Also agricultural growth and financial development has

distributional effect. In correlation analysis (Appendix Table A4.3) per capita income has

high degree of correlation with irrigated to cultivable area (0.78) and it has, as can be seen

from above figure, higher level of disparity across the districts. Similarly share of financial

indicators like banking facility and credit-deposit ratio are also much higher contributing to

the higher level of disparity among districts.

64

Figure 4.6: Decomposition of Economic Index Value

Source: Authors’ Calculation

Since these are proxy indicator used to account for distributional aspect availability of data of

income and consumption distribution is seriously required for such study. Further rural-urban

distribution of income and consumption can have profound impact in the final index value as

well. It may be possible that increase urbanisation rate in plain district is pushing up the

average per capita income while distribution in income is squeezed at the top end. It is

pointed out in chapter 3 that there exist substantial rural-urban differential in standard of

living measured through consumption expenditure even though there exist problem of

representative sample size.

4.3.3 Education Index

Education level and its quality in a region is an important component of human capital which

contributes in its economic and social development. Measuring the minimum level of

educational development necessary for development of a region is a challenging task. The

better measurement may be the indicator capturing the learning outcome as well as cognitive

development. However, the paucity of data on such indicator creates a void for measuring

actual level of educational development and we have to rely on proxy input variables. The

available data for district level that has been used are female literacy rate, enrollment ratio of

elementary level, retention rate, pupil-teacher ratio and schools in a region. The education

index is derived on these indicators in which one is outcome and others are input indicators.

The ranking of the districts according to the education index value is done which is shown in

the Figure 4.6 below. It is already discussed above that the variation in the education

65

dimension has been least across districts as compared to other dimensions of composite index

which is reflected in the heights of the histogram. Among the districts of the state, Dehradun

has the highest education index value followed by Haridwar and Pauri Garhwal. Similarly at

the bottom most is Uttarkashi preceded by Bageshwar and Pithoragarh. In the education

index value it is evident from Figure below that the gradient in index value between

Dehradun and Pithoragarh has been very low and thus it shows little variations. However, for

Uttarkashi and Bageshwar the low index value is cause of concern that requires policy

intervention which is indicated in decomposition results shown in Figure 4.7 below.

Figure 4.7: Ranking of Districts based on Education index value

Source: Authors’ Calculation

Figure 4.8: Decomposition of Education Index Value

Source: Authors’ Calculation

66

It is evident from decomposition result (Figure 4.8) female literacy rate, enrollment rate and

retention rate including population with secondary and above level of education are having

least variation across districts. The other two indicators inverse pupil-teacher ratio and

number of primary/upper primary school has variation across district. It can be seen that

Chamoli has higher value of inverse of pupil-teacher ratio than Dehradun, Haridwar and

Pauri Garhwal. Similarly, Champawat and Pithoragarh have also higher value than the top

ranked district. But for Uttarkashi and Bageshwar the value is slightly lower which is

responsible for lower index value. The reason for better pupil-teacher ratio in hill is due to

lesser number of student in the school as a result of out-migration and minimum number of

teachers in the school. Second point worth mentioning for hill districts where only public

schools are dominantly available which is included in the data. However, if we include

private school and though a quality one then probably districts in plain may have much

higher value for this indicator in decomposition results.

4.3.4 Health Index

Better health of society is a crucial component for the status of human capital in a

region. For better growth and productivity health plays an important role. However, to assess

the overall status of human health multiple indicators which includes input, output and

process is required to be monitor. Also child and maternal health is crucial indicator for a

healthy society. We have used 11 indicators to measure health status of a district which is

given in earlier chapter. Among the output indicators include female infant mortality rate,

malnutrition indicators (stunted, wasted and underweight) while health infrastructure viz.

doctors, paramedical staff, hospital, beds etc are input indicators. There are two process

indicators which is taken as proxy for maternal health include ANC and institutional delivery.

Though we selected 13 indicators but in correlation analysis 2 indicators viz. PNC (Post

Natal Care) and severely wasted has been dropped. PNC data seems to not much reliable and

it is showing negative correlation with many of the indicators. Thus we have decided to

choose ANC over PNC. Apart from it we have taken institutional delivery as development

indicator of health which is highly correlated to maternal health. Among wasted and severely

wasted the correlation coefficient is much higher (0.97) and thus only wasted has been taken.

From these indicators, composite index of health has been derived and districts are ranked on

the basis of health index which is shown in Figure 4.9 below.

67

As mentioned above, in the composite index of development health sub-component index

value has been declining with the increase in the value of overall ranking. This pattern is

clearly visible here below. In the top of rank order are Almora, Nainital, Pithoragarh and

Pauri Garhwal, none of them are in top three in composite index, whose index value are

almost similar. Similarly at the bottom rank are Udham Singh Nagar, Haridwar and Dehradun

which are top three districts in composite index. This puzzle of low performance of the

districts with better economic performance need to be under which is attempted through

decomposition health index value produced in Figure 4.10 below.

Figure 4.9: Ranking of Districts based on Health index value

Source: Authors’ Calculation

Figure 4.10: Decomposition of Health Index Value

Source: Authors’ Calculation

68

Decomposition results in Figure 4.10 shows the dominant role of infrastructure variables viz.

doctors, paramedical staff and hospital in disparity visible across the districts of the state.

Further, nutritional indicators like stunted and underweight are although relatively higher in

districts like Haridwar, Udham Singh Nagar and Dehradun but their contribution in overall

health index are lower. One of the reason is the lower weight assigned to these indicators

through PCA as variation in them across districts is lesser than infrastructure indicators. An

important output indicator viz. female infant mortality rate in Haridwar and other plain

districts is relatively higher which has contributed in their lower index value. It may be

understood as outcome of lower infrastructure than the required in these districts. There has

been least variation in indicator of maternal health shows the positive impact of government

program like Janani Suraksha Yojana (JSY). Thus improving health index of the plain

districts which also holds large share of population of the state requires infrastructure

facilities to be improved. These districts although have better infrastructure but due to

increase in population pressure the required facility are inadequate. Second reason may be

data limitations which may not include the private provision of hospital which may be

exaggerating the net effect. But even public provision of these facilities is important as they

are mostly accessed by poor household in districts.

4.3.5 Amenities Index

Provision of basic amenities is important for sustenance of life in a society and the

minimum required amenities also depends on the stage of economic development of nation.

Food, housing, minimum education and health facilities are important for life sustenance and

are including in poverty line as provided by the expert group constituted by erstwhile

planning commission. In the study, education and health facility has been tried to be captured

in dimensions discussed above while food expenditure is included through poverty ratio in

economic index. Now here in the index of amenities, we have consider basic requirement at

the household level viz. drinking water within household premises, sanitation facility,

electricity as primary source of lightening, surface road as well as electricity consumption.

We have also tried to consider some other indicator listed in earlier chapter but they have

been dropped in correlation analysis discussed below.

Percentage of village electrified was included but it has been dropped as there is almost 100

percent coverage of village in every district with little inter-district variation. Availability of

drinking water within a settlement as indicator has very weak correlation with percentage of

69

household with drinking water facility within premises and is negatively correlated with other

indicators of household amenities. So it is decided to be dropped. Regarding connectivity

indicator of road facility, there is negative correlation between total surface road per 10000 sq

km of area and total surface road per lakh population. Perhaps it is due to low density of

population in hilly districts. Among these two indicators we found positive correlation

between total surface road per 10000 sq km of area with percentage of household with

drinking water facility within premise and percentage of household with latrine facility. So it

decided to take total surface road per 10000 sq km and dropped total surface road per lakh

population. One other rationality of choosing total surface road per 10000 sq km is the need

of coverage of every location of the districts irrespective of the population density.

Amenities index is derived from these indicators by assigning weights generated through

PCA and the districts has been ranked based on the index value shown in Figure 4.11 below.

The ranking of district based on amenities index reflects similar pattern of composite index of

development and economic index as shown in Figure 4.1 and 4.3 above. In the rank order the

top four districts are Udham Singh Nagar, followed by Haridwar, Dehradun and Nainital and

at the bottom is Bageshwar preceded by Chamoli and Uttarakashi whose index values are

almost similar. There is steep fall in index value between Udham Singh Nagar and Nainital

while after that Tehri Garhwal the index value shows very little change. This pattern is more

visible in the decomposition exercise shown in Figure 4.12 below.

Figure 4.11: Ranking of Districts inverse to the Amenities index value, 2013-14

Source: Authors’ Calculation

70

In the decomposition exercise (Figure 4.12 below) shows least variation in indicator value of

latrine facility and electricity as a source of lightening. Further it is evident that surface roads

per 10000 population as well as per capita electricity consumption has been leading indicator

in widening disparity in the index value between plain and hill districts. Drinking water

within premises is important indicator of amenities and there is although little variation in

comparison to per capita electricity consumption and surface road but plain districts has

relatively higher value than hilly districts. Since all the indicators included in amenities index

are provided by state it is expected to be least variation. It may be possible that in

economically better of districts these facilities are being arranged privately by the household

themselves the data for which is not available. Surface roads per 10000 population and per

capita electricity consumption again are the indicator invariant to economic situation of a

district. For example relatively developed and urbanized districts may have higher per capita

consumption of electricity. However, as mentioned earlier one of the limitation of index

exercise is it does established causality.

Figure 4.12: Decomposition of Amenities Index in Different Indicators

Source: Authors’ Calculation

4.4 Concluding Comments

Development is multidimensional phenomenon and in the study it has been captured

through five dimensions viz. demographic, economic, education, health and basic amenities

for districts of Uttarakhand. Composite index which is a weighted summary measure of these

dimension index value is derived by assigning equal weights to each dimensions. Further for

71

each dimension, index value is derived by assigning weights generated through PCA to

selected representative indicators.

Ranking done on the basis of composite index value shows development divide among

districts of the state. Among the districts, those in the plain region and its surrounding

districts viz. Udham Singh Nagar, Haridwar, Dehradun and Nainital are at top of the rank

order while hill districts are relatively underdeveloped based on these dimensions. To see the

relative contribution of each dimension, composite index value is decomposed that gives the

respective shares of each dimension in development divide among districts. The dimension

whose index value increases with the increase in the composite index value includes

demographic, economic and basic amenities. Further while education index shows little

variation, however, health index shows decline in the index value with the increase in the

composite index value.

In demographic index population density and urbanisation rate are dominant indicator in

disparity among districts. Both these indicators need to be understood in the case of plain

districts particularly urbanisation process. Existing literature shows the role of migration from

hill to plain districts which may be a responsible factor. However, significant rural population

in these districts along with rising urbanisation rate creates a dual structure economy in these

districts which requires further investigation.

Economic attainment of plain district is definitely better in plain districts led by Udham Singh

Nagar followed by Haridwar, Dehradun and Nainital. In this not only per capita DDP which

is a average indicator of district but other indicators that is used to see the distributional

nature of the economic attainment is also better in plain districts. The decomposition exercise

shows that improving irrigated to cultivable area, financial development can play a major role

in bridging the gap. Apart from this development of small scale units in the hill districts can

also assist in balanced development of economic attainment.

Education and health are two important component of human capital. In education there is

strong paucity of data on learning outcome and cognitive development of student in

elementary school at the district level. We think it is important to measure educational

development of the district. In the education index which is measured through output

indicator and input indicator there seems to be least variation among the dimensions included

in composite index of development. However, still in the hill district the index value is lower

than plain region despite the fact that in the hill district the share of public school is higher as

72

compared to plain district. Further the plain districts which are relatively better in composite

index and economic dimension are lagging much behind in heath dimension. The main

reason is the poor outcome indicator that include female infant mortality rate and nutritional

indicator. Further in population adjusted health infrastructure facilities also these districts

have poor performance. This comment should be treated cautiously as DES data on health

infrastructure does not include private facility which may have large share in these districts.

Lastly, there are significant inter-district variations in amenities at the household level which

are essential for human development. The basic facilities like latrine and drinking needs to be

provided to every household. Index shows higher household amenities index for relatively

better off districts. The high index value for district with better composite index of

development may be due to private arranged facilities by the household but this cannot be

accounted due to data limitations of clear distinction between public and privately arranged.

However, the gap in the basic facilities needs to be filled up for achieving target of even

development of all the districts of the state.

73

Appendix Tables

Table A4.1: Index Value of different dimensions and Composite Index, 2013-14

Demographic Economic Education Health BasicAmenities

CompositeIndex

Almora 0.787 0.695 0.984 1.343 0.748 0.911Bageshwar 0.525 0.774 0.872 0.967 0.582 0.744Chamoli 0.678 0.638 1.081 1.133 0.588 0.824Champawat 0.763 0.814 1.010 0.892 0.641 0.824Dehradun 1.945 1.542 1.171 0.623 1.431 1.342Haridwar 1.910 1.788 1.083 0.449 1.805 1.407Nainital 1.271 1.292 1.021 1.325 1.276 1.237Pauri Garhwal 0.817 0.720 1.082 1.248 1.230 1.019Pithoragarh 0.679 0.753 0.967 1.264 0.668 0.866Rudraprayag 0.601 0.649 0.989 1.216 0.652 0.821Tehri Garhwal 0.779 0.747 1.010 1.056 0.760 0.870Udham SinghNagar 1.727 1.931 0.973 0.415 2.017 1.413Uttarakashi 0.517 0.657 0.756 1.070 0.600 0.720Source: Authors’ Calculation

Table A4.2: Correlation Coefficient among Demographic Indicators

Indicators

Populationdensity(per sq.km)

UrbanisationShare (%)

% Non-SCPopulation

sex-ratio(0-6)

Population density (per sq.km) 1.0000Urbanisation Share (%)

0.7657 1.0000%Non-SC Population 0.4386 0.5991 1.0000sex-ratio (0-6)

-0.5092 -0.5307 -0.0985 1.0000Source: Authors’ Calculation

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TableA4.3: Correlation Coefficient among Economic Indicators

Indicators

PCY Non-Forest Land

Irrigated tocultivatablearea

perworkeragrioutput

Agriproductivity

Workers insmallscalunitper1000pop

SSI per1000pop

Banking per100 sqkm.

Credit -depositratio

PercentageNon-poor

PCY 1.000

Non-ForestLand 0.596 1.000

Irrigated tocultivatablearea

0.785 0.503 1.000

per workeragri output 0.750 0.285 0.682 1.000

Agriproductivity 0.671 0.396 0.948 0.563 1.000

Workers insmall scalunit per1000 pop

0.403 0.389 0.313 0.499 0.075 1.000

SSI per1000 pop -0.249 0.207 -0.352 -0.265 -0.392 0.538 1.000

Banking per100 sq km. 0.822 0.513 0.715 0.683 0.616 0.372 -0.275 1.000

Credit -deposit ratio 0.454 0.437 0.816 0.295 0.913 -0.142 -0.377 0.467 1.000

PercentageNon-poor 0.475 -0.098 0.330 0.432 0.418 -0.136 -0.335 0.358 0.237 1.000

Source: Authors’ Calculation

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TableA4.4: Correlation Coefficient among Education Indicators

Indicators FemaleLiteracyRate

ElementaryEnrollment

Ratio

RetentionRate

Secondary& aboveeducationpop.

InversePupil-TeacherRatio

Numberof

primary& upperprim per100 sqkm

FemaleLiteracy Rate 1.000

ElementaryEnrollmentRatio

0.141 1.000

RetentionRate 0.487 0.268 1.000

Secondary &aboveeducationpop.

0.803 0.169 0.329 1.000

InversePupil-Teacher Ratio

0.331 0.018 -0.226 0.074 1.000

Number ofprimary &upper primper 100 sqkm

-0.143 0.096 0.252 0.119 -0.576 1.000

Source: Authors’ Calculation

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TableA4.5: Correlation Coefficient among Health Indicators

Doctors(per10000pop.)

ParaStaff(per10000pop.)

Hospital(per10000pop.)

PHC &sub-centre(per10000pop.)

TotalBeds(per10000pop.)

NotStunted

NotWasted

NotUnderweight

1/F.IMR ANC

Institutionaldelivery

Doctors (per10000 pop.) 1.000Para Staff(per 10000pop.) 0.754 1.000Hospital(per 10000pop.) 0.617 0.678 1.000PHC & sub-centre (per10000 pop.) 0.390 0.641 0.865 1.000Total Beds(per 10000pop.) 0.616 0.679 0.696 0.366 1.000

Not Stunted 0.177 0.221 0.657 0.581 0.337 1.000Not Wasted -0.080 -0.105 -0.247 -0.408 0.234 -0.361 1.000

NotUnderweight 0.087 0.093 0.044 -0.135 0.494 -0.041 0.875 1.0001/F.IMR 0.470 0.419 0.452 0.390 0.431 0.129 0.389 0.507 1.000ANC 0.505 0.429 0.089 0.190 0.074 -0.289 0.017 0.138 0.347 1.000

Institutionaldelivery -0.257 -0.281 0.060 -0.128 0.110 0.294 -0.265 -0.150

-0.181

-0.457 1.000

Source: Authors’ Calculation

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TableA4.6: Correlation Coefficient among Amenities Indicators

IndicatorsDrinking Water Latrine

facilityElectricitylightening

surfaceroad

Electricityconsumption

Drinking Water 1.000

Latrine facility 0.756 1.000

Electricitylightening 0.209 0.554 1.000

surface road 0.608 0.569 0.330 1.000

Electricityconsumption 0.875 0.599 0.169 0.838 1.000

Source: Authors’ Calculation

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Chapter-5

Measuring the Change in Development Disparities between2004-05 and 2011-12

5.1 INTRODUCTION

Relative backwardness of districts is measured through composite index of

development derived through linear aggregation of five dimensions viz. demographic,

economic, education, health and amenities. The development disparities among districts are

measured through composite index and analysed for the latest period 2013-14 in the last

chapter. However, one of the advantages of composite index is to track the development path

of a region between two points of time. Further, the quality of composite index can be

improved by rationalizing the indicators as well as through improvement in the information

for indicators over the period.

In this context, the composite index of development for districts is derived through

methodology discussed in chapter 2 at two points of time viz. 2004-05 and 2011-12. However,

the indicators chosen for these two periods are different from those considered for 2013-14

and the list of indicators is provided in Appendix table. Most of the indicators for the period

are corresponds to the year 2004-05 or earlier and similarly most of the indicators considered

for the period are representative for the year 2011-12. For the two periods the relative ranking

of the districts as well as their index values are comparatively analysed. The purpose is just to

compare the performance between these two periods and is not strictly comparable with the

index value for the representative year 2013-14.

The chapter is organised in four sections. Aggregate composite index of development has

been analysed in sections 2 which also includes decomposition of the index value. As the

aggregate index is computed from different sub-components which are aggregate of different

indicators in section 3, different sub-components are discussed in detailed and finally the last

section 4 discusses the results in a comprehensive manner.

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5.2 RANKING OF DISTRICTS ON BASIS OF INDEX

The composite index of development is derived from five dimensions and relative

ranking of districts based on index value is compared for the year 2004-05 and 2011-1219.

Districts along with composite index along with value and their ranks are given in Table

5.1.The development divide among districts is evident as the distribution of index value

extends from 0.632 in Uttarakashi to 1.626 in Haridwar which stood at bottom and top

position of rank order respectively in 2011-12. Interestingly, in 2011-12, the development

gap among districts seems to have declined slightly over the period as in comparison to 2004-

05 when the value was 0.623 and 1.727 respectively for Uttarakashi and Dehradun.

Table 5.1: Composite Index Value and Ranking of Different Districts

Districts Composite Index Ranking2011-12 2004-05 2011-12 2004-05

Almora 0.927 0.928 6 6Bagheshwar 0.708 0.668 11 12Chamoli 0.686 0.682 12 11Champawat 0.797 0.808 8 8Dehradun 1.600 1.727 2 1Haridwar 1.626 1.558 1 2Nainital 1.287 1.335 4 4Pauri Garhwal 0.956 0.959 5 5Pithoragarh 0.732 0.713 10 9Rudraprayag 0.744 0.707 9 10Tehri Garhwal 0.858 0.869 7 7Udham Singh Nagar 1.447 1.423 3 3Uttarakashi 0.632 0.623 13 13Source: Authors Calculation

In the Figure 5.1, districts are ranked in ascending order as per their respective index value

for the period 2004-05 along with respective value for 2011-12.From the figure, huge gap in

the level of development among districts is apparent. Uttarakashi remains at the bottom of the

ladder and ranked 13 while Haridwar ranked at top position. Since both Haridwar and

Dehradun have almost same index value, they could be considered at similar level of

aggregate development. Ranking of districts also shows the development gap between hilly

and plain regions. The bottom ranked which are classified as most backward districts are

fully hilly districts which include Uttarakashi, Chamoli, Bagheshwar and Pithoragarh,

19At the outset, it is requires to mention that since most of the values corresponds to the year 2011-12 and2004-05, the index is considered to be representative for the same year.

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whereas top most developed districts are those in plain area and includes Haridwar, Dehradun,

Udham Singh Nagar and Nainital. The rank order of the districts in 2011-12 is similar to the

order of 2004-05 except for Dehradun and Haridwar. Haridwar which ranked at top in 2004-

05 slips to second position and the gap in the index value between two districts has also

declined.

Figure 5.1: Ranking of Districts on The Basis of District Index of Development

Source: Based on Index CalculatedNote: In figure districts are in ascending order of index with respects to period 2004-05.

So for the policy purpose understanding the role of each dimension is of immense importance

and thus the relative contribution of different sub-components in overall composite index. In

order to examine it, the value of composite index is decomposed into different sub-

components and their percentage share is shown in Figure 5.2. Here the decomposition

results are reported only for the year 2011-12 as the results were almost similar for the year

2004-05 which is shown in Appendix.

In quantitative terms, as evident from figure below, the contribution of different sub-

components in composite index value is varying across the districts. Among different sub-

components, the contribution of demographic sub-component is the least and increases from

lower ranked districts to top rank districts. In percentage terms, it goes up from just around 1

percent in Uttarakashi to 4 percent in Haridwar. Furthermore, the percentage contribution of

access to basic amenities remained similar and shows the least variations among districts. It

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ranges from 28 percent in Almora districts followed by Udham Singh Nagar and the lowest

was 23 percent which is same in four districts.

In defining the magnitude of index value, among different dimensions economic sub-

component has a decisive role given variation of its relative shares across districts. Similar to

demographic component it increases with improvement in rank of district. In general, the

economic component’s contribution has also been similar as both Uttarakashi and Dehradun

have equal share of 26 percent while their order in overall ranking are 13th and 2nd

respectively which shows the gap in the magnitude.

Further insightful is the variation in percentage share of Health and Education sub-

components whose share varies considerably. Whereas percentage contribution of education

is higher in the bottom ranked districts like Uttarakashi, Chamoli etc but its decreases in top

ranked districts like Dehradun and Haridwar. Similarly, Health component is having higher

contribution in top ranked districts and lower in districts at bottom ranked in ladder.

Figure 5.2: Contribution of Different Components in Aggregate Index, 2011-12

Source: Based on Authors’ Calculation

5.3 SUB-COMPONENT INDEX

Here, it is attempted to measure performance of five different sub-components that

are linearly aggregated into composite index of development. As mentioned earlier, the

composite index is combined outcome of all sub-components and so for development to be

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more broad-based as well as balance it is crucial for suitable policy design to address the gap

in all the dimensions. So in order to understand the change in relative gap in different sub-

component between two points of time, ranking of different districts has been examined for

all the components separately.

5.3.1 DEMOGRAPHIC INDEX

Intuitively, population and its composition is important driver of economic growth of

a region and thus the dynamism over the period. Demographic transition theory also holds it

as dynamic component and the structure of population is dependent two way with economic

progress. As pointed out earlier, Lewis (1957) theoretically emphasizes the change in

population composition in the course of economic development and postulates increase in

urban population with the level of economic development as positive process of achieving

higher productivity and growth. Similarly rural-urban population share has also important

implication in district economic attainment as in recent paper Mcmillan and Rodrik (2011)

underlines the importance of urbanisation to the increase in productivity and overall growth

of economy of a region. To see the change in demographic index, the district is ranked as per

the value and is reported in Table 5.2 below.

Here demographic sub-component comprises of two indicators viz. population density and

urbanisation rate. It is evident that index exhibits wide range of variation also clearly visible

in the ranking order shown in Figure 5.3 below. In 2011-12, whereas the largest value which

is for Haridwar around 2.5, the lowest value is for Uttarakashi around 0.26 which is one-tenth

of the highest value. The range in composite index value between top and bottom rank

districts is around 1 while here it is double (around 2) which reflects higher variation in

demographic features as compared to aggregate pattern. Further, the rank order of different

districts in demographic index is almost similar in 2004-05. Between 2011-12 and 2004-05

the quantitative value for top ranked districts has increased with simultaneous decline for

bottom ranked districts.

The population share shows clear divide as four districts in the plain area, as per census 2011,

hold around 62 percent of total population of the state (Figure 5.8) with relatively higher

population density and urbanisation rate. Along with the increase in urban population of these

districts there is drastic fall in population share of the hill districts between census 2001 and

2011.It is as argued in literature (Mamgain and Reddy, 2016, Awasthi, 2012 etc) as the huge

rate of out-migration from the hill region of the state and one of the important cause is

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livelihood oppurtunities. At the same time it is contributing in the expansion of urban

population in plain districts that has relatively better connectivity with boundary states. Due

to limitation of data it cannot be ascertained whether the benefit of high productivity and

growth of rise in urban population in the plain districts is evenly distributed or leaving a large

section marginalised. It warrants, as also mentioned in previous chapter, in depth analysis of

rural-urban composition of population with the change in economic prospects with the

increase in population density and urbanisation rate.

Table5.2: District-wise Value of Demographic Index and Its Ranking

Districts Index Value Ranking

2012 2005 2012 2005

Almora 0.641 0.721 7 5

Bagheshwar 0.317 0.355 12 11

Chamoli 0.471 0.487 10 10

Champawat 0.657 0.720 6 6

Dehradun 2.462 2.455 2 1

Haridwar 2.499 2.327 1 2

Nainital 1.407 1.401 4 4

Pauri Garhwal 0.661 0.670 5 8

Pithoragarh 0.491 0.513 9 9

Rudraprayag 0.344 0.310 11 12

Tehri Garhwal 0.618 0.672 8 7

Udham Singh Nagar 2.169 2.068 3 3

Uttarakashi 0.263 0.302 13 13

Source: Authors’ CalculationNote: the information for calculating the index value and corresponding ranking of demographic sub-component are derived from Census (2001 and 2011). However, for comparison the period ismentioned as 2005 and 2012.

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Figure 5.3: Ranking of Districts on the Basis of Demographic Index

Source: Based on Demographic Index

5.3.2 ECONOMIC INDEX

To measure the change in performance of districts in economic attainment in a

comprehensive way, the status of economic development of district measured through

combination of certain indicators the index value of which as well as corresponding ranking,

for 2004-05 and 2011-12, is produced in Table 5.3 below. In Figure 5.4, districts are arranged

in ascending order of their respective index value at 2004-05 level. Economic sub-component

index value is combined effect of nine indicators ranging from per capita income that also

accounts for population size of the districts along with district domestic product. Apart from

it in order to account for distribution of income and its broad-based nature, structure of

income and employment in non-agriculture sector, crop yield, and financial indicators etc has

also been included. So it could be assert that economic component has tried to not only

capture the aggregate output of districts but also the level of participation of population in a

district.

Inter-district variation in economic attainment shows that in 2011-12, Haridwar was at the top

of the rank order with index value of 1.59 and Rudraprayag at the lowest rank with value of

0.71 which reflects the significant gap in economic outcome among districts of Uttarakhand.

Similar, to aggregate index, the order of ranking more or less reflects the same pattern with

the four plain districts occupying the top position. From the figure, the hill-plain divide in

economic index is clear as the bar graph jumps drastically after Garhwal upto where the size

of the histogram seems to be very flat. If we compare it with the demographic index,

variation as defined by ratio of top and bottom index value, it is two times as compared to ten

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times in the former. Between 2004-05 and 2011-12, the rank order has improved in some of

the district in hill region but the gap has widen.

Table5.3: District-wise Value of Economic Index and Its Ranking

District Name Index Value Ranking2012 2005 2012 2005

Almora 0.776 0.790 11 8Bagheshwar 0.798 0.780 9 10Chamoli 0.808 0.764 7 11Champawat 0.795 0.808 10 7Dehradun 1.531 1.666 3 1Haridwar 1.598 1.549 1 2Nainital 1.172 1.241 4 4Pauri Garhwal 0.869 0.831 5 6Pithoragarh 0.821 0.785 6 9Rudraprayag 0.706 0.703 13 13Tehri Garhwal 0.806 0.833 8 5Udham Singh Nagar 1.548 1.502 2 3Uttarakashi 0.773 0.750 12 12Source: Authors’ Calculation

Figure5.4: Ranking of Districts as per the Economic Index

Source: Based on Economic Index Value

5.3.3 EDUCATION INDEX

The crucial role of education in economic as well as social development of society is

highly recognised. It not only helps in achieving higher growth through enhancing worker

productivity and managerial efficiency but also in day to day life like recognition of one own

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right, household management, caring of children particularly from female education. The

education sub-component is based on six indicators as discussed earlier. It includes both the

outcome indicator like female literacy as well as education infrastructure like schools,

teachers etc. Here in Table 5.4 below, district-wise the value of index and its corresponding

ranking is for period 2004-05 and 2011-12 is given.

Table5.4: District-wise Value of Education Index and Its Ranking

District Name Index Value Ranking

2012 2005 2012 2005

Almora 0.980 1.017 6 5

Bagheshwar 0.977 0.907 8 12

Chamoli 0.977 0.964 9 8

Champawat 0.979 0.935 7 10

Dehradun 1.160 1.164 1 2

Haridwar 0.968 1.003 10 6

Nainital 1.099 1.026 2 4

Pauri Garhwal 1.075 1.255 3 1

Pithoragarh 0.966 0.923 11 11

Rudraprayag 0.997 0.968 5 7

Tehri Garhwal 0.887 1.059 13 3

Udham Singh Nagar 1.000 0.964 4 9

Uttarakashi 0.935 0.814 12 13

Source: Authors’ Calculation

It is evident from the table that the inter-district variation seems to be lesser in the level of

development in education component although not completely eliminated. For 2011-12, the

index value of the top and bottom ranked districts namely Dehradun and Tehri Garhwal are

1.16 and 0.88 respectively and the ratio of the two value is much less as compared to

aggregate index and other sub-components discussed above. Graphically, it is also visualized

in the Figure below. Relative ranking of Haridwar districts needs to be highlighted as

whereas in both demographic and economic index discussed above, the district remained at

the top but in education index its rank drops drastically to 10th position. Even though

Haridwar performs better in economic indicator, its poor performance in education index is a

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concern as it is considered as strong indicator and is necessary for equal oppurtunity.

Interestingly, as oppose to this, district Pauri Garhwal whose ranking was 5th in economic

index has moved up to 3rd and is only behind Dehradun and Nainital. Between 2004-05 and

2011-12, the ranking of many districts in dominantly hill region has improved.

Figure5.5: Ranking of Districts as per the Education Index

Source: Based on Economic Index Value

5.3.4 HEALTH INDEX

Health along with education is an important dimension of human development which

has crucial role in productivity and growth. The value of health index for each districts and

corresponding ranking for 2004-05 and 2011-12 is given in Table 5.5 below and further

ranking in ascending order in between two point of time is shown in figure below.

Inter-district variation in health sub-component is relatively higher than education sub-

component discussed above. It is evident from higher gap in index value between the top and

bottom ranked district namely Almora and Uttarakashi which are 1.39 and 0.72 respectively

in 2011-12. Among districts, the performance of Almora is impressive as its rank is lower in

all the other indicators than the plain districts and also its value has improved as compared to

2004-05. Almora is followed by plain districts as Nainital, Dehradun and Haridwar whose

ranking in 2011-12 has gone down as compared to 2004-05.

88

Table5.5: District-wise Value of Health Index and Its Ranking

District Name Index Value Ranking2012 2005 2012 2005

Almora 1.390 1.284 1 4Bagheshwar 0.856 0.649 11 10Chamoli 0.746 0.464 12 12Champawat 0.936 0.825 7 9Dehradun 1.299 1.645 3 1Haridwar 1.258 1.485 4 3Nainital 1.351 1.539 2 2Pauri Garhwal 0.915 1.099 9 6Pithoragarh 0.907 0.626 10 11Rudraprayag 1.070 0.857 5 8Tehri Garhwal 0.927 0.950 8 7Udham Singh Nagar 1.027 1.121 6 5Uttarakashi 0.719 0.457 13 13Source: Authors’ Calculation

Figure 5.6: Ranking of Districts as per the Health Index

Source: Based on Health Index Value

5.3.5 ACCESS TO BASIC AMENITIES

Income is an important indicator of economic development but it is mean to fulfill the

ultimate end of development that ranges from having minimum necessities like food, housing,

primary education to political, social and human rights essential for life with dignity. Also for

measurement of income is not readily available as well as it is fluctuating particular for those

with no regular working oppurtunity and business particularly those who are at the bottom

89

strata of income distribution. In this context, asset indicator has been much useful to monitor

that is used to compare the standard of living. Here index of access to basic amenities is

constructed by using 5 indicators like drinking water, sanitation facilities, electricity and

roads. The value of index with corresponding ranking for 2004-05 and 2011-12 is given in

Table 5.6 below and the ranking of districts in ascending order is given in Figure 5.7 below.

Table5.6: District-wise Value of Access to Amenities Index and Its Ranking

District Name Index Value Ranking

2012 2005 2012 2005Almora 0.888 0.827 7 7Bagheshwar 0.76 0.651 11 13Chamoli 0.749 0.731 12 10Champawat 0.764 0.752 10 9Dehradun 1.309 1.706 3 1Haridwar 1.365 1.429 2 4Nainital 1.271 1.466 4 2Pauri Garhwal 1.166 0.939 5 5Pithoragarh 0.808 0.716 9 11Rudraprayag 0.838 0.696 8 12Tehri Garhwal 0.901 0.833 6 6Udham Singh Nagar 1.444 1.462 1 3Uttarakashi 0.737 0.792 13 8Source: Authors’ Calculation

There is significant gap among districts the index of access to basic amenities. The index

value, in 2011-12, of the top and bottom ranked districts namely Udham Singh Nagar and

Uttarakashi are 1.44 and 0.74 respectively and the ratio of the two index value is two times.

Even in this index the hill-plain divide remained intact as all the four districts lying in the

plain area are at the top of the rank order vis-à-vis districts in hill region. The hill districts

Uttarakashi, Chamoli and Bagheshwar are at the lowest bottom of the rank. Against the

economic attainment indicator like per capita income the distribution of access to amenities

are expected to be more homogenously distributed particularly in rural area. So the gap

between hill and plain districts in amenities index is clear reflection of existing divide among

districts of the state. Also it need to be underlined that population size has increased

substantially in the plain districts between census 2001 and 2011 but still the top position of

plain districts are intact.

Figure5.7: Ranking of Districts as per the Access to Amenities Index

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Source: Based on Amenities Index Value

5.4 DISCUSSION

Development or backwardness is a multi-dimensional phenomenon that includes not

only the economic attainment but also education, health, social and political rights etc

essential for living life with dignity in society. The pattern from composite index of

development as well as for different sub-component is constructed for 2004-05 and 2011-12

is discussed in the chapter.

The most important fact is the relative backwardness of hilly regions of the state as compared

to districts in the plain region. Districts which are dominantly in plain are Dehradun, Udham

Singh Nagar and Haridwar with substantial part of Nainital. It has been found that between

two points of time the index value has increased in most of the districts except in Dehradun

and Nainital. The decomposition exercise of composite index shows demographic and

economic attainment as two important sub-components contributing to disparity among the

districts of the state which remained unchanged between 2004-05 and 2011-12.

Further district level analysis of sub-component provides deeper insights. In demographic

index the divide between hills and plains is much wider and has increased during this period.

In fact the urban population in plain district is much higher which contributes in higher index

value of these districts. In economic attainment all the districts but Dehradun and Nainital

have shown improvement between 2004-05 and 2011-12. However, information on intra

district distribution of income is not available which needs to be incorporated for looking at

improvement in inequality adjusted standard of living.

91

Education and health development is crucial for bridging the gap among districts. Whereas

education has little variation among districts but those with education equal or above

secondary level is higher in Dehradun, Pauri Garhwal and Nainital. Between two points of

time many of the districts in hill region has witnessed improvement in index value with

improvement in inter-districts ranking. However, it has declined slightly in Dehradun and

Haridwar, the former is at lower rank (10th position) in 2011-12. One limitation here is the

lack of information on quality of learning outcome which is crucial given the improvement in

the post RTE period in enrollment and expansion of infrastructure. So even though there may

be least gap in enrollment level and infrastructure but quality of learning outcome may have

significant variation.

In health index, the situation of plain districts like Haridwar is worse as compared to Almora,

even though they are better in other indicators. Between 2004-05 and 2011-12, the index

value of Dehradun, Haridwar, Nainital and Udham Singh Nagar has declined with

simultaneous improvement in value of Almora, Bageshwar, Champawat and Rudraprayag.

Lastly, between two points of time amenities index value is also showing decline in

Dehradun, Haridwar, Nainital and Udham Singh Nagar. It may be the effect of high level of

migration from hill to plain region that has led to net decline in available amenities.

Thus, as shown here, composite index can also be used as tool of monitoring change in

development level over the period given the comparability of indicator between two points of

time. Further, quality of index can be also be improved by improving the quality of

information as well as better outcome indicator in place of proxy indicators.

Figure 5.8: Population Distribution in Different Districts of Uttarakhand, Census-2011

92

Table A5.1: List of Indicators for Index Construction2004-05 and 2011-12

S. No. Indicators1 Density of population (population per sq. km), Census2 Urbanisation Population Share (%), Census3 Per Capita Income (Rs per person)-at current price; DES4 Forest Land; FSI5 Percentage Irrigated Land of Cultivable land; DES6 Per workers agriculture output; DES & Census7 Share of non-agricultural output (in rupees lakh at constant price); DES8 Share of non-farm workers in total workers ; Census

9 Percentage of factory workers in registered factories to total workers ;DES

10 Number of small scale units per sq km and per lakh population (no. ofunits); DES

11 Average livestock par 1000 population; Animal Census, Govt ofUttarakhand

12 Female Literacy rate; Census13 Gross Enrollment Ratio/ Net Enrollment Ratio; U-DISE14 Adult population with secondary level of education, Census15 Number of school—primary/Junior (per 1000 population); DES16 Pupil-Teacher Ratio; U-DISE17 Infant Mortality Rate; AHS

18 Number of allopathic/dispensaries and primary Health Centre per lakhpopulation; DES

19 Number of Doctors (per 1000 population); DES20 Medical Facility other than Allopathic per 1000 population; DES21 Ante Natal Checkup (ANC); AHS22 Post Natal Checkup (PNC); AHS23 Percentage of household with drinking water within premises; Census24 Percentage of household with no sanitation facilities; Census25 Number of Post Office per 1000 population, 2010-11; DES

26 Percentage of household with electricity as primary source of lighting,Census

27 Number of Banking Branches per 1000 population (2010-11); DES28 Credit-Deposit Ratio 2011-12 ; DES29 Percentage of Village Electrified (2010-11); DES

30 Total Surface road per square kilometer area (total pakka road in Km)2010-11; DES

31 Total surface road per lakh population, 2010-11; DES

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94

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Chapter-6

DISCUSSION AND COMMENTS

6.1 DISCUSSION

Development is a process of improving the quality of life of people in a society that

includes expanding economic opportunities, participation of all sections in economic process,

creating environment for inclusive social participation and providing minimum education as

well as health facilities. In a nutshell development is a multidimensional phenomenon and for

understanding development of a region we need to study certain dimensions simultaneously

at a point of time. One of the tools widely and increasingly used in the last three decades is

composite indicator that measures the net effect of certain dimensions of a phenomenon. In

the present study we have tried to measure the relative gap in development among districts of

the Uttarakhand state of India for the period 2013-14. An attempt has also been made to see

the efficacy of composite index in examining the change in relative position of districts for

two periods 2004-05 and 2011-12 for which comparable data is available. However, the

index value for these two periods is not strictly comparable with 2013-14.

The need and rationale of the study lies in the formation of the Uttarakhand itself which was

to address the uneven development of the hill areas lagging behind the plain areas of the

undivided Uttar Pradesh. In the last one and half decade since its formation, the Uttarakhand

economy, at the aggregate level, is growing at appreciable rate higher than the all India

average rate. But at least for dominant indicator like per capita income there has been

widening divide between hill and plain districts. Udham sing Nagar and Haridwar are

completely in plain areas while parts of Dehradun and Nainital may also be included in it.

Administratively the state has 13 districts divided into 45 sub-divisions and 95 development

blocks and thus being smaller in size is expected to provide better governance and ensure

rapid balanced economic development. To achieve inclusive and even development of the

state, district is an important unit of administration, planning and resource allocation. So to

tackle the challenge of widening gap between hill and plain regions, concerted intervention at

districts level is required.

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In the study level of development of a district is measured in five dimensions that include

demography, economic attainment and distribution, educational development, health facilities

and basic household amenities. Composite index which is a weighted summary measure of

these dimension index value is derived by assigning equal weights to each dimensions.

Further for each dimension, index value is derived by assigning weights generated through

PCA to selected representative indicators. Relative backwardness of a district analysed

through ranking based on composite index value: the district with higher index value is at top

order and the position decline with fall in index value. The major findings of the composite

index can discussed as follows.

Among districts of the state, there has been significant gap in the level of development

between those lying in hill and plain areas. The districts in the plain and its surrounding

region viz. Udham Singh Nagar, Haridwar, Dehradun and Nainital are at top of the rank order

while hill districts are relatively lagging behind on composite index. The decomposition of

index value in the relative share of these dimensions shows their role in disparity. The index

value of demographic, economic and basic amenities dimensions increases with the increase

in the composite index and thus contributes in disparity. On the other hand but health index

shows decline in the index value with the increase in the composite index value, while

education index shows little variation.

In demographic index, population density and urbanisation rate are dominant in favour of

plain districts contributing to disparity among districts. These indicators need to be

understood in the particularly urbanisation process as they have strong implication in

aggregate economic indicator of a district. One of the factor highlighted in literature is shows

the role of out migration from hill to plain districts. However, significant rural population in

the plain districts along with rising urbanisation rate creates a ‘dual structure economy’ in

these districts which requires further investigation. Also significant gap in urbanisation rate

and population density makes comparable analysis of the districts unviable. It would better to

analyse the rural area of the plain district with the districts where rural population share is

more than 90 percent. In fact gap in economic attainment among districts is clearly visible

which is also due to the gap in demographic structure.

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Economic attainment of is definitely better in plain districts led by Udham Singh Nagar

followed by Haridwar, Dehradun and Nainital. In this, not only per capita District Domestic

Product (DDP) which is an average indicator of income but other indicators is used to see the

distributional nature of the economic attainment is also better for plain districts. The

decomposition exercise shows wide gap in agriculture and financial indicators that can play a

major role in bridging the divide. Apart from this development of small scale units in the hill

districts can also assist in balanced development of economic attainment. Since Uttarakhand

is a unique state economic activity in hill and plain region is not uniform due to topographical

features, micro level planning is required for bridging gap in economic attainment among

districts. For additional economic oppurtunities, hill district have to specialize in their

comparative advantage like dry land farming, small scale industries, horticulture, floriculture,

medicinal plants etc. Generating gainful employment oppurtunities can also assist in

containing outmigration that will have positive impact on economic attainment dimension as

well.

Human capital development which includes education and health is crucial in bridging the

development gap among the regions. In the education index which is measured through

output indicator and input indicator there seems to be least variation among the dimensions

included in composite index of development. In the hill district the index value is still slightly

lower than plain region despite the fact that in the hill district the share of public school is

higher as compared to plain district. Furthermore, for better development outcome, it was felt

that district level information on learning outcome and cognitive development of student

would have been better indicator to capture educational development.

In health dimension, the plain districts which are relatively better in composite index and

economic dimension are lagging behind. The main reason, as decomposition shows, can be

attributed to the poor outcome indicator that includes female infant mortality rate and

nutritional indicator which are relatively worse in these districts. Further in population

adjusted health infrastructure facilities also these districts have poor performance than the hill

districts. This comment should be treated cautiously as DES data on health infrastructure

does not include private facility which may have large share in these districts. But given the

large rural population and high migrant population public health facilities is important as

private facilities has relatively higher cost.

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Household amenities index shows higher value for relatively better off districts as compared

to low ranked districts. The high index value for district with better composite index of

development may be due to private arranged facilities by the household but this cannot be

accounted due to data limitations of clear distinction between public and privately arranged.

However, the gap in the basic facilities needs to be filled up for achieving target of even

development of all the districts of the state. Minimum household amenities are considered as

essential for human development and despite lower position in economic attainment which is

constraint of different factors, households in all the districts should be assured of the basic

facilities like sanitation and drinking water.

Apart from the measuring the disparity in development among the districts for the period

2013-14, the exercise of measuring the change for the period 2004-05 and 2011-12 shows the

potential of composite index. By using relatively comparable indicator, the change in index

value for period that measure progress of atleast five year and above found least change in the

rank order of districts during 2004-05 and 2011-12. Thus one of the advantages of the index

can be monitoring of districts of effect of policy through change in indicator variable or

component index. In fact improving the desired information can lead to better index value.

6.2 LIMITATIONS OF THE STUDY

For composite index choice of indicator and assigning weights are two crucial steps. It is

attempted here to design a better index however, there are certain limitations rectifying which

may provide better comparison of the districts. It includes:

In the study for measuring certain phenomenon proxy indicators are utilised which

may not be exact representative of outcome. For example information on distribution

of income and occupation at district level may have provided better index than using

their proxy variables like share of agriculture sector in output and employment.

In assigning weights for calculating sub-component index we have derived it through

principal component analysis (PCA) but increasing the number of observations by

calculating it at block level may assign distinct weights. Although for certain indicator

like income it may not be possible to get further disaggregated information but this

97

exercise can be done for sub-component dimensions like education, health and

amenities for measuring and monitoring their progress.

There has been paucity of information regarding rural urban separately which is

essential to study the rural area of the plain district as compared to their urban area

and also with the hill district(s) which are predominantly rural.

The huge gap in topographical features and population density is a constraint for

adjusting the infrastructure variables with respect to population size of district or area.

In this situation, the utilisation rate of the education and health infrastructural

facilities if available can capture its status of availability more accurately.

6.3 COMMENTS AND RECOMMENDATIONS

Based on the analysis in the study discussed above development gap among the

districts is clearly evident which supports the hypothesis of rising inter district disparity along

with catching up of aggregate economic development with relatively developed states of

India. For better policy intervention to arrest the emerging pattern of widening development

gap within states certain observations are made here below.

In most of the literature strongly argued case of widening development divide between hill

and plain districts needs to be examined cautiously. It is found in the study that one of the

major gap among districts is of demographic characteristics measured through urbanisation

rate and population density that has implications in both analysis of other phenomenon and

policy design. Higher population growth in the plain districts is dominantly in urban area co-

existing with sizable rural population. It would be better to compare rural area of the plain

districts with the districts in hill area with dominantly rural population. The comparison of

average per capita income (PCY) of districts is misleading as in plain districts PCY is biased

in favour of urban population and economic activity. In absolute terms the rural population in

these districts is higher than total population in hill area of some of the districts.

Further in terms of economic activity the districts with pre dominantly rural population may

have different economic activity than those of highly urbanized region and thus definitely

will be reflected in economic attainment gap. However, the information on distribution of

income and consumption expenditure may reveal more accurate scenario. Thus it is suggested

98

to measure and compare inequality adjusted per capita income which is not possible currently

due to unavailability of data at the further micro level.

Industrial investment in plain districts in the last decade and half is higher than the hill

districts leading to higher growth in manufacturing. However, promoting industrial

investment in highly urbanized plain districts will be different from hill districts. Thus, as

Uttarakhand is a unique state, investment and economic activities in hill region need to be

promoted based on their comparative advantages as mentioned above.

Lastly, outmigration from the hill district is a serious problem which has both economic as

well as strategic consequences particularly for border districts. Since there is lack of informed

literature, it is suggestive to initiate a longitudinal study of the migration pattern of these

districts with sustained policy intervention to revert back the process.

99

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