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Studies Completed

 

Details of Selected Studies Completed

 

(i) Manpower Assessment for Various Offices of LIC of India” commissioned by the Life Insurance Corporation (LIC) of India

Main Objective of the Study

To assess the Manpower at different levels in various offices of LIC of India

Methodology

The study has been carried out by using primary as well as secondary sources of data. The information on indicators like manning norms, workload in terms of policies issued, claims settled, loan advanced, etc. Has been obtained from the office of LIC and its various reports. The data on actual workload in different departments have been assessed from office records and have also been observed by ‘Activity Sampling’ and ‘Time Motion Study’. Primary data have been collected through personal interviews by using structured questionnaires and ‘Focus Group Discussions’ with various stakeholders. The study has been conducted on a nationally representative sample using stratified systematic sampling procedure.

Findings

For each department, separate regression equations have been developed between the numbers of core activities performed and total time consumed on these activities. In the case of more than one core activity in a department, multiple regression equation has been developed. Thus, separate formulae have been developed for each department for various offices.

Study Team

Mr. H.K. Varshney (Team Leader), Dr. A. Kamala Devi and Mr. Vijay K. Saxena (Team Members) were involved in the preparation of proposal, developing research tools, collection of primary and secondary data, conducting Focus Group Discussion and drafting of the report.

 

(ii)Employment, Productivity and Output Growth of Labour Intensive Manufacturing Industries in India” commissioned by erstwhile Planning Commission, Government of India.

Major Objectives of the Study

1 To identify industries with high labour intensity within the registered organized manufacturing sector in India.

2. To analyze the trends of labour intensity of the selected industries over the period and find out the plausible reasons of changes in trends.

3. To discuss the growth, employment and productivity trends of labour intensive industries and use different alternative methods to estimate total factor productivity growth.

4. To analyze the employment and growth prospects and constraints faced by small and medium firms using field survey data and make suitable policy suggestions.

Methodology

The study makes an attempt to first identify labour intensive industries through calculating the labour intensity of each organized manufacturing industry using the Annual Survey of Industries (ASI) data at 3-digit level andthen uses three alternative methods – growth accounting (GA) (non-parametric), production function with correction for endogeneity – Levinsohn-Petrin (LP) (semi-parametric), and stochastic production frontier analysis (SFA) (parametric) to estimate total factor productivity growth (TFPG) to see how sensitive the results are to different methods. In the next step, the study discusses the primary survey data that have been collected through field survey and discussion.

Coverage

The study uses both secondary and primary data for analysis. Secondary data are taken from the ASIs, Central Statistical Organization (CSO), Government of India. The study period covers from 1980-81 to 2012-13 and the whole period is divided into sub-periods for doing a comparative analysis. The sub-periods are: (i) 1980-81 to 1989-90, (ii) 1990-91 to 1999-2000, (iii) 2000-01 to 2007-08 and (iv) 2008- 09 to 2012-13. The primary survey has covered five labour intensive industries such as Spinning, Weaving and finishing Textile; Wearing Apparel; Footwear; Furniture; and Sports Goods, based on purposive sampling method. The total sample size consists of 320 firms across six states such as Delhi, Gujarat, Haryana, Punjab, Tamil Nadu, and Uttar Pradesh.

Findings

1. Based on average labour-capital (L/K) ratio, 17 out of 58 industries were selected as labour intensive ones for the period 1980-81 to 2007-08. In total, 23 registered manufacturing industries have been selected as labour intensive industries for the period 2008-09 to

2012-13.

2. The rate of decline of labour intensity is more in the case of labour intensive industries than all manufacturing industries. The results suggest that labour intensive industries adopt modern technology as a substitute of labour in the production process.

3. The rate of growth of labour productivity in all labour intensive industries decline more than all manufacturing industries in the pre- and post-reforms period. There is a possible explanation that employment growth in labour intensive industries may be increased at a faster rate than output expansion. Otherwise, there could be a shortage of skilled manpower.

4. Capital productivity of labour intensive industries has declined during the post-reforms period. The reasons for decline in capital productivity is that, industries may be primarily employing more unskilled labour force which leads to sub-optimal use of machinery and equipments and low output.

5. Total factor productivity growth (TFPG) and technical efficiency of labour intensive industries have declined continuously from pre-reforms period to post-reforms period at the aggregate level of labour intensive industries as well as all manufacturing industries.

6. Lack of skilled manpower availability is the major constraints in all the labour intensive industries resulting in less productivity and efficiency in labour intensive industries.

7. Firm faces other two major constraints in doing business such as: lack of incentives from the government and heavy tax burden. These suggest that firms are expecting more incentives from government and business-friendly tax policy for the labour intensive industries which may lead to more productivity and efficiency of labour intensive industries.

Study Team

The core team consisted of Dr. Purna Chandra Parida, Director, Dr. Kailash Chandra Pradhan, Joint Director and Dr. Tapas Sarangi, Asstt. Director.

The other team members were Dr. K.S. Rao, Dr. P.K. Saxena, Mr. S.K.Yadav, Dr. Jajati Parida, Ms. Sharmistha Sinha, Mr. Bhoop Singh, Mr. Marshal Birua, Mr. J.S. Chauhan, Mr. Radhey Shyam Sharma, Ms. Neha Kumra, Dr. Kalaiyarasan A., Mr. Laxman Singh and Mr.Arun Kumar.

Data analysis and report writing was done by a team led by Dr. Purna Chandra Parida and Dr. Kailash Chandra Pradhan.

 

(iii)Human Development Indices: Development of HDI for  SCs, OBCs, PwDs, and Senior Citizens” sponsored by the Ministry of Social Justice and Empowerment, Government of India.

Objectives and Methodology

Human Development Index (HDI), first introduced in the 1990 Human Development Report (UNDP: 1990), was in response to the need for a measure that could better represent human achievements in several basic capabilities (what people can do and be) than income based indices of growth and development and could provide a credible alternative to them. The human development (HD) story of India is unique in its kind. Through the preparation of Human Development Reports (HDR), at national as well as sub-national level, India has decentralized and integrated the human development concept into its development agenda at national, state, district and municipality level. Human Development Reports use three indices to measure progress on human development. The first Human Development Report in 1990 introduced a new way of measuring development by combining indicators of life expectancy, educational attainment and income into a composite HDI. Over a period of time, the detailed composition of each index in the HD family has been subject to change as methodological advances have been incorporated. It is against this background that, at the instance of the Ministry of Social Justice and Empowerment, a study was conducted by NILERD (the then IAMR) to develop Indices for scheduled castes (SCs), other backward classes (OBCs), persons with disabilities (PwDs) and Senior Citizens. In this study, the indices for various categories are computed separately by taking a simple average of health, education and standard of living. For computing HDI for different categories, varying dimensions of these three variables are taken into account. The sources of data used are National Sample Survey Organization (NSSO), Central Statistical Organization (CSO) and National Family Health Survey (NFHS).

Findings

1. HDI for SCs, OBCs and Other Categories

Kerala was the top performer for all and General category, Maharashtra was the top ranker for SC and Punjab for OBC in 2011-12. During the same period, for rural India, Kerala was the best performer for All and General category and Himachal Pradesh was at the top for SC and OBC. In 2011-12, among the major states, for urban India, Himachal Pradesh performed the best for All categories; Karnataka scored the highest for SC, Tamil Nadu for OBC and Kerala for General category. Kerala’s position worsened for OBC category in rural as well as whole of India in terms of HDI ranking in 2011-12 as compared to 1999-2000 periods. For SC category also, Kerala’s position worsened in HDI ranking in the country in 2011-12 as compared to 1999-2000. It merits mention here that in 2011-12, for Maharashtra, West Bengal and Tamil Nadu, the index for SC was higher than that of the General category. In the same year, with the exception of Kerala and Haryana, all other states exhibited a better performance in human development for their OBC population than that of all categories.

2. HDI for Senior Citizens/Elderly Population

The HDI has been calculated for 1999-2000 and 2011-12. The education index has been calculated on the basis of literacy rate, health index on the basis of labour force participation rate (a proxy for health for senior citizens), and income index on the basis of monthly per capita expenditure (inflation and inequality adjusted). The participation rate in the labour force signifies a better health condition for the elderly/senior citizens, so labour force participation rate has been used as a proxy for health of senior citizens. Regarding health dimension, Himachal Pradesh secured the first position for rural and for All areas. Uttar Pradesh was the best performer in urban India in health related aspects of senior citizens. In terms of income dimension for senior citizens, Kerala exhibited the best performance for the country. Punjab was the best performer for rural area and Karnataka for urban area.

The education index for elderly shows that Kerala was the best performer for rural, urban and forthe country as a whole. In rural area, it was followed by Assam, while for urban areas; it was followed by West Bengal. It was found that over a period of time, there has been an improvement in HDI for senior citizens/ elderly population both in rural and urban India. In 1999-2000, the HDI for elderly population/senior citizens for All areas was 0.43, whereas for rural areas it was as low as 0.38, but urban area it was significantly high i.e.0.61. During the period 1999-2000 to 2011-12, the HDI increased quite significantly. For All areas, it increased to 0.56, whereas for rural area it remained at 0.48, but for urban area it was significantly high i.e. 0.75. In 2011-12 Kerala appeared the best performer in terms of HDI. Himachal Pradesh had occupied the second rank in HDI for elderly in rural India. Tamil Nadu was the next best performer after Kerala for urban as well as for rural and urban areas together.

3. Index for Higher Education for SCs, OBCs and Other categories

To compute the index for higher education, (a) gross enrolment ratio at graduation and above level (both technical and general), and (b) relative share of graduates and above (both technical and general) in labour force are taken into consideration. The index is computed at two points of time 1999-2000 and 2011-12 for SC, ST, OBC, General and for All categories of population. In rural India, over the period of time, the scenario for higher education for all sections of population has improved quite reasonably. Rural Himachal Pradesh was the top

performer for SC and All categories. For OBC, Kerala was the best performer, whereas, Bihar performed the best for General category. Rural Haryana performs the best for ST category. In Urban India, Himachal Pradesh was the best performer in higher education for OBC and ST category and Gujarat for SC category. The Urban area of Tamil Nadu secured the first rank for General category and Haryana for All categories. It is found that among the major states of rural India Himachal Pradesh was best performer in terms of improving higher education. In urban India, best performance has been exhibited by Haryana. The index for All categories has increased from 0.07 to 0.15 during the period 1999-2000 to 2011-12, the index for general category being the highest at both points of time. The index for SC and ST were almost equal and that of OBC was higher than that of SC and ST.

Among the states, Tamil Nadu performed the best for OBC, General and All categories. Taking rural and urban areas together, Haryana was the best performer for ST and  Maharashtra for SC for the country.

4. Human Development Index for Persons with Disabilities

The HDI for disabled persons is calculated taking health, education and income into consideration. Education index has been calculated on the basis of literacy rate for disabled population. Income index has been computed on the basis of monthly per capita expenditure (inflation and inequality adjusted) of disabled population. Health index is computed on the basis of infant mortality rate (IMR) for all (a proxy for IMR for all in the absence of IMR for disabled population). A simple average of health, education and income has been taken to compute HDI for disabled people of India. India has been able to promote human development for the disabled population over the years, both in rural and urban areas. The respective index for the country as a whole increased from 0.19 in 1999-2000 to 0.25 in 2011-12. During the same period, the respective index for rural India increased from 0.16 to 0.21. On the other hand, during the same period, the corresponding index for urban India increased from 0.30 to 0.34. For the disabled persons/differently-abled people, Kerala ranked first in human development ladder at both points of time. Kerala’s performance was the best in rural, urban as well as for the country taking rural and urban areas together in 1999-2000 and in 2011-12. Among the states, in 2011-12, Tamil Nadu was at the second position followed by Maharashtra. In rural India also, Tamil Nadu occupied the second position in terms of human development for disabled people. In urban India, Himachal Pradesh ranked second.

The distinction between consistently well performing states and the poor performing ones is evident. The poor performers in HDI have performed poor in health and education as well. In these poorly faring states, usually there is a concentration of marginalized and disadvantaged social groups. These states lack resources, infrastructure, basic health facilities, especially in the rural areas, perpetuate deprivation and inequalities for their inhabitants, in general and of backward communities, in particular. HDI ranking reflects performance in health, education and Income. Hence, the ranking in the HDI ladder has an impact on the policy of the state

governments. To improve their ranking, the state governments could bring in policy changes to improve facilities for health and education, and subsequently improve opportunities for employment and income enhancements.

Study Team

The study team consisted of Dr. Jajati K. Parida, Dr. Sanchita Bhattacharya and Ms. Neha Kumra.

 

(iv)Identifying High Growth Sectors with Greater Employment Opportunities in India: Medium-term Prospects”commissioned by Associated Chambers of Commerce & Industry of India (ASSOCHAM).

Major Objectives of the Study

The principal objective of the study is to estimate the size of labour force in 2016-17 and 2019-20 and to project future employment demand in major economic sectors that is expected to absorb the increasing labour supply in India. The specific objectives of the study are:

1. To explain the growth and employment scenario of India since 1993-94 and identify key sectors that can generate more employment.

2. To estimate the total supply of labour (labour force size) in India by the end of 2016-17 and 2019-20.

3. To project the total demand of labour by major economic sectors in India for the period 2016-17 and 2019-20.

4. To suggest policy recommendations for generating adequate employment to fill the demand and supply gaps in India.

Methodology

This study uses secondary data for the analysis. Secondary data were collected from various sources such as Census of India, National Sample Survey (NSS), Central Statistical Organization (CSO), Reserve Bank of India (RBI), Ministry of Commerce and Industry and Ministry of Agriculture. Employment data were collected from NSS. Output and investment data for various sectors were collected from the National Accounts Statistics, CSO. Both Census and NSS data were used to estimate and project the total supply of labour.

Findings

The size of labour force in India increased in a peculiar manner. During 1993-94 and 1999-2000, it increased by 25.5 million. In the next 5 years, from 1999-2000 and 2004-05, it had shown a remarkable increase of 60 million with an increase of 12 million per annum. However, in the last half of the decade, post 2004-05, it did not increase at all and remained constant at 469.9 million. Surprisingly, in the next two years, during 2009-10 to 2011-12, labour force increased by 15 million to reach 484.8 million, thus a rise of 7.5 million per annum.

The increase in the labour force size during 1999-2000 and 2004-05 was mainly distress driven, with a rise in employment mainly in the low productive agriculture sector. The stagnation of the labour force during 2004-05 to 2009-10 was due to a massive increase in pursuance of education as well as withdrawal of females from the labour force owing to mechanization in agriculture, increase in rural wages, thus raising the household income.

An average of 7.5 million increase in the labour force during 2009-10 to 2011-12 suggests the fact that those who were participating in education have begun to join the labour force. Keeping this in mind, the total labour force has been projected using their age, sex and education-specific labour force participation (LFPR) rates.

Projection

Assuming that LFPR of illiterates and persons with primary levels of education would decline further by 2 percent (as it is showing declining trends), whereas the LFPR of the persons with secondary, graduate and above levels of education would increase by 5 percent (as it is showing increasing trends over the years). The projected labour force size in 2016-17 would be 514.5 million with 373.9 million males and 140.5 million females, and in 2019-20, it would be 539 million with 391.8 million males and 147.1 million females. This indicates that the size of the labour force would increase by 28.9 million from 2011-12 to 2016-17 and by 57.5 million from 2011-12 to 2019-20 with an average increase of 7.2 million per annum.

The projected employment demand in various sectors on the other hand suggests that if  sectoral growth rate, growth elasticity, capital output ratio and employment elasticity remain constant as in 2011-12, the size of the workforce will increase to 485.6 million by 2016-17 and to 495.1 million by 2019-20. In other words, the workforce size will increase by 11.4 million during 2011-12 and 2016-17 and by 20.9 million between 2011-12 and 2019-20 respectively (about 2.5 million per annum). The upper limit of the employment demand is set by assuming that employment elasticity of agriculture and allied sector remains constant as in 2011-12 and all other things will increase to their respective maximum since 1999-2000. Under this assumption the size of workforce would increase to 491.8 million by 2016-17 and to 505.6 million by 2019-20. In other words, the workforce size would increase by 17.6 million during 2011-12 and 2016-17 and by 31.4 million during 2011-12 and 2019-20 respectively with an average increase of 4 million per annum.

The sectors that generated substantial volume of non-agricultural employment in India are Manufacturing (particularly the labour intensive manufacturing like food processing units, wearing apparel, garments, leather and wood products etc.), Construction and Services (like Transport, communication, hotel trade, real estate, education, health and other social services). The sectors with high growth elasticity and employment elasticity are the major contributors of the employment growth in India. It is important to note that about 37 million

workers have left agriculture in India. To sustain this Lewisian transition, we need to create enough employment in the non-agriculture sector.

Given the estimates that about 2.5 million jobs would be available based on the 2011-12 economic scenario, additional 5 million jobs need to be created in the non-agriculture sector to absorb the average of 7.2 million per annum increase of labour force. The following policy measures would be helpful for creating adequate number of jobs (both skilled and unskilled) so as to reduce the volume of open unemployment in India by 2016-17 and 2019-20.

Policy Recommendations

To generate employment in the Manufacturing Sector:

1. Policy constraints, such as labour laws that have limited the growth of employment in labour intensive manufacturing sub-sectors (like food processing units, wearing apparel, textiles, leather and wood products etc.) need to be eliminated.

2. Physical infrastructure in terms of improved transportation, road connectivity, uninterrupted power supply and adequate land needs to be made available.

3. Flexible regulations (with respect to bureaucratic controls) regarding safety, pollution, inspections, licensing, and labour conditions will improve the competitiveness of the manufacturers.

4. Special focus on developing indigenous technology for domestic manufacturers and higher expenditure on R&D are prerequisites for expanding the manufacturing sector.

5. Private sector needs to be encouraged through addressing the anomalies in the duty structure. Domestic manufacturers often face higher raw material costs at home and unfavourable/inverted duty structure (higher duty on intermediate goods as compared to final/finished goods often enjoying concessional custom duty under some schemes). There is a serious need to review the customs and excise duty structure.

6. Institutional support mechanism to be developed to encourage and facilitate employment in manufacturing sector such as marketing, credit etc.

7. A Technology Acquisition and Support Fund in a PPP arrangement need to be set up in lines with the proposal of the Industry Chapter of the 12th Five Year Plan. Some support from the government for creating a manufacturing ecosystem (developing standards, supporting MSMEs through common facilities and Cluster Development) can boost manufacturing growth and employment.

To generate employment in the Services Sector:

8. Markets should remain open to international competition, for example, by reducing barriers to foreign direct investment.

9. Multilateral action is needed to ensure expansion of markets and a wider distribution of benefits.

10. Labour taxes that affect the job prospects for low skilled workers and development of personal services in the country need to be rationalized.

11. Employment protection legislation needs to be reformed in the country to help in improving the capacity of the economy to create employment and enhance productivity growth in services.

To generate employment in the Construction Sector:

12. Tier II, Tier III cities need to be developed to generate employment in construction sector and in other sectors such as electricity, water, ICT, transport and various other services.

13. Major roadblocks such as bidding process reforms, contracting, approvals and clearances, effective monitoring, dispute resolution and financing need to be removed.

Study Team

The core team consisted of Ms. Sharmistha Sinha, Dr. Jajati K. Parida and Ms. Neha Kumra.

 
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