Tag: National Sample Survey Office

Lockdown Is Disrupting a Generation’s Education. What Can Be Done?

An interesting idea has been in the works in Uttar Pradesh which is planning to use Doordarshan, All India Radio and community radio to promote audio-based learning among students.

The last few days have finally seen a flurry of activities by the Ministry of Human Resource Development (HRD) and various regulators including CBSE, NCERT etc. to find alternatives to ensure the continuation of education. While this may not be a good time to address the seriously low expenditure on education in India, the lack of seriousness towards the sector can be gauged from the fact that HRD was kept in Category C (the lowest category) for expenditure – i.e. the said department will have to restrict expenditure to within 15% of that budgeted for Q1, 2020-21.

Amidst this background, the department and regulators have started moving towards developing an online mode of education – as, hopefully, a stop-gap arrangement. NCERT has released an Alternative Academic Calendar for four weeks of home-based activities for different subjects. For example, activities like categorising objects including eraser, pencils, cloth, pulses etc. to teach the concepts of colours, shapes and sizes have been suggested for the students of Class I-IV.

For Class V students, teachers will be conducting classes through internet-based platforms, in the absence of which SMS and voice recordings can be sent. However, it has not been explained how voice recordings can be sent without the internet. Certain states including Uttar Pradesh are planning to launch high-quality ed-tech applications along with using e-resources suggested by the MHRD including e-pathshala etc.

However, amongst this whole discourse of moving education online, there has hardly been any discussion surrounding the practical issues of implementation, as well as various socio-economic factors which define the Indian education ecosystem.

Limited internet availability

The 75th report of the National Sample Survey Office (NSSO) for 2017-18 highlights some of the major issues that this new model would have to address. All India percentage of households having internet facilities stands at 23.8% with rural availability at 14.9% and urban at 42%.

Also read: Rethinking Education in the Age of the Coronavirus

The problem does not end there, as having a facility does not mean it would be used. The percentage of people who were able to use the internet (all-India) stood at 20.1% with rural at 13% and urban at 37.1%. Additionally, only 10.8% of people in India had used the internet in the last 30 days. It is important to note that these statistics vary vastly among different states across the country. For instance, Bihar stands at the lowest (9.1%) for individuals who have used the internet in 30 days, while Delhi has the highest number (49.1%) of such individuals with bigger states like Maharashtra (26%), Rajasthan (15.3%), Andhra Pradesh (14.8%) etc. being in the middle.

These statistics strike at the core rationale of using the internet as a mode to impart education, and highlight how a majority of the country would be left out of the quest to achieve basic education in the months to come.

Increased responsibility of parents to educate their wards

Another important pillar of the new model is the increased role that parents play in educating their wards. Take, for example, the NCERT guidelines which – surprisingly has progressive methods of teaching to improve the analytical, quantitative, and logical reasoning abilities of the students – all key factors which our regular model of teaching and learning does not have. However, the guidelines presume that the parents will have the academic intellect to impart education to their students. But statistics highlight otherwise.

The same NSSO survey, quoted above, highlights that 26.1% of the population above 15 years of age is ‘not literate’, while a further 18.9% have attended formal education up till primary school, 16.2% each have attended middle (Class V) and secondary (until Class VIII). This constitutes a whopping 77.4% of total India’s population – who may not have the adequate level of education needed to teach children in the house. The situation at the rural level is even more dire, with 69.6 % of the population being in the spectrum of ‘not-literate’ to ‘middle school’.

Loss of nutrition due to school closure 

While the above factors touched upon the modality of the education, there is an even more basic issue at stake. The closure of schools has serious implications on the daily nutrition of students as the mid-day meal schemes have temporarily been shut. As of March 31, 2019, close to 12 crore students across the country were provided with food under with mid-day meal schemes.

Guardians of students stand in queues to collect mid-day meal at a school during a nationwide lockdown imposed to curb the spread of novel coronavirus, in Murshidabad district, Monday, April 20, 2020. Photo: PTI

This is close to 60% of the total students enrolled throughout K-12 education (the actual percentage is likely to be more, as mid-day meal only caters to students till Class VIII). Various studies have pointed out that mid-day meals are an important contributing factor for increased enrolment (~30%) in the schools.

Also read: A Viral Education? Into the Future of Our Locked Classrooms and Shut Campuses

These factors highlight a very dangerous scenario for the K-12 education sector in our country. Our learning outcomes in K-12 education do not inspire much confidence, in the first place, as has been pointed out repeatedly in various ASER reports by Pratham.

The loss of possibly half a year – if not the full academic year of 2020-21 – is going to further deteriorate the situation, as students would have difficulty in resuming schooling again after a huge gap. Additionally, the loss of income for a considerable population in India is going to further exacerbate the situation – CMIE’s data suggests that 11.9 crore people have lost employment in the two weeks of the COVID-19 lockdown. Investment in education is not going to be a priority, amongst disadvantaged households, and we might see a dip in enrolment, when and if, schools are opened.

Possible solutions

While the damage to the sector is similar to the damage every sector across the world is facing, it is possible that with some careful planning, we might be able to limit the long-term consequences of this prolonged shutdown.

To begin with, the districts in the green zone should be allowed to open schools – after analysing them further over the next few days. So far, there are 318 districts such in the country – which will likely cover a majority of the school children. Eventually, 292 yellow zone districts – which may turn to the green zone in the next few days or weeks – should also allow schools to open.

Strict social distancing measures should be implemented, and to limit the number of students, classes may run in two four-hour shifts. While this strategy would not result in finishing the quarterly curriculum, this will at least reduce the gap in learning that students are likely to experience if schools continue to remain shut for long. This may also help in addressing the possible increase in drop-outs due to the long shutdown.

This leaves the government with 107 districts which are in red and orange zones. It might not be possible to open schools in these areas any time soon, thus, there is a need to deploy public funds to fix the internet gap and ensure that students continue to learn. Some state government have come up with ideas to address this concern.

The Delhi government had mooted an interesting idea to provide data packages to the students of Class X and XII. While this is likely to have certain implementation challenges – particularly the misuse of data for objectives other than learning – smart technology solutions can be found. Use of the internet can be restricted to specific applications prepared by the government. Similarly, another interesting idea has been in works in the state of Uttar Pradesh which is planning to use Doordarshan, All India Radio and community radio to promote audio-based learning among students who do not have access to the internet.

Also read: Teaching in the Time of Isolation

Additionally, there is a need to develop a financial stimulus for the education sector primarily targeting low cost private aided and unaided schools – which are likely to witness a reduction in fee collections, due to income losses. Various states, including Rajasthan, Punjab, Haryana, and Uttar Pradesh have already announced that schools should not pressurise parents to pay fees.

However, a move like this is going to have a spillover impact on the incomes of teachers at such low-cost private schools. As of 2016-17, close to 28 lakh teachers were employed in private unaided schools, and further 8.3 lakh in government-aided schools. A more rational system could be to allow a reduced percentage of the fee in schools which are partially working (red and orange zone), with full fees for schools which would be fully functional (green and yellow).

Wherever relevant, a grant-in-aid could be issued for specific schools on a case-to-case basis to bridge income and expenditure. The powers for the same can be devolved to the district authorities to ensure a more localised approach.

While understandably, India as a developing country does not have unlimited resources, certain core sectors including education cannot simply be left as the last priority. Similar to other sectors, which are witnessing a staggered opening, the education sector – particularly the K-12 education system – needs to be opened in a staggered way.

These 12 years of education are crucial for every student and are the base years that will support the upward social and economic mobility of disadvantaged classes. A long and unplanned hiatus is likely to shatter the dreams of many and further harm the country in the long-term with a less-educated workforce. We need more talented and skilled individuals to get us out of the possible recession that the world is going to face and dropping the ball on education, is not going to help the cause.

Rohit Kumar is a government and regulatory affairs professional based out of Delhi. He works in driving public policy and advocacy campaigns across various sectors in India. He is also a former LAMP fellow. He tweets at @rsachdeva735

200+ Scholars Ask Modi Govt to Release Data From Consumer Expenditure Survey

“To prevent release of data that are adverse, and diverge from [the government’s] own understanding, is neither transparent nor technically sound,” the signatories have said.

New Delhi: A group of 214 scholars, largely economists, working around the world have written an open letter asking the Centre to release all reports and data from the National Sample Survey Office that have been internally approved.

The statement comes in light of the Modi government recently saying that it will not be releasing the Survey of Consumer Expenditure, 2017-18 because of “data quality” issues. A leaked version of the report shows that it paints a rather unflattering picture of India’s economy, which the government may not want to have in the public domain.

The leaked National Statistical Office (NSO) survey, titled ‘Key Indicators: Household Consumer Expenditure in India’, reportedly shows that the average amount of money spent by an Indian in a month fell by 3.7% to Rs 1,446 in 2017-18 from Rs 1,501 in 2011-12.

Watch: Why is Modi Govt Suppressing Unemployment Data?

The statement is signed by renowned economists including Nobel laureate Angus Deaton, Thomas Piketty, Prabhat Patnaik, Barbara Harris-White, Ashwini Deshpande, Jayati Ghosh and others. They have said that the Centre has “repeatedly shown its disinclination to make public any information that may show its own performance in a poor light” and urged the government to release all data “without delay and irrespective of what the results are”.

The full text of the statement and list of signatories is reproduced below.

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We the undersigned demand that the Government of India releases the report and data of all NSSO Surveys that have been completed and approved by the NSSO’s internal systems, including the results of the 75th round Survey of Consumer Expenditure, 2017-18.

A media leak published in Business Standard has revealed that the 2017-18 Consumer Expenditure Survey shows a sharp decline in average consumption. It has been suggested that the survey results are not being released because they support other evidence that the economy is experiencing a downturn. The Ministry of Statistics and Programme Implementation has now announced that the results of the survey will not be released at all, because they show a higher divergence with the “administrative data” than for earlier surveys.

It should be noted that consumption surveys are known to give results that diverge from macroeconomic estimates of the National Accounts. Also, National Accounts estimates are based not only on administrative data but on a combination of sources including NSSO and other surveys. Several committees have looked into these discrepancies. While further work can be done to identify sources of and reduce these discrepancies, the common understanding has been that the flaws lie as much in the methods deployed for arriving at macroeconomic estimates as they do in surveys.

Consumption surveys are crucial for monitoring trends in poverty and inequality, and are also of critical value for national income accounting, and for updating macro-economic data such as price indices. They can provide an important check on administrative and macroeconomic data, which is important both for policy makers and the general public. The fact that data on supply of goods and household consumption are diverging points to the need for questioning supply side data (which are being widely questioned within and outside India) as much as it points to the continuing need for improving survey methods.

Also read: Under RTI Act, RBI Finally Discloses Details of Major Wilful Defaulters 

It is of fundamental importance for the nation that statistical institutions are kept independent of political interference, and are allowed to release all data independently. The record of the present government on this score has been very poor. Until recently, India has good cause to be proud of its statistical system, and the sample surveys conducted by the NSSO have served as a shining example and a model to the rest of the world. While there has been much discussion and debate about the methodology of the surveys, these have been scientific and technical in nature, devoted to trying to improve the system to enable better measures of crucial indicators.

However, this government has chosen to attack the credibility of this pre-eminent statistical institution simply because the results of the surveys do not accord with its own narrative about the economy, without providing any adequate reasons, and by misrepresenting essential features of the surveys. It has repeatedly shown its disinclination to make public any information that may show its own performance in a poor light. Last year, before the parliamentary elections, the results of the Periodic Labour Force Survey were not allowed to be released until the parliamentary elections were over, despite the resignation of two members of the National Statistical Commission, and a leak in the media. Subsequently, results of other surveys including the 75th round (Consumer Expenditure), 76th round (Drinking water, Sanitation, Hygiene, and Housing Conditions) and more recent quarterly data of the PLFS surveys, have not been released.

This suppression of essential data is terrible for accountability and for ensuring that citizens have the benefit of official data collection that is paid for with their taxes. It is also counterproductive for the government, which may be kept in the dark about actual trends in the economy and therefore not be able to devise appropriate policies. Undermining the objectivity and credibility of an independent statistical system is fundamentally against the national interest.

In the interest of transparency and accountability, all data must be released without delay and irrespective of what the results are. The government may wish to defend itself against interpretations of the statistics that it disagrees with. But this is best done through technical papers and seminars. To prevent release of data that are adverse, and diverge from its own understanding, is neither transparent nor technically sound.

Indeed, in order to produce transparent and robust information on distribution, it is also important for the government to grant researchers access to (anonymous) tax microfiles.

We therefore demand that the government should immediately release the report and unit-level data of the 75th Consumer Expenditure Survey. The government should also commit to release all other survey data after the usual processes to check for possible errors have been concluded.

Signed

  1. A Vaidyanathan, Former Member, Planning Commission
  2. A K Shiva Kumar, Ashoka University
  3. A V Jose, Visiting Fellow, CDS, Thiruvananthapuram
  4. Abhijit Sen, former Member, Planning Commission
  5. Abhirup Sarkar, ISI Kolkata
  6. Achin Chakraborty, IDS, Kolkata
  7. Aditya Bhattacharjea, Delhi School of Economics
  8. Aijaz Ahmad, University of California, Irvine
  9. Ajit Zacharias, Levy Institute, Bard College, New York
  10. Alejo Julca, Independent researcher
  11. Alex M. Thomas, Azim Premji University
  12. Alicia Puyana, Flacso, Mexico City
  13. Alpa Shah, London School of Economics
  14. Aman Bardia, New School for Social Research, New York.
  15. Amit Basole, Azim Premji University
  16. Amit Bhaduri, Emeritus Professor, JNU
  17. Amitabha Bhattacharya
  18. Amiti Sen, Journalist
  19. Amiya Bagchi, Emeritus Professor, Institute of Development Studies Kolkata
  20. Anamitra Roychowdhury, JNU
  21. Andres Lazzarini, Goldsmiths University, London
  22. Angus Deaton, Princeton University
  23. Anita Dixit, Pratichi Institute
  24. Anjana Thampi, IWWAGE, New Delhi
  25. Anup Sinha Retired Professor of Economics IIM Calcutta
  26. Anwar Shaikh, New School for Social Research
  27. Arindam Banerjee, AUD, Delhi
  28. Arjun Jayadev, Azim Premji University
  29. Arthur MacEwan, University of Massachusetts Boston
  30. Ashok Kotwal, The University of British Columbia, Vancouver
  31. Ashwini Deshpande, Ashoka University
  32. Astha Ahuja, University of Delhi
  33. Atul Sood, JNU
  34. Atul Sarma, Visiting Professor, ISID, New Delhi
  35. Atulan Guha, IIM, Kashipur
  36. Ayushya Kaul, Jamia Millia Islamia
  37. Avinash Kumar, JNU
  38. Awanish Kumar, St. Xavier’s College, Mumbai
  39. B Srujana, Tricontinental Institute for Social Research
  40. Barbara Harriss-White, Emeritus Professor, Oxford University, and Emeritus Fellow of Wolfson College, Oxford
  41. Ben Fine, SOAS
  42. Bhanoji Rao, Governing Board Member, GITAM and IFHE Universities
  43. Bharat Ramaswami, ISI Delhi
  44. Bibhas Saha, Durham University
  45. Bindu Oberoi, University of Delhi
  46. Biswajit Dhar, JNU
  47. Byju, V, Thiruvananthapuram
  48. C P Chandrasekhar, Retired Professor, JNU
  49. C Saratchand, University of Delhi
  50. Carlo Cafiero, Senior Statistician, FAO
  51. Chalapati Rao KS, ISID, Delhi
  52. Chirashree Das Gupta, JNU
  53. Chris Baker, Editor, Siam Society
  54. Chrostophe Jeffrelot, Sciences Po and King’s College London
  55. D Narasimha Reddy, University of Hyderabad
  56. D Narayana, Former Director, Gulati Institute of Finance and Taxation
  57. Daniela Gabor, University of West England, Bristol
  58. David Kotz, Professor Emeritus, University of Massachusetts, Amherst
  59. Debabrata Pal, JNU
  60. Debraj Ray, New York University
  61. Deepak K Mishra, JNU
  62. Dev Nathan, Institute for Human Development
  63. Devaki Jain, ISST, New Delhi
  64. Devika Dutt, University of Massachusetts, Amherst
  65. Dilip Mookherjee, Boston University
  66. Dinesh Abrol, ISID, Delhi
  67. Dipa Sinha, AUD
  68. Dipankor Coondoo, Retired Professor, ISI
  69. Dipankar Dey, Dept of Business Management, Calcutta University
  70. Ahmet Tonak, University of Massachusetts, Amherst
  71. E Bijoykumar Singh, Manipur University
  72. Emanuele Citera, The New School For Social Research
  73. Farzana Afridi, ISI, Delhi
  74. Francesco Saraceno, Sciences Po
  75. Gaurav Khanna, University of California, San Diego
  76. Giovanni Andrea Cornia, University of Florence
  77. Hanjabam Isworchandra Sharma, Manipur University
  78. Haroon Akram-Lodhi, Trent University, Canada
  79. Hema Swaminathan, IIM Bangalore
  80. Himanshu, JNU
  81. Indra Nath Mukherji, JNU
  82. Indraneel Dasgupta, Indian Statistical Institute, Kolkata
  83. Indranil Chowdhury, University of Delhi
  84. Indranil Mukhopadhyay, OP Jindal University
  85. Ingrid Kvangraven, York University
  86. Iqbal Singh, Akal University, Bathinda
  87. Ishan Anand, Ambedkar University, Delhi
  88. Ishita Mukhopadhyay, University of Calcutta
  89. Mohan Rao, University of Massachusetts at Amherst
  90. Jan Breman, University of Amsterdam
  91. Jan Kregel, Levy Institute
  92. Jason Hickel, Goldsmith College, London
  93. Jayan Jose Thomas, Economist, New Delhi
  94. Jayati Ghosh, JNU
  95. Jens Lerche, SOAS
  96. Jesim Pais, SSER
  97. John Harriss, Professor Emeritus, Simon Fraser University, Vancouver
  98. Jose Antonio Ocampo, Columbia University
  99. Joydeep Baruah, OKD Institute of Social Change and Development, Guwahati
  100. Kalyani Menon-Sen, Feminist Learning Partnerships
  101. Kathleen McAfee, San Francisco State University
  102. K J Joseph, Gulati Institute of Finance and Taxation
  103. K N Harilal, Member, Kerala State Planning Board
  104. K Nagaraj, Retired Professor, MIDS
  105. K P Kannan, Retired Professor, CDS
  106. K V Ramaswamy, IGIDR
  107. Kumarjit Mandal, University of Calcutta
  108. Kunibert Raffer, retired Associate Professor, University of Vienna
  109. Lawrence King, University of Massachusetts, Amherst
  110. Lucas Chancel, Co-Director, World Inequality Lab
  111. M S Bhatta, Retired Professor, Jamia Millia Islamia
  112. M S Sriram, Indian Institute of Management Bangalore
  113. M Vijayabaskar, MIDS
  114. Maitreesh Ghatak, LSE
  115. Mahalaya Chatterjee, Calcutta University
  116. Malabika Majumdar, Retd. Professor, University of Delhi
  117. Mandira Sarma, JNU
  118. Martin Ravallion, Georgetown University
  119. Mary E John, CWDS
  120. Mira Shiva, Public Health Physician
  121. Mridul Eapen, Member, Kerala State Planning Board
  122. Mritiunjoy Mohanty, IIM, Kolkata
  123. Mustafa Özer, Anadolu University
  124. Mwangi wa Githinji – University of Massachusetts, Amherst
  125. Nalini Nayak, SEWA, Kerala
  126. Naveed Ahmad, Department of higher education Jammu and Kashmir (cluster University Srinagar)
  127. Narender Thakur, University of Delhi
  128. Nisha Biswas, Scientist
  129. Nishith Prakash, University of Connecticut
  130. Nitin Sethi, Independent journalist
  131. Oliver Braunschweig, The New School for Social Research
  132. Padmini Swaminathan, independent researcher, Chennai
  133. Parthapratim Pal, IIM Calcutta
  134. Pasuk Phongpaichit, Professor, Faculty of Economics, Chulalongkorn University, Bangkok
  135. Prabhat Patnaik, Emeritus Professor, JNU
  136. Pranab Bardhan, University of California, Berkeley
  137. Pranab Kanti Basu, Retired Professor, Visva Bharati University
  138. Praveen Jha, JNU
  139. Priya Mukherjee, William & Mary, Virginia
  140. Pulin B Nayak, Retired Professor of Economics, Delhi School of Economics
  141. R Nagaraj, IGIDR
  142. R Ramakumar, TISS
  143. R V Ramana Murthy, University of Hyderabad
  144. Ragupathy, Goldsmiths University, London
  145. Rahul Roy, ISI, Delhi
  146. Rajah Rasiah, University of Malaya
  147. Rajesh Madan, Noida
  148. Rajeswari Sengupta, IGIDR
  149. Rajesh Bhattacharya, IIM, Kolkata
  150. Rajiv Jha, University of Delhi
  151. Rakesh Ranjan, University of Delhi
  152. Ramaa Vasudevan, Colorado State University
  153. Rammanohar Reddy, Editor, The India Forum, and Visiting Professor, Goa University
  154. Ranjan Ray, Monash University
  155. Ranjini Basu, Focus on the Global South
  156. Ratan Khasnabis, Adamas University, and Retired Professor, Calcutta University
  157. Ravindran Govindan, Laurie Baker Center for Habitat Studies, Trivandrum
  158. Ritu Dewan, Director (retd), Dept of Economics, University of Mumbai
  159. Rohit Azad, JNU
  160. Romar Correa, University of Mumbai
  161. Rosa Abraham, Azim Premji University
  162. Runa Sarkar, IIM Calcutta
  163. S Krithi, TISS, Hyderabad
  164. Sagari R Ramdas, Food Sovereignty Alliance
  165. Saikat Sinha Roy, Jadavpur University
  166. Samarjit Das, ISI, Kolkata
  167. Sanjay Reddy, The New School for Social Research
  168. Santosh Das, ISID, New Delhi
  169. Saradindu Bhaduri, JNU
  170. Sarmistha Pal, Surrey Business School
  171. Satish Deshpande, Delhi University
  172. Satyaki Roy, ISID, Delhi
  173. Saumyajit Bhattacharya, Delhi University
  174. Seema Kulkarni, SOPPECOM, Pune
  175. Servaas Storm, Delft University of Technology, Netherlands
  176. Shambhu Ghatak, Senior Associate Fellow, Inclusive Media for Change
  177. Shantanu De Roy, TERI University
  178. Shiney Chakraborty, ISST, New Delhi
  179. Shipra Nigam, Consultant Economist, New Delhi
  180. Shouvik Chakraborty, University of Massachusetts, Amherst
  181. Shyjan Davis, University of Calicut
  182. Siwan Anderson, Vancouver School of Economics, University of British Columbia, Vancouver
  183. Smita Gupta, Economist
  184. Smitha Francis, ISID, New Delhi
  185. Snehashish Bhattacharya, SAU
  186. Sona Mitra, IWWAGE, New Delhi
  187. Stefano Zambelli, Provincial University of Trento
  188. Suchetana Chattopadhyay, Jadavpur University.
  189. Subin Dennis, Tricontinental Institute for Social Research
  190. Sudhir Kumar Suthar, JNU
  191. Sudip Chaudhuri, IIM, Kolkata
  192. Sudipta Bhattacharyya, Visva Bharati
  193. Sujata Patel, NIS, Shimla
  194. Sukanta Bhattacharya, University of Calcutta
  195. Sushil Khanna, IIM, Kolkata
  196. Sripad Motiram, University of Massachusetts Boston
  197. Sunanda Sen, Retired Professor, JNU
  198. Surajit Das, JNU
  199. Surajit Mazumdar, JNU
  200. Suresh Aggarwal, Former Professor, Department of Business Economics, University of Delhi
  201. Suranjan Gupta, New Delhi
  202. T Sabri Öncü, Former Head of Research, CAFRAL
  203. Takahiro Sato, Kobe University
  204. Taposik Banerjee, Ambedkar University, Delhi
  205. Thomas Piketty, Paris School of Economics
  206. Upasak Das, University of Pennsylvania
  207. Utsa Patnaik, Emerita Professor, JNU
  208. Uttam Bhattacharya, Institute of Development Studies, Kolkata
  209. Vamsi Vakulabharanam, University of Massachusetts, Amherst
  210. Velupillai Kumaraswamy, former Professor, University of Trento and New School University
  211. Venkatesh B Athreya, Professor of Economics (Retired), Bharathidasan University
  212. Vikas Rawal, JNU
  213. Yogendra Yadav, Swaraj India, and former member, UGC
  214. Yoshifumi Usami, University of Tokyo

Only 20% of Mudra Loan Beneficiaries Started New Businesses, Govt Survey Shows

A report on the Pradhan Mantri Mudra Yojana Survey also found that half of the additional jobs created under the scheme were “self-employed or working owners.”

New Delhi: A draft labour ministry report accessed by the Indian Express has raised doubts over the success of the government’s flagship Pradhan Mantri Mudra Yojana in generating employment and encouraging entrepreneurship. A survey conducted between April-November 2018 by the ministry, which is yet to be released, revealed that just one of every five beneficiaries of Mudra loans used the funds for setting up new businesses, while the rest used it to expand their existing businesses.

Out of 94,375 surveyed beneficiaries, 19,396 (20.6%) set up new businesses while the remaining 74,979 (79.4%) expanded existing ones.

“The Draft Report on Pradhan Mantri Mudra Yojana Survey found that 1.12 crore additional jobs were created during April 2015-December 2017,” the English daily stated. This is less than 10% of the number of loans offered. Under the scheme, 12.27 crore loan accounts received a total of Rs 5.71 lakh crore in the first three years (2015-2018). The draft report, however, does not share the break-up of additional jobs created by new and existing units.

Services and trading accounted for more than two-thirds (67.57%) of the additional jobs created while manufacturing added just 11.7%.

Also read: How Many Jobs Are Really Being Created by the Modi Govt’s Mudra Scheme?

The survey also offers a glimpse into the types of these additional jobs created under the scheme. Out of the 1.12 crore jobs, almost half (51.06 lakh) were “self-employed or working owners, which also included unpaid family members while 60.94 lakh were employees or hired workers.”

“The government had planned to use the findings of this survey to counter the National Sample Survey Office (NSSO) report on unemployment, which estimated the unemployment rate to be 6.1% in 2017-18, a record high,” it added.

Launched in 2015, the Mudra scheme facilitates collateral-free loans for small businesses under three categories: Shishu (up to Rs 50,000), Kishor (Rs 50,000-5 lakh) and Tarun (Rs 5 lakh-10 lakh).

The daily said a detailed questionnaire sent to the Ministry of Labour and Employment remained unanswered.

Does India Really Know What its GDP Is?

Everyone knew India’s organised sector contained a large number of shell companies. We should have worked out its impact and whether a blowing up factor was required before launching the new GDP series.

Another controversy about India’s gross domestic product (GDP) data has broken out, with a National Sample Survey Office (NSSO) report casting doubt on the quality of a key database of Indian companies.

According to the report, 36% of India’s ‘active’ companies are not traceable or are incorrectly classified. This is the latest incident in a string of controversies over India’s economic growth data in the last year.

GDP, simply put, is a measure of the total output in the economy. This is one number which is supposed to represent society’s welfare. The larger it is, the greater the pie which can be divided between the citizens of the country. Of course, the pie may be very unevenly divided so that the people at the bottom may gain little from the increase in the GDP, but generally it is argued that due to trickle down effects, those at the bottom are also able to improve their standard of living to some extent.

Governments also use GDP as a means to showcase their success. For instance, currently, the NDA-II government’s spokespersons repeatedly emphasise that India is the fastest growing economy and has become the sixth largest economy in the world ahead of France and will soon become larger than even Great Britain, our former ruler.

But what if the way we measure growth  is simply not correct? Doubts about our growth data started soon after the new series of GDP was announced in 2015.

The new series, which came with a number of fundamental underlying changes, showed that the Indian economy was larger and growing faster than what the earlier data indicated.

It was created with the base year 2011-12 as compared to the earlier base of 2004-05. Also, for the private organised sector, a new data series called ‘MCA-21’ was used. This series consists of the data of all the companies registered with the ministry of corporate affairs (MCA). Each company is allotted a 21-digit code, hence the name MCA-21.

Also read: Why India’s ‘Modi-fied’ GDP Math Lacks Credibility

The new controversy strikes at the heart of the second change, raising questions over 36% of the companies in the MCA-21 database.

So, two things happened in the new GDP series. One, the base year was changed to reflect the changing composition of the economy and two there was a major change in the data to measure the contribution of the private organised sector of the economy.

There is nothing new in changing the base year of GDP since the economy is always changing with new products coming and technology changing the nature of products. For instance, earlier typewriters were important but now it is computers that are the defacto instruments for information processing. Landlines have been replaced by mobile phones. Cars of today are very different from cars of two decades back and so on. So, a change in base year takes care of the changing composition of the economy and the products it is making.

Credit: PTI

Furthermore, a modern day economy is very complex and produces a huge number of things. These can be divided between different sectors called the primary, secondary and tertiary. These are further divided into agriculture, mining, manufacturing, trade, transport, etc. Thus, GDP must account for all of them. To do so, it has to add up the production in each of these sectors and sub sectors.

In other words, GDP is a sum of production in each of these components of the economy. So, we need to know the production in each of the components. That is not easy because mostly the production by individual producers is not reported by them to some agency which can then add up everything to give the contribution of each of the components.

So, there are official agencies for collecting/reporting data from each of the components to estimate the contribution to GDP. Based on the partial data available from each component, an estimate of the total has to be made. For this, methods are devised for each component, based on the availability of data. For instance, every farmer does not go to some agency and inform it about how much he/she is producing. The relevant ministry has to estimate what the agricultural production is by some method.

There are committees that go into the method and database to be used for each component of the economy. Each time the base year is changed, some advance is made in the method used as more and better data become available. The United Nations has been devising a standard method for such estimation called System of National Accounts (SNA), the latest having come in 2008.

India has been trying to implement these recommendations.

New Delhi, however, has a huge problem – much bigger than in most other countries. It has a very large unorganised sector which does not report data to any agency. Data on this component has to be obtained through periodic surveys; once every five years.

Also read: Growth at 8.2%, Yet All Is Not Well in India’s Unorganised Sector

In between these years – called the reference years – a proxy is used. This is often based on the organised sector data.

Is this kosher?

Not quite, but is broadly acceptable provided there is no shock to the economy which changes the crucial ratios in the economy. But, the Indian economy has had two big shocks in the last three years – demonetisation and then GST – both of which have adversely impacted the unorganised sector so that the proxy based on the organised sector data is no more applicable. Thus, GDP calculated on the old basis is no more valid.

But with the latest controversy about the missing companies in the formal sector database used for estimation, now there is a question mark regarding the organised sector data also. If the organised sector data for companies has become suspect, then the data for the unorganised sector becomes doubly suspect. In other words, a very large part of India’s GDP has become unreliable.

NSSO and MCA

What is the impact of the missing data from MCA21 on GDP? There are two components to the missing data. First, companies were not found at the registered address and second they were not doing the work that they had officially stated they were doing.

A company is said to be ‘active’ if it files with the government certain types of financial reports at least once in three years.

If a company is active, its data should be included in the GDP. If it is inactive, then the data does not exist and it does not contribute to the GDP.

Companies that are filing their returns regularly get counted every year. The companies that have ceased operations do not impact the GDP.

Therefore, it is the companies that exist but do not regularly file their data that pose a problem. How are their contribution to the GDP to be calculated when they do not file their data? Further which are these companies that file data intermittently?

India has a very large black economy and companies, big and small, resort to black income generation. To do this they set up what are called shell companies. As the name suggests, they are merely a ‘shell’ and are not active in production. They serve several purposes. They may be used to divert profits from the main company so as to lower tax liability. They are often the conduits for under and over invoicing. They may also be conduits for siphoning out capital from the main company to some other entity or even abroad. At times these companies are registered in the name of some other entities so are ‘benami’. Again the idea is to siphon out profits and split it to lower tax liability.

The net result is that in some cases, production that should have been shown by the main company is shown as output of the ‘benami’ or shell company. So, adding these companies gives a more complete picture of the output of the economy.

Also read: As We Debate GST Collections, Is the New Tax Regime Curbing India’s Black Economy?

On the other hand, there are many cases in which these companies are mere conduits for transfer of funds with little production. If these companies get counted then there will be an inflation of output and growth rate may also get increased. This would especially happen if a blowing up factor is applied to the MCA-21 data to account for the companies which do not file their return every year.

So, the problem with GDP due to the switch to the new corporate database is a result of using a series without fully comprehending its nature. It needed to be tested before hand. It has been known for a long time that a large number of shell companies exist in India. These are registered and are a part of the MCA database. Thus, before using this data, it was necessary to work out what would be the impact of the existence of such fictitious companies and whether a blowing up factor was required or not. What this factor should be applied to and what its magnitude should be is an important question.

At stake is our GDP data, which is even less than reliable than earlier thought because of errors in both organised and unorganised sector estimates.

Arun Kumar is Malcolm Adiseshiah Chair Professor, Institute of Social Sciences and Author of `Ground Scorching Tax’. 2019. Penguin Random House.

NSSO Questions Quality of Key Database Now Used to Calculate GDP

A study has raised questions raised over the quality of the MCA-21 database, which is a crucial part of the new GDP series math.

New Delhi: A National Sample Survey Office (NSSO) report has highlighted deficiencies in key government database of Indian companies, an observation that economists say could have consequences for the credibility of India’s GDP numbers.

A technical study conducted by the NSSO between June 2016 and June 2017, which examined parts of the corporate affairs ministry’s database, found that 37% of the companies could either not be traced, had shut down or were wrongly classified in terms of what sector they belonged to.

This study was recently uploaded on the survey office’s website.

The quality of ministry’s company database, often referred to as ‘MCA-21’, has been in the spotlight over the last four years, after the Central Statistics Office made it a key component of how it calculated the new GDP series.

While the government has maintained that using the MCA-21 database allowed for a more granular approach, as it drills down to the level of balance sheet data, some economists have argued that it is untested and may have unknowingly boosted India Inc’s contribution to the country’s growth figures.

“The use of MCA database in particular could have misleadingly enlarged the private corporate sector’s share in the Indian economy and its growth rate,” R. Nagaraj, a professor of economics at the Indira Gandhi Institute of Development Research, wrote in The Wire last year.

Also read: What Explains India Inc’s Corporate Earnings Conundrum?

The NSSO’s findings appear to tip the scale in favour of the database’s critics.

The survey office observations on the MCA-21 database, however, came while it was collecting a wide range of economic and operational data on service sector enterprises in India, a broad effort that the organisation says was conceived as “a prelude to a proposed Annual Survey on Services Sector”.

The company database was used as a survey sampling frame along with two other sources of data – a list of establishments as per the Economic Census (EC) and list of establishments as per the Business Register (BR).

“List of active private non-financial companies of 2013-14, as available from National Accounts Division (NAD), CSO, along with data for some additional companies based on such a list for 2014-15 were used as an additional frame to augment EC/ BR frame. This list was obtained by NAD from the Ministry of Corporate Affairs,” the report says.

According to the NSSO, there were 3,49,500 enterprises in the frame of the MCA, out of which 35,456 were selected for survey.

Also read: The Lonely Path to an Alternative Form of GDP Measurement

The examination of this sample threw up a variety of troubling non-responses, which was a “major setback” for the survey.

Over 20% of the firms were found to be “out of coverage”, which means that they likely no longer operate in the services sector, even though that is how they are registered and thus captured in India’s national accounts.

Nearly 15% of the firms were either untraceable or had closed down. To top it off, almost 7% of the companies had been correctly identified but simply chose not to respond to survey questions or had not maintained accounts in a proper format.

The NSSO notes the issue of non-responses was far worse in the MCA frame when compared to the “EC/BR frame”.

“The problem of non-response was severe in case of units chosen from MCA frame… About 45% of MCA units were found to be out-of-survey/casualty while EC/BR frame had about 18% of such cases,” the report notes.

Former NSSO boss P.C. Mohanan told Livemint, which reported on the study first, that the MCA-21 database didn’t receive enough scrutiny.

““The CSO should have done some kind of critical scrutiny and validation before using the MCA-21 database in the new GDP series, either through quick surveys or by comparing with other databases, or consultations with accountants familiar with company filings,” Mohanan told the publication.

Lack of Jobs May Not be Govt’s Fault Alone, But Denying Data on It Certainly Is

NSSO data is a crucial piece to India’s employment puzzle. Turning a blind eye to it is helping no one.

It is a capital mistake to theorise before you have data, British author Arthur Conan Doyle one said.

Nowhere is this truer than in India. Attempts by the government to sell a sparkling story on the jobs front – when statistics point otherwise – is no less than a valiant effort at selling snake oil.

The stubborn effort to run down the government’s own data as inadequate is to deny the evidential value of national statistics.

Studies carried out by the National Sample Survey Office (NSSO) are based on methodologies that are robust, time-tested and in line with methods adopted internationally for job data collection. The current government, however, has frowned down upon this and refused to release the results of one periodic labour force survey (PLFS).

What is instead put forth as proxies are industry data, industry-sponsored surveys and anecdotes of how Mudra loans are creating a hunky-dory job picture. Perhaps there is a belief that if evidence can be trumpeted to be absent, it is possible to argue that the problem is absent – though that is unimpeachably untrue.

The NSSO survey is based on random sampling, which is an unbiased technique for estimating the behaviour or characteristic of an entire population from a chosen sample. Every individual/household is chosen randomly so that each unit has the same probability of being chosen. Average sample here will accurately represent the population. Hence, based on the sample, valid conclusions can be drawn for the entire population.

But the PLFS report emerging from this survey has not been released on the ground that it is a draft study – and will need further approval before it is made public. It is true that a laid-down process should be followed. But it appears unlikely that the survey will be replaced by a new study or that the results will change dramatically.

Also read: A (Failed) Quest to Obtain India’s Missing Jobs Data

Meanwhile, other figures are bandied about to create a fog of confusion. It has also not helped that participants in India’s nightly TV news debates are people who have little knowledge of statistics or the actual subject.

The current rebuttals to the leaked NSSO report are a CII survey of 1,00,00 MSME firms which shows 3.2 lakh net jobs have been created in the last four years.

Another employment narrative is that over two million jobs have been created by new tech firms like Uber and Ola and that 40 million Mudra loans have been given to first-timers. Simply put, there are many jobs that have been created, but they can’t quite be counted.

To understand the broader context, we need to take a step back. First of all, employment and unemployment rates are arrived at on the basis of people who are employed and people who are actively seeking employment, respectively. If someone is self-employed but still actively looking for a job, he will show up as unemployed even though he could nominally be self-employed. Labour Bureau statistics show that in two-thirds of the states, self-employed get less than Rs 7,500 per month and 42% of them get below Rs 5,000.

Most kinds of self-employment, therefore, are not real jobs but ragtag jigs and do not provide either a steady life or living wages or social security. Hence, many ostensibly self-employed show up when better employment is advertised.  

In the best of situations, jobs are a part of the ‘creative destruction’ process.  Some jobs get created and some get destroyed and the net accretion is the addition to jobs. Industry reports will not capture this. Moreover, there is a selection and survival bias as only those industries which have added jobs are taken up for a survey. Industries which have folded up don’t figure in the calculus.

It is only in random sampling that one can capture these details. One can also capture other vital information such as whether the Uber drivers were earlier driving a taxi, auto or doing some other job. This would indicate mobility in the value chain but not the net addition of jobs.

The other piece of the job puzzle is India’s payroll data. The EPFO database showing an ‘increase’ of 20 million jobs is perhaps because the definition of EPF being mandatory for firms having 20 workers changed to firms with ten workers and all units which were earlier not under the purview of the worker security net came under its fold. This, as other experts have noted, is the formalisation of jobs although some new units (with ten workers) may figure in this picture. As for 40 million Mudra loans, an average of Rs 60,000 per unit often do not create jobs but only enable enlargement of existing units to slightly bigger ones.

Also read: In India, Rising Joblessness is a Tinderbox Waiting to Catch Fire

Why is a survey based on random sampling better than payroll statistics in India? This is because we have a dualistic economy with a large informal component and a smaller formal component. Payroll stats are good barometers only when the economy is largely formal. Households and farming disguise unemployment and underemployment and they are best captured in the random sample survey.

The passionate intensity in the debate regarding employment comes from those spokespeople who despite their burnished CVs and high-decibel arguments do not understand the basics of statistics, surveying and the nuances between different reporting formats. They can be forgiven because they don’t have the knowledge nor the basic tool kit and their passionate intensity is the surround sound of talking from a pre-determined script.  

But what about the more knowledgeable people, who have chosen to keep quiet? The reason is that they want to put up a show that their version is not divergent from the prepared script. There is a huge incentive to keep quiet or mumble here and there rather than take on the issue squarely. Much worse, there is a disincentive for telling the truth beyond the diktats of apparatchiks of the establishment. The latter feels that any figure which does not support the narrative of relentless growth will go against the government.

The blame for low job creation, in a country as diverse as India, cannot solely be placed on the Central government. However, collecting and releasing truthful statistics is certainly an important duty. This is the starting point for course correction. But as long as you do not recognise the data, nothing can be measured and no correctives can be launched.

Satya Mohanty is a Former Secretary to GOI and Adjunct Professor, JMI.

For First Time Since 1993-94, India’s Male Workforce Is Getting Smaller: Report

For men and women combined, the national workforce shrunk by 4.7 crore since 2011-12.

New Delhi: The National Sample Survey Office’s periodic labour force survey 2017-18 has reportedly found that the size of India’s male workforce – or men who are working – has reduced for the first time since 1993-94.

According to the Indian Express, the unreleased report says there are 28.6 crore employed men – a decline from 30.4 crore in 2011-12, when the last NSSO survey was conducted. This downward trend is even stronger in rural areas than in urban, the newspaper reported, with a 6.4% decline in the number of employed men in rural areas against 4.7% in urban.

For men and women combined, the national workforce shrunk by 4.7 crore, Indian Express quoted the NSSO data as saying. “While the employment loss in the rural segment hurt the women most (68 per cent), men suffered more (96 per cent) job losses in the urban areas,” the newspaper said.

The NSSO report puts the urban unemployment rate at 7.1% and the rural rate at 5.8%.

Also read: A (Failed) Quest to Obtain India’s Missing Jobs Data

The government’s decision not to publish this report yet has been subject to controversy, and two expert members including the acting chairman quit the National Statistical Commission in protest after it was not released despite their okay.

The report was first leaked in the Business Standard in January. The NSSO reportedly found that unemployment in India was at a 45-year high in 2017-18. This was the first job survey conducted after Narendra Modi’s demonetisation move.

Data on employment – or the lack thereof – has been a controversial subject during the Modi regime. It was recently reported that a survey on jobs created under the Mudra scheme won’t be released before the 2019 elections. This is the third report on jobs data that the government has failed to release yet. In addition to the PLFS and the Mudra reports, the Labour Bureau’s sixth annual employment-unemployment survey too has not been released.

A group of 108 economists and social scientists recently released a joint statement on “political interference” in statistical data, which said the government is attempting to “suppress uncomfortable data”.

No Budget for India’s Invisible Women Farmers

Fund utilisation for women farmer oriented schemes has systematically declined with marginal increases in allocation in years 2018-19 and 2019-20.

In India, land rights structurally escape women. This is a fundamental issue in why women’s work as farmers is largely invisible. However, the large-scale migration of men towards pursuing other non-farm employment opportunities due to the worsening agrarian crisis has pushed more women into this sector.

Work is not homogenous and neither are women or their work. Perceiving work through economic lens, the policy framework falls short in addressing the invisible and open unemployment of women along with increasing trends of feminisation in the sector. The first ask for women in the sector remains being identified as a ‘farmer’.

According to previous reports by the National Sample Survey Office (NSSO), the agrarian sector nearly employs 80% women workers. Despite such high numbers, both the sector and the macroeconomic policy framework are yet to recognise them as farmers. Moreover, 81% of the female agricultural labourers belong to Dalit, Adivasi and OBC communities (ILO, 2010). The largest share of casual and landless labourers also come from these social groups. The burden of the agricultural debt has also inadvertently fallen upon women.

Also read: For India’s Farmers, Budget 2018 Is Nothing but a Hoax

When unpacking schemes for women farmers, the Mahila Kisan Sashaktikaran Pariyojana (MKSP) is the only sub-programme under the Deendayal Antyodaya Yojana-National Rural Livelihood Mission (DAY-NRLM) particularly aimed at women farmers.

The NRLM aims to reach out eight-nine crore rural poor households in an effort to organise one woman per household into Self Help Groups to take up organic farming in clusters. As per the ministry of rural development’s circular “a total of 57,270 Mahila Kisan have been registered through 5816 Local Groups for taking up organic farming”.

The same circular also identifies nearly 14.03 lakh women farmers under the programme State Rural Livelihoods Mission and MKSP. As one can see, women are at the centre of implementing the objectives of organic farming of the government. However, the total budgetary allocation for MKSP in the year 2018-19 (BE) was a mere Rs 1,000 crore.

As MKSP is a sub-programme the allocations for 2019-20 (BE) are yet to be published. While the allocations for NRLM have increased over the years, increase in allocations for MKSP have yet to be realised effectively. In fact, the trends since 2014 show that not only does the policy framework suffers low levels of allocation and spending but also show how misplaced the government’s priorities continue to be in the agrarian sector with respect to women farmers.

Do the numbers and the plans really live up to the talk? Credit: Reuters

Do the numbers and the plans really live up to the talk? Credit: Reuters

The sub-programme under NRLM for Mahila Kisan needs to be scaled up and backed with improved resource mobilisation plans, which may not be enough as work for women farmers also varies regionally. Moreover, transparency in the process of identifying and registering women farmers is crucial for better outcomes.

As per the reply given by the ministry of agriculture and farmers’ welfare in Lok Sabha (August 2, 2016), budgetary allocations for women farmers have been made at 30% on various schemes* like Sub-Mission on Agriculture Mechanisation, National Food Security Mission, Rashtriya Krishi Vikas Yojana, National Mission on Oilseeds and Oil Palm, Sub-Mission on Seed and Planting Material and Mission for Integrated Development of Horticulture (National Mission on Horticulture).

Also read: Expert Gyan: Is Budget 2018 Really Putting Farmers First?

The trend from 2014 shows that the fund utilisation for these schemes has systematically declined with marginal increases in allocation in years 2018-19 and 2019-20. It should be noted that the budget for the ministry of agriculture increased from Rs 57,600 crore in 2018-19 (BE) to Rs 1,40,764 crore in 2019-20 (BE).

The total allocations for women farmers in 2018-19 (BE) under the above-mentioned schemes is 2% of the total expenditure under the Ministry of Agriculture and Farmers’ Welfare for the same year.

This amount is too little to provide coverage even for the conservative number of 8-9 crore households.

Source: Compiled by CBGA from Union Budget Documents, various years

Source: Compiled by CBGA from Union Budget Documents, various years

The income guarantee scheme of Rs 6,000 per annum under the Pradhan Mantri Kisan Samman Nidhi Yojana for farmers owning less than 2 hectares of land announced in the Interim Budget is also outside the framework as most women in agriculture do not own lands.

In the absence of schemes designed especially for women farmers, there is little the Government has been able to achieve to minimise gaps in land and asset ownership, wage gap, supportive infrastructure, access to credit, subsidised fertilisers, recognition to entitlements among others.

This piece was previously published at Governance Now. Sakshi Rai works with Centre for Budget and Governance Accountability (CBGA), New Delhi. She can be reached at sakshi@cbgaindia.org.

Budgetary Sops Will Do Little to Fix Unemployment and Poverty in India

The disparate performance of the three major sectors of the Indian economy has left us with an under-utilised workforce, facing a life ridden with poverty.

The recent interim budget clearly reflects concerns that a majority of India’s people, especially in the agricultural and informal sector, have experienced hardship despite the on-going and relatively high growth in the economy.

Palliatives designed to lessen these hardships include an annual grant of Rs 6,000, which is to be paid in three instalments to farmers owning land upto two hectares. Palliatives for the poor also include a rather unworkable plan of a contributory pension scheme of Rs 3,000 per month for workers in the  informal sector. In addition to these two steps, the budget also offers substantial tax relief to the tax paying middle income persons.

Leaked data shows that unemployment, as calculated by the National Sample Survey Office (NSSO), is at a record high of 6.1%. The urban unemployment rate it appears to be even higher than its rural counterpart and the urban female labour force is in a worse position when compared to males. All together, it provides a scary picture which has not been accepted by official agencies.

Official denials, however, should not provide an excuse to sideline or even ignore the relevance of unemployment as an indicator of poverty and a lack of inclusive development.  

Also read: Where Is the Indian Economy Headed?

Underemployment is an even trickier problem. It does not require much to observe that those reported as ‘employed’ often have  jobs which extract a premium in terms of work pressure but hardly provide enough for subsistence.

At the same time, those who are identified as unemployed or  have few options other than to rely on the scanty resources of the family, usually borrowed at stiff rates from village moneylenders. Finally, beyond those reported as unemployed, both in the rural and urban areas, there remain those who reluctantly withdraw from the labour force to swell the ranks of the unnamed participants in the informal sector in various capacities.

Instead of expecting any solution to help poverty, even as a temporary palliative in the form of budgetary announcements, one needs to consider the  expanse of poverty in the country.

None of the sops – which include the cash dole-outs for small land-holders (as proposed in the recent temporary budget) or the promised pension scheme for those with jobs in the informal sector or even one-time debt relief for farmer loans – can provide a lasting and effective solution to poverty experienced by the major sections of people in the country.

What then can explain the dearth of employment opportunities and the related trap of poverty in India? Much of above can be related to a lack of aggregate effective demand in the economy and the consequent slag in production of output and related employment demand. This can be traced to the shortfall in public expenditure, which in turn is restricted by the budgetary limits on fiscal deficits set by the Fiscal Restraint and Budgetary Management Act (FRBMA).

Reduced fiscal spending is further impacted by interest liabilities in the budget on official borrowings from the markets, which replaces the earlier pattern on deficit spendings. As a consequence much of capital expenditure as well as social sector spendings in the budget have been subject to reductions. However, explanations of jobless growth as observed in the Indian economy needs to incorporate, along with the fiscal austerity and cuts in aggregate spending, the structural changes in the sectoral pattern of growth in the economy over the last couple of decades.

Also read: Will the Budget’s Charm Offensive Quell Distress Before the General Elections?

To enumerate joblessness in the face of the rather rapid growth rate of GDP, as calculated in a report from Azim Premji University of Bangalore, the cumulative average growth rate (CAGR) over 1993-94 to 2011-12  has been at around 6%, while the corresponding CAGR for employment in the economy as a whole, stands at a mere 1%.

During the more recent years between 2011 to 2015, the respective CAGR for GDP and employment are calculated at 6.8% and 0.6%. The employment elasticity of output (real GDP), as provided  in the same report, seems to have gone down from 0.18% of 2011 to 0.08% in 2015.

The shrinking pace of  employment growth relative to and despite the high growth in the GDP, needs further inquiry. One notices that the reduced job availability in organised industry is also adversely affected by technology. As it has been observed, capital-output ratios went up in the majority of industries between 1999 and 2012, a trend which has been continuing  since then. Thus in the early 1980s, Rs 1 crore worth of real fixed capital (in 2015 prices), as calculated in the same report, supported jobs around 90 persons in the organised manufacturing sector, and by 2010 the number had fallen to 10.

Joblessness in industry is also related to the fact that growth rates have been higher for capital as well as in skill-intensive products as compared to the average industrial growth in the country. While formal jobs as are generated in organised manufacturing, and provide 12% or less of aggregate jobs, much of those are outsourced or on a contractual basis.

As a consequence, the labour force relies on sources of jobs in agriculture and services. It is of further concern that there has been a  drop in labour absorption as jobs provided in agriculture, especially when organised industry provides little relief in terms of job opportunities.

A rupee coin is seen in this picture illustration taken in Mumbai April 30, 2012. Credit: Reuters/Vivek Prakash/Files

Services, providing more than one-half of the GDP,  have a rather marginal contribution as a provider of jobs. Data available from the Labour Bureau indicate that of an aggregate 140-150 million jobs in the services sector during 2015, only 26 million were with the organised sector. The remaining jobs , mostly in petty production units and self-employment, contained large numbers  facing disguised unemployment – which has been described in the report mentioned above as ‘surplus’. As estimated, the service sector accounts for 55% of such ‘surpluses’ as defined above, which in the aggregate was around 11% for employment in the country as a whole.

Services, which remain as the major component of GDP, include the Information Technology-Business Processing Organisations ( IT-BPO) with units which have been promising in terms of their growth. However, their contribution to jobs has  been rather marginal, as can be expected with the use of capital and skill intensive technology in those sectors. Growth in the services sector is concentrated in activities related to finance, real estate and business services (FINREBS) , shares of   which , both of the service sector and of the GDP, have escalated over time. Even more surprisingly, their share in GDP has continued to rise even with declining GDP growth rates. Much of the above growth can be related to the pace of financial deregulation which picked up over last few years. However, growth  of the FINREBS failed to contribute much in terms of employment or real activity. The remaining activities in the services sector including trade, transport and community services, while more labour-intensive, had a smaller role in the overall performance of the sector as a whole.

The disparate performance of the three major sectors of the Indian economy narrate a process of structural changes over recent years. The pattern, traced back  to the mid-seventies, shows tendencies for the contribution of the service sector to outstrip that of industry as well as agriculture, attaining a share which has been  50% or above of the GDP since late 1990s. Agriculture, which contributed more than 30% of GDP during the early years of the seventies, continues to provide only 20% or less in recent years.

Also read: Budget 2019 Is About Winning Over Hearts, Even if the Math Doesn’t Add Up

Not much has been forthcoming from the slow growing industrial sector as well, with its share  to GDP rising very modestly from a range between 10% to 15% after Independence, to a little above 20% in recent times.

While structural transformations of economies have  generally been associated with the Kuznets or the Lewis pattern of sectoral shifts in output and employment, none of the two models can interpret the specific pattern of structural changes in the Indian economy. The pattern  in India tells a story of structural change which is different in terms of the sequential changes in the economy, with agriculture giving way to industry and then the latter to services.

There is one major consequence of these structural changes, with agriculture as well as industry providing little relief in terms of gainful employment and the service sector with its nominal contribution to employment. It is India’s  large informal and unorganised sector, both in the rural and urban areas, which provides the destination for those who seek better jobs.

The pattern has resulted in a mass of underutilized and un-utilized labour force, facing a life ridden with poverty, the redressal of which lies beyond the use of palliatives and sops as tried in the current budget.

Sunanda Sen is former professor, Centre for Economic Studies and Planning, Jawaharlal Nehru University, New Delhi. She can be reached at sunanda.sen@gmail.com.

Govt Blinks on Fixing Procurement Infrastructure, Announces Price Support Schemes

The government’s announcements on Wednesday to ensure that farmers get the benefit of the MSP have done little to reinforce the procurement infrastructure which was a key recommendation of the Centre’s NITI Ayog.

New Delhi: On Wednesday, the Union cabinet announced a range of measures aimed at ensuring that farmers benefit from the minimum support prices (MSP) announced by the Centre. It announced a new umbrella scheme called the ‘Pradhan Mantri Annadata Aay Sanrakshan Abhiyan’ and abbreviated to PM-AASHA to be implemented with effect from the new kharif marketing season starting October. The new scheme will have three components: a price support scheme (PSS) that has existed for several years; a price deficiency payment scheme (PDPS) modelled on Bhawantar Bhugtan Yojana; and a private procurement and stockist scheme (PPPS). As much as Rs 15,053 crore has been allocated under this scheme.

“The PM-AASHA is aimed at ensuring remunerative prices to farmers for their produce as announced by the union budget for 2018. This is a historic decision,” said Radha Mohan Singh, minister for agriculture.

Under the PSS, Central nodal agencies will procure pulses, oilseeds and copra with ‘proactive role of state governments’. The Food corporation of India (FCI) and the National Agricultural Cooperative Marketing Federation of India (NAFED) will help implement the scheme. The cabinet also announced that the government will procure 25% of the marketable surplus of farmers for eligible crops. The Centre has made a provision of Rs 16,550 crores to be provided as bank guarantee for the agencies to procure from farmers.

Under the PDPS, the state will provide the difference between the prices prevailing in mandis and the MSP. All oil-seeds are to be covered under PDPS. This scheme is modelled on the Bhawantar Bhugtan Yojana that has been implemented by the Madhya Pradesh state government. There will be no physical procurement of crops.

In lieu of PSS and PDPS, in certain pilot districts the PPPS will be tried out. Empanelled private agencies will procure oilseeds in coordination with the government. The private agencies will be provided certain concessions. To begin with, the scheme will be tested in eight districts.

However, the Cabinet’s announcements did little to reinforce the procurement mechanism infrastructure in the country which largely only works for two crops – wheat and rice. According to a survey conducted by the National Sample Survey Office (NSSO) in the 70th round in 2013, only 6% of farmers are able to sell their produce at MSP.

A 2017 study by K.S. Aditya, S.P. Subhash, K.V. Praveen, M.L. Nithyashree, N. Bhuvana and Akriti Sharma found that only 24% households, at the most, were aware about the MSP of crops grown by them. “Although MSP is announced for the whole of India, the operation is limited only to few states where the designated government agencies procure the produce from farmers,” the study stated. “Except for crops like rice and wheat, quantity procured is very limited leading to low level of awareness,” it added.

According to a 2016 NITI Ayog evaluation report 79% farmers responded with ‘no’ when asked if they were satisfied with the MSP regime. Some of the reasons for their dissatisfaction were delay in payments, lack of infrastructure at procurement centres, distance to procurement centres and delayed announcement of MSP rates. NITI Aayog’s evaluation also found that there were several states where the procurement infrastructure facilities were ‘inadequate’.

The NITI Ayog recommended that steps be taken to improve “facilities at procurement centres, such as drying yards, weighing bridges, etc. should be provided to the farmers. More godowns should be set up and maintained properly for better storage and reduction of wastage. The procurement centres should be in the village itself to avoid transportation costs”.

However, the government’s announcements on Wednesday to ensure that farmers get the benefit of the MSP have done little to reinforce the procurement infrastructure which was a key recommendation of the Centre’s NITI Aayog.