
Numbers are tricky. They can illuminate as much as they can mislead; inform as much as they can disinform; and reveal hidden problems but also mask real issues. However, one common attribute is that they emit an aura of credibility – even when they are deployed to mislead audiences, distort findings, or conceal facts. Statistics, a field earlier confined to either the ivory towers of research institutions or the obfuscating corridors of governance, is now a common form of public communication. However, a key question is lost in this avalanche of data that is fed to the people: What is the extent of statistical literacy in India? In this Issue Brief, P.C. Mohanan, former Acting Chairman, National Statistical Commission (NSC), writes on the need to evaluate and enhance statistical literacy – a competence that is required for a knowledge society but has not attracted the attention of policy makers, enumerators, the academia, and pedagogues. Although this form of literacy is elusive to define and measure, the manner in which citizens emerge as active constituents of an informed society depends in good measure on their ability to grapple with the numerals that they encounter on a daily basis. The rising relevance of data journalism, and the widespread use of numbers as a tool to enhance public messaging, should be met by increasing the popular awareness of statistics and its nuances.This Issue Brief highlights the increased use of data in India’s public communications and emphasises the need to ensure that data-based statements are presented in a clear, correct, and unambiguous manner. Mohanan shines the spotlight on some common errors that distort results and emphasises the importance of accuracy of language, logic, and context in conveying statistical results. He highlights the role of data journalists in identifying lapses and correctly conveying the messages revealed by the numbers, as wider dissemination of statistical literacy will result in a better understanding of key issues and facilitate the emergence of a discerning citizenry. CONTENTS I. INTRODUCTION II. STATISTICAL LITERACY: THE EMERGING CONTEXT III. DEFINING AND MEASURING STATISTICAL LITERACY IV. THE IMPORTANCE OF STATISTICAL LITERACY AND SOME ASPECTS OF STATISTICAL CONTENT V. CONCLUSION I. INTRODUCTION Literacy is generally defined as the ability to read and write a simple sentence in any language with understanding. This can be tested by asking the respondent to write a dictated sentence to test writing ability, and to answer questions after reading a given paragraph to test reading and understanding. Although literacy skills can be graded on a scale, a simple binary of literate and not literate is what is used to measure the literacy levels of populations. Similarly, numeracy can be defined as the skill necessary to perform simple arithmetic computations using the basic operations of addition, subtraction, multiplication, and division. Simple arithmetic exercises can be devised to measure this ability which is sometimes also termed as arithmetic ability.Related articles from The Hindu Group:1. Latest Data Point News, Photos, Latest News Headlines about Data Point-The Hindu, The Hindu2. Desai, S. 2022. Beyond the statistical soundbites: why data matter, The Hindu, September 21.3. Data Card, Frontline.4. Data Stories, The Hindu BusinessLine.5. Rajalakshmi, T. K. 2023. Why is the government delaying Census 2021?, Frontline, January 26.These two skills are considered basic abilities not only for carrying out meaningful economic transactions required for modern living but are also considered important for economic and social development. Literacy rate is most often used to understand the stage of development of a country. However, numeracy is not generally assessed or measured on a regular basis essentially due to difficulties in measurement. Literacy can be probed by directly asking the concerned persons or proxy respondents or guessed from the person’s educational attainments. Numeracy, however, requires some extent of direct assessment of the respondent’s ability.There are also other kinds of literacy now evolving. Financial literacy is now considered an ability essential for economic decision making by individuals and households. This includes quantitative understanding of one’s income, expenditure, saving and similar concepts that would help individuals set financial goals and plan their savings and investments. It is easy to see that financial literacy goes beyond simple literacy and numeracy.Statistical literacy is a term of recent origin but is rapidly gaining currency in the context of data emerging as a key factor in policy making and public debates. In December 2014, then then United Nations Secretary-General, Ban Ki-moon, declared that “the world must acquire a new ‘data literacy”’. This new skill set was needed, he said, to meet the requirements of the UN’s Sustainable Development Goals, covering everything from poverty reduction to gender equality and economic growth. Becoming data literate would help equip the international community with “the tools, methodologies, capacities, and information necessary to shine a light on the challenges of responding to the new agenda”.1In common parlance the words information, data, and statistics are used interchangeably. The more specific contextual meanings are the nuanced understanding that while data consist of numbers collected for a purpose and having specific contextual meaning, information is processed data. The word ‘statistics’ means “the science of collecting, analysing and interpreting”2 data following statistical principles. Therefore, one can talk about data literacy, statistical literacy, and information literacy as separate skills. The starting point is data, which are processed to arrive at some summarised figure(s) using statistical concepts. The data and the derived figures are not stand-alone values but have several underlying dimensions attached to it which is now called meta-data. The meta-data add value and meaning to the numbers.The term statistical literacy is used to mean a correct understanding of statistical information emanating from data.This Issue Brief attempts to understand statistical literacy and discuss its different dimensions. Broadly the term statistical literacy is used to mean a correct understanding of statistical information emanating from data. The data could be on population, economy or societal issues or any other topics of current concern. Statistical information could be simple statements that have data content or references to data. This simplification is necessary as any efforts to further segregate the skills will lead to more complex measures impacting the utility of the measure. The level of complexity would also cloud any understanding of the basic skills required to understand statistical measures used in public debates. Unlike literacy or numeracy, the use of statistical literacy occupies a more restricted space. This space often revolves around issues of public interest. Such issues are most often found in the news media, debates or discussions, and other printed works such as propaganda or advertising material or datasheets. For obvious reasons, research based uses of statistics are outside the purview of this Issue Brief as statistical literacy is an inherent requirement. Finally, the discussion on statistical literacy relates to a domain that includes only those with verbal and numerical skills. Return to Contents II. STATISTICAL LITERACY: THE EMERGING CONTEXT Popular use of the internet as it is known today is over three decades old, although it was available much earlier to restricted groups. According to the World Bank3, by 2020, an estimated 60 per cent of individuals in the world use the internet. For this purpose internet users are individuals who have used the internet (from any location) in the last three months. The internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV, and similar devices. This was almost nil in 1990. It is now reported by Global Digital Review (2022) that a total of 5.07 billion people around the world use the internet — equivalent to 63.5 per cent of the world’s total population.4 The latest report of the Department of Telecommunications (DoT) notes that in India the number of internet subscribers (both broadband and narrowband put together), which was 776.45 million at the end of September 2020 has increased to 834.29 million by the end of September, 2021.5The emergence of social media and the widespread use of the internet has led to the creation of content beyond what could be conceived in the past. Clearly, a large part of such content is not under any governmental or editorial control. While in the past, carefully edited newspapers and journals provided information to the public, this now flows from a variety of sources with a substantial part of such content being unvetted.This flood of uncensored information can impact independent rational opinion making threaten social cohesion.With a drastic lowering of entry costs, access to sources of information has multiplied and the speed of its transmission has increased tremendously. This flood of uncensored information available to individuals round the clock has the potential to impact independent rational opinion making and can become a threat to social cohesion. These could take the shape of false claims, wrong interpretation of data in order to advance specific viewpoints, or other similar misuses, which have now become common recurrences.The information deluge and its universal accessibility multiply the possibility of widespread misinterpretation of data. This is something all too familiar now. In sharp contrast to the past when the speed at which information spread was slow and restricted, it is now instant and uncontrolled.Statisticians are trained to present data in the form of tables, graphs and other forms of visualisation, after ensuring that all relevant information are correctly depicted. However, the public use of such information is to see the story behind the numbers or graphs. This is what appears as headlines in the media. In this, apart from the numbers, the use of language plays a major part. A large part of statistical literacy will have something to do with the use of correct vocabulary while explaining or presenting statistical information.Then there are clever ways of presenting charts and graphs so as to plant a particular picture in the minds of the viewer. This is also a common abuse of statistics. There is also the intent behind presentation of facts. It can be to present or explain a situation better, to defend or oppose an argument, or, in the worst-case scenario, to deliberately mislead.The flood of data-based information through different forms of media clearly points to the need to promote statistical literacy among the public. A sustained attempt to spread the concept of statistical literacy would also bring in better analysis and presentation of data by the media and factual appreciation of the issues.One of the functions assigned to the National Statistical Commission, when it was set up in 2005, was the promotion of public trust in official statistics. This indirectly involves enabling statistical agencies to draw correct conclusions from official data, without which public trust cannot be gained and restored. While the official data themselves are presented in a neutral manner, most often these are used in a way that pays scant attention to data limitations. For example, poverty estimates in the country are usually estimated from the household consumption expenditure surveys, which have a very detailed item list for collecting expenditure data. The last such survey was conducted by the National Statistical Service Organisation (NSSO) in 2011-12.However, the NSSO also records household consumption expenditure in a highly aggregated form from households in other surveys based on four or five questions. The basic purpose of such data is to rank households according to the level of expenditure. However, researchers have also used such data to derive poverty estimates disregarding the limitations of collecting expenditure data with a few questions versus the detailed questionnaire in the usual consumer expenditure surveys. Advanced analytical capabilities, now easily accessible and widely taught, are sometimes applied to datasets that are not originally intended for such analysis reminding one of the dictum: ‘if you torture data enough it will say anything’. Return to Contents III. DEFINING AND MEASURING STATISTICAL LITERACY A direct measure of statistical literacy would require an unambiguous idea of what constitutes ‘statistical literacy in terms of some ability’ as a starting point. The simple ability to read and write or perform arithmetic operations can be tested or assumed on the basis of educational achievements or qualifications as there is a clear idea of what it means to possess this ability. Statistical literacy combines ordinary literacy, numeracy, and reasoning in some form or the other.One can, therefore, consider statistical literacy as the ability to comprehend and communicate data-based information. This pre-supposes a somewhat more advanced level of literacy and numeracy. A data-literate person would be expected to understand and apply basic statistical principles, grasp the limitations in data-based statements, and reach conclusions. The W. M. Keck Statistical Literacy Project defines statistical literacy as critical thinking about numbers, about statistics used in arguments, including the ability to read and interpret numbers in statements, surveys, tables, and graphs; and study how statistical associations are used as evidence for causal connections6. Statistical literacy as explained in the works of Prof. Milo Schield qualifies knowledge of statistics as different from the approach to statistical literacy by using the conventional cautionary phrase: ‘Take CARE’. Each of the four letters in CARE stand for a kind of influence on the size of a statistic:• Context (comparisons, ratios, study design and confounding), • Assembly (how statistics are defined and presented), • Randomness (chance, margin of error and statistical significance) and • Error or bias.The development of statistical literacy is built on the first of the two influences. His book has substantial sections on explaining commonly used statistical statements and interpretations that can take forward the meaningful and correct understanding of the underlying statistical facts.A better understanding of statistical reasoning can contribute to statistical literacy and efforts to do that would advance statistical literacy. This would require some tweaking of the ways in which statistical education is imparted. This is one aspect of the issue. One way to assess the level of statistical literacy would then be developing some tests and administering them on the target population. This can at best be done in a classroom set up or in small groups.There are currently no indications of such surveys having been conducted anywhere.An alternative approach to study statistical literacy or general interest in statistics-based information would be through direct surveys of individuals asking them if they understand statistical measures like average and know about some ordinary and publicly available statistical indicators such as inflation, GDP, population, and other important macro indicators. There are currently no indications of such surveys having been conducted anywhere.The question of defining a standard measure of statistical literacy, therefore, hinges on a functional definition of the term without which comparable measures cannot be developed. This, as seen from the above discussions, is not readily possible. Statistics is often used to support a viewpoint and the level of statistics used would depend on the context and the settings.As an indirect understanding of the level of statistical literacy, one can think of public engagements with data-related issues in the media. An approximate way of understanding this could be through the news media by looking at the reporting of data-based news.One effort to define and measure statistical literacy is the project undertaken by PARIS 21 as a follow up of the Busan Action Plan for Statistics.7 The statistical literacy indicator attempted here measures the use of and critical engagement with statistics in national newspapers. The target population are journalists and newspaper readers. The source materials used are the RSS [Really Simple Syndication] feeds of national newspapers, primarily based on the global news aggregator Google News. The indicator used by them is a three-dimensional composite indicator of the equally weighted percentages of national newspaper articles that contain references to statistics at three different levels. The first level is the consistent, non-critical use of statistics based on keywords in the articles that refer to statistical data sources such as census, surveys or statistical indicators like CPI, GDP or any reference to statistical projects or institutions. Levels 2 and 3 are critical engagement with statistics, and critical mathematical engagement with statistics.To derive the indicator, the project analysed a total of 8,880 articles during a three-month period in 2016 for the use of statistics in general news (Level 1). This corresponds to an average of 261 articles per country. For Levels 2 and 3, a total of 3,067 articles with explicit references to specific words such as ‘statistics’, ‘data’, ‘study’, ‘research’ or ‘report’ were analysed. For each of the three levels of statistical literacy, the resulting score gives the percentage of articles that contain at least one search term from the keyword lists. The score for each level thus ranges between 0 and 100 and the maximum total score over all three levels is 300. The results place Mexico and the UK jointly at the top slot, while the Philippines is ranked third for the Anglophone developing countries. The explanations for these somewhat unexpected results is possibly due to three reasons.Firstly, two statistical institutes — el Instituto Nacional de Estadística, Geografía e Informática (INEGI) [the National Institute of Statistics, Geography and Informatics] in Mexico and the Philippine Statistics Authority (PSA) in the Philippines — are very engaged in monitoring the use of statistics by journalists. The INEGI reports the impact and value of statistics based on daily monitoring of newspapers and media resources, and the PSA tracks references to their institutions via Google news subscriptions, and engages with the media.A second explanation is the differences in the nature of the audience of the main newspapers by country. In such instances, a good degree of ‘scurrilous coverage’ may explain a lower score. Finally, but importantly from the perspective of this Issue Brief, in many of the developing countries newspapers use press releases from statistical/governmental agencies verbatim, without making them digestible for a general audience through simple and meaningful explanations. This points to a weakness in this indicator, in that it rewards top level keywords related to the critical mathematical category but good journalism should actually avoid verbatim reproduction. The detailed findings can be seen in the source quoted above.8Newspapers are a very good source to understand statistical awareness or interest in society.Newspapers are a very good source to understand statistical awareness or interest in society. Despite variations in reach and readership of newspapers in societies, readership is a function of the levels of the overall literacy in the society. For example, as per data published by the Registrar of Newspapers in India (RNI)9, the number of Dailies published in India as on March 31, 2021 was 9,750. The claimed circulation of Dailies was 22.6 crores copies per publishing day for a population of around 135 crores. Hindi had 4,349 Dailies, claiming a circulation of 10.4 crore copies, while 1,107 Urdu dailies, 1,083 Telugu dailies, and 820 English dailies claimed circulation figures of 2.2, 1.5 and 2.1 crore copies per publishing day respectively. Out of the total 22,930 periodicals, 19,608 mainly covered News and Current Affairs.A careful analysis of the newspaper feeds can be used to tabulate data related news reports and these newspaper feeds can give an idea of the general interest in data-based news/topics. These would depict one aspect of the statistical literacy. Although it can be indicative of the public interest in data related news, it may not be very helpful in arriving at the actual readership and reader interest in data related issues. Often, newspapers merely republish the government releases, most of which contain statistical information or conclusions. This is noted by the authors of the PARIS 21 study as a weakness of using newspaper feeds to study statistical literacy.Broadly speaking, defining and then measuring statistical literacy is multi-layered. While one can roughly gauge public engagement with data through their presence in the media, the depth of this engagement cannot be easily quantified. The efforts should, therefore, be focussed on ensuring that such engagement provides unbiased conclusions from the underlying data.The next section discusses some examples on how statistical information could present a biased picture either by economising or twisting facts to support a specific argument or sometimes just to make the resulting story interesting. Return to Contents IV. THE IMPORTANCE OF STATISTICAL LITERACY AND SOME ASPECTS OF STATISTICAL CONTENT News media are indeed the main source for public engagement with statistical information. Usually, there is little external check on what information is presented by newspapers or how it is portrayed, and correctly so, although it is expected that they do conform to ethical principles while publishing, keeping in mind the legal consequences of spreading inaccuracies or falsehoods. That said, nothing stops them from presenting publicly available data and information in a way that projects a particular view or leads the reader to a particular conclusion, sometimes statistically untenable.The growing public interest in data-based issues is evident from major newspapers now publishing specialised sections providing data insights by qualified data journalists. These are usually meant for experts or those who have special interest in the relevant fields and not generally for ordinary readers. In recent years, there has been an increasing interest in data-based reports. During the recent COVID-19 crisis, large sections of the public keenly followed the infection related data as it impacted their livelihood directly. On the flip side, in the process of creating exciting headlines, the media quite often give misleading interpretations to data. Usually, they are not expected to critically examine the data collection and aggregation methodology or the manner of administering the relevant questions in the case of survey data.This heightened public interest in data is persistent and needs to be studied to understand the depth of public involvement with statistical information and possible ways of improper use. This will lead to developing appropriate dissemination/communication strategies at different levels. Correct appreciation of statistical information would require the introduction of statistical literacy beyond the usual statistics theory taught at the school level. One might say that such modules should become part of journalism courses.In a paper presented in the World Statistical Congress10 titled Statistical Literacy for Policy Makers, Milo Schield proposed seven simple questions while presenting and understanding statistical data: How big? Compared to what? Why not rates? Per what? Defined, counted or measured how? What was controlled for? What should have been controlled for? Although these questions are fundamental in understanding and explaining data, statistical theory classes do not engage their students on such issues.Reporting findings of surveysSurvey results are fertile grounds for making headlines. Understanding any statistics requires some knowledge of the process of generating these numbers. This is more so in the case of sample surveys where reliability of the results is highly dependent on the correct application of statistical theory. In particular, survey estimates have sampling and non-sampling errors. Most often, while reporting the findings, people tend to forget this and quote the numbers with an air of unquestionable authority. For example, we see how public debate hinges on small changes in the unemployment rates derived from labour force surveys, conveniently forgetting the error margins of the estimates. Changes can even follow from simple rounding off while authoring the report.There are other questions about survey estimates that are important. Some examples include possible exclusion of a section of the population in the survey coverage, definitional, and conceptual changes. For the sake of brevity, this Issue Brief does not go into the details.Understanding the context of questionsUsers of survey data are generally advised to carefully study the survey questionnaire and the instructions for the field data collectors or other material used during the survey operations — what is now called the meta-data of the survey. All such information are expected to be part of the survey catalogue or archive and disseminated along with the survey data. Understanding this is often a pre-requisite for researchers before analysing the survey data. Quite often media and the public use the information from the published survey reports where all these meta-data are not usually provided. There are many examples of survey responses getting influenced by the way the questions are framed or the response options managed by the survey designers. A little more on this.Usually in surveys, questions are either open-ended, where respondents provide a response in their own words, or are closed-ended, where they choose from a list of choices provided by the individual/organisation conducting the survey. The Pew Research Center, the internationally known survey agency, in their methodological note on survey questionnaire design, gives some examples. In a poll11 conducted after the U.S. 2008 Presidential election, people responded very differently to two versions of the same question: “What one issue mattered most to you in deciding how you voted for President?” One was closed-ended and the other open-ended. In the closed-ended version, respondents were provided five options and could volunteer an option not on the list.When explicitly offered ‘the economy’ as a response, more than half of respondents (58 per cent) chose this answer; only 35 per cent of those who responded to the open-ended version mentioned ‘the economy’. Moreover, among those asked the closed-ended version, fewer than one-in-ten (8 per cent) provided a response other than the five they were read. In sharp contrast, as high as 43 per cent of those asked the open-ended version provided a response that was not listed in the closed-ended version of the question. All of the other issues were chosen at least slightly more often when explicitly offered in the closed-ended version than in the open-ended version. There are other similar examples in the referred link.12The National Family Health Survey (NFHS) is one of the most relied upon official sources for data on a wide range of health and behavioural information of the population in India. Domestic violence, for instance, is a topic covered by the NFHS and usually not covered in other national surveys. Based on one of the questions in the latest NFHS survey, newspaper headlines gave the startling finding that wife-beating was justified by a significant number of wives/husbands. The actual question was: “In your opinion, is a husband justified in hitting or beating his wife in the following situations:”. Seven circumstances were then listed. Each of the seven situations in the closed-ended question had three possible responses: “Yes”, “No”, “Do not know”. The findings were headlines in the media, with most news reports reproducing the findings more or less verbatim as given in the survey report.The news reports suggested that both men and women had almost identical opinions on the issue. The survey did not explicitly ask the husband/wife if they justified husbands beating wives nor whether they ever beat or received beatings. This would have provided an unconditional opinion on husbands beating wives or on the actual incidence of wife-beating. However, in this context, the responses are only for the given situations, which could have resulted in the almost identical reporting by men and women. Although these responses pertain to the ‘justification’ in the opinion of the respondents; the results presented could confuse the readers giving them the wrong impression that the numbers reflect the actual state of violence against wives. The other side of the argument could be that these are sensitive questions unlikely to elicit truthful responses. The following is a news report based on the survey:Forty-five per cent of women and 44 per cent of men believe that a husband is justified in beating his wife in at least one of seven specified circumstances. Women and men are both most likely to agree that a husband is justified in hitting or beating his wife if she shows disrespect for her in-laws (32% and 31%, respectively), and are both least likely to agree that a husband is justified in hitting or beating his wife if she refuses to have sex with him (11% and 10%, respectively). For both women and men, agreement with wife-beating is lower in urban than rural areas and it tends to decrease with schooling and wealth.Sources:Sengar, S. 2021. An Alarming Number Of Women Justify Getting Beaten By Husbands In The Country, Indiatimes.com, November 29. [https://www.indiatimes.com/news/india/indian-women-justify-getting-beaten-by-husbands-555421.html].Sengar, S. 2022. 45% Of Women Justify Husband Beating Wife If She Argues, Doesn’t Cook Properly Or Refuses Sex, Indiatimes.com, May 10. [https://www.indiatimes.com/news/india/45-of-women-justify-husband-beating-wife-if-she-argues-national-family-health-survey-569175.html]. Opinion polls, therefore, require carefully constructed questions and are poor substitutes for providing statistics depicting actual reality.Comparing absolute numbersThe RNI report quoted earlier states that, “Even among dailies also, Uttar Pradesh (U.P.), with a total circulation of 3,83,98,144 copies per publishing day retained its top position and was followed by Maharashtra with 3,10,70,720 copies per publishing day ...”. As U.P. and Maharashtra are two of the top States in terms of population, this statement is not anything unexpected. This is a case of comparing absolute numbers that are not comparable without some normalisation.Index-based ranking and ratingNowadays a lot is heard about different kinds of indices to summarise multi-dimensional issues. These catch the public attention very quickly and are used in debates as they show advancement or regression of outcomes. One familiar index is the Human Development Index (HDI), which was constructed after very careful practical and technical considerations as“a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalised indices for each of the three dimensions.”13There are many other indices released nowadays of which the World Hunger Index was recently in the news essentially because, among other things, it showed India in a very bad light. The fact that the index has severe technical limitations to depict hunger was then brought to the fore. It is anyone’s guess whether the index would have been acceptable by its critics, as it is, if it had shown India in a very bright position. Another case in point is the Ease of Doing Business Index that the World Bank used to compile. This was one highly complimented and oft-quoted index as the country’s position showed significant improvements over the years. However, the World Bank14 subsequently discontinued the index after internal investigations reported data irregularities, although these ‘irregularities’ did not pertain to India.Preparing a multi criteria based index has advantages in comparing rankings of States or countries or time periods, but the basis for selection of variables and the weights assigned for combining the variables are not usually highlighted.What is the denominator or base?Shoppers are all too familiar with advertisement like ‘SALE: 50% off’. Nothing would be said further like 50 per cent of what price or for what items, if it is a flat 50 per cent or “up to 50 per cent”, and so on. Clearly these are attention grabbers and not meant to be taken seriously. At times there are claims that a party’s vote share has increased by 50 per cent. This could be an increase from 10 per cent to 15 per cent or 2 per cent to 3 per cent.A leading claim in the newspapers from the CEO of the eyewear company, Lenskart15 was, “In the next five years, we aspire to have 50 per cent of India wearing our specs.” He further elaborated,“At Lenskart, we are obsessed with our customers, technology, and making the world a better place through easily accessible, best-quality eyewear. More than 600 million people in India and 4.5 billion people globally need vision correction, but only a fraction of them use it due to a lack of access, awareness, and high-quality, affordable solutions.”This, of course, sounds to be a very laudable objective; but from where do these numbers come or is it just business optimism?A Government of India press release on December 15, 2022,16 quoting a reply given to a question in Parliament on the Jal Jeevan Mission programme for providing tap water to every rural household, states:“At the time of announcement of Jal Jeevan Mission, 3.23 Crore (17%) households were reported to have tap water connections. So far, around 7.48 Crore (38%) rural households have been provided with tap water connections in last 3 years. Thus, as on 12.12.2022, out of 19.36 Crore rural households in the country, around 10.71 Crore (55%) households are reported to have tap water supply in their homes”.It then quoted the findings of an assessment survey,“Department of Drinking Water & Sanitation undertakes annual assessment of the functionality of household tap water connections provided under the Mission, through an independent third-party agency, based on standard statistical sampling. During the functionality assessment 2021-22, it was found that 86% of households had working tap connections. Out of these, 85% were getting water in adequate quantity, 80% were getting water regularly as per the schedule of water supply for their piped water supply scheme, and 87% of households were receiving water as per the prescribed water quality standards.”Parliamentary replies are carefully vetted at many levels of the bureaucracy. These two paragraphs look somewhat contradictory in terms of the numbers given. Possibly the survey findings refer only to the connections provided under the scheme and not to the whole of rural households. Can someone using only the second part of the reply be faulted?In a report on Breast Cancer related issues, The Hindu17 quoted an ICMR report which said, “100.5 out of 1,00,000 women were being diagnosed with breast cancer. From the approximately 1,82,000 cases of breast cancer at present, the report has projected cases to rise to 2,50,000 by 2030.”Most probably the number 100.5 refers to those getting screened and not to the whole population, in which case the expected number should be around 6.5 lakhs for a female population of roughly 65 crores in the country now. The use of correct syntax while using per cent and percentages generally receive less attention than it deserves.Question of causalityIn statistics classes, students are always reminded that correlation does not imply causality. In many cases there are claims of achievement directly linked to some new government initiatives. These statements, however, do not provide any information about what would have been the achievement without these initiatives in place; possibly the new initiative has brought only a marginal improvement.Use of language is an important part of statistical explanation either in statistical tables or statements. Statistics education revolves around textbook descriptions using statistical terminology. This does not provide scope for specialising in proper communications of statistical results. At the same time, the use of statistics in the media has to be in a way that captures the reader’s attention. The media, therefore, cannot be faulted if they move away from drab presentation of facts that will not engage the audience. Most Government statistical reports come with paragraphs that are just table reading and escape being caught with incorrect conclusions from wrong syntax. On the other extreme are eye-catching headlines from journalists that are far removed from what the data actually says. Return to Contents V. CONCLUSION The idea of statistical literacy has acquired some importance in recent times. The widespread use of official statistics to show how a government is doing better than the previous one is now an all-too-familiar tool of propaganda.Developing statistical literacy is crucial for healthy public debates in a knowledge driven society.However, a clear cut definition of statistical literacy is still elusive, and measuring this important metric of a knowledge society at the individual level remains problematic. That said, the existing literature on the topic provides the following conclusion: the extent of citizens’ engagement with data needs to be understood to improve the usage of statistics, particularly official statistics, to convey information. Needless to emphasise, developing statistical literacy is crucial for healthy public debates in a knowledge driven society. It is, therefore, an urgent requirement to improve public trust in data, especially official data.Currently, the starting point for building statistical literacy rests with data users, in particular with those who present data-based arguments in media, in public forums, and in official statements. The need to start with data-users is because the subject of statistical literacy is not adequately addressed in basic statistics courses taught in institutions. This would involve understanding the proper grammar of the language while making data-based statements. This Issue Brief highlights the increased use of data in India’s public communications and emphasises the need to ensure that data-based statements are presented in a clear, correct, and unambiguous manner. A wider discussion on the correct use of data in statements is beyond its scope, except to the extent of citing a few examples.Although the case of creating awareness about statistical literacy is very strong, measuring it can only be attempted indirectly at present. Currently, data journalists have a major role to play in the correct use of data and to point out statistically invalid claims in public debates and media. The role of the data journalist gains centre-stage as official statisticians will naturally be constrained in going public on any possible improper usage and interpretation of government data by data users.Finally, this Issue Brief is exploratory in its scope, and is meant to encourage wider discourse on the topic. The examples given are only indicative and more serious examples of incorrect usages of data or misleading statements based on data can be cited by the readers. Return to Contents Also by the Author Policy Watch No. 16: Credible Data for the Public Good: Constraints, Challenges, and the Way Ahead, October 7, 2022. [P.C. Mohanan is former Acting Chairman, National Statistical Commission (NSC). He was earlier a Member of the NSC from June 2017, and the Acting Chairman of the Commission from October 2018 until his resignation from the position in January 2019. He entered the Indian Statistical Service ranked Second in the 1979 batch and worked in both the NSSO and the CSO until his retirement in 2015. He has been a member of important technical committees that have addressed issues in India’s socio-economic sectors and has held international consultancy assignments in the Asian Development Bank, UNDP, Food and Agricultural Organisation, and International Labour Organisation. He is currently Chairman of the Kerala State Statistical Commission. He can be contacted at [email protected]]. Endnotes: 1. United Nations. 2014. The Road to Dignity by 2030: Ending Poverty, Transforming All Lives and Protecting the Planet - Synthesis Report of the Secretary-General On the Post-2015 Agenda, New York, December, p. 38. [https://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf]. Return To text.2. OECD Statistics Portal. [Online]. Glossary of Statistical Terms. [https://stats.oecd.org/glossary/detail.asp?ID=3847]. Return to Text. 3. The World Bank. [Online] Individuals using the Internet (% of population). [https://data.worldbank.org/indicator/IT.NET.USER.ZS?end=2020&start=2020&view=chart]. Return to Text. 4. Datareportal. 2022. Digital 2022: October Global Statshot Report, October 20. [https://datareportal.com/global-digital-overview#:~:text=A%20total%20of%205.07%20billion,12%20months%20to%20October%202022]. Return to Text. 5. Department of Telecommunications. 2022. Annual Report. 2021-22, Ministry of Communications, Government of India, New Delhi. [https://dot.gov.in/sites/default/files/Final%20Eng%20AR%20Min%20of%20Tele%20for%20Net%2009-02-22.pdf]. Return to Text. 6. Schield, M. 2009. Statistical Literacy Text book, W. M. Keck Statistical Literacy Project, Augsburg College. [http://www.statlit.org/Schield.htm]. Return to Text. 7. Klein, T, Galdin, A, and Mohamedou, E.I. 2016. An Indicator for Statistical Literacy Based on National Newspaper Archives, Partnership in Statistics for Development in the 21st Century (PARIS21), France. [https://iase-web.org/documents/papers/rt2016/Klein.pdf]. Return to Text. 8. Ibid. Return to Text. 9. Registrar of Newspaper for India. [n.d] Press in India – 2020-21 65th Annual Report – Volume-I, Ministry of Information and Broadcasting, Government of India. (Vide: Chapter 5) [http://rni.nic.in/all_page/pin202021.html]. Return to Text. 10. Schield, M. 2021. Statistical Literacy for Policy Makers, Proceedings - 63rd ISI World Statistics Congress, 11 - 16 July 2021[Virtual]. [https://www.isi-web.org/files/docs/papers-and-abstracts/225-day5-ips087-statistical-literacy-for-polic.pdf]. Return to Text. 11. Pew Research Center [Online]. Writing Survey Questions, Washington. [https://www.pewresearch.org/our-methods/u-s-surveys/writing-survey-questions/]. Return to Text.12. Ibid. Return to Text.13. United Nations Development Programme [UNDP]. [Online]. Human Development Reports: Human Development Index [https://hdr.undp.org/data-center/human-development-index]. Return to Text.14. The World Bank. 2021. World Bank Group to discontinue Doing Business Report, September 16. [https://www.worldbank.org/en/news/statement/2021/09/16/world-bank-group-to-discontinue-doing-business-report]. Return to Text.15. Gupta, V. 2021. “In the next five years, we aspire to have 50 pc of India wearing our specs,” says Lenskart, IndianRetailer.com, May 17. [https://www.indianretailer.com/news/lenskart-to-enhance-digital-offerings-for-customers-omnichannel-experience.n10820]. Return to Text.16. Ministry of Jal Sakthi. 2022. Impact of Jal Jeevan Mission, Press Information Bureau, Government of India. December 15. [https://pib.gov.in/PressReleasePage.aspx?PRID=1883851]. Return to Text.17. Koshy, J. 2022. Government says breast cancer not a matter of ‘national’ or ‘extreme’ urgency, The Hindu, December 18. [https://www.thehindu.com/news/national/breast-cancer-not-a-health-emergency-govt/article66272052.ece]. Return to Text.

Dr. C. Rangarajan (right), the Chairman of the National Statistical Commission, presenting the Report of the Commission to Prime Minister Atal Bihari Vajpayee, on September 5, 2001. The Commission had recommended far-reaching restructuring of the country’s statistical system, which remain elusive. File Photo: Kamal Narang / The Hindu BusinessLineBits and bytes of information propel today’s knowledge society. This data revolution is as transformational as it is multi-dimensional. India, however, remains a laggard and is yet to harness the full potential of data for the public good. In this Policy Watch, P. C. Mohanan, former Acting Chairman, National Statistical Commission (NSC), takes the reader through the data collection, analysis and dissemination process in India. In particular, he points out the deficiencies in the institutional, implementational, and procedural elements of the country’s official statistics machinery. For a country endowed with a multiplicity of resources that are matched by the problems that confront it, the scientific use of data to address peoples’ issues has often been subverted for either political reasons or because of the inability of the structures that are in place to deliver timely and credible data for decision-makers.As the rest of the world races ahead by adapting newer technologies and creating independent bodies that ensure credibility of data, India appears to not only stagnate, but regress as well. The way out, Mohanan says, is to harness the available technologies in a meaningful manner, improve statistical literacy, and insulate the statistical system from political vested interests. Keywords: National Statistical Commission, Census of India, Rangarajan Committee, Statistical Literacy, Ministry of Statistics and Programme Implementation, India Statistics, Data Governance Policy. CONTENTS I. INTRODUCTION VI. DATA AS A POLITICAL TOOL II. THE DEVELOPING FRAMEWORK OF OFFICIAL DATA VII. THE FEDERAL DISCONNECT III. EVOLUTION OF OFFICIAL STATISTICS IN INDIA VIII. INSTITUTIONAL INADEQUACIES: CAUSES AND IMPLICATIONS IV. GROWING CONCERN OVER DATA INADEQUACIES IN INDEPENDENT INDIA IX. WAY FORWARD V. REVIVING INTEREST IN OFFICIAL DATA I. INTRODUCTION “Statistics is a gateway to knowledge and the progress a country makes is vitally dependent on the efficiency of its statistical system.” C. Radhakrishna Rao (Statistics, Statisticians and Public Policy Making, 1983)1Any observer of independent India’s development process cannot ignore the role played by statistics in the country’s development process. The centrality of empiricism in planning and policy making has been highlighted by many eminent international commentators who were privy to these developments and are now being rediscovered through careful research using archival materials2. What is striking in these narratives is the close interlinking of statistical theory and practice and the official patronage it received, leading to the establishment of outstanding institutions and mechanisms for generation, analysis, and dissemination of official data. Related Resources: 1. Ministry of Statistics and Programme Implementation. 2001. Report of the National Statistical Commission, Government of India, September 5.2. International Bank for Reconstruction and Development / The World Bank. 2021.World Development Report 2021 – Data for Better Lives, World Bank Group. Washington DC. Seventy five years of independence, now marked by national celebrations, is also an appropriate timescale to critically assess the state of India’s official statistics. The digital revolution and the all-pervading use of ‘data science’ substantially impact discussions comparing the past with the present. This vastly expanded scope of data-centric discussions, therefore, needs to be somewhat pruned to highlight specific issues plaguing official data systems. This Policy Watch aims to critically examine the current state of official statistics and the mechanism that produces it. The developments in Statistics as a science, where the contribution of Indian Statisticians continue to be of a very high order, is beyond the scope of this article.This Policy Watch first takes note of the key developments relating to data over the past decade which saw transformative advances in Information Technology. It then briefly recalls the evolution of the statistical system before and after independence especially in the light of the political structure that India gave itself through its constitution. The changing role of the Union and the States becomes critical in this political framework, which has an impact on the administrative agencies involved in data collection. It, thereafter, outlines the slow decline in the outcomes that were expected from the initial developments and the failure of the statistical system to meet the growing demand for data from within and outside the government. The next two chapters provide the current state of affairs, including some recent debates on data, and some prescriptions. Finally, another important context is the sudden interest in world of data, in particular the sharing of data collected both as part of official statistics and through the nearly ubiquitous digital platforms.While the former is still confined to traditional modes of collection, the latter are largely ‘harvested’ from individuals when they use digital platforms to purchase and pay for goods and services.In addition, the digital footprints left by visitors to the internet constitute a large body of data that are already used by private sector to increase their markets and capture new ones.Against this backdrop, the Policy Watch also looks at the proposed Draft Data Governance Framework.Return to ContentsII. THE DEVELOPING FRAMEWORK OF OFFICIAL DATA “Indian Official Statistical System is wrapped in the cobweb of time, which needs thorough revamping. The silos as legacy systems of the past are not only not efficient but have often created hurdles on absorption of technology to modernize the systems and processes which are needed to maintain high quality, consistency, coherence and timeliness of collected data. It is necessary to support decision making at all levels of governance and also inculcate a collaborative approach giving a better problem solving ability on credible and responsive public policy. The stake holders, that is, enterprises, people in general, civil societies and international institutions also need credible data as public good. …[T]here is a pressing need for … strengthening of state statistical systems”.National Statistical Commission Annual Report, 2017-18(p.57-58, para.6.10)When the world of knowledge is in the thick of a data revolution, it would only be appropriate to first present an update on “what is happening” in the use of statistics for social and economic development. The awareness that official statistics has a role that goes beyond public administration gained currency with the latest phase of globalisation. Technological advancements in the field of information processing and dissemination accelerated the demand for data from beyond ‘official’ boundaries, creating newer dynamics between data owners, creators, and users. The UN General Assembly, on January 29, 2014, endorsed the Fundamental Principles of Official Statistics which were adopted by the UN Economic and Social Council on July 24, 2013. The first of these 10 principles, which defines the relationships between data, society, and official agencies, states:“statistics provide an indispensable element in the information system of a democratic society, serving the Government, the economy and the public with data about the economic, demographic, social and environmental situation. To this end, official statistics that meet the test of practical utility are to be compiled and made available on an impartial basis by official statistical agencies to honour citizens’ entitlement to public information”.3National statistical agencies in most countries have adopted the UN’s Fundamental Principles of official statistics as the guiding principles in articulating their mission. In 2016, the Government of India also adopted these principles. It claimed that doing so will bring professional independence, impartiality, accountability, and transparency in collection, compilation, and dissemination of official statistics in accordance with international standards. This, however, has been easier said than implemented, owing to a multiplicity of factors.The World Development Report (WDR)4 2021, titled Data for Better Lives5, highlights the importance of the Enlightenment era of the 18th century for a major transition in the use of data: from using it merely to collect revenue, organise the military, and monitor employment to further illuminate political and popular understanding of societies. It goes on to provide examples of the use of data to understand the spread of poverty (by the British Sociologist, Rowntree) and the incidence of diseases (the Yellow Fever outbreak in New York city at the end of the 18th century, and the 1854 cholera outbreak in London).More importantly, WDR 2021 also makes the difference between data collected for “commercial purposes”, which it calls “private intent data” and those for “public purposes, called public intent data”.6 This distinction gains importance with the widespread use of digital technologies and the reach of social media for various transactions. The collection of data harvested through such new modes has opened up many interesting prospects and raised important questions. One such issue concerns the control of personal data. This explosion in the use of personal data for planning, decision making, and increasing markets is propelled by the rapid expansion in technologies in mobile telephony, data transmission, and the usage of spectrum (owned by sovereign states) for telecommunication services (provided by a few entities) to harness data from a large base of (millions of) individual subscribers.The once-in-a-century COVID-19 pandemic, which led to lockdowns of varying degrees, also saw the increased utilisation of telecommunications to gather personal data through smartphones or by tracking the location of individuals using call detail records (CDRs). Although data protection laws date back to the 1970s7, the pandemic, which touched every part of the world, its inhabitants and governments, further emphasised the need for data protection. This is because the unprecedented use of data (as was evident during COVID-19) straddles two important areas: the responsibilities of a state towards its citizens and the right of citizens to individual privacy.In the early days of the pandemic, experts rang out a warning on the “responsible” use of data as “[f]ailing to do so will undermine public trust, which will make people less likely to follow public-health advice or recommendations and more likely to have poorer health outcomes”. 8This is one clear and present example of the need for governments to win the confidence of the public in the use of data for overall development. The argument for treating official data as a public good is of recent origin. This comes from the nature of official data and its increasing use by different agents in the society.This concept of public good is not intrinsic to the data itself. Data are unlike other physical goods whose public provision is seen or sought by all sections of society. Data take different forms, meanings, and uses to different people and cannot be free from the context in which they are utilised. The pandemic and the manner in which governments used personal data to track and control its spread prompted the World Health Organization (WHO) to consider health data as “a public good.”9Although the WHO considers health data as a “public good”, it is also important to factor in the caution expressed by the WDR-2021 that “data are not a pure public good” as they are “excludable”. There are “examples across the public sector of the unwillingness of data holders to share data with other government entities and the public”. Similarly, in the “private sector, firms may not want to sell or exchange their data with others” for reasons relating to either capacity constraints, security issues in sharing data, lack of incentives, or legal constraints.10A new social contractThis recognition and utilisation of individual data by governments requires a new understanding between citizens and states. This was reflected in the WDR 2021, when it called for“a new social contract for data—one that enables the use and reuse of data to create economic and social value, promotes equitable opportunities to benefit from data, and fosters citizens’ trust that they will not be harmed by misuse of the data they provide. A well-designed data governance framework allows countries to capture the full economic and social value of both public intent and private intent data and leverage synergies between them. This involves creating trust in the integrity of the data systems, while ensuring that the benefits of data are equitably shared.”11In India, the push towards mainstreaming data for development was expressed in the Economic Survey presented in Parliament in 2019, which devoted a chapter titled ‘Data “Of the People, Data By the People and Data For the People”’.12 Large quantities of data are furnished “by the people” as part of enrolment and participation in beneficiary schemes or while availing public services like health, education etc. These are, to use terminology from the Economic Survey, 2018-19, “data of the people” collected by the government, most often with a specific intent known to the respondents. In practice, however, most of these data gathering processes lack explicit voluntary consent of the respondents.The data ‘by the people’ originate from the day-to-day transactions or interactions of people with other agencies as is the case of ‘digital footprints’ left behind after such transactions. The digital nature of these transactions using standard identifiers such as PAN, Aadhaar, mobile number, email and social media identity makes the collection, scrutiny and processing of such data easier than other data-gathering mechanisms. However, all these forms invariably come with the “lack of explicit consent”.It would appear that the old paradigms such as the use of data as inputs in evidence-based policymaking or data for development are getting replaced by the new one: ‘data is development’. As India moves to a digital economy, Statisticians are expected to generate enough data that will find huge private markets which require these inputs for effective decision making. For this to materialise, there should be proper data infrastructure in place to capture every need and deed of the citizens. With the cost of building this infrastructure getting cheaper, the talk of data as ‘the new oil’ hinges on the possibility of monetising data by converting it into ‘subscribed’ services.The above framework is a far cry from the days when country had practically little knowledge of its population, economy and society. The next Chapter looks at how the data collection system evolved over the years.Return to Contents III. EVOLUTION OF OFFICIAL STATISTICS IN INDIA The common people of India are, it may be repeated, anything but statisticians by nature. About such a vital matter as the rent he pays for his land, the ordinary villager is often as vague as he is on the subject of his age. Ask a young peasant how old he is, and he will, as likely as not, reply “Twenty or thirty”; ask his mother her age, and the old lady will hazard “Forty or fifty”; while the grandfather will invert the order and probably tell you that he is “a hundred or eighty.” If you are riding down the countryside and try to get a passing rustic to tell you how far you are from your next camp, he will give you “Eight or ten miles,” and the chances are that you will find it fifteen.”Lord MestonPresident of the Royal Statistical SocietyInaugural Address to the Royal Statistical Society,November 15, 193213Lord Meston’s remarks made 90 years ago, in some measure, signal the approach towards data by the commoner, with significant exceptions, even in present day India. This points to an important failing, perhaps not unique to India: that of a prevalence of ‘statistical illiteracy’. This will be dealt with briefly in the final chapter, ‘The Way Forward’. The history of India’s official statistics before independence is closely linked to the colonial administration’s efforts to record the state of society and economy of the colony, against the backdrop of the near absence of any such systematic records for the country. Most of these came out in the form of Gazetteers at the district or national levels. This is not to ignore the ancient prescriptions for governance in ‘Arthashastra’ or the land administration system initiated during the Mughal period. Most of colonial administrative efforts are meticulously reported in S. Subramaniam’s paper published in the Journal ‘Sankhya’ in 196014. Subramanian was with the Directorate General of Commercial Intelligence and Statistics (DGCI&S), the first agency set up for official trade statistics in the country in 1869.The other key pre-independence developments included the Conference on Agricultural Statistics in 1884, the creation of a Statistical Bureau in Calcutta [now Kolkata] in 1895 and its absorption by a Directorate General of Commercial Intelligence and Statistics (DGCI&S) in 1905, the appointment of a Director of Statistics-in 1914, Reports of the Economic Enquiry Committee for 1925, the Royal Commissions on Agriculture and Labour of 1928 and 1930, the Bowley-Robertson Report of 1934, and the Inter-Department Committee on Official Statistics of 1945. The population censuses starting from 1861 need special mention in view of the meticulous care and importance attached to them. All these were important steps intended to contribute to the understanding of the political, social and economic aspects of the country, spanning a period of close to 100 years. It is interesting to note that land record system whose origin goes back to the Mughal period survives to this day in most parts of the country. There are also cases where the government’s control of production and trade of key items such as salt and tea ensured that the concerned administrative authorities maintained records of production, exports and consumption. The Second World War also brought in certain systems for collecting data possibly to aid war efforts. The developments after the independence of India are briefly noted in the Report of the National Statistical Commission headed by C. Rangarajan15 that reviewed the statistical system and the entire gamut of official statistics in the country. Free India’s government, its commitment to improve the lives of people, and the introduction of Five Year Plans, ushered in a development centric focus to data collection efforts. The National Income Committee, appointed in 1949, and published its report in 195116, highlighted key gaps in the statistical database of the country, but “decided to discuss these problems in greater detail before making definite recommendations”.17 In the following years of independent India, exclusive statistical offices like the Central Statistical Unit which later converted into the Central Statistical Organisation (CSO, 1951), the Directorate of the National Sample Survey (1950) and the Computer Centre (1967) were established. The National Income unit that functioned under the Ministry of Finance was transferred to the CSO in 1954. The Indian Statistical Institute (ISI) set up by the late P.C. Mahalanobis in 1932 at Kolkata grew into an institution of international repute. Path-breaking research in statistical theory and practice by the ISI provided direct inputs to the development of the official statistical system. This came in the form of pioneering work on large-scale sample surveys, design of agricultural experiments, statistical quality control, economic planning, and use of electronic computers in statistical work. Renowned experts in Statistics and Economics were associated with these developments and the efforts to improve the statistical system gave the country a head start among developing countries. It also placed India in the forefront of leadership in statistical theory and practice internationally. Official statistics in a federal frameworkThe Indian federal structure influenced the organisation of the statistical system as well. The division of administrative functions between the Government of India and the State Governments on the basis of the Union, State, and Concurrent Lists also determines the roles and responsibilities of statistical organisations. At the centre, the responsibilities are further divided amongst the various ministries and departments, according to the Allocation of Business Rules. The collection of statistics on any subject generally vests in the authority (Union Ministry or Department or State Government Department) responsible for that subject according to its classification under Seventh Schedule of the Constitution. This system assumes that the flow of statistical information originates from the States to the Centre except in cases where the State-level operations are an integral part of centrally sponsored schemes or data are collected through national sample surveys. The collection of statistics for different subject-specific areas, like agriculture, labour, commerce, industry, etc. vests with the corresponding administrative ministries. Some of the Union ministries, for instance, Agriculture, Water Resources, and Health have full-fledged statistical divisions, while most others have only a nucleus cell. Large-scale statistical operations like the Population Census, Annual Survey of Industries, Economic Census, are generally centralised. More about this later. The statistical systems in the States are similar in structure to that at the centre.It is generally decentralised laterally over the Departments of the State Government, with major Departments, such as Agriculture or Health, having large statistical divisions for their work.At the apex level is the Directorate of Economics and Statistics (DES), which is formally responsible for the coordination of statistical activities in the State.The DESs have organisations at the State headquarters, with statistical offices in the districts and taluks.The statistical activities of the DES in various States are more or less uniform.They publish statistical abstracts and handbooks, annual economic reviews or surveys, district statistical abstracts, and state budget analysis, estimates of the State Domestic Product and Retail Price Index Numbers, and engage in other statistical activities specific to the State.A cursory look at their outputs shows that even in the current digital age most of these are available only in the form of print publications with very few in open digital formats.Return to Contents IV. GROWING CONCERN OVER DATA INADEQUACIES IN INDEPENDENT INDIA “The operational efficiency of the Indian Statistical System today is compromised by serious deficiencies with respect to credibility, timeliness and adequacy.” Report of the National Statistical Commission (Paragraph 1.1.2)While the pre-independent initiatives are more of historical interest, the developments after independence need to be looked into in more detail. As these institutions and processes are still in place and the manner in which these are coping with the new data architecture is critical to understand the present state of the official statistical machinery. From another perspective – that of the role played by statistics in state policies – there is a difference between why data are collected by a colonial regime and a sovereign democratic republic: “[T]hroughout the British period the statistical development was geared towards administration, trade, commerce and such other activities. It is only after the independence in 1947 that the country saw an urgent need for a statistical framework suitable for economic and social development”18. One of the key features of the newly created institutions and systems in independent India was the nodal role played by the central agencies in their formulation and implementation. Given that the States were first formed through integration of the British Presidencies, Princely States and French and Portuguese colonies with the Union, and their subsequent re-organisation along linguistic lines, it was clearly not expected of them to develop these systems on their own. The beginning of the planning era also required data at the Union and State levels without time delay. Thus, the central statistical bodies and the Ministries were the ones that initiated important surveys and censuses. In areas such as agriculture, where the States had the key role, schemes to improve data reporting were centrally sponsored. However, most of the systems were built along the style of other administrative initiatives with conventional style bureaucracy where the entire statistical machinery was subordinate to the administrative hierarchy. The exceptions were the National Sample Survey (NSS) and the Central Statistical Organisation (CSO), created in 1950 and 1951, respectively, which enjoyed a great degree of autonomy. While the former was to serve as “a multi-faceted fact-finding body”, the latter’s objective was “to coordinate the statistical activities of independent India”, and among others, to “prepare and publish” monthly and annual Statistical Abstracts, “to act as a liaison with [the] United Nations Statistical Office and to disseminate annual statistics by graphs and charts as well as tables for public use”19. The Computer Centre was started in 1967 “as an office attached to the then Department of Statistics, Cabinet Secretariat, to cater to the data processing needs of the Statistics Department and other Departments in the Government of India”20. It was one of the first computer centres anywhere in the Government and possessed the latest machines then available. Initially, India’s statistical system was not subject to critical scrutiny, though it was always felt that it was not keeping pace with the data needs, especially those required by the Planning Commission. The Planning Commission had all along given a helping hand to the statistical system as a responsible care giver. Mahalanobis, who guided the statistical system also played a key role in the formulation of the Five Year Plans in the early years of the planned economy. Nikhil Menon (2022) brings out the role played by the Mahalanobis and the ISI in the evolution of the economic planning21. Futility of chasing administrative dataThe administration of various statutes were expected to generate large quantities of data as a by-product. Most statutes had provisions for submitting periodical reports by the agencies using the powers vested in these laws. However, by the 1980s, it was clear that the administration of most of these statutes could not produce timely and complete reports. For example, the setting up of employment exchanges all over the country and the prescription that all employment and vacancies were to be reported by organisations and all unemployed looking for jobs expected to register themselves in these exchanges was expected to produce the necessary statistics for employment and unemployment22. In reality, however, the statistics coming out from this process were highly incomplete and failed to measure the actual employment situation in the country. There were also inevitable delays in bringing out official statistics considering the size and complexity of the country – an example of a critical gap in official data that has direct impact on policy making. There are many other examples of the failure of administrative systems as a source of official statistics that persist. Pandemic exposes the chronic malaise The COVID-19 pandemic brought to the fore the limitations of the Registration of Births and Deaths Act, 1969, and its amendments to provide timely statistics on the pandemic-related deaths. The failure of the official machinery to reflect accurately the enormity of human loss as a result of the COVID-19 pandemic was not an entirely new revelation. Two decades earlier, the Rangarajan Commission had sounded a note of warning in its Report: ‘over the years, the Administrative Statistical System has been deteriorating and has now almost collapsed in certain sectors. The deterioration had taken place at its very roots namely, at the very first stage of collection and recording of data, and has been reported so far in four sectors: agriculture, labour, industry and commerce. The foundation on which the entire edifice of Administrative Statistical System was built appears to be crumbling, pulling down the whole system and paralysing a large part of the Indian Statistical System. This indisputably is the major problem facing the Indian Statistical System today’23. Although the civil registration system was introduced long back during the colonial period, registration was voluntary, different provinces had different legislations, and there was no standardisation of concepts, definitions and classifications. The enactment of the ‘Registration of Births and Deaths (RBD) Act, 1969’ replaced the diverse laws that existed and brought a uniform legislation in the country. The Act provided for a hierarchical set-up for the registration starting at the panchayat level. As noted by the Rangarajan Commission, a combination of administrative factors is responsible for the poor registration levels of vital events. Except for a few States and UTs, generally multiple agencies are involved in the registration work at the sub-national level. Moreover, for the functionaries at all levels, the work related to registration of births and deaths is in addition to their other normal duties, and is generally performed in an honorary capacity. In addition to possible administrative apathy, other factors that lead to incomplete registrations are a general lack of public awareness about the statutory requirements and procedures of registration, lack of demand of birth and death certificates in rural areas, and the perception that there is nothing to gain from registration. Computerisation of the registration work has substantially reduced the reporting the delays. The requirement of providing birth certificate for school admissions and death certificates for settling claims after death of an individual however ensures registration process will eventually reach perfection in the coming days. That said, even in States with 100 per cent civil registration, its use by local authorities in their work is problematic. For instance, even now almost all local authorities report the population in their jurisdiction based on the 2011 Census because of procedural difficulties in changing the information gathered at the time of census collection, specifically the ‘present address of usual residence’ and other vital events. Even earlier, the Registrar General of India had initiated a Sample Registration System (SRS) from the 1960s to provide reliable estimates of birth and death which now includes medical certification of cause of death as well. The SRS was introduced because the birth and death registration system was not able to provide reliable vital statistics. Although based on a sample, the SRS is still considered to be far superior to the universal registration system as it is based on sound statistical methodology with a strong supervisory mechanism for data quality and coverage. The main objective of the SRS is to provide reliable estimates of birth rate, death rate and infant mortality rate at the national and state level. It consists of a base line survey of the sample units to obtain demographic details of the usual resident population of the sample areas and a continuous (longitudinal) enumeration of vital events of usual resident population. There are independent retrospective half-yearly surveys for recording births and deaths which occurred during the half year under reference and updating the population details. Further matching of events recorded during continuous enumeration and those listed in course of the half-yearly survey, and a field verification of unmatched and partially matched events ensure complete recording of all vital events. The large scale nationwide sample surveys or the NSS was a novel arrangement initiated under the aegis of the ISI, Kolkata, for meeting national data needs in economic and efficient way. The NSS was reorganised in 1974 and all activities, including those performed by the ISI, were brought under one umbrella. This was expected to bring speed and efficiency in survey operations and report generation. The NSS was expected to remedy the weakness of administrative statistics in many areas and to this extent its surveys covered a large number of topics. However, despite the reorganisation, it failed to bring out its reports on time and substantial parts of the data that were collected remained unanalysed.Hesitancy on the part of the statistical agencies, including the CSO and NSS, to absorb or adopt modern computing technologies – without which they would not be able to meet the emerging data needs – is one of the reasons for this lag in data collection and finalisation of reports. In the 1980s, the National Informatics Centre (NIC) took over the responsibility of integrating data from different Ministries and providing computing resources for most of the data processing activities of the Government. The role played by the NIC will be discussed later in this report. Failure of the coordination mechanism The CSO has a major responsibility to coordinate statistical activities across domains. This, as noted by the Rangarajan Commission, depended on two factors: the degree of its initiative and ability to persuade various ministries and departments to share data, and the co-operation of the ministries, in particular, their willingness to participate in this process as a team and to be persuaded to accept the conclusions of the team about their statistical work. However, given the historical background, India’s statisticians had a less flexible mode of thinking, averse to change and ‘outside influence’. An example of this lies in an earlier attempt to infuse responsibility and oversight in the statistical mechanism. As early as the 1980s, the unsatisfactory experience in Indian statistical system in coordinating with the Ministries led the CSO to search for an institution outside of itself and the ministries from which it could derive authority. The idea was concretised by the Committee to Review the National Statistical System (1980), also referred to as the Kripa Narain Committee, in two of its recommendations24. One required the Government of India to formally declare by Executive Order that the Department of Statistics, to which the CSO belonged, was the “Nodal” department for undertaking integration of data required for Government’s decision making, for setting and maintaining standards, and for improvement and development of statistics in all respects. The other was to create a National Advisory Board on Statistics (NABS) with the Deputy Chairman or Member-in-charge for Statistics of the Planning Commission as the Chairman and the Director General of CSO as the Vice-Chairman. The Government constituted NABS in 1982. From 1992, the NABS was chaired by the Minister in charge of Statistics. However, this Board turned out to be ineffective, primarily because of lack of official or legal support. One available institutional arrangement for coordination was the Conference of Central and State Statistical Organisations which provided a forum for the States and central agencies to interact once a year.This mechanism, vibrant till the 1970s, nearly stopped functioning until it was revived in 2000 after the NSC started functioning.Return to Contents V. REVIVING INTEREST IN OFFICIAL DATA “The need for timely and reliable statistics for policy formulation and planning cannot be over emphasised. There is reason to believe that with progressive dismantling of the system of economic controls, the quality of data flows has weakened. Government has decided to establish a National Statistical Commission to critically examine the deficiencies of the present statistical system with a view to recommending measures for a systematic revamping of the system”.Yashwant Sinha(former Union Finance Minister) Union Budget speech, 199925The genesis of the Rangarajan Committee can be traced to this Union Budget Speech on February 27, 1999, by the then Finance Minister, Yashwant Sinha. These reforms in the official statistics sector were proposed as the economic reforms initiated in the early 1990s were expected to widen the scope of data for the user community.The setting for this announcement to establish a National Statistical Commission (NSC) was also due to the increasing integration of Indian economy with the global economy, putting pressure on government to take a hard look at the official data systems. An additional need came from international agencies to improve the compilation of economic data by member countries. Multilateral bodies like the IMF and World Bank started taking a keen interest in developing statistics and their timely reporting by member countries especially after the East Asian crisis of Nineties through the introduction of the Special Data Dissemination Standards (SDDS) and General Data Dissemination Standards (GDDS) for statistical reporting.The key recommendations by the Rangarajan Commission Report submitted in 2001, included the setting up of a “permanent and statutory National Commission on Statistics”26 and the reorganisation of a State’s statistical activities under their respective Directorates of Economics and Statistics, with the latter to be headed by “a professional statistician or a professional economist”.27Acting on this report, in June 2005, the Government set up the NSC with a part-time Chairman and Members and an entity called National Statistical Organization (NSO) with the National Sample Survey Organisation (NSSO) and the CSO as two separate wings of the NSO. The NSO was to act as per the policies and priorities set by NSC. The Chief Statistician of India, in the rank of Secretary to the Union government, was to head the NSO and also to function as Secretary of the Commission. Oversight of the technical functioning of the NSSO was also transferred to the NSC from the NSSO’s Governing Council, which was dissolved. These measures gave hope that the Indian statistical system would transform into a modern entity enjoying autonomy and national importance. There were similar efforts in countries like UK to reform their statistical system through structural legislation that would insulate official statistics from political considerations and bring in technical considerations alone in the functioning of the statistical system. The NSC was first set up through a cabinet resolution with the idea that it would become a statutory commission in due course. Despite the Suresh Tendulkar Committee’s recommendations in 2008, this was not acted upon. Subsequently, in 2011, a committee under the late Madhava Menon submitted another blueprint to the Commission, including a Draft Bill28. This exercise too did not succeed. Thus, no visible efforts have been made till now to empower the Commission by providing a statutory framework for its functioning. The Government in an order dated May 23, 2019, restructured the Statistics wing of the Union Ministry of Statistics and Programme Implementation (MoSPI) with the objective to streamline and strengthen the nodal functions of MoSPI and to integrate the administrative functions29. For one, the order created doubts among experts as to how the NSC will play its role and set policies and priorities for the NSO when all its divisions are integral part of the Ministry. Second, the concerns were more in the case of the NSSO which, since its inception, had external technical oversight and catered to the data needs of not just the CSO but of other Ministries as well, and took care of the needs of researchers30. The Government was also ambivalent on the functional status quo and how the independence and autonomy of the CSO and the NSSO will be protected under the new organisational arrangement. A subsequent clarification31 does not appear to have resolved the matter as vacuum continues to persist at the leadership of the national apex statistical body, and the efforts initiated by the Rangarajan Commission to isolate the statistical system from Government have lost their focus. The need to insulate the statistical system from Government influence is a fundamental requirement to ensure its autonomy. In addition, the enormous advancements in the field of data collection, processing, and dissemination call for statistical organisations to be more nimble than conventional government bureaucratic agencies. As is the norm nowadays, when the UN’s Sustainable Development Goals have become national and global benchmarks, the need to monitor these outcomes and absorb new IT-based solutions to meet data gaps to ensure that India’s data architecture is synchronised with international releases of indicators also call for statistical agencies to be dynamic and innovative in their attitude and approach. Somehow, however, India’s statistical mechanisms appear to be caught in a time warp. The ‘latest’ data made available for most indicators inevitably come with a lag of a few years, if not more. Alternatively, they are projections, which, howsoever corrected using statistical techniques, will not be as reflective as the latest primary data that have been analysed and released on time. Although the efforts to restructure India’s statistical system started in 1999, more than 20 years later it is not very far from where it started.The more things change the more they appear to be the same.However, now there are many models available to ensure that India can join the list of nations that have clear and usable data.As noted earlier, the UK Statistics Authority which describes itself as “an independent body at arm’s length from government”32 is a statutory office that oversees the functions of the Office of National Statistics that produces almost all key national statistics. The authority also does independent monitoring and assessment of official statistics through a Code of Practice for Statistics, which “sets standards that producers of official statistics should adhere to”. The Office of Statistics Regulation, an independent regulatory arm of the UK Statistics Authority, “assesses compliance of these statistics against the Code of Practice for Statistics”33.Return to Contents VI. DATA AS A POLITICAL TOOL “In the original sense of the word, ‘Statistics’ was the science of Statecraft; to the political arithmetician of the eighteenth century, its function was to be the eyes and ears of the central government. It could tell the Prince how many able-bodied men he might mobilise, how many would be needed for the essentials of civil life; how numerous or how wealthy, were sectarian minorities who might resent some contemplated change in the laws of property, or of marriage; what was the taxable capacity of a province, his own, or of his neighbours.”R.A. Fischer Presidential Address to the First Indian Statistical Congress, 193834Data has found three major uses in India’s political environment. In governance, they are key indicators to set targets and monitor progress. During electioneering, contestants cherry-pick information either to woo voters or to run down their electoral opponents. For researchers, journalists and others contributing to the enhancement public knowledge, data – both public and private – come in handy to either support their arguments or to counter opposing narratives. These three political uses of data are not exclusive of each other and at times, with official data become a matter of public debates. One such debate, in which official data entered the public consciousness through a series of front page newspaper reports, and across visual and online media was the controversy over two leaked reports NSSO – one of which was released later and the other withheld due to ‘quality issues’. The first of these two NSS reports was on the employment-unemployment situation in the country based on the first Periodic Labour Force Survey (PLFS) slated for release in December 2018. This was the first national survey on employment-unemployment by NSSO after 2011-12. As per the established procedure, NSS reports are released after approval by the NSC. However, although this report was approved by NSC for release in December, it was not released. A leaked version of the report was published by a journalist in the daily, Business Standard, after which the NITI Aayog came out in defence of the withholding of the report. The same report was, however, released by the NSS a few months later by which time Government had got elected afresh. A few months later, another NSS report on household consumer expenditure, a report that forms the basis for estimating poverty numbers, was published by the same journalist. Once this report was leaked, the Government came out with a statement mentioning ‘quality issues’ for abandoning the survey altogether without any comment of on what these quality issues are. The leaked version of the report showed a much bleaker picture of poverty as there was hardly any improvement in household expenditure since the previous survey in 2011-12. Before this, there were debates over the new GDP series and the back-series computation. Changing the base year for GDP computations is a standard practice to accommodate methodological innovations, availability of new data sets, uniformity with other indicators etc. The CSO changed the base year from 2004-05 to 2011-12 and the new base year was adopted from 2015-16. For time series comparisons, the usual practice is to prepare a GDP back-series using the methodology and data sets as used in the new base year. Back series preparation was problematic for the new series due to non-availability of corporate data for the earlier years. The NSC had got an exercise undertaken by experts to prepare the back-series. However the CSO prepared a back series that produced a different picture of the economy compared with the NSC exercise. This could be explained in view of the different methodology used in the two exercises. The controversy arose due to the fact that the new back series was released by the NITI Aayog instead of the CSO. This clearly brought out the political interest indirectly influencing data based narratives. It is important, therefore, to look at the relationship between statistical agencies and the government in the light of these debates. Official statistics and the Government Some of these controversies drew several commentaries from economists, the media, and the political establishment on the integrity and autonomy of Indian official statistics. The official reactions came from NITI Aayog, a generalist body created as a diluted version of the Planning Commission, rather than the specialist statistical agencies involved – a visible subordination of these agencies by the political establishment who have a stake in creating a particular narrative. The argumentative nature of the Indian polity, the widespread access to independent media and the failure of the Government to address what in effect are technical issues made the debate a novelty for the country’s public. This was not the case at the international level, where there are examples of governments dealing with uncomfortable statistics in a variety of ways. A publication by the Economic Governance Support Unit of European Parliament, Statistical Governance in Greece – Recent Developments, in November 2016, deals with certain developments in the Greek statistical system. It also covers legal proceedings before Greek courts against the Head of the Greek Statistical Office, ELSTAT during 2010 to 2015, among others on the ground of the accusation that he inflated the 2009 budget deficit for “undermining the national interest.”35It is well known that one of the major causes of the collapse of the Greek economy was attributed at that time to the frequent revisions of official statistics. The frequent revisions made them unreliable according to the inspection reports of the Eurostat, the European agency overseeing the official statistics for European Union. Although prosecuting the chief statistician for following the established procedure of compiling official data on the ground of falsifying numbers, as these were not to the taste of the government, was a strange and extreme case, it evocatively brings out the centrality of official data. William Seltzer in a report prepared for the UN Statistical office (UN, 1994) deals with many possibilities of politics coming in to play in official statistics. The report quotes the Norwegian Central Bureau of Statistics (CBS) as saying: “…CBS tries to avoid provocative comments relating to issues which are hotly debated between our political parties. However the CBS will (as a policy) not refrain from putting its statistics in a relevant social or economic context, irrespective of how this will be interpreted by various parties…..The CBS will show circumspection, while Ministries and Ministers occasionally will have to live with some statements from the CBS that they do not like; extreme caution would lead to a reduction in the social usefulness of the CBS [emphasis by author].” This is another side. Coming to the Indian context, it is instructive to see how data entered the jobs narrative. In an interview to the magazine, Swarajya, the Prime Minister was quoted as saying “…On this issue, more than a lack of jobs, the issue is a lack of data on jobs. Our opponents will naturally exploit this opportunity to paint a picture of their choice and blame us”. He went on to give more details to indicate where and how jobs are being created: “…If we look at numbers for employment, more than 41 lakh formal jobs were created from September 2017 to April 2018 based on EPFO payroll data. According to a study based on EPFO data, more than 70 lakh jobs were created in the formal sector last year.…. In just one year, 48 lakh new enterprises got registered. Will this not result in more formalisation and better jobs? More than 12 crore loans have been given under Mudra (micro loans). Is it unfair to expect that one loan would have created or supported means of livelihood for at least one person? More than one crore houses have been constructed in the last one year; how much employment would this have generated? If road construction has more than doubled per month, if there is tremendous growth in railways, highways, airlines, etc., what does it indicate?”36Quoting sources with poor statistical legitimacy as evidence of rise in employment could be due to the absence of independent data from labour force surveys or their weakness in countering the claims. However, these ‘leading’ and ‘loaded’ questions coming from the Prime Minister can put the national statistical agencies involved in measuring employment under extreme caution. In the past, debates on data – whether on GDP, poverty, or employment – were mostly confined to the technicalities of the data generating processes and the interpretation.This was understandable given that access to reports and data was limited and the tools of analysis and dissemination poor.The heightened interest of print and digital media in official numbers is somewhat a new phenomenon.Added to this is the instant circulation of information through social media.The vast pool of bright researchers with data analytics tools at their fingertips now provides abundant scope for discussions and instant publication of findings.These challenging scenario needs to be fully integrated in to the statistical system.The official agencies however are not trained to tackle this new scenario.The most unfortunate fallout of these changes has been that the agencies withdrew into a shell; adopting a wall of caution in collecting and releasing data.Return to Contents VII. THE FEDERAL DISCONNECT “The States under our Constitution are in no way dependent upon the Centre for their legislative or executive authority. The Centre and the States are co-equal in this matter.”B.R. Ambedkar, November 25, 1949 Concluding remarks in the Constituent Assembly on the ConstitutionA key feature of governance in India is the sharing of powers and responsibilities under the Seventh Schedule, which provides for Union, State and Concurrent Lists. The Chairman of the Constitution’s Drafting Committee, B.R. Ambedkar, in his concluding remarks in the Constituent Assembly, highlighted the significance of these three lists and the sharing of powers. “The chief mark of federalism, as I said, lies in the partition of the legislative and executive authority between the Centre and the Units by the Constitution. This is the principle embodied in our constitution. There can be no mistake about it. It is, therefore, wrong to say that the States have been placed under the Centre. Centre cannot by its own will alter the boundary of that partition. Nor can the Judiciary.”37Ambedkar’s formulation of separation of powers faces a challenge when it comes to official statistics because of overlapping domains and administrative interdependence between the Union and the States. It was mentioned earlier that most key statistical initiatives originates from the centre, even where the subject matter actually fell in to the State List. Under this sharing of powers, ‘Census’ and ‘Inquiries, surveys and statistics for the purpose of any of the matters in the Union List’ come under the exclusive domain of the centre. The Concurrent List includes ‘vital statistics including registration of births and deaths’ and ‘inquiries and statistics for the purposes of any of the matters not specified in state list and concurrent list. Given that definition of what constitutes the statistics on any matter is somewhat blurred, in reality there appears to be no hard and fast rule on the agencies that can collect data on any specific data in the country and publish it. Given the structure of India’s polity, where States are the implementing agencies, even for centrally funded schemes, a clear understanding between the Union and the States, backed by a mechanism for data collection, analysis, publication, and dissemination, gains critical importance. The “Census”, which is listed under the Union List gives a clear example of the need for the Union and the States to work together for the creation of a credible, reliable, and timely statistical data architecture. Most of the data are required at the national level with disaggregation at the State or possibly district levels. Therefore, it is seen as essential for the Centre to carry out all pan-India statistical operations that include all Censuses. This creates a structure ensuring adoption of uniform definitions enabling comparability and aggregation. It is possibly this aspect that has made the States take a back seat in statistical initiatives, as they would have to follow what the Union ministries set out as guidelines. This current administrative mechanism raises several issues. In many sectors like agriculture, education, and health to name a few, one would expect the data to be generated at the field or at the local institutional level and aggregated to successive higher domains and produce national level estimates. But inter-State variations in statistical capacity and administrative efficiency create deficiencies in the basic building block of the statistical framework – data collection. We thus have the respective central ministries playing a major role in guiding and directly involving in the collection and dissemination of statistics on these topics. One cannot, therefore, fault the Central agencies in exercising control of data generation and dissemination. The flip side of this arrangement has been the decline of the State’s role in the official statistical system. Estimating the Gross State Domestic Product (GSDP) for the State is one of the important activities for DES, which is a State body. The GSDP has assumed critical importance in recent times as the successive Finance Commissions have tied the devolution of funds and the borrowing limits to the GSDP. As with national GDP, estimating the GSDP is a complex exercise and calls form data from all economic agents including households, government, corporates, and non-profit organisations. A standard national prescription is not always possible given the diverse economic structure of States. For a large number of sectors controlled by the Central government, such as the railways, air transport, and post and telecommunication, the States have to depend on the CSO. In the latest revision of the GDP, with 2011-12 as the base year, the CSO started using the corporate financial data from the corporate filings with the Ministry of Corporate Affairs. The new GDP series, for the most part, replaced the Annual Survey of Industries with corporate financial data as the source for estimating manufacturing value added. This data is not available at the State level and this procedure has meant that the States have to look to the CSO for data for their GSDP compilation. This creates not only a complex process, but further restricts the States – where all the economic activities take place – from gaining clear insights on their economic activities, adding to the constraints on States to chart economic policies that are specific to their needs. State participation in NSS A key feature of the NSS since its inception was the replication of its surveys on separate and independently selected samples in all States. The idea was that the State’s sample will augment the central sample and help the pooling of data to generate sub-State level estimates. This did not materialise as barring a few, States failed to process their data. However, the Governing Council that managed the NSS had representation from the States ensuring the selection of survey subjects also kept in mind the interest of the States. The recent approach appears to be that the issues that are to be surveyed are selected by NSS without any consultations with States and their participation is not desired in most surveys. Digitisation of reporting Over the years, several schemes for the benefit of individuals or households have been initiated as ‘mission mode projects’, which generate information at the household/individual/local government levels.Infusion of IT in monitoring allows the data to be directly fed in to a central server that can generate instant web enabled data points available as Dash Boards.Clearly the custodianship of all these data rests with the central agency in charge of the project.These dash board statistics often come with limited meta-data and the unavailability of the underlying database for research purposes limits their utility.Return to Contents VIII. INSTITUTIONAL INADEQUACIES: CAUSES AND IMPLICATIONS “An institutional gap that has been highlighted recently is the need for a strong national statistical system, independent of control by the government. India has a high reputation in this area, but this has been eroded…If we want the economy to appear attractive to private capital – domestic and foreign – then the Indian statistical system must meet the highest standards observed in emerging market countries.”Montek Singh Ahluwalia Backstage – The Story Behind India’s High Growth Years, 202038Statistical frameworks for any country require a strong institutional foundation. In addition to the overlap between the Union and the States in the administrative process, a new impediment to professionalism in the official statistical machinery is overt interference in the release of official data, exemplified by the pole position of the NITI Aayog (the successor body to the Planning Commission, albeit with a narrower remit) in the recent data narratives. For instance, in 2017, the revised GDP estimates were released in the presence of NITI Aayog officials and it was the same officials from NITI Aayog who defended the Government when the employment report was leaked. The entry of NITI Aayog is a pointer to the direct control over data by a body that is a creation of the government of the day. The reforms suggested by the Rangarajan Commission wanted a professional as the head of the statistical system and the NSC to function as an independent body to play a nodal role which ought to have been brought in to the picture. These aims, as discussed earlier, have not been met and continue to remain elusive. The NITI Aayog now has a ‘flagship’ initiative to develop a National Data and Analytics Platform (NDAP) within it to improve access and use of government data. It is expected to be a user-friendly web platform that aggregates and hosts datasets from across India’s statistical system. According to the objectives it has set, NDAP seeks to ‘democratise data delivery by making government datasets readily accessible, implementing rigorous data sharing standards, enabling interoperability across the Indian data landscape, and providing a seamless user interface and user-friendly tools’. This is indeed a laudable initiative and the programme should become a major data initiative with the resources and access to high-end IT infrastructure that the organisation has at its command. One may remember that the Government of India, through the Department of Science and Technology had released a National Data Sharing and Access Policy (NDSAP)39 with somewhat similar ambitions. The preamble of this Policy issued in 2012 stated: ‘data collected or developed through public investments, when made publicly available and maintained over time, their potential value could be more fully realized. There has been an increasing demand by the community, that such data collected with the deployment of public funds should be made more readily available to all, for enabling rational debate, better decision making and use in meeting civil society needs’. The NDSAP covered all types of data including geo-spatial data over which the government had absolute control till then. Under this Policy, each organisation was to share all shareable datasets in an open data format. The technology backup for this is provided by the NIC under Department of IT. The open data portal data.gov.in has been successful in bringing together close to a half a million data resources under different catalogues. It is a state-of-the-art data portal, highly interactive and allowing several API for public use. One might say that NDSAP is India’s response to the global Open Government Data movement. Not to be left out, the MoSPI also developed a National Integrated Information Platform (NIIP) which has uploaded several survey data of NSS providing online retrieval facility of survey estimates by domains and offering limited cross tabulations, including graphic visualisations. These efforts to democratise data through technological innovations are extremely important given that the statistical agencies were rather slow in assimilating IT. As noted earlier, most programme-implementing Ministries now have web-based data capturing mechanisms and dynamic dashboards. The past experience, however, has been that many such initiatives are supply driven and are not flexible enough meet changing user needs and lean towards showcasing government achievements rather than enabling a critical understanding of the reality. However, the disproportionate efforts to showcase data on the back of modern IT architecture without strengthening the underlying data generation processes raise questions. The Economic Census is one such case in point. This is a mammoth exercise to list all non-agricultural establishments in the country with the ultimate objective of creating a directory of business activities which can be used as a register for surveys. Until now these were conducted with the help of State Statistical offices. It did help in understanding the growth and spread of economic activities but failed in the basic objective of creating a business register in the face of highly defective data gathered by the enumerators, mostly poorly trained unemployed graduates, anganwadi workers, etc. In spite of the past experience, the Government went ahead with the latest survey mobilising the Common Service Centres (CSC) spread all over the country and the high end technology that the CSC has at its command. Though a lot of time has lapsed since the Seventh Economic Census which was launched in 2019, the results have not been released. The MoSPI explained away the reason for the delay on State/UT governments not providing the mandatory approvals for the provisional results. Refusing to accept this reason, the Standing Committee on Finance, on July 28, 2022, noted: “The Committee are constrained to note that the Ministry has furnished a routine reply regarding the growing delay in the release of the Economic Census (EC) making the data on some of the items/samples irrelevant or outdated for stakeholders to be used as parameters. They have now sought to shift the onus on the [S]tates/UTs for not being forthcoming in giving approval of provisional results of the 7th EC. The Committee are, however, of the view that it is the responsibility of the Ministry to follow up with the States/UT for early examination of provisional results by the State level coordination committees to enable Ministry in releasing all-India results at the earliest. Thus the Ministry has to squarely assume responsibility in this regard as it is being conducted by them as a central sector sub scheme. It is high time that the Ministry should expedite the process of enumeration and produce reliable data publicly. The Committee desire that the identification of [S]tates should be done which are lagging behind in giving their approvals and accordingly corrective measures to be taken to identify the problem areas and difficulties encountered by these [S]tates and appraise the Committee in this regard.”40 [Emphases by author.] With most administrative activities moving online, sourcing such data has now become easier.In contrast, direct ground level data collection through surveys and censuses have become extremely problematic with dwindling cooperation from respondents, access restrictions, and data quality issues arising from a lack of trust in the ultimate objective of such data collection by official agencies.However, these factors are not to be seen as unsurmountable hurdles given that a large number of censuses and surveys continue to be done by government agencies.The unresolved issue remains the reluctance on the part of governments to open up their administrative data bases for research.Return to Contents IX. WAY FORWARD “Statistics appeals to our rational side, our hears providing a balance to our sometimes wayward hearts. In the data rich world that is emerging as our future, those nations, governments, businesses and individuals who use the power of numbers will prosper. Those who ‘get stats’ will get on. Those who do not will get left behind.”John Pullinger Presidential Address to The Royal Statistical Society, 201341An already strained Indian statistical system now faces a crisis of credibility as the autonomy of statistical agencies become visibly subordinate to political expediency. This is a major cause for concern, not only for the immediate implications but also for the long-term consequences of both data integrity and correctly charting the nation’s progress. A case in point is the decennial population census that in normal times would have been held in 2021 but has not yet commenced due to the COVID-19 pandemic. The delay was understandable; but not its indefinite postponement. One can feel the absence of current census population figures has on local level planning, with most local bodies either using outdated 2011 data or making some crude projections. Even before the pandemic, the Census process for 2022 was in the news with the Government tagging the preparation of the National Population Register (NPR) and the preparation of the National Register of Citizens (NRC) based on the data collected during the census operations. Elsewhere, I have struck a note of caution on how this will have serious ramifications on the quality of census data and the need to detach it from an already stretched-out census process42. The Union government’s position is that the Census will be a “dynamic” one after proposed amendments to the Registration of Birth and Death Act, 1969,43 but there is no indication of the Census of India, which has reportedly been postponed “until further orders”,44 commencing anytime soon. This does not auger well for India’s statistical architecture that is already an international laggard. The decision to put off the Census “until further orders” also makes citizens even less aware of the state of affairs in India, in numerical terms, which the decennial exercises were providing. This would only go on to create a less-informed citizenry, vulnerable to claims that cannot be substantiated with any degree of credibility of authenticity. This, in turn will deeply erode the central space occupied by India’s official data agencies. The primacy of space occupied by Indian statistical agencies to collect and disseminate data in conformity with international commitments and scrutiny needs to be restored. While political use of data is unavoidable and the reticence of official agencies in entering such debates is understandable, the remedy lies in transparent dissemination of data with full meta-data. The need to revitalise state statistical systems Given India’s size and complexities, large scale data gathering exercises are costly. A defining moment in the development of India’s official data system was when Mahalanobis successfully articulated the need for scientific sample surveys as a cost effective solution. Underlying this was the careful planning and execution that went behind the process. One has only to peruse the correspondences between Prof. Mahalanobis and Prof. Dandekar45 to understand the meticulous care these two stalwarts took in framing the questions and methodology in the first national sample survey. Unfortunately, we do not find such careful designing of surveys now. An example for this is the MoSPI withholding the results of 2017-18 household consumer expenditure survey citing data quality issues. The consumer expenditure survey is one survey that got worldwide recognition and became the flagship Living Standard Measurement Surveys actively recommended by World Bank. For such an important source of information, which has direct implications on the nation’s economy, the people are still in the dark as to what the “quality issues” are that made it necessary for the government to withhold its release. While it is fashionable to ask for censuses of all kinds, socio-economic changes can be better understood thorough careful sample studies. Over the years, most major statistical surveys are outsourced to private agencies or done with the help of contract workers slowly dissipating the expertise and legacy built on solid scientific foundations. India is also a pioneer in the dissemination of micro-data. Moreover, a large number of data archives have been built within India and elsewhere, through which researchers can now access these data. While anonymisation is rather easy for survey data, large administrative and census based data requires careful curating to retain respondent confidentiality. In this context, the proposed data protection statute needs to balance research needs an individual privacy of data. All these point to the need for more resources and specialization for the statistical agencies. Revitalising the state statistical systems is also key to strengthening the federal system. Most of the state statistical systems have not kept pace with the changing times and function with poor infrastructure and severe budgetary constraints. Recently the governments of Kerala and Madhya Pradesh constituted State-level Statistical Commissions that are expected to address the issues. The centrally sponsored and funded operations like the Livestock Census, Agricultural Census, Economic Census, Irrigation Census etc. are usually done by State agencies. But these data remain with the central agencies and are not put to careful, analysis by the State agencies either due to lack of expertise or demand. In some cases the national level planning for these exercises also means that specific State-level issues are not addressed reducing their utility for the concerned State. Proposed draft Data Governance Framework The growing digitisation of economic and social intercourses among economic units and in access and delivery of public services has resulted in expansion of the data horizon like never before. This expansion is producing a diverse variety of data, with ever increasing velocity and volume of data generation. Unlike in the past, not all of this is in the hands of Government; rather a substantial part of this new data is generated by private agencies and at times with no centralised agency in control. In this set up, the question of data protection and privacy has therefore acquired urgency. Many national governments have come out with legal structures to manage, control and protect privacy of data as the unrestricted use of such data could be detrimental to national and personal interests. The Government of India has also been grappling with myriad issues connected with data protection and had tabled a Bill in parliament. Statistical agencies need impersonal data but with as much details as possible keeping in view the wider interest in socio-economic changes. Conventional government thinking not only gave primacy to individual organisations but also assumed that they would own whatever data collected by them. It is only in recent times that governments are urged to think along the lines that provision of data for public use is considered their solemn duty. We have already mentioned the efforts through the National Data Access and Sharing Policy (NDSAP 2012) that mostly dealt with data owned by Government agencies. Recently the Government has also come out with a Draft National Data Governance Framework Policy. This draft has a much wider connotation of data, in particular the kinds of data emanating from the digital platforms that are noted as ‘empowering citizens, enhancing government-citizen engagement, and driving data-driven governance’. The policy rightly admits that the digital government data is currently managed stored and accessed in a non-optimal way and proposes a new National Data Governance Framework Policy (NDGFP) that aims to realize the full potential of Digital Government. This policy proposes a new office responsible for framing, managing and periodically reviewing and revising the policy called the “India Data Management Office (IDMO)” under Ministry of Electronics and IT. Each Ministry/Department shall have separate Data Management Units headed by a designated data officer. State Governments are also to be encouraged to designate/appoint State Level Data Officers. The IDMO shall coordinate closely with line Ministries, State Governments, and other schematic programs to standardize data management by building up capacity and capabilities in each Ministry. Further it will accelerate inclusion of non-personal datasets housed with ministries and private companies into the India Datasets programme. It has among other objectives the standardisation of data management and security standards across the whole of Government, creation of common standard based public digital platforms. For purposes of safety and trust, any non-personal data sharing by any entity can be only via platforms designated and authorised by IDMO. It will also build a platform that will allow dataset requests to be received and processed. The proposed IDMO will have say in data storage and retention, Government-to-Government Data Access, and many other standard data management activities cutting across all agencies and domains. The proposed data governance framework is a clear indication of the deficiencies in the present system in deriving the full potential of the new information technology based architecture in various government programs and processes. This should also be seen as a part of the historical evolution of data management in the government starting with the post-independence statistical architecture based on surveys, censuses and administrative data and the setting up of the first Government Computer Centre in the Ministry of Statistics to cater to the computing needs of the Government. Subsequently, the National informatics Centre (NIC) was set up to help Ministries to computerise their activities and develop and host data bases. Later we had the NDSAP under the aegis of the Department of Science and Technology and the Open Government Data portal supported by NIC. Efforts were also made to develop a National Spatial Data Infrastructure (NSDP) to bring all spatial data with common meta-data standards under one platform. The need for a common meta-data standard for all e-governance projects was also recognized through the development of a separate portal for this. There is no doubt that the evolving ‘platformisation strategy for delivery of public services and governance offers, immense scope for data based research and increased efficiency in governance. The data generated would also make significant contributions to the official statistical system. The basic premise on which official statistical system is built has been that such statistics should represent the whole country and its population or at least cover clearly defined domains. It is this that makes survey agencies take care to include all groups and geographical territories so that the estimates truly represent the whole country and its economy. The inclusivity of the data generated from the new strategy would therefore depend on the inclusivity of the process. Any systematic exclusion would clearly bias the conclusions derived from such data. Does this all pervasive digitalisation exclude economically and socially marginalised people? Another issue of concern to statistical agencies is the need to produce data consistently and on a continuing basis. It is well known that the administrative processes are susceptible to changes and that such changes are not always linked to the past processes. Statistical agencies would find it difficult to use these data sources if their coverage or definitions get updated or changed. One of the reasons the statistical agencies have not been able to make use of the MIS of various Ministries and their programs could be attributed this. Given the compartmentalisation of subjects dealt by Ministries both at the centre and States, one cannot be too optimistic about the proposed framework bringing perceptible improvements in the statistical system. The statistical system depends on a variety of concepts adopted from different fields for generating indicators; be it the macroeconomic aggregates or demographic parameters or any other social or developmental indicators. Domain knowledge has to be the basis for the underlying design in the proposed integration of platforms.Statistical literacy and role of official statistical agenciesBefore concluding, a short note on the importance of statistical literacy will be in order. This is more so given the importance of a general understanding of statistics by the citizenry. There is an oft repeated “paraphrased” quote attributed to H.G. Wells that says, ‘statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write’46,47,48. Though this observation was made in a Presidential Address to the American Statistical Association in 1950, it finds renewed relevance when data has become all pervading. With the advancement in general literacy and the advent of social media an individual is flooded with information, or rather processed data from diverse sources with presented and interpreted to suit specific purposes. A certain amount of statistical literacy would be a real necessity to assess the claims and counter claims on issues affecting the public.Gunnar Myrdal, the Swedish economist in his work on South Asian countries had noted that the ability not only to read and write figures with understanding but also to add, subtract, multiply and divide is of importance in economic and social development. In the current context one can safely add that certain basic statistical literacy is also essential, at least for those joining public debates. While there is no way to measure this, not to talk of enforcing any of it, the national statistical agencies have a duty to explain official numbers and provide the right perspective. A case in point is the press release issued by the Ministry of Health and Family Welfare to refute the WHO estimates of COVID-19 mortality for India49. The Ministry on behalf of the Government strongly objected to the use of mathematical models for projecting excess mortality estimates in view of the availability of authentic data and said that the validity and robustness of the models used and methodology of data collection are questionable. While the there is no doubt that the Union Ministry of Health and Family Welfare has adequate statistical resources, such a technical matter relating to national data should have been left to the NSO to deal with rather than giving a political colour to the statistical method used by WHO by the involvement of the concerned Ministry. Explaining numbers is not always very easy and is not usually part of the statistical curriculum in universities. While statistical communication has not been a very strong point for government statisticians, a silver lining is the evolution of data journalism. Major newspapers are now devoting space for explaining data, particularly those from government sources, by a new generation of qualified data journalists. The increased public interest in data is also seen in the popularity of books on data. The importance of statistical literacy is reflected in the Rangarajan Committee Report in which mentions “increasing and promoting public awareness of official statistics” as one of the functions of the NSC in order to “improve public trust in statistics”50In summary, the Indian statistical system faces many challenges; none of which are insurmountable. There is a confluence of many developments which create a favourable environment to mainstream statistics in not only policy making but also shaping informed public decisions: technology for data acquisition and processing are available like never before, interest in data is at its highest, access to data through the internet is more affordable and nearly ubiquitous. What is wanting, however, is the wider political awareness about the importance of data among India’s citizens.Institutional mechanisms that insulate the country’s data collection, analysis, release, and dissemination processes are an important requirement.Return to ContentsEndnotes:[All URLs are last accessed on October 6, 2022 ]1. Rao, C.R. 1983. Statistics, Statisticians and Policy Making, Sankhya: The Indian Journal of Statistics, Series B (1960-2002), Indian Statistical Institute, August, Vol 45. No 2, pp. 151-159. [https://www.jstor.org/stable/25052287]. Return To text.2. Dandavate, M. 1999. Statistics, Planning and Development, Sankhya: The Indian Journal of Statistics, Series B (1960-2002), Indian Statistical Institute, August, Vol 61, No. 2, pp. 229-236. [https://www.jstor.org/stable/25053080]. Return to Text.3. United Nations. 2014. Fundamental Principles of National Official Statistics, United Nations Statistics Division, January 29. [https://unstats.un.org/unsd/dnss/gp/FP-Rev2013-E.pdf]. Return to Text.4. One of the major global landmarks in the mainstreaming of data for the study of economic growth and development was launch of the annual Word Development Reports (WDR) by the International Bank for Reconstruction and Development (the World Bank) in 1978. Every WDR was specific to a theme, and contained a large body of information, including data – both general and specific to the theme of that particular year. Return to Text.5. International Bank for Reconstruction and Development / The World Bank. 2021. World Development Report 2021 – Data for Better Lives, World Bank Group. Washington DC. [https://www.worldbank.org/en/publication/wdr2021]. Return to Text.6. Ibid. p.25-27. Return to Text.7. United Nations. 2016. Data protection regulations and international data flows: Implications for trade and development, United Nations Conference on Trade and Development (UNCTAD), Switzerland, p.2, 129. [https://unctad.org/system/files/official-document/dtlstict2016d1_en.pdf]. Return to Text.8. Ienca, M., and Vayena, E. 2020. On the responsible use of digital data to tackle the COVID-19 pandemic, Nature Medicine, March 27, 26, pp. 463–464. [https://doi.org/10.1038/s41591-020-0832-5]. Return to Text.9. World Health Organization. 2021. Health Data as a global public good – a call for Health Data Governance 30 September, September 29. [https://www.who.int/news-room/articles-detail/health-data-as-a-global-public-good-a-call-for-health-data-governance-30-september]. Return to Text.10. In Economics, a Public Good must meet three criteria: non-rivalry, non-exhaustibility, and non-excludability. Return to Text.11. International Bank for Reconstruction and Development/World Bank. 2021. Op. cit. p. xi. Return to Text.12. Government of India, 2019. Economic Survey 2018-19. Volume 1, Chapter 4, Ministry of Finance, Department of Economic Affairs. New Delhi. pp 78-97. [https://www.indiabudget.gov.in/budget2019-20/economicsurvey/doc/vol1chapter/echap04_vol1.pdf]. Return to Text.13. Meston, L. 1933. Statistics in India, Journal of the Royal Statistical Society, 1933, Vol. 96, No. 1, pp. 1-14. [https://www.jstor.org/stable/2341867]. Return to Text.14. Subramanian, S. 1960. A Brief History of the Organization of Official Statistics in India during the British Period, Sankhyā: The Indian Journal of Statistics (1933-1960), Indian Statistical Institute, January, Vol. 22, No. 1/2 (Jan., 1960), pp. 85-118, Indian Statistical Institute. Return to Text.15. Ministry of Statistics and Programme Implementation. 2001. Report of the National Statistical Commission, Government of India, September 5. [https://www.mospi.gov.in/report-dr-rangarajan-commission]. Return to Text.16. Department of Economic Affairs. 1951. First Report of the National Income Committee, April 1951, Ministry of Finance, Government of India, April. [https://tinyurl.com/5xctk4xa]. Return to Text.17. Ibid. p. 51. Return to Text.18. Ghosh, J.K., et al. 1999. Evolution of Statistics in India, International Statistical Review/Revue Internationale de Statitique, Vol. 67, No. 1, April, pp.13-34. International Statistics Institute. [https://www.jstor.org/stable/1403563]. Return to Text.19. Ibid. Return to Text.20. Ministry of Statistics and Programme Implementation. 2001. Report of the National Statistical Commission, Government of India, p. 447. Return to Text.21. Menon, N. 2022. ‘Planning Democracy: How a Professor, an Institute, and an Idea Shaped India, Penguin Viking, India. Return to Text.22. The Employment Exchanges (Compulsory Notification of Vacancies) Act was enacted in 1959 to provide for compulsory notification of vacancies to the Employment Exchanges and for the rendition of returns relating to Employment situation by the employers. This act came into force with effect from 1st May, 1960. Return to Text.23. Ministry of Statistics and Programme Implementation. 2001. Op. cit. p.450. Return to Text.24. Department of Statistics. 1980. Report of the Committee to Review the National Statistical System, Ministry of Planning, Government of India, June, p.97. [https://tinyurl.com/2fuxntmw]. Return to Text.25. Sinha, Y. 1999. Budget 1999-2000, (Speech made at the Lok Sabha.) Ministry of Finance, Government of India, February 27. [https://www.indiabudget.gov.in/budget2021-22/doc/bspeech/bs19992000.pdf]. Return to Text.26. Ministry of Statistics and Programme Implementation. 2001. Op. cit. p. 456. Return to Text.27. Ibid. p. 469. Return to Text.28. National Statistical Commission. 2011. Report of the Committee on Legislative Measures in Statistical Matters as Adopted by the National Statistical Commission, National Statistical Commission Secretariat, October. [http://164.100.161.63/sites/default/files/committee_reports/legislative_measure_stat_matter_18jan12.pdf]. Return to Text.29. Press Trust of India. 2019. MOSPI orders revamp, merges NSSO, CSO into NSO, The Times of India, May 25. [https://timesofindia.indiatimes.com/business/india-business/mospi-orders-revamp-merges-nsso-cso-into-nso/articleshow/69496878.cms]. Return to Text.30. Shetty, S.L. 2020. Indian Statistical System in a Troubled State, Economic and Political Weekly, Vol. LV N 3, January 18. [https://www.epw.in/journal/2020/3/perspectives/indian-statistical-system-troubled-state.html]. Return to Text.31. Ministry of Statistics and Programme Implementation. 2019. Restructuring of Ministry of Statistics & Programme Implementation (MoSPI), Press Information Bureau, Government of India. [https://archive.pib.gov.in/newsite/PrintRelease.aspx?relid=190141]. Return to Text.32. UK Statistics Authority. 2022. What we do. [https://uksa.statisticsauthority.gov.uk/what-we-do/]. Return to Text.33. UK Statistics Authority. 2022. Code of Practice for Statistics – Ensuring official statistics serve the public, (Edition 2.1, Code revised on May 5, 2022) [https://code.statisticsauthority.gov.uk/wp-content/uploads/2022/05/Code-of-Practice-for-Statistics-REVISED.pdf]. Return to Text.34. Fisher, R. A. 1938. Presidential Address, Sankhyā: The Indian Journal of Statistics (1933-1960), 4(1), pp. 14–17. [http://www.jstor.org/stable/40383882]. Return to Text.35.. European Parliament. 2017. Statistical governance in Greece – recent developments [Briefing], November. [https://www.europarl.europa.eu/RegData/etudes/BRIE/2017/614481/IPOL_BRI(2017)614481_EN.pdf]. Return to Text.36. Jagannathan, R. 2018. Swarajya Interviews Prime Minister Modi – Part I: The State of Indian Economy, Swarajya, July 3. [https://swarajyamag.com/economy/swarajya-interviews-prime-minister-modi-the-state-of-indian-economy]. Return to Text.37. Ambedkar, B.R. 1949. Dr. B.R. Ambedkar’s Concluding remarks in the Constituent Assembly on Constitution on November 25, 1949. In B.R. Ambedkar, Selected Speeches, Prasar Bharti. pp. 39-40. Return to Text.38. Ahluwalia, M.S. 2020. Backstage: The Story behind India’s High Growth Years, Rupa Publications India, New Delhi. p. 408. Return to Text.39. National Data Sharing and Accessibility Policy-2012 (NDSAP-2012), Department of Science & Technology, Ministry of science & Technology, Government of India. Return to Text.40. Lok Sabha Secretariat. 2022. Standing Committee on Finance (2021-22), Seventeenth Lok Sabha, Ministry of Statistics and Programme Implementation, Fifty First Report, August, p.11. [http://164.100.47.193/lsscommittee/Finance/17_Finance_51.pdf]. Return to Text.41. Pullinger, J. 2013. Statistics making an impact. Journal of the Royal Statistical Society, Series A (Statistics in Society), October. Vol. 176. No. 4. Pp.818-839 [https://www.jstor.org/stable/43965360]. Return to Text.42. Mohanan, P.C. 2019. NPR: A statistical nightmare, Financial Express, December 31. [https://www.financialexpress.com/opinion/npr-a-statistical-nightmare/1807957/]. Return to Text.43. Singh, V. 2021. COVID-19 curbs off, but Census still on slow burner, The Hindu, July 16. [https://www.thehindu.com/news/national/covid-19-curbs-off-but-census-still-on-slow-burner/article65647312.ece]. Return to Text.44. Hindustan Times. 2022. Census 2021 deferred until further orders: Centre, July 27. [https://www.hindustantimes.com/india-news/census-2021-deferred-till-further-orders-centre-101658861900488.html]. Return to Text.45. Dandekar, V.M. 1953. Report on the Poona Schedules of the National Sample Survey (1950-51), Gokhale Institute of Politics and Economics, Publication No. 26, Paperback January 1. [https://dspace.gipe.ac.in/xmlui/handle/10973/13909]. Return to Text.46. Wilks, S.S. 1951. Undergraduate Statistical Education, Journal of the American Statistical Association, March 1951, Vol 46, No. 253, P.5. [https://www.jstor.org/stable/2280089]. Return to Text.47. The Consortium for the Advancement of Undergraduate Statistics Education (CAUSE), points out that Wilks was “paraphrasing” what H.G. Wells wrote in his 1903-book, Mankind in the Making. [https://www.causeweb.org/cause/resources/library/r1266]. Return to Text.48. Wells, H.G. 2003. Mankind in the Making, The Project Gutenberg, March 3. [https://www.gutenberg.org/files/7058/7058-h/7058-h.htm]. Return to Text.49. Ministry of Health and Family Welfare. 2022. Excess Mortality Estimates by WHO – India Strongly objects to the use of mathematical models for projecting excess mortality estimates in view of the availability of authentic data, [Press Release] Press Information Bureau, May 05. [https://pib.gov.in/PressReleasePage.aspx?PRID=1823012]. Return to Text.50. Ministry of Statistics and Programme Implementation. 2001. op. cit. p. 83. Return to Text.

One of independent India’s successes, improving life expectancy at birth from about 30 years in the 1950s to the 70s in the 2000s, has also exposed a

Policy making, to be effective, requires assessments of magnitudes and trends of major events based on evidence. One of the objectives of government policy interventions is—or should be—to pick up and stem slides in standards of living when they occur. For a stubbornly poverty-stricken country such as India, this function of the state assumes even greater significance when calamities, such as the COVID-19 pandemic, descend on the populace. Although the Government of India is yet to release data on the population pushed into poverty as a result of the pandemic, research organisations—both national and international—have attempted to study this important link. These studies throw light on the important issue of arriving at estimates of the numbers of people that might have been pushed into poverty as a consequence of COVID-19, and therefore on the magnitude of the problem confronting any conscientious policy-maker. The first of the two estimates assessed in this essay is due to researchers at the Pew Research Centre (PRC) in the U.S., and the second to researchers at the Centre for Sustainable Employment at Azim Premji University (APU) in India. In this Issue Brief, S. Subramanian, Economist, and author of Inequality and Poverty: A Short Critical Introduction, and other books on poverty, seeks to reconstruct the assumptions and data inputs that have gone into the making of the estimates under review. Analysing the estimates, which suggest vastly differing outcomes, he discusses the manner in which poverty figures are arrived at to provide a quantitative picture of economic deprivation. In the immediate context, and on the basis of such data as are available, he concludes that it could be reasonably estimated, in line with the APU study, that anywhere upward of 200 million people may have slid into poverty after the first wave of the COVID-19 pandemic. This finding assumes importance as an aspect of evidence-based assessment of the economic devastation that has accompanied the pandemic. It points even more specifically to the role of the state, or its relative absence, in safeguarding its peoples from a once-in-a-century, long-drawn out catastrophe which has persisted for over a year. Behind these numbers are real people, whose predicament would have been better served by a state with a mind to basing policy intervention on evidence, not least when such research evidence is available in the public domain. Even based on a partial assessment, the two main pandemic responses by the government – a hastily declared lockdown and reluctantly ad-hoc relief measures – have resulted in “grievously harsh” consequences for India and its fight against poverty. By highlighting the outcomes of two earlier significant research efforts, Subramanian invites attention to importantly required numbers that would enable policy makers to get a sense of the enormity of the deprivation that has been caused by the COVID-19 pandemic. CONTENTS I. INTRODUCTION II. THE PRC ESTIMATE III. THE APU ESTIMATE IV. DIFFERENCES BETWEEN THE PRC AND APU ESTIMATES V. CONCLUDING NOTE I. INTRODUCTION This will be, for the most part, a data-and-methodology-related essay concerned with a seemingly antiseptic assessment of the possible impact of the first wave of the coronavirus pandemic on the magnitude of income-poverty in India. The concern is not only with a pandemic of historic magnitude, but also of a policy orientation that may have resulted in anywhere upwards of 200 million Indians sliding into poverty as a result of COVID-19 and the response to it.The focus of this essay will be on numbers and counting, and on the assumptions underlying these in an environment of scanty data accessed from different sources. In order to tell a narrative involving numbers, one can either focus on the manner in which they are derived, or shine the spotlight on the story that lies behind, and is reflected by, the data. In the present essay, the relative weight of emphasis is laid on the first of these two orientations, just so that the restricted focus of the exercise is preserved in the manner of its treatment. I shall confine commentary to a few observations, and not least because the numbers leave little room for any elaborately articulated opinion that is not immediately suggested by the quantitative evidence.In what follows, I shall try and spell out, as clearly as I am able to, the method by which the poverty numbers dealt with in this essay can be derived. These poverty numbers relate to the estimates that have been advanced in two earlier studies. 1.1 Different estimates of people pushed into poverty The first study is one by the Pew Research Center (PRC), Washington, D. C., USA (Kochhar, 2021), and the second is due to the Azim Premji University (APU), Bengaluru, India (APU, 2021). The two studies come up with vastly differing estimates of the additional numbers of people precipitated into poverty during the course of the first wave of the coronavirus pandemic in India. This, as might be expected, is on account of the differing data sets employed in the two studies. My effort is essentially to try and reconstruct these data sets, on the basis of the methodological guidelines available in the two respective studies. At one level, the effort may be justified simply in terms of the importance of keeping alive, in the public domain, the findings on pandemic and poverty revealed by the studies. They are of such vital contemporary significance that they must not be allowed to simply slip into forgetfulness or past history. Apart from this, there is a case for a painstaking—even plodding—expository exercise aimed at enabling laypersons and younger researchers to get a sense of the manner and method by which estimates of the sort discussed here are arrived at. In this justification, the focus is on the intrinsic utility of explanation, appraisal, and criticism. The two studies come up with vastly differing estimates of the additional numbers of people precipitated into poverty during first wave of the pandemic. My reconstruction does not yield results identical to the studies’ results, but the relevant sets of results are close enough to those in the originals. I should clarify, and reiterate, that the assumptions and input data sets I have attributed to the two studies are a product of my reconstruction of the methodological directions provided in the two studies, and any deviation there may be of my reconstruction from the actually employed methodology is certainly not due to wilful misattribution, but rather to obvious imperfections in my reconstruction. In particular, when I speak of the ‘Pew Research Centre’ and the ‘Azim Premji University’ data sets, I refer to my reconstructions of these data sets. Links to the studies by these organisations are provided under References. 1.2 Constructing poverty ratios With these preliminary clarificatory remarks out of the way, it is useful to begin by asking: what, typically, are the data one would need in order to estimate the headcount ratio of poverty (the proportion of the population that is poor)? It is useful to address this question because there are software computing packages available which can convert the requisite data into processed summary statistics of relevance to one’s interest. One such package is a readily accessible programme maintained by the World Bank, ‘POVCALNET’, which enables its user to feed in certain relevant data, which the programme processes. It then returns, by way of output, the headcount ratio of poverty (apart from a host of other related statistics on measures of central tendency and dispersion, such as the mean, the Gini coefficient of inequality, and a number of poverty indices). There are, typically, three items of data which the POVCALNET programme seeks, as enumerated and explained below 1 : The Income Distribution ( D ) . There are different ways in which income distribution data can be presented. A particularly convenient form is one which indicates the cumulative income share of each cumulated decile of the population, arranged from poorest to richest. That is, the data are presented in such a way that we have information on the income share of the poorest 10 per cent of the population, the income share of the poorest 20 per cent, the income share of the poorest 30 per cent,…, and so on, until we have accounted for all 100 per cent of the population. The income distribution is thus essentially depicted in a two-column table in which the first column lists the cumulated deciles of the population in ascending order of income and the second provides the cumulated income share corresponding to each cumulated population share. The Poverty Line (z) . The poverty line is a level of income such that all persons with incomes less than this level are considered to be poor . The Mean Income of the Distribution (m) . This is just the average income of the reference population. Once we feed these three inputs—namely D , z, and m —into the POVCALNET programme, it will tell us the associated headcount ratio of poverty for the given combination of income distribution, poverty line and mean income 2 . All of this is simple enough. The practical problem is to find the data on D and m , and to construct a reasonably convincing poverty line, z , which does the intended job of specifying a level of income that experience and judgement would endorse as an acceptable poverty line. These inputs are not readily available in the forms in, and for the time-periods for, which they would be required for constructing a picture of the impact of COVID-19 on the magnitude of poverty. Therefore, in order to assemble the needed information on the vital triad ( D , z , and m ) for any appropriate period (in this instance, the pre- and post-pandemic periods), a researcher would need to make certain assumptions and have resort to alternative sources of data.. Return to Contents II. THE PRC ESTIMATE As noted at the end of the introductory chapter, any assessment of changes in poverty on account of COVID-19 would depend crucially on our precise choice of the data inputs D , z, and m . At least one earlier effort at such an assessment for India (and indeed for other countries and the world as a whole) is due to the work of social scientists at the PRC, an institution which describes itself as a ‘non-partisan fact tank’, located in Washington, D.C., U.S. (see Kochhar, 2021) The Pew study estimates that an additional 75 million Indians may have been pushed into poverty after the first wave of the COVID-19 pandemic. The income distribution employed in this study is India’s 2011 consumer expenditure distribution, as available from the National Statistical Office (NSO), and the poverty line is taken to be the World Bank’s international poverty line of $2 at 2011 Purchasing Power Parity prices (converted to national currency and updated to take account of inflation). The ‘pre-COVID-19’ mean income is calculated on the basis of the World Bank’s (relatively optimistic) projection, made in January 2020, of the annual growth rate for 2019-2020. The ‘post-COVID-19’ mean is calculated on the basis of the World Bank’s (considerably depressed) estimate of this growth rate, made in January 2021. On the basis of these assumptions regarding D , z, and m , the Pew study estimates that an additional 75 million Indians may have been pushed into poverty after the first wave of the COVID-19 pandemic. The following three sub-sections present, in slightly greater detail, what I take to be the assumptions regarding the data inputs D , z, and m used in the PRC study. 2.1 The PRC Income Distribution Input As is well known, there are no systematic data available on the distribution of incomes in India. What we do have is information, from the quinquennial surveys conducted by the Central Statistical Organization’s (CSO’s) NSO, on the distribution of household consumption expenditure 3 . The latest official survey data pertain to the 68 Round of the NSO for the year 2011-12. It is these distributional data which seem to have been employed in the PRC analysis as a proxy for India’s 2020 income distribution. It should be mentioned that the 68 Round survey employs three ‘recall periods’, referred to, respectively, as the ‘uniform recall period’ (URP), the ‘mixed recall period’ (MRP), and the ‘modified mixed recall period’ (MMRP). Recall periods are important building blocks as they provide information on the expenses incurred by a household over specific time blocs, say a month or a year 4 . I take it that the distributional data employed in the PRC study correspond to the MRP estimates. The distributions are assumed to be the same for both the pre-COVID-19 and the post-COVID-19 periods. Table 1, which is derived from the National Sample Survey Organization’s 2011-12 data on rural and urban consumption distributions, summarises our data input on D . Table 1: Imputed Rural and Urban Income Distributions for 2020 Based on Corresponding Consumer Expenditure Distributions from National Sample Survey Data for 2011-12 (PRC) Table-1page-0002jpg Note: G stands for the Gini coefficient of inequality. Gini coefficients range from 0 to 1, representing perfect equality and inequality, respectively. Therefore, the higher the Gini coefficient, the greater the inequality. Source: Derived from data in Tables 1BR and 1BU of National Sample Survey (2014): Level and Pattern of Consumer Expenditure 2011-12 , NSS 68 Round, National Sample Survey Office, MoSPI, GoI, February 2014. It should be added that there are obvious caveats that must be issued about the use of consumption expenditure distributions as proxies for income distributions, which the PRC study acknowledges. For one thing, consumption distributions are typically less unequal than income distributions. For another, the same distributions are employed for both ‘pre-COVID-19’ and ‘post-COVID-19’ situations, which does not take into account the possibility that the impact of the pandemic on inequality might have been regressive. Thirdly, the consumption distribution data pertain to 2011-12, and the consumption distribution—especially in the urban areas of the country—has displayed a tendency to become more unequal over time. Having said this, there are situations in which—after a due observation of the attendant limitations of the exercise—one is constrained to employ the data that are available, in a spirit of not allowing the feasible ‘mixed good’ to defeat an unattainable ‘first best’. On this score, at least, the PRC study cannot be faulted. 2.2 The PRC Poverty Line Input (z) The World Bank’s international poverty line is pegged at $1.90 per person per day at 2011 Purchasing Power Parity Exchange (PPP) rates. The PRC study employs a poverty line of $2.00. (A discussion of the merits of this poverty line is deferred to a later stage.) From Table 2.4 of World Bank (2015) 5 , we find that $1 was equivalent, in PPP exchange terms, to ₹15.11 in 2011. An international poverty line of $2.00 would, therefore, translate to ₹30.22 per person per day, or, multiplying by 30 days, to ₹906.60 per person per month. This is taken to be the poverty line for both rural and urban India. Applying the Consumer Price Index of Agricultural Labourers (CPIAL), we obtain a rural poverty line of ₹1,478 per person per month at 2020 prices. Applying the Consumer Price Index of Industrial Workers (CPIIW), we obtain an urban poverty line of ₹1514 per person per month at 2019 prices. (The rural price index is estimated to have increased by a factor of 1.63 from 2011 to 2020, and the urban price index by a factor of 1.67 from 2011 to 2019: these factors are derived from RBI data on prices. 6 Our reconstruction of the poverty line ( z ) input data in the PRC study is summarised in Table 2: Table 2: Rural and Urban Poverty Lines per Person per Month (in ₹) in 2020 at Current Prices (PRC) Rural Poverty Line Urban Poverty Line 1,4781,514 Source: Author’s calculations. 2.3 The PRC Mean Incomes Input (m) Here is my reconstruction of the PRC methodology for deriving rural and urban ‘pre-COVID-19’ and ‘post-COVID-19’ means for 2020, on the basis of my interpretation of the methodology as outlined in Kochhar (2021). First, we note that the 68 Round NSO estimates of average per capita consumption expenditure in 2011-12, at 2011-12 prices, are: ₹1,287.17 for rural India, and ₹2,477.02 for urban India 7 . The PRC method consists, first, in using these estimates in the benchmark year, 2011-12, to estimate what their values might have been in 2019 if they had grown at the same rate as real per capita GDP over the period 2012 to 2019. World Bank data 8 suggest that India’s per capita GDP at constant local currency units increased by a factor of 1.4644 from 2012 to 2019: applying this growth factor to the 2011-12 NSO estimates of mean consumption yields rural and urban estimates for 2019 of ₹1,885.58 and ₹3,628.59 respectively, at 2011-12 prices. It remains to proceed from 2019 to 2020, which requires us to consider the World Bank’s projections in this regard. In January 2020 before the outbreak of the pandemic, the World Bank projected a growth rate of 5.8 per cent on the 2019 per capita GDP for 2020, which, in the light of the economic effects of the outbreak, was revised downward to (-) 9.6 per cent in January 2021. We can now envisage a counterfactual situation of what the rural and urban means might have been in 2020 in the absence of the pandemic, by applying the growth-rate of 5.8 per cent to the estimated 2019 rural and urban means of ₹1,885.58 and ₹3,628.59 respectively, to yield ₹1,994.94 and ₹3,839.05 respectively, at 2011-2012 prices. By applying the inflation factors, mentioned earlier, of 1.63 for the rural areas and 1.67 for the urban areas respectively, we can postulate the counterfactual ‘pre-COVID-19’ means, in 2020 prices, to be ₹3,251 (= ₹1,994.94x1.63) for rural India and Rs. 6410 (= 3839.05x1.67) for urban India. In similar manner, and after applying the growth rate of (-) 9.6 per cent to the 2019 estimates of means, followed by adjustment for inflation, we can obtain estimates of the ‘post-COVID-19’ means, in 2020, at 2020 prices, of ₹2,778 for rural India and ₹5,477 for urban India. Table 3 summarises what I take to be the PRC estimates of the rural and urban means in 2020, pre-and post-COVID-19: Table 3: Pre- and Post-COVID-19 Rural and Urban Average Incomes (in ₹) in 2020 at Current Prices (PRC) Pre-COVID-19 Rural Mean Post-COVID-19 Rural Mean Pre-COVID-19 Urban Mean Post-COVID-19 Urban Mean 3,2512,7786,4105,477 Source: Author’s calculations as indicated in text. 2.4 Results from the PRC Input Data I first summarise my reconstruction of the PRC study’s input data in Table 4. Table 4: Summary of PRC Study’s Reconstructed Input Data on Distributions, Poverty Lines and Means: 2020 Table-4page-0001jpg Source: Based on the numbers in Tables 1-3. The POVCALNET software programme returns the relevant headcount ratios, as furnished in Table 5, for the input data summarised in Table 4, from which one can calculate the changes in both the headcount ratios and aggregate headcounts attributable to the COVID-19 pandemic, separately for the rural and the urban areas. I have assumed an all-India population of 1,360 million for 2020, split between the rural and urban areas in the proportions of 65 per cent and 35 per cent respectively. Table 5: Levels and Changes in Headcount Ratios and Aggregate Headcounts Attributable to COVID-19, using the PRC Study’s Reconstructed Input Data Rural Pre-COVID-19 Rural Post-COVID-19 Rural Change Urban Pre-COVID-19 Urban Post-COVID-19 Urban Change Total Change HeadcountRatio.0723.1499.07760.0162.0162.0561 AggregateHeadcount(in millions)63.91132.5168.6007.717.7176.31 Source: Author’s calculations based on the input data summarised in Table 5. The incremental number of persons plunged into poverty by the COVID-19 pandemic is 76.31 million (final entry in Table 5), which tallies quite closely with the PRC study’s estimate of 75 million. Further comments are reserved for a later part of this note. We now attempt to reconstruct the APU study’s estimate. Return to Contents III. THE APU ESTIMATE The APU estimate of incremental poverty attributable to the COVID-19 pandemic is contained in the report State of Working India 2021: One Year of Covid (APU, 2021). This remarkable production is the third in a series on the ‘ State of Working India’ ; earlier reports having appeared in 2018 and 2019. The present (2021) edition places a special emphasis on the impact of and policy response to the COVID-19 pandemic. Work on these reports has been carried out under the coordination of a group of researchers in Azim Premji University’s Centre for Sustainable Employment (CSE). The engagement is with the condition of the labouring poor, and the 2021 report provides an extraordinarily detailed account of the general state of the economy, with a focus on lives, livelihoods, incomes, nutrition and living standards, as these have been affected by the pandemic, together with an analysis of policy response (mainly policy failure) and recommendations for meaningful government intervention. This report, and the series of which it is a part, will stand out as an exemplary model of the collection, collation, processing and analysis of data drawn from diverse sources, and of serious scholarly application, humane engagement, and committed effort in the cause of understanding the condition of India’s labouring poor. A particularly compelling measure of its worth is that the work in the report has been carried out in an environment of scanty and unreliable data, not to mention a generalised culture of official obfuscation and prevarication. Returning to our more immediate concerns, the APU study’s methodology is available in Chapter 5 of the State of Working India 2021 report, and is discussed, in what follows, with respect to the input data employed in the study. 3.1 The APU Income Distribution Input (D) The distributional data employed in the study are drawn from the Centre for Monitoring Indian Economy-Consumer Pyramid Household Surveys (CMIE-CPHS). What we earlier referred to as the ‘pre-COVID-19’ and ‘post-COVID-19’ periods correspond, in the APU study, to the eight-month period July 2019-February 2020 and the eight-month period March 2020-October 2020, respectively. The study accumulates the incomes in each income-class across the eight months in each period, to arrive at a consolidated picture of the ‘pre-COVID-19’ and ‘post-COVID-19’ distributions. These data are not explicitly presented in the State of Working India report but have been kindly made available to me by the report’s authors upon request. The relevant data are furnished in Table 6. Table 6: Pre- and Post-COVID-19 Rural and Urban Income Distributions (APU) Table-6page-0001jpg Note: G stands for the Gini coefficient of inequality. Source: Data supplied to the present author by the authors of the APU study. Some observations are in order. Surprisingly, (a) the APU estimates of the urban income-Gini in 2020 are slightly lower than the NSO urban consumption-Gini in 2011-12; and (b) there is only a minor suggestion of worsening of inequality from before to after COVID-19, in both rural and urban India. 9 Secondly, and as noted by the authors of the APU report, the earnings data in the CMIE-CPHS are substantially larger than those reported by the Periodic Labour Force Employment-Unemployment Survey of 2018-19. This issue will be briefly revisited later in this article. 3.2 The APU Poverty Line Input Data (z) The basis for the poverty lines employed in the APU study is explained thus in their report (APU, 2021: p.16): The Expert Committee on Determining the Methodology for fixing the National Minimum Wage (Ministry of Labour and Employment 2019) proposed a wage such that the expenditure on minimum recommended food intake, essential non-food items (namely clothing, fuel and light, house rent, education, medical, footwear, and transport) and other non-food items for the wage earner and their dependents can be met. The recommendation was ₹375 per day (₹104 per capita per day) for rural areas and ₹430 (₹119 per capita per day) for urban areas as of July 2018 10 . This works out to ₹2,900 per capita per month and ₹3,344 per capita per month respectively, after adjusting for inflation in Jan 2020 terms. The poverty line input data are summarised in Table 7. Table 7: Rural and Urban Poverty Lines per Person per Month (in ₹) in 2020 at Current Prices (APU) Rural Poverty Line Urban Poverty Line 2,9003,344 Source: APU (2021) Table 8, which combines data from Tables 2 and 7 shows that the APU rural and urban poverty lines are twice as large as the ones in the PRC study. This is a major source of deviation in the assessment of the impact of COVID-19 on poverty in India and calls for some discussion. Table 8: Rural and Urban Poverty Lines per Person per Month (in ₹) in 2020 at Current Prices (PRC and APU) Rural Poverty Line (in ₹) Urban Poverty Line (in ₹) PRCAPUPRCACU 1,4782,9001,5143,344 Source: From Tables 2 and 7. 3.2.1 A pragmatic assessment of the poverty norm India’s official poverty lines are derived on the basis of that level of consumer expenditure at which some stipulated calorific norm of food consumption is found to be achieved in some reference year, and the reference year poverty line is then ‘updated’ for other years by means of a consumer price index to reflect price changes. The World Bank’s ‘dollar-a-day’ type poverty lines are based on the poverty lines of some of the income-poorest countries of the world many of which were prescribed by the World Bank itself.Neither approach is based on any explicit accounting of commodity requirements (and their costing) for achieving a well-defined list of human functionings at levels that might be deemed to just avoid deprivation. The result is that both official Indian poverty lines and the World Bank’s international poverty line have tended to understate the poverty threshold, by failing to provide a basis for these lines’ adequacy in the matter of meeting a set of basic needs in a measure that could be construed as necessary to escape poverty. The serious limitations of working with the World Bank’s international poverty line have been discussed by other commentators, including Reddy and Pogge (2010) and Reddy and Lahoti (2015), and will not be repeated here. Both official Indian poverty lines and the World Bank’s international poverty line have tended to understate the poverty threshold. What is suggestive is that often a combination of practical knowledge and common sense is a more reliable guide to identifying the poverty line than methods which involve plotting graphs and reading off threshold levels, or squinting at scatter diagrams of some of the poorest countries’ poverty thresholds. Most of us who are familiar with the environments in which we live must be expected to have a reasonably accurate idea of the income required to achieve some minimally acceptable standard of living.In the spirit of pragmatism just alluded to, Jayaraj and Subramanian (2017) have made an attempt to derive a poverty line for urban Tamil Nadu for the year 2014-15. In doing so, they consider both food and non-food necessities. Based on nutrient requirements and recommended dietary allowances for Indians as advanced by the Indian Council of Medical Research (2010) with reference to a low-cost ‘Indian vegetarian balanced diet’, the authors cost the items that might be expected to constitute the diet in question, while also taking account of the subsidiary ingredients that would typically enter a Tamil vegetarian diet of the type under consideration.In the matter of non-food requirements, they make essentially conservative estimates of what it would cost to achieve some elementary standard of living with respect to shelter, education, energy needs, healthcare, transport and communication, clothing and footwear, entertainment and socialization, and personal hygiene. The costing is done on a monthly basis for a family of five, and the poverty line which the authors come up with amounts to ₹14,000 for such a family, which most urban residents of India would view, from personal experience and practical knowledge, as a by no means unreasonable figure.On a per capita basis, the poverty line is a monthly income of ₹2,800—considerably higher than, for instance, the Rangarajan Committee’s recommended urban poverty line which, at 2014-15 prices, would be of the order of just ₹1,600. The poverty line suggested here is admittedly a rough-and-ready one, but it probably relates to what we know about poverty better than one assiduously derived from employing slide-rule-and-compass, which however bears little obvious relation to poverty as we might be expected to understand that condition.Continuing in this vein of uniform simple-mindedness, one could advance the cause of a poverty line (at 2014-15 prices) of ₹2,800 per person per month for urban India, and one for rural India of ₹2,240, which is 80 per cent of the urban poverty line: a swift (and brutal) concession to lower rural prices. Employing the CPIAL and CPIIW prices indices, the rural and urban poverty lines at 2020 prices are of the order of ₹2,839 per person per month for rural India, and ₹3,597 for urban India. These numbers are not far from the rural and urban poverty lines—₹2,900 and ₹3344 respectively—used in the APU study. The APU poverty lines surely appear to reflect a substantially more acceptable standard of what constitutes deprivation thresholds than the World Bank line adopted in the APU study (even allowing for the qualifier of ‘extreme’ for the poverty implied by the World Bank line). 3.3 The Mean Income Input (m) The APU study’s mean income estimates are based on a periodisation of pre- and post-COVID-19 India in two eight-month stretches—July 2019-February 2020 and March 2020-October 2020 respectively. The mean income for the pre-COVID-19 period is taken to be the average of the seasonally-adjusted monthly incomes from July 2019 to February 2020, and that for the post-COVID-19 period to be the average of the seasonally-adjusted monthly incomes from March 2020 to December 2020. The authors of the report state (APU, 2021: p.11):The seasonally-adjusted cumulative income in the months of March to October was 22 per cent less compared [with] the preceding eight months of July 2019 to February 2020. The cumulative decline was higher in urban areas than rural areas (26 per cent versus 21 per cent). For an average household in urban areas this amounts to losing 2.1 months of income (about ₹64,000 for a family of four) and in rural areas losing 1.7 months of income (about ₹34,000 for a family of four). From the quoted paragraph, one can infer 11 the magnitudes of the per capita monthly average income in the pre- and post-COVID-19 periods, for each of the rural and urban areas, and these are summarised in Table 9. Table 9: Pre- and Post-Covid Rural and Urban Average Incomes (in ₹) in 2020 at Current Prices (APU) Pre-COVID-19 Rural Mean Post-COVID-19 Rural Mean Pre-COVID-19 Urban Mean Post-COVID-19 Urban Mean 5,0603,9977,6925,692 Source: Based on APU (2021), as explained in the text. It is possible, as stated in Section 3.1, that the CMIE-CPHS estimates of income on which the APU study’s estimates are based are uniformly exaggerated versions of the corresponding actual incomes—arising possibly from under-sampling of the poorest classes (see Dreze and Somanchi, 2021). However, the declines in average incomes on account of the pandemic appear to be realistic in relation to what one knows about the differential impacts of the pandemic and the lockdown on rural and urban livelihoods in the context of employment and earnings. The APU estimate points to a substantial difference between declines in average urban and rural incomes. The decline in average urban income, at 26 per cent, is higher than the decline in average rural income, at 21 per cent. In contrast, the PRC study suggests a single, undifferentiated, and much lower reduction in average income of 14.6 per cent for both the rural and the urban areas (see Table 3). In view of this, and in view of the restricted choices available, there is a case for favouring the APU study-based estimates in Table 9. The case against what one might call uniform ‘data-nihilism’ is also made by Dhingra and Ghatak (2021) when they say: ‘Despite [certain] statistical concerns, the CPHS does provide consumption numbers for a large sample of individuals, which can provide insights into changes in consumption levels arising from the pandemic.’ 3.4 Results from the APU Input Data Table 10 summarises the reconstructed APU input data on distributions, poverty lines and mean incomes. Table 10: Summary of APU Study’s Reconstructed Input Data on Distributions, Poverty Lines and Means: 2020 Table-10page-0001jpg Source: Based on the numbers in Tables 7 and 9. Table 11, following, presents the POVCALNET results on headcount ratios, aggregate headcounts, and changes in these, for the input data on distributions, poverty lines and mean incomes attributed to the APU study. (As earlier, we take India’s 2020 population to be 1,360 million, with shares of 65 per cent and 35 per cent for the rural and urban areas respectively.) Table 11, relating to the ‘APU results’, corresponds to Table 5, which is a summary of the ‘PRC results’. Table 11: Levels and Changes in Headcount Ratios and Aggregate Headcounts Attributable to Covid-19, using the APU Study’s Reconstructed Input Data Rural Pre- COVID-19 Rural Post-COVID-19 Rural Change Urban Pre-COVID-19 Urban Post-COVID-19 Urban Change Total Change HeadcountRatio.2646.4187.1541.1631.3391.1760.1618 AggregateHeadcount(in millions)233.91370.13136.2277.64161.4183.77 219.99 Source: Author’s calculations based on the input data summarised in Table 10. The reconstructed APU data are compatible with an estimate of an increased aggregate poverty headcount, attributable to the COVID-19 pandemic, of 220 million—which falls short of the APU study’s estimate of 230 million, but not by much: the one estimate is nearly 96 per cent of the other. Return to Contents IV. DIFFERENCES BETWEEN THE PRC AND APU ESTIMATES Now, let us consider the incremental numbers of people pushed into poverty as a consequence of the pandemic and the accompanying lockdown. The APU estimate of this incremental number, at 230 million people, exceeds the PRC incremental estimate, at 75 million people, by a factor of 3! From what we know of the differential impacts of the pandemic-and-lockdown combination of events on rural and urban areas, it was the latter that were most severely affected. This is reflected in the reconstructed APU estimate which suggests that the incremental aggregate urban headcount (84 million) is about 38 per cent of the overall increase (220 million). The PRC estimate, on the other hand, suggests that the urban areas, with an additional (roughly) 8 million in poverty, account for less than 11 per cent of the overall change (76 million). This is not the only reason for judging the APU estimate as being vastly more plausible than the PRC estimate, as can be seen from the detailed evidence presented in the APU report on unemployment, job losses, losses in earnings, increased levels of hunger in the aftermath of the covid-inspired lockdown, and the extremely poor policy responses to these events of distress. In terms of the impact of the input data employed on the resulting outputs, it would appear that the distributions employed in the two studies were least instrumental in explaining the differing estimates of the two studies; differences in the mean incomes data employed by the two studies have greater explanatory significance; and differing assumptions about the poverty lines the greatest influence. Thus, if we preserve the APU data inputs on mean incomes and poverty lines but vary only the distributions by switching to those employed in the PRC study, we find that the resulting estimate of the change is 233 million: if anything, changing the distribution causes the estimate of the incremental change to increase , but not by much. If we preserve the APU data inputs on distributions and poverty lines but replace the APU mean incomes by the PRC mean incomes, we find a more substantial deviation in the change: it declines from 220 million to 147 million. Finally, if we preserve the APU data inputs on distributions and mean incomes but switch from the APU poverty lines to the PRC poverty lines, we discover a massive fall in the estimate: from 220 million to just 76 million. Our reservations on the widespread use of the World Bank’s international poverty line would seem to be well-founded: in the instant case, as in a general way, it is misleading to employ unrealistically low poverty lines, even when qualified by the notion of conveying a sense of ‘extreme’ poverty. Return to Contents V. CONCLUDING NOTE Everything considered, a count of upwards of 200 million additional people plunged into poverty, as estimated by the APU study, seems eminently plausible. We are speaking only of the first wave of the pandemic which, by all accounts, was less devastating than the second wave. The outcome, even when confined to a partial assessment of the impact on poverty, has been grievously harsh, accompanied, as it has been, by aspects of government policy that have been a combination of misplaced over-zealousness in the matter of implementing an abrupt, draconian lockdown and immutable reluctance in the matter of affording relief to the country’s affected citizens. A count of upwards of 200 million additional people plunged into poverty, as estimated by the APU study, seems eminently plausible. In this context, it is striking (even allowing for ‘adaptive expectations’) that we have not, apparently, had any state-sponsored attempt at providing or seeking evidence on the impact of COVID-19 on poverty. This is the more striking in the face of generalized and intense global awareness of, and concern with, the likely devastating consequences of the pandemic for national and international economic outcomes. Such engagement is easily seen in the research and opinion put out by various multilateral agencies such as the World Bank, the IMF and UNICEF, think-tanks like UNU-WIDER, professional journals like The Economist , and individual researchers. A small and illustrative list of studies on poverty and the pandemic would include: Kharas (2020), Kharas and Dooley (2021), Sumner et al (2020), Lakner et al (2021), IMF (2020), UNICEF (2020) (which contains both global and country-level studies on the impact of COVID-19 on child poverty in Africa, Europe and Central Asia, Latin America and the Caribbeans, South Asia, and East Asia and the Pacific), and several articles in The Economist (including in the issues of May 23, 2020; September 26, 2020; October 23, 2020; April 10, 2021; and May 15, 2021). The evidence on the impact of COVID-19 on living standards can be only as sound as the data on which it is based. But what evidence there is, combined with informed general awareness and the application of common sense, suggests both the need for and the possibility of well-founded policy intervention. This is a major reason why the available evidence needs to be appraised, systematised, and repeatedly put out in the public domain. Hence also this essay, however forlorn might be the hope that inspires it. Acknowledgement: The author is indebted to Amit Basole and Rahul Lahoti for very helpful comments on an earlier version of this essay . Return to Contents Also by the Author 1. Letting the Data Speak: Consumption Spending, Rural Distress, Urban Slow-Down, and Overall Stagnation, Dec. 11, 2019. 2. Some Basic Issues Underlying Basic Income, Feb. 7, 2019. 3. Some Views on Public Policy Outcomes in India - Is it the Message or the Messenger?, Nov. 12, 2018. [ S. Subramanian is a retired professor of Economics from the Madras Institute of Development Studies, and a former Indian Council of Social Science Research National Fellow. He has research interests in the fields of poverty, inequality, demography, welfare economics, social choice theory, and development economics. He is an elected Fellow of the Human Development and Capabilities Association, and was a member of the advisory board of the World Bank’s Commission on Global Poverty (2015-16). He is the author of, among other books, The Poverty Line (Oxford University Press: 2012), Inequality and Poverty: A Short Critical Introduction (Springer: 2019), and Futilitarianism (Routledge, Delhi: 2020). He can be contacted at [email protected] ]. Endnotes: 1. For a sample illustration, see the POVCALNET web-page titled ‘ Estimate Your Own Distribution’ , The World Bank here: [http://iresearch.worldbank.org/PovcalNet/PovCalculator.aspx]. Return To text. 2. Just for completeness of record, here is how the input data are converted into the corresponding output results. (This methodological summary can be ignored by the general reader without any significant loss in the narrative of this Issue Brief.) The distributional data, D , are essentially in the form of distinguished ordinates of the Lorenz curve , and there is a software programme which uses these data to estimate the equation of the Lorenz curve; once that is done, it is a simple matter to derive the value of the Gini coefficient of inequality, which is just twice the area enclosed by the Lorenz curve and the diagonal of the unit square in which the curve is plotted. As for the headcount ratio, the software programme exploits the fact that the slope of the Lorenz curve at any point corresponding to an income level of x is just x/m , where m , to recall, is mean income; so the headcount ratio of poverty can be inferred as that value on the Lorenz curve’s horizontal axis at which the slope of the Lorenz curve (computable from the already derived equation of the Lorenz curve) is equal to z/m , z being, of course, the poverty line. The POVCALNET software resorts to two estimating equations of the Lorenz curve—the so-called General Quadratic Lorenz and the Beta Lorenz. All estimates in this note are based on the relevant General Quadratic Lorenz’s. Return to Text. 3. Household consumption expenditure is “the sum total of monetary values of all the items (i.e. goods and services) consumed by the household on domestic account during the reference period.” Expenses that are actually made only on consumption are included, and therefore, imputed expenses, such as rents of owner-occupied houses, or expenses incurred on productive enterprises are excluded. (Summarised from ‘ India - Household Consumer Expenditure, Type 1 : July 2011 - June 2012, NSS 68th Round ’, Technical Documents, Concepts and Definitions, P A-11.) [http://microdata.gov.in/nada43/index.php/catalog/1/related_materials]. Return to Text. 4. The URP method refers to consumption data collected by asking “people about their consumption expenditure across a 30-day recall period” Under MRP, “data on five less-frequently used items are collected over a one-year period, while sticking to the 30-day recall for the rest of the items. The low-frequency items include expenditure on health, education, clothing, durables etc.” Under MMRP “for some food items, instead of a 30-day recall, only a 7-day recall is collected. Also, for some low-frequency items, instead of a 30-day recall, a 1-year recall is collected. This is believed to provide a more accurate reflection of consumption expenditures.” Misra, U. 2015 . “ Meaning URP, MRP, MMRP “, The Indian Express, October 7. [https://indianexpress.com/article/explained/meaning-urp-mrp-mmrp/]. Return to Text. 5. World Bank. 2015 . Purchasing Power Parities and Real Expenditures of World Economics: A Comprehensive Report of the 2011 International Comparison Program , Washington, DC. © World Bank . [https://openknowledge.worldbank.org/handle/10986/20526]. License: CC BY 3.0 IGO. Return to Text. 6. For data on CPIAL, see https://rbi.org.in/scripts/BS_ViewBulletin.aspx?Id=13884 for 2011-12, and https://rbi.org.in/scripts/BS_ViewBulletin.aspx?Id=20342# for April 2020; and for data on CPIIW, see https://rbi.org.in/scripts/BS_ViewBulletin.aspx?Id=13882 for 2011-12, and https://rbi.org.in/scripts/BS_ViewBulletin.aspx?Id=18666 for October 2019. Return to Text. 7. Tables 1BR and 1BU of National Sample Survey (2014): Level and Pattern of Consumer Expenditure 201-12 , NSS 68 Round, National Sample Survey Office, MOSPI, GOI, February 2014. Return to Text. 8. The World Bank . nd . GDP per capita (constant LCU) – India . [https://data.worldbank.org/indicator/NY.GDP.PCAP.KN?locations=IN]. Return to Text. 9. Note: However, in both cases, for each cumulated decile of the population, the cumulated income share in the pre-COVID-19 period is greater than or equal to the corresponding cumulated income share, post-COVID-19, reflecting a case of what in the technical literature is called ‘Lorenz dominance’. Return to Text. 10. I take it that the recommended daily rural and urban allowances of ₹375 and ₹430, respectively, are for a household of four, so that the daily per capita allowances become ₹93.75 (or ₹2,812.50 per month) and ₹107.50 (or ₹3,225 per month) at 2018 prices. The reported daily allowances of ₹104 and ₹119 translate to monthly levels of ₹3,120 and ₹3,570 respectively at 2018 prices, in excess the poverty lines for 2020 specified in the Report. One suspects there is an error in reporting the daily per capita allowances. Return to Text. 11. For example, for the rural areas, a 21 per cent loss of ₹34,000 suggests pre-and post-COVID-19 incomes of ₹161,905 (= 34000/.21) and ₹127,905 (= 161,905 – 34,000); on a per capita basis, given a family of four, this works out to ₹40,476 and ₹31,976 respectively; averaging out over eight months, yields per capita monthly means for the pre- and post-COVID-19 periods of ₹5,060 and ₹3,997 respectively. Similar computations can be made for urban areas. Return to Text. References: [ All URLs were last accessed on August 17, 2021. ] Azim Premji University. 2021. State of Working India 2021: One year of Covid-19 , Centre for Sustainable Employment. [https://cse.azimpremjiuniversity.edu.in/wp-content/uploads/2021/05/State_of_Working_India_2021-One_year_of_Covid-19.pdf]. Dhingra, S. and Ghatak, M. 2021. ‘ How has Covid-19 affected India’s economy? ’, Economics Observatory . [https://www.economicsobservatory.com/how-has-covid-19-affected-indias-economy]. Dreze, J. and Somanchi, A. 2021. ‘ The Covid-19 Crisis and People’s Right to Food ’, SocArXiv. June 1. doi:10.31235/osf.io/ybrmg. [https://osf.io/preprints/socarxiv/ybrmg/]. Indian Council of Medical Research. 2010. Nutrient Requirements and Recommended Dietary Allowance for Indians: A Report of the Expert Group of the Indian Council of Medical Research 2010 , National Institute of Nutrition, Hyderabad, India. [https://www.enacnetwork.com/files/pdf/ICMR_RDA_BOOK_2010.pdf]. International Monetary Fund. 2020 . ‘ A Crisis Like No Other, An Uncertain Recovery ’, World Economic Outlook Update, June 2020. [https://www.imf.org/en/Publications/WEO/Issues/2020/06/24/WEOUpdateJune2020]. Jayaraj, D. and Subramanian, S. 2017. ‘The Iniquity of Money-Metric Poverty in India’, Basic Income Studies , 12 (1): pp. 1 – 26. Kharas, H. 2020. ‘ The Impact of Covid-19 on Global Extreme Poverty ’, Brookings , October 21. [https://www.brookings.edu/blog/future-development/2020/10/21/the-impact-of-covid-19-on-global-extreme-poverty/]. Kharas, H. and M. Dooley, M. 2021. ‘ Long-Run Impacts of Covid-19 on Extreme Poverty ’, Brookings , June 2. [https://www.brookings.edu/blog/future-development/2021/06/02/long-run-impacts-of-covid-19-on-extreme-poverty/]. Kochhar, R. 2021. ‘ In the pandemic, India’s middle class shrinks and poverty spreads while China sees smaller changes ’, Pew Research Centre , March 18. [https://www.pewresearch.org/fact-tank/2021/03/18/in-the-pandemic-indias-middle-class-shrinks-and-poverty-spreads-while-china-sees-smaller-changes/]. Lakner, C., Yonzan, N., et. al. 2021. ‘ Updated Estimates of the Impact of Covid-19 on Global Poverty: Looking Back at 2020 and the Outlook for 2021 ’, World Bank Blogs, January 11. [https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty-looking-back-2020-and-outlook-2021]. (An updated analysis by the same authors is available at ‘ Updated estimates of the impact of COVID-19 on global poverty: Turning the corner on the pandemic in 2021? ’, World Bank Blogs, June 24.) [https://blogs.worldbank.org/opendata/updated-estimates-impact-covid-19-global-poverty-turning-corner-pandemic-2021]. Ministry of Labour and Employment. 2019. Report of the Expert Committee on Determining the Methodology for Fixing the National Minimum Wage , Government of India. [https://labour.gov.in/sites/default/files/Commitee_on_Determination_of_Methodology.pdf]. Reddy, S. and Lahoti, R. 2016. ‘$1.9 a Day: What Does it Say?’ New Left Review , Jan-Feb 2016, 97: 106-127. Reddy, S. and Pogge, T. 2010. ‘How Not to Count the Poor,’ in S. Anand, P. Segal and J. Stiglitz (eds): Debates on the Measurement of Global Poverty, Oxford University Press: New York. Sumner, A., Hoy, C., et. al. 2020. ‘ Estimates of the Impact of Covid-19 on Global Poverty ’, WIDER Working Paper No. 2020/43, April. [https://www.wider.unu.edu/sites/default/files/Publications/Working-paper/PDF/wp2020-43.pdf]. UNICEF. nd. Covid-19 Impacts on Child Poverty: Social Policy Analysis to Inform the Covid-19 Response . [https://www.unicef.org/social-policy/child-poverty/covid-19-socioeconomic-impacts].

In the space of two weeks in July, two decisions resurrected the policy focus on cooperatives in India. The first, by the executive, was to constitute an independent Union Ministry of Cooperation (MoC). The second, by the judiciary, was a verdict of the Supreme Court of India declaring that cooperative societies as a subject matter belong “wholly and exclusively to the State legislatures to legislate upon”. In this Issue Brief, H.S. Shylendra, Professor, Social Science Area, Institute of Rural Management (IRMA), Anand, draws out the legal and constitutional implications of these two developments, presents the relevance of a cooperative-based economy, and identifies the pathways for its success in the light of India’s experience with cooperatives and the prevailing political economy. The best prescription, he concludes, would be a movement, more than a ministry, to support India’s ailing cooperative sector proactively in diverse ways without hurting its autonomy. CONTENTS I. INTRODUCTION II. BRIEF HISTORY LEADING TO A MINISTRY III. ‘EMBRACE-OF-DEATH’ TO REFORMS IV. BUILDING A COOPERATIVE-BASED ECONOMY V. PATHWAYS TO COOPERATIVE SUCCESS I. INTRODUCTION Two recent developments have brought the policy focus back on India’s cooperatives. The first is the decision of the Government of India (GoI) on July 6, 2021, to constitute an independent Union Ministry of Cooperation (MoC) and the second is the judgement of the Supreme Court of India delivered on July 20, 2021, declaring that “Co-operative societies as a subject matter belongs wholly and exclusively to the State legislatures to legislate upon...” 1 Coming as they did from different sources, these two decisions have clear consequences on each other. The creation of the MoC has raised hackles over the real intention of the Union government as it is perceived to open out space for the centre to interfere in the working of the cooperatives that are under the jurisdiction of State governments. The Supreme Court judgement on a writ appeal on the 97 th Constitutional Amendment Act (CAA) clarifies the constitutional position on the domain over cooperatives and by virtue of that gives credence to the concerns of the States over possible meddling by centre in their domain, 2 be it through amendments to the law or by creating a Ministry. The newly established MoC has also raised a debate over the potential role such a Ministry can play in promoting the cooperative sector given the current socio-economic milieu. The Government of India on its part has called the step an ‘historic move’ that will strengthen cooperatives as a true peoples’-based movement. The government in its press note 3 has emphasised that a ‘Co-operative based economic development model is very relevant....’ and the Ministry through its ‘administrative, legal and policy framework’ will streamline the processes of ‘Ease-of-doing-business for cooperatives.’ Thus, apparently, the Ministry has set a larger goal for itself of working towards a cooperative-based-economy through a multi-pronged strategy. Those supportive of the new idea feel that it can help bring about uniform development of the cooperative movement in the country given its uneven spread 4 and address the much-needed inter-State coordination in the working of cooperatives 5 . However, sceptics are clear that in addition to compromising on the norms of federalism, the Ministry may work more as a front for dispensing political patronage 6 . Given such mixed concerns, it would be worthwhile to examine critically not only the legal position of such a Union Ministry but also the larger issue of promoting a cooperative-based-economy. Return to Contents II. BRIEF HISTORY LEADING TO A MINISTRY In the constitutional scheme of things, a clear demarcation has been drawn regarding cooperatives by placing them in the State List (List II of Seventh Schedule). The States have exclusive powers to legislate on and govern the cooperatives registered within their boundaries. Although the first cooperative societies act passed in 1904 under the colonial rule (and amended in 1912) was a central act, the 1919 administrative reforms transferred cooperatives to the provinces (GoI 2005) 7 . Since then Provinces and, after Independence, States have taken over the subject of cooperatives and have framed their own Acts to regulate cooperatives. In the meanwhile, the Multi-Unit Co-Operative Societies Act, 1942, was passed by the Government of India in 1942 (re-enacted in 1984 and 2002) giving the Union government jurisdiction over multi-State cooperatives. Overall, since the first Act of 1904, there has been a fair amount of clarity about the jurisdiction of the Union and the State governments over cooperatives, although the Reserve Bank of India as a federal level monetary authority could exercise some powers over the cooperative banks especially in the interest of the depositors as per the Banking Regulation Act 8 . Having been assigned primacy over cooperative governance, State governments created a separate ministry or department of cooperation for administering the cooperatives. Given the importance of this sector, State-level cooperative Ministries have also enjoyed a fair amount power and autonomy. The Government of India on its part has been working with cooperatives mainly through a minor department created as a part of some major Ministry to discharge its responsibilities pertaining to multi-state cooperatives and the general development of cooperatives in the country through various developmental schemes. Since 1904, there has been clarity about the jurisdiction of the Union and the State governments. The developmental role pursued by the Union government includes promotion of cooperatives, provision of financial assistance, capacity building through training of members and staff, infrastructure and technology development, and revival plans. Such schemes, framed under the Five Year Plans, were implemented through the concurrence of the respective State’s cooperative ministry or department. Given this two-fold role, over the years the cooperative department has been attached to or placed with diverse Union ministries such as food and civil supplies or community development or commerce or agriculture. Such linkages with a multiplicity of Ministries underscore the broader scope of the cooperative sector, combining both agriculture and non-agricultural cooperatives. Given that agriculture has been the prominent sector for cooperatives, since 1979 the Department of Cooperation has been attached to the Union agricultural ministry. Until the creation of the new MoC, issues pertaining to cooperatives were overseen by a division within the Department of agriculture, cooperation and farmers’ welfare coming under a larger ministry for agriculture 9 . Hence, the sudden up-gradation of a division/department with a relatively limited role into a full-fledged Union Ministry is a curious development, triggering concerns. An argument being put-forth is that the cooperative division in the agricultural department was unable to look after adequately the cooperatives, more so with regard to such entities operating in the non-agricultural sector which in the recent days constitute a significant proportion 10 . However, even if the non-agricultural cooperatives have grown in numbers, a bulk of them are working under the jurisdiction of State governments (see Table 1). Such an argument, hence, may not be fully tenable. Explicating New Delhi’s rationale for such a step and clarifying the concerns of the cooperative stakeholders would require going beyond the mere legal or administrative angle. Such an explanation is attempted in the following section which highlights the issues connected with the larger politics and governance about the cooperatives, and the likely compulsions that may have arisen in the sector in context of the economic reforms. Table 1: Sector-wise Distribution of Cooperatives in India (%, 2016-17) Sector % (Total in numbers) Credit & Thrift20.79 Housing17.83 Dairy17.79 Labour5.50 Agri-Allied & Livestock3.50 Consumer3.08 Women/Tribal/SC&ST2.52 Textile & Handloom2.05 Industrial2.03 Multi-Purpose1.75 Others23.02 Multi-State Coops0.15 Total Cooperatives (number) 8,54,355 Source: National Cooperative Union of India (NCUI), 2018 . Return to Contents III. ‘EMBRACE-OF-DEATH’ TO REFORMS India’s first Prime Minister, Jawaharlal Nehru, who wanted to ‘convulse India with Co-operation’ was equally emphatic that government control over cooperatives is like an ‘embrace-of-death’ (as quoted in Dwivedi 1989). 11 Cooperatives, which are democratic institutions by form, have been treated as potential training grounds for developing and nurturing grassroots leaders who can then move into the larger political domain. Given the competitive politics over the decades, this double-edged intention, howsoever noble, unfortunately has degenerated into a wily strategy of political parties and leaders to capture cooperatives to advance their own prospects in the guise of cooperative development. The sector has emerged as an avenue for dispensing patronage to the supporters of ruling parties. Ruling parties and the governments have openly made use of such opportunities to seize positions or suspend the committees of opposing groups or appoint bureaucrats to run the cooperatives under the tutelage of a government department (GoI 2009) 12 (Jain and Coelho 1996) 13 . No doubt in several places, irrespective of political opportunities which were there for the taking at local or regional levels, many leaders have worked more broadmindedly and in a neutral manner to develop cooperatives as successful ventures for the benefit of the wider section of the membership. Given the developmental role assigned to cooperatives under the planning process and the resources deployed for the purpose, the cooperatives sector has emerged as an avenue for dispensing patronage to the supporters of ruling parties, either by way of nomination to the governing boards or sanctioning schemes specific or common to the cooperatives. The policy of contributing to the share capital of the cooperatives and providing various financial assistance like loan and guarantees have enabled State governments, in the name of public interest, to directly intervene in the working of cooperatives which are legally autonomous. The role of the State governments has only worked to the detriment of the cooperative movement in general despite leading to some localised successes (Baviskar and Attwood 1991) 14 . Such a top-down approach deprived the cooperatives of their vitality in meeting the needs of their members and losing credibility in the process. The prevailing social-economic inequalities as reflected in illiteracy, poverty, and caste-differences also had not helped the cause of the cooperatives. Horace Plunkett, the pioneer of Irish cooperatives, had aptly observed: ‘there is no cooperative movement in India, there is only the cooperative policy of government’ 15 . The poor outcomes of the state-driven interference in the cooperative movement and the emerging realities in the post-reforms era resulted in some serious policy level introspections about the cooperatives. Given also their structural constraints related to scale of operations and ability to access capital, cooperatives had struggled to thrive in the liberalised economy despite growing in physical numbers (see Table 2). For example, the share of credit cooperatives in the ground level credit disbursed which was 62 per cent in 1992-93 plummeted to 34 per cent in 2002-03 16 . Table 2: Progress of Cooperatives Indicator 1950-51 17 1991-92 2016-17 Total Number 181190318700854355 Total Members (Million)13.7148.0290.1 % of members to total population3.8 %17.5%22.2% Source: NCUI, 2018. Although the cooperative sector had shown some hesitancy to accept economic reforms, the emerging realities forced them and the government to evolve relevant strategies to face up to the challenge. Simultaneously, there were strident calls to give autonomy to cooperatives to function more independently and to respond adequately to the signals of a market-economy. Reforming cooperatives assumed greater importance under the ongoing economic liberalisation. Some of the reforms initiated included opening-up the dairy sector to players other than cooperatives, application of prudential norms to cooperative banks, and enactment of liberal cooperative Acts by the States. Many civil society organisations had already started organising collectives outside the cooperative laws in the form of trust or societies to avoid state control. There were also efforts to form informal cooperatives and self-help groups (SHGs) under the growing influence of the design-principles based on institutional economics (Agarwal 2010). 18 Many cooperative leaders wanted more liberal cooperative laws. This came in the form of the enactment of the mutually aided cooperative society Acts starting from 1995 onwards by seven State governments. Andhra Pradesh was the pioneer which had passed The Andhra Pradesh Mutually Aided Co-operative Societies Act, 1995, considered as a path-breaking law. These new liberal laws encouraged formation of cooperatives delinked from the government patronage and control. The second major legal measure was the amendment in 2002 to the Companies Act of 1956, to create a new kind of cooperatives called Producers’ Companies’ (PCs) as hybrid organisations combining the strengths of cooperatives and the corporate entities. At the same time, given the growing prominence of multi-state cooperatives in terms of their number and business, the Union government came up with a more enabling legislation called the Multi-State Cooperative Societies Act in 2004, replacing the 1984 Act 19 . The next major step in the direction was the enactment of the 97 th CAA in 2012, which conferred a fundamental right on formation of a cooperative, and introduced, to quote from the Statement and Objects of the Bill, “fundamental reforms to revitalize these institutions in order to ensure their contribution in the economic development of the country and to serve the interests of members and public at large and also to ensure their autonomy, democratic functioning and professional management.” 20 A major reason attributed by the Union government to justify the CAA was that despite incentivising institutional and legal reforms through cooperative revival schemes, State governments were not forthcoming proactively to change the legal framework because of their own compulsions (GoI 2009) 21 . State governments, hence, were to be compelled to change their laws in tune with uniform constitutional norms. Incidentally, it is these uniform provisions of the 97 th CAA which the Supreme Court has struck down in its judgement of July 20, 2021, in their application to States whose concurrence was not taken for the same. It is now left to the State governments to decide whether they would like to retain or not the amendments made in their cooperative Acts pursuant to the 97 th CAA. Thus, both the Union and the States have made several attempts in the post-reforms period to restructure the cooperative legal framework with mixed outcomes. The former, particularly, has taken legal and constitutional measures to alter the governance scenario despite cooperatives being prominently in the State domain. In the process, while the centre saw the States as reluctant reformers, it was, in turn, perceived by States as obtruding in the guise of reforms. This gives a clear perspective as to why the formation of a new MoC is contentious, if not untenable. The State governments, in particular those ruled by opposition parties, are bound to perceive that Union government may have some other plan up its sleeve. Although part of provisions of the 97 th CAA pertaining to State-level cooperatives have been struck down by the Supreme Court, the role of Union government regarding multi-state cooperatives has been clearly recognised. The developmental role of the centre continues to be relevant even as the legal forms of cooperatives have been getting diversified. In addition, more women are coming forward to be part of the cooperative movement. The centre, no-doubt, has a prerogative to restructure its administrative framework to streamline its activities. The formation of the MoC is legally and constitutionally tenable even though the up-gradation looks disproportionate to the current level of engagement of the Union government with the cooperative sector. However, it may want to play a bigger role proactively going beyond the current mandate given the potential that the cooperative sector holds for building political constituencies. According to a newspaper report, the centre may even explore amending the Constitution to add cooperatives in the Concurrent List to enhance its mandate more legitimately 22 . The press note issued about the MoC, however, does not clarify many of these issues except identifying some hazier goals including talking about the relevance of cooperative-based economic development. Given the common interests that are at play, the Union Government’s apparent keenness to play a larger role in the cooperative sector can become relevant provided it can come out clearly with its plan and seek the cooperation of the States. In India’s federal structure, establishing partnership with the States becomes necessary for this new Union Ministry to work towards building a cooperative-based economy that it has visualised. Return to Contents IV. BUILDING A COOPERATIVE-BASED ECONOMY The real challenge of building a cooperative-based economy, however, lies in making cooperatives thrive on a wider basis, assuming that the Union and the States would be working together for such a cause. The more pertinent question, however, is: How to build a cooperative-based economy in a system which is moving towards strong capitalism? A cooperative-based economy could be defined as one where all major economic activities are prominently carried out by cooperatives and that cooperative way of life is the norm in the society. An overbearing state did not help as cooperatives lost autonomy and got excessively politicised. India’s efforts under the Five Year Plans in the post-independence period hold some lessons here. The planning era started with the goal of creating ‘Cooperative Socialism’ with the thrust being on ‘cooperativising the rural economy’ along Gandhian lines. The state had adopted a proactive approach to support cooperatives through various means. Given the fact that the economy was in a nascent stage of development, cooperatives were able to make some dent in sectors such as credit, milk, sugar, and fertilizers. The policy of favouring cooperatives in some of these sectors helped them grow significantly. Cooperatives in the dairy and sugar sectors succeeded to a considerable extent due to adoption of integrated models, which helped control the value chain and ensure member loyalty through assured price and services (Attwood and Baviskar 1988) 23 (Shah 1996). 24 However, despite some pockets of success, cooperativisation could not go the desired extent. As identified earlier, the overbearing nature of the state did not help the cause either as the field not only lost autonomy but got excessively politicised as well. Moreover cooperatives, in general, suffered from other factors that have a bearing on their sustainability, such as constraints in achieving scale, lack of professional support and lack of adequate capital. Inter-group conflicts and domination by local elite were also found to be common among cooperatives. The arrival of reforms in the 1990s only exacerbated the inherent challenges. Private enterprises entered sectors such as dairy, sugar, and credit that were earlier dominated by cooperatives. Having lost some of their advantages and in the absence of any level playing field, cooperatives faltered despite growing in numbers. Resilient cooperatives and those operating in certain sectors such as fertilisers, milk, sugar, and textiles managed to retain some significant share (Table 3), albeit dwindling over the years 25 . Much of the market share in all the sectors currently is held by entities other than cooperative enterprises. Cooperatives overall play only a minor role in economies like India. A global survey conducted for the United Nations in 2014 (Dave Grace & Associates 2014) 26 revealed that cooperatives’ gross revenue to GDP in Asia was 3.25 per cent as against 7.08 per cent for Europe and 4.12 per cent for North America. Table 3: Sector Specific Share (%) of Cooperatives (2016-17) Sectors % Agri-Credit13.4 Fertilizer Production28.8 Fertilizer Distribution35.0 Sugar Produced30.6 Milk Procurement17.5 Storage Capacity14.8 Spindleage29.3 Direct-Employment13.3 Source: NCUI (2018) Given such a situation, it would be an enormous challenge for cooperatives to regain their position and relevance. The mere slogan ‘ Sahakar se Samruddhi’’ (prosperity from cooperation) of the MoC may not help unless a radical shift takes place in the situation of the cooperatives supported by right kind of ideology and policy stance. As a policy, the announcement of MoC by the present government comes off as one that is more spontaneous like the Atmanirbhar Bharat (self-reliant India) launched in the wake of the COVID-19-induced economic crisis. Both steps – the formation of the MoC and self-reliant India – are inherently contradictory to the stated policy position and ideological commitments of the main ruling party. The present administration is committed more to globalisation and neoliberal reforms to deepen the capitalist footprints in the country based on private investment and entrepreneurship. A careful reading of the NITI Aayog’s two policy documents viz. ‘The Three-Year-Action Agenda:2017-20’ (NITI Aayog 2017) 27 and ‘Strategy for New India@75’ (NITI Aayog 2018) 28 clearly brings out that cooperatives are nowhere in the picture of making India a $4-trillion-economy by 2022-23, as visualised in the strategy. Even in the agricultural sector, where cooperatives have conventionally played a significant role in some of the fields, there is focus mainly on the private investment to promote agribusiness as a way of resolving the agrarian crisis involving a majority of the small and marginal farmers. One can see only a perfunctory mention of cooperatives or farmers’ producer organisations (FPOs) to play a peripheral role. The cooperatives which had struggled to blossom even in the heydays of planning are bound to shrivel in an era devoid of any ideological heft. Creation of a new ministry hence sounds rhetorical being not backed up by relevant policy and ideology to make any significant dent. Return to Contents V. PATHWAYS TO COOPERATIVE SUCCESS Apart from the ideological conviction, real pathways to the success of cooperatives would go with the following strategies. Cooperatives, despite their varied global success, remain relevant from the point of view of human welfare. Their social and economic relevance has been recognised even in capitalist economies, while they have played a significant role as part of the planning process in socialist economies. The social significance of cooperatives emerges both due to their intrinsic value and the instrumental role they can play in overcoming the social and economic crises wrought by capitalism. Solidarity among humans has become essential in view of growing challenges like alienation, atomism, inequality, and ecological rift (Ray 2021). 29 The logic of capitalism based on profit-maximisation and accumulation is at the root of many of these crises. As suggested by Marcel Mauss, “[c]ooperative economic organisations guarantee the perpetuation of the future society” (quoted in Nash et.al. 1976,p 3). 30 Economically, cooperatives offer several advantages although they come along with certain inherent limitations. The first advantage is that they enable members with small means to reap the benefits of collective action. In the absence of such a scope, the poor and disadvantaged become highly vulnerable to potentially exploitative market forces. Cooperatives offer bargaining strengths to withstand such vulnerabilities and obtain needy services at cost (Roy 1981). 31 This is the primary reason as to why cooperatives are strongly advocated for the poor (Shylendra 2013). 32 Similarly, for certain perishable commodities or areas crucial for livelihoods such as milk, vegetables, microcredit, and natural resources, cooperatives are seen as the ideal form of business because they enable easier mobilisation of members with scope for scale and cost reduction. Workers’ cooperatives are another sector of high relevance and advantage. Moreover, given their focus on mutual benefit over profit, cooperatives can help moderate monopolistic tendencies to ensure fair prices and practices. This is one of the primary reasons as to why cooperatives have grown in strength even in capitalist economies. For example, cooperative membership in Europe and North America accounted for 45.6 per cent and 38.6 per cent of the population respectively (Dave Grace & Associates 2014) . Thus, both socially and economically, cooperatives have merits justifying their relevance in any form of economy. Their social and economic relevance has been recognised in capitalist and socialist economies. State and civil society must support cooperatives proactively in diverse ways through suitable law, education, finance, technology, and policies without hurting their autonomy. The best prescription for ‘good governance’ in cooperatives is to promote cooperatives actively without compromising on their basic principles. In addition to such a proactive approach, efforts must be made to help cooperatives overcome some of their structural limitations in attaining the required scale and viability.New ways of organising cooperatives must be necessarily evolved to ensure their economic success. The inconsiderate aim of forming an independent and formal cooperative for every village or local habitation, irrespective of size, has embedded a structural hindrance to achieve the needed scale for many of the cooperatives. Hence, many cooperatives at the primary level remained unviable. Efforts to revive them through amalgamation or capital infusion has been ridden with difficulties given the top-down approach of such polices. If a cooperative remains unviable, it faces challenges of credibility and sustainability of services. The primary unit need not necessarily be a stand-alone cooperative unit always. In case of size constraint, it should try and function more as a branch of a larger unit to economise. In other words, there can be a multi-village cooperative working as a hub for remote villages having branches. One related possibility in this direction is careful selection and prioritisation of sectors and areas for cooperative formation. Although cooperatives may be organised for specific commodities or services, wherever relevant and feasible, multi-purpose cooperatives could be organised to attain viability. In recent days a cluster-based approach is being advocated, including adoption of ‘one-district-one-crop’ for FPOs. However, both may work in a top-down way, resulting in the exclusion of many producers and crops falling outside such a design. The attempt should be to include all potential members needing service in the jurisdiction and overcome viability challenge through innovative design. Again, the commonly advocated three-tier structure for all cooperative sectors need not be imposed in a top-down way. A multi-tier cooperative structure may evolve more organically as per its economic need to enable control over the value chain as well as to have clear division of functions at different levels of integration. Such integration of cooperatives into a multi-tier system must necessarily be promoted on the principle of democratic federalism which respects the mutual autonomy and accountability of each tier. Legally, cooperatives may assume any form at different levels provided they adhere to basic cooperative norms and are not discriminated by policies because of their legal form.Another crucial area which is often neglected is the professional support for cooperatives to work efficiently in the competitive environment. Apart from having their own professionals trained for their needs, cooperatives must be enabled to access, in innovative ways, the support of empathetic professionals and technical services through collectives or social enterprises which are organised specifically for such services.Agriculture, which is afflicted by growing fragmentation of operational holdings and ridden with innumerable crises, continues to remain a sector that is ripe for a vibrant revival movement to organise collectives. The efforts to build a cooperative-based economy can start with agriculture and extend to other sectors logically, as advocated by the late V. Kurien, the founding Chairman of the Gujarat Cooperative Milk Marketing Federation (GCMMF), which is popular internationally as Amul. To conclude, a cooperative-based economy is the need of the times and is worthy of serious consideration, more so in the economic and social world which will emerge after COVID-19, which has put enormous pressure on existing business models. What is required is a more coordinated and planned effort involving various levels so that cooperatives can re-emerge in a bottom-up way to grow into viable and valuable social enterprises. What India needs is a real movement for cooperatives than the mere creation of a Ministry of Cooperation. Return to Contents [ H.S. Shylendra is Professor in the Social Science Area, Institute of Rural Management, Anand (IRMA). His areas of interests include Development Theories, Rural development, Gender, Local Governance, and Cooperatives. He has nearly three decades experience combining research, teaching, policy engagement and academic administration. He was a member of the Reserve Bank of India’s Expert Committee on Credit Cooperatives. He can be contacted at [email protected] ]. Endnotes: 1. Supreme Court of India. 2021. “ Union of India v. Rajendra N. Shah “, p.38, Civil Appeal Nos.9108-9109 of 2014, July 20. [https://main.sci.gov.in/supremecourt/2013/21321/21321_2013_32_1501_28728_Judgement_20-Jul-2021.pdf]. Last accessed on August 3, 2021. Return To text. 2. Economic and Political Weekly . 2021. “ A New Ministry for Cooperation “ , Vol. 56, Issue. 30, July 24. [https://www.epw.in/journal/2021/30/editorials/new-ministry-cooperation.html?0=ip_login_no_cache%3D9c8b8f0e47df5cfc4513a1b7d579a36d]. Last accessed on July 27, 2021. Return to Text. 3. Cabinet Secretariat. 2021 . “ Modi Government creates a new Ministry of Co-operation “, Posted by PIB Delhi, July 6. [https://pib.gov.in/PressReleasePage.aspx?PRID=1733225]. Last accessed on July 23, 2021. Return to Text. 4. Biswas, P. 2021 . “ Explained: Why a Ministry of Cooperation “, The Indian Express , July 15. [https://indianexpress.com/article/explained/explained-why-a-cooperation-ministry-7395784/]. Last accessed on July 23. Return to Text. 5. Gulati, A . 2021 . “ What the new Ministry of Cooperation needs to achieve “, The Indian Express , July 19. [https://indianexpress.com/article/opinion/columns/new-ministry-of-cooperation-agenda-pm-modi-7410968 / ]. Last accessed on July 23, 2021. Return to Text. 6. Rajashekhar, M. 2021 . “ Why Exactly Did India Need a Brand New Ministry for Cooperatives, With Amit Shah As Head? “, The Wire , July 11. [https://thewire.in/government/ministry-for-cooperatives-amit-shah-bjp-nda-narendra-modi]. Last accessed on July 23, 2021. Return to Text. 7. Government of India. 2005 . “Report of the Task Force on Revival of Rural Co-operative Credit Institutions”, Ministry of Finance, New Delhi. Return to Text. 8. Concerns have been raised over the recent amendments made in 2020 to the Banking Regulation Act, 1949 about the possibility of RBI’s role undermining the autonomy of states with regards to cooperative banks. Return to Text. 9. Government of India . 2020 . “Annual Report: 2019-20”, Department of Agriculture, Co-operation, Farmers’ Welfare, Ministry of Agriculture and Farmers Welfare, New Delhi. Return to Text. 10. Supra Note No 6. Return to Text. 11. Dwivedi, R.C. 1989 . “Jawaharlal Nehru: His Vision of Cooperatives”, The Co-op Times, New Delhi. Return to Text. 12. Government of India . 2009 . “Report of the High Powered Committee on Cooperatives”, Ministry of Agriculture, New Delhi. Return to Text. 13. Jain, L.C. and K. Coelho.1996 . “In the wake of Freedom: India’s Tryst with Cooperatives”, Concept Publishing Company, New Delhi. Return to Text. 14. Baviskar, B. S. and D.W Attwood. 1991 . “Fertile Grounds: Why do Cooperatives Flourish in Western India?”, IASSI Quarterly 9, no. 4: 82–99. Return to Text. 15. As quoted in “ Co-operative Societies In India (undated) “ p.176. [http://lib.unipune.ac.in:8080/xmlui/bitstream/handle/123456789/2745/10_chapter%204.pdf?sequence=10&isAllowed=y]. Last accessed on July 28, 2021. Return to Text. 16. Supra Note No 7. Return to Text. 17. Supra Note No 15, pp. 164-65. Return to Text. 18. Agarwal, B. 2010 . “ Rethinking Agricultural Production Collectivities “, Economic and Political Weekly, Vol. 45, Issue. 9, pp. 64–78, February 27. [https://www.epw.in/journal/2010/09/special-articles/rethinking-agricultural-production-collectivities.html]. Return to Text. 19. As per available information there were 67,755 mutually aided cooperative societies, 7374 producers’ companies, and 1277 multi-state cooperatives. Return to Text. 20. Supra Note No 1, p.6. Return to Text. 21. Supra Note No 12. Return to Text. 22. Mishra, P. 2021. “ Change of Law: Plans to bring co-ops under Concurrent List “, Financial Express , July 22. [https://www.financialexpress.com/economy/change-of-law-plans-to-bring-co-ops-under-concurrent-list/2295153/]. Last accessed on July 28. Return to Text. 23. Attwood, D. W. and Baviskar B. S . 1988 . “Who shares? Cooperatives and Rural Development”, Oxford University Press , New Delhi. Return to Text. 24. Shah, T. 1996 . “Catalysing Co-operation: Design of Self-Governing Organisations”, Sage Publications , New Delhi. Return to Text. 25. For example, the share of Credit Cooperatives has declined from 62% in 1992-93 to 13.4 % 2016-17. Return to Text. 26. Dave Grace & Associates. 2014 . “ Measuring the Size and Scope of the Cooperative Economy: Results of the 2014 Global Census on Co-operatives “, April. [ https://www.un.org/esa/socdev/documents/2014/coopsegm/grace.pdf]. Last accessed on July 23, 2021. Return to Text. 27. NITI Aayog . 2017 . “India: Three Year Action Agenda,2017-18-2019-20”, NITI Aayog, New Delhi. Return to Text. 28. NITI Aayog. 2018 . “Strategy for New India@75”, NITI Aayog, New Delhi. Return to Text. 29. Ray, S. 2021 . “ Birth of an Alternative Development Paradigm: Unfolding of Transformative Mode of Production “, ICAS:MP Occasional Paper Series-1. [https://micasmp.hypotheses.org/occasional-paper-1]. Last accessed on June 25, 2021. Return to Text. 30. Nash, J. et al. (ed). 1976 . “Popular Participation in Social Change: Cooperatives, Collectives and Nationalised Industry”, Mouton Publisher, The Hague. Return to Text. 31. Roy, E.P. 1981 . “Cooperatives: Development, Principles and Management”, The Interstate Printer & Publishers, Danville. Return to Text. 32. Shylendra, H.S. 2013 . “Microfinance and Cooperatives in India: Can the poor gain from their coming together?” International Journal of Rural Management , Vol. 9, Issue. 2, pp.151-181. Return to Text.

One of the consequences of the COVID-19 pandemic is the change in the way in which societies - individuals and groups, businesses and governments - op