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Agricultural Revival and Reaping the Youth Dividend While a lot has been written on agriculture in India, the purpose of this article is to revisit the relevant ones to bring the question of youth in agriculture into focus.

We ask: what do we know about young people in farming in India? In spite of a large share of rural youth involved in farming, there is limited research or policy attention on the issues and challenges they face around farming, non-farm opportunities, succession, and intergenerational transfer of resources and knowledge.

One problem is that data are not always available by age, making it challenging to draw inferences specific to young farmers, and this is even more so with respect to young women farmers.3 We draw upon statistical data and scholarly material to examine the situation of young farmers in India.

• Although the paper implicitly understands a farmer as someone with access (ownership, shared, renting, etc) to land (or a productive resource), who invests a large part of her time and labour in farming, actual definitions vary.
• We adopt a youth studies perspective to understand the generational dimensions of social reproduction of rural communities, the lives of young people within the agrarian economy, and their paradoxical (apparent) turn away from farming in this era of mass rural un(der)employment, and youth subjectivities.
• A youth studies perspective also provides an important reminder of the need and the right of young people to be properly researched, not as objects, but as subjects.
• In doing so, the paper also engages with developmentalist and policy discourses that view movement of people out of agriculture as a transitional imperative (Chenery 1979; Lewis 1954), even as global sustainability discourses place the family farm as a bulwark against incursions of industrialised and corporatised agriculture (McMichael 2008; FAO no date).
• Despite the realisation that conventional routes of labour transition out of agriculture are not available to many, policy initiatives to make agriculture attractive for youth livelihoods have been few and far between.
• To be clear, the purpose of the paper is not to argue that all (rural) youth undertake or remain in farming, but it is to make a case for improving the livelihood prospects within agriculture, in a context of changing youth aspirations.
• We argue that a clearer understanding of issues is essential to frame a nuanced approach to support the role of youth in agriculture and the role of agriculture in youth livelihood strategies.

Profile of Farmers in India

• Agriculture and allied sectors on which 54.6% of India’s workforce relies, have registered a rapid decline as a share of national income, accounting for only around 16.1% of the gross domestic product (GDP) in 2014–15.
• Evidence from two National Sample Survey Office (NSSO) rounds suggests that over the decade spanning 2002–03 and 2013,5 the median as well as mean age of the head of an agricultural household has increased by around two years, indicating a decline in younger household heads. However, the change does not seem rapid .
• Heads of agricultural households need not be full-time farmers; other members of the household could be participating in farming, even if they are not identified as farmers themselves or as being engaged in full-time farm work.
• Data at the individual level may therefore be more relevant to gauge the extent of youth participation in farming (Figure 1a, p 10). In 2002–03, an overwhelming proportion of those below 25 years of age in farm households did not participate in farming.
• It is only among the age group 25–60 years that the proportion of household members engaged in farming exceeds those not farming. Unfortunately, the 2013 survey does not explicitly capture similar information to enable comparison.
• There are clear differences across social groups (Figure 1c, p 10). A greater proportion of youth among the Scheduled Tribes is likely to farm than those from the Scheduled Castes/Other Backward Classes; young people from other general castes are, comparatively, much less likely to be farmers.
• These differences seem to disappear among the older cohorts, but only beyond 65 years. Gender gaps exist, and the proportion of women who participated in farming is consistently less than those of men in farming (Figure 1b, p 10).
• It seems that while the generational crisis in farming is not yet evident in terms of the average age of a farmer, there is a distinct pattern of rural youth, even in farm households, being disproportionately disengaged from farming.
• In terms of education, in 2012–13, it was less likely that someone who is illiterate or completed primary school or less, would be a farmer and it was more likely that someone whose educational attainment was high school or beyond is a farmer, relative to 2002–03 .
• This might reflect a general trend that more people are now studying more, so that farmers in 2012–13 are on average more educated than they were in 2002–03. This trend seems to undermine conventional understandings about Indian agriculture that attributed its relatively lower productivity to lower literacy levels of farmers.
• There is also an indication that there is a lower preference for formal training in agriculture among youth (Census of India 2011; Figure 2 (a, b, c)). Among the younger cohorts, technical training in agriculture accounts for the lowest share of all those with technical degrees, while those with engineering degrees is much larger among the younger cohorts relative to older cohorts.
• The preference for training in engineering over training in agriculture is likely a reflection of the declining importance of agriculture. While this pattern is the same for men and women, the difference between cohorts in the proportion trained in agriculture relative to engineering is larger for men.
• The gender gap appears larger for agriculture than for other disciplines, including engineering.
• Staying In, Exiting and Entering Agriculture An oft-cited statistic from the NSSO 59th round survey of farm households (2002–03) is that as much as 40% of respondents said they would quit farming if they had a choice.
• Although the survey did not focus on youth, it suggested that in general, low profitability and risk associated with incomes were the main reasons cited for preferring to exit from farming.
• Researchers have noted that this preference is higher among resource-constrained farmers (Agarwal and Agrawal 2017; Birthal et al 2015). Exit preference was also correlated negatively with the age of the farmer-respondent (Agarwal and Agrawal 2017).
• But who leaves, who stays behind, and who enters is, however, quite complex and not always captured in macro-level data (Sharma and Bhaduri 2009). Micro-level studies suggest that there are significant differences in patterns of youth engagement with farming across space, caste, and class.
• Sharma (2007) and Sharma and Bhaduri (2009) offer some insights based on what is perhaps the only survey on the youth question in Indian agriculture.
• Sharma’s (2007) study based on a sample of 1,609 youth in the age group 18–30 years from across 13 states found that part-time farming is a rising trend, especially among small- and medium-scale farmers who tend to combine farming with non-farm activities, including urban activities based on seasonal migration.
• Youth from large landholding families tend to be full-time farmers given the economies of scale that large landholdings afford. While youth from small and marginal farm families are mobile, given the limited prospects in farming, such families are also able to lease in more land.
• Sharma (2007) also points out that those who report to be full-time farmers were older than part-time farmers and youth showing no involvement in farming were younger then: both with a mean age of 24.4 years.
• This could imply that perhaps as one grows older and has one’s own family, many return to full-time farming. The other possibility is that youth return to take up farming when non-farm options are unattractive. Djurfeldt et al (2008) argue based on evidence from Tamil Nadu that with education and industrial employment opportunities, landless and large landowning families exit farming at a faster rate, which results in less skewed distribution of land and rural incomes.
• Leasing in or buying of land then becomes possible for small and marginal landowning families, thus consolidating family farming. Sharma (2007) and Sharma and Bhaduri (2009) suggest that part-time farmers and youth not involved in farming are generally from the higher castes, have a higher number of years of schooling, and are more skilled.
• These youth are also generally from villages close to urban areas, indicating the impact of urbanisation on de-agrarianisation (see also Djurfeldt et al 2008). These patterns seem to be stronger in regions with a low value of agricultural production per capita and in villages close to towns.
• While proximity to markets is a key factor affecting returns to farming and in turn in retaining youth in rural areas, it also has the effect of enabling youth to take up more non-farm activities.
• As Krishna (2017) poignantly demonstrates, villages that are at a distance of more than 5 km from a town or a city tend to be much poorer than those that are located closer to urban settlements.
• At the individual or household level, the pattern is stronger among castes higher in social hierarchy, better educated and youth with non-farm skills.
• Interestingly, both small and marginal landholders and the large landholders show an inclination to withdraw. While small and marginal farmers are perhaps, at least in part, being pushed out of farming, big farmers appear to take advantage of non-farm opportunities, being better off in terms of education and access to capital.
• In Bundelkhand, Narain et al (2016) found that the marginal farmers were more likely to want to exit farming than the medium landholding size class. Somewhat differently, in Gujarat, Patel (1985) studied the aspirations of youth to emigrate and found that neither the rich nor the dismally poor showed a propensity to emigrate, albeit for different reasons; it was people in the “middle” who were mobile.
• She attributed this to pressure on land. Given the difficulties of land reform, the pressure on land made the surplus population restive (Patel 1985). Given that the study is somewhat dated, it is possible that the profiles of who wanted to leave and who stayed are today different from that in the 1980s.
• Jeffrey (2010) in his ethnographic work in Uttar Pradesh describes the emergence and experiences of the “educated unemployed,” a generation of youth from rural landowning families.
• Better-off landowning families increasingly send their children away for urban education and jobs, a phenomenon noted by Balagopal (2011) in the context of coastal Andhra Pradesh in the 1980s.
• Many of these youth, however, cannot find jobs in the current context and given their newfound (educational) status are reluctant to engage in farming.
• At the same time, in relatively developed states, such as Tamil Nadu and Punjab, where youth withdrawal from agriculture may be occurring at a faster pace than in other states due to urbanisation and other related processes, we are beginning to witness a small stream of well-educated, urban middle-class youth turning to farming as a lifestyle choice or as an enterprise (Shandal 2016).
• Within agriculture, field research shows that youth tend to find certain activities more attractive than others (such as dairy, poultry, orchards and horticulture); these are areas where returns are relatively higher. However, youth in rural areas believe that cultivation of field crops is the least difficult to enter, given that one does not require costly investments upfront, if land is available (Umunnakwe et al 2014).
• Studies on contract farming and contemporary supply chains suggest that on average younger farmers are more likely to participate in new marketing forms (Singh 2012). Overall, it appears that certain subsectors within agriculture appeal more to youth than others, but access to such avenues may be limited.
• Village studies provide evidence for entry of segments of lower castes into farming. For example, Rao and Nair (2003) conclude that in Andhra Pradesh, the landownership pattern among caste groups has undergone a significant change—while the dominant castes have lost land, the backward castes and Scheduled Castes are reported to have gained land.
• Sharma (2007) notes that in Bihar, the traditional farming castes like Bhumihars were selling land, which was increasingly being acquired by backward caste groups such as Yadavs. While such land transfers can be seen to be socially progressive, the low returns to agriculture particularly in relative terms and the growing crisis in the sector (Vasavi 2012; Deshpande and Arora 2010) may warrant a different reading of this phenomenon, wherein the lower castes are trapped in low-return occupations.
• Movement out of agriculture is also tied to non-economic aspirations. Agricultural labour is ascribed low status in the caste-based division of labour, historically associated with Scheduled Castes and other castes lower in caste hierarchy. Upward mobility, as Tilche (2016) notes in her study of the Patidars, is therefore associated with movement out of such manual work. Farm work may therefore not be appealing.

Structural and Policy Issues within Agriculture

• Existing studies thus identify several recurring themes that emerge in the context of youth entry and continuation in agriculture, some better understood than others. A few of these can be characterised as structural conditions associated with agriculture.
• Unremunerative agriculture constitutes one of the strongest push factors prompting exit. Research has confirmed the negative effects of green revolution such as depletion in quality of soils, increase in the use of purchased inputs, and extensive extraction of groundwater through private investments (Reddy and Mishra 2009), which have led to a process of capital intensification of agricultural production without commensurate increases in yields and/or returns.
• Accompanying these agroecological factors are a series of policy shifts such as reduced public investments in research and development, and a lack of technological breakthrough in rain-fed and drought-prone agriculture, which accounts for 60% of cropped area.
• For much of the post-reform period, terms of trade were against agriculture except for the period 2004–05 to 2010–11, when high world prices led to prices of agricultural produce remaining higher relative to non-agricultural produce (Dev and Rao 2015).
• Unviable size of holdings: The shrinking size of landholding has been a major structural factor contributing to smallholder vulnerability. The average size of landholding has declined by half, from 2.28 ha in 1970–71 to 1.16 ha in 2010–11 (NABARD 2014).
• There has also been a steady increase in the share of marginal and small landholdings at the national level and at present this segment accounts for 85% of all operational landholdings in the country, although accounting for only 44% of total area being cultivated.
• Marginal landholdings increased from -9% of lands cultivated in 1970–71 to 22% in 2010–11. Trends indicate that within each farm size category, marginal, small, medium, and large, the landholding size has declined implying that there has been no consolidation of holdings in any size category.
• This reduction in operational landholding size has been partly driven by a successive division (subdivision) of inherited land in the countryside. Other factors, such as distress sales, that we discuss later, have also been observed.
• Notwithstanding the evidence that smallholders in India might be more productive or efficient (Gaurav and Mishra 2015, for example), there is ample evidence that smallholdings in India are smaller than the threshold size and hence unviable, a point recognised explicitly by the Government of India (2016: 15):
• The results of the 70th Round NSS show that positive net monthly income—i e, difference between income from all sources and consumption expenditure—accrues only to the farmers with landholdings of more than 1 hectare.
• While the continued non-viability of small-scale farming and of fragmentation of land, push children from such families to move out of farming in search of urban employment, they pose an obstacle even to those (youth) who might be inclined to farm. Entry options into farming among lower-caste youth that we noted earlier, may not therefore necessarily constitute upward mobility in a phase of relative decline in incomes from agriculture.
• Rural Land Markets and Land Use: An important factor that contributes to reproduction of marginal landholdings and hence to agrarian distress, is the nature of emerging land markets. While unviable landholdings are constraining, there is little evidence of land consolidation either due to buying or leasing.
• A major factor that may have prevented owners of unviable landholdings (or for new entrants into farming) from accessing additional land is the rise in costs of rural land, especially in relation to returns from agriculture.
• As Chakravorty (2013) demonstrates, there has been an increase in the levels of activity in rural land markets since the late 1990s, followed by a tremendous increase in rural land prices during the last 10 years or so.
• Rising values of land due to growth in real estate activity consequent to higher incomes and demand for real estate from overseas Indians, attract buyers who invest in land and keep prices high. Investment of black money is another major source of demand for land (GoI 2012).
• The expansion in credit for housing in post-reform India too has increased effective demand for land and given the inelastic supply of land, generated price increases.
• As a result of such demand, Chakravorty (2013) contends that rural land prices in states such as Punjab are higher by 20–30 times (one of the highest in the world) compared to prices that would reflect agricultural productivity.
• Rural land values are therefore determined more outside of agriculture. Under such conditions of financialisation of land, active land markets may not always generate outcomes that are welfare enhancing for small and marginal farmers (Vijayabaskar and Menon 2017).
• One consequence of rising land prices is that farmers have limited capacity to expand their farms, and young (and new) farmers are put at a huge disadvantage. These entry barriers are even more acute for women, who typically do not have access to land of their own.
• Although laws provide for inheritance, it seems to be the norm that women do not stake a claim in order to preserve their relationship with their brothers, often justifying their stand by rationalising that if they did stake a claim, the already small landholdings would become non-viable (see Agarwal 1994).
• In the absence of proper insurance markets and anticipation of rising prices, land is seen as an important hedge against risk and hence property owners do not want to sell, even if their own capacity to invest in land to improve returns is limited. Sharma and Bhaduri (2009) found that more than 60% of their respondents revealed that, while complete withdrawal from farming was high on their agenda, selling land was the last option.
• The ties to land are maintained possibly because one cannot completely rely on non-farm opportunities, but also because of social meanings ascribed to owning land apart from expectations of land price increases.
• More than a third of their young respondents mentioned that they would like their children to continue farming not only because there was a lack of opportunities elsewhere but because that is what they had done for generations.
• In these instances, land does not pass to more efficient farmers; it is not the case that its sale offers an exit option for farmers. Demand for land is therefore not tied to desire to pursue farming as also pointed out in a study of rural Telangana (Jakimow et al 2013).
• In extreme cases, however, in the absence of effective policy interventions to address price and production risks, farmers end up relying on distress sales as micro-level studies of rural land markets reveal (Krishnaji 1991; Sarap 1995, 1998).
• Farming households also respond to risks by diversifying their livelihood options. Rather than invest in land to improve or stabilise returns from agriculture, they may consider investing in their children’s education or access non-farm employment, and hence a possible future career outside agriculture.
• Even before the onset of agrarian crisis and a relative decline in agricultural incomes vis-à-vis incomes from other sectors, agricultural surplus was being invested outside agriculture rather than towards expansion in agricultural investments (Balagopal 2011). But diversification has seldom meant economic mobility or reduced vulnerability for most rural youth.
• Diversification sans mobility? The Situation Assessment Survey of Agricultural Households for the crop year 2012–13 conducted by the NSSO indicates that 57.8% of households have at least one member who is self-employed in farming.
• Although a large share of households continue to rely on agriculture, many do not rely exclusively on agriculture and only 68.3% report farming to be their main source of income in that year. On average, agriculture accounted for only 60% of the income for farm households.
• While income from crop cultivation and animal husbandry account for 48% and 12% of income respectively, as much as 32% of income in the household is derived from wages (computed using data from the NSSO 70th round). These suggest that the rural is no longer synonymous with agriculture.
• Over the past two decades the contribution of the non-farm sector in rural GDP has grown significantly—from 37% in 1980–81 to 65% in 2009–10—accompanied by a marked increase in the share of non-farm employment over the same period (Papola 2013; Reddy et al 2014).
• However, the quality of employment outside agriculture has been poor, marked by either poor wages or incomes. In 2009–10, regular employment constituted only 20% of all jobs in the non-farm sector (Himanshu et al 2013).
• In terms of sectors, a bulk of employment generation has been in the construction sector which accounted for 35.74% of all jobs created during 1990–91 to 2015–16 (Bhattacharya 2018). Two aspects of the employment boom in construction are worth noting.
• First, it tends to employ men in larger numbers and relatively more mobile men at that. Second, employment is insecure and casual for most jobs.
• Thus, while the rural non-farm sector is no longer a “residual” employer, it offers “decent” exit options only for a few (Jodhka and Kumar 2017). Studies also suggest that occupational mobility is lowest in agriculture and allied occupations, and half of all children of farmers end up being farmers themselves (Motiram and Singh 2010).
• While the ratio of non- agricultural productivity to agricultural productivity has increased from 3.97% to 5.83% from 1983–84 to 2011–12, the construction sector has a labour productivity that is only 58% higher than that in agriculture indicating the poor quality of exit via this sector.
• To enable upwardly mobile pathways out of agriculture, rural households are investing considerably in education. According to the All India Survey on Higher Education (AISHE) 2014–15, 24.3% of youth in the age group of 18 to 23 years are in some form of higher education compared to 19.4% reported in 2010–11.
• Such investments have, however, not been backed by adequate openings in the job market. Despite having registered one of the highest growth rates since 2000, the growth in India continues to be accompanied by growing concerns of joblessness (GoI 2018),9 especially among the educated and those from rural households. According to a survey by the Ministry of Labour and Employment, Government of India (2013a: 43):
• Every 1 person out of 3 persons who is holding a degree in graduation and above is found to be unemployed based on the survey results …for the age group 15–29 years. In rural areas the unemployment rate among graduates and above for the age group 15-29 years is estimated to be 36.6 percent whereas in urban areas the same is 26.5 percent.
• This clearly indicates an emerging crisis in employment with available employment opportunities not commensurate with rural youth aspirations (Cross 2009; Jeffrey 2010; Jeffrey et al 2005a; Jeffrey et al 2005b; Jeffrey and Young 2012).
• Young men from rural farm backgrounds often engage in “timepass,” and enroll in one course after another waiting for their preferred employment to materialise (Jeffrey 2010).
• This is also tied to quality of education and first generation learning in the absence of social networks in landing them jobs (Jakimow et al 2013). Apart from the inferior status assigned to farm work as discussed earlier, the desire to move out of the rural areas is, therefore, also tied to a lack of access to quality education or to networks that facilitate access to better non-farm options.
• Such aspirations are belied by a lack of commensurate employment for the educated, continuing to be in farming in a context of growing income differentials between agricultural and non-agricultural sectors. In this context, micro-level studies (such as Anandhi et al 2002; Srinivasan 2015) point to a growing crisis of masculinity among rural young men, who unlike older generations of men, are not able to assert their identity based on farming.
• The unattractiveness of farming is further fuelled by the desire of rural women to marry out of farming (Bourdieu 2008; Srinivasan 2015). Overall, youth aspirations in rural areas are therefore often not built around farming but around strategies for a way out of agriculture.

Conclusions

• The paper pieced together information from secondary sources, highlighting that scarce attention was being paid to young farmers in policy and research, in order to address the question: what do we know about young farmers in India? The paper, however, does not pretend to have answers to all questions.
• With an agrarian crisis, an ageing farming population, and a bulging youth population, can the youth revive the prospects of agriculture in India? And can agriculture revive hopes of the youth? The agrarian crisis, precipitated by the non-viability of small-scale family farming (low productivity, poor market returns, low soil fertility, water scarcity, high levels of indebtedness), lack of public investment, and the continued dependence of a significant share of population on agriculture for their livelihoods, is in reality also a demographic crisis as (rural) youth have not been able to effectively move out of or move into agriculture in economically secure ways.
• If India is to reap dividends from the demographic youth bulge, revival of rural employment and in particular, of prospects in agriculture will be crucial. Likewise, prospects in agriculture cannot be revived without addressing the youth question.
• A youth or generational perspective demonstrates that we do not know much about youth in agriculture—their aspirations, variations across regions, how they access resources (land, knowledge and skills), challenges they encounter and so on, necessary to offer workable strategies.
• The article not only highlights the need for greater visibility of young farmers in research and policy but also more importantly for an intersectional approach on reviving agriculture, tackling rural poverty and youth livelihoods.
• Agarwal and Agrawal (2017) note that governments tend to assume farmers would be better off in cities while emergent farmers’ movements presume that all farmers would want to farm. The evidence on farmers’ preferences for exit is clearly more nuanced.
• Further, rural households are already showing through their adaptation strategies on what may be viable. Increasingly, households are combining incomes from self-cultivation with incomes from non-farm employment and business.
• Declining employment elasticity in agriculture (Majumdar 2017) also implies that households can undertake agriculture without much labour expenditure allowing pluri-activities. Creating non-farm employment in rural areas would enable youth to forge livelihood pathways in the countryside and in turn contribute to the revival of agriculture (Chand et al 2011).
• Similarly, ruralisation of manufacturing as noted by Ghani et al (2012) may also contribute to a “high road” to rural diversification. Efforts are necessary to quell the growing rural–urban disparities in access to quality healthcare and education that further accentuate vulnerabilities emanating from the agricultural sector.
• Possibly in response to the realisation that all is not well with the non-agrarian economy in terms of employment, the government has launched a new project, “Attracting and Retaining Youth in Agriculture” (ARYA) supported by the Indian Council of Agricultural Research (ICAR) and implemented by Krishi Vigyan Kendras (KVK), a public institution meant to provide technical support to agriculture.
• The National Commission of Farmers (NCF), constituted in 2004, was tasked with recommending measures to address agrarian distress. One of the sub-tasks was to suggest strategies to attract and retain youth in agriculture.
• In each of the six reports that the NCF submitted between 2004 and 2006, there is an explicit recognition of youth aspirations to move out of agriculture. The commission, however, restricted itself to suggesting a role for youth employment in custom hiring and skilling for animal husbandry.
• A sectoral and an economistic approach to integrating youth into farming may not work, given the complex set of factors that render the agrarian rural economy inferior. The challenge may also involve revalorisation of agricultural work without valorising caste.
• While improved returns may provide some incentives, in the absence of a reversal of social norms around labour in agriculture, such policies may be socially regressive. In addition, the gender-neutral category of youth implicitly refers to young men.
• This often leads to the neglect of young women in policies directed towards the youth. Inheritance laws and social norms around land rights also marginalise young women from policies that focus on youth participation in farming.
• The family farm as conceived in the conventional sense cannot be the unit of organising production; a flexible arrangement that can transcend sectors but spatially located in the rural will have to be envisaged.
• Further, exploring new forms of collective organisation of the agrarian economy may potentially weaken caste hierarchies, status and patriarchal relations that undergird the family farm (Agarwal and Agrawal 2017).
• Finally, there is a strong push from youth themselves to revive farming as evident, for example, in a growing number of urban youth embracing farming on their own volition. Political activity around access to land has also witnessed a rise recently, for example, Jignesh Mevani’s land to Dalits agenda (Outlook 2018) and the “Land March” in Maharashtra (Dhawale 2018).
• If visions of sustainable agricultural futures are to be realised, and if young people are going to have a place in that future, the problems that the youth face in agriculture have to be given more serious attention than has been the case in recent research and policy debate.
• This would entail a move away from viewing agriculture not merely as a source of surplus labour but as a sector that generates social values around land and work, which cannot be reduced to monetary valuations.

Shifting industries towards formality—reducing the dualism in the economy—constitutes another important form of structural transformation. Careful studies have documented large efficiency gaps between comparable manufacturing firms in the formal and informal sectors, implying large potential efficiency gains from growth of the formal economy ; Mazumdar and Sarkar 2008). Similarly, the service sector contains sharp distinctions in productivity levels between what are known as “modern” and “traditional” services. Modern services are technology-enabled, transportable, and tradable. They include financial intermediation, communication, computer services, business services and professional services.

Because of links to technology and trade, modern services perform much more like manufacturing: characterised by fast productivity growth and potential to leverage export markets for growth. In India, communications, finance, and computer-related services yield five or more times the output per worker than most traditional services.

• The modern/traditional distinction has been found across broad swaths of developing economies and in India in particular  although less distinct.
• Successfully reorienting India’s labour force towards higher productivity sectors would directly boost economic growth. Indeed, McMillan et al (2014) find that the main difference in the growth experience of Asia with that of Latin America and Africa has been due to Asia’s superior success at structural reform.
• The productivity gains imply large welfare gains for some of India’s poorest workers. The McKinsey Global Institute has estimated that an illiterate worker who moves from agriculture to light manufacturing can expect a wage increase of 40%. A worker with basic literacy can expect even better: a wage increase of 70% should he move from agriculture to heavy manufacturing (Gupta et al 2014).
• How should this structural transformation pulling labour into higher-productivity sectors occur? Economists have debated whether the best strategy for job creation in India lies in developing its service or manufacturing sectors.
• Green (2014) argues that the Indian manufacturing sector holds more growth potential in response to policy changes, and Ghose (2015) shows more employment potential for low-skilled workers in manufacturing. This study likewise explores the feasibility of boosting the manufacturing sector.
• Achieving this goal would require intra-sectoral shifts for driving faster expansion of labour-intensive activities, as well as reducing the dualism in the manufacturing sector to reap the benefits of productivity gains that are available from shifting activity and employment into the formal sector.
• This study analyses what could happen if India’s government took steps sufficient to achieve East Asia-style manufacturing growth. The vision for “Make in India” includes goals to increase the gross domestic product (GDP) share of manufacturing to 25% by 2022 and to create 100 million additional manufacturing jobs by 2022 (Department of Industrial Policy & Promotion 2016).
• Are these goals realistic? With assumptions about sector-level growth and employment elasticity, this study projects sectoral employment, productivity and output patterns over 20 years. The projection exercise presented here assumes a structural break in the manufacturing sector due to major policy changes. This implies two important shifts from the usual analysis of structural transformation in India.
• First, it implies that past patterns of the utilisation of labour (for example, labour intensity and skill intensity) will be broken, and therefore do not serve forecasts of the future.
• The experience of five East Asian economies that witnessed manufacturing-led growth booms provides a better benchmark for the parameterisation of the projections.
• Second, because of the minimal parametric restrictions and assumptions, the model can avoid the problem of false precision. Unlike other projections of sectoral employment—for example, Rangarajan et al (2007), Planning Commission (2012), Papola and Sahu (2012), Timmer et al (2014) and Gupta et al (2014)—that follows a detailed industrial classification into important subgroups,
• this paper breaks down manufacturing between informal and formal sectors, to distinguish between the fundamentally different segments of the economy that are often blended together. The projections provide a rough upper bound of possible outcomes from structural transformation, which may be informative for developing policies for structural change.

Developing the Projections

The core of the projection is a sector-wise GDP forecast. Employment figures then derive from an assumption of constant employment elasticity. Hence, the most important parameters are the assumptions of future growth and employment elasticity.

• Data: The employment data used here comes from various sources. The most comprehensive data on sector-wise employment at the four-digit level of India’s National Industrial Classification is provided by the National Sample Survey Office (NSSO). The National Sample Survey (NSS) employment data also breaks down the informal sector employment at the one-digit level. This study uses the NSS 68th (2011–12), 66th (2009–10) and 61st (2004–05) rounds.
• These are matched to the sectoral net value-added data from the national accounts such that detailed employment elasticities and productivity data can be constructed. Unfortunately, outside of manufacturing, the national accounts data only provides a formal/informal breakdown for the net value added. Hence, this study only explores the formal/informal difference for manufacturing.
• Consistent historical national accounts data are only available through 2014, so the projection begins in 2015. Formal sector manufacturing data on employment and value added also comes from the Annual Survey of Industries (ASI), which provides an alternative source to compare with for key parameters.
• The data for the East Asian countries comes from the Groningen Growth and Development Centre (GGDC) 10-sector database that has annual sector-level value added and employment data that match India’s sectoral breakdown fairly well. The main inconsistency is the inability to distinguish between the formal and informal sectors in the East Asian value-added data.6

Methodology:

The key units of observation are broad sectoral categories, namely manufacturing, other industry (construction and utilities), services and the primary sector (agriculture and mining). Manufacturing is further divided between formal and informal segments.

Services is divided between modern services (communications, financial and business services, and real estate) and traditional services (trade, transportation, public administration, hospitality, education, healthcare, entertainment, household services and other).

• A baseline scenario is constructed first to establish a “no change” scenario, in which current policies influencing sectoral transformation are held constant. It therefore relies as much as possible on parameters as currently observed in India. The International Monetory Fund (IMF) estimates India’s potential growth to be 7.75%, which underpins the baseline projection over the next 20 years (IMF 2018).
• A more difficult task is to match that growth rate to reasonable assumptions about sectoral growth. The approach here is to base sectoral growth rates on historical rates from 1994 to 2012.
• The reforms initiated in 1991 produced above-trend GDP growth starting in 1994, corroborated by the structural breaks in the growth rate found by Balakrishnan (2010). The high-growth period ends in 2012 when investor confidence and GDP growth collapses.
• In addition, the most recent available employment data comes from the NSS of 2011–12. By using 1994–2012 parameters, the baseline sets a high bar by presenting a sustained, high-growth period with which to contrast the alternate scenarios.
• The initial growth rate for each sector was taken from each sector’s compound annual growth rate (CAGR) from 1994–2012, which witnessed aggregate growth just above potential growth at 7.1%. To ensure the aggregate rate at the beginning of the projection equals 6.5% and sectoral rates are proportional to their historical pattern I trim each sectoral growth rate by 21%.
• After trimming, the growth rate for the construction and utilities and traditional services sectors lie below the rate of the general economy. However, industries like construction, trade and transportation tend to grow at the same pace as the overall economy. To account for this, a catch-up term is included in their growth projection to pull their growth rates towards the aggregate.
• For the baseline projection, their growth rate is adjusted by half of the distance between their growth rate in 2017 and the general economy growth rate.
• As time progresses in the projection, the faster-growing sectors occupy a larger share of the total economy. This means either the aggregate growth rate will climb over time, or the sectoral growth rates of faster-growing sectors must fall.
• The latter seems more realistic, given the torrid pace of growth during 1994–2012, and given that the baseline assumes no change in the policy mix to facilitate structural adjustment. The individual sectoral growth rates γit therefore decline each year by a factor δ, constant across time and sectors, which keeps the aggregate growth rate from exceeding 6.5% per year.

The annual growth rate for sector i therefore evolves according to the following process:

γit = γi0 (1– δt) …(1)

The exception noted above is for construction and utilities and traditional services, which evolve according to the equation for sector j:

γjt = [γj0 + λ(γt–1– γjt–1)](1 – δt) …(2)

where λ is the catch-up coefficient. For the baseline scenario,
λ = 0.5 and δ = 0.065%.

Sectoral Growth

With these parameters the sectoral growth rates average out to a level slightly lower than their initial rates, presented on lines 2.1 and 2.2 in Table 1.

• For the policy change scenario, the fundamental assumption is that India’s business climate for formal sector manufacturing alters sufficiently to ignite an East Asian-style growth spurt. Therefore, India’s historical pattern is not as relevant as that of East Asia.
• This study compares India with the experience of Korea, China, Indonesia, Malaysia and Thailand, five of the eight high-performing East Asian countries that experienced 20-year booms in manufacturing value added. Singapore and Taiwan were dropped due to their small population, and Japan due to its far more developed status at the time of its post-war boom.

The five countries examined all had large agrarian populations at the time that their manufacturing boom began.

The booms are measured to identify a 20-year period that followed a big bang of reforms comparable to what India might achieve. Therefore, this study matches the start to the time of major events, which admittedly can be somewhat arbitrary relative to a continuum of reform initiatives. However, the basic results are robust to small adjustments in the timing used. Korea’s boom is measured beginning with the election of Park Chung-hee in 1963.

China’s reform period begins under Deng Xiaoping in 1978. Indonesia begins with the major devaluation and banking reforms in 1978. Malaysia took major steps towards export-oriented industrialisation in 1985 and 1986, so this study uses 1985 as the start period. Thailand’s major reforms began in 1985 and continued into the next year.

• For comparison, India’s experience beginning in 1994 is included in Table 2 (p 40). Since this study involves a formal/informal breakdown, it presents India’s experience in the most recent 20-year period for all manufacturing activity as well as for just the formal sector.
• In terms of the initial share of manufacturing in the GDP, India’s full manufacturing sector falls in line with its East Asian peers. Even the formal sector does not have a smaller share of GDP than Korea in 1963. However, during the subsequent 20 years, the manufacturing sector in the other Asian countries gained on average 14 percentage points of GDP share, while India’s manufacturing only kept up with the overall GDP.
• For the projections, I assign India formal-sector manufacturing growth rates that match the country with the highest 20-year growth rate, Korea. Two reasons justify this choice. First, this study focuses on the formal manufacturing sector, which should grow faster than the overall manufacturing sector when structural reforms remove some of the barriers that previously forced firms into the informal sector.
• Since the other country data is for the overall manufacturing sector, the highest-growth country—percentage points above the average growth rate—provides a precedent for possible growth rates that India’s formal manufacturing sector might achieve.
• Second, the scenarios aim to present the potential impact of structural reforms on India’s manufacturing sector. Replicating the highest-growth country establishes a plausible upper bound of the impact on the manufacturing sector of sufficient reform treatment.
• Theoretical arguments can be made to support both positive and negative growth effects on the other sectors in response to big bang manufacturing-oriented reforms. These considerations are discussed in detail in Green (2015). With an array of possible sectoral responses to faster formal-sector manufacturing growth, the projection chooses the starkest point of contrast, assuming the remaining sectors follow their historical pattern, shown in line 3.1 in Table 1.

That set of first-year growth rates produces an aggregate growth rate of 9%, which is maintained for the full 20-year projection.

The informal manufacturing, modern services, and agriculture and mining sectors are assumed to grow according to equation 1. Because the construction and utilities and traditional services sectors are more likely to benefit from manufacturing growth, they are assumed to grow according to equation 2 with λ = 1. This value of λ means these two sectors grow at the same rate as the total economy, about 1.4% higher than their sectoral historical rates.

• The policy change scenario requires a higher level of δ than the baseline because it has two high-growth sectors. All the sectors are compressed by δ = 0.125% to ensure the entire economy’s growth rate remains constant at about 9% per year over the 20-year projection. Despite this limitation, the scenario is aggressive relative to historical growth experience.
• The overall rate at 9% slightly exceeds India’s highest five-year growth period 2003–08 and exceeds the 20-year growth rates of all the East Asian boom economies except China. The average growth rate for each sector appears on line 3.2 in Table 1.

Employment Elasticity of Growth

The economic growth rates combine with the employment elasticity of GDP to generate the core forecast of future employment. While GDP growth is quite commonly understood, the employment elasticity of GDP merits discussion to help apprehend the related assumptions in the projections.

• The employment elasticity of GDP is the percent change in employment for a 1% change in GDP, which is the inverse of marginal productivity, the change in aggregate productivity from adding one worker. Most often the elasticities are calculated from employment and GDP across several years, so they come close to the inverse of average productivity. The marginal/average distinction has three important implications.
• First, high-productivity industries will by definition have a lower elasticity than low-productivity industries. Hence, a low elasticity does not indicate a bad industry for job creation, since ultimately productivity growth lifts wages and living standards. If a high-productivity (low elasticity) industry grows fast enough it can provide a welcome source of high-quality jobs. Accordingly, very high elasticities can indicate problems with falling productivity.
• Second, it is always true that average productivity rises by adding new workers at higher marginal productivity. Because elasticity is the inverse of marginal productivity, a sector’s
  average productivity advances in the projections by adding new workers at lower elasticities. In fact, because it is adding new workers faster, a faster-growing sector will have greater productivity growth than a slower-growing sector even when both have identical elasticities.

Third, positive structural change means that higher-productivity (lower elasticity) industry output grows faster. The marginal productivity effect will cause average productivity to grow faster too.

Elasticity is typically measured as the ratio of growth rates of employment and output (arc elasticity) or as the coefficient of a log-log regression (point elasticity). For India, Misra and Suresh (2014), hereafter MS, use KLEMS methodology to construct an annual employment time series that matches GDP data frequency from 1994–2012 and perform log-log regressions for various sectors. They also use ASI data to perform industry-level panel log-log regressions to generate point estimates of employment elasticity in the formal manufacturing sector.

• Unfortunately for this study’s purposes, MS do not make the modern/traditional services distinction and do not address informal manufacturing. Their elasticities can be used for formal manufacturing, construction and utilities and agriculture and mining. Instead, for other sectors I have calculated the ratio of the CAGR for sectoral employment (from NSS data) and the CAGR for sectoral value added (from national accounts data) across the years 2005–12.
• The low frequency of employment data—every five years for NSS data—and occurrence of structural breaks in the economy hinder more precise methods. As a result, the ratio of growth rates methods used here is the most commonly used measure of employment elasticity for India (for example, Rangarajan et al [2007] and MS). These estimates are close to those of MS except for modern services, which in that study only comprises finance and real estate (Table 3).
• In recent years, formal manufacturing in India has witnessed high elasticity relative to other sectors—only construction and utilities is higher—justifying the policy focus on this sector for job growth. The MS estimate utilises industry-level data and, at 0.57, represents the middle value.
• This estimate is also close to the high end of the elasticities seen in most of the East Asian boomers (Table 4). However, those elasticities include the informal sector. When India’s informal sector is included, as in Table 4, India is an outlier in the other direction. The unusual amount of employment in the informal manufacturing sector in India pulls the overall manufacturing elasticity down.

Assuming a similar but smaller effect in the East Asian economies, a slightly high estimate for the formal manufacturing sector in India appears appropriate.

The policy change scenario assumes employment elasticity in formal manufacturing will rise slightly, to 0.7. Elasticity could theoretically rise through two channels. First, a lower effective cost of labour relative to capital because of labour market reforms could induce industries to raise their labour intensity. Second, reforms resulting in lower cost of labour or improvements in infrastructure could improve the comparative advantage of labour-intensive industries, giving them relatively higher growth. Shifting the rates of growth between industries in favour of labour-intensive ones can raise the overall sectoral elasticity. The historical range of elasticities among manufacturing industries is wide enough for the “between effect” to move the elasticity about +/- 0.2 without making unreasonable assumptions about long-run industry growth rates.

• Informal manufacturing has a lower elasticity than formal manufacturing, and formal manufacturing sector growth has been shown to lower the share of employment in informal manufacturing (Ghani et al 2013). Indeed, Unni (2003) finds that the growth of informal manufacturing employment after the 1991 reforms occurred because formal firms were restrained by labour laws.
• As the formal sector offers more jobs, disguised unemployment in the informal sector should decline. Of the two available estimates of informal manufacturing elasticity, the projections use the lower one of 0.15.
• The construction and utilities sector have high elasticity because construction is labour-intensive. Gupta et al (2014) argue that income from the Mahatma Gandhi National Rural Employment Guarantee Act (mgnREGA) programmes have generated a building boom in rural areas, meaning a greater share of construction takes place in low-wage, low-productivity areas.
• This has caused the elasticity of construction to rise. Notice the Rangarajan et al (2007) estimate, which pre-dates the mgnREGA programme, is the lowest. The mgnREGA-induced trend may not persist indefinitely, so the projection uses the MS estimate using log-log regressions, which at 0.99 is on the lower end of the range. This is not exceptionally low by the experience of the East Asian boomers shown in Table 8 (p 44).
• For modern and traditional services, this study’s estimates provide the only elasticities that distinguish between the two sectors appropriately. Most other estimates for modern services in India or East Asian economies are likely too high due to the exclusion of low-elasticity communications.
• Accordingly, the traditional services estimates are likely too low. Compared to the other studies, this study’s estimates appear appropriate. The East Asian boomers’ traditional services elasticities are much higher. Since this exercise assumes little change in this sector, estimates derived from India’s data are more appropriate.
• Agriculture and mining display declining elasticity for the same reason as informal manufacturing, namely shedding of surplus workers. Again, the East Asian boomers’ elasticities are much higher, but the Indian estimates are roughly declining over time, with the trend giving additional confidence that the pattern is not spurious.
• The baseline estimate uses the MS estimate. For the policy change scenario, a better supply of non-agricultural jobs will presumably pull excess workers out of agriculture faster, so the author’s estimate of -0.48 is applied.

Results

The simulation extends from 2014, assuming the employment situation is unchanged from the 2012 NSSO survey. The initial values for the projection are given in Table 5.

Running the simulation over 20 years produces significant differences between the baseline and the policy change scenarios (Table 6). Simple compounding of the assumed growth differential produces overall GDP that is 30% higher than what it might be without reform. Productivity (which should correlate with wages) also grows faster with reform.

• In the baseline scenario modern services follows its historical pattern of being the only high-growth sector (see Table 1). By the end of the projection, this produces a 45% share of GDP for services, which is approaching levels seen in advanced economies.
• In the policy change scenario modern services grow slightly faster, but because other sectors also grow faster, its share of GDP falls. Relatively higher growth rates in formal manufacturing cause it to grow 3.7 times larger than the no-reform scenario, yielding a substantial rise in its share of GDP.8
• Not only does the productivity in each sector expand, but because employment shifts towards higher-productivity sectors, aggregate productivity also expands faster than any individual sector. This inter-sectoral reallocation of labour is a form of structural change.
• Some productivity change due to the inter-sector labour shifts occurs in both scenarios (Table 7). However, the difference in the productivity growth between the two scenarios is mostly due to a higher degree of structural change in the policy change scenario.
• Perhaps most importantly, job growth would be substantially higher in the policy change scenario (Figure 1, p 44). Formal manufacturing employment would grow to exceed informal manufacturing (11% of employment versus 7%). The two together add 76 million new jobs by 2035 over their 2014 levels.
• Agriculture, on the other hand, is assumed to shed jobs faster in the reform scenario. Because so many work in agriculture, it takes ten years before the growth sectors overtake it for net gains over the baseline.
• Construction has a very high need for manpower, so employment in that sector would also expand rapidly. Agriculture sheds jobs, but the other sectors of the economy exhibit plenty of capacity to absorb those workers.
• Green (2014) calculates a need to create 10 million new jobs each year on top of what is needed to recoup manpower shedding in agriculture. Currently, India’s economy does not meet that mark, thereby creating a job gap that pushes people into fallback employment, underemployment, unemployment or out of the labour force.
• The baseline scenario does not reach a pace of creating 10 million jobs per year until 2030. This creates a backlog of workers (the cumulative historical gap) that do not find a job. The policy change projection hits a pace of 10 million new jobs per year by 2022, and completely covers the job gap backlog by 2027.

Alternative Specifications

• India’s economy is in a constant state of transformation, typical of developing economies with high growth rates. This makes any 20-year extrapolation risky. The data underpinning the projections is not perfect either. For instance, there may be short-run phenomena such as drought years that create misleading patterns. Employment is not a sharply defined concept, especially in an economy characterised by high rates of informality.
• Of course, almost any outcome can be achieved by selectively choosing high or low growth rates and employment elasticities. Therefore, this exercise has utilised mid-range parameters from recent estimates compared with historical patterns in India and East Asia. Nonetheless, it remains worthwhile to carry out some alternative specifications to explore how sensitive the projections are.

This section will focus only on the formal manufacturing sector—the main sector of interest—to limit the number of permutations explored.

The “Make in India” goals provide useful targets to structure alternative specifications around. These goals specify increasing the GDP share of the manufacturing sector (formal and informal together) to 25% by 2022 and creating 100 million additional manufacturing jobs by 2022. Structuring scenarios around meeting these targets allow a measure of the sensitivity of the projections to both growth and employment elasticity assumptions.

• Attaining 25% of GDP by 2022: The first alternative scenario asks what growth rate of formal manufacturing would be required for the overall sector to reach 25% of GDP by 2022, or within eight years of the start of the projection. In the original policy change scenario, manufacturing comprises 21% of GDP in 2022 and does not reach 25% of GDP until 2030.
• As discussed in the previous section, the growth rates in several other sectors may get pulled higher by faster growth in formal manufacturing. This would create headwinds for attaining a share-of-GDP target. For the purposes of simplicity, this scenario ignores such effects and assumes that growth rates in the other sectors—including informal manufacturing—remain identical to the policy change scenario.
• Making the single change of adjusting the formal manufacturing growth rate to meet the target, the projections indicate that the formal manufacturing sector would need to grow at 20% per year for the overall manufacturing sector to reach 25% of the GDP by 2022 (Table 8).
• This is about 6 percentage points higher than the growth rate assumed in the policy change scenario and 4 percentage points higher than the highest annual growth rate of formal manufacturing in the last 20 years.
• Further, the average annual productivity growth displayed by overall manufacturing in the first two scenarios is more than double the productivity of either the formal or the informal manufacturing sectors. This is a more extreme example of the inter-sectoral effect noted earlier, as the higher output growth rate in formal manufacturing shifts the proportions of economic activity from a low-productivity to a high-productivity sector.
• 100 million manufacturing jobs by 2022: The second scenario asks what parameters could yield 100 million new manufacturing jobs by 2022, compared to the 17 million as estimated from the same date in the policy change scenario.9 For illustrative purposes, the second scenario assumes that growth remains unchanged from the policy change scenario, so only the elasticity of formal manufacturing is allowed to adjust.
• In this case the elasticity would need to be 2.16. By almost tripling the elasticity from 0.70 in the policy change scenario, the projection produces a nearly sixfold rise in the number of new jobs created in the first eight years. By the end of the 20-year projection, the high elasticity yields a 38-fold increase, the product of compound growth rates of output.
• Such a rise in elasticity implies a completely unprecedented jump—by Indian or international standards—in labour intensity in formal manufacturing. As described above, theoretically this could occur either within the industries that constitute formal manufacturing, or between them as higher elasticity industries grow faster.
• However, the inter-industry channel has only limited range to impact elasticities. This would mean the labour intensity within industries would need to bear the burden of adjusting for the manufacturing elasticity to rise so fast. A rise in elasticity necessarily impacts average productivity. Applying such large quantities of labour on the same amount of output implies formal manufacturing productivity falling by more than 12% per year across the projection period.
• Combined goals: As a final exercise the two goals can be combined. If manufacturing reached 25% of GDP by 2022, what elasticity would formal manufacturing require to also reach the goal of 100 million new manufacturing jobs? This scenario repeats the assumption that the other sectors’ growth rates and elasticities remain the same as the original policy change scenario.
• In this case, the growth rate of formal manufacturing again reaches 20% per year to attain 25% of GDP for manufacturing. With that growth rate, a lower employment elasticity can achieve the same employment goal. Hence, the necessary elasticity falls to 1.6, still an unprecedented figure.
• If this growth rate and elasticity extended for the full 20 years of the projection, manufacturing would create 4,103 million new jobs, a 54-fold increase over the original policy change scenario. Formal manufacturing productivity would fall to 9.5% per year in this case.

Sensitivity Analysis

The alternative specifications, roughly demonstrate the range of possible outcomes from altering the key parameters of growth rate and employment elasticity. However, a fuller sensitivity analysis illustrates that even staying within previously observed values of GDP growth and employment elasticity generates a strikingly broad range of forecasts.

The sensitivity analysis begins by generating a variance–covariance matrix of the sectoral growth rates from the rates observed historically. For the baseline scenario, the matrix draws on India’s sectoral growth rates from 1994 to 2012. For the policy change scenario, the matrix is generated from the sectoral growth rates observed in the East Asian countries, which have been pooled together for comparison, during their growth boom years.10

• The model then runs 10,000 times each for the baseline and policy growth scenarios. While the growth rates applied in the earlier scenarios represent average values, the 10,000 runs use random draws of growth rates for each of the two distributions.
• Drawing from the covariance matrix provides some structure to allow sectoral growth rates that tend to co-move in the draws. The δ term will compress all sectors to keep the aggregate growth rate at the long-run level, so one sector’s draw will affect the other sectors’ final growth levels.
• The sectoral GDP illustrates the range of outcomes well, even if the levels have little intuitive meaning. The 95% bands of the GDP outcomes can be seen in Figure 2. While the median policy change scenario lies well above the median baseline scenario, the 95% confidence bands are wide. The range of experiences in East Asia was large enough so that the baseline median lies within the range of all policy change scenarios.
• The sensitivity of employment to parameter values also merits assessment. Employment depends on both the growth rate and elasticity, so both should be used to gauge the sensitivity of the projections. The two growth rate sensitivity analyses can produce employment levels, too.
• Applying the Indian and East Asian employment elasticities to the growth rates from the 10,000 draws of the baseline and policy change scenarios, respectively, generates a distribution of employment outcomes.
• Because employment elasticity should be measured across long time spans, India alone does not provide enough data points to construct a variance matrix and produce a sensitivity analysis. Even the data for East Asia is limited for this reason.
• For East Asia, the employment elasticity is measured across the two 10-year windows in each 20-year boom period of each East Asian economy. The random draws come from the variance measured across the observations for each sector, pooled across countries.
• Employment levels at the end of the projection period vary even more substantially than the growth rates. In Figure 3 (p 45), the two growth distributions produce employment levels that are proportional to the outcomes in Figure 1, but scaled by the employment elasticities. While the sectoral elasticities for India and Asia are not identical, the differences only substantially change the proportions for the agriculture and mining sector.
• The results using the distribution of the East Asia elasticities require some explanation. For formal manufacturing, other industry and agriculture and mining, all three distributions paint a similar picture. That is, the median policy change scenario lies well above the median baseline scenario, implying that the 95% confidence bands are wide.
• For the other three sectors, however, the East Asian elasticity results do not align with the scenarios using India’s elasticity. As noted earlier, the East Asian data does not have informal manufacturing broken out at all. Formal manufacturing is used instead, making the East Asian elasticity distribution for informal manufacturing a poor comparison.
• The East Asian elasticities for the two service sectors are much higher than the Indian elasticities. This elevates the top of the distribution far above the other two scenarios. In fact, for formal manufacturing, the mean of the baseline scenario is below the 95% confidence band for the East Asian elasticity distribution scenario.

Despite the indication of a significant difference between the baseline and reform scenarios, this sensitivity analysis, by and large, underscores the uncertainty involved in 20-year projections. The main projection results discussed in the paper use mean values, but a much wider set of outcomes might reasonably be expected to occur. That does not imply that projections based on mean values have no merit. They still provide useful central values for expectations of future outcomes.

Conclusions

This study has attempted to apply a rigorous approach for developing a 20-year projection of growth and employment in India. A realistic but ambitious parameterisation of a simple projection demonstrates the potential impact of an East Asia-style manufacturing boom in India. Growth, employment and productivity would all improve.

• This occurs because the central projection simulates the formal manufacturing sector growing to attain 27% of GDP in 2035 from the current 11%. Two implications of these results are worth noting.
• First, the policy change scenario forecasts that 15% of the workforce could be employed in high-productivity industries in the formal manufacturing sectors and modern services after 20 years. As a comparison point, Green (2014) estimates that almost half of India’s workforce will have finished high school by 2035, double the share today.
• Such a graduation rate would represent a dramatic improvement in worker quality over the current workforce. Compare this to the profile of the industries that are most likely to need workers with at least a high school education. Currently, 48% of the workers in formal manufacturing, 88% in the modern service sector and 60% in the traditional service sector have at least a high school education.
• Those three sectors employ 29% of the workers, while the remaining sectors utilise a much lower share of skilled labour.
• The potential rise in education levels above current industry need raises the question of where these workers will find work appropriate for their superior education. Another way to look at the potential mismatch is via Say’s Law that supply creates its own demand.
• Say’s Law suggest that businesses that can effectively utilise a better educated workforce will grow faster due to a growing skilled labour supply. Much better educational attainment may suggest that the projections presented here are not unrealistic.
• Second, the main policy conclusions of this study could be established with a more casual parameterisation, as the basic results are robust to a range of realistic assumptions. One point of rigorously parameterising the model is to rigorously rule out what is not realistic.
• The “Make in India” goals of the manufacturing sector reaching 25% of GDP and creating 100 million new jobs by 2022, while worthwhile for inspirational purposes, do not appear realistic. The latter does not even appear realistic in a 20-year time frame.

Notes

1. Author’s calculations based on the 14-industry aggregation in India’s national accounts. From largest to smallest, these three are chemicals and pharmaceuticals, basic metals, and transport equipment, according to the National Sample Survey (NSS) and national accounts data.
2. Author’s calculations based on NSS and national accounts data. Business services productivity stands out less clearly because high-productivity workers like call centre workers are far outnumbered by security guards and errand boys with productivity that compares more closely to workers in traditional service sectors.
3. This is a point missed by most evaluations of manufacturing versus service sector-led growth (Ministry of Finance 2015).
4. Rangarajan et al (2007), Planning Commission (2012), Papola and Sahu (2012), and Gupta et al (2014) break services into subsectors, but they follow a common national accounts breakdown in which communications—a modern service industry—is grouped together with transportation—a traditional service industry—hindering a modern/traditional distinction in their results.
5. The difference is consumption of fixed capital, akin to depreciation. All further references to value added indicate gross. Another concern with the formal/informal distinction outside manufacturing is a problem in the classification of non-manufacturing informal enterprises. Manufacturing enterprises with more than 20 employees (10 employees if power is used) must register with the government, and so are considered formal regardless of incorporation. Service sector formality derives only from incorporation. Hence, about 4% of unincorporated services firms that meet the employment threshold would be considered formal if they engaged in manufacturing, but instead are classified as informal.
6. The modern/traditional services split in the GGDC data suffers from the same problem noted in endnote 4, that modern-sector communications is aggregated with the traditional-sector transport and storage industries. Hence it is not strictly comparable to India’s data.
7. National accounts data includes a breakout for value added from services of owner-occupied dwellings, which is typically lumped with business services. Since these entail no employment component, they were excluded from value added attributed to modern services.
8. Further details are available in an online appendix (Green 2015).
9. For the exercises presented here “new jobs” means a rising headcount, net of replacing workers who leave the workforce.
10. The same limitations noted earlier about the East Asian GGDC data apply here, so formal and informal manufacturing get the same values in the covariance matrix.
11. Author’s calculations using data from Goldar (2014) and National Sample Survey Office (2011).

On 1 February 2019, a three-member delegation of Pakistani experts concluded an examination of the 1,000 megawatt (MW) Pakal Dul, 48 MW Lower Kalnai, 850 MW Ratle hydropower projects and the 900 MW Baglihar dam at the Chenab basin and found them to be operating according to the design. India also shared data about planned run-of-the-river.

hydropower projects at Balti Kalan, Kalaroos and Tamasha in the Jhelum and Indus basins. The last time India had shared such data was in 2013. Then came the terrorist strike on 14 February 2019.

Under the IWT, signed in 1960, India has control over water flowing in the eastern rivers—Beas, Ravi and Sutlej—while Pakistan has control over the western rivers of Indus—Chenab and Jhelum. Of the total 168 million acre-feet of the Indus basin, India’s share of water is 33 million acre-feet or just 20%. India uses nearly 95% of its share.

The deal was brokered by the World Bank after nine years of negotiation.

• Pakistan sought a guaranteed source of water, independent of Indian control. It interpreted sovereignty on the principles of maintaining status quo, or “prior appropriation.” In other words, since the people of Pakistan were already using this water, they have a claim on it and any curtailment by India would be an
  attack on its sovereignty.
• International water treaties do not recognise the validity of this principle. This doctrine implies “recognition of an international servitude upon the territory of one nation for the benefit of the other and would be entirely inconsistent with the sovereignty of the upper nation over its national domain.”
• The Government of India during the negotiations had adopted the position that the Indus dispute should not be settled using existing legal rights, but by accounting for potentialities of river development. India argued for an engineering, rather than a legal basis. Article XI(1) read,

“Nothing in this Treaty shall be construed as affecting existing territorial rights over the waters of any of the Rivers or the beds or banks thereof.” India has not relinquished its sovereign claims over the riverbeds.

• India’s first major concession was the agreement itself. Pakistan was allotted 80% of the Indus waters. Instead of any scientific rationale, sharing of water from the six rivers of the Indus river basin is based solely on the division of rivers.
• The second concession India made was to allow free-of-cost water flows into Pakistan. The 1948 Inter-Dominion Accord provided for Pakistan making payments for water based on the legal principle of India “owning” this water.
• The third concession was to relinquish 13 of the 16 pre-partition Punjab’s canal systems, though much of their river-heads were located in Kashmir, and to pay Pakistan $174 million for new works. The Indus Basin Development Fund Agreement specified the total cost of works in Pakistan to be $893.5 million.
• A consortium led by the United States (US) and the United Kingdom provided most of it as grants. Cold-war politics played a central role in heavily tilting the treaty in Pakistan’s favour. Pakistan was aligned with theUS while India tilted towards the Soviet Union.
• The international context is now fundamentally different. With the United Nations Security Council supporting India’s position on cross-border terrorism, the time is ripe to push for reciprocity. Climate variability is another new factor. Snowmelt and glacier melt comprise a significant portion of the water supply, and warming could increase variability of flows affecting seasonal requirements for agriculture as well as resulting in flooding.
• The fundamental flaw in the treaty is that Pakistan has not kept with its side of the grand bargain. The expectation that solving the Indus waters issue was a first step on the way to a Kashmir settlement has not been achieved.
• The treaty needs to be brought in line with other such bilateral treaties. It will not be easy as both countries have to agree, but a beginning should be made for shifting from “distribution” to “sharing” of the Indus waters.

Here in lies another disturbing aspect of India’s urban planning. Is it truly based on what is needed by the citizens and commuters, or is it motivated by political expediency and the greed of contractors?

The building of infrastructure with hardly any regard to commuter use—many of Mumbai’s sky walks and the monorail are prime examples—is a common phenomenon, and the media has time and again exposed how “blacklisted” contractors are hired for these projects.

• Is it not shameful that the vast engineering and architectural talent in the country often plays to the tune of politicians who have an eye on objectives other than the interests of citizens? Their engineering and architectural skills ought to be of service to the people and not for populist measures that help vested interests.
• Urban policies seem to encourage the lowest bidders rather than those with excellent professional credentials who can deliver the best services. This trend seems to be holding through in connection with most public infrastructure projects.
• Urban planning activists have time and again warned of the perils of policies that are automobile-centric, anti-public transport and geared towards the interests of private contractors. In fact, since the country’s rapid urbanisation has been chaotic, the urban infrastructure too tends to be random and piecemeal rather than mediated by context and public needs. Needless to say, corruption and lack of accountability both thrive in such a scenario.

The focus in Mumbai on metro systems and the coastal road project despite objections from urban transport and environmental experts is a familiar story with variations across the country.

On 3 July 2018, two persons were killed after a bridge collapsed onto the tracks near Andheri railway station in Mumbai. Immediately, a blame game followed and it transpired that while the railways had inspected the bridge between 2014 and 2017, there was no proper documentation to show for it.

• Before that, in 2017, a stampede on the Elphinstone railway bridge had claimed 23 lives and injured many others. The horrific stampede showed the complete failure of the authorities in harmonising land use with the massive and growing commuting population in this area.
• There are two other important facets to the recent bridge collapse. One is the post facto attempt made by the Shiv Sena to hold the growing population as the reason for the accident, which is a cynical attempt to shift responsibility.
• Mumbai, down the centuries, has attracted migrants and will continue to do so, given its employment potential. The city’s latest development plan itself speaks of creating eight million new jobs. Surely, urban planning should take into account the increase in commuter population that this will lead to? The other fact is the lack of any public outcry or protest campaigns over the third such tragedy in the past two years.
• It is as if citizens have simply accepted that these accidents and the connected deaths and injuries are the collateral damage of “development.” Governments have begun to simply ignore protests by citizen groups and carry on with controversial infrastructure projects.
• It is high time that infrastructure building be seen as a public service rather than as a distribution of largesse to contractors and other allied professionals. For this, it is imperative that citizens ask questions and demand answers persistently

And, yet, between them, the large internet platforms have suffered few, if any, consequences for their many misdemeanours. They have not been punished by the market (consumers and clients), they have not been swamped by competition, and they have certainly not been checked by governmental authorities. How did they get so powerful? Can they ever be held accountable for their actions? These are the pressing questions on the minds of academia and policymakers around the world. And, while Elizabeth Warren is today the most high-profile political proponent of a drastic solution, she may only be the first.

The Curse of Bigness

In calling for the likes of Facebook and Google to be broken up, Warren is echoing similar calls being made in academic circles and elsewhere. Prominent among these voices has been Tim Wu, a professor at Columbia Law School.

• Wu, famous for coining the term “net neutrality,” has, in his recent book The Curse of Bigness (2018), called for the adoption of a “neo-Brandesian” approach to the use of antitrust laws in the US, specifically in the context of internet platforms.1 Wu argues that the approach of the US Department of Justice has allowed internet platforms to amass economic power in a manner that could threaten the very future of democracy.
• He points out how in the past such concentrations of economic power, even by information technology companies (notably AT&T, IBM, and Microsoft), were effectively attacked using antitrust laws, resulting in the birth of the internet as we know it today (Kumar 2019).
• That is not the only argument that Wu makes in his book. He traces the political origins of the principal antitrust law in the US, the Sherman Act, 1896, and the underlying concerns which it was trying to address.
• He finds that the concerns were primarily political, that the amassing of such economic power as the world had never seen before in the hands of the robber barons of the so-called “gilded age” in the US were considered a threat to democracy.
• Election campaigns were fought and won on the pledge to break up the vast business empires of the likes of J D Rockefeller and J P Morgan. The speeches of the political leaders of the time, especially President Theodore Roosevelt, show that the concern was not just to address market failures, but also to stave off potential threats to democratic systems of governance posed by the so-called “trusts” which controlled businesses.
• This political aspect of antitrust, however, was lost since the 1980s as the so-called Chicago school of economists (led by Robert Bork) gained prominence. Wu argues that the Chicago school’s approach disingenuously tried to simplify antitrust law for lawyers and judges by reducing the whole field to the question of whether consumer welfare was being affected by a cartel or a monopoly.
• This approach also fit within the larger political move to shrink the state and give free rein to businesses, or the so-called “Reagonomics,” and this found favour with the governments and judges of the day. In Wu’s telling, this approach has allowed regulators in the US to ignore the harmful effects of internet platforms swallowing their competitors whole.

He points specifically to Facebook being allowed to acquire Instagram and WhatsApp without a murmur of disapproval from the authorities.

• Wu calls for a “neo-Brandesian” app­roach to the problem of tackling internet platforms’ dominance. While still short on specifics, The Curse of Bigness explains the broad outlines of the approach, harking back to Justice Louis Brandeis, a pioneering trust-busting judge.
• The approach calls for greater enforcement of existing laws to hinder the outright acquisition of competitors. Wu does not rule out the need to break up the existing tech companies to separate the various things to prevent them from getting access to more and more of our data.

For instance, he suggests a “de-merger” of Instagram and Whats­App from Facebook, allowing these platforms to compete rather than collude over users’ data.

Why Data Is Not the New Oil

Wu’s comparison of the near monopoly of present-day internet platforms with the Rockefeller-owned Standard Oil Trust might tempt one to conclude that “data is the new oil.” Inasmuch as data is a valuable resource and will continue to be so in the coming decades, it is true, but the comparison stops there. Data, unlike oil, is more valuable the more there is of it.

• While increased production of petroleum might lead to prices ­dropping, it is just the opposite with data. While petroleum can be mapped to jurisdictions and boundaries, data ­cannot. While petroleum, like all other natural resources, is finite, data is potentially infinite.
• As Wu points out, internet platforms owe their power to the network effect. With Google and Facebook offering their products free, there is little chance of a competitor being able to undercut them by price. Even when a competitor comes along with a better product, their accumulated capital allows competitors to be acquired swiftly, with little regulatory disapproval.
• Even if a competitor were to arise, they would be unable to compete on one key feature: data. Internet platforms probably know their customers better than they themselves do. The vast ecosystem of apps and devices which go along with the internet platforms means that incumbents will be virtually unassailable by entrants in the kind of service that they can provide their consumers.
• Seen in this light, Wu’s and Warren’s calls for antitrust action against internet platform companies cannot come too soon. They are of relevance for India too. While internet penetration in India is still low enough that it is possible for new entrants to compete with the incumbents (for example, the successful entry of Chinese apps into the Indian market) (Shaikh 2019), the concerns cannot be entirely brushed aside.
• The Competition Commission of India’s order in the context of Google Flights (John 2018), and the foreign direct investment in e-commerce policy limiting e-commerce companies from selling their own products (Badri Narayanan and Juneja 2019)2 are two instances of pushback on such concerns from regulators and concerned agencies.

The measures to prevent monopolisation of data in India could be both ex ante and post facto, depending on the situation and the powers of the regulator.

• Whether regulators in the US are alive to the fact or not, those around the world (especially in the European Union) are growing wary of the increasing economic clout of the internet platforms. The pushback has taken many forms, whether in the form of the European Commission levying one of its largest fines on Google for violations of EU competition law (Warren 2018), or India’s net neutrality regulations.
• If the US DoJ were to drop its laissez-faire attitude towards applying antitrust law and regulations on internet platforms, a new front may be opened in this long-running battle.

Notes

1. For the purposes of this column, the terms “competition law” and “antitrust” will be used interchangeably, though the latter is mostly used in the American context, while other jurisdictions discussed here use the former term.
2. However, the fact that this principle has not yet been extended to Indian e-commerce companies suggests protectionism rather than genuine concern over competition.

New Rural–Agrarian Agitations

• The recent farmers’ protests manifest the emergence of a new politics evolving in and around the rural–agrarian question. Although this new politics can also be seen as an amalgamation of farmers’ politics during the 1960s and 1980s, the more immediate reasons can be found in the socio-economic changes that happened in the Indian countryside after the economic reforms of 1991.
• The economic reforms did produce massive changes in various parts of India, including in regions like Malwa in Madhya Pradesh, Vidarbha in Maharashtra, or Sikar in Rajasthan, where the first wave of protests started. These regions have undergone significant economic and social changes in the last three decades (Suthar 2017a, 2017b).
• These protests initially began in the Mandsaur town in Madhya Pradesh in June 2017. The protesting farmers were demanding the waiver of agricultural loans and a rationalised MSP covering more crops as well as input costs based on the Swaminathan Committee report’s recommendations.
• This led to a nationwide farmers’ protest after the killing of six farmers in police firing. Soon, various farmers’ organisations from Maharashtra, Punjab, Rajasthan, Uttar Pradesh, among others, came in support of Mandsaur farmers.
• This common reaction was just the beginning of large numbers of agitations, which were to commence in the next few months. Several farmers from Tamil Nadu also gathered during the same time at the Jantar Mantar in Delhi, holding human skulls, bones, and dead rats in their mouths, symbolising their poor living conditions.
• A month later, farmers in Sikar and Ganganagar districts of Rajasthan also protested against the government’s negligence of farmers’ issues. The entire district of Sikar came to a standstill when people from every walk of life came in support of the protesting farmers.
• Eventually, these agitations resulted in the formation of a joint platform of around 180 farmers’ organisations working in different parts, called All India Kisan Sangharsh Coordination Committee (AIKAsCC).
• These agitations culminated in a massive protest march followed by a Kisan Sansad (farmers’ parliament) in the national capital on 20–21 November 2017, attended by thousands of farmers.
• The most significant show of strength of farmers was witnessed when around 50,000 farmers marched on foot from Nashik to Mumbai to pressurise the Government of Maharashtra to accept their demands.
• These agitations have been very diverse in their strategies and methods of mobilisation. Mostly, the protests were remarkably peaceful and well organised. The demonstrations were organised forms of political resistance with participation from diverse socio-economic groups or castes and classes.
• A large number of women along with educated youth took part in these protests. Unlike the 1980s movements, the leadership in these movements was not very traditional in outlook.
• These protests witnessed the emergence of an informed, professional new leadership—very articulate and technologically sound—along with the traditional form of leadership.
• Many of the protests also had a very successful social media campaign to garner nationwide support. These protests were also characterised by a unique mix of unorganised and decentralised forms of mobilisation as well as a highly coordinated and organised form, led by the All India Kisan Sabha (AIKS).
• In Madhya Pradesh, Punjab and western Uttar Pradesh, protests were largely coordinated by the local farmers’ organisations. In the case of Sikar (Rajasthan) and Maharashtra, on the contrary, protests were organised by the AIKS.
• In the case of Sikar, the entire city, and in the case of Maharashtra, an entire region came to a standstill. In the case of Sikar, people from all walks of life including bus and taxi drivers, rickshaw pullers, students, women, and landless labourers participated in the protests.
• A national magazine had called the protests a “farmer’s revolt.” Similarly, in an interview, Yogendra Yadav, a political activist associated with Swaraj Abhiyan also called this a historic moment and a move towards “peasant rebellion.”
• Probably, it is too premature to categorise these protests as a movement or to judge them with a yardstick of “success” or “failure,” but they do reflect a qualitative shift in the politics in rural India.
• This political shift is an outcome of specific processes of sociological and economic changes that took place since the introduction of large-scale economic reforms in 1991.

The Rural–Agrarian and the Urban

• Since independence, the rural–agrarian society in India has undergone two significant phases of socio-economic transformation. Economically, both these phases produced newer classes, whereas sociologically, they resulted in the emergence of new rural sociocultural value structures.
• These changes further led to new forms of political mobilisation in the countryside, demanding more state support for the agricultural sector. But both these phases also generated different kinds of crisis and stress situations.
• The first phase produced a rural–agrarian crisis whereas the second phase, after the 1991 economic reforms, led to a crisis in the urban. The present phase of rural–agrarian protests can be seen as an amalgamation of both these “crises” situations, which look back to the rural–agrarian as a way out.
• The first phase of rural–agrarian change occurred after the green revolution during the late 1960s. This phase was characterised by high growth in agricultural production, bringing economic development in the selective regions of India.
• Socially, this also facilitated the emergence of a new rural landed class, which was looking up to more state support for agricultural development. This class asserted its demands politically by organising massive protest rallies in New Delhi during the 1980s.
• However, these acts of political assertion were organised in a decentralised manner, and also lacked any concrete ideological basis.
• K Balagopal (1987) had called this new landed and educated class the “provincial propertied class.” Though this class had expanded itself into urban areas through its economic diversification or by investing into the properties or business, it always looked back to the rural as a support base.
• Strong ties with the rural compelled them to articulate the demands of the rural–agrarian sector politically. A major factor behind this section’s political activism in defence of the rural was a sense of nostalgia and attachment resulting from their embeddedness in the rural sociocultural spheres.
• Consequently, apart from the political economy reasoning behind demanding more state support, preservation of the rural society and its sociocultural ethos was also adopted as a major mobilisation strategy by these movements (Gupta 1997).
• This new class also criticised the existing model of development for giving precedence to urban India at the cost of the rural and agrarian India.
• However, the farmers’ politics during the 1980s largely remained confined to protection of the political and economic interests of a particular class of the rural–agrarian, especially the landed class. They hardly had any transformative agenda for the holistic development of the rural.
• The agenda of the rural poor or landless labourers or the rising inequalities due to the impact of green revolution were either sidelined or were missing from the political campaigns of these movements.
• Tom Brass (1999) and other scholars had discussed these “new farmer movements” highlighting some of the limitations of these movements, including a tendency to support the right-wing political forces.
• The second phase of the rural–agrarian transformation occurred in the aftermath of 1991 economic reforms. Unlike the late 1980s, this phase witnessed declining state investment in the agricultural sector and linking of Indian and global agricultural sectors.
• Besides, the expansion of the service sector in the Indian economy also resulted in the rise of the informal economy, small-scale business activities and enlargement of education industry (Jodhka 2014).
• These developments in the Indian economy opened up newer employment and social avenues for the people living in rural areas. Consequently, urban areas became the new sought-after destination of the youth and middle class who were educated and also desired a modern lifestyle.
• Besides, the emergence of a new informal economy in the urban areas as well as in the mandi spaces also led to the socio-economic transformation of rural society (Vasavi 2012; Gupta 2005; Jodhka 2014; Harriss-White and Shah 2011; Mohanty 2005).
• The new economy produced new sociocultural values of individualisation and led to the disintegration of caste and other social hierarchies in the rural areas.
• Overall, these structural economic changes resulted in, what Jodhka calls, “emergent ruralities.” According to Jodhka, the new rural was qualitatively different from the rural society of the late 1980s. He further explained it in the case of Punjab (Jodhka 2006: 1534):
• Growing obsession with the so-called “new economy,” information technology, media and the urban consumers led to a complete marginalisation of “rural” and agrarian sector.
• Farmers were looking at avenues to move out of agriculture as well as from the rural areas. They preferred employment as security guards or any other low-paid jobs in the city spaces than being small cultivators.
• The middle- or marginal-farmer households began investing heavily in the education of children, especially in the field of technical education or coaching economy with a dream of joining organised sector employment.
• Unlike the 1980s, when the socio-economic change had fostered political assertion of the farmers, the second phase produced some form of depoliticisation of the rural–agrarian society. Politics was not seen as a collective social activity but as an instrument either for economic gains or as a waste of time and energy.
• However, for the marginalised groups, electoral politics was the only ray of hope. The farmers’ movements of the 1980s had not incorporated the demands of socially marginalised groups into their political agenda.
• Consequently, these sections, especially the rural-educated youth coming from marginalised groups saw electoral and organised politics as the only political tool available to them for protection of their socio-economic interests.
• This political consciousness amongst the marginalised groups led to the emergence of the politics of the “bahujan” (Yadav 2002). These emerging forms of political mobilisations produced political as well as social contradictions in the rural and agrarian political realm.
• These contradictions required a new understanding of determinants of politics and also of the society and economy of the rural. Priyanshu Gupta and Manish Thakur (2017) have argued that the classical political economy approach of the rural–agrarian dominance may not be very useful in understanding the “fundamental transformation of the ‘village’ from the spatial habitat of the traditionally ‘dominant’ to the ‘waiting room’ for the aspiring and the despairing.”
• In addition to this, the rural–agrarian discourse has remained village- and agriculture-centric. With the changing nature of economy and “ruralities,” there are strong rural–urban linkages emerging.
• These linkages are producing newer interactions between the two sets of social values, putting rural–agrarian on the centre stage. In a special issue of Review of Rural Affairs in this journal, the limitations of the existing “formulations like Bharat versus India, the narratives of ‘crises’ or even the formulation of rurbanity” were highlighted (Jodhka 2016).
• Largely agreeing with these limitations, this paper argues that a new process of “rural–agrarian–urban,” linking three processes while retaining some of their essence simultaneously, is shaping the 21st-century rural India.
• The new rural–agrarian is a product of the aforementioned two phases. This new linkage has produced newer crises situations on the one hand, but also produced newer forms of political possibilities and aspirations of the rural on the other.
• This is getting consolidated in the form of a quest for a new identity. Although it is yet unclear what this identity would be, it appears to be a mix of individual as well as community values, community building around the agrarian and the land, and finally, a new rural with modern facilities.

Quest for Rural–Agrarian Identity

• The present protests are a political manifestation of the increasing quest for a new identity. This identity is a mix of an individual (being and dignity) as well as a collective sense of belonging.
• This also emerges from a sense of disillusionment from the urbanisation process but also a desire to reimagine the rural in newer ways with more space for individual freedom.
• It is also based upon newer aspirations in the rural where city-like facilities are demanded. It is this complex interplay between the rural and the urban with agrarian in the middle that makes the new process of politicisation over the agenda of rural–agrarian possible.
• Both the processes of agrarian crises and anti-urban sentiments have produced an identity crisis, not only in an individual but also in a collective sense. As an individual, this quest is driven by one’s sense of loss of self-dignity and respect resulting from economic as well as social reasons.
• But it is also linked with one’s sense of getting lost in an urban crowd. With the rise of individualised and consumerist lifestyles in the urban areas, the sense of community has gradually weakened. This has also led to an anxiety arising out of the present conditions of rural society.
• These disillusioned social groups belonging to the rural or/and urban spaces find the focus on issues plaguing the rural–agrarian as a more effective agenda for political assertion. There are two reasons behind this.
• First is a gradual realisation of sense of urban impacting upon the agrarian (Gurunani and Dasgupta 2018). This impact is strongly linked with, but not confined to, the spatial questions like land acquisition for various projects for urban expansion. Rather, it has deeper socio-economic implications on everyday life of the rural–agrarian.
• Secondly, the rural–agrarian is still seen as a cultural community with a sense of belonging and brotherhood. This makes the rural–agrarian a possible notional sight of protest and mobilisation.
• The farmers came out protesting against government policies as they are left with no other choice, as highlighted by many people during the author’s fieldwork. Some of the factors like declining prices of agricultural products, increasing expenditure on agricultural inputs and also increasing costs of education and health are responsible for making rural life miserable (Reddy and Mishra 2009; Himanshu 2018).
• What made the present protests different from earlier ones was the massive participation by all sections of the rural–agrarian society. Not only the male farmers, who have historically been a dominant group in farmer politics, but also many other social categories such as women, youth, middle-aged persons, educated and uneducated persons, tribals, and even the landless took part in these protests.
• The primary reason behind this is that the agrarian crisis is not confined to farmers and affects all sections of the rural society who are directly or indirectly associated with agriculture or allied professions.
• In the case of urban crises, it involves those who may not be involved in farming directly but are still associated with rural society through their kinship or social networks or because they continue to own a piece of land in the rural areas.
• This social group consists of those who had moved out of rural life some years ago with new aspirations. They either came to urban spaces to study and find employment or for better living conditions.
• However, these classes soon realised that even urban life had its own problems, including an expansive consumerist lifestyle, individualised life, urban forms of discriminations and so on.
• Besides, gradually declining economic opportunities, and jobs in the private sector becoming less lucrative and more exploitative have made urban spaces difficult for those who were the first-generation to move to the city from the family or the village.
• The prospect of moving to urban areas gave hope amidst the rising crisis in rural–agrarian society, resulting in massive migrations to city spaces. However, emerging challenges of survival in the urban areas are leading to a reverse push from the urban areas as well.
• Those who took part in the recent protests were not able to articulate the nature of this “new” identity, but acknowledged that rural India needs to reinvent itself, if it wanted to survive.
• Rajaram Singh, who is the national secretary of All India Kisan Mahasabha and was an active partner in the AIKScC, spoke to the author during the Kisan Sansad, a mock parliament by the people coming from rural areas, on Parliament Street in Delhi on 20–21 November 2017. He mentioned:
• Ye kisan ke liye, gaon ke liye, astitva aur pehchan ki ladai hai. Hamare pas iske siva koi rasta hi nahin hai. (It is a fight for survival and identity for the rural–agrarian India. We have no other way but to speak against this. (referring to the protests)3
• Yashwant had also come to take part in the Kisan Sansad. Yashwant, a combine driver from Sangrur district of Punjab is not a cultivator, nor did he own any land. However, in response to the question on why he was here, he said:
• We have no choice but to fight. Have you ever heard of banks taking action against an industrial defaulter? But when a farmer fails to repay a loan for few months after paying it in time for two to three or more years, the bank authorities start knocking his doors and threatening him.
• You have a job, and you get a salary. Our boys are without any work. I need to come here for them. I am not bothered about the result. But I feel satisfied that I did my due.4
• Broadly, three major reasons behind this shift in the nature of the rural–agrarian and quest for new identity can be identified. There are pull- and-push factors involved here. Push factors are forcing people to move away from urban spaces whereas pull factors are those attracting people back to the rural–agrarian.

Urban Spaces, Unfulfilled Aspirations

• The most crucial determinant of this phenomenon in the category of push factors is the emergence of negative externalities associated with urbanisation. These include lack of job opportunities, high living costs in the cities and above all, the increasing sense of alienation and exclusion.
• The individualised urban lifestyle promotes a sense of social isolation, and also an absence of the sense of belonging. Besides, the faster pace of competitive life is challenging for people coming from the rural–agrarian sector.
• The new rural–agrarian youth, who shifted to the urban spaces looking for a better life, now feels disillusioned and left out in the city.
• Prakash was amongst the farmers who had come from Tamil Nadu, and could not complete his engineering because of crop failure and his inability to pay the bank loan. He said: People tried shifting to urban areas and looking for new avenues.
• But all of them are dissatisfied. They got nothing. Even my family tried it. Many others also did. Many families are puzzled. Our future is in the villages and not in cities. What do we do in cities? We cannot even pay the rent of the house. In the village, we had a house and a respectable life. In the city, nobody knows you.
• This has further compelled people, especially the younger generation, to look back at rural areas, which had some sense of belonging, psychological association and also availability of, or at least a myth of presence of a social support system.
• What attracts them towards the rural, or keeps them associated with it, is its vibrant social life, fresh air, fresh vegetables, a relatively less expensive lifestyle and above all a sense of community.
• On the contrary, the idea of urban lifestyle is associated with multiple forms of exclusions and discrimination leading to disillusionment with the urban way of life.
• Sudhakar who holds a graduate degree and started his dairy farm was also on the streets of Delhi for a month. In response to the question on why he was here, he replied:
• I am here for my sons. I want to give them a decent life. This life is possible only in the village. They should go out, study and learn new things but village gives you a natural life. My parents gave me that and I want to give this to my children. It is not a fight for money. It is a fight for prestige and rural survival.
• Amra Ram from the AIKS, who was one of the prominent leaders of the movement, explained what made this possible:
• Everyone realises that the rural–agrarian is in a deep crisis. The fact is that India is a rural society which is largely agrarian. Youth do not see any other future and have no option other than to fight for the survival of rural India.
• They also understand the need to change if this fight has to be won. Ye smajh ki ise bachane ke liye ladayi jaruri hai, is andolan ki saflta hai (It was this realisation that resistance is necessary which made this movement a success).
• It is necessary to highlight here that this may not be a pan-India rural phenomenon. Besides, this feeling of looking back to the rural may not be present among all sections of the rural–agrarian society.

Education and Migration

• Another major push factor taking people away from the urban is the massive privatisation and commercialisation of the education sector and a simultaneous but gradual withdrawal of the state from organised sector employment.
• One major factor during the 1980s attracting people to the urban areas was the availability of educational institutions. Scholars have highlighted (Gill 1985; Balagopal 1987; Vasavi 2012; Jodhka 2014) how increasing access to education had played a crucial role in changing the rural society.
• It was one of the major causes of migration from the rural to urban areas. Dipankar Gupta (2015: 41) has argued that, “an important and necessary condition for being a ‘rurbanite’ is education.” He had further argued that it was the rural poor who were spending on education much more than what they could afford.
• The increasing youth participation in the education sector had exposed them to a new culture of consumerism and individualism in the urban areas.
• However, the growing expenditure on various educational heads, especially on the tuition fee on the one hand and increasing role of coaching centres on the other, had also put this new generation of rural youngsters under immense psychological stress.
• In Wardha city, I spoke to a 14-year-old boy in the month of June, during the school vacation period. He belonged to a nearby village and had just taken the ClassX exams.
• He was living in Wardha to attend coaching classes for Maharashtra Public Service Commission and Indian Engineering Services entrance exams, along with mathematics tuition classes for Class XI and Class XII syllabuses. I asked him why he had taken on so many assignments instead of enjoying his vacations at home. He replied:
• Sir, we are people from the village. People from the cities think that we are backward. Since we come from a rural background (gaon se aate hain) therefore, we do not know how to live a good life (achha jivan).
• I want to do well in my life. I want to achieve something and want to show these people in cities that we villagers can also progress and do well in our lives. That’s why I am doing so many things in studies now.
• Surprisingly, he also said that there are problems in the villages as there is too much of interference in personal life. However, he also added that in case of cities, no one cares for others. In the village community, one could call people immediately in case of an emergency.
• In the times of agrarian crisis, families of such youth could hardly afford to pay for their education. Simultaneously, there is a pressure to perform well in order to secure a better future.
• Increasing access to education is not commensurate to the employment opportunities available. But there is an increasing sense of anxiety due to the lack of job opportunities in urban areas.5 Most of the available jobs in the cities are in the private sector.
• These employment opportunities are contractual, short-term opportunities, and are also not very well paid. The kind of humiliation and discrimination one has to face in these workplaces further generates a sense of inferiority.
• The exploitation and marginalisation in the urban job market have worsened in the last two decades. Jitendra (aged 32), from Trivedi Ka Purwa village of Banda district, Uttar Pradesh, used to work as a security guard in Kanpur.
• He lost his hand in an accident. The company threw him out and also did not pay his remaining salary. “I thought it is better to go back and work on the land instead of facing insult in the cities.” Now he does farming in the village along with his family.6
• As a result of job-market-related challenges, the rural youth who are now educated and exposed to the urban lifestyle and its comforts, find it financially unaffordable. Mostly, these youth belong to marginal or small farmers’ households with an upper-middle caste family background.
• They do carry a sense of prestige originating from their caste–class location in the rural se-up. Profitable agriculture and rural community play the roles of support systems in case of a crisis in the city spaces.
• However, with the economic crisis in the agrarian economy on the one hand and increasing socio-economic complexities in the rural society on the other, they suddenly feel a significant loss of that support system.
• Any political mobilisation around the rural–agrarian question is a potential point of reclaiming that past. It is this mix of alienation, exclusion, and insecurities amongst urbanised youth that is leading to the rise of popular agitations in defence of the rural–agrarian.
• While conducting fieldwork in Nagpur during May 2017, I interviewed a few youngsters who were continuing their studies in the city. Although they were living in the cities since long, they wanted to go back to their villages provided they get some economic opportunities there.
• They also had the dilemma of village social life being one with too much interference in individual lives, but they juxtaposed it with the discrimination and alienation present in the urban spaces as well. They all expressed their anxieties with the prevailing conditions in rural India and therefore articulated the need to rebuild the rural–agrarian.
• While attending the massive farmers’ protests in Delhi in July and later in November 2017, various participants mentioned that although they themselves might not be farmers, they were related to the rural economy that is integral to the farming sector.
• They have relatives and friends who are farmers, and therefore they believe that it is their moral responsibility to stand with them in times of crisis.
• Santosh Lakshmanrao from Yavatmal district of Vidarbha articulated their plight, two months before these agitations started:
• Agriculture and rural society are in terrible condition. We do not know how to articulate it, but we know that the government does not take us seriously. The government works for big players (bade log) and not for farmers and the poor. This will turn explosive one day. When, and how I do not know.
• The village Donoda to which Lakshmanrao belongs had seen more than one villager committing suicide almost every year in the last one decade. When I spoke to some of the youngsters they argued: “Sir, you talk only about those who have committed suicide. What about those who died due to depression or shock because of social pressure? Is that not suicide or murder?”
• When I was clicking their pictures, one of them said: “Sir, take a picture of this person (pointing to the man standing next to him). He is going to commit suicide soon.”
• He continued, “He will commit suicide soon and one by one in the next few years, we all will. He refused to tell me his name.” The unrest was visible on their faces. Another person standing next to him added:
• We all are graduates. We did not get a job after completing our education. By the time we finished our degrees, there were no jobs. Even if there were, one has to pay a heavy amount to purchase a job position. For this, a farmer has to sell his land.
• We did not want to sell our land due to our emotional attachment to the land and village. Hence, we decided to continue with agriculture and live in the village. Now we are into agriculture with three–four acres of land. This land is not enough for leading a good life in today’s times.
• Nobody wants their daughters to marry boys like us. Because we live in a village and we do not have wealth, we remain unmarried though we are touching 30s. Sir, you tell us, what charm we have in life? Our life is a waste.8
• He laughed along with others. Such narratives reflect a sense of anxiety, alienation and above all a sense of identity crisis in people living in rural areas engaged in agriculture.

The Land Question

• Another major factor contributing to the emergence of the new identity is the question of land. The land question has been in the news since the last two decades due to increasing cases of disputes between the farmers and the government over land acquisition for various industrial or other developmental projects.
• Traditionally, land was seen as a major asset in rural society. It was a source of livelihood, social prestige and also provided a sense of security. However, this emotional attachment with land had witnessed a change in the last two decades due to the increasing inclination towards urban spaces and declining interest in agriculture.
• With a gradual decline in the number of jobs available in the market, an increasing sense of exclusion in the urban settings and a sudden jump in the land prices due to increasing demands for various urbanisation projects, the rural youth have started looking back to land as a source of support, prestige, and economic security.
• Some of the protests around the land question during late 1990s and 2000s were about the disputes over the amount offered by the government as compensation. However, the recent protests are against the very idea of land acquisition.
• Villagers have gradually started refusing to give away their land irrespective of the amount offered. The question of land is no more about the agricultural land but is also associated with the idea of natural resources and their ownership (Naga 2016).
• Vijoo Krishnan, who works with the AIKS and has been quite proactive in articulating farmers’ unrest, said in a conversation with the author:
• It is wrong to assume that these protests are sporadic and they occurred suddenly. They are just a manifestation of farmers’ unrest against the manner in which various governments have been dealing with their concerns.
• In many states, the government had been proactively involved in giving away the land to the corporates without even taking farmers’ concerns into account.
• This change of attitude is also an outcome of some of the policies adopted by various governments to create alternative sources of income in the rural areas itself. In case of Kerala, Rajasthan and Gujarat, the state governments had invested in making villages into tourist destinations with policies such as “ideal villages,” “homestays,” “weekend holidays” that generated employment opportunities and sources of income within the rural spaces (Verghese 2018).
• In the case of Punjab, many farmers have opened their farms to tourists. These experiments served twin objectives: promoting tourism and also making people aware of rural lifestyle and agriculture.
• This kind of exposure has also helped in generating a sense of confidence and self-pride amongst the villagers. With such activities, the prices of land have gone up. Besides, people also consider ownership of even a small piece of land in such areas as a major asset.

Conclusions

• The recent farmers’ protests show certain continuities as well as changes in the rural–agrarian politics. Although it is difficult and premature to measure them in terms of “success” and “failure,” these protests have given the farmers’ politics a new lease of life.
• The sudden emergence of these protests symbolises the underlying deep and silent processes of socio-economic change in the countryside. These protests also reflect certain profound changes emerging in Indian politics.
• They constitute a rather unorganised and fluid form of politics and can be interpreted as the post-ideological phase of Indian politics. How far these protests will have an impact on India’s electoral politics is an open question.
• The survival and strength of these protests will depend upon their ability to confront the few challenges that were also faced by the farmers’ movements during the 1980s, such as the questions of rural poverty and labour and resolution of the caste–class dichotomy existing in the rural areas.
• Above all, the future of these movements is contingent upon their ability to fight with the ongoing religion- and caste-based polarisation on the one hand and massive penetration of the market forces on the other.

For decades, India’s statistical machinery has enjoyed a high level of reputation for the integrity of the data it produced on a range of economic and social parameters. It has often been criticised for the quality of its estimates, but never were allegations made of political interference influencing decisions and the estimates themselves.

Lately, Indian statistics and the institutions associated with it have however come under a cloud for being influenced and indeed even controlled by political considerations.

• In early 2015, the CSO issued a new gross domestic product (GDP) series (with the revised base year 2011–12), which showed a significantly faster growth rate for 2012–13 and 2013–14 compared to growth under the earlier series.
• These revised estimates were surprising as they did not square with related macroaggregates. Since then, with almost every new release of GDP numbers, more problems with the base year revision have come to light. In January 2019, for instance, the CSO’s revised estimates of GDP growth rate for 2016–17 (the year of demonetisation) shot up by 1.1 percentage points to 8.2%, the highest in a decade.

This seems to be at variance with the evidence marshalled by many economists.

• In 2018, two competing back series for varying lengths of time were prepared separately by two official bodies, (a committee of) the National Statistical Commission (NSC) and later by the CSO.
• The two showed quite opposite growth rates for the last decade. The NSC numbers were removed from the official website and the CSO numbers were later presented to the public by the Niti Aayog, an advisory body which had hitherto no expertise in statistical data collection.

All this caused great damage to the institutional integrity of the autonomous statistical bodies.

• In December 2018, the schedule for the release of results from the Periodic Labour Force Survey (PLFS) of the NSSO was not met. This was the first economy-wide employment survey conducted by the NSSO after 2011–12 and was therefore deemed important.
• Two members of the NSC, including the acting chairperson, subsequently resigned because they felt the NSSO was delaying the release of the report, though the NSC itself had officially cleared it. Subsequently, news reports based on leaks of the report showed an unprecedented rise in unemployment rates in 2017–18; this perhaps explained why the government did not want to release the report.

There have since been news reports that the PLFS of 2017–18 will be scrapped altogether by the government.

• In fact, any statistics that casts an iota of doubt on the achievement of the government seems to get revised or suppressed on the basis of some questionable methodology.
• This is the time for all professional economists, statisticians, independent researchers in policy—regardless of their political and ideological leanings—to come together to raise their voice against the tendency to suppress uncomfortable data, and impress upon the government authorities (current and future, and at all levels) to restore access and integrity to public statistics, and re-establish institutional independence and integrity to the statistical organisations.
• The national and global reputation of India’s statistical bodies is at stake. More than that, statistical integrity is crucial for generating data that would feed into economic policy making and that would make for honest and democratic public discourse.

By providing quality information in the public domain, official statistics helps in measuring progress, analysing interplay of market forces, and in shedding light on business opportunities in the changing socio-economic, technological, and political environment.

The government owns the system to fulfil its commitment to produce statistics as a public good, make informed decisions in formulating policy, and evaluate performance.

As official statistics informs people about the state of progress of a country in various spheres, it has to follow a sound methodology and be authentic, dependable, trustworthy, transparent, and timely.

• In a democracy, a government is a contract between those who govern and those who are governed. Official statistics is expected to give concrete empirical evidence about governance.
• Governance, defined as the capacity of a country’s institutional matrix to implement and enforce public policies and to improve private sector coordination, affects the incentives of politicians, bureaucrats, and private economic agents alike and determines the terms of exchange among citizens and between them and government officials. (Ahrens 2002)
• Governance is independent of government. This separation expects a statistical system to be independent to provide impartial, verifiable empirical evidence on the quality of governance in its various dimensions.
• India is a vast country, united by collaborative federalism. The country inherited a stately history along with a fractured society. The country has seen great preachers of humanity along with worst forms of oppression that muzzled the voice of the poor and deprived.
• As Smith (2004) observed, “The impoverishment of India is a classic example of plunder-by-trade [emphasis in the original] backed by military might.” Freedom, and a democratic system of governance, provided people—for the first time—an opportunity to participate in socio-economic development and redeem themselves from centuries of subjugation and deprivation.
• Jawaharlal Nehru, the first Prime Minister of independent India, in his memorable speech on the eve of independence on 15 August 1947 said,
• Long years ago we made a tryst with destiny, and now the time comes when we shall redeem our pledge, not wholly, or in full measure, but very substantially …. We end today a period of misfortunes and India discovers herself again.
• The achievement we celebrate today is but a step, an opening of opportunity to the greater triumphs and achievements that await us.
• India has come a long way since then. But much more remains to be accomplished.
• Official statistics has discharged an important role in the country’s development effort by providing credible evidence about the state of development.
• The Indian statistical system is committed to continue providing, through professional quality data, an independent and impartial account of the country’s socio-economic progress. How can this cause be strengthened by a better system? That is the critical question.
• An appraisal of the present state of our system, to set the context, and suggestions for modernisation based on available options, form the contour of the paper.

The Indian Statistical System

• The system for official statistics in India as it exists today owes heavily to the great statistician Prasanta Chandra Mahalanobis (1893–1972) for its foundation. His birthday, 29 June, is celebrated as Statistics Day.
• The 125th birth anniversary of this great soul was celebrated on 29 June 2018. The Indian Statistical Institute, founded by Mahalanobis, is conducting a year-long programme to commemorate the anniversary.
• As honorary statistical adviser to the Cabinet, Government of India, Mahalanobis guided the process of laying a solid foundation for official statistics in the country. To him statistics was the “key technology.”
• This technology was considered a powerful means for not only scientific investigations but also for supporting the socio-economic development of the country, which was badly ravaged by colonial exploitation.
• The planning process, as part of the strategic objective of self-reliance, was conceived for the optimal use of resources for fast growth, as per the country’s priorities. This necessitated reliable data on various dimensions of the economy, which formed part of official statistics.
• Mahalanobis continued to nurture the development of official statistics as long as he lived. His contributions in the area of statistics are well-documented by Rudra (1996).
• The hallmark of excellence of PCM’s scientific work consists in the inseparable relation it represents between theory and application. He had an articulated philosophy of research in statistics. He believed statistics to be a Key Technology meant to help in the analysis of problems in the different sciences.
• All through his life, in all his research work, he remained true to this philosophy.
• The chapter in Rudra (1996) on the Indian statistical system records the creation of the two major wings of the present National Statistics Office—the National Sample Survey Organisation (NSSO) and the Central Statistical Organisation (CSO).
• No country, developed, underdeveloped, or over-developed, has such a wealth of information about its people as India has in respect to expenditure, savings, time lost through sickness, employment, unemployment, agricultural production, industrial production.
• We in this country, though accustomed to work in large-scale sample surveys were aghast at Mahalanobis’ plan for the national sample surveys of India.
• Their complexity and scope seemed beyond the bounds of possibility, if not beyond anyone else’s imagination, but they took hold and grew,” said Edward Deming (quoted in Rudra 1996), a renowned expert in sample survey methodology.
• The system developed for official statistics was then one of the best. India was among the earliest countries to adhere to international commitments on quality, comparability, and timeliness, such as the System of National Accounts (SNA), for estimating gross domestic product (GDP) in harmony with other systems on flow of funds, balance of payments, and fiscal statistics.
• This solid foundation helped it participate in the Special Data Dissemination Standard (SDDS) of the International Monetary Fund (IMF). These are positive aspects of the Indian statistical system, and provide methodological soundness for consistency and transparency, which are required for confidence in the data produced.
• However, the system required continual updating in keeping with the technological, organisational, methodological, and data-related issues and, when certain deficiencies became highly disturbing, it became necessary to review the system.

Rangarajan Commission

• The task of reviewing the system was entrusted on 19 January 2000 to a commission chaired by C Rangarajan, the then governor of Andhra Pradesh. The Rangarajan Commission submitted its report on 18 August 2001.
• The report examines the whole gamut of data quality, consistency, relevance, and timeliness of collected statistics, along with systems and processes for their administration, and is a major landmark.
• The Rangarajan Commission noted the shortcomings of the Indian statistical system and observed that its credibility, timeliness, and adequacy needed improving.
• The commission recommended that data gaps be identified and alternative techniques explored to improve the methodology of collecting, compiling, and disseminating data, and suggested reforming the administrative structure, grant it autonomy required for independence and upgrading infrastructure.
• As part of the implementation of the Rangarajan Commission’s recommendations, the National Statistical Commission (NSC) was set up in 2006. It was expected to be empowered to serve as a nodal body for all core statistical activities.
• This commission was to be backed by an act; it was drafted, but not enacted. Since 2006, the commission has considered many pressing issues confronting the generation of official statistics.

Areas of Weakness

• Several standing committees support the development of concepts and methods to maintain the quality of data collected—measurement of variables, survey sampling, updating the base of indices.
• Some of these are the Advisory Committee on National Accounts, the committees on prices and industrial production, and working groups on sample surveys. Whatever these committees did, became available in the public domain.
• Transparency remains a hallmark of the Indian statistical system but, despite these achievements, timeliness, quality, consistency, and coherence remain weak.
• It takes almost five years to revise the base period weighting diagram for price and production indices, whereas advanced economies produce chain-based indices for them, revising the weighting diagram every year. This is possible because systems are digitalised.
• Several attempts have been made in India to prepare an exhaustive register of business units to serve as a frame for drawing samples for various surveys. In a country where any business worth its salt requires to be registered, it is a pity that we cannot have a dependable business register.
• The data on business units collected through the economic census is not only expensive but also highly deficient.
• The Annual Survey of Industries (ASI) is an example of how a deficient frame contributes to erroneous estimates. The data on international trade based on customs and payments were also differing for different reasons.
• There is a wide variation between the consumption data based on the household surveys conducted by the NSSO and the estimates obtained through the national accounts. The present system is not equipped to reconcile the differences and look for solid evidence therein, which is needed.
• The estimation of gross state domestic product (GSDP) is a new, and more disturbing, issue thrown up by the latest revision in methodology for the current series of the GDP with base year 2011–12, which replaced the previous series with base year 2004–05.
• This has happened for several reasons; a major reason is the use of Ministry of Corporate Affairs (MCA 21) data on the corporate sector, which replaced the age-old ASI. The ASI data had various shortcomings, particularly under-coverage of the factory sector, and corporate-sector data were considered better for GDP estimation.
• The allocation for GSDP estimate for the year 2011–12, for the new series in respect of Gujarat was as high as 74.4%, to align the state’s estimates with those of the CSO, while the same for the GSDP estimate for the previous base of 2004–05 was only 30.0% (Dholakia and Pandya 2017). The allocation was as high as 100% for mining and quarrying, manufacturing, railways, and communications and services related to broadcasting and financial services.
• This has thoroughly disturbed the system developed painstakingly for estimating GSDP over many decades. It is particularly so for manufacturing, which should not be worked out by allocation.
• In a federal structure, where regional development is an important focus of development, the most important macro-parameter should be sufficiently credible.
• While pointing out major limitations of the revision carried out in the new series, Dholakia and Pandya (2017) observe that “most of these impacts are negative on the quality, reliability, valid usage, interpretation and meaningful analysis of long term trends of sectors and the economy at the state level in the country.”
• The story is similar for other states as well. New sources of data must be made use of. The goods and services tax (GST) system is capturing a lot of information on traded goods and services.
• An appropriate system must be developed for making use of these data for various estimates. Likewise, e-governance data need to be integrated more closely into official statistics.
• The Rangarajan Commission recommended that a data warehouse be built to consolidate fiscal data. The idea is to capture granular data for shedding much more light on the government effort on revenue generation and intervention for promoting socio-economic objectives.
• This will make available detailed information on government spending under various heads in any geography, district upwards, which could be related with other data for understanding the success of interventionist government policy.
• There is no such integrated system for fiscal statistics, though aggregated data are available under various heads for both the central and state governments.
• Even financial statistics, which is otherwise well-organised, needs to address emerging challenges on the distribution of financial assets and liabilities, and their sources and use, by geography, along with other characteristics, including riskiness and reasons thereof.
• Data on the regional distribution of some of the components of flow of funds are required on a quarterly basis to understand the nexus and dynamics of interactions between the real and financial sectors in shaping the course of the economy over space and time.
• This is because an enterprise is a product of opportunities, resources available, market conditions, and availability of risk capital. The success of government intervention for reducing imbalances in development and creation of jobs for demographic dividend can also be assessed when these data become available at lower levels of aggregation.
• Many areas of official statistics require improvement, and a major effort is needed to improve the legacy systems and processes that support the production of statistics and that are based on disparate conventional practices.
• The Indian statistical system was developed in the aftermath of independence to support development plans. The system improved over time, but the silos of decentralised systems created under the allocation of responsibilities to administrative ministries remained largely intact.
• More than one ministry collects and disseminates consumer price data. Now it is possible to meet user demand on agricultural, industrial, urban, rural and composite indices based on back-end compilation, and using the data repository on consumer prices and the corresponding weighting diagram for each type of index.
• In the past it was almost impossible to match unit-level data going into trade statistics compiled by the Directorate General of Commercial Intelligence and Statistics (DGCI&S) and the balance of payments (BoP) statistics produced by the Reserve Bank of India (RBI).
• Likewise, the establishment and enterprise approaches for the ASI and company finance data for manufacturing units were not amenable for mapping. Ideally we should have unit-level data on household production, consumption, and saving.
• While different sources provide these data at the aggregate level, it becomes difficult to compare them for regional distribution. Geocoded data for these three important household components would enable a better understanding of their inter-consistencies.
• It may be possible to examine household consumption expenditure for possible sources of divergence—households eating out but not reported in consumption surveys, midday meals supplied free of cost, and corporates providing their employees subsidised food.
• The GST data on sales at comparable regional levels could partially help. Digital payments data could be another source, though these data would not provide perfect mapping—except for some kind of dimensional checking.
• In short, data with comparable dimensional characteristics will allow for reconciliation of data available from alternative sources through a matching exercise by geography and institutional characteristics, and through a comparison of data from different sources that can be pulled using conformed dimensions in data warehousing parlance.
• It is relatively easy to reconcile differences in a smaller geography. Consider a few examples on comparability of data arising out of different sources.
• The data collected in the ASI details items manufactured, industrial classification, materials consumed, and location, but the MCA 21 focuses on financial performance in manufacturing units.
• As many large companies operate in multiple states, it is not possible to separately estimate statewise output. This is how the use of MCA 21 data in the new revision, instead of ASI data, led to very sizeable allocation of company output into different states.
• This problem can be greatly solved if the two sets of data are mapped through a common identity. The matching will not be perfect, but the census sector of the ASI will mostly be enterprises; hence, a major part of corporate manufacturing output can be identified to the respective geography.
• If no better alternative is available, allocation may be restricted to an unmatched portion. This will also relate two very important sets of data for various other analytical purposes.
• The NSSO is uniquely placed to conduct possibly the largest household survey. The NSSO uses professional, sophisticated, and statistically advanced methods to collect data from about 1,50,000 households.
• That is a large number of households but a tiny fraction of the 250 million households in India. Using the data to estimate household employment by state and occupation will reduce the number of households for each element.
• Even for the all-India estimate, there are some issues when it comes to sub-classifications. The NSSO estimate of urban population is significantly lower than the census figure. The standard error, even if low, does not help in the correction of such an estimate.
• One reason could be the deficiency in the urban frame of households the sample is drawn from. This deficiency leads to a major problem of consistency and validity as these estimates are used for value-added components for the unorganised sector.
• The Index of Industrial Production (IIP) is used to estimate quarterly GDP. The annual growth rate of value added by industry based on the IIP differs from the ASI rate, and when the quarterly estimate is derived using IIP it becomes deficient to this extent. The IIP is a measure of output, not of value added.
• Conceptually, the system for estimating national accounts follows three approaches based on production, income, and expenditure, which is useful for cross-checking consistency and coherence, in addition to other things. Many countries follow production and expenditure approaches for major components.
• Only a few countries follow all the three approaches because in these countries the income approach is also reliable. India follows the production approach predominantly. There are expenditure data on households, the government, and the corporate sector—the three major institutional sectors.
• However, the NSSO estimate of household expenditure differs widely from the CSO estimate, which is based on national accounting, and the difference is widening over time. There is no satisfactory way of verifying the various sources of this widening divergence.
• In big data parlance, production and expenditure data for the government, the corporate sector, and households under different heads at lower levels of aggregation, even if approximate, may allow for a way to cross-check each estimate for inter-consistency on dimension and direction of change.
• At present attempts are made to defend differences intellectually by arguing over possible sources of divergence. In some cases, one source is accepted as more credible than the other. Trade data is relied on to calculate exports although the RBI also collects payments data.
• As the RBI and the DGCI&S have much better systems now, it may be possible to undertake matching exercise periodically, at least on a sample basis.
• The Indian statistical system is highly decentralised. It has its own challenges. So far, some of the major concerns, which are legacy conventions, have been considered. Now, certain unconventional questions are raised on issues confronting official statistics.
• Do we understand the economy very well? It is about man and material resources, and their use over time and space; behavioural traits; market factors; impact on individuals and groups; opportunities and threats; policy prescriptions and their impacts; price formation; wins and losses; and the environment and its degradation.
• Do we understand poverty, inequality, and deprivation well enough to pursue policy to support vulnerable sections in a focused way and by geography? We need data to focus our programmes for success.
• To assess progress on the Sustainable Development Goals (SDGs) of the United Nations (UN), which span over 15 years up to 2030, India needs a system for collecting data on 17 major indicators and formulating plans and milestones.
• In a vast country of India’s size, non-linearities and heterogeneity cannot be wished away. India needs to use information and communications technology (ICT) to organise data for informing regional and subregional development.
• For an economy to function efficiently, it is necessary that authentic information about its functioning at each level is available in the public domain—that is, there is no information asymmetry.
• As pricing is a thermometer of market pressure, an understanding is necessary of how each market prices products and allows competition for efficiency and social welfare.
• Also, people need to be informed on how effectively government intervention is working to promote an environment conducive to growth, stability and equity, and how parliamentarians are performing to safeguard the welfare of the people.
• As enterprises use household savings for investment, risks thereon become a public concern. High-quality, dependable data at all levels of governance is needed to address these concerns.

Why We Failed to Overcome Known Shortcomings

• The Rangarajan Commission’s review of the Indian statistical system was a bold attempt to identify shortcomings and suggest appropriate action. In addition to methodological improvement, the commission wanted a major thrust on the use of modern technology for reporting; data processing, using data warehousing; and strengthening state statistical systems.
• There is no data warehouse yet for national accounting or large-scale sample surveys. When the world is using parallel processing in a cloud environment for processing voluminous data, India cannot fall behind any further.
• Procuring and deploying such a technology is a complex exercise. While solution providers can help with technology, there is the need to clearly spell out business requirements; source data; develop a methodology for collating data for estimation of parameters that adheres to accepted concepts and definitions; create classifications; and check for consistency, coherence, and timely dissemination as per policy.
• Conventional tabulations, spreadsheets, and traditional databases involve drudgery, and make for disjointed, relatively inflexible systems; adopting advanced technology will free us to do more worthy and stimulating work.
• The system needs to be more sensitive to public criticism. When the new GDP series was released, there was severe criticism, but remedies could not be made for want of appropriate data.
• If GDP estimates need to be tracted going into granular data and the method used for aggregation at different layers, there are few options, as inputs on these estimates are spread in such a way that verification is difficult.
• Data governance is another major issue. It includes laying down standards for maintenance of data, information technology (IT) architecture, and business continuity to ensure integrity and availability of these data.
• When spreadsheets are widely used to maintain data, it is not possible to impose rigorous data governance standards. The processes followed while processing data to check for consistency and coherence remain partial. It is not certain that estimates will withstand the test of inter-consistency and robustness. This is a major concern.

Our International Commitment on Quality Statistics

• In 2014 a UN resolution laid down 10 fundamental principles for official statistics. The first is official statistics provide an indispensable element of 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.
• The other fundamental principles relate to methods and procedures, scientific standards, proper interpretation, all sources of data, confidentiality, rules and regulations, coordination, use of international concepts, and cooperation.
• The 10 principles were notified by the Government of India through a gazette notification dated 15 June 2016 for adherence as part of our international commitment and also to improve data quality.
• It is necessary to put in place a system to review progress consistent with the commitments made. A code of practice for promoting the use of scientific methods and procedures for maintaining confidence in produced statistics is a major instrument for ensuring high quality, as will be explained later.
• The Indian statistical system can be proud of a variety of data on many dimensions of the economy, but systems and processes suffer from many legacy issues, particularly on absorption of technology.
• Many countries took up the challenge at the highest level because the pressure was very high to do so, as it was cutting at the roots of democracy.

What Are Our Options?

• There is a need for a comprehensive review of our official statistical system once again, not only to examine these major deficiencies but also to take advantage of new developments on explosive growth in digitisation of business operations and corresponding data sources along with technology, methodology, and user demand for data.
• There are countries that are moving away from well-established systems by undertaking a thorough revamping of their system. Apart from methodological and technological aspects, the organisational and administrative machinery also plays an important role, as recognised in the Rangarajan Commission report.
• If India had acted on some of the recommendations of the commission, the statistical system would have been in a much better state.
• An analysis of these recommendations should help to identify the weaknesses and courses of action to put the system on a sound footing. The major reason for advocating a thorough revamp of the Indian statistical system—“creative destruction” (Schumpeter 1976)—is not to criticise the well-established and time-tested systems existing now.
• These were developed when it was difficult and expensive to collect data and integrate them for multivariate distribution for analytical purposes. The technology and tools for processing and analysis of data were vastly different then.
• The systems, thus, remained disjointed. The main concern is that when we are going for changes, these are basically in bits and pieces. It is like an old Rolls-Royce, still roadworthy because of continual maintenance.
• However, maintenance has become costly and it does not pick up speed the way a new one will do. But we cannot abandon it unless we get a new one, which is tried and tested. This is where we need creative destruction.
• There have been major developments using advanced technology—geographical information systems (GIS); satellite remote sensing; broadband connectivity covering all gram panchayats; and GST for indirect taxes.
• Most government offices are digitalised and access to information online is widespread. How do we take advantage of these developments in building a system that allows for much more insight into the economy?

System of National Accounts

• The GDP is the single-most important barometer on the economy. Its compilation is guided by the SNA. The GDP covers all important parameters of economic statistics and is harmonised for consistency. In recognition of new possibilities in measuring GDP, the United Nations et al (2009) suggested:
• The sequence of accounts and balance sheets of the SNA could, in principle, be compiled at any level of aggregation, even that of an individual institutional unit.
• It might therefore appear desirable if the macroeconomic accounts for sectors or total economy could be obtained directly by aggregating corresponding data of individual units.
• There would be considerable analytical advantages in having micro-databases that are fully compatible with the corresponding macroeconomic accounts for sectors or the economy.
• Data in the form of aggregates, or averages, often conceal a great deal of useful information about changes occurring within populations to which they relate.
• However, a debate is going on over the importance of the GDP as a measure of the economy and over the analysis that assumes its central importance (Coyle 2014, 2016).
• In a review essay on her work, Syrquin (2016) agreed on one issue of particular relevance: “It may be correct, as argued in the book, that GDP has outlived its usefulness in the digital age.”

Micro–Macro Linkage Matters

• The two important factors contributing to strengthening an economy are productivity and competition. Both are much more relevant at micro levels. This does not mean that the macro level is not important. It is definitely useful for growth and stability.
• However, the quality of growth is equally important. Likewise, if tightening the economy for stability causes the small and marginal sections disproportionately higher suffering, a way must be found to protect them.
• Thus, macromodels and a calibrated approach may not be enough. Solow (2008) critiqued the stochastic general equilibrium model, stating that macromodels are made up of a single “representative agent.” There are many other issues (Barman 2016).
• This is where micro–macro linkages make eminent sense, though there will be challenges in analysing huge volumes of data. Porter (2002) said,
• Developing countries, again and again, are tripped up by microeconomic failures … countries can engineer spurts of growth through macroeconomic and financial reforms that bring floods of capital and cause the illusion of progress as construction cranes dot the skyline …
• Unless firms are fundamentally improving their operations and strategies and competition is moving to a higher level, however, growth will be snuffed out as jobs fail to materialise, wages stagnate, and returns to investment prove disappointing … India heads the list of low income countries with microeconomic capability that could be unlocked by microeconomic and political reforms.
• The other major issue is of poverty, inequality, and deprivation. Income inequality has worsened in the past 35 years; in 2016, the top 10% of earners cornered over half the country’s national income (World Inequality Lab 2018).
• In pursuing equilibrium for analytical elegance, glossing over basic issues of distribution and equity in a country where one-third of the world’s poor lives can lead only to peril.
• As Schelling (1978) pointed out, one’s search of equilibrium is meaningful when the dust is settled: “The body of a hanged man is in equilibrium when it finally stops swinging, but nobody is going to insist that the man is all right.”
• For India, the dust has not settled yet. In its tryst with destiny, the country needs to give vulnerable sections sufficient space to realise their potential.
• Should official statistics be burdened with these analytical issues? This can be debated. However, there must be a way to extract data to support various possibilities on analysis. There is a need for an integrated system populated with data of ultimate granularity and tools to extract relevant information flexibly.

Real Sector, Financial Sector, and Fiscal Sector Nexus

• In the analysis of an economy, the focus is on production, consumption, saving, investment, and exchange of goods and services. As a behavioural science, its concern is on explaining how society makes choices under conditions of scarcity of means of production, what enables growth and stability, and how the benefits are distributed. Reinert (2007) observed:
• Between the value of the raw material and that of the manufactured product lie much of employment, stable profits under increasing returns and much taxable income for the government.
• The benefits from manufacturing spread as “triple rents”:
•  to the entrepreneur in the form of profits;
•  to the employee in terms of employment; and
• through the government in terms of increased taxes.
• The framework for official statistics is detailed enough to provide data on these aspects, but these data are not well organised for access to microdata or for masking identity as required. The published data cater to user requirement following certain conventions.
• To understand the interplay of demand and supply, the domestic economy is divided into three sectors—real, financial, and fiscal. The real sector relates to production of goods and services; the financial sector relates to the flow of money-supporting transactions; and the fiscal sector relates to government revenue and expenditure.
• The issues relate to how market forces behave and respond to inducements, and how they approach equilibrium.
• To analyse performance, basic data is collected on these three sectors. There is also an external sector to complete the building blocks for analysis. As we have a reasonably good set of data on transactions forming part of the external sector, the focus here is on these three sectors only.
• How should data on the three sectors be organised, collected, compiled, and disseminated for analysis and for shedding light on behavioural dynamics?
• In the present system, microdata on entities are spread over many silos. There is limited data at the level of the village or village panchayat, the lowest tier of governance. We have 2,50,000 rural and urban bodies, and over three million government representatives as part of these institutions.
• The National Institution for Transforming India (NITI Aayog) has an aspirational objective to get data at this level to formulate credible plans at the village level and aggregate these progressively at higher levels, but new age IT is needed.

What New Age Information Technology Provides

• To take advantage of the new information age for official statistics, it will be necessary to seamlessly integrate conventional data collection methods with new government initiatives for capturing data digitally—direct benefit transfer, GST, tax collection, dispensation under other social benefit schemes, land record, land use, etc.
• Digitisation of payments is another exponentially growing area of data. Modernisation of systems for data on employment, health, education, etc, is on the anvil.
• How can capacity be built to integrate these data for shedding much better light on the economy and socio-economic development? It should also be possible to navigate the data repository or mine the data to pick up nuggets from the submerged mountains of data.
• Big data enables collection of audio, video, text, and digital data. These data may be structured, unstructured, or semi-structured. The concern now is mostly with structured data. Hence, the Statistical Data and Metadata eXchange (SDMX) has evolved as an internationally adopted method for data transfer for processing.

Statistical Data and Metadata eXchange

• The SDMX is a new-generation IT tool developed by the UN Statistics Division, along with other international agencies, for statistical reporting and sharing data and metadata following a common standard.
• The SDMX reduces delay in data transmission; uses less resources for processing at different levels; and improves the overall quality and timeliness of collected statistics. The taxonomy and classification for data elements, developed by user countries, can be customised for India’s purposes.
• Reporting under the MCA 21 uses the eXtensible Business Reporting Language (XBRL), which takes care of standardisation of concepts and definitions, nomenclature, classification, and hierarchical dimensions.
• Each individual item has a taxonomy and is assigned a unique computer-readable tag; precise, contextual description makes for seamless aggregation. The XBRL adheres to accounting principles for financial reporting.
• The same technology underlies the SDMX; the difference is due to its focus on statistical reporting. India needs a road map to implement the SDMX for the reporting and exchange of data.

Big Data and Data Warehouse

• As defined in the Oxford dictionary, big data is made up of “extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.”
• Wikipedia defines big data as “a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them.”
• Generally, big data originate in transactional data collected through data streaming—the way Google, Facebook, YouTube, and Amazon throw up data. However, these data are mostly unstructured.
• While these data can be useful for official statistics in certain circumstances and for certain purposes—like the spread of epidemics, volume of business, and the use of digital modes of payment—official statistics relies on data that are well-defined in terms of concepts, definitions, classification, representativeness, etc.
• The processing capability of big data technology is useful for integrating granular data, forming part of official statistics and their processing, taking advantage of parallel processing in clustered environments and analysis using advanced statistical and machine learning techniques.
• Official statistics builds out of huge amounts of microdata. Agriculture data—on land availability, land use, yield rate, area under irrigation, crops cultivated, cost of cultivation, farm gate price, wholesale price, consumer price, trade and transport margin, weather, and topography—are available in digitised form, by way of published, summarised tables, but are not well-integrated or geocoded. Researchers have to painstakingly cull these data from different sources.
• As these data are also pre-aggregated in tabular form, there is no flexibility to delve deeper into non-linearities, heterogeneity, or geography. These issues cannot be overlooked if agricultural productivity has to be examined in appropriate contexts.
• Also, data are required at much lower levels of aggregation than what is available now for evaluation of policy, monitoring of progress, and appropriate follow-up action needed to raise farms out of distress.
• Big data technology has the capability to pull out and process multivariate data of ultimate granularity stored in data lakes for much deeper insight on the issue being investigated, and for policy formulation and implementation.
• In the present illustrative example on agriculture, it is not only productivity and competitiveness that can be analysed in situational contexts but also the conditions of people engaged in agricultural labour; poverty levels; and malnutrition by age, gender, social group, and skills.
• This kind of analysis will be more purposive, relevant and penetrative, and shed much better light on human distress and possible remedies. This will not only support better, and more empirically based, decisions at all levels of governance—rather than a broad-brush approach, using aggregate data—but also better performance.
• A data warehouse is “a single, complete and consistent store of data obtained from a variety of different sources made available to end users in a way they can understand and use in a business context” (Devlin 1996).
• A data warehouse is generally subject-oriented, integrated, non-volatile, and time-stamped, which is accomplished by a data model organising data as facts along with dimensions as indexes for easy retrieval of these data as per user needs.
• The basic data elements are stored in the structure of a relational database management system (RDBMS) and then de-normalised using schema and populated in a multidimensional database (MDDB) server for quick retrieval.
• The idea is to fuse the two architectures of big data and data warehouse. There is also a need for metadata to explain the end-to-end life cycle of each data item used for different aggregates. This is the approach explained in Mohanty et al (2013).
• Its advantage is that it allows for the creation of a data repository, like a data lake, if advisable, for all data to flow into a central system or centrally connected systems. That may be a cloud-based cluster of servers from where a specific user can source data for storing in a data warehouse as per requirements.
• For example, the CSO may have a data warehouse for national accounting; line ministries can have their own data warehouse as per their requirements, drawing data from integrated system as a repository.
• Can we still have a single version of the truth? There is no easy answer to this question. Many iterations of carefully estimating various aggregates are needed over a long time, along with metadata mapping inputs with outputs, to find sources of differences and reconcile these.
• It will be a very complex exercise, and it may not always be feasible. Thus, a single version of the truth may not be easily achievable; however, much insight will be gained when our system can respond to such investigation because of virtualisation of data as part of an integrated repository.

What Kind of Technology?

• Big data technology has the capacity to handle huge volume of data, and appears very appropriate as a central system for official statistics for the country. The technology has been under development for the past decade and has now achieved maturity for adoption.
• The software for the system is largely open source, and the hardware is available like a commodity, which can be expanded at will to cater to increasing demand for storage and processing. The cluster of servers in a distributed cloud environment can support very high scalability.
• The data processed for official statistics are well defined in terms of concepts and definitions and need to meet high quality standards following sound methodology.
• Considering that the data originate from many sources spread throughout the country, sourcing data from decentralised systems following clearly defined measurement standards and integrating these can be a daunting task.
• There is a need for metadata that track the entire life cycle of data and for discipline on flow of data from these sources. The data can be administered through a system-driven process for timeliness and quality check.
• Considering the huge task at each stage, quality checks can be greatly process-driven. This system needs to work under professional supervision for both on-time and penetrative analysis regarding quality, consistency, and coherence of collected data. The outliers thrown up by the system or the missing data need to be attended to promptly.
• Considering the challenges in data collection and processing and in the management of operations, a modern data warehouse with big data technology is needed for the central system.
• This can be connected with all the other systems that form part of the national statistical system for two-way flow of data and processing. The requirements would be defined by users—data producers in central and state government ministries and coordinating offices.
• The details of such arrangements, supporting technology, and integration standards have to be worked out. The broad approach for data flow for the same is shown in Figure 1. It should be noted that this is only an illustration, not a prescription. Help is required from experts to work out appropriate technological solutions.
• The data can be captured by various means—surveys, census, web-based reporting, administrative records, automated systems for periodic sourcing from feeder systems, satellite images, Facebook, external open sources, and so on.
• Then, according to predefined concepts, the data are filtered and processed for extraction, transformation, and loading, and validation checks imposed to ensure quality. Complex event processing engines may include spatial engines.
• These data then move to the spatial big data warehouse. The spatial element is expected to take care of geography right from the village level, wherever applicable. The user interface is a facility to take control of entire operation according to specific
  user needs.
• Data warehouses can be used for predefined and ad hoc tabulations. Requirements for data tabulation are set by the SNA along with other harmonised systems for flow of funds, balance of payments, input–output tables, etc.
• These data are produced by different organisations and the processing systems are also different. Each state is responsible for estimating the state domestic product. Other line departments are responsible for production of data falling under their ambit.
• While this will continue to be so for a long time, it will be desirable to make provisions for certain checks as required for consistency. The harmonised system of official statistics expects this for quality and coherence of these data.

Dashboard and Data Visualisation

• At present, data production and dissemination for official statistics follow a predefined set pattern. While this is the primary responsibility of the national statistical system, each line ministry has a set-up to produce data specific to the needs of users.
• There are also ad hoc queries, which can be analysed for discovering regularity in those needs. These requirements can be systematised by developing dashboards.
• A dashboard is defined as an information management tool made available to users as a canned report containing data and visual graphics on key performance areas, which help in monitoring progress and evaluating performance on set objectives.
• The dashboard is easy to read and the visual is usually revealing. An intelligent dashboard can also be designed to be interactive and support further requirements for review and analysis.
• The dashboard culture is widely prevalent in professional organisations. Bloomberg is a classic example of intelligent dashboards feeding data on how global markets move, economies perform, rates change, and even opinions differ every day.
• Some of the information contained in such intelligent content can be important sources of external data for authorised users, subject to terms of agreement. The availability
  of these data can enhance the usefulness of information systems that look continuously for signals that may have policy implications.
• The Indian official statistical system has emerged out of the planned era. In the predominantly market-led economy, the requirement is varied and covers a much broader canvas.
• Price signals, forward trades, and international trade are but a few examples of demand for information in the present globalised world. It is necessary to work out how far an official statistical system can move to accommodate user requirements where external sources of data would be required.
• Official statistics is a public good; hence, data from other countries can be collected from their official sources as admissible. However, data from non-official sources may lack quality and credibility, and need to be evaluated against quality standards.

Data Quality and Code of Practice

• Official statistics needs to be compiled following standards laid down for concepts and definitions; data collection and aggregation methodology; a data dissemination policy as per our commitment; and a publicly disclosed policy on transparency to maintain high confidence.
• The Rangarajan Commission specified quality assurance standards. Our commitment on quality—as set out on 15 June 2016, and consistent with the UN resolution of 2014—stipulates certain core principles: impartiality, objectivity, integrity, sound methods, confidentiality, accessibility, accuracy, reliability, coherence, and clarity.
• A system of statistical audit is prescribed for ensuring these quality standards. Stringent data quality conditions are stipulated in the code of practice in many countries. For high-quality data, systems and processes need to be upgraded and modernised; resources strengthened; and feedback solicited from users periodically.

The Rangarajan Commission found that

• At the moment, as the system operates, there is no effective coordination either horizontally among the different departments at the Centre or vertically between the Centre and the States …
• For reform of administration of the Indian Statistical System by upgrading its infrastructure and thereby enhancing the credibility of official statistics, the Commission is of the view that an independent statistical authority free from political interference having power to set priorities with respect to Core Statistics is needed to ensure quality standards of statistical processes.
• Such an authority will also improve the coordination among different agencies collecting data. Though the National Advisory Board on Statistics was constituted with this objective, its impact has been minimal.
• In view of this, the Commission has recommended the creation of a permanent and statutory apex body—National Commission on Statistics [sic]—through an Act of Parliament, independent of the Government in respect of policy making, coordination, and maintaining quality standards of Core Statistics.
• Though the Rangarajan Commission was appointed by the Vajpayee government, and its report was implemented by the next government, some vital recommendations have not been acted upon.
• The NSC needed the backing of an act for effectiveness. Without it, the NSC remained largely handicapped. The NSC came out with many more reports, but these were not acted upon. Without accountability on implementation of well-thought-out decisions, it effectively remains helpless.
• As the NSC functions with part-time members and a small contingent of staff, it has not been effective in carrying forward its mandate. This is further weakened by its complete dependence on the ministry for administrative and financial matters, which creates various frictions.
• A way to ensure genuine independence must be found.

Learning from the United Kingdom

• The commission approach at the apex of the statistical system does not work well. Such a system in the United Kingdom (UK) was replaced by the UK Statistics Authority (UKSA), which is a board backed by the Statistics and Registration Service Act, 2007, and is directly responsible to parliament through the Ministry of Cabinet Affairs.
• A highly professional membership and robust systems and processes has made a major difference. The UKSA system ensures high standards of transparency and professionalism and makes executives responsible.
• For production of official statistics to be independent, the entire machinery must work at arm’s length from ministerial control.
• The UKSA has oversight of the Office for National Statistics (ONS), a non-ministerial government department. The UKSA is also responsible for independent monitoring and assessment of official statistics; maintaining a code of practice for official statistics; and according code-compliant statistics as “national statistics.”
• The chief executive of the ONS is the National Statistician and is directly accountable to parliament through the UKSA. The ONS started the first phase of modernisation in 2001. Its experience is well documented in Penneck (2009):
• Pressure to operate more efficiently, respond more rapidly to changing user demands, exploit data more effectively and improve statistical quality have led a number of statistics offices to seek to modernise their statistical systems in similar ways: adopting an information technological environment, using standard tools, and processes across statistical systems, with common business processes driven by metadata.
• “Together the Design Authority and IT strategy provide clear direction for modernisation” for delivering “high quality and noticeable business benefits.” This should be the most important learning point for India.
• The ONS is now going through the second phase of modernisation. Charles Bean (2016) Committee Report sets the agenda for this phase of reform, which is challenging organisationally, methodologically, and technologically. The report notes how the methodology focusing on GDP is deficient in many respects.
• The UK went about this reform as a part of its election manifesto. Parliament was fully involved in the discussion on systems, checks, and balances. This was in the making for a long time. Jack Straw, a prominent member of parliament and minister (until 2014) told the Royal Statistical Society in 1995:
• Democracy is about conceding power to those with whom you disagree; not those with whom you agree; and about ensuring that every citizen has a similar access to the information on which decisions are made, and governments are judged. In a modern democracy, the system of official statistics should be a dignified part of the constitution.
• Independence and authority for official statistics have been fortified in many other countries in the West by enactment and establishment of a statistical authority. Such an authority is free of any kind of extraneous influence and it is vested with the power to produce high-quality statistics.
• The European Union has prescribed a code of practice that member countries follow. India must follow similar organisation, systems, and processes to revamp its statistical system.

Action Points

• India needs an exhaustive list of data going into estimation of GDP, and for financial and fiscal statistics, with clear definition, classification, and sources for each item. The list may run over 3,000 items, expected to be available with the CSO.
• A system is needed to capture data from various sources. Milestones for web-based reporting may be defined following reporting standards such as SDMX or XBRL. India needs to build capacity for processing voluminous data using modern big data and data warehousing technology.
• It is desirable to have a design authority to lay down standards on technology for integration, high value on low cost, and use of standard tools so that local development is avoided.
• India needs to develop capacity for using advanced techniques for survey sampling; measurement of variables as contained in the SNA and other manuals; and undertake experimentation for discovery of patterns and dependencies using techniques of multilevel analysis, machine learning, and artificial intelligence.
• The time has come for statisticians to acquire advanced knowledge in software and domain knowledge of subject area of analysis and graduate as data scientists.
• Last but not the least is an act, to make the system really independent. Official statistics is a public good, and an important part of the democratic process. An act that provides for the creation of an independent, professional authority that can raise the quality of data and confidence in the system, will make a major difference.

Conclusions

• Official statistics is an important part of the democratic process: it informs people how the economy is progressing; how interventionist government policy helps in maintaining stability conducive to growth and advances the cause of social development, particularly in respect of vulnerable sections of the people; and how private enterprises work in a market economy.
• These data should be available from the local level and upwards to support more efficient use of resources and more responsive governance in all walks of life. The data should withstand rigorous scientific scrutiny and be made available to users as a public good.
• New techniques are needed in the social sciences to analyse the complexities of socio-economic dynamics. A prerequisite is the availability of granular data and tools for their access as per analytical needs.
• A spatial big data warehouse, which can capture the entire life cycle of data going into estimates at different levels of geography, is expected to serve as the backbone of analytics and give a new direction on research backed by solid empirical evidence. The flow chart is only an illustration, not a prescription.
• We do not suggest any specific tool. The widespread use of artificial intelligence, along with massively parallel processing in cloud environments, should lead to new breakthroughs. The prerequisite for this is relevant data extracted from various sources for such analysis.
• A new breed of researchers and data scientists with statistics and machine learning expertise, who understand business objectives and are good at handling huge volumes of data, will find interesting patterns.
• Tobler’s first law of geography is, “Everything is related to everything else, but near things are more related than distant things.” There is the option of multilevel analysis as a powerful statistical tool for a layered approach to data analysis.
• In the process our long-standing ideas based on too much of abstraction will come under scrutiny and pave the way for deeper insight on development issues. It will help in creating a new vista in our development effort in the present millennium.

Here is the most expensive infrastructure project of the country, with a record high unit cost of ₹ 1,200 crore per kilometre (km), but no functionality beyond electoral rhetoric. Whether this corridor can “decongest” the city roads is a black box given that the proposal of this project is not based on any extensive transport survey.

• If decongesting road traffic is the real intention, then why not first expedite the completion of the metro rail work across the city? The coastal road cannot be considered a silver bullet for the city’s infrastructural issues, which are not only varied but often mutually exclusive.For instance, if citizens must benefit from easy connectivity via the coastal roads, they must endure the degradation of their city’s inter-tidal ecology almost as a natural corollary.
• These hard choices, however, cannot be explained away simply as dilemmas of development. Evidences of coastal development in this country over the past two decades, and particularly in the last five years, indicate that such trade-offs are the result of an emerging partisan politics of welfare, which is characterised by a brazen display of corporate clientelism.
• From Gujarat to Kerala, vast stretches of the coastal lands are under corporate control through the state-abetted circumvention of regulations, especially in the name of special economic zone (SEZ), and the coastal regulation zone (crz) or the coastal management zone (CMZ) schemes.
• On the one hand, this encroachment has ousted traditional fishing communities from their ancestral lands, while on the other, a number of extractive industrial and construction activities in these zones are jeopardising their conventional livelihood.
• Simultaneously, it is hard to dismiss how the government policies, camouflaged by the rhetoric of “blue economy,” have “de-commonised” the sea and displaced the traditional (local) institutions of fisheries management.
• With the corporate-friendly policies framing the seas and the coasts as the new frontiers of economic opportunity and growth, private takeover of the marine resources is progressively squeezing out traditional fisherfolk from their native fishing grounds.
• Yet, be it the CMZ or the SagarMala, all schemes of the current government having to do with the fishery sector either make cursory or rhetorical references to the “blue economy.”
• Neither do these schemes or policy documents provide any comprehensive guidelines for the promised breakthrough, nor do they recognise the inherent heterogenity of the sector, for in doing so the government will have to face disconcerting questions on the eviction of the original inhabitants of the land in the name of “development.”
• In such a context, “rehabilitation” can gag those voices of concern that hold the government accountable for its failure to ensure the fundamental rights of its citizens.