Discussion Paper

This stock-taking paper assesses the current state of India’s agriculture and rural economy across multiple dimensions.

It begins by documenting shifts in household consumption patterns and nutritional outcomes, then takes stock of major policy instruments including the Public Distribution System, minimum support prices, and input subsidies—examining their scale, functioning, and impacts. The paper reviews the evidence on agricultural diversification, noting the gap between consumption patterns and cropping choices. It surveys critical challenges including groundwater extraction levels, climate vulnerability, environmental impacts of current farming practices, constraints in supply chains and farmer organizations, and India’s agricultural trade performance. The assessment also considers the fundamental structural issue: agriculture’s disproportionate share of employment relative to its contribution to GDP, and what this means for farm incomes and rural livelihoods.

This comprehensive stocktaking provides a foundation for understanding where Indian agriculture stands today and what questions merit deeper investigation going forward.

Over the past two decades, India’s economy has expanded steadily, with per capita income growing at about 5% per year (World Bank).1 But behind this headline growth lies a more sobering story about jobs. The pace of employment growth has lagged far behind, at just 1.6% per year. Official estimates suggest that catering to the expanding workforce would require about 8 million non-farm jobs to be created annually, on average, until 2030 (Economic Survey of India 2023-2024).2

Despite recent improvements in workforce participation — an increase of 10.5 percentage points since 2017 — there are key challenges to the creation of good-quality jobs. Agriculture and low-productivity self-employment still account for most jobs in India, even expanding their share in recent years. Indian growth has been services-led, but the segments of the sector that drive growth are not the ones that create jobs at scale. Manufacturing sector’s share of employment, as well as its contribution to growth, has remained unchanged. Average real wages have been virtually stagnant due to low labour productivity, and most workers lack job security and social protection. Further, job creation in India is unevenly distributed across regions, with southern states and urban centres accounting for most of the increase in non-agricultural employment.

This report lays out six key insights about jobs and employment trends in India today. We begin with the biggest challenge: while job creation outside of agriculture is on the rise, it is still quite limited. Moreover, even within non-agricultural jobs, real wages and work productivity have stagnated over the past decade. This suggests two things: a) that jobs are not being created in relatively more productive sectors, and b) that the supply of workers looking for jobs in the non-agricultural sector exceeds the availability of these jobs. This is especially true for young people under 25: many have more education than ever before, but still struggle to find good work, either because there aren’t enough opportunities, or they do not have the right skills. The jobs that do exist are often informal, meaning they lack basic protections like social security or health benefits. Women’s employment, although increasing, is still very low. Women are a major source of untapped talent, and bringing more of them into the workforce is essential for India’s future economic growth. Finally, growth has been highly uneven across the country. Just 13 cities have accounted for over half of India’s growth, leading to unequal job opportunities and driving large numbers of people to migrate to a few urban centres.

To navigate these challenges, we need to address crucial questions: what policy levers can best unlock job-rich growth, improve job quality, and expand access to opportunity across regions? We hope this report sparks wider debate on the most urgent and impactful solutions.

The scale and scope of digitalisation in public life in India are unmatched in the world, especially in large public service applications. The use of digitalisation technologies are not only restricted to the government but are growing rapidly in the private sector as well. While the potential of such digitalisation and AI applications for public good is unquestionable, they do raise some serious social, ethical and legal questions [72, 21, 24, 9] whose mitigation and compliance with regulation pose new challenges for computer science.

In fact, such systems have been difficult to operationalise anywhere in the world. Many attempts at building large public services like national digital identity systems [61], health registries [58, 12, 54, 25], national population and voter registries [71, 50, 64], public credit registries [19, 14], income [67] and tax registries [26] etc. have often been questioned on fairness, privacy and other ethical grounds. The concerns have invariably been related to the need for protective safeguards when large data integration projects are contemplated, and acknowledgment of the bias, exclusion, discrimination, privacy and security problems that these may create. In some situations they have even had to be abandoned altogether as they were unable to deal with these risks [58, 12, 23, 30, 18]. In fact, there are very few examples of successful systems free of controversies. In India too, the use of our national digital identity programme was restricted only to welfare disbursal by the majority judgement on Aadhaar; whereas the minority judgement found it to be unconstitutional in its entirety [60]. Very similar considerations in the respective constitutional courts have led to the halting of the biometric-based national identity programme in Jamaica [30] and Kenya [18]. The concerns around digitalisation and privacy [7, 29, 63, 60] not only require new computer science techniques for design, analysis and implementation, but also require dimensions beyond computer science [41]. In fact, though the much awaited data protection act [37] has recently been passed by the parliament, after several years of debate around the earlier drafts [62, 56], there still remains considerable doubts about its scope and structure [35, 5, 57].

Digital public goods in India

The unprecedented growth of digitalisation in public life in India has been centred around Aadhaar [69] and welfare delivery, the Unified Payment Interface (UPI) [70], the ongoing efforts towards the Ayushman Bharat Digital Mission (ABDM) for health [42], the Reserve Bank of India’s Account Aggregator System [51], and, of course, electronic elections.

India’s biometric-based digital identity system, Aadhaar [69], has an enrolment of over 1.3 billion, and is used for a variety of applications, including – but not limited to – welfare disbursal and direct benefit transfers, public distribution systems for ration, deduplication of income tax registries, voters’ lists and driving licenses, property registrations, Covid vaccination records and accessing healthcare. It is also used for ‘Know Your Customer’ (KYC) services for a large number of domains. Several of these applications are backed by law, but several others are not.

The Unified Payment Interface (UPI) [70] system in India is one of the largest transaction systems in the world. Just in January 2024, there were over 12 billion UPI transactions of over | 1.8 million Crores in total value. It has undeniably empowered a vast section of the working population and has enhanced their ease of doing business. Its adoption and penetration even in rural areas have been remarkable. However, the UPI transactions are not anonymous, they leave digital trails, and they do not support a proof of payment independent of the service providers. Hence the issues of privacy and trust are still moot.

The ABDM [42] is not yet fully rolled out, but universal health ids have already been generated for almost all residents of India during digitalisation of Covid vaccination records. The universal health ids have also been linked to Aadhaar. The digitalisation process turned out to be crucial for the supply chain management of Covid vaccines and generation of vaccine certificates. It is envisaged that the ABDM will provide electronic health records (EHR) for every individual in India, sharable and interoperable across hospitals and healthcare providers on the basis of consent. The ABDM certainly has the potential to transform healthcare and clinical research for the better, but there still are open questions related to the exact pathways to public good and risk assessment and mitigation.

The Reserve Bank of India’s Account Aggregator framework [51], which went live in September 2021, aims to make financial data more accessible by creating data intermediaries called Account Aggregators (AA) which will collect and share the user’s financial information from a range of entities that hold consumer data called Financial Information Providers (FIPs) to a range of entities that are requesting consumer data called Financial Information Users (FIUs) after obtaining the consent of the consumer. This will be one of the largest such systems in the world and the Securities and Exchange Board of India (SEBI) has already joined the programme [52]. The Account Aggregator Framework is based on the Data Empowerment and Protection Architecture (DEPA) of Niti Ayog [46], which however is still at the draft stage.

The digitalisation efforts such as the above undeniably are great examples of software development and project management at scale, though they may in some aspects lack the sophistication of contemporary computer science. They also have had great impact on society. Data collection and analysis is one of the biggest aims of such digitalisation. In fact, Nandan Nilekani, who pioneered the vision of digitalisation of public services in India [44], famously said that “Indians will be data rich before they’re economically rich”. However, converting a speculative data-driven theory of public good to a rigorous, safe and effective theory will require considerable research and due diligence, and the pitfalls are many [47, 21, 59, 72, 24, 9]. Required now are comprehensive academic enquiries into these digitalisation and analytics efforts, to learn from the experience, from the points of view of sociology, economics, ethics and computer science.

Design considerations for digital systems

While all digitalisation come with some inherent risks of exclusion, increased transaction costs and loss of privacy, the theory of social good based on digitalisation are often not fully developed [9]. It is undeniable that in addition to facilitating superior record keeping and audit, digitalisation also offers the possibility of using modern data analytics techniques for finding large scale correlations in data that may facilitate improved design of social policy strategies and early detection and warning systems for anomalies. For example, it may be tremendously insightful to be able to correlate education levels, family incomes and nutrition across the entire population; or disease burden with income, education, nutrition and lifestyle, work environment and exposure to environmental stress. More generally, it may enable carrying out econometric analysis, epidemiological studies, automatic discovery of latent topics and causal relationships across multiple domains of the economy [68, 31, 65, 66, 33, 6, 34, 17].

However, realising such potential requires identifying and understanding the diverse data sources and their complexity. This may involve understanding the constraints of personnel, resources and equipment at various data generation and consumption points, understanding their primary functions and ensuring that they are not hampered in any way. It also requires an understanding of the frequency of data generation, error models, access rights, interoperability, sharing, data analysis, dissemination and other usage requirements, and designing the data organisation and application programming interfaces appropriately. Most importantly, it requires the necessary due diligence to ensure that the AI and data analytics techniques are reliable and they indeed deliver what they promise [9]. Developing tools and techniques for validating and evaluating the reproducibility and reliability of AI algorithms is an active area of research that requires multidisciplinary inputs.

The following are the specific areas that require particular attention.

Use case analysis

The most problematic aspect of digitalisation in large public service applications is incomplete modelling and analysis of use cases. Such inadequate analysis may not only result in the digitalisation use cases failing to achieve their intended purposes, but may also result in harms like exclusion, denial of service and increased transactional costs for users [15, 39]. For example, it becomes imperative to analyse whether digital identity and authentication systems are sound, implying that nobody is incorrectly authenticated, and complete, implying there are no authentication failures for true users. Even if these guarantees are probabilisitic, the probabilities need to be quantified and the consequences of the probabilistic errors – especially for their potential for denial of service and exclusion – completely analysed. The use cases also need to be analysed for fairness, their potential for discrimination and their privacy and security threat models. Developing formal techniques for use case specification, modelling and analysis is an open research problem.

Privacy and security

The biggest concern is large digital applications usually is privacy. Computer science has traditionally conflated privacy with secrecy. Privacy, however, has other connotations as well, including but not limited to loss of informational self-determination, breaking silos by linking of information across multiple domains, function creep, unfair assessment using out-of-context data, public or selective disclosure resulting in unwanted attention or social stigma, defamation, unfair and discriminatory treatment etc. [55, 63, 56]. Most of these harms result from lack of effective purpose limitation, for which security against insider attacks is an important necessary condition [55, 4]. Most large public service applications fail to engender trust due to inadequate articulation of what precise privacy problems are being addressed, and how exactly do the adopted measures address the privacy concerns.

Any digitalisation will necessarily entail some privacy risks. The problem then is to precisely model the risks and develop tools and techniques to mitigate them to the extent possible. This requires rigorous modelling of the limits of anonymisation [40, 53], data security, data hiding – including differential privacy [16] – and encryption. We also require a framework to precisely articulate a privacy threat model and capture the minimum unavoidable privacy risks of an ideal functionality of an application (assuming perfect security), and clearly articulate the trust requirements.

Ethics of AI

Threats to privacy and liberty also arise from big-data analytics or machine learning algorithms, which are important reasons for collecting and recording high frequency, real-time and non-aggregated transactional data in the first place. In recent times, several commentators have pointed out the fairness risks associated with big data analytics. It is forcefully argued by O’Neil [47] that big-data analytics, by the very fact that they are designed by the privileged and often for profit, increases inequality and threatens democracy. O’Neil illustrates with a series of examples that they reinforce inequality and reward the rich and punish the poor. The bias is either present in the algorithm or in the data and sometimes even in both. The common traits of such poor fallout of predictive analytics usually are opacity, scale, and damage. They also often exacerbate inequality [21]. Thatcher et al. [59] argue that “As algorithms select, link, and analyse ever larger sets of data, they seek to transform previously private, unquantified moments of everyday life into sources of profit.” Similar concerns have also been raised by Zuboff [72].

There are also theoretical results that suggest that fairness and non-discrimination with big data analytics may be impossible to achieve unless the data is actually well-behaved and lies in a narrow manifold [13, 32, 8]. Poor AI ethics is also often reflected in shoddy and careless design of unreliable data analytics [9].

Developing techniques to model and mitigate risks of discrimination in AI algorithms, eliminate biases, ensure fairness, or even test them effectively are open research questions. The social realities and the inequities in India add new dimensions to the problem.

Digitalising elections

Electronic voting

End-to-end verifiable (E2E-V) cryptographic voting systems [10] have been around for some time. However, their adoption in large public elections is poor, seemingly because of the inaccessibility of their underlying complex cryptography to the general electorate. Moreover, the ruling of the German Constitutional Court [43] – which ruled that elections should be publicly verifiable without any special knowledge – makes relying on pure electronic elections untenable. Meanwhile, risk-limiting audits based on voter-verified paper records (VVPR) or Voter Verified Paper Audit Trails (VVPAT) [10] may be effective in bringing easy-tounderstand verifiability and recoverability to electronic voting if carried out properly, but they generally require the electorate to trust the post-election custody chain of the paper trail.

Despite decades of research it is still an open problem to design an electronic voting protocol that combines cryptographic security with VVPRs in a principled way, and provides sound and efficient methods of recovering from errors without re-running the entire election in case an election fails verification. The secret ballot requirement makes the problem harder.

A variety of proposals on internet and blockchain based voting add yet another dimension to electronic voting which requires careful and rigorous analysis.

Also, eligibility verification and ensuring that only and all eligible voters can vote, is still an open problem in voting, and it is particularly hard in a country like India.

Securing the electoral rolls and other public registries

With the rapid digitalisation of electoral rolls all around the world, it is now well understood that securing the electoral rolls is the most crucial aspect of securing elections [27, 48, 28].

On the one hand, conventional wisdom – and also election laws in most countries – require the electoral rolls to be publicly displayed, and all additions, deletion and other updates to be made available for public scrutiny. Traditionally, such public verifiability has been considered essential for the integrity of the electoral rolls (for example, see the Registration of Elector Rules [38] under the Representation of the People Act [2]). On the other hand, free access to digitised electoral rolls make profiling of individuals and groups easier, which in turn increases the possibility of targeting or even coercion of voters in closely fought constituencies. In fact, digital profiling of voters and communities are becoming common, and democracies have to soon decide the extent to which it should be allowed.

Protecting individual privacy in electoral rolls and yet making all changes publicly verifiable is as yet an open research problem. Similar considerations also apply to other public registries as well, including citizen registries, registries of social and economic status, tax registries, wealth and property registries, land records, etc.

Tools for digital governance

Recently, there have been a plethora of proposals towards using blockchain and similar other technologies to build trustworthy digital platforms for governance [45, 36], and cryptocurrencies form a special case.

Cryptocurrencies

A crucial property of cash is that transactions are anonymous, which provides a safety net in any democratic society. The basic principle of privacy demands that one should be able to transact without leaving an electronic trail, unless required by law [22]. While cryptocurrencies like bitcoin are supposed to be anonymous by design, and hence even highvalue transactions are difficult to regulate or tax, the extant UPI system in India does leave an electronic trail. It also requires a trust assumption on the providers, as opposed to a blockchain transaction which may provide a proof of the transaction validity. In view of this, there is a demand for a Central Bank Digital Currency (CBDC) in India which may support anonymous transactions [3, 20] and be zero-trust. However, how such an anonymous digital currency tied to a fiat currency may be realised and how cryptocurrencies may be regulated are still very much open questions in computer science, political science and economics.

Governance

While blockchain is a fascinating data structure, it is still unclear what exact roles do consensus and immutability – the two defining properties of blockchain – play to realise the required safety properties in governance, and precisely which aspects of the privacy and security threat models of governance do they address. For example, in most registry applications there usually is only one authority, so consensus is inapplicable. Exact requirement specification of governance related registries, understanding the suitability of blockchains for such registries, and rigorous and formal analysis of the use cases and the threat models of blockchain applications are open research questions.

Social media

Perhaps the most difficult challenges of rapid digitalisation come from the social media systems. On the one hand, they have unleashed hitherto unknown opportunities for ordinary individuals to freely communicate and express themselves without let or hindrance, thereby promoting free speech. On the other hand, the same freedom has led to negative manifestations like creating alternate realities with fake news, illegal profiling and targeting, brainwashing political campaigns, targeted and predatory advertisements, and even shaping public opinion and morality [49]. The problem of fake news has been so overwhelming in India that it has forced the government to demand traceability in end-to-end encrypted communication platforms like WhatsApp [7, 11]. While the court is seized with the traceability issue, the question of how to bring the much needed safety elements into social media and communication systems remain an open problem. It is not even clear whether social media should be regulated or not, and bringing order to social media will require thoughtful and multidisciplinary research.

Conclusion

The digitalisation of society has happened too fast, especially in India where digital literacy is still rather low. This has raised new problems of hitherto unknown dimensions. If we bring digitization to public life, we cannot leave the rigours of computer science behind.