Sekhar Bonu

Summary

Which social groups consume the least in India? In which states do these social groups reside? Who comprises the bottom of India’s pyramid? This paper attempts to answer these questions using the recently published Household Consumption Expenditure (2022-23) survey data. The study finds that scheduled tribes in Chhattisgarh, Jharkhand, and Odisha are at the bottom of India’s consumption pyramid. While the scheduled tribes are relatively the lowest consumers in most states, the scheduled castes are the second lowest. Short-term and long-term policy and programmatic action are required to boost the consumption of scheduled tribes and scheduled castes in the states that fall below the national average of consumption expenditure. This would require human capital development and increased access and productivity of economic assets for these social groups. Given the long gestation periods of human and economic development interventions, short-term focused and conditional cash transfers could be considered to enhance the living standards of the scheduled tribes and scheduled castes at the bottom of India’s consumption pyramid in specific geographical areas where poverty is concentrated.

Introduction

National Sample Survey Office conducted the Household Consumption Expenditure Survey (HCES) between August 2022 and July 2023. The HCES, 2022-23 sampled 155,014 households in rural and 106,732 in urban areas to estimate the consumption patterns in India. The last large-scale consumption expenditure survey for which data was available was conducted in 2011-12^1. 

Past surveys have established variations in consumption expenditure by social groups. This study, with its updated variations in social groups by state/union territories (UTs), holds the potential to fine-tune policy and programmatic responses to address inequalities. The study aims to identify those states and social subgroups with the lowest consumption expenditure, a key indicator of relative well-being and poverty. The paper suggests policy recommendations to address the inequalities in consumption expenditure by identifying the social groups within particular states with relatively depressed consumption. 

HCES 2022-23 recorded social groups as scheduled tribes, scheduled castes, other backward castes, and others. Household consumption expenditure was collected on food items, consumables and service items, and durable goods. After adjusting with appropriate weights, monthly per capita expenditure (MPCE) was derived by dividing household monthly expenditure by household size. 

Findings

All India scenario.  India’s average MPCE is INR 4,534, with INR 3,733 in the rural- and INR 6,459 in the urban areas. Figure 1 shows the percentile-wise distribution of MPCE in India, by rural and urban areas. In rural areas, the 10th percentile consumption is INR 1,911, 31% of the INR 6,043 MPCE reported by the 90th percentile. In urban areas, the consumption expenditure in the 10th percentile is INR 2,810, which is 25% of the INR 11,084 MPCE reported by the 90th percentile. Overall, including rural and urban areas, the 10th percentile expenditure is INR 2,042, 26% of INR 7,730 reported by the 90th percentile. In both rural and urban, the bottom 10th percentile consumes less than a third of what the 90th percentile consumes. 

Figure 1: Distribution by deciles of India’s MPCE by rural, urban and combined

Pk (for k=10, 20, 30 …, 80, 90) is the kth percentile of the distribution of persons by MPCE, that is, the MPCE level below which k% of the population lies. 
States with the lowest consumption expenditures? Figure 2 gives state/UT-wise MPCE by social groups. Table 1 shows a heat map of state and social group consumption expenditure. The state with the lowest consumption expenditure in India is Chhattisgarh, with an average MPCE of INR 2,854, followed by Jharkhand (Rs 3,166) and Odisha (INR 3,275). Bihar (INR 3,447), Uttar Pradesh (INR 3,564), Madhya Pradesh (INR 3,582), Assam (INR 3,720), West Bengal (INR 3,803), Meghalaya (INR 3,937), Ladakh (INR 4,323), and Manipur (INR 4,501) fall below India’s average MPCE of INR 4,534. 

India’s poor consumption expenditures are clustered mainly in the Eastern and Eastern parts of the North. Indian states with less than the average MPCE of India (INR 4534) are primarily in East India and Eastern parts of north and central India. Odisha, Chhattisgarh, Jharkhand, Assam, West Bengal, Meghalaya, and Manipur are in the East. States in the Eastern part of North and Central India include Bihar, Uttar Pradesh, and Madya Pradesh.

Rajasthan has moved above India’s average consumption. Many decades have passed since the BIMARU acronym was coined in the mid-1980s to highlight states with particular characteristics including consumption below India’s average (Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh). HCES 2022-23 findings show remarkable progress in Rajasthan, which has an MPCE of INR 4,655, above India’s average of INR 4,534. Odissa must be part of any discourse about India’s poorest states along with Chhattisgarh and Jharkhand. 

Interesting variation in North-East India. The general perception of the North-East is that of relative backwardness, much attributed to remoteness and logistic costs. The HCES 2022-23 findings show interesting diversity among the North East states.  North East states have interesting diversity in consumption. The states with low consumption expenditure in the Northeast include Assam (INR 3,720), Meghalaya (INR 3,837) and Manipur (INR 4,501). Sikkim (INR 8,926), Mizoram (INR 6,295), and Arunachal Pradesh (INR 5,901) have relatively higher MPCE.

Table 1: Heat map of state-wise monthly per capita expenditure by social groups (states sorted by poorest to richest by MPCE)

Source: Household consumption and expenditure survey 2022-23, Ministry of Statistics and Program Implementation. The author calculates it from the microdata.

Source: Household consumption and expenditure survey 2022-23, Ministry of Statistics and Program Implementation. 
MCPE by social groups. Across India, the scheduled tribes consume less, except in urban areas, where they consume slightly more than the scheduled castes, followed by other backward groups. The ‘Other’ social group category consumes the most in rural and urban areas (Figure 3). Across India, the MPCE of scheduled tribes is INR 3,260, 58% of the ‘Other’ category.  MPCE of scheduled castes is slightly higher at INR 3,859. In rural areas, the MPCE of scheduled tribes and scheduled cases is INR 3,016 and INR 3,472, respectively.  

Source: Household consumption and expenditure survey 2022-23, Ministry of Statistics and Program Implementation. 
Central and East India tribes are the poorest consumers in India. Further analysis of MPCE by social groups reveals even deeper pockets of poverty of consumption, as depicted in Figure 2. The scheduled tribes in India’s central and eastern parts are the poorest. The scheduled tribes living in Jharkhand (INR 2,346), Chhattisgarh (INR 2,364), and Odisha (INR 2,472) are at the bottom of India’s pyramid. Closely following them are the scheduled tribes living in Madhya Pradesh (INR 2,775), West Bengal (INR 2,823), Uttar Pradesh (INR 2,541) and Bihar (INR 2,956). Scheduled tribes living in a more advanced state like Maharashtra (INR 3,162) also fall in the poorer consumption category.

Tribes are generally the poorest consumers in most states. Except for some states with low tribal populations (Uttarakhand, Punjab, Puducherry, Goa, Chandigarh) and a few exceptions (Ladakh, Nagaland, Himachal Pradesh, Arunachal Pradesh, Haryana, Delhi, Sikkim), the tribes are the poorest in all the remaining states and UTs. 

Scheduled castes in North and East India are also relatively poorer consumers.  Next in the hierarchy of poverty of consumption are the scheduled castes from Bihar (INR 3,016), Jharkhand (INR 3,020), Odisha (INR 3,096), Uttar Pradesh (INR 3,119), Madhya Pradesh (INR 3,320), and West Bengal (INR 3,449).  Scheduled castes are also the poorest social groups in the following states: Uttarakhand, Punjab, Himachal Pradesh, Haryana, Puducherry, Andaman and Nicobar Islands, Sikkim and Chandigarh. 

Scheduled castes are generally the second poorest social group after scheduled tribes in most states. They are also the second poorest consumers in most states (except Manipur, Jammu and Kashmir, Nagaland, and Arunachal Pradesh). In West Bengal, the scheduled castes and other backward classes are at the same level of consumption. 

Other backward classes in some states are also relatively poor. The poorest OBCs live in Chhattisgarh (INR 2,931), Jharkhand (INR 3,304), West Bengal (INR 3,448), Uttar Pradesh (INR 3,472), Odisha (INR 3,503) and Bihar (INR 3,570).

The ‘Other’ category is generally the richest in all the states. The ‘Other’ category (those not scheduled castes, scheduled tribes, or other backward groups) is the richest in all states except Ladakh, Nagaland, Himachal Pradesh, Arunachal Pradesh, and the Andaman and Nicobar Islands.

Variation in poverty by social groups. The difference between the poorest, mostly scheduled tribes and the richest, primarily ‘Other’ categories, is significant. The variation is relatively lesser in some states, such as Bihar and Assam. A more substantial gap can be seen in Maharashtra, where even though the average consumption is higher, the scheduled tribes consume much less, which can be comparable to the poorest living in Chhattisgarh and Jharkhand. 

Conclusions

This note highlights the clustering of low consumption expenditures in states predominantly in East India. The scheduled tribes from the three contiguous states of Chhattisgarh, Jharkhand, and Odisha are at the bottom of India’s consumption pyramid. The scheduled tribes in most Indian states/UTs are relatively the poorest consumers in their respective states, followed by scheduled castes.

More research is needed to determine the reasons for low consumption by social groups in specific states. The scheduled tribes and scheduled castes in states with below-Indian-average consumption need special attention. Apart from Chhattisgarh, Jharkhand, and Odisha, these states include Bihar, Uttar Pradesh, Madhya Pradesh, Assam, West Bengal, Meghalaya, and Manipur. In some states with consumption above India’s average, scheduled tribes have low consumption and need special attention. These include Rajasthan, Jammu and Kashmir, Gujarat, and Maharashtra. 

India has made remarkable economic and social progress over the past several decades. As India aims to become a developed nation by 2047, one of the critical challenges it must address is bridging regional and social group inequalities. The paper identifies that relative consumption poverty is clustered in some states, predominantly in East India. Indian states are heterogeneous, with wide variation between districts and development blocks. A better understanding of the distribution of poverty by districts and development blocks is essential to do better targeted programmatic and policy interventions. We need more granular and frequent high-quality data sets to identify the reasons for the pockets of poverty and frame effective policies and programs targeted to these relatively poor areas. More frequent data sets are required to monitor and evaluate the effectiveness of policies and programs on poverty elevation. Think tanks and centres for operational research that specialise in these geographical areas need to be encouraged to build state capacities to harness data and research to enhance the effectiveness of public expenditure and galvanise private capital into these pockets. 

One of the most sustainable ways of increasing consumption is by enabling inclusive and higher economic growth. While much of the policy focuses on building more manufacturing and services-related jobs, East India also needs to boost agricultural productivity. India’s “green” and “white” revolution must expand to the Eastern parts of India, especially the poorest districts and blocks in the identified states. Animal husbandry, horticulture, and fisheries should also be prioritised as they can generate additional income and improve nutrition in these regions. Agriculture research focusing on making these regions more productive needs to be prioritised.  

Urbanisation needs to be accelerated in East India. The South has Chennai, Bangalore, and Hyderabad. The West has Ahmedabad, Mumbai, and Pune. The North has the National Capital Region, which benefits many states. Unfortunately, the East has one megacity, Kolkata, which is driving some growth. The East needs to prioritise proactive urbanisation to catch up with the rest of India and, in the process, alleviate poverty in some of the poorest areas of the states highlighted in this paper.

Building state capacities is especially critical at the district and block levels where poverty and consumption clusters exist. National programs tend to benefit the states that have higher capacity. Due to weak state capacities, the benefits of many centrally sponsored schemes, which depend on state government capacities for implementation, accrue less to some of the backward areas highlighted in the paper. It is difficult for states with weaker capacity to compete for funds from a national program that does not have internal checks to ensure proportionate funds are utilised by states with weaker capacity. While efforts should be made to build state capacity in the poorer states, it is also essential to re-examine the need for re-introducing programs specific to backward areas where funds are ring-fenced only for the most disadvantaged regions, along with funds specifically allocated to boost implementation capacity. 

Very focused programmatic interventions to improve human capital and productive capacities will be essential to accelerate the development of these social groups that lie at the bottom of India’s consumption pyramid. Improving human capital has to focus on improving the quality of education, and investments in student hostels. Further, scholarships to reduce dropouts at senior, senior secondary, and higher education levels are critical. Special purpose vehicles with focused and clear mandates for the accelerated development of these identified social groups will be required to build the capacity of the development agencies to deliver time-bound results. While most human capital and economic development initiatives will take time to deliver results, to achieve short-term tangible results, the governments could consider conditional cash transfers to the scheduled tribes and scheduled cases in select identified states. If not across the whole state due to fiscal constraints, the government could consider some of the most backward districts in these states for special cash transfers. 

Effective monitoring of government policy and program implementation improves public expenditure efficiency. Monitoring can generate real-time data across the results chain—resources, activities, outputs, outcomes, and impact—serving several public goods. Traditional paper-based monitoring has been less effective because of poor quality of data, time lag, limited analysis and insights, etc. Digitisation of program and policy implementation has improved the quality of monitoring and effectiveness of public expenditure. Yet, digitisation has not delivered its promised full potential. Often, it is because of a weakness in tracking outputs and outcomes. Digitisation of monitoring varies across different departments and levels of government. This paper discusses a framework to help deepen the digitisation of monitoring, especially at the state and local body level, based on NITI Aayog’s implementation of the Data Governance Quality Index (DGQI) in the central government. 

A data value chain for monitoring comprises data collection, cleaning and processing, quality assurance, storage, integration, analysis and visualisation, interpretation, dissemination, and archiving. The full benefits of digitising monitoring involve reviewing and strengthening the whole data value chain. A comprehensive framework approach encompassing the data value chain is critical to tapping the digitisation process’s full potential to enhance monitoring of government policies and programs. 

The framework can help agencies review and improve digitisation across the data value chain for monitoring. With complementary toolkits developed by NITI Aayog, the framework can assist agencies in self-assessing the digitisation process and developing roadmaps for digital monitoring transformation. 

A framework for digitising monitoring

The framework comprises three broad categories. Data system, the first category, handles data collection and interpretation in the value chain. The second category is data outcomes deals with data generation to track development outcomes. Institutional arrangements needed for driving the monitoring digitisation process is the third category.

Data systems

The data system includes data generation, quality assurance, data analysis, use of technology, data security, and data management protocols. Data generation deals with the level and sophistication of data generation. The gold standard for data generation is real-time transactional data with limited or no human interphase in data generation. Deploying relevant sensors generates additional data, such as geolocation. The sophistication of data generation includes the granularity, frequency, use of computer-aided personal interview (CAPI) tools, geographical information systems, transactional data, and level where data digitisation takes place when the original data is collected on paper. 

Quality assurance covers data cleaning, addressing missing data, systems to evaluate data quality, back checks and other mechanisms to validate data quality. Data analysis should maximise use and lead to meaningful insights. Various statistical tools and IT software help automate data analysis to provide patterns, trends, and correlations to assist decision-making. Building capacity and incentives for frontline workers involved in data entry and collection is equally important. Data visualisation makes it easier to interpret data by highlighting key trends. Graphs, charts, and dashboards assist in data visualisation. Data interpretation and visualisation lead to actionable insights that help in informed decision-making. 

Technology use leads to efficiencies across the data value chain. This includes the use of Aadhar and mobile phone linkages; linkages to related platforms through API, including PFMS, GSTN, and Udhyog Aadhar; use of local government directory to standardise names of towns and villages; etc. Technology should be deepened to tap into big data from social media, remote sensing, night light, mobility and other non-conventional sources. Better use of the Internet of Things, sensors, GIS, etc., needs to be explored. Data security and privacy involve access controls, regular security audits, masking and anonymisation, encryption, and security awareness training for the staff. 

Data-driven outcomes

Outputs result from activities an agency undertakes over which the agency has significant control. Outcomes and impact result from outputs on individual beneficiaries, communities, and the economy. Failure to translate outputs into outcomes is the main reason for public expenditure inefficiencies. Tracking real-time outcomes will help with mid-course correction and improve development outcomes. 

Collecting outcomes and impact data compared to outputs is a lot more challenging. For example, increased child immunisation (outputs) reduces communicable disease incidence (outcomes). Over a more extended period, immunisation leads to healthier children and better learning outcomes, which are impacts. Both outcomes and impacts are primarily outside the realm of control of the agency, mainly in individuals, households or communities, and therefore, special efforts are required to track outcomes and impacts, often through special surveys. The agency can infer the output data from the administrative data it collects with immunisation. The outcomes data on reducing childhood communicable diseases will need household survey data. 

Digitisation has the potential to help track outcomes and impacts better if agencies’ digital readiness and preparedness to triangulate multiple datasets is optimised. Agencies can track outcomes through a primary survey of beneficiaries. By leveraging technology, agencies can enhance primary surveys’ cost efficiency, quality, and timeliness. Agencies can explore other data sets generated within the agency or by different government agencies that can provide insights into the progress of related proxy outcomes. However, this will require open data protocols for sharing data across departments. Private sector-generated data sets can also help track outcomes. Data generated by social media and non-conventional sources like mobility, nightlight, and other big data can also track outcomes. Data philanthropy and regulations are required to tap private sector-generated data for the public good. Data from monitoring and triangulating with outcomes data from surveys and other sources makes it possible to track outcomes over shorter time frames. 

Institutional arrangements

Digital transformation for better monitoring requires institutional transformation. The foremost organisational change is to remove data silos within an agency. Data silos in a department result from legacy based on project and program-level monitoring. Breaking silos is very challenging. It can be done only with a firm commitment from the top.

A data and strategy unit reporting directly to the CEO or Secretary will be critical to break the data silos and building a culture of intra-agency data sharing. The role of the data and strategy unit is to link all the data generated in a department or agency and provide actionable insights through better data analytics. The role of the data and strategy unit should also include figuring out measures to track outcomes using proxy data generated by different sources, both public and private and conventional and non-conventional sources. 

Making the best use of data through data analytics will need statistics, data sciences, econometrics, and technology capabilities. A department or agency should consider different options to build the human resource capabilities of the data and strategy unit. Given the rapid pace of technological changes, it is essential to implement a human resource strategy that builds on the external ecosystem’s capabilities by hiring consultants and building strategic knowledge partnerships with think tanks, universities, and other relevant non-profits. 

Data Governance Quality Index

The Development Monitoring and Evaluation Organisation (DMEO), NITI Aayog, developed the Data Governance and Quality Index (DGQI). The various ministries and departments of the government of India utilised DGQI to assess the digitisation of schemes and monitor programs. Toolkit development by DMEO helped ministries of the Government of India undertake self-assessment and initiatives to deepen the digitisation of monitoring of various schemes and policies. Data and strategy units were established to develop roadmaps for the digital transformation of the monitoring systems. The action plans for the road maps and the digital transformation progress were tracked. DGQI, as per a report released in 2023, helped many departments of the government of India make significant progress in the digital transformation for better monitoring and delivering development results. State governments and local bodies can undertake similar efforts to improve monitoring and deliver better outcomes. 

Conclusion

A key reason for the suboptimal impact of public expenditure is the failure of public agencies to translate resources, activities, and output into development outcomes. Regular tracking of outputs and outcomes of policies and programs helps to make midcourse corrections to achieve development outcomes more effectively. Regularly tracking development outcomes can pose challenges, but organisations can overcome them by digitising monitoring. The government departments and their agencies must bolster digitisation across the whole data value chain for monitoring. The framework and the toolkit developed by NITI Aayog can help state governments and local bodies digitally transform their monitoring systems to achieve better development outcomes.

Weak state capacity is often cited as a reason for the inferior quality of service delivery and an underperforming economy. Rapid digitisation and data-centric governance have made state capacity building even more urgent. Complex development challenges like climate change, generating high-quality jobs, and making India competitive require competent civil services and an agile state at all levels of government.

How can we build state capacities faster to address the urgent development challenges more effectively through a broader ecosystem approach? The paper proposes a few ideas for governments, especially state and local governments, to pivot to a much higher equilibrium of performance. The paper focuses on how governments can leverage the strengths of the external ecosystem—private sector, think tanks, civil society organisations and academia—to achieve development goals faster. These suggestions complement efforts to strengthen the state capacities from within, such as cadre rationalisation, workflow improvement, etc.

The traditional approach to capacity building is through in-service training, allowing study leave, rewarding performance, etc. Except for the elite Indian Civil Services, most civil servants, especially frontline workers of the state government and local bodies, get limited opportunities for training. The government has taken steps to improve state capacity through the Karmayogi initiative. The recently constituted Capacity Building Commission has given a strong institutional base for successful capacity-building efforts.

Several constraints of motivation and incentives exist in scaling up state capacity in a 100% permanent civil services structure. In a permanent civil servant model, most are hired at the entry level. They are often promoted based on seniority in a career spanning over 30 years, with limited incentives for upskilling. A few motivated civil servants gain advanced skills with remarkable impact. Even if special efforts are made to build civil servant capacities through a traditional approach, delivering results at all levels of government will take time, given the scale. It is, therefore, essential to supplement ongoing efforts to build state capacity by tapping capacities in the external ecosystem.

The first step is moving from a 100% permanent civil services model to a more agile one where permanent civil servants can vary from 50% to 90% of the workforce, depending upon the agency, with a higher percentage for regulatory and law-enforcing agencies. The government can convert some vacant positions into contractual positions for lateral fixed-time hiring. Based on the emerging requirements of the government entity, lateral entrants can be hired to undertake particular tasks based on clear job descriptions and deliverables, giving the government the agility to meet the changing skill requirement.

The second step is introducing a standardised lateral hiring process at multiple levels. Lateral entrants hired can augment capacities, especially where specialised skills and knowledge are required. The Government of India and some state governments already practise lateral hiring. However, this practice needs to be scaled up. State governments’ personnel and finance departments must take more steps to encourage lateral entry. For example, they can permit concerned departments to recruit qualified professionals as short-term contractual hires using budgets from vacant positions. The finance departments can also permit using an unutilised salary budget as professional fees for contractual lateral hiring. With a good IT and managerial background, lateral hires can improve management and IT practices. The hiring department must familiarise lateral entrants with government processes through well-conceived induction training, regular mentoring, and performance management. This entails building decentralised human resources capacities at the agency and department levels.

The third step is outsourcing well-defined tasks to knowledge institutions: think tanks, universities, consulting firms, etc. However, this requires capabilities within the government to draft the terms of reference and manage the procurement of technical consultants. Because of severe capacity constraints in both these skills, the outsourcing model is used in a limited manner in most settings. Enterprising governments can break out of low equilibrium status by recruiting lateral entrants with the requisite skills to draft good terms of reference and assist in the procurement process.

The fourth step is to simplify and innovate the procurement. The open tender process is the standard method for procuring consulting services. However, this is time-consuming, entails high procurement capacities, ends in mis-procurement, and leads to re-tendering. The small-scale procurement of consulting services should be agile and innovative to meet governments’ time-bound needs. Wider use of limited tender can help government agencies to scale up their work. The General Financial Rules of the Government of India, 2017 provides for limited tender, which allows procurement from an empanelled list of firms through a simplified process for a faster turnaround for small works, goods, and services contracts. Standard guidelines on the use of limited tender issued by the finance departments will bolster the use of the limited tender route. Similar provisions and efforts are required at the state government level. This would help the government onboard the technical expertise without compromising open tenders’ fairness and transparency.

The fifth step is to build capacities in procurement. Procurement as a skill is often undermined in governments and relegated to a function of the accounts department. Governments frequently hire external consultants as transaction advisors for sophisticated and complex public-private partnerships. This entails the capacity of the government agency to procure an external consulting firm. This capacity constraint is sometimes overcome by seeking external multilateral or bilateral support. The procurement process can be standardised and simplified by adopting standard bidding documents and standardised procedures. Governments at various levels need to invest in building procurement capacities for hiring technical services, which can manifold increase the output of the government.

The sixth step is onboarding knowledge institutions for government policy and program-related work. Governments should revise the procurement guidelines to introduce a “Knowledge Institutions” open tender process. The Knowledge Institutions open tender process should limit the tender process to a pre-identified, more comprehensive list of non-profit knowledge institutions with a proven track record of serving India’s training and research needs. The Knowledge Institutions’ open tender process differs from a limited tender by removing the financial ceiling. It can mitigate procurement risks, especially for entities with weak procurement capacities, as the tenders are limited to well-established knowledge institutions.

The seventh step is to build strong partnerships to leverage the capacities in the external ecosystem for the government to tap. Partnerships with knowledge institutions can help tap talent and capacities in universities and institutions of repute and think tanks. Unfortunately, many existing partnerships between governments and knowledge institutions are ad hoc. External knowledge partners need resources for rigorous analytical work and longer-term commitment to deliver resource-intensive products. The trust-based open tender process and limited tenders can boost the participation of India’s knowledge institutions in the public administration and public policy analysis and formulation process. The lateral entrants and partnerships with knowledge institutions would help build the country’s overall capacities in public service delivery and public policy analysis as talented young minds get exposure to the public policy process.

The eighth step is to use a special-purpose vehicle to undertake technical assignments demanding multidisciplinary skills. Special-purpose vehicles give more flexibility in hiring talent from the external ecosystem and getting experienced government staff on deputation to meet the interdisciplinary requirements. An empowered and well-staffed special-purpose vehicle with exemplary leadership can be transformational: for example, the contribution of the National Highway Authority to India’s national road network, the Unique Identification Authority of India to Aadhar, the Delhi Metro Rail Corporation to Delhi Metro, etc.

To conclude, the traditional approach to capacity-building of career civil servants and other state capacity-building initiatives is \ critical. This can be augmented further by building on the strengths of the external ecosystem through lateral hiring, outsourcing, building capacities in procurement, innovations in knowledge institutions’ procurement, forging partnerships with knowledge institutions, and establishing special-purpose vehicles.