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In Quest of Inclusive Growth

R B Barman (barmanrb@gmail.com) is former chairman, National Statistical Commission. 

Economics needs an integrated approach for inclusive growth. But, this purpose is lost in the excessive focus on the separation of the subject into micro and macro and the obsession with rationality and general equilibrium for theoretical perfection. In such a context, the analysis of the factors contributing to the changes in the gross domestic product overlooks the distributional aspects, whereas statistics focuses on distribution in its search for approximating the regularity in data generated in a multivariate space. The feasibility of a new multidisciplinary framework for organising economic data in the quest of a paradigm inclusive growth is explored.

In the current information age, data- driven decision-making processes can enable the delineation of a path of inclusive growth as a strategy for sustainable and well-balanced development. In a networked economy, not only can a huge volume of data be collected, collated and accessed with relative ease, such voluminous data can also be used for extracting insights for a transparent, accountable, inclusive and efficient system of governance. The need of the hour is to embrace this data revolution in terms of a framework of data collection, management, and analysis that would reduce information asymmetry and transform the economy at all levels.

What we need for an inclusive society is to create such opportunities for people that would enable them to move from poverty to prosperity. Poverty alleviation can be attained by increasing returns through productivity gains, aided by technology improvement and shift of labour out of agriculture through structural transformation. For interventionist government policies to work in this context, knowing the extent of poverty merely will not be useful. There is also the need to know the plausible ways of empowering the poor. The problem here, however, is not just the availability of data. Even if data are available, these might be scattered. The challenge then is to use this plethora of information effectively for dealing with poverty comprehensively.

On the other hand, the fact that we are missing our target of inclusive growth—despite an excess supply of humanpower, know-how for producing high quality goods and services, and/or the access to technology—raises various pertinent questions about the efficacy of the currently available data and analysis: Is there a lack of interactive analysis at the policy level to monitor and evaluate progress for efficiency? What kind of data do we need for this purpose and how do we organise such data for analysis?

Economic statistics, for instance, is expected to inform us not only about our command over physical, human and/or energy resources, but also about their sectoral allocations. While the sectoral organisation of these data is crucial for assessing the strength of public policies, there is also the need to understand how the market-determined rate variables— that is, the interest, wage, exchange, and (un)employment rates, which drive the sectoral allocation of the resources—influence the economy. The task is huge. However, the modern statistical approaches and artificial intelligence-based deep learning have given rise to a new paradigm of data science, wherein sophisticated analytical techniques enable a data scientist/analyst to sift through huge volumes of micro data for extracting information on the patterns and dependencies in multi-variate and multi-level contexts. This approach shows the path for moving from the “parts to the whole.”

In these settings, the current article provides two significant insights. First, it explores the sectoral organisation of the economic data and also raises major issues about the analysis of each of these sectors for those who are not well initiated in economic analysis. Second, it indicates how the research facilitated by granular data holds the potential for a new paradigm in economic theory and can make a difference in policy discourse.

Sectoral Organisation of Data

The sectoral groupings of the gross domestic product (GDP) estimates into real, financial, fiscal and external sectors are implicit in our framework of System of National Accounts (SNA). In the basic set-up of macroeconomics for the analysis of the fluctuations and causalities of aggregate growth, the GDP is decomposed into consumption, investment, government expenditure on the purchase of goods and services and net exports. Though each of these components relate to different sets of motivations, the main issues remain the determination of the national income in terms of multipliers and accelerators; and the effect of the equilibration of the various markets on prices, and the interest, exchange, and the wage rates. The contribution of each component in the determination of income is analysed.

The main focus of policy is on growth and stability, that is, the so-called internal and external balances. In a welfare state, the government has a commitment to support vulnerable sections of the population. Government investments for infrastructure development are crucially important, particularly in the present state of development. For this, it is necessary to understand the nexus among the sectors, and the role of institutions in it. In this context, one is intrigued by the question of whether there is any additional advantage to understanding the interplay of the economy through this four-sector classification. Conceptually, it should help in evaluating the welfare programmes of the government and the priority sector lendings at the lower levels of governance. It is also useful for gauging whether the strength of intersectoral dependence can have an impact on the efficacy of interventionist government policies, particularly when such policies come under severe criticism of the free-market champions.

Real Sector

The real sector covers the agriculture, industry and services sectors of the economy, excluding banking and finance. Agriculture has a share of 17% in the GDP but employs about half the total labour force (www.cso.ie), while the micro, small and medium enterprises (MSME) have a share of 32% in the gross value added (GVA) and have an important place in providing an “above the poverty line” lifestyle to the people (GoI 2018).

The agrarian questions: Agricultural labourers with low wage, seasonal employment are recognised to be at the bottom of income distribution and live in the periphery of the markets. It is no wonder that markets bypass them. Under the Sustainable Development Goals (SDGs), they are amongst those who are expected to be lifted out of poverty, hunger and malnutrition by 2030. While the national government runs special schemes/programmes for employment generation, such as the Mahatma Gandhi National Rural Employment Guarantee Act (MgNREGA) programme, various market-related infirmities militate against the beneficiaries’ effective participation in gainful work. Thus, for making development inclusive we need to look for ways to influence forces of both demand and supply for overcoming the barriers and rigidities surrounding the poor. For this, we need to understand the micro structure of the markets surrounding them.

On the other hand, agriculture, with a fixed stock of land, cannot absorb additional hands gainfully, facing diminishing marginal returns on labour (NSSO 2003). While the marginal product of labour (wage) is determined by agricultural technology and the land-to-labour ratio, increase in the wages can be attained through technological improvement and a movement of labour away from the fixed factor in agriculture (Eswaran and Kotwal 1994; Kotwal and Roy Chaudhuri 2013). Any impediment to these two factors will slow down the rate of poverty decline. The important questions in this context are: Which factors determine the production and allocation in the economy? How can the government exercise its power to influence market for social welfare?

Again, both productivity and remunerative prices for agriculture are constrained by the farmers’ inability to save enough to invest in capital, and their lack of bargaining power to influence prices. In fact, they are at the mercy of the traders occupying the middle space between them and the ultimate consumers. We need to understand the structure of the returns from such distributive trade to know how the farmers can get a better deal on prices and how this will affect the traders in the mid-stream. In addition, the farmers, in general, face various problems in availing inputs such as seeds, irrigation, and extension services, which hamper their ability to benefit from agriculture. It is no wonder that, given an option, many of the farmers intend to leave agriculture (NSSO 2003). If the market fails to create alternative avenues for them, then the government needs to step in. But, to what extent, and how?

Issues for MSMEs: The MSMEs potentially occupy a place of significance in economic growth and development by producing goods and services with low investments and opening up job opportunities. These can also act as the training ground for new entrants with entrepreneurial pursuits. While the share of this sector in the GVA has gone down slightly from 32.36% in 2012–13 to 31.60% in 2015–16, its annual growth rate has evidenced a sharp decline from 15.27% to 7.62% during the same period (GoI 2018). This decline is despite the government’s policy to support this sector for the promotion of enterprises and additional jobs and, above all, the structural transformation of the labour market that can raise the income for the people with limited means of living.

Is it possible to provide more space to the MSMEs? The priority sector enterprises, except those in the large organised sector, earn their living from small vocations. If we leave out the numerically small medium enterprises, a major chunk of micro and small enterprises struggle to run their business. This sector has disadvantages in terms of (i) costs due to the lack of scale economics, (ii) availability of inputs such as funds and or capital (due to some kind of principal–agent problem that these enterprises face while dealing with government and banks), and (iii) market access and product quality. Planning activities for the skill upgradation of these small enterprises call for near real-time intelligence for being more effective in mitigating these critical concerns regarding productivity, marketing, managing financial resources and risks, while strategies for overcoming their limitation in raising funds would need a real-time feedback on their operations. Transparency on dealings with support systems is expected to improve conditions.

The large corporates also need support, but of a different kind. As an illustration, let us look at the consumer electronics sector. Though Indian companies have entered these industries they face steep entry barriers, especially in the hardware and software manufacturing sector, from their global competitors. They can neither reach the quality nor the size to compete with the imported products. On the other hand, India succeeded in automobile and information technology (IT) support services with the government’s support. This indicates that government policy plays a crucial role in supporting industrial growth. In the area of cutting-edge technology, therefore, private sector initiatives may not be sufficient without government support for research and development (R&D). We need quality data on R&D for focused policy. Similarly, for labour-intensive sectors—such as construction, textiles, leather and leather products, tourism and hospitality—that are most suitable for the generation of much-needed employment, we need data on their performance, capacity utilisation, contribution to exports, constraints on capital, market access, etc.

Unemployment is a major issue confronting the economy. Can we think of a situation in which every person’s skills and abilities will be matched with employment opportunities? In the commodity market, orders are matched with the supplies for business deals. How can this be done for the labour market? It would require data-driven coordination between the supply of and demand for labour. The data as a by-product of such a digital employment exchange system (from the various operations/industries) will be valuable for devising measures for the optimal use of human resource.

While India enjoys the advantage of a huge domestic market, there is also the need to have a balanced development that would accommodate the aspirations of people from different walks of life. This is undoubtedly a very tall order. There is a need to significantly reduce information asymmetry for the market to work efficiently and interventionist government policies to realise the desired objectives.

Financial Sector

Finance plays an important role in the allocation of resources for productive pursuit. We need to have a well-functioning financial sector that can provide financial services with stability and efficiency. While most of the activities for the production of goods and services form part of the real sector, a major part of the savings for investments in the various sectors get mobilised through the financial sector. In the process the intermediaries transform the maturities of deposits to term credit either for short-term loans for working capital or for long-term capital formation. Such a creation of products according to market appetite has risks and returns, and vulnerabilities on maturity mismatches. The main issue for analysis is how these products are priced, bubbles created, risks mitigated and resources allocated for various purposes, and how they promote social welfare or otherwise.

In India, people of modest means are at the mercy of microfinance institutions and private moneylenders for small loan amounts for which they pay high interests, but with which they can barely escape from poverty. They suffer from a credibility gap, which is accentuated by loan waivers and inefficiency of the credit delivery system. These factors also impose additional costs to borrowers. As the market does not allocate enough investable resources to sectors like agriculture and the MSME, their growth prospect gets curtailed. We need to have relevant data to carry out intensive studies on the extent of allocation of resources to them for capital formation during a time period.

The main beneficiaries of financial intermediation are the big businesses. Though constraints imposed under priority sector lending keep a check on such flows, they have the ability to raise sizeable amounts of money from the capital market. As they undertake major initiatives for investments in frontier industries serving the cause of nation-building, their needs should be met. However, their track record on non-performing assets raises serious alarm. Funds made available for real sector activities get diverted for speculative activities. This should be contained. The Reserve Bank of India (RBI) has initiated a project to assess the credit risk of borrowers that should generate early warning, deterring them from causing major damages.

There are critical sectors like power, infrastructure (including road, rail and air travel), telecommunication and broadband connectivity that require huge investment. These sectors do not generate enough returns and, hence, private capital is hard to come. As this is a collective responsibility for the greater good of the society, the government has an indispensable role in promoting these investments. Whether it crowds out the private sector or facilitates crowding in is contentious. However, we need to remember that it is not only a question of short-term gains and/or long-term interest, but also of shared prosperity. Hence, there is a need to strike a balance between the competing interests based on quantified metrics.

A financial bubble occurs when prices of financial assets are jacked up artificially by different means, such as financial engineering, including subprime. Such bubbles are basically created by financial institutions in their lust for higher profits. When these bubbles burst, they result in serious financial instability that affects the whole economy. To avoid such a situation, the government regulates the financial sector, which requires high frequency data on loans, stock prices, mutual funds, corporate performance, etc. These data form a rich repository for analysis. Linking these data with the real sector is a complex issue. At the aggregate level, data on the flow of funds provides information on sources and uses of funds by institutional classification, such as households, financial and non-financial corporations, the government, and the rest of the world. The challenge is to break it down to the state and the industry levels.

The RBI has granular data on credit and deposits to work with. There are other data sources such as microfinances, which can also be pooled. These data have to be related with households and enterprise data, including data on output, assets, market access, capacity utilisation, and profitability, for insight on the role of finance in economic development. We need to understand the distributive character of people’s command over financial assets and its impact on income. This will help in redirecting policy towards creating opportunities for a more equitable society. This is a highly challenging task. We need to build the system brick by brick, mainly focusing on sources and uses of funds at disaggregated levels of geography along with industry and size class distribution to get a handle on what is happening in this ecosystem. This will be a major step towards inclusive growth. The committee on financial sector statistics formed by the National Statistical Commission (NSC) submitted its report in June 2018, which makes recommendations towards this end. The implementation of this report could be a bold first step.

The payment system, which is an integral part of the financial system, has seen an explosive growth in recent years. Historically, whenever there was a significant change in the mode of payment for business transactions, there was a major impact on the real economy. We need to find out how the spread of digitised payments, both for retail and wholesale transactions, is helping the economy to shift to a faster gear.

Fiscal Sector

Fiscal policy not only plays the role of stabilising the economy to avoid risks associated with sharp downturns and upturns, but also a very active role for supporting development, be it infrastructure, education, health, etc. In the formulation of the policy for taxation to collect revenue, and promotion of savings and investment for growth and employment, there are concerns that need due attention. It also aims at supporting exports and reducing non-essential imports to keep the external sector in balance. Taxes should be progressive, that is, the rich pay higher income tax that can be redistributed among the poor, who need support in an aspirational society. Hence, the fiscal policy is an instrument in the hands of the government to influence the allocation of resources for a nation’s economy to follow its desired path.

It is important to maintain a balance between income and expenditure. Keynesian economics suggests expansionary fiscal policy to stimulate higher demand for economic growth and employment. On the other hand, supply-side economics advocates lower rates to incentivise entrepreneurs to invest more towards growth and employment generation. In their view, higher taxes reduce incentives for savings and investment. In between these opposing theories lie many issues on growth and welfare. It is in this context that the saying “minimum government, maximum governance” needs to be validated.

In the recent decades, the dominance of supply-side economics has increased inequality, which is now a major global concern. The fruits of growth should have gone to the workers, at least to the extent of their contribution in raising productivity. This has not happened mainly because the suppliers of capital have cornered much of the gains (Stieglitz 2015). If the poor are deprived of their due, the likely additional demand gets curtailed and social welfare also suffers. Some of the welfare programmes like the MGNREGA, the Pradhan Mantri Awas Yojana, the health and crop insurance schemes for vulnerable sections, mid-day meals, the Swachh Bharat Abhiyan, and fertiliser subsidies are major welfare schemes for the vulnerable sections. There are other schemes like Bharat Nirman for strengthening the foundation of the growth engine through government support. How such programmes promote equity should be measured to strike an optimal balance between competing theories on fiscal policy.

Fiscal data originate from many sources, including the goods and services tax, customs duty, income tax, and state taxes. It is a massive operation for revenue generation, expenditure control and allocation of resources. These data coming from the central and state governments are not integrated. There may be contentious issues. The NSC-appointed committee on fiscal sector statistics suggested setting up a new outfit to maintain the data on fiscal statistics, which is also consistent with the recent recommendations by the finance commission.

External Sector

External sector relates to the imports and exports of goods and services. These transactions are part of the current account of the balance of payments (BoP) of a country, whereas the capital account comprises the capital inflows and outflows. In a globalised world, economies are interdependent. The Indian economy too has a large international exposure, some of which, like energy and remittances, are very critical for the growth and stability of the economy. We need to promote exports to finance imports and keep the BoP in balance. Imports are also important for raw materials going into exports and sophisticated capital goods going into the manufacturing sector. The rise in exports also raises employment. These, in short, explain the importance of the external sector. The analytical questions are many. The main concerns are on international pressures and their impact on the domestic economy: output, employment, trade, flows of reserves and exchange rate.

We have a very good database on foreign trade and BoP as most of these data are by-products of regulatory controls. The Directorate General of Commercial Intelligence and Statistics has an ambitious project to go for Data Warehousing to organise these data for deeper insights into the external sector. I wish that the RBI also puts its act together in sorting out the inter-departmental issues for taking these data to their well-established versatile Data Warehousing in full. It is long overdue.

Policy for Inclusive Growth

The basic principles of economics include growth, equity and allocative efficiency. Macroeconomics is mainly concerned with growth and stability. The single-minded approach on growth and stability, willy-nilly, leaves out the others, though at aggregate-level competitiveness, production function, etc, are directed at allocative efficiency.

Allocative efficiency, in practice, has wider connotations. We need to explain the idea of production possibility frontier and the issues of consumer surplus and producer surplus by reflecting upon the concept of market power, based on which prices get determined in the market. There are ways by which markets get segmented, and/or collusions take place to raise profitability. There is a heavy dose of microeconomics in such analysis.

When we talk about equity, we look forward to the issues of poverty, deprivation, malnutrition, farmer distress, inequality and so on. The belief in the trickle-down effect of growth is not well-established by facts. There is no doubt that poverty in India has reduced, but the income distribution has worsened with the Gini coefficient of income being around 0.5. High inequality has adverse consequences on society. Hence, trickle down is a leaky bucket that may take many years to improve the lot of the poor.

In general, the return on capital outweighs that on labour, making it difficult for low-wage earners to escape poverty. We need investment in technology and high skills to increase productivity and supply of output. Such increased supply should be matched by demand for a market-clearing remunerative price. Thus, it is the demand-creating technology that is raising the productivity of the poor and giving them remunerative prices that matters. Again, an increase in income helps the poor demand more manufactured products, education, and health. This triggers structural transformation. We should be able to get a picture of this transformation by analysing the data suitable for providing such insights, preferably from the village panchayats and upwards.

Lacking Consensus Paradigm

The mainstream economics separates macro from microeconomics as aggregates are considered to have a regularity that may not be discernible from its constituents. The theoretical concept of point equilibrium exists on the blackboard, but possibly not in actual marketplaces. In the process, it misses out on the complex character of an evolving economy that is constantly experiencing technological and institutional transitions and power plays in a market economy. This separation, though challenged by many, still remains the main paradigm of macroeconomics. It is because, even if the prescriptions of macroeconomics on growth and stabilisation through the interplay of market remain contentious, the alternatives suggested elude a more sound and rigorous foundation.

A strong criticism of the prevailing paradigm was from Lucas (1976) as, in his view, “macroeconomic relations are ill defied unless they capture the microeconomic behaviour of economic agents” (Nachane 2018). Nachane (2018), further, added that

The Consensus macroeconomics that has emerged to incorporate micro behaviour in Dynamic Stochastic General Equilibrium (DSGE) model imposes five strong assumptions: (i) reductionism (ii) representative agent (iii) representative agents as optimisers (iv) existence of general equilibrium and (v) stability of general equilibrium. These assumptions are considered extreme and unrealistic.

While pointing out the limitation of DSGE model, Nachane (2018) noted an alternative: “models to be of relevance to the real world must essentially rest on two pillars: (i) the micro-behaviour of individuals, and (ii) the structure of their mutual interactions” (Colander et al 2008). Two such approaches, mentioned by him, are (i) econophysics literature, and (ii) agent-based computational economics. The search for new approaches includes models based on complex systems and stochastic process. This appears to be the future of the new paradigm on economics.

The limitations of analytical approaches in economics, as briefly explained, are succinctly summarised by Grouwe (2009) who said that

Mainstream models take the view that economic agents are superbly informed and understand the deep complexities of the world. In the jargons they have “rational expectations.” Not only that. Since they all understand the same “truth,” they all act the same way. Thus, modelling the behaviour of just one agent (the “representative” consumer and the “representative” producer) is all one has to do to fully describe the intricacies of the world. Rarely has such a ludicrous idea been taken so seriously by so many academics.

Where do we move from here? In the changed situation of the availability of micro data, there is a mounting demand for moving from the micro- to the macro-level probabilistically. We have mentioned many issues which are parts of microeconomics and hence micro–macro linkages make ample sense.

New Paradigm of Analysis

As per Schumpeter (Hardy 1945), the essential point to grasp is that it is in dealing with capitalism that we are dealing with an evolutionary process that cannot be stationary. As Dopfer et al (2004) observed, the problem is that macro is no good because it cannot analyse the process of change as it actually happens through changing connections, networks, structures and processes, which is a complex adaptive system. The new paradigm in the making is expected to replace the existing, deeply entranced paradigm. The following are some of the approaches under which research is progressing:

(i) Econophysics, as explained in Nachane (2018), focuses

away from individual equilibria to systems equilibria, wherein evolving unstable micro-dynamic interactions are consistent with macro equilibrium. Micro foundations are abandoned in favour of dimensional analysis, and the use of traditional topological methods is replaced by the methods of statistical physics. (Colander 2006)

(ii) Agent-based computational economics (ACE) modelling

allows for a variegated taxonomy of agents, including a spectrum of cognitive features ranging from passive cognition to the most sophisticated cognitive abilities. A second important aspect of ACE is that it examines the evolution of macrodynamics as the number of interacting agents increases and as their interacts become more complex. The method relies heavily on experimental designs to make inferences about the behaviour of different agents. The interactions are determined by the agents’ internal structures, information sets, beliefs and cognitive abilities. (Epstein and Axtell [1996], Tesfatsion and Judd [2006], LeBaron and Tesfatsion [2008], and others quoted in Nachane [2018])

(iii) The Complex System is a method of analytic thinking which tries to look at the larger whole based on granular data. Complex Systems approaches developed in many areas of sciences are examples of such analytics (Capra and Luisi 2014). These systems consider the nature of complexity of the processes involved in an economic system through the interplay of the elements and connections that form the network structure of the economy and the dynamic forces that change them through interactive processes (Dopfer et al 2004; Beinhocker 2006). As advocated by Haken (1989), “the economy is a dynamic system composed of many subsystems; i e, it is a genuine synergetic system.” He goes on to conclude that “in future the general concepts of synergetics, which treats the various forms of collective behaviour, will undoubtedly play an important role in economics.”

(iv) The use of stochastic processes for approximating macro dynamics using micro data is a possibility (Aoki and Yoshikawa 2007). They treat macroeconomic models as composed of large numbers of micro units or agents of several types and explicitly discuss the stochastic dynamics and the combinatorial aspects of interactions among them. In neoclassical equilibria, flexible prices lead the economy to the state of full employment, and marginal productivities are all equal. Many studies show that such equilibria are not possible in economies with a large number of agents of heterogeneous types. They treat equilibria as statistical distributions and not as fixed points. The authors employed a set of statistical tools via continuous time Markov chains, and statistical distributions of the fractions of agents by types as available in the new literature of combinatorial stochastic processes, for reconstructing the macroeconomic models.

Dimensions of Data Restructuring

The present system of organisation of data follows a set pattern for the dissemination of such data, which are mostly available at aggregate levels. This system catering to the needs of the macroeconomic framework that is currently dominating our effort for dissemination of data has to change. There are several reasons for advocating such a change, the most important being the current framework that is not only rigid on access but also leaves many questions unanswered.

The national accounts estimate both growth and the sectoral distribution of such growth, and provide a harmonised system for data on the financial, fiscal and external sectors. We intend to know how the benefits of growth accrue to the various sections of society over time, space, size, classes, etc. While the non-agriculture sectors grow at a faster pace than agriculture, the share of population engaged in agriculture changes at a relatively slow pace. This has major impact on the living conditions of the different groups of people. While we get to know about the pattern of changes in consumption, savings and investments, we would also like to know more clearly about how the various sections of people in the country get affected.

The organisation of data should keep in view the users’ requirement, such as decision-making throughout the system, right from a district to the highest level of governance. Such a structured system should be capable of decomposing aggregates seamlessly. As a district covers around two million people, and has an administrative set-up to monitor developmental activities, the availability of intelligence on the fly will make a major difference for them. This intelligence can also be used to assign responsibilities on expected outcome through specifically developed dashboards to aid those who are responsible to deliver. The key results (KR) can be compared with objectives (O), under so-called OKR. This may be an important initial step to closely monitor and evaluate performance for a major improvement in governance. As reported by Doerr (2017), OKR is a simple idea that has the potential to deliver up to 10 times of the base rate of growth for private sector heavyweights.

The NITI Aayog is committed to a bottom-up approach for economic development. To translate this vision into reality we need to restructure data with the help of Data Warehousing technology to decompose aggregates in a flexible manner to assess how each section benefits and what remains to be done for the vulnerable sections.

Modernisation of Official Data

In tandem with the current trend of modernising data systems, we need a metadata supported integrated system for data management. The system should be developed in such a way that the data conform to specified concepts and definitions, classifications, and spatial codes. These data when populated in Data Warehousing—following dimensional modelling for multidimensional view and access—will provide the flexibility to drill down (up) from (to) aggregate to (from) lower levels of granularity and drill across for deeper insight on the economy. This will then become a very important tool to provide empirically rich output for understanding the patterns and dependencies to support decision-making at all levels of governance and informing other stakeholders about the state of the economy and policy concerns. The contextual insight on how market operates and rewards various sections of people and what needs to be done at different levels of administration to support inclusive growth will be a game changer. In this process, knowledge will be imparted for the efficient functioning of markets and interventionist government policies for inclusive growth.

Metadata is a data dictionary that tracks the entire lifecycle of data and hence is important for efficient maintaining of databases. Its governance requires serious commitment on the administration of the system and the development of taxonomy or data dictionary in a holistic manner. There are a host of other issues including data quality, harmonisation, profiling, and lineage, which are important components of large, distributed database systems. The data should be amenable to contextual search, flexible audit, and effective access control to be exercised by data stewards.

We also need to take advantage of big data technology in a cloud environment for reducing response time when the volume of data is very large (Barman 2018). There are certain international standards, namely the General Statistical Information Model (GSIM) and Generic Statistical Business Process Model (GSBPM) for rationalising, standardising and integrating statistical production processes to modernise information systems.

As a final expectation on official statistics, we look forward to high quality in terms of consistency, coherence, validity and integrity that enhances trust in the produced data. The test of coherence using various alternative sources of data is sacrosanct for enhanced confidence. This requires a multipronged action right from reorganising, restructuring, retraining and empowering the organisation for advanced knowledge, high level of professionalism and genuine independence. The NSC had constituted five committees in 2016 to cover all aspects of data requirement and processing for modernising official statistics. These committees relate to real sector, financial sector, fiscal sector, online reporting and analytics. Their reports were submitted to the NSC in 2018 and are available in the public domain. If the recommendations of these committees are implemented it will be a very good beginning for translating the ideas set out above.

Bringing Professional Expertise

The analysis has to be a multi-level exercise, starting from the districts, to the states and finally at the national level; shedding enough light about heterogeneity and non-linearity. A statistician is trained to deal with distribution, intra- and intercorrelation, stochastic process and transition processes for the analysis of data. We need tools to deal with complex systems, asymmetric distribution and path dependence, to get insights into the forces of growth and development in a dynamic context. Machine learning and artificial intelligence exploit voluminous data for extracting patterns and dependencies free from distributional assumptions. This also has merit.

We need an approach of analysis of economic data that will explore the regularity hidden in the apparent complexity of a behavioural context. We should encourage a multidisciplinary approach keeping broad-based welfare in mind. Though statistics is used by all, a specialist statistician’s touch should be worthwhile.

Conclusions

The mainstream economics focuses on growth using an analytical framework that assumes symmetric distribution of income, expenditure, and assets. The reality is that these variables follow highly skewed distribution. In addition, the theory assumes rational expectation and equilibrium for theoretical elegance. These assumptions are challenged, both theoretically and empirically, as mentioned above. As inclusive growth is important for equity and general welfare, we need to consider distribution as an important component of analysis. This will help in giving due focus on inclusive growth.

The interventionist government policy covers many welfare schemes as important components of social justice. There are deficiencies in their implementation; the identification of appropriate schemes for beneficiaries, inefficient handling, rent seeking, and insufficient accountability. Hence, a modern integrated information system which can extract information on such beneficiaries covering various socio-economic dimensions including variables under SDGs can be very useful for effective administration and monitoring of schemes under inclusive growth.

The ability to pick up signals early is a sign of smartness. The formulation of policy and effective implementation of decisions is a sign of good governance. An efficient market needs an efficient information system and its timely analysis for follow-up actions on policy. We need to develop a data repository that is vast, and data handling tools that are capable of going to the most granular level possible, thereby reducing information asymmetry and allowing flexibility and ease of access.

“Economics now is all about data” (Fox 2016). This article has tried to suggest an indicative approach to overcome major drawbacks in information system and analytics. Grouwe (2009) observed that macroeconomics must be revamped fundamentally. In pursuit of mounting criticisms, a theory needs to be tested empirically for its validity. The modernisation of statistical system to find out how far market outcome is explained empirically needs analytics both for accessing of data and testing of hypothesis. Statisticians have a unique space in this debate which they should play effectively. However, a multidisciplinary approach is needed for synergy to solve complex issues on economic development.

References

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Updated On : 12th Aug, 2019

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