Modernisation of official statistics

We need to eliminate information asymmetry at all levels. Here is a road map

In the pursuit of collaborative federalism with focus on balanced regional development, formulation of credible plans right from village level and their execution and monitoring at different levels of governance is an aspirational objective of the Niti Aayog. We need a spatial big data warehouse (analytics) having micro data, geo-coded, along with tools for extraction of relevant information, their quantitative analysis along with dashboards for assisting governance at all levels. We should at least have district at base level which has about two million people and a well-established administration.

The political economy has stipulated doubling of farm income in seven years, which amounts to 10.4 per cent compound annual growth. Make in India required a model for expanding the domestic market while being globally competitive. Sustainable Development Goal requires banishing of poverty and hunger by 2030, which implies inclusive growth, not explained by conventional theory. The reality is that the yield rate of major crops produced in India is almost half that of the global best. Manufacturing contributes 15-18 per cent, with technology largely stuck at basic or intermediate levels not providing the edge needed for rapid structural transformation.

Service sector-led structural transformation did not create enough jobs in spite of high GDP growth during the post-liberalisation era. The widening rural-urban divide has contributed to restlessness among those who feel left out. To realise the demographic dividend we need a rise in rural income to unleash the forces of demand for the growth of the manufacturing sector, supporting employment and urbanisation.

The United Nations, by a resolution on official statistics expects us to produce quality statistics to ‘honour citizens’, which is accepted by us. We need to produce statistics as public good for the government, enterprises and general public at sufficient levels of disaggregation, shedding enough light about material and human resources which can be combined to raise productivity for a quantum jump in income and support the market micro-structure for efficiency. We need to eliminate information asymmetry at all levels. In this connection, we need to evaluate existing legacy systems from the point of view of relevance, adequacy and cost effectiveness. There are systems that have outlived their utility and others that use outdated systems and processes and also lacks modern data governance practices.

We have to revalidate the requirement and undertake gap analysis, area by area, on comprehensiveness and comparability, taxonomy, classification, reporting system etc for modernisation.

INTEGRATED SYSTEM: Data once captured should be carried wherever required

We need to capture entire life-cycle of data along with a formula for aggregation to enforce a code of practice to ensure data quality, consistency and coherence. We may follow this principle for national accounts, flow of funds and fiscal statistics. For employment statistics, we would need to reconcile data from both households and enterprises for cross validation. Health, education and demography are other important areas we should cover by web-based reporting for timely collection, collation and dissemination.

With a view to achieving this objective, we have set up three committees to clearly define data metrics as core statistics relating to the real sector, financial sector and fiscal sector of the economy. We have two more committees to address issues on web-based reporting and analytics. The idea is to have a top- down approach on data needs and bottom-up approach on collection, processing, validation and generation of output for integrated information system as the backbone of official statistics. The nexus of the three sectors capturing the dynamics of socio-economic forces propelling the economy right from the district level can be a complete game-changer for creating a learning society to pursue a higher trajectory of growth along with social justice for a long time, taking India to its well deserved summit. 

We need to explain the word ‘nexus’ for clear understanding. If we have granular data for real and financial sectors that can be related at, say, the district level, we would be able to understand the distribution of production, cost of production and financing of these activities, where action matters the most. The gap analysis using data envelopment analysis, which estimates the best combination of inputs maximising output that defines a frontier will guide a producer to understand where he stands vis-à-vis the best to take steps to improve productivity, be it agriculture or industry. We may require loan to invest in capital and other inputs. Financial data will shed light on how this demand for credit is met by the financial system. With the world flush with money, genuine investment in the real sector, particularly MSMEs, should not suffer.

A part of government intervention goes for welfare of vulnerable sections. The data captured as part of e-governance and fiscal expenditure on developmental schemes when superimposed on real and financial sector data can be a rich repository for evaluation of policy, monitoring progress and understanding dynamic forces. We can access this data at all levels from bottom to top. All stakeholders, be it the government, enterprise and general public, can gain from this data.

What are the challenges? The ideas set above are no doubt highly challenging, but doable. We do not expect this to be accomplished at one go or the way it is envisaged. Based on data required for an integrated system, data once captured should be carried wherever required. With this it will be possible to move to designing and system integration stages for analytics, allowing for our decentralised systems.
 

The author is chairman, National Statistical Commission. Views are personal