Statistics day B'day of Mahalanobis Removing Data Constraints in Indian Statistical System

1. All data/ generated by government departments/organizations under the control of the government should share the same by hosting it on websites to be accessible to public, that can be downloaded free of cost in spreadsheet/database format. Presently, a large part of the time for research is spent on collecting the data from scattered data sets and converting it into a time series. Improved data availability, shall increase research/ quality of research based on easier availability of data sets.

2. Literature on the methodology adopted/ reports of groups should also be made available at the same web-address/ location.

3. MoSPI should offer access to their own data series (as detailed above) and also links to data of other organizations by setting up a virtual format of a pan India data bank, which can also operate at a disaggregated level.

4. Data series should be user friendly and offer simple analysis, basic regression, graphs and charts to make the same as competitive as data from private sources through starts-ups, mobile and computer applications, etc.

5. There should be convergence of definitions adopted in the different data sets, to improve comparability.

6. Wherever there is classification of data, the same should be used from input, manufacturing, trade both domestic and international and for purposes of taxation. In case, the same classification at any disaggregated level is not possible, it should be matched at the next level of aggregation/ disaggregation.

7. There is a need to build a concordance between different data sets brought out especially trade and industrial production data. This shall help to support analysis, research and draw policy conclusions, especially in respect of substitutability of domestic production with imports, assessment of impact of FTAs/ PTAs etc.

8. There is delay in introduction of revised index number series. Before the advent of computers, most index number series were revised every ten years. However, even after the advent of computers, the period of revision is not less than 10 years. Index number series should be revised every five years and revision be made effective 12months of the revised base year.

9. A new of services price index, an index of output of the services sector (which has a share of over 55 per cent of the GDP) is pending for more than a decade.

10. The system of data collection needs to be rely on technology to increase reliability, reduce validation needs, data processing time and offer real time data. Field surveys should be replaced by drone and satellite imaging especially for agricultural data. Data on market transaction, trade quantities/ volumes and prices/discounts should also emanate as a by-product of transactions from on-line sources.

11. Progress (physical and financial) of government spending on schemes/programmes needs to be monitored on a real time basis to facilitate timely correction, especially for the social sector. The present system of disaggregated collection and compilation at each stage needs to be replaced with on-line updation directly to a central/ virtual system to avoid delays and to capture progress without any tampering.

12. In several sectors progress is measured as either an input or an output. However, for each sector in addition to input and output, there is need to measure efficiency (both physical and financial) quality and also outcomes. The same may at times cover more than one parameter, all of which should be measured and made available as a time series.

13. Disaggregation of the GDP series in Manufacturing does not go beyond registered and unregistered units, whereas the IIP is able to cover manufacturing sub-sectors such as Machine Tools, Plastic Machinery, Earthmoving and Mining Machinery, Metallurgical Machinery, Textile Machinery, Process Plant equipment, Dies, Moulds & Press Tools, Heavy Electrical Equipment. Such disaggregation would help to provide progress on the relevant sub-sectors and also help validate data from the two sets. Similar sub-sectoral computation may also be considered for Agriculture and the Services sector.