Be skeptical. Be very skeptical.

By Ajay Shah, on December 14th, 2011
In recent months, we’ve had a few slip-ups by the official statistical system in India:

  • Yesterday’s IIP release was preceded by a mistake. Mint says: On Monday, the government was guilty of a similar error in its factory output data. Till it corrected the number pertaining to capital goods output, analysts were left scrambling for explanations as to how this had grown 25.5% while overall factory growth had shrunk 5.1%. (The answer: it hadn’t, and had actually shrunk by 25.5%).
  • On 9 December, we discovered there were important mistakes in the exports data.
  • In December 2010, RBI modified the numbers that it releases about its trading on the currency market.
  • In September 2010, there was a mistake in the quarterly GDP data released by CSO.
These examples are part of a larger theme, of problems of the official statistical system. The Indian statistical system is afflicted by three levels of problems:
  1. The first level is conceptual problems and analytical errors. As an example, the weights of the WPI basket are wrong; the estimation methods used in the IIP are likely to be wrong, etc. Quarterly GDP measurement does not have a demand side (which requires a quarterly household survey, which the government does not know how to do).
  2. The second level is the lack of rugged IT systems. The production of statistics requires high quality enterprise IT systems. The government does not have the ability or incentive to roll these out. As an example, the September 2010 mistake in quarterly GDP data seems to have come about because quarterly GDP data is produced in a spreadsheet. As with all usage of spreadsheets, this is highly error prone.
  3. The third level is the problems of truant front-line staff. In a country which is not able to get civil servants to show up at school to teach, it is not surprising that front-line staff of statistical agencies are untrustworthy in going out into the field and filling out survey forms.
The mistakes that we’re seeing are merely a reflection of #2 (the lack of rugged enterprise IT systems). But there is much more going on which holds back the usefulness of official statistics.
Government officials in this field have pinned a lot of hope on the implementation of the report of the statistical commission (headed by C. Rangarajan, 2001). I am personally not optimistic about this. The report seems to emphasise an incremental agenda of building the statistical system, emphasising the interests of the incumbents. What is required is a ground-up rethink about the statistical system, from first principles, so as to address the three difficulties above.
Turning to the users of official statistics, most economists attach enormous prestige to phrases like GDP, IIP, CPI, etc. But in India, we cannot unthinkingly use some numbers just because they come with the label `GDP’ from some government agency. We have to always skeptically ask first principles questions about how the data is generated. All too often, the standard Indian government data is useless.
In the class of government data that I know of, I feel the CPI is reasonably okay. The WPI is a fairly useful database about prices but useless as a price index. The quarterly GDP data, IIP, NSSO, ASI are untrustworthy.
Decision makers in government and in the private sector need to struggle with these issues, carefully thinking about what statistics are allowed to influence their decision processes. Academic users of data need to be much more careful about avoiding garbage-in-garbage-out problems.
For more on this subject, you might like to look at the label `statistical system’ on this blog.



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Should Governments Collect Subjective Well-being Data?

By Winton Bates, on October 1st, 2009

The idea of governments collecting data on our subjective well-being might seem slightly Orwellian to many people. It could bring to mind images of officials from the government statistics office knocking at your front door and telling you that they are from the government and they have come to help you by collecting information about what is going on in your mind.

However I don’t think anyone needs to worry a great deal about the implications for their personal liberty of proposals for government collection of subjective well-being data, such as in the recently published book, “Well-being for Public Policy” by Ed Diener, Richard Lucas, Ulrich Schimmack and John Helliwell. As discussed in an earlier post, such data would be unlikely to increase the influence that paternalistic interventionists may have on the policy making process.

The important issue is whether the collection of this additional information is warranted in terms of its potential contribution to discussion of policy issues.

In their concluding chapter the authors ask themselves whether enough is known about subjective well-being for government agencies “to initiate systematic programs for measuring it”. This is how they summarise their reasons for answering “yes”:
“The measures are sufficient to reveal some of the groups in society that are suffering, and they also tell us which groups are thriving. The measures already provide strong clues about the characteristics of nations that lead to the experience of a satisfying life for citizens, along with those that predict the opposite. The measures give clear clues about the activities and circumstances that tend to lead to ill-being and well-being. And when national accounts of well-being are instituted our understanding of these issues will only grow.”

Do we really need systematic programs for collection of information on subjective well-being to tell us about such matters? The measures of subjective well-being generally tend to confirm what we know already from information on incomes and other objective indicators of the quality of life. It seems to me that the important issue is whether collection of more data on subjective well-being would add reliable information that is not available from other sources.

The book discusses the potential contributions of subjective well-being measures in providing new information that could be relevant to discussion of policy issues relating to externalities, non-market goods, taxation, setting fines and compensation for lost welfare. Some specific examples caught my eye. It is possible that information on the extent of misery caused by different diseases could result in better allocation of public funds for medical research (p 134). Some research findings suggest that effects of airport noise on well-being of people in affected areas may currently be under-stated by its effects on residential land values (p 147). Subjective well-being information may help in assessing the value of public facilities such as parks to residents of cities who have access to such facilities (p 155).

The critical issue in considering the contribution that subjective well-being data can make to public discussion is whether this information is reliable (yields consistent results) and valid (actually measures well-being). My assessment of the relevant literature (in my draft paper on Gross National Happiness) is somewhat less optimistic than the view presented in this book. Despite all the noise in this data, however, I think the authors may be correct that enough randomness washes out in large samples to make the responses to single item questions sufficiently reliable for the purpose of creating national indicators (p74). Multiple item questionnaires such as those suggested by Ed Diener and Robert Biswas-Diener to measure “psychological wealth” (in their recent book, “Happiness”) could provide much more reliable information.

I think the authors make a fairly strong case that the surveys are measuring an aspect of well-being although I think it is an over-statement to claim that “the measures behave as they would be expected to behave given widely accepted ideas about what well-being is” (p 93). For example, the measures show a decline in well-being when people have children, despite the widely accepted idea that having children has something to do with well-being.

There is a risk that subjective well-being measures will cloud public discussion of policies rather than shed additional light on relevant issues if they come to be viewed as definitive measures of overall well-being. In interpreting these measures it is important to bear in mind that it is quite possible for people to make rational decisions to sacrifice some of their current satisfaction with life, in order to improve their own future well-being or that of their families.



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