- <u>Uniform Reference Period (URP): All expenditure data used to estimate monthly</u> per-capita expenditure are based on the 30-days reference period.

- <u>Mixed Reference Period (MRP): Expenditure data used to estimate the monthly</u> per-capita expenditure are based on the 365-days reference period in the case of clothing and consumer durables and the 30-days reference period in the case of other items.

With rare exceptions, monthly per-capita expenditure associated with the MRP turns out to be higher than that associated with the URP. The original Planning Commission estimate of poverty, which had employed the Lakdawala poverty lines, had relied on the URP monthly per-capita expenditures. At some time prior to the Tendulkar Committee report, the Planning Commission decided, however, to shift to the MRP estimates. Therefore, while recommending revisions that led to an upward adjustment in the rural poverty line, the Tendulkar Committee also shifted to the MRP monthly percapita expenditures in its poverty calculations. Therefore, the revised poverty estimates available for 1993-94, 2004-05 and 2009-10 are based on the Tendulkar lines and the MRP estimates of monthly per-capita expenditures.

# **3. The NSSO versus NAS Expenditure Estimates**

We note an important feature of the NSSO expenditure surveys at the outset. The average monthly per-capita expenditure based on the surveys falls well short of the average private consumption expenditure separately available from the National Accounts Statistics (NAS) of the Central Statistical Office (CSO). Moreover, the proportionate shortfall has been progressively rising over successive surveys. These two observations hold regardless of whether we use the URP or MRP estimate of monthly per-capita expenditure available from the NSSO. Figure 1 graphically depicts this phenomenon in the case of URP monthly per-capita expenditure, which is more readily available for all quinquennial surveys since 1983.

Precisely what explains the gap between the NSSO and NAS expenditures has important implications for poverty estimates. For example, if the gap in any given year is uniformly distributed across all expenditure classes as Bhalla (2002) assumes in his work, true expenditure in 2009-10 is uniformly more than twice what the survey finds. This would imply that many individuals currently classified as below the poverty line are actually above it. Moreover, a recognition that the proportionate gap between NSSO and NAS private expenditures has been rising over time implies that the poverty ratio is being over-estimated by progressively larger margins over time. At the other extreme, if the gap between NSSO and NAS expenditures is explained entirely by under-reporting of the expenditures by households classified as non-poor, poverty levels will not be biased upwards.

There are good reasons to believe, however, that the truth lies somewhere between these two extremes. The survey underrepresents wealthy consumers. For instance, it is unlikely that any of the billionaires and most of the millionaires are covered by the survey. Likewise, the total absence of error among households below the poverty line is highly unlikely. For example, recall that the expenditures on durables are systematically under-reported for the 30-days reference period relative to that for 365days reference period. Thus, in all probability households classified as poor account for a 4