Growth forecasts are more important than is commonly understood. Firms use them for planning sales. Financial institutions use them in their investment allocation templates. Multilateral agencies use them for global outlook assessments and governments use them for annual budgeting. The advance estimate of national income for 2016-17, released by the Central Statistics Office (CSO) on 6 January, provides the basis for computing the revised estimate of the fiscal deficit and other key fiscal ratios realized in 2016-17 in the forthcoming budget. More importantly, the advanced estimate also provides the basis for setting fiscal targets for 2017-18. Incorrect forecasts can mess up the government’s fiscal planning.

Like all forecasts, economic growth forecasts are also by their very nature probabilistic statements. Actual outcomes often turn out to be different from the forecast. The aim of the forecaster is to minimize the probability of getting it wrong. In a recent exercise undertaken at the National Institute of Public Finance and Policy, my colleague Parma Devi Adhikari and I have used an automatic leading indicator (ALI) model to forecast the growth rate of the Indian economy for 2016-17. This approach has come to be recognized globally as being possibly the most effective for accurately forecasting gross domestic product (GDP). Without getting into technicalities, let me just say that in this approach the forecaster starts with a whole set of collateral variables which she considers most linked to growth and uses them in running the ALI model to derive a growth forecast. The skill of the forecaster lies in selecting the appropriate set of initial collateral variables.

In using this model to forecast GDP, we faced a major hurdle, as would any macroeconometric research requiring time-series GDP data for India. There is a break in the GDP time series in 2011-12. In that year, the CSO launched a new series of national accounts, which has generated a great deal of controversy. New series need to be issued from time to time to reflect changes in the structure of the economy, new sources of data, new concepts. It is standard practice that when a new series is issued, it is also extrapolated backwards for earlier years to have a continuous, comparable time series. The CSO used to do this in the past. However, till date it has not produced the back numbers of GDP for earlier years consistent with the new 2011-12 series. This has compromised any econometric exercise that requires a reasonably long GDP time series.

Analysts have had no option but to use a time series with an abrupt non-comparability of data before and after 2011-12. Fortunately, the CSO has provided GDP estimates based on both the old series and the new series for three overlapping years—2011-12 to 2013-14. The GDP growth rate for these overlapping years is significantly higher with the new series compared to the old series. This shift requires a correction factor to be applied to any forecast that is perforce mostly based on the old GDP time series. With this adjustment we arrived at a preliminary forecast of 6.8% real GDP growth for 2016-17, which is slightly lower than the CSO’s advance estimate of 7.1%.

However, neither our preliminary forecast nor the CSO’s advance estimate take into account the impact of demonetization since November 2016. There is plenty of evidence, not all of it anecdotal, of a sharp decline in economic activity. The Society of Indian Automobile Manufacturers, for instance, reported that automobile sales in December declined the most in 16 years. Housing sales in the October-December quarter fell by a massive 44% in the largest eight cities, again the lowest in 16 years. The All India Manufacturers’ Organization, which largely represents small and medium enterprises, undertook a sample survey 34 days after demonetization. It indicated that revenue had dropped by 50% and jobs by 35% among its member enterprises.

Reflecting this decline in productive employment, the labour ministry has reported an increase of around 20% in demand for relief employment under the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA).

Such data are clearly indicative of a significant decline in economic activity post demonetization. However, they do not provide a basis for estimating the impact of demonetization on aggregate GDP. One source of data usable for this purpose is the Reserve Bank of India’s fortnightly data on the outstanding credit of scheduled commercial banks. There is a strong statistical relationship between outstanding non-food credit, a good proxy for bank credit to the private sector, and GDP (technically, it is statistically significant at the 1% level). There is no such statistically significant relationship between GDP and food credit, which mainly goes to the public sector to maintain foodgrain stocks.

This relationship between non-food credit and GDP can be exploited, without asserting any direction of causality, to infer movements in one from the other. Between 30 October and 25 December of 2015, outstanding non-food credit increased by Rs1.85 trillion. In contrast, between 28 October and 23 December of 2016, outstanding non-food credit declined by Rs39,200 crore, a massive negative change in the last two months of 2016 compared to the same period in 2015. This change is reflected in a decline in the annual growth of outstanding non-food credit in December from 10.7% in 2015 to only 5.4% in 2016. Factoring this into our growth forecast via the statistical relationship cited earlier, our growth forecast would have to be adjusted down to 6.5% to take account of demonetization up to the end of December.

However, the process of remonetization is not over. The squeeze on economic activity driven by the rationing of cash withdrawals is expected to continue till end-February, if not later, hence also the deceleration in credit growth. This requires a further downward adjustment of our forecast. Our ALI model-based forecast, after taking into account the adverse impact of demonetization, comes to 6.1% as compared to the official advance estimate of 7.1%.

This growth deficit, if our forecast turns out to be correct, can undermine ongoing fiscal calculations. The budget for FY 2016-17 was prepared based on a projected nominal GDP of Rs150.76 trillion, which assumed an 11% nominal growth. However, adding the CSO’s implicit GDP deflator of 2.5% to our real GDP growth forecast of 6.1% would imply a nominal GDP growth of only 8.6%.

This 2.4 percentage point growth deficit would translate to a lower nominal GDP in 2016-17, lower revenue, and a larger deficit. The government will find it hard to meet its 3.5% fiscal deficit target under these conditions. However, this will not be revealed in the 2016-17 revised estimate in the forthcoming budget. That is because the budget will be presented two months before the end of the financial year, so the revised estimate will be based on assumed GDP and revenue-growth rates. And these will be made consistent with the 3.5% fiscal deficit target. The actual size of the fiscal deficit will be known only two or three months later.

There is a further problem with fiscal planning for 2017-18. The ongoing budget calculations are being based on the official nominal GDP baseline of Rs151.9 trillion. However, if the actual nominal GDP baseline for 2017-18 is lower at Rs147.4 trillion as per our forecast, this would bias the fiscal projections for 2017-18 as well. Specifically, the budget is likely to overestimate the expected revenue and the absolute level of the permissible fiscal deficit within the fiscal responsibility and budget management (FRBM) target of 3% of GDP for 2017-18.

To summarize, there is likely to be a deficit in actual growth compared to the official projection for 2016-17. As a consequence, the actual fiscal deficit is likely to overshoot the target in both 2016-17 and 2017-18. What is the implication of breaching these targets from a fiscal policy perspective?

In answering that question, it is important to recognize that macroeconomic stability requires a counter-cyclical fiscal policy stance, i.e., allow the deficit to go up when growth dips below the desired norm and compress it when growth spikes above the norm.

Most advanced economies and several emerging market economies now target a structural deficit, which serves as an automatic counter-cyclical stabilizer. The structural deficit is the deficit consistent with sustainable public debt under conditions of normal growth. The actual deficit is allowed to exceed or fall below this target when growth is too low or too high.

The FRBM targets which have been set from the outset as a fixed percentage of GDP do just the opposite. The deficit shrinks when growth dips and balloons when growth rises. This pro-cyclical target setting has forced successive finance ministers to look for creative ways of getting around a dysfunctional FRBM straitjacket. In extreme situations, it has even been abandoned, as during the financial crisis of 2008. The report of the FRBM committee will hopefully set this right. Meanwhile, if the fiscal deficit target of 3.5% for 2016-17 is breached and the 3% FRBM target or 2017-18 is eased in the forthcoming budget, this would not be a bad thing. Given the benign inflation outlook, such pump priming would be a welcome corrective after the adverse growth shock of demonetization.

Sudipto Mundle is emeritus professor at the National Institute of Public Finance and Policy, and was a member of the 14th Finance Commission.

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