smoothing parameter for annual data. In the robustness section, we also present results for a different filter that avoids many of the shortcomings of the typical procedure (Hamilton, 2018).

When GDP growth accelerates, most governments in South Asia also tend to spend more. The cyclical components of public spending and GDP move strongly together in Nepal (0.40), Pakistan (0.37), Bhutan (0.36), and Bangladesh (0.33). The analysis of the co-movements between growth rates, rather than cyclical components, yields similar results. By this metric, however, Pakistan’s government spending is the most procyclical in the region (0.37). Except for Sri Lanka, the procyclicality of public spending increased after the Global Financial Crisis (see Table A3 in the Appendix).

The inflation tax seems acyclical in most South Asian countries, as no statistically significant relationship in either direction emerges. The two exceptions are Bhutan and Nepal, in which the inflation tax declines with stronger economic activity. In terms of deviations from trend, the elasticities are -0.33 and -0.44 respectively. In terms of growth rates, the relationship is only significant in Nepal.

**Table 1** <u>Correlation coefficients between fiscal variables and GDP</u>

|**Country**|<br>|**Government**<br>**expenditure**|Obs.|**Inflation**<br>**tax**|<br>Obs.|**Tax**<br>**revenue**|Obs.|
|---|---|---|---|---|---|---|---|
|_Bangladesh_|cyclical components|0.33*|28|-0.25|24|-0.07|28|
||growth rates|0.31|27|-0.09|23|0.06|27|
|_Bhutan_|cyclical components|0.36*|28|-0.33*|28|0.19|28|
||growth rates|0.36*|27|-0.13|27|0.06|27|
|_India_|cyclical components<br>|-0.28|28|0.04|28|0.66***|28|
||growth rates|0.02|27|0.05|27|0.65***|27|
|_Nepal_|cyclical components|0.40**|28|-0.44**|28|0.48**|28|
||growth rates|0.33*|27|-0.33*|27|0.43**|27|
|_Pakistan_|cyclical components|0.37**|28|0.16|28|0.20|28|
||growth rates|0.37*|27|0.16|27|0.25|27|
|_Sri Lanka_|cyclical components|0.05|28|0.11|27|0.27|28|
||growth rates|0.12|27|0.01|27|0.12|27|

**Note:** Based on annual data from 1990 to 2017. Cyclical components are calculated as the deviation of the actual data from a trend computed using the Hedrick-Prescott filter with the standard smoothing parameter for annual data (6.25). ***, **, * denote significance at the 1, 5 and 10 percent levels respectively.

**Sources:** ADB, IMF, World Bank, and own calculations.

Like for public expenditure, the cyclical components of tax revenue and GDP move closely together in most countries in the region. In India, for example, the correlation between the two variables is 0.66 and in Nepal it is 0.48. In Bangladesh, on the other hand, there is no correlation between the two series (-0.07). The results are similar when analyzing the coefficient between growth rates of the tax revenue and GDP (0.65, 0.43, and 0.06 respectively). The correlation between these two variables is statistically significant in India and Nepal. It is also positive in Bhutan, Sri Lanka, and Pakistan, but not statistically significant.

# _4.2. Regression analysis_

In analyzing fiscal cyclicality, we additionally rely on regression-based results. As pointed out by Lane (2003), the reduced-form relation may be the most appropriate concept, since there is no strong reason to exclude any equilibrium feedback from fiscal policy to the level of output. Short-term tax buoyancy 6