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Nepal: Selected Issues

Author(s):
International Monetary Fund. Asia and Pacific Dept
Published Date:
February 2019
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Executive Summary

Following prolonged tepid growth, economic activity in Nepal has picked up, amid greater political stability and enhanced supply of electricity. The improved outlook provides an opening to address deep-seated structural weaknesses and boost long-term growth prospects. However, fiscal spending pressures, including related to the rapid implementation of fiscal federalism, and excessive credit growth pose challenges to macroeconomic and financial stability. This background paper for the 2018 Article IV consultation with Nepal presents supporting analysis which underlies the policy recommendations in the staff report.

The first chapter shows that the considerable co-movement in headline inflation rates between India and Nepal is driven almost exclusively by food-inflation co-movement. By contrast, the role for inflation spillovers emanating from India in driving non-food inflation in Nepal appears limited. The implication is that Nepal should rely on domestic monetary policy rather than stable inflation in India to achieve stable domestic inflation.

The second chapter analyzes Nepal’s newly-established fiscal federalism framework. It highlights that the rapid transition to fiscal federalism carries several pitfalls and can complicate fiscal and macroeconomic management. Managing effective fiscal federalism in Nepal will require strong efforts to strengthen public financial management and enhance the implementation capacity at all levels of government to mitigate risks to fiscal sustainability and improve allocative efficiency gains.

The third chapter highlights that Nepal’s long-term economic development requires sustainable financial sector development. However, despite steps taken to address financial sector weaknesses, Nepal’s banking sector remains exposed to several risks and vulnerabilities, exacerbated by the prolonged rapid credit expansion of the last few years. The financial cycle can be moderated, and risks contained by a combination of measures discussed in the chapter.

Financial inclusion is an important vehicle to promote inclusive growth and reduce poverty. The fourth chapter highlights that financial inclusion in Nepal has improved gradually over the past several years, but there are still large gaps between urban and rural areas and across genders. Going forward, it would be important for the implementation of Nepal’s financial inclusion plan to focus on the underserved and progressively address financial inclusion gaps.

Inflation Co-Movement Between India and Nepal1

Many countries in South Asia have strong economic ties to India—paramount among these, Nepal. This note examines the degree to which inflation co-moves between India and a panel of countries in Asia. Unlike much of the previous analysis on inflation co-movement, the approach taken here treats food and core inflation as distinct processes and allows for the possibility that apparent co-movement in food prices may reflect common shocks to weather patterns across countries—as such, inflation ‘spillovers’ may not be as large as some studies suggest.

A. Stylized Facts

1. Although food-inflation co-movement between India and Nepal is relatively strong, for core inflation it is much weaker, especially in the recent period. An examination of correlations between food and core inflation rates in India and a set of countries in Asia suggest that this result extends beyond just the India-Nepal case. In particular, Table 1 shows that for countries with strong trade ties to India (those for whom imports from India comprise a large share of total imports) headline inflation rates tend to be more highly correlated. However, this finding appears to be driven exclusively by higher correlation in food inflation rates. For core inflation, the countries who depend most on import from India (especially Bhutan and Nepal) do not exhibit core inflation rates which are highly correlated with those in India.

Table 1.Inflation Correlation with India
Import shareFood CPIHeadline CPICore CPI
Bhutan74.30.450.520.09
Nepal57.80.730.650.18
Sri Lanka20.40.270.24N/A
Bangladesh14.00.170.400.25
Pakistan3.20.740.510.50
Indonesia2.20.020.010.17
Singapore2.1-0.370.310.42
Vietnam1.9-0.200.020.50
Hong Kong SAR1.8-0.54-0.170.02
Malaysia1.6-0.150.11-0.16
Thailand1.30.270.090.14
China1.1-0.45-0.32-0.08
Philippines1.10.080.120.39
South Korea1.1-0.21-0.150.21
Cambodia1.00.500.350.38
Laos0.40.400.14N/A
Mongolia0.4-0.23-0.04N/A
top 1/3
middle 1/3
bottom 1/3
Sources: Haver Analytics; National Authorities; IMF Information Notice System, International Financial Statistics, and Direction of Trade Statistics; The World Bank Group; IMF staff calculations.
Sources: Haver Analytics; National Authorities; IMF Information Notice System, International Financial Statistics, and Direction of Trade Statistics; The World Bank Group; IMF staff calculations.

Core CPI

(Seasonally Adjusted, 3MMA)

Sources: Haver Analytics, Nepali Authorities, and IMF staff calculations.

Consumer Pries Index: Food

(Seasonally Adjusted, 3MMA)

Sources: Haver Analytics, Nepali Authorities, and IMF staff calculations.

B. Empirical Analysis of Inflation Co-Movement Determinants

2. To assess the determinants of inflation co-movement between India and other countries in Asia, an instantaneous quasi-correlation measure is used, as employed by Duval and others (2016), among others:

Here, co-movement between inflation (π) in each country vis-à-vis India (i,India) is assessed in each month (t) based on the product of deviations of inflation in that period from some notion of equilibrium (π*) in the two countries, normalized by the product of the volatility of inflation in the two countries (σiπ).2

3. A panel regression is estimated to quantify the determinants of inflation co-movement between India and its neighbors, similar to that of Auer and Mehrotra (2014):

where Importijtis each (i) country’s share of imports from India (j), relative to its total imports at each time, t. Other variables are quasi-correlations of broad money growth (M2), industrial production growth (IP), and deviations of rainfall from seasonal (monthly) norms, between India and other countries in the sample; γt captures time (monthly) fixed effects, which could be associated with common movements in global inflation determinants, such as demand conditions, or global energy or food prices. Analysis is conducted for food and core inflation, separately, and controls for time (monthly) fixed effects.

4. The main takeaway from the results is that food inflation co-movement between India and other countries is higher in cases where the co-movement in rainfall deviations from seasonal norms is highest (Table 2). Greater trade integration with India also plays a role in driving inflation co-movement in the case of food price inflation, as does nominal effective exchange-rate co-movement Regression results for the determinants of core inflation co-movement are much less precisely estimated—neither rainfall (through possible second-round effects) nor trade integration, nor exchange-rate co-movement with India are found to have a statistically significant impact on core inflation co-movement This suggests that core inflation is primarily determined by domestic (idiosyncratic) factors.3

Table 2.Inflation Co-Movement Regression Results
(1) Food Inflation(2) Core Inflation
Rainfall co-movement1.095*

(0.580)
0.651

(0.443)
Exchange-rate co-move0.056*

(0.030)
0.052

(0.032)
Import share, from India0.003*

(0.002)
-0.002

(0.002)
Observations870556
Number of countries1611
Time fixed effects?yesyes
Robust standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1Source: IMF staff calculations.
Robust standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1Source: IMF staff calculations.

C. Policy Implications

5. Despite the appearance of considerable co-movement in headline inflation rates between India and Nepal, this phenomenon is driven almost exclusively by food-inflation co-movement. Analysis using a panel of Asian countries shows that food inflation co-movement is partly related to trade integration, as has been argued by others in the literature, but is also partly a function of similar responses to common weather shocks.

6. The role for inflation spillovers emanating from India in driving non-food inflation in Nepal appears limited, suggesting a crucial role for monetary policy in tackling domestic inflationary pressures. Although the results of this note do nothing to diminish the widely held view that headline inflation co-movement is strong between India and Nepal, they do suggest a need for nuance in this message, as food inflation co-movement is strong while core inflation co-movement is weak. In addition, common shocks such as rainfall patterns are likely partly responsible for food-inflation co-movement, suggesting that this is not exclusively driven by spillovers from India. Since core inflation co-movement is weak, idiosyncratic domestic factors such as economic slack, exchange-rate movements, and differing degrees of passthrough from food- and energy-price shocks (second-round effects) play an important role. This finding is critically important for monetary policy, especially since domestic policy is primarily effective only in controlling core inflation. Thus, domestic monetary policy needs to be calibrated to domestic inflationary pressures—Nepal cannot necessarily rely on stable inflation in India to achieve stable domestic inflation.

References

    AuerR. andA.Mehrotra2014Trade Linkages and the Globalisation of Inflation in Asia and the PacificJournal of International Money and Finance Vol. 49 pp. 129151.

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    BlagraveP.2018Inflation Co-Movement in Asia: Blame it on the Rain?IMF Working Paper forthcoming.

    DuvalR.N.LiR.Saraf andD.Seneviratne2016Value-Added Trade and Business Cycle SynchronizationJournal of International Economics Vol. 99 pp. 251262.

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1Prepared by Patrick Blagrave (APD). Methodology based on Blagrave, 2018, “Inflation Co-Movement in Asia: Blame it on the Rain?” IMF Working Paper, forthcoming.
2Quasi-correlations for dependent variables are calculated in the same way. The variable-specific notion of equilibrium used here is simply the period average, but results are robust to instead using the HP-filtered series to represent equilibrium values.
3In both food and core-inflation regressions, M2 and IP co-movement have no statistically significant impact on inflation co-movement (results not shown in Table 2, for brevity).

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