Information about Asia and the Pacific Asia y el Pacífico
Chapter

II. Inflation Dynamics in Asia

Author(s):
International Monetary Fund. Asia and Pacific Dept
Published Date:
October 2010
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Information about Asia and the Pacific Asia y el Pacífico
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Although inflation in Asia is still relatively moderate, it has picked up in some countries and is becoming an important consideration as policymakers seek to manage their exits from stimulus, and in particular to normalize policy conditions while guarding against risks to the recovery. A key input for managing this exit is an assessment of the forces that drive inflation, or so-called inflation dynamics. This chapter presents a quantitative analysis of inflation dynamics in Asia and shows how the nature and origin of inflation pressures differ across economies and have changed over time. The chapter also discusses more specifically the inflation drivers in the two largest emerging Asian economies—China and India.

A. Introduction

Inflation pressures have risen in some Asian countries since late 2009. Headline inflation accelerated markedly in the first quarter of 2010 and reached 4½ percent (year-on-year) in the second quarter on average across the region excluding Japan (Figure 2.1). The increase in headline inflation has been mainly driven by commodity prices.1 However, core inflation has also picked up, although it is still at low levels (Figure 2.2).

Inflation pressures have varied across the region. In India, headline inflation recently reached double digits, and core inflation since April 2010 has been close to its precrisis peaks. In Indonesia, headline inflation increased to 4½ percent in the second quarter of 2010, from 2½ percent in the fourth quarter of 2009, and core inflation has remained at about 4 percent since the end of 2010. On the other hand, in China, notwithstanding the rapid economic recovery and credit growth, inflation has remained relatively moderate at about 3 percent.

Figure 2.1.Asia (excl. Japan): Headline Consumer Price Index

(Year-on-year percent change)

Sources: CEIC Data Company Ltd.; and IMF staff calculations.

Figure 2.2.Asia (excl. Japan): Core Consumer Price Index

(Year-on-year percent change)

Sources: CEIC Data Company Ltd.; and IMF staff calculations

Against this background, an important consideration for policymakers is what forces drive inflation dynamics across the region. In order to assess inflation prospects, and determine the appropriate monetary policy response, it is important to determine the extent to which inflation in Asia is driven by supply and demand pressures as well as the extent to which these pressures are caused by foreign versus domestic sources. Identifying the relative contributions of different factors to inflation is complicated by the fact that these factors usually coexist. For example, the run-up in Asian inflation before the global crisis, to nearly 8 percent in 2008, coincided with both surging world commodity prices and strong Asian growth. To determine the relative contributions of various factors to inflation it is thus necessary to conduct an empirical analysis, as this chapter does below. The analysis examines the relative impacts of supply shocks and demand shocks, as well as their origins in terms of foreign and domestic sources. Supply factors comprise commodity prices and producer prices, while demand factors comprise monetary shocks (to money supply, interest rates, and exchange rates) and output gaps.

Two main conclusions emerge from the empirical analysis:

  • Over the past two decades, the main driving forces of inflation in Asia have been supply shocks and monetary shocks, while output gaps have played a relatively smaller role. There are, however, variations in the importance of these various factors across economies. Among ASEAN economies other than Indonesia, commodity prices play a particularly important role in driving inflation, perhaps owing to the openness of these economies and their dependence on oil and food imports. By contrast, in some of the higher-income economies (Australia, Hong Kong SAR, Japan, and New Zealand), output gaps tend to be more important. Across the region, while foreign factors sometimes play an important role, most shocks are domestically driven.

  • The relative roles of key inflation drivers appear, however, to be changing over time. The role of supply shocks in driving inflation appears to have fallen slightly in recent years, while the role of output gaps has increased. The impact of monetary shocks on inflation in Asia has diminished, particularly in economies that have relatively clear monetary objectives and flexible exchange rate regimes (such as Indonesia, Korea, the Philippines, and Thailand).

B. Explaining Inflation Dynamics in Asia

The Role of Food and Energy Prices

Food and energy prices are a particular focus of attention in Asia, as they constitute a larger share of CPI baskets compared with other regions. The shares of food and energy in the average emerging Asian CPI basket are nearly 40 percent and 10 percent, respectively, both of which are higher than the average for emerging economies worldwide (Figure 2.3). In India and Indonesia, the CPI shares of food and energy are higher than the Asian average.

Figure 2.3.Emerging Asia: Food and Energy Weights in Consumer Price Index Baskets

(In percent)

Sources: CEIC Data Company Ltd.; Hong Kong and Shanghai Banking Corporation; and IMF staff calculations.

Moreover, changes in food and energy prices tend to have significant second-round effects on inflation in Asia. In particular:

  • Over the last decade, simple contemporaneous correlations between headline inflation and core inflation, on the one hand, and between core inflation and food and energy prices on the other hand, have been quite high (at 0.8 and 0.4, respectively; Figure 2.4). This suggests that changes in food and energy prices feed through quickly to core inflation, possibly through inflation expectations, wages, and other input costs.

  • Core inflation has tended to follow headline inflation in Asia, rather than the other way around, suggesting that the overall inflationary impact of changes in commodity prices has been relatively persistent. This has been the case especially in India, Indonesia, Malaysia, the Philippines, and Thailand.2

  • The strength of second-round effects in Asia seems to depend on demand conditions. In an empirical estimation of core inflation, in which core inflation depends on commodity prices, the output gap, expected and past inflation, and an interaction term between commodity prices and the output gap, the latter term turns out to be significant on average in the region, suggesting that the output gap influences the impact of commodity prices on inflation (Table 2.1).3 This may be because when demand conditions are weak an increase in commodity prices and production costs is more likely to be reflected in narrower profit margins, while when demand conditions are strong firms have more scope to pass higher production costs on to consumers.

Figure 2.4.Asia (excl. Japan): Headline Inflation and Global Commodity Price Inflation

(Year-on-year, in percent)

Sources: CEIC Data Company Ltd.; and IMF, WEO database, and staff calculations.

Table 2.1.Asia: Pass-Through from Output Gap to Core Inflation 1
Estimated coefficients

(1991:Q1-2010:Q2)1
Estimated coefficients

(2001:Q1-2010:Q2)
Output gapInteraction

dummy of

output gap

with

commodity

price inflation
Output gapInteraction

dummy of

output gap

with

commodity

price inflation
Australia0.15 *0.04 *0.29 *0.24 *
China0.08 *-0.040.12 **-0.03
Hong Kong SAR0.02 *0.40 *0.02 *0.77 *
India0.37 *0.31 *0.78 *0.93 *
Indonesia0.36 **0.100.63 *0.43 *
Korea0.19 **0.12 **0.23 *0.13 *
Malaysia0.02 **0.02 *0.02 *0.08 **
New Zealand0.29 **0.22 **0.38 *0.46 *
The Philippines0.07 *0.38 *0.10 *0.75
Singapore0.06 *0.000.21 *0.02
Taiwan Province of China0.03 *0.08 *0.03 *0.32 *
Thailand0.04 *0.10 *0.06 *0.20 *
Average0.140.140.240.36
Sources: CEIC Data Company Ltd.; and IMF staff calculations.

Sample period starts from 1994:Q1 for China, 1993:Q1 for Indonesia and Thailand, 1996:Q2 for India, 1995:Q1 for Malaysia, and 1999:Q1 for the Philippines. * and ** denote significance at 5 and 10 percent levels, respectively.

Sources: CEIC Data Company Ltd.; and IMF staff calculations.

Sample period starts from 1994:Q1 for China, 1993:Q1 for Indonesia and Thailand, 1996:Q2 for India, 1995:Q1 for Malaysia, and 1999:Q1 for the Philippines. * and ** denote significance at 5 and 10 percent levels, respectively.

A separate point worth noting at this stage is that Asia accounts for a substantial share of the global demand for commodities. Asian demand may therefore have an important influence on world commodity prices. Emerging Asia accounted for 25 percent of global oil demand as of 2008, a threefold increase from its share during the 1980s (Figure 2.5). Asian demand accounts for more than 50 percent of world demand for aluminum and copper, and for 35 percent of world soy demand (Figure 2.6). The high share of Asia in world demand for commodities suggests that developments in the region may have an increasing influence on world commodity prices (see IMF, 2008b).

Figure 2.5.United States and Emerging Asia: Oil Demand

(Share in world demand, in percent)

Sources: U.S. Energy Information Association; and IMF staff calculations.

Figure 2.6.Emerging Asia: Metal and Soy Demand

(Share in world demand, in percent)

Sources: World Bureau of Metal Statistics; and U.S. Department of Agriculture.

Empirical Analysis

The contributions of the various drivers of inflation, including food and energy prices but also other factors, can be assessed in a framework that takes into account international linkages. The analysis is done through a global VAR (GVAR) model (see Appendix 2.1), in which changes in headline inflation in 12 Asian economies are explained by supply and demand shocks. Supply shocks include changes in production costs, proxied by producer price indexes, and in commodity prices. Demand shocks refer to changes in monetary variables (money supply, nominal interest rates, and nominal effective exchange rates), and in the output gap. In addition to domestic factors (the impact of domestic supply and demand shocks on domestic inflation), the model also allows an assessment of the relative roles of regional and global factors. Regional factors refer to the impact on inflation in Asian economies from supply and demand shocks in other Asian economies. Global factors refer to the impact on inflation in Asian economies of supply and demand shocks in the 21 non-Asian economies in the model.

The results from the empirical analysis suggest that supply shocks and monetary shocks account for most of the variation in Asia’s inflation during the last two decades. In particular:

  • Supply shocks explain about 45 percent of the inflation fluctuations in Asia, of which about three-quarters reflect commodity price shocks (Figure 2.7). The contribution of commodity prices is particularly significant among ASEAN economies (except Indonesia), Japan, and Korea, which are among the largest oil importers in Asia. In general, commodity prices contribute more to inflation in economies that have higher oil intensity (defined as barrels of oil consumption divided by GDP in constant U.S. dollars) (Figure 2.8). The contribution of commodity prices to inflation is smaller for high-income commodity exporters (Australia and New Zealand), where they contribute less than 10 percent to the fluctuations in inflation. In these economies, higher commodity prices drive up the terms of trade, but this tends to be accompanied by exchange rate appreciation that mitigates the inflationary impact of higher food and fuel prices.

  • Demand shocks explain 55 percent of fluctuations of inflation in Asia, of which nearly three-quarters reflects the impact of monetary shocks and one-quarter reflects the effect of output gaps. In particular, changes in money supply and interest rates explain about 25 percent of inflation fluctuations; changes in exchange rates explain about 15 percent, although they play a more important role in those economies (such as Indonesia and Korea) that experienced relatively large currency swings during the sample period (Figure 2.9); and changes in the output gap account for about 15 percent of Asia’s inflation fluctuations.

Figure 2.7.Selected Asia: Contribution of Supply Shocks to Inflation Variation1

(In percent)

Source: IMF staff estimates.

1 Generalized forecast error variance decomposition for endogenous changes in prices over 10 quarters.

Figure 2.8.Selected Asia: Contribution of Commodity Price Shocks to Inflation Variation and Oil Intensity

Sources: IMF, WEO database, and staff estimates

Figure 2.9.Selected Asia: Contribution of Aggregate Demand Shocks to Inflation Variation1

(In percent)

Source: IMF staff estimates.

1 Generalized forecast error variance decomposition for endogenous changes in prices over 10 quarters.

The role of output gaps in driving inflation has, however, grown over time. In emerging Asia, the correlation between core inflation and the output gap rose to 0.7 over the past decade, from 0.2 in the previous two decades (Figure 2.10). On average in Asia over the last decade, output gaps explained about 20 percent of inflation fluctuations, from about 5 percent over the previous decade (Figure 2.11). By contrast, the contribution of monetary shocks to inflation has diminished over time, particularly in economies such as Indonesia, Korea, the Philippines, and Thailand. The impact of output gaps on core inflation can also be assessed within the inflation equation of Table 2.1. On this basis, estimates using data for the past decade suggest that a 1 percentage point decrease in output gaps in Asia leads to a ¼ percentage-point increase in core inflation, which is twice the size of the elasticity over the whole period. The association between the output gap and core inflation is particularly significant in India, Indonesia, and New Zealand.

Figure 2.10.Asia (excl. Japan): Year-on-Year Inflation and Output Gap

(In percent)

Sources: CEIC Data Company Ltd.; and IMF staff calculations.

Figure 2.11.Change in the Relative Contribution of Shocks between 1986-99 and 2000-101

(In percentage points)

Source: IMF staff estimates.

1 Generalized forecast error variance decomposition for endogenous changes in prices over 10 quarters.

In terms of the geographic origins of shocks, the analysis suggests that inflation fluctuations in Asia are driven mainly by domestic factors (see also Jongwanich and Park, 2009). In particular:

  • More than 60 percent of inflation fluctuations in Asia have a domestic origin (Figure 2.12). The contribution of domestic factors is more pronounced for economies that have large domestic demand bases (China, India, and Indonesia) and for those that are more advanced (Japan, Korea, and New Zealand). On the other hand, domestic factors account for a lower share of inflation fluctuations in ASEAN economies such as Malaysia and Thailand, which are relatively more open and exposed to global inflationary shocks (Figure 2.13).

  • Global factors account for about 30 percent of inflation in Asia, and regional factors account for slightly less than 10 percent. The contribution of regional factors may, however, be larger than this, if account is taken of the indirect impact of regional demand on domestic inflation via its impact on commodity prices. Indeed, demand from Asia explains about 45 percent of the demand-driven changes in world fuel prices, and 30 percent of demand-driven fluctuations in food prices (Figure 2.14). Once this indirect effect is taken into account, the contribution of regional factors to Asia’s inflation fluctuations increases to about 20 percent.

Figure 2.12.Selected Asia: Relative Contributions of Domestic, Regional, and Global Factors to Inflation Variation1

(In percent)

Source: IMF staff estimates.

1 Generalized forecast error variance decomposition for endogenous changes in prices over 10 quarters.

Figure 2.13.Selected Asia: Contribution of Domestic Demand Shocks to Inflation and Openness

Source: IMF staff estimates.

Figure 2.14.Contribution of Regional Demand Factor to Fuel and Food Price Inflation1

(In percent)

Source: IMF staff estimates.

1 Generalized forecast error variance decomposition for endogenous changes in prices over 10 quarters.

C. A Closer Look at Inflation Dynamics in China and India

Inflation dynamics have differed quite substantially in China and India:

  • In China, inflation has been surprisingly moderate over the past decade, with headline inflation generally below 5 percent since the late 1990s. Inflation has been moderate despite economic growth being very rapid during this period, and credit growth outpacing nominal GDP growth in most years. Food inflation has been relatively volatile, with spikes often coinciding with supply disruptions (Figure 2.15). Nonfood inflation, however, has been subdued and has rarely risen above 2 percent. The reasons usually cited for the low rate of nonfood inflation have been the rapid growth in manufacturing capacity, combined with the slow rate of consumption growth relative to income.

  • In India, after averaging 5 percent in 2000—07, headline (wholesale) inflation has risen and become more volatile (Figure 2.16). In 2008, inflation rose sharply to more than 9 percent following unprecedented increases in international commodity prices. As commodity prices fell subsequently and domestic growth weakened, inflation declined sharply to 2¼ percent in 2009. In 2010, inflation rose once again to double digits in the first half of the year as a result of strong growth that has eliminated the slack in the economy. Furthermore, CPI inflation, in which food prices have a higher weight, has been in double digits for more than two years.

Given the large size and systemic nature of their economies, the next two subsections take a closer look at the determinants of inflation dynamics in China and India.

Figure 2.15.China: Consumer Price Inflation

(Year-on-year, in percent)

Sources: IMF, WEO database, and staff calculations.

Figure 2.16.India: Headline Inflation (WPI) and Inflation Volatility

(In percent)

Sources: CEIC Data Company Ltd.; and IMF staff estimates.

China

This section examines the factors that drive nonfood inflation (a measure of core inflation) in China. It relates movements in nonfood inflation to aggregate demand factors, such as movements in the output gap and monetary conditions, and to supply factors, such as movements in input prices, global prices, the occurrence of natural disasters, and fluctuations in productive capacity. The analysis focuses on the estimation of a New Keynesian Phillips Curve (NKPC), which links nonfood inflation to expected and past inflation, domestic cost pressures (proxied by the domestic output gap), and foreign cost pressures (proxied by import deflators); and a Bayesian variance autoregression (BVAR) model, which assesses the inflationary impact of external variables (including the U.S. output gap, commodity prices, and the nominal effective exchange rate), domestic variables (including domestic output gap and producer price inflation) and monetary policy variables (including the one-year lending rate and broad money growth).

A key conclusion is that domestic demand pressures have played a limited role in driving inflation in China, but foreign demand pressures have been important. The relatively large role played by foreign demand is an unconventional finding, but perhaps it is unsurprising given China’s history of externally oriented growth and limited consumption demand. The results also suggest that input prices are important drivers of producer prices and nonfood inflation. In particular:

  • The impact of the output gap on nonfood inflation is limited in China. This could, however, reflect difficulties in measuring the output gap for such a rapidly changing economy as China. By contrast, inflation expectations, lagged inflation, and relative foreign cost pressures all significantly increase nonfood inflation (Table 2.2).4 There seems to be a modest underlying deflationary pressure (indicated by a negative constant of about 0.2 percentage points in the Phillips curve), possibly reflecting China’s large labor force and the expansionary impact on productive capacity from rapid productivity growth.

  • By contrast, the foreign (U.S.) output gap and commodity prices are important drivers of inflation dynamics in China. A 1 percent shock to the foreign output gap raises producer prices by more than 2 percent, food prices by about 1 percent, and nonfood prices by about ½ percent in the first year (Figure 2.17). World commodity prices affect both producer prices and nonfood inflation, but they have little impact on food price inflation.

  • Monetary policy has a mixed effect on inflation (Figure 2.18). Money growth appears to have surprisingly little impact on inflation. On the other hand, interest rates do affect food inflation within 1—2 years, although they have little impact on nonfood inflation. Nominal exchange rate appreciation seems to have a modest pass-through effect on producer prices (but little effect on consumer prices), possibly because imports are dominated by intermediate goods and consumer goods imports are relatively small.

Table 2.2.China: NKPC-Baseline GMM Estimates with Nonfood Inflation1
VariableCoefficientst-statistic
Constant-0.002 **-1.81
Foreign price gap0.31 *15.74
Expected inflation0.12 *2.69
Output gap20.020.93
Lagged inflation0.63 *21.38
R-squared= 0.84, adjusted R-squared= 0.75
Source: IMF staff estimates.

The sample period is 1996—2008. * and ** denote significance at 5 and 10 percent levels, respectively.

Output gap is estimated through a growth accounting model.

Source: IMF staff estimates.

The sample period is 1996—2008. * and ** denote significance at 5 and 10 percent levels, respectively.

Output gap is estimated through a growth accounting model.

Figure 2.17.China: Impact of Foreign Output Gap

(In percent)

Source: IMF staff estimates.

Figure 2.18.China: Impact of Monetary Policy

(In percentage points)

Source: IMF staff estimates.

The importance of foreign shocks for China’s inflation is highlighted if one decomposes the volatility of the inflation series in the BVAR model (Figure 2.19). A quarter of the variance in producer prices is explained by changes in world commodity prices, and a further 20 percent by movements in foreign demand. Commodity prices explain around one-third of the variance of nonfood consumer price inflation, while the foreign output gap accounts for about 10—15 percent. Other prices and the domestic output gap appear to have a relatively small influence on nonfood inflation. The variance of domestic food inflation, on the other hand, appears to be relatively unaffected by both domestic and foreign supply and demand shocks. Rather, it is lagged food prices that are most important, indicating that food price supply shocks have a highly persistent effect over time.

Figure 2.19.China: Variance Decomposition of Inflation

(In percent)

Source: IMF staff estimates.

India

Headline inflation in India is significantly correlated with international commodity prices, but it is also correlated with the output gap. First, the energy (fuel) component of the WPI moves closely in line with international oil prices after a lag. Second, the domestic energy component of the WPI is significantly correlated with domestic core inflation, with a correlation coefficient generally higher than 0.5, suggesting that movements in domestic underlying inflation have occurred in tandem with shocks to international oil prices. The correlations of core and headline inflation (both in quarter-on-quarter seasonally adjusted annualized terms) with various measures of the output gap range from 0.15 to 0.22 (Figure 2.20).

Figure 2.20.India: Inflation and Output Gap

(In percent)

Source: IMF staff calculations.

The empirical analysis suggests that both demand and supply conditions affect inflation in India. The key driver is commodity prices, but demand conditions also have a significant impact. An NKPC is estimated both for headline (WPI) and core (WPI, excluding food and energy) inflation, using quarterly data from 1996:Q2 to 2010:Q1. Different measures of the output gap (factor costs GDP, factor costs GDP excluding agriculture, and market price GDP) are used in the estimations.

The main results are as follows:

  • The effect of the output gap on inflation is for the most part statistically significant. Its significance depends, however, on the measure of inflation used and, to a lesser extent, on the measure of the output gap. In the case of core inflation, the impact of the output gap is statistically significant with a coefficient of 0.98 (Table 2.3). In the case of headline inflation, however, the coefficient of the output gap loses statistical significance, but remains economically relevant as a 1 percentage point increase in the output gap leads to a 0.77 percentage point increase in headline inflation (Table 2.4).5

  • International commodity prices exert an effect on inflation above and beyond their effect on expectations or past inflation (Table 2.5). A one percentage point increase in commodity prices is associated with a 0.35 percent increase in headline inflation.

  • Lagged inflation is particularly important, as its coefficient is generally large (positive) and statistically significant, implying substantial inflation inertia. The estimated effect of lagged inflation on current inflation typically exceeds 0.70 and is much larger than that of expected inflation.

  • Expected inflation also has an effect on current inflation, but quantitatively it is generally small across specifications. A one percentage point increase in expected inflation leads to a 0.2-0.4 percentage point increase in inflation depending on the specification. Also, the effect of expected inflation tends to be larger when foreign variables are included.

Table 2.3.India: NKPC-Baseline GMM Estimates with Core Inflation1
VariableCoefficientst-statistic
Expected inflation0.25 *2.08
Output gap0.98 *2.28
Lagged inflation0.75 *6.34
J-statistic = 0.10 (p-value=0.81), adjusted R-squared = 0.36
Source: IMF staff estimates.

The sample period is 1996:Q2—2010:Q1. * denotes significance at 5 percent level. Estimated constant is not shown here. Coefficients on lagged and forward inflation are constrained to add up to one.

Source: IMF staff estimates.

The sample period is 1996:Q2—2010:Q1. * denotes significance at 5 percent level. Estimated constant is not shown here. Coefficients on lagged and forward inflation are constrained to add up to one.

Table 2.4.India: NKPC-Baseline GMM Estimates with Wholesale Price Inflation1
VariableCoefficientst-statistic
Expected inflation0.27 *2.25
Output gap0.771.45
Lagged inflation0.73 *6.31
J-statistic = 0.08 (p-value=0.93), adjusted R-squared = 0.24
Source: IMF staff estimates.

The sample period is 1996:Q2—2010:Q1. * denotes significance at 5 percent level. Estimated constant is not shown here. Coefficients on lagged and forward inflation are constrained to add up to one.

Source: IMF staff estimates.

The sample period is 1996:Q2—2010:Q1. * denotes significance at 5 percent level. Estimated constant is not shown here. Coefficients on lagged and forward inflation are constrained to add up to one.

Table 2.5.India: NKPC-Open Economy GMM Estimates with Core Inflation1
VariableCoefficientst-statistic
Expected inflation0.32 *4.57
Output gap0.501.50
Relative commodity price index0.15 *3.50
Lagged inflation0.68 *9.51
J-statistic = 0.13 (p-value=0.66), adjusted R -squared = 0.35.
Source: IMF staff estimates.

The sample period is 1996:Q2—2010:Q1. * denotes significance at 5 percent level. Estimated constant is not shown here. Coefficients on lagged and forward inflation are constrained to add up to one.

Source: IMF staff estimates.

The sample period is 1996:Q2—2010:Q1. * denotes significance at 5 percent level. Estimated constant is not shown here. Coefficients on lagged and forward inflation are constrained to add up to one.

The relatively important role of food prices in driving inflation in India becomes clearer once inflation is disaggregated into its food and nonfood components. In India, and developing countries in general, the volatility of food shocks is higher and large upward shocks are more common and persistent, and the transmission mechanism between food and nonfood prices is stronger than in rich countries (Box 2.1). The kinds of large food price shocks observed in recent years in India could thus be expected to have a relatively large effect on overall inflation. For example, between September 2008 and July 2010, India experienced unusual food price shocks. In India, the mechanism transmitting food shocks to nonfood prices is stronger than in countries such as the United States. This mechanism thus led to higher inflation throughout 2008 and early 2009 than such shocks would have generated in the United States (Figure 2.21). By 2010, as nonfood shocks were declining, the spillover of food inflation into nonfood inflation, enabled by India’s stronger transmission mechanism, led to year-on-year inflation of 5-7 pecentage points higher than the United States would have faced under similar food price increases.

Figure 2.21.India: Actual and Simulated Inflation

Source: IMF staff estimates.

D. Conclusions and Policy Implications

Although inflation dynamics across Asia, including in China and India, are mainly driven by domestic supply shocks, the contribution of demand factors has risen in recent years. Looking ahead, if the influence of demand factors on inflation continues to grow, policymakers will need to give increasing priority to managing inflation relative to promoting growth.

The contribution of monetary shocks to inflation has diminished over time, perhaps reflecting the improvements in monetary frameworks in many countries. These improvements have included greater clarity and transparency with respect to monetary objectives and instruments as well as greater exchange rate flexibility. Additional moves in this direction may help to further reduce the level and volatility of inflation across the region.

Developments in Asia seem also to have a growing influence on global commodity prices, which is consistent with the high and rising share of Asia as a source of demand for key commodities. As this share grows over time, policymakers will need to pay increasing attention not only to the influence of global commodity prices on domestic prices, and indeed domestic economic conditions, but also to the implications of domestic conditions for global prices.

Inflation dynamics are also different between China and India, which are of particular interest as the largest emerging economies. In China, for the past several years, investment has grown more rapidly than consumption, resulting in a buildup of supply capacity that has held down inflation pressures. Inflation pressures in this environment are driven mainly by supply shocks, which largely comprise shocks to food prices. In India, meanwhile, more traditional mechanisms seem to be at work, where both supply and demand forces play a role in driving inflation. There is also some new evidence that, in India, the persistence of food inflation is higher and food price shocks feed more strongly into nonfood prices than in other advanced and emerging economies (Box 2.1).

Box 2.1.Persistence of Food Price Inflation

In developing countries, the volatility of shocks to food prices is higher than in more advanced countries, large upward shocks are more common, these shocks are more persistent, and the transmission mechanism from food to nonfood prices is stronger. A closer examination of each of these features informs that food prices shocks affect nonfood inflation much more strongly in developing economies.

We analyze some characteristics of food and nonfood price inflation in a sample of 91 countries, comprising advanced economies, emerging markets, and low-income countries. Food inflation on average is significantly higher in the developing economies than in the advanced economies, while for nonfood inflation the differences are less pronounced. Similarly, the standard deviation of food price inflation is much lower among the richer economies. Finally, food price inflation is right-skewed (meaning more large upward shocks to food prices than downward shocks) in most countries, and to a greater extent than nonfood inflation.

Higher volatility does not make food price shocks an important issue when policymakers think about price stability. If these shocks dissipate quickly, then their effect on overall inflation will be transitory and muted, and the time during which these shocks can propagate into the broader price index will also be limited. However, if high volatility is accompanied by high persistence, then proportionately larger food price shocks will be maintained in the economy for a long period of time, and can propagate into nonfood prices.

Food and Nonfood Inflation(Month-on-month; in percent)
Food inflationNonfood inflation
Income groupMeanStandard

deviation
Skewness

(percent

positive)
MeanStandard

deviation
Skewness

(percent

positive)
High income2.22.491.31.91.247.8
Middle income6.14.270.15.22.365.2
Low income11.214.876.07.14.968.0
Sources: CEIC Data Company Ltd.; Haver Analytics; and IMF staff calculations.
Sources: CEIC Data Company Ltd.; Haver Analytics; and IMF staff calculations.

The degree of persistence can be measured in various ways, each with its own shortcomings, but a starting point for most specifications is estimating the equation:

where πtX represents inflation for X, a basket of either food or nonfood items, at time t. We use two different methods. In the first method, the sum of autoregressive coefficients (SARC) is the sum of the q coefficients in the above equation. A higher sum of the q coefficients means a series in which more of the initial shock is maintained over time. The second method, the largest autoregressive root (LAR) method, reformulates equation (1) as a lag polynomial, and calculates the largest root of this polynomial. The closer this root is to one, the closer the series comes to having a unit root in which all shocks are permanent.

Persistants ol Food and Nonfood Inflalion by GDP Per Capita

SARC measure

Source: IMF staff estimates.

By both measures, food inflation shocks are more persistent than nonfood shocks in most of the countries in the sample. And both measures are correlated with income: inflation persistence in both food and nonfood categories is less in richer countries than in poorer ones, with food price persistence being close to zero in rich countries (justifying their exclusion from core inflation) but not in poorer ones.

Finally, the degree to which food price inflation feeds into nonfood inflation is significant. Food price shocks that dissipate quickly can still have large effects on nonfood prices if the link between food and nonfood prices is strong. In general, these linkages are stronger among poorer countries than richer ones. Estimating the degree of transmission of food price shocks into nonfood prices can be done by estimating a VAR for the following equations relating food and nonfood prices:

As above, these effects are larger in developing economies than for developed economies. In poorer countries, the average response of nonfood prices to a shock to food prices is stronger at the outset than in richer countries. While the effects on nonfood prices tend to dissipate at about the same rate, the long-term effect of a food price shock is greater in poorer countries than in richer ones: on average, a unit shock to food prices leads to a long-term increase in nonfood prices of about 0.1 percentage points higher in a poor country than in a richer one.

Histogram of SARC Estimates

Source: IMF staff estimates.

The combination of these three factors—relatively volatile inflation, greater long average persistence, and relatively strong transmission into nonfood prices—means that the kinds of large food price shocks observed in recent years across the world will have a more important effect on the overall price level of poor countries than in richer countries, where these features are more muted.

Response of Nonfood Prices to Unit Food Price Shock

Source: IMF staff estimates.

The fact that food price shocks in developing countries feed strongly into nonfood prices has a number of policy implications. Countries with low volatility in food price shocks and weak transmission mechanisms can afford to regard such shocks as temporary supply-side distortions, but this may not be the case in poor countries. In these cases, food price shocks eventually work their way into the price of nonfood goods and services, adding to nonfood and overall inflation. This greater severity of food price shocks means that central banks in developing countries should be vigilant when supply shocks hit food.

Note: The main author of this box is James Walsh.
Appendix 2.1. Global VAR

A number of macroeconomic variables are modeled; let xit denote the vector collecting these variables for country i = 0,1, 2, …. N. Given the general nature of interdependencies that might exist in the world economy, all country-specific variables (xit) and observed global factors (such as oil prices) are treated endogenously. Denote the observed global and unobserved global factors by dt and ft, respectively. Then

for i = 0,1, 2, …. N and t = 1,2, …. T, where ξit is a vector representing country-specific factors. On the other hand, δio and δi1 represent the coefficients of the deterministic intercept and time trend, respectively. Unit root and cointegration properties between variables can be accommodated by allowing for the global and country-specific factors to have unit roots. Without unobserved common factors, the model for the i-th country decouples from the rest of the country models, and each country model can be estimated separately. But when unobserved common factors are included, the model is quite complex, particularly for large N.

An alternative strategy is to proxy the unobserved global factor (ft) by the cross-section averages of country-specific variables Xit, and the observed common effects (dt) (Pesaran, 2006; and Pesaran, Schuermann, and Weiner, 2004). After some algebraic manipulation, the model in equation (1) can be re-expressed as follows:

where

The weights wij capture the importance of economy j for economy i. The use of country-specific weights allows us to specify a different model for each country (by attaching zero weights to missing variables from country j’s model).1 For each country, we include output, consumer and producer price inflation, money supply, the nominal exchange rate, and the short term interest rate, as endogenous variables. Global oil and food prices are assumed to be exogenous global factors for all countries except for China, India, and the United States. We use quarterly data for the period 1986 through 2010 (first quarter).

Appendix 2.2. Structural VAR (SVAR)

In order to check the robustness of our analysis using GVAR, and to test for structural changes in the inflation process, we also estimate a structural VAR which allows for the identification of structural shocks through a Choleski decomposition. We employ seven variables: GDP, consumer and producer price inflation, the bilateral U.S. dollar exchange rate, real narrow money (or short-term interest rate), a food and oil commodity price index, and foreign (trade weighted) GDP. To ensure stationarity of variables we take their first differences.

For the largest economies of the region, China and India, we impose an ordering in which economic growth can have an impact on global commodity prices directly through its own demand effect or indirectly through its impact on global demand. For the smaller economies, global demand and commodity prices are assumed to be exogenous, as is commonly assumed in the literature.

The results of the country-specific SVAR models are broadly consistent with the GVAR estimates. Variance decomposition of different shocks suggests that the contributions of the shocks to inflation differ by less than 5 percent for all economies between the two methodologies.

Following the robustness check, we split the sample in two subsamples, 1986-99 and 2000-09, to examine the evolution of importance of supply and demand factors for inflation dynamics in Asia.

Note: The main authors of this chapter are Roberto Guimaraes, Carolina Osorio Buitron, Nathan Porter, D. Filiz Unsal, and James Walsh. Yiqun Wu provided research assistance.

Oil prices have risen to about $80 as of mid-September 2010, after falling to $61 a barrel in 2009, although food prices have eased since early 2010.

The convergence of the two measures of inflation has been tested using the methodology followed in OECD (2005).

The output gap is defined as a deviation of output from its trend, calculated using Hodrick-Prescott filter.

The results are robust for different output gap measures including measures based on statistical filters (such as Hodrick-Prescott, Baxter-King, and Christiano-Fitzgerald filters) as well as a measure based on a simple growth accounting exercise.

The statistical significance of the output gap depends in part on the instrument set used and on the lag structure of the estimated equation. For instance, preliminary estimates indicate that the lagged output gap may also have a direct impact on inflation.

Before estimating the model we conduct unit root and cointegration tests, to identify and take account of long term relationships between macroeconomic variables for each country. We also test for weak exogeneity of xit* as well as for global observed factors (such as oil and food prices), since these are the main assumptions that underline the estimation strategy.

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