Information about Asia and the Pacific Asia y el Pacífico

6 Modeling and Forecasting Inflation in India1

Tim Callen, Christopher Towe, and Patricia Reynolds
Published Date:
February 2001
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Introduction and Motivation

Maintaining a reasonable degree of price stability while ensuring an adequate expansion of credit to assist economic growth have been the primary goals of monetary policy in India (Rangarajan, 1998). The concern with inflation emanates not only from the need to maintain overall macroeconomic stability, but also from the fact that inflation hits the poor particularly hard as they do not possess effective inflation hedges. Prime Minister Vajpayee recently stated that “inflation is the single biggest enemy of the poor.” Consequently, maintaining low inflation is seen as a necessary part of an effective antipoverty strategy.

By the standards of many developing countries, India has been reasonably successful in maintaining an acceptable rate of inflation. Since the early 1980s, inflation has not exceeded 17 percent (measured by the year-on-year change in the monthly wholesale price index (WPI)) and has averaged about 8 percent. While this is only on par with other countries in the Asian region, it compares well with an average inflation rate in all developing countries of around 35 percent over this period.

The Reserve Bank of India (RBI) has largely conducted monetary policy through an intermediate target for credit or broad money growth. Prior to 1985/86, the implementation of monetary policy was based on an annual target for credit expansion (either a nominal target for total or nonfood credit, or a targeted incremental credit-to-deposit ratio). In 1985/86, the RBI switched to an annual target for broad money growth. Initially this was fixed on the basis of actual monetary growth in the preceding year (or average of several years). In 1990/91, the target became more forward-looking based on projections for real GDP growth, inflation, and velocity. Success in achieving the announced targets, however, was limited, with the target being hit in only four years between 1985/86 and 1997/98 (Mohanty and Mitra, 1999).

In its April 1998 Monetary and Credit Policy Statement the RBI announced a move away from the broad money target toward a “multiple indicators” approach to the conduct of policy (although it still announced a projected range for M3 growth). As Kannan (1999) points out, however, this change was not really a discrete shift in the way policy is conducted, but rather the formal recognition of changes that had gradually taken place in the policy framework over several years. The appropriateness of using broad money as the intermediate target for monetary policy had become a growing issue. While the majority of empirical studies still point to a stable money demand function in India despite the ongoing process of financial deregulation (Arif, 1996; and Reserve Bank of India, 1998),2 the experiences with monetary targets in other countries following financial deregulation and India’s own limited success in meeting its announced targets raised doubts about the continued usefulness of the monetary target.

In adopting a multiple indicators approach, it is necessary to know which of the many potential indicators provide the most reliable and timely indications of future developments in the target variable(s), and which should therefore be most closely monitored. In this paper, we assess which variables are the most useful indicators of future inflation developments. We approach this in two ways. First, we estimate two models of the inflation process in India (one based on a monetary approach, the other using an output gap) and then assess their ability to forecast recent inflation developments. Second, we use a series of vector autoregressions (VARs) to identify the indicators that contain predictive information about future inflation.

The remaining sections of the chapter outline the measures of inflation available in India; describe recent inflation developments; discuss studies of inflation in India, outline the models used in the estimation work, and present the results; and conclude with a discussion of the implications of the results.

Measures of Inflation in India

Three different price indices are published in India: the wholesale price index (WPI); the consumer price index (CPI), which is calculated for three different types of employees (those in the industrial, urban nonmanual, and agricultural/rural sectors); and the GDP deflator. The WPI is available weekly, with a lag of two weeks for the provisional index and eight weeks for the final index. The CPI is available monthly, with a lag of about one month, and the GDP deflator is available only annually (and is not considered further in this paper).

In most countries, the main focus for assessing inflationary trends is placed on the CPI because it is usually the index where most statistical resources are placed and because it most closely represents the cost of living (and is therefore most appropriate in terms of the welfare of individuals in the economy). In India, however, the main focus is placed on the WPI because it has a broader coverage and is published on a more frequent and timely basis than the CPI. However, the CPI remains important because it is used for indexation for many wage and salary earners.

The WPI is heavily weighted toward manufactured products, which comprise 64 percent of the index; primary articles, consisting mainly of food items, account for 22 percent of the index; and Fuel and energy the remaining 14 percent (the prices of many of these items are administered by the government). However, being a combination of primary and intermediate products, the WPI is not representative of the consumption basket of the average Indian household. The CPI is more relevant in measuring inflation as it impacts on households, but its coverage and quality are often questioned. The CPI for industrial workers, the most commonly quoted of the three CPI measures, is more heavily weighted toward food items, which account for nearly 60 percent of the total index.

In recent years, a number of countries have developed measures of “underlying” or “core” inflation that attempt to identify permanent trends in inflation by eliminating from the index temporary price fluctuations and elements that are under government control.3 Core inflation is generally associated with the demand pressure component of measured inflation and is often viewed as being important for the determination of inflation expectations.

In India, no measure of “core” inflation is publicly available. Given the importance of supply shocks on primary prices, however, (see discussion below) and the presence of price controls on a number of items in the measured indices, a measure of underlying inflation is important from a monetary policy perspective. An obvious and immediately available proxy is the manufactured price subcomponent of the WPI. This eliminates primary products (whose prices are most likely to be subject to temporary supply shocks) and fuel and energy (whose prices are mainly administered, although these are being liberalized) from the WPI. It is not a perfect proxy, however, because agricultural processing is an important aspect of Indian manufacturing, so agricultural supply still impacts the manufacturing price index. Further, the mean of primary product inflation has exceeded that of manufacturing inflation, indicating that a focus on manufacturing inflation alone will likely underestimate “permanent” inflation.

Inflation During the 1980s and 1990s4

WPI inflation was relatively stable during the 1980s, averaging about 6¾ percent, recording a low of 3 percent in early 1986, and a high of a little over 10 percent in early 1988 (Figure 6.1 and Table 6.1). In the 1990s, inflation was, on average, higher at 8 percent, and considerably more variable. Inflation rose sharply in the early 1990s, reaching a peak of a little over 16 percent in late 1991, as primary product prices rose sharply and the balance of payments crisis resulted in a sharp depreciation of the rupee and upward pressure on the price of industrial inputs. As the agricultural sector rebounded, however, industrial activity slowed, and financial stability was restored, while inflation declined to 7 percent by mid-1993. It accelerated again to over 10 percent during 1994 and 1995 as economic activity recovered. In response, the RBI moved to tighten monetary policy, and inflation was brought down gradually. Inflation reignited in the second half of 1998 as adverse supply conditions in key commodity markets put upward pressure on food prices (although overall monsoon conditions were regarded as normal). As these conditions eased, inflation again fell sharply to 2 percent by mid-1999, before again rising to around 6½ percent in mid-2000 as fuel and energy prices rose sharply.

Figure 6.1.CPI and WPI Inflation, 1983-2000

(12-month percentage change)

Sources: Data provided by Indian authorities; and staff calculations.

Table 6.1.Inflation–Characteristics of Inflation, 1983–2000
Mean inflation rate
Overall WPI7.456.757.92
Primary sector7.966.399.04
Manufacturing sector7.007.236.84
Fuel and energy8.745.6810.83
Standard deviation
Overall WPI2.901.653.45
Primary sector5.384.565.68
Manufacturing sector3.242.533.66
Fuel and energy5.492.506.00

Within the three subcomponents of the WPI, prices in the manufacturing sector were lower and more stable, ranging from 2 to 13 percent (Figure 6.2). Inflation in both primary products and fuel and energy was considerably higher in the 1990s than in the 1980s. Both indices have also been volatile. Within the fuel and energy category, the sharp rise in prices in recent years was partly due to the government moving more toward market-based prices, although given the administered nature of these prices, such adjustments tended to occur at irregular intervals leading to sharp swings in the index.

Figure 6.2.Components of the WPI, 1983-2000

(12-month percentage change)

Sources: Data provided by Indian authorities; and staff calculations.

Inflation in the primary products category has ranged from a peak of over 21 percent in late 1991 to a low of negative 2 percent in 1985. While the government intervenes in the price determination of essential food items by fixing procurement or minimum support prices for producers and issue prices for consumers, at the margin, agricultural prices are determined by market forces.5 With around two-thirds of agricultural land in India still unirrigated, the monsoon remains the key factor for agricultural production and prices (imports of agricultural products are heavily restricted so that shortfalls in domestic production quickly translate into higher prices). Consequently, years of drought (1982/83, 1985/86, 1987/88, and 1990/91) have been associated with a sharp rise in inflation, while years following these droughts, or years with bumper harvests, have seen a decline in inflation.

Although the CPI has tended to give a higher estimate of inflation than the WPI or the manufacturing price index during this sample period,6 the choice between the three indices as the main policy measure of inflation has been largely irrelevant for most of the 1980s and 1990s as there has been little sustained difference in their broad trends. However, there have been several periods where this has not been the case: (i) from mid-1983 to mid-1984 when CPI inflation increased to nearly 14 percent, but the WPI rate remained broadly unchanged at around 7 percent; (ii) from early 1995 to late 1996 when WPI inflation fell sharply from 12 percent to 4 percent, but CPI inflation remained around 8–10 percent; and (iii) between 1997 and early 1999 when industrial price inflation remained broadly unchanged in the 4–5 percent range, while the rates of overall WPI and CPI inflation swung quite widely. Further, during 1998, CPI inflation accelerated much more rapidly than WPI inflation, and a significant gap opened between the two measures. Samanta and Mitra (1998) find that the difference in the commodity basket and weighting patterns in the two indices explain part of this divergence, with the much higher weighting on food items in the CPI being important. However, other factors also appear to be at work, although their study does not identify these.

Modeling Inflation in India

Recent studies of inflation in India have generally followed either a monetarist or “structuralist” approach. The monetarist approach is well known, and recent examples of its application to India include Kumar Das (1992) and Pradhan and Subramanian (1998). The structuralist approach sees inflation as the result of structural disequilibrium in the economy and examines inflation in a sector-specific manner. In general, with agricultural output given in the short run by the size of the crop (unless imports are freely allowed or significant stocks are held), agricultural prices are viewed as adjusting to clear excess demand. Within agricultural prices, the price of foodgrains (wheat and rice in particular) are considered to be the most important given their large share in family budgets (Buragohain, 1997). In the industrial sector, scope is seen for changes in output even with fixed capacity in the short run, and industrial prices are not so much driven by demand as by cost factors. Price developments in the agricultural sector have implications for industrial costs and prices: excess demand for agricultural products leads to higher agricultural prices and this, in turn, feeds into industrial prices because agricultural prices are a direct input into the production process and because wage pressures increase as the price of food items rises (as noted earlier, many wages and salaries are directly indexed to the CPI).

In applying the structuralist approach to India, Balakrishnan (1991, 1992) models industrial prices through an error-correction specification based on a markup pricing rule using annual data for 1952–80. Labor and raw material costs are both found to be significant determinants of inflation in the industrial sector. Foodgrain prices are modeled as a function of per capita output, per capita income of the nonagricultural sector, and government procurement of foodgrains through the public distribution system (PDS). Balakrishnan (1994) finds the structuralist model outperforms the monetarist model on the basis of F-tests and the Cox test when estimated on annual data over the period 1952–80. This view of the superiority of the structuralist model over the monetarist model in the case of India is supported by Bhattacharya and Lodh (1990).

Demand pull models of inflation have been less commonly applied to India. An exception is Chand (1996) who modeled the GDP deflator over the period 1972-91 using an output gap approach. His results indicated that excess demand is an important determinant of inflation, and there is some weak evidence of asymmetry in the effect of periods of excess demand and excess supply, with periods of excess demand exerting greater upward pressure on inflation than periods of excess supply exert in a downward direction.7 However, Coe and McDermott (1997), in a study of the output gap model in Asia, found that India was one of the few countries where the output gap model did not work—to derive a satisfactory model of the Indian inflation process they added a “money gap” term to the equation.

Indeed, while considerable empirical support is provided for the output gap model in studies of industrial countries, and by Coe and McDermott for a number of Asian countries, it is not clear how applicable such a specification is for India, which has a large (and protected) agricultural sector and is vulnerable to numerous supply shocks. The usual interpretation of the output gap model is that a positive demand shock causes actual output to rise above its potential level (i.e., a positive output gap develops), and this leads to an increase in inflationary pressures. If instead, output rises above its potential level because of a positive supply shock (a favorable monsoon that leads to a large increase in agricultural production), this is likely to result in lower inflation in the agricultural sector and a decline in general inflation, at least in the first round.8 The appropriateness of the output gap model is likely to depend on whether demand or supply shocks dominate in the economy.9 For India, this suggests two possible lines could be considered for estimating an output gap model: (i) the estimation could be confined to industrial prices where the output gap specification is likely to be relevant; or (ii) separate output gap terms reflecting the state of the industrial and agricultural sectors could be used with the expectation that a move in industrial output above potential will, other things being equal, result in an increase in inflationary pressures, but that a move in agricultural production above trend will result in a decline in inflation.

Most of the studies discussed above used annual data in the estimation. However, if the primary interest is to develop a model that could be used to forecast inflation as an input into the formulation of monetary policy, a major consideration is to use variables for which data are published on a regular and timely basis. This places a constraint on adopting some of the approaches discussed above; the lack of information on the cost side, particularly on wages and productivity, means that the cost-push models of industrial prices estimated by Balakrishnan could not he considered.

The main focus here is on modeling and forecasting the quarterly WPI and the manufacturing price subcomponent of the index. Two models are estimated: a monetarist model of the price level in which cointegration techniques are used to model the long-run behavior of prices, and then a dynamic equation for inflation is derived based on these results; and an output gap model.

For the monetarist model, a single cointegrating relationship between the WPI, broad money, output, and the interest rate is found, but to obtain a cointegrating vector for manufactured prices, foreign prices need to be included in the regression. The preferred dynamic specifications of the two equations are presented in Table 6.2. The error-correction (ECM) terms from the deviation of actual prices from their long-run equilibrium level were found to be significant and correctly signed with a lag of four quarters in all equations. However, the speed of adjustment to the equilibrium is slow, while the sum of the coefficients on lagged inflation in the WPI regression, at around 0.3, also suggests inertia in the inflation process. The first lags of money and output growth were found to be significant in both equations, and imposing equal and opposite coefficients on these terms was not rejected. This term can be interpreted as a “money gap,” with monetary growth in excess of output growth leading to an increase in inflationary pressures. However, its short-run elasticity is quite low in both cases (0.13 and 0.11, respectively), indicating that excess monetary growth feeds slowly into inflation.

Table 6.2.Inflation Equations
Dependent Variable DWPIM(1)



DWPI (-1)0.3060.308
DWPI (-2)-0.024-0.030
DWPI (-3)-0.3680.363
DWPI (-4)-0.3200.309
DM3 (-1)0.1610.174
DIP (-1)-0.122-0.098
MONG3 (-1)0.1260.107
DERATE (-3)0.0630.066
DUSPPI (-1)0.2300.229
DUSPPI (-3)0.3220.312
DWPIP (-3)0.2070.202
ECM (-4)-0.083-0.084-0.116-0.118
LM (1)7.498.214.544.88
Note: Sample period: 1982Q2–1998Q2, t-statistics in parentheses. All variables in logs. D denotes the first difference operator. WPI-wholesale price index; WPIM-manufacturing WPI; M3-broad money; IP-industrial production index; ERATE-dollar/rupee exchange rate; USPPI-U.S.
Note: Sample period: 1982Q2–1998Q2, t-statistics in parentheses. All variables in logs. D denotes the first difference operator. WPI-wholesale price index; WPIM-manufacturing WPI; M3-broad money; IP-industrial production index; ERATE-dollar/rupee exchange rate; USPPI-U.S.

For manufactured price inflation, imported inflation, the exchange rate, and primary product inflation were also found to be significant and correctly signed. The significance of imported inflation for manufactured inflation, but not overall inflation, reflects the greater import propensity of the manufacturing sector. Indeed, one important factor behind the relatively subdued inflation in the manufacturing sector in recent years is most likely the opening up of domestic industry to greater external competition. In fact, this equation specification will not capture the full impact of trade liberalization on industrial price inflation as it uses U.S. producer prices rather than actual Indian import prices. The significance of primary product inflation is consistent with the feed through from the agricultural sector to the industrial sector emphasized by the structuralist models. As would be expected given the difficulties in modeling the variability of primary prices, the fit of the equation for manufactured inflation is better than for overall WPI inflation.

The dynamic forecasts from the preferred equations over the period 1997Q2-1998Q2 indicate, however, that while the equations predict inflation developments moderately well, there is considerable uncertainty around the forecasts (Figure 6.3). While formal tests failed to detect a structural break following financial deregulation in the early 1990s, reestimation of the equations over the more recent time period suggested some interesting changes (although caution should be attached to these results given the short sample period). First, the fit of both equations improved. Second, the importance of the monetary terms and imported prices increased significantly. Third, for manufactured inflation, the importance of primary product inflation fell, possibly suggesting that as the domestic economy has been opened to foreign competition the links between domestic agriculture and industry have been weakened.

Figure 6.3.Out-of-Sample Forecast of Inflation

(Year-on-year percentage change)

Source: Staff calculations.

The output gap models failed to provide a satisfactory explanation of inflation developments. While the sum of the output gap terms was correctly signed and significant for overall WPI inflation, it was incorrectly signed and significant for industrial prices. Given the output gap is based on industrial output, rather than overall GDP, these results are counterintuitive.10 The output gap was also insignificant and/or incorrectly signed when included in the preferred monetary equation.

To further explore the properties of the output gap model, and to check the robustness of the other results derived from the quarterly data, the equations for DWPI were reestimated using annual data (1957/58-1997/98) with real GDP as the activity variable instead of industrial production (Table 6.3). The results were broadly similar to those obtained from the quarterly data, again showing a slow pace of adjustment to the long-run equilibrium.11

The earlier discussion suggested that, for the Indian economy, it is likely to be important to distinguish between the agricultural and industrial sectors when estimating an output gap model. Using annual data, it is possible to split the output gap into separate industrial and agricultural components. Both the agricultural and industrial output gaps were correctly signed when entered individually (i.e., agricultural output above trend results in lower inflation, while industrial output above trend leads to higher inflation), although only the agricultural gap was significant. This highlights the importance of sectoral aspects of the Indian inflation process and indicates the importance of accounting for supply shocks in explaining inflation in India.

Table 6.3.Estimates of Inflation Equations on Annual Data
DWPI (-1)0.2060.2200.0450.090
DM3 (-1)0.405
DGDP (-1)-0.571
MONG3 (-1)0.467
DOIL (-1)0.0790.0780.110.103
ECM (-1)-0.235-0.242
OGAP (-1)-0.502
OGAPA (-1)-0.413
OGAPI (-1)0.373
LM (1)
Note: Sample period: 1957/58-1997/98. t-statistics in parentheses. All variables in logs. D denotes the first difference operator. WPI-wholesale price index; M3-broad money; GDP-real GDP; OIL-oil price; OGAP-output gap, OGAPA-agricultural output gap; OGAPI-industrial output gap; MONG3=DM3-DGDP; ECM-error correction term.
Note: Sample period: 1957/58-1997/98. t-statistics in parentheses. All variables in logs. D denotes the first difference operator. WPI-wholesale price index; M3-broad money; GDP-real GDP; OIL-oil price; OGAP-output gap, OGAPA-agricultural output gap; OGAPI-industrial output gap; MONG3=DM3-DGDP; ECM-error correction term.

Results from bivariate Granger-causality tests and variance decompositions broadly support the regression results.12 The results indicate that the money gap has the highest degree of predictive content for inflation, although this comes mainly from industrial production rather than the monetary aggregates over the whole sample period. During the more recent period (1992Q2-1998Q2), however, the predictive content of the monetary aggregates, particularly M1, increases, especially at short time horizons. Foreign inflation also has some predictive content over short time horizons. For manufactured price inflation, the money gap again has significant predictive power, although in this case Ml on its own is significant. Foreign inflation also has strong predictive content, while agricultural price inflation has some predictive content. In contrast to the results for the aggregate WPI, however, the predictive content of the money gap terms weaken substantially, particularly over short time horizons, during the more recent period.

Conclusions and Policy Implications

In its recent policy statements, the RBI has indicated that it is moving away from a broad money target toward a “multiple indicators” approach to the conduct of monetary policy. This paper has assessed which of the potential indicators available give the most reliable and timely indications of future inflationary developments. This has been carried out both by developing a model of inflation and by estimating a series of bivariate VARs. The results indicate a number of important issues in modeling and forecasting inflation in India.

  • Developing an adequate model of inflation is complicated by the swings in the prices of primary articles, which, year-to-year, are largely driven by climatic conditions and by changes in administered prices. A model of manufacturing prices fits better than one for the overall WPI.
  • Developments in monetary aggregates appear to contain the best information about future inflation, particularly when judged against developments in activity. The information content of the monetary aggregates appears to have increased since financial deregulation. An output gap specification, unlike in many other countries, does not perform well on Indian data.
  • With regard to manufacturing prices, import and primary product prices and the exchange rate also provide useful information about future inflation developments.

The results also have a number of policy implications and raise a number of issues for the monetary authorities:

  • While the RBI is moving away from announcing an explicit monetary target, the monetary aggregates continue to provide important information about future inflationary developments and will need to continue to be closely monitored.
  • The manufacturing subcomponent of the WPI has been used in this paper as a measure of core inflation. Recent work at the Reserve Bank, however, has begun to develop a core inflation index that will give a clearer picture of underlying inflation developments than the currently published price indices (Samanta, 1999; Mohanty, Rath, and Ramaiah, 2000). Of course, in its current conduct of policy, the Reserve Bank at times implicitly focuses on a core measure of inflation by discounting price movements that are expected to be reversed in the short run (for example, the sharp rise in primary product prices in 1998). But the publication of an explicit core inflation index would improve the transparency of policy. However, this does not mean that developments in the headline price index can be totally discounted. The results presented here indicate that there are important links between developments in the primary sector and other areas of the economy, and the RBI needs to take this into account in its policy formation.
  • The publication of a wider measure of liquidity that includes public deposits held with financial institutions and NBFCs, as set out in the report of the Working Group on “Money Supply: Analytics and Methodology of Compilation,” would provide important additional information given the growing role of nonbank institutions.
  • The change in the RBI’s policy approach entails some risks. The broad money target has formed the backbone of monetary policy for a number of years, is well understood by the public, and provides an anchor for inflation expectations (Rangarajan, 1998). During the transition period, the RBI will need to be vigilant and quickly respond to any rise in inflation pressures to ensure there is no suggestion of weakening its commitment to maintain reasonable price stability.
  • An important question not addressed in this paper is whether the swings in primary product prices are accentuated by import restrictions and by poor distribution. If so, liberalizing the import of agricultural products and improving distribution would help macroeconomic stability.

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This chapter is based on Callen, T., and D. Chang, 1990, “Modeling and Forecasting Inflation in India,” IMF Working Paper, WP/99/119.


Financial deregulation began in earnest in the early 1990s, although important changes in the structure of the financial sector had already started in the 1980s with the growth of the Nonbank Finance Companies (NBFCs). The rapid growth of deposits into these institutions may already have begun to affect the interpretation of the monetary aggregates pre-1990.


Since prices of controlled items generally adjust slowly, and nor always completely, to changes in underlying market prices, price indices that include them may not fully reflect underlying inflationary trends.


The WPI series was revised in April 2000. The series was rebased to 1993/94 and the weights updated.


Major food grains such as rice and wheat and commodities such as sugar and edible oil are partly supplied through the public distribution system (PDS), which runs in parallel with the market system. Supplies for the PDS are purchased from producers at pre-announced procurement prices and are sold to consumers at a subsidized issue price (any individual with a registered residential address is eligible for a ration card that entitles them to buy a fixed quota of goods at the issue price). The Food Corporation of India (FCI) maintains a buffer stock of items sold through the PDS.


However, this does not appear to be true over longer time periods (see Reserve Bank of India, Annual Report, 1996/97).


Several studies on industrial countries have argued that a nonlinear Phillips curve provides a better representation of the data than the linear schedule (see Debelle and Laxton, 1997).


Of course, second round effects from the rise in incomes in the agricultural sector may lead to higher demand for manufactured products and a rise in inflation.


On similar lines, De Masi (1997) argues that “the concept of potential output is less meaningful for Countries in which a large proportion of output is accounted for by primary commodities whose production is supply determined, or which are experiencing large inflows or outflows of labor.”


Of course, this does not necessarily imply that the output gap model does nor work for India. Rather, it may he that the Hodrick-Prescott filter does not provide a good characterization of potential output in India.


Jadhev (1994) finds structural breaks in a quarterly money demand function around 1975 and in 1982/83. Such breaks were not tested for in this specification.


For further details, see Callen and Chang (1999).

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