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Inflationgrowth relationship inselected Asian developing countries:evidence from panel dataAhmed Taneem Muzaffara & P.N. (Raja) Junankarbcda School of Business, University of Western Sydney, Sydney,Australiab Industrial Relations Research Centre, University of New SouthWales, Sydney, Australiac University of Western Sydney, Sydney, Australiad IZA Institute for the Study of Labor, Bonn, GermanyPublished online: 30 May 2014.

To cite this article: Ahmed Taneem Muzaffar & P.N. (Raja) Junankar (2014) Inflationgrowthrelationship in selected Asian developing countries: evidence from panel data, Journal of the AsiaPacific Economy, 19:4, 604-628, DOI: 10.1080/13547860.2014.920594

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Inflationgrowth relationship in selected Asian developing countries:evidence from panel data

Ahmed Taneem Muzaffara* and P.N. (Raja) Junankarb,c,d

aSchool of Business, University of Western Sydney, Sydney, Australia; bIndustrial RelationsResearch Centre, University of New South Wales, Sydney, Australia; cUniversity of Western Sydney,

Sydney, Australia; dIZA Institute for the Study of Labor, Bonn, Germany

We question the empirical foundation of keeping inflation at 5% or below indeveloping economies. Using System Generalized Method of Moments we investigatethe issue in the context of 14 Asian developing countries for the period 19612010.We find no robust empirical justification for targeting inflation at such a low level.The inflation threshold for these countries is found around 13% and it may rangebetween 7% and 14% depending on the level of development. The findings suggestthat developing countries can gain from moderate levels of inflation and should not bealarmed when inflation crosses the 5% benchmark.

Keywords: Threshold level of inflation; growth; developing countries in Asia; panelestimation

JEL Classifications: E31, O40

1. Introduction

Macroeconomic policies in developing countries, in recent decades, put strong emphasis

in attaining price stability by keeping inflation low. This follows episodes of high and

accelerating inflation accompanied by growth stagnation during the 1970s and the 1980s,

especially in Latin American countries. There seems to be a strongly held belief by the

mainstream economics profession that targeting low inflation is good for achieving mac-

roeconomic stability and therefore beneficial for long term economic growth. The experi-

ences of stagflation of the 1970s and the Great Moderation during 19932007 have ledto a growing consensus on this view.1 Founded on this belief, policies targeting low infla-

tion have been spearheaded by international financial institutions such as the International

Monetary Fund (IMF). The IMF strongly advises the developing countries to pursue mac-

roeconomic policies targeted at keeping inflation rates within 5%. In response to why

inflation should be kept within 5% the IMF notes:

Inflation is the most pernicious tax on low-income households that lack the means to protecttheir salaries and scant savings against inflation . . . a large body of empirical evidence hasestablished that when (annual) inflation passes the 5 percent mark investment and economicactivity also suffer . . . [therefore] the Fund supports policies aimed at achieving or maintain-ing low inflation.2

As a result, central to the economic management of some of these countries, com-

pelled to embrace IMF suggested economic reforms, has been to restrain inflation at 5%.

Typically, these policies include tight monetary policy and reining in fiscal deficits

*Corresponding author. Email: [email protected]

2014 Taylor & Francis

Journal of the Asia Pacific Economy, 2014

Vol. 19, No. 4, 604628, http://dx.doi.org/10.1080/13547860.2014.920594

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mailto:[email protected]://dx.doi.org/10.1080/13547860.2014.920594

because of their supposed link to inflationary pressure. This policy of fighting inflation

first, keeping it as low as 5%, is suggested even at the expense of immediate adverse

impacts on growth on the ground that this would foster sustained growth in the future supposedly short term pain for long-term gain.

This paper questions the empirical validity of this conventional wisdom that inflation

beyond 5% is detrimental to economic growth in the case of developing countries. We

provide an empirical investigation on a sample of 14 Asian developing countries over the

period 19612010 and show that inflation above 5% is not necessarily harmful for eco-nomic growth. Moreover, our findings reveal that the inflation threshold, the level beyond

which it starts having a negative effect on growth, is not fixed at a particular level. It

varies according to the level of economic development with poorer countries tending to

have a higher inflation threshold. To the best of our knowledge, empirical analysis on this

issue with particular emphasis on developing countries of Asia is absent. Our contribu-

tion, as such, is twofold. First, the study adds to the literature pertaining to the

growthinflation nexus by providing econometric evidence on panel data from Asiandeveloping countries. Second, our findings have profound implications for macroeco-

nomic policies in developing countries, in terms of policy space and dealing with

the IMF. There is evidence that despite the policy application suggested by the IMF

based on this conventional wisdom, economic performance failed to improve and

poverty remained high in many countries, even though inflation fell (see e.g. Wilkinson

2000, 643).

The rest of paper is organized as follows. Section 2 discusses the issue that leads us to

this investigation the problem of keeping inflation at a low single-digit level and cross-country evidence on inflation threshold. Section 3 describes the data and selection of vari-

ables as well as summary statistics. Sections 4 and 5 provide the empirical model and

findings from the analyses on inflation threshold, respectively. Section 6 examines varia-

tions in inflation threshold according to levels of development. Section 7 provides con-

cluding remarks.

2. The low-inflation trap and cross-country evidence on inflation threshold

In recent years, a number of leading economists have put forward strong theoretical argu-

ments and empirical evidence against a very low inflationary environment. Nobel Laure-

ate in Economics, Paul Krugman in his opinion page of The New York Times in 2011

calls this the low-inflation trap.3 When inflation falls it creates a deflationary expecta-

tion and thus even if nominal interest rate is kept at a very low level,4 real interest rate

continues to rise. This leads to a higher real cost of borrowing and eventually depresses

the economy.5 A couple of decades ago, another Nobel Laureate economist James Tobin

also pointed out the danger of paying too much attention to inflation control (see Tobin

1987). The Economist notes that [e]conomists of highly divergent stripes. . .[such as]Kenneth Rogoff, Greg Mankiw, Scott Summer, Paul Krugman, Brad DeLong all haveindicated that higher inflation would be a boon to the economy.6 Criticisms also came

within the IMF. In 2011, Dominique Strauss-Kahn, the then Managing Director of the

IMF, argued the need for a wholesale re-examination of macroeconomic policy

principles and questioned the pre-crisis7 advice that keeping inflation low and stable

was the best way to secure optimal economic performance.8 A number of studies (see

e.g. Anwar and Islam 2011; Chowdhury 2006; Epstein and Yeldan 2008) in recent

years have argued against low-inflation targeting policies, both empirically and analyti-

cally, in developing countries. Chowdhury (2006, 409) argued that pursuing a very low

Journal of the Asia Pacific Economy 605

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single-digit inflation rate in developing countries may create a stabilization trap, a situa-

tion characterized by low inflation and insufficient growth for poverty reduction.

On the empirical side, a classic example of a developing economy trapped in a stabili-

zation trap could be Argentina9 (see Chowdhury 2006, 426427). In the 1980s, hyperin-flation in the country (average annual inflation rate of around 391%) had led to a crisis

and the IMF stabilization programme brought down inflation to a single-digit level, 1.5%

annually. However, as Chowdhury (2006, 427) notes that the continuation of the tight

macroeconomic policies created a deflationary-spiral and the economy plunged into

recession, causing unemployment rate to rise from 6.5% in 1991 to 17.5% in 1996. This

had dire consequences for the poverty rate (head-count ratio) as well about 13 percent-age point rise in a decade, from 21.8% in 1993 to 34.3% in 2002. Referring to the experi-

ence of Argentina, the author alerts that while inflation needs to be kept under control,

too much emphasis on a very low inflation rate may lead to an inadvertent consequence

that exacerbates poverty. Studies such as Epstein and Yeldan (2008) and Anwar and Islam

(2011) also observe the sacrifice made in terms of growth and employment in developing

countries in pursuit of a low inflationary environment. The latter study argues that low

inflation is not translated into benefits of reduced cost of borrowing, because such costs

are likely to be determined by structural factors of the economy. Contrary to the main-

stream view, nominal borrowing interest rates lagged behind inflation resulting in higher

real interest rates.

Despite the findings that targeting inflation at a very low level may be detrimental to

economic growth, the IMF policy guideline, explicitly or implicitly, tends to suggest a

target of 5% or below irrespective of country-specific circumstances. For instance, an

inflation target of 5% or less was suggested to 22 out of 32 programme countries between

1995 and early 2007 (Goldsbrough, Adovor, and Elberger 2007, 5). According to the

IMFs Independent Evaluation Office (IEO 2007), an inflation target of less than 5% was

suggested to 29 Sub-Saharan African countries during the 2000s.

To examine IMFs position on inflation we now look at the empirical findings on

inflation threshold from some selected cross-country studies. A list of such studies and

their findings in relation to developing countries is provided in Table 1 followed by

discussion.

Empirical literature, both recent and relatively old, provides strong evidence of a non-

linear relationship between growth and inflation. The question of interest, therefore, is at

what level the inflation threshold occurs for the developing countries. Bruno and Easterly

(1998) find robust evidence that growth falls sharply at discrete high-inflation (which

they propose to be 40% per annum) crises, then recovers rapidly and strongly after infla-

tion falls. They argue that the correlation between growth and inflation only exists in the

case of extreme inflation observations. However, Sarel (1996), a study carried out at the

IMF, finds evidence of a structural break of inflation at a much lower rate of 8%. He uses

annual data for 87 countries over 19701990 but does not differentiate between devel-oped and developing countries. Amato and Gerlach (2002, 788), in this connection, state

that there is little hard evidence to suggest that the level of inflation targets [in develop-

ing countries]. . .should be much different than in more advanced economies. However,not differentiating between developed and developing countries leads to potential bias in

the estimation due to combining various countries at different levels of development.

Sepehri and Moshiri (2004) support that the inverted U-shaped relationship between infla-

tion and growth vary across countries of various stages of development. As a result of

such degree of heterogeneity across countries in terms of development, the authors sug-

gest that it is inappropriate to set a single numerical policy target uniformly for all

606 A.T. Muzaffar and P.N. (Raja) Junankar

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Table1.

Selectedcross-countrystudiesonthethreshold

effectofinflationongrowth.

Study

Period

Number

ofcountries

Method

Findingsoninflationthreshold

Yilmazkuday

(2013)

19652

004

84countries

Rolling-w

indowtwo-stage-least-

squares

method.

Inflationthreshold

isbetween

8%

and15%.Apositiveeffect

oftradeandhuman

capitalon

growth

issignificantwhen

inflationisbelow8%

and

15%,respectively.

Lopez-V

illavicencio

andMignon(2011)

19612

007

44countries

Panelsm

ooth

transitionmodel

(PSTR)anddynam

icgeneralized

methodof

moments(G

MM)

Threshold

inflationdiffers

strongly

betweendeveloped

anddevelopingcountries.For

developingcountriesitis

17.5%,belowwhichthe

relationship

isnon-significant.

Bick(2010)

19602

004

40developingcountries

Generalized

panelthreshold

model.

12%

and19%

withandwithout

regim

eintercept,respectively.

Espinoza

etal.(2011)

19602

007

165countries

Logisticsm

ooth

transition

regressionmodel.

Between7%

and13%

for

developingcountries.

Pollin

andZhu(2006)

19612

000

80middle-incomeand

low-incomecountries

PooledOLS,Fixed

effects,

Random

effects,andBetween

effectspanelestimation

models.

Between15%

and18%.

Drukker

etal.(2005)

19502

000

138countries

Non-dynam

ic,fixed

effectspanel

datamodels.

Around19%

inthefullsample.

Sepehriand

Moshiri(2004)

19601

996

92countrieswith26lower-

middle-incomeand28

low-incomecountries.

Splineregressiontechnique.

15%

and11%

forlower-m

iddle-

incomeandlow-income

countries,respectively.

Burdekin

etal.(2004)

19651

992and

19671

992for

developed

and

developing

countries,

respectively

21industrialand51

developingcountries.

Generalized

least-squares

(GLS)

estimatorwithfixed

effects.

Higher

inflationthreshold

for

more

advancedcountries;8%

and3%

forindustrialand

developingcountries,

respectively.

(continued

)


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Table1.

(Continued

)

Study

Period

Number

ofcountries

Method

Findingsoninflationthreshold

Khan

andSenhadji(2001)

19601

998

140countries

Conditionalleast-squares

1%3

%and11%1

2%

for

industrialanddeveloping

countries,respectively.The

positiveeffectofinflationon

growth

ispresentupto

18%

fordevelopingcountries.

Ghosh

andPhillips(1998)

19601

996

145countries

Binaryrecursivetreesmethod.

2%3

%.

BrunoandEasterly(1998)

19611

992

26countries

Descriptiveanalysis

Nocross-sectionalcorrelation

betweenlong-runaverages

of

growth

andinflationbelow

inflationrateof40%.

Sarel(1996)

19701

990

87countries

Splineregression

8%.

Source:Adaptedfrom

Muzaffar

(2013,474

9).


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developing countries (202). Their basic model is based on Solows augmented production

function which is estimated using Sarels (1996) spline regression technique. In general,

for developing countries the study finds a threshold at a double-digit level and higher

than that of advanced economies. Another study by Ghosh and Phillips (1998, 709), also

carried out at the IMF such as Sarel (1996), believes that the threshold level of inflation is

expected to differ, at least somewhat, across countries.

An influential study, carried out at the IMF, which finds substantially different

threshold levels for developed and developing countries is Khan and Senhadji (2001).

They find a double digit inflation figure at around 11%12% for developing countriesas opposed to 1%3% for developed ones. More importantly the sensitivity analysis,performed to see the effect of inflation on growth when threshold varies from 1% to

50%, shows a positive effect of inflation on growth up to 18% for developing countries

(16). In a more recent study (outside the IMF), Lopez-Villavicencio and Mignon (2011)

also find a wide difference between the threshold figures for developed and developing

countries, 2.7% and 17.5%, respectively. The study shows that for developing countries

the relationship between inflation and growth is non-significant when inflation is below

17.5%. The results are consistent using both panel smooth transition (PSTR) model and

Generalized Method of Moments (GMMs). Findings of Burdekin et al. (2004) are at

odds with the results of these major studies. According to them the inflation threshold

for developing countries is 3%, significantly lower than 8% found for the advanced

economies. Perhaps a rationale behind this result by Burdekin et al. (2004) can be found

in the explanation by a much earlier study by Dorrance (1964). Prices are more inflexi-

ble downwards as a result of more organized trade unions in rich countries compared to

poor countries. Developing economies are also mostly dependent on a few primary prod-

ucts for exports and agricultural products. Therefore, Dorrance (1964) argues that the

appropriate rate of inflation needed to achieve relative price flexibility in advanced econ-

omies is higher than that of developing countries. One serious limitation of the study by

Burdekin et al. (2004) is that it uses previous period real GDP per capita as a regressor.

The level of real GDP per capita is likely to be non-stationary and thus may lead to a

spurious relationship.

Broadly, results from other panel studies on inflation threshold provide estimates

between 7% and 19%. The study by Espinoza, Leon, and Prasad (2011) at the IMF revis-

its the issue within a nonlinear framework and argues that the threshold level lies in the

range of 7% to 13% for developing countries. Pollin and Zhu (2006) estimate the inflation

threshold for 80 countries over the period 19612000. The authors, in addition to entiresample period, consider four separate decades and consistently find that higher inflation

is associated with moderate gains in growth up to around 15%18% inflation. Based ontheir results, they question the justification of inflation targeting policies to keep inflation

at 3%5% levels. There are, however, several limitations of this study. First, panel esti-mation models pooled OLS, fixed effects, random effects, and between effects usedare not appropriate in a dynamic setting and fail to take care of the issue of endogeneity

problem. Such problems can be taken care of by using methods such as GMMs estima-

tions which some of the other studies relied on. The study also does not include variables

relating to money as a control variable in the model.10 Money is an important covariate in

explaining the growthinflation relationship. Moreover, in the case of regression by dec-ades, estimates of the threshold are weak on a number of cases because the linear and

squared terms of inflation are not significant at 5%.

Drukker, Gomis-Porqueras, and Hernandez-erme (2005), using an unbalanced panel

dataset covering 138 countries spanning over 19502000, find a well-defined threshold


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level at 19.16% for the full and non-industrialized samples.11 One limitation of this study

is that the inference made is based on a non-dynamic fixed effects model instead of more

appropriate dynamic models such as GMM. Bick (2010) also finds a similar result, but

argues that the inclusion of regime intercept, used in his study, lowers the threshold from

19% to 12%. Inflation rates less than 12% are associated with a significant positive effect

on growth. Bicks (2010) study consists of a balanced panel dataset12 of 40 developing

countries covering the period 19602004.Taken together, the review of the existing literature on cross-country panel studies,

carried out both within and outside the IMF, provides strong evidence that inflation

threshold in developing countries is well above 5% level. Broadly, the threshold varies

between 7% and 19% with majority of the studies supporting a double-digit level for

developing countries. Moreover, most studies including the influential ones carried out

at the IMF suggest that the threshold for developing countries is significantly higher

than that of advanced economies. Higher inflation tolerance of developing countries

might be due to a number of reasons such as the BalassaSamuelson effect, indexationsystems, exchange rate policies, and experience of high bouts of inflation in these coun-

tries (see Lopez-Villavicencio and Mignon 2011, 462). In short, inflation threshold at

5% may be applicable for developed countries, but certainly not for developing coun-

tries. The implication of this finding is that developing countries can gain from moderate

levels of inflation and should not be alarmed when inflation crosses the 5% benchmark

set by the IMF.

3. Data, variables, and summary statistics

We now turn to examine the empirical foundation of IMFs policy prescription for

developing countries to keep inflation within 5% level. We gather a panel dataset of 14

Asian developing countries for the period 19612010 from standard and most widelyused sources the World Banks World Development Indicators (WDI); the IMFsInternational Financial Statistics (IFS) and World Economic Outlook (WEO). The selec-

tion of country is based on the availability of data on real Gross Domestic Product

(GDP), Consumer Price Index (CPI), and other control variables. The countries are

Bangladesh, Cambodia, India, Indonesia, Lao PDR, Malaysia, Pakistan, The Philippines,

Thailand, Vietnam, Kazakhstan, Kyrgyz Republic, Papua New Guinea, and Tajikistan.

The dataset is unbalanced because information on variables for the 14 countries is not

available for all years.

Our sample includes a longer time-frame (19612010) compared to the previousstudies and, therefore, contains more information about the growth effects of low infla-

tion. Moreover, we use annual data in our panel estimations in order to maximize sample

size and to measure the parameters of interest more precisely. This empirical approach

differs from most frequent practice in the literature smoothing data using 510 yearaverages. There are two main arguments in favour of using annual observations. First, the

relationship between inflation and growth is more evident in the case of higher frequency

of the data as noted by Bruno and Easterly (1998). They state, [the] results get stronger

as one goes from the cross-section to ten year averages to five year averages to annual

data (4). Khan and Senhadji (2001, 16) also note that high-inflation effect [on

growth] . . . is more powerful for yearly data. The second reason is based on the argumentset forth in Baltagi, Demetriades, and Law (2009). The study states that averaging out

annual data over, for instance, five year periods results in reduction in sample size thereby

raising the possibility of making most of the variables statistically insignificant. The


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authors explain that smoothing out of time series data removes useful variation from the

data, which could help to identify the parameters of interest with more precision (286).

Apart from these two reasons we also believe that taking 5 6 10-year averages to smoothout business cycle fluctuations is not appropriate because such data-smoothing technique

assumes a uniform business cycle pattern for all countries in the panel. It is unlikely that

the business cycle pattern is homogenous for all countries in the sample.

Our empirical approach, in essence, involves regressing real GDP growth on CPI

inflation, conditioned upon other variables suggested by the related literature. Growth

and inflation are derived by taking the first difference of the natural logarithm of level

variables, real GDP (constant $2000) and CPI, respectively. To preview, Figure 1

attempts to capture the relationship between real GDP growth and inflation following

Khan and Senhadji (2001, Figure 1, 4) who smoothed out data by reducing the full sam-

ple to five observations. The arithmetic mean of real GDP growth is taken for five

equal subsamples corresponding to increasing levels of inflation (see Khan and Senhadji

2001, 3). The observation from our sample differs from what was noticed by Khan and

Senhadji (2001). In both cases the relationship between inflation and growth is positive

for low levels of inflation. However, unlike Khan and Senhadji (2001) we do not see, in

our sample, growth to decline drastically when inflation moves to a moderately high

level. This also does not corroborate the findings of Ghosh and Phillips (1998) who

warn of a danger of a steep drop in growth beyond the threshold level of inflation. The

findings of Khan and Senhadji (2001) also show that the negative effect of inflation on

growth weakens at very high inflation rates, supporting Fischers (1993) findings. Our

findings, however, are different and more in line with what Bruno and Easterly (1998)

suggested. Figure 1 shows that growth declines gradually when inflation starts to move

towards a higher level. It is at a very high level of inflation that growth falls sharply.

The differences between the findings are perhaps obvious. Khan and Senhadji (2001) in

their analysis, displayed in Figure 1 (4), did not differentiate between industrial and

developing countries. Our analysis consists of a much smaller group of developing coun-

tries. This reiterates the importance of making the distinction between advanced and

developing countries while performing the analysis as pointed out by studies such as

Sepehri and Moshiri (2004).

Apart from inflation, a number of control variables which might influence the

growthinflation relationship are considered as regressors. They are as follows.

Figure 1. Relationship between real GDP growth and inflation.Sources: Authors calculations using data from World Bank, WDI and IMF, IFS, and WEO.


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Lagged growth. Growth lagged one period is taken on the right hand side to reflect the

dynamic nature of the relationship and to capture the possibility of partial adjustment

towards the steady state. Greene (2003, 307) explains that incorporating dynamics in this

way allows us to consider the entire history of the right hand side variables, so that any

measured influence is conditioned on this history. The coefficient of this variable is

expected to be positive and lie between 0 and 1.

Real household consumption per capita (HCONPC). To measure the effect of aggre-

gate demand, real household consumption per capita measured in constant $2000 is

included as a control variable. The inclusion of this variable is justified since consumption

is assumed to be the largest component of aggregate demand. Besides, higher household

consumption per capita is assumed to be growth enhancing and an indication of poverty

reduction. The expected sign of its coefficient is positive.

Financial deepening (FD). Broad moneyGDP ratio is taken as a control variable to mea-sure the effect of financial deepening. The expected sign of FD could be either positive or

negative. Earlier studies such as McKinnon (1973), Shaw (1973), and Kapur (1976) argue

that financial development has a positive impact on growth. If the ratio of broad money to

GDP is growth enhancing, money supply is expected to have a greater impact on growth

and less impact on inflation. This is because a developed financial system absorbs the

money supply and diverts it to the real sector, thus generating growth. Therefore, the sign

is positive. On the other hand, Robinson (1952) and Lucas (1988) doubt the role played by

financial development in promoting growth. Bangake and Eggoh (2011, 178) points out the

empirical example of the Asian economies which grew fast in the 1970s and 1980s without

a developed financial system. The sign of FD could be negative if an increase in M26 GDPimplies pressure on inflation and, therefore, negative impact on growth. Moreover, mone-

tary expansion may lead to bubbles (which might result into an inflationary situation) in

the financial system if monetary transmission mechanism is not effective enough to channel

the funds from the financial system to the real sector.

Government consumption expenditure (GOVCON). Government consumption (% of

GDP) is taken to determine the effect of fiscal policy. The sign of GOVCON could be

either positive or negative. A negative sign might indicate that higher government spend-

ing is inflationary (fiscal theory of price level) that is bad for growth. In addition,

increased size of the government may crowd out private sector, thus adversely affecting

growth. On the other hand, when fiscal deficits correct a deficiency in private demand the

argument of crowding out is not valid (Arestis and Sawyer 2003). Hemming, Kell, and

Mahfouz (2002) identify a number of reasons why the effect of fiscal policy tends to be

positive and quite large. These conditions include a demand constrained economy with

excess capacity; a closed economy or an open economy with a fixed exchange rate; and

households with limited horizons or liquidity constraints. Therefore, we can also expect a

positive sign of GOVCON when government spending is beneficial in raising the produc-

tive capacity of the economy and thus growth inducing.

Trade openness (OPEN). Summation of export and import as a percent of GDP is

taken as a control variable to measure the trade openness. A more open economy is sub-

ject to shocks which could be either positive or negative. Therefore, the sign of this coeffi-

cient in the growth regression is uncertain.

Agricultures share of GDP (AGR). Since agriculture is still the mainstay of the econ-

omy in most of the developing countries, the share of agriculture output (% of GDP) is

considered as a control variable. We expect that AGR would help us capture the structural

changes in the economy and any impact on growthinflation nexus as a result of the


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change. A greater level of development would mean that economic transformation is tak-

ing place and economies are less dependent on the primary sector. A negative sign of

AGR, therefore, is expected which would indicate that such transformation is taking place

and is beneficial for economic growth. However, we do not rule out the possibility of a

positive sign of the coefficient of AGR either. As Chenery (1960) observes that this over-

all relationship may not necessarily apply to every individual country. He argues that

within limits, the changing composition of domestic demand for food can be offset

through foreign trade. A country having a continuing comparative advantage in primary

production, he points out, may, therefore, reach a high level of income without an

increase in the share of industry in total output.

Oil and commodity price shocks. Developing countries are prone to supply shocks

caused by oil and commodity prices in the international markets. Fluctuations in the oil

and commodity prices may act as an incentive or disincentive for domestic production

and therefore affect growth. We create two dummy variables to assess such impacts. The

oil dummy, DUMOIL, takes the value 1 for the years 1971, 1974, 1979, 1999, 2000,

2004, 2005, and 2008 and the value 0 otherwise. In these years, growth rate of oil price

index is 20% above its historical mean that is 7.44%. The commodity price dummy,

DUMCOMPR, is constructed in a similar fashion and takes the value 1 for years 1973,

1974, 2004, and 2006 (and 0 otherwise) when growth rate of commodity price index is

above 20% of its historical mean at 3.85%. Here we are only interested to see the influ-

ence of negative supply shocks as a result of rising oil and commodity prices and there-

fore the expected signs of these dummies are negative.

We present time series graphs of the variables in Figure 2. Mean growth rate in the

sample countries shows major declines in the early 1970s and 1990s. There was one

Figure 2. Mean (unweighted) of cross-country data for each year, 19612010.


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major hyperinflationary period in the 1960s and one inflationary spike during the Asian

Financial Crisis 19971998. These episodes are mostly driven by the experiences ofIndonesia around that time. Apart from growth and inflation experiences, one revealing

fact is that real household consumption per capita shows a significant upward trend since

the early 2000s. Financial deepening shows more or less an upward trend with some set-

backs in the 1990s. On the other hand, government consumptionGDP ratio shows amore restrained trend. It peaks in the late 2000s perhaps due to the expansionary policy to

help recover the economies from the Great Recession of 20092010. Trade opennessshows a significant upward trend in the 1990s perhaps due to globalization policies domi-

nant around that time. The mean of agricultureGDP ratio shows a continuous declineover this period of time.

Inspection of the time series graphs of the control variables reveals that taking the con-

trol variables in their first differenced form is appropriate to avoid spurious regression.

Although theoretically ratio variables such as M2-GDP and government consumptionGDPshould show mean reversion process and therefore should be stationary, the visual inspec-

tion of the time series casts doubt on this. Therefore, following Kwon, Mcfarlane, and Rob-

inson (2009), in our regression analyses we use the variables in their first differences.

We provide summary statistics of the growth rates of the above variables for the

period 19612010 in Table 2. In general, long-run average (unweighted) economicgrowth in the sample countries is below 5%. The average inflation is around 17% and it is

significantly more volatile (as evident from the standard deviation) than economic growth

over this long period. Real household consumption per capita has registered less than 2%

average growth over this period. Mean growth rates in financial deepening and govern-

ment consumption (both% of GDP) are less than 5% and 1%, respectively. The latter

shows that the size of the government proportional to GDP has not grown much over the

long period. Average growth rate in trade openness is around 2%, reflecting the fact that

the countries in the sample are becoming more open economies. It is also important to

note that average growth of share of agriculture to GDP is around negative 2%, revealing

a declining trend in the primary sector of the economies. This is an indication that struc-

tural transition is taking place in these traditional agro-based economies. In the interna-

tional markets, oil price on average has increased more and remained more volatile

compared to commodity prices (Table 2).

Table 2. Summary statistics of growth rates of selected variables, 19612010.Variable Observations Mean Standard deviation Minimum Maximum

Economic growth 538 4.63 5.35 33.64 19.57CPI inflation 465 17.64 103.42 7.63 1877.37Real household

consumption per capita470 1.81 7.88 31.12 53.96

Broad money-GDP ratio 435 4.23 10.35 87.44 64.17Government

consumption-GDP ratio513 0.55 13.47 65.10 152.59

Trade openness 529 2.05 16.90 107.95 114.99Agriculture-GDP ratio 507 2.14 7.39 42.23 47.22Oil price index 50 7.43 27.50 64.86 111.20Commodity price index 50 3.84 13.57 23.22 42.46


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Table3.

Correlationmatrixofgrowth

variables.

Growth

CPI

inflation

Realhousehold

consumption

per

capita

Broad

money-G

DP

Government

consumption-G

DP

Trade

openness

Agriculture-G

DP

Oil

price

Commodity

price

Growth

1

CPIinflation

0.277

1

Realhousehold

consumptionper

capita

0.493

0.237

1

Broad

money-G

DP

0.02

0.122

0.093

1

Government

consumption-G

DP

0.016

0.048

0.086

0.266

1

Tradeopenness

0.011

0.046

0.028

0.032

0.147

1

Agriculture-G

DP

0.139

0.019

0.069

0.004

0.05

0.102

1

Oilprice

0.122

0.115

0.131

0.119

0.079

0.3

0.126

1

Commodityprice

0.232

0.071

0.187

0.127

0.134

0.259

0.066

0.449

1

Note:Bold

facedfiguresreferto

correlationsignificantat5%

levelofsignificance.


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The correlation matrix of the growth variables, presented in Table 3, also provides

some interesting facts about the possible long run relationship amongst the variables.

These associations, however, do not provide an understanding about the direction of cau-

sality. There exists a moderate negative association between inflation and GDP growth

and it is significant at 5% level.

In summary, the selection of variables to examine the growthinflation relationship isdone with a view to incorporating economic factors relating to structural, demand and

supply side shocks, and macro policies affecting the issue. From an analytical point of

view the aim is to create a parsimonious model in explaining the relationship between

inflation and growth.

4. Empirical model

To investigate the potential nonlinearity of the relationship between inflation and growth,

we develop the following panel model:

yit mi bt b1pit b2p2it Xn

j3bj Zit eit 1

(for country, i D 1, . . . , N and time, t D 1, . . . ,T), where yit is the real GDP growth, mi isan unobservable time invariant country-specific effects to capture heterogeneity in the

growthinflation relationship across countries, bt is a time-specific effect incorporatingdummies for different time periods, pit is the CPI inflation rate, Zit is a vector of control

variables, and eit is the classical error term, assumed to be independent and identicallydistributed with mean 0 and variance s2e , which varies with countries and time in the

regression. The country-specific effects, mi, and the error term, eit, have the standard error

component structure:

Emi Eeit Emieit 0 2

and the transient errors are not serially correlated:

Eeit eis 0 for s 6 t: 3

The above general version of the model can be rewritten as follows:

Growthit mi bt b1 D lnCPIit b2D ln CPIit2 b3 Growthit1b4 D lnHCONPCit b5 D lnFDit b6 D lnGOVCONit b7 D lnOPENitb8 D ln AGRit b9 DUMOILt b10 DUMCOMPRt eit; 4

where the notation is explained above.

The inclusion of time-fixed effects requires some explanations. We use time-fixed

effects in our model to make the estimations more robust. As Kumar and Woo (2010)

explain that this is to account for the possibility of any structural changes over the sam-

ple period, including changes in trends in global growth or global risk factors. Besides,

in the autocorrelation tests and robust standard errors reported later, we assume absence


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of autocorrelation across countries in the error term. Thus, including time dummies

increases the chances of this assumption to hold (see Kathavate and Mallik 2012;

Roodman 2006). In all our estimations, the time-fixed effects are found to be signifi-

cant. We also use robust standard errors in all our estimations to take care of the

heteroscedasticity.

To empirically estimate the above model using annual data, our concern is the pres-

ence of outliers very high observations of inflation for few countries which mayhave strong influence on the results. To avoid problems due to the outliers in our model,

we include cases where inflation rate is less than 40%, practised similarly by Pollin and

Zhu (2006) and de Mendoca and de Guimaraes e Souza (2012) among others. This elimi-

nation of high-inflation cases is done in line with the observation made by Bruno and

Easterly (1998) that inflation beyond 40% is indeed harmful for economic growth.

Finally, the threshold level of inflation is measured by the turning point of inflation:

coefficient of linear term of p=2coefficient of squared term of p100:

5. Empirical results

5.1. Static panel estimation results

This section presents results based on fixed effects (FE) and random effects (RE) mod-

els, the two most common models used in panel data estimations. Such panel estima-

tions are superior to simple OLS estimations as they can capture the country

heterogeneity more efficiently. We estimate the model within a static framework by

not incorporating the lagged dependent variable on the right hand side of the regression

as it may cause bias in the estimators. Kumar and Woo (2010, 12) state that in the

dynamic panel setting, the within transformation in the estimation process of FE intro-

duces a correlation between transformed lagged dependent variable and transformed

error,. . .[making] FE inconsistent. In the case of RE models, it is assumed that theobservable regressors are uncorrelated with the unobservable characteristics in both

country-specific effects and error term. This assumption is somewhat relaxed in the

case of FE model which does not impose that country-specific effects and observable

regressors are uncorrelated.

Table 4 presents the results from the FE and RE estimates. Our main interest is in the

measure of inflation turning point which is around 13% and 14% according to FE and

RE, respectively. We would consider results from FE estimates since the Hausman test

shows that the FE model is preferable to the RE one. In the case of 13% inflation thresh-

old, coefficient of the squared term of inflation is significant but the linear term is not.

Coefficients of other covariates, except for government consumption expenditure and

trade openness, are significant. They also show the expected signs, apart from

DUMCOMPR.

5.2. Dynamic panel estimation results

Estimations within a dynamic setting allow us to incorporate a lagged dependent variable

as a regressor, thus overcoming the problem of FE and RE estimates. We now face the

difficulty of finding an appropriate external instrument for inflation and other economic

variables to overcome the endogeneity problem. We use system generalized method of


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moment (SGMM) approach arising from the work of Arellano and Bover (1995) and

Blundell and Bond (1998) to take care of this endogeneity issue. This approach uses suit-

able lagged levels and lagged first differences of the regressors as their instruments. It has

recently gained popularity and is extensively used in applied economic research

(see Kumar and Woo 2010). The estimator also helps eliminate any endogeneity that

may arise because of the correlation of country-specific, time-invariant, factors, and the

right-hand side variables. Moreover, in this type of regression, lagged values of the

Table 4. Fixed and random effects panel regressions.

Dependent variable is real GDP growth

(1) (2)

FE3 RE3

Inflation 0.163 0.0751

(0.093) (0.0759)

Inflation2 0.622 0.267(0.246) (0.245)

Turning point 13.10 14.06

Dln HCONPC 0.232 0.282

(0.074) (0.0729)

Dln FD 0.0835 0.0238(0.0364) (0.0353)

Dln GOVCON 0.0188 0.0217

(0.0266) (0.0317)

Dln OPEN 0.00338 0.00388(0.016) (0.0181)

Dln AGR 0.0649 0.0869(0.0335) (0.0382)

DUMOIL 0.0303 0.0217(0.0104) (0.0202)

DUMCOMPR 0.0637 0.0144(0.0218) (0.0163)

Observations 380 380

Number of countries 14 14

R-squared overall 0.31 0.35

Within 0.36 0.33

Between 0.004 0.61

Hausman test: 45.07

Chi-square 6 (p-value) (0.00)Countries Bangladesh, Cambodia, India, Indonesia, Lao PDR, Malaysia, Pakistan,

The Philippines, Thailand, Vietnam, Kazakhstan, Kyrgyz Republic, PapuaNew Guinea, and Tajikistan.

Notes: Estimation is based on annual observations and all cases of inflation greater than 40% are excluded toavoid the outlier effect of inflation. All standard errors are robust and reported below coefficient estimates. ,, and , denote significance at 1%, 5%, and 10% respectively. In Hausman test the null hypothesis is that Ran-dom Effect model is preferable to Fixed Effect model. Constant terms and time dummies are not reported to con-serve space.


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regressors are used to prevent simultaneity or reverse causality. We also use the one-step

estimator as opposed to the two-step estimator since the latter does not produce any mean-

ingful result.

Table 5 provides results by regressing real GDP growth on different regressors.

Adding different regressors changes the threshold levels of inflation. This reveals

that the relationship between economic growth and inflation is not simple and it is

Table 5. Impact of inflation on growth, dynamic panel estimations.


(1) (2) (3) (4)

SGMM1 SGMM2 SGMM3 SGMM4

Growth (1) 0.131 0.152 0.15 0.15(0.0857) (0.0719) (0.0733) (0.0733)

Growth (2) 0.141 0.0967 0.0483 0.0483(0.05) (0.0534) (0.0736) (0.0736)

Inflation 0.0158 0.0504 0.167 0.167

(0.0465) (0.042) (0.0562) (0.0562)

Inflation2 0.0345 0.0488 0.645 0.645(0.0336) (0.0267) (0.115) (0.115)

Turning point 22.89 51.63 12.94 12.94

D ln HCONPC 0.208 0.237 0.237

(0.0552) (0.0673) (0.0673)

D ln FD 0.0596 0.0596(0.0203) (0.0203)

D ln FD (1) 0.0524 0.0524(0.0168) (0.0168)

D ln GOVCON 0.0128 0.0128

(0.0195) (0.0195)

D ln GOVCON (1) 0.0267 0.0267(0.0124) (0.0124)

D ln OPEN 0.0154 0.0154

(0.0189) (0.0189)

D ln OPEN (1) 0.075 0.075(0.0397) (0.0397)

D ln AGR 0.0565 0.0565(0.0319) (0.0319)

D ln AGR (1) 0.089 0.089(0.0349) (0.0349)

DUMOIL 0.0202

(0.0078)

DUMCOMPR 0.057

(0.0155)

Observation 435 396 370 370

(continued)


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necessary to take into account the effects of other variables into this relationship.

The results showing a threshold level at 12.94%, in columns 3 and 4, seem to be a

good estimate as both the inflation coefficients are significant at 1% level. The

results are robust since post-regression diagnostic tests are satisfactory showing that

the underlying assumptions of the model are valid. For instance, AR (1) and AR (2)

tests are performed to test first- and second-order serial correlation in the disturban-

ces. One should reject the null hypothesis of the absence of first order serial correla-

tion and not reject the absence of second order serial correlation (see Baltagi,

Demetriades, and Law 2009). Our tests satisfy these conditions. The other important

diagnostic test, Sargan test, does not reject the null hypothesis that over identifying

restrictions are valid.

6. The level of development and inflation threshold

The panel estimations, above, suggest that the inflation threshold for Asian developing

countries is at around 13%. We now want to examine if this threshold varies according to

the level of economic development. Table 6 shows the findings from subsamples based

on different regions and income groups. There is strong evidence from the results that

poorer countries have a higher threshold level of inflation. For instance, in column 1,

when we drop relatively richer East Asian countries, we find the threshold at around 14%.

The threshold reduces, in columns 2 and 3, to around 11% and 8% as the mean real GDP

per capita and mean real household consumption per capita increase. This result

Table 5. (Continued )


(1) (2) (3) (4)

SGMM1 SGMM2 SGMM3 SGMM4

Number of countries 14 14 14 14

SGMM estimation method One step One step One step One step

AR (1) test (p-value) 2.98 2.83 2.9 2.9(0.00) (0.00) (0.00) (0.00)

AR (2) test (p-value) 0.24 0.58 0.31 0.31(0.80) (0.55) (0.75) (0.75)

Sargan test (p-value) 353.18 432.82 537.81 537.81

(0.14) (0.14) (0.87) (0.87)

Countries Bangladesh, Cambodia, India, Indonesia, Lao PDR, Malaysia,Pakistan, The Philippines, Thailand, Vietnam, Kazakhstan,Kyrgyz Republic, Papua New Guinea, and Tajikistan.

Notes: Estimation is based on annual observations and all cases of inflation greater than 40% are excluded toavoid the outlier effects of inflation. All standard errors are robust and reported below coefficient estimates. ,, and , denote significance at 1%, 5%, and 10%, respectively. Regressions use the Blundell and Bond (1998)system GMM (SGMM) estimator. AR (1) and AR (2) tests are Arellano-Bond first and second order serial corre-lation tests respectively. The null hypothesis is that residuals show no serial correlation. Sargan test is for overidentifying restrictions. The null hypothesis is that over-identifying restrictions are valid. Time dummies are notreported to conserve space. Variables lagged in one period are represented by 1 within parentheses after thevariable.


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convincingly proves that threshold varies according to economic circumstances of groups

of countries.

To further strengthen our claim, we examine how the inflation threshold might vary

according to changes in agricultural dependence, financial deepening, and trade openness.

First, Table 7 groups the countries according to above and below sample mean based on

these three criteria.

We now present the regression results in Table 8, based on the above criteria. The

results are consistent with what we have argued so far. The threshold varies according to

the levels of economic development, and less-developed countries tend to have a higher

inflation threshold. For instance, in the case of agro-dependence, countries more depen-

dent on agriculture appears to have a higher turning point, at 13.51%, compared to that of

countries relatively less dependent on agriculture (turning point at around 10.7%; see col-

umns 1 and 2 in Table 8). We find similar evidence in the case of financial deepening and

trade openness. The findings on threshold vary, but remain within the range between 7%

and 14%.

Taken together, our empirical investigation finds an inflation threshold at around 13%

for a sample of 14 Asian developing countries over the period 19612010. Moreover, thethreshold varies between 7% and 14% depending on the level of development. Although,

our sample size is much smaller, our finding on inflation threshold echoes findings from

Table 6. Impact of inflation on growth, the regional effect.


(1) (2) (3)

SGMM5 SGMM6 SGMM7

Excluding EastAsia

Excluding SouthAsia

Excluding FormerCommand Economies

Inflation 0.216 0.132 0.094

Inflation2 0.756 0.596 0.571Turning point 14.28 11.07 8.31

Number ofcountries

10 11 8

Countries droppedfrom the sampleof 14 countries

Indonesia, Malaysia,The Philippines, andThailand

Bangladesh, India, andPakistan

Cambodia, Lao PDR,Vietnam, Kazakhstan,Kyrgyz Republic, andTajikistan

Mean real GDP percapita

534.24 1099.92 1268.83

Mean realhouseholdconsumption percapita

385.9 682.1 724.15

Notes: Estimation is based on annual observations and all cases of inflation greater than 40% are excluded toavoid the outlier effect of inflation. , , and , denote significance at 1%, 5%, and 10%, respectively. Regres-sions use the Blundell and Bond (1998) system GMM (SGMM) estimator. Control variables, time dummies, andpost estimation tests (AR test and Sargan test) are not reported to conserve space. Unweighted mean values ofreal GDP per capita and real household consumption per capita within each group over the period 19902010are reported.


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major recent studies such as Bick (2010), Espinoza, Leon, and Prasad (2011), Lopez-Vil-

lavicencio and Mignon (2011), and Pollin and Zhu (2006). Our results, in short, help to

conclude that empirical basis for maintaining inflation within 5% in developing countries

of Asia is weak.

Table 7. Averages of selected variables in the sample countries, 19902010.Agriculture share of GDP: sample mean 24.63

Countries below sample mean Countries above sample mean

Country Mean Country Mean

Kazakhstan 10.28 Lao PDR 47.01

Thailand 10.44 Cambodia 38.91

Malaysia 11.1 Kyrgyz Republic 35.88

Indonesia 16.18 Papua New Guinea 34.74

The Philippines 16.28 Tajikistan 27.55

India 23.28 Vietnam 25.85

Bangladesh 23.89

Pakistan 24.07

Financial deepening: sample mean 45.25



Malaysia 114.37 Tajikistan 9.9

Thailand 97 Lao PDR 14.29

The Philippines 52.34 Cambodia 15.52

India 52.03 Kyrgyz Republic 15.66

Vietnam 47.6 Kazakhstan 19.83

Papua New Guinea 34.4

Bangladesh 37.3

Pakistan 41.65

Indonesia 42.98

Trade openness: sample mean 88.08



Malaysia 188.57 India 30.23

Papua New Guinea 113.91 Bangladesh 33.23

Vietnam 113.57 Pakistan 34.61

Thailand 111.71 Indonesia 58.04

Tajikistan 108.1 Lao PDR 67.33

Cambodia 104.72 The Philippines 86.09

Kyrgyz Republic 96.13

Kazakhstan 89.37


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Table8.

Theim

pactofinflationongrowth

based

onstructuralcharacteristicsofthecountries.

DependentvariableisrealGDPgrowth

(1)

(2)

(3)

(4)

(5)

(6)

SGMM8

SGMM9

SGMM10

SGMM11

SGMM12

SGMM13

Agro

dependence

Financialdeepening

Tradeopenness

Least

Most

Most

Least

Most

Least

Inflation

0.0764

0.635

0.0903

0.651

0.114

0.171

Inflation2

0.357

2

.35

0

.576

2

.34

0

.719

0.763

Turning

point

10.7

13.51

7.83

13.91

7.92

11.2

Number

of

countries

44

44

44

Countries

Kazakhstan,

Thailand,

Malaysia,

andIndonesia

Lao

PDR,Cam

bodia,

KyrgyzRepublic,

andPapuaNew

Guinea

Malaysia,Thailand,

ThePhilippines,and

India

Tajikistan,Lao

PDR,

Cam

bodia,and

KyrgyzRepublic

Malaysia,PapuaNew

Guinea,Vietnam

,andThailand

India,Bangladesh,

Pakistan,and

Indonesia

Notes:Estim

ationisbased

onannualobservationsandallcasesofinflationgreater

than

40%

areexcluded

toavoid

theoutliereffectofinflation. ,

,and ,denotesignificance

at1%,

5%,and10%,respectively.Regressionsuse

theBlundellandBond(1998)system

GMM

(SGMM)estimator.P-valueforthelinearterm

ofinflationin

column(5)is0.153andtherefore

theestimateissignificantat16%

significance

level.Controlvariables,timedummies,andpostestimationtests(A

RtestandSargan

test)arenotreported

toconservespace.4leastand

mostagro-dependent,financialdeepening,andtradeopen

countriesareselected

from

Table7foreach

groupofestimation.


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7. Conclusion

We question the validity of the conventional wisdom that inflation beyond 5% is harmful for

economic growth in the case of developing countries. First, we critically review the existing

literature to find support for this view. We, then, provide an empirical investigation, using

panel estimation techniques such as System GMM, on a sample of 14 Asian developing

countries over the period 19612010. Findings from both critical reviews and econometricanalyses show that the empirical justification to keep inflation as low as 5% is weak. Our

empirical evidence suggests that inflation threshold for these countries is around 13%, well

above the level, 5%, advocated by the IMF. Besides, we show that poorer countries tend to

have a higher threshold, providing convincing evidence against one size fits all policy and

that country-specific circumstances matter. Our results reveal that inflation threshold may

vary within the range of 7%14% depending on the level of development. The implicationof this finding is that developing countries can gain from moderate levels of inflation and

should not be alarmed when inflation crosses the 5% benchmark set by the IMF.

The policy implications arising from this study are straightforward. The IMF should

rethink its macroeconomic policy advice to developing countries and governments in

these countries should highlight their country-specific circumstances during the negotia-

tion with the IMF officials to access IMFs support facilities. The findings also help

rethink macroeconomic policy making in developing countries that still follow a low-

inflation targeting policies. Macroeconomic policies require a broader perspective, creat-

ing a balance between the need for stabilization and development. The study suggests

that the developing countries should give priority to poverty reduction and employment

creation instead of pursuing a restrictive policy of low inflation.

Acknowledgements

We thank Anis Chowdhury, Ron Ratti, Gerald Epstein, Malcolm Treadgold, Geoffrey Harcourt, andparticipants at the 11th Annual Society of Heterodox Economists Conference held at the Universityof New South Wales, Australia for very helpful comments on an earlier draft. An earlier version ofthe article was written as a background paper for the United Nations Economic and Social Survey ofAsia and the Pacific 2013. Usual caveats apply.

Notes

1. The 1970s was a period of high inflation and stagnation, caused mainly by commodity priceshocks and the breakdown of the Bretton Woods system as a result of high US inflation. TheGreat Moderation was characterized by an unusually high degree of macroeconomic stability,with steady growth and low and stable inflation in most of the advanced economies since1993 until the Great Recession hit in 2008.

2. Source: IMF-Supported Programs Frequently Asked Questions, available at http://www.imf.org 6 external 6 np6 exr 6 faq6 progfaqs.htm#q4.

3. Paul Krugman, The Low Inflation Trap (Slightly Wonkish), The New York Times(23 September 2011), available at http: 6 6 krugman.blogs.nytimes.com 6 20116 096 236the-low-inflation-trap-slightly-wonkish 6 .

4. It cannot go below zero.5. The Japanese economy is a classic example in this regard. This idea is also captured in the

Keynesian Liquidity Trap. There is however an opposing view which considers higher infla-tion rate to cause higher cost of borrowing. Rising inflation leads to higher inflation expecta-tions. Fishers equation explains that this would lower the real return for lending and hurtlenders. As a result lenders tend to charge higher nominal interest rates.

6. The Economist (15 February 2010), Monetary policy: a healthy dose of inflation. Availableat http://www.economist.com 6 blogs 6 freeexchange 6 20106 026 monetary_policy_1 (this isbecause of the problem of the zero bound to nominal interest rate).


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http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.imf.org/external/np/exr/faq/progfaqs.htm&num;q4http://www.economist.com/blogs/freeexchange/2010/02/monetary_policy_1http://www.economist.com/blogs/freeexchange/2010/02/monetary_policy_1http://www.economist.com/blogs/freeexchange/2010/02/monetary_policy_1http://www.economist.com/blogs/freeexchange/2010/02/monetary_policy_1http://www.economist.com/blogs/freeexchange/2010/02/monetary_policy_1http://www.economist.com/blogs/freeexchange/2010/02/monetary_policy_1

7. The Global Financial Crisis of 20072008 and the Great Recession of 20092010.8. See the opening remarks by Dominique Strauss-Kahn, at the IMF conference on Macro and

Growth Policies in the Wake of the Crisis, Washington D.C., March 7, 2011, available viathe internet at http://www.imf.org 6 external.

9. Developing countries are defined according to their Gross National Income (GNI) per capita.Countries with a GNI per capita of US$ 11,905 and less are defined as developing. In 2012,Argentinas GNI per capita was around US$ 11,572. The World Bank classifies Argentina asan upper middle income country.

10. The article by Khan and Senhadji (2001) also suffers from the same problem.11. They also find two but lower threshold levels, 2.57% and 12.61%, for industrial countries.12. One may question this aspect of the dataset as it is difficult to create a balanced dataset for

developing countries due to poor availability of data.

Notes on contributors

Ahmed Taneem Muzaffar holds a PhD in economics from the University of Western Sydney, NewSouth Wales, Australia. He also studied financial economics and obtained an MSc from the Universityof Essex, UK. His key research interests include macroeconomic policies and economic development.

P.N. (Raja) Junankar (BSc (Econ), MSc (Econ), LSE; and Ph.D., University of Essex.) is an Emeri-tus Professor (University of Western Sydney); Honorary Professor (University of New SouthWales); and Research Fellow at the IZA in Bonn, Germany. He has consulted for the OECD, ILO,ESCAP, and several other organizations.

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Appendix

TableA1.

Listofvariablesandsources.

Mnem

onic

Variabledescription

Source

Indicatorcode

RGDP

GDP(constant$2000).

WDI

NY.GDP.M

KTP.KD

GDP,gross

domesticproduct,atpurchaserspricesisthesum

ofgross

valueadded

byall

residentproducersin

theeconomyplusanyproducttaxes

andminusanysubsidiesnot

included

inthevalueoftheproducts.Itiscalculatedwithoutmakingdeductionsfor

depreciationoffabricatedassetsorfordepletionanddegradationofnaturalresources.Data

arein

constant2000USdollars.Dollar

figuresforGDPareconvertedfrom

domestic

currencies

using2000officialexchangerates.Forafewcountrieswheretheofficialexchange

ratedoes

notreflecttherateeffectivelyapplied

toactualforeignexchangetransactions,an

alternativeconversionfactorisused.

RGDPCAP

GDPper

capita(constant$2000).

WDI

NY.GDP.PCAP.KD

GDPper

capitaisgross

domesticproductdivided

bymidyearpopulation.Dataarein

constant

USdollars.

CPI

Consumer

price

index

(2005D

100).

WDI

FP.CPI.TOTL

Consumer

price

index

reflectschanges

inthecostto

theaverageconsumer

ofacquiringabasket

ofgoodsandservices

thatmay

befixed

orchanged

atspecified

intervals,such

asyearly.The

Laspeyresform

ulaisgenerally

used.

INFCPI

Inflation,consumer

prices(annual%).

WDI,IFS,

WEO,SDBS

FP.CPI.TOTL.ZG

Inflationismeasuredbytheconsumer

price

index.

M2GDP(FD)

Broad

money

(%ofGDP);M2NominalGDPratio.

WDI

FM.LBL.BMNY.GD.

ZS

Broad

money

isthesum

ofcurrency

outsidebanks;dem

anddepositsother

than

those

ofthe

centralgovernment;thetime,savings,andforeigncurrency

depositsofresidentsectorsother

than

thecentralgovernment;bankandtravellerschecks;andother

securities

such

ascertificatesofdepositandcommercialpaper.

GOVCON

Generalgovernmentfinalconsumptionexpenditure

(%ofGDP).

WDI

NE.CON.GOVT.ZS

(continued

)


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TableA1.

(Continued

)

Mnem

onic

Variabledescription

Source

Indicatorcode

Generalgovernmentfinalconsumptionexpenditure

(form

erly

generalgovernmentconsumption)

includes

allgovernmentcurrentexpendituresforpurchases

ofgoodsandservices

(including

compensationofem

ployees).Italso

includes

mostexpenditure

onnationaldefence

and

security,butexcludes

governmentmilitaryexpendituresthatarepartofgovernmentcapital

form

ation.

HCONPC

Household

finalconsumptionexpenditure

per

capita(constant$2000).

WDI

NE.CON.PRVT.PC.

KD

Household


per

capita(privateconsumptionper

capita)

iscalculatedusingprivateconsumptionin

constant2000pricesandWorldBankpopulation

estimates.Household


isthemarketvalueofallgoodsand

services,includingdurableproducts(such

ascars,washingmachines,andhomecomputers),

purchased

byhouseholds.Itexcludes

purchases

ofdwellingsbutincludes

imputedrentfor

owner-occupieddwellings.Italso

includes

paymentsandfees

togovernmentsto

obtain

permitsandlicenses.Here,household

consumptionexpenditure

includes

theexpendituresof

nonprofitinstitutionsservinghouseholds,even

when

reported

separatelybythecountry.Data

arein

constant2000U.S.dollars.

OPEN

Trade(%

ofGDP)

WDI

NE.TRD.GNFS.ZS

Tradeisthesum

ofexportsandim

portsofgoodsandservices

measuredas

ashareofgross

domesticproduct.

AGR

Agriculture

valueadded

asa%

ofGDP

WDI

NV.AGR.TOTL.ZS

OIL

UKBrentcrudeoilindex

(2005D

100)

IFS

COMPR

Non-energycommodityprice

index

bytheWorldBankforlowandmiddleincomecountries

(2005D

100)

IFS


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Abstract1. Introduction2. The low-inflation trap and cross-country evidence on inflation threshold3. Data, variables, and summary statistics4. Empirical model5. Empirical results5.1. Static panel estimation results5.2. Dynamic panel estimation results

6. The level of development and inflation threshold7. ConclusionAcknowledgementsNotesNotes on contributorsReferences

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