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Introduction

Post global financial crises (2008) have forced countries
to adopt expansionary and stimulating macroeconomic
policies aiming to reduce unemployment. Some countries,
such as United Kingdom, Germany, and the United States
of America have become successful in lowering the unem-
ployment in their labor markets. However, Spain and Italy
are stuck at high rates of unemployment with rigid labor
markets (Bhattarai 2016). The unemployment could be
stabilized towards their natural rates by stimulating the ag-
gregate demand through fiscal or monetary policies with or
without some increase in price levels (Keynes 1936, Phllips
1958, Benati 2015, Blanchard 2016).

Short-term economic problems, such as inflation and
unemployment are among the most notable macroeco-
nomic problems all the time (Al-zeaud 2014, Arshad 2014,
Bhattarai 2016, Caporale and Škare 2011, Cioran 2014,
Furuoka 2007, Furuoka 2008, Israel 2015, Katria et  al.,
2011, Kogid et al. 2011, Mahmood et al. 2013, Okafor et al.

2016, Sa’idu and Muhammad 2015, Ştefan and Bratu 2016,
Thayaparan 2014, Touny 2013, Umaru and Zubairu 2012,
Zaman et al. 2011, Pallis 2006, Benati 2015, Blanchard
2016). The Indonesian government started to focus on
inflation when Indonesia experienced an economic shock
during the transition period (1965–1969). Fortunately, the
Indonesian government managed to control the inflation
rate as Indonesia only had an inflation rate of below 10%
in 1969 (Bank Indonesia 2004). However, the monetary
crisis hit Indonesia again in 1997–1998 that resulted in the
inflation rate of 58.4%. During the post-monetary crisis
period, Indonesia managed to recover that caused the infla-
tion rate to be below two digits. Further, the global financial
crisis hit the global economy in 2008, but the Indonesian
inflation rate remained stable. One of the likely factors of
this condition is the government’ various economic rescue
programs such as the tight money or contractive policy
that was effective in taming the inflation rate. Besides, the
Inflation Targeting Framework that was implemented by
Bank Indonesia (the Indonesian central bank) since July

THE CAUSALITY BETWEEN INFLATION AND UNEMPLOYMENT:
THE INDONESIAN EVIDENCE

Gatot SASONGKO1, Andrian Dolfriandra HURUTA2

Universitas Kristen Satya Wacana, Salatiga, Indonesia
E-mails: 1gatot.sasongko@staff.uksw.edu (corresponding author); 2andrian.huruta@staff.uksw.edu

Received 21 June 2018; accepted 05 September 2018

Abstract. Two closely watched indicators of economic performance are inflation and unemployment. This study empirically
analyzes the causality between inflation and unemployment in Indonesia during 1984 to 2017. The data were gathered from
the Indonesian Central Bureau of Statistics. Methodologically, this study employed the Granger Causality test and Vector
Autoregression to determine the causality between inflation and unemployment. The results show that there is a one-way causality
between inflation and unemployment. The findings imply that unemployment causes inflation, but not vice versa. Next inflation
and unemployment are also closely related to other determining factors, such as season, household income, and the decisions
to attend school or to perform the housekeeping.

Keywords: granger causality, inflation, unemployment, vector autoregression.

JEL Classification: E600, E610.

http://creativecommons.org/licenses/by/4.0/

https://doi.org/10.3846/btp.2019.01

https://doi.org/10.3846/btp.2019.01

mailto:gatot.sasongko@staff.uksw.edu

mailto:andrian.huruta@staff.uksw.edu

2005 (Bank Indonesia 2017) and the empowerment of the
Regional Inflation Monitoring Team in each local region fa-
cilitated further the inflation control (Sasongko and Huruta
2018).

The high inflation rate in 1965 also caused a high un-
employment rate (read: stagflation). Since 1965, the unem-
ployment rate has increased by 5–6% per year. However,
similar to the inflation rate, the Indonesian government
managed to reduce the unemployment rate to less than 10%
(Bank Indonesia 2004). Every government closely monitor
inflation and unemployment as the two main economic
performance indicators. Statisticians combine inflation and
unemployment data to develop the misery index that aims
to measure the health of an economy. One of the economic
principles is the short-term trade-off between inflation and
unemployment. If fiscal and monetary policymakers in-
crease aggregate demands and economy along the short-run
aggregate demand curve, they can reduce unemployment
temporarily, albeit with an increase in inflation rate. On
the other hand, if monetary and fiscal policymakers reduce
aggregate demands and economy along the short-run aggre-
gate demand curve, they can curb inflation but also increase
unemployment temporarily (Mankiw et al. 2013).

This study aims to investigate the trade-off between in-
flation and unemployment as found by Phllips (1958) espe-
cially on the causality between inflation and unemployment
in Indonesia during 1984 to 2017. It is clear the importance
to recognize the relationship between inflation and unem-
ployment when determining the macroeconomic policies
for an economy. Despite the availability of several studies
that examined the Phillips curve hypothesis, there is still a
shortage of applied studies that investigate this hypothesis
under developing countries where the majority of research
has concentrated on the developed nations. The outcomes
of this study may help policymakers to formulate better
policies that can achieve their objectives of price stability
and full employment in Indonesia.

1. Literature review

Several studies have investigated the relationship between
inflation and unemployment. Al-zeaud (2014) does not find
a causal relationship between inflation and unemployment
in Jordan because the study does not include foreign labor
when measuring the unemployment level, thus inhibiting
the trade-offs between these two variables in the short term.
Further, Furuoka (2008) also does not find the causality
between inflation and unemployment in the Philippines.
The socioeconomic factors such as the output gaps likely
explain the Phillips curve better in the Philippine context.
In Nigeria, Umaru and Zubairu (2012) indicate that there
is no causality between inflation and unemployment. The
findings suggest that the Phillip curve does not apply in

Nigeria and it is necessary to use the unemployment or
inflation theory that is based more on the Nigerian data
and situation.

Besides studies that show no causal relationship between
inflation and unemployment, Caporale and Škare (2011)
demonstrate that there is a one-way causal relationship
between inflation and job opportunities. Their findings,
based on the study on the Organisation for Economic Co-
operation and Development (OECD) countries, thus sug-
gest that inflation affects job opportunities, but not the way
around. This condition recommends policymakers to pay
more attention to the short-term and long-term job and
output growth. In Malaysia, Furuoka (2007) also finds the
one-way causality between inflation and unemployment,
implying that inflation leads to unemployment but not the
way around. The study also demonstrates the cointegration
and causal relationship between inflation and unemploy-
ment in Malaysia. In other words, the results confirm the
existence of the Phillips curve. Still in the same country,
Kogid et al. (2011) document the one-way causality be-
tween inflation and unemployment. Their findings imply
that inflation causes unemployment, but not vice versa. The
results also confirm the trade-off relationship between infla-
tion and unemployment in Malaysia and the government
needs to ensure that the economic policies will facilitate
sustainable economic growth in the future. Using the US
data, Ştefan and Bratu (2016) also find the one-way rela-
tionship between inflation and unemployment, suggesting
that inflation explains unemployment but not vice versa.
Their findings suggest that policymakers should develop
programs that reduce unemployment such as productive
labor projects while at the same time also control inflation.
Besides, the programs should focus on replacing foreign
labors with local labors and on ensuring that the aggregate
demands reach the optimal unemployment and inflation
levels that will eventually support the long-term economic
growth. The Pakistani study of Mahmood et al. (2013)
demonstrate the one-way causality between inflation and
unemployment, implying that inflation affects unemploy-
ment but not the way around. The study also suggests that
the Pakistani policymakers maintain the equilibrium point
between inflation, unemployment, and interest rate to con-
trol for economic shocks. Lack of focus on one of the three
variables likely affects the economy. Still using the Pakistani
data, Zaman et al. (2011) find the long-term relationship
and one-way causality between inflation and unemploy-
ment, denoting that inflation causes unemployment but not
vice versa. The results also indicate that increasing inflation
likely increases employment opportunities that eventually
facilitates growth. The study empirically confirms the ex-
istence of the Phillips curve in Pakistan, both in the short-
term and in the long-term. Nigeria also exhibits the one-way
causality between inflation and unemployment by Sa’idu

2 G. Sasongko, A. D. Huruta. The causality between inflation and unemployment: the Indonesian evidence

and Muhammad (2015) that indicating inflation leads to
unemployment but not the other way around. Their results
recommend the joint efforts of all policymakers to restruc-
ture the economy to manage price instability and improve
the infrastructures.

Further, Katria et al. (2011) who analyze the South Asian
Association for Regional Cooperation (SAARC) countries,
find a negative relationship between inflation and unem-
ployment. Their results indicate that the collaboration be-
tween monetary and fiscal policies manages to stabilize the
business cycle. Next, the Nigerian study by Okafor et al.
(2016) indicates that inflation negatively affects unemploy-
ment. Their results recommend that policymakers not only
rely on monetary targets but also on output targets through
the economy deepening to maintain the optimal inflation
rate and the minimal unemployment level. In a similar vein,
Cioran (2014) demonstrates that inflation negatively affects
unemployment in Romania and the European Union (EU).
The findings suggest that inflation rate is an effective instru-
ment to prevent increasing unemployment in the EU and
Romania.

Besides the negative results, other studies find the pos-
itive relationship. For example, using the Egyptian data,
Touny (2013) documents that unemployment positively
affects inflation in the long run. The results recommend
that policymakers implement their monetary policies
to overcome the inflationary pressure regardless of the
negative effects of unemployment. Further, Israel (2015)
who analyzes several developed countries such as France,
Germany, the UK, and the US, show the long-term posi-
tive relationship between inflation and unemployment.
This positive relationship is closely related to the political
intervention. The condition causes two problems, namely:
(1) monetary expansion on the income and wealth distribu-
tion leads to the increasing gap between the poor and the
rich. The increasing gap causes the labor market to be less
flexible and increases unemployment, (2) monetary expan-
sion causes less fluctuation but eventually increases unem-
ployment. Using the Uni European Countries data, Pallis
(2006) investigated the relationship between inflation and
unemployment in the 10 new European Union countries
find that in almost all countries the interaction between the
price inflation rate and the unemployment level took place
in a rather long time period, reaching in some cases the
lag of year four. In Pakistan, Ul-Haq et al. (2012) provided
further support for the existence of a long-term relationship
between unemployment and inflation. On the other hand,
the outcomes of VECM revealed a positive and significant
correlation between inflation and unemployment either in
the long term or the short term.

Other studies demonstrate the two-way causality be-
tween inflation and unemployment. For example, Arshad
(2014) shows the two-way causality between inflation rate

and unemployment in Pakistan, implying that inflation
causes unemployment, and unemployment causes infla-
tion. The data suggest that inflation rate explains the vari-
ance of unemployment better than economic growth while
unemployment contributes to the variance of inflation more
than economic growth. In Sri Lanka, Thayaparan (2014)
finds the two-way causality between inflation and unem-
ployment, implying that inflation causes unemployment
and unemployment causes inflation. The findings indicate
that both unemployment and inflation significantly affect
the Sri Lankan macroeconomic conditions. Next Bhattarai
(2016) finds bidirectional causality as well as cointegrating
relationships between unemployment and inflation among
the OECD countries. Estimates of a vector autoregression
(VAR) model on these trade-offs also support such hypoth-
esis.

Overall, these studies show varying results such as
one-way causality, two-way causality, and no causal rela-
tionship between inflation and unemployment. Further,
these studies also use different analytical models, such as
Granger Causality, Johansen Cointegration, Autoregressive
Distributive Lag, Error Correction Model, Vector Error
Correction Model, Panel Data, Vector Autoregression, and
etc. It can be concluded from the previous discussion that
there is an uncertain relationship between inflation and
unemployment of different economies in the certain period.

2. Research methods

This study uses the secondary data from the central bu-
reau of statistics and the world bank publication. More
specifically, the study relied on the time-series data from
1984 to 2017. Further Granger Causality and Vector
Autoregression used to analyze the data. Before running
the Granger Causality and Vector Autoregression model,
this study initially ran the stationary and the lag length test.
The following are the models for the stationary test and the
test statistic (Brooks 2008).

ΔYt = ϕYt – 1 + ϕt; (1)

. (2)

After running the stationary test, this study ran the lag
length test. There are various approaches to select the opti-
mal lag length, such as Likelihood Ratio, Final Prediction
Error, Akaike Information Criterion and Schwarz
Information Criterion (Rosadi 2012). This study uses the
Akaike Information Criterion (AIC). The minimum value
of the AIC suggests the optimal lag (Ivanov and Kilian
2005). After completing the lag length test, this study ran
the Granger Causality test (Rosadi 2012):

(3)

Business: Theory and Practice, 2019, 20: 1–10 3

(4)

The above equation indicates that Xt is inflation, and
Yt is unemployment, while μt and Vt are error terms that
are assumed to contain no serial correlation and m = n =
r = s. The Granger Causality test produces four possible
results as represented by the following equations:

1. If Σaj ≠ 0 and Σbj = 0, then there is a one-way
causality from inflation to unemployment.

2. If Σaj = 0 and Σbj ≠ 0, then there is a one-way
causality from unemployment to inflation.

3. If Σaj = 0 and Σbj = 0, then there is no causal re-
lationship between inflation and unemployment.

4. If Σaj ≠ 0 and Σbj ≠ 0, then there is a two-way
causality between inflation and unemployment.

Further, this study ran the Vector Autoregression
after completing the Granger Causality test. The Vector
Autoregression (VAR) is commonly used for forecast-
ing systems of interrelated time series and for analyz-
ing the dynamic impact of random disturbances on the
system of variables. The reduced form VAR approach
sidesteps the need for structural modeling by treating
every endogenous variable in the system as a function
of p-lagged values of all of the endogenous variables in
the system. The following is the equation in the Vector
Autoregression (p) with k-endogen variable yt = (y1t , y2t ,
…,ykt) (Lütkepohl 2006).

yt = A1yt–1 + … + Apyt–p + Cxt + ∈t , (5)

where:
yt = (y1t , y2t ,…,ykt)′ is a k × 1 vector of endogenous

variables;
xt = (x1t , x2t ,…,xdt)′ is a d × 1 vector of exogenous

variables;
A1, …, Ap are k × k matrices of lag coefficients to be

estimated;
C is a d × k matrix of exogenous variable coefficients

to be estimated;

∈t = (∈1t , ∈2t , …, ∈kt)′ is a k × 1 white noise innova-
tion process, with E(∈t) = 0, E(∈t ∈t′) = ∑∈, and E(∈t
∈s′) = 0 for t ≠ s.

3. Results

Table 1 below shows the results of the stationarity test using
the Augmented Dickey-Fuller (ADF) method.

Table 1 indicates that inflation is stationary at the order
of integration of level or I(0) while the unemployment level

Table 1. Stationarity test

Variable p-value Conclusion
Inflation 0.0000* I(0)
Unemployment 0.3012 the series is not stationary
DUnemployment** 0.0000* I(1)

*indicates the rejection of the null hypothesis at 5% of significance
level.
** DUnemployment implies that Unemployment at the first diffe-
rence [I(1)].

Table 2. Lag length test

Lag LogL LR FPE AIC SC HQ
0 –151.4651 NA  197.5714 10.96179 11.05695 10.99088
1 –150.5083 1.708562 245.9338 11.17917 11.46464 11.26644
2 –145.1985 8.723246 225.3365 11.08561 11.56140 11.23106
3 –125.0476 30.22635* 72.08667* 9.931972* 10.59807* 10.13561*
4 –121.2335 5.176270 74.94396 9.945251 10.80167 10.20707
5 –120.3520 1.070367 97.66239 10.16800 11.21473 10.48800

*indicates the optimal lag.

is not stationary at the order of integration of level, prompt-
ing us to have the first difference technique. The order 1
or I(1) differencing shows that DUnemployment does not
contain the unit root anymore because it is now stationary.
Further, determine the optimal length of lag by using Lag
Length Test as can be seen in Table 2.

Table 2 suggests the optimal lag to indicate the depen-
dence of a variable on its lagged value and other endogenous
variables is lag 3, implying that we have to use lag 3 to inves-
tigate the causality between inflation and DUnemployment.
This decision is indicated by the Akaike Information
Criterion (AIC) value of 9.931972 that is smaller than the
AIC values at the other lags. After ran the lag length test, this
study ran the Granger Causality test using lag 3. The results
of Granger Causality test can be seen in Table 3.

Table 3 reveals that the null hypothesis proposing
that DUnemployment does not Granger Cause infla-
tion is rejected, implying that DUnemployment exhibits
the Granger Cause on inflation. The results suggest that
DUnemployment granger cause inflation, but not vice

4 G. Sasongko, A. D. Huruta. The causality between inflation and unemployment: the Indonesian evidence

versa. The decision of rejecting the null hypothesis is based
on the probability value of 2.E-08 that is lower than α = 5%.
After ran the Granger Causality, this study ran the Vector
Autoregression. Variables in a Vector Autoregression
model are determined simultaneously and rely more on
historic patterns of data to establish relations between
unemployment and inflation than economic theories
(Bhattarai 2016). The results of Vector Autoregression
can be seen in Table 4.

Table 4 indicates that a simple Vector Autoregression
model with three lags on inflation and DUnemployment
shows that Inflation is significantly influenced by DUnem-
ployment(–2), DUnemployment(–3) and inflation(–1). It
implies that the influence of DUnemploy ment(–2), DUnem-
ployment(–3) and inflation(–1) have a large contribution to
the movement of inflation in Indonesia. Estimates of Vector
Autoregression also support by the Impulse Response
Functions (IRFs). The results of the Impulse Response
Functions (IRFs) can be seen in Figure 1.

Impulse Response Functions (IRFs) was calculated for
DUnemployment and inflation to address the reaction of
the economy to external changes (shocks). The results of
the IRFs analysis show that there is a trade-off between
inflation and DUnemployment as shown by the IRFs of

DUnemployment to inflation. Overall, estimate results of
Granger Causality, Vector Autoregression, and Impulse
Response Functions (IRFs) prove that the DUnemployment
is more instrumental to explain inflation in Indonesia.

Further, this result is supported by Touny (2013) who
finds the positive effect of unemployment on inflation. The
normalized cointegration equation reveals that unemploy-
ment gap has a long-run positive effect on the changes in
the inflation rate, which is consistent with “Lucas Critique”
where a policy of inflation would fail to reduce the unem-
ployment rate in the long run, because workers would
eventually adjust their expectations of inflation. Further,
the more rapid the reduction in the unemployment rate,
the less disinflation is achieved at each unemployment rate
level. Even at the cases where the unemployment rate is very
high, the inflation rate falls little and thus the economy is
moving too rapidly out of the recession (Pallis 2006). Other
findings by Ul-Haq et al. (2012) also suggest that policy
makers should pay special attention to this relationship
between inflation and unemployment when they are going
to design macroeconomic policies.

Thus, when inflation does not support DUnemployment,
it is necessary to analyze factors that affect DUnemployment.
Table 5 below presents the information on the number of

Table 3. Granger causality test

Pairwise Granger Causality Tests
Sample: 1984 2017
Lags: 3
Null Hypothesis: Obs F-Statistic Prob. 
Inflation does not Granger Cause DUnemployment  30  0.72869 0.5454
Dunemployment does not Granger Cause Inflation  33.0657 2.E-08

Table 4. VAR Model of inflation and DUnemployment for Indonesia (1984–2017)

DUnemployment Inflation
Coefficients t-prob Coefficients t-prob

DUnemployment(–1) –0.135523 0.20747 0.709939 1.30180
DUnemployment(–2) –0.181229 0.20753 –6.937364 1.30223
DUnemployment(–3) 0.217161 0.24175 11.60203 1.51692
Inflation(–1) 0.024945 0.01742 0.421936 0.10934
Inflation(–2) 0.003714 0.01524 0.042180 0.09564
Inflation(–3) –0.006079 0.01513 0.021545 0.09495
Constant –0.186910 0.34132 4.400302 2.14168

R2 0.124570 0.813392
F-statistic 0.545466 16.70883
Log-likehood –39.33832 –94.43436
AIC 3.089221 6.762290
Swarz SC 3.416167 7.089236

Business: Theory and Practice, 2019, 20: 1–10 5

Figure 1. Impulse responses to DUnemployment and inflation shocks

Table 5. The Population 15 Years of age or over by the main employment status (2001–2017) (source: Badan Pusat Statistik
(2017a), processed))

Number Main Employment Status
2001 2017

Amount % Amount %

1 Self-employed 17,451,704 19.22 21,849,573 17.54

2 Employer Assisted by Temporary/ Unpaid Worker 20,329,073 22.39 21,275,899 17.08

3 Employer Assisted by Permanent/ Paid Worker 2,788,878 3.07 4,446,024 3.57

4 Employee 26,579,000 29.27 47,420,633 38.08

5 Casual Agricultural Worker 3,633,126 4.00 5,360,306 4.30

6 Casual Non-Agricultural Worker 2,439,035 2.69 6,021,760 4.84

7 Family/ Unpaid Worker 17,586,601 19.37 18,164,654 14.59

8 No Answer – – – –

Total 90,807,417 100.00 124,538,849 100.00

the population 15 years of age or over who worked by the
main employment status.

Table 5 indicates the sharp increase in the number of
the working-age population with the employee status, both
in absolute and relative terms. The number of employees
in 2001 was 26,579,000 or 29.7% of the total working age

population. The number increased to 47,420,633 or 38.08%
of the total working age population in 2017. Increased in-
vestment mainly drives the increasing number of employ-
ees. Djambaska and Lozanoska (2015), Yelwa et al. (2015),
Touny (2013), Israel (2015), Ul-Haq et al. (2012), Bhattarai
(2016), and Pallis (2006) support the results by arguing that

6 G. Sasongko, A. D. Huruta. The causality between inflation and unemployment: the Indonesian evidence

investment is a determining factor in reducing unemploy-
ment.

Further, another factor that affects the number of un-
employment is the industry in which the population work.
Table 6 below shows the data on the population 15 years of
age or over who worked by industry.

The proportion of the population above 15 years who
worked at the primary sectors (agriculture, plantation,
forestry, hunting, and fishery) declined sharply. In 1991,
53.29% of the working population worked in the primary
sectors, and proportion declined to 31.74% in 2016. The
agricultural sector dominates the primary sectors because
of most population work in this sector (Thayaparan 2014,
Yelwa et al. 2015, Kebschull 1987, Israel 2015). Edelman
(2013) confirms the findings by suggesting that farmers
in Latin America and Indonesia live in communities with
exclusive land rights and most of them use the lands for
agricultural activities. The agricultural works in Indonesia
are heavily affected by the season factor, especially before
2005 because of the less developed irrigation system. More
specifically, the agricultural sector greatly depends on the
sufficient availability of rainfall. Farmers begin to culti-
vate their soils after rain falls. Rain usually starts to fall in

October and the dry season starts in April. The following
Table 6 displays the open unemployment level based on
the February and August surveys. February is in the rainy
season while August is in the dry season. The open unem-
ployment rate was higher in August, a month in the dry
season than in February, a rainy month. The average August
unemployment rate was 8,599,944 while in February the
average unemployment level was 8,599,676. The propor-
tion of the population working in the agricultural sector
was so high that the season factor significantly affected the
unemployment rate. However, the annual difference of the
unemployment level between these two months tended to
decline. There was even no difference of the open unemploy-
ment level between February and August in 2017 (Badan
Pusat Statistik 2017d). Further estimates of an Independent
Sample T Test also support the data. Estimate results can
be seen in Table 7.

The significance value (2 tailed) of 1.000 is bigger than
the tolerance value of 5% (0.05) implies that there is no
difference between February and August unemployment
rate. The more developed irrigation system reduce the farm-
ers’ dependence on rainfall and eventually on the season.
Next, the Indonesian working-age population who attended

Table 6. Population 15 years of age or over who worked by main industry (1991–2016) (source: Badan Pusat Statistik (2017b),
processed))

Number Main Industry
1991 2016

Amount % Amount %
1 Agriculture, Plantation, Forestry, Hunting, and Fisheries 39,385,946 53.29 38,291,111 31.74
2 Mining and Quarrying 551,581 0.75 1,311,834 1.09
3 Manufacturing Industry 7,712,468 1.43 15,975,086 1.24
4 Electricity, Gas, and Water 148,480 0.20 403,824 0.33
5 Construction 2,415,002 3.27 7,707,297 6.39
6 Trade, Restaurants, and Accomodation Services 11,190,391 1.14 28,495,436 23.62
7 Transportation, Warehousing, and Communication 2,475,803 3.35 5,192,491 4.30
8 Financial, Real Estate, and Business Services 515,401 0.70 3,481,598 2.89
9 Community, Social, and Personal Services 9,377,036 12.69 19,789,020 1.40

10 Undefined 139,516 0.19 – –
  Total 73,911,624 100.00 120,647,697 100.00

Table 7. Independent Sample T Test for Unemployment on February and August (1986–2017)

Levene’s Test
for Equality of

Variances
t-test for Equality of Means

F Sig. t Df
Sig.

(2-tailed)
Mean

Difference
Std. Error
Difference

95% Confidence Interval of
the Difference

Lower Upper

Unmpl

Equal variances
assumed

.002 .967 .000 24 1.000 –268.00000 6.06551E5 –1.25213E6 1.25159E6

Equal variances
not assumed

.000 23.945 1.000 –268.00000 6.06551E5 –1.25228E6 1.25174E6

Business: Theory and Practice, 2019, 20: 1–10 7

school or performed the housekeeping increased. The num-
ber of economically inactive women due to housekeeping
increased both in absolute and relative terms (Ehrenberg
and Smith 2012). In 2005, the number of the working age
population who performed the housekeeping (mostly wom-
en) was 17,275,478 or 17.08% of the total labor force. The
number increased to 36,078,772 or 18.78% of the labor force
in 2017 (Badan Pusat Statistik 2017c). The estimates of an
Independent Sample T Test also support the data. Estimate
results can be seen in Table 8.

The significance value (2 tailed) of 0.000 is lower than the
tolerance value of 5% (0.05) implies that there is a difference
between the Indonesian working-age population who at-
tended school and performed the housekeeping. Ehrenberg
and Smith (2012) confirm the data by arguing that women
spend a significant portion of their time to housekeeping
such as cooking or taking care of their children. Women
prefer housekeeping to enter the labor market because
housekeeping is also a productive activity.

Further, the number of the population 15 years of age
or over who attended school increased from 9,147,830 in
2005 to 15,244,852 in 2017. However, the proportion of
the working-age population who attended school decreased
from 9.04% in 2005 to 7.94% in 2017. Higher school atten-
dance decreases the number of unemployment. Hubacek
et al. (2007) support the findings by demonstrating that
the economic success of the developing Asian countries
enhances the quality of life of their population. Most Asian
population experience the transition from poverty to suf-
ficient fulfillment of food and clothes. Further, they aspire
to not only meet their basic needs of food and clothes, but
also to enjoy a higher quality of life from highly nutritious
food, life comfort, medical treatments, and other highly
qualified services.

Conclusions

This study suggests the one-way relationship between inf-
lation and DUnemployment. More specifically, the Granger
Causality, Vector Autoregression, and Impulse Response

Functions (IRFs) model show that from 1984 to 2017,
DUnemployment causes inflation, but not vice versa. The
results imply that the Phillips model (Phllips 1958) that
proposes the reciprocal relationship between inflation and
unemployment is not empirically supported in Indonesia.
These findings also different with the most recently by
Blanchard (2016) who find that The US Phillips curve is
alive and well (or at least as well as it has been in the past).

Various factors affect the Indonesian unemployment
rate, such as: (1) The season factor significantly affects un-
employment, albeit with the declining magnitude, because
the agricultural sector still absorbs a significant portion of
the Indonesian labor force; (2) Increased income encour-
ages young labor force (15–19 years) to delay entering the
labor market but to continue their studies; and (3) Better
economic condition also increases the number of non-labor
force. More specifically, women prefer becoming house-
wives (caring for their households) in entering the labor
market because caring for households is also a productive
activity (Ehrenberg and Smith 2012).

Inflation is a less effective policy instrument to over-
come the unemployment problem in Indonesia. This ar-
gument implies that increasing the inflation rate is inef-
fective to reduce the unemployment rate. Numerous facts
indicate that other variables affect the Indonesian unem-
ployment rate. However, it is viable to increase the unem-
ployment rate to control inflation, although this policy
has to be implemented carefully. Further, the Indonesian
geographical condition that consists of thousands of is-
lands likely causes the implementation of macro policies
to take a longer time because of the greater needs to adjust
for the inter-region differences. Thus, the use of the panel
data model likely accounts for the possible inter-region
variances better.

References

Al-zeaud H (2014) The trade-off between unemployment and
inflation evidence from causality test for Jordan. International
Journal of Humanities and Social Science 4 (4): 103-111.

Table 8. Independent Sample T Test for Working Age Between Attend School and Housekeeping (1986–2017)

Levene’s Test
for Equality of
Variances
t-test for Equality of Means

F Sig. T df
Sig.

(2-tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper

WorkAge

Equal varian-
ces assumed

4.160 .046 –48.096 60 .000 –9.88226 .20547 –10.29326 –9.47126

Equal va-
riances not

assumed
–48.096 51.170 .000 –9.88226 .20547 –10.29472 –9.46980

8 G. Sasongko, A. D. Huruta. The causality between inflation and unemployment: the Indonesian evidence

Arshad I (2014) Examining relationship between macroeconomic
variables using Var Approach. In: Proceedings of the 2014
International Conference on Industrial Engineering and
Operations Management. Bali, 2393-2401 http://ieomsociety.
org/ieom2014/pdfs/510

Badan Pusat Statistik (2017a) Penduduk 15 Tahun Ke Atas
Menurut Status Pekerjaan Utama 1986–2017 https://www.
bps.go.id/statictable/2009/04/16/971/penduduk-15-tahun-ke-
atasmenurut-%0Astatus-pekerjaan-utama-1986—2017.html

Badan Pusat Statistik (2017b) Penduduk 15 Tahun Ke Atas yang
Bekerja menurut Lapangan Pekerjaan Utama 1986–2017
https://www.bps.go.id/statictable/2009/04/16/970/penduduk-
15-tahun-ke-atas-yang-bekerja-menurut-lapangan-pekerjaan-
utama-1986—2017.html

Badan Pusat Statistik (2017c) Penduduk Berumur 15 tahun ke atas
menurut jenis kegiatan tahun 1986–2017 https://www.bps.
go.id/statictable/2009/04/16/969/penduduk-berumur-15-ta-
hun-ke-atas-menurut-jenis-kegiatan-tahun-1986—2017.html

Badan Pusat Statistik (2017d) Tingkat Pengangguran Terbuka
(TPT) Menurut Provinsi, 1986–2017 https://www.bps.go.id/
statictable/2014/09/15/981/tingkat-pengangguran-terbuka-
tpt-menurut-provinsi-1986—2017.html

Bank Indonesia (2004) Laporan Perekonomian Indonesia Tahun
1998/1999. Jakarta. https://www.bi.go.id/id/publikasi/laporan-
tahunan/perekonomian/Pages/LapTah 1998 1999.aspx

Bank Indonesia (2017) Tujuan Kebijakan Moneter Bank In-
donesia http://www.bi.go.id/id/moneter/tujuan-kebijakan/
Contents/Default.aspx

Benati L (2015) The long-run Phillips curve: A structural VAR
investigation. Journal of Monetary Economics 76: 15-28.
https://doi.org/10.1016/j.jmoneco.2015.06.007

Bhattarai K (2016) Unemployment-inflation trade-offs in OECD
countries. Economic modelling 58 (2016): 93-103. https://doi.
org/10.1016/j.econmod.2016.05.007

Blanchard O (2016) The Phillips curve: Back to the ’60s? Ameri-
can Economic Review 106 (5): 31-34. https://doi.org/10.1257/
aer.p20161003

Brooks C (2008) Introductory econometrics for finance (2nd
ed) Cambridge University Press. https://doi.org/10.1017/
CBO9780511841644

Caporale GM, Škare M (2011) Short- and long-run linkages
between employment growth, inflation and output growth :
evidence from a large panel. Economic and Finance Wor-
king Paper. London https://www.brunel.ac.uk/__data/assets/
pdf_file/0011/127487/1117

Cioran Z (2014) Monetary policy, inflation and the causal relation
between the inflation rate and some of the macroeconomic
variables. Procedia Economics and Finance 16 (May): 391-
401. https://doi.org/10.1016/S2212-5671(14)00818-1

Djambaska E, Lozanoska A (2015) Foreign direct investment and
unemployment evidence from the republic of Macedonia.
International Journal of Economics, Commerce and Mana-
gement United Kingdom, III(12): 73-85.

Edelman M (2013) What is a peasant? What are peasantries? A
Briefing paper on issues of definition. Intergovernmental Wor-
king Group on a United Nations Declaration on the Rights of
Peasants and Other People Working in Rural Areas, Geneva,
15-19 July 2013. Geneva http://www.ohchr.org/Documents/
HRBodies/HRCouncil/WGPleasants/MarcEdelman

Ehrenberg RG, Smith RS (2012) Modern Labor Economics:
Theory and Public Policy (Donna Battista, Ed) (11th ed)
Boston: Pearson Education, Inc. http://fac.ksu.edu.sa/sites/
default/files/Modern_labor_economics__theory_and_pub-
lic_policy_0

Furuoka F (2007) Does the “Phillips curve” really exist? New
empirical evidence from Malaysia. Economics Bulletin 5 (16):
1-14. http://economicsbulletin.vanderbilt.edu/2007/volume5/
EB-07E20006A

Furuoka F (2008) Unemployment and Inflation in the Philippines:
new evidence from Vector Error Correction Model. Philippo-
ne Journal of Development XXXV (1): 93-106. https://dirp4.
pids.gov.ph/ris/pjd/pidspjd08-1unemployment

Hubacek K, Guan D, Barua A (2007) Changing lifestyles and
consumption patterns in developing countries: A scenario
analysis for China and India. Futures 39 (9): 1084-1096.
https://core.ac.uk/download/pdf/51098

Israel K (2015) Reconsidering the long-run relationship between
inflation and unemployment http://www2.gcc.edu/dept/econ/
assc/Papers 2015/ASSC 2015 – Israel, Karl-Friedrich

Ivanov V, Kilian L (2005) A practitioner’s guide to lag order
selection for VAR impulse response analysis. Studies in Non-
linear Dynamics & Econometrics 9 (1): 1-36.

Katria S, Bhutto NA, Butt F, Domki AA, Khawaja HA, Khalid J
(2011) Tradeoff between inflation and unemployment. In:
Proceedings of 2nd International Conference on Business
Management, 1-18 https://www.umt.edu.pk/icobm2012/
pdf/2C-90P

Kebschull D (1987) The role of the agricultural sector in the
South by. Intereconomics, 125-126. https://doi.org/10.1007/
BF02932233

Keynes JM (1936) The general theory of employment, interest,
and money. MacMillan and Cambridge University Press.

Kogid M, Asid R, Mulok D, Lily J, Loganathan N (2011) Inf-
lation-unemployment trade-off relationship in Malaysia.
Asian Journal of Business and Management Sciences 1 (1):
100-108 http://medcontent.metapress.com/index/A65RM-
03P4874243N %5Cnhttp://www.ajbms.org/articlepdf/
ajbms_2011_1124

Lütkepohl H (2006) New introduction to multiple time series
analysis. New York: Springer-Verlag.

Mahmood Y, Bokhari R, Aslam M (2013) Trade-off between
Inflation, Interest and unemployment rate of Pakistan: A coin-
tegration analysis. Pakistan Journal of Commerce and Social
Sciences 7 (3): 482-492 http://www.jespk.net/publications/140

Mankiw NG, Euston Q, Wilson P (2013) Principles of economics:
an Asian edition (2nd ed) Singapore: Cengage Learning Asia.

Okafor IG, Chijindu EH, Ugochukwu US (2016) Responsiveness
of unemployment to inflation: empirical evidence from Ni-
geria. International Journal of Scientific Research in Science
and Technology 2 (4): 173-179 https://www.researchgate.
net/publication/311349523_Responsiveness_of_Unemploy-
ment_to_Inflation_Empirical_Evidence_from_Nigeria

Pallis D (2006) The trade-off between inflation and unemploy-
ment in the New European Union Member-States. Interna-
tional Research Journal of Finance and Economics (1): 80-88.

Phllips AW (1958) The relation between unemployment and the
rate of change of money wage rates in the United Kingdom.
Economica 25 (100): 283-299.

Business: Theory and Practice, 2019, 20: 1–10 9

Rosadi D (2012) Ekonometrika dan Analisis Runtun Waktu
Terapan Dengan Eviews. Yogyakarta: ANDI.

Sa’idu BM, Muhammad AA (2015) Do Unemployment and
inflation substantially affect economic growth? Journal of
Economics and Development Studies 3 (2): 132-139 https://
doi.org/10.15640/jeds.v3n2a13

Sasongko G, Huruta AD (2018) Monetary policy and the causality
between inflation and money supply in Indonesia. Business:
Theory and Practice 19: 80-87. https://doi.org/10.3846/
btp.2018.09

Ştefan C, Bratu A (2016) The inflation-unemployment tradeoff
in a macroeconometric model. British Journal of Economics,
Finance and Management Sciences 12 (1): 22-31 http://www.
ajournal.co.uk/EFpdfs/EFvolume12(1)/EFVol.12 (1) Article 3

Thayaparan A (2014) Impact of inflation on economic growth
in Sri Lanka: a study of time series analysis. Global Jour-
nal of Management and Business Research: B Economics
and Commerce 13 (5): 45-54. https://doi.org/10.11648/j.
jwer.20160501.11

Touny MA (2013) Investigate the long-run trade-off betwe-
en inflation and unemployment in Egypt. International

Journal of Economics and Finance 5 (7): 115-125. https://
doi.org/10.5539/ijef.v5n7p115

Ul-Haq I, Khan S, Khan A, Ahmed E (2012) Phillips curve or
locus critique: time series evidence from Pakistan. Journal of
Economics and Behavioral Studies 4 (4): 190-193 https://www.
researchgate.net/publication/301551781_Revisiting_the_Phil-
lips_Curve_and_the_Lucas_Critique

Umaru A, Zubairu AA (2012) An empirical analysis of the re-
lationship between unemployment and inflation in Nigeria
from 1977–2009. Economics and Finance Review 1 (12):
42-61. https://doi.org/10.1111/1467-8268.00061

Yelwa M, David OOK, Omoniyi AE (2015) Analysis of the re-
lationship between inflation, unemployment and economic
growth in Nigeria: 1987–2012. Applied Economics and
Finance 2 (3): 102-109. https://doi.org/10.11114/aef.v2i3.943

Zaman K, Khan MM, Ahmad M, Ikram W (2011) Inflation,
unemployment and the NAIRU in Pakistan (1975–2009).
International Journal of Economics and Finance 3 (1): 245-
254. https://doi.org/10.1016/S2212-5671(12)00015-9

10 G. Sasongko, A. D. Huruta. The causality between inflation and unemployment: the Indonesian evidence

RethinkingThe Current Inflation Target Range In South

Africa
Lumengo Bonga-Bonga, Ntsakeseni Letitia Lebese

The Journal of Developing Areas, Volume 53, Number 2, Spring 2019, pp.
13-27 (Article)

Published by Tennessee State University College of Business
DOI:

For additional information about this article

Access provided by Ebsco Publishing (11 Sep 2018 13:19 GMT)

https://doi.org/10.1353/jda.2019.001

8

https://muse.jhu.edu/article/702993

https://doi.org/10.1353/jda.2019.0018

https://muse.jhu.edu/article/702993

T h e

J o u r n a l o f D e v e l o p i n g A r e a s
Volume 53 No. 2 Spring 20

19

RETHINKING THE CURRENT INFLATION

TARGET RANGE IN SOUTH AFRICA

Lumengo Bonga-Bonga

Ntsakeseni Letitia Lebese

University of Johannesburg, South Africa

ABSTRACT

Critics argue that inflation targeting is not an appropriate monetary policy framework for

developing countries. They maintain that developing countries are more susceptible to the negative

effects of external shocks due to the uncertainty perceived by investors with respect to their

political and economic stability. It is in this line that this paper assesses whether the 3%-6%

inflation target is the optimal inflation target band in South Africa. To determine the optimal level

of inflation target in South Africa, this paper follows the methodology developed by Ball and

Mankiw (2002), which rests on the premise that there is a short run trade-off between inflation and

unemployment. Ball and Mankiw (2002) show that there exists a level of unemployment that is

consistent with stable inflation. The unemployment level that corresponds with a stable inflation is

known as the non-accelerating inflation rate of unemployment (

NAIRU

). Thus, this paper uses an

expectations-augmented Phillips curve to estimate a time-varying NAIRU for South Africa from

1980 to 2015. We use the headline inflation rate and the official unemployment rate based on the

narrow definition to evaluate the appropriateness of the current inflation target range. Quarterly

data from 1980 to 2015 sourced from Quantec is used to this end. The results of the empirical

analysis indicate that, if South Africa were to put in place an inflation target range based on the

NAIRU, it would have to target an inflation rate that ranges from 1.4 to 11.5 percent. This range is

different to

the official inflation target of 3% to 6% adopted by the South African Reserve Bank

(SARB). Furthermore, this paper finds that the Phillips curve is not vertical in South Africa, as

actual inflation does not depend solely on inflation expectations. The policy implication of the

findings of this paper is that the South African Reserve Bank should think about revising its current

inflation target, as it is too narrow for an emerging economy. The current low range of inflation

target could have a negative effect on output and unemployment in the country. This paper

recommends that the SARB should rely on the realities of the South African economy rather than

on external concerns when defining the range of inflation target.

JEL Classifications: E52, C13
Keywords: inflation target, NAIRU, unemployment, South Africa

Corresponding Authors’ Email Addresses: lbonga@uj.ac.za

INTRODUCTION

South Africa had double-digit inflation rates from the 1970s to the early 1990s

(Casteleijn 1999; Rossouw & Padayachee, 2008). In 1994, following its re-integration

into the international economy, South Africa was subjected to increased political pressure

to reduce its inflation rate to levels which were commensurate with its trading partners

(Banerjee, Galioni, Levinsohn, McLaren & Woolard 2008; van der Merwe 2004;

Padayachee, 2001; Ricci, 2005). Subsequently, the government introduced inflation

targeting as a monetary policy framework in 2000 (Mboweni 1999) Inflation targeting

policy aimed at stabilising prices in South Africa. A number of studies support the view

14

that the pursuit of price stability has been successful in reducing the high inflation rate in

South Africa (National Labour and Economic Development Institute, 2004 Ricci, 2005).

South Africa managed to achieve single digit inflation rates in the mid-1990s for the first

time since the 1960s, (Casteleijn, 1999). However, while the South African Reserve Bank

(SARB) succeeded in reducing inflation, the government failed to realise the

development objectives as set out in GEAR (National Labour and Economic

Development Institute, 2004). South Africa experienced a continued increase in the

unemployment rate from 1980 to 2010, reaching 27% in 2003 (Department of Economic

Development, 2010). In 2010, South Africa was ranked amongst the top 10 countries in

the world that have high unemployment rates (Department of Economic Development,

2010). According to McCord and Bhorat (2003), South Africa has the highest

unemployment rates compared to other emerging countries in Africa, Asia and Latin

America. The rising unemployment may be attributed to the change in monetary policy as

international empirical evidence (Akerlof, Dickens, Perry, Gordon & Mankiw 1996;

Fortin, 2001; Lundborg & Sacklẻn, 2006) indicates that a change in the inflation

rate

from a high level to a low level is associated with perpetual increases in the

unemployment rate. Some experts reinforce this observation by noting that at low

inflation rates, the Phillips curve is negatively sloped, implying a trade-off between

inflation and unemployment (Akerlof et al., 1996; Holden, 2002; Lundborg and Sacklẻn,

2006; Padayachee, 2001). The existence of trade-off means that reductions in the

inflation rate are directly correlated with increases in the unemployment rate (Erceg,

2002). Hence, it is essential for a central bank to establish the correct shape of the Phillips

curve for that particular economy and the consequent dynamics of the

inflation/unemployment trade-off before deciding, on the one hand, on the suitable

monetary policy regime to pursue or, on the other, if inflation targeting is the choice, on

the targeted band.

Critics argue that inflation targeting is not an appropriate monetary policy

framework for developing countries (Kahn, 2008). They maintain that developing

countries are more susceptible to the negative effects of external shocks due to the

uncertainty perceived by investors with respect to their political and economic stability

(Ricci, 2005). Investor uncertainty and the requirement of a flexible foreign exchange

market expose small developing economies to negative external shocks more than it does

big and developed economies (Kahn, 2008; Ricci, 2005). In addition, some scholars

argue that if the central bank makes the pursuit of price stabilisation its sole objective,

economic growth and development are inhibited (Kahn, 2008). However, recent studies

(Arkerlof, Dickens, Perry, Bewley and Blinder, 2000; Ball and Mankiw, 2002; Fortin,

2001; Lundborg and Sacklẻn, 2006; Hsing, 2009; Fortin, 2001), on the relationship

between inflation and unemployment indicate that there is a particular point on a non-

linear Phillips curve where the trade-off is optimised and central banks should set the

target for inflation at this specific point. Since a number of authors suggest that inflation

targeting derives its justification from the theory of natural rate (Bernanke, et al., 1999;

Bernanke, 2003), it is clear that the optimal target for inflation rate should be set by

taking into account the level of natural rate of unemployment, which determines the

shape of the Phillips curve. However, the widespread implementation of low-inflation

targets assumes, without proper verification, the principle of a vertical long-run Phillips

curve, whereby inflation has no long-run effect on unemployment. This paper shows that

15

setting inflation targets simply by relying on the idea of vertical Phillips curve may be

misleading if the actual shape of the Phillips curve is not vertical.

Given the fact that unemployment has maintained a growing trend in South

Africa even after the adoption of the inflation targeting policy, the policy has become

very controversial (Department of Economic Development, 2010; Banerjee, Galioni,

Levinsohn, McLaren & Woolard, 2007; Klasen & Woolard, 2008), this paper aims to

evaluate whether the 3%-6% inflation target band is at a level that optimised the trade-off

between inflation and unemployment. An optimal inflation ban should refrain from

generating excess unemployment. The remainder of this paper is organised as follows: In

Section 2, the literature is reviewed. This is followed by the discussion of methodology in

Section 3. The estimation of results is detailed in section 4 while in section 5 we provide

the conclusion and policy implications of the research.

LITERATURE REVIEW

Theoretical Discussion

The debate on the relationship between inflation and unemployment dates back to 1958

when Phillips (1958) found a nonlinear relationship between unemployment and changes

in wages by conducting empirical tests on data from the United Kingdom for the period

1861 to 1951. Based on the results of his study, Phillips concluded that by accepting

some degree of inflation, central banks could maintain lower rates of unemployment

(Van der Merwe, 2004). Phillips’ conclusion led to major disagreements among

economists on the existence of a relationship between inflation and unemployment and,

consequently, the actions that should be taken to address the trade-off between these

economic indicators (Friedman, 1968; Burger and Marinkov, 2006; Fischer, 1996;

Mankiw, 2001).

Monetary economists argue that the Phillips curve is vertical in the long run

(Friedman, 1968; Fischer, 1996; Michie, 2003). According this group of economists, the

trade-off between inflation and unemployment is temporary and mainly results from the

behaviour of workers as they adjust their wage expectations following an increase in the

unemployment rate (Hodge, 2002; Friedman, 1968). In the long run, following the full

incorporation of inflation expectations in wage negotiations; there is an increase in the

inflation rate and the unemployment rate remains unchanged as nominal wages adjust

towards their real rate (Hodge, 2002). The unemployment rate thus returns to its natural

rate and hence, remains stable in the long run (Friedman, 1968:8 and Michie, 2003). A

vertical Phillips curve, as hypothesised by the monetarists, implies that there is no

permanent relationship between inflation and unemployment. Post-Keynesian economists

contest this tenet of monetary economists. They are of the view that workers base their

wages on adaptive expectations; and that a trade-off between inflation and unemployment

depends on how quickly workers adopt their future inflation expectations following

disturbances in the economy and on the bargaining power of employers during wage

negotiations (Holden, 2002; Michie, 2003; Hodge, 2002 and Michie, 2003). The post-

Keynesian economists maintain that at any unemployment rate, besides the natural rate of

unemployment, there will be a trade-off between inflation and unemployment (Michie,

2003). The debate between the Keynesian, monetary and post-Keynesian economists can

16

only be resolved by evaluating empirical evidence to conclude whether or not there is a

relationship between inflation and unemployment or whether the Phillips curve is vertical

or not. The empirical evidence is presented below.

INTERNATIONAL EMPIRICAL EVIDENCE

Existence of a Trade-Off

Empirical evidence supports the existence of a trade-off between inflation and

unemployment. Research-conducted for European countries (Holden, 2002); certain

member countries of the Organisation for Economic Corporation and Development (Ball,

Mankiw & Nordhaus, 1999) as well as for the United States of America (Karanassou,

Sala & Snower, 2010; Banarjee et al., 2008; Akerlof et al., 2000)- demonstrates that there

is a trade-off between inflation and unemployment. The trade-off results from, inter alia,

expectations about future inflation coupled with rigidities in nominal wages (Akerlof et

al., 1996; Akerlof et al., 2000); regulated and highly unionised labour markets (Holden,

2002) and shocks to economies (Svenssons, 1999). In their study, Akerlof et al. (2000)

find that employees’ expectations deviate from rational behaviour at low inflation levels

and that workers and employers tend to ignore the impact of low inflation when they

negotiate wages and prices. Akerlof et al. (2000:4) use survey data based on inflation

expectations of workers and employers to determine the inflation rate and the

unemployment level, which optimises the trade-off between

inflation and unemployment.

In contrast to models based on rational expectations; Akerlof et al. (2000) find that the

Phillips curve is not vertical at low inflation levels, and that there is a trade-off between

inflation and unemployment.

In his study on European countries, Holden (2002) find that countries with

regulated labour markets, and where the majority of workers are unionised, face a trade-

off between inflation and unemployment when they target low inflation rates. This

situation arises because, in unionised labour markets, wages can only be changed through

mutual consent between employers and workers (Holden, 2002). Hence, workers and

their labour unions possess bargaining power, which they can use to prevent employers

from effecting cuts in nominal wages when inflation is low (Holden, 2002) The inability

of employers to reduce nominal wages unilaterally means that, in the long run, increases

in nominal wages lead to increases in inflation rate (Holden, 2002).

Determination of an Optimal Inflation Rate

The existence of a trade-off means that there is an optimal inflation target. An appropriate

inflation target would ideally be close to the optimal inflation rate on a nonlinear Phillips

curve. Given the presence of a trade-off, studies have been conducted in certain

developed countries (Akerlof et al., 2000; Fortin, 2001; Lundborg & Sacklen, 2006;

Maugeri, 2010; Wyplosz, 2000) to evaluate whether the inflation target is set at a level

that optimises the trade-off between inflation and unemployment. For example, Fortin

(2001:7) in his analysis of inflation in Canada over the period 1992 to 2002 find that the

inflation target, which is set by the Bank of Canada exhibits a trade-off between inflation

17

and unemployment. While the Bank of Canada has set an inflation rate band of 1%-3%,

Fortin (2001) find that an optimal inflation target band for Canada would be 2%-3%.

In a study of inflation targeting policy in Sweden, Lundborg & Sacklen (2006)

find that the inflation target of 2 percent, which is pursued by the European Central Bank,

is not at a level that optimises the trade-off between inflation and unemployment.

Lundborg & Sacklen (2006) analyse data from 1963 to 2000 and find that the trade -off

would be optimised if the European Central Bank targeted an inflation rate of 4 percent.

A similar study was conducted by Maugeri (2010) for Italy for the period 1960to2003.

Maugeri (2010) finds that the inflation target of 2 percent, which is pursued by the

European Central Bank, is not optimal for Italy. Maugeri notes that the inflation rate that

would minimise the trade-off between inflation and unemployment in Italy is between

15% and 20% (Maugeri, 2010). Making use of survey data on inflation expectations of

economic agents in regulated and unionised labour markets of France, Germany, the

Netherlands and Switzerland from 1960 to 1999, Wyplosz (2000) investigates the

inflation rate that optimises the trade-off of the two variables in these countries. Wyplosz

finds that the 2 percent inflation target, which is pursued by the European Central Bank,

results in trade-off between inflation and unemployment, that is, the low inflation target

leads to the increase in unemployment in these countries. The results from Wyplosz’s

study (2000) suggest that an optimal inflation rate for France, Germany, the Netherlands

and Switzerland should be 5 percent. While an optimal inflation target range should

refrain from generating excess unemployment, Mishkin and Westelius (2008) suggest

that the higher the uncertainty about the inflation process, the wider should the target

range be. This is true for emerging market countries, which are more vulnerable to

external shocks and likely to have more uncertainty about inflation outcomes.

Empirical Evidence in South Africa

Most of the literature on inflation targeting in South Africa (e.g. du Plessis and Burger,

2006; Fedderke and Schaling, 2005; Nell, 2002 and Pretorius and Smal, 1994) indicates

the existence of trade-off, in the short run, between inflation and unemployment or

between inflation and proxies for demand effects such as marginal costs and output gaps.

Early studies, which were conducted in the 1960s and 1970s, find trade-off between

inflation and unemployment (e.g. Gallaway, Koshal and Chapin (1970); Hume (1971);

Hodge (2002); du Plessis and Burger (2006)) – and between inflation and output gaps

(e.g. Krogh (1967); Truu (1975); Strydom and Steenkamp (1967);du Plessis and Burger

(2006)). Recent studies based on output gaps (Pretorius & Smal, 1994; Fedderke &

Schaling, 2005; Nell; 2002) mainly find evidence of trade-off between inflation and

output gaps. Studies, based on expectations-augmented Phillips curve of South Africa

(Pretorius and Smal, 1994 and Fedderke and Schaling, 2005), find that the trade-off

occurs indirectly through labour costs rather than through prices.

More recent studies (Leshoro, 2012; Phiri, 2010; Gupta and Uwilingiye, 2008)

demonstrate that the 3%-6% inflation target range limits the level of economic growth in

South Africa. Leshoro (2012) asserts that an inflation rate greater than 4 percent has a

negative effect on GDP growth rates. Gupta and Uwilingiye (2008) assert that a 3% to

6% inflation target range results in a welfare loss, which ranges from 0.34 percent to 0.67

percent of GDP. Phiri (2010:354) find an inflation threshold of 8 percent and concludes

18

that any inflation rate below and above the threshold will have an adverse effect on

growth.

Methodology

To determine the optimal level of inflation target in South Africa, this paper follows the

methodology developed by Ball and Mankiw (2002), which rests on the premise that

there is a short run trade-off between inflation and unemployment. Given the existence of

the short run trade-off, Ball and Mankiw (2002) show that there exists a level of

unemployment that is consistent with stable inflation. The unemployment level that

corresponds with a stable inflation is known as the non-accelerating inflation rate of

unemployment (NAIRU) (Gordon, 1997 and Ball & Mankiw, 2002). Thus, this paper

uses an expectations-augmented Phillips curve to estimate a time-varying NAIRU for

South Africa from 1980 to 2015. The NAIRU is not directly observable but is estimated

based on variables which are used to determine an expectations-augmented Phillips curve

(see Staiger, Stock, and Watson, 1997; Boone, Giorno, Meacci, Rae, Richardson and

Turner, 2003). To determine if the SARB target is set at a level that optimises the trade-

off between inflation and unemployment, the estimated NAIRU is used to determine a

stable inflation rate for South Africa. The inflation rate, based on the estimated NAIRU,

is then compared to the inflation target range that is adopted by the South African

Reserve Bank. .

The short-run trade-off between inflation and unemployment can be expressed

as:

U  (1)

Where ? represents the inflation rate; ? represents the unemployment rate;  is a
parameter which represents a constant term in the equation; and  >0 is a parameter

which measures how the inflation rate responds to changes in the unemployment rate.

Nonetheless, the amended version of the inflation-unemployment dynamics is

represented as:

    *UUe (2)

Where
e

 is expected inflation,
*

U is the NAIRU and  is the supply shock. Equation
2 shows that the actual inflation depends on the expected inflation and how U deviates

from
*

U . Supply shocks, such as oil crisis and changes in the exchange rate, may affect
also the level of inflation.

Ball and Mankiw (2002:118) acknowledge that economic agents base their

decisions on adaptive expectations
1
. Thus, the expectations-augmented Phillips curve,

which is based on adaptive expectations, is thus presented as follows:

19

   


UU
*

1 (3)

Where ?−1 represents the inflation rate observed during the previous period.
The expectations-augmented Phillips curve, as represented by equation 3 above,

is used as a base to derive an estimate of a time-varying NAIRU that is used to determine

a stable inflation rate. Ball and Mankiw (2002) show that to estimate the NAIRU,

Equation 3 should be rewritten as:

  UU
*

(4)

Suppose that the value of parameter  is estimated, Equation 4 becomes:










 *
UU (5)

Given that  and U are observed from the data, the left-hand side of Equation 5 can be

computed to provide the estimate of





*
U . Ball and Mankiw (2002) suggest the use

of Hodrick-Prescot (HP) filter to obtain
*

U , which represents the longer-term trend and




, the shorter-term supply shock or cyclical movement. The inflation rate, which

corresponds to the natural rate of unemployment, is estimated by imputing values for

parameters derived above into Equation 4.

Data and Empirical Results

In this study, we use the headline inflation rate ( )2 and the official unemployment rate
based on the narrow definition ( U ) to evaluate the appropriateness of the current
inflation target range. Quarterly data from 1980 to 2015 is used and data for  and U

are sourced from Quantec. In order to estimate the NAIRU (
*

U ) we estimate the

coefficient  from Equation 1. However, given that both  and U have a unit root, we
have to test if there is a cointegrating relationship between these variables by making use

of the Engel-Granger cointegration test. It is important to note that the choice of the

Engle-Granger cointegration over other cointegration techniques, such as the Johansen

technique, is justified by the fact the Engle-Granger cointegration technique is robust

even when series are fractionally integrated or depict long memory (Gonzalo and Lee,

2000). Gonzalo and Lee (2000) show that it is very difficult to distinguish between series

20

that are fractionally integrated and those that have unit root (I(1)). By applying the Engle-

Granger cointegration technique, we believe that the results obtained will be robust even

though the series are fractionally integrated or have long memory. Moreover, Silvapulle

and Podivinski (2000) indicate that modellers should not be concerned by the possibility

of small departure from the condition of non-normality when using cointegration

technique even in finite samples. However, the authors emphasise that ARCH and

GARCH effects may be more problematic and compromise the results obtained from a

cointegration technique. The results of the Engle-Granger cointegration test are reflected

on Table 1. The results show that the null hypothesis of no cointegration between  and
U is rejected when inflation is an endogenous variable.

The study adopts the results of the Engle-Granger cointegration test and

estimated the cointegrating parameter in the relationship between unemployment and

inflation. The estimation, as per Equation 1, yields a value of 0.66 for parameter  . The

estimated equation is represented as:

U66.02.23  (6)

TABLE 1. RESULTS OF THE ENGLE-GRANGER COINTEGRATION TEST

Note: the null hypothesis of no cointegration is rejected when  is endogenous

Given the above suggestion by Silvapulle and Podivinski (2000), we need to make sure

that there is no ARCH effect in the results obtained. Thus, we perform the

heteroscedasticity test to examine the presence of the ARCH effect in the obtained

results. Table 2 presents the results of the ARCH test for heteroscedasticity in the

residual. This test aims to detect the presence of time-varying volatility or ARCH effect

in the residual. The results presented in Table 2 show that the null hypothesis of no

ARCH is not rejected with the F-statistics and the observed R-Squared. This indicates

that the residual is homoscedastic and there is no need to account for time-varying

volatility in the estimated model.

TABLE 2. HETEROSCEDASTICITY TEST: NULL HYPOTHESIS OF NO ARCH

F-Statistics 1.707

Probability 0.2001

Obs R-Squared 1.7

24

Probability Chi-Square 0.1892
Note: The probabilities show the rejection of the null hypothesis

Knowing the value of  , we proceed to estimate the natural rate of unemployment as per
the procedure described above by using the HP filter. Figure 1 below provides a

diagrammatic representation of South Africa’s NAIRU, together with the headline

inflation and actual unemployment rate, from 1980 to 2015.

Variable z-statistic Probability
 -29.6342 0.0214

U -18.20813 0.2031

21

FIGURE 1. GRAPHICAL REPRESENTATION OF SOUTH AFRICA’S

ESTIMATED NAIRU, INFLATION AND UNEMPLOYMENT FROM 1980 TO

2015

Figure 1 shows that the estimated time-varying NAIRU increases substantially from

single digit figures in the 1980s to double digit figures from the late 1980s to 2000. From

the year 2000 to 2015, the NAIRU stabilises around 23 to 25 percent. In Kabundi et al.

(2015) and Viegi (2015) we find support for these findings as both studies show that the

NAIRU stabilises around these rates in South Africa. Figure 1 further shows that the

unemployment rate is below the NAIRU during the period 1994 to 1997. This period

corresponds to increasing economic activities in South Africa. However, the

unemployment rate in South Africa trended above the NAIRU during the period 1998-

2000 marking the period of contagion from the Russian and Latin American financial

crises and during the period post 2008, signifying the effects of global financial crisis o n

the South African labour market. The trend of unemployment compared to the NAIRU is

an evidence of the contribution of external shocks to the labour market and economic

activities in South Africa.

It is clear from Figure 1 that the relationship between inflation ( ),
unemployment and NAIRU is far from supporting the evidence of a vertical Phillips

curve in South Africa, especially after 2004. Figure 1 shows that deviation of

unemployment rate from NAIRU translates to changes in inflation rate. For example, in

the period 2004-2008 the unemployment rate in South Africa is below the NAIRU. This

deviation of unemployment rate from the NAIRU coincides with the increase in inflation

rate in South Africa. However, during the period of global financial crisis and the

afterward of the crisis, unemployment soars above the NAIRU as inflation rate decreases.

This finding indicates thatthe difference between the NAIRU and unemployment

coincide with the change in inflation in South Africa as per the theory of the augmented

Phillips curve. This occurrence indicates that actual inflation is not only influenced by

inflation expectations but also by the deviation of the unemployment rate from the

NAIRU. This further shows that the Phillips curve is not vertical in South Africa, as

actual inflation does not depend solely on inflation expectations. The final step of the

analysis is to obtain estimates of the stable inflation rate that would minimise the trade-

8

12

16

2

0

2

4

28 0

4
8
12
16

20

1980 1985 1990 1995 2000 2005 2010 2015

inflation

unemployment

NAIRU

22

off between inflation and unemployment. The estimates from Equation 3 yield a rate of

inflation, which ranges from 1.4 percent to 11.5 percent from 2000 to 2015. Figure 2

compares the actual inflation rate (  ) and the inflation rate derived from the South
African NAIRU ( InflaN ).

FIGURE 2. SOUTH AFRICA’S INFLATION RANGE BASED ON ESTIMATES

OF NATURAL RATE OF UNEMPLOYMENT (INFLAN) AND THE ACTUAL

INFLATION RATE

0
4
8
12
16
20
1980 1985 1990 1995 2000 2005 2010 2015

Actual inflation InflaN

The results show that InflaN and Actual inflation (  ) have similar trends, but, in

spite of this, they have different ranges. Moreover, these results indicate that, if South

Africa were to put in place an inflation target range based on the NAIRU, it would have

to target an inflation rate that ranges from 1.4 to 11.5 percent. This range is different to

the official inflation target of 3% to 6% adopted by the South African Reserve Bank

(SARB). It is a reality that when South Africa implemented inflation targeting, it chose

an inflation range that coincided with its trading partners rather than a range that could

optimise the trade-off between unemployment and inflation.

It is clear that monetary policy makers in South Africa are adopting the inflation

targeting policy on the premise of a vertical Phillips Curve without sound evaluation of

the domestic context and reality. It is not surprising that stakeholders such as the trade

unions continue to believe that the South African Reserve Bank has been too restrictive in

the conduct of monetary policy. Although South Africa’s trading partners have set

inflation targets at similarly lower rates, these countries have lower unemployment rate

and NAIRU than South Africa’s which gives them the leverage to target inflation rates

over narrow bands. Table 3 indicates that a vast majority of these countries have low and

stable inflation rate interval, and with the exception of Brazil, all the countries, Thailand,

Peru and Israel target inflation at rates which are slightly below South Africa’s inflation

target range. It is then difficult to understand why South Africa has a lower upper bound

inflation rate and a tighter inflation target interval than one of his important trade partner,

Brazil. A wider inflation target, like the one of Brazil is ideal for emerging market

economies that are vulnerable to external shocks. A wider inflation target range could

prevent monetary authorities from frequently reacting to external shocks. Stringent anti-

inflationary policies might have caused persistent and high unemployment in South

Africa. Literature shows that a number of emerging market economies have harmed their

economies from unnecessarily reacting to external shocks. For example, Mackowiak

(2007) shows that United States (US) monetary policy shocks affect a larger fraction of

23

the variance in the aggregate price level and aggregate output in emerging economies

than of the variances in the same variables in the US itself. Moreover, Kaminsky et al.

(2005) indicate that emerging market economies are overwhelmingly procyclical in their

conduct of monetary policy and often deepen downturns when reacting to external

shocks. South African monetary authority may have caused reduced economic activities

as it responds to the effects of external shocks on the domestic economy with restrictive

measures. Indeed a number of studies have found that restrictive monetary policy

reactions often fail to reduce inflation in South Africa (Bonga-Bonga and Kabundi,

2011). Moreover, in a historical perspective, the 3% to 6% inflation range adopted by the

SARB is strikingly low, as Figure 1 shows that in the 1980s when South Africa had one

digit unemployment rate, inflation rate was between 12% and 14%.

TABLE 3. EMERGING MARKETS’ SOUTH AFRICA’S COMPETITORS WHO

ADOPTED INFLATION TARGETING

Country
Inflation

Target range

Unemployment

rate

Chile 2 – 4% 5.90%

Israel 1 -3 % 6.20%

Brazil 1.5 – 8.5% 6.50%

Czech Republic 2 – 4% 7%

Thailand 0.90% 0.80%

Source: Levin, Natal & Piger (2004) and Fraga, Goldfajn & Minella (2003), World Bank (2015)

Note: unemployment rate is for the period 2006-2010.

CONCLUSIONS

This paper aims to assess whether the SARB 3% to 6% inflation target range is at a rate

that optimises the trade-off between inflation and unemployment. Empirical work shows

that countries with low levels of inflation and/or whose Phillips Curve is vertical (many

of which are developed economies) may adopt low inflation rate/ ranges targets. Studies

also show that despite South Africa having non-vertical Phillips Curve, significant high

levels of unemployment, and high-income inequality, its inflation targeting policy is

modelled around a narrow inflation rate band with a lower upper bound, unlike countries

at similar level of development such as Brazil. Furthermore, while South Africa’s

inflation target range compares well with that of other emerging market countries, the

level of unemployment in South Africa is higher than that of these emerging market

countries. In this research work, we estimate the stable inflation rate based on the

expectations-augmented Phillips curve for South Africa. Given the magnitude of the

computed NAIRU, the estimation results provide an inflation range that is wider than the

current inflation target range set by the South African Reserve Bank. The paper

concludes that the current tighter inflation target policy in South Africa is based on

unsound fundamentals and may have led to some unnecessary responses by the monetary

authority to apply restrictive measures which in turn have been detrimental to output

growth and reduced employment, among other things.

The policy implication of this paper is that the South African Reserve Bank

(SARB) should rethink the current inflation target range of 3% to 6%. Such a narrow

24

range of inflation target could have a negative effect on output and unemployment in the

country given that monetary authority may be forced to apply unnecessary contractionary

monetary policy. Moreover, the findings of this paper that SARB should broaden the

inflation target range could even suggest that dual targets, inflation and output targets,

may be appropriate for South Africa. The poor output growth that South Africa

experiences warrants scrutiny by policymakers.

ENDNOTES

1
Ehlers and Steinbach (2007) show that economic agents in South Africa make use of adaptive

expectations to a certain extent in forming their expectations of future inflation.
2
It is important to note that the South African Reserve Bank (SARB) was targeting CPIX-inflation

from 2000 t0 2008 instead of the headline CPI. In 2008, the SARB reverted to targeting headline

CPI inflation.

REFERENCES

Arkerlof, GA, Dickens, WT, Perry, GL, Gordon, RJ, Mankiw, NG (1996) The

macroeconomics of low inflation. Brookings Papers on Economic Activity 1999: 1 – 76.

Arkerlof, GA, Dickens, WT, Perry, GL, Bewley, TF, Blinder, AS (2000) Near-

Rational Wage and Price Setting and the Long-Run Phillips Curve. Brookings Papers on

Economic Activity 2000: 1 – 60.

Ball, L, Mankiw, NG, Nordhaus, WD (1999) Aggregate Demand and Long-run

Unemployment. Brookings Papers on Economic Activity 1999: 189 – 2

51.

Ball, L, Mankiw, NG (2002) The NAIRU in theory and practice. Journal of

Economic Perspectives 16: 115 – 136.

Banerjee, A, Galioni, S, Levinsohn, J, McLaren, Z, Woolard, I (2007) Why has

unemployment risen in the new South Africa? National Bureau of Economic Research,

NBER Working Paper Series, Working Paper No. 13167.

Banerjee, A, Galioni, S, Levinsohn, J, McLaren, Z, Woolard, I. (2008). Why has

unemployment risen in the new South Africa? Economics of Transition 16: 715 – 740.

Bernanke, BS, Laubach, TS, Mishkin, FS (1999) Inflation targeting. Princeton

University Press, Princeton

Bernanke, BS (2003) Panel discussion, October 17. www.federalreserve.

gov/boarddocs/speeches/2003. Accessed 12 Decembre 2015.

Bonga-Bonga, L, Kabundi, A (2011) Monetary policy action and inflation in

Siuth aFrica: an empirical analysis, African Finance Analysis 13: 25-37.

Boone, L, Giorno, C, Meacci, M., Rae, D, Richardson, P, Turner, D (2003)

Estimating the structural rate of unemployment for the OECD countries. OECD

Economic Studies 2002/2: 172 – 211.

Burger, P, Marinkov, M (2006) The South African Phillips curve: How

applicable is the Gordon model? South African Journal of Economics 74: 172 – 189.

Casteleijn, AJH (1999) The Viability of Implementing an Inflation Targeting

Monetary Policy Framework in South Africa. South African Reserve Bank:

Pretoria.

Department of Economic Development (2010). New Growth Path Framework.

Department of Economic Development: Pretoria Department of Finance (1996) Growth,

25

Employment and Redistribution: A Macroeconomic Strategy. Department of Finance:

Pretoria.

Du Plessis, S, Burger, R (2006) A New Keynesian Phillips Curve for South

Africa. Paper presented at the South African Reserve Bank Conference: Pretoria.

Ehler, N, Steinbach, R (2007) The formation of inflation expectations in South

Africa. Working paper no. 3243, South African Reserve Bank.

Erceg, CJ (2002) Choice of an Inflation Target Range in a Small Open

Economy. AEA Papers and Proceedings 92: 85 – 89.

Fedderke, JW, Schaling, E ( 2005) Modelling Inflation in South Africa: A

multivariate cointegration analysis. South African Journal of Economics 73: 79 – 92.

Fischer, S (1996) Why are central banks pursuing long-run price stability?

Federal Reserve Bank of Kansas City: Kansas City.

Fortin, P, (2001) Inflation targeting: the three percent solution. Policy Matters 2:

1 – 16.

Fraga, A, Goldfajn, I, Minella, A (2003) Inflation targeting in emerging market

economies. National Bureau of Economic Research, NBER Working Paper Series,

Working Paper No. 10019.

Friedman, M, (1968) The Role of Monetary Policy. The American Economic

Review 58:1 – 17.

Gonzalo, J and Lee, TH (2000). On the robustness of cointegration tests when

series are fractionally integrated. Journal of Applied Statistics, 27(7): 821-827.

Gordon, RJ (1997) The time-varying NAIRU and its implications for economic

policy. Journal of Economic Perspectives 11:11 – 32.

Gupta, R, Uwilingiye, J (2008). Measuring the Welfare Cost of Inflation in

South Africa. Economic Research Southern Africa, Working Paper No. 68.

Hodge, D (2002) Inflation versus unemployment in South Africa: Is there a

trade-off? South African Journal of Economics 70: 418 – 443.

Holden, S (2002) The costs of price stability – downward nominal wage rigidity

in Europe. The National Bureau of Economic Research, Working Paper No. 8865.

Hsing, Y (2009) Estimating the time-varying NAIRU for Germany and policy

implications. Applied Economic Letters 16:469 – 473.

Kabundi, A, Schaling, E, Some, M (2015) Estimating a Phillips Curve for South

Africa: A Bounded Random Walk Approach. ERSA working paper no. 568.

Kahn, B (2008) Challenges of Inflation Targeting for Emerging-market

Economies: The South African Case. Paper presented for the South African Reserve Bank

Conference Series on Challenges for monetary policy-makers in emerging markets,

Pretoria.

Kaminsky, GL, Reinhart, CM, Vegh, CA (2005) When it rains, it pours:

procyclical capital flow and macroeconomic policies, in NBER macroeconomic Annual

2004, Vol.19, ed. By Gertler, M. and Rogoff, K., Cambridge: NBER.

Karanassou, M, Sala, H, Snower, DJ (2010) Phillips curves and unemployment

dynamics: A critique and holistic perspective. Journal of Economic Perspectives 24: 1 –

51.

Klasen, S, Woolard, I. (2008) Surviving Unemployment without State Support:

Unemployment and Household Formation in South Africa. Journal of African Economies

18: 1 – 51.

26

Leshoro, TLA. (2012) Estimating the inflation threshold for South Africa.

Economic Research Southern Africa. ERSA, Working Paper 285.

Levin, AT, Natalucci, FM, Piger, JM (2004) The macroeconomics effects of

inflation targeting. Conference proceeding of the 28
th

annual economic policy conference

on “Inflation Targeting: Prospects and Problems” held in St Loius. Conducted by the

Federal Reserve Bank of St Louis. St Louis: Federal Reserve Bank of St Louis.

Lundborg, P, Sacklẻn, H (2006) Low-inflation targeting and long run

unemployment. The Scandinavian Journal of Economics 108:397 – 418.

Mackowiak, B (2007) External shocks, US monetary policy shocks and

macroeconomic fluctuations in emerging market. Journal of monetary economics 54:

2512-2520.

Mankiw, NG (2001) The Inexorable and Mysterious Trade-off between Inflation

and Unemployment. The Economic Journal 111: C45 – C61.

Maugeri, N (2010) Macroeconomic Implications of Near Rational Behaviour:

An Application to the Italian Phillips Curve. Unpublished doctoral thesis. Siena:

Università di Siena.

Mboweni, TT (1999) Inflation targeting in South Africa. The South African

Journal of Economics 67: 401 – 409.

McCord, A, Bhorat, H (2003). Employment and Labour Market Trends. In:

Kraak A, Perold, H (eds) Human Resources Development Review. HSRC Press: Cape

Town

Michie, J (2003) The Institutional Underpinnings of the Unemployment-Inflation

Relationship: A Review Paper. Human Sciences Research Council: Pretoria:

Mishkin, FS, Westelius, NJ (2008) Inflation Band Targeting and Optimal

Inflation Contracts. Journal of Money, Credit and Banking 40: 557-582

National Labour and Economic Development Institute (2004). Highlights of the

current labour market conditions in South Africa. Johannesburg: National Labour and

Economic Development Institute.

Nell, KS (2002) Is low inflation a precondition for faster growth? The case of

South Africa. University of Kent, Department of Economics, Working Paper No. 0011.

Padayachee, V (2001) Central bank transformation in a globalized world: The

Reserve Bank in post-apartheid South Africa. Journal of International Development 13:

741 – 765.

Phillips, AW (1958) The Relation between Unemployment and the Rate of

Change of Money Wage Rates in the United Kingdom, 1861 – 1957. Economica 25:283-

299.

Phiri, A (2010) At what level is inflation least detrimental towards finance-

growth activity in South Africa? Journal of Sustainable Development in Africa 12: 354 –

364.

Pretorius, CJ and Smal, MM (1994) A macro-economic examination of the

price-formation process in the South African economy. The South African Reserve Bank

Quarterly Bulletin 191: 25 – 36.

Ricci, LA (2005) Bringing inflation under control. In: Nowak, M, Ricci, LA

(eds) Post-Apartheid South Africa: The First Ten Years. International Monetary Fund:

Washington.

http://ideas.repec.org/a/mcb/jmoncb/v40y2008i4p557-582.html

http://ideas.repec.org/a/mcb/jmoncb/v40y2008i4p557-582.html

27

Rossouw, J, Padayachee, V (2008) An Analysis of inflation from a central

banking perspective: The South African experience since 1921. University of Kwazulu

Natal: School of Development Studies. Working Paper No. 50.

Silvapulle, PS and Podivinsky, JM (2000). The effect of non-normal

disturbances and conditional heteroskedasticity on multiple cointegration tests. Journal of

Statistical Computation and Simulation, 65(1-4): 173-189

Staiger, D, Stock, JH, Watson, MW (1997) How Precise are Estimates of the

Natural Rate of Unemployment? The Journal of Economic Perspectives 11: 34 – 49.

Svenssons, LEO (1999) Price-level targeting versus inflation targeting: A free

lunch? Journal of Money, Credit and Banking 31:277 – 295.

Van der Merve, EJ (2004) Inflation targeting in South Africa. The South African

Reserve Bank Occasional Paper No. 19.

Viegi, N (2015) Labour Market and Monetary Policy in South Africa. SARB

Working Paper 15/01.

World Bank (2015) World Development Indicators. http://beta.worldbank.org.

Accessed 27 january 2016.

Wyplosz, C (2000) Do we know how low should inflation be? Conference

proceeding of the first Central Banking Conference on “Why Price Stability” held in

Geneva. Conducted by the European Central Bank. Geneva: Graduate Institute of

International Studies.

http://www.tandfonline.com/doi/abs/10.1080/00949650008811997#.VSaJJ_mUe50

http://www.tandfonline.com/doi/abs/10.1080/00949650008811997#.VSaJJ_mUe50

http://beta.worldbank.org/

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