Rethinking The Current Inflation Target Range In South Afric.docx

Rethinking The 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:
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https://doi.org/10.1353/jda.2019.0018
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
2019
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: [email protected]
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:

Where � represents the inflation rate; � represents the
parameter which represents a constant term in the equation; and
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:
Where
e
*
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:
*
(4)
becomes:
UU (5)
-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
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
are sourced from Quantec. In order to estimate the NAIRU (
*

U ) we estimate the
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
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
estimated equation is represented as:

TABLE 1. RESULTS OF THE ENGLE-GRANGER
COINTEGRATION TEST
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 1.724
Probability Chi-Square 0.1892
Note: The probabilities show the rejection of the null hypothesis
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
20
24
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
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
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.
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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: [email protected] (corresponding author);
[email protected]
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/
mailto:[email protected]
mailto:[email protected]
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

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
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

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
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.
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