Inflation Persistence
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Transcript of Inflation Persistence
Grup Riset Ekonomi
Departemen Kebijakan Ekonomi dan Moneter
Desember 2016
MHA Ridhwan
Angsoka Yorintha Paundralingga
Melva Viva Grace
Juan Samuel
CR/2/GRE-DKEM/2016
CATATAN RISET
Regional Inflation Persistence
Dynamics: A Case of Indonesia Alt: A Regional Analysis of Inflation Persistence Dynamics in Indonesia
1-1
1. Introduction
As an archipelagic economy, Indonesia’s economy has historically experienced
relatively high and persistent inflation rate (Ridhwan, 2014). Nonetheless, in early
2000s, a number of Asian central banks, including Indonesia, have adopted explicit
inflation targeting approach as their monetary policy frameworks (Filardo & Genberg,
2010a). This new anchor for monetary policy following the abandonment of a fixed
exchange rate regime was needed to tame this high inflation.
Second properties of Indonesia’s inflation, high persistence, is also a central
issue to understand the inflation process. Undeniably, quantifying inflation
persistence is essential for central bank to achieve its goal. Meller and Nautz (2012)
survey monetary policies taken by central banks where inflation persistence had been
increasingly used as an indicator of policy effectiveness.
Benati (2008) evaluates estimated persistence across different monetary
regimes from several developed countries over long samples. His finding is that regimes
that clearly anchor inflation generate less persistence inflation. The clear anchor
inflation could be in the form of gold standard or inflation targeting framework. His
research is in-line with Levin, Natalucci & Piger (2004) who found that inflation has
become less persistent in countries that implement inflation targeting framework.
Therefore, after the implementation of inflation targeting framework, the measure of
inflation persistence serves other purposes, namely the ability of the central bank to
control inflation. In this regard, Phiri (2016) argues that high inflation persistence
indicates the inability of central banks to control inflation such that any deviations of
inflation from its steady-state will ensure that inflation does not easily adjust back [to]
its long-run equilibrium. In other words, high inflation persistence has
profound consequences, since it can weaken the central bank’s credibility and public
trust in the monetary policymakers. Jain (2013) explains that if the public did not believe
that the central bank’s policy is credible, inflation that is driven by simple shock and
1-2
supposed to only rise temporarily, then fall back, would instead rise for a long time,
despite the central bank’s attempt to control it.
Roache (2014) compares inflation targeting countries to find relationship
between persistence and ability of monetary policy to accommodate temporary price
shocks, which later be defined as policy space. He found that the higher the persistence,
the smaller the policy space.
Several researchers, such as Fuhrer (1994), Mishkin (2007), Ascari and Ropele
(2012) introduced the term “sacrifice ratio” which measures output growth lost
required to reduce inflation by a percentage point. In short, this ratio represents lower
costs of disinflation. They argue that higher level of inflation persistence is related to
higher sacrifice ratio. Furthermore, this ratio has implications for the output cost of
monetary policy, in which higher persistence implies much costly disinflation monetary
policy.
Cecchetti and Debelle (2006) argue that if inflation persistence has changed to
a lower level, monetary policy’s attempt to reduce inflation will become easier, since
people’s tendency to anchor inflationary expectations to the past will be lessened. In
other words, lower persistent inflation will sustain low inflation rate given that a shock
boosting inflation now would have a less extended impact on the rate of later inflation.
Thus, lower persistent inflation will reduce the cost of disinflation. Fundamentally, the
nature of inflation persistence dynamics indicates important implications for monetary
policy.
It is also important for the central bank to have a good understanding of the
underlying patterns and determinants of regional inflation persistence. A good
understanding of the underlying patterns and determinants which led to the
heterogeneity across region would enable the monetary policymaker to more
accurately forecast regional inflation and construct appropriate monetary policy. It
could also help the regional government in constructing complementary structural
policies in regions whose inflationary processes are less responsive to a common
monetary policy. Inflation persistence varies in its level, magnitude, and variance
2-3
among regions. Then explaining this phenomenon remains a challenge, particularly for
a country that is as large and spatially diverse as Indonesia. This heterogeneity reflects
different regional structural rigidities which could reduce each region’s capacity to
adjust to common and national shocks and the corresponding policy responses. Several
literatures suggest that inefficiency in a regional labor market may explain different
responsiveness of inflation to a common shock.
In this context, the objective of this paper is to estimate the degree of
persistence for Indonesia’s inflation. The degree of persistence in inflation in every
region provides an important test concerning the effectiveness of monetary policy in
controlling inflation. Unfortunately, only few papers investigate the heterogeneity in
the inflation process at a regional level. Several papers, such as Cecchetti et al. (2002),
study the regional dynamics of relative prices across the US. Beck and Weber (2005a,b)
analyze inflation rate dispersion across regions in the US, Japan, Canada and EMU
regions. Studying Italy, Vaona (2009) was the first researcher to study regional inflation
persistence. With its huge disparity between northern and southern region, Italy is an
ideal laboratory to study how inflation persistence may interact with regional
disparities. Benigno (2004) argues that central banks should overweight regions, within
their target index, with stickier price developments and underweight more flexible
regions in order to avoid the former ones that bear a disproportionate part of the
adjustment process following a monetary shock.
The rest of the paper is organized as follows. The next section explains our
methodology used to examine the regional inflation persistence. Section 3 presents the
empirical results. Section 4 concludes.
2. Estimating Persistence at the Regional Level
Fuhrer (2009) explains the persistence using physics theory of inertia, which is
defined as the tendency of a body with some mass to resist acceleration and to remain
at rest, unless acted upon by a force. The greater the mass, the greater the inertia, i.e.
the greater the force required to accelerate the body. Similarly, inflation shows a
2-4
tendency to stay near its equilibrium value. To move inflation from its current level, it
requires economic “force”. Thus the greater the economic force required to move it
from its recent level, the more persistent it is. Willis (2003) defines persistence as the
speed of adjustment of inflation returns to its equilibrium after a shock or after a
deviation. Technically, European Central Bank establishes Inflation Persistence Network
(IPN) research project and define inflation persistence as “the tendency of inflation to
converge slowly (or sluggishly) towards its long-run value following a shock which has
led inflation away from its long-run value”. Vladova and Pachedjiev (2008) argue that
this definition implies to two important subject, i.e., the speed of adjustment and the
equilibrium level of inflation.
The measurement of inflation persistence has two types of analysis. First,
structural persistence represents inflation persistence that comes from known
economic disturbances. Second, reduced-form persistence refers to the empirical and
econometric property of inflation without any economic interpretation. In this paper,
we study reduced-form persistence which is represented using a univariate
autoregressive (AR) function.1 This approach lies on the assumption of stationary
inflationary process. Technically, a non-stationary variable will vary a lot and cross the
estimated mean infrequently. Halunga, Osborn and Senseir, (2009) argue that inflation
targeting is meaningless when inflation is more likely to contain a unit root. If inflation
follows a random walk pattern, then the best forecast of tomorrow’s inflation is today’s
inflation as the most recent observed inflation. Nevertheless, the average value could
not be the predictor of inflation level. Hence, it has been the standard practice in
the empirical works to firstly diagnose the integration properties of a univariate
autoregressive series of inflation. Moreover, several measures of persistence will be
derived from its autocorrelation function for inflation. Examples of such measures
include the integer order of integration, half-life of responses to shocks, the largest
autoregressive root, and the sum of the autoregressive coefficients. Pivetta and Reis
1 While the reduced-form analysis is related with the univariate model, the structural analysis is associated with the multivariate models, such as vector autoregressive models. To obtain measures of persistence in multivariate models, other variables are also used to make inference about the persistence of inflation.
2-5
(2001) define half-life as the number of periods in which inflation is at least half as large
as the initial shock. Other researchers, such as Cogley and Sargent (2001), use the
largest autoregressive root as persistent indicator. Andrews and Chen (1994) suggest
that the sum of autoregressive coefficients (SARC) is still the best scalar measure of
persistence. The inflation series is said to contain a unit root if SARC is greater than or
equal to unity. In other words, shocks to inflation has a permanent effect on the price
level. In this case, the inflation is persistent and will never returns to its mean value or
its equilibrium level. Therefore, following Altissimo, Ehrmann and Smets (2006), we
defined inflation persistence as a tendency for the inflation to converge into the long-
run equilibrium slowly after a shock that brings inflation afar from its long-run
equilibrium.
Inflation Persistence
Inflation persistence that exists in every province in Indonesia is measured
assuming that inflation follows a stationary autoregressive process of order or AR(m) as
follow:
𝜋𝑖,𝑡 = 𝛼𝑖 + ∑ 𝛽𝑖,𝑗𝜋𝑖,𝑡−𝑗 + 휀𝑖,𝑡
𝑚
𝑗=1
(1)
In which:
𝜋𝑖,𝑡 = Inflation rate in a province 𝑖 at time 𝑡 (% yoy)
𝑚 = Selected optimal lag length by means of a standard information criterion (SIC)
휀𝑡 = Random disturbance error term
Afterwards, to look at the degree of inflation persistence in the context of AR coefficient
summation, equation (1) can be written as follow:
𝜋𝑖,𝑡 = 𝛼𝑖 + 𝜌𝑖𝜋𝑖,𝑡−1 + ∑ 𝛿𝑖,𝑗∆𝜋𝑖,𝑡−𝑗 + 휀𝑖,𝑡
𝑚−1
𝑗=1
(2)
In which:
𝜌𝑖 = ∑ 𝛽𝑖,𝑘
𝑚
𝑘=1
= Sum of AR coefficient (SARC), inflation persistence parameter that is achieved from the result of AR coefficient calculation using time series model
2-6
𝛿𝑖,𝑗 = − ∑ 𝛽𝑖,𝑘
𝑚
𝑘=1+𝑗
= Simple form of 𝛽
Parameter 𝜌 can be interpreted as an inflation velocity to return to the equilibrium after
a shock. Equation (2) can be reparameterized as follow:
∆𝜋𝑖,𝑡 = (𝜌𝑖 − 1) [𝜋𝑖,𝑡−1 − 𝜏𝑖] + ∑ 𝛿𝑖,𝑗∆𝜋𝑡−𝑗 + 휀𝑡
𝑚−1
𝑗=1
(3)
where 𝜏𝑖 =𝛼𝑖
1−𝜌𝑖. Equation (3) has a basic specification from the Augmented Dickey
Fuller (ADF) to assess the unit root existence. Zero hypothesis to test ADF is a process
to get unit root (𝜌𝑖 = 1), when 𝜌𝑖 = 1 has a series that are random walk, non-
stationary, and has high persistency because there is an autocorrelation with that lag
series. A shock that occurs will achieve a long-run effect and need time to converge into
its long-run equilibrium or be divergent from its long-run path permanently. Dorsche
(2005) said if 𝜌𝑖 value < 0.5, then inflation data can be assumed to have low persistency.
To test the robustness’ result for the test that was done, Levin and Piger (2004)
consider a change in the average inflation rate after calculating the structural changes
that happen all the time. To explain the structural changes, we can add time- varying
mean as follow:
∆𝑍𝑖,𝑡 = (𝜌𝑖 − 1) 𝑍𝑖,𝑡−1 + ∑ 𝛿𝑖,𝑗∆𝑍𝑖,𝑡−𝑗 + 휀𝑖,𝑡
𝑚−1
𝑗=1
(4)
where 𝑍𝑖,𝑡 = 𝜋𝑖,𝑡 − 𝜇𝑖,𝑡 , a difference in inflation using time-varying mean. To get time-
varying mean, we use Hodrick- Prescott (HP) filter for inflation data, get subtracted by
inflation data, and then the filtered data will be used as an input data. Afterwards, half-
life from a shock to inflation will be calculated using this formula.
−ln (2)
ln (𝜌𝑖) (5)
To evaluate the persistence of inflation, there are two problems that need to be
considered: the first is the number of lags and the second is time variation. Roache
2-7
(2014) demonstrates that the persistence change does not happen suddenly, but by
degrees as credibility in the new policy regime is accumulated over time. To capture the
time variation in persistence, we use regression estimated over 36 overlapping rolling
period samples of monthly data. This approach is better known as “rolling regression”.
The window size was chosen to balance two objectives, i.e., adequately assessing time-
variation and sufficiently estimating large sample periods. In particular, the sample of
the first series is January 2003 through December 2005. The second series then starts
and finishes one month later, from February 2003 through January 2005 and so on.
Regional Inflation Persistence Determinant
To identify inflation persistence determinant, we use panel data model to
explain determinant variation of inflation persistence among provinces for a research
time period as follow:
𝜌𝑖,𝑡 = 𝛼 + 𝛽′𝑋𝑖𝑡 + 𝑢𝑖𝑡 (6)
Where 𝜌𝑖,𝑡
is the inflation persistence level in province i for t time period, 𝑋𝑖𝑡 is an
observation vector from the explanation variable. The advantages of this data panel,
according to Baltagi (2004), are the ability to control unobserved heterogeneities among
individual units, can avoid omitted variables problems that are not changing over time,
and the low possibility to encounter autocorrelation and multicollinearity problems, like
in time-series data.
There are two types of the basic data panel, fixed effect model and random
effect model. For fixed effect model, we assume that individual heterogeneity is caught
by intercept. Each individual has an intercept, but the slope from each individual is
considered constant. For the fixed effect model, 𝜇𝑖 can be assumed by fixed Model fixed
effect to be written like:
𝑌𝑖,𝑡 = 𝛼 + 𝛽′𝑋𝑖,𝑡 + 𝑢𝑖,𝑡 (6)
𝑢𝑖,𝑡 = 𝜇𝑖 + 𝑣𝑖,𝑡
2-8
Then, fixed effect model can be written using dummy variable to sign an
individual as follow:
𝜌𝑖,𝑡 = ∑ 𝛼𝑗
𝑁
𝑗=1
𝑑𝑖,𝑡𝑗
+ 𝛽′𝑋𝑖,𝑡 + 𝑢𝑖,𝑡
where 𝑑𝑖𝑡𝑗
is a dummy, 𝑑𝑖,𝑡𝑗
= 1 if i=j, else is 0. The Parameter can be estimated using OLS.
Estimator of 𝛽′is called Least Square dummy variable (LSDV) estimator. LSDV estimator
includes a bunch of dummy variable for N-1 to sign each individual. Explanation variable
that has not changed over time will be dropped because of perfect collinear and
individual dummy variable.
Data
Data that is used for this research includes time period from 2002 until 2015
with monthly, quarterly, and yearly frequencies for 33 provinces in Indonesia as it is
written on the table below.
Table 1. Variable and Data
Number Variable Data Highlights Source of Data
1 Inflation
Monthly inflation level (year-on-year) that is measured by Consumer Price Index for 33 provinces in Indonesia. Beside the general inflation level, we use raw food category, processed food inflation category, and transportation inflation category.
BPS
2 Shock
Shock is measured using the growth rate of GRDP for 33 provinces that are estimated using AR, then there will be calculation of the standard deviation from the residual.
BPS
3 Competition Competition is measured by the price dispersion. The calculation using standard deviation from the price index of each
BPS
3-9
price category that is normalized into first period.
4 Rainfall Rainfall in 33 provinces BMKG
5 Infrastructure % of village with paved road BPS
6 Institution Institution is estimated by the child mortality level
BPS
7 Output Gap Output Gap is gotten from HP Filter approach to get actual Output from the farming GRDP
BPS
3. Regional Inflation Persistence Analysis
To analyze the degree of persistency in every region, we use rolling regression
technique in order to discover whether the inflation persistence is changing over time.
The variation degree of inflation persistence that exists among regions can give
important input for constructing a structural policy in every region. From the estimation
result, we get the persistency parameter for a full sample from 2005 until 2015 and a
sub sample that is divided into three periods in every province: 2003-2006, 2007-2010,
and 2011-2015.
Based on the result of a full sample period, the general inflation persistence
parameter is around 0.7 – 0.9 with half-life value around 3-8 months. Most of the
provinces have high inflation persistence that is above 0.8. The highest inflation
persistence is located at West Java province, following DIY and DKI Jakarta. The different
persistence level among regions shows different structural rigidity level that exists in
every province and reflects the respond of any change or shock that occurs.
The estimation result in a sub sample period shows the tendency of decreasing
level of inflation persistence in most provinces. Even though there is an increase level
of inflation persistent in most provinces (except Aceh) in 2007-2010 sub sample, there
were certain number of shocks that happened in all provinces, such as the rapid
increase in domestic oil price and international commodities. In the sub-sample 2011-
3-10
2015 period, many provinces experienced simultaneous decrease in the degree of
persistency. Some provinces, arguably, were no longer persistent, such as Gorontalo
and South Kalimantan whose degree of persistence between 0.4-0.5. The decline of
persistence level is also reflected by the decline of half life that captures time period
that is needed for a province to get its inflation back to the long-run equilibrium.
Table 2. General Inflation Persistence
Province Full Sample 2003-2006 2007-2010 2011-2015
Half Life
Half Life
Half Life
Half Life
Aceh 0.857 4.5 0.967 20.7 0.862 4.7 0.862 4.7
Babel 0.827 3.6 0.850 4.3 0.946 12.6 0.633 1.5
Bali 0.857 4.5 0.843 4.1 0.901 6.7 0.825 3.6
Banten 0.901 6.6 0.935 10.3 0.967 20.9 0.900 6.6
Bengkulu 0.874 5.1 0.944 12.0 0.943 11.9 0.875 5.2
Jateng 0.891 6.0 0.952 14.1 0.962 17.8 0.865 4.8
Kalteng 0.846 4.2 0.900 6.6 0.957 15.6 0.636 1.5
Sulteng 0.859 4.6 0.963 18.4 0.921 8.5 0.763 2.6
Jatim 0.891 6.0 0.944 12.0 0.964 18.7 0.841 4.0
Kaltim 0.901 6.6 0.954 14.7 0.950 13.4 0.863 4.7
NTT 0.856 4.5 0.900 6.6 0.898 6.4 0.767 2.6
Gorontalo 0.752 2.4 0.956 15.4 0.928 9.2 0.471 0.9
Jakarta 0.904 6.9 0.954 14.7 0.958 16.1 0.908 7.1
Jambi 0.829 3.7 0.870 5.0 0.940 11.1 0.787 2.9
Lampung 0.899 6.5 0.948 13.0 0.953 14.4 0.845 4.1
Maluku 0.820 3.5 0.919 8.2 0.878 5.3 0.717 2.1
Malut 0.827 3.6 0.904 6.9 0.931 9.7 0.813 3.3
Sulut 0.855 4.4 0.961 17.4 0.940 11.2 0.837 3.9
Sumut 0.880 5.4 0.935 10.3 0.954 14.7 0.888 5.8
Papua 0.791 3.0 0.823 3.6 0.908 7.2 0.820 3.5
Riau 0.873 5.1 0.894 6.2 0.963 18.3 0.880 5.4
Kepri 0.901 6.7 0.934 10.2 0.947 12.7 0.939 11.0
Kalsel 0.795 3.0 0.898 6.4 0.914 7.7 0.555 1.2
Sulsel 0.882 5.5 0.950 13.5 0.927 9.2 0.869 4.9
Sumsel 0.876 5.3 0.905 6.9 0.980 35.0 0.843 4.1
Sultra 0.818 3.5 0.921 8.4 0.934 10.2 0.756 2.5
Jabar 0.925 8.9 0.973 25.3 0.973 25.0 0.905 6.9
Kalbar 0.847 4.2 0.945 12.3 0.958 16.0 0.783 2.8
3-11
Province Full Sample 2003-2006 2007-2010 2011-2015
Half Life
Half Life
Half Life
Half Life
NTB 0.860 4.6 0.932 9.8 0.941 11.5 0.787 2.9
Sumbar 0.837 3.9 0.899 6.5 0.940 11.2 0.835 3.8
Yogyakarta 0.913 7.7 0.973 25.3 0.973 24.9 0.880 5.4
Besides measuring general inflation persistence level, we also measure CPI
inflation persistence level based on certain inflation categories: Raw Food, Processed
Food, and Transportation. These three groups are chosen because of the high inflation
level in the inflation basket.
Based on the estimation result from the full sample period, the inflation
persistence degree of raw food category in some provinces, particularly in Java and
Sumatera, have high level of inflation, which is above 0.8. On the other hand, in KTI
sector, the persistence level is considered low. This situation is also reflected by the half
life level, which means that the more persistent level of inflation in a province, the half-
life is going to be high.
For the sub sample period, the estimation result shows a similar pattern like the
general inflation, which has a decreasing pattern in the degree of inflation persistence.
There were a tendency of high level of persistence in 2003-2006 period and in 2007-
2010 period, and then reached low level of persistence in 2011-2015 period. Provinces
in Java island generally have higher degree of persistence compare to other regions.
Table 3. Inflation Persistence in the Raw Food Category
Province Full Sample 2003-2006 2007-2010 2011-2015
Half Life
Half Life
Half Life
Half Life
Aceh 0.806 3.2 0.962 17.7 0.742 2.3 0.802 3.1
Babel 0.762 2.6 0.634 1.5 0.930 9.5 0.623 1.5
Bali 0.819 3.5 0.775 2.7 0.827 3.7 0.834 3.8
Banten 0.870 5.0 0.882 5.5 0.956 15.4 0.800 3.1
Bengkulu 0.831 3.7 0.881 5.5 0.881 5.5 0.810 3.3
Jateng 0.860 4.6 0.929 9.4 0.953 14.5 0.820 3.5
Kalteng 0.849 4.2 0.913 7.6 0.914 7.7 0.655 1.6
Sulteng 0.756 2.5 0.792 3.0 0.866 4.8 0.578 1.3
Jatim 0.886 5.7 0.896 6.3 0.964 19.1 0.877 5.3
3-12
Province Full Sample 2003-2006 2007-2010 2011-2015
Half Life
Half Life
Half Life
Half Life
Kaltim 0.853 4.4 0.931 9.8 0.934 10.2 0.731 2.2
NTT 0.832 3.8 0.924 8.8 0.849 4.2 0.683 1.8
Gorontalo 0.721 2.1 0.894 6.2 0.818 3.5 0.542 1.1
Jakarta 0.877 5.3 0.948 13.1 0.952 14.1 0.863 4.7
Jambi 0.818 3.5 0.760 2.5 0.938 10.9 0.755 2.5
Lampung 0.869 4.9 0.823 3.6 0.941 11.4 0.847 4.2
Maluku 0.724 2.1 0.815 3.4 0.785 2.9 0.674 1.8
Malut 0.689 1.9 0.668 1.7 0.872 5.1 0.423 0.8
Sulut 0.793 3.0 0.876 5.2 0.881 5.5 0.716 2.1
Sumut 0.827 3.6 0.879 5.4 0.927 9.2 0.827 3.7
Papua 0.637 1.5 0.899 6.5 0.918 8.2 0.319 0.6
Riau 0.824 3.6 0.882 5.5 0.937 10.7 0.807 3.2
Kepri 0.799 3.1 0.888 5.9 0.908 7.2 0.855 4.4
Kalsel 0.816 3.4 0.897 6.4 0.851 4.3 0.618 1.4
Sulsel 0.837 3.9 0.901 6.6 0.880 5.4 0.859 4.6
Sumsel 0.851 4.3 0.857 4.5 0.962 17.8 0.768 2.6
Sultra 0.752 2.4 0.823 3.6 0.927 9.2 0.721 2.1
Jabar 0.882 5.5 0.947 12.7 0.947 12.8 0.871 5.0
Kalbar 0.793 3.0 0.813 3.4 0.917 8.0 0.604 1.4
NTB 0.848 4.2 0.900 6.6 0.914 7.7 0.819 3.5
Sumbar 0.809 3.3 0.840 4.0 0.898 6.4 0.769 2.6
Yogyakarta 0.877 5.3 0.896 6.3 0.966 20.0 0.911 7.4
For the processed food category, the estimated persistence in a full sample is varied
between 0.7 and 0.9. Similar to raw food category, provinces in Java Island are more
persistent than other regions. Banten and Kepri, which are known as industrial cities,
have high level of persistence. On the other hand, Bali and Gorontalo have low level of
persistence. In sub-sample period, the estimation result shows that in 2003-2006
period, almost all provinces tend to increase in the level of persistence, decrease in
2007-2010 period, and bounce back in 2011-2015 period. The persistence level in Bali
and Kepri for those three sub-sample periods continue to increase.
Tabel 4. Inflation Persistence in the Processed Food category
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Province Full Sample 2003-2006 2007-2010 2011-2015
Half Life
Half Life
Half Life
Half Life
Aceh 0.841 4.0 0.898 6.5 0.944 12.1 0.715 2.1
Babel 0.835 3.8 0.894 6.2 0.932 9.8 0.717 2.1
Bali 0.791 3.0 0.748 2.4 0.842 4.0 0.778 2.8
Banten 0.931 9.7 0.860 4.6 0.928 9.3 0.960 16.8
Bengkulu 0.805 3.2 0.972 24.7 0.946 12.6 0.449 0.9
Jateng 0.912 7.5 0.981 35.6 0.892 6.1 0.929 9.4
Kalteng 0.837 3.9 0.829 3.7 0.896 6.3 0.846 4.1
Sulteng 0.886 5.7 0.866 4.8 0.934 10.1 0.894 6.2
Jatim 0.902 6.7 0.981 36.8 0.936 10.4 0.949 13.2
Kaltim 0.902 6.7 0.971 23.7 0.865 4.8 0.853 4.3
NTT 0.860 4.6 0.858 4.5 0.703 2.0 0.866 4.8
Gorontalo 0.730 2.2 0.751 2.4 0.897 6.4 0.518 1.1
Jakarta 0.916 7.9 0.975 27.5 0.833 3.8 0.962 17.7
Jambi 0.897 6.3 0.919 8.2 0.867 4.9 0.908 7.2
Lampung 0.833 3.8 0.944 12.1 0.785 2.9 0.856 4.4
Maluku 0.849 4.2 0.928 9.3 0.877 5.3 0.893 6.2
Malut 0.871 5.0 0.930 9.5 0.877 5.3 0.897 6.4
Sulut 0.887 5.8 0.910 7.3 0.888 5.8 0.918 8.1
Sumut 0.857 4.5 0.934 10.1 0.882 5.5 0.771 2.7
Papua 0.882 5.5 0.730 2.2 0.889 5.9 0.960 16.9
Riau 0.845 4.1 0.907 7.1 0.954 14.7 0.883 5.6
Kepri 0.919 8.2 0.850 4.3 0.950 13.4 0.972 24.6
Kalsel 0.833 3.8 0.958 16.1 0.775 2.7 0.861 4.6
Sulsel 0.869 4.9 0.954 14.6 0.927 9.2 0.848 4.2
Sumsel 0.891 6.0 0.941 11.4 0.960 17.0 0.955 15.2
Sultra 0.887 5.8 0.984 43.8 0.946 12.6 0.909 7.2
Jabar 0.889 5.9 0.870 5.0 0.923 8.7 0.900 6.6
Kalbar 0.879 5.4 0.915 7.8 0.886 5.7 0.942 11.7
NTB 0.899 6.5 0.940 11.1 0.892 6.1 0.931 9.7
Sumbar 0.842 4.0 0.967 20.9 0.898 6.4 0.895 6.2
Yogyakarta 0.910 7.3 0.993 96.6 0.931 9.7 0.933 10.0
The estimated result of the inflation persistence in transportation category in
full sample period shows that Java and other region in Sumatera are more persistent
than other region. But for the sub sample period, all provinces show high level of
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persistence in 2003-2006 period, which was above 0.9. This level of persistence, in
general, tends to decrease in 2007-2010 sub sample period and 2011-2015.
Tabel 5. Inflation Persistence in Transportation Category
Province Full Sample 2003-2006 2007-2010 2011-2015
Half Life
Half Life
Half Life
Half Life
Aceh 0.840 4.0 0.916 7.9 0.750 2.4 0.874 5.1
Babel 0.817 3.4 0.924 8.8 0.840 4.0 0.729 2.2
Bali 0.871 5.0 0.917 8.0 0.883 5.6 0.898 6.4
Banten 0.875 5.2 0.916 7.9 0.898 6.4 0.909 7.2
Bengkulu 0.870 5.0 0.928 9.3 0.859 4.6 0.907 7.1
Jateng 0.863 4.7 0.926 9.0 0.867 4.9 0.881 5.5
Kalteng 0.873 5.1 0.915 7.8 0.902 6.8 0.877 5.3
Sulteng 0.837 3.9 0.940 11.2 0.880 5.4 0.892 6.1
Jatim 0.868 4.9 0.926 9.0 0.877 5.3 0.899 6.5
Kaltim 0.880 5.4 0.933 10.1 0.864 4.7 0.903 6.8
NTT 0.846 4.1 0.925 8.9 0.932 9.9 0.783 2.8
Gorontalo 0.863 4.7 0.930 9.6 0.873 5.1 0.848 4.2
Jakarta 0.876 5.2 0.933 10.0 0.861 4.6 0.900 6.6
Jambi 0.889 5.9 0.940 11.2 0.882 5.5 0.911 7.5
Lampung 0.882 5.5 0.931 9.7 0.889 5.9 0.899 6.5
Maluku 0.874 5.2 0.918 8.1 0.892 6.1 0.878 5.3
Malut 0.861 4.6 0.930 9.6 0.835 3.8 0.847 4.2
Sulut 0.861 4.6 0.928 9.3 0.834 3.8 0.915 7.8
Sumut 0.879 5.4 0.930 9.5 0.913 7.7 0.876 5.3
Papua 0.849 4.2 0.907 7.1 0.818 3.5 0.892 6.1
Riau 0.872 5.1 0.934 10.1 0.861 4.6 0.899 6.5
Kepri 0.866 4.8 0.922 8.5 0.868 4.9 0.894 6.2
Kalsel 0.856 4.4 0.921 8.4 0.879 5.4 0.858 4.5
Sulsel 0.865 4.8 0.925 9.0 0.853 4.4 0.890 5.9
Sumsel 0.867 4.9 0.918 8.1 0.824 3.6 0.907 7.1
Sultra 0.813 3.4 0.936 10.4 0.796 3.0 0.892 6.1
Jabar 0.892 6.1 0.911 7.5 0.939 11.1 0.911 7.4
Kalbar 0.775 2.7 0.925 9.0 0.855 4.4 0.635 1.5
NTB 0.890 5.9 0.926 9.0 0.903 6.8 0.911 7.4
Sumbar 0.849 4.2 0.931 9.7 0.845 4.1 0.889 5.9
Yogyakarta 0.882 5.5 0.962 17.9 0.841 4.0 0.868 4.9
3-15
Determinant Analysis of Regional Inflation
To explain the persistence variation that exists among region, we do the
estimation using panel data method in three time periods, which are 2003-2006, 2007-
2010, and 2015-2022. This estimation is done in 33 provinces using seconder data with
the model as follow:
𝝆𝒊𝒕 = 𝜶 + 𝜷𝟏𝑺𝒉𝒐𝒄𝒌 + 𝜷𝟐𝑷𝒓𝒊𝒄𝒆𝑫𝒊𝒔 + 𝜷𝟑𝑰𝒏𝒔 + 𝜷𝟒𝑮𝒂𝒑 + 𝜷𝟓𝑹𝒂𝒊𝒏 + 𝜷𝟔𝑻𝒓𝒂𝒏𝒔
+ 𝜷𝟕𝑫𝒖𝒎𝒎𝒚 + 𝜺
Where, 𝝆𝒊𝒕 is the persistence level of raw food, 𝑺𝒉𝒐𝒄𝒌 reflects the shock that exists in
each province’s economy, 𝑷𝒓𝒊𝒄𝒆𝑫𝒊𝒔 is the price dispersion that consists of competition
proxy, 𝑰𝒏𝒔 is an institution variable, 𝑮𝒂𝒑 is an output gap from farming sector, 𝑹𝒂𝒊𝒏 is
the level of rainfall, 𝑻𝒓𝒂𝒏𝒔 is a transportation variable that uses a data of village with
paved roads. This estimation uses OLS Panel, LSDV, and Panel Fixed Effect approaches.
Table 6. OLS Panel Estimation Result
Variable Model 1 Model 2
Shock 0.002
0.00007
(0.002) (0.004)
Disperse -0.759 *** -0.633 **
(0.237) (0.276)
Curjan -0.002
0.016
Transport -0.00001
(-0.0005)
Inst -0.002 **
(0.001)
Agri_gap -0.002
(0.002)
Konstanta 1.078 *** 0.935 ***
(0.073) (0.045)
Number of obs 97 97
R-squared 0.1961 0.0768
Root MSE 0.11035 0.11571
The estimation result with OLS Panel approach shows that variable shock is positively
correlated with inflation persistence level of a province. A shock that occurs in the
economy of a region will impact the inflation, and the average inflation velocity will go
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back to its long-run equilibrium. The type and size of the expected shock will influence
inflation persistence level, such as, shock of the rise in domestic oil price in 2005
increases the level of inflation persistence in that period, including general inflation and
group inflation. Besides, supply shocks oftentimes increase persistence level in several
regions that experience deficit budget.
A price dispersion variable also proves to significantly influence the inflation
persistence level. Price dispersion is a proxy from the competitive market that has been
indicated to impact positively on the inflation persistence. But the estimation result
shows that a price dispersion variable is negatively correlated with the level of
persistence. Similar result is also found by Babetski, Coricelli, dan Horvath (2007)1 in
their research about inflation persistence determinant using price dispersion.
Transportation variable that uses a percentage sum of villages with paved road
compare to the total villages is found to negatively correlated with inflation persistence
level. This is in accordance to the first hypothesis, which believes that the good quality
of existing transportation will expedite good flow of goods and services. The
smoothness of goods distribution will stabilize price level and reduce supply shock that,
at the end, creates low inflation rate and impersistent result.
Another approaches that are used to estimate inflation persistent determinant
are LSDV and Panel Fixed Effect. These approaches are useful to discover another
variable that affects inflation rate by adding island dummy that represents idiosyncratic
factor from each region.
Table 7. LSDV Panel and Fixed Effect Estimation Result
Variable LSDV Fixed Effect Panel
shock 0.003 0.006
0.003 0.004
disperse -0.587 ** -0.822 ***
0.246 0.304
curjan 0.003
0.017
transport -0.001 -0.005 **
0.001 0.002
d_sumatra -0.045
0.028
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Variable LSDV Fixed Effect Panel
d_kalimantan -0.094 *
0.052
d_sulawesi -0.114 ***
0.039
d_kti -0.158 ***
0.046
Konstanta 1.032 *** 1.232 ***
0.076 0.125
Number of obs 97 97
R-squared 0.2307 0.25
Root MSE 0.10917
The estimation result using LSDV and Panel Fixed Effect approaches confirms the
estimation result using OLS Panel. Shock variable, price dispersion, and transportation
influence the inflation persistence level. Rainfall variable is positively correlated with
inflation persistence among regions. High rainfall level will impact planting season and
harvest season of various farming commodities in a region. Also, the high rainfall level
could influence production result and commodity availabilities that are important to
fulfill society daily needs. The common level of defective harvest, as a result of high
rainfall, will impact the commodity supply, which also create the high level of
persistence.
Idiosyncratic factor that shows the characteristic of region can influence the
level of persistence in regions that are represented by the island dummy variable. This
shows that the economy characteristic of a region will influence the inflation
persistence variation that occurs among regions. This is confirmed by the calculation of
inflation persistence in the region with high level of processed food category, such as
Banten, a province with industrial region. On the other hand, the persistence level of
inflation in raw food category in Java Island, which is the central production of farming
commodities, is relatively higher than other regions.
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4. Conclusion
In this paper, we analyzed Indonesia’s regional inflation persistence using
univariate models of inflation over the period 2003-2016. In this univariate framework,
we examined whether regional inflation goes back to its equilibrium value after any
shock. We also analyzed the determinant of inflation persistence.
The results on regional inflation persistence in Indonesia can be summarized as
follows: (a) First, using a simple univariate approach, this study shows a tendency of
decreasing level of inflation persistence in most provinces, (b) Second, provinces in Java
Island are more persistent than other regions. This is not only true for general inflation,
but also for raw food inflation and processed food inflation, (c) Third, factors which
determined high persistence are shock variable, price dispersion, and transportation
variable. This is not unexpected since prices of Indonesia’s commodity adjust sluggishly
to change in aggregate Indonesia, especially food. This rigidity is an important
characteristics of Indonesia’s price function. High rigidity and persistent are also related
with high prices. In a limited supply situation and a crawling upward of the commodity
price, the sellers who have an acceptable profit margin in mind, will keep the price in
high level in order to save their stocks, since they already bought their commodities
when the prices, at the moment, are in overbought condition. This finding is also in-line
with our recent survey that the wholesaler is the price maker in the food market. Thus,
this study indicates high market power of the middlemen.
5-19
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