Testing for convergence clubs in real wage acrossIndonesian provinces from 2008 to 2020
Muhamad Rifki Maulana This
Bank Indonesia,
Jakarta, Indonesia
E-mail: muhamad_rm@bi.go.id
Harry Aginta
Bank Indonesia
Jakarta, Indonesia;
Nagoya University,
Nagoya, Japan
E-mail: harry_ag@bi.go.id
E-mail:aginta.harry@c.mbox.nagoya-u.ac.jp

Keywords:
regional wage,
club convergence,ordered logit model,
Indonesia

study evaluates empirically theconvergence in real wages across 34
Indonesian provinces. The authors apply theclub convergence test on real wage data at theprovince level from 2008 to 2020. An overall
convergence in real wages has not beenfound. Instead, three significant clubconvergences
have
been
identified.
Furthermore, regional factors that influenceclub convergence formation are investigatedby using the ordered logit model. It has beenfound that club convergence formation is
jointly influenced by the following factors:
share of employment in the manufacturingsector, investment share to GDP, labour
force participation rate, and the initial level ofwage. The findings support the evidence ofclub convergence studies that emphasise therole of the initial condition and regional
characteristics on the formation of clubconvergence. The results should alert
national and provincial governments tosynchronise policies promoting sound andcompetitive labour markets across provincesfrom a policy standpoint.

Introduction
According to modern economic theories, people with indistinguishable talents willobtain equal remuneration in a wholly integrated labour market (Hicks 1963, Marshall1920). Adhering to the same premise: many studies have tested for wage convergencein the context of broader labour market analysis, taking labour as a factor of
production (Galizia 2015, Rosenbloom 1998, Rosenbloom‒Sundstrom 2002).
According to Dayanandan–Ralhan (2005), given the unrestricted mobility of people(in addition to lower transportation costs and the use of a common currency), testing
wage convergence as the price of labour within a country is more reasonable thanacross countries. While wage convergence is generally expected, the absence ofRegional Statistics, Vol. 12. No. 3. 2022: 104–130; DOI: 10.15196/RS120305

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convergence at the intra-national level implies the presence of regional imbalances,
resource misallocation, and differences in the cost of living (González 2020).
In Indonesia, analysing wage convergence is relevant because it often becomes themain factor for many people to migrate. Based on theory, the economic reason is one
aspect that could influence migration, and several approaches that underlie thisamong them were delivered by Mantra (1992) and Todaro–Smith (2003). Both agree
that economic motives are reasons for migration, especially migration from rural tourban areas. The Indonesian Central Bureau of Statistics (BPS) recorded that the
population in urban areas in 2020 is 56.7%, an increase from the 2010 figure of 49.8%.
Furthermore, due to substantial urban expansion, which necessitates many workers,
the figure is expected to rise to 66.6% by 2035. Another report from BPS shows thatthe number of lifetime migrants in Indonesia in 2019 is up to 29.8 million people,
with migrant workers of 5.4 million people. Java island dominates the population of
migrants in Indonesia, with approximately 51.2% of lifetime migrants and 56.5% of
migrant workers residing in Java. The high number of migrants in Java is mainly
influenced by wage conditions, living costs, and the availability of living facilities.
Improvements in people’s mobility due to the rapid development oftransportation infrastructure in Indonesia in the last decade add relevance to the studyof regional wage convergence. For example, the number of airports in Indonesia
increased from 148 in 2004 to 235 in 2018. Consequently, domestic passenger traffic
increased dramatically during that period, rising from 34 million passengers per year
in 2004 to 94 million passengers per year in 2018. Similarly, there has also been a rapid
development in communication infrastructure due to the massive base transceiver
station (BTS) constructed by Indonesia's telecommunication state-owned enterprisefrom approximately 26 thousand units in 2008 to 231 thousand units in 2020. This
improvement in connectivity is expected to reduce disparity across Indonesian
regions in many economic and social dimensions, including wages.
Against this backdrop, the present study focuses on convergence patterns of thelong-run dynamics in wages across Indonesian provinces and the influencing factorsof converging behaviour. Despite numerous studies on regional income convergence,less is known about regional wage convergence in Indonesia. Furthermore, this study
uses the average net income per month of employees and labourers in 34 provincesfrom 2008 to 2020 as the primary indicator instead of the regional minimum wage
(Upah Minimum Regional – UMR). In many cases, regional minimum wage is usually
influenced by local government policy and other unconditional factors, so it does not
optimally represent the real market situation. We also remove the effect of inflation
on regional wages by converting the data from nominal to real terms.
We find three significant convergence clubs of regional wages by applying the clubconvergence technique (Phillips‒Sul 2007). Interestingly, the composition of the
clubs using real wage is very similar to the one we obtained using nominal wage,
implying the existence of a price-adjusted mechanism in regional wages. Provinces

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Muhamad Rifki Maulana – Harry Aginta

that converge into the higher wage clubs (Clubs 1 and 2) share similar characteristics:
having a large proportion of manufacturing industries and high traffic of migrant
workers, and locations of main national strategic infrastructure projects to promote
investment. Our further analysis using an ordered logit model suggests that theformation of club convergence is significantly explained by the following variables:
share of employment in the manufacturing sector, investment share to gross domestic
product (GDP), labour force participation rate, and the wage level in the initial period.
These findings also confirm the assumption of similar characteristics mentioned
above.
The remainder of this paper is organised as follows. A brief review on wage
convergence studies and the contribution of the present study section reviews therelated literature, the Methods and data section discusses the methodologies and data.
The results of club convergence identification and the influencing factors arepresented in the Results and discussion section. Finally, the paper presents theconclusions.

A brief review on wage convergence studies and contribution of
the present study
In labour market literature, wage convergence is generally evaluated from two
perspectives: first, convergence in wages across workers and, second, across locations.
Among others, the study of Fang–Yang (2011) is a well-appreciated work evaluating
wage convergence across unskilled and skilled workers in China. Their results indicatethat the wages of unskilled and skilled workers in China have converged, mainly due
to the acceleration of structural change in the Chinese economy. Furthermore, therapid growth in labour demand in China has exceeded the capacity of the labour
market to supply, causing the wages of unskilled workers to escalate, known as the
Lewis turning point.
However, studies investigating wage convergence across locations have gained
popularity since the advancements in transportations and communication
technologies increased labour mobility (Prado et al. 2020). The notion of free labour
mobility across administrative borders is a necessary condition for the wage
convergence mechanism to work. Regional wage differentials tend to decrease when
there are no migration barriers, particularly from low-wage to high-wage regions(Collin et al. 2019).
A collection of studies shows evidence of regional wage convergence concerning
flexible labour mobility. For example, using panel data covering 203 regions(Nomenclature of Territorial Units for Statistics or NUTS-2 level) in the EU from1996 to 2006, Naz et al. (2017) find wage convergence only across internal regions(regions within the same country) but no evidence of convergence for border regions(neighbouring regions across international borders). Using a similar approach, Enflo

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et al. (2014) apply panel fixed effect models and show that internal and externalmigrations contributed to wage convergence across Swedish counties before WorldWar I, where internal migration occurred mainly during the interwar years.
As shown in other empirical studies, the mechanism of wage convergence acrossregions assumes a significant advancement in transportation and communications
that can enlarge the scope of the labour market across geographical boundaries. With
these theories and empirical backgrounds, it is natural to expect appealing findings
from the analyses of regional wage convergence in Indonesia due to the developmentof transportation and communications infrastructure in the last decade.
The present study contributes to the existing literature by focusing on wage
convergence analysis across regions that can be used to evaluate the degree of labour
market integration in Indonesia. Previously, several studies examined convergence inIndonesia but mainly focused on variables related to income, such as GDP, GDP per
capita, and total factor productivity (TFP). For example, applying the dynamic panel
data approach, Firdaus–Yusop (2009) analyse convergence in income using provincelevel data of Indonesia. Applying the system generalized method of moments (GMM)
estimation technique, they show convergence among Indonesian provinces during
1983–2003. However, the convergence speed is relatively slow (0.29%), much lower
than the convergence speed observed in most regional convergence studies: 2%(Barro et al. 1991, Barro‒Sala-i-Martin 1992). Using classical absolute and conditional
convergence frameworks, Kharisma–Saleh (2013) analyse income convergenceamong 26 provinces in Indonesia during 1984-2008. They find a strong indication ofabsolute convergence and conditional convergence and refer to this evidence as thecatching-up process, where provinces with lower income levels in 1984 tend to growfaster relative to the provinces with higher levels of income. Based on the system
GMM estimation, they also find that the speed of convergence in Java is faster thanthat outside Java. The other study has been implemented by Vidyattama (2006) using
a more extended dataset since the 1970s. Evidence from this study shows thatsignificant changes in specific policies and economic development in Indonesia,
including macroeconomic conditions and structural changes, affect the pattern ofregional income convergence. Using the most recent data available, Aginta et al.
(2020) analyse income convergence across 514 Indonesian districts from 2000 to 2017using the club convergence framework. Their findings support the view of a lack ofconvergence in per capita income during the post-decentralisation era. Finally,
Purwono et al. (2021) show that between 2011 and 2017, TFP convergence occurredin 33 Indonesian provinces, with intra-provincial exports having a greater impact on
TFP convergence than international exports. Probably the closest study to ours is
Aginta's (2021) analysis, which identifies club convergence in regional prices acrossprovinces in Indonesia and further investigates the conditioning factors influencing
club convergence formation. Using consumer price index (CPI) data from 2012:01 to
2019:12 aggregated at the province level, this study shows the absence of overall

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Muhamad Rifki Maulana – Harry Aginta

108

convergence at the regional price level, and the dynamics of regional prices arecharacterised by four-club convergence. This extended research, which employs theordered logit model, demonstrates that a one-unit change in labour productivity,
inflation expectation, consumption growth, and spatial externalities considerably
impacts the probability of provinces clustering into a different club.
We have shown that empirical research on wage convergence in Indonesia is
scarce. The present article attempts to close the research gap by providing new
evidence of regional wage convergence and its influencing factors.

Methods and data
Econometric methods: Testing for club convergence
Without the necessity of co-integration in time series, the log t-test developed byPhillips–Sul (2007) can investigate the existence of multiple convergence clubs(Bartkowska‒Riedl 2012). In other words, although evidence of co-integration in timeseries is lacking, it does not automatically disprove convergence. With this advantage,
many researchers have applied this method in various convergence analyses on
different focuses, including income, productivity, financial development, and othersocioeconomic indicators.
To identify the presence of club convergence on regional wage: this study applies
Phillips–Sul’s (2007) modern test of club convergence. According to the model, we
consider a panel data variable, for instance, 𝑦 is expressed as:
𝑦𝑖𝑡 = 𝛿𝑖𝑡 𝜇𝑡(1)where i refers to individual units 1, 2, …., N across time t = 1, 2, …, T, 𝑦 is thedependent variable, 𝛿 indicates individual unit and time-specific components, or atime-varying idiosyncratic element. 𝜇 is not unit-specific and thus characterizes thecommon pattern of 𝑦 . The dynamics of the idiosyncratic element, 𝛿 , can be
expressed as:
𝛿𝑖𝑡 = 𝛿𝑖 +𝜎𝑖 𝜉𝑖𝑡log(𝑡)𝑡 𝛼(2)where 𝛿 is a time-invariant individual-specific effect, and 𝜉 is unnecessarily
influenced by time, with a mean of 0 and variance of 1 across individual units.
Departing from equation 2, the null hypothesis states that convergence exists if all
individual units collectively approach the common transition path, such that:𝑙𝑖𝑚 𝛿𝑖𝑡 = 𝛿𝑖 𝑎𝑛𝑑 𝛼 ≥ 0(3)𝑡→∞Intuitively, the alternative hypothesis is 𝐻 : 𝛿 ≠ 𝛿 for all i and 𝛼 ≥ 0. To evaluate
the convergence over the long-run time horizon, the relative transition parameter of
an individual unit, ℎ , is formulated as follows:
ℎ𝑖𝑡 =

𝑦𝑖𝑡
𝛿𝑖𝑡
=
1 𝑁
1 𝑁
∑
∑
𝑦
𝛿
𝑁 𝑖=1 𝑖𝑡 𝑁 𝑖=1 𝑖𝑡(4)Regional Statistics, Vol. 12. No. 3. 2022: 104–130; DOI: 10.15196/RS120305

Testing for convergence clubs in real wage acrossIndonesian provinces from 2008 to 2020109

ℎ represents the specific behaviour of individual unit i against the cross-sectional
average. In the state of convergence under equation 3, 𝛿 → 𝛿 , then ℎ → 1. This
also implies that the cross-sectional variance of ℎ converges to 0 (𝐻 → 0),
1
𝐻𝑡 =
𝑁

𝑁

(ℎ𝑖𝑡 − 1)2 → 0,

𝑡→∞

(5)

𝑖=1

where 𝐻 = 𝜎 .
To empirically investigate the presence of convergence: the null hypothesis istested with a log t regression model based on the variance ratio :𝐻1− 2 log{log(𝑡)} = 𝑎 + 𝑏 log(𝑡) + 𝜖𝑡
𝐻𝑡𝑓𝑜𝑟 𝑡 = [𝑟𝑇], [𝑟𝑇] + 1, … , 𝑇 𝑤𝑖𝑡ℎ 𝑟 ∈ (0,1)

log

(6)

From the Monte Carlo simulation, Phillips–Sul (2007) argue that setting 𝑟 = 0.3 isrecommended. The null hypothesis is rejected when 𝑡 < −1.65; if that is the case,the next step is to identify relative or club convergence.

Identifying club convergence and investigating conditioning factors of clubconvergence
The method of Phillips–Sul (2009) can identify whether different club convergences
exist in the sub-sample in the absence of overall convergence in the full sample.
Hence, after testing the overall convergence using log t regression, we use theclustering algorithm of Phillips–Sul (2009) for club convergence identification. A
summary of this clustering algorithm is provided in Appendix 1.1
The literature proposes an important discussion on the conditioning factors ofclub convergence from two different convergence perspectives. On the one hand, theclub convergence hypothesis emphasises the importance of the initial condition foran economy's transition. On the other hand, conditional convergence argues thatstructural characteristics completely affect the long-run growth path, while the initialcondition is exogenous (Von Lyncker‒Thoennessen, 2017).
Although the club convergence method by Phillips–Sul (2007) clusters individual
units according to their transition path estimates, it does not explain the factors thatdrive club formation as Azariadis–Drazen (1990) and Galor (1996) specify the clubconvergence hypothesis. To complete our analysis, we investigate the conditioning
factors of club convergence formation. For this purpose, similar to Bartkowska–Riedl(2012), we apply the ordered logit model. Based on the theoretical considerations
discussed previously, we test the initial condition and structural characteristics as
explanatory factors in the estimation.
In practice, we denote each club convergence as 𝑐 = 1, 2, … 𝑐, where 𝑐 iscategorical according to the number of club convergence identified. Since the methodof Phillips–Sul (2007, 2009) ranks the clubs according to the long-run trend of each1

See Phillips–Sul (2009) for further detailed discussion.

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Muhamad Rifki Maulana – Harry Aginta

individual in the respective club, we are allowed to arrange 𝑐 as an ordinal variable.
We assume that there is an unobserved variable 𝑦 ∗ that is related to the long-run wage
dynamics of provinces that force provinces to be clustered in a certain club. Thus, we
can write the specification as𝑦𝑡∗ = 𝑋𝑖 𝛽 + 𝜖𝑡(7)where 𝑋 is a vector consisting potential explanatory variables for club convergencemembership, with 𝑖 = 1, … ,34, indicating the province and 𝜖 have a logistic
distribution. The model uses a maximum likelihood (ML) estimator to compute theprobabilities of observing values of 𝑐. Note that although one can assess the
directional effect of explanatory variables towards club membership with the sign of𝛽 coefficients, the magnitude does not contain any economic information. Therefore,
in addition to the directional information given by the sign of 𝛽 coefficients, we
further compute the marginal effects of a given unit change in each explanatory
variable on the predicted probability, holding other variables constant.

Data
As a proxy for regional wage, we use the average net nominal income per monthreceived by a general worker (in thousand Indonesian rupiah [IDR]) published by the
BPS. According to BPS, the net nominal income per month is defined asremuneration received during the last month in the form of money or goods by aperson considered an own-account worker, a casual employee in agriculture, or acasual employee in the non-agriculture sector. The original wage data is in nominal
terms, and its statistical measurement is uniform across provinces and consistent overtime. Ideally, to reflect labour’s purchasing power differential across provinces, thenominal wage should be deflated using each province's estimated absolute cost ofliving, similar to the approach used by Collin et al. (2019). However, due to data
limitations at the province level, we could not estimate the absolute cost of living.
Instead, we deflate the nominal wage using the provincial CPI of 2005 as the baseyear (2005=100).2 In this way, we remove the nominal effects from the change inregional wage over time, but we acknowledge that our real wage data does not reflect
regional variation in labour’s purchasing power. Figure A1 in the Appendix 2
demonstrates the evolution of provincial real wages across time, and the summary
statistics are provided in Table A1 in the Appendix 2.

2 Since the range of our observation is from January 2008 to December 2020, we intentionally select the year
outside of our investigated interval as the base year to avoid using the same wage level (nominal equals real wage) ata particular year.

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Results and discussion
Regional wage disparities across Indonesian provinces
Before implementing the club convergence test, it is important to document the
dynamics of wage disparity across provinces over time. Figure 1 plots the coefficientof variation (CV) of regional wages in real and nominal terms. The plot shows theabsence of sigma convergence in both regional real and nominal wages; that is, thedispersion of wages in the final period is higher than in the initial period.3
Interestingly, the trend of regional wage dispersion in Indonesia is different from thatin China and India. As shown in Figure 2, dispersion in regional wages in these twodeveloping and most populated countries in Asia exhibits a declining trend during thesame observation period. Although the difference in regional wage disparity at theend of the period is relatively subtle (0.185 in China, 0.205 in Indonesia, and 0.216 inIndia), the difference in the long-run trend is more recognisable, with regional wage
disparity in Indonesia being more persistent than in China and India.

Figure 1

Dispersion of regional real and nominal wage in Indonesia
CV of real and nominal wage
0.25
0.23
0.21
0.19
0.17
0.15

CV, real

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

0.00

CV, nominal

3 Derived from Barro–Sala-i-Martin (1992), sigma convergence refers to the decrease in the dispersion of thelevels of a given variable across countries or regions over time.

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Figure 2

Dispersion of regional nominal wage in China and India*
CV nominal wage
0.35
0.30
0.25
0.20
0.15

CV, China

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

0.00

CV, India* CV for China and India is computed from 295 prefectural-level cities and 31 states and union territories,
respectively. Regional wage data for China is available until 2018, while the data for India is available until 2019. Datafor both countries are collected from CEIC.

We also illustrate the evolution of regional wage disparities among Indonesian
provinces over the years. As seen in Figure 3, generally, the quantiles of the
distribution show persistent gaps over time, indicating the tendency of steady regional
wage disparities, similar to what is shown in Figure 1. Particularly, the persistent gap
between quantile 95 and the rest of quantiles and the widening gap between quantiles
75 and 50 after 2017 implies a systematic difference between high-wage provinces
and the rest of the provinces, which might be related to the structural differences.
This dynamic of quantile distribution in provincial wage helps us understand that
there is a strong symptom of lack of convergence in regional wage across Indonesian
provinces despite the massive efforts from the government to enhance regional
connectivity. However, the econometric method that we use in the present study
makes it possible to identify club convergence, if any, albeit divergence in the entire
sample. Therefore, in the next section, we will test the temporary conclusion of wage
divergence with a formal econometric framework.

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Testing for convergence clubs in real wage acrossIndonesian provinces from 2008 to 2020

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Figure 3

Dispersion of Indonesian provincial real wage
Real wage (thousand IDR)
2,000
1,800
1,600
1,400
1,200
1,000
800
600

Quantile
0.05

0.25

0.50

0.75

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

0

0.95

Testing and identifying for convergence clubs
We begin the formal test for convergence by applying log t regression on real wages
across 34 Indonesian provinces over the 2008:01–2020:12 period. As reported inTable 1, the results suggest that the null hypothesis of overall convergence is rejected.
Therefore, we can support our findings from preliminary inspection and conclude
that Indonesian provinces do not converge to a common equilibrium in terms of real
wages during the observation period. As real wage is partially linked to the price levelin each province, this result is consistent with the evidence from previous studies
where overall convergence is not observed in regional price dynamics acrossIndonesia prices (Jangam‒Akram 2019, Aginta 2021). It is important to emphasise
that, given the method used to build real wage data in this analysis as described, theabsence of overall convergence from log t regression does not necessarily imply
divergence in labour’s purchasing power among Indonesian provinces.
Table 1

Test of overall convergence
Log(t)

CoefficientStandard error

t-statistics–1.1300.014–79.721

Note: t-statistic < –1.65 implies the rejection of the null hypothesis of convergence.

We continue the analysis with the clustering algorithm by Phillips–Sul (2009) to
identify club convergence. Table 2 presents the results.

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114

Table 2Club convergence test
CoefficientStandard error
t-statistics
Number of provinces

Club 1Club 2Club 3

0.113
0.232
0.486
3

0.745
0.242
3.081
9

–0.0140.108–0.12622

Note: t-statistic < –1.65 implies the rejection of the null hypothesis of convergence.
Club 1: Jakarta, Riau Islands, Banten. Club 2: Papua, East Kalimantan, North Kalimantan, West Java, West
Papua, North Sulawesi, Bali, Central Kalimantan, South Sulawesi. Club 3: North Maluku, Riau, Maluku, West
Sulawesi, South Kalimantan, Bangka Belitung, Southeast Sulawesi, West Sumatra, Aceh, Gorontalo, East Java, North
Sumatra, Bengkulu, Yogyakarta, Central Java, South Sumatra, West Nusa Tenggara, Jambi, Lampung, Central
Sulawesi, West Kalimantan, East Nusa Tenggara.

We find three significant initial clubs representing regional wages' convergence
dynamics across Indonesian provinces.4 These results are similar to those Neagu(2020) reported for the Romanian case. Next, we use the merging method of Phillips–
Sul (2009) described in Appendix 2 to test whether the initial clubs can merge with
their adjacent club and thus generate higher club convergence. The results from themerging test suggest rejecting the convergence hypothesis in any merging pair (𝑏 < 0
and t-statistics < –1.65).5 Hence, we confirm the initial three clubs as the final clubconvergence. The clubs are ordered from the highest to the lowest wage; club 1
consists of higher-wage provinces, while the lowest-wage provinces are clustered inclub 3. As mentioned before, the club convergence method estimates the transition
path of clubs and all individual units. Taking this advantage, in Figure 4, we show theevolution of the computed clubs’ transition paths over time. Unlike using the absolutevalue of wages on the Y axes (similar to Figure 3), in Figure 4, we plot the relativetransition path of each club to the cross-sectional average of all three clubs.
Interestingly, there is an indication of gap reduction between clubs 1 and 2 from 2008
until 2012. However, the transition path of club 1 exhibited an increasing trend witha significantly higher slope than that of club 2, resulting in a larger gap between thetwo clubs. In other words, the pattern of expanding differences among the clubs’
transition paths supports the identification of significant club convergence overoverall convergence. Instead of forming a converging shape, reflected in smaller gaps
between clubs over time, the three transition paths demonstrate increasing dispersion
between clubs, where club 1 is systematically above the average, club 2 steadily moves
from below towards the average, while club 3 is consistently below the average.

4 The evaluation of club convergence is executed using the club convergence package in R developed by Sichera–
Pizzuto (2019).
5 We also implement the merging procedure according to Von Lyncker–Thoennessen (2017). The test gives
identical results from the merging test of Phillips–Sul (2009).

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Testing for convergence clubs in real wage acrossIndonesian provinces from 2008 to 2020

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Figure 4

The transition path of convergence clubs
Relative transition path of all clubs
1.075
1.050
1.025
1.000
0.975

Club1

Club2

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

0

Club3

We then analyse the within-club transition dynamics by plotting the transition pathof provinces in each club, as shown in Figure 5. Unlike the diverging pattern shownin Figure 4, the transition paths in Figures 5 a), b), and c) demonstrate a clear
convergence pattern, with a smaller gap between provinces in the final period. In club
1, although real wages in Banten and Jakarta improve over time, the convergence islargely driven by the declining real wage in the Riau Islands, particularly since 2015.
It is worth noting that the Riau Islands is renowned as the country's industrial hub,with shipbuilding, oil and gas, and electronics manufacturing among its mainstays.
Despite its proximity to Singapore, the performance of the islands' industrial sector
has deteriorated, with diminishing levels of foreign direct investment (FDI) andexports (Negara–Hutchinson, 2020). Meanwhile, Jakarta is the country’s capital citywhere large business and financial services are centred, and Banten is a province thatshares a border with Jakarta and relies heavily on the manufacturing industry tosupport its economy (approximately 37% of GDP).
The convergence in club 2 is due to a combination of real wage decline in miningbased provinces (e.g., East Kalimantan, Papua, and West Papua) and improved
conditions in the industrialised province of West Java and the newly establishedprovince of North Kalimantan. Similarly, the consistent growth of real wages in Java's
other two industrialised provinces (Central Java and East Java, with manufacturing
shares of 30% and 34% of GDP, respectively) dominates the club 3 convergenceprocess. Conversely, the wage condition in East Nusa Tenggara continues to decline.
Agriculture's prolonged dominance – most of which is low-tech and subsistenceoriented, plays a significant role in this phenomenon (ILO 2013).

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Figure 5

The transition path of Indonesian provinces within each convergence cluba) Club1
Relative transition path of provinvies in Club1
1.02
1.01
1.00
0.99
0.98
0.97
2018

2019

2020

2019

2020

2017

2016

2015

Jakarta

2018

Banten

2014

2013

2012

2011

2010

2009

2008

0.00

Riau Islandsb) Club2
Relative transition path of provinvies in Club2
1.06
1.04
1.02
1.00
0.98
0.96

Bali
Central Kalimantan
East Kalimantan
North Kalimantan
North Sulawesi
Papua
West JavaWest Papua

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

0.00

South Sulawesi

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Testing for convergence clubs in real wage acrossIndonesian provinces from 2008 to 2020

117c) Club3
Relative transition path of provinvies in Club3
1.04
1.02
1.00
0.98
0.96

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

0.00

Aceh

Bangka Belitung

Bengkulu

Central Java

Central Sulawesi

East Java

East Nusa Tenggara

Gorontalo

Jambi

Lampung

MalukuNorth Maluku

North Sumatra

Riau

South Kalimantan

South SumatraSoutheast Sulawesi

West Kalimantan

West Nusa Tenggara

West Sulawesi

West Sumatra

Yogyakarta

Finally, we visualised the geographical distribution of club convergence in Figure
6. It is worth noting that we capture geographical effects of club convergence (Barroet al. 1991, Quah 1996), similar to what has been documented in the study of Aginta
et al. (2020) and Aginta (2021) when studying regional income and price convergencein Indonesia. These geographical effects are apparent on Sumatra Island, where aprovince and its neighbouring provinces are clustered in the same club (club 3). A
similar pattern is also observed in the distribution of club 2 (in Kalimantan and Papua
islands) and club 1 (where Jakarta and its neighbour Banten clustered together).

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Figure 6

The geographical distribution of club convergence in Indonesia
Clubs
Club 1 (3)Club 2 (9)Club 3 (22)The club convergence test results show the presence of a persistent gap in regional
real wages across Indonesian provinces. The results also reflect wage rigidity andheterogeneity in macroeconomic and labour market conditions across provinces.
However, to date, we do not know which components of macroeconomic and labour
market conditions explain regional wage disparity across Indonesian provinces.
Therefore, in the following section, we investigate the important factors contributing
to persistent regional wage disparity. More specifically, we aim to provide empiricalevidence to address the following question: what regional factors influence theformation of club convergence?

Factors influencing the club convergenceThis section examines and discusses the important conditioning factors thattheoretically influence club convergence formation. The club convergence hypothesis
places a considerable weight on the crucial roles of the initial condition and structuralcharacteristics in influencing the convergence process; that is, countries or regions
will only converge to a common steady state if they depart from similar initialconditions and share the same structural characteristics (Galor 1996).6 Therefore, in
addition to the level of real wage in 2008 to control for the initial condition, we also
include sectoral and labour market indicators to capture the role of structuralcharacteristics in club convergence formation. To be consistent with the theoretical
6 In the context of the neo-classical framework, multiple steady-state equilibria could arise from the variation infactor endowments. In a particular case, the initial level of capital-labour ratio can be used as a proxy of factor
endowments that determine the shape of the steady-state path of an economy. Meanwhile, economies that use similar
production technology tend to evolve toward a common steady state. See Galor (1996) for a more in-depth look at
the theoretical models behind convergence clubs.

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119

foundation of the convergence framework, the selection of variables in our orderedlogit model is also comparable with previous club convergence studies (Bartkowska‒
Riedl 2012, Cutrini 2019, Von Lyncker‒Thoennessen 2017).
The ordered logit specification requires variables to be explained in an ordinalmanner (McKelvey‒Zavoina 1975). Hence, we re-arrange the clubs by using the wage
level of each club as a reference to order the clubs from 1, 2, and 3 as high-, middle-,
and low-wage clubs, respectively. Finally, as in the previous literature, we use theseordered clubs as the dependent variable of the regression, while independent variablesconsist of the main factors that theoretically influence club convergence. Table 3
contains the definitions and sources of the variables.
Table 3

Variables in ordered logit estimation
Variable

Definition

SourceInitial value of wage (2008)

Real wage in 2008 (in thousand IDR)BPS

Manufacture employment
share

Number of people employed in manufacturing sector
Total number of people employed

BPS

Investment share to GDP

Total investment
Total GDPBPS

Labour force participation
rate

Number of persons in the labor force
Population aged ten (10) years and over

BPS

Real GDP (2010 = 100) in log form

BPS

GDP

We report the marginal effects on the probabilities computed from the orderedlogit model in Table 4.7 The individual marginal effect measures how much theprobability of a province being included as a member of a specific club changes withrespect to a small change in the explanatory variables. In this way, our results can
explain how a unit change in the independent variable affects the probability ofprovinces clustered into club 2 (middle-wage) and club 3 (low-wage). However, ourmodel cannot precisely compute the marginal effects on the probability of club 1(high-wage). We consider this to be the problem of insufficient samples in club 1(high-wage).8 Nonetheless, the model clarifies how the selected factors influence themembership of the convergence club.

7
8

See Long–Long (1997) for a discussion on interpreting the results of ordered logit models.
Aginta (2021) and Bartkowska–Riedl (2012) also encounter a similar problem in their respective studies.

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120

Table 4

The results from ordered logit estimation
Variables
Initial value of wage (2008)
Manufacture employment
share
Investment share to GDP
Labour force participation rate
GDP (in logs)
Number of provinces

Marginal effects on probabilities
Club 1 (high)
0.000(0.000)0.000(0.000)0.000(0.000)–0.000(0.000)0.000(0.000)3Club 2 (middle)
0.009**(0.004)0.189**(0.106)
0.162**(0.078)–0.046**(0.022)0.242(0.211)9Club 3 (low)–0.009**(0.004)–0.189**(0.106)–0.162**(0.078)0.046**(0.022)–0.242(0.211)22

Note: Numbers in parentheses are standard errors. ***, **, * show significant level at 1, 5, and 10%, respectively.

All the ordered logit coefficients show the expected signs. Additionally, themagnitude of the coefficients deserves special attention. Our results point to thesignificance of structural characteristics in explaining club convergence formation.9
Specifically, the share of employment in the manufacturing sector is the mostimportant structural element driving club convergence. A one-point increment in the
manufacturing employment ratio would significantly raise the probability of aprovince converging to the middle-wage club (club 2) by 18% while reducing the
likelihood of a province converging to the low-wage club (club 3). In this respect, ourfindings appreciate the conventional view that claims labour productivity is generally
higher – therefore, higher wages – in the manufacturing sector. The investment share
in GDP and labour force participation rate is the other important structural
determinant, to a lesser extent than the share of employment in the manufacturingsector, but has a larger effect than the initial level of real wage in 2008. Overall, ourresults imply that the mechanism of club convergence formation in real wages acrossIndonesian provinces mainly works through underlying attributes in regional labour
market conditions. Next, we elaborate on the effects of each factor in detail.
The manufacturing employment share has a positive effect for clubs 1 and 2,
whereas it shows an adverse effect for club 3, which means that the province with ahigher manufacturing employment share has a higher wage than the rest. This result
is also consistent with Felipe et al. (2019) finding, in which high-tech manufacturingfirms generally pay higher wages in Indonesia. Furthermore, they also reveal thatdifferences are likely due in part to differences in the skill requirements of the
manufacturing sector, with average levels of education and training being significantly
9 Cutrini (2019) empirically finds comparable results on the relative importance of structural variables than theinitial level of income per capita in influencing club membership across 274 European regions on a NUTS-2 level.

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121

higher. Moreover, the high productivity rate in the manufacturing sector often
becomes the main reason labour in the manufacturing sector often earns a higher
wage than labour in other sectors. Strain (2019) finds evidence of a strong link
between productivity and wages. In detail, he describes that when properly measured,
productivity and compensation show very similar trends in the last few decades.
Similar to the share of manufacturing employment, the investment-to-GDP ratio
also demonstrates a positive effect on higher wage clubs. This result is also similar to
the finding of Baskoro et al. (2019), who conclude that the relatively higher wage in
FDI companies is possibly explained by the higher productivity of labour, which
represents an improvement of labour skill and in line with the shift in Indonesianindustrial character. Lipsey–Sjoholm (2001) highlight the higher level of workers'
education in foreign-owned firms as the main factor explaining why foreign-owned
firms in Indonesia might pay a high price for labour. Another reason is that foreignowned firms wish to reduce employee turnover to secure their technologicaladvantages from being copied by their competitors. The significance of investment
in affecting regional wage also implies a regional imbalance in economic development,
where investment activities are largely concentrated in a few provinces with better
infrastructure, strategic geographical position, and natural resource endowment. For
example, provinces like Jakarta, Banten, and Riau islands are in club 1 and have better
infrastructures than other provinces. These provinces also have strategic geographicallocations surrounded by well-managed transportation infrastructure, and thus will
induce higher labour and capital mobility.
Meanwhile, the labour force participation rate shows a different effect in which
the sign of the coefficient is negative in clubs 1 and 2, whereas it is positive in club 3.
This means that a higher labour force participation rate decreases the probability of
being in higher wage clubs, reflecting the standard labour supply and demandconditions. A higher labour supply relative to its demand leads to downside pressure
on wages. Similar to what is mentioned by Herr (2002), we find evidence of a negative
relationship between wages and labour supply.
The initial wage level shows a positive effect for clubs 1 and 2, which means thatthe probability of the province belonging to clubs 1 and 2 is higher when the province
has a higher initial wage level. Inversely, the adverse effect for club 3 means that theprovince with a higher initial value of wage has a small probability of belonging toclub 3. The effect of the initial condition in our study is also in line with Bartkowska–
Riedl (2012) findings, which shows that the region's initial state plays a crucial role in
the European areas to determine which club they will belong.
As for the last variable, although statistically insignificant, the size of the economy,
or GDP, shows a positive effect on higher-level wage clubs. This implies that thecross-sectional variation in regional wages in Indonesia is less connected to the sizeof the economy. Instead, regional economic and labour market structures are the main
factors shaping the level of regional wages.

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Muhamad Rifki Maulana – Harry Aginta

122

Conclusions
This study empirically investigates the convergence of regional wages in Indonesia, alarge and geographically diverse developing country. Specifically, we address two
crucial questions in the empirical analysis. First, can we identify club convergence inregional wages in Indonesia, despite the presence of prolonged wage disparity?
Second, to what extent do region-specific characteristics influence club convergence?
To achieve these goals, we divide our strategy into two main steps. First, we test
whether regional wages converge to a common steady-state using log t regression
developed by Phillips–Sul (2007, 2009). In the absence of overall convergence, we
further check for the presence of club convergence. Second, we investigate theessential factors that influence club convergence formation.
Our results from the first step show three significant clubs representing theconvergence dynamics of regional wage across Indonesian provinces: three provinces
clustered in club 1, nine provinces in club 2, and 22 provinces in club 3. Overall, theresults from our initial investigation imply that, based on the long-run dynamics ofregional real wages from 2008 to 2020, Indonesian provinces can be clustered into
three club convergences. The presence of club convergence from our results is similar
to the finding of Neagu (2020) in the context of regional wage analysis in Romania.
In the second part, the results from the ordered logit model show that regional
characteristics related to labour market conditions largely explain the formation ofclub convergence in provincial wages. Variables such as manufacturing employment
share, investment-to-GDP ratio, labour force participation, and the initial conditionof wage significantly influence the convergence club formation, while the size of theeconomy, or GDP, exhibit insignificant effects. Our findings are compatible with thetheoretical underpinnings of the convergence concept and comparable to previous
club convergence investigations (Bartkowska‒Riedl 2012, Cutrini 2019, Von
Lyncker‒Thoennessen 2017).
Taken together: our results suggest four key points concerning policy implications
for reducing wage disparity across Indonesian provinces. First, it is imperative topromote the development of manufacturing industries in all provinces.
Manufacturing sectors would attract skilled workers from different places and bringpositive technical spillovers to local workers. In turn, this would create a trickle-down
effect that tends to help reduce wage disparity, as in the case of India (Jain, 2018).
Second, boosting investment is equally important, not to mention attracting inward
FDI. Technology spillovers and demand creation effects brought by FDI firms would
positively influence the productivity of local firms and workers, leading to improved
wage levels. Third, reducing wage differentials across regions requires convergence ineducation. Therefore, improving education quality in less developed regions should
become a priority and promote industrialisation and investment to guarantee theprovision of educated labour and skilled workers. Finally, the Indonesian government

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Testing for convergence clubs in real wage acrossIndonesian provinces from 2008 to 2020123

needs, both national and local, to continue enhancing healthy competition in theregional labour market to promote efficiency in resource allocation across regions.
However, one limitation of this study is the relatively short observation timeframe
used to study wage convergence. This may impact the estimation of club convergence,as the power of the log t-test decreases as the time dimension shortens (Phillips‒Sul
2007, 2009, Von Lyncker‒Thoennessen 2017). Furthermore, based on thegeographical distribution of the clubs, one is tempted to conclude that real wages in
Sumatra Island converge perfectly to club 3, except for the Riau Islands. This might
not be the case when the spatial unit used is at the district level, as observed in thecontext of regional income convergence in Indonesia (Santos-Marquez et al. 2021).
Hence, future studies could investigate regional wage convergence across Indonesia's
district level, subject to data availability. Such studies would allow us to look moredeeply at the role of spatial dependence within a province and between adjacentdistricts belonging to different provinces in shaping regional wage. Finally, depending
on data availability, future studies could examine regional wage convergence inIndonesia by utilising real wage data that capture cross-sectional differentials inlabour’s purchasing power.

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124

Appendices
Appendix 1: Clustering algorithm for club convergence
When the results from log t-test regression reject the null hypothesis of overall
convergence, the application of the clustering algorithm introduced by Phillips–Sul(2009) can be applied for club convergence identification. The following steps briefly
summarize the mechanism of the algorithm:
Step 1: Ordering based on final observation
All individual units (in our study, provinces) are arranged in descending order
based on their last observation in the time-series dimension of the panel.
Step 2: The formation of the core group
Apply log t regression to the first 𝑘 individual units (provinces), where 2 ≤ 𝑘 < N.
The core group is established when the 𝑡 > −1.65. If the 𝑡 in the first 𝑘 unit
< −1.65, the first unit is dropped, and the log t regression is applied for the second
and third units. This step continues until the condition where 𝑡 of the pair units> −1.65. In the case where no pairs of units showing 𝑡 > −1.65 in the entire sample,
the conclusion is that there are no convergence clubs in the panel.
Step 3: Filter the data for club membership
When the core group of a club is successfully identified, the remaining individual
units (provinces) that do not belong to the core group will be added one at a time andevaluated using log t regression. If the inclusion of an additional unit results in
𝑡 < −1.65, then the club convergence only has the core group. Otherwise, a newgroup is formed when 𝑡 > −1.65.
Step 4: Repetition and stopping rule
Apply log t regression to the remaining individual units (provinces). If the results
suggest rejecting the null hypothesis of convergence, repeat steps 1 to 3. If there isno core group identified for which 𝑡 > −1.65, label the remaining individual units(provinces) as divergent and the algorithm stops.
Appendix 2: Brief description of the club merging procedure
Apply log t regression to the first two adjacent groups identified by the initialclustering mechanism. If 𝑡 > −1.65, a new club convergence is formed from these
two groups. Next, repeat the step by adding the next group one at a time until thecondition of 𝑡 > −1.65 holds. If the null of convergence is rejected, we conclude
that all previous groups converge, except for the last added one. Restart the merging
algorithm from the club where the convergence hypothesis does not hold.

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125
Table A1

Descriptive statistics of monthly real wage in 34 Indonesian provinces
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

Province
Aceh
Bali
Bangka Belitung
BantenBengkulu
Central Java
Central Kalimantan
Central Sulawesi
East Java
East Kalimantan
East Nusa Tenggara
Gorontalo
JakartaJambi
Lampung
MalukuNorth Kalimantan
North Maluku
North Sulawesi
North Sumatra
Papua
Riau
Riau Islands
South Kalimantan
South Sulawesi
South SumatraSoutheast Sulawesi
West JavaWest Kalimantan
West Nusa Tenggara
West Papua
West Sulawesi
West Sumatra
Yogyakarta

Mean
1,057
1,205
1,017
1,371
1,049
885
1,173
989
929
1,537
938
1,000
1,706
941
874
1,191
1,290
1,205
1,243
1,009
1,612
1,188
1,744
1,066
1,145
987
1,079
1,233
954
936
1,551
1,170
1,061
968

Std Dev
947
1,920
1,339
2,831
685
1,484
1,886
824
1,553
1,583
486
1,603
3,334
975
1,389
793
2,435
1,060
2,143
829
1,656
976
2,329
1,402
1,978
912
1,495
2,490
696
985
1,053
1,304
990
1,254

Min

(thousand IDR)
Max

968
1,001
784
987
955
668
857
849
734
1,324
880
683
1,295
755
724
1,075
880
985
929
872
1,280
1,036
1,365
830
876
818
810
942
845
785
1,384
986
885
784

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1,231
1,529
1,248
1,797
1,178
1,123
1,452
1,120
1,185
1,839
1,037
1,226
2,210
1,092
1,079
1,370
1,613
1,373
1,571
1,142
1,900
1,322
2,051
1,269
1,452
1,105
1,350
1,645
1,077
1,115
1,732
1,377
1,235
1,189

Muhamad Rifki Maulana – Harry Aginta

126

Figure A1

The time-series of monthly real wage acrossthe 34 Indonesian provinces based on region*

Sumatra

Java

2,000

2,000

1,500

1,500

1,000

1,000

500

500

0

0

1
15
26

3
20
33

5
22

14
23

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Monthly real wage (thousand IDR)
2,500

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Monthly real wage (thousand IDR)
2,500

4
13

Bali-Nusa

628

Kalimantan

2,000

2,000

1,500

1,500

1,000

1,000

500

500

0

0

11

30

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Monthly real wage (thousand IDR)
2,500

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Monthly real wage (thousand IDR)
2,500

2

9
34

7
24

10
29

17

(Figure continued the next page.)

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127
(Continued.)

Sulawesi

Maluku-Papua

2,000

2,000

1,500

1,500

1,000

1,000

500

500

0

0

8
25

12
27

19
32

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Monthly real wage (thousand IDR)
2,500

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Monthly real wage (thousand IDR)
2,500

16
31

18

* The names of the provinces see in Table A1.

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21

128

Muhamad Rifki Maulana – Harry Aginta

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