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Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
MINIMUM WAGE POLICY AND ITS IMPACT ON
EMPLOYMENT IN THE URBAN FORMAL SECTOR
Asep Suryahadi , Wenefrida Widyanti , Daniel Perwira & Sudarno Sumarto
To cite this article: Asep Suryahadi , Wenefrida Widyanti , Daniel Perwira & Sudarno Sumarto
(2003) MINIMUM WAGE POLICY AND ITS IMPACT ON EMPLOYMENT IN THE URBAN FORMAL
SECTOR, Bulletin of Indonesian Economic Studies, 39:1, 29-50, DOI: 10.1080/00074910302007
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Date: 19 January 2016, At: 20:16
Bulletin of Indonesian Economic Studies, Vol. 39, No. 1, 2003: 29–50
MINIMUM WAGE POLICY AND ITS IMPACT ON
EMPLOYMENT IN THE URBAN FORMAL SECTOR
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto*
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SMERU Research Institute, Jakarta
Since the late 1980s, minimum wages have become an important plank of the Indonesian government’s labour policy. Their levels have increased faster in real terms
than those of average wages and per capita gross domestic product and, as a result,
minimum wages have become binding for the majority of formal sector workers.
This study finds that the imposition of minimum wages has a negative and statistically significant impact on employment in the urban formal sector. The disemployment impact is greatest for female, young and less educated workers, while the
employment prospects of white-collar workers are enhanced by increases in minimum wages. Some workers who lose jobs in the formal sector and have to relocate to
the informal sector face lower earnings and poorer working conditions.
INTRODUCTION
Although first introduced in Indonesia
in the early 1970s, minimum wages did
not gain much attention until the late
1980s, when the government began to
make them an important plank of its
labour market policies. In the first half
of the 1990s, the government tripled
minimum wages in nominal terms and
more than doubled them in real terms
in just five years. During the second half
of the decade, their nominal levels continued to increase, but in real terms they
began to taper off after 1996 and fell significantly in 1998, owing to the high inflation that swept the country during the
economic crisis.1
Minimum wages began to re-emerge
as a key element of economic and social policy in 2000. In three consecutive
years, from 2000 to 2002, they were
raised significantly. As a result, their
levels in real terms in 2002 were already
higher than at their pre-crisis peak in
1997. Critically, this occurred against
the backdrop of an economy still struggling to recover from a major economic
crisis. Following a massive contraction
of 13.7% in 1998 and near zero growth
in 1999, the economy grew by around
5% in 2000 and 3.5% in 2001 and 2002.
Theory suggests that the impact of a
minimum wage varies with different
product and labour market conditions.
A competitive labour market model
predicts that a minimum wage established above the equilibrium market
wage will cause a reduction in employment and create unemployment. On the
other hand, a monopsonistic labour
market model predicts that a minimum
wage set above the monopsony wage
level (but below the competitive wage
level) will increase employment.
Whether a country’s labour market
is closer to the competitive or the
monopsonistic model is an empirical
ISSN 0007-4918 print/ISSN 1472-7234 online/03/010029-22
© 2003 Indonesia Project ANU
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30
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
matter. Although we do not rule out the
possibility of imperfect competition, observers generally have maintained that
the Indonesian labour market is competitive. This was particularly evident
during the recent economic crisis,
whose impact was significant in terms
of the fall in real wages, but relatively
small in terms of the increase in open
unemployment (Feridhanusetyawan
1999; Manning 2000). In general, there
is little evidence to characterise the Indonesian labour market as monopsonistic, with the minor possible exception
of some large, isolated employers in the
outer islands.2
The first serious attempt to assess the
impact of minimum wage policy on the
labour market in Indonesia was conducted by Rama (1996),3 who found that
minimum wages have a modest impact
on labour market outcomes, and concluded that the doubling of the minimum wage in the first half of the 1990s
had led to an increase in average wages
in the range of 5–15%, and a decline in
urban wage employment in the range
of 0–5%. However, he suggested that the
disemployment impact appeared to be
considerable in small manufacturing
firms.
These findings have been challenged
by Islam and Nazara (2000), who argue
that the Indonesian regional minimum
wage policy has not impaired employment prospects, and propose that if Indonesia achieves annual economic
growth of 4%, real minimum wages can
be increased by 24% annually without
net job losses being incurred. They
contend there is no evidence that minimum wage-induced increases in domestic labour costs have eroded
business profitability in large and
medium-scale manufacturing. With reasonably competitive factor and product
markets this result is hardly surprising,
since firms can be expected to respond
to the negative impact on profits of
higher input costs by substituting capital and higher productivity labour for
low skill labour, and by increasing their
product prices. But whether unskilled
labour will be negatively affected by the
imposition of minimum wages is a different issue.
The current study offers a reassessment of the impact of minimum wage
policy on employment in Indonesia between 1988 and 1999, focusing on the
urban formal sector.4 The employment
impact of minimum wages is investigated by means of an econometric approach, using data collected through
the National Labour Force Surveys by
Statistics Indonesia (BPS). In contrast
to previous studies of the labour market impact of minimum wages in Indonesia, which have focused only on
workers in aggregate, this study also
assesses the impact for different types
of workers.5
MINIMUM WAGE POLICY
During the 1970s and 1980s, the Indonesian government rarely intervened in
wage determination, and neither did it
enforce regulations on the laying off of
workers. In addition, the government
tightly controlled the labour movement
by allowing only one government sanctioned labour union. As a result, there
was little effective direct government or
union involvement in wage setting
(Manning 1994).
The late 1980s witnessed many
changes in the Indonesian labour market, two of which are especially important. First, several independent labour
unions were established, despite the
government’s efforts to disband them
and declare them illegal. Second, the
government began to enforce the implementation of regional minimum wages,
which were updated annually. It has
been argued that this minimum wage
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
policy was a form of compensation for
the policy of tightly restricting labour
rights, and was an effort to appease
workers.
These changes also occurred in response to both internal and external
pressures, however. The internal pressures came from groups concerned
about the conditions of workers in an
increasingly industrialised Indonesian
economy, including many senior policy
makers worried that labour was not
sharing in the high growth that had
taken place (Agrawal 1996; Edwards
1996; Manning 1994).
The external pressures were an indirect result of the growing level of Indonesian exports to North America and the
European Union (EU), regions where
concern was being aired in many quarters about labour market conditions in
developing countries. The focus was on
workers in export sectors, where, it was
claimed, there were poor working conditions, low wages and a denial of the
fundamental right to form labour unions.
This belief led to calls for a ‘social clause’
to be inserted in trade agreements between developed and developing countries, stipulating that favoured access to
the markets of developed countries
would not be granted to those third
world countries where labour standards
remained unsatisfactory (Addison and
Demery 1988).
Indonesia was one of the countries
targeted by this concern. In the early
1990s, several complaints were filed
against it, threatening to deprive it of
low tariffs under the Generalised
Scheme of Preferences (GSP) on its exports to the US market. Potentially
even more significant was a threat by
the US government to withdraw investment guarantees to US companies
investing in Indonesia (Rama 2001).
As part of its response, the Indonesian government revamped the mecha-
31
nism for setting minimum wages in
1989, and then on several occasions during the 1990s. Its objective has been to
set minimum wages with reference to a
range of factors, including ‘minimum
subsistence needs’ (kebutuhan hidup
minimum or KHM); the cost of living;
the capacity and sustainability of companies; existing market wage rates;
labour market conditions; and economic
and income per capita growth.6
Prior to 1996, minimum wages were
calculated with reference to what was
termed ‘minimum physical needs’
(kebutuhan fisik minimum or KFM) rather
than KHM.7 Both KFM and KHM are
bundles of consumption items deemed
essential for the livelihood of a single
worker. The KHM bundle consists of 43
items, ranging from food, clothing,
housing, transport and health to recreation.8 KHM is a broader consumption
bundle, and represents a higher standard of living than KFM. For example,
the food bundle of KFM was set to
achieve an intake of 2,600 calories per
day, while that of KHM was set to
achieve a daily intake of 3,000 calories.9
Until 2000, most provinces had just
one level of minimum wage, which was
applied throughout the entire province.
Exceptions were to be found in the provinces of Riau, South Sumatra, West Java,
East Java and Bali, where several minimum wages existed for different regions
within the province. In addition, some
provinces had different minimum wages
for different sectors of the economy. In
such cases, the sectoral minimum wages
could not be set at a lower level than the
general minimum wage that applied in
the region.
Until the end of 2000, regional minimum wages were established by a
decree issued by the Minister of Manpower. In determining minimum wage
levels, the minister received recommendations from provincial governors, who
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32
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
in turn took advice from tripartite provincial councils made up of representatives of employees, employers and the
government. In practice, employee and
employer representatives were usually
government appointees.
Beginning in 2001, as part of the regional autonomy policy adopted and
implemented throughout the country,
the power to set minimum wage levels
was transferred to governors, and in
some cases to mayors and regents
(bupati), as the respective heads of provinces, cities and districts (kabupaten).10
As of 2000, Indonesia had 341 cities and
districts. In setting minimum wages,
mayors and regents also receive recommendations from tripartite councils in
their regions.
DATA
The data analysed in this study are
drawn mainly from the National
Labour Force Surveys (Sakernas) conducted annually by BPS. The Sakernas
is a nationally representative repeated
cross-section survey conducted in August each year and covering all provinces in Indonesia.11 This study uses the
Sakernas data from the 1988 to 2000
surveys, except for the 1995 data,
which are derived from the labour
force module of the Intercensal Population Survey (Supas).
The Sakernas and the labour force
module of the Supas collect information
on individual main employment activities, earnings, and hours of work on the
primary job, as well as data on individual
characteristics such as gender, age and
level of education. The sample size of the
Sakernas each year ranges from 40,000
to 80,000 households, covering 135,000
to 240,000 individuals aged 10 years and
over. Since the present study focuses on
the urban formal sector, only data from
households that supply labour to that
sector are included in the analysis.
The minimum wage data were obtained from the Department of Manpower and Transmigration. The study
also uses data published by BPS on real
regional gross domestic product
(RGDP) for each province. To make
them comparable across years, we deflate nominal wages and minimum
wages by the annual provincial CPI, also
from BPS.
LABOUR MARKET STRUCTURE,
MINIMUM WAGES AND
WAGE DISTRIBUTION
Changes in Labour Market Structure
During the 1990s, the structure of the
Indonesian labour market underwent
considerable change, both quantitatively and qualitatively. Table 1 presents
in summary form some key statistics on
the Indonesian labour force from 1988
to 2000. The labour force grew by 33%,
from 71.9 million in 1988 to 95.7 million
in 2000—an average annual growth rate
of 2.4%. During the entire period, however, the labour force participation rate
was relatively steady at around 67%,
suggesting that the increase in labour
force size was driven mainly by natural
population growth.
The gender composition of the
labour force was also relatively stable.
The proportion of females remained
close to 39–40%. Similarly, the proportion of young workers—those aged
between 15 and 24 years—changed
little during the period, declining only
slightly from around 23% to 21% in the
12-year period. On the other hand,
there was a notable trend towards
urbanisation of the labour force, with
the urban proportion increasing by
more than two-thirds, from 23.6% in
1986 to 39.3% in 2000. The table also
shows a clear trend in favour of formalisation within the economy, at
least before the crisis, when the formal
component of the workforce increased
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
33
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TABLE 1 Summary Statistics of the Indonesian Labour Market, 1988–2000
Labour Market Characteristics
1988
1992
1996
2000
Size of labour force (millions)a
Labour force participation rate (%)b
Female labour force (%)
Youth labour force (%)c
Urban labour force (%)
Formal workforce (see endnote 4) (%)
Less educated labour forced
Blue-collar workforce (%)
Part-time workforce (%)e
Unemployment rate (%)f
71.9
66.9
40.1
23.1
23.6
26.9
87.9
–
28.9
2.8
76.2
67.8
39.0
22.8
28.7
30.7
84.4
–
29.6
2.8
88.2
66.9
38.5
22.3
33.9
37.9
78.8
81.2
33.3
4.9
95.7
67.8
38.6
20.7
39.3
35.1
75.9
79.2
26.7
6.1
a
The labour force consists of those who work (the workforce) and the unemployed.
b
The labour force participation rate is the labour force as a share of the total population aged
15 years and over.
c
Youth are defined as those aged 15–24 years.
d
The less educated are defined as those who have completed no more than junior secondary
education.
e
Those who work less than 30 hours per week are considered to be working part time.
f
Unemployment rates from 1994 onward are not comparable with the preceding period owing
to a change in job search length.
Source: Sakernas.
from 26.9% in 1988 to 37.9% in 1996.
There is evidence that the economic crisis reversed this trend, presumably
temporarily, with the formal proportion of the workforce falling back to
35.1% in 2000.
There was a significant improvement
in the education level of the labour
force, with the less educated declining
from 87.9% in 1988 to 75.9% in 2000. The
employment share of blue-collar workers also tended to decline. Before the
crisis, more and more people chose to
work part time, with this group reaching a peak of one-third of the workforce
in 1996. It appears that the crisis reversed this trend, as only 26.7% of the
workforce worked part time in 2000.
Presumably some part-timers were
forced to work full time in response to
falling real income. Meanwhile, the unemployment rate increased during the
crisis period, from 4.9% in 1996 to 6.1%
in 2000.12
Trends in Minimum Wages
As a result of changes in labour market
policy in the late 1980s, minimum wages
have become an important plank of
labour policy, as is evident from the
speed at which the government has increased their levels. Figure 1 compares
the trend in the real minimum wage between 1989 and 2002 with trends in the
average real wage and in real per capita
GDP during the same period.13 It shows
that minimum wages have increased
much faster in real terms than both the
34
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 1 Trends in the Real Minimum Wage, Real Average Wage
and Real Per Capita GDP, 1989–2000
(1989 = 100)
300
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200
100
Real minimum wage
Real average wage
Real per capita GDP
0
1989
1991
1993
1995
average wage and per capita GDP. The
real minimum wage in 1994 was around
2.4 times its 1989 level, mainly as the
result of large minimum wage increases
in 1990 and 1994. Interestingly, figure 1
also indicates that these two increases
coincided with declines in the average
wage. In 1990, when the real minimum
wage was raised by almost 50%, the real
average wage declined by more than
12%. Similarly, when the real minimum
wage was increased by 30% in 1994, the
real average wage declined by around
2%. During the other years before the
crisis, when the increases in real minimum wages were more modest, real
average wages generally also increased,
more or less in line with increases in real
per capita GDP.
In 2000, the real minimum wage was
raised substantially, by significantly
more than rises in the real average
wage and real GDP. The increase in the
1997
1999
2001
weighted average of real regional minimum wages in that year was more than
17%, while the real average wage rose
by around 13% and real per capita GDP
by around 3.5%. Real minimum wages
were substantially increased in the following two years as well, bringing the
2002 minimum wage to a new peak,
higher than the pre-crisis peak of 1997.
Because minimum wages rose more
rapidly than average wages, the ratio
of minimum to average wages increased
markedly, from about 20% in 1989 to
50% in 1994 (figure 2). This ratio remained at around that point thereafter,
with only a slight decline during the crisis. Figure 2 also shows that the proportion of workers whose monthly earnings
were lower than the minimum wage
tended in general to increase until 1994,
and then to fall steadily. It rose from
around 7% in 1989 to around 21% in
1994 and 1995, and then continuously
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
35
FIGURE 2 Ratio of Minimum Wage to Average Wage and Proportion of Workers
Earning Less than the Minimum Wage, 1989–2000
60
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40
Ratio of minimum to average wage
Proportion of workers earning less
than the minimum
20
0
1989
1991
1993
declined to reach less than 11% by 2000.
This implies that there has been a tendency toward greater compliance with
minimum wage regulations since the
mid 1990s.
Minimum Wages and the
Wage Distribution
Some studies have shown that minimum
wages have an impact not merely on
wages around the minimum but on the
whole wage distribution (e.g. Maloney
and Nuñez 2001; Neumark, Schweitzer
and Wascher 2000). Figure 3 presents a
set of kernel densities of the wage distribution in the urban formal sector, showing how minimum wages have affected
the wage distribution over time.14 In this
series of diagrams, the wage of each
worker is measured as a ratio of the
worker’s nominal wage to the nominal
minimum wage applying in the region
where the worker lives. The vertical line
1995
1997
1999
at point 1 in each of the graphs represents the minimum wage level.
Ideally, the impact of minimum
wages on the wage distribution should
be assessed by comparing the wage distribution with and without minimum
wages being imposed. In reality, however, the wage distribution without
minimum wages, which is the counterfactual, can only be guessed at. Assessing the impact of minimum wages on
the wage distribution therefore requires
some informed judgment, for which the
following guides are useful. First, if the
minimum wage is only ‘on paper’ and
is not enforced, then the wage distribution can be expected to be relatively
smooth, including the distribution
around the minimum wage level. Second, if the minimum wage is fully enforced, then the wage distribution will
begin at the minimum wage level, because no worker will earn less than this
36
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 3 The Impact of Minimum Wages on Wage Distribution
in the Urban Formal Labour Force, 1988–2000a
(b) 1992
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(a) 1988
Density
0.8
Density
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
0
10
1
2
3
(c) 1996
4
5 6
Ratio
7
8
9
10
7
8
9
10
(d) 1999
Density
0.8
Density
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
0
10
1
2
3
4
5 6
Ratio
(e) 2000
Density
0.8
0.6
0.4
0.2
a
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
The vertical line in each graph represents
the minimum wage. Observations represent
the ratio of each worker’s nominal wage to
the region’s nominal minimum wage.
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
amount. The reality will probably lie
between these two extremes, with some
spikes expected to occur around the
minimum wage level. If the level of the
minimum wage is sufficiently high, it
may become the mode of the distribution, since most workers will earn
around this amount.
Figure 3 reveals that in 1988, a year
before the minimum wage regulations
were revamped, minimum wages had
very little impact on the wage distribution in Indonesia. There was no apparent spike in the wage distribution
around the minimum wage. But this has
changed over time. By 1992, the impact
of minimum wages on the wage distribution had become more apparent.
Spikes at and around the minimum
wage occurred in the distribution. In
1996, the mode of the wage distribution
was only slightly higher than the minimum wage. By 1999, and also in 2000,
the minimum wage had become the
mode of the distribution, indicating that
minimum wages have become binding
for the majority of workers.
The graphs in figure 3 show the wage
distribution for all urban formal sector
workers. Since minimum wage regulations may have a different impact on different groups of workers, we also graph
their impact on the wage distribution
across various groups of formal sector
wage workers in 2000 (figure 4). Again
the vertical line at point 1 in each graph
represents the minimum wage level.
Figure 4a reveals that the wage distributions of both male and female workers are affected by minimum wages, but
it appears that the effect is greater for
female workers. The proportion of workers on the minimum wage is higher for
females than for males. In addition, while
the mode of the wage distribution for female workers is at the minimum wage,
the mode for male workers is slightly
higher than the minimum.
37
Figure 4b indicates that the wage distributions of both adult and young
workers are affected by minimum
wages. The graph reveals that the wages
of most young workers are at or around
the minimum, with only a few earning
more than double the minimum. The
impact of minimum wages on adult
workers is also significant, with the
mode of their wage distribution only
slightly higher than the minimum.
Education is an important determinant of earnings. In figure 4c, workers
are grouped into two categories, the
‘educated’ and the ‘less educated’, where
the latter are defined as those with lower
secondary education or below. As expected, the graph reveals that the wage
distribution of less educated workers is
more strongly affected by minimum
wages than that of educated workers.
However, the impact of minimum wages
in altering the shape of the wage distribution of educated workers remains significant, as the mode of their distribution
is only slightly higher than the minimum
wage.
When workers are separated into
white-collar and blue-collar groups as
shown in figure 4d, it becomes clear that
minimum wages have very different consequences for the wage distribution of
each group. The graph suggests that the
wage distribution of white-collar workers is not greatly affected by the presence of minimum wages, because their
earnings are generally far above the minimum. On the other hand, the majority
of blue-collar workers are clearly affected
by minimum wages, as indicated by the
fact that the minimum is the mode of the
wage distribution for these workers.
Finally, figure 4e shows the wage distribution for full-time and part-time
workers. A worker is classified as parttime if he or she works less than 30
hours per week. Minimum wages are
only binding for full-time workers and
38
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 4 The Impact of Minimum Wages on Wage Distribution across
Categories of Workers in the Urban Formal Labour Force, 2000a
(a) By Gender
(b) By Age Group
Density
0.8
Density
0.8
0.6
0.4
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Youth
0.6
Female
0.4
Male
0.2
Adult
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
0
1
2
(c) By Education
3
4
5 6
Ratio
7
8
9 10
8
9
(d) By Type of Job
Density
0.8
Density
0.8
0.6
0.6
Less educated
Blue collar
0.4
0.4
White collar
Educated
0.2
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
0
1
2
3
(e) By Full-time/Part-time Status
Density
0.8
Part time
0.6
0.4
Full time
0.2
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
a
As for figure 3.
4
5 6
Ratio
7
10
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
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the graph shows that the minimum is
the mode of their wage distribution. For
part-time workers, minimum wages are
not binding and are of no consequence
for the wage distribution, as indicated
by the fact that most part-time workers
earned less than the minimum.
THE EMPLOYMENT IMPACT
OF MINIMUM WAGES
The purpose of minimum wage regulations is to lift the incomes of those workers who currently earn wages that are
‘too low’. Unfortunately, the reality is not
as simple as that. The imposition of a
minimum wage affects both the supply
and the demand sides of the labour market. Consequently, its impact is not limited to wages, for there is also an effect
on employment. It is this employment
impact that often becomes a source of
controversy when minimum wages are
imposed or increased. Of equal importance, minimum wages can also be expected to have a different employment
impact on different groups of workers.
The Controversy over the
Minimum Wage Impact
In the context of developed countries,
controversy about the impact of minimum wages on employment arose in
the 1990s as a result of the findings of
Card and Krueger (1994). Using data
collected through a telephone survey of
fast-food restaurants, they compare
employment changes in two states in the
US. They find that restaurants in New
Jersey, where the minimum wage was
increased, expanded their workforce
relative to restaurants in Pennsylvania,
where there was no change in the minimum wage. This finding has been challenged by many, notably by Neumark
and Wascher (1995). They re-evaluate
Card and Krueger’s evidence using different data, obtained from actual payroll records. In contrast to the findings
39
of Card and Krueger, they conclude that
the minimum wage increase in New Jersey actually led to a fall in employment
in this state relative to the Pennsylvania control group.
A similar controversy occurred in the
context of developing countries. CastilloFreeman and Freeman (1992) analyse the
imposition of US minimum wage norms
in Puerto Rico. They estimate that the
elasticity of employment with respect to
the minimum wage in this country is
around –0.5, meaning that a 10% increase
in minimum wages causes a reduction
in employment of 5%. Hence, they assert that the imposition of the US minimum wage has led to massive job losses
on the island. Krueger (1995) disputes
this finding on methodological grounds,
however, arguing that the evidence of a
negative employment impact from the
imposition of the US minimum wage in
Puerto Rico is statistically fragile.
Meanwhile, Bell (1997) contrasts the
cases of Mexico, where minimum wages
are very low relative to average wages,
and Colombia, where minimum wages
are much closer to the mean. She finds
that the disemployment impact of minimum wages is zero in Mexico but substantial in Colombia. Similarly, in a
study of eight Latin American countries,
Maloney and Nuñez (2001) find that
minimum wages have significant implications for employment, and affect the
wage distribution not only in the neighbourhood of the minimum wage but
also in the higher reaches of the distribution and in the informal sector.
Theoretical Framework
In the context of developed countries,
the impact of minimum wages on
labour market outcomes is generally
analysed using a competitive labour
market framework. In this framework,
a minimum wage imposed above the
equilibrium market wage will cause a
40
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 5 The Impact of the Minimum Wage in the Formal and Informal Sectora
WI
WF
DF
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DI
WM
W*
W*
W1
O
N1
N*
N
a
See text for an explanation of terms
reduction in employment and create unemployment (Stigler 1946). This framework is appropriate for use in the
context of countries with integrated
labour markets. For developing countries that are still characterised by dual
labour markets, including Indonesia, an
alternative framework is required.
The framework used in this study is
illustrated in figure 5. The economy consists of a formal sector and an informal
sector. The labour supply is fixed in total, and its allocation to the formal and
informal sectors, and the level of wages,
are determined by the demand for
labour in both sectors. The horizontal
axis (ON) represents the total amount
of labour available. The left vertical axis
indicates the level of wages in the formal sector, and the demand for labour
in this sector is represented by the curve
DF. The right vertical axis indicates the
level of wages in the informal sector,
and the demand for labour in this sector is DI, measured from right to left.
If there is no distortion in the labour
market, equilibrium is achieved at the
intersection of DF and DI. The equilibrium wage level is W*, which prevails
in both the formal and informal sectors.
Of ON total labour, ON* is allocated to
the formal sector, and the remainder,
N*N, to the informal sector. Suppose
now that a minimum wage level of WM
is imposed in the formal sector. This will
reduce the amount of labour absorbed
in the formal sector to ON1, such that
the informal sector now has to absorb
more labour, N1N. As a result, the wage
level in the informal sector is depressed
to W1.
In this framework, the imposition of
minimum wages will reduce employment in the formal sector and lower
wages in the informal sector. Workers
lucky enough to keep their jobs in the
formal sector benefit at the expense of
other workers—both those who need to
relocate to the informal sector and those
already there—all of whom suffer a de-
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
cline in earnings. There is also an
implied shift in the structure of the
economy, with the formal sector contracting and the informal sector expanding; this will be driven by an increase in
product prices in the formal sector relative to the informal sector.
In reality, of course, workers are differentiated by their productivity. The
demand curves for higher productivity
workers lie above those for the less
skilled, so the equilibrium market wage
is likely to be above the minimum wage.
In this case the minimum wage would
have no direct effect on higher productivity workers. Indirectly, however, to
the extent that there is substitutability
between workers with differing degrees
of productivity, firms will now have a
greater incentive to increase their employment of higher productivity workers, who can expect some upward
pressure on their earnings as a result.
The Method for Empirical
Estimation and Data Construction
Using the above framework, this study
sets out to examine the impact of minimum wages in Indonesia on employment levels in the urban formal sector.
Since minimum wages are determined
at the provincial level, one strategy for
estimating the impact of minimum
wages on urban formal sector employment is to use variations in the levels of
minimum wages and employment
across provinces as well as over time.
This strategy was adopted by Rama
(2001) and is also used here.
Following Rama (2001), data on urban
formal sector employment from the Sakernas are aggregated at the provincial
level. These data, which are calculated
for all workers as well as for certain segments of the workforce, are then combined across years to form a panel data
set with the province as the unit of ob-
41
servation. This data set is then merged
with other provincial-level data on minimum wage levels, the CPI, RGDP and
demographic variables. The complete
panel data set can be constructed for all
26 provinces in Indonesia,15 covering the
12 yearly observation points from 1988
to 1999, so that in total there are 312 province level annual points of observation.
The exceptions are data for white-collar
and blue-collar workers, which are available only from 1994.
Using the model discussed in the appendix, we then estimate the impact of
minimum wages on employment by regressing provincial level employment
data on the minimum wage level and
on some control variables. Other independent variables used in the estimation include the size of the population
aged 15 years and over, to represent the
factors that affect labour supply, and
real RGDP, to represent the factors that
affect labour demand. Province dummy
variables are included to take into account individual province fixed effects
that do not vary across time. Meanwhile, year dummy variables are included to measure specific time effects
that have an impact on all provinces in
any given year.
In addition, ‘degree of compliance
to the minimum wage’ is included as
an independent variable in the estimation. As shown in figures 2 and 3, compliance with the minimum wage in
Indonesia has changed over time. This
has important consequences for how
minimum wages affect labour market
outcomes. It is therefore necessary to
control for the impact of varying degrees of compliance in order to measure the true impact of minimum
wages.16 In the estimations, the proportion of workers who earn above the
minimum wage is used as a proxy for
the degree of compliance variable.17
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42
TABLE 2 Results of OLS Estimation of Employment Regressiona
(Dependent variable: Log of employment)
All
Workers
Male
Female
Adult
Youth
Educated
Less
Educated
Whitecollar
Bluecollar
Fulltime
Parttime
–0.065
(–1.87)
–0.137
(–0.86)
1.004**
(38.26)
0.020*
(1.94)
Yes
Yes
–0.471
(–0.83)
–0.307**
(–4.64)
–1.177**
(–3.88)
0.949**
(18.41)
0.013
(0.65)
Yes
Yes
2.895**
(2.60)
–0.066
(–1.80)
–0.165
(–0.98)
0.975**
(35.66)
0.018
(1.60)
Yes
Yes
–0.108
(–0.18)
–0.307**
(–3.35)
–1.414**
(–3.37)
1.052**
(15.30)
–0.004
(–0.13)
Yes
Yes
1.762
(1.17)
–0.017
(–0.48)
0.059
(0.36)
0.960**
(37.69)
–0.001
(–0.07)
Yes
Yes
–0.262
(–0.45)
–0.196**
(–3.79)
–0.838**
(–3.54)
1.038**
(26.43)
0.034*
(2.19)
Yes
Yes
0.174
(0.21)
1.000*
(2.09)
0.009
(0.01)
1.145*
(2.11)
–0.127
(–1.18)
Yes
Yes
–13.879
(–1.38)
–0.140
(–0.70)
–0.609
(–1.69)
0.779**
(3.46)
0.047
(1.06)
Yes
Yes
2.786
(0.67)
–0.086*
(–2.25)
–0.217
(–1.24)
1.007**
(34.13)
0.010
(0.83)
Yes
Yes
–0.532
(–0.83)
–0.364*
(–2.56)
–1.958**
(–3.00)
0.911**
(8.30)
0.068
(1.58)
Yes
Yes
2.037
(0.85)
0.998
0.998
0.994
0.998
0.989
0.998
0.996
0.966
0.995
0.998
0.964
2973.0**
3198.8**
1038.1**
2894.3**
606.8**
2771.7**
1741.1**
102.4**
744.8**
2806.4**
179.5**
312
312
312
312
312
312
312
156
156
312
312
Log of real minimum wage –0.112**
(–3.03)
–0.371*
Degree of complianceb
(–2.19)
Log of population group
0.997**
aged 15 years and over
(35.02)
Log of real RGDP
0.014
(1.28)
Province dummies
Yes
Year dummies
Yes
Constant
0.055
(0.09)
R-squared
F-test
Number of observations
a
Numbers in parentheses are t-values.
Measured as the proportion of workers who earn above the minimum wage.
**Significant at 1% level.
*Significant at 5% level.
b
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
Independent
Variable
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
The Results of the Estimation
The employment regression discussed
above is estimated using the Ordinary
Least Squares (OLS) method on the
provincial panel data set for all workers, as well as for various segments of
the workforce. The results of the estimations are presented in table 2. The
table shows that the coefficients of the
minimum wage variable for all workers and all segments of the workforce
are negative, except in the case of
white-collar workers. This is consistent
with the prediction from the theoretical framework that minimum wages
will reduce the employment of unskilled workers in the formal sector.
The coefficient for all workers indicates that the elasticity of total employment to the minimum wage is –0.112
and is statistically significant. This implies that every 10% increase in real
minimum wages will result in a reduction of more than 1% in total employment. Similarly, the coefficients for
female, young, less educated, full-time
and part-time workers are also all
negative and statistically significant.
Their employment elasticities with respect to minimum wages are –0.307 for
female workers and young workers, –
0.196 for less educated workers, –0.086
for full-time workers, and –0.364 for
part-time workers.18
The only group of workers who benefit from the minimum wage in terms
of employment opportunities are
white-collar workers. The employment
elasticity for this group with respect to
the minimum wage is 1.0 and is statistically significant. This implies that a
10% increase in the real minimum wage
will increase the employment of
white-collar workers by 10%, and may
suggest a substitution effect of minimum wages on the employment of different types of workers. As the level of
minimum wages is raised, firms reduce
43
the employment of unskilled workers
and employ more white-collar workers with relatively higher skill levels.
This also provides an indication that
firms change technologies in response
to increases in minimum wages. Owing to capital–skill complementarity, a
higher proportion of white-collar
workers employed usually indicates
that more capital-intensive technologies have been adopted (Baldwin 1994;
Berman, Bound and Griliches 1994).
The coefficients of the degree of compliance variable indicate that higher compliance tends to strengthen the negative
impact of minimum wages on employment. For most groups of workers their
signs are the same as the signs of the coefficients of the minimum wage variable.
The coefficients of the degree of compliance variable for all workers and for female, young, less educated and part-time
workers are all statistically significant.
However, higher compliance has no impact on the employment of white-collar
and more educated workers.
Sensitivity Analysis
The inclusion of the degree of compliance variable in the employment regression is an innovation in this study.
Hence, a sensitivity analysis is conducted by re-running the employment
regression without this variable. The
results are presented in table 3. Confirming the previous results, all coefficients in table 3 have the same signs as
those in table 2. However, the employment elasticities with respect to minimum wages in table 3 are in general
smaller than those in table 2, and the
t-values of the coefficients are also
smaller.
Comparison of the results of the sensitivity analysis with those in table 2
provides evidence that controlling for
the degree of compliance results in a
stronger estimate of the impact of mini-
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All
Workers
Male
Female
Adult
Youth
Educated
Less
Educated
Whitecollar
Bluecollar
Fulltime
Parttime
Log of real minimum wage
–0.063*
(–2.13)
–0.047
(–1.70)
–0.155**
(–2.83)
–0.044
(–1.51)
–0.123
(–1.64)
–0.025
(–0.86)
–0.087*
(–2.04)
0.999*
(2.14)
–0.073
(–0.37)
–0.058
(–1.88)
–0.109
(–0.94)
Log of population group
aged 15 years and over
0.988**
(34.79)
1.001**
(38.59)
0.926**
(17.64)
0.971**
(35.88)
1.028**
(14.76)
0.961**
(38.12)
1.020**
(25.66)
1.144*
(2.14)
0.834**
(3.71)
1.003**
(34.24)
0.867**
(7.86)
0.012
(1.07)
0.019
(1.86)
0.006
(0.28)
0.017
(1.51)
–0.012
(–0.44)
–0.000
(–0.03)
0.029
(1.84)
–0.127
(–1.19)
0.052
(1.14)
0.008
(0.71)
0.056
(1.28)
Province dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
–0.697
(–1.34)
–0.749
(–1.61)
0.452
(0.48)
–0.441
(–0.88)
–1.234
(–1.00)
–0.139
(–0.29)
–1.562*
(–2.20)
–13.848
(–1.45)
0.694
(0.17)
–0.971
(–1.81)
–1.934
(–0.96)
0.998
0.998
0.993
0.998
0.989
0.998
0.996
0.966
0.995
0.998
0.962
3006.9**
3284.0**
1012.1**
2968.8**
599.3**
2851.8**
1713.0**
106.4**
755.9**
2872.7**
178.6**
312
312
312
312
312
312
312
156
156
312
312
Log of real RGDP
Constant
R-squared
F-test
Number of observations
a
Numbers in parentheses are t-values.
**Significant at 1% level.
*Significant at 5% level.
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
Independent
Variable
44
TABLE 3 Results of OLS Estimation of Employment Regression Without Degree of Compliance Variablea
(Dependent variable: Log of employment)
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Independent
Variable
Log of real minimum wage
Lagged degree of
complianceb
Log of population group
aged 15 years and over
Log of real RGDP
Province dummies
Year dummies
Constant
R-squared
F-test
Number of observations
a
All
Workers
Male
Female
Adult
Youth
Educated
Less
Educated
Whitecollar
Bluecollar
Fulltime
Parttime
–0.074*
(–2.36)
–0.124
(–0.89)
0.990**
(31.24)
0.012
(1.04)
Yes
Yes
–0.475
(–0.84)
–0.065*
(–2.29)
–0.117
(–0.93)
0.995**
(35.20)
0.022*
(2.13)
Yes
Yes
–0.406
(–0.82)
–0.127*
(–2.16)
–0.034
(–0.13)
0.955**
(16.14)
–0.001
(–0.03)
Yes
Yes
–0.055**
(–0.05)
–0.051
(–1.63)
–0.129
(–0.94)
0.964**
(31.62)
0.015
(1.35)
Yes
Yes
–0.141
(–0.26)
–0.147
(–1.84)
0.263
(0.75)
1.057**
(13.86)
0.003
(0.11)
Yes
Yes
–1.861
(–1.40)
–0.035
(–1.13)
–0.138
(–1.01)
0.953**
(34.14)
0.000
(0.04)
Yes
Yes
0.160
(0.31)
–0.100*
(–2.26)
–0.025
(–0.13)
1.024**
(23.68)
0.027
(1.70)
Yes
Yes
–1.400
(–1.85)
0.891
(1.86)
–0.863
(–1.01)
1.084*
(2.02)
–0.120
(–1.11)
Yes
Yes
–11.141
(–1.12)
–0.093
(–0.46)
–0.158
(–0.44)
0.823**
(3.63)
0.053
(1.17)
Yes
Yes
1.191
(0.28)
–0.073*
(–2.24)
–0.082
(–0.57)
0.986**
(30.30)
0.005
(0.45)
Yes
Yes
–0.483
(–0.83)
–0.059
(–0.49)
–0.188
(–0.35)
1.020**
(8.46)
0.072
(1.70)
Yes
Yes
–4.450*
(–2.07)
0.998
0.998
0.993
0.998
0.989
0.998
0.996
0.967
0.995
0.998
0.965
2878.8**
3339.2**
929.0**
2821.8**
574.8**
2679.3**
1699.9**
103.3**
728.8**
2771.1**
175.4**
286
286
286
286
286
286
286
156
156
286
286
Numbers in parentheses are t-values.
Measured as the proportion of workers who earn above the minimum wage.
**Significant at 1% level.
*Significant at 5% level.
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
TABLE 4 Results of OLS Estimation of Employment Regression with Lagged Degree of Compliance Variablea
(Dependent variable: Log of employment)
b
45
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46
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
mum wages on employment. This is an
expected result, since variance in compliance means less certain effectiveness
of the minimum wage regulations in influencing supply and demand in the
labour market. These results emphasise
the need to control for the effect of varying degrees of compliance in estimating the impact of minimum wages,
particularly in the context of developing countries, where compliance cannot
be taken for granted.
There is a possibility, however, that
the compliance variable is endogenous,
creating bias in the estimates in table 2.19
To take this possibility into account, the
estimations are run again with the compliance variable lagged one period. The
results are presented in table 4, and
again show that the coefficients of minimum wages are negative, except in the
case of white-collar workers. As before,
the magnitudes of the coefficients and
the t-values are in general also smaller
than in table 2. The coefficients of the
lagged degree of compliance variable are
also mostly negative, but none is significant, suggesting that lagged compliance
is not a good instrument for measuring
current compliance. The fact that the coefficients of the minimum wage variable
in tables 3 and 4 in general do not
change in sign from table 2 indicates that
endogeneity is not a serious problem in
the estimation.
CONCLUSIONS
Between 2000 and 2002, the central and
regional governments in Indonesia vigorously pursued a minimum wage
policy, and regional minimum wages
were increased significantly; as a result,
real minimum wages in 2002 were already higher than their peak pre-crisis
levels in 1997. This was done while the
economy was still struggling to recover
from a severe economic crisis. Given the
environment of slow economic growth,
there is increasing concern that further
large increases in minimum wages may
slow employment growth in the modern industrial sector.
This study finds that as minimum
wages continued to rise during most of
the 1990s, compliance also steadily increased from the middle of the decade,
altering the entire wage distribution of
urban formal sector workers. In 1988, a
year before minimum wage regulations
were revamped, minimum wages had
very little impact on the wage distribution. But by 1999 the minimum wage
had become the mode of the distribution, indicating that minimum wages
had become binding for the majority of
workers.
The results of the analysis in this
study show that increases in minimum
wages have a negative impact on urban
formal sector employment, except in the
case of white-collar workers. For all
workers, the estimated elasticity of total employment to the minimum wage
is around –0.1 and is statistically significant. This implies that for every 10% increase in real minimum wages, there
will be a reduction of around 1% in total employment, controlling for other
factors affecting employment, such as
economic growth and growth in the size
of the working population.
Significantly, the negative employment impact of minimum wage increases is greatest for those groups that
are most vulnerable to changes in labour
market conditions, such as female,
young and less educated workers, who
make up the bulk of Indonesia’s labour
force. For female and young workers,
the employment elasticities with respect
to minimum wages are around –0.3,
while for less educated workers they are
around –0.2.
White-collar workers are the only
group to have benefited from minimum
wages in terms of employment. The em-
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
ployment elasticity of white-collar workers with respect to the minimum wage
is 1.0 and is statistically significant, which
perhaps indicates that the imposition of
minimum wages leads to the substitution of different types of workers: as the
level of minimum wages is increased,
firms reduce the employment of other
types of workers and repla
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
MINIMUM WAGE POLICY AND ITS IMPACT ON
EMPLOYMENT IN THE URBAN FORMAL SECTOR
Asep Suryahadi , Wenefrida Widyanti , Daniel Perwira & Sudarno Sumarto
To cite this article: Asep Suryahadi , Wenefrida Widyanti , Daniel Perwira & Sudarno Sumarto
(2003) MINIMUM WAGE POLICY AND ITS IMPACT ON EMPLOYMENT IN THE URBAN FORMAL
SECTOR, Bulletin of Indonesian Economic Studies, 39:1, 29-50, DOI: 10.1080/00074910302007
To link to this article: http://dx.doi.org/10.1080/00074910302007
Published online: 17 Jun 2010.
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Date: 19 January 2016, At: 20:16
Bulletin of Indonesian Economic Studies, Vol. 39, No. 1, 2003: 29–50
MINIMUM WAGE POLICY AND ITS IMPACT ON
EMPLOYMENT IN THE URBAN FORMAL SECTOR
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto*
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:16 19 January 2016
SMERU Research Institute, Jakarta
Since the late 1980s, minimum wages have become an important plank of the Indonesian government’s labour policy. Their levels have increased faster in real terms
than those of average wages and per capita gross domestic product and, as a result,
minimum wages have become binding for the majority of formal sector workers.
This study finds that the imposition of minimum wages has a negative and statistically significant impact on employment in the urban formal sector. The disemployment impact is greatest for female, young and less educated workers, while the
employment prospects of white-collar workers are enhanced by increases in minimum wages. Some workers who lose jobs in the formal sector and have to relocate to
the informal sector face lower earnings and poorer working conditions.
INTRODUCTION
Although first introduced in Indonesia
in the early 1970s, minimum wages did
not gain much attention until the late
1980s, when the government began to
make them an important plank of its
labour market policies. In the first half
of the 1990s, the government tripled
minimum wages in nominal terms and
more than doubled them in real terms
in just five years. During the second half
of the decade, their nominal levels continued to increase, but in real terms they
began to taper off after 1996 and fell significantly in 1998, owing to the high inflation that swept the country during the
economic crisis.1
Minimum wages began to re-emerge
as a key element of economic and social policy in 2000. In three consecutive
years, from 2000 to 2002, they were
raised significantly. As a result, their
levels in real terms in 2002 were already
higher than at their pre-crisis peak in
1997. Critically, this occurred against
the backdrop of an economy still struggling to recover from a major economic
crisis. Following a massive contraction
of 13.7% in 1998 and near zero growth
in 1999, the economy grew by around
5% in 2000 and 3.5% in 2001 and 2002.
Theory suggests that the impact of a
minimum wage varies with different
product and labour market conditions.
A competitive labour market model
predicts that a minimum wage established above the equilibrium market
wage will cause a reduction in employment and create unemployment. On the
other hand, a monopsonistic labour
market model predicts that a minimum
wage set above the monopsony wage
level (but below the competitive wage
level) will increase employment.
Whether a country’s labour market
is closer to the competitive or the
monopsonistic model is an empirical
ISSN 0007-4918 print/ISSN 1472-7234 online/03/010029-22
© 2003 Indonesia Project ANU
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30
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
matter. Although we do not rule out the
possibility of imperfect competition, observers generally have maintained that
the Indonesian labour market is competitive. This was particularly evident
during the recent economic crisis,
whose impact was significant in terms
of the fall in real wages, but relatively
small in terms of the increase in open
unemployment (Feridhanusetyawan
1999; Manning 2000). In general, there
is little evidence to characterise the Indonesian labour market as monopsonistic, with the minor possible exception
of some large, isolated employers in the
outer islands.2
The first serious attempt to assess the
impact of minimum wage policy on the
labour market in Indonesia was conducted by Rama (1996),3 who found that
minimum wages have a modest impact
on labour market outcomes, and concluded that the doubling of the minimum wage in the first half of the 1990s
had led to an increase in average wages
in the range of 5–15%, and a decline in
urban wage employment in the range
of 0–5%. However, he suggested that the
disemployment impact appeared to be
considerable in small manufacturing
firms.
These findings have been challenged
by Islam and Nazara (2000), who argue
that the Indonesian regional minimum
wage policy has not impaired employment prospects, and propose that if Indonesia achieves annual economic
growth of 4%, real minimum wages can
be increased by 24% annually without
net job losses being incurred. They
contend there is no evidence that minimum wage-induced increases in domestic labour costs have eroded
business profitability in large and
medium-scale manufacturing. With reasonably competitive factor and product
markets this result is hardly surprising,
since firms can be expected to respond
to the negative impact on profits of
higher input costs by substituting capital and higher productivity labour for
low skill labour, and by increasing their
product prices. But whether unskilled
labour will be negatively affected by the
imposition of minimum wages is a different issue.
The current study offers a reassessment of the impact of minimum wage
policy on employment in Indonesia between 1988 and 1999, focusing on the
urban formal sector.4 The employment
impact of minimum wages is investigated by means of an econometric approach, using data collected through
the National Labour Force Surveys by
Statistics Indonesia (BPS). In contrast
to previous studies of the labour market impact of minimum wages in Indonesia, which have focused only on
workers in aggregate, this study also
assesses the impact for different types
of workers.5
MINIMUM WAGE POLICY
During the 1970s and 1980s, the Indonesian government rarely intervened in
wage determination, and neither did it
enforce regulations on the laying off of
workers. In addition, the government
tightly controlled the labour movement
by allowing only one government sanctioned labour union. As a result, there
was little effective direct government or
union involvement in wage setting
(Manning 1994).
The late 1980s witnessed many
changes in the Indonesian labour market, two of which are especially important. First, several independent labour
unions were established, despite the
government’s efforts to disband them
and declare them illegal. Second, the
government began to enforce the implementation of regional minimum wages,
which were updated annually. It has
been argued that this minimum wage
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
policy was a form of compensation for
the policy of tightly restricting labour
rights, and was an effort to appease
workers.
These changes also occurred in response to both internal and external
pressures, however. The internal pressures came from groups concerned
about the conditions of workers in an
increasingly industrialised Indonesian
economy, including many senior policy
makers worried that labour was not
sharing in the high growth that had
taken place (Agrawal 1996; Edwards
1996; Manning 1994).
The external pressures were an indirect result of the growing level of Indonesian exports to North America and the
European Union (EU), regions where
concern was being aired in many quarters about labour market conditions in
developing countries. The focus was on
workers in export sectors, where, it was
claimed, there were poor working conditions, low wages and a denial of the
fundamental right to form labour unions.
This belief led to calls for a ‘social clause’
to be inserted in trade agreements between developed and developing countries, stipulating that favoured access to
the markets of developed countries
would not be granted to those third
world countries where labour standards
remained unsatisfactory (Addison and
Demery 1988).
Indonesia was one of the countries
targeted by this concern. In the early
1990s, several complaints were filed
against it, threatening to deprive it of
low tariffs under the Generalised
Scheme of Preferences (GSP) on its exports to the US market. Potentially
even more significant was a threat by
the US government to withdraw investment guarantees to US companies
investing in Indonesia (Rama 2001).
As part of its response, the Indonesian government revamped the mecha-
31
nism for setting minimum wages in
1989, and then on several occasions during the 1990s. Its objective has been to
set minimum wages with reference to a
range of factors, including ‘minimum
subsistence needs’ (kebutuhan hidup
minimum or KHM); the cost of living;
the capacity and sustainability of companies; existing market wage rates;
labour market conditions; and economic
and income per capita growth.6
Prior to 1996, minimum wages were
calculated with reference to what was
termed ‘minimum physical needs’
(kebutuhan fisik minimum or KFM) rather
than KHM.7 Both KFM and KHM are
bundles of consumption items deemed
essential for the livelihood of a single
worker. The KHM bundle consists of 43
items, ranging from food, clothing,
housing, transport and health to recreation.8 KHM is a broader consumption
bundle, and represents a higher standard of living than KFM. For example,
the food bundle of KFM was set to
achieve an intake of 2,600 calories per
day, while that of KHM was set to
achieve a daily intake of 3,000 calories.9
Until 2000, most provinces had just
one level of minimum wage, which was
applied throughout the entire province.
Exceptions were to be found in the provinces of Riau, South Sumatra, West Java,
East Java and Bali, where several minimum wages existed for different regions
within the province. In addition, some
provinces had different minimum wages
for different sectors of the economy. In
such cases, the sectoral minimum wages
could not be set at a lower level than the
general minimum wage that applied in
the region.
Until the end of 2000, regional minimum wages were established by a
decree issued by the Minister of Manpower. In determining minimum wage
levels, the minister received recommendations from provincial governors, who
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32
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
in turn took advice from tripartite provincial councils made up of representatives of employees, employers and the
government. In practice, employee and
employer representatives were usually
government appointees.
Beginning in 2001, as part of the regional autonomy policy adopted and
implemented throughout the country,
the power to set minimum wage levels
was transferred to governors, and in
some cases to mayors and regents
(bupati), as the respective heads of provinces, cities and districts (kabupaten).10
As of 2000, Indonesia had 341 cities and
districts. In setting minimum wages,
mayors and regents also receive recommendations from tripartite councils in
their regions.
DATA
The data analysed in this study are
drawn mainly from the National
Labour Force Surveys (Sakernas) conducted annually by BPS. The Sakernas
is a nationally representative repeated
cross-section survey conducted in August each year and covering all provinces in Indonesia.11 This study uses the
Sakernas data from the 1988 to 2000
surveys, except for the 1995 data,
which are derived from the labour
force module of the Intercensal Population Survey (Supas).
The Sakernas and the labour force
module of the Supas collect information
on individual main employment activities, earnings, and hours of work on the
primary job, as well as data on individual
characteristics such as gender, age and
level of education. The sample size of the
Sakernas each year ranges from 40,000
to 80,000 households, covering 135,000
to 240,000 individuals aged 10 years and
over. Since the present study focuses on
the urban formal sector, only data from
households that supply labour to that
sector are included in the analysis.
The minimum wage data were obtained from the Department of Manpower and Transmigration. The study
also uses data published by BPS on real
regional gross domestic product
(RGDP) for each province. To make
them comparable across years, we deflate nominal wages and minimum
wages by the annual provincial CPI, also
from BPS.
LABOUR MARKET STRUCTURE,
MINIMUM WAGES AND
WAGE DISTRIBUTION
Changes in Labour Market Structure
During the 1990s, the structure of the
Indonesian labour market underwent
considerable change, both quantitatively and qualitatively. Table 1 presents
in summary form some key statistics on
the Indonesian labour force from 1988
to 2000. The labour force grew by 33%,
from 71.9 million in 1988 to 95.7 million
in 2000—an average annual growth rate
of 2.4%. During the entire period, however, the labour force participation rate
was relatively steady at around 67%,
suggesting that the increase in labour
force size was driven mainly by natural
population growth.
The gender composition of the
labour force was also relatively stable.
The proportion of females remained
close to 39–40%. Similarly, the proportion of young workers—those aged
between 15 and 24 years—changed
little during the period, declining only
slightly from around 23% to 21% in the
12-year period. On the other hand,
there was a notable trend towards
urbanisation of the labour force, with
the urban proportion increasing by
more than two-thirds, from 23.6% in
1986 to 39.3% in 2000. The table also
shows a clear trend in favour of formalisation within the economy, at
least before the crisis, when the formal
component of the workforce increased
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
33
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TABLE 1 Summary Statistics of the Indonesian Labour Market, 1988–2000
Labour Market Characteristics
1988
1992
1996
2000
Size of labour force (millions)a
Labour force participation rate (%)b
Female labour force (%)
Youth labour force (%)c
Urban labour force (%)
Formal workforce (see endnote 4) (%)
Less educated labour forced
Blue-collar workforce (%)
Part-time workforce (%)e
Unemployment rate (%)f
71.9
66.9
40.1
23.1
23.6
26.9
87.9
–
28.9
2.8
76.2
67.8
39.0
22.8
28.7
30.7
84.4
–
29.6
2.8
88.2
66.9
38.5
22.3
33.9
37.9
78.8
81.2
33.3
4.9
95.7
67.8
38.6
20.7
39.3
35.1
75.9
79.2
26.7
6.1
a
The labour force consists of those who work (the workforce) and the unemployed.
b
The labour force participation rate is the labour force as a share of the total population aged
15 years and over.
c
Youth are defined as those aged 15–24 years.
d
The less educated are defined as those who have completed no more than junior secondary
education.
e
Those who work less than 30 hours per week are considered to be working part time.
f
Unemployment rates from 1994 onward are not comparable with the preceding period owing
to a change in job search length.
Source: Sakernas.
from 26.9% in 1988 to 37.9% in 1996.
There is evidence that the economic crisis reversed this trend, presumably
temporarily, with the formal proportion of the workforce falling back to
35.1% in 2000.
There was a significant improvement
in the education level of the labour
force, with the less educated declining
from 87.9% in 1988 to 75.9% in 2000. The
employment share of blue-collar workers also tended to decline. Before the
crisis, more and more people chose to
work part time, with this group reaching a peak of one-third of the workforce
in 1996. It appears that the crisis reversed this trend, as only 26.7% of the
workforce worked part time in 2000.
Presumably some part-timers were
forced to work full time in response to
falling real income. Meanwhile, the unemployment rate increased during the
crisis period, from 4.9% in 1996 to 6.1%
in 2000.12
Trends in Minimum Wages
As a result of changes in labour market
policy in the late 1980s, minimum wages
have become an important plank of
labour policy, as is evident from the
speed at which the government has increased their levels. Figure 1 compares
the trend in the real minimum wage between 1989 and 2002 with trends in the
average real wage and in real per capita
GDP during the same period.13 It shows
that minimum wages have increased
much faster in real terms than both the
34
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 1 Trends in the Real Minimum Wage, Real Average Wage
and Real Per Capita GDP, 1989–2000
(1989 = 100)
300
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200
100
Real minimum wage
Real average wage
Real per capita GDP
0
1989
1991
1993
1995
average wage and per capita GDP. The
real minimum wage in 1994 was around
2.4 times its 1989 level, mainly as the
result of large minimum wage increases
in 1990 and 1994. Interestingly, figure 1
also indicates that these two increases
coincided with declines in the average
wage. In 1990, when the real minimum
wage was raised by almost 50%, the real
average wage declined by more than
12%. Similarly, when the real minimum
wage was increased by 30% in 1994, the
real average wage declined by around
2%. During the other years before the
crisis, when the increases in real minimum wages were more modest, real
average wages generally also increased,
more or less in line with increases in real
per capita GDP.
In 2000, the real minimum wage was
raised substantially, by significantly
more than rises in the real average
wage and real GDP. The increase in the
1997
1999
2001
weighted average of real regional minimum wages in that year was more than
17%, while the real average wage rose
by around 13% and real per capita GDP
by around 3.5%. Real minimum wages
were substantially increased in the following two years as well, bringing the
2002 minimum wage to a new peak,
higher than the pre-crisis peak of 1997.
Because minimum wages rose more
rapidly than average wages, the ratio
of minimum to average wages increased
markedly, from about 20% in 1989 to
50% in 1994 (figure 2). This ratio remained at around that point thereafter,
with only a slight decline during the crisis. Figure 2 also shows that the proportion of workers whose monthly earnings
were lower than the minimum wage
tended in general to increase until 1994,
and then to fall steadily. It rose from
around 7% in 1989 to around 21% in
1994 and 1995, and then continuously
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
35
FIGURE 2 Ratio of Minimum Wage to Average Wage and Proportion of Workers
Earning Less than the Minimum Wage, 1989–2000
60
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40
Ratio of minimum to average wage
Proportion of workers earning less
than the minimum
20
0
1989
1991
1993
declined to reach less than 11% by 2000.
This implies that there has been a tendency toward greater compliance with
minimum wage regulations since the
mid 1990s.
Minimum Wages and the
Wage Distribution
Some studies have shown that minimum
wages have an impact not merely on
wages around the minimum but on the
whole wage distribution (e.g. Maloney
and Nuñez 2001; Neumark, Schweitzer
and Wascher 2000). Figure 3 presents a
set of kernel densities of the wage distribution in the urban formal sector, showing how minimum wages have affected
the wage distribution over time.14 In this
series of diagrams, the wage of each
worker is measured as a ratio of the
worker’s nominal wage to the nominal
minimum wage applying in the region
where the worker lives. The vertical line
1995
1997
1999
at point 1 in each of the graphs represents the minimum wage level.
Ideally, the impact of minimum
wages on the wage distribution should
be assessed by comparing the wage distribution with and without minimum
wages being imposed. In reality, however, the wage distribution without
minimum wages, which is the counterfactual, can only be guessed at. Assessing the impact of minimum wages on
the wage distribution therefore requires
some informed judgment, for which the
following guides are useful. First, if the
minimum wage is only ‘on paper’ and
is not enforced, then the wage distribution can be expected to be relatively
smooth, including the distribution
around the minimum wage level. Second, if the minimum wage is fully enforced, then the wage distribution will
begin at the minimum wage level, because no worker will earn less than this
36
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 3 The Impact of Minimum Wages on Wage Distribution
in the Urban Formal Labour Force, 1988–2000a
(b) 1992
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(a) 1988
Density
0.8
Density
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
0
10
1
2
3
(c) 1996
4
5 6
Ratio
7
8
9
10
7
8
9
10
(d) 1999
Density
0.8
Density
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
0
10
1
2
3
4
5 6
Ratio
(e) 2000
Density
0.8
0.6
0.4
0.2
a
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
The vertical line in each graph represents
the minimum wage. Observations represent
the ratio of each worker’s nominal wage to
the region’s nominal minimum wage.
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
amount. The reality will probably lie
between these two extremes, with some
spikes expected to occur around the
minimum wage level. If the level of the
minimum wage is sufficiently high, it
may become the mode of the distribution, since most workers will earn
around this amount.
Figure 3 reveals that in 1988, a year
before the minimum wage regulations
were revamped, minimum wages had
very little impact on the wage distribution in Indonesia. There was no apparent spike in the wage distribution
around the minimum wage. But this has
changed over time. By 1992, the impact
of minimum wages on the wage distribution had become more apparent.
Spikes at and around the minimum
wage occurred in the distribution. In
1996, the mode of the wage distribution
was only slightly higher than the minimum wage. By 1999, and also in 2000,
the minimum wage had become the
mode of the distribution, indicating that
minimum wages have become binding
for the majority of workers.
The graphs in figure 3 show the wage
distribution for all urban formal sector
workers. Since minimum wage regulations may have a different impact on different groups of workers, we also graph
their impact on the wage distribution
across various groups of formal sector
wage workers in 2000 (figure 4). Again
the vertical line at point 1 in each graph
represents the minimum wage level.
Figure 4a reveals that the wage distributions of both male and female workers are affected by minimum wages, but
it appears that the effect is greater for
female workers. The proportion of workers on the minimum wage is higher for
females than for males. In addition, while
the mode of the wage distribution for female workers is at the minimum wage,
the mode for male workers is slightly
higher than the minimum.
37
Figure 4b indicates that the wage distributions of both adult and young
workers are affected by minimum
wages. The graph reveals that the wages
of most young workers are at or around
the minimum, with only a few earning
more than double the minimum. The
impact of minimum wages on adult
workers is also significant, with the
mode of their wage distribution only
slightly higher than the minimum.
Education is an important determinant of earnings. In figure 4c, workers
are grouped into two categories, the
‘educated’ and the ‘less educated’, where
the latter are defined as those with lower
secondary education or below. As expected, the graph reveals that the wage
distribution of less educated workers is
more strongly affected by minimum
wages than that of educated workers.
However, the impact of minimum wages
in altering the shape of the wage distribution of educated workers remains significant, as the mode of their distribution
is only slightly higher than the minimum
wage.
When workers are separated into
white-collar and blue-collar groups as
shown in figure 4d, it becomes clear that
minimum wages have very different consequences for the wage distribution of
each group. The graph suggests that the
wage distribution of white-collar workers is not greatly affected by the presence of minimum wages, because their
earnings are generally far above the minimum. On the other hand, the majority
of blue-collar workers are clearly affected
by minimum wages, as indicated by the
fact that the minimum is the mode of the
wage distribution for these workers.
Finally, figure 4e shows the wage distribution for full-time and part-time
workers. A worker is classified as parttime if he or she works less than 30
hours per week. Minimum wages are
only binding for full-time workers and
38
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 4 The Impact of Minimum Wages on Wage Distribution across
Categories of Workers in the Urban Formal Labour Force, 2000a
(a) By Gender
(b) By Age Group
Density
0.8
Density
0.8
0.6
0.4
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Youth
0.6
Female
0.4
Male
0.2
Adult
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
0
1
2
(c) By Education
3
4
5 6
Ratio
7
8
9 10
8
9
(d) By Type of Job
Density
0.8
Density
0.8
0.6
0.6
Less educated
Blue collar
0.4
0.4
White collar
Educated
0.2
0.2
0
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
0
1
2
3
(e) By Full-time/Part-time Status
Density
0.8
Part time
0.6
0.4
Full time
0.2
0
0
1
2
3
4
5 6
Ratio
7
8
9
10
a
As for figure 3.
4
5 6
Ratio
7
10
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
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the graph shows that the minimum is
the mode of their wage distribution. For
part-time workers, minimum wages are
not binding and are of no consequence
for the wage distribution, as indicated
by the fact that most part-time workers
earned less than the minimum.
THE EMPLOYMENT IMPACT
OF MINIMUM WAGES
The purpose of minimum wage regulations is to lift the incomes of those workers who currently earn wages that are
‘too low’. Unfortunately, the reality is not
as simple as that. The imposition of a
minimum wage affects both the supply
and the demand sides of the labour market. Consequently, its impact is not limited to wages, for there is also an effect
on employment. It is this employment
impact that often becomes a source of
controversy when minimum wages are
imposed or increased. Of equal importance, minimum wages can also be expected to have a different employment
impact on different groups of workers.
The Controversy over the
Minimum Wage Impact
In the context of developed countries,
controversy about the impact of minimum wages on employment arose in
the 1990s as a result of the findings of
Card and Krueger (1994). Using data
collected through a telephone survey of
fast-food restaurants, they compare
employment changes in two states in the
US. They find that restaurants in New
Jersey, where the minimum wage was
increased, expanded their workforce
relative to restaurants in Pennsylvania,
where there was no change in the minimum wage. This finding has been challenged by many, notably by Neumark
and Wascher (1995). They re-evaluate
Card and Krueger’s evidence using different data, obtained from actual payroll records. In contrast to the findings
39
of Card and Krueger, they conclude that
the minimum wage increase in New Jersey actually led to a fall in employment
in this state relative to the Pennsylvania control group.
A similar controversy occurred in the
context of developing countries. CastilloFreeman and Freeman (1992) analyse the
imposition of US minimum wage norms
in Puerto Rico. They estimate that the
elasticity of employment with respect to
the minimum wage in this country is
around –0.5, meaning that a 10% increase
in minimum wages causes a reduction
in employment of 5%. Hence, they assert that the imposition of the US minimum wage has led to massive job losses
on the island. Krueger (1995) disputes
this finding on methodological grounds,
however, arguing that the evidence of a
negative employment impact from the
imposition of the US minimum wage in
Puerto Rico is statistically fragile.
Meanwhile, Bell (1997) contrasts the
cases of Mexico, where minimum wages
are very low relative to average wages,
and Colombia, where minimum wages
are much closer to the mean. She finds
that the disemployment impact of minimum wages is zero in Mexico but substantial in Colombia. Similarly, in a
study of eight Latin American countries,
Maloney and Nuñez (2001) find that
minimum wages have significant implications for employment, and affect the
wage distribution not only in the neighbourhood of the minimum wage but
also in the higher reaches of the distribution and in the informal sector.
Theoretical Framework
In the context of developed countries,
the impact of minimum wages on
labour market outcomes is generally
analysed using a competitive labour
market framework. In this framework,
a minimum wage imposed above the
equilibrium market wage will cause a
40
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
FIGURE 5 The Impact of the Minimum Wage in the Formal and Informal Sectora
WI
WF
DF
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DI
WM
W*
W*
W1
O
N1
N*
N
a
See text for an explanation of terms
reduction in employment and create unemployment (Stigler 1946). This framework is appropriate for use in the
context of countries with integrated
labour markets. For developing countries that are still characterised by dual
labour markets, including Indonesia, an
alternative framework is required.
The framework used in this study is
illustrated in figure 5. The economy consists of a formal sector and an informal
sector. The labour supply is fixed in total, and its allocation to the formal and
informal sectors, and the level of wages,
are determined by the demand for
labour in both sectors. The horizontal
axis (ON) represents the total amount
of labour available. The left vertical axis
indicates the level of wages in the formal sector, and the demand for labour
in this sector is represented by the curve
DF. The right vertical axis indicates the
level of wages in the informal sector,
and the demand for labour in this sector is DI, measured from right to left.
If there is no distortion in the labour
market, equilibrium is achieved at the
intersection of DF and DI. The equilibrium wage level is W*, which prevails
in both the formal and informal sectors.
Of ON total labour, ON* is allocated to
the formal sector, and the remainder,
N*N, to the informal sector. Suppose
now that a minimum wage level of WM
is imposed in the formal sector. This will
reduce the amount of labour absorbed
in the formal sector to ON1, such that
the informal sector now has to absorb
more labour, N1N. As a result, the wage
level in the informal sector is depressed
to W1.
In this framework, the imposition of
minimum wages will reduce employment in the formal sector and lower
wages in the informal sector. Workers
lucky enough to keep their jobs in the
formal sector benefit at the expense of
other workers—both those who need to
relocate to the informal sector and those
already there—all of whom suffer a de-
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
cline in earnings. There is also an
implied shift in the structure of the
economy, with the formal sector contracting and the informal sector expanding; this will be driven by an increase in
product prices in the formal sector relative to the informal sector.
In reality, of course, workers are differentiated by their productivity. The
demand curves for higher productivity
workers lie above those for the less
skilled, so the equilibrium market wage
is likely to be above the minimum wage.
In this case the minimum wage would
have no direct effect on higher productivity workers. Indirectly, however, to
the extent that there is substitutability
between workers with differing degrees
of productivity, firms will now have a
greater incentive to increase their employment of higher productivity workers, who can expect some upward
pressure on their earnings as a result.
The Method for Empirical
Estimation and Data Construction
Using the above framework, this study
sets out to examine the impact of minimum wages in Indonesia on employment levels in the urban formal sector.
Since minimum wages are determined
at the provincial level, one strategy for
estimating the impact of minimum
wages on urban formal sector employment is to use variations in the levels of
minimum wages and employment
across provinces as well as over time.
This strategy was adopted by Rama
(2001) and is also used here.
Following Rama (2001), data on urban
formal sector employment from the Sakernas are aggregated at the provincial
level. These data, which are calculated
for all workers as well as for certain segments of the workforce, are then combined across years to form a panel data
set with the province as the unit of ob-
41
servation. This data set is then merged
with other provincial-level data on minimum wage levels, the CPI, RGDP and
demographic variables. The complete
panel data set can be constructed for all
26 provinces in Indonesia,15 covering the
12 yearly observation points from 1988
to 1999, so that in total there are 312 province level annual points of observation.
The exceptions are data for white-collar
and blue-collar workers, which are available only from 1994.
Using the model discussed in the appendix, we then estimate the impact of
minimum wages on employment by regressing provincial level employment
data on the minimum wage level and
on some control variables. Other independent variables used in the estimation include the size of the population
aged 15 years and over, to represent the
factors that affect labour supply, and
real RGDP, to represent the factors that
affect labour demand. Province dummy
variables are included to take into account individual province fixed effects
that do not vary across time. Meanwhile, year dummy variables are included to measure specific time effects
that have an impact on all provinces in
any given year.
In addition, ‘degree of compliance
to the minimum wage’ is included as
an independent variable in the estimation. As shown in figures 2 and 3, compliance with the minimum wage in
Indonesia has changed over time. This
has important consequences for how
minimum wages affect labour market
outcomes. It is therefore necessary to
control for the impact of varying degrees of compliance in order to measure the true impact of minimum
wages.16 In the estimations, the proportion of workers who earn above the
minimum wage is used as a proxy for
the degree of compliance variable.17
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42
TABLE 2 Results of OLS Estimation of Employment Regressiona
(Dependent variable: Log of employment)
All
Workers
Male
Female
Adult
Youth
Educated
Less
Educated
Whitecollar
Bluecollar
Fulltime
Parttime
–0.065
(–1.87)
–0.137
(–0.86)
1.004**
(38.26)
0.020*
(1.94)
Yes
Yes
–0.471
(–0.83)
–0.307**
(–4.64)
–1.177**
(–3.88)
0.949**
(18.41)
0.013
(0.65)
Yes
Yes
2.895**
(2.60)
–0.066
(–1.80)
–0.165
(–0.98)
0.975**
(35.66)
0.018
(1.60)
Yes
Yes
–0.108
(–0.18)
–0.307**
(–3.35)
–1.414**
(–3.37)
1.052**
(15.30)
–0.004
(–0.13)
Yes
Yes
1.762
(1.17)
–0.017
(–0.48)
0.059
(0.36)
0.960**
(37.69)
–0.001
(–0.07)
Yes
Yes
–0.262
(–0.45)
–0.196**
(–3.79)
–0.838**
(–3.54)
1.038**
(26.43)
0.034*
(2.19)
Yes
Yes
0.174
(0.21)
1.000*
(2.09)
0.009
(0.01)
1.145*
(2.11)
–0.127
(–1.18)
Yes
Yes
–13.879
(–1.38)
–0.140
(–0.70)
–0.609
(–1.69)
0.779**
(3.46)
0.047
(1.06)
Yes
Yes
2.786
(0.67)
–0.086*
(–2.25)
–0.217
(–1.24)
1.007**
(34.13)
0.010
(0.83)
Yes
Yes
–0.532
(–0.83)
–0.364*
(–2.56)
–1.958**
(–3.00)
0.911**
(8.30)
0.068
(1.58)
Yes
Yes
2.037
(0.85)
0.998
0.998
0.994
0.998
0.989
0.998
0.996
0.966
0.995
0.998
0.964
2973.0**
3198.8**
1038.1**
2894.3**
606.8**
2771.7**
1741.1**
102.4**
744.8**
2806.4**
179.5**
312
312
312
312
312
312
312
156
156
312
312
Log of real minimum wage –0.112**
(–3.03)
–0.371*
Degree of complianceb
(–2.19)
Log of population group
0.997**
aged 15 years and over
(35.02)
Log of real RGDP
0.014
(1.28)
Province dummies
Yes
Year dummies
Yes
Constant
0.055
(0.09)
R-squared
F-test
Number of observations
a
Numbers in parentheses are t-values.
Measured as the proportion of workers who earn above the minimum wage.
**Significant at 1% level.
*Significant at 5% level.
b
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
Independent
Variable
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
The Results of the Estimation
The employment regression discussed
above is estimated using the Ordinary
Least Squares (OLS) method on the
provincial panel data set for all workers, as well as for various segments of
the workforce. The results of the estimations are presented in table 2. The
table shows that the coefficients of the
minimum wage variable for all workers and all segments of the workforce
are negative, except in the case of
white-collar workers. This is consistent
with the prediction from the theoretical framework that minimum wages
will reduce the employment of unskilled workers in the formal sector.
The coefficient for all workers indicates that the elasticity of total employment to the minimum wage is –0.112
and is statistically significant. This implies that every 10% increase in real
minimum wages will result in a reduction of more than 1% in total employment. Similarly, the coefficients for
female, young, less educated, full-time
and part-time workers are also all
negative and statistically significant.
Their employment elasticities with respect to minimum wages are –0.307 for
female workers and young workers, –
0.196 for less educated workers, –0.086
for full-time workers, and –0.364 for
part-time workers.18
The only group of workers who benefit from the minimum wage in terms
of employment opportunities are
white-collar workers. The employment
elasticity for this group with respect to
the minimum wage is 1.0 and is statistically significant. This implies that a
10% increase in the real minimum wage
will increase the employment of
white-collar workers by 10%, and may
suggest a substitution effect of minimum wages on the employment of different types of workers. As the level of
minimum wages is raised, firms reduce
43
the employment of unskilled workers
and employ more white-collar workers with relatively higher skill levels.
This also provides an indication that
firms change technologies in response
to increases in minimum wages. Owing to capital–skill complementarity, a
higher proportion of white-collar
workers employed usually indicates
that more capital-intensive technologies have been adopted (Baldwin 1994;
Berman, Bound and Griliches 1994).
The coefficients of the degree of compliance variable indicate that higher compliance tends to strengthen the negative
impact of minimum wages on employment. For most groups of workers their
signs are the same as the signs of the coefficients of the minimum wage variable.
The coefficients of the degree of compliance variable for all workers and for female, young, less educated and part-time
workers are all statistically significant.
However, higher compliance has no impact on the employment of white-collar
and more educated workers.
Sensitivity Analysis
The inclusion of the degree of compliance variable in the employment regression is an innovation in this study.
Hence, a sensitivity analysis is conducted by re-running the employment
regression without this variable. The
results are presented in table 3. Confirming the previous results, all coefficients in table 3 have the same signs as
those in table 2. However, the employment elasticities with respect to minimum wages in table 3 are in general
smaller than those in table 2, and the
t-values of the coefficients are also
smaller.
Comparison of the results of the sensitivity analysis with those in table 2
provides evidence that controlling for
the degree of compliance results in a
stronger estimate of the impact of mini-
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All
Workers
Male
Female
Adult
Youth
Educated
Less
Educated
Whitecollar
Bluecollar
Fulltime
Parttime
Log of real minimum wage
–0.063*
(–2.13)
–0.047
(–1.70)
–0.155**
(–2.83)
–0.044
(–1.51)
–0.123
(–1.64)
–0.025
(–0.86)
–0.087*
(–2.04)
0.999*
(2.14)
–0.073
(–0.37)
–0.058
(–1.88)
–0.109
(–0.94)
Log of population group
aged 15 years and over
0.988**
(34.79)
1.001**
(38.59)
0.926**
(17.64)
0.971**
(35.88)
1.028**
(14.76)
0.961**
(38.12)
1.020**
(25.66)
1.144*
(2.14)
0.834**
(3.71)
1.003**
(34.24)
0.867**
(7.86)
0.012
(1.07)
0.019
(1.86)
0.006
(0.28)
0.017
(1.51)
–0.012
(–0.44)
–0.000
(–0.03)
0.029
(1.84)
–0.127
(–1.19)
0.052
(1.14)
0.008
(0.71)
0.056
(1.28)
Province dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
–0.697
(–1.34)
–0.749
(–1.61)
0.452
(0.48)
–0.441
(–0.88)
–1.234
(–1.00)
–0.139
(–0.29)
–1.562*
(–2.20)
–13.848
(–1.45)
0.694
(0.17)
–0.971
(–1.81)
–1.934
(–0.96)
0.998
0.998
0.993
0.998
0.989
0.998
0.996
0.966
0.995
0.998
0.962
3006.9**
3284.0**
1012.1**
2968.8**
599.3**
2851.8**
1713.0**
106.4**
755.9**
2872.7**
178.6**
312
312
312
312
312
312
312
156
156
312
312
Log of real RGDP
Constant
R-squared
F-test
Number of observations
a
Numbers in parentheses are t-values.
**Significant at 1% level.
*Significant at 5% level.
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
Independent
Variable
44
TABLE 3 Results of OLS Estimation of Employment Regression Without Degree of Compliance Variablea
(Dependent variable: Log of employment)
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Independent
Variable
Log of real minimum wage
Lagged degree of
complianceb
Log of population group
aged 15 years and over
Log of real RGDP
Province dummies
Year dummies
Constant
R-squared
F-test
Number of observations
a
All
Workers
Male
Female
Adult
Youth
Educated
Less
Educated
Whitecollar
Bluecollar
Fulltime
Parttime
–0.074*
(–2.36)
–0.124
(–0.89)
0.990**
(31.24)
0.012
(1.04)
Yes
Yes
–0.475
(–0.84)
–0.065*
(–2.29)
–0.117
(–0.93)
0.995**
(35.20)
0.022*
(2.13)
Yes
Yes
–0.406
(–0.82)
–0.127*
(–2.16)
–0.034
(–0.13)
0.955**
(16.14)
–0.001
(–0.03)
Yes
Yes
–0.055**
(–0.05)
–0.051
(–1.63)
–0.129
(–0.94)
0.964**
(31.62)
0.015
(1.35)
Yes
Yes
–0.141
(–0.26)
–0.147
(–1.84)
0.263
(0.75)
1.057**
(13.86)
0.003
(0.11)
Yes
Yes
–1.861
(–1.40)
–0.035
(–1.13)
–0.138
(–1.01)
0.953**
(34.14)
0.000
(0.04)
Yes
Yes
0.160
(0.31)
–0.100*
(–2.26)
–0.025
(–0.13)
1.024**
(23.68)
0.027
(1.70)
Yes
Yes
–1.400
(–1.85)
0.891
(1.86)
–0.863
(–1.01)
1.084*
(2.02)
–0.120
(–1.11)
Yes
Yes
–11.141
(–1.12)
–0.093
(–0.46)
–0.158
(–0.44)
0.823**
(3.63)
0.053
(1.17)
Yes
Yes
1.191
(0.28)
–0.073*
(–2.24)
–0.082
(–0.57)
0.986**
(30.30)
0.005
(0.45)
Yes
Yes
–0.483
(–0.83)
–0.059
(–0.49)
–0.188
(–0.35)
1.020**
(8.46)
0.072
(1.70)
Yes
Yes
–4.450*
(–2.07)
0.998
0.998
0.993
0.998
0.989
0.998
0.996
0.967
0.995
0.998
0.965
2878.8**
3339.2**
929.0**
2821.8**
574.8**
2679.3**
1699.9**
103.3**
728.8**
2771.1**
175.4**
286
286
286
286
286
286
286
156
156
286
286
Numbers in parentheses are t-values.
Measured as the proportion of workers who earn above the minimum wage.
**Significant at 1% level.
*Significant at 5% level.
Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
TABLE 4 Results of OLS Estimation of Employment Regression with Lagged Degree of Compliance Variablea
(Dependent variable: Log of employment)
b
45
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46
Asep Suryahadi, Wenefrida Widyanti, Daniel Perwira and Sudarno Sumarto
mum wages on employment. This is an
expected result, since variance in compliance means less certain effectiveness
of the minimum wage regulations in influencing supply and demand in the
labour market. These results emphasise
the need to control for the effect of varying degrees of compliance in estimating the impact of minimum wages,
particularly in the context of developing countries, where compliance cannot
be taken for granted.
There is a possibility, however, that
the compliance variable is endogenous,
creating bias in the estimates in table 2.19
To take this possibility into account, the
estimations are run again with the compliance variable lagged one period. The
results are presented in table 4, and
again show that the coefficients of minimum wages are negative, except in the
case of white-collar workers. As before,
the magnitudes of the coefficients and
the t-values are in general also smaller
than in table 2. The coefficients of the
lagged degree of compliance variable are
also mostly negative, but none is significant, suggesting that lagged compliance
is not a good instrument for measuring
current compliance. The fact that the coefficients of the minimum wage variable
in tables 3 and 4 in general do not
change in sign from table 2 indicates that
endogeneity is not a serious problem in
the estimation.
CONCLUSIONS
Between 2000 and 2002, the central and
regional governments in Indonesia vigorously pursued a minimum wage
policy, and regional minimum wages
were increased significantly; as a result,
real minimum wages in 2002 were already higher than their peak pre-crisis
levels in 1997. This was done while the
economy was still struggling to recover
from a severe economic crisis. Given the
environment of slow economic growth,
there is increasing concern that further
large increases in minimum wages may
slow employment growth in the modern industrial sector.
This study finds that as minimum
wages continued to rise during most of
the 1990s, compliance also steadily increased from the middle of the decade,
altering the entire wage distribution of
urban formal sector workers. In 1988, a
year before minimum wage regulations
were revamped, minimum wages had
very little impact on the wage distribution. But by 1999 the minimum wage
had become the mode of the distribution, indicating that minimum wages
had become binding for the majority of
workers.
The results of the analysis in this
study show that increases in minimum
wages have a negative impact on urban
formal sector employment, except in the
case of white-collar workers. For all
workers, the estimated elasticity of total employment to the minimum wage
is around –0.1 and is statistically significant. This implies that for every 10% increase in real minimum wages, there
will be a reduction of around 1% in total employment, controlling for other
factors affecting employment, such as
economic growth and growth in the size
of the working population.
Significantly, the negative employment impact of minimum wage increases is greatest for those groups that
are most vulnerable to changes in labour
market conditions, such as female,
young and less educated workers, who
make up the bulk of Indonesia’s labour
force. For female and young workers,
the employment elasticities with respect
to minimum wages are around –0.3,
while for less educated workers they are
around –0.2.
White-collar workers are the only
group to have benefited from minimum
wages in terms of employment. The em-
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Minimum Wage Policy and Its Impact on Employment in the Urban Formal Sector
ployment elasticity of white-collar workers with respect to the minimum wage
is 1.0 and is statistically significant, which
perhaps indicates that the imposition of
minimum wages leads to the substitution of different types of workers: as the
level of minimum wages is increased,
firms reduce the employment of other
types of workers and repla