The Implications of Minimum Wage Policy on School Enrollment:

An Empirical Analysis of Indonesia

by

Prapon Wongsangaroonsri

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

Bachelor of Arts, Honours in Economics

THE UNIVERSITY OF BRITISH COLUMBIA

(Vancouver)

April 2016

ii

I. Abstract1

This paper investigates the effect of minimum wage on school enrollment in Indonesia between

1997 and 2007, exploiting a policy initiative which more than quadrupled the minimum wage

rate. Using a fixed effect regression with difference-in-difference approach, this study finds that

minimum wage policy had diverging implications on school enrollment for each demographic

group. The result indicates a positive effect of minimum wage on school enrollment for

individuals below the legal working age and a less-robust indication of negative effect on for

individuals above the legal working age. In the context of developing countries, this study

further argues that the more stringent household income constraints allows minimum wage to

have positive effects on school enrollment in addition to the negative effects previously found in

the literature.

1 Acknowledgement: I am immensely grateful toward my thesis advisor, Professor Jamie

McCasland, for her valuable insights and encouragement. Her advices had led many significant

improvements of the model and specification used in this paper. I am also deeply indebted to

Professor Nicole Fortin, who had generously provided assistance and feedbacks through the

entire writing process. I would like to thank Professor David Green for his insightful comments

regarding the economic model used in this paper. Special thanks to my family and friends, who

had been a source of constant emotional supports and discussions.

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II. Table of Content

I. Abstract………………………………………………….…………...ii

II. Table of Content………….……………………………………......iii

1. Introduction….......……..………………………………………........1

2. Background……..……..……………………….………………........3 2.1 Minimum Wage System in Indonesia….…………………..……….,……3

2.2 Adjustments for Non-Homogeneous Labour Market……….………...….5

3. Literature Review…. .....………………………………………........6

4. Economic Framework.………………………………….................12 4.1 Intergeneration Model of Education………………………..……….,…..12

4.2 Adjustments for Non-Homogeneous Labour Market……….………...…16

5. Data……..….…..…………………………………….......................18 5.1 Data Source and Description…………………………………………….18

5.2 Summary Statistics……………………………………….….………..…21

6. Estimation Strategy...……………………………….......................24

7. Results Presentation...……………………………….......................31 7.1 Minimum Wage Effect by Age Group.………………………………….33

7.2 Minimum Wage Effect by Gender Group….…………….….………..…37

7.3 Analysis of Results……...………………….…………….….………..…39

8. Conclusion………......……………………………….......................41

Bibliography.………......……………………………….......................43

Figures and Tables……......………………………………..................45

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1. Introduction

The controversy surrounding minimum wage policy is a subject of many debates in both

the academics and political arenas. The policy is celebrated by its proponents as one of the

means in alleviating income disparity and denounced by its opponent for the risk of displacing

workers and choking firms’ competitiveness with labour cost. While there is little dissent

regarding the positive effect of the policy on formal sector wage rate (albeit an unclear

implication on distribution), the effect of the policy on the labour market is by far more

controversial and hence a topic of extensive study. However, in addition to the much studied

labour market effects, the policy may also results in household-side adjustments, which may

amplify or weaken the intended policy’s effect. In contrast to the relatively numerous researches

in the literature that focuses on the direct impact on income and employment, this paper argues

that household spending particularly on educational investment is another channel which is

affected by the policy. In turn, by affecting the level of human capital of the subsequent

generations of workers, the effect on education investment also hold long-run implication over

the economy through the quality of labour supplied in the labour market. A similar query into

this dynamics is investigated in Neumark and Wascher (1995), which stresses the linkage

between schooling, employment, and minimum wage for labour market analysis.

This study is conducted to investigate the manner in which household adjustments to

minimum wage policy may lead to unintended consequence on education and, in turn, hold long-

run implications over labour market. As a result of the dynamics between education and labour

market, any policy that affect education holds implication to the labour market structure and

productivity. Due to the lag in which changes in education outcome manifests on the economy,

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this linkage between minimum wage and education may not produce an apparent effect during

the period in which minimum wage policy is evaluated and is not often included into policy

evaluation. The assumption of relationship between minimum wage and educational investment

implies that the use of the minimum wage without considering these additional effects may lead

to sub-optimal outcome for the economy. This implication then posit the need for a more

integrated outlook and robust criteria for evaluating policy impacts of minimum wages beyond

the short-term outlooks of workers’ welfare and the economy.

In order to investigate this linkage, this study focuses to Indonesia, which is a fertile

ground for minimum wage research due to the country’s long-term reliance on the policy to

achieve social and distributional outcomes. Within this setting, this paper aims to illustrate two

specific ideas. First is that minimum wage policy has an indirect implication on the labour

market over the long-run by influencing households’ decision to invest in education. Second,

the households’ educational adjustments are not uniformed over different demographic groups,

which in turn hold implications over the distribution human capital among the population in

addition to level effect. Conditioned on demographic groups, this paper hypothesizes the

minimum wage has two opposing effects on school enrollment in developing countries due to the

more stringent income constraints on households toward investment in education. The net effect

to be either positive or negative, depending on the effect that prevails for each demographic

group.

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2. Background

2.1 Minimum Wage in Indonesia

The first minimum wage legislation in Indonesia was implemented in the early 1970s, following

the creation of the national wage council in 1969. However, the rate set by council was not

binding, and the lack of enforcement and monitoring resulted in the legislation only achieving

symbolic status (Hohberg & Lay 2015). The minimum rate wage only became binding

following the reform to the legislation in 1989, whereupon the minimum wage is calculated

based on a government-determined minimum consumption basket. Variations of the rate at the

provincial level is allowed to account for differences in price-level of goods in the basket, local

labor market conditions, and economic growth (Rama 2001). However, while the price of the

consumption goods did not differ significantly across Indonesia, the considerations for local

labour market still led to large variations of the minimum wage across provinces. Additionally,

considerations are given for some variations at district levels from the provincial baseline.

Another subsequent reform to the minimum wage system occurred in 2001, in which the tasks of

determining each province’s minimum wage were relegated to the provincial government. The

recommended rate of minimum wage in each province is chosen annually by regional wage

council on an annual basis during the last three months of the previous year. Once approved, the

new rate is announced by the provincial governor via a press release and is effective for the

duration of the coming year. The minimum wage is set for monthly full time work but also

affects the part-time wage, which is calculated as a by-day proportion of full-time work (Widarti

2006). Another initiative in enforcing the policy came with the Indonesian Manpower Act in

2003, which allows non-compliance with the policy to be subjected to criminal sanctions. The

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Article (185) of the Act 13 of 2003 concerning Manpower stated that the punishment of paying

wage lower than the minimum rate to involve a maximum of 4 years imprisonment and a fine of

4 hundred million rupiah, a much harsher development in comparison to the punishment scheme

in the 1990s, where non-complying firms were simply black-listed (Rama 2001).

However, despite the efforts to achieve a universal minimum wage rate, the enforcement

and monitoring of the policy is mostly limited to the occupations in the formal sector, whereas

the informal sector may not be subjected to the same minimum constraint. In the context of

developing countries, where the informal sector tends to be large relative to the economy, this

implies that a large share of population may still be working below minimum wage. This idea is

supported by Hohberg & Lay (2015), which estimated the share of informal worker to be roughly

between 50-65% over the 1997 to 2007 period and found the no statistical significant evidence of

minimum wage effect on informal incomes. Despite the limitation in enforcement, minimum

wage had been widely utilized in Indonesia as a tool to combat inequality following the 1989

legislation. Coinciding with a period of large economic growth in the first half of 1990s,

minimum wage increased significantly in the nominal term during the period. The nominal

minimum wage shows a strong annual growth since the early 1990s, although the real minimum

wage fell in the second half of 1990s following massive inflation after the 1997 Asian Economic

Crisis. The growth in the real minimum wage started catching up to the inflation by early 2000s

and only exceeded the pre-crisis level in 2002, painting an even more dubious picture over the

actual effectiveness of the policy in raising the universal standard of living in the country.

5

2.2 Education System in Indonesia

The education system in Indonesia is divided into 4 general levels: primary school for 7

to 12 years old, junior secondary for 13 to 15, senior secondary for 16 to 18, and tertiary for

above 18 years old. Relative to developing economies, the country has a high enrollment at a

primary school level. The enrollment rate is partly a result of the huge primary school

construction project undertaken by the Indonesian government in the 1970s, which doubled the

number of primary schools across country (Duflo 2001). Corresponding to this policy initiative,

public schools still provided up to 93% of primary education in Indonesia in 2014 (World Bank

2014). However, compared to primary education, a large falloff takes place during the transition

from primary into secondary education and once more during the transition from secondary to

tertiary education. Similar reversal is seen in the public-private school ratio, where only 44% of

junior secondary and 33% of senior secondary education was provided by public school in 2014.

Similarly, nearly all of tertiary education is provided by private institutions. Nevertheless,

government efforts have been made in addressing this falloff. Coinciding with the continuing

rise in real minimum wage, early 2000s in Indonesia was also a period of massive educational

expansion. Particularly, government spending on education had increased markedly between

2001 and 2006 with large sum of spending directed toward supply-side expansion of public

schools (OECD 2015). These transitional falloffs from primary to secondary school and from

secondary to tertiary school warrant further investigations, especially in regards to whether the

dynamics has any relationship with the minimum wage that were subjected to constant annual

increase during the same period.

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3. Literature Review

Across various geographic and demographic settings, the effect of minimum wage policy

on employment is shown to be varying across different labour markets. Earlier researches into

the field such as Brown et al. (1982) noted the adverse effect on minimum wage as a result of

distortions in labour market using time series analysis. While the outcome is consistent with

traditional model of competitive labour market, it is challenged by a number of more recent

empirical studies using panel regression analysis with a difference-in-difference strategy, where

minimum wage is shown to have negligible or even positive effect on employment. One of such

studies is Card & Krueger (1994), which investigate the effect of the 1992 increase of minimum

wage in New Jersey on employment in fast-food chains. The study applied a difference in

difference approach, using fast-food chain employment in Pennsylvania as a control group due to

the comparable seasonal employment pattern and lack of changes in minimum wage during the

time period of the study. The study found the difference term on employment to be positive and

significant, indicating a positive employment effect in New Jersey from the increase in minimum

wage. The study is challenged in identifying the specific factor that had driven the result, but

offered an explanation that the increase of fast-food price in New Jersey following minimum

wage allows the burden to be passed on to consumers. Similar divergence of employment from

competitive labour market model’s prediction is found in Katz & Krueger (1992) and Dickens et

al (1999) after the increase of minimum wage in Texas and United Kingdom, respectively. Taken

together, these studies indicate the limitation of the standard competitive labour market model in

analyzing the employment effect of minimum wage.

The conflicting results of the studies on minimum wage suggest the need for more

detailed researches into the mechanism within the labour market, which may have driven this

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effect. On the labour market level, the divergence of employment outcome from the theoretical

prediction suggests market imperfections such as hiring firms’ monopsony power (Dickens et al

1999) or employee’s shirking (Shapiro & Stiglitz 1984). Alternatively, on the level of firms, it is

possible that the employment effect is weakened by non-layoff adjustment dynamics, such as

firm-sponsored training to improve workers’ productivity (Acemoglu& Pischke 1999). Aside

from labour market side analysis, another channel of adjustments that needed to be explored is

on the side of the households, who are affected by the policy both through increasing income as

consumers of goods and changing labour market conditions as suppliers of labour. In turn,

household’s behavioral adjustment that impact spending pattern and entry into labour market can

be the driver of changes in the economy. One of the adjustment dynamics, particularly the

interaction between household spending and labour market outcome is investigated in Magruder

(2013), using individual level panel data of households in Indonesia. The study took advantage

of provincial variation in minimum wage and utilized a difference-in-difference and spatial

regression approach to investigate the effect on minimum wage on employment and real

expenditure of each worker. The study found that the negative employment effect is alleviated

through the expansion of some local firms resulting from increased household expenditure.

Taking the perspective of households as the supplier of labour, the amount of educational

investment is another crucial adjustment channel of the households, which can affect the human

capital and hence the quality of labour that they will supply. However, the effect of minimum

wage on this channel still remains ambiguous, and previous researches on the topic had not

reached a complete agreement on the direction of this adjustment mechanism. Cahuc and Michel

(1996) suggested that the more competitive labor market environment in the lower wage sector

as a result of minimum wage can incentivize individuals to increase their human capital

8

investment to mitigate the risk of being unemployed. In contrast, Neumark and Wascher (1995)

have found the opposite effect between minimum wage and school enrollment for teenagers.

The study constructed a state-level panel of the United States with proportion of teenagers who

are in school, in employment, or both as the dependent variable. Using conditional logit model

with state-specific controls such as compulsory schooling laws and teachers’ wages, the study

found a significant negative effect of minimum wage on proportion of teenagers in school. The

study noted that the negative relationship is mainly a result of the increase in employment

compensation as a result of minimum wage, which induces incentives for teenager to transition

from education to employment. Given these two diverging effects, the relationship between the

minimum wage and educational attainment of household is not clearly established in the

literature. To a certain extent, the relationship appears dependent on the dynamics between the

household and labour market, which warrants further investigation. Additionally, most of the

previous studies of the interaction between minimum wage and education are concentrated in

developed countries. These studies provide limited perspective into the adjustment mechanism

of the households in developing countries, where labour market condition and income

distribution are different.

In addition to the dynamics discussed above, studies of minimum wage effect in

developing countries is further complicated by the presence a large informal sector, where policy

enforcement is limited. In addition to the policy aspect, several studies highlights the need for

distinction between formal and informal firms and workers due largely differing characteristics.

Porta and Schleifer (2008) investigated productivity differences across formal and informal

sector with cross-sectional data from World Bank. The study utilized fixed effect regression on

formality dummy on several measures of productivity such as sales, gross profit, and output per

9

employee, controlling for firm-specific characteristics such manager’s level of education and

physical capitals. The study found that on average informal firms have less and capital and

access to external finance, which is highly correlated with productivity. Similar need for

distinction between the formal and informal is also stated Banerjee (1983), which studied the

earning workers from rural India in both formal and informal sector following migration into the

urban area. Controlling for age, education, and experience, the study found that being in

informal sector lead to workers having lower absolute income, although the returns to education

is found to be similar in both sectors.

The limitation in policy enforcement and differing characteristics imply the need for

separate policy analysis for each sector. Early theoretical model of minimum wage with

incomplete policy coverage is discussed in Mincer (1976). The model suggests that the wage

outcome is dependent on the supply effect on informal worker as a result of movement across

sectors. In this context, there are two opposing supply effects as formal workers who became

unemployed start moving into the informal sector and informal workers start quitting their job to

queue for a job in the formal sector. The outcome of wage and employment in the informal

sector is thus dependent on the elasticity of substitution between sectors. Likewise on the

empirical side, the results of previous studies on introduction of minimum wage into labour

market with segmentation is mixed and appears to differ across settings. Hohberg & Lay (2015)

investigated divergence in the outcome of minimum wage legislation in the formal and informal

sector of Indonesia between 1997 and 2007. The study utilized panel data of individuals and ran

a regression of minimum wage on employment dummy and income, controlling for individual-

specific factors such as age and education. In line with the theoretical prediction, the study

found a significant positive effect of minimum wage on formal sector workers’ earning,

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especially for the workers who started below the minimum wage level, and insignificant

minimum wage effect on the informal sector workers’ income. However, on the employment-

side, the minimum wage effect is shown to be significant positive in the formal sector and

significant negative in the informal, which indicates a transfer of worker from informal into

formal sector. However, the study is limited in its ability to explain this phenomenon, which

once again may be dependent on the underlying adjustment dynamics of the firms and

households. Additionally, the result may have limited applicability over different labour markets

as the opposite effect on informal sector wage is found in a series of studies of the Latin America.

Maloney & Mendez (2004) analyzed a panel data of workers in Colombia, separated into formal

and informal sector with a regression of minimum wage on workers’ real earning. The study

found a positive effect of minimum wage increase on the informal sector wage rate in Columbia

in addition to the formal sector. The study attribute the increase as a result of minimum wage

being used as a numeraire in wage-setting for the informal sector as well as the formal, despite

non-enforcement. Similar result is found in Fajnzylber (2001), which investigated the effect of

minimum rises in Brazil. Due to this divergence in findings of the previous studies, further

studies of underlying adjustment dynamics at the level of households are essential for

understanding and coming up with a better estimate of minimum wage policy across labour

markets.

To contribute to the current literature, this paper will focus on household-side educational

investments as one of the adjustment dynamics to minimum wage, using Indonesia as a setting.

In regards to minimum wage research, Indonesia provides a fertile setting for a study,

particularly due to the country’s reliance on minimum wage policy as a key social policy since

the early 1990s (Rama 2001). Various aspects of the Indonesian minimum wage policies had

11

been investigated in the previous studies such as divergence of income and employment effect of

minimum wage across formal and informal sector (Hohberg & Lay 2015), employment effect

across firm sizes and industries (Alatas & Cameron 2008), and the relationship between

employment and household consumption in (Magruder 2013). In the context of education, Duflo

(2001) used the number of primary schools constructed as a result of aggressive educational

policy in 1973-1978 Indonesia as an instrumental variable on wage regression. The study found

a significant positive effect that this additional education have on workers’ income, indicating

potential gain from education. However, to my knowledge, no previous study of Indonesia had

synthesize the two fields of study together in the form of minimum wage effect on household

investment in education, which will be the main goal of this study. Comparatively, there are

fewer researches that address the relationship between minimum wage and educational

attainment as well as a lack of universal consensus among previous researches. Additionally, I

have not found any previous researches linking education and minimum wage that was

conducted at an individual-level, especially in the setting of developing economy. By

investigating the relationship at the level of household, I hope to uncover some individual level

concentration of minimum wage effect that may not be visible from the previous state level

regressions.

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4. Economic Framework

4.1 Intergenerational Model of Education

This section will formalize the relationship between the two variables of interest:

minimum wage and school enrollment. Due to the lack of formal model on the subject matter,

an ad hoc model of intergeneration households based on basic economic rationality will be used.

Consider a basic two generations model with 𝑛 household, comprising of one adult and one child.

In each period, the adult works and earns either high-skilled wage or minimum wage. The adults

also decides whether to send the child to school (in which case, the child makes zero income) or

to work at minimum wage. In each subsequent period, the child becomes an adult, have a child

of his own, and find employment in the low-skilled sector if he is uneducated. To capture

uncertainty in the labor market, the educated adult will find employment in high-skilled sector

with some probability or will end up in low-skilled sector otherwise.

To state the model formally, let WMW stands for minimum wage, which is a treated as a

similar parameter for all households. Let p be the probability of the event that an educated adult

finds a job in the high-skilled sector and let WiHS be the high-skilled wage for household i

WiHS = WMW + Bi

Where Bi stands for skill premium in high-skilled sector. To capture variations the household

and individual characteristics, let Bi be a random variable that can takes some positive value so

that high-skilled wage is always higher than the minimum.

WMW < WiHS for all i = 1 … n

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Each individual gain utility from both their lifetime income and the expected lifetime

income of their immediate offspring. In this framework, the benefit of education is the

probability of gaining the difference between wage of high-skilled labour and minimum wage.

The cost of education is the foregone minimum wage from not sending the child to work plus

school enrollment fee.

The unique feature of the model is that the decision component is made across generation

in which only the parents decide whether to send their child to school. Since the parents’

education investment is decided in the previous generation, each parent will then treat the utility

derived from their own occupation as fixed and the decision component only involves the

potential utility to be gained from the offspring’s income. Hence, for simplicity, only the

potential income from the offspring will be used to model the decision process. Let UiO be the

utility that parent in household i would receive from their offspring with β discount is each

period.

E[UiO|School = 0] = WMW + βWMW

E[UiO|School = 1] = −C + β(pWHS + (1 − p)WMW) = β(WMW + pBi) − C

Hence household i will invest in the child’s education if the condition below is satisfied

β(WMW + pBi) − C ≥ WMW + βWMW

βpBi − C ≥ WMW

Bi ≥(WMW+C)

βp

The condition requires that the expectation of the discounted skill premium of the child in

household i must be greater than the foregone gain of minimum wage and the cost of schooling.

14

In this context, only the households whose offspring’s skill premium is high enough to satisfy the

above condition will invest in education.

In addition to households’ optimality, let us assume another constraint on educational

decision in which each household’s income in each period cannot fall below some minimum

subsistence need. Let W̅i stands for some arbitrary minimum basket of consumption for

household i that follows some distribution over a positive range, where the variation can be

thought to be a result of factors such as household-specific consumption preferences. For

simplicity, the model will assumes that (WHS > WMW > max W̅i). The lack of credit market

and household saving mechanism in the model means that households, whose disposable income

after spending on minimum subsistent is less than C, will be unable to send the child to school,

even if doing so would be optimum in the term of lifetime income.

Using this analytical framework, a successful enforcement of higher minimum wage has

two implications on educational investment from the households’ perspective (assuming no spill-

over effect on the wages above the minimum). First, an increase in minimum wage reduces the

potential gain from education as well as increasing the cost of foregone minimum wage.

Consequently, some of the affected households might find it optimal to substitute the child’s

education for work. By itself, this analysis indicates a negative consequence of increasing

minimum wage in the form of lower human capital in the labour market on the aggregate level

and as an increasing difficulty for the new cohorts of less educated workers to move on to higher

pay skilled employment on the household level. However, without the access to credit market

for household to borrow against their potential human capital, it is possible that a number of

households are constrained from making their optimal investment in education. In which case, a

positive shock to their wage earners’ income through minimum wage can lead to an increase in

15

educational investment as the household shift their allocation closer toward their optimal

investment plan. Hence, the number of households that decide to invest in education are affected

by two opposing dynamics. The ultimate direction of the minimum wage effect on education is

thus dependent whether the positive income effect or the negative substitution effect induces a

stronger change.

In this context, each individual i who was born in period will only make one decision in

whether to send their child to school based on both expected level of income and income

constraint. Both conditions are formally stated below.

1. E[U|S = 1] − E[U|S = 0] ≥ 0 Bi ≥(WMW+C)

βp

2. W̅i ≥ WMW − C

Following an exogenous increase in the minimum wage, the above conditions produce two

opposing effects on the proportion of households decide to invest in education. As observed

from condition (1), an increase in WMW as an opportunity cost of education will cause some

households to find it no longer worthwhile to send the child to school and hence start substituting

education for work in the formal sector. On a fixed range of Bi distribution, this substitution

effect will reduce the proportion of Bi which satisfies condition (1) and hence reduces

households which will invest in education. Conversely, increasing WMW causes the income

constraint in condition (2) to become less binding. Again, on a fixed range of W̅i, this will

increases the proportion of the households that are not bound by the income constraint and hence

potentially increasing school enrollment. With the presence of both effects, it is generally not

possible to disentangle and quantify each separately.

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4.2 Adjustments for Non-Homogeneous Labour Market

The above model is valid for a labor market where a single rate of minimum wage is

homogeneously applicable to all workers. However, as noted in Hohberg & Lay (2015) and

Rama (2001), a significant proportion of jobs in the Indonesian labor market is in the informal

sector, where minimum wage does not apply.

To model this feature in the labor market, let WIF replaces WMW as income of the parent

who works in the informal sector. Assuming that the child of an informal sector parent is still

only capable of working for minimum wage, WIF will only be substituted into the household

income constraint condition. Tthe equations below shows the conditions for the households with

the adult in the informal sector to make investment in the child’s education.

1. E[U|S = 1] − E[U|S = 0] ≥ 0 Bi ≥(WMW+C)

βp

2. W̅ ≥ WIF − C

Unlike the previous conditions, WMWonly enters condition (1) and hence would only produce

negative effect on education for households that fall under this category. Given this setup and

the additional assumption that the magnitude substitution effect is homogeneous across formal

and informal sector, then it is possible to decouple the income and substitution effect by

subtracting effect of minimum wage in the informal sector from the formal (provided that the

extent of the substitution effect is similar).

The last modification that can be made to the model involves the assumption that the

substitution effect may have an asymmetric effect across the formal and informal sector. For

instance, a household with at least one member working in the formal sector may find it easier

for the other members to access formal sector job by using this connection. In which case,

17

having a family member who is working in a formal sector is positively correlated with another

household member holding an occupation formal sector. Conversely, individuals in informal

households are constrained from finding formal employment. Following this analysis, the

substitution effect of education for employment will be asymmetrically concentrated within the

formal sector. In the most extreme case, where parental connection to the formal sector is

required for the formal employment, minimum wage will have no effect on school enrollment in

the informal sector whether through income or substitution. Both substitution and income effect

will be concentrated within the formal and, once again, identification of both effect is not

possible. This model will be used as a framework for analysis in the remaining sections of the

paper.

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5. Data

5.1 Data Description

The main data set will cover post-1990s Indonesia, where provincial minimum wage

rates were subjected to continual above inflation increase (Rama 2001). In addition to the policy

background, Indonesia has two main significances as the setting of this research. First, the level

of income inequality tends to be higher in developing countries, implying a more stringent

budget constraint on households at the lower end of the income distribution and hence a more

visible income effect. Second, the presence of large informal labour sector, which is not covered

by the minimum wage, can be utilized as a control group for the income effect. The assumption

underlying the use of the informal sector as a control group is supported by Hohberg & Lay

(2015), which indicated that the policy has statistically insignificant effect on the income of

workers in informal sector and will be subjected to additional tests in this paper.

The household data is obtained from the Indonesia Family Life Survey (IFLS) conducted

in 1993, 1997, 2000, and 2007, available from the RAND Corporation website. The IFLS is an

individual-level longitudinal survey with numerical code indexing individuals by their household

across survey waves. The survey covers 13 out of 27 provinces in Indonesia, although the

coverage is occasionally extended to track households who had moved out of the surveyed

province. The 13 provinces surveyed is representative of 83% of the country’s population. For

this research, the IFLS has two particular advantages. First, the data set allows individuals to be

categorized by household units, which makes it possible to associate the education variable on

individual observation with the household level characteristic as additional controls. Second, the

IFLS allows inclusion of individuals in the informal labour sector and their income approximated

19

through reported monthly profit. Formal and informal sector workers are categorized based on

self-reported work category in the survey. Categories such as “self-employed” and “unpaid

family workers” are considered to be representative of the informal. The IFLS forms the

backbone of the data set that will be used for analysis, containing the both the dependent variable

of educational status (whether the individual is currently in school) and the controls for

individual and familial characteristic (eg. household income). The IFLS set a minimum cut-off

for work survey at the age of 15, which coincides with Indonesia’s minimum working age of 15.

The macro-economic variables are obtained from Indonesian Bureau of Statistics, particularly

the independent variable of Indonesia’s provincial monthly minimum wage from 1997-2007 and

other macroeconomic variables, including consumer price index. Real minimum wage variables

are constructed by dividing the nominal wage with CPI in each year to index the variables in

terms of 1997 Rupiah.

From these two data sets, I constructed an unbalanced panel of individuals with trackable

household id, using the last three waves of IFLS in 1997, 2000, and 2007 in which both

household and minimum wage data are available. The data set contains 144,407 observations

overall and 51,443 observations of school-age individuals over the three available years. Of

which, 7,107 observations which reported to have migrated across province during the period

between each wave of survey, constituting 4.9% of the entire sample. These migrated

individuals are not included in the analysis to prevent endogeneity from the possibility that the

migration was motivated by incentives to pursue higher minimum wage.

The IFLS categorizes an individual to be school age if they were between under 24 years

old at the time of the survey. They are then directed to a survey section regarding current

schooling status. As a result, the schooling dummy dependent variable is applicable only to

20

individual age under 25. Additionally, so as to only include the schooling age observations and

to diminish the effect of birth-rate on school enrollment percentage, this paper set 7 years old as

a lower bound on the age of each observations to be included in the panel in addition to the 24

years old upper bound. While the schooling variable used in the paper is binary, the schooling

status inquiry in the IFLS actually allows 3 replies “In School,” “Not In School,” and “Not Yet

In School.” To remove potential bias from possible delayed in school enrollment, observations

that reported “Not Yet In School” are not included for the analysis. Provincial macroeconomic

variables, particularly real and nominal GDP, are attached to each observation based on province

and year. The provincial location of each survey respondent is obtained from the reported

provincial address in the survey. In addition to the variables available in the survey, a number of

household variables are also generated as controls for each observation in the panel, including

monthly family income, parents’ education level, number of children in the household, and

formality indicator.

Monthly family income variable is generated at the household level by summing over all

income of all individuals in the family in a specific year. The individual income balance used for

the summation is reported as an aggregate from all sources over one month period prior to the

survey. Incomes of individuals reported as “Not in Household” or “Servant” are excluded from

the family income total. To equalize this variable across families of different sizes, the

aggregated income is divided by √𝑛, where 𝑛 is the number of members in the corresponding

household. The square-root scale is to capture the economy of scale in a household in a sense

that the marginal cost of living for the second householder is cheaper than the first and so on.

This square-root equivalence scale is similar to the one used in the more recent OECD

publication in comparing household income of different sizes (OECD 2011). Individuals’

21

income variable is constructed from reported wage for formal sector workers (both private and

government) and from reported profit for self-employed informal workers. For observations

without monthly data, their income is estimated using annual wage or profit divided by 12.

Since Indonesian minimum wage regulation is effective on monthly wage, this variable is

reported on a monthly scale for comparability. Number of children in the household is generating

by counting the number of observations age below 25 in each specific household. Observations

are excluded from the count if they reported as being “Head” of the household or if they are “Not

in Household.” Lastly, the indicator for formal and informal sector for each household is

generated based on sector of working household members. Households are considered formal if

at least one worker in family is a “private worker” or “government worker.” Conversely,

household are consider informal if all working member are “self-employed” or “family worker.”

To prevent potential multicollinearity if households is made formal by individuals in the sample

for school enrollment regression, only the formal workers whose age is 25 or above can satisfy

the requirement to make a household formal (Otherwise, there would be a built-in negative

relationship between formal and school enrollment as the individual in the sample would have

drop out and start working in the formal sector). A weakness using this definition is that the

categorization process will be dependent on the self-perceived work category of the survey

respondent and hence may not reflect the intended division between the formal and the informal

sector. Nevertheless, this definition of formal and informal households will be used for the

remaining part of the paper.

22

5.1 Summary Statistics

The summary statistics will be divided mainly into two sections: the first summarizing

minimum wage data and the second summarizing the household data.

Figure (1) shows the level of minimum wage in Indonesia by year, averaged across all

provinces. The horizontal dash line shows the level of the starting minimum wage in the series

in 1997 for comparison with the subsequent years. During the 1997-2007, nominal minimum

wage grew markedly, increasing roughly 4.5 times on average across all provinces. However,

the increase in real minimum wage had been far less impressive by comparison with only 32%

on average over the 10 years period. As previously mentioned, the real minimum wage in 2000

is lower than the 1997 rate as a result of the massive inflation following the 1997 financial crisis.

All provinces in the sample were subjected to this drop, although to varying degrees. Real

minimum wage only caught up to the pre-crisis level by 2002 and had been subjected to steady

annual increase over the rest of the available time period. Similar trend of over time changes

across provinces and the difference between the nominal and real wage is confirmed in table (1).

The limited source of over-time variation is a cause for concern. However, as shown in figure

(2), the growth of real minimum wage across province does not follow a similar time trend.

Differences in cross-provincial growth can then be exploited as sources of identifying variation.

Figure (3) shows the general enforcement level of minimum wage in the data set across the

formal and informal sector. The histogram is constructed with the difference between

log(income) of each worker in the data set and log(minimum wage) which is attached to each

observation by year and province. The difference term is positive for all observations whose

income is higher than minimum and negative for observations whose income is lower than

minimum wage. The large density of the difference term in the negative region indicates

23

imperfect implementation of policy in both sectors. Regardless, the clustering of the difference

in the formal sector at the level slightly above and exactly at the level of minimum wage

suggests that the policy had some degree of successful enforcement. A similar clustering is not

visible is the informal sector. This interpretation of the distribution is in line with the initial

assumption that minimum wage policy does have statistically significant positive effect on

informal wages, which is needed for identification of income and substitution effect. This

assumption will be subjected to be more rigorous test in the later section of the paper.

Table (2) shows summary statistics of education variables. Using the school enrollment

dummy, the table shows the proportion of the individuals between 6 and 24 years old that were

enrolled in school at the time of the survey with sub-categorization based on household sectors,

age groups, gender, and income quantiles. For the three available years, school enrollment is

higher for households with at least one member working in the formal sector compared to

households with no member working the formal sector to a statistically significant degree, which

is consistent with the model’s prediction. Similarly, school enrollment for male is also

statistically higher than female for all the three years. In the term of age group, the proportion of

the individuals enrolled in school decreased significantly across all years for each subsequent

education level. Proportion in primary education age observation that were in school remained

relatively static across the three years, although there had been a significant increase in

enrollment among secondary school age population and a significant decrease among tertiary

school age population from 2000 to 2007. Similar fall-off is seen for the group below and above

legal working age. The correlation between income quartile and school enrollment is not quite

as apparent for 2nd

to 4th

quartile, although it is worth noting that the 1st quartile consistently had

the highest school enrollment proportion over all 3 years.

24

6. Estimation Strategy

Earlier in the paper, I posited an assumption that the minimum wage only affect the

income of workers in the formal sector. This assumption will be tested, utilizing regression of

minimum wage on family equivalence income with controls for household and time fixed effect

as indicated in equation (1) and (2) below

ln 𝑓𝑎𝑚𝑖𝑛𝑐𝑘,𝑡 = 𝜋0𝐹 + 𝜋1

𝐹 ln 𝑀𝑊𝑗,𝑡 + 𝜋2𝐹𝑍𝑘,𝑡 + 𝜃𝑘 + 𝛿𝑡 + 휀𝑖,𝑡 (1)

ln 𝑓𝑎𝑚𝑖𝑛𝑐𝑘,𝑡 = 𝜋0𝐼𝐹 + 𝜋1

𝐼𝐹 ln 𝑀𝑊𝑗,𝑡 + 𝜋2𝐼𝐹𝑍𝑘,𝑡 + 𝜃𝑘 + 𝛿𝑡 + 휀𝑖,𝑡 (2)

The superscript 𝐹 categorizes individuals who belong to a household with at least one income-

earner working in formal sector, and 𝐼𝐹 for individuals whose entire household was working in

informal sector at the time of the survey. The unit of observation 𝑘 is on the level of household.

The independent variable is ln 𝑀𝑊𝑗,𝑡, where 𝑀𝑊𝑗,𝑡 is real minimum wage of province 𝑗 at time 𝑡,

and the dependent variable is the log of family equivalence income. 𝑍𝑘,𝑡 stands for vector of

time-varying household characteristics, added in as controls. 𝜃𝑘 and 𝛿𝑡 control for household

and time fixed effect, respectively. For the assumption to hold, the coefficient 𝜋1𝐹 has to be

positive and significant, indicating positive income effect on households with formal worker.

Conversely, 𝜋1𝐼𝐹 has to be insignificant and hence would not be indicative effect of minimum

wage into the informal sector.

In contrast to most papers in the literature which focus on the effect of minimum wage on

individual’s primary income, the regression has the observation unit of households and uses

family equivalence income as the dependent variable. In the context of household income

constraint, family equivalence income is the more relevant measure of the capability of each

25

household unit to finance a child’s education. Hence, the measure is also more relevant in

investigating whether the possible spill-over in the informal sector will affect school enrollment.

There are two potential concerns with simply testing for the effect of minimum wage on family

members’ primary wage or profit (particularly household’s head). First, using individual income

does not take into account the family structure. For instance, the positive income shock from a

single formal worker would be effectively diluted in household with more financially dependent

members. Second, primary income balance alone does not take into account the manner in

which member of the household member may adjust their time allocation in response to

minimum wage. For instance, a householder who is self-employed may not experience a direct

increase in his profit but might have started finding part-time employment in the formal sector,

which nonetheless induces a positive income shock that is applicable for the income effect on

schooling. Additionally, to account for possibility of spill-over income shock on particular sub-

group of informal households, the regression will be repeated with a sample test group, whose

income in the starting year is well at the lower end of the income distribution (20th

– 40th

percentile), and a placebo group, whose income is at the upper end of the distribution (70th

to 90th

percentile). Theoretically, the minimum wage policy should only affects individuals whose

starting wage is below the minimum level (although in practice it is also possible that individuals

just above the minimum wage level would also receive an upward bump). Hence, a possible

absence of minimum wage effect from informal sector regression of the entire sample size may

also be a result of having two different effects that is experienced differently by high and low

income households.

26

Next, to capture the possible relationship between minimum wage and education through

the income and substitution effect as specified earlier, I will utilize a linear probability model of

schooling with controls for individual fixed effect.

𝑠𝑐ℎ𝑙𝑖,𝑡 = 𝛽0 + 𝛽1 ln 𝑀𝑊𝑗,𝑡 + 𝛽2Formal𝑖,𝑡 + 𝛽3(Formal𝑖,𝑡 ∗ ln 𝑀𝑊𝑗,𝑡) + 𝛾1𝑋𝑖,𝑡 + 𝛼𝑖 + 𝛿𝑡 + 휀𝑖,𝑡 (3)

𝑠𝑐ℎ𝑙𝑖,𝑡 𝜖 {0,1}

The dependent variable 𝑠𝑐ℎ𝑙𝑖,𝑡 is a dummy variable which takes value of 1 if an individual 𝑖

under 25 years old was in school during period 𝑡. Formal𝑖,𝑡 is a dummy variable taking the

value of 1 if at least one member of the individual’s family reported working in the formal sector

at time 𝑡. 𝑋𝑖,𝑡 is a vector of individual specific control variables that varies over time. 𝛼𝑖, 𝛿𝑡

captures fixed effect on the level of individual and time respectively. 휀𝑖,𝑡 is the generic error

term. While the usage of linear probability model is limited by the assumption of constant

marginal effect which may result in the predicted probability being outside of the [0,1] range, I

assume that the model should still be suitable as the study is mainly concern with the sign of the

minimum wage coefficient. An important feature of the model is the interaction

term(Formal𝑖,𝑡 ∗ ln 𝑀𝑊𝑗,𝑡), which is utilized to capture the additional effect of minimum wage

on school enrollment specific to the formal sector. The summative of effect of the minimum

wage on school enrollment is (𝛽1 + 𝛽3) for formal household and 𝛽1 for informal households.

Under the specification of equation (3), 𝛽1 will captures the magnitude of minimum wage

effect that is common across all sectors as well as the total effect of minimum wage on

observation in informal household. 𝛽3 from the interaction term will capture the minimum wage

effect that is specific to the household with at least one member working the formal sector.

Using both of the beta terms, it may be possible to distinguish the extent of income and

27

substitution effect across both sectors. Identification of both effects relies on two assumptions:

first, the substitution effect must equally apply to both formal and informal households and,

second, the income effect must only affect formal households. Under the assumption that

formal sector jobs are equally accessible across formal and informal, the coefficient 𝛽1 will

capture the magnitude of the substitution effect for both sectors and hence should be negative.

Also, if the income effect is shown to only apply to households with member working in the

formal sector, then 𝛽3 will capture the formal specific income effect, and should be positive.

Alternately, under the family connection model where having a family member working the

formal sector is positively correlated with an individual finding employment in the formal sector,

both the substitution and the income effect will be embedded within the coefficient 𝛽3. In which

case, 𝛽3 can either be negative if the magnitude substitution effect from increasing minimum

wage overtakes the income effect or positive if the opposite is true. Also, 𝛽1 should be

insignificant as neither the income effect nor the substitution effect will affect the income of the

household where all members were working in the informal sector. Lastly, if the income spill-

over assumption is violated, then both the income and substitution effect will be embedded

within 𝛽1. In the last two cases, it wouldn’t possible to decouple the income and substitution

effect and estimate the magnitude of each.

In addition to formal and informal subdivision, it would be plausible to assume that the

householders of different age and income group will experience different magnitude of effect

from minimum wage. For instance, it makes intuitive sense that the substitution effect is much

smaller or practically non-existent for the younger subset of the sample due to the presence of

legal constraints such as minimum working age. Likewise, households with income well-above

the minimum wage threshold (and hence less income constraint on education) should be much

28

less likely to experience the income effect. To control for both of these variations in the

substitution and income effect across groups, the regression with equation (3) will be repeated

for each sub-group of population as separated by age and income to test for these potential

differences in minimum wage effect. Also, as indicated in the institution research of the

education system, there is an abundance of public primary school in Indonesia, which provides

education at a relatively lower cost compared to secondary or tertiary school, which are mostly

private. In this sense, it is possible that income effect would be smaller at the primary school

level, thus it would be possible to further refine the regression for income effect by limiting the

age group to individuals within the age range that were in and were entering junior secondary

school (who were still too young to legally work).

Lastly, I expand the model by adding another gender-specific dimension to the

regression analysis. The interaction between female dummy as minimum wage, formal dummy,

and wage-formal interaction as shown are added to equation (3). However, as the paper employs

a fixed effect model at the individual level, it is not possible to add another female dummy as a

regressor to measure the baseline average difference between male and female in school

enrollment. Since fixed effect uses over-time variation for identification, female dummy which

is time-invariant will be included within the individual fixed term and drop from the model.

The use of fixed effect model allows potential sources of endogeneity in the regression

to be limited. The individual fixed effect controls for individual’s time-invariant unobservable

characteristics such as intelligence or work ethics. The time fixed effect controls for

macroeconomic performance that is common across all regions, potentially absorbing the impact

of the economic shocks following the Asian financial crisis during the time period (with the

assumption that all observations are equally affected in each particular year). Although

29

provincial fixed effect term is not explicitly added into the model, any province-specific time-

invariant characteristics should be captured by the individual fixed effect term. Since the data set

does not include observations that have migrated across province between each time period,

adding a fixed effect term at a provincial level is redundant with the individual fixed effect (the

two fixed effects are essentially collinear). Additionally, since the minimum wage is set at a

provincial level, the model should be able circumvent issue of reverse causality by using an

individual level regression. Despite these advantages, the model still suffer from a possibility of

underlying endogeneity with time-varying unobservable factors in the data set such as province-

specific economic performance that may cause the province minimum wage to rise along with

access to education (such as large-scale investment projects that lead to concentration of

population in a denser community) or if some provinces were particularly more or less

vulnerable to the financial crisis.

As described above, the study has a difference-in-difference design, where the

overtime change in real minimum wage level across provinces can be thought of as treatment

effect. Subsequently, if the no spill-over assumption is satisfied, then individuals belonging to

formal households can be thought of as the test-group and individuals in informal households as

control group. In regards to difference-in-difference, Bertrand et al (2004) indicates that the

researches using difference-in-difference specification tend to overestimate the result without

taking into account intra-group serial correlation. Additionally, Magruder (2013), which utilized

the same IFLS data set for analysis, pointed out the need to consider serial correlation within

each minimum wage cluster, which is determined at the provincial level. Both of these studies

indicate the need to use clustered-robust standard errors for the coefficient estimates. As is the

convention, the standard error will be clustered at the policy level, which in this case is the

30

province. The IFLS surveys at the level of 23 provincial units, including some major provinces

that had been sub-divided into North-South region. However, in practice, five of the provinces

only enter the data-set by the means of tracking households that had migrated to provinces not

originally part of the survey. These additional provinces contain less than 10 population and

hence are omitted from the analysis. With only 18 clusters, there is a concern that the

asymptotically normal critical value would not be representative. Since the theory on asymptotic

convergence with clusters relies on the assumption that the number of clusters in the sample goes

to infinity, there is a need to correct for small sample size. This paper will use the method

suggested in Cameron et al (2008) in replacing the critical values with the values from student-t

distribution with the (𝐺 − 2) degree of freedom, where 𝐺 is the number of available clusters.

The relevant critical values for 𝑡16 distribution are 1.75 for 10%, 2.12 for 5%, and 2.92 for 1%

significance. The results reported in the paper are robust to these critical values at the 10% level.

7. Estimation Result

31

To test the assumption that minimum wage only affects the income of workers in the

formal sector, the fixed effect regressions from equation (1) and (2) is used to check for possible

differences in how minimum affect households with or without formal sector workers. I ran

each regression twice, once with OLS standard error and once with clustered-robust standard

error. Column [1] and [2] of table (3) shows the regression result with OLS standard error. The

estimates in both columns appears to indicate that minimum wage have positive effect on family

equivalence income, which is statistically significant at 1% level for formal and 10% level for

informal (but are not robust to the possible intra-cluster serial correlation between observations).

However, in the context of policy level treatment effect, it is not possible to assume that all

observations are independent and the clustered-robust standard error would be a better suitable

for the estimates. Column [3] and [4] reports the same estimate but with clustered-robust

standard error. Compared to column [1] and [2], the coefficients lost significance level. The

minimum wage effect became significant at 10% level for the formal sector and became

insignificant for informal household. This development is in line with Bertrand et al (2004),

which noted that the lack of control for this serial correlation using clustered robust standard

error causes many overrejection of the null hypothesis using a difference-in-difference design.

The regression result, as reported in table (3), appears to support the idea that the increase in

minimum wage level led to an increase of income workers in the formal sector. However, no

evidence is shown on the spill-over effect on the income of workers in the informal sector. The

coefficient of 0.326 for the formal sector means that a 1% increase in minimum wage will lead to

0.326% increase in household equivalent income on average. This finding is in concurrence with

Hohberg and Lay (2015) which found that there is no evidence of spill-over effect on the income

of informal workers in Indonesia, using the same period of increases in minimum wage. Hence,

32

it appears to be the case that the increase in income for a formal household is driven by the

formal worker in the household unit, who experienced a positive income shock.

However, the repeated income regression using the test and the placebo group among

informal household seems to be pointing to the opposite result. The test-group is defined as the

informal households who were between 20th

and 40th

percentile of the distribution of household

equivalent income in 1997. A dummy variable is generated to keep track of them over all three

years. This is to keep constant the test-group over the available time period and allows for the

possibility that they may shift to different percentile of income distribution over time. Hence, it

is not possible keep track of the group by simply running a regression with households in the 20th

to 40th

percentile of each time period. The placebo group is generated similarly and is defined as

the informal households whose income is between 70th

and 90th

percentile on the income

distribution. The result of these regressions are shown in column [5] and [6]. For the test group,

minimum wage has a coefficient of 0.542, which is statistically significant at 10% level. For the

placebo, the minimum wage coefficient is insignificant. This value of 0.542 is in fact higher

than the coefficient of the regression with the entire formal household demographic and, thus,

suggests that the policy effect is localized toward particular informal household subgroup in the

income distribution. However, more importantly, the regression result indicates that there is a

strong spill-over effect of minimum wage on informal households’ income, which only affects

some particular sub-group and hence is not detected using the regression of all informal

households. This subgroup analysis now gives a result contrary to Hohberg & Lay (2015),

where no significant evidence of minimum wage effect is found on informal workers’ income.

Hence, the assumption that the positive income shock resulting from minimum wage only exist

for formal sector workers cannot be rejected by this preliminary test, at least when using

33

household income as a measure. The second part of the analysis with school enrollment will then

proceed with this assumption. As a consequence, the identification strategy with formal and

informal sector separation would not work in this setting.

7.1 Minimum Wage Effect by Age Group

The result of the main school enrollment regression using equation (3) for each sub

age-group is shown in table (4) to table (6). The schooling dependent variable only includes

observations that either reported as “In School” or “Not In School.” Observations that reported

“Not Yet In School” are not included in the regression to remove potential bias from difference

in enrollment timing. Additionally, for result comparison across the 4 columns, only the

observations with all the control variables are included in the regression. The sample is

subdivided by age-group due to possible difference in the effect on minimum wage on the

younger and the older age group in the sample. For this purpose, younger age-group is defined

to be 6 to 14 years old and the older from 15 to 22 years olds. The decision to set the cut-off

point between 14 and 15 years old stems from the minimum legal working age of 15 years old as

well as IFLS’s specification for employment survey which only include respondents who are 15

years in age or older. Instead of 24 years old, which maximum age available on schooling

variable, the maximum age for the regression of the older age-group is chosen to be 22 years old,

which is the age where majority of population graduate from tertiary education with a bachelor

degree. Under this new specification, the 6 to 14 age group is expected to show zero or very

small substitution effect compared to the 15 to 22, who are legally capable of substituting

education for employment. On the other hand, income effect should be weaker for the older age-

group as the higher cost of education would make it less likely for households that were

34

operating on the margin of minimum wage to be able to afford higher education even with the

income boost.

Table (4) shows the regression result for the 6-14 age group, where column [1] to [3]

report the coefficient with OLS standard error and column [4] to [6] with clustered standard error.

As before, the statistical significance of the estimates disappeared with using clustered standard

error for all the main variables and hence it is not possible to reject the null hypothesis that both

the income and substitution effect are zero. However, another possible interpretation is that the

income effect was much weaker for primary school age children, where most of the school are

publicly-provided. Hence, the inclusion of primary school level observation would then dilute

the possible income effect on junior secondary schoolers. This intra-group heterogeneity means

that the 6-14 years age group has to be further subdivided. As an alternate specification to

reduce to effect of primary schoolers on the regression, table (5) shows the regression result with

children between 10 and 14 years old. The ideal targeted age range for junior secondary school

would have been 11 to 14 years old. However, due to sample size issue, it is not possible to

squeeze the age range of the regression to the ideal range. Nevertheless, this specification is

sufficient to indicate that the income effect had indeed been taking place within this sub-sample

group. As shown in column [6], the 𝛽1 coefficient is positive and significant (5% level) with

clustered-robust standard error. Compared with the estimate in table (4), the magnitude of the

coefficient increases from 0.108 to 0.611, which lends support to the idea that the income effect

was diluted by including primary schooler into the age-group. On the other hand, 𝛽2 of formal

dummy and 𝛽3 of interaction coefficient are both shown to be statistically insignificant, which is

consistent with the spill-over effect assumption. Taken together, the regression result from table

(5) is indicative of the income effect, shown as significant positive effect on school enrollment of

35

individuals in the 10 to 14 age group that is common across both sector. The magnitude of 𝛽1

indicates that for 1% percent increase in real minimum wage, the each individual in the 10 to 14

age group will on average experience 0.611% increase in the probability of being enrolled in a

school. This translates to a 19.5% percent increase in school enrollment, resulting from 32%

increase in real minimum wage over the 10 years period.

To test whether it is the additional income from minimum wage that had driven the

result, the regression is repeated with separation of the 10 to 14 age-group into subset based on

family income. The intuition is that the impact of the positive income shock would mostly affect

household with individuals whose wage is below the original rate of minimum wage. On the

other hand, households whose members are earning well above the minimum should experience

none or comparatively smaller effect on their income. Hence, I will define the households who

were earning well to the right of the income distribution as the placebo group and the households

to the left of the distribution as test group and separately run a fixed effect regression in equation

(3) on each. The results for these regressions are shown column [7] and [8], where the

observations are subdivided into group based on whether they belong to households whose

income is below the median family income and above the median. Family incomes are sorted

into above and below for each respective year rather than as a pooled group. Comparing column

[7] and [8], the coefficient 𝛽1 is statistically significant for the below median group at 10% level

and is insignificant for the above median group. In fact, 𝛽1 for below-median is higher than the

estimate from all income group. The increase in magnitude of the estimate from 0.611 to 1.079

now indicates more than a one-to-one percentage increase in probability of school enrollment

from increase in real minimum wage for households below the median of family income

distribution, which translate to 34.5 percent increase in probability of school enrollment over 10

36

years (that is only applicable to a small subset of the population). The result implies that the

income effect does exist for children in the junior secondary school age range, but only for lower

income households. This interpretation is consistent with the model prediction that it is more

likely for budget constraint condition to be binding for lower income household as well as the

intuition that the minimum wage policy is more effective for people and thus households on the

lower end of the income distribution. It would be plausible to assume that the positive income

shock toward education is the driving force behind the income effect for this subgroup.

Table (6) shows the result of the regression with the 15 to 22 age group, which is over

minimum working age and hence may have experienced the substitution effect. As shown in

column [4] to [8], the 𝛽1 and 𝛽3 are coefficient is consistently insignificant with clustered

standard error. The statistical significance in column [1] to [3] disappears after using the

clustered-robust standard error. Taken together, the result indicates that neither substitution nor

income effect on school enrollment can be verified using this data-set. Aside from the

straightforward conclusion that there is no actual effect of minimum wage for this age group (in

both formal and informal households), this outcome is also consistent with the prediction from

the model with spill-over income effect, where depending on their magnitude the opposing

substitution and income effect might have canceled out one another. Interestingly, the result

does not differ even after separating the observations in above and below median household

income group, despite the assumption that poorer households are more susceptible to the

substitution effect as a result of greater financial needs. Nevertheless, this specification alone

does not make it possible to verify whether income and substitution effect actually exist for this

age-group. Additionally, even if they exist, the effects are not separately quantifiable.

37

7.2 Minimum Wage Effect by Gender Group

Two final adjustments are made to the model in this section. To identify possible

gender bias in the schooling and minimum wage dynamics. Interaction terms between all three

variables of interest with female dummy are added to the regression equation (3). Second, as a

robustness check to the existence of possible substitution effect, employment dummy will be

used as a dependent variable to verify whether the substitution into employment had led the

population to fall into unemployment while queuing for as opening in the job market as

discussed in Mincer (1976) or had occurred without a loss in school enrollment. The employed

dummy is constructed to include employment in both formal and informal sector since the result

in table (3) indicates spill-over effect of minimum on informal income, which means informal

sector jobs are also viable choices for substitution. The result of the regressions is reported in

table (7) for the 10 to 14 age group and table (8) for 15 to 22 age group.

For the 10-14 age group, there appears to be no gender dynamics in the income

effect found in the previous regression as indicated by the absence of statistical significance in

column [2]. Separation of household group into higher and lower income also does not yield

any statistically significant result and hence does not show any difference in the gender-specific

effect. This seems to imply that the school enrollment at the junior secondary school level

increased equally on average for both gender group, which seems to give a positive implication

toward equity in education at this level. The regression for the 15 to 22 age group in table (8)

gives a similar result with the lack of the statistical significance with cluster-robust standard error

(although the coefficient estimates with OLS standard error are significantly negative for both

minimum wage term and female minimum wage interaction). The result of employment

regressions is shown in column [5] to [7]. In spite of the lack of statistical significance to the

38

minimum wage effect on school enrollment, there is a significant positive effect on minimum

wage on employment dummy that is specific to female as shown in column [5]. The coefficient

of 0.0527 translate to a modest gain of 1.7% in female employment for the 15 to 22 age group

over 10 years of investigation period. A possible explanation for this gender-specific maybe due

to inherent preferences within households for males children to have higher education as a result

of gender norm, which leads to less available male population for the gain in employment to take

place. However, the effect disappears with the separation of the investigation group into below

and above median group. This result may have stemmed from the limitation on the part of the

data-set rather as separation of the sample into the two groups causes standard error to become

much larger compared to the grouped regression, which may indicates an issue from limited

identifying time-variation for the fixed effect model. Taken together, this seems to indicate that

a rising minimum wage leads to a positive employment gain for female without loss of schooling.

This result may implies that higher minimum wage enticed the female population who were

initially in neither education nor employment to seek out job. However, it is worthy of note that

estimate minimum wage coefficient for the 15 to 22 age group on school enrollment is

consistently negative throughout all specifications. Similarly, despite the lack of statistical

significance with cluster-robust standard error, the interaction between minimum wage and

female dummy is also consistently negative throughout all specifications. This result to a

certain extent hints the possible existence of negative substitution effect which more

concentrated for female population, albeit it not possible to verify the effect using the current

data set.

7.3 Analysis of Result

39

To summarize the findings, the paper found that the positive minimum wage effect spill-

over into lower income subgroup of the informal households. This result is consistent with the

result that income and substitution effects does not vary between the formal and informal sector,

although both effects are only visible within subgroup of the population. The income effect is

only quantified for individuals between 10 and 14 years old, and the substitution effect for

female between 15 and 22 years old. The coefficient 𝛽2 of formal dummy is insignificant

throughout all regressions, indicating that the differences in the level of school enrollment

between formal and informal shown in summary statistics could not be explained very well by

differences in income level and minimum wage enforcement alone. It might be possible that

these differences, which persisted across all time periods, were driven by household specific

characteristics that is captured by the fixed effect term and hence does not show in the regression.

Lastly, coefficient 𝛽3 is insignificant throughout all regressions as anticipated from the

conclusion regarding the spill-over effect on informal income. Overall, the study provide a more

benign policy implication toward equality between the formal and informal sector in the term of

educational access (although an inherent inequality in that is embedded within the fixed effect is

still possible). The female-specific increases in employment also has a slight positive

implications on gender equality, especially if the gain in employment occurred without the loss

in school enrollment, although this cannot be shown by the regression results.

Regardless, the study suffers from issues in possible endogeneity from economic

condition, which is time-varying and may only affect some sub-groups of the population. Since

the minimum wage rate is chosen locally based on consumption need as well as labor market

condition, it would be the case that the minimum wage is correlated with local economic

40

performance. In turn, economic performance may provide other channel of affecting schooling

decision that is not simply captured through minimum wage. Part of this endogeneity is

addressed through the use of the within estimator with fixed effect model, adding provincial

fixed effect to control for time-invariant characteristics that may influence school decision.

However, the lack of time-varying macro data particularly the provincial GDP made it difficult

to appropriately address this issue. Issues of time-varying changes in educational facilities

across provinces also presents a major validity concern to the result of the research, where

correlation between minimum wage, economic performance, and provincial investment in

education might be plausible. However, the Indonesian Bureau of Statistics provided a limited

source of data in constructing this additional category of control variables as the period of

available data set on provincial education facilities does not coincide with the investigation

period. Given availability in additional data, this could provide a way to improve this research.

Lastly, the limitation in the identifying sources of time variation proves to be a challenge

for the study as population are sub-divided into smaller groups. Lower variation source in turn

leads to large standard error and lowers quality of the estimate, which makes it more difficult to

find statistical significance in the result. As previously stated, although the study uses time

panel data, the data set only have three available time periods with a modest variation in real

minimum wage (as opposed to the nominal). However, it will be possible to revise the study to

potentially reduce this weakness in the future, since the IFLS is an ongoing study in Indonesia

and, in fact, the fifth wave of the survey was fielded in 2015 and will be published within 2016.

A possible extension of the research would then be to enlarge the data to four time periods to

improve the quality of the results.

41

8. Conclusion

The paper introduces two new factors in studying minimum wage impact on school

enrollment, specifically by using household and individual level regressions and using a

developing country as the setting of the study. The use of individual level regressions makes it

possible to show concentration of the income and substitution effect within particular sub-group

of the population. The use of developing country, namely Indonesia, allows for the income

effect to be more visible as a result of more stringent budget constraint, compared to previous

research on developed countries. Although the magnitudes of the income and substitution effects

are not quantified for the all relevant sub-groups, the paper does achieve the main goal of

identifying and verifying the existence of these households’ adjustment on education. In turn,

these adjustments give ambiguous implications on social and economic outcomes for each

subgroup of the population over the long-run.

Three main conclusions can be reached from this study. Firstly, in the setting of

developing country, the study found strong evidence for the income effect for the lower income

group below legal working age. Hence, the use of minimum wage policy may lead to positive

spill-over effect for this subset of the population. Second, the result indicates that household

adjustments through the substitution effect is very specific to the setting, particularly in

comparison between developed and developing countries. With the details of Indonesia’s

education system and labour market, the exact effect via the income and substitution channel is

limited in its generalizability to other settings. However, the predominance of negative

substitution effect found in Neumark & Wascher (1995) is not found in this research. This

development suggests that the trade-off in the term of lower school enrollment from minimum

wage policy is to a certain extent alleviated in the setting of developing countries, where there

42

are more potential gains on school enrollment as a result of positive income shock from

minimum wage. Lastly, both the income and substitution effect, in which households adapt to

the policy, are shown to be very specific and localized in the term of demographic. A regression

at the aggregate level may causes the estimate for these subgroup to lose statistical significance.

This outcome indicates that the studies that explore household adjustment mechanisms with

state-level regression such as Neumark & Wascher (1995) may have left out household-level

dynamics that is not visible at the macro level

The complexity of minimum wage policy’s influences household’s educational decision

suggests that policy may have lingering implications on distributional outcome between each

demographic group such as gender in addition to economic outcome. Overall, as shown in this

paper, the existence of this channel of interaction between minimum wage, education, and

eventually labour market implies the need for a more demographically-specific investigation into

household-side adjustments to the minimum wage dynamics in the future.

43

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45

Figures and Tables

Figure 1. Minimum Wage in Indonesia

Figure 2. Real Minimum Wage Growth By Province

46

Figure 3. Distribution of Difference between Income and Minimum Wage by Sector

0.1

.2.3

.4.5

-10 0 10 20 -10 0 10 20

Informal Sector Wage Density Formal Sector Wage Density

den

sity

log(income) - log(minw)Source:IFLS & Indonesian Bureau of Statistics

47

[1] [2] [3] [1] [2] [3]

1997 Nominal 2000 Nominal 2007 Nominal 1997 Real 2000 Real 2007 Real

North Sumatra 151.0 254.0 761.0 151.0 127.2 203.1

West Sumatra 119.0 200.0 750.0 119.0 100.2 200.1

South Sumatra 127.5 190.0 753.0 127.5 95.2 200.9

Lampung 126.0 192.0 555.0 126.0 96.2 148.1

Jakarta 172.5 286.0 816.1 172.5 143.3 217.8

West Java 172.5 230.0 447.7 172.5 115.2 119.5

Central Java 113.0 185.0 500.0 113.0 92.7 133.4

Yogyakarta 106.5 194.5 460.0 106.5 97.4 122.8

East Java 132.5 214.5 448.5 132.5 107.4 119.7

Bali 141.5 214.0 622.0 141.5 107.2 166.0

West Nusa Tengara 106.0 184.0 600.0 106.0 92.2 160.1

South Kalimantan 125.0 200.0 745.0 125.0 100.2 198.8

South Sulaw esi 112.5 200.0 673.2 112.5 100.2 179.6

Provinces

Table 1: Minimum Wage (Thousands Rupiah)

* Real Minimum Wage in 1997 Rupiah

48

[1] [2] [3]

1997 2000 2007

0.647 0.625 0.641

(0.0045) (0.0043) (0.0043)

0.615 0.606 0.630

(0.0066) (0.0063) (0.0066)

0.676 0.641 0.649

(0.0060) (0.0058) (0.0058)

0.659 0.641 0.660

(0.0063) (0.0060) (0.0061)

0.635 0.609 0.622

(0.0063) (0.0061) (0.0062)

0.089 0.096 0.000

(0.0037) (0.0036) (0.0000)

0.329 0.290 0.000

(0.0062) (0.0065) (0.0000)

0.844 0.880 0.000

(0.0050) (0.0045) (0.0000)

0.897 0.915 0.939

(0.0048) (0.0044) (0.0038)

0.406 0.406 0.395

(0.0067) (0.0059) (0.0065)

0.658 0.626 0.615

(0.0081) (0.0073) (0.0073)

0.630 0.578 0.592

(0.0083) (0.0075) (0.0074)

0.606 0.555 0.581

(0.0085) (0.0076) (0.0077)

0.608 0.559 0.592

(0.0098) (0.0093) (0.0095)

0.188 0.135 0.081

(0.0029) (0.0021) (0.0014)

0.453 0.423 0.375

(0.0037) (0.0030) (0.0024)

0.288 0.337 0.406

(0.0033) (0.0029) (0.0025)

0.065 0.086 0.112

(0.0018) (0.0017) (0.0016)

Primary School Age (6-12)

Secondary School Age(13-18)

Tertiary School Age (19-24)

Tertiary

*Standard error in parentheses.

** Percentage of schooling by income quartile exclude families w ith zero income

3rd Family Income Quartile

4th Family Income Quartile

Panel B: Education Background (Proportion, Indv age > 24)

No Education

Primary

Secondary

1st Family Income Quartile

2nd Family Income Quartile

Male

Female

Under Minimum Working Age (10-14)

Above Minimum Working Age (15-22)

Total

Informal

Formal

Table 2: Education

Panel A: School Enrollment (Proportion, Indv age 6 - 24)

49

[1] [2] [3] [4] [5] [6]

Informal Formal Informal Formal Informal - Test Group Informal - Placebo Group

0.174* 0.326*** 0.174 0.326* 0.542* -0.112

(0.102) (0.084) (0.237) (0.159) (0.259) (0.312)

-0.005 0.009 -0.005 0.009 0.194 -0.050

(0.080) (0.060) (0.161) (0.075) (0.361) (0.199)

0.047 0.084 0.047 0.084 -0.095 -0.081

(0.075) (0.082) (0.157) (0.204) (0.201) (0.267)

0.079 0.097 0.079 0.097 0.235 -0.101

(0.110) (0.107) (0.202) (0.208) (0.324) (0.399)

0.062 0.111 0.062 0.111 -1.304** 0.386

(0.236) (0.133) (0.398) (0.274) (0.461) (0.538)

8.703*** 7.485*** 8.703*** 7.485*** 4.293 13.38***

(1.207) (0.998) (2.824) (1.946) (3.074) (3.597)

N 10270 11704 10270 11704 2094 1879

Dependent Variable is Log(Real Household Equivalent Income)

Constant

Test-group is defined as individual belonging to households betw een 20th and 40th percentile of household income distribution. Similar definition for 70th-90th percentile is used for control-group

Table 3: Household Income and Minimum Wage

Household Head Education: Secondary

Household Head Education: Tertiary

Household Fixed Effect

Year Fixed Effect

Clustered Robust Standard Error No No Yes Yes

Log(Real Minimum Wage)

Urban Indicator

Household Head Education: Primary

Yes

Yes

Standard error in parentheses. Clustered-Robust w here indicated

* p<0.10, ** p<0.05, ***p<0.01

A household is regarded as formal if at least household member reported w orking in the formal sector

Yes Yes Yes

YesYesYes

Yes Yes

Yes Yes

Yes Yes

50

[1] [2] [3] [4] [5] [6] [7] [8]

School School School School School School School, Faminc<Median School, Faminc>Median

0.0761** 0.110*** 0.108*** 0.0761 0.1100 0.1080 0.2670 0.1020

(0.0309) (0.0344) (0.0344) (0.1360) (0.1650) (0.1640) (0.3220) (0.0774)

0.718** 0.725** 0.7180 0.7250 0.2540 1.5770

(0.3170) (0.3170) (0.8280) (0.8040) (1.4430) (0.9140)

-0.0612** -0.0618** -0.0612 -0.0618 -0.0206 -0.136*

(0.0271) (0.0271) (0.0707) (0.0685) (0.1250) (0.0771)

0.0286*** 0.0286* 0.0633 -0.0054

(0.0065) (0.0140) (0.1020) (0.0431)

-0.0018 -0.0018 -0.0082 0.0047

(0.0034) (0.0074) (0.0216) (0.0088)

0.0048 0.0048 0.0091 0.0143

(0.0035) (0.0077) (0.0162) (0.0280)

0.1250 -0.2720 -0.4600 0.1250 -0.2720 -0.4600 -2.3690 -0.3930

(0.3650) (0.4050) (0.4080) (1.6000) (1.9480) (1.9070) (3.7960) (0.7820)

N 17459 17459 17455 17459 17459 17455 8644 8811

Table 4: Minimum Wage Regression on Schooling (6-14 years old)

Log(Household Equivalent Income)

Clustered-Robust Standard Error No No No

Number of Children In Household

Log(Real Minimum Wage)

Formal

Formal*Log(Real Minimum Wage)

Age

Constant

YesYes Yes Yes Yes

* p<0.10, ** p<0.05, ***p<0.01

Clustered Robust standard error in parentheses.

Controlled for individual and time fixed effects

51

[1] [2] [3] [4] [5] [6] [7] [8]

School School School School School School School, Faminc<Median School, Faminc>Median

0.553*** 0.629*** 0.611*** 0.553** 0.629** 0.611** 1.079** 0.4890

(0.1160) (0.1250) (0.1250) (0.2600) (0.2610) (0.2740) (0.4930) (0.5760)

1.592* 1.5250 1.5920 1.5250 -1.6620 4.0630

(0.9490) (0.9530) (1.6290) (1.8520) (3.8330) (4.6350)

-0.136* -0.1300 -0.1360 -0.1300 0.1420 -0.3440

(0.0812) (0.0815) (0.1380) (0.1570) (0.3260) (0.3990)

0.0294* 0.0294 0.0697 0.0125

(0.0157) (0.0744) (0.1650) (0.0688)

-0.0092 -0.0092 -0.0228 0.0118

(0.0088) (0.0248) (0.0693) (0.0401)

0.0060 0.0060 0.0229 -0.0010

(0.0086) (0.0217) (0.0569) (0.0755)

-5.500*** -6.380*** -6.473*** -5.500* -6.380* -6.473* -12.10* -4.8570

(1.3680) (1.4650) (1.4740) (3.0740) (3.0930) (3.2670) (5.9210) (6.4390)

N 10234 10234 10231 10234 10234 10231 5106 5125

Log(Real Minimum Wage)

Yes

Formal

Formal*Log(Real Minimum Wage)

Age

Number of Children In Household

Log(Household Equivalent Income)

Yes Yes

Table 5: Minimum Wage Regression on Schooling (10-14 years old)

* p<0.10, ** p<0.05, ***p<0.01

Controlled for individual and time fixed effects

Clustered Robust standard error in parentheses.

Constant

Yes Yes Clustered-Robust Standard Error No No No

52

[1] [2] [3] [4] [5] [6] [7] [8]

School School School School School School School, Faminc<Median School, Faminc>Median

-0.1360 -0.180* -0.190** -0.1360 -0.1800 -0.1900 -0.2000 -0.2400

(0.0857) (0.0932) (0.0935) (0.2210) (0.1940) (0.2010) (0.3910) (0.2940)

-1.1190 -1.1760 -1.1190 -1.1760 0.2290 -1.6270

(0.8830) (0.8820) (1.6880) (1.7400) (7.2360) (3.8120)

0.0949 0.1000 0.0949 0.1000 -0.0138 0.1390

(0.0752) (0.0751) (0.1460) (0.1510) (0.6180) (0.3230)

-0.0425*** -0.0425 -0.0684 0.1230

(0.0132) (0.0314) (0.2070) (0.1150)

0.0031 0.0031 -0.0082 -0.0024

(0.0078) (0.0169) (0.0783) (0.0316)

-0.0172* -0.0172 -0.0024 -0.0390

(0.0102) (0.0212) (0.0626) (0.0564)

2.435** 2.955*** 3.842*** 2.4350 2.9550 3.8420 3.5370 4.9000

(1.0120) (1.0980) (1.1300) (2.6360) (2.3240) (2.5450) (4.7130) (3.8890)

No No No Yes Yes Yes

N 13629 13629 13629 13629 13629 13629 5635 7994

Clustered-Robust Standard Error

Log(Real Minimum Wage)

Formal

Formal*Log(Real Minimum Wage)

Age

Number of Children In Household

* p<0.10, ** p<0.05, ***p<0.01

Controlled for individual and time fixed effects

Clustered Robust standard error in parentheses.

Yes Yes

Log(Household Equivalent Income)

Constant

Table 6: Minimum Wage Regression on Schooling (15-22 years old)

53

[1] [2] [3] [4]

School School School, Faminc<Median School, Faminc>Median

0.5430*** 0.5430 0.8820 0.5630

(0.1380) (0.3610) (0.7350) (0.8900)

0.2200* 0.2200 0.3650 -0.1320

(0.1210) (0.2940) (1.0680) (0.7270)

1.7140 1.7140 -1.5080 5.3210

(1.3070) (3.6780) (7.7960) (8.7760)

0.2930 0.2930 -1.3370 -2.7390

(1.9090) (6.0960) (11.4300) (10.9300)

-0.1480 -0.1480 0.1310 -0.4560

(0.1120) (0.3120) (0.6600) (0.7540)

-0.0233 -0.0233 0.1090 0.2450

(0.1630) (0.5220) (0.9720) (0.9280)

0.0279* 0.0279 0.0280 -0.0021

(0.0157) (0.0761) (0.1380) (0.0354)

0.0352 0.0352 0.0809 0.0110

(0.0561) (0.1490) (0.1870) (0.0757)

-0.0064 -0.0064 -0.0225 0.0114

(0.3430) (0.5060) (0.0709) (0.0404)

-6.929*** -6.929* -4.9440 -11.90**

(1.4760) (3.3860) (5.2560) (7.0230)

N 10189 10189 5065 5124

Clustered Robust standard error in parentheses.

Controlled for individual and time fixed effects

* p<0.10, ** p<0.05, ***p<0.01

Table 7: Minimum Wage Regression on Schooling With Female Dummy (Age 10 to 14)

Yes No Yes Yes

Female*Formal*Log(Real Minimum Wage)

Age

Number of Children In Household

Log(Household Equivalent Income)

Clustered-Robust Standard Error

Log(Real Minimum Wage)

Female*Log(Real Minimum Wage)

Formal

Female*Formal

Formal*Log(Real Minimum Wage)

Constant

54

[1] [2] [3] [4] [5] [6] [7]

School School School, Faminc<Median School, Faminc>Median Employed Employed, Faminc<Median Employed, Faminc>Median

-0.187* -0.1870 -0.1380 -0.1130 0.2820 0.3050 0.2590

(0.0968) (0.2000) (0.4200) (0.3310) (0.3030) (0.9290) (0.3210)

-0.0695* -0.0695 -0.1650 -0.2650 0.0537* -0.1030 0.2760

(0.0390) (0.0412) (0.6400) (0.3060) (0.0281) (0.8660) (0.4750)

-0.3030 -0.3030 2.5640 0.5260 0.1460 2.3180 -1.0580

(1.0710) (1.9570) (8.4820) (5.1680) (3.4030) (13.6600) (5.6660)

-2.0020 -2.0020 -4.6780 -4.3870 3.4450 1.0130 6.0210

(1.2700) (1.8790) (11.3200) (5.6470) (3.7460) (23.9900) (8.0010)

0.0289 0.0289 -0.2070 -0.0432 -0.0191 -0.2130 0.0853

(0.0911) (0.1670) (0.7220) (0.4360) (0.2910) (1.1650) (0.4800)

0.1660 0.1660 0.3890 0.3720 -0.2840 -0.0632 -0.5090

(0.1080) (0.1540) (0.9670) (0.4730) (0.3190) (2.0530) (0.6700)

-0.0423*** -0.0423 -0.0259 -0.0596 0.0469 0.0476 0.0478

(0.0133) (0.0290) (0.0597) (0.0508) (0.0515) (0.1120) (0.0714)

0.0387 0.0387 -0.0659 0.1260 0.0114 0.0064 -0.1260

(0.0513) (0.0706) (0.1960) (0.1110) (0.1320) (0.4910) (0.3450)

0.0049 0.0049 -0.0075 -0.0026 0.0096 0.0632 0.0108

(0.1350) (0.2800) (0.5620) (0.5400) (0.5020) (1.1480) (0.0403)

4.186*** 4.1860 3.7250 5.0480 -4.8110 -4.1550 -5.8360

(1.1410) (2.4710) (5.6440) (3.8350) (3.6720) (10.7300) (4.2340)

N 13620 13620 5555 8065 10134 3932 6202

Yes

Table 8: Minimum Wage Regression on Schooling With Female Dummy (Age 15 to 22)

* p<0.10, ** p<0.05, ***p<0.01

Female*Log(Real Minimum Wage)

Yes

Formal*Log(Real Minimum Wage)

Female*Formal*Log(Real Minimum Wage)

Clustered Robust standard error in parentheses.

Controlled for individual and time fixed effects

Yes Yes Yes

Number of Children In Household

Log(Household Equivalent Income)

Clustered-Robust Standard Error No Yes

Constant

Log(Real Minimum Wage)

Formal

Female*Formal

Age