THEORETICAL REVIEW Human Capital Theory

DO WAGES REFLECT PRODUCTIVITY UNDER MINIMUM WAGES SETTING?

Evidence from Indonesia Firm Level Survey

Rifai Afin

Universitas Trunojoyo Madura Abstrak

Penelitian ini menginvestigasi pengaruh pendidikan pada produktifitas tenaga kerja dan upah pada skema kebijakan upah minimum pada sektor manufaktur di Indonesia. Menggunakan data tingkat perusahaan, studi ini mengidentifikasi faktor yang mempengaruhi output dan upah pada tingkat perusahaan dan menggunakan data tingkat regional untuk menginvestigasi pengaruh upah minimum pada perubahan upah.

Temuan pertama, tenaga kerja dengan 9 dan 12 tahun pendidikan mengalami diminishing marginal produktifitas dan tingkat pendidikan lain konsisten dengan human capital theory dan sebagian besar varibel kontrol secara positif berpengaruh terhadap output dan upah. Kedua, pendidikan dan variabel lain mempengaruhi output dan upah dengan pola yang sama yang mengindikasikan dengan kuat bahwa pengusaha membayar upah berdasarkan kinerja para pekerjanya. Ketiga, upah minimum secara statistik signifikan mempengaruhi upah. Hal ini menunjukkan bahwa upah minimum adalah pedoman bagi pengusaha dalam memberikan pembayaran minimum atas jasa pekerja, tetapi jika pekerja ingin lebih maka harus berbuat lebih baik.

Kata kunci: Produktivitas,Upah, Kebijakan Upah Minimum

policy. Considering economic situation In competitive market, wage is a

INTRODUCTION

such as price or inflation, economic result of market clearing process of the

unemployment, labour market, the intersection between

growth,

and

government formulate nominal wage labour supply and demand function.

Market competition Nominal wage is as a result of

moderately.

determines wage through interaction multiplication of product price and

between employer, in the demand side, marginal productivity of labour or in

and workers in the supply side but in the other words is the value of marginal

minimum wage law context, this may productivity of labour. Increasing

not always work.

nominal wage might reflect increasing Wage is the value of labour price or marginal productivity of labour,

productivity. Theoretically, Education which is in the demand side, can make

both formal and informal, working movement along demand function of

experience, age, and wage with its labour downward. In the contrary,

endogeneity have responsibility for labour supply function has positive

workers quality reflected in their slope which means that the higher the

productivity. Many studies prove the wage the higher the motivation of

effect of education, labour to join in labour market.

significant

experience, and job training on earnings Government, as the regulator, sets

that is commonly called returns to nominal wage that satisfies both firm

education for examples Angrist and and labour through minimum wage

Newey (1991), Vaillancort (1995),

Rifai Afin, Do Wages Reflect Productivity Under Minimum Wage Setting? 71

Kling (2001), Blackburn and Neumark minimum wage set by the local (1995), Altonji (1993), Harmon and

government compared to productivity Walker (1995), Ashenfelter and

and nominal wages?, and does the Zimmerman (1997), and Bonjour,

minimum wage setting affect wages?. Cherkas, Haskel, Hawkes, and Spector

This paper is structured as (2003). This evidence shows that

follows: the following section describes education has important role to generate

theoretical foundation on how education earnings but according to Chevalier,

affects productivity and earnings and Harmon, Walker and Zhu (2004), this is

how minimum wage policy affects not clear whether the effect of education

wages. Section three explores data and on wages is because educated workers

empirical investigation method by have high productivity or signalling of

econometric model, education level on worker’s ability.

proposing

estimation techniques, the strategy how Some other studies such as Besen

the outcome of empirical methods (1968), Griliches (1968), Khaldi (1975),

answer the questions, and data sources. Lockheed, Jamison, and Lau (1980),

Section four explores the results of Pudasaini (1983), and Weir (1999),

empirical study examined in section investigate effect of education on

three. Last section, five, brings us to the productivity.

concluding remarks. Most studies on the relationship of education, wage, and productivity have

sort of lack the interconnection of this THEORETICAL REVIEW three components. In the one side, some Human Capital Theory

Human investment firstly has not studies partially focus on the obviously considered as a kind of relationship between education and real capital accumulation process until labour productivity but in the other side Schultz (1961) and Becker (1962) concern about the effect of education on formally identified education as an earnings by arguing that earnings activity affecting future income. represent labour productivity. Jones Education is basically general concept (1994) tried to identify the effect of of which does not influence income; the education on both productivity and truth is skills that generate income. earnings in industrial sector. This paper Correa (1963) defines skills as the contributes to the further question by learned responses that contribute to the investigating

the interrelationship efficiency of behaviour. Education is between the triangle of education, only a formal process of acquiring skills productivity, and earnings as well as in educational institutions like schools how minimum wage set by the or colleges. Even though workers have government behaves in the two labour improved their productivity, this does performance indicator, productivity and not automatically raise wages unless earnings. More specifically, this paper workers could convincingly threaten to has several questions to be addressed. get a job elsewhere (Cahuc and Firstly, how is the effect of education Zylberberg, 2004). In equilibrium and other control variables on labour competitive goods and labour market, productivity and earnings? Secondly, do the labour income or wage is the same other factors, besides education, have as value of marginal productivity of the same pattern as predicted by level of labour which is called marginal education on affecting productivity and productivity theory. Regarding this wages? Third, where is the position of theory, Addison and Siebert (1979) the interrelationship efficiency of behaviour. Education is between the triangle of education, only a formal process of acquiring skills productivity, and earnings as well as in educational institutions like schools how minimum wage set by the or colleges. Even though workers have government behaves in the two labour improved their productivity, this does performance indicator, productivity and not automatically raise wages unless earnings. More specifically, this paper workers could convincingly threaten to has several questions to be addressed. get a job elsewhere (Cahuc and Firstly, how is the effect of education Zylberberg, 2004). In equilibrium and other control variables on labour competitive goods and labour market, productivity and earnings? Secondly, do the labour income or wage is the same other factors, besides education, have as value of marginal productivity of the same pattern as predicted by level of labour which is called marginal education on affecting productivity and productivity theory. Regarding this wages? Third, where is the position of theory, Addison and Siebert (1979)

human capital is that the assumption of ability of

education

or

accumulation has significant effect on employers to measure marginal cost and

increasing real productivity of labour marginal revenue is implausible and this

and wages as well. At this level, the is supported by empirical evidence by

connection of education to productivity Lester (1946) who strongly attacked the

and wages seems not straightforward marginality concept. The argument to

like general views on this particular refuse marginal productivity idea is the

topic. In the first line, a problem starts assumption of profit maximization.

from education effect on productivity. They argue that this assumption is

In this step, trouble arises when we invalid in the general areas of economy

define education such as general or especially in the public sector. This

specific training (Becker, 1975) and argument seems to be a common issue

skills measure (level of education or that shows the limitation of market

scoring test). In the second line, the clearing process. The third objection of

problem is how to represent accepting marginal productivity theory

productivity in terms of wages like is about the nature of technology. They

previously discussed that there are four argue ‘’specific marginal product value

objections to marginal are not related to specific employment

main

productivity theory so that it is levels but to ranges of employment’’. At

presumably more complicated. this rate, the marginal productivity of

We have discussed the effect of labour positively depends on the

human capital in micro economic employment ranges (discontinuity of

perspective accompanied by some labour demand function).The last but

contradictory ideas and empirical not least of the main objection of

the other side, marginal productivity theory. According

studies.

In

macroeconomic studies on human to Oi (1962), labour is not perfectly

capital theory have slowly but sure have variable but has fixed cost in part, such

been increasing. Some economists as hiring and training cost, which is

investigate human capital theory in called labour as a quasi-fixed factor,

macroeconomic perspective e.g. Nelson consequently, employer rationally

and Phelps (1966), Jorgenson and decides allocation of labour cannot only

Griliches (1967), Griliches (1963), and

be based on the relation of wages and Lucas (1988) that pioneered to marginal value of product but also fixed

investigate theoretically and empirically cost that must be paid in the future of

how the quality of labour affects that quantity of labour. These four main

economic progress through increasing objections of marginal productivity

productivity. According to Aghion and theory are very critical in this particular

Hewitt (1998), the human capital theory research.

can be divided into two different views Regardless the objections to

which are Nelson-Phelps approach and marginal productivity theory, many

Lucas approach. Delsen and Schone- studies, some of them have already been

wille (1999) argues that in the Nelson mentioned in the section I, concern

and Phelps approach, economic growth about empirical test on the factors

is more depending on the accumulation affecting marginal productivity and

of human capital endowment relative to wages in micro economic level that is

the current human capital accumulation. basically from marginal productivity

This means the higher the level of This means the higher the level of

quality of the data. Cohen and Soto use Moreover, in this approach, the only

the survey data in which the factor of production in the production

classification of education system is function is physical capital or past

uniform. At this stage, we may conclude innovation. Therefore, if the physical

that the effect of education on economic capital increases economic growth, it

growth faces more technical problems means that that human capital which is

rather that debating among existing included in the innovation has

theories.

significant effect on growth. In the other side, Lucas (1988)

Signalling Theory

proposes model focusing on mechanism The most courageous theory of the effect of human capital on

challenging human capital theory is economic growth through schooling and

signalling theory. Basically, signalling learning by doing. Workers who are just

theory assumes that there is no clear hired perform their job depending on

information on individual worker when the skills that they bring before being

employers hire them and in the short run hired but in the meantime they learning

after hiring them. This means there is and experience that make them perform

asymmetric information in the process better. According to Delsen and

of transaction in the job market. In other Schonewille (1999), Lucas identifies

words, education does not inform that there is direct effect of human

employer the true ability of workers capital on production process, and

therefore the relationship of education assuming that human capital is another

and productivity and earnings might not factor of production process besides

be causal (Cahuc and Zylberberg, labour and physical capital. Thus, the

2004). According to Spence (1973) higher the level of human capital stock

employers only identify workers based the higher the economic growth.

on their personal data, such as education Estimating the effect of human capital

level, race, sex, and age rather than effect on the production function which

measuring marginal product of workers. is based on the Lucas approach will be

Besides that, there are always some more straightforward that Nelson and

potential sources of information that are Phelps approach.

needed by employers to categorize their However, empirical tests of

wages such as previous job position, human capital theory in the

wages, criminal and or reference from macroeconomic context result in

previous employer, medical condition. different conclusion among studies.

Let us discuss what Spence (1974) Some studies such as Benhabib and

had done in explaining signalling theory Spiegel (1994), Barro (2001), and Islam

formally. Suppose there are two groups (1995) conclude that there is no

of people with different job and significant effect of human capital

different productivities and both of them indicator on economic growth. In the

can do investment in education as well contrary, Temple (1999), Ciccone and

as the costs of education between those Papaioannou (2005), Cohen and Soto

two groups are different. The years of (2006) in their studies summarize that

schooling for group 1 and 2 are E1 and human capital has significant impact on

E2 and schooling costs for both groups economic growth. Some arguments that

are CE1 and CE2 respectively and C2 < have been proposed regarding these

C1 by assumption. For separating the C1 by assumption. For separating the

will happen in the signalling theory. In (1-a1) or a2 be the proportion of people

signalling, productivity of workers does in group 2 and there are two kinds of

not depend on the education, so that we jobs in the labour market for

can write f i1 =f i2 =f i for group i = 1, 2. If simplifying. Productivity of group i in

E* is the number of schooling that job j with education E is represented by

satisfies the inequalities: the f ij (E).

f 2  f 1 f 2  f 1 Some assumptions need to be

(1) c 1 c 2 imposed to make the model works.

Firstly, employers make decision on By assumption, f2>f1 and c2>c1 and hiring workers based on their

the wages offered if f1 if E<E* and f2 if observation on obtained education of

E ≥E* the equilibrium can be written in workers without knowing their

the Table 1 as follows. productivity. Secondly, in the labour

The essential finding from this side, workers find the job that is suitable

model is that the private and social with their expected productivity

returns to education differ. As its conditional on their education level and

consequence, group two invest more in finally, in the equilibrium of the job

education whereas the optimum is just market, labour receives income in

when E=0 for both groups. Wages which their productivity is used as a

would be  1 f 1   2 f 2 for every group. It basis. In this such situation, individual

benefit group 1 but not group two therefore invest in education based on

because group 1 must invest more in what they expect from future income

education. In signalling, people pursue and allocates their educational fund as

more education not because of they will much as the amount of expected

be paid more but to distinguish among income.

others.

Table 1. Pure Signalling Equilibrium

Group

Wages Education E=0

Productivity

Education

Spending Group 1

E = E*

Cost

f1 f1 E1 f1 0 Group 2

f2 f2 E2 f2 C2E* Note: Bold indicate equilibrium productivities

Table 2. Pure Human Capital Equilibrium

Group

Wages Education E=0

Productivity

Education

Expenditure Group 1

E = E*

Cost

f1 f1 E1 f1 0 Group 2

f2 f2 E2 f2 C2E* Note: Bold indicate equilibrium productivities

In human capital model, assume on this particular theoretical and that f ij ( E )  f ( E ) for both group and

empirical study.

both kinds of jobs and consider that

f ( 0 )  f 1 and f ( E *)  f 2 as well as E

DATA AND METHODOLGY

satisfies the equation 2.1 above then the

Research Framework

equilibrium is similar to in pure The strategy which is used in this signalling model as shown in Table 2

study emerges from the basic idea that and the only difference from pure

employer should know how qualified signalling is that off diagonal terms so

their employees represented by their that it has another implication which is

productivity that their wages can be wages. In human capital equilibrium,

based on. By identifying the effect of wages is offered at different levels

education on real productivity and depending on the level of education.

wages and make comparison of those Educated worker is more productive.

effects we may come to the conclusion that, intuitively, employers, rationally,

Minimum Wage Setting

pay their workers based on their In market clearing mechanism,

productivity regardless initial working wage is determined by the value of

contract, employers can make revision marginal productivity of workers

of contract based on the evaluation of themselves. Employers offer wages

workers actual productivity. If there is a based on their observation to their

worker who is caught shirking or the worker’s performance. The government

worker performance is not like what could do intervention labour market

they expect, the employer will make process through setting the minimum

some revision on wage payment so that wage policy.

the wage must always represent the minimum wages as anchor for price

Keynesian views

actual productivity of workers. The level. Minimum wages force the wage

question is whether the effect of structure and cause movement of

education on productivity increasing income distribution among workers. In

with the level of education and does the the contrary, classical view of minimum

effect of education on productivity is wages argues that minimum wages have

followed by the proportional increase in negative effect on employment. Some

wages. The next question is where does empirical studies confirm those theories.

the minimum wages that has been set by Bryan, Salvatori, and Taylor (2012) find

the government lies on between value of that minimum wage negatively affect

productivity and received wages. the earnings for young workers even

though they still question the imprecise

Data Sources

estimation results. Maloney and Mendez This study explores cross section (2004) support empirically that

data of manufacturing survey and labour minimum wages strongly affect real

individual survey and regional level wages. Meyer and Wise (1983) find that

panel data. First part of this study is there is no earnings effect because of

estimating factors affecting output and minimum wage changes. Bazen and

wages from characteristics of the firms, Martin (1991) find that minimum wages

and in this part, cross section data from has increased real wages of youth

firm level survey are used. The data of employment but it could have negative

manufacturing can be collected from effect on youth employment as well.

two kinds survey those are economic Until now, there is no single consensus two kinds survey those are economic Until now, there is no single consensus

and the only census of manufacturing typically designed but in economic

that can be accessed is census in 2006 census we will find more information

containing 25,694 firms with medium such as level of worker’s education and

and large scale of production. firm administration status but this

Table 3. Variable Statistic Summary

Minimum Maximum Variable

Std. Dev. value Value Male workers with < 6 year schooling (MNSYS) 20.95455 31.64908

Mean

1 125 Female workers with < 6 year schooling (FNSYS)

1 112 Total workers with <6 year schooling (TNSYS)

5 159 Male workers with 6 year schooling (MSYS)

2 515 Female workers with 6 year schooling (FSYS)

5 1480 Total workers with 6 year schooling (TSYS)

17 1995 Male workers with 9 year schooling (MNYS)

2 869 Female workers with 9 year schooling (FNYS)

4 2262 Total workers with 9 year schooling (TNYS)

15 3131 Male workers with 12 year schooling (MTYS)

5 2167 Female workers with 12 year schooling (FTYS)

1 4509 Total workers with 12 year schooling (TYS)

9 6393 Male workers with university degree (MUNIV)

1 245 Female workers with university degree (FUNIV) 17.27273 27.37775

1 123 Total workers with university degree (TUNIV)

2 287 Wages

791857 9.22E+07 Wages and any additional payment

2.01E+07 2.73E+07

793748 1.28E+08 Ratio of imported raw materials to total raw materials (IMP)

2.51E+07 3.47E+07

0.534171 0.304058 0.003029 0.993053 Output

4857154 2.14E+09 Capital/assets

3.96E+08 6.83E+08

1.84E+11 5.85E+11

39929 2.73E+12

In the other side, annual needs some estimation techniques and manufacturing survey does not contain

we cover this by implementing OLS that kinds of information. Mostly, other

(Ordinary Least Squares). The estimated studies use only labour individual

models of production function (Cobb- survey that cannot describe the true

Douglas) are described below: productivity of labour; moreover it

(2) cannot be compared to wages. The

Q = f (X, Z, S)

second part of this study is estimating Q represents for output variable, X the effect of minimum wage on low job

denotes input variables, Z is firm position or under supervisor wages in

Characteristics, and S denotes schooling manufacturing sector using panel

variables. In this part, the strategy used regional level data which contain eight

is estimating separately output and years and four regions those are region

wages function between male and

1 (West Java, Jakarta, Banten), region 2 female workers and total workers per (Middle Java, and Jogjakarta), region

level of education and the role of non three (East Java and Bali), region 4

labour inputs. Separation of gender aims (Sumatra, Kalimantan, Irian Jaya or

to see clearly whether gender has papua, and Sulawesi). This panel data

important role on production while are provided by Central Bureau of

separating other factors such as capital Statistics (Badan Pusat Statistik). Some

and energy. The mathematical model 2 supporting data for the qualitative

can be derived into econometric models assessment are taken and explored from

in the logarithm as follows:

some documents of Ministry of Labour

LnQ   0   1 lCapital i   2 lMNSYS i   3 lFNSYS i

and Transmigration.

  4 lMSYS i   5 lMSYS i   6 lFSYS i   7 lMNYS i   8 lFNYS i   9 lMTYS i   10 lFTYS i   11 lMUNIV i 

Econometric Model

 12 lFUNIV i   13 lenergy   14 IMP i 

There are three types of estimated

( 3 models for this study. The first is output )

i Dcontrol  

models for measuring marginal There are 13 control variables included productivity of workers. The second is

in the models to make sure that there is wages function that has the same

no omitted variable bias. Since this predictors with output function. Both

study aims to identify how well models are the same, only have different

marginal productivity proxy wages and, dependent variables, because, by

the model for estimating output and assumption, marginal productivity

wages is the same model even though reflects wages but even though they

they could have different meaning on have the same explanatory, some of

the model. The wages model is as variables have different meaning. Jones

follows:

(1994) also runs similar model to

Lnwages   0   1 lCapital i   2 lMNSYS i 

compare output function and wages and

 3 lFNSYS i   4 lMSYS i   5 lMSYS i   6 lFSYS i 

only removes capital in the wages

 7 lMNYS i   8 lFNYS i   9 lMTYS i   10 lFTYS i 

function. The empirical models applied

 11 lMUNIV i   12 lFUNIV i   13 lenergy   14 IMP i 

in this study are taken from the

production function and wages function.

  i Dcontrol   i ( 3 . 3 ) ( 4 i ) 

Both types of model are estimated in 1 The third model is used for

several forms of nested and non nested estimating the effect of minimum wages models containing five models each on earnings in manufacturing sector. type function. Running these models Bazen and Martin (1991) applied several forms of nested and non nested estimating the effect of minimum wages models containing five models each on earnings in manufacturing sector. type function. Running these models Bazen and Martin (1991) applied

approach, graphical analysis are used to also Neumark, Sehweitzer, and Wascher

describes how wages, education, (2000) approach using two time

productivity, and minimum wages difference for estimating minimum

interact and intuitively interpreted, wage effect on wage distribution. In this

quantitative approach applies some case, difference in wages is used as

econometric models and tests the effect dependent variable and current

of education on productivity and wages. minimum wages and its lag 1, price level, and lag 1 of wages as explanatory

Qualitative Assessment

variables. If price level is significant it Labour Education Level means that the wages changes are partly

Education per labour force caused by price changes and as its

represents the availability of quality of consequence, wages changes do not

human resources in manufacturing fully increase welfare. The model is

sector. Table 4.1 draws the distribution written below:

percentage of worker’s education level

of total labour and manufacturing sector   4 PI i , t  1   wages i , t   

 lwages it   1 MW it   2 MW i , t  1   3 PI it

5 1 i  u it (5)

which shows, on average, manu-

facturing is more educated than total ∆lwages is the wages changes, MW is

national workers but the pattern is minimum wage, PI denotes price level,

quietly the same, most workers, almost αi is the unknown intercept for each

80 percent have secondary school. entity, u it is the error term. The model is

There are some meanings that can be already specified in fixed effect model.

proposed based on this fact. Firstly, manufacturing sector may tend to use

RESULTS AND ANALYSIS

superior technology relative to other

Empirical Facts of Education,

sectors,

with

higher potential

Earnings, and Minimum Wages in

productivity, profits and wages (private

Manufacturing Sector

returns).Tertiary is university degree This chapter is divided into two

workers, Ssec and diploma is senior parts, those are qualitative and

high school and diploma degree of quantitaive or formal test assessment of

workers, Jsec is Junior school degree, wages, education, and productivity, as

elem denotes elementary school, <Elem well as minimum wages policy both in

denotes having no education degree. total labour markets and manufacturing

sector generally known as returns to

Graph aph 1. Distribution of Worker’s Education Leve vel

Tertiary

Ssec and

Jsec

Elem

<Elem m

Diploma

Total Labour

Manufacturing

Source: Calculated fro from Labour Individual Survey and Economic Cens nsus of Manufacturing , 2006 06

productivity is increasi asing from 2006- consequence of the first a t argument, in

The second

me meaning,

as

2007 with the increase se 54 percent in the context of returns to ed education and

2006 and 77 percent in in 2007, and in human capital theory, m manufacturing

2006 decrease at 26 p percent , while may have the higher ave verage labour

wages increase 28 perce rcent in 2006 and income, because the higher er the level of

39 percent in 2007. A And follow the education the higher the pro productivity of

productivity changes at 2 t 27 percent. workers will generates hig higher income.

In perfect comp petitive market, We will come again to t this issue in

wages are fully determi mined by worker section econometric approac oach .

productivity but graph 2 2 tell us different thing.

That is th the change of Labour Productivity and Wa Wages

productivity is always f s followed by the Strengthening eviden dence of the

changes in wages paid b d by the firm and effect of education on labour

the increase of produ ductivity is not productivity in manufactu cturing sector

responded by the incr crease of wages can be investigated from the the firm labour

proportionately. Some r e reasons may be services expenditure in term erms of labour

suspected, regardless ss the market productivity changes. Reas easonably, the

structure, the measur sure of labour increase of productivity ty should be

productivity used in tabl able 2 ignores the followed by the increase ase of wages

contribution of other f r factors such as expenditure, in other word ords, employer

capital, raw materials als, and energy pays workers based on th their working

because the value of f productivity is performance. Graph 2 depi picts the trend

derived from value of production of percentage productivity ity and wages

divided by the number ber of production from 2006-2008. Percentag tage change of

workers.

Graph 2. Percen entages of Change in Wages Expenditure and P d Productivity

Source: Calc alculated from Manufacturing Economic Census, 20 2006 -2008 This method cannot estimat ate the role of

district and municipal, w which are based workers and other factors o s of production

on proper living need eds. Ministry of accurately and independ ndently. The

labour and transmigrat ration issues the second reason, technically, lly, each firm,

minister rule no 17/ Me Men/8/2005 about has some positions of for th their workers,

technical explanation o of proper living not only production but but also non

needs of workers that m must be included production department t such as

in the labour services es payment. The marketing, finance, human an resources,

amount of payment is c s calculated from

the annual field surv urvey which is production but it is diff ifficult to be

that contribute to the he value of

conducted by the team b built by the local included in productivity ity measuring

government involving ing employers, process. The third reason n is minimum

aca cademics, and wages policy. Pricing pol olicy such as

labour

union,

Formal ally, provincial minimum wages distor torts market

government.

government announce m minimum wages mechanism in th e labour ma markets. Three

based on the lowest wag ages in distric and parties in the labour mar arkets, firms,

municipal in that province p after labour, and government, hav have their own

accomplishing price su survey in each expectation to the price ce of labour.

local area of districts. Government intervention in in the labour

Local government nt set the nominal markets by setting minim nimum wages

wages each year and i it is common ly affects labour markets thro rough demand

increasing reflecting the price of and supply side. In the n next section,

commodity movement. nt. Study of the estimation on productivity a y and wages is

effect of minimum wage ages in Indonesia, done by applying econome metric models.

Smeru (2001), shows t s that this affects Even though there is dif differences in

labour markets especi ecially from the changes but the pattern is th the same

demand side. This study dy concludes that the increase of 10 perce rcent of minimum

Minimum Wages Policy wages decreases the e labour force Indonesia governm ment set the

absorption in labour r markets by 1 minimum wages in the e local level,

percent. Since decentr ntralisation fiscal percent. Since decentr ntralisation fiscal

wages increase gradual ually from 2006- higher. Something that t cannot be

neglected is political reason son behind this

Graph 3. Minim imum Montly Wages Average of Provincial Le Level

Source: Calculated d from Data of Ministry of Labour and Transmig igration and BPS, 2009

Graph 4. M Montly Minimum and Manufacturing Wages

2008 Manufacturin turing

Minimum Wages Average

Total Labour our

Source: Calculated d from Manufacturing Economic Census and Mi Ministry of Labour and Transmigration n

Table 4 depicts the trend of translog but ignoring the education level minimum wages and manufacturing

and gender by aggregating skilled and sector. Clearly pictured that wages in

unskilled labour. The method proposed manufacturing sector is higher, on

by Moretti, translog estimation, may average, than the wages which are set

work properly in the small scale model by government and total labour. In the

which means the model contains previous part, told us that labour

relatively small number of dependent education level in manufacturing sector

variables. Translog approach derives is higher than total labour, which

main variables into additional standard probably factor that generates higher

variables in the translog model such as labour income in manufacturing or

squared and multiplication between temporarily we may conclude that

main variables. In this paper, I use productivity of labour in manufacturing

estimation instead of sector is higher than total labour.

direct

transformation logarithm, translog. Ordinary Least Squares (OLS) is applied to all models to get unbiased

Econometric Results

and consistent parameters. Estimation Results As clearly shown in the table A1, This is the second part of generally, all models indicate the same empirical facts that contains the results; all main parameters which are estimation results of econometric related to the effect of education on models followed by some tests of the labour productivity are statistically models. Those tests are hetero- significant except for female workers skedasticity, multicollinearity, model with low level of education, not specification, and normality which are completed and only six years schooling the critical tests for cross section data. we will come again to this result in next The first part is estimation results of section. In addition, some formal tests production function which is shown in to check the models performance such the table A1 and A2 in appendix. as Breusch-Pagan /Cook-Weisberg test In the output function, there are for heteroskedasticity, ovtest for four models to be alternatives which Regression Specification Error Test have the same dependent variable but (RESET), Variance Inflation Factor different predictors. The predictors are (VIF) for collinearity diagnostic, and estimated separately among the models Saphiro and Wilk test for normality of based on the gender comparison per error disturbance have been done and level of education and factors of presented in Table . 4. According to the productions besides labour such as Table 4 there is no serious problem on capital and energy used. Moretti (2004) the estimated models in terms of the estimates the effect of education on

assumption tests. productivity at firm level using both

regression

direct estimation on Cobb-Douglas and

Table . 4. Summary of Classical Assumption Tests

Tests

Multicolinearity Output

(Min and Max Models (Probability Chi Square)

Homoskedasticity

Model Specification Normality

(Probability Z) VIF) 1 0.057

(Probability F)

1.3 and 2.2 Wages

Note: the level of significant of tests is 5 percent and VIF is under 10

In the other side, wages functions Firstly, in all models labour in all levels (see table A3 and A4) perform as we

of education have significant effect but expected as well. The estimation

for junior and senior high school strategy applied in the wages function is

workers, they tend to have diminishing providing four models with two kinds of

marginal productivity. It means dependent variables those are wages and

additional labour with 9 and 12 years wages plus and two different groups

education contributes to the decrease of gender separation like what output

output changes. Lewis (1954), in the function does in output model

theory of dualism, supported this estimation. Separating wages and wages

finding by arguing that in countries plus function is aimed to know whether

where unlimited supplies of labour and additional bonus and payment besides

large population relative to capital exist, wage is also attached to the

marginal productivity of labour could be performance in production process. In

negligible, zero, or even negative. Even order to obtain the coefficient of

though this argument still has some parameters, I apply the same technique

further question, how we measure the as estimating output function, OLS. All

relativity of one production factor to the wages functions show that there is

others is still need to be redefined. In significant and positive effect of labour

other words, diminishing marginal per level of education. The assumption

labour productivity in manufacturing tests for OLS are also run and the tests

sector especially for labour with junior result in no deviation from hypothesis

and senior high school certificate could which means estimated parameters are

exist because labour with that level of unbiased and consistent which are

education is relatively more abundant presented in table 4.

than other production factors that they can work with, such as capital and

Results Interpretation energy. In Indonesian manufacturing The estimation results for

sector, the composition of drop out (<6 production function have some findings.

year schooling) workers only 2.2 year schooling) workers only 2.2

The third international aspect is foreign with 9 years schooling, 49 percent

investment status that does not workers with 12 years schooling, and

surprisingly

have positive and

2.5 percent with college and university significant effect on output. In the other degree. This composition, at least,

side, domestic status of firm has also emphasize our results that is why

diminishing in our sample. Foreign workers with 9 years and 12 years

companies, which commonly use more schooling might have diminishing

advanced technology, still have more marginal productivity. Jones (1994)

capability of doing more. found the same results when estimated

Other variables that are important both production and wages function

role for manufacturing output are which resulted in inconsistency when

location and standardised product. the results show that workers with lower

Location, as we expected, positively level of education are not always have

significant which means industrial state higher productivity. Moreover, Jones

increases manufacturing output. Some argues that in that case, education has

studies that support this results are Lall, indirect effect on labour productivity.

Shalizi, and Deichmann (2001), and Fan According to two first findings, it is

and Scott (2003) that have found difficult to say that production function

empirically that agglomeration has clearly point out how human capital

positive effect on industry performance theory works. Pudasaini (1983)

and also Fu and Ross (2010) who prove confirmed that, in his results, there was

that industrial agglomeration has the decline contribution to output by the

positive effect on wages. higher educated workers even though on

Administrative factors which are average the role of education on output

legal forms and standardisation of is positively significant.

product have also important role on Other variables characterizing the

output and wages. Legal status of the manufacturing sector output are

firm such as Government Company, international aspects those are export,

cooperatives, limited import, and foreign investment.

corporation,

partnership, etc has positive effect on Imported raw materials have positive

both output and wages and it shows that effect on output which means that the

the parameter of corporation status is higher imported raw materials the

the highest among other legal status. higher the output. Even though not

This is not surprising finding. In the many respondents or about 20 percent

other side, standardisation significantly of respondents import their raw

affects output. Intuitively, this is easy to materials, it does matter. In other words,

understand that highly standardized imported raw materials have relatively

products are usually produced by high high productivity in production process.

performing firms so that product In the other side, export has also

standardization is a good way to boost positively significant impact on output

manufacturing productivity. supporting study done by Sjoholm

In the wages function side, the (1997) that had proved the positive

results show us what we expected effect of import and export from

before. Firstly, the effect of education Indonesian manufacturing data even

and training on wages tells us that the though there is no clear consensus

pattern of parameters per level of among theoretical and empirical studies

education are the same as the pattern of education are the same as the pattern of

when

marginal

productivity of labour at a level of education decreases the wages decrease as well. Regardless job contract that is signed before knowing true productivity of workers, the expected of employers to workers performance is correct. In the other side, trained workers are paid more than that non-trained. Most firms in Indonesia apply some condition to their workers for some months before they get an extended contract and get more payment. One of the conditions is training both on the job and off the job training. For workers who have passed the training they get some beneficial including higher salary, bonus, and insurance. Employers, basically, will not know the true marginal productivity of workers but the signal in the labour markets which is easily recognised that most workers, as previously mentioned, who search jobs in the labour markets have junior and senior high school certificate so that employers decided to

accept such typical workers that make, in the production function, capital and other production factors optimally used for that education level are decreasing. From the graph 4.5, which is based on model 3 estimation results, it is clearly illustrated that the changes of the number of low education level than the changes in their productivity compared to high skilled labour with university degree and the effect of number of workers with junior and senior high school certificate on output is relatively lower than others and it is followed by the effect on wages. It exists because of the diminishing marginal productivity. This argument is supported by the fact those level of education are the biggest composition in manufacturing sector, but it does not mean that the nominal value of received wages for 9 and 12 year school is lower than 6 and <6 year schooling because it represents percentage of changes (variables estimated in logarithm). The pattern of those parameters from two functions is depicted in the Graph . 5 below:

Graph 5. The Pattern of Effect of Education on Marginal Productivity and

9 years 12 years university degree

Effect on Output

Effect on Wages

Secondly, capital and energy have status. Table A.4 summarised that those positive effect on wages which means

three variables are positive different capital and energy are complementary

from zero. Firms located on industrial factors of workers. Capital and energy

state pay higher than that of outside are complementary factors in our

industrial state. Indonesia government sample, machinery use energy in the

built some industrial states in some production process and if the capital and

cities such as Surabaya, Pasuruan, energy combination substitutes workers

Tangerang, and Bekasi which are in so the more intensive the combination

most crowded island, Java, and used the less workers will be hired and

spreading them out to other island such the expenditure for wages decreases as

as Batam in Sumatra Island, and Timika consequences of using machinery and

in Irian Jaya and some other are still in energy intensively. Generally, we may

progress. Most of firms located in conclude that workers are needed to

industrial states are big companies and operate machinery and for some cases

well publicly known as companies that some job positions related to machine

have high standard products. To sum operation cannot be replaced by other

up, graph A1 depicts the impact of machines such as installation and

control variables on output and wages maintenance.

based on model 3 estimation. The graph International

shows us that most of the effects of important role in determining industrial

factors

play

those variables on wages follow the wages. Import, exports, and foreign

trend of their marginal productivity. investment have positively significant effect on wages. In empirical literatures,

Effect of Minimum Wage Setting on there is no consensus on what effect of Manufacturing Wages import and export on wages. One When the effect of education and supporting finding is from Martins and other control variables on output and Opromolla (2009) using firm level data wages has the same pattern, so where proved that export and import have the minimum wages does lie?. Section positive impact on workers salary. In

4.1.3 provides qualitative explanation of the contrary, Alvarez and Opazo (2008) minimum wage and manufacturing used firm level data and found that labour earnings, this part apply imported goods have negative effect on econometric model instead. Table 4.3 domestic firm wages. The other shows us the effect of minimum wage international aspect is status of on manufacturing wage changes. By investment, which is foreign ownership, focusing on fixed and random effect does positively affect on wages. It is models, the results show that minimum generally

wages and lag of wages are different management system is better than that from zero at 10 percent level. of domestic so that it is not surprising Considered by the sensitive results of results that foreign pays more than fixed and random effect model, domestic firms. This result is shown in specification test, Hausman test, need to table A3 conclude that foreign

be applied. According to Hsiao (2003), ownership firms pays more than the issue of specification test is not domestic firms. whether or not individual effect fixed or Other variables that are expected random, but more important thing is to have positive effect on wages are individual effect can be considered as a location, standardisation, and Legal random draws from a common be applied. According to Hsiao (2003), ownership firms pays more than the issue of specification test is not domestic firms. whether or not individual effect fixed or Other variables that are expected random, but more important thing is to have positive effect on wages are individual effect can be considered as a location, standardisation, and Legal random draws from a common

distribution of individual effect and 2 

LM  T   r ij

i  2 j  attributes can be viewed as identical 1 across panels and suggested Hausman

Where r 2 ij is the i jth residual correlation test as an alternative method to identify

coefficient. The Breusch and Pagan satisfying model. Hausman test result (1980) test statistic is distributed 2

suggests that fixed model is more ,

appropriate in this case at 10 percent where d = Nc(Nc − 1)/2, under the null level. To confirm the result of Hausman

cross-sectional test, we test whether the variance among

hypothesis

of

The other test, panels is zero, in order to perform the

independence.

heteroskedasticity, is run by command test, Breusch and Pagan Lagrangian

xttest3 following the null hypothesis multiplier test has been applied. 2 specifying that 

i for i=1..Nc where Nc Due to fixed effect model is more

is the number of cross sectional data. appropriate for this case, further tests

Let   T i  e it be the estimator of

for fixed model result must be applied.

t  Baum (2001) argued that fixed effect 1 the ith cross-sectional unit’s error

model estimated in stata command variance, based upon the T residuals e requires OLS point estimator and its

i it

available for that unit. Then define: interval perform under classical

assumption which probably the error 2

Ti

i  T i  T  1   ( e it   i )

disturbance is independently and

identically distributed and in the case

panel data these assumptions could be as the estimated variance of  . The rejected in some ways. Two tests for

modified Wald test statistic, defined as fixed effect, Breusch and Pagan for

^ Nc 2

( 2 ) independence and modified Wald test

for groupwise heteroskedasticity, are i  1 V i suggested to check the assumptions.

 [Nc] under the 2 Breusch-Pagan

will be distributed

null hypothesis. Calculation shows us contemporaneous

test

for

the results of Breusch-Pagan tests that explored by Zellner’s seemingly

correlation

are

result in high probability chi square, unrelated regression (SUR) estimator.

0.43 and 0.63 for Breusch-Pagan Command ‘’xttest2’’ in Stata tests the

Lagrange Multiplier and modified Wald hypothesis that the residual correlation

test meaning that the two tests strongly matrix, calculated over data common to

that there no all cross-sectional units, is an identity

concluded

heteroskedasticity and no serial matrix of order Nc, where Nc is the