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Table 5.20 Employment Equation using Structural Estimate (Second-stage estimation)

Log of Real Minimum Wage

(1)

Fraction Affected (2)

Coef P value Coef P value

Ln Real W ages -1.0954 0.000 -1.3028 0.000 Urban -0.6177 0.023 -1.3121 0.000 Youth 0.1079 0.890 -0.2380 0.745 Women -0.5762 0.392 -0.5945 0.444 Industry -0.1779 0.807 -0.3949 0.581 Trade -1.0036 0.140 -0.8105 0.254 Services 2.8741 0.000 1.5240 0.030 Construction 4.3107 0.004 2.0987 0.153 High school 0.1123 0.816 1.1585 0.014 University 3.3257 0.027 9.5215 0.000 Year 1991 0.0776 0.111 -0.4211 0.000 Year 1992 0.1676 0.003 0.1167 0.053 Year 1993 0.3346 0.000 0.1149 0.059 Year 1994 0.3607 0.001 0.2001 0.003 Year 1995 0.4621 0.000 0.0568 0.364 Year 1996 0.6174 0.000 0.2061 0.007 Year 1997 0.7530 0.000 0.0379 0.621 Year 1998 0.5110 0.000 0.1621 0.009 Year 1999 0.5866 0.000 -0.3131 0.001 Year 2000 0.7972 0.000 0.0877 0.142 Year 2001 0.8765 0.000 0.2224 0.001 Year 2002 0.8322 0.000 0.1392 0.067 Year 2003 0.8170 0.000 -0.0293 0.613 Constant 25.1213 0.000 -0.0168 0.699 Observations 364 338 Number of Areas 26 26 Within group R-sq 0.489 0.336

Note: All regressions include province dummies.

Com pared to the m ethods in the previous section, the structural em ploym ent demand

equation provides evidence that the m inim um wage affects em ploym ent indirectly

through the w age rate, w hich is consistent w ith the standard theory. As m entioned

above, standard theory suggests that m inim um wage affects em ploym ent if there is a

significant effect on the wage paid to the worker. Therefore, we argue that this

specification is m ore com plete and relevant than the m odel in the previous section

using separate reduced form equations in explaining the effect o f m inim um wage on

em ploym ent. In addition, this m ethod does not suffer from the endogeneity problem

and contem poraneous correlation across equations in separate reduced form equation.

A s a result, we argue that the structural em ploym ent dem and equation is the m ore

preferred specification, showing a sim ple interaction betw een w age and em ploym ent

equations.

5.6. Conclusions

This chapter analyses the effects o f changes in the m inim um wage on wages and

em ploym ent in Indonesia using a provincial panel data set from 1989 to 2003 across 26

provinces. Com pared to the other developing country studies, the m inim um wage

policy in Indonesia provides a unique characteristic as the m inim um wage is set

differently across regions (provinces). Follow ing Lemos (2004d and 2005), this study

has applied five different m easures o f m inim um wage using four standard panel data

estim ates, including the sim ple fixed effect, Panel Corrected Standard Error (PCSE),

instrum ental variable and Dynam ic Arellano Bond estimates.

A lthough the findings vary across different m easures and m ethods, generally they

support the standard com petitive m odel, suggesting that an increase in the m inim um

wage increases average wages and causes a reduction in covered sector em ployment.

U nlike previous studies, the result shows that there is no significant impact o f the

m inim um wage on total paid em ploym ent because o f the non-com pliance problem in

Indonesia. This result implies that total paid em ploym ent is not a valid m easure for the

em ploym ent effect o f m inim um wage in Indonesia. Standard com petitive m arket model

alw ays assum ed that the m inim um wage com pliance is high and this is not the case

w ith Indonesia w here there is a high proportion o f uncovered sector em ployment. This

em ploym ent effect o f m inim um wage in Indonesia. In practice, the coefficients change

dram atically when paid em ploym ent in the covered sector is used as the dependent

variable.

Table 5.21 the Effects of Minimum Wage across Measures and Estimates

Log of Real Minimum Wage (1) T ou gh n ess (2) Fraction Affected (3) Fraction Below (4) Fraction At (5) P an el A: W age Equation

Sim ple Fixed 0.1656 - 0.2935 -0.1599 -0.9075

Effects (0.00) (0.021) (0.087) (.019) PCSE 0.1669 - 0.2881 -0.1766 -0.8040 (0.00) (0.009) (0.098) (0.042) Instrumental 0.2613 - - 0.1648 -4.839 Variable (0.002) (0.522) (0.209) Dynamic A-B 0.1221 - 0.099 - - (0.008) (0.071)

P an el B: E m p loym ent Equation (C overed Sector)

Simple Fixed -0.4597 -0.8349 -1.0463 -1.3057 -1.0175 Effects (0.000) (0.000) (0.000) (0.000) (0.081) PCSE -0.4175 -0.6851 -0.9590 -1.2063 - (0.000) (0.000) (0.000) (0.000) Instrumental - -1.5660 -6.2963 -1.7307 -11.8607 Variable (0.000) (0.251) (0.000) (0.091) Dynamic A-B -0.3010 -0.6052 - -1.0445 - (0.016) (0.000) (0.000)

Table 5.21 presents a com parison table sum m arizing the basic results o f wage and

em ploym ent equations across m easures and estim ates which were analyzed above. In

practice, the reduced form s o f wage and em ploym ent are estim ated separately

(independently) in explaining the effects o f m inim um wage on wage and em ployment.

Com paring m inim um wage m easures, the findings suggest that the log o f real

m inim um wage is superior to the other m easures. The log o f real m inim um wage

consistently shows a significant impact across different estim ates, providing well-

suited m easurem ent particularly w hen the m inim um wage varies across regions.

A lthough the log o f real m inim um wage is endogenous in the wage equation, it is

exogenously determ ined with em ploym ent, providing a “cleaner” m easure o f the

m inim um wage effect on em ployment. Toughness suffers from the endogeneity bias

m easures are endogenously determ ined w ith em ploym ent because they m easure in a

sim ilar w ay with em ploym ent in the left-hand-side equation, providing a strongly

sim ultaneous correlation with em ployment. U sing the log o f real m inim um wage, it is

suggested that an increase o f the m inim um wage by 10% increases the average wage o f

paid em ploym ent in the covered sector by 1.22% -2.61% and decreases covered sector

em ploym ent by 3.01% -4.17% , depending on the m ethod used.

In com parison across the basic m odel, the D ynam ic A rellano-B ond m ethod tends to

provide a m ore efficient result using wage and em ploym ent equation as it m easures the

adjustm ent o f em ploym ent to changes in labour dem and and supply. In contrast, the

PC SE and sim ple fixed effect estimates tend to suffer from the endogeneity problem as

they assum e exogeneity across variables.

In order to check the robustness o f the result obtained by changing specification, the

effect o f the m inim um wage on wages and em ploym ent is also estim ated using

different specifications, including the Seem ingly U nrelated Regression (SUR) and

structural em ploym ent dem and equation. SUR is estim ated due to the fact that the error

term s across w age and em ploym ent equations are likely to be correlated. However, the

SU R estim ate does not control for the endogeneity problem in the m inim um wage

m easures, suggesting that the results are potentially m eaningless.

On the other hand, the structural em ploym ent dem and equation estimate shows in a

different way that the m inim um wage affects em ploym ent indirectly through the wage

rate, show ing a sim ple correlation betw een wage and em ploym ent equations as

endogeneity problem and contem poraneous correlation across equations, providing a

m ore efficient estim ate than separate reduced form equations. As a result, we conclude

that the structural em ploym ent dem and equation is the m ore preferred specification

com pared to the previous estim ates using separate reduced form wage and em ploym ent

equations. In addition, although the specification differs from the previous estimate,

the finding supports the previous evidence using separate reduced form equations that

the m inim um wage is positively associated w ith average wages and negatively

associated w ith em ploym ent. The coefficient o f m inim um wage is also similar,

suggesting that the findings are strong across different specifications.

Thus far, this chapter showed that an increase in m inim um wage reduces paid

em ploym ent in the covered sector (as part o f the em ploym ent in the formal sector).

This chapter therefore provided a basic analysis o f the im pact o f m inim um wage on

w ages and em ploym ent in Indonesia. A further area o f research revealed by these

findings is how the m inim um wage policy affects em ploym ent in the uncovered sector.

The effect o f m inim um wage on em ploym ent in the uncovered sector shows a

displacem ent effect that m ight exist as the result o f decreasing paid em ploym ent (the

covered sector). The full effect o f m inim um wage on em ploym ent in the covered and

uncovered sectors w ill be discussed in the next chapter.

CHAPTER VI

THE EFFECTS OF CHANGES IN MINIMUM WAGE

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