Instrumental Variable Method

In order to ensure whether the minimum wage measures used in the previous estimates are endogenously determined with average wages, the Davidson- MacKinnon test o f endogeneity for panel data is reported in this section. If the null hypothesis is rejected, it would imply that the minimum wage measure is endogenously determined with wage, and therefore an instrumental variable is required, suggesting that the OLS (PCSE) estimates which assume the exogenous minimum wage are biased. In contrast, if the null hypothesis is accepted, it means that

the minimum wage measure is exogenously determined with wage and hence the previous section estimates are unbiased and consistent.

Table 5.5 Davidson-MacKinnon Tests of Endogeneity for a Panel Data regression

M in im u m W a g e M e a s u r e C h i-S q u a r e d

Log of Real Minimum Wage 1 3 .1 8 2 (0 .0 0 )

Fraction Affected 0 .7 6 1 (0 .3 8 )

Fraction At 4 .8 0 0 (0 .0 3 )

Fraction Below 4 .1 7 6 (0 .0 4 ) N otes: H 0: OLS estim ator yield a consistent estim ate

The results from Davidson-MacKinnon tests for endogeneity are reported in table 5.5 . The result confirms that the log o f real minimum wage is suffered from 22

endogeneity as the null hypothesis that OLS yield a consistent estimate is rejected at 1% level. In practice, this is consistent with the results obtained by Suryahadi et al (2001) in the previous Indonesian minimum wage study. Suryahadi et al (2001) argued that the existing average wage is considered as one o f the important factors for regional government to set their minimum wage level, suggesting that the minimum wage and the average wage are simultaneously determined. The other potential reason is because both average wage and minimum wage are employed in real terms with the same denominator (consumer price index). This implies that they potentially move together in a similar way across the business cycles, suggesting a strong simultaneous correlation between average wage and minimum wage.

On the other hand, the fraction at and the fraction below measures also tend to be endogenous. This result is similar to results obtained by Lemos (2004d), which found that the spike (the fraction at) is endogenously determined with wages in the case o f

22 T h e D a v id s o n -M a c K in n o n te s t is e s tim a te d u sin g D M E X O G X T S tata c o m m a n d w ith a lag g e d v a lu e o f th e m in im u m w a n e m e a su re as th e in stru m e n ta l v a ria b le .

Brazil. As pointed out by Lemos (2004d) the fraction at is endogenously determined with wage and employment because o f the wage bargaining process (see above).

Relating to the fraction below measure, it seems that an exogenous labour demand shift has potentially induced the extent o f non-compliance, suggesting a potential endogenous correlation between the fraction below and the labour market condition.

This extent o f non-compliance can also be viewed as a displacement effect from the covered sector to the uncovered sector as a result o f exogenous labour demand shift.

In contrast, there is no evidence that the fraction affected is endogenously determined with average wage, suggesting that the fraction affected is exogenous with respect to the average wage. In practice, the fraction affected primarily depends on the size of the minimum wage increases and are less likely to depend on the average wage. This result is consistent with Brown’s (1999) argument that the fraction affected is relatively a cleaner measure o f minimum wage than toughness as it directly measures workers affected by the minimum wage. In addition, the size o f the minimum wage increases in Indonesia primarily depend on the minimum basic living needs index changes, suggesting that the fraction affected measure is less affected by labour market conditions.

In order to deal with the endogeneity problem o f the minimum wage measures, the wage equation is re-estimated using the instrumental variable method with the fixed effects specifications. The instrumental variable method requires that the instrument is strongly correlated with the minimum wage measure (the endogenous independent variable) but it is not directly correlated with the error terms in the wage equation (in the second-stage estimation). Lemos (2005) used political variables as the

instrumental variables for the minimum wage measures in the case o f Brazil.

However, political variables at the regional level might not be a valid instrument for Indonesia as the political regime tended to be strongly centralized particularly before

1999 during Soeharto’s regime. Note: D ependent variable is the m inim um wage measure. All regressions include province dummies.

In contrast, Neumark and Wascher (1992) employed the one year lag o f minimum wage level in the bordering regions as the instrument variable for minimum wage measure. Contrary to Neumark and Wascher (1992), my study found that the one year lagged value o f the average minimum wage level in the bordering provinces is not a good instrument for Indonesian case. Table 5.6 presents the first-stage estimation o f the instrumental variable method using the one year lagged value o f the average minimum wage level in the bordering provinces as an instrument for the minimum wage measure . As presented in table 5.6, the instrument variable is not significantly 23

different from zero in all specifications, suggesting that the instrument is relatively weak because it is not strongly correlated with the minimum wage measure. The potential problem with using this instrument is that consumption packages used to measure the minimum wage level in Indonesia can be traded easily across provinces suggesting that bordering provinces might have similar business cycles (Rama, 2001).

As an alternative, following Suryahadi et al (2001) for Indonesia and Gindling and Terell (2006) for Honduras, this study instruments the minimum wage measure using one period lagged value o f its minimum wage measure in each specification. Using the lagged value o f the minimum wage measure as an instrument is theoretically appropriate as it is strongly correlated with the minimum wage measure and is not determined by the labour market in the current period. Lemos (2005) argued that the lagged value o f the minimum wage is not a valid instrument for Brazil as there is evidence o f serial correlation. However, there is no evidence o f serial correlation for the Indonesian case suggesting that the lagged value o f the minimum wage is a suitable instrument.

2' T h e fra c tio n a ffe c te d e s tim a te is e x c lu d e d fro m th e in s tru m e n ta l v a ria b le m e th o d as th e re is no e v id e n c e o f e n d o g e n e ity w ith th e a v e ra g e w ag e.

Table 5.7 Wage Equation using IV Method (First-stage estimation)

Note: D ependent variable is the m inim um wage measure. All regressions include province dummies.

Table 5.7 presents the first-stage o f the instrumental variable method results using one year lag o f its minimum wage measure as an instrument variable. As presented in table 5.7, the instrument is significant and positive in all specifications, indicating that the first-stage estimation is plausible. In addition, the F test o f joint significance of the instrument in the first-stage estimation is used to measure the quality o f instruments.

As pointed out by Staiger and Stock (1997), a large F statistic (above the rule of thumb o f ten) implies that the instrument is strong in explaining the endogenous

variable variation. The F test of the first-stage estimation for the log o f real minimum wage and the fraction below measures are relatively high indicating a strong instrumental variable. In contrast, the F test for the fraction at measure is low (below the often-used threshold o f ten), suggesting that a one year lag o f the fraction at is a weak instrument for the fraction at24. This result supports the previous section finding that the fraction at is not an effective minimum wage measure for Indonesian case.

Table 5.8 presents the second stage o f the instrumental variable method results for wage equation using different minimum wage measures. As presented in the first column, the log o f real minimum wage shows a consistently positive effect on average wage. It is suggested that an increase in the real minimum wage by 10% raises the real average wage by 2.61%. Compared to the PCSE estimate, the estimated coefficient is slightly higher suggesting a downward bias in the OLS estimator. In contrast, Williams and Mills (2001) and Gindling and Terrell (2007) argued that the estimated coefficient should be upwardly biased as governments are likely to increase the minimum wage level when the economy is in a good condition. However, this is not the case o f Indonesia as the minimum wage tends to be adjusted (increased) every year depending on the minimum basic living need index (KHM) regardless o f the regional economic performance.

2 4 1 h a v e a ls o trie d to u se a o n e y e a r lag o f the lo g o f real m in im u m w a g e as th e in s tru m e n t v a ria b le fo r th e fra c tio n at, b u t th e re su lt re m a in s th e sa m e, s u g g e s tin g a w e a k in s tru m e n t fo r th e fra c tio n at.

Table 5.8 Wage Equation using IV Method (Second-stage estimation)

On the other hand, the fraction at and the fraction below measures are not significantly different from zero using the instrumental variable method. These results, once again, suggest that these minimum wage measures are not effective in explaining the effect o f minimum wage on average wage. As a result, we conclude that they are not valid measures o f the minimum wage level for Indonesia in terms of wage equation. In other words, this conclusion suggests that the log o f real minimum wage and the

fraction affected is superior to the fraction at and the fraction below in measuring the minimum wage effect on the average wage.