individual is female; _household_head_ is a dummy, which equals 1 if the individual is a household head; _married_ is a dummy, which equals 1 if the individual is married; _child_under_5_years_old_ is a dummy, which equals 1 if the individual has at least one child under five years old; _female×married_ is an interaction term of the dummies of female and married (=1 if the individual is female and married); _female×children_count_ is an interaction term of a gender dummy and the number of children in the household; _val_ is the valued added per employee in the manufacturing sector in logarithmic form; _%industry_GDP_ is the share of the industrial sector to regional GDP; _%service_GDP_ is the share of the services sector to regional GDP; and _%SME_ is the share of small and medium size enterprises to the total establishments in the region. The island group dummies and time dummies are introduced to capture region-specific and timespecific effects (NCR and year 2003 as reference); and . To capture the non-linear effect of age on employment and daily wage, we include “age square” in the equation.

# _4.2 Empirical results_

The estimated results are shown in Table 10. Regression 1 shows the role of individual characteristics on employment and wage. The coefficients of the independent variables all carry the expected signs. Age plays a positive role in employment and wage determination in a significant way. Its positive effects decline as age increases. In general, individuals with better education have a higher chance to get employed with a higher wage.<sup>11</sup> Like in many other countries, a Filipino female worker has a lower chance of getting employed and is offered lower daily wage compared with a male worker, all other things being equal.<sup>12</sup> This finding is consistent with the statistics we have in the previous section that female workers tend to have better education compared with male workers with the same level of salary. Partly because of their stronger incentive to seek employment, those who are household head or married, controlling for other characteristics, are more likely to be employed. Marital and family status is more likely to influence women’s participation in labor market. Married women, women from households with a larger number of children or have child(ren) under five years old are less likely to be employed, mainly because they tend to choose staying home.

Regression 2 includes regional structural indicators. The results are similar to those of regression 1 except for the effects of region dummies on employment. Individuals in regions with a higher share of industrial and services sectors (hence, lower share of agricultural sector) face better employment opportunities. Regions with a higher share of industrial output tend to have higher prevalent wage as the industrial sector is in general more productive compared with the agricultural sector (the reference sector); while the same is not true for regions with a higher share of services output, probably due to the mixed wage structure of in the services workers – individuals with high skills (for example, financial specialists) may earn a high level of salary while those with low skills (like cleaning staff) may earn a low level of salary. Value added per employee in the manufacturing sector is positively associated with the daily wage level, while it

> 11 The effects of years of schooling are not quadratic when the square term was introduced into the estimations. To capture the impacts of different levels of education and test the robustness of the estimation, we will estimate a second set of Heckman regressions with three dummy variables (with “less than elementary school education” as reference) – “with elementary school certificate”, “with high school certificate”, and “with university certificate” instead of “years of schooling”. The results, available upon request, are broadly consistent with those using years of schooling as measures of education.

> 12 See Schady (2000) and Yamaguchi (2005).

12