metadata: authors: 0: author: Luo,Xubei
1: author: Terada,Takanobu
count: Philippines
docna: 0: docna: Education and wage differentials in the Philippines
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Tertiary Education,Educational Sciences,Gender and Development,Business Cycles and Stabilization Policies
teratopic: Macroeconomics and Economic Growth,Social Protections and Labor,Education
abstracts: cdata!: In the Philippines, an important part of
            income inequality is associated with the wage difference
            between the less educated and the better educated. The
            majority of the least educated are employed in low-paid
            services jobs and the agricultural sector. Tertiary
            education is to a large extent a prerequisite for high-paid
            occupations. Using the Labor Force Survey 2003-2007, this
            paper examines disparities in human capital endowment,
            returns to education, and the role of education in wage
            differentials in the Philippines. The empirical results show
            that returns to education monotonically increase - workers
            with elementary education, secondary education, and tertiary
            education earn 10 percent, 40 percent, and 100 percent more
            than those with no education. The results also show that
            education is the single most important factor that
            contributes to wage differentials. At the national level,
            education accounts for about 30 percent of the difference in
            wages. It accounts for a higher percentage of the difference
            for female workers (37 percent) than male workers (24
            percent). There are also differences across regions and
            sectors. As an economy develops, the demand for skills
            increases. In the Philippines, efforts to improve education
            to increase the supply of highly educated people are
            important not only for long-term growth, but also for
            helping to translate growth into more equal opportunities
            for the children of the current generation.
geo_reg_key: 517198,517191
repnme: repnme: Education and wage differentials in the Philippines
display_title: Education and wage differentials in
            the Philippines
listing_relative_url: /research/2009/11/11332543/education-wage-differentials-philippines
topicv3: Tertiary Education m1326349 482,Manufacturing Sectors m1326521 1281,Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Protections and Labor,Education
no_of_pages: 27
author_keys: 0: author_key: 677436
1: author_key: 988642
model_extractions: WPS5120 Policy Research Working Paper 5120 Education and Wage Differentials in the Philippines _Xubei Luo Takanobu Terada_ The World Bank East Asia and Pacific Region Poverty Reduction and Economic Management Department November 2009
Policy Research Working Paper 5120

# **Abstract**

In the Philippines, an important part of income inequality is associated with the wage difference between the less educated and the better educated. The majority of the least educated are employed in low-paid services jobs and the agricultural sector. Tertiary education is to a large extent a prerequisite for high-paid occupations.

Using the Labor Force Survey 2003–2007, this paper examines disparities in human capital endowment, returns to education, and the role of education in wage differentials in the Philippines. The empirical results show that returns to education monotonically increase— workers with elementary education, secondary education, and tertiary education earn 10 percent, 40 percent, and 100 percent more than those with no education.

The results also show that education is the single most important factor that contributes to wage differentials. At the national level, education accounts for about 30 percent of the difference in wages. It accounts for a higher percentage of the difference for female workers (37 percent) than male workers (24 percent). There are also differences across regions and sectors.

As an economy develops, the demand for skills increases. In the Philippines, efforts to improve education to increase the supply of highly educated people are important not only for long-term growth, but also for helping to translate growth into more equal opportunities for the children of the current generation.

This paper—a product of the Poverty Reduction and Economic Management Department, East Asia and Pacific Region—is prepared as a background paper for the Philippines Inclusive Growth report. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at xluo@worldbank.org and tnterada@umd.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Education and Wage Differentials in the Philippines**<sup>*****</sup>

Xubei Luo (The World Bank)<sup>**</sup> Takanobu Terada (University of Maryland)<sup>***</sup> Key Words: Wage differentials, returns to education, inequality

> * The authors would like to thank Jehan Arulpragasam, Jean-Jacques Dethier, Swati Ghosh, Ulrich Lächler, Silvia Redaelli, Erwin Tiongson, and Nong Zhu for useful comments.

> ** Email: <u>xluo@worldbank.org</u>

> *** Email: <u>tnterada@umd.edu</u>
<!-- Start of picture text --> B rs<br>><br>2<br>2 > -<br>ne2=O ° . ° ° bad a323oe° IN<br>Ze=oS .« * oMnc?,° oe ¢ae © eeoSoS Sessa° . ° “s ane¢ °<br>£ Py @PHL oo FH o HKG<br>2=5 “ec Heons<br>8 | °<br>3 B * c ¢ oo?<br>> oohen<br>ad&€ e or oo +°*}*4 “ee<br>3 °<br>“a . o eo<br>o<br>5 GDP per capita,| thousands5 of dollars10 15 20 40 6080<br><!-- End of picture text -->

<!-- Start of picture text --> 8<br>kor ¢<br>a<br>80 *. e°<br>x2ae e ° . e.° oe o 6° ae<br><€== wee ° ;eo NaedPN<br>o ry<br>=2 . MING °fetta wtfos . °<br>i ° °<br>s5 ° ‘ ° ° . ols 7 ones<br>3 bay oo ¢ PS<br>=rara °. *IN ochre* ere * o*% 6 ry<br>fa tind. * of ° . °<br>moa « . .<br>o *% Ps ~". aw foe<br>5 GDP per capita,| thousands5 of dollars10 15 20 40 6080<br><!-- End of picture text -->
<!-- Start of picture text --> Percent of Labor Force with Each Grade Completed<br>2003-2007<br>8<br>8<br>£8<br>2<br>a<br>c=)+<br>&<br>c=}<br>2003 2004 2005 2006 2007<br>ME No Grade (MOE Elementray School<br>ME High School HE, College<br><!-- End of picture text --> <!-- Start of picture text --> Percent of Labor Force with Each Grade Completed<br>by Island Group 2003-2007<br>8<br>c=]oo<br>Ps<br>a<br>+<br>&<br>Oo<br>Manila Metropolitan Area Luzon Visayas Mindanao<br>HE. No Grade ( Elementray School<br>GEE High School! [EEE College<br><!-- End of picture text --> <!-- Start of picture text --> Percent of Labor Force with Each Grade Completed<br>by Region 2003-2007<br>s<br>8<br>es<br>mo<br>a<br>°<br>MN I oll WA IVB ¥V CAR VI VII VIIL DX xX XL XIIARMMKIII<br>HEE No Grade ME Elementray School<br>MQ High School GE College<br><!-- End of picture text -->
In the Filipino labor force, females are on average better educated than males as those who are less educated tend to choose not to participate in the labor market (Table 1).<sup>2</sup> For example, 32 percent of females in the labor force have completed university education or above compared with only 14 percent of males; only 8 percent of females in the labor force have not completed elementary school education compared with 13 percent of males. As the unemployment rate and daily wage are similar between males and females, the fact that females are better educated suggests, to some extent, that females may in fact still face tougher conditions in the market compared with their male counterparts with similar education background.

**Table 1: Percentages of Labor Market Participants with Different Education by Gender**

|**Highest Degree**<br>||**Elementary**<br>**M**|**ale**||
|---|---|---|---|---|
|**Completed**|**No Gade**|<br>**School**|**High School**|**Some College**|
|2003|14.1|29.2|42.8|13.8|
|2004|13.8|29.5|43.0|13.6|
|2005|13.2|28.3|43.9|14.7|
|2006|12.7|28.3|44.2|14.8|
|2007|12.3|27.9|45.3|14.5|
|**2003-2007**|**13.2**|**28.6**|**43.9**|**14.3**|
|**Highest Degree**||**Fe**|**male**||
|<br>||**Elementar**|||
|**Completed**|**No Gade**|**y**<br>**School**|**High School**|**Some College**|
|2003|8.3|22.1|38.5|31.1|
|2004|8.0|21.3|38.7|31.9|
|2005|7.7|20.2|40.0|32.1|
|2006|6.8|19.8|40.9|32.4|
|2007|6.6|18.7|41.8|32.9|
|**2003-2007**|**7.5**|**20.4**|**40.0**|**32.1**|

_Source:_ Authors’ calculation based on LFS, 2003-2007 As in many other countries, the youth are better educated than the older generations (Table 2). For example, 49 percent of the labor force aged 15-25 has high school certificate compared with only 23 percent of those aged 55-64, although the difference might already be underestimated as some of the youth may still pursue higher schooling. The large difference in human capital between the older age group, for example 55-64, and the younger age group, for example 15-25 or 25-50, is consistent with achievements in education in the Philippines in the past decades. Less than one out of ten in the labor force younger than 50 has not completed elementary school education, compared to more than one out of five aged 50-65.

> 2 In addition to education, household environment, such as household income level, marital status, existence and number of child(ren) and age of child(ren), affects female worker’s participation in labor market.

4
**Table 2: Distribution of Human Capital by Age Group**

|**Highest Degree**<br>**Completed**|**No Gade**|**Elementary**<br>**School**|**High School**|**Some College**|
|---|---|---|---|---|
|15-24|9.8|27.7|48.7|13.8|
|25-34|8.4|21.1|45.3|25.3|
|35-44|11.1|26.2|40.8|21.9|
|45-54|14.9|28.9|34.1|22.2|
|55-64|22.8|29.7|22.5|25.0|
|**Philippines**|**11.0**|**25.5**|**42.4**|**21.0**|

_Source:_ Authors’ calculation based on LFS, 2003-2007

# **2. Education and Job Opportunities**

Job opportunities and wages are closely associated with education.<sup>3</sup> The best educated (with a university certificate) have the highest employment rate, followed by the least educated (with no elementary school certificate); the same is true for the employment rate (Table 3). The population with an elementary school certificate or high school certificate has the lowest employment rate. However, the unemployment rate is considerably higher for the better educated – over 9 percent for both with high school certificate and university certificate or above, compared to 3 percent for those with less than elementary education and 5 percent for those with only elementary education. The high unemployment rate among the best educated may to some extent reflect the frictions between supply and demand of high skilled labor and the relatively long time spent in job hunting of individuals in this group. The regional difference in labor performance, for example the low employment and high wage in NCR, may be closely associated with its higher share of better human capital.

**Table 3: Labor Market Performance Indicators by the Highest Grade Attained**

||Mean<br>**Particip**|**ation R**<br>% Cha|<br>**ate**<br>nge|Mean<br>**Employme**<br>**Worki**<br>**Popu**|**nt Rate**<br>**ng Age**<br>**lation)**<br>% Chan|**(to**<br>ge|Mean<br>**Unemplo**<br>**(to Partici**|**yment Rate**<br>**pation Rate)**<br>% Change|Mean<br>**Daily Earn**<br>**PH**|**ings (2**<br>**P)**<br>% Cha|**000**<br>nge|
|---|---|---|---|---|---|---|---|---|---|---|---|
|No grade|68.4|-0.26|*|66.6|-0.22||2.7|-0.04|106.09|-5.5|***|
|Elementary school|58.6|-1.49|*******|55.7|-1.32|***|4.9|-0.17|126.76|-8.2|***|
|High school|59.6|-2.01|*******|54.0|-2.18|***|9.4|0.57|176.47|-5.7|***|
|College|73.2|-3.88|*******|66.3|-2.22|***|9.3|-1.83|354.86|-9.5|***|
|**Philippines, 2003-**<br>**2007**|**62.4**|**-1.82**||**58.1**|**-1.76**||**6.**|**8**<br>**0.08**|**195.25**|**-4.3**||

_Note:_ Test statistics are shown for significance of change in each indicator in 2003 – 2007: *, **, and *** represent significance at 10 percent, 5 percent, and 1 percent, respectively. _Source:_ Authors’ calculation based on LFS, 2003-2007 The daily wage increases monotonically with education level. The wages of the better educated

> 3 The LFS report daily wage only of the employee at the private household, private establishment, government or government corporate and family-owned farm or business of their own household members. The self-employed, who often engage in the informal sector where the mean daily wage level is lower compared to that in the formal sector, may tend to show a particular pattern of personal characteristics.   In addition, LFS does not report on remittances. Due to the data constraint, unfortunately, we are unable to capture the effect of self-employment and remittances on income and employment.

5
<!-- Start of picture text --> Income per day by highest grade completed: 2003-07<br>~ || ——— No grade<br>——-—-—- Elementary school A<br> ||777777777- High school nd IY<br>* ||—-—-- University Pyi pa<br>got BALIn? MANi<br>mG NAR N<br>n ‘PPM<br>A a" hs, \ ~<br>0 2 4 6 8<br>Earning (in log) in 2000 PHP<br><!-- End of picture text -->
<!-- Start of picture text --> Class of worker by the highest grade attained: 2003-2007<br>iz = =<br>&<br>58<br>t=<br>oa<br>=<br>o<br>a<br>oa<br>No grade Elementary High school University<br>MEE Private household (ON Private establishment<br>ME Sovernment corporation HE Self-employed<br>HE Employer (family-operated) Employee (family-operated)<br>HE Worked w/o pay<br><!-- End of picture text -->
male labor force that has not completed any degree (54 percent) is employed in the agricultural sector, the majority of female labor with no degree (55 percent) and with an elementary degree (67 percent) is employed in the services sector (Table 6). Forty-five percent of the female labor force with no degree or with an elementary school degree is employed by private households where the daily wage (PHP 129) is far below the average daily wage of an average female worker (PHP 193). More than 80 percent of male labor force who completed college and 90 percent of female labor force work in the services sector. The majority of university graduates have better-paid jobs, working as executives, managers, or professionals.

**Table 6: Industry of Work and Type of Occupation by Highest Degree Completed**

||**No grade **|<sup>**Elementary**</sup><br>**School**<br>**Male**|**High**<br>**School**<br>|**College**|**No grade**|**Elementary**<br>**School**<br>**Female**|<br>**High**<br>**School **<br>|<sup>**College**</sup>|
|---|---|---|---|---|---|---|---|---|
|_Sector_|||||||||
|Agriculture|54.1|33.6|11.4|2.1|48.3|29.2|9.0|1.2|
|Manufacturing|24.1|32.8|32.2|17.2|6.8|14.1|24.1|8.8|
|Services|21.8|33.7|56.5|80.7|55.2|67.3|71.3|90.5|
|_Types of Occupation_|||||||||
|Executives /<br>Managers|0.5|1.0|3.2|16.5|0.3|0.5|2.4|8.7|
|Professionals|0.3|0.7|4.4|40.3|0.3|0.8|5.8|55.1|
|Workers / Laborers|99.3|98.4|92.4|43.2|99.4|98.7|91.8|36.2|

_Source:_ Authors’ calculation based on LFS, 2003-2007

# **3. Estimation and Decomposition Methodology**

In this paper, we estimate the effects on wage of personal attributes (i.e. gender, education, and experience), sectors of employment, and occupations, controlling for region-specific and yearspecific effects, and employ a regression-based inequality decomposition method to examine their respective contribution to wage differential.

Wage is estimated in a standard form of regression as below. The coefficients are used to calculate the relative significance of human capital endowment measured by the highest degree completed.

where i denotes individual, wagei daily wage of individual i; experiencei the number of years of 2 potential experience; experiencei the number of years of potential experience squared (1/10,000); sexi a dummy for sex, which equals 1 if female; elementaryi, an education dummy, which equals 1 if the individual’s highest degree completed is elementary school education; highschooli, which equals 1 if the individual’s highest degree completed is high school education; universityi , which equals 1 if the individual’s highest degree completed is university education; and, _v_ , an error term.

8
The number of years of experience is approximated by “experience ≡ age – years of schooling – 6”, as direct information is not available in LFS.<sup>5</sup> We introduced four different dummies: time dummy (2003 as reference); region dummy (NCR as reference); sector dummy (agricultural sector as reference); and occupation dummy (worker / laborer as reference), to capture time/region-/sector-/occupation-specific effects. To relax the assumption of homogeneity across regions and across sector, we conduct regression analysis and decompose the wage inequality for each region and each sector separately. Further, to relax the assumptions that each factor plays the same role in wage determination for both genders, we also conduct regressions for male and female separately.<sup>6</sup> As a robustness check, we repeat the regression analysis that replaces dummies for education groups with estimated years of schooling (results are shown in Annex 1). The results are consistent.

The relative importance of each factor to wage differentials, measured by the variance of logarithm of daily wage, is estimated as: where j indexes factor included in the model; s� denotes the relative contribution of the j’th covariates; a� the j’th element of the estimated coefficient (=α, β, ρ, τ, φ, ω) based on the above model; Z� the j’th element of explaining variables (= potential experience, potential experience squared, potential years of schooling, sex, sector dummies, occupation dummies, region dummies and time dummies) plus a constant; and Y the daily wage in log. The relative significance of the j’th element is the percentage effect of wage inequality that it is accounted for.

We also explore the contribution of each independent variable to the difference in inequality between regions. The contribution of variable j to the change in an inequality measure, I�. �, is defined as: where the subscripts 1 and 2 denote region 1 and region 2; Π� denotes the contribution of the j’th factor to the change in inequality measure.<sup>7</sup>

> 5 The LFS does not provide information of the actual years of schooling. Therefore, we assume that in general it takes 6, 10 and 15 years to complete primary, secondary and tertiary school, respectively. Using potential experience rather than actual experience, however, may produce misleading (biased) results. Potential experience may be overestimated, for example, if the individual starts school late, or has extended period of unemployment between jobs. In this case, the estimated coefficient may be biased.

> 6 The estimation of for both genders combined may be biased, because a variety of factors may influence the choice of labor market participation of an individual, especially female. Marital status, income level of household members, and the number of children, often affect female worker’s participation in the labor market to a larger extent. We may also overestimate the potential experience, more so for female than for male, because female worker tend to leave the market more frequently than male worker for family reasons. This topic is of interest of further research.

> 7 This is applicable to any inequality measures that is continuous and symmetric and for which I�μ, μ, ... , μ��0. Those inequality indexes include Gini index, Atkinson index, the generalized entropy family, etc.  For details, see

9
# **4. Education and Wage Differentials**

Education is the single most important factor that contributes to wage differentials. Table 7 presents the wage equation using dummies of the highest degree completed as measures of education level. The results suggest that all factors carry the expected signs. Other things equal, females earn less than males; experience counts although returns to experience increase at a decreasing rate; education counts and the wage premium for higher education increases with the level of the highest attainment; manufacturing workers earn the highest wage, followed by services workers, while agricultural workers earn the least; occupation counts, officials, managers, executives, and professionals earn more than laborers. Comparing 2007 with 2003, wage premium for secondary and high education increased slightly; while wage gaps across industries declined. Comparing female with male workers, wage premium for secondary and tertiary education is higher for female; while that for primary education is lower, consistent with the findings in many other countries.<sup>8</sup> The wage gap across economic sectors of employment differs significantly between males and females. The daily wage level of males in the services sector is 30 percent higher than those in the agricultural sector, while the wage level of females in the services sector is virtually the same as those in the agricultural sector. The wage gap between executives, professionals and laborers is higher for females than males.

> Fields (2003).

> 8 See Psacharopoulos, G. and H.A. Patrinos (2004), and Schady (2000) and (2003).

10
# **Table 7: Wage Equation Results (2003-2007)**

||**#1**|**#2**<br>**(Both**|**#3**|**#4**|**#5**|
|---|---|---|---|---|---|
||**(Both sexes)**|<br>**sexes,**<br>|**(Both sexes,**<br>**2007)**|**(All men)**|**(All women)**|
|**De**|**pendent Varia**|**2003)**<br>**ble = ln(Dai**|**ly Wage)**|||
|**Personal Attributes**||||||
|Gender|-0.25|-0.26|-0.27|||
||(0.003)***|(0.006)***|(0.007)***|||
|Experience|0.03<br>|0.03<br>|0.03<br>|0.03<br>|0.03<br>|
||(0.000)***|(0.001)***|(0.001)***|(0.000)***|(0.001)***|
|exp * exp (/10,000)|-0.04|-0.04|-0.04|-0.04|-0.04|
||(0.001)***|(0.001)***|(0.002)***|(0.001)***|(0.001)***|
|**_Education Dummies_**||||||
|_Elementary_|0.11|0.10|0.09|0.13|0.07|
||(0.004)***|(0.008)***|(0.010)***|(0.005)***|(0.009)***|
|_High School_|0.40|0.37|0.40|0.37|0.46|
||(0005)***|(0009)***|(0011)***|(0005)***|(0010)***|
|_College_|.<br>1.01<br>(0.006)***|.<br>0.94<br>(0.013)***|.<br>1.02<br>(0.014)***|.<br>0.86<br>(0.008)***|.<br>1.14<br>(0.010)***|
|**Sector (Agricultural as reference)**<br>Manufacturing|0.38|0.42|0.37|0.41|0.31|
||(0.004)***|(0.007)***|(0.008)***|(0.004)***|(0.009)***|
|Services|0.17|0.24|0.16|0.30|0.01|
||(0.004)***|(0.007)***|(0.009)***|(0.005)***|(0.007)|
|**Occupation (Worker/laborer as refere**|**nce)**|||||
|Officials, managers, executives|0.50|0.52|0.45|0.44|0.58|
||(0.008)***|(0.017)***|(0.018)***|(0.010)***|(0.012)***|
|Professionals|0.42|0.47|0.36|0.33|0.45|
||(0.005)***|(0.010)***|(0.011)***|(0.007)***|(0.007)***|
|**Region dummies (NCR as reference)**||||||
|Luzon|-0.199<br>|-0.165<br>|-0.218<br>|-0.182<br>|-0.211<br>|
||(0.004)***|(0.008)***|(0.008)***|(0.004)***|(0.006)***|
|Visayas|-0.413|-0.362|-0.45|-0.397|-0.422|
||(0.004)***|(0.009)***|(0.010)***|(0.005)***|(0.007)***|
|Mindanao|-0.38|-0.324|-0.427|-0.359|-0.387|
||(0.004)***|(0.009)***|(0.010)***|(0.005)***|(0.007)***|
|**Year dummies (2003 as reference)**||||||
|2004|-0.016|||-0.026|-0.005|
||(0.004)***|||(0.004)***|(0.006)|
|2005|-0.073<br>***|||-0.056<br>***|-0.097<br>***|
||(0.004)|||(0.005)|(0.007)|
|2006|-0.092|||-0.081|-0.11|
||(0.004)***|||(0.005)***|(0.007)***|
|2007|0069|||006|0087|
||-.<br>(0.004)***|||-.<br>(0.005)***|-.<br>(0.006)***|
|Constant|4.40|4.35|4.39|4.32|4.26|
||(0.007)***|(0.013)***|(0.015)***|(0.008)***|(0.012)***|
|Number of observations|156167|34380|30806|93242|64289|
|Adjusted R-squared|0.55|0.56|0.53|0.52|0.58|

_Note:_ Robust standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

Wage differences across regions suggest some segmentation in the labor market. Individuals 11
employed in NCR earn the highest wage followed by those in Luzon. For example, in average, wage in Visayas is 40 percent lower than that in NCR, others being equal. Over time, wage gaps widened. The large wage gap may be related to the unique production structure of the primary city NCR where high-paid jobs in high-value-added services industries agglomerate.<sup>9</sup> Table 8 shows relative contribution of personal attributes, sectoral and occupational factors to wage differentials, while regional and year effects are controlled for. For both genders, personal attributes account for approximately one-third of wage inequality, of which education accounts for 30 percent. Occupation accounts for 12 percent of wage differentials.<sup>10</sup> Personal attributes, in particular education level, play a more important role in wage differentials for female workers than for male workers. Interestingly, the sector of employment plays a more important role for male workers than for female workers, while the type of occupation plays a more important role for female workers than for male workers. This is likely related to the wide spectrum of jobs that female workers have within a sector (especially the services sector). Comparing the results in 2003 and 2007, the contribution of personal attributes, including education level, is stable over time; while the contribution of sector and occupation slightly declines and that of region increases. This is consistent with the findings of the slightly declining wage inequality within sectors and the widening gap between the NCR and other regions.

**Table 8: Contribution of Each Explanatory Factor to Wage Differentials (2003-2007)**

||**#1**<br>**(Both sexes) **|**#2**<br> <sup>**(Both sexes,**</sup><br>**2003)**|**#3**<br>**(Both sexes,**<br>**2007)**|**#4**<br>**(All men)**|**#5**<br>**(All women)**|
|---|---|---|---|---|---|
|**Residual**|**45.7**|**44.0**|**47.2**|**47.7**|**42.0**|
|**Personal Attributes**|**32.0**|**30.4**|**31.9**|**25.8**|**37.2**|
|_Gender_|_1.3_|_1.2_|_1.4_|_--_|_--_|
|_Experience_|_1.6_|_1.4_|_1.4_|_2.3_|_0.7_|
|_Dummies for Highest Degree_<br>_Completed_|_29.1_|_27.9_|_29.0_|_23.5_|_36.5_|
|**Sector**|**4.6**|**6.3**|**4.1**|**10.1**|**1.2**|
|**Occupation**|**11.6**|**14.3**|**9.8**|**8.8**|**14.8**|
|**Region**|**5.9**|**5.1**|**7.1**|**7.5**|**4.6**|
|**Year**|**0.2**|**--**|**--**|**0.1**|**0.3**|

> 9 The services sector in NCR employs 78 percent of labor force, accounts for approximately 68 percent of regional GDP.

> 10 As occupation is closely associated with education, the total contribution of education to wage differential could be even higher than 30 percent, as part of it is captured by occupation.

12
We experiment with similar regressions at the regional and sectoral level. Results are presented in Annex 2. The findings indicate that impacts of personal attributes and occupation differ significantly across regions and genders. The wage gap between sexes is largest in Visayas (30 percent), while smallest in NCR (24 percent). Wage premium for tertiary education is slightly higher in Mindanao for all workers. Wage gap between professional workers and unskilled workers is the smallest in NCR, while the largest in Visayas.

Within a region, wages vary widely across sectors. For example, in Luzon, those who are employed in the manufacturing sector and services sector earn 46 percent and 24 percent higher daily wage than those in the agricultural sector; while in NCR, the wage gap is 24 percent and 7 percent, respectively. This is consistent with the difference in production structure within each economic sector across regions. Wage gap between agricultural and manufacturing workers of both genders is the largest in Luzon, while the smallest in Mindanao. Especially in NCR and Mindanao, female workers in the services sector earn less than those in the agricultural sector.

Within each region, education is commonly the single most important factor that contributes to wage differentials – it accounts for 28 percent of wage inequality of all workers in NCR, 27 percent in Luzon, 36 percent in Visayas and 34 percent in Mindanao. The role of educational is less significant for male workers in all regions. Occupation is the second most important factor – it accounts for 11-14 percent of wage inequality in each region. The role of sector is limited, especially in NCR and Mindanao where the economy is dominated by a single sector (services in NCR and agricultural in Mindanao).

Within each sector, education is also the single most important factor that contributes to wage differentials. It accounts for 6 percent, 14 percent and 33 percent of wage inequality in the agricultural, manufacturing and services sectors, respectively. Wage premium for secondary and tertiary education differ significantly across sectors. Having better education, such as high school and college degree, is most rewarding in the services sector. For both genders, holding a high school diploma is associated with a 52 percent increase in daily wage and holding a college degree 120 percent compared with those who are without grade.

Wage inequality across regions is closely associated with the difference in their human capital. Education is the most important factor that explains the difference in inequality between NCR and Visayas and between NCR and Mindanao; while the difference in inequality between NCR and Luzon, where human capital is similar, mainly stem from their production structures, i.e., the share of manufacturing and services sectors. Results in Annex 3 present the relative contribution of each factor to inequality of daily wage measured by Gini and Theil indexes between NCR and other three regions based on the regression results. Education accounts for 44 percent of the difference in Gini index and 38 percent of Theil index between NCR and Visayas. Similarly, it accounts for 58 percent of Gini index and 48 percent of Theil index between NCR and Mindanao.

13
# **5. Conclusions**

Education plays an important role in wage differentials in the Philippines. A large part of inequality stems from the difference in wages between the less educated and the better educated. At the national level, education accounts for about 30 percent of the difference in wages. It accounts for a higher percentage of the difference between female workers than between male workers. Across regions, education accounts for 20-30 percent of the difference in wages between NCR and Visayas and between NCR and Mindanao; across sectors, it accounts for 6 percent, 14 percent, and 33 percent of the difference in wages in the agricultural, manufacturing, and services sectors, respectively.

Returns to education increase with years of schooling – workers with elementary, secondary, and tertiary education earn 10 percent, 40 percent, and 100 percent, respectively, more than those with no education. Tertiary education is often a prerequisite for high-paid jobs. The majority of the least educated clustered in low-paid services jobs and in the agricultural sector.

Efforts to improve education to increase the supply of highly educated people are important for economic efficiency enhancement and growth acceleration. They are important not only for longterm growth, but also for helping to translate growth into more equal opportunities for the children of the current generation.

# **References**

Asian Development Bank. (2007). Asian Economic Outlook 2007. Manila, Philippines.

- Balisacan, A.M. and N. Fuwa. (2004). “Going Beyond Cross-Country Averages: Growth, Inequality and Poverty Reduction in the Philippines”, World Development, Vol. 32, Issue 11, November 2004, pp. 1891-1907.

- Bourguignon, F., M. Fournier, and M. Gurgand. (2005). “Distribution, development, and education in Taiwan, 1979-94.” in: F. Bourguignon et al. (eds.): The Microeconomics of Income Distribution Dynamics in East Asia and Latin America, pp.313-356. World Bank, Washington D.C.

- Chiswick, B.R. (1997). “Interpreting the Coefficients of Schooling in the Human Capital Earnings Function”, Policy Research Working Paper, No. 1790. World Bank, Washington D.C.

Greene, W.H. (2004). Econometrics, Prentice Hall, Upper Saddle River, New Jersey.

- Fields, G.S. (2003). “Accounting for Income Inequality and Its Change: A New Method, with Application to the Distribution of Earnings in the United States”, Research in Labor Economics.

- Fournier, M. (2005). “Exploring Information from Path Dependency in Oaxaca-Blinder Decomposition Procedures”, Applied Economics Letters, 12, pp. 669-672.

14
- Hasan, R., and K.R.L. Jandoc. (2008), “The Quality of Jobs in the Philippines: Comparing SelfEmployment with Wage Employment”, UP School of Economics, Discussion Paper No.0811.

- Hungerford, T. and G. Solon. (1987). “Sheepskin Effects in the Returns to Education”, Review of Economics and Statistics, 69:175-177.

Lächler, U. (1998). “Education and Earnings: Inequality in Mexico”, Policy Research Working Paper, No. 1949 , World Bank, Washington D.C.

Mincer, J. (1974). Schooling, Experience and Earnings. Columbia University Press, New York.

- Psacharopoulos, G. and H.A. Patrinos. (2004). “Returns to Investment in Education: A Further Update”, Education Economics, Vol. 12, No. 2, August 2004 .

- Sakellariou, C. (2006). “Education Policy Reform, Local Average Treatment Effect and Returns to Schooling from Instrumental Variables in the Philippines”, Applied Economics, 38, 47381.

- Schady, N.R. (2000). “What Education Pay? Non-Linear Returns to Schooling Among Filipino Men”, Mimeo.

Schady, N.R. (2003). “Complexity and Sheepskin Effects in Human Capital Earnings Function: Recent Evidence for Filipino Men”, Oxford Bulletin of Economics and Statistics, 65(2), 171-196.

15
In(wage;7 a + By : ; » ; : €j 2 Tj ?i +B; 1 Ba i

|

j j Ej
# **Table A2: Contribution of Each Explanatory Factor to Wage Differentials, Using Years of Potential Schooling 2003-2007**

||**#1**<br>**(Both sexes) **|**#2**<br> <sup>**(Both sexes,**</sup><br>**2003)**|**#3**<br>**(Both sexes,**<br>**2007)**|**#4**<br>**(All men)**|**#5**<br>**(All women)**|
|---|---|---|---|---|---|
|**Residual**|**48.3**|**46.4**|**50.0**|**49.4**|**44.7**|
|**Personal Attributes**|**26.1**|**24.2**|**26.0**|**21.7**|**30.8**|
|_Gender_|_1.3_|_1.1_|_1.4_|_--_|_--_|
|_Experience_|_1.2_|_1.0_|_1.1_|_2.2_|_0.0_|
|_Years of completed schooling_|_23.6_|_22.1_|_23.5_|_19.5_|_30.8_|
|**Sector**|**4.2**|**5.8**|**3.6**|**9.9**|**0.8**|
|**Occupation**|**15.7**|**18.7**|**13.6**|**11.7**|**19.1**|
|**Region**|**5.6**|**4.8**|**6.7**|**7.1**|**4.3**|
|**Year**|**0.2**|||**0.1**|**0.3**|

17
**Table A3: Equation Results, Using Years of Potential Schooling by Region 2003-2007**

|||**NCR**|||**Luzon**|||**Visayas**|||**Mindanao**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|
|||||**D**|**ependent Va**|**riable = ln(D**|**aily Wage)**||||||
|**Personal Attribute**|**s**||||||||||||
|Gender|-0.24|_--_|_--_|-0.26|_--_|_--_|-0.29|_--_|_--_|-0.26|_--_|_--_|
||(0.006)***|_--_|_--_|(0.004)***|_--_|_--_|(0.007)***|_--_|_--_|(0.006)***|_--_|_--_|
|Experience|0.02<br>(0.001)***|0.02<br> (0.001)***|0.02<br> (0.001)***|0.02<br>(0.001)***|0.03<br>(0.001)***|0.02<br>(0.001)***|0.03<br>(0.001)***|0.03<br>(0.001)***|0.02<br>(0.001)***|0.03<br>(0.001)***|0.04<br>(0.001)***|0.03<br>(0.001)***|
|exp * exp|||||||||||||
|(/10,000)|-0.02<br>(0.002)***|-0.02<br> (0.003)***|-0.02<br> (0.003)***|-0.03<br>(0.001)***|-0.04<br>(0.001)***|-0.02<br>(0.002)***|-0.04<br>(0.002)***|-0.04<br>(0.002)***|-0.02<br>(0.003)***|-0.05<br>(0.002)***|-0.05<br>(0.002)***|-0.04<br>(0.003)***|
|_Years of completed_<br>_schooling_|0.08<br>(0.001)***|0.06<br> (0.002)***|0.10<br> (0.002)***|0.06<br>(0.001)***|0.05<br>(0.001)***|0.08<br>(0.001)***|0.07<br>(0.001)***|0.05<br>(0.001)***|0.08<br>(0.002)***|0.07<br>(0.001)***|0.06<br>(0.001)***|0.09<br>(0.001)***|
|**Sector**|||||||||||||
|Manufacturing|0.18<br>(0.055)***|0.23<br> (0.056)***|-0.03<br>(0.173)|0.43<br>(0.005)***|0.47<br>(0.005)***|0.32<br>(0.012)***|0.36<br>(0.009)***|0.39<br>(0.009)***|0.31<br>(0.023)***|0.20<br>(0.007)***|0.24<br>(0.007)***|0.11<br>(0.021)***|
|Services|0.03<br>(0.055)|0.16<br>(0.056)***|-0.26<br>(0.173)|0.24<br>(0.006)***|0.37<br>(0.007)***|0.05<br>(0.010)***|0.15<br>(0.009)***|0.28<br>(0.011)***|-0.01<br>(0.017)|0.07<br>(0.007)***|0.20<br>(0.009)***|-0.15<br>(0.014)***|
|**Occupation**|||||||||||||
|Officials, managers,<br>executives|<br>0.52|0.46|0.59|0.64|0.58|0.72|0.68|0.60|0.80|0.58|0.52|0.70|
||(0.014)***|(0.019)***|(0.020)***|(0.013)***|(0.017)***|(0.020)***|(0.023)***|(0.028)***|(0.038)***|(0.020)***|(0.025)***|(0.033)***|
|Professionals|0.37<br>(0.010)***|0.35<br> (0.013)***|0.38<br> (0.014)***|0.58<br>(0.007)***|0.48<br>(0.010)***|0.59<br>(0.010)***|0.73<br>(0.012)***|0.56<br>(0.018)***|0.77<br>(0.017)***|0.64<br>(0.010)***|0.48<br>(0.015)***|0.69<br>(0.014)***|
|**Year dummies**|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Constant|4.29<br>(0.057)***|4.42<br> (0.059)***|4.09<br> (0.174)***|4.01<br>(0.009)***|4.04<br>(0.010)***|3.73<br>(0.016)***|3.78<br>(0.013)***|3.74<br>(0.016)***|3.56<br>(0.024)***|3.81<br>(0.011)***|3.82<br>(0.014)***|3.62<br>(0.021)***|
|No. of observations|25528|13810|11718|67155|39832|27323|28534|16755|11779|36314|22845|13469|
|Adj. R<sup>2</sup>|0.42|0.34|0.47|0.47|0.46|0.51|0.50|0.46|0.56|0.51|0.46|0.59|

_Note:_ Robust standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

18
**Table A4: Contribution of Each Explanatory Factor to Wage Differentials, Using Potential Years of Schooling by Region 20032007**

||**#1**|**#2**<br>**NCR**|**#3**|**#4**|**#5**<br>**Luzon**|**#6**|**#7**|**#8**<br>**Visayas**|**#9**|**#10**|**#11**<br>**Mindana**|**#12**<br>**o**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|
|**Residual**|**58.1**|**66.0**|**52.6**|**52.7**|**54.2**|**48.8**|**49.9**|**54.1**|**44.1**|**48.8**|**54.1**|**40.5**|
|**Personal Attributes**|**27.7**|**20.0**|**32.0**|**23.5**|**19.0**|**28.9**|**27.0**|**23.9**|**30.1**|**31.3**|**27.7**|**36.0**|
|_Gender_|_3.2_|_--_|_--_|_1.5_|_--_|_--_|_1.4_|_--_|_--_|_0.9_|_--_|_--_|
|_Experience_|_1.0_|_1.5_|_0.1_|_1.3_|_3.2_|_-0.5_|_1.9_|_3.4_|_0.3_|_3.9_|_4.2_|_2.8_|
|_Years of completed schooling_|_23.5_|_18.4_|_31.9_|_20.7_|_15.8_|_29.3_|_23.7_|_20.5_|_29.9_|_26.6_|_23.5_|_33.1_|
|**Sector**|**0.5**|**-0.3**|**1.1**|**6.5**|**13.9**|**1.3**|**4.1**|**9.8**|**0.7**|**1.6**|**6.3**|**-0.9**|
|**Occuption**|**13.6**|**14.2**|**14.2**|**17.1**|**12.7**|**20.9**|**18.6**|**12.0**|**24.3**|**17.8**|**11.7**|**23.3**|
|**Year**|**0.1**|**0.2**|**0.1**|**0.1**|**0.2**|**0.1**|**0.4**|**0.1**|**0.8**|**0.4**|**0.1**|**1.1**|

**Table A5: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Gini Index)**

||**NCR **|**Luzon **<br>**Bo**|**Visayas **<br>**th Sexes**|**Mindanao**|**NCR **|**Luzon **<br>|**Visayas **<br>**All Men**|**Mindanao**|**NCR **|**Luzon **<br>**Al**|**Visayas **<br>**l Wome**|**Mindanao**<br>**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Gini Index|0.31|0.35|0.40|0.39|0.27|0.32|0.36|0.36|0.36|0.39|0.46|0.44|
|**Residual**||**0.12**|**0.23**|**0.14**||**-0.07**|**-0.08**|**-0.08**||**0.08**|**-0.46**|**-0.87**|
|**Personal Attributes**||**-0.08**|**0.25**|**0.45**||**0.14**|**0.45**|**0.68**||**-0.04**|**0.11**|**0.78**|
|_Gender_||-0.11|-0.04|-0.08||--|--|--||--|--|--|
|_Experience_||0.04|0.05|0.14||0.12|0.13|0.18||-0.07|0.02|0.31|
|_Years of completed schooling_||0.00|0.24|0.38||0.02|0.32|0.50||0.03|0.09|0.47|
|**Sector**||**0.52**|**0.16**|**0.06**||**0.88**|**0.62**|**0.40**||**0.03**|**-0.04**|**-0.23**|
|**Occupation**||**0.43**|**0.35**|**0.33**||**0.05**|**0.01**|**-0.01**||**0.92**|**1.31**|**1.20**|
|**Year**||**0.01**|**0.01**|**0.02**||**0.00**|**0.00**|**0.00**||**0.00**|**0.09**|**0.12**|

19
**Table A6: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Theil Index)**

||**NCR Luzon **<br>**Bo**|**Visayas **<br>**th Sexes**|**Mindanao**|**NCR **|**Luzon **<br>**A**|**Visayas **<br>**ll Men**|**Mindanao**|**NCR **|**Luzon **<br>**All**|**Visayas **<br>**Women**|**Mindanao**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Theil index|0.18<br>0.21|0.29|0.27|0.15|0.19|0.24|0.24|0.23|0.26|0.35|0.32|
|**Residual**|**0.20**|**0.35**|**0.29**||**0.05**|**0.04**|**0.04**||**0.18**|**-0.23**|**-0.54**|
|**Personal Attributes**|**-0.02**|**0.26**|**0.39**||**0.15**|**0.41**|**0.60**||**0.05**|**0.16**|**0.67**|
|_Gender_|-0.09|-0.02|-0.04||--|--|--||--|--|--|
|_Experience_|0.03|0.03|0.10||0.10|0.11|0.15||-0.05|0.01|0.24|
|_Years of completed schooling_|0.04|0.24|0.33||0.05|0.29|0.45||0.10|0.14|0.43|
|**Sector**|**0.43**|**0.10**|**0.04**||**0.73**|**0.52**|**0.34**||**0.03**|**-0.03**|**-0.17**|
|**Occupation**|**0.38**|**0.28**|**0.27**||**0.06**|**0.03**|**0.02**||**0.74**|**1.04**|**0.95**|
|**Year**|**0.01**|**0.01**|**0.01**||**0.00**|**0.00**|**0.00**||**0.00**|**0.07**|**0.09**|

20
# **Annex 2: Estimations at the Regional and Sectoral Levels**

# **Table A7: Equation Results, Using Dummies for the Highest Degree Completed by Region 2003-2007**

||**#1**<br>**(Both**<br>**sexes)**|**#2**<br>**(All men)**<br>**NCR**|**#3**<br>**(All**<br>**women)**|**#4**<br>**(Both**<br>**sexes)**|**#5**<br>**(All men)**<br>**Luzon**|**#6**<br>**(All**<br>**women)**|**#7**<br>**(Both**<br>**sexes)**|**#8**<br>**(All men)**<br>**Visayas**|**#9**<br>**(All**<br>**women)**|**#10**<br>**(Both**<br>**sexes)**|**#11**<br>**(All men)**<br>**Mindanao**|**#12**<br>**(All**<br>**women)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||**Depen**|**dent Variab**|**le = ln(Daily **|**Wage)**||||||
|**Personal Attributes**|||||||||||||
|Gender|-0.243|--|--|-0.266|--|--|-0.295|--|--|-0.267|||
||(0.006)***|--|--|(0.004)***|--|--|(0.007)***|--|--|(0.006)***|||
|Experience|0.022|0.022|0.021|0.026|0.029|0.022|0.031|0.035|0.027|0.035|0.035|0.032|
||(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|(0.001)***|
|exp * exp (/10,000)|-0.032<br>(0.002)***|-0.03<br> (0.003)***|-0.031<br>(0.003)***|-0.038<br>(0.001)***|-0.042<br> (0.001)***|-0.033<br>(0.002)***|-0.043<br>(0.002)***|-0.046<br> (0.002)***|-0.039<br> (0.003)***|-0.05<br>(0.002)***|-0.049<br>(0.002)***|-0.049<br>(0.003)***|
|Dummies for education|||||||||||||
|_Elementary school_|0.079|0.1|0.068|0.071|0.088|0.05|0.131|0.175|0.051|0.156|0.173|0.124|
||(0.020)***|(0.023)***|(0.035)*|(0.007)***|(0.008)***|(0.014)***|(0.009)***|(0.010)***|(0.017)***|(0.008)***|(0.008)***|(0.017)***|
|_High school_|0.39|0.317|0.48|0.347|0.304|0.407|0.434|0.417|0.42|0.472|0.45|0.483|
||(0.020)***|(0.023)***|(0.035)***|(0.007)***|(0.008)***|(0.015)***|(0.010)***|(0.012)***|(0.019)***|(0.009)***|(0.010)***|(0.019)***|
|_University_|0.954<br>(0.021)***|0.759<br> (0.026)***|1.139<br>(0.035)***|0.917<br>(0.010)***|0.772<br> (0.012)***|1.049<br>(0.017)***|1.103<br>(0.014)***|0.959<br> (0.019)***|1.179<br> (0.023)***|1.124<br>(0.012)***|0.985<br>(0.015)***|1.24<br>(0.020)***|
|**Sector**|||||||||||||
|Manufacturing|0.235<br>(0.053)***|0.261<br> (0.055)***|0.06<br>(0.176)|0.457<br>(0.005)***|0.482<br> (0.005)***|0.368<br>(0.012)***|0.41<br>(0.008)***|0.414<br> (0.009)***|0.401<br> (0.022)***|0.234<br>(0.007)***|0.257<br>(0.007)***|0.167<br>(0.021)***|
|Services|0.071<br>-0.053|0.183<br>(0.055)***|-0.193<br>(0.175)|0.244<br>(0.005)***|0.363<br> (0.007)***|0.065<br>(0.009)***|0.167<br>(0.009)***|0.278<br> (0.011)***|0.04<br> (0.015)***|0.067<br>(0.007)***|0.187<br>(0.009)***|-0.123<br>(0.013)***|
|**Occupation**|||||||||||||
|Officials, managers,<br>executives|0.447|0.401|0.519|0.524|0.478|0.611|0.522|0.478|0.627|0.454|0.43|0.545|
||(0.014)***|(0.019)***|(0.020)***|(0.013)***|(0.017)***|(0.020)***|(0.023)***|(0.029)***|(0.038)***|(0.020)***|(0.024)***|(0.033)***|
|Professionals|0.292<br>(0.010)***|0.279<br> (0.014)***|0.296<br>(0.014)***|0.42<br>(0.008)***|0.344<br> (0.011)***|0.455<br>(0.010)***|0.516<br>(0.014)***|0.398<br> (0.020)***|0.559<br> (0.019)***|0.458<br>(0.011)***|0.331<br>(0.016)***|0.524<br>(0.015)***|
|**Year dummies**|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Constant|4.594|4.607|4.489|4.216|4.172|4.054|3.914|3.813|3.802|3.978|3.914|3.911|
||(0.056)***|(0.059)***|(0.178)***|(0.009)***|(0.010)***|(0.017)***|(0.013)***|(0.015)***|(0.024)***|(0.011)***|(0.013)***|(0.021)***|
|Number of observations|25528|13810|11718|67296|39885|27411|28652|16840|11812|36629|23031|13598|
|Adjusted R-squared|0.44|0.36|0.49|0.5|0.48|0.54|0.53|0.48|0.6|0.54|0.48|0.62|

_Note:_ Robust standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

21
# **Table A8: Contribution of Each Explanatory Factor to Wage Differentials, Using Dummies for the Highest Degree Completed by Region 2003-2007**

||**#1**|**#2**<br>**NCR**|**#3**|**#4**|**#5**<br>**Luzon**|**#6**|**#7**|**#8**<br>**Visayas**|**#9**|**#10**|**#11**<br>**Mindanao**|**#12**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|
|**Residual**|**56.0**|**64.2**|**50.7**|**50.0**|**52.2**|**46.1**|**46.7**|**51.9**|**40.3**|**46.0**|**51.9**|**37.9**|
|**Personal Attributes**|**32.1**|**24.0**|**36.3**|**30.3**|**23.9**|**35.8**|**34.8**|**28.9**|**39.8**|**38.9**|**33.3**|**44.2**|
|_Gender_|_3.2_|_--_|_--_|_1.6_|||_1.4_|||_0.9_|||
|_Experience_|_1.3_|_1.7_|_0.5_|_1.7_|_3.3_|_0.2_|_2.1_|_3.4_|_1.1_|_3.9_|_4.2_|_3.3_|
|_Highest Degree Completed_|_27.6_|_22.4_|_35.7_|_27.0_|_20.6_|_35.5_|_31.3_|_25.4_|_38.7_|_34.1_|_29.1_|_40.9_|
|**Sector**|**0.6**|**-0.3**|**1.2**|**6.8**|**14.0**|**1.6**|**4.7**|**10.2**|**1.2**|**1.8**|**6.0**|**-0.7**|
|**Occupation**|**11.1**|**11.8**|**11.8**|**12.8**|**9.6**|**16.4**|**13.5**|**9.0**|**17.8**|**12.8**|**8.6**|**17.6**|
|**Year**|**0.1**|**0.2**|**0.1**|**0.1**|**0.2**|**0.1**|**0.4**|**0.1**|**0.9**|**0.4**|**0.1**|**1.1**|

22
# **Annex 3: Contributions to Wage Differentials**

**Table A 9: Equation Results, Using Dummies for the Highest Degree Completed by Sector 2003-2007**

||**#1**|**#2**<br>**Agriculture**|**#3**<br>|**#4**|**#5**<br>**Manufacturi**|**#6**<br>**ng**|**#7**|**#8**<br>**Services**|**#9**|
|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|**(Both**<br>**sexes)**|**(All men)**|**(All**<br>**women)**|
|||**D**|**ependent Va**|**riable = ln(**|**Daily Wage)**|||||
|**Personal Attributes**||||||||||
|Gender|-0.17<br>(0.006)***|||-0.165<br>(0.006)**|*||-0.286<br>(0.004)***|||
|Experience|0.015<br>(0.001)***|0.017<br> (0.001)***|0.007<br>(0.001)***|0.018<br>(0.001)**|0.023<br>* (0.001)***|0.014<br>(0.002)***|0.032<br>(0.001)***|0.036<br> (0.001)***|0.027<br> (0.001)***|
|exp * exp (/10,000)|-0.022<br>(0.001)***|-0.024<br> (0.001)***|-0.011<br>(0.002)***|-0.023<br>(0.001)**|-0.029<br>* (0.001)***|-0.033<br>(0.004)***|-0.046<br>(0.001)***|-0.052<br> (0.002)***|-0.039<br> (0.002)***|
|Dummies for education||||||||||
|_Elementary school_|0.109<br>(0.006)***|0.117<br> (0.006)***|0.086<br>(0.012)***|0.083<br>(0.008)**|0.108<br>* (0.008)***|0.063<br>(0.037)*|0.131<br>(0.009)***|0.204<br> (0.015)***|0.082<br> (0.012)***|
|_High school_|0.219|0.223|0.202|0.29|0.27|0.437|0.522|0.569|0.464|
|_University_|(0.007)*** <br>0.578<br>(0.029)***|(0.008)***<br>0.479<br> (0.032)***|(0.017)***<br>0.748<br>(0.055)***|(0.008)**<br>0.645<br>(0.012)**|* (0.008)***<br>0.618<br>* (0.014)***|(0.036)***<br>0.764<br>(0.038)***|(0.009)*** <br>1.175<br>(0.010)***|(0.014)*** <br>1.093<br> (0.015)***|(0.012)***<br>1.217<br> (0.013)***|
|**Occupation**<br>Officials, managers,||||||||||
|executives|0.862<br>(0.043)***|0.887<br> (0.045)***|0.785<br>(0.147)***|0.508<br>(0.013)**|0.513<br>* (0.016)***|0.509<br>(0.024)***|0.482<br>(0.010)***|0.408<br> (0.014)***|0.584<br> (0.014)***|
|Professionals|0.724<br>(0.043)***|0.78<br> (0.049)***|0.604<br>(0.084)***|0.412<br>(0.012)**|0.45<br>* (0.017)***|0.349<br>(0.017)***|0.388<br>(0.006)***|0.296<br> (0.008)***|0.428<br> (0.008)***|
|**Region**||||||||||
|Luzon<br>Visayas|-0.444<br>(0.053)*** <br>-0.647|-0.414<br> (0.055)***<br>-0.609|-0.568<br>(0.177)***<br>-0.8|-0.174<br>(0.005)**<br>-0.396|-0.16<br>* (0.006)***<br>-0.394|-0.226<br>(0.012)***<br>-0.425|-0.201<br>(0.005)*** <br>-0.408|-0.191<br> (0.007)*** <br>-0.39|-0.218<br> (0.007)***<br>-0.426|
||(0.054)***|(0.055)***|(0.177)***|(0.007)**|* (0.007)***|(0.018)***|(0.006)***|(0.009)***|(0.008)***|
|Mindanao|-0.516<br>(0.053)***|-0.493<br> (0.055)***|-0.617<br>(0.177)***|-0.43<br>(0.007)**|-0.432<br>* (0.007)***|-0.438<br>(0.019)***|-0.396<br>(0.006)***|-0.389<br> (0.008)***|-0.406<br> (0.008)***|
|**Year dummies**|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Constant|(0.007)*** <br>4.756|(0.008)***<br>4.7|(0.014)***<br>4.838|(0.006)**<br>4.945|* (0.007)***<br>4.866|(0.015)***<br>4.809|(0.006)*** <br>4.449|(0.008)*** <br>4.394|(0.008)***<br>4.222|
|Number of observations|(0.055)***|(0.056)***|(0.178)***|(0.012)**|* (0.012)***|(0.041)***|(0.011)***|(0.017)***|(0.014)***|
|Adjusted R-squared|29668|23348|6320|41236|31569|9667|85837|37391|48446|

**Note** : Robust standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

23
# **Table A 10: Contribution of Each Explanatory Factor to Wage Differentials, Using Dummies for the Highest Degree Completed by Sector 2003-2007**

||**#1**|**#2**<br>**Agricultur**|**#3**<br>**e**|**#4**<br>|**#5**<br>**Manufactur**|**#6**<br>**ing**|**#7**|**#8**<br>**Services**|**#9**|
|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<br> <sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|
|**Residual**|**80.4**|**82.6**|**77.0**|**63.8**|**59.2**|**65.9**|**43.7**|**52.3**|**39.5**|
|**Personal Attributes**|**9.3**|**7.1**|**11.2**|**15.8**|**16.6**|**18.2**|**39.3**|**32.3**|**41.3**|
|_Gender_|_2.1_|_--_|_--_|_0.8_|_--_|_--_|_2.9_|_--_|_--_|
|_Experience_|_0.9_|_1.9_|_-0.2_|_0.9_|_2.9_|_0.7_|_3.1_|_4.4_|_2.1_|
|_Highest Degree Completed_|_6.2_|_5.2_|_11.4_|_14.1_|_13.7_|_17.6_|_33.2_|_27.9_|_39.2_|
|**Region**|**4.4**|**4.1**|**6.1**|**9.8**|**12.1**|**7.0**|**4.4**|**5.6**|**3.8**|
|**Occupation**|**5.7**|**6.0**|**5.0**|**10.4**|**11.8**|**8.5**|**12.4**|**6.3**|**15.1**|
|**Year**|**0.3**|**0.3**|**0.7**|**0.3**|**0.3**|**0.3**|**0.3**|**0.2**|**0.3**|

**Table A11: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Gini Index)**

||NCR|Luzon <br>Bo|Visayas <br>th Sexes|Mindanao|NCR|Luzon <br>|Visayas <br>All Men|Mindanao|NCR Luzon <br>Al|Visayas <br>l Wome|Mindanao<br>n|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Gini Index|0.31|0.35|0.40|0.39|0.27|0.32|0.36|0.36|0.36<br>0.39|0.46|0.44|
|**Residual**||**0.04**|**0.16**|**0.09**||**-0.10**|**-0.12**|**-0.12**|**-0.03**|**-0.68**|**-0.97**|
|**Personal Attributes**||**0.16**|**0.44**|**0.64**||**0.23**|**0.54**|**0.81**|**0.31**|**0.77**|**1.27**|
|_Gender_||-0.11|-0.05|-0.08||--|--|--|--|--|--|
|_Experience_||0.04|0.05|0.14||0.12|0.13|0.17|-0.03|0.07|0.32|
|_Dummies for the highest_||||||||||||
|_degree completed_||0.23|0.44|0.58||0.11|0.41|0.64|0.33|0.70|0.95|
|**Sector**||**0.54**|**0.18**|**0.06**||**0.88**|**0.64**|**0.39**|**0.06**|**0.01**|**-0.21**|
|**Occupation**||**0.25**|**0.21**|**0.19**||**-0.02**|**-0.06**|**-0.08**|**0.65**|**0.82**|**0.79**|
|**Year**||**0.01**|**0.01**|**0.02**||**0.00**|**0.00**|**0.00**|**0.00**|**0.09**|**0.11**|

24
**Table A12: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Theil Index)**

||**NCR Luzon **<br>**Bo**|**Visayas **<br>**th Sexes**|**Mindanao**|**NCR **|**Luzon **<br>**A**|**Visayas **<br>**ll Men**|**Mindanao**|**NCR **|**Luzon **<br>**All**|**Visayas **<br>**Women**|**Mindanao**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Theil index|0.18<br>0.21|0.29|0.27|0.15|0.19|0.24|0.24|0.23|0.26|0.35|0.32|
|**Residual**|**0.13**|**0.30**|**0.24**||**0.02**|**0.00**|**0.00**||**0.10**|**-0.41**|**-0.63**|
|**Personal Attributes**|**0.19**|**0.40**|**0.54**||**0.23**|**0.49**|**0.72**||**0.32**|**0.67**|**1.06**|
|_Gender_|-0.09|-0.02|-0.04||--|--|--||--|--|--|
|_Experience_|0.04|0.04|0.10||0.10|0.11|0.15||-0.02|0.06|0.25|
|_Dummies for the highest_<br>_degree completed_|0.24|0.38|0.48||0.13|0.38|0.57||0.34|0.62|0.81|
|**Sector**|**0.45**|**0.12**|**0.04**||**0.74**|**0.54**|**0.32**||**0.05**|**0.01**|**-0.16**|
|**Occupation**|**0.23**|**0.18**|**0.17**||**0.00**|**-0.03**|**-0.05**||**0.53**|**0.65**|**0.63**|
|**Year**|**0.00**|**0.01**|**0.01**||**0.00**|**0.00**|**0.00**||**0.00**|**0.07**|**0.09**|

25