metadata: authors: 0: author: Luo,Xubei
count: Philippines
docna: 0: docna: Disparities in labor market performance in
            the Philippines
docty: Policy Research Working Paper
subtopic: Labor & Employment Law,Labor Markets,Educational Sciences,Employment and Unemployment,Rural Labor Markets
teratopic: Macroeconomics and Economic Growth,Governance,Social Protections and Labor,Health, Nutrition and Population
abstracts: cdata!: The Philippine economy has been growing
            rapidly, at an annual growth rate of 5 percent over the past
            five years. Such decent growth in gross domestic product,
            however, did not translate into an increase in household
            income. Wage income declined in real terms. The poverty
            headcount increased slightly. The fruits of economic growth
            were not shared equally across the country. Challenges
            remain to create more jobs to keep pace with the rapidly
            growing active population. Using the Philippines Labor Force
            Survey data (2003-2007), this paper reviews the disparities
            in labor market performance and examines the contribution of
            regional and individual characteristics. The results show
            that real wages declined and disparities widened between the
            National Capital Region and other islands. The youth, less
            educated, and women face more challenges in finding
            employment with a decent salary, other things being equal.
            Disparities in labor market performance are largely
            associated with the difference in regional structure and
            human capital endowment. Individual characteristics account
            for roughly one-third of the difference in wages between the
            National Capital Region and other regions; regional
            structures and other unobservable factors account for
            two-thirds of the difference.
geo_reg_key: 517198,517191
repnme: repnme: Disparities in labor market performance in
            the Philippines
display_title: Disparities in labor market
            performance in the Philippines
listing_relative_url: /research/2009/11/11335317/disparities-labor-market-performance-philippines
topicv3: Labor Markets m1326488 855,Education m1327048 470,Unemployment m1504051 2747,Working Age Population m1504053 2749,Employment Rate m1504079 2895
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Governance,Social Protections and Labor,Health, Nutrition and Population
no_of_pages: 24
author_keys: 0: author_key: 677436
model_extractions: WPS5124 Policy Research Working Paper 5124 Disparities in Labor Market Performance in the Philippines _Xubei Luo_ The World Bank East Asia and Pacific Region Poverty Reduction and Economic Management Department November 2009
Policy Research Working Paper 5124

# **Abstract**

The Philippine economy has been growing rapidly, at an annual growth rate of 5 percent over the past five years. Such decent growth in gross domestic product, however, did not translate into an increase in household income. Wage income declined in real terms. The poverty headcount increased slightly. The fruits of economic growth were not shared equally across the country. Challenges remain to create more jobs to keep pace with the rapidly growing active population.

Using the Philippines Labor Force Survey data (2003– 2007), this paper reviews the disparities in labor market performance and examines the contribution of regional and individual characteristics. The results show that real wages declined and disparities widened between the National Capital Region and other islands. The youth, less educated, and women face more challenges in finding employment with a decent salary, other things being equal. Disparities in labor market performance are largely associated with the difference in regional structure and human capital endowment. Individual characteristics account for roughly one-third of the difference in wages between the National Capital Region and other regions; regional structures and other unobservable factors account for two-thirds of the difference.

As labor market opportunities and returns to education and other individual talents significantly differ across regions, implementation of appropriate regional labor market policies is important. Human capital, however, remains a key determinant in employment and wages. Concerted efforts to improve education will also be important.

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 author may be contacted at xluo@worldbank.org.

_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
# **Disparities in Labor Market Performance in the Philippines**

**Xubei Luo**<sup>*****</sup> The World Bank

**Key Words:** Employment, wage, disparities.

> * The author would like to thank Jehan Arulpragasam, Jean-Jacques Dethier, Swati Ghosh, Ulrich Lächler, Silvia Redaelli, Erwin Tiongson, and Nong Zhu for useful comments, and Takanobu Terada for excellent research assistance. Email: <u>xluo@worldbank.org.</u>
# **1. Labor market performance in the Philippines**

Despite decent economic growth, labor market performance in the Philippines was less than satisfactory in the past five years. Real wages slightly declined and new job creation lagged behind the rapid increase in working-age population. Compared with other countries in East Asia, the employment rate is relatively low in the Philippines.

# _1.1 Changes over time_

Based on the Labor Force Survey (LFS) data for 2003-2007, the labor force participation rate, the employment rate, and the real wage rate all declined over the period, while the working age population rate increased marginally (Table 1).<sup>1</sup> In the context of a slight increase in workingage population, a 2 percent decline in the participation rate and the employment rate suggest a worrisome trend of limited job creation for a rapidly growing population. A 5 percent decline in real wages stands in contrast with the rapid economic growth. On average, 66 percent of the population was in the working age range, of which 62 percent participated in the labor force and 58 percent were employed. The mean daily wage was 195 PHP in 2000 constant terms.

**-** **<u>Table 1: Labor Market Indicators in the Philippines (2003 2007)</u>**

||**Working Age**<br>**Population**|**Labor force**<br>**participation rate**|**Employment rate to**<br>**working-age population**|**NSO unemployment**<br>**rate to participation**|**Daily wage**<br>**(2000 PHP)**|
|---|---|---|---|---|---|
|2003|65.5%|63.0%|59.2%|6.2%|201|
|2004|65.9%|62.2%|58.0%|6.8%|197|
|2005|66.4%|63.2%|58.5%|7.4%|194|
|2006|66.8%|62.2%|57.7%|7.3%|190|
|2007|65.8%|61.2%|57.4%|6.3%|193|
|**2003 - 2007**|**66.1%**|**62.4%**|**58.1%**|**6.8%**|**195**|

_Source:_ LFS, 2003-2007.

_Note:_ We employed the definitions of the National Statistics Office (NSO) of the Philippines for active (workingage) population, participation rate, employment rate and unemployment rate. Active population rate is the ratio of working-age population (aged 15 years and above) to the country's total population. Labor force participation rate is the ratio of the working-age population that is either employed or unemployed to the overall size of their cohort (national population of the same age range). Employment rate is the percentage of work force that works or has a job to return during the reference period. Compared with ILO, the NSO employs a broader definition of unemployment as the proportion of labor force that does not work, is available for work and (1) is actively looking for work or (2) is not looking for work because of following reasons: (a) belief that there is no job available; (b) awaiting results of previous job application; (c) temporary illness; (d) bad weather, and (e) waiting for job recall. According to the LFS, daily wage is measured as the basic pay per day of the employees of private household, private establishment, government (corporation) and own family-operated business.

# _1.2 International comparison_

From a cross-country perspective, the Philippines’ labor market indicators are middling among other East Asian countries (Table 2, Table 3 and Table 4). The participation rate in the Philippines as percent of total population in 2007 (61 percent) was low compared to other East

> 1 There are four rounds of Labor Force Survey each year in January, April, July, and October. We use only the October round for this study. We use the pooled data as if they were a single sample from a larger survey fielded over a longer period to calculate the mean level for 2003-2007; and use the single round data to calculate the mean level of each year and examine the changes over time.

2
Asian countries – it was only slightly higher than Taiwan (58 percent) but lower than most other comparator countries. The employment rate as percent of working age population of the Philippines (57 percent) was also relatively low, compared with countries such as China (79 percent) and Thailand (74 percent). The relative high unemployment rate in the Philippines (6.4 percent in 2007) may to some extent be related to its definition.<sup>2</sup>

**Table 2: Participation Rate to Total Working Age Population in Selected East Asian**

**<u>Economies</u>**

|Year|**Philippines**|China|Indonesia|Korea|Malaysia|Singapore|Taiwan|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|
|2003|**63.0**|77.1|67.2|60.9|66.9|65.5|55.8|73.0|73.7|
|2004|**62.2**|77.8|67.4|61.5|66.9|65.5|56.1|73.4|73.2|
|2005|**63.2**|78.4|67.6|61.4|66.9|65.4|56.2|73.7|73.0|
|2006|**62.2**|78.9|66.7|61.3|66.8|65.3|57.9|72.9|72.9|
|2007|**61.2**|79.4|67.7|60.9|67.0|65.0|58.2|73.6|72.6|

_Source:_ LABORSTAT, ILO _Note:_ Working Age Population is defined as the population over 15 years old except in Malaysia where it is defined as the population over 15 years old and under 65 years old.

**Table 3: Employment Rate to Total Working Age Population in Selected East Asian**

**<u>Economies</u>**

|Year|**Philippines**|China|Indonesia|Korea|Malaysia|Singapore|Taiwan|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|
|2003|**59.2**|74.5|59.4|58.0|62.7|48.0|53.0|71.7|72.4|
|2004|**58.0**|74.1|58.9|58.5|62.0|47.8|53.6|72.2|72.5|
|2005|**58.5**|73.7|58.0|58.7|60.9|N/A|53.9|72.6|N/A|
|2006|**57.7**|73.3|57.9|58.8|60.9|50.5|55.7|71.9|N/A|
|2007|**57.4**|73.0|59.7|58.9|61.0|50.6|56.0|72.6|N/A|

_Source:_ LABORSTAT, ILO

**<u>Table 4: Unemployment Rate to Total Labor Force in Selected East Asian Economies</u>**

|Year|**Philippines**|China|Indonesia|Korea|Malaysia|Singapore|Taiwan|Thailand|Vietnam|
|---|---|---|---|---|---|---|---|---|---|
|2003|**6.2**|4.3|9.7|3.6|3.6|5.9|5.0|1.5|2.3|
|2004|**6.8**|4.2|9.9|3.7|3.5|5.8|4.4|1.5|2.1|
|2005|**7.4**|4.2|11.2|3.7|3.5|N/A|4.1|1.4|N/A|
|2006|**7.3**|4.1|10.3|3.5|3.3|4.5|5.1|1.2|N/A|
|2007|**6.3**|4.0|9.1|3.2|3.2|4.0|5.1|1.2|N/A|

_Source_ : LABORSTAT, ILO _Note_ : The definition of unemployed is slightly different across countries. See LABORSTAT for details.

# **2.  Disparities in labor market performance across regions**

Labor market performance varies widely across regions in the Philippines, especially between the National Capital Region (NCR) and the other three island groups (Luzon, Visayas, and Mindanao). This is related to their significant difference in industrial structure and sources of employment (Table 5).<sup>3</sup> In NCR, the share of agriculture is negligible, and the industrial and

> 2 According to the NSO definition, the unemployed in the Philippines includes discouraged worker, the temporary laid-offs, temporary disabled and etc. See Annex 1 for details.

> 3 Following the official administrative division of Philippines, Manila National Capital and 16 subregions are

3
services sectors account for about 30 percent and 70 percent of regional GDP (20 percent and 80 percent of employment), respectively. In Luzon, Visayas, and Mindanao, one-sixth to one-third of regional GDP and one-third to half of employment came from agriculture, while less than half of regional GDP and employment came from services.<sup>4</sup>

**<u>Table 5: Regional GDP and Source of Employment by Industry Group</u>**

||**GDP per capita**<br>**(2000 PHP)**|**Share of**<br>**GDP**<br>**Agri**|**Share of**<br>**Employment**<br>**culture**|**Share of**<br>**GDP**<br>**In**|**Share of**<br>**Employment**<br>**dustry**|**Share of**<br>**GDP**<br>**S**|**Share of**<br>**Employment**<br>**ervice**|
|---|---|---|---|---|---|---|---|
|NCR|109,326|0.0%|0.9%|31.4%|21.4%|68.6%|78.1%|
|Luzon|27,979|21.8%|35.1%|34.8%|17.6%|43.4%|47.3%|
|Visayas|31,048|16.4%|40.7%|30.3%|13.8%|53.3%|45.4%|
|Mindanao|26,680|31.3%|50.7%|28.5%|9.3%|40.2%|40.0%|
|**Philippines**|**38,861**|**14.5%**|**35.8%**|**31.8%**|**19.5%**|**53.7%**|**49.0%**|

_Source:_ Philippine National Statistical Coordination Board and Bureau of Labor and Employment Statistics. Employment data are for 2006 and GDP data are for 2007.

The unique labor market environment in NCR is characterized by high active population rate, low participation rate, low employment rate, high unemployment rate, and high daily wage (Table 6).<sup>5</sup> The scenarios in Mindanao and Visayas are at the other end of the spectrum, characterized by low active population rate, high participation rate, high employment rate, low unemployment rate, and low daily wage.

The level of daily wage varies widely across regions: wage level is the highest in NCR and the lowest in Visayas (Figure 1). In Luzon, Visayas, and Mindanao the level of daily wage of the employed clusters at two separate low levels; while in NCR it concentrates at a higher level. Within region, there is sharp difference in wage distribution. With daily wage one-third higher and unemployment rate almost doubling the national average, individuals in NCR face a tougher competition in getting a job but are likely to have better remuneration if employed.

> classified into four island region groups – National Capital Region (NCR), Luzon, Visayas and Mindanao – based on their geographic location. See Map 1 for details.

> 4 See Manasan and Chatterjee (2003) and Balisacan and Fuwa (2004).

> 5 See Annex 2 for illustrations.

4
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**<u>Table 6: Labor Market Performance Indicators by Regions</u>**

||**Wor**<br>**Pop**<br>**(**|**king Age**<br>**ulation**<br>**15+)**|**Wor**<br>**Popul**|**king Ag**<br>**ation (1**<br>**64)**|**e**<br>**5-**|**Wor**<br>**Popul**|**king Ag**<br>**ation (6**|**e**<br>**5+)**|**Par**|**ticipatio**<br>**Rate**|**n**|**Em**<br>**Rate (t**<br>**Age P**|**ploymen**<br>**o Work**<br>**opulati**|**t**<br>**ing**<br>**on)**|**Unem**<br>**R**<br>**Part**|**ploym**<br>**ate (to**<br>**icipati**|**ent**<br> <br>**on**|**Daily W**<br>**P**|**age (2**<br>**HP)**|**000**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|%Δ|Mean|%Δ||Mean|%Δ||Mean|%Δ||Mean|%Δ||Mean|%Δ||Mean|%Δ||
|**NCR (Manila)**|**70.7**|**-0.07**|**67.3**|**0.16**||**3.4**|**-0.22**||**59.2**|**-0.12**||**51.9**|**-0.23**||**12.3**|**0.2**||**262.4**|**-2.5**||
|NCR|70.7|-0.07<br>***|67.3|0.16|***|3.4|-0.22|***|59.2|-0.12||51.9|-0.23||12.3|0.2||262.4|-2.5||
|**Luzon**|**66.3**|**0.53**|**61.7**|**0.62**||**4.6**|**-0.09**||**61.7**|**-2.24**||**57.5**|**-2.21**||**6.8**|**0.19**||**195.2**|**-2.7**||
|I: Ilocos|67.7|1.07<br>***|61.9|0.8|***|5.8|0.27|***|58.9|-2.25|*******|54.9|-2.42|***|6.8|0.54||176.4|-3.9||
|II: Cagayan Valley|68.7|1.09<br>***|63.8|1.26|***|4.9|-0.17|**|65.5|-1.72|**|64.0|-2.12|**|2.4|0.67|**|151.9|-7.4|**|
|III: Central Luzon|67.7|0.21<br>***|63.5|0.45|***|4.2|-0.23|***|59.0|-2.74|*******|53.9|-3.15|***|8.7|1.07|**|194.5|-4.4|**|
|IVA: CALABARZON|67.7|0.65<br>***|63.6|0.64|***|4.1|0.01|***|61.2|-2.09|*******|55.7|-1.17|*|9.1|-1.21|***|221.2|0.1||
|IVB: MIMAROPA|61.1|0.34<br>***|57.0|0.49|***|4.1|-0.14|**|67.2|-1.69||64.9|-2.34|**|3.4|1.07|**|168.0|-0.3||
|V: Bicol|60.6|-0.74<br>***|55.7|-0.32|**|4.9|-0.41|*|64.6|-2.43|*******|61.8|-2.52|***|4.4|0.31||167.7|-10.2|***|
|CAR|66.4|2.56<br>***|61.4|2.06|***|5.0|0.49|***|63.4|-0.6||60.9|-0.54||4.0|-0.07||226.0|-9.0|**|
|**Visayas**|**65.2**|**0.95**|**59.7**|**0.96**||**5.5**|**-0.02**||**63.5**|**-2.38**||**59.8**|**-2.36**||**5.8**|**0.17**||**157.4**|**-2.7**||
|VI: Western Visayas|66.5|1.23<br>***|60.6|1.25|***|5.9|-0.02|***|62.8|-3.24|*******|59.3|-3.93|***|5.6|1.37|***|141.1|0.8||
|VII: Central Visayas|66.6|0.5<br>***|61.4|0.48|***|5.2|0.01|***|62.3|1.08|*|58.0|1.32|**|6.9|-0.48|*|170.7|-3.2||
|VIII: Eastern Visayas|61.1|0.96<br>***|55.7|1|***|5.4|-0.05|***|66.5|-6.38|*******|63.5|-5.37|***|4.5|-1.16|***|166.8|-12.3|***|
|**Mindanao**|**63.9**|**-0.12**|**60.4**|**-0.08**||**3.6**|**-0.04**||**64.6**|**-1.67**||**61.7**|**-1.48**||**4.5**|**-0.18**||**164.6**|**-9.1**||
|IX: Zamboanga Peninsula|63.0|0.57<br>***|59.0|0.66|***|4.0|-0.09|**|63.2|-1.92|*|61.3|-1.47||3.1|-0.63|******|172.2|-20.4|***|
|X: Northern Mindanao|65.3|1.39<br>***|61.0|1.18|***|4.3|0.21|***|71.0|-1.86|**|67.7|-2.74|***|4.7|1.36|***|164.6|-7.2|**|
|XI: Davao|65.6|1.08<br>***|62.0|1.46|***|3.6|-0.37||65.1|-1.31||61.4|-0.56||5.6|-1.04|***|159.7|-6.9|**|
|XII: SOCCSKARGEN|64.2|0.79<br>***|61.0|0.8|***|3.2|-0.01|***|64.8|-2.79|*******|61.4|-2.11|*|5.2|-0.82|***|149.9|-7.7|***|
|ARMM|61.2|-5.82<br>***|59.5|-6.04|***|1.8|0.22|***|54.6|-0.35||52.7|-0.27||3.5|-0.12||232.7|-15.7|***|
|XIII: Caraga|63.1|0.56<br>***|58.6|0.37|***|4.5|0.18|***|66.0|-1.16||63.3|-1.12||4.1|0.02||170.7|-4.0||
|**Philippines**|**66.1**|**0.35**<br>***** **|**61.7**|**0.42**|*******|**4.4**|**-0.07**|***** **|**62.4**|**-1.82**|*******|**58.1**|**-1.76**|*******|**6.8**|**0.08**||**195.3**|**-3.5**|*******|

Source: LFS, 2003-2007

**Note:** Changes of active population rate, participation rate, employment and unemployment are measured as percentage changes over the 2003 – 2007 period. A change in mean daily wage is measured as the absolute change in PHP during the period.

Test statistics are shown for significance of change in each indicator during the 2003 – 2007 period. A positive sign of changes indicates that the level of the indicator is higher in 2007 than in 2003. *, **, and *** represent significance at 10 percent, 5 percent and 1 percent, respectively.

6
<!-- Start of picture text --> foe] Daily wage by region: 2003-07<br>S —— Manila -—--—-—- Luzon<br>seceesenene Visayas —-—-- Mindanao<br>A<br>8 ab<br>| JAA<br>83] i} sant<br>i=Bo] oeyp oT.<br>So) ye MN<br>i; we<br>4 I<br>° = ==. .<br>0 200 400 600 800 1000<br>Daily Wage in 2000 PHP<br><!-- End of picture text -->

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<!-- Start of picture text --> 8© Daily wage by region (Mindanao): 2003-07<br>S —— Zamboanga 9 ———-—- Northern Mindanao<br>j strseeseene——-— DavaoCordillera ——-—-—-- "> AutonomousSOCCSKSARGENRegion<br>g| $<br>fa} Ihe y<br>fel A<br>cs> HaeWF PYAR<br>==Sa) Wii VMoe H'<br>5 iON 4<br>N / ¥,<br>=) 3 oo<br>é M7. \<br>Ss ~<br>sae Comata<br>0 200 400 : 600 800 1000<br>Daily Wage in 2000 PHP<br><!-- End of picture text -->
<!-- Start of picture text --> Changes in Wage - Philippines 2003-2007<br>wo<br>oO<br>e<br>z<br>(a)<br>“<br>=)<br>0 20 40 Percentiles. 60 80 100<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in NCR<br>- Philippines 2003-2007<br>oO<br>oO 20 40 _ 60 80 100<br>Percentiles<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in Luzon<br>- Philippines 2003-2007<br>oO<br>0 20 40 Percentiles_ 60 80 100<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in Visayas<br>- Philippines 2003-2007<br>6<br>,<br>0 20 40 60 80 100<br>Percentiles<br><!-- End of picture text --> <!-- Start of picture text --> Changes in Wage of Laborforce in Mindanao<br>- Philippines 2003-2007<br>Sw<br>0 20 40 60 80 100<br>Percentiles<br><!-- End of picture text -->
participate in the labor force, 15.2 percent are unemployed – the youth unemployment rate is more than twice that of the national average (6.8 percent) or three times that of 25-50 years old (4.6 percent) and almost six times of that of the 50-65 years old (2.6 percent). The mean level of daily wage for the youth (143 PHP) is 25 percent lower than the national average (195PHP). In 2003-2007, the youth unemployment rate increased by 0.6 percent and daily wage declined by 3.9 percent. This suggests that the youth, many of whom have limited working experience, face the most challenging situation in the labor market.

**<u>Table7: Labor Market Performance Indicators by Age Group</u>**

||**Part**<br>|**icipatio**<br>**Rate**|**n**|**Empl**<br>**Ra**<br>**Work**|**oyme**<br>**te (to**<br>**ing A**|**nt**<br>**ge**|**Unemplo**<br>**Rate**<br>**Particip**|**yment**<br>**(to**<br>**ation **<br>**Daily Ear**<br>**P**|**nings (2**<br>**HP)**|**000**|
|---|---|---|---|---|---|---|---|---|---|---|
||Mean|%Δ||Mean|%Δ||Mean|%Δ<br>Mean|Δ||
|Age group (15<=age<25)|45.8|-3.48|*******|40.5|-3.1|***|15.2|0.6<br>*<br>142.9|-3.9|***|
|Age group (25<=age<50)|73.1|-1.35|*******|70.1|-1.3|***|4.6|0.0<br>213.2|-9.9|***|
|Age group (50<=age<65)|70.7|-0.62|**|67.5|-0.9|***|2.6|0.4<br>242.7|-17.5|***|
|Age group (65<=age)|38.8|-1.17|**|35.0|-1.0|*|1.0|0.1<br>165.4|5.7||
|**Philippines**|**62.4**|**-1.82**||**58.2**|**-1.8**||**6.8**|**0.1**<br>**195.3**|**-8.3**||

_Source:_ LFS, 2003-2007 _Note:_ Test statistics are shown for significance of change in each indicator during the 2003 – 2007 period: *, **, and *** represent significance at 10 percent, 5 percent and 1 percent, respectively.

The wage rate is similar between males and females, but the participation and employment rates differ widely (Table 8). For those employed, the daily wage level is similar – the average daily wage of both males and females is 195PHP. As a result of the large difference in participation, the chances for female workers who choose to participate in the labor force to get a job are not much different from those for male workers – both with an unemployment rate of about 7 percent.

The distribution of daily wages is, however, more unequal for females and youth. While a similar share of females who participate in the labor force find a job as their male counterparts, they typically have a wider range of wages. In other words, although female and male workers have a similar level of average wages, female workers are more likely to be in both ends of the spectrum and have high-paid jobs or low-paid jobs than male workers. The P90/P10 ratio (4.8) and the Gini coefficient (0.35) of male workers’ daily wages are lower than those of female workers (8.3 and 0.42, respectively) (Table 9A). Wages increased with age/experience for white-collar jobs and declined for blue-collar jobs after a certain age. The P90/P10 ratio (5.4) and the Gini coefficient (0.34) of the age group 15-25 and 25-50 are lower than those of the age group 50-65 (Table 9B).

> daily wage of those aged 15 to 65. Labor market conditions for the elderly group (over 65) are different from other age groups as a large share of the elderly chooses not to participate in labor force. For example, about two out of five of the elderly population (aged over 65 years old) participates in the labor market; only 1 percent of the elderly in labor force is unemployed.

9
**<u>Table 8: Labor Market Performance Indicators by Gender</u>**

||**Work**<br>**Pop**<br>**(**|**ing A**<br>**ulatio**<br>**15+)**|**ge**<br>**n**|**Work**<br>**Popul**<br>|**ing A**<br>**ation (**<br>**64)**|**ge**<br>**15-**<br>**Wor**<br>**Popul**|**king A**<br>**ation (6**|**ge**<br>**5+)**|**Part**|**icipatio**<br>**Rate**|**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|%Δ||Mean|%Δ|Mean|%Δ||Mean|%Δ||
|Male|65.5|0.56|***|61.5|0.52|***<br>3.9|0.02|***|77.3|-2.43|*******|
|Female|66.7|0.13|***|61.9|0.31|***<br>4.8|-0.17|***|47.5|-1.28|*******|
|**Philippines**|**66.1**|**0.35**||**61.7**|**0.42**|**4.4**|**-0.07**||**62.4**|**-1.82**||
||**Emp**|**loyme**|**nt**|**Unem**|**ploym**|**en**<br>**Daily**|**Earnin**|**gs**||||
||Mean|%Δ||Mean|%Δ|Mean|Δ|||||
|Male|72.0|-2.5|***|6.8|0.3|195.5|-7.6|***||||
|Female|44.3|-1.1|**|6.7|-0.3|*** 194.8|-9.3|***||||
|**Philippines**|**58.1**|**-1.8**||**6.8**|**0.1**|**195.3**|**-8.3**|||||

_Source:_ LFS, 2003-2007 _Note:_ Test statistics are shown for significance of change in each indicator during the 2003 – 2007 period: *, **, and *** represent significance at 10 percent, 5 percent and 1 percent, respectively.

**Table 9: Inequality in daily wage distribution by age and gender groups**

**(2003-2007)**

A. Inequality of Daily Wage distribution <u>by Age Group</u>

||**P90/P10 ratio **|**Gini index**|
|---|---|---|
|Age group (15<=age<25)|5.4|0.34|
|Age group (25<=age<50)|5.4|0.35|
|Age group (50<=age<65)|8.0|0.43|
|Age group (65<=age)|6.3|0.46|
|**Philippines**|**6.3**|**0.38**|

B. Inequality of Daily Wage distribution <u>by Gender</u>

||**P90/P10 ratio **|**Gini index**|
|---|---|---|
|Male|4.8|0.35|
|Female|8.3|0.42|
|**Philippines**|**6.3**|**0.38**|

_Source:_ LFS, 2003-2007

# **4. The role of individual and regional characteristics in employment and wages**

# _4.1 Description of the Model_

This study employs the Heckman correction estimation instead of Ordinary Least Squares estimation in order to address the potential problem of selection bias as participation in the labor force is not random. Individuals who choose to work may be different from those who choose not to work – only those whose reservation wage does not exceed the wage offered by the 10
<!-- Start of picture text --> by + + b,age, +b, age" + +b, age" + age" + + b, school, + p,sex, + bzhousshold,..3, + bzhousshold,..3, bzhousshold,..3, +, married, married,<br>+ $-child_under_S_yearg_old+ p,female, x married, + + b,female,<br>% children_count, + Vy ln (vail, Vy ln (vail, ln (vail, (vail, } 7 Vy, Solndustry_S DP, TU toservice_GDF,, 7 Vy, Solndustry_S DP, TU toservice_GDF,, Vy, Solndustry_S DP, TU toservice_GDF,, Solndustry_S DP, TU toservice_GDF,, DP, TU toservice_GDF,, TU toservice_GDF,, toservice_GDF,,<br>7 Uy Uy PeSME, ». gregiondummy, > » T,Ume_dummy, + ¢ gregiondummy, > » T,Ume_dummy, + ¢ > » T,Ume_dummy, + ¢ » T,Ume_dummy, + ¢ T,Ume_dummy, + ¢ + ¢ ¢ = U U<br><!-- End of picture text --> by + + b,age, +b, age" + +b, age" + age" + + b, school, + p,sex, + bzhousshold,..3, + bzhousshold,..3, bzhousshold,..3, +, married, married, + $-child_under_S_yearg_old+ p,female, x married, + + b,female, % children_count, + Vy ln (vail, Vy ln (vail, ln (vail, (vail, } 7 Vy, Solndustry_S DP, TU toservice_GDF,, 7 Vy, Solndustry_S DP, TU toservice_GDF,, Vy, Solndustry_S DP, TU toservice_GDF,, Solndustry_S DP, TU toservice_GDF,, DP, TU toservice_GDF,, TU toservice_GDF,, toservice_GDF,, 7 Uy Uy PeSME, ». gregiondummy, > » T,Ume_dummy, + ¢ gregiondummy, > » T,Ume_dummy, + ¢ > » T,Ume_dummy, + ¢ » T,Ume_dummy, + ¢ T,Ume_dummy, + ¢ + ¢ ¢ = U U In(wage,) = = ty + + age, + mage” mage” + a,school, + a,school, + + a,sex, + + By, In (val, (val, } = = 6. Yolndustry_G@DR,. “> Ba %service Ba %service %service GDR, - By %SME, + - By %SME, + By %SME, + %SME, + + » yreglon.dummy, yreglon.dummy, > y é,time_dummy, é,time_dummy, <!-- Start of picture text --> In(wage,) = = ty + + age, + mage” mage” + a,school, + a,school, + + a,sex, + + By, In (val, (val, } = = 6. Yolndustry_G@DR,.<br>“> Ba %service Ba %service %service GDR, - By %SME, + - By %SME, + By %SME, + %SME, + + » yreglon.dummy, yreglon.dummy, > y é,time_dummy, é,time_dummy,<br><!-- End of picture text --> <!-- Start of picture text --> (= {2,3,4))<br><!-- End of picture text --> <!-- Start of picture text --> (= (2004,2005,2006,2007})<br><!-- End of picture text --> (= {2,3,4)) (= (2004,2005,2006,2007})
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
does not affect employment. This is consistent with the fact that workers in regions with higher labor productivity tend to have higher level of wage, others being equal. The prevalence of SMEs has a significant positive impact on employment but a negative impact on wage. This is consistent with the fact that many SMEs are labor intensive with less stringent skill requirement, and hence lower paid.

The significance of regional dummy and year dummy variables suggests the unobserved regionspecific characteristics and difference over time both play a role in employment and wage determination.<sup>13</sup> To relax the assumptions that the role of individual characteristics in the labor market is similar across regions, we estimate Heckman selection and income equations for each region separately. The results are shown in Table 11. In each region, individual characteristics – age, education, gender, marital status, household head, child(ren) under 5 years old, interaction terms of female and marital status and of female and children headcount – play significant roles in determining one’s employment status and the first three also in wage level. In each region, wage is positively associated with age, while the positive effect is declining with the increase in age. Returns to education on employment and on wage level are the highest in the NCR. Interestingly, while the wage level is lower for females than for males in all regions, a female has a higher chance of getting employed than a male in NCR, others being equal. This is probably related to the unique characteristics of female labor supply in NCR and the high share of services sector there. Wage gap between male and female workers is the smallest in Luzon.

> 13 If the regional characteristics, such as value added per worker, share of industry, share of services, and share of SMEs, are not controlled for, their impacts will be captured in the regional dummies. In the selection equation, regional dummies have different signs in regression 1 and regressions 2.

13
# **Table 10: Determination of Employment and Daily Wage at the National Level (2003-2007)**

|**Estimation of income equation**|_Reg_<br>**(dependen**|_ression 1_<br>**t variable = dail**|_Re_<br>**y wage in**|_gression 2_<br>**log)**|
|---|---|---|---|---|
|age|0.05|(47.38)***|0.05|(50.55)***|
|age * age|-0.0004|(35.15)***|-0.0004|(37.40)***|
|year of shool|0.09|(238.45)***|0.08|(236.98)***|
|sex (female=1)|-0.22|(51.80)***|-0.23|(56.71)***|
|value added per employee(in log)|||0.03|(13.00)***|
|share of industry (% of GDP)|||0.004|(17.37)***|
|share of service (% of GDP)|||0.0001|(0.44)|
|% of SMEs|||-0.06|(39.49)***|
|Island group dummies (NCR as reference)|||||
|Luzon|-0.21|(55.26)***|-0.21|(29.88)***|
|Visayas|-0.40|(85.08)***|-0.38|(60.84)***|
|Mindanao|-0.38|(87.89)***|-0.30|(35.58)***|
|year dummies (2003 as reference)||yes||yes|
|constant|3.69|(171.35)***|9.35|(55.38)***|
|**Estimation**|**of selectio**|**n equation**|||
|age|0.15|(134.02)***|0.15|(134.18)***|
|age * age|-0.002|(133.37)***|-0.002|(133.51)***|
|year of shool|0.02|(33.05)***|0.02|(31.67)***|
|sex (male = 0, female=1)|-0.06|(9.79)***|-0.06|(9.65)***|
|househoold head (household head =1)|0.32|(40.01)***|0.31|(39.46)***|
|married (married =1)|0.22|(26.68)***|0.22|(26.46)***|
|child of younger than 5 years old|-0.04|(7.61)***|-0.04|(7.48)***|
|female * married|-0.73|(68.11)***|-0.73|(68.14)***|
|female * headcount of child(ren)|-0.07|(32.08)***|-0.07|(31.81)***|
|value added per employee(in log)|||-0.005|(1.32)|
|share of industry (% of GDP)|||0.01|(27.23)***|
|share of service (% of GDP)|||0.01|(15.47)***|
|% of SMEs|||0.02|(7.11)***|
|Island group dummies (NCR as reference)|||||
|Luzon|-0.04|(6.64)***|0.08|(7.65)***|
|Visayas|-0.01|(0.72)|0.09|(9.36)***|
|Mindanao|-0.05|(6.88)***|0.13|(10.29)***|
|year dummies (2003 as reference)||yes||yes|
|constant|-2.75|(141.86)***|-5.10|(19.31)***|
|Log likelihood|-3|96834.1|-3|94553.5|
|Number of observations||467,501||467,501|

Note: *, ** and *** stand for significance at 10 percent, 5 percent and 1 percent, respectively.

14
**Table 11: Determination of Employment and Daily Wage at the Regional Level in the Philippines (2003-2007) –** **<u>Using Years of School as an Indicator of Human Capital</u>**

|||_NCR_|_L_|_uzon_|_V_|_isayas_|_Mi_|_ndanao_|
|---|---|---|---|---|---|---|---|---|
|**Estimati**|**on of inc**|**ome equation(d**|**ependent**|**variable = dail**|**y wage i**|**n log)**|||
|age|0.04|(19.06)***|0.04|(19.46)***|0.05|(22.63)***|0.06|(31.93)***|
|age * age|-0.0004|(14.28)***|-0.0003|(13.76)***|-0.001|(17.30)***|-0.001|(23.98)***|
|year of school|0.09|(76.92)***|0.08|(142.09)***|0.08|(110.22)***|0.08|(119.18)***|
|sex (female=1)|-0.24|(31.01)***|-0.17|(19.76)***|-0.24|(23.83)***|-0.25|(26.07)***|
|year dummies (2003 as reference)||yes||yes||yes||yes|
|constant|3.60|(72.51)***|3.67|(82.86)***|3.18|(62.65)***|2.97|(72.91)***|
|||**Estimation of**|**selection**|**equation**|||||
|age|0.16|(48.76)***|0.15|(88.56)***|0.15|(58.94)***|0.15|(68.89)***|
|age * age|-0.002|(48.29)***|-0.002|(88.95)***|-0.002|(58.99)***|-0.002|(67.58)***|
|year of school|0.04|(23.57)***|0.02|(17.33)***|-0.002|(2.15)**|0.03|(26.06)***|
|sex (male = 0, female=1)|0.25|(15.28)***|-0.10|(9.76)***|-0.04|(2.41)**|-0.29|(21.32)***|
|househoold head (household head =1)|0.32|(17.51)***|0.27|(21.99)***|0.32|(18.08)***|0.36|(20.02)***|
|married (married =1)|0.33|(16.30)***|0.20|(15.30)***|0.23|(12.41)***|0.25|(13.50)***|
|child of younger than 5 years old|-0.02|(1.48)|-0.03|(4.55)***|-0.04|(3.26)***|-0.06|(6.19)***|
|female * married|-0.91|(35.11)***|-0.69|(42.34)***|-0.67|(27.85)***|-0.78|(33.15)***|
|female * headcount of child(ren)|-0.09|(15.16)***|-0.07|(19.86)***|-0.07|(14.69)***|-0.04|(10.78)***|
|year dummies (2003 as reference)||yes||yes||yes||yes|
|constant|-3.34|(60.94)***|-2.66|(96.45)***|-2.58|(62.79)***|-2.93|(78.45)***|
|Log likelihood|-58|765.12|-1|70018|-7|4502.09|-9|1684.48|
|Number of observations||66,654||200,976||85,428||114,443|

_Note:_ *, ** and *** imply significance at 10 percent, 5 percent and 1 percent, respectively.

15
The empirical results show that wages and employment opportunities considerably differ across regions. We use Blinder-Oaxaca decomposition method (Blinder, 1973 and Oaxaca, 1973) to identify the relative role of individual and regional characteristics in wage determination in different regions. Wage differential is decomposed into two parts, one part that can be explained by differences in individual characteristics, such as age, education and gender, and one residual part that may capture the effects regional structural characteristics and unobservable characteristics.<sup>14</sup> Table 12 presents the results of the Oaxaca decomposition study, taking NCR as reference. The results offer support to the significant difference in wage structure between NCR and the other regions. Individual characteristics account for roughly one-third of the difference between NCR and other regions while other characteristics account for about twothirds.

**Table 12: Decomposition of the Effects of Individual and Other Characteristics on Regional –** **<u>Wage Differentials Using Years of School as An Indicator of Human Capital</u>**

||**Mean Daily Wage**<br>**(pesos)**|**Effects of Individual**<br>**Characteristics (Age,**<br>**Education, and Gender) on**<br>**Mean Wage Differential**<br>**between the Region in**<br>**Question and the NCR**|**Effects of Other Characteristics on**<br>**Mean Wage Differential between**<br>**the Region in Question and the**<br>**NCR**|
|---|---|---|---|
|NCR|262|--|--|
|Luzon|195|35.7%|64.3%|
|Visayas|157|32.0%|68.0%|
|Mindanao|165|28.4%|71.6%|

# **5. Conclusions**

The findings suggest large disparities in labor market performance across regions and individual groups in the Philippines. Development gaps widened between the Manila Metropolitan Area and other islands. Compared with other regions, NCR is characterized by high active population rate, low participation rate, low employment rate, high unemployment rate, and high daily wage. The youth, less educated, and women face more challenges in getting employed and earning a good salary, others things being equal.

Disparities in labor market performance are largely associated with the difference in regional structure and human capital endowment. Individual characteristics account for roughly one-third of the difference in wage between NCR and other regions; while regional structures and other unobservable factors account for two-thirds. Regional structural characteristics, such as industrial composition, labor productivity and prevalence of SMEs, are significantly associated with employment opportunities and wage levels. As expected, workers in regions with higher productivity and higher share of industry production are more likely to have higher wage, others being equal. The share of SMEs is positively associated with employment opportunities but negatively with wage, which is probably related to the nature of jobs created in SMEs. For similar reasons, a higher share of services to regional GDP is positively associated with higher employment opportunities, but not with wage. Individual characteristics, such as age, education

> 14 Further examination of roles of regional characteristics and that of unobservable characteristics is of interest for future studies.

16
level, gender, and family status, play an important role in employment and wage. As expected, the well-educated have better chances of getting jobs and earning higher salaries. Interestingly, although the average wage difference between male and female is small, female workers in general have higher education than their male counterparts.

As labor market opportunities and returns to education and other individual talents significantly differ across regions, implementation of appropriate regional labor market policies is important. Human capital, however, remains a key determinant in employment and wage determination. Concerted efforts to improve education will also be important. The design and implementation of relevant policies are of interest for future work.

# **References**

- Balisacan, A.M. and N. Fuwa. (2004, May). “Changes in Spatial Income Inequality in the Philippines.” Research Paper No.2004/34, World Institute for Development Economics Research. (United Nations University, Helsinki).

- Balisacan, A.M. (2003), “Poverty and Inequality”, in: The Philippine Economy: Development, Policies and Challenges, New York Oxford University Press; Quezon City, Ateneo de Manila University Press , 2003

- Blinder, A.S. (1973). “Wage Discrimination: Reduced Form and Structural Estimates. Journal of Human Resources, 8: 436-455.

- Manasan R.G. and S. Chatterjee (2003), in: The Philippine Economy: Development, Policies and Challenges, New York Oxford University Press; Quezon City, Ateneo de Manila University Press , 2003

- Oaxaca, R. (1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review 14: 693–709.

- Schady, N.R. (2000, September). “What Education Pays? Non-Linear Returns to Schooling among Filipino Men” World Bank (mimeo).

- Yamaguchi, F. (2005). “Why Do Schooling Returns Differ? Screening, Private School, and Labor Markets in the Philippines and Thailand.” _Economic Development and Cultural Change, 53_ : 4, 959-81.

17
# **Annex 1: Comparison of employment and unemployment indicators using ILO and NSO definitions**

There is difference in employment rate and in unemployment rate according to the International Labor Organization (ILO) definitions and the Philippines National Statistics Office (NSO) definitions. This annex provides statistics using both definitions for nuance.

Employment rate is defined as the proportion of the working-age population that work or have a job to return during the reference period. The difference in employment (to working age population) rates between ILO and NSO lies in the difference between their definitions of working age population. ILO defines 15 to 65 years old as working age while NSO defines 15 years and above. In this study, the NSO employment rates for the Philippines as well as for each of the 17 regions are slightly lower than the ILO employment rates.

Larger difference lies in ILO and NSO unemployment to participation rates, which results not only from the difference in working age definitions but also to a larger extent from the difference in unemployment definitions. ILO defines unemployment as the working age individual who does not work, is available for work and is actively looking for work. NSO employs a broader definition of unemployment as the proportion of labor force that does not work, is available for work and (1) is actively looking for work, or (2) is not looking for work because of following reasons: (i) belief that there is no job available (discouraged worker); (ii) awaiting results of previous job application (labor in transition); (iii) temporary illness (temporarily disabled); (iv) bad weather; and (v) waiting for job recall (laid-off).

The national unemployment rate by the NSO definition is twice that by the ILO definition (Table A1). This suggests that the NSO unique unemployment, including the discouraged, temporarily disabled, laid-off, in transit, and not looking for work because of bad weather, accounts for 50 percent of total NSO unemployment. A higher ratio of the unemployed falls into the NSO unique unemployment in some regions than others. For example, NSO unique unemployment accounts for 70 percent of NSO unemployment in ARMM, while only 37 percent in NCR. The trends of changes in ILO unemployment and the NSO unemployment also differ across regions. For instance, while the ILO unemployment rate in Western Visayas and in Eastern Visayas did not change significantly over the period, the NSO unemployment increased in Western Visayas and decreased in Eastern Visayas.<sup>15</sup>

> 15 The changes of NSO unemployment rate in Western Visayas and Eastern Visayas are both significant in statistical sense.

18
Composition of NSO unique unemployment – proportions of working age population who is available but do not actively look for work because of the five reasons listed above – is shown in Table A2. In the Philippines, 42 percent of the individuals in working age who are not employed but do not look for a job are discouraged worker – they do not search for jobs because of their beliefs that there is no work available for them; 24 percent are in job transition; 10 percent are the temporarily disabled; 22 percent are the temporarily laid-off; and 2 percent do not look for jobs because of bad weather. These proportions vary across regions. For example, less than 30 percent of those who were available but did not look for jobs in NCR were discouraged; while the ratio is 47 percent in Mindanao. This may imply nuances in different policy challenges to improve labor market performance at the regional level.

**<u>Table A1: Comparison of the ILO and NSO Employment and Unemployment</u>**

||**Empl**<br>**active**|**ILO**<br>**oyment**<br>**popula**|**(to**<br>**tion**|<br>**Unemp**<br>**particip**|**ILO**<br>**loymen**<br>**ation r**|**t (to**<br>**ate)**|<br>**Empl**<br>**active**|**NSO**<br>**oyment**<br>**populat**|**(to**<br>**ion**|<br>**Unem**<br>**(to pa**|**NSO**<br>**ploym**<br>**rticipa**|**ent**<br>**tion**|**NSO's**<br>**unique**<br>**unemployed**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|%Δ||Mean|%Δ||Mean|%Δ||Mean|%Δ||(%)|
|**Manila Metropolitan Area**|**53.3**|**-0.42**||**8.0**|**0.38**||**51.9**|**-0.23**||**12.3**|**0.2**||**37.2%**|
|NCR|53.3|-0.42||8.0|0.38||51.9|-0.23||12.3|0.2||37.2%|
|**Luzon**|**59.0**|**-2.24**||**3.0**|**-0.2**||**57.5**|**-2.21**||**6.8**|**0.19**||**53.6%**|
|I: Ilocos|57.1|-2.51|***|2.2|1.16|***|54.9|-2.42|***|6.8|0.54||68.2%|
|II: Cagyas|65.8|-1.99|**|0.9|0.55|**|64.0|-2.12|**|2.4|0.67|**|61.6%|
|III: Central Luzon|55.4|-3.09|***|4.6|-0.5|*|53.9|-3.15|***|8.7|1.07|**|49.6%|
|IVA: CALABARZON|57.1|-1.35|*|4.8|-0.85|***|55.7|-1.17|*|9.1|-1.21|***|50.3%|
|IVB: MIMAROPA|65.9|-2.53|**|1.6|0.35||64.9|-2.34|**|3.4|1.07|**|52.5%|
|V: Bicol|63.1|-2.3|***|2.0|-0.39|*|61.8|-2.52|***|4.4|0.31||57.1%|
|CAR|62.0|-0.81||1.7|-0.54|*|60.9|-0.54||4.0|-0.07||58.7%|
|**Visayas**|**61.5**|**-2.59**||**2.4**|**0.01**||**59.8**|**-2.36**||**5.8**|**0.17**||**59.5%**|
|VI: Western Visayas|61.4|-4.19|***|2.2|0.45||59.3|-3.93|***|5.6|1.37|***|62.4%|
|VII: Central Visayas|59.5|1.13|**|3.4|-0.46|*|58.0|1.32|**|6.9|-0.48|*|52.9%|
|VIII: Eastern Visayas|64.8|-5.64|***|1.5|-0.25||63.5|-5.37|***|4.5|-1.16|***|68.9%|
|**Mindanao**|**62.5**|**-1.5**||**2.0**|**-0.3**||**61.7**|**-1.48**||**4.5**|**-0.18**||**55.3%**|
|IX: Zamboanga Peninsula|62.1|-1.24||1.2|-0.14||61.3|-1.47||3.1|-0.63|******|61.5%|
|X: Northern Mindanao|68.7|-3.13|***|2.3|0.79|*|67.7|-2.74|***|4.7|1.36|***|53.5%|
|XI: Davao|62.5|-0.47||3.2|-1.14|***|61.4|-0.56||5.6|-1.04|***|44.6%|
|XII: SOCCSKARGEN|62.4|-2.01|*|2.3|-0.79|***|61.4|-2.11|*|5.2|-0.82|***|57.2%|
|ARMM|52.9|-0.41||1.0|-0.26||52.7|-0.27||3.5|-0.12||70.7%|
|XIII: Caraga|64.5|-1.13||1.6|-0.25||63.3|-1.12||4.1|0.02||62.7%|
|**Philippines**|**59.5**|**-1.84**||**3.1**|**-0.12**||**58.1**|**-1.76**||**6.8**|**0.08**||**50.9%**|

19
# **Table A2: Composition of NSO** **<u>unemployment</u>**

||**Uni**|**Labor in**<br>**que unemplo**|**Temporarily**<br>**yment by the**|**Bad**<br>**NSO defini**|**Temporarily**<br>**tion**|
|---|---|---|---|---|---|
||**Discouraged**<br>(%)|<br>**Transition**<br>(%)|<br>**disabled**<br>(%)|<br>**Weather**<br>(%)|<br>**laid-off**<br>(%)|
|**NCR**|**29.2**|**33.0**|**8.8**|**0.4**|**28.6**|
|NCR|29.2|33.0|8.8|0.4|28.6|
|**Luzon**|**42.3**|**25.6**|**8.5**|**1.2**|**22.5**|
|I: Ilocos|55.8|21.4|5.6|1.2|16.0|
|II: Cagyas|58.8|18.4|11.4|0.7|10.9|
|III: Central Luzon|42.8|21.0|7.7|0.8|27.8|
|IVA: CALABARZON|31.5|34.5|7.9|0.9|25.3|
|IVB: MIMAROPA|40.1|17.9|16.8|3.3|21.9|
|V: Bicol|55.2|15.6|11.9|3.0|14.4|
|CAR|39.0|33.2|13.1|1.8|12.9|
|**Visayas**|**50.3**|**15.0**|**12.0**|**3.5**|**19.3**|
|VI: Western Visayas|56.1|12.4|13.0|1.3|17.3|
|VII: Central Visayas|38.8|19.6|11.4|6.1|24.1|
|VIII: Eastern Visayas|59.8|11.7|11.0|2.9|14.6|
|**Mindanao**|**46.7**|**20.8**|**14.1**|**1.9**|**16.5**|
|IX: Zamboanga Peninsula|44.7|26.6|10.0|2.0|16.7|
|X: Northern Mindanao|33.8|22.8|17.2|1.4|24.9|
|XI: Davao|42.0|23.9|14.2|1.0|18.9|
|XII: SOCCSKARGEN|50.3|19.7|13.1|2.9|14.0|
|ARMM|69.7|15.0|9.5|1.2|4.5|
|XIII: Caraga|51.7|14.1|18.4|3.2|12.7|
|**Philippines**|**42.4**|**23.9**|**10.2**|**1.6**|**21.9**|

20
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