unemployment rate research paper philippines

• Corpus ID: 153230357

Unemployment and Inflation in the Philippines: New Evidence from Vector Error Correction Model

• Published 2009
• Philippine Journal of Development

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Contributor Notes

Unemployment has remained high in the Philippines, at almost twice the level of neighboring countries, despite relatively fast employment growth in the past decade. Employment growth was not sufficient to reduce unemployment because of rapid population growth and increased labor force participation. This paper shows that Philippine employment growth and unemployment declines were positively correlated with real GDP growth and, to a lesser extent, negatively with the real minimum wage. The key policy implications are that higher economic growth and moderation of increases in the real minimum wage are required to reduce unemployment.

F actors B ehind H igh U nemployment in the P hilippines

I. i ntroduction.

The unemployment rate in the Philippines remains high relative to other countries in the region. Unemployment fell to a cyclical low of 8 percent in 1996, prior to the onset of the Asian crisis, but rose to more than 11 percent in 2000 and 2001, more than twice the level of a number of neighboring countries. While employment growth has been higher than in many other countries in the region, this has not been sufficient to lower the unemployment rate given high population growth and a rise in labor force participation.

This paper analyses the factors behind unemployment in the Philippines, by estimating equations to explain employment growth and the unemployment rate. The paper first discusses the performance of the labor market (including a comparison with neighboring countries) and its institutional features. It then presents the econometric analysis and discusses some policy conclusions.

II. L abor M arket P erformance in the P ast T wenty Y ears

The unemployment rate 2 has fluctuated in the range of 7-14 percent over the past twenty years (see chart). In 1996, the rate fell to a cyclical low of about 8 percent, prior to the slow down in economic activity associated with the Asian crisis. Unemployment did not rise sharply as a result of the Asian crisis, but jumped in 2000 to more than 11 percent, as employment contracted due to a sharp fall in agricultural employment ( Table 1 ). By October 2001, however, the seasonally adjusted unemployment rate had declined to 10½ percent, due to some recovery in agricultural employment. In the late 1990s, the under-employment rate 3 was more than twice the unemployment rate, but fell to 17¼ percent in 2001.

PHILIPPINES: Unemployment Rate 1/

Citation: IMF Working Papers 2002, 023; 10.5089/9781451844054.001.A001

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Philippines Labor Market Indicators

(In percent)

1/ Defined as employed and unemployed persons as a percent of the population over 15 years old.

Unemployment has been higher among females than males. The female unemployment rate was about 2-2½ percentage points higher than the male unemployment rate in the 1980s and early 1990s, but the gap has narrowed to about 1 percentage point in recent years. Underemployment, however, has been significantly higher among males than females, averaging 24 percent in 2000 for males compared with almost 18 percent for females.

Urban unemployment has been almost twice the rural rate. The urban unemployment rate in 2001 was 14 percent, while the rural unemployment rate was only 8¼ percent (the labor force is about evenly split between rural and urban areas). However, the under–employment rate in rural areas has been higher than in urban areas, suggesting that rural workers are more likely to want additional hours of work than urban workers. Most of the rural workers are employed in the agricultural sector, where part-time and seasonal work is more prevalent than in urban areas.

Employment growth has been strongest in the service sector. Employment in services grew by almost 50 percent in the 1990s, well above growth in the industry sector (30 percent) and agriculture (8 percent). By 2001, the service sector had the largest share of employment at about 47 percent of the total.

Labor force participation has increased by 6 percentage points over the past twenty years, mainly because of a large increase in participation by women. Nonetheless, the participation rate for males, at 82 percent in 2001, remains well above that for women, at 52 percent.

An important feature of the labor market is the large number of Filipinos working overseas. Partly in response to the lack of job opportunities at home, the number of workers formally deployed overseas has increased substantially and reached 2.9 million by 1999, about 9 percent of the labor force. Worker remittances were equivalent to US$6.8 billion in 1999 (about 8½ percent of GNP), up from less than US$1.2 billion in 1990.

III. C omparison W ith O ther C ountries in the R egion

The unemployment rate in the Philippines is about twice the rate of other selected countries in the region ( Figure 1 ). While Philippine unemployment began to decline in the late 1980s, the recession in the early 1990s contributed to a jump in the unemployment rate. The Philippines did not experience the sharp increase in unemployment associated with the Asian crisis seen in other countries, but unemployment in the Philippines increased in 2000, as employment growth slowed in the agricultural sector. The sources and methods for calculating unemployment in the Philippines and neighboring countries differs in some respects, but the measurement basis appears broadly comparable (see data annex ).

SELECTED ASIAN COUNTRIES: Labor Market Indicators, 1985-2000

Employment growth in the Philippines has been faster over the past decade than in most other countries in the region, with the notable exception of Malaysia. Employment in the Philippines grew by almost 30 percent in the 1990s, compared with growth of less than 20 percent in Korea, Thailand and Indonesia over the same period. In Malaysia, however, employment grew by about 40 percent in the 1990s, contributing to a significant fall in the unemployment rate from more than 8 percent in 1988 to about 3 percent in 2000.

Employment growth has not been sufficient to reduce the unemployment rate given the increase in population and the rise in the labor force participation rate. Population growth in the Philippines was among the fastest of Asian countries, and was only slightly slower than in Malaysia ( Figure 2 ). The increase in the labor force participation rate in the Philippines was about in line with that experienced in other countries in the region.

SELECTED ASIAN COUNTRIES: Population, Productivity, and Real GDP Growth

The relatively fast employment growth contrasts with relatively slow real GDP growth in the Philippines. In the 1990s, real GDP expanded by only one-third in the Philippines, compared with increases of 50-100 percent in Indonesia, Korea, Malaysia, and Thailand. The high employment growth combined with the slow GDP growth is reflected in the relatively poor labor productivity performance in the Philippines. Labor productivity increased by less than 7 percent in the past decade in the Philippines, 4 compared with increases of 30-50 percent in the other selected Asian countries.

The comparison with Malaysia is perhaps the most interesting. Malaysia and the Philippines had similar rates of population growth over the past two decades, and unemployment in Malaysia was also relatively high in the 1980s. However, the unemployment rate fell in Malaysia in the 1990s while it rose in the Philippines, as employment growth was not high enough to offset the labor force growth, in contrast to Malaysia where employment growth was high enough to more than offset labor force growth. The higher population base in the Philippines (at 76 million in 2000, compared with about 23 million in Malaysia) meant that the number of new jobs required in the Philippines to reduce unemployment was about three times that in Malaysia.

IV. I nstitutional S tructure of the L abor M arket

Philippine labor policy supports tripartism, with involvement of the government, employers and unions in labor issues. Tripartism was declared a national policy with the passage of the Labor Code in 1974, itself a product of tripartite discussions. The code prescribed the convening of regular national tripartite conferences on labor issues (which have been held about once a year) together with the establishment of tripartite agencies to deal with labor matters. Also, the Labor Code requires the government to assist in job training, job search, monitoring of conditions of employment, health and safety, and labor relations (with involvement of the employers and unions in all these areas).

Wage bargaining centers on the setting of minimum wages, with limited collective bargaining. Since 1989, Regional Tripartite Wages and Productivity Boards have been responsible for setting minimum wages, and comprise representatives of the government, employers and unions ( Box 1 ). A National Wages and Productivity Commission (also with a tripartite composition) reviews the decisions of the regional boards to ensure they are in accordance with the criteria to be used for setting the minimum wage. Collective bargaining at the enterprise level is advocated by government as the ideal way of setting wages and other terms and conditions, but the coverage of collective bargaining has remained relatively limited. 5

Labor has the right to organize but union membership is relatively small. About 3¾ million workers (12 percent of the labor force) belonged to some 9,000 unions in 1998. The extent of union membership among workers in the sizable electronics sector is limited, as it is in the agriculture sector. Union representatives in the Philippines have expressed concern that employers in some sectors (including the electronics sector) do not treat them as partners in seeking improvements in labor productivity and working conditions.

The National Labor Relations Commission is in charge of resolving labor disputes. The commission has exclusive jurisdiction to decide on disputes related to unfair practices; wages and other terms and conditions of employment; and violation of the legality of strikes. Man days lost to strike action in 1998 (550,000) were about half the level of the early 1990s. Despite this improvement, observers note that the process of resolving labor disputes remains overly legalistic and adversarial.

A Congressional Commission on Labor has been reviewing the Labor Code to ensure that developments in the labor market and the demands of globalization are reflected. Employer groups are seeking a less legalistic approach to the resolution of labor disputes and greater flexibility in working hours (currently restricted to five eight–hour days a week). The Department of Labor also notes the need for the Labor Code to conform with International Labor Organization (ILO) conventions ratified by the Philippines. 6

Determination of the Minimum Wage

Prior to 1989, the Philippine Congress determined minimum wages, with no variation by region or industry. The Legislature would set the minimum wage after public hearings and consultations with employers, unions, and government agencies. Wage adjustments were made irregularly, depending on the extent of public demands for an increase. The main objective of setting a minimum wage was to protect workers from exploitation that may arise from imperfections in the labor market. The minimum wage was to be set at a level sufficient for workers to meet basic living requirements and thereby enable a more equitable sharing of national income.

In 1989, the Regional Tripartite Wage and Productivity Boards were established to take over responsibility for setting minimum wages from the Philippine Congress, in an effort to have the minimum wage better reflect regional and industry variation in economic conditions. The regional wage boards are comprised of three government representatives, two employer group representatives and two union representatives. The Boards conduct public hearings before deliberating on the minimum wage adjustment for their region. The Philippine Congress, however, can still legislate the minimum wage, if it were an across-the-board adjustment for the nation as a whole.

In determining minimum wages, the regional boards must consider a range of criteria, including the following:

the demand for living wages;

wage adjustment vis–à–vis the consumer price index;

the need to induce industries to invest in the countryside (where the cost of living is lower);

the fair return on capital invested and capacity to pay of employers; and

effects on employment growth and family income.

The frequency of wage adjustments is usually not more than once every 12 months (usually in October-December). Adjustments could be more frequent if there are extraordinary increases in petroleum prices or basic goods and services.

The regional boards are authorized to exempt certain firms from paying the minimum wage. These typically include new establishments, distressed firms, and establishments with less than 10 workers.

Compliance with the minimum wage requirements, however, is a problem. Of the almost 50,000 establishments inspected by government in 1999, 19 percent were found to have been violating the minimum wage order for their region.

There is no formal government–funded unemployment income support scheme in the Philippines. The tradition of strong family ties, together with the sizable flow of remittances from workers abroad, provides income support for the unemployed. The government, however, provides a number of services, including job search facilities through an internet–based job matching services, and employment creation assistance through the Public Employment Service Office. The government also sponsors a number of job training initiatives in the private sector and administers the deployment of Filipino workers overseas through the Philippine Overseas Employment Administration.

V. F actors A ffecting E mployment G rowth in the P hilippines

The factors behind employment growth in the Philippines are examined in a simple model that regresses the level of employment on output and the minimum wage (deflated by product prices), as follows. 7

The equation is estimated using the cointegration approach (the Johannsen method in PC FIML). The advantage of this approach is that it enables analysis of the variables in level form (which all tested as non–stationary, I (1) variables). 8

The data is explained in the data annex . Employment is derived from the quarterly household labor survey and the minimum wage was used because of the absence of better wage data (a weakness that needs to be addressed to facilitate better analysis). The use of the minimum wage may imply weaker statistical results for the wage variable in the equation if decisions by firms are based on wages that are higher than the minimum (or reflect non–wage costs), 9 that are not correlated with the minimum wage.

The results from estimating the above equation for aggregate employment show a close correlation between output and employment ( Table 2 ). The coefficient on real GDP is estimated at 0.68-0.93, with the results depending on the sample period (with a somewhat lower coefficient for the 1990s only) and on whether the GDP deflator (a proxy for prices faced by the firm) or the consumer price index (CPI) is used to deflate the minimum wage. The results imply that a 10 percent increase in real GDP is correlated with a 7-9 percent increase in employment. Importantly, the results should not be misinterpreted as implying causality between output and employment—they merely indicate a correlation without testing for the direction of causality.

Philippines: Aggregate Employment Equations 1/

(Cointegration Analysis)

1/ The employment equation is specified as follows: log(employment) = α log (real GDP) + β log (minimum wage/price index) + constant + seasonals.

2/ Indicates whether the GDP deflator or the Consumer Price Index was used to deflate nominal wages to obtain a measure of real wages.

3/ The maximum and trace statistics provide a test of the null hypothesis of no cointegration using the Johannsen approach. If the test statistic is significant, the null hypothesis can be rejected.

** indicates that the coefficient is statistically significant at the 5 percent level

* indicates significance at the 10 percent level.

The estimation results are less robust for the minimum wage. For the full sample period, the minimum wage was not found to be significantly correlated with employment. However, for the 1990s, the period when the minimum wage was set regularly by Regional Boards, a significant negative correlation was found. The results for the equation based on the 1990s suggest that a 10 percent rise in the minimum wage is correlated with a fall in employment of about 5-6 percent over the long run. The overall statistical results for the shorter sample period are not as strong, however, given that the cointegration test is not significant (in contrast, for the full sample, the null hypothesis of no cointegration could not be rejected).

Employment equations were also estimated for the three major sectors of the economy, and show similar results to the aggregate equations ( Table 3 ). 10 The coefficients for real value added in the respective sectors range from 0.73 for agriculture to 1.0 for services, which implies that a 10 percent increase in output is correlated with a 7-10 percent increase in employment in these sectors.

Philippines: Employment Equations by Sector 1/

1/ The employment equation is specified as follows: log(employment) = α log (real GDP) + β log (minimum wage/price index) + seasonals.

The minimum wage was found to be significantly and negatively correlated with employment for the agriculture and services sectors. The results suggest that a 10 percent rise in the real minimum wage is correlated with a 4.3 percent decline in employment in agriculture and a 2.8 percent decline in employment in services. The significance of the minimum wage for agriculture is somewhat surprising, given that about three–quarters of the workers in this sector are not directly subject to minimum wage legislation, as they are unpaid family workers or work on their own account. However, the statistical results suggest either that: (1) employment of the one–quarter of workers in agriculture subject to the minimum wage is sensitive to changes in the minimum wage; and/or (2) that an increase in the minimum wage in other sectors may draw workers away from agriculture (which is often seen as a fall–back occupation when employment in non–agriculture sectors is not available). The result for services is not surprising given that almost two–thirds of workers in this sector are in formal employment (and subject to minimum wage legislation). For the industry sector, the minimum wage was not significantly correlated with employment.

The following table summarizes the findings of the regression analysis and shows that the change in real GDP was the main factor correlated with the change in employment. In the 1990s, total employment increased by 28¾ percent, with the equation suggesting that 34½ percentage points of the change was related to real GDP growth and negative 3 percentage points was related to the increase in real minimum wages ( Figure 3 ). A remaining almost 3 percentage points is unexplained by the equation. The negative relationship with the minimum wage is the most significant for the agriculture sector equation, with a negative 10 percentage point contribution from the real wage to employment growth. The fall in the real minimum wage in the service sector (by about 14 percent in the 1990s, due to a larger increase in the GDP deflator in this sector than in other sectors) was related to a 4 percent increase in employment. Nonetheless, a sizable unexplained residual remains for several equations, which may be because the equations are specified in levels and take no account of the dynamic adjustment to the long term equilibrium relationship.

PHILIPPINES: Employment, Real GDP, and Real Wages, 1990-2000

Philippines: Factors Related to Employment Growth

1/ From 1989:2 to 2000:2, unless otherwise stated.

Direct estimation of the unemployment rate in a reduced form equation is consistent with the above results ( Table 4 ). 11 The results suggest that the unemployment rate was:

positively autocorrelated, with the first lag of the unemployment rate the most significant;

negativity related to lagged output growth, showing that an increase in output growth is related to a fall in unemployment (with the first lag the most significant); and

positively related to changes in the minimum wage (deflated by the CPI), suggesting that an increase in the minimum wage is related to a rise in the unemployment rate (with the first and third lags the most significant).

Philippines: Unemployment Equation

(Ordinary Least Squares Analysis)

* indicates significance at the 10 percent level. Figures in brackets, i.e, (), indicate significance levels.

There is also some evidence that the increase in remittances from Filipino workers overseas in the 1990s was positively related to the unemployment rate ( Table 5 ). This suggests that remittances from abroad provide income while workers search for a new job. The results for this equation, however, show that inclusion of remittances makes the minimum wage variable less significant than in the earlier equation (in Table 4 ).

Philippines: Unemployment Equation (including remittances)

* indicate significance at the 10 percent level. Figures in brackets, i.e., ( ), indicate significance level.

VI. F actors A ffecting the M inimum W age

Given the negative correlation between the minimum wage and employment, it may be useful to understand the factors influencing the determination of the minimum wage. A wage equation is estimated as follows (following Layard, Nickell, and Jackman (1991) ):

The estimation results (again using cointegration) suggest that the minimum wage is correlated with the CPI and unemployment rate but less so with productivity ( Table 6 ). The results show that a 10 percent increase in the CPI is correlated with an 11–13 percent increase in the minimum wage rate, depending on the sample period used. The unemployment rate is negatively correlated with the minimum wage, suggesting that regional wage boards take into account labor market conditions in setting the minimum wage (as required by the criteria used to set the minimum wage). 12 Productivity is significant in the equation for the 1990s sample period when the unemployment rate is excluded, suggesting that the regional wage boards may have taken some account of productivity developments in setting the minimum wage.

Philippines: Minimum Wage Equation 1/

1/ The employment equation is specified as follows: log(nominal minimum wage) = α log (consumer price index) + β log (productivity) + C log(unemployment rate) + constant + seasonals

VII. C onclusion

Unemployment has remained high in the Philippines, at almost twice the level of some neighboring countries, despite a relatively strong rise in employment. Job growth was not fast enough to reduce the unemployment rate, given rapid population growth and increased labor force participation in the Philippines.

This paper found that employment growth and the unemployment rate were strongly correlated with real GDP growth in the Philippines. In particular, a 10 percent increase in real GDP was correlated with a rise in total employment of around 7-9 percent. Similar results were shown for the agriculture, industry and services sectors. The unemployment rate was also found to be negatively correlated with real GDP growth.

A less robust correlation was found between employment and the minimum wage. The results show that a 10 percent increase in the real minimum wage was correlated with a 5-6 percent decline in aggregate employment in the 1990s. The analysis by sector showed a somewhat weaker relationship, with employment in agriculture and services more sensitive to the minimum wage than employment in the industry sector. The unemployment rate was also positively correlated with increases in the real minimum wage.

A key policy implication is that higher economic growth and moderate increases in the real minimum wage are required to reduce unemployment to a level more consistent with other countries in the region. In turn, this will require sustained implementation of a comprehensive policy package focused on macroeconomic stability, structural reform, poverty reduction, and better governance (see for example, IMF Occasional Paper 187, Philippines: Toward Sustainable and Rapid Growth , 1999). Moreover, the results suggest that excessive increases in the minimum wage, not justified by price inflation or productivity increases, will likely adversely affect employment growth, especially in the agriculture and service sectors.

A reduction in population growth may reduce pressures on the job market, and on economic infrastructure more generally, but the impact would only be felt in the long run. Even if the birth rate fell sharply in coming years, the population entering the workforce (i.e., those over 15 years old) would likely grow relatively quickly for the next 15 years (at about 2 percent per year) given that more than one-third of the current population is less than 15 years old. The continued growth in the work force underscores the need for strong policies to support sustained economic growth and thereby create jobs for the new entrants.

Ferguson , C. , 1969 , The Neoclassical Theory of Production and Consumption , ( Cambridge , Cambridge University Press ).

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Jackman , Richard , Richard Layard , and Stephen Nickell , 1991 , Unemployment, Macroeconomic Performance and the Labour Market , ( Oxford University Press ).

Rodlauer , M. et al. , Philippines: Toward Sustainable and Rapid Growth, Recent Developments and the Agenda Ahead , Occasional Paper 187 , International Monetary Fund , Washington DC 2000 .

Labor Force Survey

Data on the labor force, employment and unemployment (presented in Table 1 ) is derived from the Labor Force Survey. The survey covers 41,000 households and is conducted four times a year, in January, April, July and October. The reference period for the survey is the week prior to the survey interview. The definitions used in the survey are as follows:

Labor force: persons 15 years old and over who are either employed or unemployed.

Employed: persons 15 years old and over who: (1) worked for one hour or more during the reference period for pay or profit (or without pay on the farm or business enterprise operated by a member of the same household); or (2) have a job but are not at work because of temporary illness/injury, vacation, or other reasons.

Unemployed: persons who have no job/business and are actively looking for work. Also considered unemployed are persons without a job or business who are not looking for work because of their belief that no work was available or because of temporary illness/disability, bad weather, or pending job interviews.

Underemployed: employed persons who express the desire to have additional hours of work in their present job or an additional job, or to have a new job with longer hours.

Comparison with neighboring countries

The statistical basis of the above data is similar to that in neighboring countries such as Indonesia, Korea, Thailand, and Malaysia. 13

In Indonesia, data is derived from a quarterly National Labor Force Survey, based on concepts that are being similar to the Philippine data except that unemployment is defined more narrowly. Unemployment excludes those not actively seeking work because of the belief that no jobs are available, whereas these persons are recorded as unemployed in the Philippines. Employment is defined broadly the same as in the Philippines, except that it covers persons aged 10 years and over (compared with 15 years and over in the Philippines).

In Malaysia, data is derived from a quarterly Labor Force Survey, with unemployment defined in the same way as in the Philippines. The main difference is that persons aged 65 and over are excluded from the employment and unemployment data, whereas they are included in the Philippines. Unemployment is defined in much the same way as in the Philippines, with a distinction made between those actively seeking work and those “inactively unemployed” who did not seek work during the reference week (because they did not believe work was available or those who would have looked for work if they had not been temporarily ill, faced bad weather, or were awaiting answers to job applications).

In Korea, a monthly survey of the labor force is the basis for the data, with the definitions broadly comparable with the Philippines. The survey covers those aged 15 years and over, with the main difference with the Philippines being for unpaid family workers (they need to work for 18 hours or more to be counted as employed in Korea, compared with only one hour in the Philippines). Unemployment is defined much as in the Philippines.

In Thailand, data are based on a survey taken three times a year and the definitions are similar to the Philippines. The survey covers those aged. 15 years and over since 1996 (and 13 years and older before that) who worked for at least one hour for wages, dividends or other forms of payment (or those temporarily out of work). The unemployed are defined much the same way as in Korea, Malaysia, and the Philippines.

Data used for modeling

Employment and unemployment data used in the paper are derived from the labor force survey outlined above, and is available from 1982.

Wage data used for modeling is based on a weighted average of the minimum wage 14 as a no other consistent and timely data for labor costs is available. Average monthly compensation of paid employees is available from the Annual Survey of Establishments conducted by the National Statistics Office, but the latest data is for 1995. An index of compensation per employee is also published by the National Statistics Coordination Board, but is derived simply as total compensation divided by total employment and therefore is unadjusted for changes in skill levels of the workforce. 15 Better wage data is needed to facilitate labor market analysis. In particular, a labor cost index is needed that adjusts for quality changes in the labor force.

Real gross domestic product data is derived from the Philippine National Accounts, as is data for the GDP deflator. Seasonally adjusted GDP data is used.

The author is grateful to: Bas Bakker, Nigel Chalk, Maple Kongsamut, Sean Nolan, and Markus Rodlauer for helpful comments; and to Clara Eulate, Ioana Hussiada, and Nong Jotikasthira for research and secretarial support.

Derived from a quarterly household labor survey (see data annex for details).

Defined as employed persons seeking additional hours of employment.

In part, the slower productivity growth in the Philippines may reflect statistical problems with the national accounts. The national accounts data has not adequately measured the rapid growth of value added in the electronics and information technology sector in the Philippines in the past 5-6 years, yet the household labor force survey has likely measured the increased employment in these sectors. Therefore, growth in both value added and labor productivity are likely understated in the 1990s.

Only about 500,000 out of 29 million workers are covered by Collective Bargaining Agreements.

The Philippines recently ratified the anti-child labor conventions No. 138 and No. 182, bringing to seven the number of conventions ratified out of the eight fundamental ILO conventions. The Philippines is working toward ratification of the remaining fundamental convention (No. 29) on forced labor.

This approach assumes that the underlying technology is a CES production function, following Ferguson (1969) . A cost function for labor can be derived from the production function, as specified in the equation.

The use of simple Ordinary Least Squares analysis of levels data would likely give rise to spurious regression results, hence the use of cointegration. Moreover, analysis of the change in employment (to ensure stationarity of the variables) would not pick up information in the trends that are clearer in the level data.

A survey conducted by the Bureau of Labor and Employment Statistics that covered non-agricultural establishments employing at least 20 persons in 1998, found that wages and salaries comprised almost 80 percent of total labor costs. The remaining labor costs included bonuses and gratuities, social security expenses, and payments in kind. The annual salary and wage costs per employee were about 107,000 pesos in 1998, compared with the annualized minimum wage of 26,000–46,000 (assuming 250 working days annually—the range reflects the regional variation in the minimum wage in 1998, with a low of 104 pesos per day and a high of 187 pesos per day).

Data are only available for employment by sector since the late 1980s. For the industry and service sector equations, the sample period was shortened to begin in 1991:1 and 1990:2 respectively, due to instability in the coefficients for the full period from 1989:2.

The reduced form equation is specified as follows: log unemployment rate = α log (unemployment rate) + β Δlog (real GDP) + γ Δlog (minimum wage/cpi) + constant + seasonals. Given that statistical tests suggest that the unemployment rate may be stationary, the quarterly change in real GDP and the minimum wage were used to obtain stationary explanatory variables, and the equation was estimated using Ordinary Least Squares.

The unemployment rate is not a strongly trending variable (i.e., it is likely a stationary or I(0) variable), hence it strictly should not be included in the cointegration equation. Therefore, the results of the cointegration equation including unemployment should be treated with caution.

See the International Labor Organization website laborsta.ilo.org for further details.

The minimum wage for Manila and outside Manila are weighted together using employment. A distinction is made between the minimum wage for agriculture and non-agriculture (with the latter used in the industry and services employment equations).

For example, the index of compensation per employee for manufacturing fell by about half in the 1990s (in constant price terms), apparently because a growing share of lower paid jobs lowered the average compensation for the sector as a whole.

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• Republic of the Philippines Labor Market Review: Employment and Poverty.
• Unpacking Youth Unemployment in Latin America
• Unemployment and the earnings structure in Latvia
• The Sri Lankan Unemployment Problem Revisited
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Table of Contents

• I. Introduction
• II. Labor Market Performance in the Past Twenty Years
• III. Comparison With Other Countries in the Region
• IV. Institutional Structure of the Labor Market
• V. Factors Affecting Employment Growth in the Philippines
• VI. Factors Affecting the Minimum Wage
• VII. Conclusion
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The Applicability of Okun’s Relationship Between Unemployment and GDP Growth in the Philippines

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iRASD Journal of Economics

Dr. Farhat Rasul

This study investigates the validity and asymmetry of output-unemployment relationship for three groups: high income, upper middle income and lower middle income Asian Economies over the period of 1980-2018. This study investigates whether the behavior of labor markets is rigid or flexible in these economies over the sample period. By using the Hodrick and Prescott filter, the study finds a statistically significant relationship between cyclical output and cyclical unemployment; hence provides the evidence of the existence of Okun’s Law with more sensitive results for the lower middle economies as compared to other groups of countries. The study also discovers the evidence of asymmetric relationship of output-unemployment during the recessionary and expansionary period of economic growth. Although the value of coefficient varies due to asymmetry but the variation is found to be small across the three groups of the countries. The study concludes that sample economies have rigid labor...

Saqib Masood

This research work is on the topic of an empirical estimation of Okun’s law in context of Pakistan. Coefficient of Okun’s law is estimated to check whether this law exist in Pakistan’s economy or not. Okun’s law shows three to one link between real GDP and rate of unemployment. Time series data of real GDP and rate of unemployment of Pakistan have been used to find the validity of Okun’s law. Duration of data is 1972- 2012. Different three versions of Okun’s law gap version, difference version and dynamic version are used to calculate the Okun’s coefficient. Ordinary least square method is applied for analysis. The empirical results show that there is no existence of Okun’s law in Pakistan’s economy. Coefficients estimated by using all the three versions are very small and reject the presence of this law in Pakistan.

towseef mohi ud din

gatot sasongko

This study aims to confirm the existence of Okun's coefficients in Indonesia. Authors conducted panel data of 34 provinces in Indonesia for the period from 2014 to 2019, obtained from the Central Bureau of Statistics. The data were analyzed by using the panel data model and Panel Granger Causality. The Panel Granger causality analysis results show that there was one-way causality between the economic growth and the unemployment. It was caused by several influencing factors, such as education level, population, and employment opportunities. Based on the Fixed Effect Model and Panel Granger Causality results, authors conclude that Okun's Law has not been proven for 34 provinces in Indonesia.

A New Perspective in Social Sciences

Even though economies creating not enough employment have been examined for a long time, researchers do not reach any consensus about how to solve it out. In the developing countries, under growing world economy theories on jobless growth are being developed since it is not opening new jobs to reduce unemployment rate even if the growth rate of population has been decreased after the World War II. Politicians are also concerned about unemployment since it has a potential threat to the social environment and security in society. Also, it is inevitable to lead not only to decreasing wealth but also increasing psychological problems with unemployed. This may lead to detrimental troubles which are more difficult to figure them out. To avoid these, politicians and economists implement some economic policies to cease to rising unemployment rate or reduce it. It is generally accepted that economic growth results in reducing the rate of unemployment. To reduce unemployment, there will be an increase in production in an economy which causes rising demand for factors of production and since labour is a factor of production so this process leads to increase employment and reducing the rate of unemployment rate. Thus, unemployment decreases in an economy which has a positive growth rate. On the other hand, although many countries have grown by high rate after the 1980s, yet the rate of unemployment has not declined by satisfying amount. Especially, employment rate has become steady or declined in some industry which has registered increased productivity. In that case, unemployment rate may not decrease enough if economic growth is caused by productivity. Okun (1962) pointed out the relation between economic growth and unemployment rate. If economic growth exceeds its potential by 1%, then unemployment rate goes down by 0.5%. Later, this relation is called Okun’s Law. After the 1980s, one of the resources of economic growth was productivity, especially in developing countries. Due to the increase in productivity, the employment rate remained as before or could decrease even if the countries’ economies grew. Moreover, the growth rate of the population was high which led to decrease employment rate or less than economic growth performance. The aim of this paper is that investigate Okun’s law for five fragile economies: Brazil, Indonesia, India, South Africa and Turkey. This paper is organised as follows. Section 1 describes basic theoretical background of Okun’s Law. In Section 2, the paths for growth rate and unemployment rate from 1990 to 2017 for five fragile countries are discussed. Then, some empirical examples for five fragile economies will be presented in section 3. In the following part, data and economic models will be discussed. The results of applications are reported in Section 5. Finally, Section 6 makes concluding remarks.

Research Papers in Economics

Stefan Marth

We examine Okun´s Law on the basis of new growth perspectives and in the context of the WWWForEurope project. By comparing Okun´s work from 1962 with the latest IMF and OECD surveys, a connection is set up and Okun´s theoretical framework is updated and its persistence is examined. In addition, the implicit logic of Okun´s Law is expressed and stressed, and literature is reviewed from this point of view as well. Furthermore, the main implicit component of Okun´s Law - productivity - is taken into account when discussing its breakdown in the WWWForEurope context. After applying the „Difference method“, we derive the conclusion that the Okun coefficients and intercepts vary substantially between countries.

Sema Yılmaz Genç

One of the most important macroeconomic problems which have gone on from the past to today is that the effort among the production factors is not utilized. In this sense, one of the studies for the solution of unemployment problem is whether the increase in economic growth will decrease the unemployment or not. The American economist Okun is the first economist who reviewed the relation between the economic growth and unemployment. Okun's theorem which explained the negative correlation between the economic growth and unemployment in America in 1948-1960 has included as Okun's Law in the economics literature. Okun's Law indicates that the unemployment rate decreases in the periods when the growth rate is high, and the unemployment rate increases in the periods when the growth rate is low. In this study, the validity of Okun's Law in Turkey has been reviewed. The concept and types of unemployment have been firstly included with this purpose. Secondly, the relationship between the unemployment and economic growth has been analyzed with Granger Causality test with the use of data between 1988-2016.

Syed hassan Raza

This research work is on the topic of an empirical estimation of Okun's law in context of Pakistan. Coefficient of Okun's law is estimated to check whether this law exist in Pakistan's economy or not. Okun's law shows three to one link between real GDP and rate of unemployment. Time series data of real GDP and rate of unemployment of Pakistan have been used to find the validity of Okun's law. Duration of data is 1972-2012. Different three versions of Okun's law gap version, difference version and dynamic version are used to calculate the Okun's coefficient. Ordinary least square method is applied for anaylsis. The empirical results show that there is no existence of Okun's law in Pakistan's economy. Coefficients estimated by using all the three versions are very small and reject the presence of this law in Pakistan. • INTRODUCTION Arther Okun in 1962 presented a law about the relationship between real GDP and unemployment rate. He suggested a three percent growth in output is related with a one percent decrease in the rate of unemployment, ceteris paribus. When there will increase in production, more workers will be required to produce extra units. And there will be reduction in unemployment, so with the increase in the GDP, unemployment will decrease. But both the variables do not change with same proportion. GDP changes more rapidly rather than to change in unemployment. At the earlier studies Okun estimated the three present increase in GDP cause one present decrees in unemployment. But the later studies which were based on the latest data showed that two present GDP growths are associated with one present decrease in unemployment. The value of the coefficient may vary from country to country and economy to economy depending on the circumstances and economic situations. (Lal at el. 2010) Estimation of Okun's coefficient which is a measure of the responsiveness of unemployment to output growth is important because it shows the cost of unemployment in terms of output. Okun's law is often used as a scale for computing the cost of unemployment. However, it is also essential to examine and watch out if the impact of the variations in the country's growth to the variations of unemployment is harmful to the citizen of Pakistan and in which the appropriate policies are needed. This paper aimed at the estimation of the coefficient of Okun's law to check the existence of Okun's law from Pakistan economy and to give some policy recommendations in the light of estimated coefficient. The purpose of this paper is to calculate Okun's coefficient, and discover the validity of Okun's law for Pakistan. The inspiration for doing this work is straight forward, if Okun's law is valid for Pakistan this will give an idea about the kind of unemployment which would then suggest whether or not unemployment can be decreased by increasing growth. This research paper estimates the validity of Okun's law in Pakistan's economy whether both unemployment rate and GDP growth are linked with each other or not to find the intensity of this relationship as well as direction of association. According to Okun's formulation, GDP growth causes diminishing trend in unemployment rate. The growth in GDP means an increase in real GDP over time and real GDP means value of all goods and services formed in any economy adjusted for price changes. This research is important not only in order to know how much the output of this country causes changes in unemployment rate but also the mechanism through which these effects take place. For a country that has suffered considerably from the persistence of high regional unemployment dispersion, the knowledge of this relationship for is important from the point of view of the implementation of appropriate economic policies. This paper includes the three versions of Okun's law. Dynamic version is also used for the estimation of coefficient. This version has not been used by any researcher before in context of Pakistan's economy. Extended time period from 1972 to 2012 is used. The implementation of adequate policies to continue with the reduction of unemployment and then with a higher growth of output is one of the main goals of Pakistani national and regional policy makers. In order to devise these policies it would be essential to explain if there is a relationship

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Philippines Unemployment Rate

The unemployment rate in the philippines declined to 3.1% in june 2024 from 4.5% in the corresponding month of the previous year, pointing to the lowest level since last december. the number of unemployed persons dropped to 1.62 million from 2.33 million in june 2023 and in may 2024 at 2.11 million. meanwhile, employment increased to 50.28 million from 48.84 million in the same month a year earlier and 48.87 in may 2024. among industry groups, employment grew the most in construction (938 thousand), wholesale and retail trade; repair of motor vehicles and motorcycles (527 thousand), and accommodation and food service activities (396 thousand); manufacturing (353 thousand). by contrast, employment dropped the most in agriculture & forestry (-916 thousand); public administration and defense; compulsory social security (-340 thousand), and fishing & aquaculture (-81 thousand). in the meantime, the labor force participation rate edged down to 66.0% in june 2024 from 66.1% a year earlier. source: philippine statistics authority, unemployment rate in philippines decreased to 3.10 percent in june from 4.10 percent in may of 2024. unemployment rate in philippines averaged 7.98 percent from 1986 until 2024, reaching an all time high of 17.60 percent in june of 2020 and a record low of 3.10 percent in december of 2023. this page provides - philippines unemployment rate - actual values, historical data, forecast, chart, statistics, economic calendar and news. philippines unemployment rate - data, historical chart, forecasts and calendar of releases - was last updated on august of 2024., unemployment rate in philippines decreased to 3.10 percent in june from 4.10 percent in may of 2024. unemployment rate in philippines is expected to be 4.90 percent by the end of this quarter, according to trading economics global macro models and analysts expectations. in the long-term, the philippines unemployment rate is projected to trend around 5.00 percent in 2025, according to our econometric models..

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Us job market may be near tipping point, research shows.

By Howard Schneider

JACKSON HOLE, Wyoming (Reuters) - As inflation fell fast in 2023 and continued to slow this year, Federal Reserve officials were cheered that the steam seemed to come out of the U.S. economy not through rising unemployment but rather a decline in the large number of job openings businesses posted during the peak of the pandemic-era labor shortage.

But the economy may now be near a tipping point where a continued drop in job openings will translate into faster increases in unemployment, an argument in favor of the Fed beginning to cut interest rates to guard the labor market, according to new research presented on Friday at the Kansas City Fed's annual economic conference in Jackson Hole, Wyoming.

"Policymakers face two risks: being too slow to ease policy, potentially causing a 'hard landing' with high unemployment ... or cutting rates prematurely, leaving the economy vulnerable" to rising inflation, economists Pierpaolo Benigno of the University of Bern and Gauti B. Eggertsson of Brown University wrote in their research paper. Based on their new analysis of the job market, "our current assessment suggests the former risk outweighs the latter."

Fed officials appear to have reached the same conclusion, with reductions to the U.S. central bank's benchmark policy rate expected to begin at the upcoming Sept. 17-18 meeting and likely continue in subsequent sessions.

Still, the new research adds further detail to several ongoing Fed debates by combining in a single economic model two key relationships; one between the unemployment rate and rate of inflation, known as the Phillips Curve, and one between the job vacancy rate and the unemployment rate, known as the Beveridge Curve.

The paper suggests, for example, that when labor markets are loose, policymakers can continue to regard supply shocks as of less consequence to underlying inflation and to appropriate monetary policy. It takes a combination of supply problems and tight labor markets, they conclude, to generate the sort of persistent inflation surge the U.S. just experienced.

It also adds a dose of caution to a debate that has been underway at the Fed now for years over what constitutes the maximum level of employment that is consistent with the central bank's 2% inflation target - Congress has made the Fed responsible for both objectives - and what risks policymakers may need to take with the job market to keep inflation low and stable.

The answer, the research suggests, is that it depends heavily on the underlying demand and supply of labor, which Benigno and Eggertsson capture by focusing less on the unemployment rate itself and more on the ratio of job openings to the number of people looking for work.

When the number of openings and the number of unemployed jobseekers is close to balance, taming an inflation outbreak involves a large rise in joblessness, as happened in the 1970s when the U.S. experienced high inflation and unemployment simultaneously.

When the labor market is tight, by contrast, with demand for workers high relative to their numbers, "the cost of reducing inflation in terms of increased unemployment is relatively low," the researchers concluded.

The job-openings-to-unemployed metric became important in recent U.S. central bank discussions, a focus of policymakers and Fed Chair Jerome Powell in particular when it spiked above the 2-to-1 mark during the reopening from the COVID-19 pandemic, with firms posting two jobs for every available body.

RISKS OF RUNNING 'HOT'

Analysis by Fed Governor Christopher Waller and staff economist Andrew Figura in 2022 suggested that bringing that ratio closer to balance could lower inflation without the unemployment rate rising much, if at all, a counter to predictions by other top economists that unemployment rates as high as 10% might be needed to tame the worst U.S. inflation outbreak in 40 years.

Their findings have been proven in practice, with the ratio now down to 1.2, the Fed's preferred measure of inflation down to 2.5% from a peak of more than 7% in June of 2022, and the unemployment rate until recently remaining below 4%.

Yet even the current ratio is above the one-to-one level that the researchers say seems to mark the breakpoint - at least approximately - between labor market conditions that generate inflation and those that don't. Since World War One, they found, most inflation outbreaks have involved job openings rising above the number of people who are out of work and looking for a job.

After years of persistently low inflation and falling unemployment in the decade before the pandemic, Fed officials had felt they could potentially run the economy "hot," to the benefit of workers, with little chance of rising prices. The new research suggests there are risks in that approach.

The researchers also warn that if the openings-to-unemployed ratio continues to slide, the economy is at a point where unemployment could rise fast, a fear Waller himself has recently raised.

In the current situation, they project the Fed could achieve its inflation objective, with the number of job openings in balance with the number of unemployed, at a jobless rate of around 4.4% - still below the long-term average for the U.S. but significantly higher than the experience of the past roughly two years.

Once the one-to-one threshold is passed, "further reductions in inflation are likely to be more costly," they wrote, with a job openings-to-unemployed ratio of 0.8 - with fewer jobs than people looking - causing unemployment to rise above 5%.

(Reporting by Howard Schneider; Editing by Paul Simao)

Assessing Patterns and Trends in Urbanization and Land Use Efficiency Across the Philippines: A Comprehensive Analysis Using Global Earth Observation Data and SDG 11.3.1 Indicators

• Original Article
• Open access
• Published: 13 August 2024

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• Jojene R. Santillan   ORCID: orcid.org/0000-0002-5895-8647 1 , 2 &
• Christian Heipke   ORCID: orcid.org/0000-0002-7007-9549 1  

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Urbanization, a global phenomenon with profound implications for sustainable development, is a focal point of Sustainable Development Goal 11 (SDG 11). Aimed at fostering inclusive, resilient, and sustainable urbanization by 2030, SDG 11 emphasizes the importance of monitoring land use efficiency (LUE) through indicator 11.3.1. In the Philippines, urbanization has surged over recent decades. Despite its importance, research on urbanization and LUE has predominantly focused on the country’s national capital region (Metro Manila), while little to no attention is given to comprehensive investigations across different regions, provinces, cities, and municipalities of the country. Additionally, challenges in acquiring consistent spatial data, especially due to the Philippines’ archipelagic nature, have hindered comprehensive analysis. To address these gaps, this study conducts a thorough examination of urbanization patterns and LUE dynamics in the Philippines from 1975 to 2020, leveraging Global Human Settlement Layers (GHSL) data and secondary indicators associated with SDG 11.3.1. Our study examines spatial patterns and temporal trends in built-up area expansion, population growth, and LUE characteristics at both city and municipal levels. Among the major findings are the substantial growth in built-up areas and population across the country. We also found a shift in urban growth dynamics, with Metro Manila showing limited expansion in recent years while new urban growth emerges in other regions of the country. Our analysis of the spatiotemporal patterns of Land Consumption Rate (LCR) revealed three distinct evolutional phases: a growth phase between 1975–1990, followed by a decline phase between 1990–2005, and a resurgence phase from 2005–2020. Generally declining trends in LCR and Population Growth Rate (PGR) were evident, demonstrating the country’s direction towards efficient built-up land utilization. However, this efficiency coincides with overcrowding issues as revealed by additional indicators such as the Abstract Achieved Population Density in Expansion Areas (AAPDEA) and Marginal Land Consumption per New Inhabitant (MLCNI). We also analyzed the spatial patterns and temporal trends of LUE across the country and found distinct clusters of transitioning urban centers, densely inhabited metropolises, expanding metropolitan regions, and rapidly growing urban hubs. The study’s findings suggest the need for policy interventions that promote compact and sustainable urban development, equitable regional development, and measures to address overcrowding in urban areas. By aligning policies with the observed spatial and temporal trends, decision-makers can work towards achieving SDG 11, fostering inclusive, resilient, and sustainable urbanization in the Philippines.

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1 Introduction

Rapid urbanization is a global phenomenon with significant implications for sustainable development. Because urbanization is inevitable, the United Nations Sustainable Development Goal 11 (SDG 11) was established, aiming to “make cities and human settlements inclusive, safe, resilient and sustainable”. One of the objectives included within this goal is SDG 11.3, which sets out to improve inclusive and sustainable urbanization by 2030, enhancing the capability for participatory, integrated, and sustainable planning and management of human settlements across all nations (UN Department of Economic and Social Affairs 2024 ). Within this context, sustainable urbanization entails the improvement of land use efficiency and urban planning strategies. Understanding the patterns and dynamics of built-up area expansion and population growth is crucial for effectively meeting this objective and advancing the broader aim of SDG 11.

Monitoring the achievement of the land use efficiency (LUE) target is facilitated through the SDG 11.3.1 indicator, known as LCRPGR, which represents the ratio of the land consumption rate (LCR) to the population growth rate (PGR) (UN Statistics Division 2021 ). Analyzing the LUE of urban and other settlement areas offers numerous benefits. Firstly, it provides insights into the evolution of urban settlements, aiding in understanding their development over time. Secondly, it assists authorities and decision-makers in identifying new areas for growth and helps formulate policies for the optimal use of land (Zhou et al. 2021 ). Monitoring progress against the SDG indicator 11.3.1 allows decision-makers and stakeholders to access timely information, facilitating accelerated progress towards inclusive and sustainable urbanization (UN-Habitat 2018 ).

The Philippines, like many other developing countries, has experienced substantial urban growth over the last decades. In fact, the Philippines stands out as one of the pioneers of urbanization within the East Asia and Pacific region, with a significant surge in urbanization gaining momentum during the 1960s (The World Bank Group 2017 ). By 2050, it is estimated that 84% of Filipinos reside in urban areas. This growth is anticipated to occur not only within the nation’s capital region (Metro Manila), but predominantly within smaller and mid-sized cities (UN-Habitat 2024 ). This phenomenal shift towards urbanization has led to various challenges related to land use, infrastructure development, environment, and the well-being of urban populations (Berse 2024 ). The UN-Habitat reported that in numerous cities across the Philippines, the ramifications are already evident: limited access to land and adequate housing for the vulnerable and poor, burgeoning slums, outdated infrastructure, poverty, unemployment, safety concerns, crime rates, environmental pollution, health issues, and the repercussions of both natural and man-made disasters, including those exacerbated by climate change and conflict (UN-Habitat 2024 ). Within the context of sustainable urbanization, addressing these issues is imperative. However, it requires a thorough understanding of the country’s urbanization process.

The complex dynamics shaping urban development in the Philippines has been studied through the years (Boquet 2023 ; Bravo 2017 ; Dumayas 2015 ; Estoque 2017 ; Jolipa 1980 ; Murakami 2000 ; Nemeth and Smith 1983 ; Ortega et al. 2015 ; Pernia 1976 ; Von Einsiedel 1992 ). A recurring theme across these studies is the significant role of economic opportunities in driving migration to urban centers, particularly in Metro Manila, which serves as a primate megacity dominating the country’s urban hierarchy. The spatiotemporal aspects of urbanization have also garnered significant attention from researchers seeking to understand the dynamics of land use/land cover change, population distribution, and urban growth (Bagarinao 2017 ; Estoque 2017 ; Estoque and Murayama 2011 , 2015 ; Malaque and Yokohari 2007 ; Murakami et al. 2005 ; Murakami and Palijon 2005 ; Olfato-Parojinog et al. 2023 ; Zoleta-Nantes et al. 2008 ). There are also investigations examining the social and environmental impacts of urbanization in different localities of the Philippines (Almadrones-Reyes and Dagamac 2023 ; Constantino-David and Valte 1994 ; Dado and Narisma 2022 ; Estoque and Murayama 2013 ; Flores et al. 2024 ; Jago-on et al. 2009 ; Moriwake et al. 2002 ). It is notable that the majority of these studies benefitted from remote sensing, Geographic Information System (GIS) and related geospatial technologies. However, most of these studies have focused on Metro Manila. Not enough attention is given to comprehensive investigations across different regions, provinces, cities, and municipalities, thus limiting our insight into how urbanization varies across the Philippines. With the exception of Santillan and Heipke ( 2023 ), existing studies only provide insights into urbanization dynamics at specific points in time. There is a need for longitudinal studies tracking urbanization trends over extended periods to identify temporal variations and patterns of change associated with urbanization at the local scales. Additionally, there is a notable gap in studies assessing the impacts of urbanization on LUE at local levels, particularly within the context of SDG 11.3.1. Understanding how urbanization influences land consumption and efficiency is crucial for informing sustainable development strategies and promoting inclusive urban growth.

In this work, we extended the earlier study conducted by Santillan and Heipke ( 2023 ) to finely examine the patterns and trends in built-up area expansion, urbanization characteristics, and LUE from 1975–2020 in the Philippines using the Global Human Settlement Layers (GHSL). In addition to conducting analysis at city and municipal levels, unique aspects of this study are the use of SDG 11.3.1 secondary and supplementary indicators (Table  1 ). Specifically, this study aims to answer the following key research questions:

What are the spatial patterns and temporal trends of urbanization, built-up land consumption, population growth and LUE across Philippine cities and municipalities?

What do the secondary and supplementary indicators associated with SDG 11.3.1 reveal about the characteristics of LUE across the Philippines?

The remainder of this paper is structured as follows. Section 2 discusses related work, including an overview of global EO datasets for urbanization and LUE efficiency studies, and a summary of the state-of-the-art of SDG 11.3.1 monitoring. Section 3 presents the methodology, including a description of the scope and level of analysis, input datasets, as well as the processing and analytical steps employed. Sections 4 and 5 present the results and its discussion. Finally, Sect. 6 provides the conclusions and outlook of the study.

2 Related Work

2.1 global eo datasets for urbanization and lue studies.

A major limitation for conducting comprehensive urbanization and LUE studies over large geographical areas is the availability of suitable spatial datasets. In the case of the Philippines, its archipelagic nature complicates the collection of consistent data on land-use/land cover and population in cities and municipalities which are spread across multiple islands. In addition, its geographical location in the tropics often results in a significant presence of cloud cover in optical remote sensing images, making it challenging to generate complete land use/land cover maps. These complexities result in data gaps and variations in type and quality, and consequently limit detailed and accurate analysis of urbanization and land use efficiency. Fortunately, recent advancements in remote sensing and other geospatial technologies have led to the generation of global EO datasets suitable for SDG 11.3.1 monitoring. Global land cover datasets, like GlobeLand30 (Jun et al. 2014 ), Esri Landcover (Karra et al. 2021 ), ESA WorldCover (Zanaga et al. 2021 ), ESA CCI Land Cover (ESA 2017 ), Global Urban Footprint (GUF) (Esch et al. 2017 ), Global Artificial Impervious Area (GAIA) (Gong et al. 2020 ), World Settlement Footprint (WSF) (Marconcini et al. 2021 ), and the GHS-BUILT‑S of the GHSL (European Commission 2023 ), among others, are vital sources of multitemporal built-up area information, especially for the Philippines. Spatially disaggregated population datasets such as GHS-POP of the GHSL, Gridded Population of the World (GPW) (CIESIN 2016 ), and WorldPop ( 2024 ) are also available, providing globally consistent and spatially explicit data for urbanization and land use efficiency studies.

Among global EO data products, the GHSL has been considered as state-of-the-art data source for SDG 11.3.1 monitoring (Estoque et al. 2021 ; Melchiorri et al. 2019 ). The GHSL project, managed by the European Commission–Joint Research Centre (JRC), has generated and examined data on global built-up surfaces, population density, and thematic maps of human settlements to gain insights into human presence on the planet. Over the years, JRC has made significant investments in research and methodological advancements, resulting in the generation of these innovative products (Pesaresi et al. 2024 ). Spatial data mining technologies were employed for the automatic processing and analysis of vast amounts of satellite images, census data, and volunteered geographic information (European Commission 2023 ). The GHSL is distinguished by its availability across varying spatial resolutions (e.g., 1 km, 100 m), gridded data structure, comprehensive multi-temporal coverage (from 1975–2020, including projections for the years 2025 and 2030), unrestricted open access, and a distinct balance between local specificity and global uniformity (European Commission 2023 ). These intrinsic attributes render the GHSL exceptionally suitable to addressing the data requirements to assess urbanization and monitor SDG 11.3.1. This is exemplified in recently published works (Estoque et al. 2021 ; Holobâcă et al. 2022 ; Laituri et al. 2021 ; Li et al. 2022 ; Melchiorri et al. 2019 ; Santillan and Heipke 2023 ; Schiavina et al. 2019 , 2022 a).

The error assessment of the recently released GHSL Data Package 2023 (GHS P2023) data products per epoch is on-going and will be reported in peer-reviewed publications (European Commission 2023 ). Nevertheless, initial assessments have already been conducted by JRC. For instance, the GHS-BUILT‑S representative of the year 2018 showed a 94.8% overall accuracy, 94.1% precision, and 93.9% recall for built-up area classifications in Southeastern Asia where the Philippines is located. For GHS-POP, an earlier work (Santillan and Heipke 2023 ) established its good correspondence with official census data when examined at the country level ( R 2  ≈ 1). There were also assessments performed in previous GHSL releases. An assessment conducted by Abella and Ranido ( 2023 ) for the previous GHSL release (GHS P2022) in Butuan City, Philippines using LiDAR-derived built-up data showed accuracies larger than 90% for the 2015 and 2020 epochs of GHS-BUILT‑S. Liu et al. ( 2020 ) also evaluated the GHS-BUILT‑S for the year 2014 over China. Although they noted a need for further improvement in accuracy, they found that the GHS-BUILT‑S can provide quantitative information about built-up areas, suitable for low-resolution analysis. For parts of the US territory, Leyk et al. ( 2018 ) evaluated the first edition of the GHSL and concluded that regions characterized by larger development intensity were accurately classified and demonstrated high reliability. In another study, Togiti et al. ( 2023 ) obtained an average accuracy of more than 90% and individual (per year) accuracy of more than 80% when they evaluated the GHS-POP in Hyderabad City, India. Investigations done by Archila Bustos et al. ( 2020 ) and Calka and Bielecka ( 2020 ), as cited in Macmanus et al. ( 2021 ), have also demonstrated that GHS-POP provides the most precise population estimates at the pixel level when compared with local data for urban regions. These studies, in addition to a growing number of works where the GHSL was employed, and the continued improvement of the products by JRC through the years, established the reliability of the GHSL products for examining urbanization and LUE dynamics over long time periods, especially in countries where spatiotemporally consistent data required for such an analysis is often limited.

2.2 SDG 11.3.1 Monitoring

Several investigations have been carried out to monitor SDG 11.3.1 across multiple scales, helping to understand the complex relationship between urban expansion, population growth, and LUE. These studies highlight the diverse development trajectories and land take efficiency of different human settlements around the world and describe how regions evolve over time and revealed shifting trends in LUE (Estoque et al. 2021 ; Melchiorri et al. 2019 ; Schiavina et al. 2019 , 2022 b). They also demonstrate the complex balance between urban growth and population dynamics within specific regions, as well as the impact of urban population size, expansion rate, compactness, and border complexity on LCRPGR (Faye et al. 2022 ; Li et al. 2021 a, 2021 b). SDG 11.3.1 monitoring at country-level also reveals unique and complex relationships between land consumption and population growth, resulting in different magnitudes and directions of land use efficiency (Bhandari et al. 2023 ; Calka et al. 2022 ; Jiang et al. 2021 ; Koroso et al. 2021 ; Nicolau et al. 2019 ; Santillan and Heipke 2023 ; Wang et al. 2020 ). Studies focusing on cities and municipalities (Cai et al. 2020 ; Ghazaryan et al. 2021 ; Ling et al. 2023 ; Wang et al. 2022 ; Zhou et al. 2021 ) underscore the spatially diverse dynamics of land use efficiency, highlighting that local urban centers can exhibit different land use efficiencies due to their unique spatial configurations and the varying coordination between LCR and PGR over time. All these studies emphasize the need for targeted land use policies that address the dynamics between urban expansion and socio-economic factors across various levels to enhance efficient land utilization.

Alongside these achievements, various scholars have highlighted several limitations inherent in the current SDG 11.3.1 methodology. One limitation lies in methodological issues and the interpretability of the calculated LCRPGR value. The formula can yield abnormal values from small population changes combined with significant land consumption changes (Cai et al. 2020 ; Li et al. 2021 a). Indeterminate values occur when there is no change in population over a period, making LUE calculation impossible (Calka et al. 2022 ; Nicolau et al. 2019 ). Also, a single LCRPGR value cannot discern whether population or land has grown, rendering it inadequate for monitoring LUE (Calka et al. 2022 ; Estoque et al. 2021 ; Nicolau et al. 2019 ; Wang et al. 2020 ; Zhou et al. 2021 ). Moreover, the lack of spatial explicitness of the LCRPGR has also become a major discussion among SDG 11.3.1 monitoring researchers. A single LCPRGR value cannot fully capture the spatial aspects of urbanization and LUE dynamics (Cimini et al. 2023 ; Han et al. 2022 ; Melchiorri et al. 2019 ; Schiavina et al. 2022 b; Wang et al. 2020 ). Besides, LCRPGR is substantially subject to path dependency in quantifying LUE of new development so that even in different land take situations, the same LRPGR value can be obtained (Schiavina et al. 2019 ).

A few approaches have been put forward to address these identified limitations of the SDG 11.3.1 indicator. To properly interpret the LCRPGR value and provide insights into the urbanization and LUE dynamics, it is recommended to include the values of LCR and PGR (and their signs) (Cimini et al. 2023 ; Estoque et al. 2021 ; Wang et al. 2020 ). To address the lack of spatial explicitness, several secondary and supplementary indicators have been proposed to be computed and support the interpretation of the calculated LCRPGR values (see Table  1 for a summary). So far, one or more of these secondary indicators have only been utilized in few studies (Bhandari et al. 2023 ; Han et al. 2022 ; Melchiorri et al. 2019 ; Nicolau et al. 2019 ; Schiavina et al. 2019 , 2022 b), complementing and characterizing LUE analysis. Considering that they were utilized in different geographical contexts, further studies are needed to evaluate the importance of these new indicators for LUE analysis. In an archipelagic country like the Philippines, these new indicators may provide fresh insights into the urbanization and LUE processes prevalent in the country over the past years until the present.

3.1 Geographical Scope and Levels of Analysis

The Philippines is an archipelagic country in Southeast Asia with a total area of approximately 300,000 km 2 , inclusive of inland water bodies (Fabian Jr. 1991 ). It comprises of 7641 islands (Barile 2017 ), clustered into three major groups, namely Luzon, Visayas, and Mindanao (Fig.  1 ). In terms of administration and governance, the country is structured into four hierarchical levels, with each level containing the subsequent one: (i.) regions, (ii.) provinces (iii.) cities and municipalities, and (iv.) barangays. All economic and social development efforts are coordinated and set at the regional level. The lower three levels are defined as local government units (LGUs). It is important to note that the classification of cities in the Philippines differs notably from that in other countries (Dumayas 2015 ). Independent cities are those that operate autonomously and are not under the jurisdiction of any specific province in which they are geographically located. Conversely, component cities are those that are under the administrative umbrella of a particular province (NSCB 2012 ). As of 2020, there are 17 regions, 82 provinces, 149 cities, and 1485 municipalities in the Philippines (PSA 2021 ). Of the 149 cities, 38 are independent and 111 component cities. Among the 38 independent cities, 33 are designated as highly urbanized (HUCs). This classification is determined by specific criteria, including a minimum population of 200,000 residents and an annual income of at least Fifty Million Pesos based on 1991 constant prices (NSCB 2012 ). Moreover, 16 of the HUCs are in Metro Manila.

Map of the Philippines highlighting its three major island groups: Luzon, Visayas, and Mindanao. The inset map shows the geographical location of the Philippines within East and Southeast Asia

In this study, the analysis of urbanization and LUE was conducted at the city and municipality level. We used the administrative boundaries as our spatial unit of analysis. This is useful in the context of policy formulation and implementation considering that administrative boundaries in the Philippines correspond to political jurisdictions with distinct policy frameworks and governance structures. Analyzing urbanization and LUE within these boundaries helps policymakers tailor interventions and policies to specific contexts.

3.2 GHSL Datasets Used

Three datasets from the GHSL were mainly utilized in this study, namely GHS-BUILT‑S, GHS-POP, and GHS-SMOD.

The GHS-BUILT‑S dataset (Pesaresi and Politis 2023 ) portrays the distribution of built-up surfaces at 5‑year intervals from 1975 onwards, categorized into residential (RES) and non-residential (NRES) areas. This data is derived from spatiotemporal interpolation of Landsat (MSS, TM, ETM) and Sentinel‑2 satellite imagery, with built-up area quantified in square meters per pixel. For this study, the total built-up surface component (i.e., RES + NRES) was utilized to estimate built-up areas. In the context of GHSL, the built-up surface corresponds to areas occupied by buildings, where a building is “any roofed structure erected above ground for any use” (European Commission 2023 ).

GHS-POP (Freire et al. 2016 ; Schiavina et al. 2023 a) illustrates the spatial distribution of residential population, indicating the number of people per pixel. Population estimates are sourced from the Gridded Population of the World, version 4.11 (GPWv4.11) by the Center for International Earth Science Information Network (CIESIN). GHS-POP was generated using a dasymetric mapping approach (Freire et al. 2016 ) that disaggregates population data from census or administrative units into grid cells, aligning with the distribution, density, and classification of built-up areas as mapped in corresponding GHSL global layers. We selected the GHS-POP dataset over other similar gridded population datasets, such as LandScan and WorldPop, due to its specific strengths and alignment with the goals of our study. GHS-POP provides consistent global population data at a high spatial resolution (up to 100 meters) for multiple time points (every 5 years, from 1975 to 2020). In contrast, LandScan offers a coarser resolution of approximately 1 km and focuses on ambient population distribution, making it more suitable for emergency response applications (Dobson et al. 2000 ). Additionally, LandScan data is only available from the year 2000 onwards. While WorldPop provides higher resolution data (up to 100 meters) and detailed population estimates, its temporal coverage is also limited to the period from 2000 to 2020. Furthermore, neither LandScan nor WorldPop is spatially consistent and integrated with built-up area data from GHS-BUILT‑S, which is essential for our LUE analysis.

The GHS-Settlement Model (SMOD) (Schiavina et al. 2023 b) delineates and classifies various settlement types based on clusters of cells, incorporating population size, density, and built-up area density as defined by the Degree of Urbanisation Stage I (European Commission & Statistical Office of the European Union 2021 ). This raster grid dataset assigns specific settlement classifications to each grid cell, including urban centers, dense urban clusters, semi-dense urban clusters, suburban or peri-urban areas, rural areas, low-density rural areas, very low-density rural areas, and water bodies (European Commission 2023 ).

3.3 Data Acquisition and Processing

Raster tiles of GHS-BUILT‑S, GHS-POP, and GHS-SMOD from the GHSL Data Package 2023 (GHS P2023), covering the Philippines at 5‑year interval (1975–2020), were downloaded from the GHSL website at https://ghsl.jrc.ec.europa.eu/download.php . The GHSL products are given in the Mollweide coordinate reference system (CRS), with a spatial resolution of 100 m, except for GHS-SMOD, which has a resolution of 1 km.

We generated a mosaic of each dataset per epoch using ArcGIS—ArcMap 10.8 software. The original settlement classifications in the GHS-SMOD were simplified into two categories: urban and rural. For subsequent analysis, we utilized a Shapefile of Philippine cities and municipalities downloaded from the United Nations Office for the Coordination of Humanitarian Affairs (OCHA)—Humanitarian Data Exchange ( https://data.humdata.org/dataset/cod-ab-phl ). It was processed to be spatially consistent with the GHSL datasets (i.e., in terms of CRS and spatial resolution). The mosaicked GHSL data products and the city/municipal boundaries were then used as inputs to the “Zonal Statistics as Table: SUM” tool in ArcGIS-ArcMap10.8 to extract the total built-up area and the total population. These metrics were calculated separately for urban and rural areas within each of the 149 cities and 1485 municipalities with the aid of the reclassified GHS-SMOD. In further steps, we excluded one municipality, Kalayaan, which falls under the jurisdiction of the province of Palawan in the Spratly Islands, because it reported zero built-up area and population data in the GHSL.

3.4 Urbanization and LUE Analysis

We extended the methodology employed in an earlier work (Santillan and Heipke 2023 ) to analyze urbanization and LUE analysis across Philippine cities and municipalities.

Using the tabular outputs of the GIS data processing discussed in the previous section, we computed the 5‑year values and changes in built-up area and population in each city and municipality. Built-up population density ( BUPD ) and level of urbanization ( LOU ) were computed according to the following equations (see Table  1 for the variable definitions). These additional metrics can aid in better characterization of the urbanization process in each locality.

To analyze LUE, we computed the LCR, PGR, and LCRPGR, along with the secondary and supplementary indicators that aims to complement and address the lack of spatial explicitness of the LCRPGR. These indicators were computed in the same 5‑year interval using appropriate equations listed in Table  1 .

In addition to estimating built-up area and population, as well as computing the values of the indicators and additional metrics in individual cities and municipalities, we also employed aggregations at national, municipality and city levels for macro-level analysis. Doing so provides a holistic view of the overall trends and patterns across cities and municipalities. It is also particularly useful in understanding the broader dynamics of urbanization and LUE at these scales. Aggregating data helps in minimizing the influence of outliers or extreme values that may exist within individual cities or municipalities. By consolidating data across multiple cities and municipalities, we aim to obtain a more stable and representative estimate of the overall trends. This is also one way to reduce the impact of anomalous values, especially in LCRPGR computations which can lead to extreme values (Li et al. 2021 a).

For analyzing the characteristics and trends in individual cities and municipalities, we utilized the mean statistic. In computing the mean, cities and municipalities with extremely positive and negative LCRPGR values in any of the time periods were first excluded using the 3‑sigma rule (Ghilani 2017 ). This has been an identified issue with the current SDG 11.3.1 indicator formulation and occurs when there are small changes in population growth over a period (Li et al. 2021 a). In this study, there were 58 municipalities and 1 city that exhibited abnormal LCRPGR values, mostly due to the small changes in population in one or more periods. The computed LCRPGR values reached as low as about −1000 and as high as about 10,000. Although we do not discount its possibility of happening, we decided to exclude these localities in statistical computations that aims to characterize individual cities and municipalities (such as the means and standard deviations of SDG 11.3.1 indicators).

To facilitate comparison between the different geographical regions of the country, we grouped the cities and municipalities into the following categories: Metro Manila (MM), Luzon municipalities (excluding MM), Luzon cities (excl. MM), Visayas municipalities, Visayas cities, Mindanao municipalities, and Mindanao cities. Also, we clustered the cities and municipalities into four categories based on their LCRPGR values (Table  2 ). This will aid in characterizing the calculated values of the LCRPGR, including its spatially explicit interpretation with respect to the secondary and supplementary indicators.

For context, we briefly describe the additional indicators as follows. The Built-up Area per Capita ( BUpC ) quantifies the average amount of built-up space available for each person in a spatial unit (like a city or urban area). This metric provides insights into both overcrowding and sparse population conditions within an urban environment. On the other hand, the Total Change in Built-up Area ( BUChange ) measures the overall growth of constructed spaces in a spatial unit over a specific period and can reveal trends in urban densification (UN Statistics Division 2021 ). The Abstract Achieved Population Density in Expansion Areas ( AAPDEA ) and Marginal Land Consumption per New Inhabitant ( MLCNI ) were proposed by Schiavina et al. ( 2019 ) to significantly support the interpretation of LUE because of the latter’s insufficiency for comparing development trajectories. These new indicators are computed based on the absolute difference between the spatial expansion of the settlement (built-up areas) and the change in population in the same spatial unit (Schiavina et al. 2019 ). Using these indicators helps identify the relationship between new population and the new built-up areas within a spatial unit (Schiavina et al. 2022 b).

4.1 Trends and Patterns of Urbanization in the Philippines

Figure  2 illustrates the built-up area and population of Philippine cities and municipalities in both 1975 and 2020. A notable observation is the substantial built-up area and population in Metro Manila as early as 1975. Additionally, beyond Metro Manila, several cities and municipalities also exhibited considerable built-up areas and population. In 2020, we observed a significant increase in built-up areas and population across the country, particularly in regions adjacent to Metro Manila and in municipalities surrounding cities that already exhibited high built-up areas and population in 1975.

Built-up area and population of the Philippine cities and municipalities in 1975 and 2020, estimated from the Global Human Settlement Layers (GHSL)

The Philippines’ total built-up area increased from 824.50 km 2 in 1975 to 2401.77 km 2 in 2020 (Table  3 ). This is equivalent to a substantial increase of approximately 191%. On average, built-up area expands throughout the country at a rate of 35.05 km 2 /year. During the same period, the population increased by ~165%, from 41.92 million in 1975 to 111.27 million in 2020, resulting in an annual increase of 1.54 million people per year. These GHSL-derived estimates of population closely mirror official census data released by the Philippine Statistics Authority, which reports a similar annual growth rate of 1.49 million people per year.

In municipalities, built-up areas and population grow at rates of 23.56 km 2 /year and 0.92 million people per year, respectively. Cities, on the other hand, exhibit built-up area expansion and population growth at 11.49 km 2 and 0.62 million people per year, respectively. In Metro Manila, built-up area expands at a rate of 0.84 km 2 /year while its population grew at a rate 0.14 million people per year. In 1975, Metro Manila comprised approximately 20% of the nation’s total built-up area. However, this contribution steadily decreased over subsequent years. By 2020, it accounted for only around 8% of the total built-up areas. Especially from the year 2000 until 2020, Metro Manila’s built-up area appears to have changed a little. The decline in Metro Manila’s contribution suggests that cities and municipalities outside of Metro Manila are experiencing significant expansion, contributing more to the overall built-up area expansion across the country.

Figure  3 a, b depicts the proportion of built-up areas in cities and municipalities. Approximately 49% of the total built-up area belongs to the municipalities in 1975. This trend increases through the years, reaching ~61% in 2020. The opposite trend is found for the total built-up area in cities, whose contribution to the total built-up area has been decreasing over the years. In 1975, built-up areas in cities accounted for ~51% of the country’s total built-up area and this was reduced to ~39% in 2020. On average, ~57% of built-up areas are in municipalities and ~43% are in cities during the period of 1975–2020. It is also notable that most of the built-up development occurred in urban areas, showing an increasing trend through the years across cities and municipalities. In municipalities, urban built-up areas covers approximately 42% of the land in 1975, rising to around 77% by 2020. Similarly, in cities, urban built-up areas constituted 90% of the land in 1975, climbing to 94% by 2020.

Built-up area ( a ) and population ( b ) of the Philippines from 1975–2020, estimated from the Global Human Settlement Layers (GHSL) and aggregated at country, municipality and city levels, including their relationships across different geographic regions ( c,   d ). As exemplified in  c , the initial point on each line corresponds to the built-up area and population data from 1975, with subsequent points occurring at 5‑year intervals until 2020

The municipalities’ contribution to the national population is relatively stable, with an average of ~60%. The population in cities constitutes a lower proportion than in municipalities, averaging ~40% over the same period. A higher proportion of population in urban areas is notable across different levels. As expected, urban population in cities is the highest, ranging from ~88% in 1975 and reaching ~96% in 2020 of the cities’ total population. In municipalities, the proportion of population living in urban areas is lower, but still significant, ranging from ~53% in 1975 to ~77% in 2020.

Through the years, the relationships between built-up area and population at the country, municipality and city levels (Fig.  3 c) follows a linear trend. It is evident that at the national level, there is a shift in the slope of the line beginning 1990, where the trend is towards higher population. This characteristic is also evident at the municipality level. For cities, the trend towards higher population came late during the year 2000. The same pattern can be found when we grouped the cities and municipalities into geographical regions (Fig.  3 d), except for Metro Manila where we can already observe an inclination towards higher population after 1975. This is an indication that Metro Manila is becoming more urbanized through time without significant built-up expansion, while cities and municipalities outside Metro Manila are continuing to grow both in terms of built-up areas and population.

Based on our calculations (Table  3 ), there has been an increasing level of urbanization across the country over the years. The country’s overall LOU is already high in 1975 at 67%, and this increased to 85% in 2020. In municipalities, the LOU ranges from 53 to 77%, which is relatively lower than the country LOU. A higher LOU is found in cities, ranging from 88% in 1975 to 96% in 2020. Metro Manila has the highest LOU of 100% which has been consistent since 1975.

In terms of built-up population density (BUPD), a decreasing trend is evident during the period of 1975 to 2000 in municipalities and in the country overall, which is indicative of higher rates of built-area expansion than population growth during those periods. After the year 2000, BUPD stabilized in municipalities, accommodating ~46,000 people per km 2 . In cities, BUPD has an increasing trend and peaks in 2020, and has almost equalized with those of municipalities from 2010 to 2020. Metro Manila’s BUPD values also showed an increasing trend, rising from approximately 45,000 people per km² to around 67,000 people per km² in 2020. This final value is significantly higher than the rest of the country for the same year.

4.2 LCR and PGR Characteristics

Both the LCR and PGR have exhibited a decreasing trend at the national, municipal, and city levels (Table  4 ). In 1975–1980, the national LCR and PGR were 3.56 and 2.67%, respectively. These values decreased to 1.46 and 1.70%, respectively, in 2015–2020. The highest values of national LCR occurred in 1980–1985 and 1990–1990 at more than 4%, while the highest PGR was recorded between 1975 and 1980. An important finding is the abrupt decrease in national LCR by almost 2% between 1985–1990 and 1990–1995.

LCR in all the cities and municipalities in all periods exhibited positive values. Looking at aggregated statistics (Table  4 ), municipalities consistently exhibit higher LCR compared to cities, sometimes surpassing city LCRs by more than double during certain periods. The peak LCR for municipalities occurred during 1980–1985 at 5.53%, remaining relatively stable until 1985–1990, before abruptly declining by more than 3% in subsequent periods. Conversely, city LCRs remained relatively stable above 2% between 1975 and 2000 before experiencing a significant decrease in the following periods, particularly in 2020 where LCR was observed at 0.84%. LCRs of Metro Manila remained below 1% through the years, reaching their peak at 0.97% during 1995–2000. It declined considerably in the following periods, leading to only 0.05% in the most recent period.

The national PGR was highest during 1975–1980 and has been declining through the years. In the most recent period, the national PGR was estimated at 1.70%. PGRs in cities peaked at 3.25% during 1975–1980. They consistently exceeded those in municipalities from 1975 to 1995, but this trend reversed beginning in 1995–2000 where PGRs of municipalities dominated. Metro Manila’s PGR exhibit fluctuating characteristics, achieving only the highest value of 2.58% during 1975–1980.

The observed overall trends in LCR and PGR manifest themselves clearly in the corresponding LCR and PGR maps (Figs.  4 and  5 ). The proportion of the cities and municipalities with distinct LCR and PGR ranges are summarized in Fig.  6 , while a comparison of cities and municipalities in terms of their mean LCR and PGR values are depicted in Fig.  7 a, b, d and e. Both municipalities and cities follow a similar trend over time in their average PGR. However, they differ significantly in their average LCR, which results in markedly different average values of LCRPGR.

Spatial patterns and temporal trends of LCR of Philippine cities and municipalities. Each value range includes its upper limit. All cities and municipalities exhibited positive LCR values in all periods

Spatial patterns and temporal trends of PGR of Philippine cities and municipalities. Each value range includes its upper limit

Proportion of municipalities and cities based on their LCR ( a ,  b ), PGR ( c ,  d ), and LCRPGR ( e ,  f ) values. Each value range in the legend includes its upper limit

a–c  Trends of mean values of LCR, PGR, and LCRPGR of cities and municipalities; error bars represent 95% confidence interval of the mean. d–f  Comparison of mean values of LCR, PGR, and LCRPGR of cities and municipalities grouped into geographical regions

In Fig.  4 , we can identify three distinct phases in the evolution of the Philippines’ LCR: a growth phase between 1975–1990, followed by a decline phase between 1990–2005, and a resurgence phase from 2005–2020. Several cities and municipalities maintained consistently high LCR values throughout most periods, notably in northern Luzon, western Visayas, and western Mindanao. These regions also experienced a resurgence in LCR values, particularly evident from the 2000–2005 period onwards. Moreover, the PGR maps illustrate a general decline in PGR nationwide over the years, while also highlighting areas with markedly high and low population growth rates. Cities and municipalities consistently exhibiting very low (< 1%) PGR are predominantly located in western Mindanao. Cities and municipalities in the main island groups of the Philippines showed high variability in their mean LCR and PGR values in the periods from 1975 to 2000 (Fig.  7 d, e).

4.3 LCRPGR and LUE Characteristics

The country’s LCRPGR exhibits a general declining trend over the years, indicating efficient land utilization for built-up purposes (Table  4 ). Although it fluctuated and leaned towards inefficiency during the early periods between 1975 and 2000, the national LCRPGR consistently remained below 1 in the subsequent periods. The peak efficiency occurred during 2000–2005, with an LCRPGR value of 0.68. Moreover, when considering all municipalities as a single spatial unit, their LCRPGR value during the periods between 1975 and 1990 indicates inefficient land utilization, with land consumption occurring at a rate more than twice that of population growth. However, in the subsequent periods, LCRPGR values approached or fell below 1, indicating that municipalities have somehow synchronized their LCR with PGR. In aggregated terms, cities consistently exhibited LCRPGR values below 1 for most periods until 2020. Metro Manila, as a single unit, consistently showed low LCRPGR values throughout, except during 1995–2000 when it became inefficient.

A more informative trend can be observed when analyzing the mean LCRPGR of cities and municipalities across geographical regions (Fig.  7 f). Metro Manila cities consistently displayed mean LCRPGR values below 1 throughout the years, indicating efficient LUE. Mindanao cities and municipalities also showed efficiency over the years, except during 1980–1985 and 1985–1990. In contrast, Luzon cities were generally inefficient between 1975 and 1995 but became efficient in the following periods. Luzon municipalities exhibited inefficiency from 1975 to 1990, transitioning to efficiency from 1990 to 2010, and then reverting to inefficiency. A similar pattern is observed for Visayas municipalities, which were only efficient during 1990 to 2005. Visayas cities appeared to be the most inefficient over the same periods. These variations in mean LCRPGR values indicate differing levels of coordination between LCR and PGR. Focusing on the mean LCRPGR for the most recent period (2015–2020) and applying the classification scheme (Table  2 ), the LUE dynamics in Metro Manila cities and municipalities are characterized by a demographic decline concurrent with spatial expansion, as indicated by a negative mean LCRPGR. However, its LCRPGR as an aggregated (mega)region (Table  4 ) is leaning towards the second category, which is population densification. Population densification is also a common trend for Luzon cities, as well as for Mindanao cities and municipalities. Luzon municipalities exhibit a higher rate of spatial expansion than demographic growth. Lastly, Visayas cities and municipalities experience spatial expansion at a rate at least twice as fast as their population growth.

An illustration of the spatial patterns and temporal trends of LUE across the country is provided by Figs.  6 e, f and  8 . From 1975 to 1990, inefficiency dominated the country, where more than 70% of the municipalities and more than 50% of the cities had LCRPGR values larger than 1. In the succeeding periods, efficient land utilization took place in more than 50% of the municipalities and cities. Echoing our findings in the LCR trends, we can discern three distinct phases in the evolution of LCRPGR across the country: an inefficiency phase from 1975–1990, succeeded by an efficiency phase between 1990–2005, and a resurgence of inefficiency from 2005–2020. In the third phase, cities and municipalities with efficient land utilization continue to dominate. However, we are beginning to observe the emergence of clusters of category 4 LCRPGR in different parts of the country. For lack of a shorter term, we refer to these clusters as “rapidly growing urban hubs”, and they are mostly found in northern Luzon, central Visayas, and in the perimeters of mainland Mindanao. Localities under category 3 LCRPGR are also eminent, and we refer to them as clusters of “expanding metropolitan regions”. Localities with Category 2 LCRPGR are still dominant, and we consider them to already possess urbanization characteristics of “densely inhabited metropolises” because of their higher (and positive) PGR compared to their LCR. Lastly, cities and municipalities on the western side of Mindanao, including island cities and municipalities in the Sulu archipelago, exhibited category 1 LCRPGR. We refer to these localities as “transitioning urban centers” because they experience demographic decline despite positive LCRs.

Spatial patterns and temporal trends of LCRPGR of Philippine cities and municipalities. Each value range includes its upper limit

4.4 Urbanization and LUE Characteristics Based on Secondary and Supplementary Indicators

Table  5 provides a summary of the secondary and supplementary indicator values aggregated at the country, municipality and city levels.

The BUpC for all levels generally followed a similar trend, with differences in the earlier years. Initially, there were some variations, with municipalities starting at lower values (16 m 2 /person) compared to the cities (26 m 2 /person). However, over time, these differences diminished, and the BUpC values converged to ~22 m 2 /person across different levels.

The country maintains positive BUChange values, signifying ongoing expansion of built-up areas, albeit at a slower pace compared to municipalities. The country experienced its highest expansion rate during 1985–1990 at 21.27%. Municipalities exhibit higher BUChange values, indicating more rapid expansion of built-up areas over time compared to cities. Cities follow a similar pattern in BUChange, though with slightly lower rates. Across all levels, expansion rates demonstrate a declining trend towards the later periods. Despite its dense population, Metro Manila demonstrates the lowest BUChange values.

The Abstract Achieved Population Density in Expansion Areas (AAPDEA) values at the country level indicate a generally increasing number of people for each square kilometer of built-up area expansion. Starting at 41,000 persons/km 2 in 1975–1980, it rose to 56,000 persons/km 2 in 2015–2020, with the highest value observed during 2000–2005 at 67,000 persons/km 2 . These trends are also reflected in the AAPDEA of municipalities, although the number of persons accommodated is lower than the national level. Throughout the periods, cities consistently accommodated a larger number of people per square kilometer of built-up area expansion. Metro Manila, as a megaregion, displayed over five times the national AAPDEA during 1975–1980. Although values decreased towards 1995–2000, they later showed an increasing trend, surpassing 1 million people per km 2 of expansion. These results are indicative of overcrowding.

At the country level, the Marginal Land Consumption per New Inhabitant (MLCNI) indicates varying levels of built-up area consumption per new inhabitant. Beginning at 24 m 2 /person in 1975–1980, it surged to 38 m 2 /person in 1985–1990 before declining to 18 m 2 /person by 2015–2020. Similar trends are mirrored in municipalities, but they are at slightly higher levels. Cities, on the other hand, display MLCNI values lower than municipalities. They reached 26 m 2 /person during 1995–2000 before dropping to 11 m 2 /person by 2015–2020. Metro Manila exhibited the lowest MLCNI values among all levels. Despite a spike to 38 m 2 /person during 1995–2000, MLCNI values significantly decreased to 1 m 2 /person in 2015–2020.

For the 2015–2020 period, we compared the mean AAPDEA and MLCNI of cities and municipalities per LCRPGR category to see how it is related to LUE (Fig.  9 ). Cities and municipalities with LCRPGR < 0 exhibited negative AAPDEA, which is expected due to their negative PGRs. Cities and municipalities with 0 < LCRPGR ≤ 1 (i.e., densely inhabited metropolises) have the highest AAPDEA. For each km 2 of built-up expansion, a corresponding average population of 419,761 and 213,793 are accommodated in cities and municipalities, respectively. These values are significantly higher than the aggregated AAPDEA for cities and municipalities (Table  5 ), potentially bloated by the high AAPDEA of Metro Manila cities and municipalities. In the same localities, each new inhabitant is associated with a land consumption of 11 m 2 (cities) and 12 m 2 (municipalities).

Comparison of mean AAPDEA ( a ) and MLCNI ( b ) in Philippine cities and municipalities per LCRPGR category for the 2015–2020 period

In expanding metropolitan regions composed of cities and municipalities with 1 < LCRPGR ≤ 2, the average AAPDEA are significantly lower, accommodating 36,674 and 43,576 people for every km 2 of built-up expansion, respectively. New inhabitants in cities under this category consume an average of 31 m 2 of built-up area. For municipalities, an average of 30 m 2 of land is consumed for each new inhabitant.

Rapidly growing urban hubs composed of cities and municipalities with LCRPGR > 2 have the lowest AAPDEA and largest MLCNI. Cities under this category accommodate an average population of 19,067 per km 2 built-up expansion, and where each new inhabitant consumes 64 m 2 of built-up area. Conversely, municipalities accommodate 20,165 people for each km 2 of the new built-up area and a new inhabitant consumes 72 m 2 of land.

5 Discussion

The first key research question addressed in this study pertained to the spatial patterns and temporal trends of urbanization, built-up land consumption, population growth, and LUE across Philippine cities and municipalities. Our findings indicate a notable surge in built-up areas and population across the country. This expansion trend, observed over the past four decades, underscores the persistent urbanization dynamics shaping the Philippine landscape. Notably, while Metro Manila exhibited substantial built-up area and population as early as 1975, its contribution to the national built-up area steadily declined over subsequent years. This characteristic signifies the decentralization of urban growth beyond the capital region. The shift in urban growth dynamics coincides with earlier observations made by Ortega et al. ( 2015 ). Moreover, there has been a declining trend observed in both the LCR and PGR at the national, municipal, and city levels despite the continuous expansion of built-up areas and population growth over the past 45 years. Such observation aligns with the findings of Estoque et al. ( 2021 ) when they analyzed LCR and PGR at the global level, indicating that this characteristic is not unique to the Philippines.

We have identified three distinct phases in the country’s LCR trend. The growth phase from 1975 to 1990 indicates rapid urban expansion, followed by a decline phase from 1990 to 2005, possibly due to improved land use planning and management practices. The resurgence phase from 2005 to 2020 suggests renewed urbanization pressures and development activities. These observed phases in the LCR evolution reflect changing trends in built-up land utilization over time and require further research to investigate their underlying causes. Moreover, the variability in LCR and PGR values across different cities and municipalities underscores the diverse urbanization dynamics and land use patterns within the Philippines. These findings emphasize the importance of tailored urban planning and development strategies to address the specific needs and challenges of different administrative divisions in the country.

Another major finding from our analysis is that the Philippines has been undergoing efficient built-up land utilization through the years, reflecting the same results reported in an earlier work (Santillan and Heipke 2023 ). A major difference, however, is that our earlier work failed to analyze LUE characteristics across different levels. Across the country, most cities and municipalities are inefficient from 1975–1990, but they started to become efficient in the following years. While we cannot specify the primary reason for the better LUE after 1990, it is likely due to improved land use planning and management practices following the enactment of Republic Act 7160 (also known as the Local Government Code of the Philippines) in 1991. Prior to this law, planning and zoning were centrally controlled by the national government (Von Einsiedel 1992 ). The 1991 law decentralized political and fiscal authority, granting cities and municipalities regulatory powers (Pagsanghan 1993 ), including the responsibility for comprehensive land use planning and zoning ordinances (Lech and Leppert 2018 ). This shift likely contributed to more efficient land utilization starting after 1990.

Several studies have evaluated SDG 11.3.1 at national and global scales, providing a useful benchmark for our findings. When comparing our results with those of Schiavina et al. ( 2019 ), notable differences and similarities emerge. Schiavina et al. observed that globally, the LCRPGR between 1990 and 2015 was 1.2, indicating a rate of spatial expansion prevailing over population growth, with specific values of 1.6 for 1990–2000 and 1.0 for 2000–2015. In contrast, our study found that the Philippines experienced similar trends earlier, with a country-aggregated LCRPGR of 1.33, 1.68, and 1.85 (average 1.62) for the period 1975–1990 (see Table  4 ). Between 1990–2000, the Philippines had balanced LCRPGR values ranging from 0.99 to 1.05 (average 1.02), and a declining trend from 2000–2015 with values of 0.68, 0.87, and 0.97 (average 0.84). Regionally, Asia’s aggregated LCRPGR was 1.7 for 1990–2000 and 1.3 for 2000–2015, while Europe exhibited extremely high LCRPGRs of 20.2 and 7.8 for the same periods, respectively. The Philippines’ LCRPGR values are much lower and more consistent during these later periods, indicating less aggressive land consumption compared to regions like Europe and Northern America, which had values of 2.2 and 1.2 for the same periods, respectively. This comparison shows that while the global and regional trends typically indicate land consumption prevailing over population growth, the Philippines exhibited a similar pattern earlier and transitioned to a more balanced and consistent pattern of land use efficiency over the comparison period.

The Philippine’s LCRPGR characteristics also possess some unique characteristics when compared to nearby countries. For instance, Bhandari et al. ( 2023 )’s land use efficiency analysis of Thailand provinces exhibited trends that varies from that of the Philippines. During 2005–2010, Thailand experienced an average LCRPGR of 1.6, indicating urban expansion outpacing population growth, whereas the Philippines observed a more moderate LCRPGR of 0.87. By 2010–2015, Thailand’s average LCRPGR declined to 0.40, reflecting urban compactness due to higher population growth, in contrast to the Philippines’ relatively stable LCRPGR of 0.97. From 2015 to 2020, Thailand demonstrated inefficient land use with average LCRPGR of 1.12, while the Philippines maintained a consistent LCRPGR around 0.86. On the other hand, comparing the Philippines’ LUE with mainland China, as studied by Wang et al. ( 2020 ), also shows significant differences. From 1990–2000, China had a high LCRPGR of 1.69, indicating urban expansion outpacing population growth. In contrast, the Philippines maintained an average LCRPGR of 1.02. By 2000–2010, China’s LCRPGR increased to 1.78, while the Philippines reduced to 0.77, indicating improved land use efficiency. Before 1990, the Philippines had an average LCRPGR of 1.62, aligning closely with China’s subsequent land use efficiency. These comparisons suggest that the Philippines adopts a more balanced approach to built-up land utilization relative to population dynamics, contrasting with the dynamic urban expansion patterns as observed in Thailand and China.

Philippine cities and municipalities demonstrate better land use efficiency compared to their Asian counterparts during certain periods. For example, the analysis by Jiang et al. ( 2021 ) reveals that cities in China had an average LCRPGR of 1.34 from 1990 to 1995, which increased to 1.59 from 2005 to 2010, indicating inefficient land utilization. In contrast, Philippine cities showed a similar average LCRPGR of 1.31 during 1990–1995 but displayed a decreasing trend towards more efficient land use in subsequent years up to 2010 (see Fig.  7 c). Philippine municipalities exhibited nearly identical average LCRPGR values compared to Chinese cities but during earlier periods (1975–1990). During the same periods as Chinese cities (1990–2010), Philippine municipalities had average LCRPGR values below 1, indicating more efficient land utilization practices.

The LCRPGR spatiotemporal maps applying the LCRPGR categorization by Jiang et al. ( 2021 ) were useful in identifying clusters of distinct LUE. In this study, we referred to these clusters as transitioning urban centers, densely inhabited metropolises, expanding metropolitan regions, and rapidly growing urban hubs. Understanding these clusters helps policymakers design targeted policies that promote sustainable urban development while addressing the unique needs of different urban areas. For example, transitioning urban centers, exhibiting positive LCR and negative PGR, may benefit from policies that involve revitalizing urban spaces, promoting mixed-use development, and encouraging economic activities that attract residents and businesses to counteract population decline. In densely inhabited metropolises, policies leaning toward sustainable transportation systems and high-rise development can be a favorable strategy to accommodate the growing population density while optimizing LUE. Rapidly growing urban hubs may require interventions to ensure equitable access to basic services and infrastructure for the growing population.

The assessment of LUE characteristics based on secondary and supplementary indicators associated with SDG 11.3.1 provided valuable insights into the efficiency of built-up area expansion in accommodating population growth. For instance, the level of efficiency coincides with overcrowding issues as revealed by AAPDEA and MLCNI. Compared to the global average (15.8 × 10 3 people per km 2 ), the AAPDEA we obtained for Philippine cities and municipalities (as aggregated regions) are significantly higher, even surpassing the average AAPDEA (17.8 × 10 3 people per km 2 ) of urban centers in Asia (Schiavina et al. 2019 ).

Within the context of SDG 11.3.1, achieving efficient LUE is desirable as it indicates optimal utilization of limited urban space and promotes sustainability through compact development. However, the high level of efficiency we observed for the Philippines signifies overcrowding. This is especially true for Metro Manila where existing built-up areas are accommodating a larger population without significant expansion. Therefore, achieving efficient land use must be balanced with considerations for livability, quality of life, and equitable access to resources and services to address potential challenges associated with overcrowding.

6 Conclusions and Outlook

The comprehensive analysis presented in this study reveals complex patterns and evolving trends of urbanization and LUE across the Philippines from 1975 to 2020. By leveraging the GHSL dataset and incorporating SDG 11.3.1 indicators, our research provided a detailed understanding of built-up area expansion, urbanization characteristics, and LUE dynamics at the national, city and municipal levels. Implications for the formulation of sustainable development policies were also suggested. In addition, this study contributes to addressing challenges in data availability for urbanization and LUE studies in the Philippines, emphasizes the importance of global EO datasets like GHSL, exemplifies the significance of incorporating secondary and supplementary indicators to overcome limitations of the current SDG 11.3.1 methodology, and offers valuable insights and methodologies for analyzing urbanization and LUE dynamics in diverse regions like the Philippines. One of the aspects that we have not fully investigated is identifying the underlying causes of the observed trends in urbanization and LUE.

The current conceptualization of the SDG 11.3.1 indicator overlooks the inclusion of the vertical dimension (e.g., building heights and/or built-up volumes) in land use efficiency analysis (Estoque et al. 2021 ; Jalilov et al. 2021 ). Incorporating data on built-up volumes or heights may clarify or further characterize the LUE characteristics we obtained in this study, particularly for cities and municipalities with LCRPGR below 1 where overcrowding is assumed to dominate.

Like other researchers utilizing global EO datasets such as the GHSL (Li et al. 2022 ), we recognize that the accuracy of our study results is contingent upon the quality of the input datasets. Thus, caution should be exercised when interpreting and utilizing our research findings. While we have previously established the advantages and reliability of the GHSL based on prior works by other researchers, we still emphasize the need to validate the datasets used in this analysis thoroughly to provide more confidence in the results. For instance, the spatiotemporal built-up area information provided by GHS-BUILT‑S showed positive LCRs in all cities and municipalities across time periods. This implies that once an area is identified as built-up, it is generally expected to remain as such throughout subsequent periods. However, there could be localities in the Philippines experiencing significant changes that do not follow the typical pattern of urban expansion. In regions affected by natural disasters or other disruptive events, the incremental growth assumption of GHS-BUILT‑S may lead to inaccuracies in built-up area mapping and SDG 11.3.1 monitoring. Furthermore, the dasymetric mapping approach used in generating GHS-POP can over-concentrate population in built-up areas, potentially resulting in an overestimation of urban resident numbers (Macmanus et al. 2021 ). This is because GHS-POP relies on GHS-BUILT‑S for its methodology. In regions where built-up area data is sparse or inaccurate (e.g., rural areas), this approach may introduce anomalies, misrepresenting population distribution. This could explain why several municipalities were found to exhibit abnormal LCRPGR values due to negative or minimal population changes detected by GHS-POP. Future work will investigate errors and uncertainties in the built-up area and population estimates from the GHSL datasets and incorporating them in the derived values of SDG 11.3.1 indicators to strengthen any conclusions derived from the analysis.

Availability of data and material

All of the datasets used in this research are publicly available through the respective websites of the data providers indicated in the paper.

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Acknowledgements

The lead author acknowledges the support of the Philippines’ Department of Science and Technology—Science Education Institute (DOST-SEI) through its Foreign Graduate Scholarships in Priority S&T Fields Program, and Caraga State University, Philippines, for providing a doctoral scholarship and fellowship. We are also grateful to the European Commission—Joint Research Center (EC-JRC) and the United Nations Office for the Coordination of Humanitarian Affairs (OCHA) for providing the datasets utilized in this work free of charge. Furthermore, we appreciate the valuable input and constructive suggestions provided by the two anonymous reviewers, which have improved the quality and clarity of this manuscript.

This work was financially supported by the Department of Science and Technology—Science Education Institute (DOST-SEI), Philippines & Caraga State University, Philippines.

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J.R. Santillan : Conceptualization, Methodology, Formal analysis, Writing—original draft, Writing—review & editing, Visualization, Funding acquisition. C. Heipke : Writing—review & editing, Supervision.

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Santillan, J.R., Heipke, C. Assessing Patterns and Trends in Urbanization and Land Use Efficiency Across the Philippines: A Comprehensive Analysis Using Global Earth Observation Data and SDG 11.3.1 Indicators. PFG (2024). https://doi.org/10.1007/s41064-024-00305-y

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The Relationship between Unemployment and Inflation: Indication from the Philippine Economy

12 Pages Posted: 26 Mar 2024

Kingboy Chino Carandang

World Citi Colleges - Quezon City - Graduate School Department

Rene Boy Bacay

WORLD CITI COLLEGES

Florinda Vigonte

Marmelo v. abante.

Date Written: February 26, 2024

The study explores the relationship between unemployment and inflation as indicated by the existing and prevalent situations and circumstances in the Philippine economy. The two economic indicators are explored and discussed to be able to show how they affect the quality of life of Filipinos belonging to various sectors and social status. With the utilization of PRISMA or the Preferred Reporting Items for Systematic Reviews and Meta-analysis, the study has determined the relationship between unemployment and inflation as well as the factors underlying their association with one another. Also, the study has discussed the positive and negative effects of these two economic indicators that will transpire future actions and plans that the government should implement.

Keywords: Unemployment, inflation, PRISMA, economic indicators

JEL Classification: A23

Suggested Citation: Suggested Citation

Kingboy Chino Carandang (Contact Author)

World citi colleges - quezon city - graduate school department ( email ).

Philippines

WORLD CITI COLLEGES ( email )

Quezon City, NCR 1008 Philippines

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