Empirical Analysis and Test of the Relationship of Output Growth and Unemployment in the Philippines 1964 - 2013

THE RELATIONSHIP OF OUTPUT AND UNEMPLOYMENT IN THE PHILIPPINES

EMPIRICAL ANALYSIS AND TEST OF THE RELATIONSHIPOF OUTPUT GROWTH AND UNEMPLOYMENT IN THE PHILIPPINES FROM 1964-2013

A Research Paper Presented to the Economics Department

De La Salle University-Manila

In partial fulfillment of the course requirements

in MACREC2 V24

Submitted to:Mr. Angelo Taningco

Submitted by:Castro, Francis Angelo Lorenzo A.

Diamada, Weddy Anne R.Esteban, Shiena Jane M.

Guevarra, Katrina Louise T.


Santos, Vladimir C.

August 18, 2014

ABSTRACTThe primary objective of the study is to examine the relationshipof Gross Domestic Product (GDP) and Unemployment Rate in the Philippines from 1964-2013 by inducing a comprehensive analysis on the nature of the individual data and by using a model specification of an economic theory. By examining such relationship between these two macroeconomic variables, the Okun’s Law was used as a framework to test the hypothesis that there exists a negative relationship between the two. To achieve the objectives of this study, first, an empirical analysis of their individual dynamics was relayed upon the variables by obtaining their relevant descriptive statistics throughout the given period and by looking into published journal articles to explain the volatilities from economic and socio-political problems like the Asian Financial Crisis, implementation of the Martial law, EDSA revolutions and other significant events in thePhilippine history. Subsequently, the model specification called the First Difference Model is employed to the data consisting of GDP Growth dY/Y and successive changes in unemployment dU by method of Ordinary Least Squares and data-analysis software utilization. The results show that from the sample size consisting of both time-series and cross-sectional data from 1964-2013 implies that there is no statistically significant relationship that exists between the changes in GDP and the Unemployment rate annually. The same model was then reapplied to


a ten-year interval of the same data and the results are still consistent about the irregularity and lack of explanatory power of the apparent relationship between the two macroeconomic variables.

Keywords: Okun’s Law, First Difference Model, Unemployment, GrossDomestic Product, Regression


TABLE OF CONTENTS

I. Introduction …….………………………………………………………………….………1Background of the Study…………………………………………………..……1Significance of the Study………………………………………………..………3-4Research Gap and Objectives…………………………………………………...4Scope and Limitations of the Study……………………………………….….....4

II. Unemployment-Output Growth Relationship- Review of Related Literature…………5

A. The Okun's Law…………………………………………………………….5-6B. Direct Empirical Studies Concerning the

Relationship……………...….….6-8

III. Methodology…………………………………………………….…………………………9A. Method of Analysis and Sources of Data…………………………………..9B. Presentation of the Model………………………………………………..…9-10

IV. Presentation and Analysis of Individual Data ………………………………………...11

A. Unemployment in the Philippines (50 years) ………………………………11-12

B. Gross Domestic Product of the Philippines (50 years) …….…………….…12-16

C. Unemployment vs. GDP Growth Rate……………………………………...16-17D. Comparison to Other Countries/Economies………………………………...17-

18

V. Encountering the Model and Results……………………………………………………19A. Employing First Difference Model for the Last 50

years…………………..19-23B. Encountering the Model Again: Breaking Down into

Decades…….….…...23-26


VI. Further Implications of the Model and Results…………………..…………………27-28

VII. Conclusion……………………………………..…………………………………………29

VIII. References……………………………………………………………………………30-33

APPENDIX ………………………………………………………………………..……….34-37

THE RELATIONSHIP OF OUTPUT AND UNEMPLOYMENT IN THE PHILIPPINES 1

INTRODUCTION

Background of the Study

Unemployment

According to National Statistical Coordination Board, in general, “unemployment is the total lack of work at a given point in time. It is a situation of those persons who want work and are able to take suitable jobs but cannot findthem. There is an international standard in the measurement of unemployment. The current international standard measurement is contained in Resolution No. 1 adopted by the 13th ICLS (1982), which defines “unemployed” to comprise allpersons above a specified age who satisfy simultaneously thefollowing 3 criteria: (1) without work, i.e., were not in paid employment or self-employment as specified by the international definition of employment; (2) currently available of work, i.e., were available for paid employment or self-employment during the reference period; and (3) seeking work, i.e., had taken specific steps in a specified recent period to seek paid employment or self-employment. There is a need to adopt the international standard in measuring unemployment because the deviation raises certain issues such as international comparability. This precludes ameaningful comparison of the country’s labor market performance vis-à-vis other countries. Moreover, the currentmeasurement (which is based only on 2 criteria) produces higher unemployment rate for the Philippines as can be gleaned from the comparative unemployment statistics selected ASEAN countries. In October 2004, National Statistical Coordination Board (NSCB) has approved Resolution No. 15 series of 2004 concerning the “Adoption ofa New Official Definition of Unemployment”. Specifically, the new official definition of the unemployed is as follows:


The unemployed include all persons who are 15 years old and over as of their last birthday and are reported as: (1) without work, i.e., had no job or business during the basic survey reference period; and (2) currently available for work, i.e., were available and willing to take up work in paid employment or self-employment during the basic survey reference period, and/or would be available and willing to take up work in paid employment or self-employment within 2 weeks after the interview date; and (3) seeking work, i.e., had taken specific steps to look for a job or establish a business during the basic survey reference period; or not seeking work due to the following reasons; (a) tired/believeno work available i.e., discouraged workers who looked for work within the last 6 months prior to the interview date; (b) awaiting results of previous job application; (c) temporary illness/disability; (d) bad weather, and (e) waiting for rehire job/recall. The basic survey reference period for the Labor Force Survey (LFS) is the past week or the week before the interview.” (NSCB, 2005)

Whenever people are unemployed, production of goods andprovision of services falls off, and simultaneously, the unemployed lack the capacity to purchase goods and services.People who still have money, investors, are averse to investany money in the production of goods or the provision of services because when production and consumption are down, there is no return on the investment. The ability of government to provide for people is compromised. When thereis high unemployment, people pay less in income taxes and also pay less in sales taxes because they purchase fewer goods and services which leads to less in the way of public services.

‘You don’t live when you are unemployed—you exist’ (Jackson & Crooks 1993). The personal and social costs of


unemployment include severe financial hardship and poverty, debt, homelessness and housing stress, family tensions and breakdown, boredom, alienation, shame and stigma, increased social isolation, crime, erosion of confidence and self-esteem, the atrophying of work skills and ill-health. Most of these increase with the duration of unemployment (Dixon 1992; EPAC 1992; Cass 1988; White 1991; Victorian Social Justice Consultative Council (VSJCC) 1992). Unemployed people report that being unemployed is one of the worst things that can happen to them (White 1991).

Okun’s Law

Typically, growth slowdowns coincide with rising unemployment. This negative correlation between GDP growth and unemployment has been named “Okun’s law,” after the economist Arthur Okun who first documented it in the early 1960s. In reality, though, Okun’s law is a statistical relationship rather than a structural feature of the economy. Okun’s law is only a rule of thumb-contrary to connotations of the word “law”. As with any statistical relationship, it may be subject to revisions in an ever-changing macro-economy. Okun’s relationship between changes in the unemployment rate and output growth has varied considerably over time and over the business cycle. First possible explanation is that it documents that Okun’s law has been sensitive to the state of the business cycle. Because the economic expansions that have occurred since theonset of the Great Moderation have been longer than average by historical comparison, this has helped to drive some of the observed changes in Okun’s coefficient toward the end ofthe sample. Second, there have also been recent changes in the dynamics of the relationship between output and unemployment. Nevertheless, it can still be useful as a


forecasting tool by the policymakers and ecocomists-providedthat they takes its instability into account. Comparing alternative forms of Okun’s law with a common baseline forecasting model, the results show that incorporating instability in producing more accurate unemployment forecasts. While Okun’s simple difference relationship can improve on the baseline forecasting model, on average the dynamic version of Okun’s relationship produces the most accurate forecasts. To make these forecasts with Okun’s law,one must also have forecasts of output growth in the future.In the contemporaneous relationship, for instance, the one-quarter forecast for the change in the unemployment rate would depend on a one-quarter forecast for output growth.


The Long-Run Perspective

There are real, concrete benefits of long-run perspective. Moreover, the value concept is simple: If you understand that changes are coming, you can be better prepared to deal with those changes. If you understand whereyou want to be in the future-your destination-that knowledgemay help you achieve that future, that vision. Thinking about the future and thinking from the future create a valuable perspective-the long-run perspective. The long –runperspective provides power to individuals and organizations:power over individuals’ lives, power over the fortunes and prospects of organizations, and power to change the future. Long-run perspective is not only visioning, but also how to develop strategies to achieve that vision, as well as strategies to deal with anticipated high-impact events. It will also create an action plan and making changes when appropriate or necessary.

A long-run perspective changes the way individuals perceive the future and the world around them, and there is often a gain in self-confidence, a sense of power over theirown lives. Entrepreneurs and small businesses can benefit greatly from developing a long-run perspective and applying futures methods to develop scenarios, vision, strategies, and plans. These entities will probably benefit from exploring both internal and external forces to determine thedriving forces that are bringing about change.

Significance of the Study

In recent years, the Philippines have been experiencinghigh levels of output growth. In fact, among the ASEAN-4 countries, which include Indonesia, Malaysia, Thailand, and the Philippines, the Philippines had the highest and fastest


output growth with the exception of Malaysia. However, despite the relatively high output growth in recent years, the Philippines still has one of the highest unemployment rates in the ASEAN region, ranging at around 7-8 percent. According to Okun’s Law, this shouldn’t be the case. The intuition is that, when output growth is high for any given country, unemployment must decrease as more jobs are createdby the vibrant economic environment. Looking back at the numbers, there is indeed an increase in employment in the Philippines for this time period as employment grew by 40%. However, this increase in employment was cancelled out partly because employment can’t keep up with the rapid increase in the labor force (International Monetary Fund, 2011). Other than that, the researchers must now look for the other reasons why this case for the Philippines happens to be so.

This study contains very crucial information regarding the relationship of unemployment and output growth in the Philippines. Fully understanding their relationship in the Philippine setting would help determine the proper steps andactions that should be taken by the policy makers in order to translate this high output growth into more jobs and income opportunities for the masses, thus, achieving genuineand inclusive growth. In addition to this, fully understanding this relationship for the case of the Philippines would provide people with the information neededin order for them to better adapt to the economic environment and maximize the opportunities that are presented to them as a result of this high growth in output.

Research Gap and Objectives


The relevant studies for the existence of the relationship of Gross Domestic Product and Unemployment Rateare mostly constituted to other foreign countries favored tosuch objectives and issues that are addressed to their own economies. There are very little studies available unto which the Philippines are the main focus of the study other than using the ASEAN region as the point of academic interest.

In an attempt to fill such gaps in the academic field,the researchers aim:

To present and empirically analyze the dynamics of Unemployment Rate and Output of the Philippines from 1964-2013;

To quantify the sensibility of the Gross Domestic Product in response to the changes in the Unemployment under the same time period;

To give necessary inferences by utilizing other similar andrelevant studies, and by comparing results with other countries.

Scope and Limitations of the Study

This study analyses the relationship of Gross Domestic Product (GDP) and Unemployment of the Philippines from the years 1963-2013. To fulfil such primary objective, this study employs an empirical analysis on the dynamics of the two areas concerned, thus, will heavily align itself to the principle behind Okun’s Law. First, GDP and Unemployment rates are investigated by evaluating its dynamics using secondary resources and their respective descriptive statistics during the period mentioned. Thereafter, a linearregression is generated to represent the relationship. Out of the other models that could validate the existence of


Okun’s law, the First Difference Model was utilized in whichvariables gathered are the real Gross Domestic Product Growth (%) and the successive changes in the Unemployment rate (%) for the past half century.

UNEMPLOYMENT-OUTPUT GROWTH RELATIONSHIP – REVIEW OF RELATED LITERATURE

A. The Okun’s Law

For centuries, economic study and thought has already been around and evolving according to how humans interact with each other. One of the most important things discussed by economists in their studies throughout the years is the relationship between economic growth and unemployment. Many of these economists noted that, indeed, there is a relationship between economic growth and unemployment. In the 1962, an economist by the name of Arthur Okun started discussing this relationship in his research and since then,his research became known as Okun’s Law.

Arthur Okun was born in 1928 and became a professor andan economist at Yale University by the time he was conducting his research. The results from his research was published in the early 60’s and since then was named after him as his very own law, Okun’s Law. He passed away at a relatively young age of 51 in 1980. In its most basic form,


Okun’s Law supposes that there would be a negative relationship between the change in economic growth (real GDP) and the unemployment rate. This negative relationship is clearly seen in this equation:

This more detailed equation of Okun’s Law says that fora specific country, unemployment decreases when the real GDP growth is higher than the expected or trend rate of growth (Stock and Von-Ludwig, 2010, p. 19). It has to be taken into account that the unemployment rate can increase even with positive growth. Thus, for a specific country, unemployment increases when the real GDP growth is lower than the expected or trend rate of growth . This is the reason why the trend growth rate is also called the unemployment threshold (Schalk et al. 1997). Meanwhile, the coefficient β, also known as Okun’s coefficient, measures the impact of GDP growth rate on the unemployment rate. β orOkun’s Coefficient is explained as being determined by a number of variables that regulate the flexibility of the labor markets such as laws created to protect workers from abuse, laws that prevent monopolies, laws that prevent hoarding, etc. These laws/regulations make the labor market less flexible as they prevent the labor market to adapt quickly to any changes or fluctuations in the economy. A country with a less flexible labor market would have a lowerOkun’s Coefficient, therefore, a lower impact of GDP growth rate on the unemployment rate. A country with a more flexible labor market would have a higher Okun’s Coefficient, therefore, a higher impact of the GDP growth rate on the unemployment rate. (Stock and Von-Ludwig, 2010, p. 19)


The growth trend cannot be observed and reflects the change of potential output. This depends on the growth rate of the labor force, productivity, technical progress and cost efficiencies (Blanchard and Illing 2004, p. 271).

In a previous talk from former Federal Reserve Chairman, Ben Bernanke, he summarized Okun’s Law basic concepts by saying that: “Okun noted from his observations that because of the continuous growth of the labor force andthe level of productivity, real GDP growth must be kept close to the rate of growth of its potential to hold the unemployment rate steady.” Thus, from this, the economy mustgrow at a pace above its potential in order to decrease the unemployment rate. To be more specific, in order to attain a1 percentage point decrease in the unemployment rate, real GDP must grow at approximately 2 percentage points faster than the rate of potential GDP growth for that period. As anexample, Ben Bernanke illustrated that “if the potential rate of GDP growth is 2%, Okun's law says that GDP must growat about a 4% rate for one year to achieve a 1 percentage point reduction in the rate of unemployment."

B. Direct Empirical Studies Concerning the Relationship

In a study back in 2010 called "Test of Okun's Law in Some Asian Countries Co-Integration Approach" by Lal, Irfan,Muhammad, Sulaiman D., Jalil, M. Anwer, and Hussain, Adnan, all from the University of Karachi, the researchers tested the validity of Okun's Law on five developing countries in Asia, namely, Bangladesh, China, India, Pakistan, and Sri Lanka using time series annual data during the period 1980-2006. In order to find out the long run association between variables and error correction mechanism (ECM) used for short run dynamic, the Engle Granger (1987) co integration


technique was used. The researchers used the two standard model specifications of Okun's law, namely, the "First difference model" and the "Gap model". Econometrics modelingsuch as ordinary least squares (OLS) and Augmented Dickey Fuller (ADF) was used in order to give a more scientific andplausible approach. In the end, the results show that Okun'sLaw is not applicable in developing countries because of asymmetric problems. At the very least, it is sure that it cannot be applied to the five countries used in the research.

In a study in 2014 called "Potential Growth in EmergingAsia" by Anand, Rahul, Cheng, Kevin C., Rehman, Sidra, and Zhang, Longmei, using data taken from 1993-2013, although the issue is not purely about Okun's Law, the researchers denied the existence of it for all five countries.

A study made by Amy Faizah Nain in 2012 entitled "Does Okun's Law Exist in Selected ASEAN Countries?" uses a less rigorous approach in terms of Mathematics. Pedroni cointegration technique is used in order to find long run relation between unemployment rates and real GDP. Furthermore, the vector error correction model (VECM) is to find the short term behaviour of real GDP to its long run value. The simple regression is to examine the relationship between unemployment rate and real GDP. Statistical data taken was from 1980-2009 for five ASEAN countries: Indonesia, Malaysia, Philippines, Singapore, and Thailand. The research verifies the relationship between unemployment and GDP but only for three countries: Malaysia, Singapore, and Thailand. The remaining two countries Philippines, and Indonesia did not support the Okun's Law.

“At earlier studies, Okun found that the relationship was about 3 to 1: that is 1 point of unemployment for every 3 points of GDP gap. However, modern data and advanced


econometrics techniques found that the 2 to 1 (or perhaps 2.5) ratio between output and the unemployment rate is more representative for recent periods”. [Samuelson and Nordhaus,15th Ed.]

Clifford L. F. Attfield and Brian Silverstone (1997) found the strong evidence of long run equilibrium association between output and unemployment gap with an estimate of Okun’s coefficient approximately to -2.25 in U.Seconomy.

From a study “Output and Unemployment Relationship: How Applicable is the Okun's Law to Nigeria?” by Oluwatobi, S., & Sodipe, O. A., it is discovered that a long run relationship exists between unemployment and GDP and an inverse relationship exists between them in Nigeria, thus, Okun’s Law is present there. In other words, a unit decrease in the level of unemployment brings about an increase in GDP by 1.75%. Since the Okun’s Law is applicable to this, job creation and entrepreneurial development can lead to a reduction in the level of unemployment. These are the activities that the researchers recommend to policymakers to focus on. In addition, unemployment in Nigeria may be due to the employers that are reluctant to employ Nigerian graduates because of the unbelief in their education system. Thus, investment in education is a must. Dabalen, Oni, and Adekola (2000) in their study postulated that employers complain that graduates are poorly prepared for work and academic standards have fallen considerably making Universitydegree void of technical competence. A talent mismatch occur which causes labor inefficiency. Addressing this challenge may contribute to the reduction of unemployment, which will ultimately increase the level of output.

A study on Okun's Law and Long Term Co-Integration Analysis for OECD Countries by Bilal Kargi showed that thereare some special cases for Okun’s Law. Increase of working


hours of current employees may create increase in GDP if it does not cause increase in unemployment (Knotek 2007; Levine, 2013). Unemployment rates increase while GDP continue its characteristic movements because developments in the use of technology and directing to non-OECD countrieswith cheap facilities and labor force. Technically, Okun’s Law suggest that %3 increase in GDP causes an %1 decrease inunemployment (Caraiani, 2010; Elshamy, 2013). Relation between unemployment and growth states the positive effects of power of trade associations and labor costs when unemployment rates are low and growth rates are high, besides, efficient trade union bargains in labor market may create negative effects on growth rate (Adjemian, Langor andRojas 2010). In the study, unemployment and growth data of 23 OECD countries are used. Firstly, Okun Difference Equation (ODE) is used to calculate needed GDP increase to have constant unemployment rate. Then, with the use of time series analysis, long term relation of the two variables is studied. In conclusion of the study, Okun’s law indicates reverse relation between unemployment and growth. This meansthat as unemployment increases, GDP decreases. Result of calculation related to OECD countries showed that Okun’s lawis valid for 23 countries. Growth performance of countries, especially with high employment rate, is quite low. Countries, especially with high Okun coefficient need, at least, this much economic growth to keep unemployment at current rate. Long term co-integration is valid for 14 of 23OECD countries and variables are long term related. Similar results of Huang and Yeh (2013) for long term relation are found. Finally, the finding of Tillmann (2010) that reverse relation between unemployment and growth in long term is getting weak is supported. For 34 OECD countries average unemployment is calculated as 6.87 and average growth as 2.43. These results show that long term relation of


unemployment and growth data are suggesting validity of Okun’s law.

According to a research in Cleveland entitled An Elusive Relation between Unemployment and GDP Growth: Okun’sLaw, the slow GDP growth questions the recent sustainabilityimprovement of the labor market. There is a suspicion that the unemployment rate can improve only so much given the modest growth of economic activity. This idea is based on Okun’s law, a simple rule of thumb, which associates the growth rate in real GDP to changes in the unemployment rate.The improvement in the labor market which is measured by theunemployment rate is consistent with the recovery in GDP. Itshows that the recession had a larger impact on unemploymentthan one might have anticipated. A simple version of Okun’s law regresses the change in the unemployment rate over a period in time (usually a quarter, or in the picture below, a year) on a constant and the change in real GDP growth overthe same period. Intuitively, there is a natural, robust relationship between changes in unemployment and changes in output. However, what exact form it takes is a complicated problem that requires going beyond the simple rule of thumb given by Okun’s law.

METHODOLOGY

A. Sources and Method of Analysis

The data used in the regression analysis is from thelatest datasets published by the World Bank, and statistical tables in the Yearbook of Labor Statistics


(2013) by the Philippine Statistics Authority Bureau of Labor and Employment Statistics. The analysis begins by presenting the individual data on the Gross Domestic Product and Unemployment Rate of the Philippines from 1963 to 2013 and obtaining its descriptive statistics while simultaneously incorporating inferences by using secondary sources. To further conclude the implications of the data, the GDP and Unemployment Rate is also compared to other countries. The main data analysis software used for this case study is Stata11 where the econometric model used is a log-log analysis of the relationship of the two variables of interest. This modelis further discussed in the next part that is still undermethodology. The researchers also briefly tested the samemodel to period ten-year intervals to scrutinize the stability or instability of the outcome from the fifty-year perspective.

Subsequently, to further examine the resulting estimated coefficients and the volatility of the variables, outcomes were supported by secondary sources such as journal articles, books, government credited websites, and academically reliable informative websites.

B. Presentation of the Model

After comprehensively analyzing the dynamics and fluctuations on the individual data on GDP, GDP Growth and Unemployment rate, the relationship among the macroeconomic variables are then investigated. Utilizing the information for long run analysis, a sample data for the unemployment and Gross Domestic Product of the Philippines (GDP) for fifty (50) years (1964-2013) is obtained for the model.


The model used for such objective is captured by the equation

(∆YY )t=❑0−❑1∆Ut+εt

where (∆YY ) is the year-on-year percentage change in real GDP and∆Ut is the year-on-year change in the rate of unemployment.

The same representative equation can also be presented as a log-log or double-log model, or simply

logYt=❑0−❑1logUt+εt

Shown above is one of the model specifications of Okun’s Law which is the “First Difference Model” as defined by Lal in 2010 (as cited in Elshamy, 2013). This encapsulates an apparent relationship between the log of thereal Gross Domestic Product (GDP) (logYt ) and the log of the Unemployment(logUt) where ❑1 can also be interpreted as theelasticity of GDP with respect to Unemployment. To interpret the resulting coefficient, it is the percentagechange in output given a minimal change in the rate of unemployment (Gujirati & Porter, 2009). As established inthe former, the relationship between the two points of interest is expected to be an inverse one as shown by thenegative sign of ❑1.


PRESENTATION AND ANALYSIS OF INDIVIDUAL DATA

A. Unemployment in the Philippines (50 years)

The unemployment rate is one of the indicators that gage an economy’s condition enabling itself to compare its own state to neighboring countries. It is the ratio of unemployed individuals and the total labor force. A high unemployment rate is an indication that a specific economy may not be maximizing its capability of producing goods and services since not all individuals in the labor force participate through the acquisition of jobs.

Although the term “unemployed” literally refer to those who don’t have work, it should be made clear that those persons who are able and willing to work and are also actively seeking for work are considered as unemployed. From the data gathered by the Bureau of Labor and Employment Statistics and through the use of Stata, the unemployment rate from 1963 to 2012 can be seen in thegraph below.

46

810

12Un

emplo

yment R

ate (%

)

1960 1970 1980 1990 2000 2010YearSource: www.bles.dole.gov.ph | Bureau of Labor Em ploym ent and Statistics

(1963-2012)Long-Run Unem ploym ent Rate in the Philippines


Evident in the graph, the unemployment rate often fluctuatesbut generally increases overtime. The sudden increase or decreaseof the unemployment rate is more often than not due to an intensification of many already apparent social, economic and political problems. Sudden shifts in leaders, ideologies, etc. are factors that affect work distribution of firms. Also, the increase in population contributes to a high unemployment rate since jobs are limited and companies filter out employees, leaving out the others jobless. Other factors causing unemployment include business depressions, changes in demand for kinds of workers, as mentioned earlier, seasonal variations, personal maladjustments, changes in people’s habits, wants and needs and last but not the least, technological changes.


Among the ASEAN countries, the Philippines was reported to have the highest unemployment rate as reported by the International Labor Organization (ILO) at 7.3% in 2013. “Almost 202 million people were unemployed in 2013 around the world, an increase of almost 5 million compared with the year before,” the ILO said in the executive summary of its report publishedJanuary2014.

A rationalization of long-term unemployment was provided by John Maynard Keynes and his followers in terms of an excess of the amount of money people tried to save over the amount of moneybusinessmen were willing to invest whenever the economy neared a full-employment level of income. He argued that the government has the right to intervene in the market when the aggregate demand for goods and services drops. Without increased public spending to make up for decreased private spending, an economy will slide into a vicious circle of low demand and output, ensuring a prolonged period of high unemployment. Government thrift at such times will only deepen the problem (Springer, 2012).

B. Gross Domestic Product of the Philippines (50 years)

Gross Domestic Product (GDP) is usually used as an indicatorof the economic health of a country. It is a measure of a

050

100

150

Real

GDP (in

billion

USD

)

1960 1970 1980 1990 2000 2010YearSource: www.m ultpl.com | W orld Bank

(1963-2012)Long-Run Real GDP in the Philippines


country’s standard of living which is composed of the aggregate output in the national income accounts. The higher GDP a country has, the more it is advanced compared to those that have low GDP (Blanchard, 2011). Although it should be noted that it does not take depreciation and degradation of assets and natural resourcesinto account.

The cause of these fluctuations is mainly attributed to political events such as the implementation of Martial Law in 1972, the assassination of Ninoy in 1983, the first EDSA revolution in 1986, the turnover of military coups in 1989, the election of Ramos in 1992, 1991 power blackouts, the Asian crisisin 1997, election of Erap in 1998, and the second EDSA revolution.

The administration lead by former president Ferdinand Marcosduring the mid-70s focused on improving the Philippine economy and its image by spending more so as to have appeal to foreign eyes. It mostly funded infrastructures such as the Philippine International Convention Center, the Cultural Center of the Philippines, the Lung Center of the Philippines and Nayong Pilipino to name a few.

The sector that benefited the most was the tourism industry since it was in the Philippines where international events were hosted. This included the Miss Universe beauty pageant and the IMF forums. Since Marcos’ goal was to better the international status of the country, this policy continued onto the 1980s despite the global stagflation, debt crisis and the abrupt interest rate increases (De Dios,1984).

Overall, the early effects of increased government spending were positive until Marcos outdid it to a point of no return. Today, the Philippine debt is humongous which roots to the first


debts acquired in the Marcos regime. The initial debts did not plainly stack itself over another but also earned its own interest. Thus, a bigger cumulative debt left to today’s generation.

During martial law, GDP rised from P55 million to P193 million in about 8 years due to the massive lending from commercial banks, contributing around 62% of external debt. The assassination of Ninoy Aquino resulted in huge reforms for the country, which resulted to loss of confidence and credit rating, which made it harder for the Philippines to borrow money from other banks when it needed it the most. The government plunged into further death and by 1983, decline was contracted by 6.8%. Thus, there was a reduction in balance of payments and national account deficits and the economy slid down by about 7% in 1984-1985.

After the socio-political disasters, the People Power revolution, and the Aquino administration took over, it was seen that the previous administration (Marcos) incurred debt more thanit can finance, therefore leaving the Aquino administration to shoulder the heavy burden, placing the Philippines into a very bad position: the worst recession since its post-war era. (History Central, n.d.).

The erratic recovery from 1987-1991 helped stabilize the Philippine economy and better management set the course for long-run growth according to 1994-1997 analysis. However, this was interrupted due to the Asian Crisis, which made the Philippines take risky measures which eventually led to decline in growth. Fortunately, the Ramos administration was able to handle it better than neighbouring countries.



GDP Growth Rate

Retrieving the relevant descriptive statistics with the use of Stata, the mean across this distribution is 4.0272 which denote that for the long-run, GDP continues to increase by an average of 4.0272%. This increase in GDP presents the Philippinesas a developing country. The standard deviation is a measure of how spreads out the data is. A standard deviation of 3.054275 indicates that the Philippines’ GDP growth rate is almost close to each other each year. Comparing it to the data on real GDP, itconfirms that in the long-run, it increases at a rate not far from its preceding value.

The variance is defined as the average of the squared differences from the mean. A small value of the variance indicates that the distribution of the observations is concentrated to an area around the mean while a large value

-10

-50

510

GDP Grow

th Rate (%

)

1960 1970 1980 1990 2000 2010YearSource: www.indexm undi.com | W orld Bank

(1963-2012)Long-Run GDP Growth Rate in the Philippines


indicates that the distribution is further away from the mean. The 9.328596 value of variance is not much of a big number which means that individual data are closer to each other and thus, does not vary too much.

Skewness is a measure of symmetry. It may be positive, negative or undefined. A negative skewness is illustrated by a longer tail located at the left side of the probability density function than the right side. It shows that the bulk of the observations lies at the right of the mean. The opposite is true for positive skewness where the tail is longer on the right side and the bulk of the observations lies at the left side of the mean. Lastly, a zero value indicates that the observations are relatively evenly distributed on both sides of the mean. The value of skewness is -2.028789 which means it is skewed to the left and that most of the observations are located to the right of the mean. Related to this is kurtosis which is an indicator used in distribution analysis as a sign of flattening or "peakedness" of a distribution. A value of 8.583313 classifies the data as a Leptokurtic distribution, sharper than a normal distribution, with values concentrated around the mean and thicker tails. This means high probability for extreme values.

gdpgrowthrate------------------------------------------------------------- Percentiles Smallest 1% -7.32 -7.32 5% -.58 -7.3110% .745 -.58 Obs 5025% 3.42 -.58 Sum of Wgt. 50

50% 4.615 Mean 4.0272 Largest Std. Dev. 3.05427575% 5.45 7.0690% 6.725 7.63 Variance 9.32859695% 7.63 8.81 Skewness -2.02878999% 8.92 8.92 Kurtosis 8.583313


C. Unemployment Rate vs. GDP Growth Rate

The graph below shows the dynamics of GDP growth rate and Unemployment of the Philippines from 1963-2013.

As seen in the graph above, there is a divergence in the patterns of GDP growth and the unemployment rate. The most variation with high GDP and low employment was in 1984, 1991, 1998 and 2001. This gap is termed jobless growth because despite the increase in aggregate output, the numberof people who are employed is low. Furthermore, the unemployment rate does not only stay stagnant but even increases at some point implying that it is possible to havea high GDP growth without additional provision of jobs.

D. Comparison to Other Countries/Economies

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

-10

-5

0

5

10

15

Output Growth and Unemployment Rate of the Philippines (1963-2013)

Unemployment Rate GDP Growth


To compare GDP across countries, GDP per capita at PPP exchange rates was used instead of converting each output per person using plain exchange rates. This is to avoid inconsistencies due to the simultaneous variations in exchange rates. According to Blanchard (2011), PPP or Purchasing Power Parity numbers are adjusted real GDP numbers which can be thoughtof as measures of purchasing power across time or across countries. The use of PPP numbers is much more efficient than using exchange rates as it saves time of computing and because ittakes into account the level of consumption of an average person in a specific country.

As evidenced by the graph above, among the ASEAN countries, the Philippines’ rank is in the lower half of the rest. Accordingto Rappler, the country mentioned had the highest GDP in Q1 of 2013 at 7.8% but has remained one of the most impoverished

SOURCE: Growing Capacity, Inc. 2013 | EIU


countries in the world. On the other hand, Singapore has the largest gap among the others. This is due to a combination of itssteady economic growth and a low population growth. Also, according to ICAEW’s quarterly Economic Insight Report, by the year 2020, the gap between Singapore and other ASEAN countries isexpected to increase.

Looking at the six largest countries, despite Philippines having the largest GDP growth, it is not ranked first in terms ofPPP exchange rates. This is because the Philippines’ increasing population hinders high per capita income growth rates. As more people are present, dividing the total income by a large number would result to a small per capita income.

However, forecasting measures explain that “ASEAN nations will continue to do well by international standards, as investment and domestic consumption continue to fuel growth. While we believe that GDP per capita will rise across all markets, it should be noted that income per head does not take account of the distribution of wealth. Though GDP per capita is growing, and will continue to grow in ASEAN, it is possible that a large section of the population will not immediately benefit. However, consistently rising national output ultimately raises personal living standards,” said Douglas McWilliams, ICAEW chief economic advisor and chief executive of Cebr.


ENCOUNTERING THE MODEL AND RESULTS

A. Employing First Difference Model for The Last 50 years

As mentioned earlier, this study employs one of the measures of Okun’s law specifications which is the first-difference model. This model merely measures the sensitivityof the growth rate of Output and the change in Unemployment at time t.

The table below shows the relevant economic data of thearea of interest where it includes the real GDP measured in billions of dollars in constant 2000 prices of the Philippines for the last fifty (50) years, the annual growthrate of GDP as denoted by dY/Y, unemployment rate, and lastly, the successive and annual change in unemployment rate as represented by the column dU.

Table 1.Real GDP, GDP growth, Unemployment Rate, and Change in Unemployment Rate (1964-2013)

Year Real GDP(Billion US $) dY/Y Unemployment

Rate dU


1964 22.41 3.45 6.4 0.21965 23.59 5.27 7.2 0.81966 24.63 4.43 7.1 -0.11967 25.95 5.32 8.0 0.91968 27.23 4.95 7.8 -0.21969 28.5 4.66 6.7 -1.11970 29.57 3.76 7.7 11971 31.17 5.43 5.2 -2.51972 32.87 5.45 6.3 1.11973 35.8 8.92 4.9 -1.41974 37.08 3.56 4.0 -0.91975 39.14 5.56 3.9 -0.11976 42.59 8.81 5.2 1.31977 44.98 5.60 5.1 -0.11978 47.3 5.17 4.9 -0.21979 49.97 5.64 4.2 -0.71980 52.54 5.15 4.9 0.71981 54.34 3.42 5.3 0.41982 56.31 3.62 5.7 0.41983 57.36 1.87 5.8 0.11984 53.16 -7.32 7.1 1.31985 49.28 -7.31 6.8 -0.31986 50.96 3.42 6.7 -0.11987 53.16 4.31 9.7 31988 56.75 6.75 9.6 -0.11989 60.27 6.21 9.2 -0.41990 62.1 3.04 8.4 -0.81991 61.74 -0.58 10.6 2.21992 61.95 0.34 9.9 -0.71993 63.26 2.12 9.3 -0.61994 66.04 4.39 9.5 0.21995 69.13 4.68 9.5 01996 73.17 5.85 8.6 -0.91997 76.96 5.19 8.8 0.21998 76.52 -0.58 10.3 1.5


1999 78.87 3.08 9.8 -0.52000 82.35 4.41 11.2 1.42001 84.74 2.89 11.2 02002 87.83 3.65 11.4 0.22003 92.19 4.97 11.4 02004 98.37 6.70 11.8 0.42005 103.07 4.78 7.8 -42006 108.47 5.24 8.0 0.22007 115.65 6.62 7.3 -0.72008 120.45 4.15 7.4 0.12009 121.83 1.15 7.5 0.12010 131.13 7.63 7.4 -0.12011 135.9 3.91 7.0 -0.42012 145.16 4.57 7.0 02013 155.56 6.62 7.3 0.3

Studying the relationship of dY/Y and dU, the specification modelreinstates

dYY

=❑0−❑1dU+εt

where dYY is the same as (∆YY )t, and dUisalso∆Ut , the

estimations results using Stata11 is obtained Source | SS df MS Number of obs = 50-------------+------------------------------ F( 1, 48) = 1.82 Model | 16.565578 1 16.565578 Prob > F = 0.1842 Residual | 438.056497 48 9.12617702 R-squared = 0.0364-------------+------------------------------ Adj R-squared = 0.0164 Total | 454.622075 49 9.27800153 Root MSE = 3.021------------------------------------------------------------------------------ dyy | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- du | -.5406831 .4013137 -1.35 0.184 -1.347578 .2662121 _cons | 4.030295 .427319 9.43 0.000 3.171113 4.889477------------------------------------------------------------------------------


As expected, the Okun’s Coefficient (❑1) is negative, supposedly denoting an inverse relationship between the two variables. This result means that for every 1% point increase in unemployment, on average, the output growth of the Philippines is slowing down by the rate of 0.54%. Although the results show a negative response from the successive changes in Unemployment dU to the annual growth of the Gross Domestic Product of the Philippines dYY for the last fifty years, the explanatory power of the model is given at a low level – that is, only 0.03% of the variations in the annual GDP growth rate is actually explained by the variations in the changes in Unemployment annually. Moreover, the probability value isnot statistically significant given a p-value of 0.05. Inother words, this model does hold a strong statistical evidence in the supposedly responsiveness of output to changes in unemployment where the expectation of such responsiveness is inverse.

The constant (❑0¿ is clearly significant and denotes that, on average, the economy is growing at 4.030295% at thefull-employment output. To further visualize the relationship, the following figure captures this association between the two variables including the resulting fitted model by means of Ordinary Least Squares:


-5 -4 -3 -2 -1 0 1 2 3 4

-10

-8

-6

-4

-2

0

2

4

6

8

10

dU

dY/Y


(∆YY )t=4.030295−0.5406831dU

r2=0.0364

Therefore quantitatively representing the relationship of the

growth rate in Output (∆YY )t, and the successive changes in Unemployment ∆Ut in the Philippines.

B. Encountering the Model Again: Breaking Down into Decades

The aforementioned results for the relationship of the sensitivity of the changes of in GDP with respect to the changes in annual unemployment rate show insignificant outcome. Furthermore, the explanatory power of the model – r2 – is also given at a very small rate. In this section, the relationship of the same variables is then explored but relaxing the Central Limit Theorem that requires the degreesof freedom (n-k) to be at least thirty (30). Specifically, the same First Difference Model is then applied to the data but only a span of ten (10) years is observed for each test.This is for further analysis of the apparent correlation if there is any significant result using only this period.

Table 2.Average Gross Domestic Products, Growth Rates, Unemployment Rates, and Change in Annual Unemployment Rates.

YearsReal GDP

(Billions US$)

GDP GrowthRate

Unemployment Rate

∆Unemployment

Rate1964-1973 28.172 5.164 6.73 -0.13


1974-1983 48.161 4.84 4.9 0.091984-1993 57.263 1.098 8.7 0.351994-2003 78.78 3.853 10.17 0.212004-2013 123.559 5.14 7.85 -0.41

Using the averages in a ten-year period, as shown in the table above, the data fairly depicts how GDP Growth Rates and the Change in Unemployment Rates move in the same way that they are expected - which is supposedly an inverse relationship. Whenever the average growth rate is positive for a given decade, the average of the successive changes inunemployment is negative or relatively in a decreasing rate.However, this mere observation from the averages does not give a strong validation for an inverse relationship becauseas shown earlier, in a span of fifty (50) years, the apparent “negative” relationship is insignificant with a p-value of 0.184.

Employing the same model from Part A, the results of the regression is summarized in the table below. Refer to Appendix for the complete regression results from Stata.

Table 3.Summary of Regression Results for Each Decade.

Year Coefficient

StandardError t P>|

t| R-sqaured

1964-1973

-0.496869

0.4023043 -1.24 0.252

0.1601


41974-1983 1.162048 0.9358816 1.24 0.25 0.1616

1984-1993

-0.432928

7 1.309957 -0.33 0.75 0.01351994-2003

-1.403576 0.7067092 -1.99

0.082 0.3302

2004-2013

0.0801033 0.5098441 0.16

0.879 0.0031

The Okun’s Coefficients are unstable in the fifty-year span with respect to ten year intervals. In addition to that, the slopes are also insignificant. Both results implicating that the relationship of the two variables is unstable and does not validate the null hypothesis that there really exists a relationship between the two. The values of the r2 are expected to be low since the data set used for each set is rather very small.

The figures below show the estimated regression lines for each decade of interest.

y = -0.4969x +5.0994

R² = 0.1601

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.50246810

dU

dY/Y


1964-1973


1974-1983

y = 1.162x + 4.7354R² = 0.1616

1984-1993

y = -0.4329x +1.2495

R² = 0.0135

y = -1.4036x +4.1478R² = 0.3302

-1.5 -1 -0.5 0 0.5 1 1.50246810

dU

dY/Y

-1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5

-10

-6

-2

2

6

dU

dY/Y

-1.5 -1 -0.5 0 0.5 1 1.5 2-101234567

dU

dY/Y


1994-2003

2004-2013

y = 0.0801x + 5.1698R² = 0.0031

As shown above, there are very insignificant and unstable relationships that exist between GDP Growth and Unemployment rates in these ten period intervals. The two variables minimally explain each other’s variations and their apparent trend interchanges from negative to positive and vice versa. These results resound too in the long-run where a period of fifty years is taken into account. These results are consistent with a study where it was validated that Okun’s law is only confirmed in some ASEAN countries

-1.5 -1 -0.5 0 0.5 1 1.5 2-101234567

dU

dY/Y

-5 -4 -3 -2 -1 0 10.002.004.006.008.0010.00

dU

dY/Y


and the Philippines do not have the same characteristic withthe Unemployment-Output relationship. In other words, the study implies that the Okun’s Law is “non-existent” in the country by using the data from 1980-2009 (Nain, 2012, p. 1).

FURTHER IMPLICATIONS OF THE MODEL AND RESULTS

The results show that in the fifty (50) year perspective the slope of the regression line is negative. This negative slope may indicate that there is an inverse relationship between the growth rate of output and unemployment in the Philippines. However, the results also show that this is not statistically significant. This is very important because it means that although the slope of the regression line is negative, the null hypothesis that Okun’s Law does not necessarily hold true in the case of thePhilippines will be accepted.

There are a lot of factors why an inverse relationship between the growth rate of output and unemployment holds true across different countries. In the case of the Philippines, although it is shown and proven that this inverse relationship is not statistically significant, theoretically, there are also many explanations for the negative slope of the fitted regression line. First, it is important to note that employment has a very important role to play in driving economic growth and keeping the economy afloat during recessions. A very high unemployment rate would imply that the resources at hand are not used efficiently as a result of workers not being able to performtheir tasks/duties and therefore, the losses from it cannot anymore be recovered and realized.


This very high unemployment rate would eventually lead to a decrease in the aggregate demand in an economy as the unemployed workers are not able to afford the goods that they once are able to purchase or are significantly slashingtheir consumption in order to brace for the tough times ahead. This would, in turn, lead to an overall reduction in consumption, and a lower consumption would mean less economic activity and movement in the markets. Finally, thisstagnation would lead to a lower output growth in the economy as consumption usually contributes a large portion when computing for an economies’ current GDP (Castells-Quintana and Royuela, 2012).

In addition to this, a very high unemployment rate would also imply that investments, which also make up a veryhuge chunk of GDP, would decrease. This is primarily due to the lower demand and consumption in the economy. Investors would see that investing both human and physical capital in the economy would not be beneficial for them and thus, they won’t invest (Castells-Quintana and Royuela, 2012).

Parallel to this, a very high unemployment would also lead to a higher government debt. Since there are lower revenues from taxes as a result of lower income revenues of the people, and higher spending by the government to keep its basic services and other projects afloat, public debt will no doubt increase.

Looking at the economies’ exports, which is also part of computing for the GDP (imports less exports), a high unemployment rate will also cripple local industries that export goods and services to other countries as there would be little to no physical and human capital investments to fuel these industries.


As compared to the short run difficulties the economy would face as a result of a lower consumption; a lower investment, a larger government debt, and a deteriorating export industry wouldlead to long run difficulties and complications as the economy slowly becomes crippled.

Aside from these direct negative impacts of a high unemployment rate to output and ultimately the economy as a whole, another negative effect is the “de skilling” of the long time unemployed workers. As time passes by, newer techniques and processes are created, adopted, and institutionalized. Since these workers are not employed and not practicing their craft for a very long time, they are left out with outdated and obsolete techniques and skills that are useless in a rapidly changing technological and working environment (Pissarides, 1992). As a result of the gap in knowledge due to “de skilling”, most of these long time unemployed workers decrease their chances of finding future jobs, which would further increase unemployment and decrease output as it becomes a cycle. In relation to this, the “unemployment hysteresis hypothesis” states that small increases in unemployment over time may lead to long term unemployment for those who were unemployed. This happens because people who are long time unemployed do not search hard enough for new jobs and opportunities, thus, the downward pressure to wages becomes insufficient (Layard, Nickell and Jackman, 1991). Furthermore, a study conducted by Martin and Rogers (2000) says that when growth is fuelledby “learning-by-doing”, the short term macroeconomic instability reduces human capital accumulation and thereforegrowth.

Other important studies that further explained the supposed to be inverse relationship between unemployment andthe growth rate of output were made by Andrienko and Guriev


(2004) who found that high unemployment would create liquidity constraints. These liquidity constraints would limit labor migration and create more unemployment. More unemployed workers would then lead to lower economic growth.

High unemployment would also damage one’s self esteem, life satisfaction, and confidence in society (Ochsen and Welsch, 2011). Lower confidence and trust in society will create a lot of socio economic problems that would lead to unrest and conflict as time passes by (ILO, 2011). The performance of the labor market would be hardly hit as a result of the unrest, thus, hampering long run economic growth.

Aside from the inverse or negative relationship betweenthe growth rate of output and unemployment being not significant or not necessarily holding true for the Philippines, the results also showed that this same relationship is weak. The explanatory power of the model is given at a low level of only 0.03%. This means that only 0.03% of the variations in the annual GDP growth rate is explained by the variations in the changes in unemployment. The reason for this weak relationship is probably due to structural issues in the Philippine labor market. One of these structural issues is the high proportion of self-employed Filipino workers and the unpaid family members thatwork for them. From the 2013 figures released by the National Statistics Office, 3 out of 10 are self-employed Filipino workers. In addition to this, redundancy costs or costs related in compensating an employee for losing his or her job, is particularly high in the Philippines. Heavy regulations are also imposed by the government with regards to hiring and firing practices in the Philippines (World Economic Forum, Global Competitiveness Report 2010–11).



CONCLUSION

This study empirically discusses the dynamics of unemployment rate and Gross Domestic Product individually and the relationship between the two simultaneously in the case of the Philippines for the past half century. The volatility of the individual macroeconomic variables are also affected by microeconomic factors such as work distributions of the firms, and significant socio-political events such as Martial Law in 1972, the assassination of Ninoy in 1983, EDSA revolutions and the like. Nevertheless, this does not necessarily imply the relationship of GDP and unemployment. To further examine the relationship of the aforementioned variables, one of the model specifications ofOkun’s Law – the First Difference Model – was employed to the data. The results show that if the method of Ordinary Least Squares is applied to the successive changes in Unemployment rate and in the growth rate of Output, the signof the slope that signifies the relationship between the twois negative. However, it cannot be concluded that there exists a negative relationship between the variables mentioned merely because the estimated coefficients are insignificant. A study conducted back in 2012 that used a more sophisticated model for Okun’s Law to examine the relationship in the case of selected ASEAN Countries including the Philippines in a span thirty (30) years also found out that there exist no significant relationship between GDP and unemployment in the country.

To conclude the research findings, there exists no statistically significant relationship between the GDP Growth and the Rate of Unemployment in the last fifty (50) years for the Philippines. Even though this study also attempted to examine the ten period interval relationships


of the two variables, the results still came insignificant and very unstable. In other words, efforts of reducing the unemployment rate in the country do not give a positive causality effect on economic growth such that the GDP would increase by some percentage. This implies that there is a greater need for the country to realign the more specific actions for keeping the economy grow throughout the long-runas merely reducing the rate of unemployment would not give such a drastic impact on GDP growth. This study is able to contribute a form of verification on such an economically and socially relevant issue that would certainly impose a challenge to the policymakers into being consistent with theeconomy’s robust performance last 2013.


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APPENDIXRegression Results generated from Stata11 on Ten-Year intervals using data from 1964-2013

1964-1973

Year dU dY/Y1964 0.2 3.451965 0.8 5.271966 -0.1 4.431967 0.9 5.321968 -0.2 4.95


1969 -1.1 4.661970 1 3.761971 -2.5 5.431972 1.1 5.451973 -1.4 8.92

Source | SS df MS Number of obs = 10-------------+------------------------------ F( 1, 8) = 1.53 Model | 3.20967691 1 3.20967691 Prob > F = 0.2518 Residual | 16.8335639 8 2.10419548 R-squared = 0.1601-------------+------------------------------ Adj R-squared = 0.0552 Total | 20.0432408 9 2.22702675 Root MSE = 1.4506

------------------------------------------------------------------------------ dyy | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- du | -.4968694 .4023043 -1.24 0.252 -1.424585 .4308459 _cons | 5.099407 .4616869 11.05 0.000 4.034755 6.164059------------------------------------------------------------------------------

1974-1983

Year dU dY/Y1974 -0.9 3.561975 -0.1 5.561976 1.3 8.811977 -0.1 5.601978 -0.2 5.171979 -0.7 5.641980 0.7 5.151981 0.4 3.421982 0.4 3.621983 0.1 1.87

Source | SS df MS Number of obs = 10-------------+------------------------------ F( 1, 8) = 1.54


Model | 5.11649887 1 5.11649887 Prob > F = 0.2495 Residual | 26.5495043 8 3.31868804 R-squared = 0.1616-------------+------------------------------ Adj R-squared = 0.0568 Total | 31.6660032 9 3.5184448 Root MSE = 1.8217

------------------------------------------------------------------------------ dyy | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- du | 1.162048 .9358816 1.24 0.250 -.9960987 3.320195 _cons | 4.735416 .5822056 8.13 0.000 3.392847 6.077984------------------------------------------------------------------------------

1984-1993

Year dU dY/Y1984 1.3 -7.321985 -0.3 -7.311986 -0.1 3.421987 3 4.311988 -0.1 6.751989 -0.4 6.211990 -0.8 3.041991 2.2 -0.581992 -0.7 0.341993 -0.6 2.12

Source | SS df MS Number of obs = 10-------------+------------------------------ F( 1, 8) = 0.11 Model | 3.01101919 1 3.01101919 Prob > F = 0.7495 Residual | 220.538543 8 27.5673178 R-squared = 0.0135-------------+------------------------------ Adj R-squared = -0.1098 Total | 223.549562 9 24.8388402 Root MSE = 5.2505

------------------------------------------------------------------------------ dyy | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- du | -.4329287 1.309957 -0.33 0.750 -3.453694 2.587836 _cons | 1.249525 1.722481 0.73 0.489 -2.722523 5.221573------------------------------------------------------------------------------



1994-2003

Year dU dY/Y1994 0.2 4.391995 0 4.681996 -0.9 5.851997 0.2 5.191998 1.5 -0.581999 -0.5 3.082000 1.4 4.412001 0 2.892002 0.2 3.652003 0 4.97

Source | SS df MS Number of obs = 10-------------+------------------------------ F( 1, 8) = 3.94 Model | 9.74966325 1 9.74966325 Prob > F = 0.0823 Residual | 19.7737452 8 2.47171815 R-squared = 0.3302-------------+------------------------------ Adj R-squared = 0.2465 Total | 29.5234084 9 3.28037872 Root MSE = 1.5722

------------------------------------------------------------------------------ dyy | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- du | -1.403576 .7067092 -1.99 0.082 -3.033251 .2260979 _cons | 4.147751 .518842 7.99 0.000 2.951299 5.344203------------------------------------------------------------------------------

2004-2013Year dU dY/Y2004 0.4 6.702005 -4 4.782006 0.2 5.242007 -0.7 6.622008 0.1 4.152009 0.1 1.152010 -0.1 7.632011 -0.4 3.91


2012 0 4.572013 0.3 6.62

Source | SS df MS Number of obs = 10-------------+------------------------------ F( 1, 8) = 0.02 Model | .098102417 1 .098102417 Prob > F = 0.8790 Residual | 31.7939063 8 3.97423828 R-squared = 0.0031-------------+------------------------------ Adj R-squared = -0.1215 Total | 31.8920087 9 3.54355652 Root MSE = 1.9935

------------------------------------------------------------------------------ dyy | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- du | .0801033 .5098441 0.16 0.879 -1.095599 1.255806 _cons | 5.169842 .6641686 7.78 0.000 3.638267 6.701418------------------------------------------------------------------------------

Empirical Analysis and Test of the Relationship of Output Growth and Unemployment in the Philippines 1964 - 2013

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