A Project Paper on Relationship Between Hourly Compensation In

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Masters in International Economics and Finance

Faculty of Economics,

Chulalongkorn University, Phayathai Road,

Bangkok-10330,Thailand.Tel: (662) 218 6295, (662) 218 6218,Fax:(662) 218 6295,

E-mail: [email protected], http://www.econ.chula.ac.th/programme/ma_inter.html

Paper#2

Application of Dummy Variable models

2940605: Quantitative Methods in Economic Analysis

BY

SK. ASHIQUER RAHMANID#4585974929

A Thesis Submitted In Partial Fulfillment of the Requirement for the Degree of Masters in

International Economics and Finance

TO,

DR.BANGORN TUBTIMTONGASSISTANT PROFESSOR

A Paper

OnRelationship Between Hourly Compensation in

Manufacturing and Unemployment Rate in the

United States, Canada and the United Kingdom


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UNEMPLOYMENT RATE AND COMPENSATION 2002

ii

Letter of TransmittalJune 3, 2002

Dr.Bangorn TubtimtongAssistant Professor,Masters in International Economics and FinanceFaculty of Economics,Chulalongkorn University,Phayathai Road, Bangkok, Thailand

Subject: Letter of Transmittal.

Dear Madam,

Here is my paper on "Relationship between Hourly Compensation in Manufacturing andUnemployment Rate in the United States, Canada and the United Kingdom that I was

assigned. It was a great opportunity for me to acquire practical knowledge of the Quantitative

Methods in Economic Analysis and forecasting

I have concentrated my best effort to achieve the objectives of the report and hope that my

endeavor will serve the purpose.

I believe that the knowledge and experience I have gathered during my paper preparation will

immensely help me in my professional life. I will be obliged if you kindly approve this effort.

Sincerely yours,

Sk. Ashiquer RahmanId#4585974929Chulalongkorn UniversityMasters in International Economics and FinanceBangkok, Thailand


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iii

PrefaceAny institutional education would not be completed if it were confined within theoretical aspects.

Every branch of education has become more competed by their practical application and

accomplishment of full knowledge. We shall be benefited by our education if we can effectively

apply the institutional education in practical fields. Hence, we all need practical education to apply

theoretical knowledge in real world. By considering this importance faculty of economics

arranges the Quantitative Methods in Economic Analysis courses for the students of Masters in

International Economics and Finance. As a part of this program my topic was selected as

Relationship between Hourly Compensation in Manufacturing and Unemployment Rate in

the United States, Canada and the United Kingdom.

I tried my best to conduct an effective study by arrange and analysis data. There may be some

mistakes, which are truly unintentional. So, I would request to look at the matter with merciful

mind.



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AcknowledgementFirst, all praises go to almighty Allah, the most gracious, the most merciful to give me the ability for

all these I have done.

Then I would like to thank Ms. Wanwadee Wongmongkol. Now I would like to thank

Dr.Bangorn Tubtimtong Assistant Professor, Chulalongkorn University, Phayathai Road,

Bangkok,Thailand to give me the opportunity to do this project.

I would also like to thank Professor. Paitoon Wiboonchutikula, Ph.D ,Associate professor and

Chairperson of Faculty of Economics, Chulalongkorn University & Professor Salinee. Secretatery

international economics and finance. My striking thanks go to honorable sir Dr. MN.Sirker who

has helped me in all aspect to prepare the report.

I would like to thank lab incharge Ms. Mink . Last but not the least I wish to thank my

friends, William Lloyed ,Nakarin and Athipat, for their very helpful discussions.



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v

Table of ContentTitle Page

Letter of Transmittal iiPreface iiiAcknowledgement ivTable Of Content vList Of Tables viList Of Figures viiAcronyms And Abbreviation/ Contraction/Symbols viii

Statement Of The Problem 1-1Literature Review 1-1Formulation Of General Model 1-2Data Sources &Description 2-11Model Estimation And Hypothesis Testing 12-16Interpretation Of The Results And Conclusions 17-17Limitations Of The Study And Possible Extensions 17-17References 17-17 Appendix


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vi

Title Page

Table 1. Descriptive Statistics 2

Table 2. Least Squares Regressions Results 13

Table 3 ML-ARCHRegressions Results 13

List of Tables


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vii

Title Page

Fig:1: Compensation in Canada 3

Fig:2: Compensation in UK 3

Fig:3: Compensation in USA 4

Fig:4: Unemployment in Canada 4

Fig:5: Unemployment in UK 5

Fig:6: Unemployment in USA 5

List of Figure


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viii

Title Page

Graph:1: Compensation in Canada 6

Graph :2: Compensation in UK 6

Graph :3: Compensation in USA 7

Graph :4: Unemployment in Canada 7

Graph :5: Unemployment in UK 8

Graph :6: Unemployment in USA 8

Graph :7. Residual, Actual, Fitted 14

Graph: 8. Pooled Result 15

List of Graph


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ix

Title Page

Chat:1: Compensation in Canada 9

Chat :2: Compensation in UK 9

Chat :3: Compensation in USA 10

Chat :4: Unemployment in Canada 10

Graph :5: Unemployment in UK 11

Chat :6: Unemployment in USA 11

List of Chat


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Unemployment Rate and Compensation

Page 1

StatementoftheProblem

This paper studies the relationship between hourly compensation in manufacturing

and unemployment rate in the United States, Canada and the United Kingdom. Since each

country provides only 20 observations, the parameters from the regression may not be valid.

I further study whether the method of pooling data can be applied to this case by using

dummy variable technique to test the difference of intercepts and slopes in each country.

LiteratureReview

In Branson (1989), the compensation should negatively correlated with

unemployment rate because if (inns pay more compensation, labors will have more

incentive to continue working, so unemployment rate is low. But when firms reduce

compensation for labors, unemployment rate will be higher because labors have little

incentive continuing their jobs.

FormulationofGeneralModel:

The linear regression model is set as follows

Yit = 1+ 2 Xit+ Uit (1)

Where Y;t is civilian unemployment rate, and X;t is manufacturing hourly compensation

in U.S. dollars (index, 1992 = 100). i denotes country-the United States, Canada and

the United Kingdom and t denotes time period. In this case, i = 3 and t = 20.


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Page 2

DataSourcesandDescription

I used an annual data from 1980 to 1999 of the United States, Canada and theUnited Kingdom. There are 20 observations for each country, so 60 observations in total.

The data was from Gujarati 2002 (See Appendix) and its descriptive statistics is presented

in here

Table 1. Descriptive Statistics


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Page 3

Histogram and Stats

Fig: 2: Compensation in UK

Fig:1: Compensation in Canada


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Page 4

Fig:3: Compensation in USA

Fig:4: Unemployment in Canada


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Page 5

Fig:5: Unemployment in UK

Fig:6: Unemployment in USA


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Page 6

Graph1. Compensation in Canada

Graph :2: Compensation in UK


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Page 7

Graph3: Compensation in USA

Graph :4: Unemployment in Canada


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Page 8

Graph :5: Unemployment in UK

Graph :6: Unemployment in USA


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Page 9

Chat 1. Compensation in Canada

Chat :2: Compensation in UK


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Page 10

Chat 3: Compensation in USA

Chat :4: Unemployment in Canada


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Page 11

Chat :5: Unemployment in UK

Chat :6: Unemployment in USA


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ModelEstimationandHypothesisTestingThe usual OLS was assigned to estimate equation (1) and 60 observations are

pooled disregarding the space and time dimensions. The results are as follows

= 12.439 - 0.053X (2)

Se (0.818) (0.010)

t (15.202) (-5.424)

Rz

= 0.3366, d = 0.4806

n=60, df=58

Clearly, compensation is negatively correlated with unemployment rate as

expected and t statistic is statistically significant but RZ value is quite low. Also

Durbin-Watson statistic suggests that perhaps there is autocorrelation in the data. However`,

there are highly restricted assumption in equation (1) because the differences across each

country's data, such as intercept and slope, are not considered. So, the regression results in

(2) may not capture the different characteristics between the cross-sectional unit. If this is to

be the case, maybe each country's data cannot be pooled


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Page 13

One way to take into account the individuality of each country is to let the

Table 2. Least Squares Regressions Results

Table 3 ML-ARCHRe ressions Results


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Page 14

intercept and slope coefficients vary across countries. So the fixed effects model (FEM) is

set by using dummy variables as in equation (3) to test whether the intercepts and slope

Cefficients are statistically different.

Yit =

1+

2D

2i+

3D

3i

X

it+

1(D

2iX

it)+

2(D

3iX

it)+ U

it(3)

Where D2i = 1 if the observation belongs to Canada, 0 otherwise and D3i = 1 if the

observation belongs to the United Kingdom, 0 otherwise. Therefore, the United States is

the comparison country. The results of estimating equation (3) are as follows

= 11.524-- 2.181 D2i + 1.029D3i -0.056X;tt + 0.049(D2i Xit)+ 0.009(D3i Xit) (4)

se 1.510 2.173 1.778 0.016 0.025 0.020t (7.627) (-1.004) (0.578) (-3.400) (1.951) (0.463)

As you can see from the model above, all t statistics of the dummy variables added are

not statistically significant at( 0.05) level of significance suggesting that, the intercepts and

slope coefficients of Canada and the/ United Kingdom are not statistically different

from the United States.

R2 = 0.5582, d = O.6764

n = 60, df = 54

Graph :7. Residual, Actual, Fitted


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If the comparison country is changed, regression model (3) will yield different

results. Let D2i = 1 if the observation belongs to the United States, 0 otherwise and D3i

= I if the observation belongs to the United

Kingdom

, 0 otherwise; i.e. Canada is a

Then let D2i = 1 if the observation belongs to the United States, 0 otherwise and D3;

-1 if the observation belongs to Canada, 0 otherwise; i.e. the United Kingdom is a

comparison country, the estimation is as follows

Y= 12.554 1.029D2i - 3.211D3i - 0.046Xit - 0.009(D2i Xit t) + 0.040(D3i Xit) (6)

se (0.938) (1.778) (1.822) (0.012) (0.020) (0.022)t (-0.578) (-1.762) (-3.847) (-0.463) (1.758)

R2 = 0.5582, d = 0.6764

n = 60, df = 54

Y= 9.342 - 2.181 D2i + 3.211D3- 0.006X;t- 0.049(D2i Xit) - 0.040(D3i Xit) (5)se (1.561) (2.173) (1.82 (0.019) 0.025) (0.022)

t (5.981 (1.004 (1.76 (-0.341 -0.463 (-1.758R'= 0.5582, d = 0.6764n = 60, df = 54

Graph: 8. Pooled Result


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Page 16

All t statistics for dummy variables in both (5) and (6) are statistically insignificant

as in model (4). It can be concluded that the intercepts and slopes of the three countries

are not statistically different suggesting that they can be pooled. However, the RZvalue

from model (2) is very low compared with model (4). To do a formal test whether

model (4) is better, F statistic is calculated as follows

(R2

UR-R2

R)/q (0.5582-0.336)/4

F = = =6.771 (7)(1-R

2UR)/n-k (1-0.5582)/54

Where q is the number of parameter restrictions. The critical value of F with 4 numerator

df and 54 denominator df is 3.16, so F= 6.7713 exceeds the critical value. This proves

Fig:7. Pooled Result


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Page 17

that model (4) can explain the variation in unemployment rate better than model (2),

although coefficients of dummies are statistically insignificant.

InterpretationoftheResultsandConclusionsIf we accept that all data sets from the three countries can be pooled and using

regression (2), it can be concluded that if an hourly compensation in manufacturing

increases US$ 1, the unemployment rate will reduce 0.053 percent. But the interpretation

here is subject to some limitations since RZ is very low. However, if we consider model (4)

with higher R2 value, it would yield similar conclusions except that the unemployment

rate will decrease 0.056 percent when there is a US$1 rise in hourly compensation. This

time, the intercepts and slopes from different country will change, but these coefficients

are statistically insignificant. For example, if there is a US$ 1 increase in compensation,

the unemployment rate will drop 0.056, 0.007 and 0.047 percent in the United States,

Canada and the United Kingdom, respectively.

LimitationsoftheStudyandPossibleExtensions

Since RZ value from the regression (2) is very low suggesting an invalidity of the

model, the assumption that the data from 3 countries can be pooled, which is proved by

FEM, may have to be relaxed. Another method to test whether we can use the panel data

should be considered, for example, use random effects model (REM) or allow all

coefficients vary over individuals as well as time

References:

Branson, William H. (1989).Macroeconomic Theory and Policy. Third edition,Harper & Row.

Gujarati, Domadar N. (2002).Basic Econometrics. Fourth edition, McGraw-Hill. p 6


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Appendix

Unenrplolvuent rate (UNEM) and hourly compensation in manufacturing (COMP) in the United

States, Canada and the United Kingdom, 1980-1999

Obs UNEM? COMP?

-US-1980 7.100000 55.600000

-US-1981 7.600000 61.100000

-US-1982 9.700000 6700000

-US-1983 9.600000 68.800000

-US-1984 7.500000 71.200000

-US-1985 7.200000 75.100000

-US-1986 700000 78.500000-US-1987 6.200000 80.700000

-US-1 988 5.500000 8400000

-US-1989 5.300000 86.600000

-US-1990 5.600000 90.800000

-US-1 991 6.800000 95.600000

-US-1992 7.500000 10000000

-US-1993 6.900000 102.700000

-US-1994 6.100000 105.600000

-US-1995 5.600000 107.900000

-US-1996 5.400000 109.300000

-US-1997 4.900000 111.400000

-US-1998 4.500000 117.300000

-US-1 999 4.000000 123.200000

CAN-1980 7.200000 4900000

-CAN-1981 7.300000 54.100000

-CAN-1982 10.60000 59.60000

-CAN-1983 11.500000 63.900000

-CAN-1984 10.900000 64.300000

_CAN-1985 10.200000 63.500000

-CAN-1 986 9.200000 63.300000

-CAN-1 987 8.400000 6800000

-CAN-1988 7.300000 7600000

-CAN-1989 700000 84.100000

-CAN-1 990 7.700000 91.500000

-CAN-1991 9.800000 100.100000


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-CAN-1992 10.600000 10000000

-CAN-1993 10.700000 95.500000

-CAN-1994 9.400000 91.700000

-CAN-1995 8.500000 93.300000

-CAN-1996 8.700000 93.100000

-CAN-1997 8.200000 94.400000

-CAN-1998 7.500000 90.600000

-CAN-1999 5.700000 91.900000

-UK-1980 7.000000 43.700000

-UK-1981 10.500000 44.100000

-UK-1982 11.300000 42.200000

-UK-1983 11.800000 3900000

-UK-1 984 11.70000 37.200000

_UK-1985 11.200000 3900000

_UK-1986 11.200000 47.800000

_UK-1987 10.300000 60.200000

UK-1988 8.600000 68.300000

UK-1989 7.200000 67.700000

UK-1990 6.900000 81.700000

UK-1991 8.800000 90.500000

UK-19992 10.100000 10000000

UK-1993 10.500000 88.700000

U K-1994 9.700000 92.300000

UK-1995 8.700000 95.900000

UK-1996 8.200000 95.600000

UK-1997 7.000000 103.300000

UK-1998 6.300000 109.800000

UK-1999 6.100000 112.200000

Documents

A Project Paper on Relationship Between Hourly Compensation In