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7/26/2019 Econ131 Research Paper. Topic: Unemployment
1/23
Ella Lorraine Obra Economics 131
Charmagne Anne Rimando Introduction to Quantitative Research
Ma. Monserrat Veloso 12 October 2012
Mary Joice Zamora
The Unemployment Condition in the Philippines (1990-2009)
Introduction
According to researches, the largest part of the peoples income is coming from paid jobs.
Thus, it is important to study what factors affect the unemployment rate in a certain country.
However, variables that may explain unemployment may differ from country to country. In this
paper, we will use the imports, labor force participation, and GDP as independent variables in
explaining unemployment rate in the Philippines.
In this research paper different studies conducted will be presented in Chapter 2; so as to
show that such relationship between our chosen dependent and independent variables exist. It is
defined that unemployment is present when those who want to work and are seeking for job
cannot find one. There are studies that argue that there exists a positive relationship between
imports and unemployment rate and there are those that do not. The focus of this paper would be
the positive relationship. The presence of a huge amount of imports in country may affect the
different sectors which provide jobs for the employment pool. In addition, it is known that as
GDP increases, the unemployment rate decreases. Labor force participation is also one of the
important variables that may aggravate the unemployment rate in a certain country (refer to
Chapter 2).
In the definition of terms part of the paper, the variables that were defined are the only
ones that were present in the final model which does not contain any problems of CLRM
(Classical Linear Regression Model); but the reader will expect to see on the review of the
literature part the definitions of the other variables that were at first included in the model but
were found to have problems of CLRM to justify why these particular variables were included
in the preliminary model in the first place.
Objective
Even though we have found literature saying that the variables imports, GDP, and labor
force are related to unemployment rate, we are still not certain whether these relationships are
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present in the condition of the Philippines. Also, in consideration of the mentality of the people
that imports are better than the local products, it is then important to show, if it is applicable in
the Philippines, if there is a positive relationship between imports and labor force and
unemployment rate and that there is a negative relationship between GDP and unemployment
rate.
Research Question
Are the factors of unemployment stated earlier present in the context of Philippines?
Do the relationships presented in the literature are also the relationships expected in the
country?
Methodology
In doing this paper, we will use secondary data from the National Statistics Coordination
Board (NSCB) and World Bank. Furthermore, our group used a time-series data from 1990 to
2009 in order to see the trends of unemployment rate as a function of the independents variables
we have stated above. Observations have been increased since we have three explanatory
variables and we need to subtract it from the numbers of observations in order to get the degree
of freedom. Degrees of freedom means the total number of observations in the sample (= n) less
the number of independent (linear) constraints or restrictions put on them. In other words, it is
the number of independent observations out of a total of n observations (Gujarati, 2004: 77)
If we will just use little observations, the degree of freedom will be low which can be a source
of a problem. A regression model will be used, generated by using GRETL (Gnu Regression,
Econometrics, and Time-Series Library) using the method of Ordinary Least Squares. Also, the
linear model will be transformed into logarithmic model so as to determine which kind of model
will better show the relationships that we intend to study.
Definition of Terms
1.
Unemployment Rate- According to Rudiger Dornbusch. et.al (2008: 604), unemploymentrate is the fraction of the labor force that is out of work and looking for a job or
expecting a recall from a layoff, in terms of percentage.
2. GDP- or Gross Domestic Product is the value of all final goods and services produced in
the country within a given period (Dornbusch, et.al, 2008: 23). GDP in the model is
measured in terms of current US dollars .The logarithm of GDP in the model depicts the
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relative change or percentage change in the unemployment rate when the GDP changes
by 1%.
3. Labor Force- are the people who are looking for work and are working (Dornbusch,
2008: 596). The logarithm of labor force shows the relative change in the unemployment
rate when the labor force changes by 1%.
4. Imports- from the perspective of the country that is importing, are the commodities that
are consumed in that country but produced abroad (Dornbusch, 2008: 280). In the models
presented in this paper, imports are measured in millions of dollars at constant 1985
prices. The logarithm of Imports represents the percentage change in the unemployment
rate as the imports changes by 1%.
Chapter II
Review of Related Literature
Introduction
The problem of unemployment is one of the many things that the countries are facing
including our country, the Philippines. Since it is one the major things to consider when one talks
about development it is crucial that we solve this problem. This chapter presents existing studies
regarding variables that can explain unemployment rate in a country. It will begin through giving
a brief definition of unemployment which highlights the Philippine description of those
individuals who are not employed. The next section will be offering different explanatory
variables for unemployment. Then, delimitation of indicators of unemployment in the
Philippines will follow. The last section will be providing a conclusion.
The total absence of work for a certain period of time defines the broad concept of
unemployment. It takes place when such individuals cannot get an appropriate job despite theirwanting to have one (Stat Informer 2012). Unemployment rate measures the fraction of the
workforce that is out of work and looking for a job or expecting a recall from a layoff
(Dornbusch, Fischer, and Startz 2008:42). A kind of living for an individual without work is
tough, he is financially incapable of buying the basic goods and services he needs. In this regard,
with high unemployment rate finding a job is a lot more difficult and thus unemployment rate is
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an essential tool to measure economic progress of a specific nation (Dornbusch, Fischer, and
Startz 2008).
In the Philippines, the National Statistical Coordination Board (NSCB) has specifically
defined those people that are unemployed,
The unemployed include all persons 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 two weeks after the interview date; AND
3.
seeking work, i.e., had taken specific steps to look for a job or establish business during the basic
survey reference period; OR not seeking work due to the following reasons: (a) tired/believe no work
available, i.e., the discouraged workers who looked for work within the last six 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 (Virola 2005).
This is the international standard accepted as a new measurement of unemployment.
International standard is needed aiming to make the unemployment rate of countries comparable.
Generating a significant assessment of countries labor market would then be possible (Stat
Informer 2012).
Unemployment rate of a country is explained by several factors and is seen through
different perspectives. One of which are Structuralist theory, Non-Accelerating Inflation Rate of
Unemployment (NAIRU), and the chain reaction theory. Structuralist theory argues that lengthy
variations in unemployment rate are significantly explained by the financial wealth of thecountry. NAIRU approach says otherwise, it points out the role played by some fundamental
shocks and a set of unemployment-prone labor market institutions, but it is evolving to a pure
institutionalist view (Agnese and Sala 2008:2). For instance, merely looking to changes in labor
market body swings on unemployment can be understood. On the other hand, capital
accumulation and productivity or working age population as examples of growth variables plays
a vital role in determining how well the labor market performs. This argument is lifted by the
chain reaction theory (Agnese and Sala 2008).
In addition, Foreign Direct Investment (FDI) and government size are able to explain
unemployment. Industrial countries find the size of the government as an essential factor
affecting unemployment. Theory says that increased in unemployment rate can be caused by a
large government. Private sectors will be excluded such as the investment area. This results to a
decline of technical progress and growth productivity and thus decreasing the global economic
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competitiveness of the country. A large government sector also implies greater government
expenditures which raises labor taxes. In this regard, a higher real cost of labor is prominently
seen resulting to falling demands for labor. Accordingly, it gives rise to high unemployment rates
(Feldmann 2006:452). Conversely, FDI refers to the net inflows of investment to acquire a
lasting management interest (10 percent or more of voting stock) in an enterprise operating in an
economy other than that of the investor (World Bank 2012). Based on a comparative study
conducted by Medina Arango (2010: 6) which involving Colombia and the Philippines, FDI has
a positive effect in the economy of a country: economic growth and employment generation;
technology and knowledge; access to goods and services; and filling the savings gap of the
country. Because we want to know the relationship between FDI and unemployment, we shall
focus on the effect of FDI in job creation. For employment generation, as a country becomes
more productive, its competitiveness increases thus creates employment (Arango, M. 2010: 6).
Indeed indicators for unemployment are many and varied that this paper cannot capture
them all. Economic researches regarding the unemployment in the Philippines are able to present
different indicators significantly affecting unemployment rate. This paper will only focus on four
economic variables explaining variations on the rate of unemployment. These are imports,
growth rate, and labor force participation rate.
On the other hand, according to Dornbusch (2008:280), imports, from the perspective of
the country that is importing, are the commodities that are consumed in that country but
produced abroad. Although at a first glance, imports as a variable does not affect unemployment
there are studies conducted that present relationships between them. Here we show a positive
relationship between these two; positive in a sense that when the imports increase, the
unemployment increases (Autor 2012). Examples of studies that present this particular
relationship are done by David H. Autor, et.al (2012) which talked about how imports from
China affected the level of unemployment in the United States. The authors mainly talked about
how imports affect the labor market and wages in the United States and concluded that the
employment in terms of manufacturing industry is decreasing at the presence of imports,
specifically those coming from China. In addition, studies on the relationship of imports and
unemployment focusing on the productivity sector of United States has found out that rising
real imports are responsible for approximately 1.3 million of the jobs lost between 2007 and
2011, or almost one-third of total private non-construction job loss (Mandel and Carew 2012:
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1). Michael Mandel and Diana Carew focused on the productivity sector of United States. The
difference of this study on the one just mentioned is that their paper asserts that not only the
unemployment and imports have relationship but that the latter influences the former greatly
(Mandel and Carew 2012).
Okuns law, which was named after Arthur Okun, explains the relationship between
unemployment and GDP growth rate. This theory assumes that there is a negative relationship
between these two variables. However, Knotek presented a case in the United States in 2003 to
the first quarter of 2006 where the real GDP growth rate is increasing and the unemployment rate
is decreasing; however, it is observed that in the latter part of 2006, the growth rate has been
decreasing while the unemployment rate is also decreasing. Thus, it is argued that the
relationship between these two variables is statistical rather than a structural feature of the
economythe application of Okuns may vary across the country (Knotek 2007: 73). In
addition, Levine (2012:2) stated that
If the rate of output exceeds the rate of labor force growth,
some of the new jobs created by employers to satisfy the growing
demand for their goods and services will be filled by drawing
from the pool of unemployed workers.
Another study was conducted by Andrei, Vasile, and Adrian (2012) to test the
applicability of Okuns law in Romania. In doing this, they used a quarterly data of
unemployment rate and GDP growth rate from 2000 to 2008, and deseasonalized it. It was
presented in models used that high growth rates are accompanied by low unemployment rates.
Furthermore, it is also observed that there is a high value of R2and the value derived from the
Durbin-Watson test is around 2 which means that the problem of serial correlation is not lurking
in the model that they used. However the problem of heteroskedascticity is present in their model
as found out by the Whites test. Also, Kitov and Kitov (2012) conduc ted the same study
wherein they replaced unemployment rate by employment per population ratio; since it is argued
that these two are complementary. Also, they focused on cross-country cases, but delimited it to
largest economies in the world. Furthermore, the model that they used is a lin-log model (the
dependent variable is linear, while the independent variables are in the logarithmic form), since
economic growth is measured through percentage changes. As a result of their study, they proved
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that one of the strong forces that have an impact to the employment per population ration is the
real GDP growth rate.
In a study done by Brooks (2002) it is presented that unemployment is high and is
twice the unemployment rate in the Philippines due to high population growth and increasing
labor participation. The researcher found out that the labor force participation in the country has
been increasing from 1982 to 2001. This increase can be attributed to the growing involvement
of womens participation. Furthermore it is observed that the labor productivity is relatively low
since it is also accompanied by a slow GDP growth. Another study that presents the relationship
between was from Hamilton Place Strategies, however in the context of trends of unemployment
rate during Election Day. This organization presented that the unemployment rate is a function of
unemployed looking for work and labor force, given the formula:
Unemployment rate= unemployed looking for work
Labor force
Source: Hamilton Place Strategies, (2012),Jobs Preview 2012: The Year of the Missing Worker.
Two arguments were made in this research, that is: First, unemployment rate is . . . a function of
those who are actively looking for work and have not found it. Second, those that are
unemployed are not captured in the unemployment rate, but are reflected in the labor force
participation rate, which declines as people drop out of the workforce or choose not to pursue
work at all (Hamilton Place Strategies, 2012:3).
Summary and Conclusion
In conclusion, unemployment is a very broad economic concept which is also explained
by numerous variables. Since unemployment is one of the several economic phenomena
indicating how rich the economy is, it is then necessary to determine explanatory variables for
unemployment. With the knowledge of such indicators countries can generate actions that can
help diminish the rate of unemployment. In studying the unemployment rate in the Philippines
this paper has only chosen three explanatory variables which are imports, growth rate, and labor
force participation rate. Employing regression analysis, this paper can identify if these variables
can significantly explain variations in unemployment.
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Chapter III
Data Presentation and Analysis
Model 1 shows that only the p-value of the coefficient of the constant is significant at
1%. Other variables were found to be insignificant. The adjusted R-squared is equal to -
0.160337, which means that there is/are a variable/s included in the model which does not help in
explaining the dependent variable. The sign of the coefficient of GDP growth is not consistent
with that of the literature, which led us to testing if there is a presence of problems of Classical
Linear Regression Model (CLRM). The tests conducted were test for multicollinearity (Variance
Inflation Factor), test for heteroscedasticity (Whites Test for Heteroscedasticity), test for
normality of residuals, test for serial correlation (Durbin-Watson test and Breusch-Godfrey test
for first-order autocorrelation), and test for specification error (Ramseys RESET). The testsshowed that the model is clear of problems on multicollinearity, heteroscedasticity, and
specification error, but there is a problem of serial correlation in the model.
In Model 2 the variables FDI was dropped to solve the problem in Model 1. FDI was
chosen to be dropped since in the case of the Philippines it does not really explain
unemployment. In this model the p-value of the coefficient of the constant is the only variable
that is significant; it is significant at 1%. The adjusted R-squared is equal to -0.117405, which
means that there is still a variable that does not help in explaining the dependent variable or the
current variables present in the model are not enough to explain unemployment rate. The
negative value of the adjusted R-squared of Model 2 is less than that of Model 1. This means that
a certain extent some of the variables that need to removed were removed therefore making this
new model more acceptable than that of the first.
In Model 3, after FDI was dropped there was still a problem in the model thus another
variable was dropped which is the GDP growth. Even though we have removed the variable FDI
and GDP growth this does not necessarily mean that we will let the model to contain just two
variables for we know for a fact that there are other variables that can explain the unemployment
rate in the Philippines. According to the literature (see the review of the related literature), labor
force and GDP can explain unemployment rate. So now we add these two particular variables.
After adding the variable labor force and GDP, the adjusted R-squared became 0.657184,
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because the adjusted R-squared increased, we have the basis of including labor force and GDP in
the model. The variables with significant p-values in the model are GDP and labor force, being
significant at 1%. The test for multicollinearity shows that labor force has a VIF greater than 10
which indicates that multicollinearity is present in the model.
Model 4 shows that the p-value of the coefficient of both lnGDP and lnLaborForce are
significant at 1%. The adjusted R-squared is 0.644902, which means that the model explains
64.49% of variability in the dependent variable. The problem of multicollinearity has been
removed when the functional form of the variables was changed. Compared to the other models,
Model 4 is the best model that has been generated. The following regression model was derived,
Unemployment rate= -7.55919 + 1.00673 lnImport + 19.4568 lnLF1.87094 lnGDP
the constant term and the import variable are not significant at any level. For a span of 20 years (1990 to
2009), if the labor force participation increases by 1% it is expected that the unemployment rate will also
increase by 19.4568%, ceteris paribus. It is also showed that if the lnGDP decreases by 1%, the
unemployment rate will decrease by 1.87094% while holding the level of lnimports and lnlabor force
participation constant. Based on the value of the adjusted R-squared, we argue that it is a good model
since it explains 64% of the variation in the unemployment rate. Based from the F-statistic of 12.50 and
the critical value of 3.24 we can conclude that the coefficients in this model are significantly different
from zero.
Conclusion
This study presented some of the variables that can explain unemployment in the
Philippines but in reality there are more variables that are not included in the final model. Even
though the literature says that the preliminary variables like FDI and GDP growth explain and/or
are correlated with unemployment rate, in this particular study these variables are not applicable.
For further study, increase in observations may be needed for the other variables affecting
unemployment to be applicable to the Philippine context. Ultimately, the variables included in
the final model was able to explain at a certain extent the unemployment rate as part of the
unemployment condition in the country from 1990-2009 and from this we can say that in terms
of improving the welfare of the country, the independent variables can be manipulated as to
lessen the unemployment rate in the Philippines.
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Appendix
Actual Data.
YEARUNEMPLOYMENTRATE IMPORTS
LABORFORCE GDP
1990 8.1 269148 24244 44311595230
1991 9 266139 25631 45417505303
1992 8.6 289273 26290 52976363148
1993 8.9 322548 26879 54368183872
1994 8.4 369325 27654 64084543195
1995 8.4 428475 28380 74119868202
1996 7.4 500194 29733 82848194395
1995 7.9 567682 28901 82344374414
1998 9.4 484235 29674 72207022472
1999 9.4 470673 30759 82995145601
2000 11.2 490768 30911 81026294681
2001 11 508044 32809 76261998623
2002 11.5 536535 33936 81357657790
2003 11.2 594603 34571 83908205720
2004 11.9 628911 35862 91371236939
2005 7.7 643839 35286 1.03066E+11
2006 8 655706 35464 1.22211E+11
2007 7.4 628664 36213 1.4936E+11
2008 7.3 633770 36805 1.73603E+11
2009 7.5 621543 37892 1.68334E+11
*The data on unemployment rate and imports were taken from National Statistical Coordination Board
while the labor force was taken from the Department of Labor and Unemployment (website). The Gross
Domestic Product was taken from World Bank (website).
From the data given above, we use the statistical package GRETL to obtain thispreliminary regression output using the method of Ordinary Least Squares.
Model 1: OLS, using observations 1990-2009 (T = 20)Dependent variable: unemployment_ra
Coefficien
tStd. Error t-ratio p-value
const 9.422 1.67966 5.6095 0.00004 ***
foreign_direct_
-0.289015 0.474491 -0.6091 0.55101
GDP_growth 0.0112176 0.213829 0.0525 0.95881
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imports 2.05965e-08
3.51303e-06
0.0059 0.99539
Mean dependent var 9.010000 S.D. dependent var 1.530703Sum squared resid 43.49969 S.E. of regression 1.648857R-squared 0.022874 Adjusted R-squared -0.160337
F(3, 16) 0.124851 P-value(F) 0.944039Log-likelihood -36.14899 Akaike criterion 80.29797Schwarz criterion 84.28090 Hannan-Quinn 81.07548Rho 0.635402 Durbin-Watson 0.721617
White's test for heteroskedasticity -Null hypothesis: heteroskedasticity not presentTest statistic: LM = 7.24865with p-value = P(Chi-square(9) > 7.24865) = 0.611249
Test for normality of residual -Null hypothesis: error is normally distributed
Test statistic: Chi-square(2) = 5.73632with p-value = 0.0568035
LM test for autocorrelation up to order 1 -Null hypothesis: no autocorrelationTest statistic: LMF = 13.3367with p-value = P(F(1,15) > 13.3367) = 0.0023604
All the variables included in the model are not significant, not even at 10%. The next to do
would be to test if there are any problems in CLRM present in this current model. We have the
following GRETL output for the different tests that were made.
1.
Test for Multicollinearity
Variance Inflation FactorsMinimum possible value = 1.0Values > 10.0 may indicate a collinearity problem
foreign_direct_ 1.002GDP_growth 1.510imports 1.510
VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlationcoefficient between variable j and the other independentvariables.
Properties of matrix X'X:1-norm = 5.2431077e+012Determinant = 4.7836727e+015Reciprocal condition number = 1.593951e-013
2. Test for Heteroscedasticity
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White's test for heteroskedasticityOLS, using observations 1-20Dependent variable: uhat^2
coefficient std. error t-ratio p-value----------------------------------------------------------------
const 5.93698 10.6870 0.5555 0.5907foreign_direct_ 0.516710 5.05529 0.1022 0.9206GDP_growth 0.332128 1.90017 0.1748 0.8647imports 3.18533e-05 4.91902e-05 0.6476 0.5319sq_foreign_di 0.580412 1.04745 0.5541 0.5917X2_X3 0.0141654 0.453006 0.03127 0.9757X2_X4 3.78463e-06 9.32261e-06 0.4060 0.6933sq_GDP_growth 0.0217692 0.190176 0.1145 0.9111X3_X4 1.37605e-06 4.07276e-06 0.3379 0.7424sq_imports 2.61907e-011 5.69762e-011 0.4597 0.6556
Warning: data matrix close to singularity!
Unadjusted R-squared = 0.362432
Test statistic: TR^2 = 7.248648,with p-value = P(Chi-square(9) > 7.248648) = 0.611249
3. Test for Normality of Residual
Frequency distribution for uhat1, obs 1-20number of bins = 7, mean = 2.13163e-015, sd = 1.64886
interval midpt frequency rel. cum.
< -1.5469 -1.9246 3 15.00% 15.00% *****-1.5469 - -0.79154 -1.1692 5 25.00% 40.00% *********-0.79154 - -0.036193 -0.41387 4 20.00% 60.00% *******-0.036193 - 0.71915 0.34148 2 10.00% 70.00% ***0.71915 - 1.4745 1.0968 1 5.00% 75.00% *1.4745 - 2.2298 1.8522 2 10.00% 85.00% ***
>= 2.2298 2.6075 3 15.00% 100.00% *****
Test for null hypothesis of normal distribution:Chi-square(2) = 5.736 with p-value 0.05680
4.
Test for Serial Correlation
Durbin-Watson statistic = 0.721617p-value = 0.000105588
Breusch-Godfrey test for first-order autocorrelationOLS, using observations 1990-2009 (T = 20)Dependent variable: uhat
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coefficient std. error t-ratio p-value----------------------------------------------------------------const 0.496714 1.26945 0.3913 0.7011foreign_direct_ 0.494015 0.381343 1.295 0.2147GDP_growth 0.165103 0.166915 0.9891 0.3383imports 4.79090e-07 2.64303e-06 0.1813 0.8586
uhat_1 0.787691 0.215691 3.652 0.0024 ***
Unadjusted R-squared = 0.470652
Test statistic: LMF = 13.336732,with p-value = P(F(1,15) > 13.3367) = 0.00236
Alternative statistic: TR^2 = 9.413035,with p-value = P(Chi-square(1) > 9.41303) = 0.00215
Ljung-Box Q' = 8.26774,with p-value = P(Chi-square(1) > 8.26774) = 0.00404
5. Test for Specification Error
RESET test for specification (squares and cubes)Test statistic: F = 3.503171,with p-value = P(F(2,14) > 3.50317) = 0.0584
RESET test for specification (squares only)Test statistic: F = 6.062699,with p-value = P(F(1,15) > 6.0627) = 0.0264
RESET test for specification (cubes only)Test statistic: F = 6.117924,with p-value = P(F(1,15) > 6.11792) = 0.0258
From above, we can see that the problem of multicollinearity does not exist. Also in terms of
heteroscedasticity, from the GRETL output, we can say that the model is acceptable. In the test
for normality of residual, since the null hypothesis is normal distribution and the given chi-square is 5.736 with a p-value of 0.05680 we accept the null hypothesis.
The GDP for the previous model does conform to the literature given above and inaddition to that the variables included in the model are all not significant. The following model is
more acceptable than the previous model but it is underspecified because there are only two
variables in this new model.
Model 2: OLS, using observations 1990-2009 (T = 20)Dependent variable: unemployment_ra
Coefficien
tStd. Error t-ratio p-value
Const 8.94214 1.45575 6.1426 0.00001 ***GDP_growth 0.0059488 0.209664 0.0284 0.97770Imports 9.36904e-
083.44541e-
060.0272 0.97862
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Mean dependent var 9.010000 S.D. dependent var 1.530703Sum squared resid 44.50837 S.E. of regression 1.618067R-squared 0.000216 Adjusted R-squared -0.117405F(2, 17) 0.001840 P-value(F) 0.998162Log-likelihood -36.37822 Akaike criterion 78.75644
Schwarz criterion 81.74364 Hannan-Quinn 79.33957Rho 0.696330 Durbin-Watson 0.609766
White's test for heteroskedasticity -Null hypothesis: heteroskedasticity not presentTest statistic: LM = 7.59469with p-value = P(Chi-square(5) > 7.59469) = 0.180033
Test for normality of residual -Null hypothesis: error is normally distributedTest statistic: Chi-square(2) = 7.46847with p-value = 0.0238915
LM test for autocorrelation up to order 1 -Null hypothesis: no autocorrelationTest statistic: LMF = 15.2515with p-value = P(F(1,16) > 15.2515) = 0.0012596
1. Test for Multicollinearity
Variance Inflation FactorsMinimum possible value = 1.0Values > 10.0 may indicate a collinearity problem
GDP_growth 1.508
imports 1.508VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlationcoefficient between variable j and the other independentvariables
Properties of matrix X'X:1-norm = 5.2430918e+012Determinant = 3.9614224e+014Reciprocal condition number = 2.3026665e-013
2. Test for Heteroscedasticity
White's test for heteroskedasticityOLS, using observations 1-20Dependent variable: uhat^2
coefficient std. error t-ratio p-value----------------------------------------------------------------const 6.80678 7.44573 0.9142 0.3761GDP_growth 0.654480 1.22842 0.5328 0.6025
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imports 3.49007e-05 3.78969e-05 0.9209 0.3727sq_GDP_growth 0.0287942 0.143722 0.2003 0.8441X2_X3 2.23527e-06 3.18983e-06 0.7007 0.4949sq_imports 3.71737e-011 4.59478e-011 0.8090 0.4320
Warning: data matrix close to singularity!
Unadjusted R-squared = 0.379735
Test statistic: TR^2 = 7.594694,with p-value = P(Chi-square(5) > 7.594694) = 0.180033
3. Test for Normality of Residual
Frequency distribution for uhat5, obs 1-20number of bins = 7, mean = 1.5099e-015, sd = 1.61807
interval midpt frequency rel. cum.
< -1.3441 -1.7262 4 20.00% 20.00% *******-1.3441 - -0.57989 -0.96199 6 30.00% 50.00% **********-0.57989 - 0.18433 -0.19778 3 15.00% 65.00% *****0.18433 - 0.94854 0.56643 2 10.00% 75.00% ***0.94854 - 1.7128 1.3306 0 0.00% 75.00%1.7128 - 2.4770 2.0949 3 15.00% 90.00% *****
>= 2.4770 2.8591 2 10.00% 100.00% ***
Test for null hypothesis of normal distribution:Chi-square(2) = 7.468 with p-value 0.02389
4.
Test for Serial Correlation
Durbin-Watson statistic = 0.609766p-value = 3.02846e-005
Breusch-Godfrey test for first-order autocorrelationOLS, using observations 1990-2009 (T = 20)Dependent variable: uhat
coefficient std. error t-ratio p-value--------------------------------------------------------------const 0.744194 3.34239 0.2227 0.8268
imports 7.58279e-07 4.62108e-06 0.1641 0.8718labor_force 4.22222e-05 0.000183927 0.2296 0.8215GDP 2.28944e-012 1.18537e-011 0.1931 0.8494uhat_1 0.143257 0.294577 0.4863 0.6338
Unadjusted R-squared = 0.015522
Test statistic: LMF = 0.236502,with p-value = P(F(1,15) > 0.236502) = 0.634
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Alternative statistic: TR^2 = 0.310442,with p-value = P(Chi-square(1) > 0.310442) = 0.577
Ljung-Box Q' = 0.271872,with p-value = P(Chi-square(1) > 0.271872) = 0.602
5. Test for Specification Error
RESET test for specification (squares and cubes)Test statistic: F = 0.053155,with p-value = P(F(2,15) > 0.0531552) = 0.948
RESET test for specification (squares only)Test statistic: F = 0.091955,with p-value = P(F(1,16) > 0.0919549) = 0.766
RESET test for specification (cubes only)Test statistic: F = 0.091887,with p-value = P(F(1,16) > 0.0918871) = 0.766
In the following model, the variable labor force is added (see the literature) to try to
remedy the problem of underspecification in the model 2.
Model 3: OLS, using observations 1990-2009 (T = 20)Dependent variable: unemployment_ra
Coefficien
tStd. Error t-ratio p-value
Const -4.15938 2.89974 -1.4344 0.17072Imports -5.8416e-
064.24502e-
06-1.3761 0.18775
labor_force 0.000696623
0.00015823 4.4026 0.00044 ***
GDP -6.50145e-011
1.0616e-011
-6.1242 0.00001 ***
Mean dependent var 9.010000 S.D. dependent var 1.530703Sum squared resid 12.85177 S.E. of regression 0.896234R-squared 0.711313 Adjusted R-squared 0.657184F(3, 16) 13.14111 P-value(F) 0.000139Log-likelihood -23.95626 Akaike criterion 55.91253Schwarz criterion 59.89546 Hannan-Quinn 56.69004Rho 0.108573 Durbin-Watson 1.778832
White's test for heteroskedasticity -Null hypothesis: heteroskedasticity not presentTest statistic: LM = 7.54334with p-value = P(Chi-square(9) > 7.54334) = 0.580738
Test for normality of residual -Null hypothesis: error is normally distributed
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Test statistic: Chi-square(2) = 12.483with p-value = 0.00194689
LM test for autocorrelation up to order 1 -Null hypothesis: no autocorrelationTest statistic: LMF = 0.236502
with p-value = P(F(1,15) > 0.236502) = 0.633772
1. Test for Multicollinearity
Variance Inflation FactorsMinimum possible value = 1.0Values > 10.0 may indicate a collinearity problem
imports 7.461labor_force 10.329GDP 3.696
VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation
coefficient between variable j and the other independentvariables
Properties of matrix X'X:1-norm = 1.8586671e+023Determinant = 2.106367e+042Reciprocal condition number = 5.1392367e-025
2. Test for Heteroscedasticity
White's test for heteroskedasticityOLS, using observations 1990-2009 (T = 20)
Dependent variable: uhat^2
coefficient std. error t-ratio p-value---------------------------------------------------------------const 143.383 92.3403 1.553 0.1515imports 0.000230709 0.000142004 1.625 0.1353labor_force 0.0146942 0.00888184 1.654 0.1290GDP 6.34895e-010 7.86352e-010 0.8074 0.4382sq_imports 5.28123e-011 1.51496e-010 0.3486 0.7346X2_X3 9.71346e-09 7.25126e-09 1.340 0.2100X2_X4 0.000000 0.000000 1.235 0.2451sq_labor_forc 3.72793e-07 2.18861e-07 1.703 0.1193
X3_X4 0.000000 0.000000 1.348 0.2074sq_GDP 0.000000 0.000000 0.2087 0.8389
Warning: data matrix close to singularity!
Unadjusted R-squared = 0.377167
Test statistic: TR^2 = 7.543342,with p-value = P(Chi-square(9) > 7.543342) = 0.580738
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3. Test for Normality of Residual
Frequency distribution for uhat1, obs 1-20number of bins = 7, mean = -9.76996e-016, sd = 0.896234
interval midpt frequency rel. cum.
< -1.9081 -2.2598 1 5.00% 5.00% *-1.9081 - -1.2047 -1.5564 0 0.00% 5.00%-1.2047 - -0.50122 -0.85294 2 10.00% 15.00% ***-0.50122 - 0.20222 -0.14950 8 40.00% 55.00%
**************0.20222 - 0.90566 0.55394 8 40.00% 95.00%
**************0.90566 - 1.6091 1.2574 0 0.00% 95.00%
>= 1.6091 1.9608 1 5.00% 100.00% *
Test for null hypothesis of normal distribution:Chi-square(2) = 12.483 with p-value 0.00195
4. Test for Serial Correlation
Durbin-Watson statistic = 1.77883p-value = 0.105984
Breusch-Godfrey test for first-order autocorrelationOLS, using observations 1990-2009 (T = 20)Dependent variable: uhat
coefficient std. error t-ratio p-value--------------------------------------------------------------const 0.744194 3.34239 0.2227 0.8268imports 7.58279e-07 4.62108e-06 0.1641 0.8718labor_force 4.22222e-05 0.000183927 0.2296 0.8215GDP 2.28944e-012 1.18537e-011 0.1931 0.8494uhat_1 0.143257 0.294577 0.4863 0.6338
Unadjusted R-squared = 0.015522
Test statistic: LMF = 0.236502,
with p-value = P(F(1,15) > 0.236502) = 0.634
Alternative statistic: TR^2 = 0.310442,with p-value = P(Chi-square(1) > 0.310442) = 0.577
Ljung-Box Q' = 0.271872,with p-value = P(Chi-square(1) > 0.271872) = 0.602
5.Test for Specification Error
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RESET test for specification (squares and cubes)Test statistic: F = 1.531411,with p-value = P(F(2,14) > 1.53141) = 0.25
RESET test for specification (squares only)Test statistic: F = 3.243041,
with p-value = P(F(1,15) > 3.24304) = 0.0919
RESET test for specification (cubes only)Test statistic: F = 3.272001,with p-value = P(F(1,15) > 3.272) = 0.0906
In the Model 3, the VIF for the variable labor force is greater than 10. If the variable is
greater than 10 according to Danao ( : 231) there is a serious multicollinearity. Labor force has a
VIF of 10. 329. In the following and last model presented in this paper, Model 4, the remedy that
was done is to change the functional form of the variables. The variables now are in thelogarithmic form.
Model 4: OLS, using observations 1990-2009 (T = 20)Dependent variable: unemployment_ra
Coefficien
tStd. Error t-ratio p-value
Const -7.55919 22.1679 -0.3410 0.73754
l_imports 1.00673 1.86562 0.5396 0.59689l_labor_force 19.4568 4.79581 4.0570 0.00092 ***l_GDP -7.87094 1.30675 -6.0233 0.00002 ***
Mean dependent var 9.010000 S.D. dependent var 1.530703Sum squared resid 13.31220 S.E. of regression 0.912147R-squared 0.700970 Adjusted R-squared 0.644902F(3, 16) 12.50214 P-value(F) 0.000183Log-likelihood -24.30826 Akaike criterion 56.61651Schwarz criterion 60.59944 Hannan-Quinn 57.39402Rho 0.187524 Durbin-Watson 1.612281
White's test for heteroskedasticity -Null hypothesis: heteroskedasticity not presentTest statistic: LM = 7.7552with p-value = P(Chi-square(9) > 7.7552) = 0.558992
Test for normality of residual -Null hypothesis: error is normally distributedTest statistic: Chi-square(2) = 17.053with p-value = 0.000198144
LM test for autocorrelation up to order 1 -Null hypothesis: no autocorrelationTest statistic: LMF = 0.655874with p-value = P(F(1,15) > 0.655874) = 0.430681
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1. Test for Multicollinearity
Variance Inflation FactorsMinimum possible value = 1.0Values > 10.0 may indicate a collinearity problem
l_imports 7.291l_labor_force 9.580
l_GDP 5.831VIF(j) = 1/(1 - R(j)^2), where R(j) is the multiple correlationcoefficient between variable j and the other independentvariables
Properties of matrix X'X:1-norm = 24926.499Determinant = 0.84960181Reciprocal condition number = 5.5585008e-008
2. Test for Heteroscedasticity
White's test for heteroskedasticityOLS, using observations 1990-2009 (T = 20)Dependent variable: uhat^2
coefficient std. error t-ratio p-value------------------------------------------------------------const 4209.73 6273.80 0.6710 0.5174l_imports 380.872 599.310 0.6355 0.5394l_labor_force 2196.74 2282.14 0.9626 0.3585
l_GDP 371.448 648.031 0.5732 0.5792sq_l_imports 13.0121 37.8395 0.3439 0.7381X2_X3 103.304 119.877 0.8618 0.4090X2_X4 40.6016 66.1751 0.6135 0.5532sq_l_labor_fo 406.722 264.428 1.538 0.1550X3_X4 193.477 134.621 1.437 0.1812sq_l_GDP 21.8963 21.3588 1.025 0.3294
Warning: data matrix close to singularity!
Unadjusted R-squared = 0.387760
Test statistic: TR^2 = 7.755203,with p-value = P(Chi-square(9) > 7.755203) = 0.558992
3. Test for Normality of Residual
Frequency distribution for uhat2, obs 1-20number of bins = 7, mean = 2.54019e-014, sd = 0.912147
interval midpt frequency rel. cum.
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< -1.9749 -2.3462 1 5.00% 5.00% *
-1.9749 - -1.2324 -1.6037 0 0.00% 5.00%-1.2324 - -0.48988 -0.86114 2 10.00% 15.00% ***-0.48988 - 0.25265 -0.11861 11 55.00% 70.00%
*******************
0.25265 - 0.99518 0.62391 5 25.00% 95.00% *********0.99518 - 1.7377 1.3664 0 0.00% 95.00%
>= 1.7377 2.1090 1 5.00% 100.00% *
Test for null hypothesis of normal distribution:Chi-square(2) = 17.053 with p-value 0.00020
4. Test for Serial Correlation
Durbin-Watson statistic = 1.61228p-value = 0.0463899
Breusch-Godfrey test for first-order autocorrelationOLS, using observations 1990-2009 (T = 20)Dependent variable: uhat
coefficient std. error t-ratio p-value-------------------------------------------------------------const 2.33204 22.5944 0.1032 0.9192l_imports 0.0835794 1.88883 0.04425 0.9653l_labor_force 1.38548 5.14121 0.2695 0.7912l_GDP 0.433893 1.42555 0.3044 0.7650
uhat_1 0.223423 0.275878 0.8099 0.4307
Unadjusted R-squared = 0.041893
Test statistic: LMF = 0.655874,with p-value = P(F(1,15) > 0.655874) = 0.431
Alternative statistic: TR^2 = 0.837864,with p-value = P(Chi-square(1) > 0.837864) = 0.36
Ljung-Box Q' = 0.814197,with p-value = P(Chi-square(1) > 0.814197) = 0.367
5.
Test for Specification Error
RESET test for specification (squares and cubes)Test statistic: F = 0.659016,with p-value = P(F(2,14) > 0.659016) = 0.533
RESET test for specification (squares only)Test statistic: F = 1.219366,with p-value = P(F(1,15) > 1.21937) = 0.287
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RESET test for specification (cubes only)Test statistic: F = 1.266486,with p-value = P(F(1,15) > 1.26649) = 0.278
In this part of the paper, the different ways on how to remedy the problems on CLRM were
presented in relation to the explaining unemployment rate. The last model is free of all the
problems of CLRM but we know that the variables included in the last model are not the only
variables that explain unemployment. There is no multicollineraity in this model given that the
Variance Inflation Factors (VIF) is less than 10. Also, the Durbin-Watson p-value shows that we
should accept the null hypothesis, that there is no serial correlation in this model, at 5% alpha
level. This finding conforms to the rho value, 0.187, which is not significantly different from
zero. In connection to the earlier findings, the Durbin-Watson statistic is also near the value of 2.
Thus, the problem of first order serial correlation is not present or is not severe in this model.
It can also be further improved or not improved if the number of observations increased
but that way of trying to improve the model is not part of this paper.
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