On the Importance of Labour Productivity Growth: · PDF fileUNIVERSITE DE LAUSANNE ECOLE DES...
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UNIVERSITE DE LAUSANNE ECOLE DES HAUTES ETUDES COMMERCIALES
Macroeconomic Modelling
On the Importance of Labour Productivity Growth:
Portugal vs. Ireland
Cátia Felisberto
July 2003
Professor: Jean-Christian Lambelet
Assistant: Alexander Mihailov
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Abstract
The purpose of this paper is to analyse the importance of labour productivity and relate it with economic growth, living standards and inflation. Two particular countries are considered: Portugal and Ireland. In a first part, a growth accounting analysis is carried out, for both countries, in order to give us some insight about the behaviour of these economies and about the determinants of their economic growth. In a second part, a vector autoregressive analysis is performed, which will allow the study of the relationships between labour productivity and economic growth, living standards and inflation.
We conclude that total factor productivity has been a major engine of growth over the 1990s in Ireland. Factors such as increased labour productivity and efficiency, use of labour in an intensive way and foreign investment (whose repercussions are spread to the whole economy and through time) have largely been contributing to Irish economic growth. On the other hand, in Portugal, labour and capital as major production factors account for a large portion of the “explained” growth. Portugal needs to increase his average level of education and professional formation to achieve a higher labour productivity. Another problem seems to be the type of investment made in Portugal which is not very fruitful in the long run.
The above conclusions are corroborated by all the estimation methods applied.
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I. Introduction
Many studies have been dedicated to the importance of labour
productivity growth. The reason is that “the rate of productivity growth can
have an enormous effect on real output and living standards” (Steindel and
Stiroh, 2001).
The aim of this paper is to analyse the importance of labour
productivity in the case of Portugal and Ireland. The choice of these two
countries was mainly related with the fact that during many decades both
countries have had similar economic growth rates and suddenly, in the 90s,
Ireland started to show an annual growth rate of GDP (Gross Domestic Product)
higher than the one presented by Portugal. The choice of Portugal and Ireland
was also motivated by the fact that both countries belong to the European Union,
Ireland joined in 1973 and Portugal in 1986.
Firstly, a growth accounting analysis is carried out for both countries in
order to give some insight about the behaviour of these economies and about the
sources of their economic growth. In this analysis two estimation methods are
applied: simple growth accounting and growth country regressions. Secondly,
the importance of labour productivity as a growth determination factor is
analysed. This is done performing a vector autoregressive analysis (VARs).
More precisely, three VARs are estimated for each country, which will allow
the study of the relationships between labour productivity and economic growth,
labour productivity and living standards and finally labour productivity and
inflation. Complementarily, an analysis of the correlation, the Impulse
Responses and the Granger Causality between those pairs of variables (again for
both countries) is performed. Finally, we compile the major lessons from
Ireland and we leave some policy recommendations for Portugal.
This paper is organised into five main sections. The next section is
dedicated to the growth accounting for Portugal and Ireland. In section III, the
importance of labour productivity for Portugal and Ireland is analysed through
the VARs. Within each of these 2 sections a brief description of the data used is
provided, the estimation method used is presented and the main results are
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described and analysed. Section IV comprises the compilation of the main
lessons from Irish performance and the suggestion of some policies which we
believe to be fundamental for the Portuguese economy. Finally, in section V the
main conclusions are presented.
II. Growth Accounting
In this section, a growth accounting analysis for Portugal and Ireland is
performed, with the objective to acquire some insight about the economic
growth of these countries. This analysis will be based on the neoclassical
growth accounting. Before proceeding, some weaknesses of this theory should
be mentioned. On the one hand, this theory does not explain the strengths that
are behind capital and labour input (two growth sources). On the other hand, the
so called total factor productivity (interpreted as technological progress)
remains exogenous and unexplained by the model.
In the next lines a short explanation is given about the neoclassical
growth accounting.
Output is assumed to grow through increases in the production factor
inputs – labour and capital – and through improvements in technology. To
investigate the sources of growth the following production function is used:
Y = A F(K,L) (1)
where Y denotes output, K denotes capital input, L represents labour input and
A is total factor productivity (technological progress). Labour and capital are
assumed to be homogeneous. The growth rate of the total factor productivity is
given by the equation presented bellow:
d ln Y = vK d ln K + vL d ln L + d ln A (2)
where vK = (1 - vL), is capital’s share of national income and vL is labour’s share
of national income. The last term of equation (2) corresponds to the total factor
productivity, which accounts for the growth not explained by capital
accumulation or increased labour input (Solow’s residual). The components
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usually assumed to be included in this unexplained growth are: advances in
knowledge, efficiency stand out, research, education and training.
The capital stock can be estimated using the standard perpetual
inventory method:
Kt = It + (1 –� ���t-1 (3)
Growth theory and empirical work on growth tell us that the growth of
a country is mainly related with the following four aspects. First, the investment
in physical capital, equipment and infrastructures is very important. Countries
that invest a greater share and that have a high private investment tend to grow
faster, even if only for a transitory period. Another very important issue is the
investment in human capital, education and training. A third aspect is related to
productivity growth. The literature suggests that free market policies (small
governments with open markets that encourage foreign trade) are more likely to
produce faster productivity growth. Finally, incentives (e.g. tax incentives) for
research and development can also be determinant.
Data
As shown before, to perform the growth accounting the following data
are needed: real output, labour input, investment, average depreciation rate and
the capital’s share of national income.
Since the data to compute the real value of labour’s or capital’s share of
income were not available, they were assumed to be approximately 70% and
30%, respectively (as we know labour’s share of income is relatively larger than
the capital’s share). Computations were done with shares of 80% - 20% and
60% - 40% and the results did not change significantly. For the same reasons
average depreciation rate was assumed to be 5%. The previous computations
were done as well with a depreciation rate of 10% and again the results
underwent insignificant modifications.
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As a measure of labour input two series were used: employment and
total hours of work. This last series was constructed from the number of weekly
hours of work. Portuguese weekly hours of work were supplied by Portuguese
Ministry of Social Security and Work, while Irish weekly hours of work were
obtained through Datastream, OECD Economic Outlook. As a proxy for
investment the Gross Fixed Capital Formation (GFCF) was used. Real GDP
was used to measure real output. GFCF and GDP series were also collected
from Datastream.
Estimation Procedures
Two methods were used to perform the growth accounting. The first
one, the simple growth accounting analysis, consists in computing total factor
productivity using the equations (2) and (3).
The second method comprises the estimation of the following equation:
d ln Yt =� 1��� 2 d ln Kt + 3 d ln Lt��� t (4)
where 2 denotes to the capital’s share of national income,� 3 applies for the
������� � ��� ��� ��������� ������� ���� t corresponds to the Total Factor
Productivity (unexplained growth).
Since the regression using first differences was unsatisfying for
Portugal, the same regression was run in levels (using logged variables, without
computing the first differences) plus a time trend. This procedure prevents the
loss of information which might happen with the use of first differences.
Results
Until 1991 the behaviour of the Portuguese and Irish economies was
similar. From 1991 the Irish GDP grew, in average, a 4,7% more per year than
the Portuguese GDP (Chart 1 to 5). The results suggest that this increase was
mainly due to an increase in the total factor productivity - accelerating total
factor productivity growth has led to a notable growth revival (Chart 4 and 5).
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In the 90’s the Irish growth rate of labour and capital inputs were also above the
ones of Portugal (Chart 2 and 3), but their contribution to the increase in GDP
was not as important as the contribution of total factor productivity (Chart 5).
This is in accordance with the majority of the authors, whose works highlight
technical progress. Of course capital investment is necessary since it is not
possible to have technological improvement without new type of equipment.
Indeed, Ireland had, in the last decade, a lot of investment from abroad which
played an important role. Another important finding is that Ireland has faced an
increased efficiency, which has been in the base of its economic growth in the
last years. It is worth to note that until 1994 the percentage of the GDP growth
not explained was always higher for Portugal than for Ireland (Chart 4). In 1994
this trend is reversed and in 2002 the percentage of the unexplained GDP
growth was 2,5% for Portugal and 4,6% for Ireland.
In what concerns the behaviour of the labour and capital inputs,
between 1960 and 2002 employment has grown 52% in Portugal, whereas in
Ireland it grew 67%. On the other hand, the capital stock in Portugal grew 462%,
in the same period, while in Ireland it grew 384%. From the analysis of these
numbers one can infer that the investment realised in Portugal was not as
efficient as the one realised in Ireland (the investment does not seem to generate
any increase in efficiency). Clearly, investment also plays a role for growth and
development but it is not sufficient.
By estimating equation 4 we expect to obtain positive coefficients for
2 and 3, moreover their values should be close to 0,3 and 0,7 respectively (the
capital’s share of national income and the labour’s share of national income).
Relatively to the estimation for Portugal1 (Table 1), the first regression presents
a low R2, a negative sign for 3 (which was not expected) and despite that, this
coefficient does not seems to be significant. On the other hand, 2 is significant
at a 5% level but it is too high relatively to what was expected. Regressing
employment and capital separately does not improve the results, moreover the
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results are quite similar to the previous ones (in terms of goodness of fit,
magnitude, significance level and signs of the coefficients). In fact, there is no
evidence of a correlation between employment and capital (the correlation is
only 7%). The regression in levels presents a very good R2. 2 is statistically
significant (at a 5% level) and it explains almost all the GDP growth. 3 is not
statistically significant. For Ireland (Table 2), the value of 2 is lower than
expected and it is not significant. The value of 3 is slightly higher than the
prevision (but not so high as the one for Portugal) and it is very significant. The
R2 is not very high, but since we are working with first differences we can
consider the result obtained very good. The Durbin-Watson statistic gives us
evidence of no correlation in the residuals.
These regressions confirm the results presented before: in the case of
Ireland, the induced increase in efficiency is more important than the increase in
capital growth and labour growth (Irish economic growth is mostly due to an
increase in total factor productivity). In fact, the regression (Table 2) shows that
50% of the growth on output is not explained by growth in capital or in labour.
In what concerns Portugal, again we verify that economic growth is explained,
almost entirely, by the increase in the inputs. These extreme results for Portugal
and Ireland confirm the different performance these two countries had over the
1990s.
III. Importance of Labour Productivity – Vector Autoregression
Analysis
This section is devoted to the importance of labour productivity growth.
Before continuing it is worth to mention and describe the most common
measures of productivity. One is average labour productivity, which is defined
as real output per hour of work. In the present paper another definition for this
1 We have incorporated an AR(1) process in the error term to eliminate the correlation in the residuals (without the AR(1) term the Durbin-Watson statistic was low so we could not reject the hypothesis of serial correlation).
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concept is also used: real output per employee2. Finally, we still have total
factor productivity. This concept is more complicated and it is defined as real
output per unit of all inputs. As already mentioned before, it accounts for the
growth not explained by capital accumulation or increased labour input. Total
factor productivity is assumed to comprise advances in knowledge, efficiency
stand out, technology, economies of scale, research, education and training.
The concepts of labour productivity growth and total factor productivity
by their own are not very meaningful. As Steindel and Stiroh (2001) argued, it
is more interesting to analyse the relationship between those concepts and
economic growth, growth in real per capita income, and inflation. To
accomplish this objective we used Vector Autoregression analysis (VAR). Each
VAR is performed with labour productivity growth and one of the following
three variables: GDP growth, GDP per capita growth and inflation.
Data
To carry out the proposed vector auto regression analysis the following
data is required, for Portugal and Ireland: output, population, inflation rate,
employment and total number of hours worked. Employment and total number
of hours worked are used as labour input measures.
The minimum number of observations desirable to perform a vector
autoregressive analysis is approximately eighty observations. For Portugal, it
was possible to obtain quarterly data beginning in 1980, for the variables
mentioned, except for population. Between 1980 and 1988 Portuguese
population was only available in an annual basis. Hence, it was necessary to
convert this annual data into quarterly data3. On the other hand, for Ireland it
was impossible to find quarterly data for the previous variables. According to
the National Statistics Office they have started to collect quarterly data only
after 1997. Therefore, it was necessary to change the frequency of all Irish
2 We have decided to work with productivity per employee because worked hours were not available in a quarterly basis for the desired period. 3 This was done using the spline procedure in E-Views 4.1 (multiplicative method).
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series from annual (which were available) to quarterly4. The only exception to
this procedure was the series of weekly hours worked since it was available in a
quarterly basis. For both countries this variable was accessible just from 1985
on. In what concerns the data that was converted from annual to quarterly their
sources are the ones mentioned in section II-Data. The remaining variables were
obtained from Datastream, however they have different sources: GDP and GDP
deflator source is the International Financial Statistics and population and
employment source is the National Statistics Office. As already mentioned
before, Irish weekly hours worked source is the Central Statistics Office and the
Portuguese weekly hours worked were obtained in the Portuguese Ministry of
Social Security and Work. All the variables in growth rates are plotted in Chart
6, 7, 8, 9 and 10.
For all the series, tests for the presence of seasonality were executed,
and the series that showed evidence of seasonality were deseasonalised5 . The
four tests available (through X12-EViews) are the followings: test for the
presence of seasonality assuming stability, nonparametric test for the presence
of seasonality assuming stability, moving seasonality test and the combined test
for the presence of identifiable seasonality. A series was taken to be seasonal
when two tests among the first three show evidence of seasonality. The series
that have shown evidence of seasonality were: GDP and employment for
Portugal and weekly hours of work for both countries.
To obtain the growth rates of the variables logarithms were taken from
the series and then first differences were taken from the logged data. In general,
the resulting series are stationary. Nevertheless, to check, the Augmented
Dickey-Fuller Test was executed for all the variables (Table 3 and 4). The
results are that the Portuguese series are stationary at all significance levels,
whereas for the Irish case just the log-difference of productivity per employee
and per hour worked are stationary at all levels of significance. For the log-
difference of Irish GDP and Irish GDP per capita the hypothesis of the existence
of unit roots can be rejected only at a 10% significance level. Surprisingly, the
4 Again, this transformation was done using E-Views 4.1.
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Irish inflation rate is not stationary. Hence, it was necessary to do the second
differences for this series (the resulting series is stationary at a 1% level).
VAR Estimation
As already mentioned, the VARs for both countries were performed
with two different measures of productivity: GDP per hour worked and GDP
per employee. In principle, total hours worked should be a more precise
measure of productivity. However, the results obtained using this measure are
worst than the results obtained using simply total employment (see Table 3).
Besides that, for total hours worked the data is available only after 1985.
Therefore, it was decided to continue the work only with productivity per
employee.
In order to choose the best lag structure specification the (unrestricted)
VARs were performed with 2, 4, 6, 8 and 10 lags. Looking at the Akaike
information criterion (which gives the best lag) and the Adjusted R2 (which
measures the validity of additional variables) (Table 5) we can see that in the
case of Portugal the best results are obtained with 4 lags. Although, the results
obtained with 6 lags are quite similar. In what concerns Ireland, for the VAR
with productivity growth and GDP growth the best result is obtained with 10
lags and for the other regressions the best results are obtained with 6 lags. Since
for Portugal the results do not differ a lot with 4 or 6 lags and for Ireland 6 lags
seems to be the best choice, for the sake of simplicity all the further analysis
were based in the VARs with 6 lags. From the economic point of view this also
seemed an adequate choice since 6 lags correspond to 1 year and a half.
Consider a larger period does not appeared to be necessary because it is not
likely that the variables will exert some effect after 1 year and a half and
consider a smaller period would be too restrictive.
After analysing the relationships between productivity growth and the
other three variables, the study proceeds with the analysis of: the Impulse
5 Using the X12, procedure E-Views.
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Responses6, the Granger Causality Test7, and of the Variance Decomposition8,
for the chosen models (productivity per employee and VARs with 6 lags).
Results
First of all, it is worth to examine the evolution of labour productivity
between 1980 and 2002. In Chart 9, we can observe that Portuguese and Irish
growth of the labour productivity per employee have a regular evolution over
the years. The only aspects to stress are that the Portuguese productivity growth
has a negative peak in 1983 and two positive peaks in 1988 and 1992. However,
these peaks just stand by one year and are not observed thereafter. Chart 10,
shows a very similar evolution for productivity per hour worked, in the case of
Portugal. In the Irish case, the growth of the productivity per hour worked
shows some peaks that before were not observed. One is negative and it occurs
in 1988 and the other one is positive and it occur in 1994. But again it is not
possible to distinguish medium/long periods of productivity slowdown or of a
strengthening in productivity growth.
(i) Productivity Growth and Economic Growth
Without any previous analysis one would say that productivity growth
and economic growth are closely related. By definition, output growth is the
sum of the growth of labour hours and of labour productivity growth (Steindel
and Stiroh, 2001). Thus, a higher labour productivity growth seems to imply a
higher GDP growth. When a long period is analysed demographic changes can
affect output growth independently of productivity growth.
Observing Chart 11, which presents the Portuguese growth rate of
output and the Portuguese labour productivity for each decade and each half
decade, we can verify that GDP growth and productivity growth follow the
6 The Impulse Response function shows the effect that a shock in a specific moment and variable has on current and future values of the endogenous variables. 7 The Granger Causality Test tests whether an endogenous variable can be treated as exogenous. 8 The Variance Decomposition accounts for the proportion of the variance of an endogenous variable due to the different structural shocks.
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same path; no matter whether we look at the first or second chart. As expected,
decreases/increases in productivity growth are accompanied by
decreases/increases in economic growth. Nevertheless, when we look at Chart
12, which plots the same information but now for Ireland, there is no evidence
of a relationship between those two variables. The most illustrative example of
this lack of link are the 90s, where output growth doubles relatively to the
output growth in the 80s and the productivity growth decreases slightly. In the
second half of the 70’s again productivity decreases whereas the output growth
increases.
To get some insight about the effects of time in the relation between
productivity growth and output growth the correlation between these two
variables is plotted in Chart 13. For Portugal it is possible to observe a high
correlation in the first quarter that vanishes quite fast. In the long run no
significant correlation is detected. In what concerns Ireland, a short run
correlation is also observed, however it is not as strong as the one before.
Surprisingly, between the fifteenth quarter and the eighteenth quarter we
observe a considerable negative correlation between Irish productivity growth
and output growth.
It is worth to refer that for the United States Steindel and Stiroh (2001)
have also found this short term relationship, followed by a flat correlation up to
five years, which then rises.
(ii) Productivity Growth and Per-Capita Income Growth
The main justification to believe in a relationship between productivity
growth and income growth relies on the statement, defended by many authors,
that the productivity growth and the real wages are equal (Steindel and Stiroh,
2001). In fact, when we observe Chart 14, either for Portugal or Ireland, this
relationship does not seem to exist. For each country the evolution of the
correlation between per-capita income growth and productivity growth is very
close to the evolution of the correlation between output growth and productivity
growth. This result goes completely against to what is found by Steindel and
Stiroh (2001) for the United States. Indeed, they found a very weak correlation
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in the short term (which for Portugal and Ireland is strong) and they justify this
fact with no constant returns to scale, with the slippage between growth in
wages per hour and growth in income per-capita and with the inexistence of
fixed relationship between hours worked and population. They argue that in the
long term these divergences tend to disappear, which explains the increasing
correlation between per-capita income and productivity growth that is observed
for the United States. However, for Portugal and Ireland this is not the case.
(iii) Productivity Growth and Inflation
In principle, there is no obvious reason for the existence of a
relationship between these two variables. However, we can see, in the
Portuguese data (Chart 15), a negative correlation in the short run between
productivity growth and inflation. Thereafter, it does not exists a significant
correlation between these two variables.
In the case of Ireland (Chart 15), in the short run no important
correlation is observed, but in the third year and a half it starts to arise a positive
correlation between productivity growth and inflation, which decreases through
time and reaches the value of -5,5% in the sixth year.
For the United States, Steindel and Stiroh (2001) found persisting
negative correlation between productivity growth and inflation (“it seems to be
the case that periods of higher productivity growth are periods of lower
inflation” 9 ). The justifications presented for this fact are: a) since higher
inflation rates can distort the price mechanism they can also reduce the
economic efficiency, having a negative impact on capital accumulation and
technological progress; b) since the periods of high productivity growth are
periods of relatively fast economic growth, the monetary authorities can profit
and implement anti-inflationary policies.
9 Steindel and Stiroh (2001).
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(iv) VAR Estimations and Impulse Responses
All the VAR estimations for Portugal are very poor. The R2 and
Adjusted R2 are very low, the coefficients are not statically significant and the
F-statistic is also very low in all the regressions. The reason for this may be just
the lack of variability in the data. For Ireland the VAR estimation results are
always very good. The R2 and Adjusted R2 are high, the F-statistic is also high
and some of the coefficients show a high level of significance.
The impulse responses allow us to clarify the linkages between
productivity growth and each of the other variables.
For Portugal (Chart 16), a shock in productivity does not influence
significantly either the output growth, or the per-capita income growth. On the
other hand a shock on GDP growth, or in per-capita income growth, has an
instantaneous influence on productivity, but this relation quickly disappears.
For Ireland (Chart 17), the short-term impulse response relations from
productivity growth to output growth and per-capita income growth and from
output growth and per-capita income growth to productivity growth are weak in
magnitude. For Ireland we observe smooth impulse responses, which are not
very common. This is related with the fact that the quarterly data for Ireland is
obtained from annual data. To check if this fact was having any influence in the
magnitude of the impulse responses this analysis was performed also with
productivity per hour worked (“truly” quarterly data). With this productivity
measure the magnitude of the shocks does not change significantly. The change
is moreover in the pattern of the impulse responses, which comes closer to what
we observe for Portugal.
The influence of productivity on inflation, in the Irish case, is very
weak. Nevertheless, inflation seems to influence productivity two years after the
shock; however this is not a very strong relation. This is also the case for
Portugal, with the only difference that the influence of inflation on productivity
appears 16 months after the shock on inflation.
(v) Granger Causality Test
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Using the Granger Causality test (Table 12) it is possible to see that for
Portugal there is no evidence of influence of productivity growth on economic
growth or vice-versa. In the case of Ireland (Table 13) productivity growth
predicts GDP growth at the 1% level and GDP growth predicts productivity
growth but just at the 5% level. These results are consistent with what we have
found with growth accounting analysis, i.e., once more we have evidence that
Irish economic growth is largely due to increasing productivity whereas for
Portugal this is not observable.
The results of the Granger Causality test for productivity growth and
per-capita income growth are very similar to the ones presented for productivity
growth and economic growth. Once more, for Portugal, there is no evidence of
influence of productivity growth on per capita income growth, neither from this
one on productivity growth. For Ireland, the causality flow is again bi-
directional. However, the magnitude of the flow from productivity growth to
per-capita income growth is stronger than in the reverse direction.
When productivity growth and inflation are considered the result from
the Granger Causality test is similar for both countries: there is no evidence of
prediction between productivity growth and inflation or vice-versa.
IV. Lessons for Portugal
In this section we summarize the main lessons we can withdraw from
the evolution of the Irish economy and we present some policy
recommendations for Portugal.
The strong Irish economic growth in the 1990s was mainly due to the
interaction between the following factors: increase in labour supply and in
labour productivity, increase in foreign investment and as well a long period of
social consensus and a favourable fiscal regime. The importance of two former
factors is not observable from the analysis presented in section II and III
nevertheless they also contributed for the Irish economic growth.
The increase in the quantity and quality of labour supply was
determinant. This increase results from the entrance in the active population of a
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large number of young people with relatively high qualifications. Nowadays,
Ireland is among the OECD countries with a larger number of scientists and
engineers since almost half of the students go to the university.
As we have mentioned before the foreign investment also played an
important role in the economic revival. The most modern sector in the economy
(computing, chemistry and drinks without alcool) grew more than 2 thirds
between 1994 and 1997. Moreover, only the foreign companies working in the
sector of manufactory produce approximately 30% for GDP.
The social consensus among social partners (namely the “Program of
Competitiveness and Work” signed in 1996) was also important since it made
possible to sustain the Irish competitiveness relatively to her commercial
partners. Finally, the moderation in wages achieved through healthy budget and
monetary policies and efficient revenue management was as well important for
the Irish economic growth.
We already stated that one of the biggest problems that Portugal faces is
the lack of qualifications of the labour market. To illustrate the magnitude of the
problem we report the percentage of the population between 25 and 64 years old,
for Portugal and for Ireland, that has attained at least upper secondary education
in 1998. In Portugal only 18% of males and 22% of females have attained the
referred degree of education while in Ireland the numbers are respectively, 48%
and 54%. Despite the scenario for Ireland being much better than for Portugal
still Ireland remains in the second half of the OECD ranking, (which comprises
28 countries). It is urgent that Portugal approaches his level of education and
professional formation to the average level of OECD countries.
Some measures concerning labour productivity that we think to be
important to implement in Portugal are the followings:
� reduce the number of students that give up from school prematurely;
� extend the possibilities of formation for those who do not intend to
continue one’s studies;
� create formation programs addressed to the active population (this
competence should be shared with the employers);
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� create and promote technical education considering the country needs
and direct the university degrees to the prior areas in the country;
� create more mechanisms to pass from the school system to the active
life;
� invest in technology and research.
V. Conclusions
We have applied three methods of estimation to investigate the
differences in the Portuguese and Irish economic performance. Those methods
were: simple growth accounting, growth country regressions and VARs by
country. These three procedures lead us to same conclusion: Irish economic
growth is largely due to increasing (in the 1990s) total factor productivity (TFP)
whereas in Portugal changes in labour and capital account almost entirely for
the evolution of real GDP.
When we analyse more closely the characteristics of the labour and
investment in both countries we realize that Ireland has had the great capacity of
increasing her efficiency through advances in education, training, knowledge,
technology, research and use of labour intensively. This has been the principal
engine of the Irish growth and it seems to be what is missing to Portugal. It is
very urgent that Portugal approaches its level of education and professional
formation to the average level of OECD countries in order to achieve a higher
labour productivity.
The success of the Irish strategy to attract foreign investment has also
stimulated the economy in a very important way. Clearly, investment also plays
a role for growth and development but it is not sufficient. It must be carried
through the right sectors to generate the desired effects. The type of investment
made in Portugal in the last decade does not seem to be very fruitful in the long
run.
The correlation analysis between the two European countries under
study, on the one hand, and the United States in the background article by
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Steindel and Stiroh (2001), on the other hand, does not allow us to take
conclusions and would need a further study.
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Switzerland’s economy: did the Swiss economy really stagnate in the
1990s, and is Switzerland all that rich?”, Analyses & Prévisions, Institute
Créa of Applied Macroeconomics, University of Lausanne
20
Romer, David, 2001, Advanced Macroeconomics, 2nd edition, McGraw-Hill
Steindel, Charles and Stiroh, Kevin J., 2001, “Productivity: What is it, and
why do we care about it?”, Journal of Economic Literature
21
APPENDIX 1 Chart 1: GDP growth for Portugal (on the left) and Ireland (on the right) -
year-over-year percentage change
-.04
.00
.04
.08
.12
60 65 70 75 80 85 90 95 00
DLNGDP_IR
Chart 2: Employment growth for Portugal (on the left) and Ireland (on the
right) - year-over-year percentage change
-.04
-.02
.00
.02
.04
.06
.08
.10
60 65 70 75 80 85 90 95 00
DLNEMP_PT
Chart 3: Capital growth for Portugal (on the left) and Ireland (on the right) - year-over-year percentage change
.00
.01
.02
.03
.04
.05
.06
.07
60 65 70 75 80 85 90 95 00
DLNCAPITAL_PT
22
Chart 4: Total factor productivity for Portugal and Ireland (using employment to measure labour contribution to growth)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Years
Tot
al f
acto
r pr
oduc
tivity
PT IR
Chart 5: GDP index decomposition for Portugal and Ireland (using
employment to measure labour contribution to growth)
Portugal
0
1
2
3
4
5
6
7
8
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
inde
x (1
960=
1)
GDP index labor contribution to GDP labor + capital contribution to GDP
Ireland
0
1
2
3
4
5
6
7
8
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
inde
x (1
960
=1)
GDP index labor contribution to GDP labor + capital contribution to GDP
23
Table 1: Growth Accounting Estimations for Portugal (1960-2002)
Dependent Variable: DLNGDP_PT Method: Least Squares Date: 07/29/03 Time: 14:19 Sample(adjusted): 1962 2003 Included observations: 42 after adjusting endpoints Convergence achieved after 19 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C -0.009080 0.021145 -0.429438 0.6700 DLNCAPITAL_PT 1.246845 0.492163 2.533400 0.0155
DLNEMP_PT -0.309382 0.263759 -1.172973 0.2481 AR(1) 0.269551 0.165137 1.632287 0.1109
R-squared 0.289049 Mean dependent var 0.039483 Adjusted R-squared 0.232921 S.D. dependent var 0.031286 S.E. of regression 0.027401 Akaike info criterion -4.266092 Sum squared resid 0.028531 Schwarz criterion -4.100600 Log likelihood 93.58793 F-statistic 5.149834 Durbin-Watson stat 1.987304 Prob(F-statistic) 0.004369
Inverted AR Roots .27
Dependent Variable: DLNGDP_PT Method: Least Squares Date: 07/29/03 Time: 14:21 Sample(adjusted): 1962 2003 Included observations: 42 after adjusting endpoints Convergence achieved after 16 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C -0.011215 0.021822 -0.513904 0.6102 DLNCAPITAL_PT 1.220591 0.511282 2.387314 0.0219
AR(1) 0.295730 0.161896 1.826667 0.0754
R-squared 0.263509 Mean dependent var 0.039483 Adjusted R-squared 0.225740 S.D. dependent var 0.031286 S.E. of regression 0.027529 Akaike info criterion -4.278418 Sum squared resid 0.029556 Schwarz criterion -4.154299 Log likelihood 92.84678 F-statistic 6.976904 Durbin-Watson stat 1.954727 Prob(F-statistic) 0.002569
Inverted AR Roots .30
24
Dependent Variable: DLNGDP_PT Method: Least Squares Date: 07/29/03 Time: 14:22 Sample(adjusted): 1962 2003 Included observations: 42 after adjusting endpoints Convergence achieved after 11 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 0.041534 0.007843 5.295719 0.0000 DLNEMP_PT -0.254870 0.284927 -0.894512 0.3765
AR(1) 0.381841 0.152431 2.505013 0.0165
R-squared 0.169255 Mean dependent var 0.039483 Adjusted R-squared 0.126652 S.D. dependent var 0.031286 S.E. of regression 0.029237 Akaike info criterion -4.157992 Sum squared resid 0.033338 Schwarz criterion -4.033873 Log likelihood 90.31783 F-statistic 3.972901 Durbin-Watson stat 1.949617 Prob(F-statistic) 0.026892
Inverted AR Roots .38
Dependent Variable: LNGDP_PT Method: Least Squares Date: 07/29/03 Time: 14:23 Sample(adjusted): 1961 2003 Included observations: 43 after adjusting endpoints Convergence achieved after 37 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 2.421882 6.188611 0.391345 0.6977 @TREND -0.004548 0.014242 -0.319351 0.7512
LNCAPITAL_PT 0.972608 0.412900 2.355552 0.0238 LNEMP_PT -0.194109 0.268174 -0.723819 0.4736
AR(1) 0.882810 0.092508 9.543120 0.0000
R-squared 0.997343 Mean dependent var 10.85367 Adjusted R-squared 0.997063 S.D. dependent var 0.482446 S.E. of regression 0.026146 Akaike info criterion -4.341323 Sum squared resid 0.025977 Schwarz criterion -4.136532 Log likelihood 98.33845 F-statistic 3565.599 Durbin-Watson stat 1.546009 Prob(F-statistic) 0.000000
Inverted AR Roots .88
25
Table 2: Growth Accounting Estimations for Ireland (1960-2002)
Dependent Variable: DLNGDP_IR Method: Least Squares Date: 07/25/03 Time: 13:13 Sample(adjusted): 1961 2003 Included observations: 43 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C 0.034053 0.007568 4.499379 0.0001 DLNCAPITAL_IR 0.109493 0.202462 0.540809 0.5916
DLNEMP_IR 0.800718 0.152148 5.262755 0.0000
R-squared 0.494275 Mean dependent var 0.047909 Adjusted R-squared 0.468988 S.D. dependent var 0.027415 S.E. of regression 0.019978 Akaike info criterion -4.921187 Sum squared resid 0.015964 Schwarz criterion -4.798313 Log likelihood 108.8055 F-statistic 19.54715 Durbin-Watson stat 1.943892 Prob(F-statistic) 0.000001
26
APPENDIX 2 Chart 6: GDP growth for Portugal (on the left) and Ireland (on the right) –
quarter over quarter percentage change
-.04
.00
.04
.08
.12
.16
1980 1985 1990 1995 2000
DLNGDP_IR
Chart 7: GDP per capita growth for Portugal (on the left) and Ireland (on the
right) – quarter over quarter percentage change
-.08
-.04
.00
.04
.08
.12
.16
1980 1985 1990 1995 2000
DLNGDPPERCAPITA_IR
Chart 8: Inflation for Portugal (on the left) and Ireland (on the right) – quarter over quarter percentage change
-4
0
4
8
1985 1990 1995 2000
INFLATION_IR
27
-4
0
4
8
1985 1990 1995 2000
DINFLATION_IR
Chart 9: Productivity (per employee) growth for Portugal (on the left) and
Ireland (on the right) – quarter over quarter percentage change
-.08
-.04
.00
.04
.08
.12
1980 1985 1990 1995 2000
DLNPRODUCTIVITYE_IR
Chart 10: Productivity (per hour worked) growth for Portugal (on the left)
and Ireland (on the right) – quarter over quarter percentage change
-.04
-.02
.00
.02
.04
.06
.08
.10
.12
1980 1985 1990 1995 2000
DLNPRODUCTIVITYH_PT
-.04
.00
.04
.08
.12
1980 1985 1990 1995 2000
DLNPRODUCTIVITYH_IR
28
Table 3: Augmented Dickey-Fuller Unit Root Test for the Portuguese Series
Null Hypothesis: DLNGDPPERCAPITA_PT has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -10.31424 0.0000 Test critical values: 1% level -3.506484
5% level -2.894716 10% level -2.584529
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNGDPPERCAPITA_PT) Method: Least Squares Date: 05/03/03 Time: 20:41 Sample(adjusted): 1980:3 2002:2 Included observations: 88 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNGDPPERCAPITA_PT(-1) -1.105563 0.107188 -10.31424 0.0000 C 0.008191 0.002493 3.285740 0.0015
R-squared 0.552976 Mean dependent var 3.85E-05 Adjusted R-squared 0.547778 S.D. dependent var 0.032982 S.E. of regression 0.022180 Akaike info criterion -4.756823 Sum squared resid 0.042306 Schwarz criterion -4.700520 Log likelihood 211.3002 F-statistic 106.3834 Durbin-Watson stat 1.966587 Prob(F-statistic) 0.000000
Null Hypothesis: DLNGDP_PT_SA has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -10.69172 0.0000 Test critical values: 1% level -3.506484
5% level -2.894716 10% level -2.584529
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNGDP_PT_SA) Method: Least Squares Date: 05/03/03 Time: 20:42 Sample(adjusted): 1980:3 2002:2 Included observations: 88 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNGDP_PT_SA(-1) -1.141064 0.106724 -10.69172 0.0000 C 0.009226 0.002136 4.320085 0.0000
R-squared 0.570672 Mean dependent var 2.99E-05 Adjusted R-squared 0.565680 S.D. dependent var 0.027824 S.E. of regression 0.018337 Akaike info criterion -5.137327 Sum squared resid 0.028917 Schwarz criterion -5.081024 Log likelihood 228.0424 F-statistic 114.3129 Durbin-Watson stat 1.942023 Prob(F-statistic) 0.000000
Null Hypothesis: DINFLATION_PT has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -9.821737 0.0000 Test critical values: 1% level -3.506484
5% level -2.894716 10% level -2.584529
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DINFLATION_PT) Method: Least Squares Date: 05/03/03 Time: 20:42 Sample(adjusted): 1980:3 2002:2 Included observations: 88 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DINFLATION_PT(-1) -1.056215 0.107539 -9.821737 0.0000 C 1.375978 0.194551 7.072571 0.0000
R-squared 0.528681 Mean dependent var 0.012500 Adjusted R-squared 0.523200 S.D. dependent var 1.851735 S.E. of regression 1.278636 Akaike info criterion 3.351930 Sum squared resid 140.6022 Schwarz criterion 3.408233 Log likelihood -145.4849 F-statistic 96.46652 Durbin-Watson stat 1.986066 Prob(F-statistic) 0.000000
Null Hypothesis: DLNPRODUCTIVITYE_PT has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -9.330319 0.0000 Test critical values: 1% level -3.506484
5% level -2.894716 10% level -2.584529
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNPRODUCTIVITYE_PT) Method: Least Squares Date: 05/03/03 Time: 20:41 Sample(adjusted): 1980:3 2002:2 Included observations: 88 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNPRODUCTIVITYE_PT(-1) -1.006054 0.107826 -9.330319 0.0000 C 0.004773 0.002291 2.082871 0.0402
R-squared 0.503048 Mean dependent var 0.000107 Adjusted R-squared 0.497269 S.D. dependent var 0.029586 S.E. of regression 0.020978 Akaike info criterion -4.868240 Sum squared resid 0.037846 Schwarz criterion -4.811937 Log likelihood 216.2026 F-statistic 87.05486 Durbin-Watson stat 1.991772 Prob(F-statistic) 0.000000
Null Hypothesis: DLNPRODUCTIVITYH_PT has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -9.033564 0.0000 Test critical values: 1% level -3.530030
5% level -2.904848 10% level -2.589907
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNPRODUCTIVITYH_PT) Method: Least Squares Date: 05/03/03 Time: 20:36 Sample(adjusted): 1985:3 2002:2 Included observations: 68 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNPRODUCTIVITYH_PT(-1) -1.105576 0.122385 -9.033564 0.0000 C 0.008132 0.002652 3.065780 0.0031
R-squared 0.552862 Mean dependent var -2.35E-05 Adjusted R-squared 0.546087 S.D. dependent var 0.030526 S.E. of regression 0.020567 Akaike info criterion -4.901336 Sum squared resid 0.027917 Schwarz criterion -4.836056 Log likelihood 168.6454 F-statistic 81.60528 Durbin-Watson stat 1.985620 Prob(F-statistic) 0.000000
29
Table 4: Augmented Dickey-Fuller Unit Root Test for the Irish Series
Null Hypothesis: DLNPRODUCTIVITYH_IR has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=10)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -7.820458 0.0000 Test critical values: 1% level -3.540198
5% level -2.909206 10% level -2.592215
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNPRODUCTIVITYH_IR) Method: Least Squares Date: 05/03/03 Time: 20:41 Sample(adjusted): 1985:3 2000:4 Included observations: 62 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNPRODUCTIVITYH_IR(-1) -1.011100 0.129289 -7.820458 0.0000 C 0.009491 0.002016 4.708148 0.0000
R-squared 0.504785 Mean dependent var 0.000114 Adjusted R-squared 0.496532 S.D. dependent var 0.017983 S.E. of regression 0.012760 Akaike info criterion -5.853293 Sum squared resid 0.009769 Schwarz criterion -5.784676 Log likelihood 183.4521 F-statistic 61.15956 Durbin-Watson stat 1.993825 Prob(F-statistic) 0.000000
Null Hypothesis: DLNPRODUCTIVITYE_IR has a unit root Exogenous: Constant Lag Length: 9 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.585957 0.0003 Test critical values: 1% level -3.513344
5% level -2.897678 10% level -2.586103
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNPRODUCTIVITYE_IR) Method: Least Squares Date: 05/03/03 Time: 20:41 Sample(adjusted): 1982:4 2002:4 Included observations: 81 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNPRODUCTIVITYE_IR(-1) -0.223638 0.048766 -4.585957 0.0000 D(DLNPRODUCTIVITYE_IR(-1)) 1.152534 0.097604 11.80831 0.0000 D(DLNPRODUCTIVITYE_IR(-2)) 0.119140 0.135278 0.880707 0.3815 D(DLNPRODUCTIVITYE_IR(-3)) -0.052064 0.133469 -0.390082 0.6977 D(DLNPRODUCTIVITYE_IR(-4)) -0.677280 0.132719 -5.103109 0.0000 D(DLNPRODUCTIVITYE_IR(-5)) 0.798948 0.141176 5.659250 0.0000 D(DLNPRODUCTIVITYE_IR(-6)) 0.177680 0.131820 1.347897 0.1820 D(DLNPRODUCTIVITYE_IR(-7)) 0.064739 0.127951 0.505964 0.6145 D(DLNPRODUCTIVITYE_IR(-8)) -0.587937 0.127768 -4.601599 0.0000 D(DLNPRODUCTIVITYE_IR(-9)) 0.534872 0.099185 5.392692 0.0000
C 0.001846 0.000416 4.441300 0.0000
R-squared 0.909431 Mean dependent var 2.57E-05 Adjusted R-squared 0.896492 S.D. dependent var 0.002733 S.E. of regression 0.000879 Akaike info criterion -11.10954 Sum squared resid 5.41E-05 Schwarz criterion -10.78437 Log likelihood 460.9363 F-statistic 70.28899 Durbin-Watson stat 1.876945 Prob(F-statistic) 0.000000
Null Hypothesis: DINFLATION_IR has a unit root Exogenous: Constant Lag Length: 4 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -1.666920 0.4443 Test critical values: 1% level -3.508326
5% level -2.895512 10% level -2.584952
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DINFLATION_IR) Method: Least Squares Date: 05/03/03 Time: 20:43 Sample(adjusted): 1981:3 2002:4 Included observations: 86 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DINFLATION_IR(-1) -0.049248 0.029545 -1.666920 0.0994 D(DINFLATION_IR(-1)) 0.468030 0.099764 4.691348 0.0000 D(DINFLATION_IR(-2)) 0.259080 0.109996 2.355364 0.0210 D(DINFLATION_IR(-3)) -0.092691 0.108310 -0.855791 0.3947 D(DINFLATION_IR(-4)) -0.365675 0.100093 -3.653346 0.0005
C 0.048171 0.032951 1.461923 0.1477
R-squared 0.572264 Mean dependent var -0.005777 Adjusted R-squared 0.545530 S.D. dependent var 0.184808 S.E. of regression 0.124587 Akaike info criterion -1.260409 Sum squared resid 1.241755 Schwarz criterion -1.089176 Log likelihood 60.19760 F-statistic 21.40621 Durbin-Watson stat 1.928058 Prob(F-statistic) 0.000000
Null Hypothesis: DLNGDP_IR has a unit root Exogenous: Constant Lag Length: 9 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.592793 0.0986 Test critical values: 1% level -3.513344
5% level -2.897678 10% level -2.586103
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNGDP_IR) Method: Least Squares Date: 05/03/03 Time: 20:42 Sample(adjusted): 1982:4 2002:4 Included observations: 81 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNGDP_IR(-1) -0.039690 0.015308 -2.592793 0.0116 D(DLNGDP_IR(-1)) 1.114376 0.099765 11.16999 0.0000 D(DLNGDP_IR(-2)) 0.002615 0.149232 0.017522 0.9861 D(DLNGDP_IR(-3)) -0.210576 0.148130 -1.421560 0.1596 D(DLNGDP_IR(-4)) -0.846916 0.149521 -5.664191 0.0000 D(DLNGDP_IR(-5)) 0.897245 0.151692 5.914924 0.0000 D(DLNGDP_IR(-6)) 0.110722 0.151705 0.729856 0.4679 D(DLNGDP_IR(-7)) -0.087820 0.149089 -0.589043 0.5577 D(DLNGDP_IR(-8)) -0.557689 0.150684 -3.701045 0.0004 D(DLNGDP_IR(-9)) 0.540900 0.105635 5.120478 0.0000
C 0.000515 0.000226 2.277655 0.0258
R-squared 0.891754 Mean dependent var 3.72E-05 Adjusted R-squared 0.876290 S.D. dependent var 0.002619 S.E. of regression 0.000921 Akaike info criterion -11.01611 Sum squared resid 5.94E-05 Schwarz criterion -10.69094 Log likelihood 457.1525 F-statistic 57.66726 Durbin-Watson stat 1.913009 Prob(F-statistic) 0.000000
Null Hypothesis: DLNGDPPERCAPITA_IR has a unit root Exogenous: Constant Lag Length: 9 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.625808 0.0922 Test critical values: 1% level -3.517847
5% level -2.899619 10% level -2.587134
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DLNGDPPERCAPITA_IR) Method: Least Squares Date: 05/03/03 Time: 20:42 Sample(adjusted): 1982:4 2001:4 Included observations: 77 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DLNGDPPERCAPITA_IR(-1) -0.045824 0.017451 -2.625808 0.0107 D(DLNGDPPERCAPITA_IR(-1)) 1.093586 0.106376 10.28041 0.0000 D(DLNGDPPERCAPITA_IR(-2)) 0.030932 0.156567 0.197566 0.8440 D(DLNGDPPERCAPITA_IR(-3)) -0.185425 0.157147 -1.179946 0.2423 D(DLNGDPPERCAPITA_IR(-4)) -0.881835 0.167573 -5.262392 0.0000 D(DLNGDPPERCAPITA_IR(-5)) 0.873239 0.165853 5.265147 0.0000 D(DLNGDPPERCAPITA_IR(-6)) 0.132387 0.160914 0.822721 0.4136 D(DLNGDPPERCAPITA_IR(-7)) -0.048747 0.159688 -0.305265 0.7611 D(DLNGDPPERCAPITA_IR(-8)) -0.593836 0.167736 -3.540293 0.0007 D(DLNGDPPERCAPITA_IR(-9)) 0.535472 0.116377 4.601202 0.0000
C 0.000546 0.000231 2.358720 0.0213
R-squared 0.888293 Mean dependent var 5.73E-05 Adjusted R-squared 0.871368 S.D. dependent var 0.002738 S.E. of regression 0.000982 Akaike info criterion -10.88222 Sum squared resid 6.37E-05 Schwarz criterion -10.54739 Log likelihood 429.9655 F-statistic 52.48334 Durbin-Watson stat 1.900819 Prob(F-statistic) 0.000000
Null Hypothesis: DDINFLATION_IR has a unit root Exogenous: Constant Lag Length: 3 (Automatic based on SIC, MAXLAG=11)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -7.695925 0.0000 Test critical values: 1% level -3.508326
5% level -2.895512 10% level -2.584952
*MacKinnon (1996) one-sided p-values.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(DDINFLATION_IR) Method: Least Squares Date: 05/13/03 Time: 18:06 Sample(adjusted): 1981:3 2002:4 Included observations: 86 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
DDINFLATION_IR(-1) -0.829350 0.107765 -7.695925 0.0000 D(DDINFLATION_IR(-1)) 0.294113 0.098954 2.972216 0.0039 D(DDINFLATION_IR(-2)) 0.534365 0.099450 5.373222 0.0000 D(DDINFLATION_IR(-3)) 0.410412 0.097481 4.210179 0.0001
C -0.001977 0.013588 -0.145500 0.8847
R-squared 0.438446 Mean dependent var -0.002019 Adjusted R-squared 0.410715 S.D. dependent var 0.164069 S.E. of regression 0.125948 Akaike info criterion -1.249522 Sum squared resid 1.284885 Schwarz criterion -1.106827 Log likelihood 58.72944 F-statistic 15.81064 Durbin-Watson stat 1.946821 Prob(F-statistic) 0.000000
29
30
Tab
le 5
: Su
mm
ary
of R
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ared
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31
Chart 11: Output Growth and Productivity (per employee) Growth for Portugal
Average percentage change over 10 years’ periods
Average percentage change over 5 years’ periods
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
1960 1965 1970 1975 1980 1985 1990 1995 2000
GDPgrow th_PT ProductivityEgrow th_PT
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
1960 1970 1980 1990 2000
GDPgrow th_PT ProductivityEgrow th_PT
1960-70 1970-80 1980-90 1990-2000 2000-02
60-65 65-70 70-75 75-80 80-85 85-90 90-95 95-00 00-02
32
Chart 12: Output Growth and Productivity (per employee) Growth for Ireland
Average percentage change over 10 years’ periods
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
1960 1970 1980 1990 2000
GDPgrow th_IR ProductivityEgrow th_IR
Average percentage change over 5 years’ periods
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
1960 1965 1970 1975 1980 1985 1990 1995 2000
GDPgrow th_IR ProductivityEgrow th_IR
60-65 65-70 70-75 75-80 80-85 85-90 90-95 95-00 00-02
1960-70 1970-80 1980-90 1990-2000 2000-02
33
Chart 13: Correlation of Output Growth and Productivity (per employee) Growth for Portugal and Ireland (1980:Q1 to 1987:Q3)
Portugal
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Number of Quarters
Ireland
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Number of Quarters
34
Chart 14: Correlation of Output Per-Capita Growth and Productivity (per employee) Growth for Portugal and Ireland (1980:Q1 to 1987:Q3)
Portugal
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Number of Quarters
Ireland
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Number of Quarters
35
Chart 15: Correlation of Inflation and Productivity (per employee) Growth for Portugal and Ireland (1980:Q1 to 1987:Q3)
Portugal
-0.7
-0.5
-0.3
-0.1
0.1
0.3
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Number of Quarters
Ireland
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2-0.1
0
0.1
0.2
0.3
0.4
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Number of Quarters
36
Table 6: VAR Output – Productivity growth and GDP growth - Portugal
Vector Autoregression Estimates Date: 04/24/03 Time: 19:18 Sample(adjusted): 1981:4 2002:2 Included observations: 83 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
DLNPRODUCTIVITYE_PT DLNGDP_PT_SA
DLNPRODUCTIVITYE_PT(-1) 0.156331 -0.010787 (0.20703) (0.18404) [ 0.75511] [-0.05861]
DLNPRODUCTIVITYE_PT(-2) 0.183880 -0.005352 (0.20877) (0.18559) [ 0.88076] [-0.02883]
DLNPRODUCTIVITYE_PT(-3) 0.076410 0.045605 (0.20411) (0.18145) [ 0.37435] [ 0.25133]
DLNPRODUCTIVITYE_PT(-4) -0.287523 -0.182911 (0.20307) (0.18052) [-1.41590] [-1.01325]
DLNPRODUCTIVITYE_PT(-5) -0.133383 -0.056072 (0.19362) (0.17212) [-0.68889] [-0.32577]
DLNPRODUCTIVITYE_PT(-6) 0.210124 0.131732 (0.19090) (0.16970) [ 1.10072] [ 0.77626]
DLNGDP_PT_SA(-1) -0.183731 -0.151451 (0.23488) (0.20880) [-0.78224] [-0.72534]
DLNGDP_PT_SA(-2) -0.153599 0.220766 (0.23524) (0.20912) [-0.65295] [ 1.05570]
DLNGDP_PT_SA(-3) 0.167794 0.186599 (0.24306) (0.21608) [ 0.69033] [ 0.86358]
DLNGDP_PT_SA(-4) 0.099965 0.168940 (0.24384) (0.21676) [ 0.40996] [ 0.77937]
DLNGDP_PT_SA(-5) 0.149053 0.028393 (0.23080) (0.20517) [ 0.64582] [ 0.13839]
DLNGDP_PT_SA(-6) -0.051408 -0.048038 (0.22797) (0.20266) [-0.22550] [-0.23704]
C 0.003785 0.005358 (0.00338) (0.00301) [ 1.11918] [ 1.78222]
R-squared 0.152420 0.140009 Adj. R-squared 0.007121 -0.007418 Sum sq. resids 0.031594 0.024968 S.E. equation 0.021245 0.018886 F-statistic 1.049009 0.949683 Log likelihood 208.9835 218.7521 Akaike AIC -4.722493 -4.957882 Schwarz SC -4.343638 -4.579027 Mean dependent 0.005056 0.008420 S.D. dependent 0.021321 0.018816
Determinant Residual Covariance 5.27E-08 Log Likelihood (d.f. adjusted) 459.9548 Akaike Information Criteria -10.45674 Schwarz Criteria -9.699033
37
Table 7: VAR Output – Productivity growth and GDP per capita growth - Portugal
Vector Autoregression Estimates Date: 04/24/03 Time: 19:19 Sample(adjusted): 1981:4 2002:2 Included observations: 83 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
DLNPRODUCTIVITYE_PT DLNGDPPERCAPITA_PT
DLNPRODUCTIVITYE_PT(-1) 0.045363 -0.012029 (0.25634) (0.27941) [ 0.17696] [-0.04305]
DLNPRODUCTIVITYE_PT(-2) 0.401826 0.312336 (0.25534) (0.27831) [ 1.57369] [ 1.12225]
DLNPRODUCTIVITYE_PT(-3) 0.244490 0.270369 (0.25376) (0.27659) [ 0.96349] [ 0.97752]
DLNPRODUCTIVITYE_PT(-4) -0.508415 -0.454914 (0.25453) (0.27743) [-1.99747] [-1.63974]
DLNPRODUCTIVITYE_PT(-5) -0.336240 -0.332789 (0.24290) (0.26476) [-1.38426] [-1.25696]
DLNPRODUCTIVITYE_PT(-6) 0.277482 0.256054 (0.23962) (0.26118) [ 1.15800] [ 0.98037]
DLNGDPPERCAPITA_PT(-1) -0.036247 -0.107736 (0.23619) (0.25744) [-0.15347] [-0.41849]
DLNGDPPERCAPITA_PT(-2) -0.300809 -0.056229 (0.23649) (0.25777) [-1.27195] [-0.21814]
DLNGDPPERCAPITA_PT(-3) -0.060688 -0.099065 (0.24320) (0.26508) [-0.24954] [-0.37371]
DLNGDPPERCAPITA_PT(-4) 0.302751 0.328066 (0.24306) (0.26493) [ 1.24556] [ 1.23830]
DLNGDPPERCAPITA_PT(-5) 0.342454 0.326880 (0.23499) (0.25613) [ 1.45732] [ 1.27622]
DLNGDPPERCAPITA_PT(-6) -0.114957 -0.116793 (0.23427) (0.25535) [-0.49070] [-0.45739]
C 0.003347 0.005413 (0.00309) (0.00337) [ 1.08203] [ 1.60575]
R-squared 0.186727 0.146525 Adj. R-squared 0.047308 0.000215 Sum sq. resids 0.030315 0.036016 S.E. equation 0.020811 0.022683 F-statistic 1.339327 1.001471 Log likelihood 210.6981 203.5475 Akaike AIC -4.763811 -4.591506 Schwarz SC -4.384956 -4.212652 Mean dependent 0.005056 0.007826 S.D. dependent 0.021321 0.022685
Determinant Residual Covariance 4.82E-08 Log Likelihood (d.f. adjusted) 463.6836 Akaike Information Criteria -10.54659 Schwarz Criteria -9.788883
38
Table 8: VAR Output – Productivity growth and Inflation - Portugal
Vector Autoregression Estimates Date: 05/03/03 Time: 18:24 Sample(adjusted): 1981:4 2002:2 Included observations: 83 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
DINFLATION_PT DLNPRODUCTIVITYE_PT
DINFLATION_PT(-1) -0.048586 -0.000118 (0.12100) (0.00194) [-0.40154] [-0.06090]
DINFLATION_PT(-2) 0.105718 0.000787 (0.12294) (0.00197) [ 0.85991] [ 0.39905]
DINFLATION_PT(-3) 0.091216 0.000850 (0.12240) (0.00196) [ 0.74523] [ 0.43326]
DINFLATION_PT(-4) 0.240586 -0.001824 (0.12202) (0.00196) [ 1.97176] [-0.93226]
DINFLATION_PT(-5) -0.148430 0.002114 (0.12415) (0.00199) [-1.19561] [ 1.06177]
DINFLATION_PT(-6) 0.070275 0.002385 (0.12592) (0.00202) [ 0.55809] [ 1.18119]
DLNPRODUCTIVITYE_PT(-1) -4.424330 0.020900 (7.42903) (0.11912) [-0.59555] [ 0.17545]
DLNPRODUCTIVITYE_PT(-2) 2.778979 0.118104 (7.39461) (0.11857) [ 0.37581] [ 0.99607]
DLNPRODUCTIVITYE_PT(-3) -3.387925 0.177727 (7.23093) (0.11595) [-0.46853] [ 1.53285]
DLNPRODUCTIVITYE_PT(-4) 11.88228 -0.287607 (7.23859) (0.11607) [ 1.64152] [-2.47792]
DLNPRODUCTIVITYE_PT(-5) -5.349654 -0.018626 (7.46470) (0.11969) [-0.71666] [-0.15561]
DLNPRODUCTIVITYE_PT(-6) 4.501406 0.184941 (7.46217) (0.11965) [ 0.60323] [ 1.54564]
C 0.895245 -0.001373 (0.36853) (0.00591) [ 2.42925] [-0.23240]
R-squared 0.134844 0.178027 Adj. R-squared -0.013469 0.037117 Sum sq. resids 119.1699 0.030640 S.E. equation 1.304771 0.020922 F-statistic 0.909188 1.263412 Log likelihood -132.7829 210.2566 Akaike AIC 3.512840 -4.753170 Schwarz SC 3.891694 -4.374316 Mean dependent 1.343373 0.005056 S.D. dependent 1.296072 0.021321
Determinant Residual Covariance 0.000713 Log Likelihood (d.f. adjusted) 65.18996 Akaike Information Criteria -0.944336 Schwarz Criteria -0.186627
39
Table 9: VAR Output – Productivity growth and GDP growth - Ireland
Vector Autoregression Estimates Date: 04/24/03 Time: 19:20 Sample(adjusted): 1981:4 2002:4 Included observations: 85 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
DLNPRODUCTIVITYE_IR DLNGDP_IR
DLNPRODUCTIVITYE_IR(-1) 1.825618 -0.140555 (0.12979) (0.12847) [ 14.0663] [-1.09408]
DLNPRODUCTIVITYE_IR(-2) -0.992128 0.078693 (0.27903) (0.27619) [-3.55569] [ 0.28492]
DLNPRODUCTIVITYE_IR(-3) -0.157962 -0.037367 (0.31247) (0.30930) [-0.50552] [-0.12081]
DLNPRODUCTIVITYE_IR(-4) 0.008839 0.436002 (0.31119) (0.30803) [ 0.02840] [ 1.41546]
DLNPRODUCTIVITYE_IR(-5) 0.340952 -0.780042 (0.27806) (0.27524) [ 1.22619] [-2.83408]
DLNPRODUCTIVITYE_IR(-6) -0.233391 0.339626 (0.13200) (0.13066) [-1.76810] [ 2.59929]
DLNGDP_IR(-1) 0.010263 2.036920 (0.13226) (0.13092) [ 0.07760] [ 15.5589]
DLNGDP_IR(-2) 0.065267 -1.070837 (0.29328) (0.29031) [ 0.22254] [-3.68866]
DLNGDP_IR(-3) -0.007806 -0.073180 (0.33089) (0.32754) [-0.02359] [-0.22343]
DLNGDP_IR(-4) -0.514882 -0.745792 (0.33141) (0.32804) [-1.55362] [-2.27344]
DLNGDP_IR(-5) 0.846287 1.550708 (0.29386) (0.29088) [ 2.87986] [ 5.33107]
DLNGDP_IR(-6) -0.382196 -0.712563 (0.13197) (0.13063) [-2.89605] [-5.45473]
C 0.001480 0.001048 (0.00035) (0.00034) [ 4.26314] [ 3.04864]
R-squared 0.972073 0.988591 Adj. R-squared 0.967418 0.986689 Sum sq. resids 7.01E-05 6.87E-05 S.E. equation 0.000987 0.000977 F-statistic 208.8450 519.8833 Log likelihood 474.7374 475.6045 Akaike AIC -10.86441 -10.88481 Schwarz SC -10.49083 -10.51123 Mean dependent 0.008236 0.013257 S.D. dependent 0.005467 0.008466
Determinant Residual Covariance 5.46E-13 Log Likelihood (d.f. adjusted) 958.8185 Akaike Information Criteria -21.94867 Schwarz Criteria -21.20151
40
Table 10: VAR Output – Productivity growth and GDP growth - Ireland
Vector Autoregression Estimates Date: 04/24/03 Time: 19:21 Sample(adjusted): 1981:4 2001:4 Included observations: 81 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
DLNPRODUCTIVITYE_IR DLNGDPPERCAPITA_IR
DLNPRODUCTIVITYE_IR(-1) 1.805259 -0.145893 (0.13305) (0.13489) [ 13.5682] [-1.08160]
DLNPRODUCTIVITYE_IR(-2) -0.965536 0.080898 (0.28368) (0.28760) [-3.40359] [ 0.28129]
DLNPRODUCTIVITYE_IR(-3) -0.155973 -0.019154 (0.31721) (0.32159) [-0.49170] [-0.05956]
DLNPRODUCTIVITYE_IR(-4) -0.032121 0.387553 (0.31651) (0.32088) [-0.10149] [ 1.20780]
DLNPRODUCTIVITYE_IR(-5) 0.377577 -0.732416 (0.28339) (0.28730) [ 1.33238] [-2.54935]
DLNPRODUCTIVITYE_IR(-6) -0.246917 0.322271 (0.13512) (0.13698) [-1.82739] [ 2.35261]
DLNGDPPERCAPITA_IR(-1) 0.027122 2.039446 (0.13466) (0.13652) [ 0.20141] [ 14.9386]
DLNGDPPERCAPITA_IR(-2) 0.043081 -1.073619 (0.29736) (0.30146) [ 0.14488] [-3.56137]
DLNGDPPERCAPITA_IR(-3) -0.020475 -0.098142 (0.33540) (0.34003) [-0.06105] [-0.28863]
DLNGDPPERCAPITA_IR(-4) -0.454290 -0.673105 (0.33770) (0.34236) [-1.34526] [-1.96610]
DLNGDPPERCAPITA_IR(-5) 0.793572 1.472291 (0.30057) (0.30472) [ 2.64019] [ 4.83160]
DLNGDPPERCAPITA_IR(-6) -0.365897 -0.682951 (0.13633) (0.13821) [-2.68393] [-4.94142]
C 0.001506 0.001072 (0.00036) (0.00036) [ 4.23215] [ 2.97047]
R-squared 0.971988 0.987368 Adj. R-squared 0.967045 0.985138 Sum sq. resids 6.99E-05 7.19E-05 S.E. equation 0.001014 0.001028 F-statistic 196.6302 442.9192 Log likelihood 450.5364 449.4264 Akaike AIC -10.80337 -10.77596 Schwarz SC -10.41907 -10.39167 Mean dependent 0.008324 0.012086 S.D. dependent 0.005587 0.008434
Determinant Residual Covariance 6.46E-13 Log Likelihood (d.f. adjusted) 906.8810 Akaike Information Criteria -21.75015 Schwarz Criteria -20.98156
41
Table 11: VAR Output – Productivity growth and Inflation - Ireland
Vector Autoregression Estimates Date: 05/13/03 Time: 18:13 Sample(adjusted): 1982:1 2002:4 Included observations: 84 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ]
DDINFLATION_IR DLNPRODUCTIVITYE_IR
DDINFLATION_IR(-1) 0.476391 -0.001188 (0.11454) (0.00096) [ 4.15933] [-1.24206]
DDINFLATION_IR(-2) 0.291024 0.000570 (0.12844) (0.00107) [ 2.26587] [ 0.53106]
DDINFLATION_IR(-3) -0.142630 -0.000885 (0.11925) (0.00100) [-1.19607] [-0.88882]
DDINFLATION_IR(-4) -0.452597 -0.000173 (0.11621) (0.00097) [-3.89455] [-0.17846]
DDINFLATION_IR(-5) -0.039708 0.000520 (0.12049) (0.00101) [-0.32957] [ 0.51633]
DDINFLATION_IR(-6) 0.183127 -0.000696 (0.11018) (0.00092) [ 1.66213] [-0.75668]
DLNPRODUCTIVITYE_IR(-1) -16.44607 1.848146 (12.5028) (0.10441) [-1.31539] [ 17.7001]
DLNPRODUCTIVITYE_IR(-2) 40.41721 -1.012410 (26.0963) (0.21794) [ 1.54877] [-4.64541]
DLNPRODUCTIVITYE_IR(-3) -34.84259 -0.080950 (29.5710) (0.24696) [-1.17827] [-0.32779]
DLNPRODUCTIVITYE_IR(-4) 15.50106 -0.371312 (29.7990) (0.24886) [ 0.52019] [-1.49205]
DLNPRODUCTIVITYE_IR(-5) -1.248136 0.890182 (26.4064) (0.22053) [-0.04727] [ 4.03661]
DLNPRODUCTIVITYE_IR(-6) 0.151640 -0.461052 (12.5482) (0.10479) [ 0.01208] [-4.39960]
C -0.034045 0.001538 (0.04339) (0.00036) [-0.78466] [ 4.24532]
R-squared 0.608499 0.968840 Adj. R-squared 0.542329 0.963573 Sum sq. resids 1.119763 7.81E-05 S.E. equation 0.125584 0.001049 F-statistic 9.196091 183.9626 Log likelihood 62.15257 464.1211 Akaike AIC -1.170299 -10.74098 Schwarz SC -0.794101 -10.36478 Mean dependent -0.008295 0.008213 S.D. dependent 0.185634 0.005495
Determinant Residual Covariance 1.73E-08 Log Likelihood (d.f. adjusted) 512.3117 Akaike Information Criteria -11.57885 Schwarz Criteria -10.82646
42
Chart 16: Impulse Responses for Portugal
-.010
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_PT to DLNPRODUCTIVITYE_PT
-.010
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_PT to DLNGDP_PT_SA
-.004
.000
.004
.008
.012
.016
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDP_PT_SA to DLNPRODUCTIVITYE_PT
-.004
.000
.004
.008
.012
.016
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDP_PT_SA to DLNGDP_PT_SA
Response to Cholesky One S.D. Innovations
-.010
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_PT to DLNPRODUCTIVITYE_PT
-.010
-.005
.000
.005
.010
.015
.020
.025
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_PT to DLNGDPPERCAPITA_PT
-.004
.000
.004
.008
.012
.016
.020
.024
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDPPERCAPITA_PT to DLNPRODUCTIVITYE_PT
-.004
.000
.004
.008
.012
.016
.020
.024
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDPPERCAPITA_PT to DLNGDPPERCAPITA_PT
Response to Cholesky One S.D. Innovations
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10 11 12
Response of DINFLATION_PT to DINFLATION_PT
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10 11 12
Response of DINFLATION_PT to DLNPRODUCTIVITYE_PT
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_PT to DINFLATION_PT
-.01
.00
.01
.02
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_PT to DLNPRODUCTIVITYE_PT
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLNPRODUCTIVITYE_PT to DLNPRODUCTIVITYE_PT Response of DLNPRODUCTIVITYE_PT to DLNGDP_PT_SA
Response of DLNGDP_PT_SA to DLNPRODUCTIVITYE_PT Response of DLNGDP_PT_SA to DLNGDP_PT_SA
Response of DLNPRODUCTIVITYE_PT to DLNPRODUCTIVITYE_PT Response of DLNPRODUCTIVITYE_PT to DLNGDPPERCAPITA_PT
Response of DLNGDPPERCAPITA_PT to DLNPRODUCTIVITYE_PT Response of DLNGDPPERCAPITA_PT to DLNGDPPERCAPITA_PT
Response of DLNPRODUCTIVITYE_PT to DINFLATION_PT Response of DLNPRODUCTIVITYE_PT to DLNPRODUCTIVITYE_PT
43
Chart 17: Impulse Responses for Ireland
-.002
-.001
.000
.001
.002
.003
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_IR to DLNPRODUCTIVITYE_IR
-.002
-.001
.000
.001
.002
.003
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_IR to DLNGDPPERCAPITA_IR
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDPPERCAPITA_IR to DLNPRODUCTIVITYE_IR
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDPPERCAPITA_IR to DLNGDPPERCAPITA_IR
Response to Cholesky One S.D. Innovations
-.002
-.001
.000
.001
.002
.003
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_IR to DLNPRODUCTIVITYE_IR
-.002
-.001
.000
.001
.002
.003
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_IR to DLNGDP_IR
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDP_IR to DLNPRODUCTIVITYE_IR
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNGDP_IR to DLNGDP_IR
Response to Cholesky One S.D. Innovations
-.10
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10 11 12
Response of DDINFLATION_IR to DDINFLATION_IR
-.10
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10 11 12
Response of DDINFLATION_IR to DLNPRODUCTIVITYE_IR
-.003
-.002
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_IR to DDINFLATION_IR
-.003
-.002
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10 11 12
Response of DLNPRODUCTIVITYE_IR to DLNPRODUCTIVITYE_IR
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of DLNPRODUCTIVITYE_IR to DLNPRODUCTIVITYE_IR Response of DLNPRODUCTIVITYE_IR to DLNGDP_IR
Response of DLNPRODUCTIVITYE_IR to DLNPRODUCTIVITYE_IR Response of DLNPRODUCTIVITYE_IR to DLNGDPPERCAPITA_IR
Response of DLNGDPPERCAPITA_IR to DLNPRODUCTIVITYE_IR Response of DLNGDPPERCAPITA_IR to DLNGDPPERCAPITA_IR
Response of DLNPRODUCTIVITYE_IR to DDINFLATION_IR Response of DLNPRODUCTIVITYE_IR to DLNPRODUCTIVITYE_IR
44
Table 12: Granger Causality Test for Portugal
Table 13: Granger Causality Test for Ireland
VAR Pairwise Granger Causality/Block Exogeneity Wald Tests Date: 05/03/03 Time: 20:51 Sample: 1980:1 2002:4 Included observations: 81
Dependent variable: DLNPRODUCTIVITYE_IR
Exclude Chi-sq df Prob.
DLNGDPPERCAPITA_IR 12.74623 6 0.0472
All 12.74623 6 0.0472
Dependent variable: DLNGDPPERCAPITA_IR
Exclude Chi-sq df Prob.
DLNPRODUCTIVITYE_IR 18.25435 6 0.0056
All 18.25435 6 0.0056
VAR Pairwise Granger Causality/Block Exogeneity Wald Tests Date: 05/03/03 Time: 20:46 Sample: 1980:1 2002:4 Included observations: 85
Dependent variable: DLNPRODUCTIVITYE_IR
Exclude Chi-sq df Prob.
DLNGDP_IR 13.51089 6 0.0356
All 13.51089 6 0.0356
Dependent variable: DLNGDP_IR
Exclude Chi-sq df Prob.
DLNPRODUCTIVITYE_IR 21.46327 6 0.0015
All 21.46327 6 0.0015
VAR Pairwise Granger Causality/Block Exogeneity Wald Tests Date: 05/03/03 Time: 20:48 Sample: 1980:1 2002:4 Included observations: 83
Dependent variable: DLNPRODUCTIVITYE_PT
Exclude Chi-sq df Prob.
DLNGDP_PT_SA 1.996513 6 0.9200
All 1.996513 6 0.9200
Dependent variable: DLNGDP_PT_SA
Exclude Chi-sq df Prob.
DLNPRODUCTIVITYE_PT 1.542980 6 0.9566
All 1.542980 6 0.9566
VAR Pairwise Granger Causality/Block Exogeneity Wald Tests Date: 05/03/03 Time: 20:53 Sample: 1980:1 2002:4 Included observations: 83
Dependent variable: DLNPRODUCTIVITYE_PT
Exclude Chi-sq df Prob.
DLNGDPPERCAPITA_PT 5.033537 6 0.5395
All 5.033537 6 0.5395
Dependent variable: DLNGDPPERCAPITA_PT
Exclude Chi-sq df Prob.
DLNPRODUCTIVITYE_PT 6.180060 6 0.4033
All 6.180060 6 0.4033
VAR Pairwise Granger Causality/Block Exogeneity Wald Tests Date: 05/03/03 Time: 20:58 Sample: 1980:1 2002:4 Included observations: 83
Dependent variable: DINFLATION_PT
Exclude Chi-sq df Prob.
DLNPRODUCTIVITYE_PT 3.899370 6 0.6903
All 3.899370 6 0.6903
Dependent variable: DLNPRODUCTIVITYE_PT
Exclude Chi-sq df Prob.
DINFLATION_PT 4.239390 6 0.6443
All 4.239390 6 0.6443
VAR Pairwise Granger Causality/Block Exogeneity Wald Tests Date: 05/13/03 Time: 18:15 Sample: 1980:1 2002:4 Included observations: 84
Dependent variable: DDINFLATION_IR
Exclude Chi-sq df Prob.
DLNPRODUCTIVITYE_IR 5.0093 6 0.5426
All 5.0093 6 0.5426
Dependent variable: DLNPRODUCTIVITYE_IR
Exclude Chi-sq df Prob.
DDINFLATION_IR 4.4527 6 0.6156
All 4.4527 6 0.6156