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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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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



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



24



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



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


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



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


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


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


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.*


5% level -2.894716 10% level -2.584529


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


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.*


5% level -2.894716 10% level -2.584529


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


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.*


5% level -2.894716 10% level -2.584529


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


DLNPRODUCTIVITYE_PT(-1) -1.006054 0.107826 -9.330319 0.0000 C 0.004773 0.002291 2.082871 0.0402


Null Hypothesis: DLNPRODUCTIVITYH_PT has a unit root Exogenous: Constant Lag Length: 0 (Automatic based on SIC, MAXLAG=10)

t-Statistic Prob.*


5% level -2.904848 10% level -2.589907


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


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.*


5% level -2.909206 10% level -2.592215


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


DLNPRODUCTIVITYH_IR(-1) -1.011100 0.129289 -7.820458 0.0000 C 0.009491 0.002016 4.708148 0.0000


Null Hypothesis: DLNPRODUCTIVITYE_IR has a unit root Exogenous: Constant Lag Length: 9 (Automatic based on SIC, MAXLAG=11)

t-Statistic Prob.*


5% level -2.897678 10% level -2.586103


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


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.*


5% level -2.895512 10% level -2.584952


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


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.*


5% level -2.897678 10% level -2.586103


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


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


Null Hypothesis: DLNGDPPERCAPITA_IR has a unit root Exogenous: Constant Lag Length: 9 (Automatic based on SIC, MAXLAG=11)

t-Statistic Prob.*


5% level -2.899619 10% level -2.587134


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


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


Null Hypothesis: DDINFLATION_IR has a unit root Exogenous: Constant Lag Length: 3 (Automatic based on SIC, MAXLAG=11)

t-Statistic Prob.*


5% level -2.895512 10% level -2.584952


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


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

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rcap

itadl

npro

duct

ivity

hdi

nfla

tion

dlnp

rodu

ctiv

ityh

0,08

0,03

0,02

0,02

0,02

-0,0

4-0

,04

-0,0

4

0,18

0,19

0,11

0,12

0,06

0,07

-0,0

1-0

,01

0,21

0,21

0,23

0,17

0,02

0,02

0,04

-0,0

2

0,21

0,27

0,41

0,29

-0,0

80,

000,

190,

03

0,31

0,36

0,42

0,44

-0,0

50,

020,

110,

15

dlnp

rodu

ctiv

ityh

dlnG

DP

perc

apita

dlnp

rodu

ctiv

ityh

dinf

latio

ndl

nGD

Ppe

rcap

itadl

npro

duct

ivity

hdi

nfla

tion

dlnp

rodu

ctiv

ityh

0,07

0,97

0,00

0,90

0,01

0,97

-0,0

70,

90

0,14

0,98

0,05

0,92

-0,0

00,

98-0

,10

0,90

0,26

0,98

0,11

0,93

0,06

0,98

-0,1

20,

91

0,51

0,98

0,29

0,94

0,30

0,98

-0,0

10,

91

0,56

0,99

0,35

0,94

0,29

0,98

-0,0

50,

91

-0,9

3

-10,

70

-1,3

2

-9,8

9-1

,21

-1,1

3

-10,

46-1

,57

-6,1

6

-5,6

6

-15,

70-5

,22

-5,9

1

-15,

82-6

,47

-15,

85

-10,

18

-15,

97

-9,5

0

-15,

85

30

31

Chart 11: Output Growth and Productivity (per employee) Growth for Portugal

Average percentage change over 10 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


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


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(-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]



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


DLNPRODUCTIVITYE_PT DLNGDPPERCAPITA_PT







DLNGDPPERCAPITA_PT(-1) -0.036247 -0.107736 (0.23619) (0.25744) [-0.15347] [-0.41849]



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]


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


38

Table 8: VAR Output – Productivity growth and Inflation - Portugal


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(-3) -3.387925 0.177727 (7.23093) (0.11595) [-0.46853] [ 1.53285]




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


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]



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


40

Table 10: VAR Output – Productivity growth and GDP growth - Ireland


DLNPRODUCTIVITYE_IR DLNGDPPERCAPITA_IR







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(-5) 0.793572 1.472291 (0.30057) (0.30472) [ 2.64019] [ 4.83160]


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


41

Table 11: VAR Output – Productivity growth and Inflation - Ireland


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]







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


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


-.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


-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 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


-.002

-.001

.000

.001

.002

.003

1 2 3 4 5 6 7 8 9 10 11 12


-.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


-.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 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


DLNPRODUCTIVITYE_IR 18.25435 6 0.0056

All 18.25435 6 0.0056




DLNGDP_IR 13.51089 6 0.0356

All 13.51089 6 0.0356

Dependent variable: DLNGDP_IR



All 21.46327 6 0.0015


Dependent variable: DLNPRODUCTIVITYE_PT


DLNGDP_PT_SA 1.996513 6 0.9200

All 1.996513 6 0.9200

Dependent variable: DLNGDP_PT_SA


DLNPRODUCTIVITYE_PT 1.542980 6 0.9566

All 1.542980 6 0.9566




DLNGDPPERCAPITA_PT 5.033537 6 0.5395

All 5.033537 6 0.5395

Dependent variable: DLNGDPPERCAPITA_PT



All 6.180060 6 0.4033


Dependent variable: DINFLATION_PT



All 3.899370 6 0.6903



DINFLATION_PT 4.239390 6 0.6443

All 4.239390 6 0.6443


Dependent variable: DDINFLATION_IR



All 5.0093 6 0.5426



DDINFLATION_IR 4.4527 6 0.6156

All 4.4527 6 0.6156

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