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Exchange Rates, the Competitiveness of Nations and Unemployment

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 Faculdade de Economia
da Universidade de Coimbra

Grupo de Estudos Monetários e Financeiros

(GEMF)
Av. Dias da Silva, 165 – 3004-512 COIMBRA,
PORTUGAL

gemf@fe.uc.pt
http://www.uc.pt/feuc/gemf

PEDRO BAÇÃO, ANTÓNIO PORTUGAL DUARTE

& DIANA MACHADO
Exchange Rates, the Competitiveness of Nations and
Unemployment
ESTUDOS DO GEMF
N.º 14 2016

Este trabalho é financiado por Fundos Nacionais através da

FCT – Fundação para a Ciência e a Tecnologia no âmbito do
projeto UID/ECO/00031/2013.
 Exchange Rates, the Competitiveness of Nations
and Unemployment1

Pedro Bação

António Portugal Duarte

Diana Machado

Faculdade de Economia da Universidade de Coimbra

Grupo de Estudos Monetários e Financeiros (GEMF)
Av. Dias da Silva, 165
3004-512 Coimbra, Portugal
Tel. +351 239790500
Fax +351 239403511

Abstract

The main goal of this paper is to assess the impact of exchange rate fluctuations on
economic activity, namely on unemployment. We estimate a Vector Autoregressive (VAR) model
for each of the following major economies: Australia, Brazil, China, Germany, Japan, Switzerland,
the United Kingdom and the United States of America. The VAR model comprises the following
variables: the unemployment rate, the GDP growth rate, the inflation rate, the interest rate, and the
real effective exchange rate. We use the Cholesky decomposition to identify the shocks that drive
these variables. The results obtained using the Cholesky decomposition depend on the ordering of
the variables; therefore we discuss the results obtained when different orderings are employed. Our
results suggest that an exchange rate appreciation leads to an increase in unemployment in relatively
small countries such as Australia, Switzerland and the UK, but not in Japan or in the USA. The
results for Brazil, China and Germany do not appear to be satisfactory, possibly because the sample
covers a period of important structural change in those countries.

Keywords: competitiveness, currency wars, exchange rate, monetary policy, unemployment, VAR
model.

JEL classification: F16, F31, F41

1
This paper has been prepared for the book “Competitiveness of Enterprises and National Economies” edited by
Bojan Krstić and Zbigniew Paszek, published by Faculty of Economics, University of Niš.
1
1. Introduction

The relationship between exchange rates and employment is the subject of intense debate,
straddling economics and politics (see, e.g., Delgado, Ketels, Porter and Stern, 2012). The exchange
rate, or, more precisely, the real exchange rate, guides the allocation of resources between sectors,
according to the relative strength (competitiveness) of domestic producers vis-à-vis international
competitors (Montiel, 2011; Nelson, 2015). This means that changes in the exchange rate can
impact the competitiveness of certain domestic sectors and thus labor markets via trade flows.
However, it is usually exchange rate appreciations that worry policymakers, in view of their
potential detrimental impact on international competitiveness and consequently on domestic
economic growth and employment (Bussière, Lopez and Tille, 2014).
Nevertheless, the relevance of the concern about “national competitiveness” is itself
controversial. Krugman (1994) has dubbed it “a dangerous obsession” and argues that concerns
about competitiveness are “almost completely unfounded”. De Grauwe (2010) also expresses some
skepticism, partially due to the policies that are put forward to promote competitiveness, such as
currency devaluation or “strategic” industrial policy. On the other hand, Mishkin (2007) clearly
expresses worry about the competitiveness mechanism, arguing that an appreciation of the dollar
restrains exports and stimulates imports, which implies a reduction in aggregate demand with
negative consequences for economic growth and employment in the United States of America.
Based on the case of France, Noyer (2007) gives a more balanced view. For this author, while it is
clear that the euro's appreciation in recent years penalizes export sectors whose competitors are
located in other monetary areas, it is also evident that it benefits those sectors which are large
consumers of imported commodities. Therefore, the overall effect on France's growth and external
balance is not clear. Amiti, Itskhoki and Konings (2012) highlight the fact that large exporters are
simultaneously large importers, a situation which may lead to incomplete pass-through of exchange
rate shocks into international prices, thereby calling into question the positive effect of exchange
rate fluctuations in terms of competitiveness.
The connection between exchange rates and economic activity has received renewed
interest in recent years with the debate around the problematic of currency wars, initiated by Brazil's
Finance Minister Guido Mantega in September 2010 (Cline and Williamson, 2010). According to
the International Monetary Fund (IMF), this situation happens when currencies are held artificially
low to boost exports, a policy action popularly referred to as “currency manipulation”.
Indeed, following the Great Financial Crisis of 2008-9, which has lowered growth rates
throughout the world, some countries, especially China, Malaysia, Singapore, Switzerland, Hong
Kong, Taiwan and other East Asian countries, are purposely using various policies to weaken the
value of their currency to increase exports and create jobs. However, there is the risk of this being
a zero-sum game, in which some must end up disappointed. A weaker currency in one country
implies a stronger currency in some other country or countries. Therefore, these policies come at
the expense of other nations, including the United States, Japan and the Euro area, which, if unable
to retaliate through the same channel, will observe a destruction of jobs and consequently an
increase in their unemployment rate, albeit temporarily.
To overcome the worrying economic signs that their economies still exhibit, the United
States of America, the United Kingdom and, to a lesser extent, the Euro area adopted in recent years
expansionary monetary policies. In addition to the cut in short-term interest rates, the monetary
authorities have used quantitative easing to provide further monetary stimulus to the economy.
Quantitative easing is an unconventional form of monetary policy that expands the money supply
through government purchases of assets, usually government bonds (Fawley and Neely, 2013;
Nelson, 2015).

2
 However, as the U.S. dollar, the British pound, and the euro are floating currencies, some
emerging countries, such as Brazil, argue that these expansionary policies have caused these
currencies to depreciate against the currencies of emerging markets, therefore operating as “beggar-
thy-neighbor” policies (Bussière, Lopez and Tille, 2014; Nelson, 2015). In response to the concerns
of emerging-market economies, the Federal Reserve, the Bank of England, and the European
Central Bank argue that the net effect of quantitative easing on trading partners is not necessarily
negative. These policies are adopted only for domestic purposes (stimulate domestic consumption
and investment), so that any effect on the currencies of emerging countries will be a side-effect or
by-product of the policy (Economist, 2013).
The debate about the effects on domestic output and employment resulting from exchange
rate manipulation thus remains far from over. The main goal of this paper is to analyze the effects
of exchange rate variations on economic activity, namely on the unemployment rate. Our
contribution to the literature stems from the fact that we estimate a model which also incorporates
an equation for unemployment, to quantify the reaction of this variable to exchange rate shocks.
The remainder of the paper is organized as follows. Section 2 presents a brief review of the
literature around the relationship between exchange rate movements, competitiveness and
unemployment. This is followed, in Section 3, by a description of the model, data and empirical
strategy. Section 4 shows the results of the econometric analysis and discusses their implications.
Section 5 discusses some limitations of our approach and concludes.

2. Literature Review

Porter (1990) contradicts the prevailing thinking about competitiveness, which views
labor costs, interest rates, exchange rates, and economies of scale as the most potent
determinants of competitiveness. To Porter (1990), a nation's competitiveness depends on the
capacity of its industry to innovate and upgrade. Countries gain advantage against the world's
best competitors because of pressure and challenge. Domestic companies benefit from having
strong domestic and foreign rivals, aggressive home-based suppliers, and demanding local
customers. Competitive advantage is also created and sustained through a highly localized
process. Differences in national values, culture, economic structures, institutions, and history
all contribute to competitive success.
In general, Porter (1990) identifies four broad attributes of a nation, that individually
and as a system constitute the famous “diamond of national advantage”. These attributes are
the following: i) Factor conditions, such as skilled labor or infrastructure necessary to compete
in a given industry; ii) the nature of home-market demand for the industry's product or service;
iii) the presence or absence in the nation of supplier industries that are internationally
competitive and iv) the conditions in the nation governing how companies are created,
organized, and managed, as well as the nature of domestic rivalry (Porter, 1990, p. 78).
However, if historically the term competitiveness has been used mostly to draw attention
to the cost position of firms or countries, as it is indicated by Aiginger, Barenthaler-Sieber and
Vogel (2013), the concept of competitiveness is still often used today when an economy (or a
firm or industry) is challenged by new low-cost competitors, as e.g., from China, India,
Indonesia or Vietnam. It is this narrow focus on costs (“cost competition” or “price
competition”) that was criticized by Krugman (1994) as “elusive and meaningless” at the
conceptual level and “misleading or even dangerous” at the policy level, since this narrow
perspective implies that cost reduction is the only effective policy response to ensure the
competitiveness of nations.

3
 In a more applied perspective, Delgado et al. (2012) build and estimate a model to
explain foundational competitiveness across countries. The authors distinguish between the role
of macroeconomic and microeconomic influences on competitiveness. Macroeconomic factors
set general conditions that create opportunities for higher productivity but do not directly link
to company productivity and labor mobilization. On the other hand, microeconomic
determinants of competitiveness are related with specific attributes of the national business
environment (e.g. whether business regulation enhances or inhibits investment and growth), the
structure of economic activity (e.g. the extent of local rivalry and the extent of agglomeration
spillovers from cluster development), and the use of sophisticated business management
practices (Delgado et al., 2012, p. 3).
Based on a Schumpeterian perspective about economic growth, Fagerberg, Srholec and
Hnelll (2005) identify technology, capacity, costs and demand as the main sources of the
competitiveness of nations. Working on a sample of 100 countries in the period 1993-2002, the
authors argue that these four elements are relevant sources of competitiveness and consequently
of economic growth. For example, the authors view technology as one of the main explanations
behind the continuing good growth performance of the Asian tigers.
On the contrary, Garelli (2006) assumes that economic value is only created by
enterprises. Nations can establish an environment that hinders or supports the activities of
enterprises. However, a nation does not directly generate economic added value. The author
concludes that competitiveness strategies succeed when they balance the economic imperatives
imposed by world markets with the social requirements of a nation formed by history, values
systems, and tradition (Garelli, 2006, p. 3).
As is clear from this review of the literature, there is no general consensus on
competitiveness. The term encompasses several dimensions. Changes in the exchange rate,
especially when carried out intentionally, emerge as an important issue under the general
heading of competitiveness because of the effects that they may have on economic activity and
unemployment, although effects these may be of a temporary nature.
In what concerns the analysis of the impact of exchange rate movements on economic
activity, we can say that most of the existing studies predominantly focus on episodes of
weakening currencies, more specifically sharp depreciations, currency wars, or currency crises.
This is understandable given that currency crises generally have powerful adverse
effects on economic activity, as documented by Cerra and Saxena (2008), Bussière, Saxena and
Tovar (2012) and Bussière, Lopez and Tille (2014), among others. A different perspective is
presented by Kappler et al. (2012) who look at the impact of an appreciation on the current
account balance and on real output. The authors find that large real exchange rate appreciations
lead to a deterioration of the current account through lower savings and lower exports, the
impacts being larger in emerging market and developing countries. However, they find little
impact on overall GDP as domestic demand and net exports move in opposite directions.
Alexandre, Bação, Cerejeira and Portela (2011) show that both the degree of openness
and the technology level mediate the impact of exchange rate movements on labor market
developments. According to their estimations, whereas employment in high-technology sectors
seems to be relatively immune to changes in real exchange rates, these appear to have a sizable
and significant effects on highly open low-technology sectors. The analysis of job flows also
suggests that the impact of exchange rate fluctuations on these sectors occurs through
employment destruction.
Branson and Love (1988) also found evidence that for the United States of America,
during the 1970s and 1980s, real exchange rate movements had a strong impact on
manufacturing employment. The authors conclude that the appreciation of the U.S. dollar in the
first half of the 80s had a strong effect on employment destruction. Evaluating the effects of
4
real exchange rate fluctuations on employment at the industry level for a set of seven
industrialized countries, Burgess and Knetter (1998) observe that real exchange rates
appreciations were associated with declines in employment in most of the countries analyzed.
Namely, these authors verify that employment rates in the United States of America, Canada,
and Italy are more affected by exchange rate fluctuations than in Japan, Germany and France.
More recently, Klein, Schuh and Triest (2003) also confirm that real exchange rate fluctuations
have significant effects on job reallocation between the various sectors of activity.
Based on different concepts of exchange rate equilibrium, and consequently identifying
various exchange rate misalignments, Cassino and Oxley (2013) examine the relationship
between exchange rate movements and the real economy. The authors find that there is no single
answer to how exchange rate fluctuations affect the economic activity. Exchange rates respond
to many different types of shocks. These shocks may be fundamental shocks which have a
persistent impact on the real equilibrium exchange rate, and consequently on economic activity
and employment, or non-fundamental disturbances which push the exchange rate away from its
equilibrium level. Under these circumstances, exchange rate misalignments may be persistent
or extremely transitory. For this reason, policymakers need to have a clear understanding of the
type and nature of the exchange rate shocks when deciding on appropriate responses, for
example, to maintain or increase country's competitiveness.
Eichengreen (2007) also refers to “other narratives” which give to real exchange rate
further prominence. The author mentions the literature on export-led growth as a strategy to
keep the prices of tradable goods high enough to make it attractive to shift resources into their
production and thereby avoid adverse effects on unemployment rate.
In our paper the focus is on the impact of exchange rate fluctuations on unemployment.
We present our approach to this issue in the following sections and our results in Section 4.

3. Model and Data

In order to obtain a quantitative estimate of the impact of exchange rate movements on

unemployment, we will employ the VAR methodology inroduced by Sims (1980). Specifically, we
will estimate, separately for each of the countries included in our sample, a set of five equations,
one for each of the following (endogenous) variables: the unemployment rate, the growth rate of
the Gross Domestic Product (GDP), the inflation rate, the interest rate and the real effective
exchange rate.
These variables were chosen because they are the main focus of attention in economic
policy and in macroeconomic research; they are the main aggregate indicators of economic activity.
However, conventional contemporary macroeconomic models tend to focus on the output gap
rather than on GDP growth, a change of focus that reflects the difference between analysing “growth
cycles” – by looking at deviations from trend or potential output – or analysing “classical cycles” –
by looking at the growth rate of GDP (Mintz, 1972). Additionally, those models also tend not to
explicitly analyse the behaviour of the unemployment rate. In fact, if one wishes to move beyond
the sort of mechanical relation exemplified by Okun's Law, 80 years after John Maynard Keynes's
General Theory, unemployment (or, more precisely, “involuntary unemployment”) still represents
a challenge for macroeconomic modelling – see, e.g., the recent textbook treatments in Shimer
(2010) and Galí (2011).
One important recent example of this kind of macroeconomic modelling is given in Dees,
Pesaran, Smith and Smith (2014), which presents a framework for the construction of modern
macroeconomic models using the global vector autoregression (GVAR) approach – see also di
Mauro and Pesaran (2013), Garratt, Lee, Pesaran and Shin (2012) and Smith and Galesi (2013).
5
The GVAR approach, first proposed by Pesaran, Schuermann and Weiner (2004) and extended,
along multiple dimensions, by Dees, di Mauro, Pesaran and Smith (2007), attempts to model in an
explicit and consistent way the mechanisms that lead to the international transmission of shocks.
Such modelling effort is especially important in the context of forecasting.
Dees, Pesaran, Smith and Smith (2014) present a multi-country New Keynesian (MCNK)
model, which builds on a version of the traditional New Keynesian model (see, e.g., Clarida, Galí
and Gertler, 1999). Thus, the basic single-country three-equation New Keynesian model (the three
equations being the Phillips curve equation, an aggregate demand – IS curve – equation and the
Taylor rule equation) is augmented with an equation for the real effective exchange rate. In addition,
the set of individual country models is supplemented with a first-order autoregressive equation for
the price of oil. As a consequence, this model includes the following variables: inflation, the output
gap, the interest rate, the real effective exchange rate and the oil price. In this paper we restrict
ourselves to the core macroeconomic variables (substituting GDP growth for the output gap) and
thus omit the oil price from our model. On the other hand, we add the unemployment rate to our
model, so that we can estimate the response of unemployment to exchange rate shocks. Since the
main ingredient of Okun's Law (GDP growth, in the original formulation of the Law) is present in
our model, we have reasons to believe that our model should be able to provide at least a reasonable
account of unemployment fluctuations. Finally, we included a dummy variable to account for the
possible effect of the international financial crisis in 2009, the year in which the spread of the crisis
to other countries (besides the USA) was most visible.
According to the VAR methodology, in each equation of the model the explanatory variables
(besides the constant and possibly other deterministic terms) will be lags of all the endogenous
variables. The GVAR approach augments the VAR model for each country (or region) with
“foreign” variables, these being either global variables (such as oil prices and “global unobserved
factors”) or weighted averages (with country specific weights) of the domestic variables of the other
countries included in the GVAR model. Therefore, the GVAR approach provides a much finer
model of the macroeconomic linkages that exist between countries.
In this paper, we do not aim at providing such a detailed view of the international
macroeconomic linkages. Rather, as mentioned above, we intend to evaluate the importance for
unemployment of shocks to the exchange rate – the variable that is often viewed as the international
transmitter (or signaller) of shocks par excellence, as discussed in the previous sections of this paper.
Thus, we will estimate national VAR models separately for each country and focus on the impulse-
response function of unemployment to shocks associated with the real effective exchange rate.
Our dataset comprises annual data for the period 1981-2014 for the five variables, across
the eight countries, in our VAR model. The main descriptive statistics of our dataset are presented
in Table 1. The data sources are the following:
- Unemployment: the unemployment rate is the series with WEO subject code LUR from the
World Economic Outlook database of the International Monetary Fund (April 2015 edition).
- GDP growth: the growth rate of real GDP is the series with WEO subject code NGDP_RPCH
from the World Economic Outlook database of the International Monetary Fund (April 2015
edition).
- Inflation: the inflation rate for all countries except Brazil was computed as the growth rate of
the GDP deflator, series with WEO subject code NGDP_D taken from the World Economic
Outlook database of the International Monetary Fund (April 2015 edition). For Brazil, the
growth rate of the GDP deflator is the series with code NY.GDP.DEFL.KD.ZG from the
World Development Indicators of the World Bank (accessed on September 9, 2015).
- Real effective exchange rate: the original data for the real effective exchange rate for all
countries except Brazil is the series with code PX.REX.REER from the World Development
Indicators of the World Bank (accessed on September 9, 2015). For Brazil the series comes
6
 from OECD, “Main Economic Indicators - complete database”, Main Economic
Indicators (database), http://dx.doi.org/10.1787/data-00052-en (accessed on September
10, 2015, via the Federal Reserve Economic Data of the St. Louis Federal Reserve Bank,
series ID CCRETT01BRM661N). In the estimations we used the logarithm of the original
data for all countries.
- Interest rate: the interest rate series comes from the International Monetary Fund's
International Financial Statistics. From 1981 until 2012, it is the yearly average of the
quarterly series with code Rshort included in the GVAR dataset (2013 Vintage). This dataset
was prepared by Rodrigo Mariscal, Ambrogio Cesa Bianchi and Alessandro Rebucci at the
Inter-American Development Bank, Washington DC, US, and is available for download at
https://sites.google.com/site/gvarmodelling/data (accessed on September 9, 2015). For 2013
and 2014, the annual interest rate data was collected from Datastream.

When the identification of the shocks in a VAR model is carried out by means of the
Cholesky decomposition, the ordering of the variables (more precisely, the positioning of the
variances and covariances of the estimated VAR residuals in the residual variance-covariance
matrix that will be decomposed) matter for the results. Different orderings will give different
impulse-response functions because the identification obtained by the Cholesky decomposition
imposes a recursive structure on the model: the first variable will react contemporaneously to the
first shock only; the second variable will only react contemporaneously to the first and the second
shocks; the last variable will react contemporaneously to all shocks. The ordering of the variables
must therefore make sense from the point of view of economic theory.
Following the discussion in the literature (e.g., Christiano, Eichenbaum and Evans, 1999,
2005), we divide our variables in two groups. The first group (of “slow-moving” variables – see
Bernanke, Boivin and Eliasz, 2005) includes unemployment, GDP growth and inflation. These are
the variables that, as discussed in the literature, are expected to react with a lag to all or most other
shocks. The second group (of “fast-moving” variables) includes the interest rate and the exchange
rate. These variables are consistently placed last in VAR models, given that they should react very
quickly to shocks, independently of their source. Nevertheless, it is not clear whether the interest
rate or the exchange rate should come first in the ordering. In view of this, we applied the Cholesky
decomposition to both cases. We will thus discuss the results obtained under the two orderings of
these variables.
There is still the issue of the ordering of the slow-moving variables. However, our focus
here is merely on the response of unemployment to exchange rate shocks. Proposition 4.1 in
Christiano, Eichenbaum and Evans (1998) implies that the impulse-response function of a slow-
moving variable with respect to a shock to a fast-moving variable in invariant to the ordering of the
slow-moving variables. This means that, in this paper, we do not need to worry about how we order
unemployment, GDP growth and inflation; it is only the ordering of the interest rate and of the
exchange rate that matters, and we will experiment with the two possible orderings.

7
 Table 1: Descriptive statistics
Australia U_AUS g_AUS i_AUS R_AUS E_AUS
mean 7.0439 3.1993 4.0908 7.6927 4.4391
st.deviation 1.8370 1.6184 3.0665 4.1195 0.14083
maximum 10.858 6.3460 12.081 16.750 4.6994
minimum 4.2420 -1.1130 -0.29984 2.5000 4.2021
Brazil U_BRA g_BRA i_BRA R_BRA E_BRA
mean 6.8054 2.5748 342.42 1051.7 4.3637
st.deviation 2.4875 3.0947 692.45 2971.8 0.19971
maximum 12.317 7.9010 2700.4 15779. 4.6520
minimum 3.3500 -4.4000 4.9153 8.1800 3.9829
China U_CHN g_CHN i_CHN R_CHN E_CHN
mean 3.2276 9.8576 5.3602 5.3643 4.6783
st.deviation 0.85082 2.7482 5.0295 3.0348 0.31808
maximum 4.3000 15.200 20.203 11.340 5.4782
minimum 1.8000 3.8000 -2.1296 1.9800 4.2432
Germany U_DEU g_DEU i_DEU R_DEU E_DEU
mean 7.7244 1.6974 2.2261 4.1727 4.6425
st.deviation 1.5648 2.0761 2.5986 2.8320 0.049378
maximum 11.000 5.7230 14.626 11.262 4.7744
minimum 4.8310 -5.5800 -0.68694 0.050000 4.5608
Japan U_JPN g_JPN i_JPN R_JPN E_JPN
mean 3.5689 2.0187 0.049734 2.3577 4.5917
st.deviation 1.0712 2.5074 1.4662 2.8282 0.16356
maximum 5.3580 7.1470 3.1969 7.5253 4.8866
minimum 2.0920 -5.5270 -2.1646 0.00085083 4.2535
Switzerland U_CHE g_CHE i_CHE R_CHE E_CHE
mean 2.5264 1.7646 1.7863 2.3250 4.5399
st.deviation 1.4697 1.6623 1.8848 2.3927 0.065745
maximum 5.1990 4.3760 7.4712 8.3275 4.6988
minimum 0.19000 -2.1140 -0.32736 -2.0000 4.4200
UK U_UK g_UK i_UK R_UK E_UK
mean 7.8360 2.2664 3.7398 6.3563 4.6727
st.deviation 2.2109 1.9523 2.4297 4.0251 0.088606
maximum 11.750 5.9340 11.636 14.093 4.8341
minimum 4.7500 -4.3110 1.0913 0.30000 4.5079
USA U_USA g_USA i_USA R_USA E_USA
mean 6.4331 2.7419 2.6701 4.5116 4.7086
st.deviation 1.6403 1.9356 1.5840 3.3924 0.11165
maximum 9.7080 7.2590 9.3359 14.078 5.0038
minimum 3.9670 -2.7760 0.75973 0.040000 4.5550
Notes: U: unemployment rate; g: GDP growth rate; i: GDP-deflator inflation rate; R: interest
rate; E: real effective exchange rate.

8
4. Results and Discussion

One of the first things to decide when building a VAR model is the order of the model, that
is to say, the number of lags to include in the model. Given the length of our sample (34 years), the
number of variables (five) and the fact that the data are annual and therefore long lags are not
expected to be necessary, we set two as the maximum number of lags to be tried. To select the
number of lags we used the Schwartz (1978) Bayesian information criterion, as implemented in the
econometrics software Gretl, version 1.9.90, which we employed to perform all the computations
reported here. For all countries, the criterion selected one lag. The results of the estimations are in
Tables 2-9.
In general, the results show a good fit of the model: high R-squared (usually above 80%),
significant F-statistic and Durbin-Watson statistic close to 2. There are, nevertheless, some
deviations. In fact, the model seems to perform noticeably worse for Brazil than for the other
countries, in terms of the coefficient of determination and of the significance of the explanatory
variables. Nevertheless, even in the case of Brazil, the errors do not appear to be autocorrelated. It
also appears to be the case that the equation for GDP growth appears to fit less well than the other
equations: either annual GDP growth is harder to forecast with a VAR model than the other
variables, or our model misses something of relevance for the modelling of GDP growth. We do
not pursue the issue here, but research building upon this paper should look into that question.
The performance of the model for Brazil should be related to the fact that the sample
covers a very heterogeneous period in the economic history of Brazil. Namely until 1994
inflation and interest rates were above 100%, while in recent years these indicators have
presented one-digit values. Figure 1 illustrates the difference between the behavior before and
since 1995: the second period simply appears to be absent from the plot.

Figure 1: GDP-deflator inflation (left) and the short-term interest rate (right) in Brazil.

Let us now turn to the main topic of our paper: the response of unemployment to
exchange rate shocks. Figures 2-9 show, for each of the eight countries included in our sample,
the impulse-response functions of unemployment when there is a one standard-deviation
impulse in the exchange rate equation, together with the 90% confidence intervals computed
by bootstrap. The first thing to notice is that, except for Brazil, the shape of the impulse-
response function is similar regardless of whether the ordering is (slow-moving variables,
exchange rate, interest rate), or (slow-moving variables, interest rate, exchange rate).
Nevertheless, in the case of Brazil, the impulse-response function under either of the orderings
is never significantly different from zero.
Notice also that, when analyzing the impulse-response functions, one must bear in mind
that an increase in the value of the real effective exchange rate series that we are using means
that exports become more expensive relative to imports, that is, that domestic producers become
less competitive.
9
 Therefore, one would expect to see an increase in domestic unemployment following an
adverse shock to the exchange rate. That is indeed the pattern we find for Australia, Switzerland
and the United Kingdom. The maximum impact arrives earlier in the UK (two periods after the
shock) than in Australia and in Switzerland (three or four periods after the shock). Interestingly,
the largest magnitude of the impact appears to be similar across the three countries: the
unemployment rate increases by at most 0.10 to 0.15 percentage points. Nevertheless, it must
be noted that, given that the standard deviation of the Australian exchange rate is roughly twice
the standard deviation of the Swiss and of the UK exchange rates (see Table 1), it appears that
Australian unemployment is less responsive to a 1% change in the exchange rate than the Swiss
and the UK unemployment rates.
The impulse-response functions for Japan also show an increase in unemployment
following an increase in the exchange rate. However, in this case the effect on unemployment
is very small and not statistically significant.
In the other three countries (China, Germany and the USA), the impulse-response
function does not display the expected positive sign. In the case of the USA, the impulse-
response function is close to the horizontal axis and is never significantly different from zero.
In the case of Germany, the impulse-response function is never significant, but the estimated
magnitude is close to that reported above for Australia, Switzerland and the UK, although with
the opposite sign. In the case of China, the impulse-response function also attains that same
order of magnitude, with the “wrong” sign, and it is significantly different from zero. One
possible explanation is that our model is inadequate for China and Germany. Perhaps even more
than Brazil, China has seen many important changes happen during the period covered by our
sample. That our simple model may fail to capture the complexity of the structural change that
has taken place in China – see, e.g., Zhu (2012) – is therefore not surprising. Germany has also
been through the process of reunification, which may help explain the estimates reported here.
Our results suggest that exchange rate fluctuations appear to be a matter of concern for
workers in some countries but not for all. Depending on the trade-offs involved in welfare
maximization in each country, benign neglect of shocks to the exchange rate may not be the
optimal policy. Therefore, it will be interesting for policymakers concerned with the detrimental
effect of exchange rate movements on domestic unemployment to know how other countries
manage to insulate unemployment from exchange rate shocks. It is possible that the different
responses derive from different degrees of openness, in which case nothing of practical
relevance will be learned. But it is also possible, for instance, that it is the reaction of policy
instruments that is giving rise to the observed differences. In this case, a detailed analysis of the
behavior of the more successful policymakers may be helpful for other countries. Nevertheless,
we venture to suggest that relatively small countries with their own currencies, such as
Australia, Switzerland and the UK, are more susceptible to the destabilizing effects of exchange
rate fluctuations.

10
 Table 2: Estimates of the VAR model for Australia
U_AUS g_AUS i_AUS R_AUS E_AUS
constant -3.47631 4.88372 11.3492 -12.9198 1.34001***
(4.69373) (9.56888) (12.7522) (10.1177) (0.401782)
U_AUS_1 0.798366*** 0.513933*** -0.204604 0.338562* -0.0123879*
(0.0810494) (0.165232) (0.220199) (0.174708) (0.0069378)
g_AUS_1 -0.27221*** 0.219469 0.297048 0.773867*** -0.00984364
(0.0767866) (0.156541) (0.208618) (0.165519) (0.0065729)
i_AUS_1 -0.0967327 0.182374 0.750782*** 0.432288*** 0.0123218**
(0.0633838) (0.129218) (0.172204) (0.136628) (0.0054256)
R_AUS_1 0.167897*** -0.250402** 0.0222883 0.609832*** -0.008782**
(0.0468907) (0.0955939) (0.127395) (0.101076) (0.0040138)
E_AUS_1 1.09273 -1.09015 -2.27976 2.05121 0.729186***
(1.00337) (2.04552) (2.72601) (2.16283) (0.0858882)
d09 0.968664 -0.663604 -5.58796*** -3.33183** -0.0902687
(0.727768) (1.48367) (1.97724) (1.56875) (0.0622968)
R-squared 0.889867 0.412924 0.673237 0.896073 0.858531
F 35.01297*** 3.047880** 8.928070*** 37.36258*** 26.29772***
D-W 1.652987 1.651031 1.811955 2.428896 1.453495
Notes: The VAR model is estimated by applying ordinary least squares to each equation. The
dataset is annual and covers the period 1981-2014. U is the unemployment rate; g is the GDP
growth rate; i is the inflation rate (computed from the GDP deflator); R is the interest rate; E
is the real effective exchange rate; d09 is a dummy variable which equals 1 in the year 2009
and is zero otherwise. R-squared is the usual coefficient of determination. F is the F-statistic
for the test of the null hypothesis that all the lags and the dummy variable in the corresponding
equation have null coefficients. D-W is the Durbin-Watson statistic. The values in parentheses
are the standard deviations associated to the estimate of each coefficient. *: the coefficient (or
the F statistic) is significant at the 10% significance level. **: the coefficient (or the F statistic)
is significant at the 5% significance level. ***: the coefficient (or the F statistic) is significant
at the 1% significance level.

11
 Table 3: Estimates of the VAR model for Brazil
U_BRA g_BRA i_BRA R_BRA E_BRA
constant 2.58574 10.9125 4349.90 19510.1 1.02893
(6.02246) (13.2738) (2891.63) (14029.9) (0.674697)
U_BRA_1 0.864297*** 0.320713 -99.6130* -465.146* -0.00278437
(0.108958) (0.240149) (52.3154) (253.828) (0.0122066)
g_BRA_1 -0.0156321 0.120279 -32.3553 -32.1881 0.00506785
(0.0783720) (0.172735) (37.6296) (182.575) (0.0087800)
i_BRA_1 -0.00065387 0.00224983 0.721484** 0.454099 5.48154e-05
(0.0006622) (0.0014596) (0.317965) (1.54273) (7.4190e-05)
R_BRA_1 0.000143199 -0.00047419 -0.0851936 0.186061 -8.1121e-06
(0.0001546) (0.0003407) (0.0742204) (0.360109) (1.7318e-05)
E_BRA_1 -0.378323 -2.48388 -778.783 -3559.64 0.762249***
(1.27892) (2.81881) (614.064) (2979.37) (0.143278)
d09 0.444137 -3.17919 86.5872 259.587 0.0309381
(1.29059) (2.84452) (619.666) (3006.55) (0.144585)
R-squared 0.805592 0.280971 0.424456 0.264621 0.614484
F 17.95658*** 1.693311 3.195781** 1.559318 6.906999***
D-W 1.609714 1.899984 2.068555 2.066080 1.646964
Notes: See Table 2.

12
 Table 4: Estimates of the VAR model for China
U_CHN g_CHN i_CHN R_CHN E_CHN
constant 4.20091*** 2.32782 6.45226 -1.92137 0.328856
(0.759809) (9.80703) (13.7298) (3.45170) (0.417279)
U_CHN_1 0.700642*** 0.139858 -1.23775 -0.790064** 0.0565706
(0.0723560) (0.933916) (1.30748) (0.328702) (0.0397371)
g_CHN_1 -0.0142159 0.631358*** 0.582347* 0.0855433 0.0126799
(0.0161142) (0.207990) (0.291185) (0.0732045) (0.0088498)
i_CHN_1 0.0281199** -0.149353 0.537032** 0.187039*** -0.00846083
(0.0122949) (0.158693) (0.222169) (0.0558539) (0.0067522)
R_CHN_1 -0.08669*** 0.133093 -0.119223 0.549592*** 0.0118839
(0.0256081) (0.330529) (0.462739) (0.116334) (0.0140637)
E_CHN_1 -0.59419*** 0.224424 -1.06324 1.06945* 0.855334***
(0.118402) (1.52825) (2.13954) (0.537885) (0.0650253)
d09 0.102342 -0.0889181 -4.84662 -1.75981* 0.0317719
(0.199984) (2.58123) (3.61371) (0.908495) (0.109829)
R-squared 0.960883 0.323553 0.635409 0.937444 0.896304
F 106.4456*** 2.072692* 7.552132*** 64.93740*** 37.45539***
D-W 1.704632 1.411358 1.432340 1.371868 1.883561
Notes: See Table 2.

13
 Table 5: Estimates of the VAR model for Germany
U_DEU g_DEU i_DEU R_DEU E_DEU
constant 4.00142 -24.0737 14.9817 1.40698 1.48400**
(11.6936) (31.8136) (38.2947) (17.0927) (0.551670)
U_DEU_1 0.973030*** 0.0459165 -0.528302* 0.148770 0.00426926
(0.0911446) (0.247967) (0.298483) (0.133227) (0.0042999)
g_DEU_1 -0.17943*** 0.220323 0.320894* 0.312160*** -2.4910e-05
(0.0542554) (0.147607) (0.177677) (0.0793056) (0.0025596)
i_DEU_1 0.0665327 -0.0580804 0.00773908 0.0678610 0.00390730
(0.0587778) (0.159910) (0.192487) (0.0859161) (0.002773)
R_DEU_1 0.151052** -0.0861885 0.410866** 0.763856*** 0.00414490
(0.0544682) (0.148186) (0.178374) (0.0796166) (0.0025696)
E_DEU_1 -0.923137 5.55825 -2.37218 -0.537668 0.667154***
(2.58892) (7.04340) (8.47829) (3.78425) (0.122137)
d09 0.250438 -7.45508*** -0.164320 -2.61633*** 0.0183564
(0.639673) (1.74029) (2.09482) (0.935015) (0.0301778)
R-squared 0.860463 0.466209 0.505777 0.898918 0.721197
F 26.72169*** 3.784707*** 4.434639*** 38.53604*** 11.20931***
D-W 1.179567 1.337826 2.106617 1.484428 1.639763
Notes: See Table 2.

14
 Table 6: Estimates of the VAR model for Japan
U_JPN g_JPN i_JPN R_JPN E_JPN
constant -0.567088 -12.2254 7.66537 2.36226 0.422217
(1.85056) (13.9450) (4.69928) (5.73211) (0.622109)
U_JPN_1 0.877709*** 0.427116 -0.642838** 0.178914 -0.0244295
(0.109852) (0.827795) (0.278956) (0.340266) (0.0369292)
g_JPN_1 -0.057821** 0.00861968 0.200284*** 0.207058** -0.019051**
(0.0258517) (0.194807) (0.0656474) (0.0800757) (0.0086907)
i_JPN_1 0.111885 -0.622648 0.267560 0.351545 -0.0563217*
(0.0964151) (0.726542) (0.244835) (0.298646) (0.0324122)
R_JPN_1 -0.0370430 0.856602** -0.0782469 0.686609*** 0.0387240**
(0.0503837) (0.379670) (0.127943) (0.156063) (0.0169376)
E_JPN_1 0.266130 2.33898 -1.23020 -0.630530 0.914705***
(0.349383) (2.63280) (0.887217) (1.08221) (0.117453)
d09 1.03689*** -6.64271*** 0.483523 0.195554 0.0669874
(0.292327) (2.20285) (0.742331) (0.905484) (0.0982726)
R-squared 0.951073 0.507326 0.813311 0.928938 0.758620
F 84.23346*** 4.462201*** 18.87821*** 56.64633*** 13.61897***
D-W 1.199783 1.618049 1.665323 1.521698 1.544231
Notes: See Table 2.

15
 Table 7: Estimates of the VAR model for Switzerland
U_CHE g_CHE i_CHE R_CHE E_CHE
constant -9.11616 31.5313 24.7275 2.60836 1.15491
(7.86873) (23.8438) (18.1247) (21.8153) (0.731661)
U_CHE_1 0.779070*** 0.0595139 -0.383234* -0.169237 0.00260674
(0.0839483) (0.254380) (0.193365) (0.232738) (0.0078058)
g_CHE_1 -0.22648*** 0.264633 0.195121 0.191435 -0.00125601
(0.0538023) (0.163031) (0.123927) (0.149162) (0.0050027)
i_CHE_1 -0.0958641 -0.123811 0.321598* 0.111524 -0.00198634
(0.0799734) (0.242335) (0.184210) (0.221718) (0.0074362)
R_CHE_1 0.164948*** -0.290592** 0.0867916 0.779352*** 0.00226725
(0.0431306) (0.130694) (0.0993466) (0.119575) (0.0040104)
E_CHE_1 2.17915 -6.45806 -5.11438 -0.502756 0.745412***
(1.71519) (5.19738) (3.95076) (4.75521) (0.159484)
d09 1.40271*** -4.58121*** -1.27440 -1.68043 0.0272788
(0.466304) (1.41299) (1.07408) (1.29278) (0.0433585)
R-squared 0.918099 0.458253 0.720473 0.780782 0.636316
F 48.57584*** 3.665480*** 11.16905*** 15.43387*** 7.581768***
D-W 1.202881 1.510987 2.131735 1.594610 1.561689
Notes: See Table 2.

16
 Table 8: Estimates of the VAR model for the United Kingdom
U_UK g_UK i_UK R_UK E_UK
constant -17.8709** -14.3500 27.5398* 25.9974 2.74209***
(6.62778) (18.3984) (15.5310) (16.2637) (0.592565)
U_UK_1 0.986913*** 0.386749** -0.140820 -0.120282 -0.02051***
(0.0626054) (0.173790) (0.146704) (0.153626) (0.0055973)
g_UK_1 -0.238*** 0.273669* 0.185336 0.450557*** 0.00679424
(0.0489790) (0.135963) (0.114773) (0.120188) (0.0043790)
i_UK_1 0.0110205 -0.240195 0.646838*** 0.351543** 0.0138514**
(0.0690143) (0.191580) (0.161722) (0.169352) (0.0061703)
R_UK_1 0.0707192 0.00731675 0.0795558 0.722407*** -0.006719*
(0.0424516) (0.117844) (0.0994775) (0.104171) (0.0037954)
E_UK_1 3.82342*** 3.01388 -5.63342* -5.54232 0.443088***
(1.35791) (3.76950) (3.18201) (3.33214) (0.121406)
d09 1.45343*** -5.59293*** -0.403852 -2.69834** -0.14677***
(0.507171) (1.40788) (1.18846) (1.24453) (0.0453442)
R-squared 0.962621 0.607177 0.751782 0.927364 0.809648
F 111.5957*** 6.697934*** 13.12440*** 55.32468*** 18.43150***
D-W 1.635690 1.613292 1.989232 2.488248 2.027909
Notes: See Table 2.

17
 Table 9: Estimates of the VAR model for the United States of America
U_USA g_USA i_USA R_USA E_USA
constant 2.21956 -16.6832 0.822677 5.74802 1.09693**
(4.90400) (13.1411) (4.55068) (10.6733) (0.469002)
U_USA_1 0.695905*** 0.386816** 0.00416664 0.117805 0.00338670
(0.0649938) (0.174161) (0.0603111) (0.141455) (0.0062158)
g_USA_1 -0.23279*** 0.148987 0.115374** 0.322432** 0.00654820
(0.0597937) (0.160227) (0.0554857) (0.130137) (0.0057185)
i_USA_1 0.205662* -0.88701*** 0.648224*** -0.0125731 0.0101368
(0.117368) (0.314506) (0.108912) (0.255444) (0.0112247)
R_USA_1 0.0373572 0.246820 -0.0405713 0.790736*** 0.00427095
(0.0621946) (0.166660) (0.0577135) (0.135363) (0.0059481)
E_USA_1 -0.102899 3.80252 -0.0495938 -1.44179 0.747399***
(1.07268) (2.87441) (0.995392) (2.33461) (0.102587)
d09 2.97472*** -4.40749*** -1.03734* -0.673830 0.0655217
(0.579736) (1.55350) (0.537967) (1.26176) (0.0554440)
R-squared 0.914203 0.564651 0.825700 0.875660 0.832602
F 46.17368*** 5.620361*** 20.52805*** 30.51729*** 21.55314***
D-W 1.654749 1.702055 1.097997 1.640789 1.054236
Notes: See Table 2.

18
 Figure 2: Impulse-response function of unemployment to the exchange-rate shock in Australia when the
interest rate is ordered last (left) and when the exchange rate is ordered last (right).

Figure 3: Impulse-response function of unemployment to the exchange-rate shock in Brazil when the interest
rate is ordered last (left) and when the exchange rate is ordered last (right).

Figure 4: Impulse-response function of unemployment to the exchange-rate shock in China when the interest
rate is ordered last (left) and when the exchange rate is ordered last (right).

Figure 5: Impulse-response function of unemployment to the exchange-rate shock in Germany when the
interest rate is ordered last (left) and when the exchange rate is ordered last (right).

19
 Figure 6: Impulse-response function of unemployment to the exchange-rate shock in Japan when the interest
rate is ordered last (left) and when the exchange rate is ordered last (right).

Figure 7: Impulse-response function of unemployment to the exchange-rate shock in Switzerland when the
interest rate is ordered last (left) and when the exchange rate is ordered last (right).

Figure 8: Impulse-response function of unemployment to the exchange-rate shock in the United Kingdom when
the interest rate is ordered last (left) and when the exchange rate is ordered last (right).

Figure 9: Impulse-response function of unemployment to the exchange-rate shock in the United States of
America when the interest rate is ordered last (left) and when the exchange rate is ordered last (right).

20
5. Conclusion

The estimates presented in this paper show considerable differences across countries in the
response of unemployment to exchange rate shocks. Relatively small countries, such as Australia,
Switzerland and the UK, appear to experience an increase of about 0.10-0.15 percentage points
when the exchange rate appreciates by 0.06-0.14 percentage points, that is to say that the elasticity
is not very far from unity. Other countries, most clearly Japan and the USA, do not appear
significantly affected, in terms of unemployment, by exchange rate movements. Our results suggest
that further research into the possible explanations of these differences may be useful for
policymakers.
Our results also suggest other lines of research. First, our VAR model does not appear to fit
the Brazilian data well, while the results for China and Germany appear counter-intuitive. Since this
countries experienced major structural changes in the period covered by our sample, it is possible
that our model needs to be supplemented with elements related to those structural changes in order
to produce better results. One first step in this direction would be to test for structural breaks in the
model, but the reduced size of our sample relatively to the number of variables limits the usefulness
of those tests. A dataset with quarterly data, if available could be helpful. Unfortunately, the GVAR
database is not complete for the countries used in our study. Second, we opted to use GDP growth
rather than the output gap. The robustness of the results to this option should be investigated. Finally,
some plots suggested that the impulses may have permanent effects in some countries, or, in other
words, that there are unit roots in some variables. A more detailed study of this issue could lead to
the imposition of restrictions suggested by the results of unit root tests.

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23
 ESTUDOS DO G.E.M.F.
(Available on-line at http://www.uc.pt/feuc/gemf )

2016-14 Exchange Rates, the Competitiveness of Nations and Unemployment

- Pedro Bação, António Portugal Duarte & Diana Machado
2016-13 Portfolio Choice with High Frequency Data: CRRA Preferences and the Liquidity Effect
- Rui Pedro Brito, Hélder Sebastião & Pedro Godinho
2016-12 International portfolio selection on European stock markets based on time-varying betas
- José Soares da Fonseca
2016-11 Modeling the determinants of emotional intelligence, self-motivation and individual success.
Evidence from Portugal
- Diane Silva & Elias Soukiazis
2016-10 Alternative Sources of Dutch Disease: A Survey of the Literature
- Nuno Baetas da Silva, João Sousa Andrade & António Portugal Duarte
2016-09 The relationship between social transfers and poverty reduction: A nonparametric approach
for the EU-27
- Nuno Baetas da Silva & João Sousa Andrade
2016-08 The Greek economy under the twin-deficit pressure: a demand orientated growth approach
- Elias Soukiazis, Micaela Antunes & Ioannis Kostakis
2016-07 New-Issues Markets as behavioral barriers to entry: an agent-based model of choices and
market structure
- Ulisses L. Morais, Adelino M. G. Fortunato & Ernesto J. F. Costa
2016-06 Collecting new pieces to the regional knowledge spillovers puzzle: high-tech versus low-tech
industries
- Carlos Carreira & Luís Lopes
2016-05 Impact of European Integration Process on Value Added Creation in Chosen Member
Countries
- Jozef Kubala
2016-04 Are the transition economies balance-of-payments constrained? An aggregate and multi-
sector approach applied to Central and Eastern Europe.
- Peter Leško, Elias Soukiazis & Eva Muchova
2016-03 Dimensions of the welfare state and economic performance: a comparative analysis-
- João A. S. Andrade, Adelaide P. S. Duarte & Marta C. N. Simões
2016-02 Entry and exit in severe recessions: Lessons from the 2008–2013 Portuguese economic
crisis
- Carlos Carreira & Paulino Teixeira
2016-01 Collective Bargaining Systems and Macroeconomic and Microeconomic Flexibility: The
Quest for Appropriate Institutional Forms in Advanced Economies
- John T. Addison

2015-21 A remuneração dos administradores nas sociedades cotadas: determinantes e

enquadramento jurídico
- Daniela Machado, Maria Elisabete Ramos & Pedro Godinho
2015-20 Applying Thirlwall’s Law to the Portuguese economy: a sectoral analysis
- Jeanete Dias & Micaela Antunes
2015-19 Industry based equity premium forecasts
- Nuno Silva
2015-18 Pacts for Employment and Competitiveness as a Role Model? Their Effects on Firm
Performance
- John T. Addison, Paulino Teixeira, Katalin Evers, Lutz Bellmann
2015-17 On the Forecasting of Financial Volatility Using Ultra-High Frequency Data
- António A. F. Santos
2015-16 Social Spending, Inequality and Growth in Times of Austerity: Insights from Portugal
- Marta N. C. Simões; Adelaide P. S. Duarte & João A. S. Andrade
 Estudos do GEMF

2015-15 Portfolio Management with Higher Moments: The Cardinality Impact

- Rui Pedro Brito, Hélder Sebastião & Pedro Godinho
2015-14 The Determinants of Entrepreneurship at the Country Level: A Panel Data Approach
- Gonçalo Brás & Elias Soukiazis
2015-13 Budget, Expenditures Composition and Political Manipulation: Evidence from Portugal
- Vítor Castro & Rodrigo Martins
2015-12 The Occupational Feminization of Wages
- John T. Addison, Orgul D. Ozturk & Si Wang
2015-11 Economies to Scale and the Importance of Human Capital in the Moulds Industry in
Portugal: A Micro Panel Data Approach
- Fátima Diniz & Elias Soukiazis
2015-10 The Evolution of the Volatility in Financial Returns: Realized Volatility vs Stochastic
Volatility Measures
- António A. F. Santos
2015-09 The Impact of the Chinese Exchange Policy on Foreign Trade with the European Union
- Ana Cardoso & António Portugal Duarte
2015-08 The links between the Companies` Market Price Quality and that of its Management and
Business Quality: A system panel data approach
- Dinis Santos & Elias Soukiazis
2015-07 Education and Software piracy in the European Union
- Nicolas Dias Gomes, Pedro André Cerqueira & Luís Alçada-Almeida
2015-06 A Monetary Analysis of the Liquidity Trap
- João Braz Pinto & João Sousa Andrade
2015-05 Efficient Skewness/Semivariance Portfolios
- Rui Pedro Brito, Hélder Sebastião & Pedro Godinho
2015-04 Size Distribution of Portuguese Firms between 2006 and 2012
- Rui Pascoal, Mário Augusto & Ana M. Monteiro
2015-03 Optimum Currency Areas, Real and Nominal Convergence in the European Union
- João Sousa Andrade & António Portugal Duarte
2015-02 Estimating State-Dependent Volatility of Investment Projects: A Simulation Approach
- Pedro Godinho
2015-01 Is There a Trade-off between Exchange Rate and Interest Rate Volatility? Evidence from an
M-GARCH Model
- António Portugal Duarte, João Sousa Andrade & Adelaide Duarte

2014-25 Portfolio Choice Under Parameter Uncertainty: Bayesian Analysis and Robust Optimization
Comparison
- António A. F. Santos, Ana M. Monteiro & Rui Pascoal
2014-24 Crowding-in and Crowding-out Effects of Public Investments in the Portuguese Economy
- João Sousa Andrade & António Portugal Duarte
2014-23 Are There Political Cycles Hidden Inside Government Expenditures?
- Vítor Castro & Rodrigo Martins
2014-22 The Nature of Entrepreneurship and its Determinants: Opportunity or Necessity?
- Gonçalo Brás & Elias Soukiazis
2014-21 Estado Social, Quantis, Não-Linearidades e Desempenho Económico: Uma Avaliação
Empírica
- Adelaide Duarte, Marta Simões & João Sousa Andrade
2014-20 Assessing the Impact of the Welfare State on Economic Growth: A Survey of Recent
Developments
- Marta Simões, Adelaide Duarte & João Sousa Andrade
2014-19 Business Cycle Synchronization and Volatility Shifts
- Pedro André Cerqueira
2014-18 The Public Finance and the Economic Growth in the First Portuguese Republic
- Nuno Ferraz Martins & António Portugal Duarte
 Estudos do GEMF

2014-17 On the Robustness of Minimum Wage Effects: Geographically-Disparate Trends and Job
Growth Equations
- John T. Addison, McKinley L. Blackburn & Chad D. Cotti
2014-16 Determinants of Subjective Well-Being in Portugal: A Micro-Data Study
- Sara Ramos & Elias Soukiazis
2014-15 Changes in Bargaining Status and Intra-Plant Wage Dispersion in Germany. A Case of
(Almost) Plus Ça Change?
- John T. Addison, Arnd Kölling & Paulino Teixeira
2014-14 The Renewables Influence on Market Splitting: the Iberian Spot Electricity Market
- Nuno Carvalho Figueiredo, Patrícia Pereira da Silva & Pedro Cerqueira
2014-13 Drivers for Household Electricity Prices in the EU: A System-GMM Panel Data Approach
- Patrícia Pereira da Silva & Pedro Cerqueira
2014-12 Effectiveness of Intellectual Property Regimes: 2006-2011
- Noemí Pulido Pavón & Luis Palma Martos
2014-11 Dealing with Technological Risk in a Regulatory Context: The Case of Smart Grids
- Paulo Moisés Costa, Nuno Bento & Vítor Marques
2014-10 Stochastic Volatility Estimation with GPU Computing
- António Alberto Santos & João Andrade
2014-09 The Impact of Expectations, Match Importance and Results in the Stock Prices of European
Football Teams
- Pedro Godinho & Pedro Cerqueira
2014-08 Is the Slovak Economy Doing Well? A Twin Deficit Growth Approach
- Elias Soukiazis, Eva Muchova & Pedro A. Cerqueira
2014-07 The Role of Gender in Promotion and Pay over a Career
- John T. Addison, Orgul D. Ozturk & Si Wang
2014-06 Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy
- João Sousa Andrade & António Portugal Duarte
2014-05 Software Piracy: A Critical Survey of the Theoretical and Empirical Literature
- Nicolas Dias Gomes, Pedro André Cerqueira & Luís Alçada Almeida
2014-04 Agriculture in Portugal: Linkages with Industry and Services
- João Gaspar, Gilson Pina & Marta C. N. Simões
2014-03 Effects of Taxation on Software Piracy Across the European Union
- Nicolas Dias Gomes, Pedro André Cerqueira & Luís Alçada Almeida
2014-02 A Crise Portuguesa é Anterior à Crise Internacional
- João Sousa Andrade
2014-01 Collective Bargaining and Innovation in Germany: Cooperative Industrial Relations?
- John T. Addison, Paulino Teixeira, Katalin Evers & Lutz Bellmann

2013-27 Market Efficiency, Roughness and Long Memory in the PSI20 Index Returns: Wavelet and
Entropy Analysis
- Rui Pascoal & Ana Margarida Monteiro
2013-26 Do Size, Age and Dividend Policy Provide Useful Measures of Financing Constraints? New
Evidence from a Panel of Portuguese Firms
- Carlos Carreira & Filipe Silva
2013-25 A Política Orçamental em Portugal entre Duas Intervenções do FMI: 1986-2010
- Carlos Fonseca Marinheiro
2013-24 Distortions in the Neoclassical Growth Model: A Cross-Country Analysis
- Pedro Brinca
2013-23 Learning, Exporting and Firm Productivity: Evidence from Portuguese Manufacturing and
Services Firms
- Carlos Carreira
2013-22 Equity Premia Predictability in the EuroZone
- Nuno Silva
2013-21 Human Capital and Growth in a Services Economy: the Case of Portugal
- Marta Simões & Adelaide Duarte
 Estudos do GEMF

2013-20 Does Voter Turnout Affect the Votes for the Incumbent Government?
- Rodrigo Martins & Francisco José Veiga
2013-19 Determinants of Worldwide Software Piracy Losses
- Nicolas Dias Gomes, Pedro André Cerqueira & Luís Alçada Almeida
2013-18 Despesa Pública em Educação e Saúde e Crescimento Económico: Um Contributo para o
Debate sobre as Funções Sociais do Estado
- João Sousa Andrade, Marta Simões & Adelaide P. S. Duarte
2013-17 Duration dependence and change-points in the likelihood of credit booms ending
- Vitor Castro & Megumi Kubota
2013-16 Job Promotion in Mid-Career: Gender, Recession and ‘Crowding’
- John T. Addison, Orgul D. Ozturk & Si Wang
2013-15 Mathematical Modeling of Consumer's Preferences Using Partial Differential Equations
- Jorge Marques
2013-14 The Effects of Internal and External Imbalances on Italy´s Economic Growth. A Balance of
Payments Approach with Relative Prices No Neutral.
- Elias Soukiazis, Pedro André Cerqueira & Micaela Antunes
2013-13 A Regional Perspective on Inequality and Growth in Portugal Using Panel Cointegration
Analysis
- Marta Simões, João Sousa Andrade & Adelaide Duarte
2013-12 Macroeconomic Determinants of the Credit Risk in the Banking System: The Case of the
GIPSI
- Vítor Castro
2013-11 Majority Vote on Educational Standards
- Robert Schwager
2013-10 Productivity Growth and Convergence: Portugal in the EU 1986-2009
- Adelaide Duarte, Marta Simões & João Sousa Andrade
2013-09 What Determines the Duration of a Fiscal Consolidation Program?
- Luca Agnello, Vítor Castro & Ricardo M. Sousa
2013-08 Minimum Wage Increases in a Recessionary Environment
- John T. Addison, McKinley L. Blackburn & Chad D. Cotti
2013-07 The International Monetary System in Flux: Overview and Prospects
- Pedro Bação, António Portugal Duarte & Mariana Simões
2013-06 Are There Change-Points in the Likelihood of a Fiscal Consolidation Ending?
- Luca Agnello, Vitor Castro & Ricardo M. Sousa
2013-05 The Dutch Disease in the Portuguese Economy
- João Sousa Andrade & António Portugal Duarte
2013-04 Is There Duration Dependence in Portuguese Local Governments’ Tenure?
- Vítor Castro & Rodrigo Martins
2013-03 Testing for Nonlinear Adjustment in the Portuguese Target Zone: Is there a Honeymoon
Effect?
- António Portugal Duarte, João Soares da Fonseca & Adelaide Duarte
2013-02 Portugal Before and After the European Union
- Fernando Alexandre & Pedro Bação
2013-01 The International Integration of the Eastern Europe and two Middle East Stock Markets
- José Soares da Fonseca

2012-21 Are Small Firms More Dependent on the Local Environment than Larger Firms? Evidence
from Portuguese Manufacturing Firms
- Carlos Carreira & Luís Lopes
2012-20 Macroeconomic Factors of Household Default. Is There Myopic Behaviour?
- Rui Pascoal
2012-19 Can German Unions Still Cut It?
- John Addison, Paulino Teixeira, Jens Stephani & Lutz Bellmann
2012-18 Financial Constraints: Do They Matter to R&D Subsidy Attribution?
- Filipe Silva & Carlos Carreira
 Estudos do GEMF

2012-17 Worker Productivity and Wages: Evidence from Linked Employer-Employee Data
- Ana Sofia Lopes & Paulino Teixeira
2012-16 Slovak Economic Growth and the Consistency of the Balance-of-Payments Constraint
Approach
- Elias Soukiazis & Eva Muchova
2012-15 The Importance of a Good Indicator for Global Excess Demand
- João Sousa Andrade & António Portugal Duarte
2012-14 Measuring Firms' Financial Constraints: A Rough Guide
- Filipe Silva & Carlos Carreira
2012-13 Convergence and Growth: Portugal in the EU 1986-2010
- Marta Simões, João Sousa Andrade & Adelaide Duarte
2012-12 Where Are the Fragilities? The Relationship Between Firms’ Financial Constraints, Size and
Age
- Carlos Carreira & Filipe Silva
2012-11 An European Distribution of Income Perspective on Portugal-EU Convergence
- João Sousa Andrade, Adelaide Duarte & Marta Simões
2012-10 Financial Crisis and Domino Effect
- Pedro Bação, João Maia Domingues & António Portugal Duarte
2012-09 Non-market Recreational Value of a National Forest: Survey Design and Results
- Paula Simões, Luís Cruz & Eduardo Barata
2012-08 Growth rates constrained by internal and external imbalances and the role of relative
prices: Empirical evidence from Portugal
- Elias Soukiazis, Pedro André Cerqueira & Micaela Antunes
2012-07 Is the Erosion Thesis Overblown? Evidence from the Orientation of Uncovered Employers
- John Addison, Paulino Teixeira, Katalin Evers & Lutz Bellmann
2012-06 Explaining the interrelations between health, education and standards of living in Portugal.
A simultaneous equation approach
- Ana Poças & Elias Soukiazis
2012-05 Turnout and the Modeling of Economic Conditions: Evidence from Portuguese Elections
- Rodrigo Martins & Francisco José Veiga
2012-04 The Relative Contemporaneous Information Response. A New Cointegration-Based Measure
of Price Discovery
- Helder Sebastião
2012-03 Causes of the Decline of Economic Growth in Italy and the Responsibility of EURO.
A Balance-of-Payments Approach.
- Elias Soukiazis, Pedro Cerqueira & Micaela Antunes
2012-02 As Ações Portuguesas Seguem um Random Walk? Implicações para a Eficiência de
Mercado e para a Definição de Estratégias de Transação
- Ana Rita Gonzaga & Helder Sebastião
2012-01 Consuming durable goods when stock markets jump: a strategic asset allocation approach
- João Amaro de Matos & Nuno Silva

A série Estudos do GEMF foi iniciada em 1996.

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