eastern europe and trade liberalization: a vulnerability...
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University of Rome “La Sapienza”
Eastern Europe and Trade Liberalization: A
Vulnerability Approach *
Alessandro Federici
Pierluigi Montalbano Carlo Pietrobelli
Umberto Triulzi
[email protected] [email protected]
ABSTRACT The aim of this paper is to detect empirically the phenomenon of macrovulnerability linked to trade openness, underlining the role of the integration process and policy reforms in reducing exposure to external shocks. This is not an attempt to constitute a case against liberalization but, on the contrary, it is a way to make people more conscious about the “ex ante” risks in return for higher expected returns The analysis is focused on Eastern Europe, in consideration of the dramatic and unprecedented trade liberalization process experienced in the area at the beginnings of ‘90s, and the on going accession process towards EU. The difficulty of properly measuring macrovulnerability to trade shocks at the macro level is acknowledged and emphasized. The paper proposes a measure to combine trade shocks, volatility and human development. The main result of the analysis is to demonstrate that trade liberalization in Eastern Europe, if not associated with adequate tools or consistent reforms, could be harmful for CEECs’ human development and sustainable livelihood and that the apparent association in Eastern Europe between trade liberalization and socio-economic performance could be misleading. The social and economic convergence process, currently in place, between CEECs and EU member countries could in fact hidden that actually trade liberalization negatively influences the human development levels in most of CEECs. This preliminary evidence provides a substantive contribution to the debate, currently in place, about the role of international trade on the socioeconomic performance of emerging countries, as well as the determinants and effects of volatility. It suggests that policy tools or consistent reforms may play an effective role in reacting to such events and, in the case of CEECs, to catch up, if not the EU average levels of well-being, at least their own potential well-being. JEL C82 E17, F40
This paper has been produced for the Research Program on Macroeconomic Vulnerability of the GDN, Ipalmo and University of Rome “La Sapienza, and directed by Umberto Triulzi. And sponsored by the Italian Ministry of Foreign Affaires (DCDG). Preliminary drafts were presented at the Fifth Annual Global Development Conference “Understanding Reform”, New Delhi, India January 28-30, 2004, at University of Reading, Department of Economics; University of Rome La Sapienza, Department of Economic Theory. We wish to thank Andrea Cornia, .Robert Holzman, Luca De Benedictis and Silvia Nenci for their useful comments. Responsibility for errors and omissions remains our own.
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1. Introduction
Openness to trade, factor and capital flows offers remarkable opportunities for economic and political progress of countries. However, such openness also spreads out a feeling of insecurity and vulnerability.1 Is this feeling justified in economic terms? Should nations – and their policy-makers – really get worried? Actually, the countries most integrated in the world economy have been particularly affected by international crises (Easterly e Kraay, 1999). According to the World Bank, during the ‘90s the occurrence of economic crises was, along with natural disasters and conflicts, one of the main reasons for aggregated shocks and increases in the incidence of poverty at the international level.2 Developing countries seem to be more susceptible to further negative externalities, especially when internal markets are not working well (Dercon, 2001). In fact, in a globalized world, many endogenous and natural shocks are becoming less important than “man-made” external shocks. This implies, especially in developing countries, that traditional social relations and local market structures have to face an entirely new sets of incentives and shocks. Hence, traditional coping mechanisms are under pressure and a vast proportion of population does not have the means to benefit from competition at the international level (Dercon, 2001).
A crucial question is: where to strike the balance between the advantages of an open economy, and the drawbacks of a greater exposure to shocks – that in turn bring about major socioeconomic costs, and may harm the people’s livelihood in social and economic contexts characterized by a weak institutional development - ?
In this respect, the case of Central and Eastern European Countries (CEECs) is particularly instructive. CEECs have experienced, since the early 1990s, a dramatic and unprecedented process of political change, economic liberalization and institutional reform (Svejnar, 2002). As a result, these countries have experienced, at the beginning of the transition process, an economic slowdown of a magnitude never seen during non-war years (Mundell, 1995), to recover only after some years, following a U-shaped “transition” curve. Some authors have argued that CEECs’ recession was due to several negative trade shocks, such as the collapse of the Council for Mutual Economic Assistance (Comecon)3, the cut of traditional trade linkages with former USSR, the immediate shift to world prices in foreign trade (Blanchard, 1997). May negative trade shocks have actually influenced CEECs economic performance? And if so, do they determine once-for-all or permanent effects? In other words, does all this make countries more vulnerable in their socioeconomic conditions?
The aim of this paper is to develop a methodology and use it to offer a preliminary answer to some of these questions. We explore the phenomenon of “macro-vulnerability” related to trade openness with evidence on Eastern and Western Europe. The aim is to question whether CEECs’ trade openness, that occurred as a “big bang”, may have caused long term negative effects related to the greater vulnerability to future negative shocks that is has induced. Trade integration and policy reforms have been relevant issues in this context, and are also explored here.
1 On these issues, seeYusuf (2001) and also several essays in Ocampo et al., 2000. 2 Between 1990 and 1997, more than 80% of developing countries experienced at least one year of negative per capita output growth as a result of an economic crisis, natural disaster or conflict (World Bank, 2000). 3 The Comecon was established in 1949 with the aim to favor economic, scientific and technological cooperation and develop the economic integration among the following Socialist member countries: USSR; Bulgaria, Czechoslovakia, Hungary, Poland, Romania, Albania (1949); DDR (1950), Mongolia (1962); Cuba (1972) and Vietnam (1978).
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The paper is organized as follows: section two presents a brief review of the theoretical literature drawn from so-far apparently distant areas of research. Stylized facts on the socioeconomic performance of transition economies, both in terms of growth and volatility, are reviewed in Section 3. Section 4 presents an empirical model on the relationship between trade openness and macro-volatility in Europe during the period 1992-2001. In Section 5 we move towards a forward looking vulnerability analysis of European countries. Section 6 draws some conclusions and policy implications of the analysis.
2. Review of the literature
Mainstream international economics based on the Heckscher-Ohlin theory asserts that international trade produces benefits for all participants. Countries and individuals specialize according to comparative advantage, and goods and factors relative prices tend to converge.
Moreover, over time a huge number of studies have explored additional issues on the relationships between trade openness and other economic dimensions. Such an ample spectrum of studies include, among the others: the evaluation of the impact of trade liberalization on poverty (Timmer, 1997; Delgado et al., 1998; Mellor and Gavian, 1999) and on inequality between and within countries (Frankel 2000, Ben-David 1993, Dollar and Kraay 2001, Milanovic 2003); the relationship between trade integration and economic growth (Edwards, 1993; Frenkel and Romer, 1999; Dollar and Kraay, 2001); the role of policies and institutions (Krueger, 1990, Ades and Di Tella, 1997, 1999, Lall and Pietrobelli, 2002), and, last but not least, the nature of the relationship between trade openness, volatility and vulnerability (Atkins and Mazzi, 1999; Hnatkovska and Loayza, 2003; Wolf, 2003; Kose at al., 2003). These studies have been carried out both at the macro and at the micro level.
In the present context, we are especially interested in the socio-economic effects that international trade (and its shocks) may have. To this aim, Winters (2000) points out the following line of reasoning, that is likely to occur: trade shocks operate primarily via prices;4 government policies could prevent them from being transmitted internally; trade shocks are likely to affect different households in different manners; a change in trade policy could influence the incentives for enterprises to produce specific goods, with different bearings on poverty;5 if trade liberalization causes falling tariff revenues, this in turn may reduce social expenditures and hurt the poor (McCulloch, et al., 2001).
The path-breaking work of Winters (2000) on international trade and poverty, while rightly and solidly founded on microeconomic analysis, has so far adopted a static approach. The aim here is to add a forward-looking dimension to the analysis as well as specifically address, from a macro point of view, the relationship between trade liberalization, its associated risks and macro vulnerability.
4 If factor supplies show some elasticity, part of the trade shock will show up as changes in employment rather than in factor prices. With a perfectly elastically supplied factor only employment effects would emerge (Winters, 2000) 5 Applying the Stolper-Samuelson theorem, trade reform could be associated with decreasing poverty if opening up to trade increases the price of unskilled-intensive goods, that require the type of labor developing countries are most endowed with. However, the validity of this theorem in the context of developing countries has been heavily criticized (Winters, 2000).
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In order to adopt a forward looking approach, we need to include risk into the analysis.Starting from the simplest analysis of risk, it is reasonable to expect that at low levels of trade, further trade liberalization would tend to reduce risk exposure, because (larger) world markets with many players tend to be more stable than (smaller) domestic ones (Winters, 2000).6 However, Glick and Rose (1999) show – with empirical evidence - that trade linkages should be first among the suspects in explaining regional contagion during currency crises. This issue has been analyzed in depth by Forbes (2001), who underlines that trade can transmit crises internationally via three distinct, and possibly counteracting, channels: a competitiveness effect (when changes in relative prices affect a country's ability to compete abroad); an income effect (when a crisis affects incomes and the demand for imports); and a cheap-import effect (when a crisis reduces import prices and acts as a positive supply shock). She suggests that trade effects are not only statistically significant, but also quantitatively relevant (Forbes, 2001). However, it has also bee remarked that these channels could counteract and balance each other, and that the resulting aggregate impact of trade linkages could be small (Corsetti et al., 2000, Wincoop and Yi, 2000).7
Notwithstanding the interest and relevance of the above studies, we still lack a solid and comprehensive methodology to assess whether the more opened economies are also more vulnerable (in socio-economic terms, see section 2.1) to international trade shocks. The extreme approach to condemn any shock that causes even one individual to suffer a reduction of income is inevitably bound to fail, given the wide heterogeneity of households and the strongly redistribute nature of trade shocks. For our purpose, in the next section we borrow some key elements from current socio-economic vulnerability analyses, and try to overcome the problems due to the wide range of definitions of vulnerability (Triulzi and Montalbano, 2002) and their microeconomic focus.8
2.1. Vulnerability: theoretical and methodological references
Vulnerability can be defined as the result of the risk exposure of the unit of analysis (e.g. households, individuals, communities or countries), coupled with the unit's socioeconomic characteristics and its ability to adequately respond to shocks so as to avoid declines of well being below a certain threshold. More specifically, vulnerability is the "continuous forward-looking state of expected outcomes" (Alwang et al., 2001) which are in themselves determined by the assets of a household, the correlation, frequency, timing and severity of shocks, and the risk management instruments applied (Heitzmann et al 2002)
It is fundamental to stress that while well-being and poverty are ex-post outcomes, vulnerability is an ex-ante condition which could potentially lead to a negative outcome. Consequently, what really matters to
6 However, it is worth reminding that at the same time, if foreign shocks are much greater than domestic ones, we could get the opposite effects. 7 Another strand of literature that has addressed this issue, even if with different techniques, is related to the hypothesis of “small states”, which are supposed to be more open and fragile (Atkins and Mazzi, 1999, Easterly and Kraay, 1999). However, much less evidence is available on this specific hypothesis. 8 A partial exception is the tentative analysis of vulnerability from a macro point of view proposed by Thomas (2003).
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assess vulnerability is not the current values of the phenomena, but the ability to understand their future dynamics and intervene when needed (Dercon, 2001).9
Vulnerability is indeed a complex subject. It is not determined by one, easily measurable factor.10 There are many sources of risk that interact with each other, as well as many different types of risk management strategies. Moreover, risk management instruments need to be aimed at not only preventing risks, but more importantly at encouraging individuals to take risks in a more conscious, beneficial and profitable manner with a long-term outlook..
Diagram 1. The Logic of Socio-economic Vulnerability and its Determinants
Risk Exposure + Response Tools + Unit's SE Characteristics = Socio-economic Vulnerability
(exogenous f
Source: adapte
Despite existend to consfactor), the managemen
2.2 Tow
Though the micro (housspite of themacroeconofor a macro
9 This is a centraproper vulnerabiorganizations (eproliferation of msecurity, conflict10 There is, geneempirically validcomparable acromeasurement of outcome shocksvariables. This imare many, while gaps, such as for11 Most approachouseholds and methodologies (correlation. This“stress” in ordemanagement toodivided into redstrategies and lon
actors) (endogenous factors) (negative outcome)
d from Heitzmann at al., 2001.
ting differences in definitions and measurement methodologies, most vulnerability analyses ider the following key elements (Diagram 1): the degree of exposure to a risk (exogenous
role of the ability to adequately respond to shocks, based on the actual availability of risk t tools and on the unit's socioeconomic characteristics (endogenous factors). 11ards a macro approach of vulnerability
concept of vulnerability has been discussed both at the macro (national) level and at the ehold/individual) level, most of the vulnerability analyses usually adopt a micro approach, in widespread globalization process, that would necessarily compel to consider also its mic dimensions and implications. Moreover, the following considerations justify our search approach to vulnerability.
l point. Some disciplines call vulnerability something which is very similar to ex-post poverty outcome assessments. Instead, a lity assessment requires an ex-ante analysis of exogenous risk factors and risk management tools. While many international .g. FAO, World Bank, UNDP, USAID) have made significant strides in improving our understanding of vulnerability, a
ultiple methodologies, terminology and approaches to vulnerability is apparent, involving as diverse areas of interest as food prevention, etc.
rally speaking, an intrinsic incompatibility between the completeness of the definition of vulnerability and its ability to be (Alwang et al, 2001). The problem for a quantitative analysis is to isolate a simple measure (or set of measures) that is ss time and location (Gamanou and Morduch, 2002). The information requirements are high and no straightforward hypothetical situations is possible via survey data. Currently, most of the applications used infer the distributions of possible
from the error process in cross-section regression models explaining consumption outcomes by household and community plies strong assumptions about how shocks evolve over time and space. The data needed to construct outcome-based measures
they do not give much insight about how the poor cope with vulnerability (Dercon, 2001). Other measures may help to fill these example Sustainable livelihoods approaches, that focus on assets. hes place particular emphasis on elaborating classification of risks, risk response strategies and livelihood characteristics of communities. It is widely agreed that risks derive from a variety of natural, political, social and economic sources. Some e.g. the World Bank) also distinguish between the characteristics of the risk, such as frequency, magnitude, intensity and depth of risk classification, however, is not widespread. Some experts prefer to use the term “life event” instead of “shocks” or r to allow for the inclusion of an active component, in contrast to a perception of the poor as passive social actors. Risk ls are also analyzed and grouped into specific categories in most vulnerability approaches. These instruments are generally uction, mitigation and coping mechanisms. The sustainable livelihoods approach, for example, focuses on short-term coping g-term adaptive behavioral changes (UNDP, 1999).
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First of all, the incidence of macro shocks at the international level has been particularly evident and increasing in the last decade. These shocks, the result of a perverse combination of international turmoil and political economy mismanagement, have manifested themselves in various forms (public budget, balance of payments, currency and banking crises; hyperinflation; etc.) and in various countries.12
The second reason for a macro approach is related to the policy instruments. In fact, recent events proved a lack of “ex-ante” international macroeconomic policies capable of properly recognizing and coping with the systemic nature of macroeconomic crises, and with their actual effects. Thus, for example, the negative impacts of the crisis proved to be much more devastating for those who are poor or nearer the poverty line, even if they do not affect the local population disproportionately (Lustig, 2000). This is due to the poor’s lower ability to save, and lack of full access to general public or private safety net systems (World Bank, 2000). In addition, current policies and “ad hoc” interventions usually fail to take into account of a large percentage of the population with a large chance of falling below the poverty line in the near future. Policies need to be redesigned and redirected to address such issues (Holzman, 2001).
Thirdly, current vulnerability studies have often largely ignored a number of relevant macro issues, such as those related to a lack of policy credibility, or the inconsistency between short-term strategies and long-term commitments, and the relationship between conflicts and vulnerability (Triulzi and Montalbano, 2001, 2003).
In our efforts to measure vulnerability we try to extend the basic insights of the available literature to the macroeconomic level. So far, the most important achievements on the role of trade shocks and covariate risks have been given by the literature on macro volatility. In turn, most episodes of extreme volatility have had remarkable welfare implications in developing economies. To our aims, the rationale of the analysis goes from the impact of trade shocks on macroeconomic volatility, and then to its effects on socio-economic vulnerability.
2.3 Studies of Macroeconomic Volatility: theory and methodology
Traditionally, the issue of volatility has been considered essentially as a business cycle phenomenon, with only secondary effects for emerging economies, with the exclusion of extreme crises. However, in recent years, the effects of volatility on growth and poverty alleviation has attracted the interest of many scholars (World Bank, 2003a).
We may divide current volatility literature into a strand which analyzes the effects of volatility, and another one that focuses on its determinants.
The latter strand of the literature explores the determinants of particularly high or low (i.e. extreme) volatility, typically in cross section analyses. Cross-country differences in the volatility of aggregate variables, such as consumption or GDP growth, can arise either from differences in the incidence of
12 Although some of them received quite a bit of attention from the media (Mexico 1995, Southeast Asia 1997; Brazil and Russia 1998), these represent, as highlighted also by the World Bank (WDR, 2000), merely the tip of the iceberg of a much vaster and more complex phenomenon. It is sufficient to look at recent events in Argentina to see how much we still have to understand of the full impact of these crises at all levels.
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shocks or from differences in transmission mechanisms. On the first cause, Gavin and Hausmann (1996) conclude that long run growth volatility of a sample of developing countries has been importantly influenced by the volatility of terms of trade and the real exchange rate. Others focus on the transmission mechanisms, and emphasize the role of the ability of domestic institutions to reduce or magnify the effects of external shocks (Acemoglu et al., 2002, Rodrik, 1999).
On the effects of volatility, most of the literature suggests a positive relation between volatility and (average) growth. However, there is an alternative view, notably applied to emerging markets which suggests a negative link, and explains it with the greater uncertainty that lowers investments in physical and human capital thereby reducing long term growth. 13
In sum, we argue that the whole link between trade shocks, volatility and vulnerability has not been explored thoroughly so far. This is what we try to analyze for CEECs.
3. CEECs Performance and Volatility: Some Stylized Facts
CEECs have not performed as well as many had expected. And the situation has been even worse for the Baltic States and the other European countries (Fig. 1).
Fig. 1. CEECs GDP per capita Growth Rate (period 1991-2000)
-30
-25
-20
-15
-10
-5
0
5
10
15
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
PECO7 P.Baltici Altri Europa
Source: World Bank, SIMA-GDF, GFS & WDI Central CEECs 7 include Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic and Slovenia. Other European include Albania, Belarus, Macedonia FYR and Russian Federation. Baltic States: Estonia, Latvia and Lithuania
13 See for example Ramey and Ramey, 1995, Martin and Rogers, 1997; Talvi and Vegh, 2000, Easterly et al. 2001; Rodrik, 1999; Acemoglu et al, 2002; Hnatkovska and Loayza, 2003.
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At the same time, during the 90s, candidate CEECs to EU enlargement have showed a clear process of convergence towards the income levels of the European Economic Area (EEA) member countries.14 Consistently with Barro and Sala-i-Martin (1991 and 1995) hypothesis, we detect a clear negative relationship between per capita income growth rate of period 1992-2001 and natural log of its initial level (Fig. 2). 15 The OLS regression results are the following:
Fig. 2 ß convergence in Europe in the period 1992-2001: the case of the candidate CEECs
AustriaBelgium
Czech RepublicDenmark
Finland
France
Germany
Greece
Hungary
Iceland
Italy
Luxembourg
Netherlands
Norway
Poland
Portugal
Slovak Republic
Slovenia
SpainSweden
Switzerland
United Kingdom
.1
.2
.3
.4
.5
PC
GD
P G
row
th ra
te
7 8 9 10 11
LN PC GDP 1990
PC GDP Growth rate = 0,917216 – 0,0687172(LN PC GDP 1990)Adjusted R2= 0,2446
3.1 Volatility in Europe
However, this convergence process has been associated, for the entire group of CEECs, included those candidate to EU enlargement, with an higher degree of volatility with respect to the performance of EEA member states. If the standard deviation is used as a metric, we can see that CEECs show an higher degree of volatility for almost all the reported macroeconomic variables (see Appendix A). This finding is particularly evident for trade variables, per capita consumption and per capita GDP growth rates. Fig. 3 shows this evidence about the volatility of the annual growth rate of per capita GDP and consumption,
14 Members of EEA (the agreement is in place since 1994) are the 15 EU member States together with 3 member states of the European Free Trade Association (Iceland, Norway and Liechtenstein). The forth member of Efta, Switzerland, has been considered in the analysis as well, since it signed a bilateral agreement with Eu in 1999. Liechtenstein has been not considered in the anaysis. 15 This process of convergence, namely “ß convergence”, does not imply the existence of a reduction of the relative distribution of incomes over time, as in the case of the so called “σ convergence“.
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respectively. In the majority of CEECs, the ratio of per capita consumption growth rate over per capita GDP growth rate is greater than 1. This result is consistent with a number of empirical analysis (see Kose et al., 2003; Wolf, 2003, World Bank Latin American and Caribbean Studies, 2000). They show that, while the volatility of output growth has declined, on average, in the 1990s relative to the three earlier decades, the volatility of consumption growth relative to that of income seems to have increased, especially in the case of the more financially integrated developing economies. This relationship, however, seems to be not linear, as the relative volatility of consumption declines after a certain threshold of income. A further complication is due the evidence that, while within countries and across sectors faster growth seems to be linked to higher volatility, across countries the link turns negative – i.e. higher volatility is associated to slower growth. This is a very important point: countries more integrated in the international economy seem to be more volatile and record lower growth rates. Volatility in this respect should be considered as a proxy of uncertainty. Something similar could be happened in the case of CEECs. This situation has been particularly evident both in the case of volatility of per capita GDP, consumption, as well as in the case of volatility of the main trade variables (see Appendix B).
Fig. 3 Ratio between volatility of per capita GDP and per capita consumption: An European picture
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
Albania
Bulgaria
Croatia
Czech Republic
Estonia
Hungary
LatviaLithuania
Poland
Romania
Slovak Republic
Slovenia
Eunewmember
BalticEEA
Other European
The central question posed by these stylized facts calls for an analysis of whether the volatility of macroeconomic variables may have adverse effects on the countries’ socioeconomic development, reflecting inability to smooth consumption over time, rising unemployment, disruption of economic activity, etc. (Coricelli and Ianchovichina, 2003). More specifically, the aim of our analysis is to empirically relate the dynamics of CEECs in the midst of trade liberalization - the type, magnitude and frequency of trade shocks they have to face -, with their macroeconomic volatility, and with the impact on their ability to cope with these shocks and on their socio-economic performance.
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3.2 A Measure of Well-being
In order to analyze the incidence of trade shocks on CEECs’ socioeconomic vulnerability, we need a measure of well-being comparable across countries and years. The obvious reference is the Human Development Index (HDI), produced by the UNDP and reported annually in the Human Development Report (UNDP, various years). HDI is a measure of the countries’ achievements in terms of three distinct dimensions of human development (longevity; education and standard of living), measured, respectively, by life expectancy; total enrollment and per capita GDP. In our case, however, cross-country differences in the volatility of per capita GDP growth, can arise either from differences in the incidence of shocks and differences in transmission mechanisms, and do not give us much information about the ability of people to react to shocks. A better measure is volatility of per capita consumption, that gives us information on the ability of people to smooth consumption over time.
As a result, we decided to adapt the HDI and use, consistently with vulnerability analysis, per capita consumption instead of per capita GDP. Thus, we compute HDIR (where R states for Revised) for each year and each country as a linear combination of life expectancy at birth; total enrolment, and household final consumption expenditure per capita (in constant 1995 US$ (eq.1a), rescaled between 0 and 116 in order to make them homogenous and comparable. We compute HDIR as follows:
HDIRti= wx1X1ti + wx2X2ti + wx3X3ti (1a)
where HDIRti is the composite index of human development in period t and country i; w is the contribution of each variable to the component, X is the index of the variable, whose range of value is between 0 and 1.17
In addition, in order to take into account sustainability in the measurement of welfare level, we propose here a concave function of data. The sustainability of development considered here stems from the assumption of non replaceability among the various dimensions of HDIR, in that only development that takes place with harmony among them can be considered sustainable over time. Our attempt is to implicitly measure such sustainability, giving it a lower value in correspondence to a greater distance of the country point from a locus of ideal balance in the variable space and to incorporate this measure into HDIR by penalizing the latter with the lack of sustainability. With the above considerations about balanced and sustainable development in mind, we then postulate that the ideal balance between the three components of HDIR occurs when they are all equal; in other words, the ideal balance locus is the diagonal straight line passing through the points (0, . . . , 0) and (1, . . . , 1) in the three-dimensional real
16 Applying the affine function f(t) = (t-m)/(M -m), where M and m are the maximum and minimum value, respectively, of the variables considered. 17 In order to calculate the weight of each each variable we adopted a Multiway Analysis called Dynamic Factor Analysis (Coppi R., Di Ciaccio A., 1994; Corazziari I., 1997). In our analysis, we consider the threedimensional matrix X(I,J,T) = {xijt}, i=1…I, j=1…J, t=1…T, where i is the unit; j the variable and t the time dimension. Once aggregated two of the three indices we get 2x2 arrays, considering the array X(IT,J) and decomposing its variability into three components by Principal Component Analysis and Regression Methods.
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variable space R3.We decided to decrease, or “penalize”, the value of the HDIR of each country by a coefficient a times the variance of the sum of the components (a>0) as follows:
HDIRti= (wx1X1ti + wx2X2ti + wx3X3ti) - aVAR(wx1X1ti; wx2X2ti; wx3X3ti) (1b).
The concavity of our proposed welfare index reflects the non replaceability of variables and penalizes progressively their unbalances: the higher the dissimilarity between components, the higher the total penalization. The value of parameter a the above formula determine the entity of penalization: to make things simple we adopted in our analysis the value a=1.
HDIR shows the relative position of each country within the sample. This is particularly useful for our purposes, as it shows that a country could be worse off even with a positive performance, if the others do better. HDIR ranges between 0 and 1: the larger the index, the higher is the level of human development of the country. As this measure includes the performance of per capita consumption, it remains coherent with the current practice of most vulnerability analyses. However, HDIR gives us richer information on the actual countries’ standard of living.
Consistently with expectations, the average HDIR of the EEA countries is 0,76, twice that of CEECs 7 (0,38) and that of Baltic (0,33) and almost three times greater than that of other European countries (0,29) and the situation remains stable over the entire decade (Tab. 1).
Tab. 1 – HDIR levels by groups of countries (period 1990-2000)
owever, if we take the HDIR rate of change the situation is much more differentiated. Except in the
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Average
Baltic 0,422 0,387 0,316 0,268 0,257 0,266 0,313 0,339 0,337 0,361 0,355 0,329
CEECs 7 0,389 0,374 0,375 0,385 0,385 0,390 0,385 0,381 0,381 0,380 0,386 0,383
EEA 0,727 0,742 0,759 0,768 0,773 0,770 0,764 0,757 0,757 0,754 0,755 0,757
Other 0,323 0,312 0,295 0,282 0,271 0,270 0,271 0,283 0,285 0,282 0,275 0,286
Hcase of EEA countries, other European countries show an unbalanced path, which is particularly evident in the case of Baltic countries (Fig. 4)
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Fig. 4 – HDIR rate of change by groups of countries (period 1991-2000)
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
bal tic ceecs eea other
Using HDIR instead of per capita GDP, we can test again the convergence hypothesis for candidate CEECs. As Fig. 5 shows, the convergence process is actually in place also in socio-economic terms. The OLS regression results are the following:
Fig. 5 ß convergence in Socio-economic terms in Europe in the period 1992-2001: the case of the candidate CEECs
If we take the standard deviation of HDIR rate of change, (Fig. 6) we can get a measure of the volatility of human development for each country in relative terms (i.e. a country could result more volatile than
Austria
Belgium
Czech Republic
Denmark
Finland
France GermanyGreece
Hungary
Iceland Italy
Luxembourg
Netherlands Norway
Poland
Portugal
Slovak Republic
Slovenia
Spain
Sweden Switzerland United Kingdom
-.01
0 .0
1 .0
2 .0
3 H
DIR
Gro
wth
rate
.2 .4 .6 .8 1 HDIR 1990
HDIR Growth rate = 0,1144433 – 0,1489038(HDIR 1990) Adjusted R2= 0,1288
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others also if it remains more stable in absolute terms)18. The index correctly takes into account the relative size of the variation i.e. the relative human development volatility in each country.
Fig. 6 – HDIR volatility (standard deviation): an European Picture
Once we get our HDIR index, we can focus now our attention on the determinants of its volatility. The r trade volatility, which has been
cal Analysis in Europe from 1990 to 2000
Our analysis of trade determinants of volatility has been carried out for 32 Western and Eastern European entation
Establishing a sound methodology to analyze macro socioeconomic vulnerability is not an easy task and requires an holistic approach to capture its multi-dimensional nature. Given the lack of reliable time
0 0,025 0,05 0,075 0,1 0,125 0
LithuaniaLatvia
EstoniaTurkey
Russian FederationMacedonia, FYR
CroatiaBelarusAlbania
SloveniaSlovak Republic
RomaniaPoland
HungaryCzech Republic
BulgariaUnited Kingdom
SwitzerlandSweden
SpainPortugalNorway
NetherlandsLuxembourg
ItalyIrelandIcelandGreece
GermanyFranceFinland
DenmarkBelgiumAustria
EEA average
Baltic averageOther European averageCEECs 7 average
aim of our empirical analysis is, specifically, to investigate whetheassociated with consumption volatility in the case of CEECs during 90’s, could be considered as one of the principal determinants of HDIR volatility.
4. Trade Openness and Volatility: An Empiri
countries in the period 1990-2000; the decade of dramatic trade liberalization and of the implemof the major “first type” reforms for the CEECs (Svejnar, 2002)19.
18 In this case, the country is likely to lose positions within the rank, resulting more volatile than countries that share the same growth path. 19 The country groupings are the following:
European Economic Area: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom.
“Candidate” countries (CEECs): Bulgaria, Czech Republic, Estonia, Hungary, Layvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia.
Others: Albania, Belarus, Croatia, Macedonia, Russian Federation and Turkey .
13
series for these countries, we use cross section data. This is justified by the idea that cross section differences in the volatility of a number of aggregate variables (e.g. per capita consumption) can alternatively arise from differences in the exposure of shocks or from differences in the availability of coping mechanisms, showing different vulnerability conditions in different countries. These will be reflected by different elasticities of income, consumption or other elements of well-being with respect to a given shock (Wolf, 2003).
The methodology adopted here is subject to a number of caveats. Firstly, as the focus is on aggregate variables in cross-country comparisons, we deal only with covariant macro shocks (i.e. shocks affecting
te aim of our analysis is to propose a methodology applicable across countries and periods, we decide to force ourselves to use macroeconomic data already available from the official
4.2 Trade Shocks and Macro Tools
of HDIR rates of change is associated with trade shocks. To this aim, consistently with the methodology used by the World Bank-HDSP for assessing socio-economic
the variables on average). The results we obtain may thus differ among social groups within each country. Indeed, the relative income position of households is like to have an important effect on their ability to access to adequate tools and coping mechanisms. Secondly, the model is not able to distinguish the effects of a permanent shock from a temporary one. This distinction would have, indeed, substantial implications. In fact, while in the case of a permanent shock volatility could be considered as an optimal choice in face of a structural change, in the case of a temporary shock, volatility may reflect only inability to smooth.
Finally, as the ultima
international sources.20 We acknowledge the risk of missing out a number of relevant country-specific issues, but enjoy the benefits and insights of a comparative approach.
Next step is to check if the volatility
vulnerability (Heitzmann et al, 2002, World Bank, 2003b), we build a “Trade Matrix” (see annex A and B) to map the numerous direct and indirect, endogenous and exogenous macro relationships between trade and welfare, singling out three groups of explanations: Trade Shocks; Macro Tools and Countries’ Characteristics (Diagram 1).
On trade risk exposure, we select the most significant sources of trade instability, and synthetise the information above in an Index of Trade Shocks (ITS): using a Principal Components Analysis (PCA)
technique21, we obtained a weighted average of the standard deviation of trade openness, terms of trade, current account balance, import price and per capita GDP growth rate, with weights given by the contribution of each variable to the explanation of the whole volatility22.
20 In this analysis, we use GDF&WDI central database, the primary Wold Bank database for development data from officially-recognized
Rp into a new space Rk with k<p, obtaining k linear combinations of the standardized
).
international sources. The database is updated quarterly. 21 The PCA reduces the dimension of the original space original variables that synthesize in the best way all the information of the phenomenon. The number of the significant principal components can be selected using different criteria: the eigenvalues’ method (i.e every principal components whose eigenvalue > 1) and the percentage method (i.e. selection of a number of Principal Components to explain a good percentage of the total variability). 22 More specifically, the contribution of each variable to the volatility of the chosen principal component(s
14
Moreover, since vulnerability is a combination of the risk exposure (exogenous factor) with the available tools and socioeconomic characteristics (endogenous factor) (Diagram 1), expressing the ability to
23
mitigate the risk and cope with the shock, we consider in the matrix also a set of policy instruments (annex B) . As in the case of Trade Shocks, we then set up, using the standard deviation of the original variables, an Index of Public Macro Tools (IPMT) applying the Principal Components Analysis (PCA). To this aim we use the following variables: net current transfers from abroad (% of GDP); general government final consumption expenditure (% of GDP); real interest rates; public spending on education (% of GDP) and private spending on health (% of GDP). The weights of these five components are obtained by the PCA.
4.3 Econometric Evidence
Using the indices constructed above, we finally test the following linear relationship between the ocks and public policy instruments with a cross country Tobit Regression
Model24:
i = 1,…,N and N is the e; VolHDIR is the standard deviation of the average HDIR rate of change;
x of trade shocks;
The erro ally distributed and uncorrelated with zero mean Var(εI) = σ2 .
The res (Tab.2): the fit of the model (Eq.2) is good, with
combination of trade shocks and volatility of Public Tools. The estimates bear the expected signs of the
volatility of HDIR, trade sh
VolHDIRi = β0 + β1 ITSi+ β2 IPMTi + εi (2)
Where: number of countries included in the sampl
ITS is an indeIPMT is an index of public policy instruments;
r terms εI are assumed to be norm
ults for all coefficients are robust and significant an adjusted R2 equal to 0.72. Thus, the model explains the volatility of HDIR rate of change as a linear
coefficients, denoting a positive and significant relation between the volatility of HDIR rates of change and trade shocks, and a negative relation with the volatility of Public Tools. It provides us with a clear and strong result: trade shocks raise HDIR volatility and policy tools may partly offset this effect.
However, given the substantial diversity across countries within the sample, it could be useful to control also for the role of the various countries’ structural characteristics. We selected a great number of relevant countries’ characteristics and divide them into three broad categories: structural, development,
regression fit highlighting the importance of the basic regression.
openness (for a complete list of them see annex C). The idea here is that while all economies have the potential to be integrated into the international economy, some economies by virtue of their trade, development or other structural characteristics can be disadvantaged. We test the basic model above by adding all of them, separately. The results, not reported in this paper, are fair good. It improves the
23 We are aware that if individuals do not have access to equivalent tools, the volatility of the aggregate HDIR could not reflect properly the volatility experienced by the different households. 24 A Tobit regression accounts for the presence of censored data. In our case, we have to take into account that HDIR ranges from 0 to 1 (by construction) while its standard deviation goes from 0 to infinite (by definition).
15
Tab. 2 Effects of trade shocks, trade tools and country characteristics on volatility of HDIR growth rates Dependent Variable: VolHDIR Method: ML - Censored Normal (TOBIT) Included observations: 32 Left censoring (value) at zero Convergence achieved after 5 iterations
cient Std. Error z-Statistic Prob. QML (Huber/White) standard errors & covariance
CoeffiConstant 0.062314 0.026762 2.328448 0.0199
ITS 0.088491 0.012307 7.190030 0.0000 987 0.028786 -1.910212 0.0561
ribution IPMT -0.054
Error Dist
nso bsred oed obs
032
ight censootal obs
obs 0 32
potential heter scedasticity using Huber – Wation
ity to Vuln ysis: Empirics from the Eu ountries
rward in our an est whether the lity
SCALE:C(4) 0.016051 0.001650 9.725605 0.0000 R-squared 0.754403 Adjusted R-squared 0.728089 Left ce R red Uncensor T
Notes: p-values of t-statistics are reported below the corresponding coefficient Standard errors are corrected for o hite procedure Source: Authors’ estim
5. From Volatil erability Anal ropean C
The next step fo alysis points to t higher levels of volati (resulting from lly worsen socioeconomic vulnerability
dual (a household) to fall into poverty.
between the
c Prob.
higher trade shocks or lower ability to cope with them) may reai.e. raise the probability of an indivi
A simple regression analysis reveals a negative, systematic and significant relationshipvolatility of HDIR and its average rate of change (Tab. 3). More specifically, higher volatility of HDR rates of change are systematically associated to lower HDR rates of change.
Tab. 3 - Effect of volatility on average HDIR rate of change Dependent Variable: GROWTHHDIR Method: Least Squares Included observations: 32
Variable Coefficient Std. Error t-StatistiConstant 0.007595 0.003822 1.986962 0.0561
VOLATILITY -0.243000 0.073236 -3.318019 0.0024 justed R-squared 0.244073 R-squared 0.268458 Ad
orrected for potential heteroscheda
hts the negat ole of ec mic unc inty on ome autho macroeconomic instability and Orph
Notes: p-values of t-statistics are reported below the corresponding coefficient Standard errors are c sticity using Huber - White procedure Source: Authors' estimation
This result highlig ive r ono erta countries’ socioeconomic performance. S rs relate it to (Judson anides, 1996),
tical insecurity (Alesina et al., 1996), or on a sk aversion and irreversibility of wrong choices (Hnatkovska, Loayza, 2003).
The phenomenon is even more significant if we operate a decomposition of HDIR rates of change total volatility into “normal”, “boom” and “crisis volatility”. This decomposition provides us with a new variable, namely “extreme volatility”, which reflects the impacts of sharp negative
institutional weakness (Serven 2000, Rodrik, 1991), politheoretical basis, to ri
and positive fluctuations. This variable may be considered totally distinct from “normal volatility”, which represents
16
the repeated and small cyclical movements around the mean25. For instance, if we examine the cases of France and Latvia, we notice that while the former never experiences boom or crisis HDIR volatility, the latter is characterized by two distinct instances of crisis volatility (1995) and boom volatility (1996) (Fig. 7).
The use of such notions allows us to argue that while, on average, EU member countries never experienced either boom or crisis HDIR rates of change volatility during this period, the CEECs on average have gone through three separate events of the extreme HDIR rates of change volatility (‘crisis’ in 1995 and 1997-98, and ‘boom’ in 1996 and 1999) (Fig. 8).
Fig. 7 Volatility decomposition of HDIR rates of change: a comparison between France and Latvia
100%
Fig. 8 Volatility decomposition of HDIR rates of chan
-40%1993 1994 1995 1996 1997 1998 1999 2000 2001
France HDIR Rate of change Average growth rate Threshold
5%
10%
15%
20%
25%
30%
-20%
0%
20%
40%
60%
80%
100%
ge: a comparison between EU and CEECs
-40%1993 1994 1995 1996 1997 1998 1999 2000 2001
Latvia HDIR Rate of change Average growth rate Threshold
80%
0%
20%
40%
60%Boom Volatility
-20%Crisis Volatility
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
1993 1994 1995 1996 1997 1998 1999 2000 2001
CEECs HDIR rate of change Average growth rate Threshold
Crisis Volatility Crisis Volatility
Boom Volatility
-15%
-10%
-5%
0%
1993 1994 1995 1996 1997 1998 1999 2000 2001
EU HDIR rate of change Average growth rate Threshold
If we focus on the more worrying events of “extreme volatility” and related them to the average level of HDIR rate of change, the relationship between the two variables remains significant (Tab. 4). Indeed, “extreme volatility” turns out to be the most significant explanation of the (lower) average HDIR levels.
25 “Extreme volatility” is calculated for each country as the portion of the standard deviation of HDIR that corresponds to downward and upward deviations with respect to a lower and upper threshold, equal to the country’s average HDIR rate of change minus and plus the average HDIR rate of change volatility within the sample (a narrower range could be calculated taking into consideration only the average volatility of EEA countries’ HDIR rates of change) See also Hnatkovska and Loayza (2003).
17
Tab. 4 Effect of "extreme" volatility on average HDIR rate of change Dependent Variable: GROWTHHDIR Method: Least Squares Included observations: 32 White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable Coefficient Std. Error t-Statistic Prob. C 0.005007 0.002182 2.294505 0.0289
EXTREME volatility -0.224101 0.088707 -2.526314 0.0170 R-squared 0.282154 Adjusted R-squared 0.258226
Notes: p-values of t-statistics are reported below the corresponding coefficient. Standard errors are corrected for potential heteroscedasticity using Huber - White procedure Source: Authors' estimation
These results strongly argue that it is necessary to go beyond the apparent association between trade beralization and positive socio-economic performance in Eastern Europe (European Commission, 2003,
of change that can be directly attributed to the “total” and “extreme” volatility, respectively. To this aim, we estimate, using the model above, the levels of HDIR rate of change with zero volatility - a measure of the potential av mpare them with the actual levels of average HDIR rate of change. T in Tab. 5 (column 1). It is easy to see that the effect of volatility has been particu (about 2.6% and 1.75% of their potential HDIR annual rate of change has been l ctiv coun ost harmed by volatility are La .2% .64% otential annual HDIR rate of change, respectively). In n the CEECs7 the new er States) and, bove all, EEA member countries, the effect of volatility is less relevant.
re, d been able to reduce their degree of volatility, probably they would have ached better levels of HDIR during the ‘90s. This is precisely a measure of the vulnerability of Baltic to
the range of trade shocks that have determined their own volatility levels.
e easily calculated as the difference between the current
liEBRD, 2003). Trade liberalization, with its new set of shocks and incentives, may have actually raised socioeconomic vulnerability in most CEECs.
In order to prove this statement more convincingly, we measure the reduction of human development rate
erage HDIR rate of change - and cohese values are reportedlarly relevant for the Baltic and the Other European countries
ost, respe ely). The tries mtvia and Albania (-3 and –2 of its p
contrast, i (the group that includes also EU memba
Therefo if the Baltic hare
Thus, if we consider in our estimated model, ceteris paribus, the occurrence of a trade shock (for instance, equal to 1%) we can actually measure the degree of vulnerability to trade – a covariate shock – for every country in our sample. This may blevels of HDIR and the estimated ones (Table 5, column 2): Baltic and, in particular, Other European countries turn out to be highly vulnerable to trade shocks. On average, trade shocks lower the actual HDIR annual rate of change of 0,001% for the CEECs 7, about 0,002% in the Baltic countries, and about 0,004% in Other European. The same exercise focusing only on “crisis volatility” is reported in columns 3 and 4: Column 4 accounts for the role of trade shocks in causing sharp fluctuations26.
26 Austria, France, Greece, Iceland and Slovenia didn’t experiment periods of “extreme volatility”. Thus, the difference between current HDIR rate of change and potential HDIR rate of change is equal to zero.
18
Tab. 5 Vulnerability to total and crisis volatility Cross - sectional Tobit regression analysis, 1990-2000
Diff. Current HDIR Growth Diff. Current HDIR Growth Diff. Current HDI
Vulnerability caused by total volatility Vulnerability caused by extreme volatility
and Potential HDIR Growth and HDIR Growth with 1% Trade Shock
R Growth and Potential HDIR Growth
Diff. Current HDIR Growth and HDIR Growth with 1%
Trade Shock
-0,00514%
0109% -0,91% -0,00132% Republic -0,88% -0,00091% -0,82% -0,00111%
Estonia -2,30% -0,00153% -2,42% -0,00186%Latvia -3,20% -0,00312% -3,45% -0,00378%Lithuania -2,27% -0,00191% -2,39% -0,00232%
Baltic -2,59% -0,00219% -2,75% -0,00265%
Albania -2,64% -0,01673% -2,86% -0,02029%Belarus -2,24% -0,00263% -2,38% -0,00318%Croatia -1,18% -0,00180% -1,19% -0,00218%Macedonia, FYR -0,71% -0,00091% -0,67% -0,00111%Russian Federation -2,12% -0,00262% -2,29% -0,00317%Turkey -1,59% -0,00073% -1,52% -0,00088%
Other -1,75% -0,00424% -1,82%
Bulgaria -0,89% -0,0
19
Source: Authors' estimation
This is precisely what we are aiming to demonstrate. The empirical exercise on CEECs, during the 1990s, reveals a significant level of vulnerability to trade shocks via an increased level of volatility in their socioeconomic performances. Trade shocks significantly and directly affect countries’ socioeconomic vulnerability, with strong effects on countries characterized by weak structure and lacking adequate policy tools.
The analysis carried out so far has been based on past observations. We still need to distinguish between and expected volatility. Since vulnerability is, by definition, a forward looking approach of
ungary -0,89% -0,00117% -0,73% -0,00142%Poland -0,44% -0,00060% -0,23% -0,00073%Romania -0,66% -0,00089% -0,53% -0,00108%Slovak Republic -0,80% -0,00141% -0,80% -0,00171%Slovenia -0,30% -0,00092% 0,00% -0,00112%
CEECs 7 -0,70% -0,00100% -0,57% -0,00121%
Austria -0,34% -0,00015% 0,00% -0,00018%Belgium -0,94% -0,00023% -0,86% -0,00028%Denmark -0,55% -0,00012% -0,40% -0,00015%Finland -0,47% -0,00086% -0,27% -0,00104%France -0,54% -0,00002% 0,00% -0,00003%Germany -0,54% -0,00002% -0,50% -0,00003%Greece -0,31% -0,00032% 0,00% -0,00038%Iceland -0,37% -0,00051% 0,00% -0,00062%Ireland -0,57% -0,00064% -0,34% -0,00078%Italy -0,50% -0,00016% -0,29% -0,00020%Luxembourg -0,35% -0,00077% 0,00% -0,00093%Netherlands -0,41% -0,00018% -0,32% -0,00022%Norway -0,51% -0,00078% -0,34% -0,00094%Portugal -0,81% -0,00046% -0,83% -0,00056%Spain -0,86% -0,00021% -0,75% -0,00025%Sweden -0,48% -0,00054% -0,36% -0,00065%Switzerland -0,49% -0,00043% -0,33% -0,00052%United Kingdom -0,55% 0,00000% -0,18% 0,00000%
EEA -0,53% -0,00036% -0,32% -0,00043%
CzechH
realized
analysis, we need to make statements in terms of expected vulnerability. In particular, this measure may iffer substantially for the acceding CEECs to EU 27 . Are these countries likely to experience a
tural change linked to their access to the European Economic Community (EEC).
how the impact of volatility of HDIR rate of change due to the access, respectively
dsynchronization with the EEA member countries’ socioeconomic performance and a stabilization of its degree of volatility?
To test for this hypothesis we have explored the evidence for the Mediterranean entrants into the EU, i.e. Greece, Spain and Portugal, and computed the HDIR for the 1960s, 1970s and 1980s. It is easy to detect the presence of a strucThus, measuring the change of their HDIR volatility, we find an increased synchronization of their volatility patterns with those of EU countries28. More specifically, Portugal experienced a -5% reduction of the volatility of its HDIR relative to that of EEC, and Greece (-54%) and Spain even larger reductions (-71%).
Table 6 describes the effect of EU access on average HDIR rate of change for the period 2001-2011. Columns from 1 to 3 sin the case of a small reduction of volatility (as experimented by Portugal), medium reduction (as in the case of Greece) and large reduction (as in the case of Spain).
Table 6 Accession effect on average HDIR rate of change (2001-2011)
Cross - sectional Tobit regression analysis, 1990-2000 Cross - sectional Tobit regression analysis, 1990-2000
Portugal Effect Greece Effect Spain Effect Portugal Effect Greece Effect Spain Effect (-5%) (-54%) (-71%) (-5%) (-54%) (-71%)
Bulgaria -0,85% -0,41% -0,26%
Average HDIR Growth (Total Volatility) Average HDIR Growth (crisis Volatility)
-0,86% -0,42% -0,26%Czech Republic -0,84% -0,41% -0,26% -0,78% -0,38% -0,24%
tonia -2,18EsH
% -1,06% -0,67% -2,30% -1,11% -0,70%ungary -0,85% -0,41% -0,26% -0,70% -0,34% -0,21%
Latvia -3,04% -1,47% -0,93% -3,28% -1,59% -1,00%Lithuania -2,15% -1,04% -0,66% -2,27% -1,10% -0,69%Poland -0,42% -0,20% -0,13% -0,22% -0,11% -0,07%Romania -0,63% -0,30% -0,19% -0,50% -0,24% -0,15%
6. Conclusion
This paper offers a substantive contribution to the debate on the role of international trade on the development of emerging countries. More specifically, it tries to fill a missing link in the theory between trade shocks and countries’ socioeconomic performance and vulnerability. It explores, both conceptually and empirically on the case of Eastern Europe, the rationale of the relationship between trade shocks, greater macroeconomic volatility, and socioeconomic vulnerability.
Slovak Republic -0,76% -0,37% -0,23% -0,76% -0,37% -0,23%Slovenia -0,28% -0,14% -0,09% 0,00% 0,00% 0,00%
27 Poland, Hungary, Czech Republic, Slovak Republic, Slovenia, Estonia, Latvia and Lithuania already joined EU in May, 1st 2004 28 Greece entered the EEC in 1981, and Portugal and Spain in 1986. Also Mexico appears to have recorded a larger synchronization of its macro volatility with the US and Canada with the implementation of the NAFTA Agreement (Kose, 2004).
20
The main result of the analysis shows that, in spite of some beta-convergence, Eastern European countries have experienced a worsening of their socioeconomic vulnerability following the trade shocks
f the early 1990s. This is due to their poor use of adequate policy tools to mitigate shocks and their n the domestic economies.
shocks on socioeconomic vulnerability. Thus, countries with
pervasive shocks coming from the international economy.
orepercussions o
The paper presents a methodology to compute backward and forward looking measures of socioeconomic vulnerability to trade shocks, and distinguishes between “normal” and “extreme” volatility. These empirical results spur some general and relevant policy implications. First of all, countries need to limit the impact of trade shocks on the volatility of their macroeconomic framework, as this is likely to increase their countries’ socioeconomic vulnerability.
Secondly, national economic policies still matter also in a globalized setting. They may counterbalance negative trade shocks to limit exposure to risk and enhance the response capabilities of their populations.
Thirdly institutions - both at a multilateral and regional level - and structural reforms, may importantly help to offset the negative effects of trade weak institutions, whose internal markets are not working well, are always worse off. This makes EU member countries as well as acceding countries in a privileged standing relative to Eastern Europe. This, in turn, calls for a deeper reflection on the governance of the globalization process, and on the role that multilateral agreements and international institutions may play to help emerging countries and transition countries to limit their socioeconomic vulnerability to the growing and
21
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ANNEX A
TRADE SHOCK VARIABLES 92-2001Classification TARGET VARIABLE TARGET INDICATORS AIM Database WDI 2003 DEFINITION WDI
Exogenous effects on volatility Output volatility
% change of GDP per capita at market prices
(constant local currency)
It is a proxy of the external shocks on the domestic economy owing to trade liberalization.
GDP per capita growth (annual %)
Annual percentage growth rate of GDP per capita based on constant local currency. GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.
Trade Openness Volatility of Trade Openness Ratio of Trade to GDP (%)
It measures trade orientation of a coutry and is a variable more directly related to the trade liberalization process
Trade of goods and services (% of GDP)
Trade of goods and servicesin percentage of GDP is the sum of exports and imports of goods and services measured as a share of gross domestic product.
Exogenous effects on purchase power Volatility of Terms of Trade
Ratio of national accounts exports price index to imports price index
It measures the exogenous change of the ratio of export to import prices owing to trade openness. It is common knowledge this ratio deteriorates in the weakest countries in the long run.
Terms of trade (goods and services, 1995 = 100)
The terms of trade effect equals capacity to import less exports of goods and services in constant prices.
Exogenous effects on external trade balance
Volatility of current account balance
Ratio of current account balance to GDP (%)
It measures the exogenous effects of trade openness on the external trade balance in relation to the total output of the country. The volatility pattern of this variable is a good measure of the ability of the country to maintain equilibrium in its trade balance over time
Current account balance (% of GDP)
Current account balance is the sum of net exports of goods, services, net income, and net current transfers.
Exogenous effects on purchase power Volatility of Import Prices % change import prices
(annual average)
It measures the first and most obvious advantage of trade liberalization: the reduction of the domestic prices owing to lower tariffs (assuming that the country is unable to affect world prices). This variable gives us important insights about price effects of trade liberalization in target countries.
Imports price index (goods and services, 1995 = 100)
Imports price index is derived by dividing the national accounts exports of goods and services in current U.S. dollars by imports of goods and services in constant 1995 U.S. dollars, (1995 = 100).
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ANNEX B
RISK TOOLS TARGET VARIABLE Database WDI 2003 DEFINITION WDI
Volatility of Net current transfers from abroad
Net current transfers from abroad (current US$, % of GDP)
Current transfers comprise transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net current transfers from abroad is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents.
Volatility of General government final consumption expenditure
General government final consumption expenditure (% of
GDP)
General government final consumption expenditure includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation.
Volatility of Gross national expenditure
Gross national expenditure (% of GDP)
Gross national expenditure is the sum of household final consumption expenditure (private consumption), general government final consumption expenditure (general government consumption), and gross capital formation (gross domestic investment).
Volatility of Gross National expenditure - Military
expenditure
Gross Ntl expenditure (% GDP)-Military expenditure (%GDP)
It is the difference between gross national expenditure and military expenditure (as percentages of GDP)
Volatility of Real interest rate Real interest rate (%) Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator.
Volatility of public spending on Education
Public spending on education, total (% of GDP)
Public expenditure on education consists of public spending on public education plus subsidies to private education at the primary, secondary, and tertiary levels.
Volatility of Health expenditure Health expenditure, total (% of GDP)
Total health expenditure is the sum of public and private health expenditures. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation.
Volatility of Health expenditure per capita
Health expenditure per capita (current US$)
Total health expenditure is the sum of public and private health expenditures as a ratio of total population. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities, and emergency aid designated for health but does not include provision of water and sanitation.
Volatility of Real effective exchange rate index
Real effective exchange rate index (1995 = 100)
Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs.
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ANNEX C
TRADE COUNTRY CHARACTERISTICSTARGET VARIABLE Database WDI 2003 DEFINITION WDI
Central government debt Central government debt, total (% of GDP)
Total debt is the entire stock of direct, government, fixed term contractual obligations to others outstanding at a particular date. It includes domestic debt and foreign debt. It is the gross amount of government liabilities not reduced by the amount of government claims against others. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year
Agricultural raw materials exports
Agricultural raw materials exports (% of merchandise
exports)
Agricultural raw materials comprise SITC section 2 (crude materials except fuels) excluding divisions 22, 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap)
Agricultural raw materials imports
Agricultural raw materials imports (% of merchandise
exports)
Agricultural raw materials comprise SITC section 2 (crude materials except fuels) excluding divisions 22, 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap).
Energy imports, net Energy imports, net (% of commercial energy use)
Net energy imports are calculated as energy use less production, both measured in oil equivalents. A negative value indicates that the country is a net exporter. Commercial energy use refers to apparent consumption, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.
Fuel exports Fuel exports (% of merchandise exports) Fuels comprise SITC section 3 (mineral fuels).
Fuel imports Fuel imports (% of merchandise exports)
Fuels comprise the commodities in SITC section 3 (mineral fuels).
Foreign direct investment by reporting economy
Foreign direct investment by reporting economy (IMF-BoP,
current US$)
Short-term debt Short-term debt (% of total external debt)
Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt.
Population, total Population, total
Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.
Roads, total network Roads, total network (km) Total road network includes motorways, highways, and main or national roads, secondary or regional roads, and all other roads in a country.
Fixed line and mobile telephones Fixed line and mobile telephones (per 1,000 people)
Mobile phones Mobile phones (per 1,000 people)
Mobile phones refers to users of portable telephones subscribing to an automatic public mobile telephone service using cellular technology that provides access to the public switched telephone network, per 1,000 people.
Internet hosts Internet hosts (per 10,000 people)
Employment in industry Employment in industry (% of total employment)
Employment in industry is the proportion of total employment recorded as working in the industrial sector. Employees are people who work for a public or private employer and receive remuneration in wages, salary, commission, tips, piece rates, or pay in kind. Industry includes mining and quarrying (including oil production), manufacturing, electricity, gas and water, and construction, corresponding to major divisions 2-5 (ISIC revision 2) or tabulation categories C-F (ISIC revision 3).
Trade Trade (% of GDP) Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product.
Private capital flows Private capital flows, total (% of GDP)
Private capital flows consist of private debt and nondebt flows. Private debt flows include commercial bank lending, bonds, and other private credits; nondebt private flows are foreign direct investment and portfolio equity investment.
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Employment in agriculture Employment in agriculture (% of total employment)
Employment in agriculture is the proportion of total employment recorded as working in the agricultural sector. Employees are people who work for a public or private employer and receive remuneration in wages, salary, commission, tips, piece rates, or pay in kind. Agriculture includes hunting, forestry, and fishing, corresponding to major division 1 (ISIC revision 2) or tabulation categories A and B (ISIC revision 3).
Domestic financing, total Domestic financing, total (% of GDP)
Domestic financing (obtained from residents) refers to the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. It includes all government liabilities--other than those for currency issues or demand, time, or savings deposits with government--or claims on others held by government and changes in government holdings of cash and deposits. Government guarantees of the debt of others are excluded. Data are shown for central government only.
Domestic credit to private sector Domestic credit to private sector (% of GDP)
Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.
Agriculture, value added Agriculture, value added (% of GDP)
Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.
Agricultural employment Agricultural employment (FAO)
Financing from abroad Financing from abroad (% of GDP)
Financing from abroad (obtained from nonresidents) refers to the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. It includes all government liabilities--other than those for currency issues or demand, time, or savings deposits with government--or claims on others held by government and changes in government holdings of cash and deposits. Government guarantees of the debt of others are excluded. Data are shown for central government only.
External debt External debt (% of exports of goods and services)
Rural population Rural population (% of total population)
Rural population is calculated as the difference between the total population and the urban population.
Labor force, female Labor force, female (% of total labor force)
Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises all people who meet the International Labour Organization's definition of the economically active population.
GINI index GINI index
Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of zero represents perfect equality, while an index of 100 implies perfect inequality.
Income share held by lowest 10%
Income share held by lowest 10%
Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.
Mortality rate, infant Mortality rate, infant (per 1,000 live births)
Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.
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Appendix A- Volatility of the main macroeconomic variables 1990-2000 (Standard Deviation)
Net current transfers from abroad (% of
GDP)
Military expenditure (% of central government expenditure)
Public health expenditure
(%GDP)
Public spending on
education (%GDP)
Central government
debt, total (% of GDP)
Real interest rate Real exchange rate Inflation rate
Albania 2,980 15,729 0,391 na 5,862 29,771 na 66,063
Bulgaria 0,958 4,407 0,770 1,038 0,000 25,511 32,917 304,082
Croatia 1,658 7,214 0,912 0,441 0,000 28,268 28,289 462,541
Czech Republic 0,343 2,536 0,480 0,449 2,515 2,952 32,651 2,964
Estonia 2,128 4,971 1,247 0,401 1,203 26,354 na 26,878
Hungary 0,870 2,446 0,462 0,853 12,681 2,150 30,712 6,362
Latvia 1,829 8,377 0,407 0,663 1,798 2,758 na 72,796
Lithuania 0,897 1,787 0,382 0,533 3,752 19,015 na 124,942
Poland 1,715 3,145 0,267 21,721 9,140 4,840 29,501 13,080
Romania 0,689 1,945 0,667 0,225 0,000 0,000 19,263 78,079
Slovak Republic 0,373 5,285 0,424 0,585 5,671 7,174 28,725 2,705
Slovenia 0,071 3,306 0,322 0,146 2,808 4,492 na 7,846
CEECs 7 0,717 3,296 0,484 3,574 6,563 7,853 28,962 59,303
Baltic 1,618 5,045 0,679 0,533 2,251 16,042 na 74,872
EEA 0,215 1,582 0,305 0,321 4,995 2,021 23,278 1,250
Other European 2,037 6,538 0,718 0,374 14,507 23,578 25,912 252,047
Source: World Bank, SIMA-GDF, GFS & WDI Central CEECs 7 include Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic and Slovenia. Other European include Albania, Belarus, Macedonia FYR, Russian Federation and Turkey.
Appendix B Volatility of the main macroeconomic variables 1990-2000 (Standard Deviation) Per capita
consumption growth rate (annual %)
GDP per capita growth rate (annual
%)
PC cons growth rate /
PC GDP growth rate
Life expectancy Enrollment Trade
(% of GDP)Terms of
TradeImport price
index
Current account balance
Albania 9,484 6,439 1,473 1,11 0,106 16,661 21,466 18,895 3,583
Bulgaria 5,283 5,263 1,004 0,31 0,039 11,358 5,316 9,187 3,932
Croatia 5,064 6,817 0,743 0,73 0,047 8,224 3,441 7,371 5,718
Czech Republic 3,218 2,695 1,194 0,91 0,043 13,110 5,503 7,047 2,520
Estonia 9,947 8,419 1,181 1,26 0,053 20,952 3,581 10,159 3,939
Hungary 3,303 2,475 1,335 0,83 0,065 23,435 2,813 6,559 3,409
Latvia 15,635 12,867 1,215 1,41 0,074 17,976 16,306 9,989 6,190
Lithuania 3,169 10,285 0,308 1,40 0,053 30,257 6,196 3,459 3,759
Poland 1,988 1,764 1,127 0,77 0,045 6,795 3,378 4,749 2,792
Romania 5,875 4,792 1,226 0,30 0,058 7,691 5,990 10,135 1,670
Slovak Republic 4,283 4,183 1,024 0,39 0,058 14,509 4,113 8,341 5,086
Slovenia 4,611 2,851 1,618 0,84 0,038 3,674 3,819 9,342 3,113
CEECs 7 4,080 3,432 1,189 0,62 0,050 11,510 4,419 7,909 3,217
Baltic 9,584 10,524 0,911 1,36 0,060 23,062 8,694 7,869 4,629
EEA 1,70 1,717 0,991 0,54 0,057 8,989 2,411 7,670 2,048
Other European 7,64 6,302 1,212 0,75 0,068 13,226 9,038 17,954 3,160
Source: World Bank, SIMA-GDF, GFS & WDI Central CEECs 7 include Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic and Slovenia. Other European include Albania, Belarus, Macedonia FYR, Russian Federation and Turkey.
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