A. Introduction

112. In the coming years, Slovenia will face increasing challenges in maintaining its export competitiveness. Slovenia’s high income level relative to the other new EU member states and emerging markets puts it at a disadvantage in terms of cost competitiveness. The loss of the exchange rate instrument will pose a further challenge to its competitive position. For a high–wage economy such as Slovenia, quality upgrading and specializing in higher–value–added niche markets will become an increasingly important strategy to withstand competition from low–cost economies and sustain its export market shares. In this regard, its ability to improve the quality and technological content of exports will be a key determinant of its long–run growth prospects and living standards.

113. Although most competitiveness indicators seem adequate for now, Slovenia’s trade performance appears to have been lagging behind its regional peers. Most indicators of cost competitiveness are favorable, and exports have been growing at a robust pace. Export market shares have also held stable. But the increase in export market share has been more limited than in the other new member states, where market shares have grown much more rapidly. This trend raises questions as to whether Slovenia is gradually falling behind in its export competitiveness.

114. In this context, this chapter aims to assess Slovenia’s competitive position by examining its trade patterns and analyzing whether its exports show evidence of technological and quality upgrading. In particular, it asks the following two questions:

• Is there evidence of technological and quality upgrading of Slovene exports?

• To what extent does the technological and quality upgrading help explain Slovenia’s export market performance?

115. The analysis finds that, although Slovenia has been increasing the share of hightechnology goods in its exports, including to the more developed markets of the EU–15, this increase is smaller than in most other emerging markets. While this trend is partly explained by Slovenia’s favorable initial position, its slower pace of quality upgrading also appears to have played a role. This slow growth, in turn, appears to be related to a catching–up process, as well as to limited opportunities for market linkages and technological spillover.

116. The chapter is organized as follows. Section B presents the background on overall competitive indicators; Section C discusses the literature on technological and quality upgrading on export competitiveness; Section D examines specialization trends in Slovenia’s exports and provides indicators of competitiveness of high–technology goods; Section E presents evidence on the quality content of exports; and Section F analyses the factors that could explain the market competitiveness trends and the determinants of quality upgrading.

B. Background

117. Most indicators suggest that the competitiveness of the Slovene economy is adequate. CPI–based and cost–based real effective exchange rates have been broadly stable over the past five years (text figure). Studies on equilibrium exchange rates (Bulir and Smidkova, 2004; and Egert and Lommatzsch, 2003) also do not suggest an overvaluation. Declining wage growth has slowed unit labor cost growth, although productivity–adjusted gross manufacturing wages, on average, are still high by regional standards. Export growth, as well as export profitability, remains robust. The variety of export products are on the rise. Slovenia also ranks favorably in competitiveness rankings (Lopez–Claros, Porter, and Schwab, 2006).

118. Despite these positive trends, Slovenia’s export performance is still lagging behind that of other new member states, and remains heavily dependent on a few export markets. While Slovenia has been maintaining its market share, other new member states (EU–8) have been making more significant gains in the EU–15 and world markets. Whereas the EU–8 market share in the EU–15 more than doubled from 2.2 percent to 4.7 percent between 1993 to 2004, Slovenia’s market share has remained relatively constant throughout this period, at about 0.25 percent. Exports to the markets outside of the EU–15, Balkans, and the USA have been steadily increasing at the expense of the EU–15 exports. But diversification within the EU–15 market appears to be relatively limited. The four biggest trading partners—which comprise in order Germany, Italy, Austria, and France—accounted for 83 percent of total goods exports to the EU–15 and 49 percent of total exports to the world, respectively. The sluggish growth in market share has been partly attributed to the slowdown in these key export markets—in particular, Germany and France—in the early half of this decade (IMAD, 2005). Nevertheless, these trends raise concerns about whether Slovenia is gradually losing ground to its regional peers and emerging market countries due to a loss of competitiveness.

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Market share in EU-15, 1994-2004

(In percent)

Citation: IMF Staff Country Reports 2006, 250; 10.5089/9781451835786.002.A006

Source: COMTRADE; and IMF staff calculations.

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The Structure of Slovenia’s Export Markets

(In percent)

Citation: IMF Staff Country Reports 2006, 250; 10.5089/9781451835786.002.A006

Source: COMTRADE; and IMF staff calculations.

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Slovenia: Competitiveness Indicators, 1996–2005

Citation: IMF Staff Country Reports 2006, 250; 10.5089/9781451835786.002.A006

Sources: International Financial Statistics, IMF; COMTRADE; Country authorities; Statistical Office of the Republic of Slovenia; and IMF staff calculations.1/ An increase indicates appreciation.

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C. Literature

119. The trade literature has highlighted the role played by quality and technological upgrading in export competitiveness. Theoretical models have discussed the importance of differentiation, through the production of greater varieties of goods or goods of higher quality, for maintaining export markets, especially for larger and richer countries (Krugman, 1980; Flam and Helpman, 1987; and Grossman and Helpman, 1991). One way to achieve this differentiation is through product innovation. When innovation leads to creation of a new product, the economy will have a competitive advantage in this product. In line with the theories of technological gaps and product life cycle (Posner, 1961; and Vernon, 1966), this product will be traded internationally to exploit innovation–driven monopoly profits, until imitation and standardized mass production reduce the competitive advantage. Thus, more advanced economies with higher wages and a more skilled labor force will specialize in new and rising industries that are driven by product innovation, such as high–technology industries, where they can have a competitive advantage. Alternatively, by exporting highquality goods, countries are also able to differentiate their products and obtain a higher price. Empirical evidence also shows that richer countries export goods with high–quality (Brooks, 2003, Schott, 2004), and that quality differences account for a substantial share of country differences in real income per worker (Hummels and Klenow, 2005). While the early literature focused on the role of preference–driven demand for high–quality goods, recent studies have also highlighted the need to upgrade the quality of exports to meet minimum quality requirements and to minimize transaction costs, given the incomplete contracts in international transactions (Hallak and Sivadasan, 2006). Against this background, we examine next the trends in the technological and quality content of Slovene exports as a key measure of the country’s ability to sustain export competitiveness.

D. Is There Increasing Specialization in High–Technology Products?

120. In this chapter, the specialization pattern of Slovenia’s exports is analyzed by examining the structure of exports and indicators of revealed comparative advantage (RCA). Developed by Balassa (1965), the RCA is defined as the ratio of the share of “product B” in the country’s total exports to the share of the “product B” in world exports. ⁴² For products in which the index is unity or greater, the country is deemed to have a comparative advantage. The main advantage of the RCA index is that it allows a more disaggregated analysis of the competitiveness of export products than the more standard measures of competitiveness discussed above (see Pitigala, (2005); Mahmood, (2000); Fertö and Hubbard, (2003); and World Bank, (1998)). In this chapter, the RCA is calculated based on the factor–intensity content of Slovenia’s exports and the imports in EU–15 markets, which have close trade links with Slovenia. The SITC three–digit (Rev. 3) export data between 1994 and 2004 from the United Nations COMTRADE database are used. Following Krause (1984) and Hinloopen and Van Marrewijk (2005), these data are classified into five different groups based on the factor intensity: (i) human capital (skilled) labor–intensive products; (ii) technology–intensive products; (iii) unskilled labor–intensive products; (iv) natural resource–intensive products; and (v) primary products.⁴³

121. The structure of Slovenia’s exports to the EU–15 shows specialization in technology–intensive and skilled–labor–intensive goods (text tables). Together, they account for around two–thirds of total exports. This specialization is in line with Slovenia’s high level of technological advancement and stock of skilled human capital. Slovenia’s ratio of number of students in tertiary education to the total population was around 5 percent in 2003, which is higher than the EU–25 average of 3.7 percent (Eurostat, 2005). Furthermore, Slovenia is ranked among the top four countries in the EU–25 in terms of youth education attainment level in 2004. The RCA indicators also show that over the past decade the competitiveness of technological–intensive exports has been gradually increasing, while that of unskilled labor–intensive exports has been declining (text table). Furthermore, since 2003, the RCA for technology–intensive exports has been greater than 1 suggesting that Slovenia’s exports to the EU–15 have been more competitive than the rest of the world’s.

Structure of Slovenia’s Exports to the EU–15 by Factor Content

(In percent)

Source: COMTRADE; and IMF staff calculations.

Structure of Slovenia’s Exports to the EU–15 by Factor Content

(In percent)

		Skilled		Natural	Unskilled
	Primary	Labor	Technology	Resource	Labor
	Products	Intensive	Intensive	Intensive	Intensive
1996	4	33	27	10	27
1997	4	33	28	10	25
1998	4	35	28	9	24
1999	4	33	30	9	24
2000	4	33	32	9	22
2001	4	32	32	9	22
2002	4	34	33	9	20
2003	4	33	35	9	20
2004	5	33	34	9	19

Source: COMTRADE; and IMF staff calculations.

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Structure of Slovenia’s Exports to the EU–15 by Factor Content

(In percent)

		Skilled		Natural	Unskilled
	Primary	Labor	Technology	Resource	Labor
	Products	Intensive	Intensive	Intensive	Intensive
1996	4	33	27	10	27
1997	4	33	28	10	25
1998	4	35	28	9	24
1999	4	33	30	9	24
2000	4	33	32	9	22
2001	4	32	32	9	22
2002	4	34	33	9	20
2003	4	33	35	9	20
2004	5	33	34	9	19

Source: COMTRADE; and IMF staff calculations.

Revealed Comparative Advantage Indices of Slovenia’s Exports to the EU-15

(Ratio)

Source: COMTRADE; and IMF staff calculations.

Revealed Comparative Advantage Indices of Slovenia’s Exports to the EU-15

(Ratio)

	Primary	Skilled	Technology	Natural	Unskilled
	Products	Labor	Intensive	Resource	Labor
		Intensive		Intensive	Intensive
1996	0.40	1.28	0.87	1.80	2.07
1997	0.44	1.27	0.87	1.82	1.91
1998	0.44	1.26	0.83	1.76	1.91
1999	0.45	1.20	0.84	1.84	1.98
2000	0.45	1.32	0.86	1.85	1.95
2001	0.45	1.26	0.89	1.86	1.91
2002	0.45	1.25	0.93	1.81	1.76
2003	0.45	1.22	1.01	1.92	1.69
2004	0.42	1.26	1.01	2.22	1.69

Source: COMTRADE; and IMF staff calculations.

View Table

Revealed Comparative Advantage Indices of Slovenia’s Exports to the EU-15

(Ratio)

	Primary	Skilled	Technology	Natural	Unskilled
	Products	Labor	Intensive	Resource	Labor
		Intensive		Intensive	Intensive
1996	0.40	1.28	0.87	1.80	2.07
1997	0.44	1.27	0.87	1.82	1.91
1998	0.44	1.26	0.83	1.76	1.91
1999	0.45	1.20	0.84	1.84	1.98
2000	0.45	1.32	0.86	1.85	1.95
2001	0.45	1.26	0.89	1.86	1.91
2002	0.45	1.25	0.93	1.81	1.76
2003	0.45	1.22	1.01	1.92	1.69
2004	0.42	1.26	1.01	2.22	1.69

Source: COMTRADE; and IMF staff calculations.

122. Under alternative classifications of high–technology export products, it is also evident that Slovenia’s exports of goods with high technological content are increasing as a share of total exports to the world. A total of six different classifications of high–tech and high–skill exports are examined. Following the taxonomies in Peneder (2001), export products are classified under two different criteria by: (i) by factor intensity; and (ii) labor skill intensity. In line with Hatzichronoglou (1997), manufacturing industries are also classified under various levels of technology intensity. Another taxonomy, following Dulleck and others (2005), classifies industries under different levels of technological content. Under this classification, industries under machinery, equipment, and transport are included as high–technology industries. In addition, the shares of exports of information and communication technology (ICT) goods and high–technology product goods are also calculated.⁴⁴ Under each of these categories, the share of the high–technology segments has increased between the years 1994 and 2004 (text figures). For example, the shares of exports with high–technology intensity and of high skill intensity each increased by around 5 percent. Exports of high–technology industries goods, have also increased by around 8 percent as a share of total exports. The share of ICT industries, however, has remained at the same level over this time period.

123. While this specialization is in keeping with the trend in other European countries, Slovenia seems to be shifting to high–technology exports more slowly than the other EU–8 countries. Aside from the high–tech–product exports and the high–skill–intensive exports, where Slovenia has advanced the most, the rate at which Slovenia is shifting into high–technology goods is generally slower than that of other EU–8 members, particularly the Central European countries (CECs) of Hungary and the Czech and Slovak Republics. These findings are consistent with the studies on technological upgrading (Dulleck and others 2005; and Landesmann, 2003), which also show a structural shift toward high–technology industries of the EU–8 in its exports to the EU, with the CECs leading the group.

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Europe: Increase in Share of High–Technology Exports to World, 1994–2004 ¹/

(In percent)

Citation: IMF Staff Country Reports 2006, 250; 10.5089/9781451835786.002.A006

Sources: COMTRADE database; and staff calculations.¹/ EU–15 data excludes Belgium, Ireland, and Luxembourg.

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124. An examination of the RCA indicators also suggests that Slovenia’s competitive gains vis–à–vis the rest of the world are not as strong as the CECs’. A look at the RCA indicators between 1994 and 2004 shows some regularities (text figures):

• Slovenia held a more favorable initial competitive position than the other EU–8 countries. In 1994, Slovenia’s RCA was among the highest in the EU–8, with the RCA furthest to the right. This is the case under all technological classifications, except for the ICT industries.

• Over the past decade, Slovenia’s position has been relatively stable, as demonstrated by its position near the 45–degree line. The CECs, in particular, are placed above the 45–degree line, suggesting their competitive position is advancing more rapidly.

• During this decade, other new member states have been rapidly catching up. Indeed, by 2004, many of these countries have RCAs above 1, indicating a stronger competitive position in these products. The CECs tend to be positioned more upper left than to Slovenia. However, in the high–technology product and high skill intensity products (not shown), Slovenia’s position has improved and is close to that of the EU–8.

• A partial explanation for the relatively slow gain in the RCA of Slovene exports could be its initial position. A simple regression line on the RCA levels for the high technology exports for 56 developed and emerging markets in the sample has a slope less than the 45–degree line. This suggests that the catch–up effect may play a significant role in the growth rate of market share. This also indicates that specialization has not increased in products that did not hold an initial competitive advantage existed. These factors are tested more formally in Section F.

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Cross–Country Comparison: Revealed Comparative Advantage, 1994 vs. 2004

(Ratio)

Citation: IMF Staff Country Reports 2006, 250; 10.5089/9781451835786.002.A006

Sources: COMTRADE database; and staff calculations.

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E. Is There Evidence of Quality Upgrading?

125. In this study, quality is proxied using unit value ratios (UVRs) of manufacturing export goods Following studies on quality upgrading by Dulleck and others (2005), Hallak and others (2005), and Landesmann (2003), this ratio tries to capture the quality content of the export product that is implicit in its price. This approach is based on the assumption that, for the same product, a higher price is paid for greater quality. The UVR is calculated as the ratio of unit value for a Slovene export product to that of the world export of the same product.⁴⁵, ⁴⁶ To achieve homogeneity of goods to the maximum extent possible, the unit values are calculated at the six–digit level of the Harmonized System Classification from the COMTRADE database. The product level UVRs are then aggregated using current–year shares as weights. To ensure comparability, the methodology also ensures a common basket of goods for both the country and the benchmark of total world exports.

126. Since 1994, Slovenia appears to have improved the quality content of its exports, as measured by an increase in UVRs, but the pace of improvement is lagging behind the EU–8, especially in high–technology exports. The change in the country–level UVR between 1994 and 2004 shows a positive improvement (text figures). This is in keeping with the trend in most developed and emerging market countries and ranks about average, compared with the rest of the EU–8 countries. A closer look at the high technology exports reveals, however, that Slovenia’s pace of quality improvement is lagging behind many of the EU–25 countries. This trend is observed under all the technological classifications.

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Europe: Increase in UVR of Manufacturing and High Technology Exports, 1994–2004 ¹/

(Ratio)

Citation: IMF Staff Country Reports 2006, 250; 10.5089/9781451835786.002.A006

Sources: COMTRADE and staff calculations.¹/ Several EU–8 countries whose data are missing are excluded. EU–15 excludes Belgium, Ireland, and Luxembourg.

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127. The increase in UVRs over time could be driven by a quantitative shift toward high–quality products or by higher prices for the same exports. Under the assumption of scale economies in the production of high–quality goods, an exogenous increase in demand—for example, through trade openness—of a relatively high–quality good would generate larger output. Thus, even though prices have remained unchanged, the increasing share of the high quality good in the export composition would lead to an increase in UVRs. UVRs would also increase if improving cost competitiveness enabled new entrants to focus on producing higher–quality goods. One method to determine whether the increase in the UVR is being driven by a price increase or by a shift into products with a higher–quality content is by calculating the UVRs using the base–year shares as constant weights. In Slovenia’s case, the increase in the UVR of high–tech products under current weights is higher than under constant weights, using 2000 as the base year. This indicates a shift in the composition of exports, with an increasing share of products with higher UVRs. This trend is observed under most of the technological classifications. In the CECs, this trend is less evident, suggesting that the increase in UVRs could be driven more by price effects in these countries. The price increase, in turn, could reflect higher quality content, owing to technical improvements or even externalities from market perception or preference for quality (such as labeling or brand image). This may be particularly relevant for countries that exhibit a faster catch–up effect. The following section tests more formally the different factors driving the increase in UVRs and the improvement in market competitiveness.

F. Empirical Analysis

128. To better understand the factors behind market share performance and to test whether quality upgrading explains export market performance, we estimate the change in market share under the following specification:

${\mathrm{\Delta }Share}_{j,t}=\alpha +{\beta }_1{\mathrm{\Delta }Share}_{j,t-1}+{\beta }_2{\mathrm{Sh}are}_{j,0}+{\beta }_3{\mathrm{\Delta }UVR}_{j,t}+{\beta }_4{\mathrm{\Delta }REER}_{j,t}+{\beta }_5{Tradeopenness}_{j,t}+{\beta }_6{FDI}_{j,t}+{\epsilon }_{i,j,}$

where j represents an individual country in time–series panel data. The equation is estimated using a fixed–effects methodology across country observations. A negative coefficient β₂ on the initial share indicates a catch–up effect, as countries with a small initial shares grow faster. The coefficient β₃ estimates the effect of improved quality, contained in the UVR indicator, in gaining market share. We also introduce control variables such as the real effective exchange rate to capture the impact of price competitiveness. Similarly, to proxy for the impact of market linkages and trade policies, we include FDI and trade openness, both as a percent of GDP. The equation is also estimated using changes over different time windows. The data in the full sample cover 129 countries over the period 1994–2004.

129. The empirical estimation for market performance points to several findings:

• Catch–up plays a significant role in gaining market share (text table). This relationship is robust to alternative specifications of time windows and country samples. The speed of convergence, implicit in the coefficient for the initial laggedlevel market share, is faster for the sample of developing economies compared than for emerging markets, in line with the catch–up hypothesis. However, the coefficient is quite large for the developed economies. Among these countries, the market share—as a relative concept—is being lost as other countries catch up. This relationship holds even after controlling for factors such as trade openness and price competitiveness. In the case of Slovenia, this could partly explain the export performance of its high–tech industries, where Slovenia held a comparative advantage in the mid–1990s. However, this still does not explain the total export performance because even though Slovenia’s initial aggregate export market share was smaller than the CECs, the growth in its market share was still smaller.

• The strong relation of trade openness to market share gain among the emerging markets could suggest a positive impact of growing linkages through trade integration. However, this relationship is not very robust and is sensitive to the choice of country groups.

• A positive relationship between REER appreciation and market share improvements is particularly strong for emerging markets and developing economies. Interestingly, one would have anticipated that a REER appreciation would have led to a loss of competitiveness and market share. But the opposite sign could be a reflection of reverse causality, as a growing trade share is linked to productivity improvement and Balassa–Samuelson effects.

• For emerging markets, quality upgrading is also important for improving export performance. In this group of countries, the increase in UVR shows a strong positive relationship with gains in market share. This result supports the role of quality upgrading as a key determinant among emerging markets since export market shares are increasing even as unit prices are going up. Among the developed economies, however, this relationship is negative and significant. For these countries, market share has been declining on average despite an improvement in UVRs. This development may reflect more difficulty in retaining market share for a given level of UVR growth, as other developing and emerging market countries catch up faster. Indeed, an interaction term between initial share and UVR growth enters the regression positively for the sample of developed economies (not shown), suggesting that enhancing quality, for a given level of initial share, helps to retain market share. Among the developing economies, the negative relationship despite the catching–up effect could suggest either underlying structural problems or a capturing of the price effect in the UVR indicators. Below, we test for the factors driving the UVR growth in the data.

130. Given the differential impact of UVR growth on market share, we examine in more detail some of the factors that determine growth in the UVR index. We test the following specification, again using a fixed–effects model:

${\mathrm{\Delta }UVR}_{i,t}=\alpha +{\beta }_1{\mathrm{\Delta }UVR}_{i,t}+{\beta }_2{\mathrm{UV}R}_{i,o}+{\beta }_3{\mathrm{\Delta }REER}_{i,t}+{\beta }_4{Tradeopenness}_{i,t}+{\beta }_5{FDI}_{i,t}+{\epsilon }_{i,t\mathrm{.}}$

UVR growth is regressed on the initial UVR to examine the catch–up effect. In the presence of scale economies in producing high quality goods, one would expect a positive value of the coefficient β₂. But if factors other than technological intensity, such as improved image and consumer preferences are driving higher demand and prices, the scope for catch–up could be greater. Real exchange rate appreciation is also included to examine whether a rise in prices is driving UVRs. Finally, to test whether cross–border technological spillovers and learning through market integration play a role, we include FDI and trade openness, measured as the total exports and imports as a percent of GDP. Trade openness can also be an important indicator of quality upgrading as production of higher–quality goods would require more imports of intermediate goods. All variables are expressed in logs.

131. The results indicate several empirical regularities, as follows:

• The catch–up effect is again significant, as countries with a lower initial UVR experience faster UVR growth. The coefficient on initial UVR is negative and highly significant across the different time windows and country groups (Text Tables 2a and 2b). In keeping with the growth literature, the speed of convergence is found to decline faster among the emerging market and developed economies than among the developing economies. This negative relationship is also observed when using a subsample for the various technological segments.

• Market spillover effects are important for quality upgrading. Trade openness enters the regression positively and is significant across most samples and time periods. FDI is also positively related to UVR growth and is significant, in particular, for the emerging markets group. This outcome indicates that knowledge spillover and market linkages have played an important role in enhancing quality in this group of countries.

Table 1.

Dependent Variable: Change in World Market Share

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

Table 1.

Dependent Variable: Change in World Market Share

	All countries, 1 yr	All countries, 3 yr	Emerging, 1 yr	Developed, 1 yr	Developing, 1 yr
Lagged dependent variable	−0.133	−0.034	−0.195	0.01	−0.136
	(1.73	(0.56)	(3.14)**	(0.11)	(1.62)
Lagged level market share	−0.835	−0.924	−0.546	−0.867	−0.849
	(9.97)**	(10.53)**	(4.21)**	(5.52)**	(8.82)**
Change in country UVR	−0.122	−0.148	0.241	−0.132	−0.2
	(1.32)	(1.61)	(4.29)**	(2.03)	(1.37)
Change in REER	0.44	0.14	0.548	−0.219	0.44
	(3.13)**	(1.04)	(1.85)	(0.70)	(2.58)*
FDI	−0.021	0.011	−0.028	0.02	−0.038
	(0.75	(0.34)	(1.18)	(1.07)	(0.75)
Trade openness	0.224	−0.062	0.56	−0.428	0.165
	(1.78)	(0.21)	(4.16)**	(1.76)	(0.98)
Constant	−5.666	−4.957	−5.08	−1.602	−6.274
	(7.83)**	(3.77)**	(7.99)**	(1.91)	(6.41)**
Observations	997	763	270	186	540
R-squared	0.51	0.45	0.48	0.52	0.51
Number of Countries	118	119	31	22	65

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

View Table

Table 1.

Dependent Variable: Change in World Market Share

	All countries, 1 yr	All countries, 3 yr	Emerging, 1 yr	Developed, 1 yr	Developing, 1 yr
Lagged dependent variable	−0.133	−0.034	−0.195	0.01	−0.136
	(1.73	(0.56)	(3.14)**	(0.11)	(1.62)
Lagged level market share	−0.835	−0.924	−0.546	−0.867	−0.849
	(9.97)**	(10.53)**	(4.21)**	(5.52)**	(8.82)**
Change in country UVR	−0.122	−0.148	0.241	−0.132	−0.2
	(1.32)	(1.61)	(4.29)**	(2.03)	(1.37)
Change in REER	0.44	0.14	0.548	−0.219	0.44
	(3.13)**	(1.04)	(1.85)	(0.70)	(2.58)*
FDI	−0.021	0.011	−0.028	0.02	−0.038
	(0.75	(0.34)	(1.18)	(1.07)	(0.75)
Trade openness	0.224	−0.062	0.56	−0.428	0.165
	(1.78)	(0.21)	(4.16)**	(1.76)	(0.98)
Constant	−5.666	−4.957	−5.08	−1.602	−6.274
	(7.83)**	(3.77)**	(7.99)**	(1.91)	(6.41)**
Observations	997	763	270	186	540
R-squared	0.51	0.45	0.48	0.52	0.51
Number of Countries	118	119	31	22	65

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

Table 2a

Dependent Variable: Change in Country UVR,

(1, 3 and 5 year windows)

Robust t statistics in parentheses

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

Table 2a

Dependent Variable: Change in Country UVR,

(1, 3 and 5 year windows)

	All, 1yr	All, 1yr	All, 3 yr	All, 3 yr	All, 5 yr	All, 5 yr
Lagged Dependent Variable	−0.305	−0.361	0.141	0.151	−0.038	−0.019
	(2.09)*	(2.16)*	(1.57)	(1.42)	(0.87)	(0.35)
Lag level (initial UVR)	−0.391	−0.295	−0.886	−0.847	−0.847	−0.519
	(4.11)**	(3.45)**	(9.64)**	(8.72)**	(3.75)**	−1.34
Change in REER	0.09	0.054	−0.044	0.042	0.092	0.127
	(1.37)	(0.84)	(1.18)	(1.00)	(1.13)	(1.33)
FDI		0.027		0.051		−0.031
		(1.78)		(2.57)*		(0.68)
Trade Openness	0.213		0.396		−0.099
	(2.90)**		(2.08)*		(0.64)
Constant	−0.791	0.076	−1.448	0.172	0.735	0.127
	(2.57)*	(4.14)**	(1.84)	(8.90)**	(1.12)	(1.33)
Observations	1127	999	873	765	620	541
Number of Country	127	118	127	119	125	115
R-squared	0.29	0.28	0.32	0.31	0.11	0.04

Robust t statistics in parentheses

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

View Table

Table 2a

Dependent Variable: Change in Country UVR,

(1, 3 and 5 year windows)

	All, 1yr	All, 1yr	All, 3 yr	All, 3 yr	All, 5 yr	All, 5 yr
Lagged Dependent Variable	−0.305	−0.361	0.141	0.151	−0.038	−0.019
	(2.09)*	(2.16)*	(1.57)	(1.42)	(0.87)	(0.35)
Lag level (initial UVR)	−0.391	−0.295	−0.886	−0.847	−0.847	−0.519
	(4.11)**	(3.45)**	(9.64)**	(8.72)**	(3.75)**	−1.34
Change in REER	0.09	0.054	−0.044	0.042	0.092	0.127
	(1.37)	(0.84)	(1.18)	(1.00)	(1.13)	(1.33)
FDI		0.027		0.051		−0.031
		(1.78)		(2.57)*		(0.68)
Trade Openness	0.213		0.396		−0.099
	(2.90)**		(2.08)*		(0.64)
Constant	−0.791	0.076	−1.448	0.172	0.735	0.127
	(2.57)*	(4.14)**	(1.84)	(8.90)**	(1.12)	(1.33)
Observations	1127	999	873	765	620	541
Number of Country	127	118	127	119	125	115
R-squared	0.29	0.28	0.32	0.31	0.11	0.04

Robust t statistics in parentheses

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

Table 2b

Dependent Variable: Change in Country UVR

(one year window)

Robust t statistics in parentheses

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

Table 2b

Dependent Variable: Change in Country UVR

(one year window)

	All	All	Developing	Developing	Emerging	Emerging	Developed	Developed
Lagged Dependent Variable	−0.361	−0.305	−0.392	−0.274	−0.129	−0.09	−0.158	−0.21
	(2.16)*	(2.09)*	(1.75)	(1.30)	(0.90)	(0.62)	(1.77)	(2.92)**
Lag level (initial UVR)	−0.295	−0.391	−0.407	−0.578	−0.2	−0.249	−0.274	−0.276
	(3.45)**	(4.11)**	(2.11)*	(2.76)**	(3.57)**	(5.77)**	(2.57)*	(2.34)*
Change in REER	0.054	0.09	0.023	0.014	0.082	0.231	0.155	0.267
	(0.84)	(1.37)	(0.27)	(0.17)	(0.66)	(1.89)	(0.55)	(1.30)
FDI	0.027		0.016		0.056		0.029
	(1.78)		(0.79)		(1.93)		(1.18)
Trade Openness		0.213		0.128		0.397		0.963
		(2.90)**		(1.72)		(2.53)*		(3.11)**
Constant	0.076	−0.791	0.065	−0.436	0.043	−1.646	0.125	−3.837
	(4.14)**	(2.57)*	(2.22)*	(1.44)	(1.50)	(2.39)*	(4.03)**	(3.05)**
Observations	999	1127	542	655	270	279	186	193
Number of Country	118	127	65	74	31	31	22	22
R-squared	0.28	0.29	0.36	0.37	0.12	0.14	0.17	0.22

Robust t statistics in parentheses

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

View Table

Table 2b

Dependent Variable: Change in Country UVR

(one year window)

	All	All	Developing	Developing	Emerging	Emerging	Developed	Developed
Lagged Dependent Variable	−0.361	−0.305	−0.392	−0.274	−0.129	−0.09	−0.158	−0.21
	(2.16)*	(2.09)*	(1.75)	(1.30)	(0.90)	(0.62)	(1.77)	(2.92)**
Lag level (initial UVR)	−0.295	−0.391	−0.407	−0.578	−0.2	−0.249	−0.274	−0.276
	(3.45)**	(4.11)**	(2.11)*	(2.76)**	(3.57)**	(5.77)**	(2.57)*	(2.34)*
Change in REER	0.054	0.09	0.023	0.014	0.082	0.231	0.155	0.267
	(0.84)	(1.37)	(0.27)	(0.17)	(0.66)	(1.89)	(0.55)	(1.30)
FDI	0.027		0.016		0.056		0.029
	(1.78)		(0.79)		(1.93)		(1.18)
Trade Openness		0.213		0.128		0.397		0.963
		(2.90)**		(1.72)		(2.53)*		(3.11)**
Constant	0.076	−0.791	0.065	−0.436	0.043	−1.646	0.125	−3.837
	(4.14)**	(2.57)*	(2.22)*	(1.44)	(1.50)	(2.39)*	(4.03)**	(3.05)**
Observations	999	1127	542	655	270	279	186	193
Number of Country	118	127	65	74	31	31	22	22
R-squared	0.28	0.29	0.36	0.37	0.12	0.14	0.17	0.22

Robust t statistics in parentheses

^*significant at 5%;

^**significant at 1%; significance above 10 % threshold marked in bold.

132. These results suggest that, despite its comparative advantage with a high–skill workforce, weak market linkages may have contributed to the slower quality upgrading in Slovenia. Given that Slovenia’s FDI has been much lower than in all other EU–8 and some EU–15 countries (Text Table 3), this could have played a role in the slower quality upgrading. The role of relatively weak market linkages in Slovenia is also suggested by the fact that, despite spending substantially higher amounts on research and development than the CECs, Slovenia has produced a much smaller number of commercial applications in the form of patents. Furthermore, despite a very high tertiary school enrollment rate, the number of Slovenes with a tertiary education who are in the labor force is much smaller than the average in both the EU–8 and EU–15 countries.

Table 3:

Indicators of Investment and Technological Progress, 2000-04

Sources: Eurostat; World Economic Outlook, IMF; World Development Indicators, World Bank; OECD; US Patent and Trademark Office.

^1/EU-15 data excludes Belgium, and Luxembourg.

^2/Data reflects average over the sample period. The time periods for individual country data may vary within this range depending upon data availability.

Table 3:

Indicators of Investment and Technological Progress, 2000-04

		FDI, 2000-04,	R&D, 2000-02,	US Patents, 2000-	School enrollment,	Labor force with tertiary	Manufacturing
		(Percent of GDP)	(Percent of GDP)	04, (Total)	tertiary, 2000-02 (In	education, 2000-01, (In	Productivity Growth,
					percent of Gross)	percent of total)	2000-04 (In percent)
Slovenia		2.3	1.5	18	65	17	6.3
EU-8 average		5.0	0.8	16	54	23	7.8
	Czech	7.3	1.2	28	33	11	7.7
	Hungary	4.9	0.9	53	45	16	5.3
	Poland	3.5	0.6	15	58	14	8.1
	Slovakia	7.5	0.6	5	32	29	7.0
	Estonia	7.9	0.6	3	64	36	10.0
	Latvia	3.4	0.4	2	68	20	9.1
	Lithuania	3.3	0.6	2	65	45	9.3
EU-15 average		4.6	2.0	1,951	60	21	0.0
	Austria	2.4	2.1	551	51	12	4.8
	Denmark	5.7	2.5	457	63	25	1.1
	Finland	3.6	3.4	788	86	32	6.7
	France	2.8	2.2	3,829	54	26	0.3
	Germany	2.8	2.5	11,000	50	19	3.6
	Greece	0.7	0.6	20	68	24	−2.2
	Ireland	16.1	1.1	148	50	26	na
	Italy	1.2	1.1	1,696	53	12	−0.1
	Netherlands	8.0	1.9	1,312	57	12	3.1
	Portugal	3.9	0.9	13	53	9	2.4
	Spain	4.3	1.0	283	59	27	0.4
	Sweden	5.0	4.3	1,561	77	27	4.9
	UK	3.8	1.9	3,712	62	26	na

Sources: Eurostat; World Economic Outlook, IMF; World Development Indicators, World Bank; OECD; US Patent and Trademark Office.

^1/EU-15 data excludes Belgium, and Luxembourg.

^2/Data reflects average over the sample period. The time periods for individual country data may vary within this range depending upon data availability.

View Table

Table 3:

Indicators of Investment and Technological Progress, 2000-04

		FDI, 2000-04,	R&D, 2000-02,	US Patents, 2000-	School enrollment,	Labor force with tertiary	Manufacturing
		(Percent of GDP)	(Percent of GDP)	04, (Total)	tertiary, 2000-02 (In	education, 2000-01, (In	Productivity Growth,
					percent of Gross)	percent of total)	2000-04 (In percent)
Slovenia		2.3	1.5	18	65	17	6.3
EU-8 average		5.0	0.8	16	54	23	7.8
	Czech	7.3	1.2	28	33	11	7.7
	Hungary	4.9	0.9	53	45	16	5.3
	Poland	3.5	0.6	15	58	14	8.1
	Slovakia	7.5	0.6	5	32	29	7.0
	Estonia	7.9	0.6	3	64	36	10.0
	Latvia	3.4	0.4	2	68	20	9.1
	Lithuania	3.3	0.6	2	65	45	9.3
EU-15 average		4.6	2.0	1,951	60	21	0.0
	Austria	2.4	2.1	551	51	12	4.8
	Denmark	5.7	2.5	457	63	25	1.1
	Finland	3.6	3.4	788	86	32	6.7
	France	2.8	2.2	3,829	54	26	0.3
	Germany	2.8	2.5	11,000	50	19	3.6
	Greece	0.7	0.6	20	68	24	−2.2
	Ireland	16.1	1.1	148	50	26	na
	Italy	1.2	1.1	1,696	53	12	−0.1
	Netherlands	8.0	1.9	1,312	57	12	3.1
	Portugal	3.9	0.9	13	53	9	2.4
	Spain	4.3	1.0	283	59	27	0.4
	Sweden	5.0	4.3	1,561	77	27	4.9
	UK	3.8	1.9	3,712	62	26	na

Sources: Eurostat; World Economic Outlook, IMF; World Development Indicators, World Bank; OECD; US Patent and Trademark Office.

^1/EU-15 data excludes Belgium, and Luxembourg.

^2/Data reflects average over the sample period. The time periods for individual country data may vary within this range depending upon data availability.

133. The analysis can be expanded in several dimensions. First, in understanding trade patterns, gravity models have played a significant role. Thus, the impact of trade distance and its interaction with measures of technological and quality gaps with trading partners can be used. Second, in order to disentangle the effect of prices from that of quality improvement, export data based on volume (tons) can be used. Third, since the link between quantities exported and prices, as represented by the UVRs, may be distorted by the presence of a greater variety of products (Hallak and Schott, 2005), the effect of variety in isolating the role of quality can also be examined. Fourth, empirical analysis can also be done at a more disaggregated level to examine more directly whether catching up, market spillover, and investment lead to higher quality and market performance. Answering this question would be particularly important since the literature has noted the differential impact across different industries and quality segments. Finally, with many emerging market countries, particularly the EU members, focusing on increased research spending in order to achieve faster technological progress, the impact of this greater spending on quality, as well as market performance, can also be analyzed.

G. Conclusions and Policy Implications

134. This paper focuses on two key questions, the answers to which provide important indicators of Slovenia’s future prospects for export competitiveness and growth enhancement. First, it examines whether there is evidence of technological and quality upgrading of Slovene exports. Second, it tries to analyze to what extent the technological and quality upgrading helps explain Slovenia’s export market performance. This is particularly interesting, given that Slovenia is gaining export market shares more slowly than the other new EU member states. The paper also takes a comparative look at Slovenia’s technological upgrading process vis–à–vis other European countries.

135. The paper finds increasing specialization trends in high–technology products, as well as quality upgrading, in Slovenia’s exports to the world. This is observed from the data on Slovenia’s export composition, revealed comparative advantage, and unit value ratio indicators, all of which show improvements in high–technology and high–skilled export products over the past decade. This trend is also consistent across a number of different types of classification of high–technology products.

136. Nevertheless, the pace of technological and quality upgrading is slower than that of the EU–8 countries. The Central European countries, in particular, are making much faster gains in terms of shifting their export composition to high technology exports, as well as upgrading the quality content of these exports. The empirical analysis suggests that this could be related to their faster catching–up process, as well as to stronger market spillover and learning effects. These findings suggest that, in order to enhance its export market performance, Slovenia will need to improve its market linkages by creating a more conducive environment for investment, which will, in turn, enable technological upgrading and productivity improvements.