Abstract
Summary
Summary
Since 1995, NMS-62 as a group has experienced a spectacular increase in exports to the EU. However, this masks considerable heterogeneity across these countries: the Czech Republic, Hungary and Poland have steadily increased exports to the EU as a share of GDP, while the performance of Bulgaria and Romania has been less stellar.
Structural and institutional factors explain a significant part of the variance in export performance. In particular, human capital, labor skills, foreign investment environment, and wage competitiveness are found to be highly significant in explaining export performance of NMS in the EU market as are reforms that help countries link up with global supply chains. For countries where there is a significant room for increasing exports to the EU, these reforms can help maximize benefits of access to the larger market.
While higher exports help growth, quality improvement is important for sustained income convergence. For NMS-6, overall quality of exports is relatively high when compared to that of other exporters of similar products in the world market, but less so when assessed in the EU market. In both markets, Romania and Bulgaria show the largest room for quality improvement while Hungary and Czech Republic, countries specializing in mid-quality products, show the least room tend for improvement among the NMS.
Policy priorities to improve export quality need to be mindful of initial conditions. For countries specializing at lower end products, priorities include a better foreign investment regime and higher links with supply chains that would allow access to technology. For countries specializing in higher quality products, innovation via sustained pursuit of higher education and R&D spending is key to further improvement of quality.
Significant boost in exports can come from further liberalization of services trade within the EU. The implementation of the EU Services Directive (SD) resulted in a sizable reduction in import restrictions and the NMS-6 seems to be benefiting more than other EU members. However, significant barriers remain in products where the NMS-6 hold a comparative advantage, including in professional and technical services product. Further liberalization by EU members and a new impetus to liberalize services trade will help NMS-6 enhance their exports to the single market.
A. Introduction
1. Being part of the European Union (EU) allows new member states (NMS) access to a larger market for their products and provides an anchor for growth and convergence.3 The EU single market provides opportunities for firms to grow, and, at the same time, subjects them to stronger competition raising incentives to improve productivity. The open trade and investment regime in turn also acts as a conduit for technology transfer that over time improves quality of exports. Higher exports and quality create a virtuous cycle of growth and convergence (Hausmann et al. (2007), see text chart).
Export Quality and GDP Per Capita, 2010
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
Source: WDI, Henn, Papageorgiou, and Spatafora (2013), and IMF staff calculations.2. In this paper, we examine export performance of NMS in the EU single market, with a focus on the six new member states which are not yet part of the euro area (NMS-6): Bulgaria, Croatia, Czech Republic, Hungary, Poland and Romania. We focus on the following questions: How successful have these countries been in taking advantage of their unrestricted access to the EU single market?4 What structural factors matter most? Has export integration with the EU been associated with improvement in export quality? What role has services sector played in exports? And going forward can services exports play a bigger role?
B. Evolution of Exports to EU: Relative Success and its Determinants
3. NMS-6 shows a varying degree of success in taking advantage of the EU single market. As a group, NMS-6 export more goods and services to the EU than other members measured in both gross and value added terms (scaled by GDP) (Figure 1). However, within NMS-6, there is a range. The Czech Republic and Hungary, being among the most open economies in the world, derive a quarter of their GDP from value added exports to the EU, while this share is less than one tenth for Romania. Since the crisis, exports have played a stronger role in growth counting for a higher share of GDP in all NMS-6 countries except Croatia. This has been driven by higher exports to the EU, as well as outside the EU (Figure 1).
NMS: Gross and Value Added Exports of Goods and Services
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
Note: More up-to-date data for value added exports of goods and services could not be shown as the World Input Output Table does not go beyond 2011.Source: Staff calculations using World Input Output Table, Eurostat and Haver Analytics.4. What factors explain the varying export performance in the single market? We investigate this question empirically in a sample of ten NMS for the period 2003-11.5 Our variable of interest is value added exports to the EU. Scaled by GDP, this variable tells us what share of economic activity in NMS is generated by import demand from the EU single market We chose value added as opposed to gross exports since the former measures exports more accurately taking out re-exports and imported inputs.6
5. In what follows, we examine the role of structural factors in export performance. The choice of structural variables draws on trade literature which emphasizes the importance of human capital (Bougheas and Riezman 2007, and Bombardini et al. 2012) and institutional quality (e.g. Anderson and Marcouiller 2002, and Levchenko 2007). These factors affect competitiveness and export performance of a country by influencing the overall environment in which firms operate. In selecting variables, we started with a large set that include human capital, labor market efficiency and flexibility, foreign investment, physical and virtual infrastructure, and governance. The final selection was made based on data availability and statistical significance. Below are the five variables that were included in our preferred regression specification, all of which show a strong correlation with value added exports to the EU.7
Human capital. Better human capital improves a country’s exports through expansion of productive capacity over time. Human capital is proxied by two variables: higher education (upper secondary or tertiary education attainment) and the share of employed participating in continuous vocational training and skills upgrade. The second variable, which takes into account on-the-job training and skill upgrade, also implicitly captures the degree of skills match in the economy (for example, Card and others (2009) found that vocational and on-the-job training programs tend to lead to better labor market outcomes).
Labor market efficiency. A well-functioning labor market is critical for ensuring an efficient allocation of labor force and providing incentives to work. We proxy labor market efficiency with the two variables: inactivity trap, which captures incentives to stay out of work force either because after-tax income is too low or social benefits are too generous (a larger value indicates weaker incentive to work), and minimum wage relative to gross average wage, which captures wage competitiveness of low-skilled labor.
Foreign investment environment. The importance of foreign direct investment in promoting exports and technology transfer is well-known. This is particularly so for NMS where foreign capital from the EU has been a main driver of growth in the last decade. We use foreign investment environment as the fifth structural variable. This variable is an index based on a survey that captures prevalence of foreign ownership in a country as well as sentiment regarding whether current regulations discourage foreign ownership. A higher index indicates a more conducive environment for foreign ownership.
Participation in supply chains. In addition, we also include a measure of supply chain integration (the share of exports processed through upstream and downstream supply chains) given the strong role of supply chains in global and EU exports in the past decade (IMF, 2013; Rahman and Zhao, 2013). The degree of supply chain integration, which varies across time and country, captures the effects of other structural and institutional variables that may have important bearing for FDI and foreign firms’ decisions to locate operations but could not be included in the regression due to lack of data: quality of export processing infrastructure, unobserved regulations or obstacles hindering business operation, availability/cost of utilities and other inputs, and tax advantages. By including this variable, we have a more complete coverage of structural factors that are relevant for exports.
6. What about gravity factors that are typically found to be important determinants of trade flows in the literature? In our regression, the measure of supply chain integration is already capturing many of the gravity factors, such as distance from destination, domestic market size and income level. According to Rahman and Zhao (2013), about 88 percent of explained variance in integration with supply chains in a sample of 40 countries over 15 years is captured by these factors. Nevertheless, we also include main gravity variables —income per capita, weighted distance from export partners, and population of the exporting country (which also controls for the size of the domestic market and the bias that smaller countries typically have a higher exports-to-GDP ratio compared to larger countries) – in our regression analysis. In addition, we control for demand growth in partner countries (proxied by weighted PPP real GDP growth or weighted consumer sentiment in partner countries), and price competitiveness (unit labor cost based real effective exchange rate, REER-ULC).
7. Estimation results show that structural factors explain much of the variance in value added exports of the NMS to the EU. The estimation results from the panel OLS are shown in Table 1. A system regression could not be estimated due to the small sample size. We used two alternative dependent variable: value added exports to the EU in percent of a country’s own GDP (column 1, Table 1), and this variable’s distance from the NMS-10 average (column 2, Table 1). The five structural variables are all statistically highly significant and together capture more than 80 percent of the explained variance in both versions of the regression.
Determinants of Value-Added Exports of Goods and Services to EU: NMS-10, 2003–11
Higher values indicate lower degree of restrictions.
Determinants of Value-Added Exports of Goods and Services to EU: NMS-10, 2003–11
| Estimation results | |||
|---|---|---|---|
| In levels | Relative to NMS-10 average |
||
| Structural variables | |||
| Upper secondary or tertiary educational attainment | 0.16** | 0.33*** | |
| Participation in continuous vocational training and skills upgrade | 0.15*** | 0.14*** | |
| Inactivity trap | -0.09*** | -0.03* | |
| Relative minimum wage | -0.08** | -0.16*** | |
| Foreign investment environment 1/ | 1.0** | 2.2*** | |
| Control variables | |||
| Share of exports processed by supply chain | 0.44*** | 0.34*** | |
| PPP GDP per capita | 0.00 | 0.00 | |
| Weighted real GDP growth of trading partners | 0.06 | 0.13 | |
| Real effective exchange rate (ULC-based) | -0.03 | -0.07** | |
| Population | 0.57 | -0.68 | |
| Constant | -29.4*** | 0.03 | |
| Observations | 73 | 73 | |
| R-squared | 0.858 | 0.859 | |
Higher values indicate lower degree of restrictions.
Determinants of Value-Added Exports of Goods and Services to EU: NMS-10, 2003–11
| Estimation results | |||
|---|---|---|---|
| In levels | Relative to NMS-10 average |
||
| Structural variables | |||
| Upper secondary or tertiary educational attainment | 0.16** | 0.33*** | |
| Participation in continuous vocational training and skills upgrade | 0.15*** | 0.14*** | |
| Inactivity trap | -0.09*** | -0.03* | |
| Relative minimum wage | -0.08** | -0.16*** | |
| Foreign investment environment 1/ | 1.0** | 2.2*** | |
| Control variables | |||
| Share of exports processed by supply chain | 0.44*** | 0.34*** | |
| PPP GDP per capita | 0.00 | 0.00 | |
| Weighted real GDP growth of trading partners | 0.06 | 0.13 | |
| Real effective exchange rate (ULC-based) | -0.03 | -0.07** | |
| Population | 0.57 | -0.68 | |
| Constant | -29.4*** | 0.03 | |
| Observations | 73 | 73 | |
| R-squared | 0.858 | 0.859 | |
Higher values indicate lower degree of restrictions.
8. Differences in educational attainment, vocational training and skills upgrade and foreign investment environment seem to be most significant in explaining differences in exports from NMS to the EU. The relative importance of structural variables included in the regression is illustrated in Figure 2, which shows the increase in value added exports to the EU brought about by a one-standard-deviation improvement (LHS panel). The strongest impact comes from human capital, in particular continuous participation in vocational training and skills upgrade. This is consistent with the empirical literature that suggests significant productivity gains from vocational training (for a survey of literature see OECD (1998) and Descy and Tessaring (2005)). In the version that uses the distance of value added exports from the group average, foreign investment environment shows the largest impact with higher education and skills also contributing strongly (Figure 2, RHS panel).
Structural Factors: Relative Importance for Exports to the EU
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
9. We also find participation in supply chains and price competitiveness to be statistically significant (Table 1). Links with supply chains increase exports with the impact being the second highest when compared to the impact of structural variables included in the regression (Figure 3 2, LHS). This highlights the role of supply chains as an important conduit for increasing exports. Higher REER-ULC decreases exports by eroding competitiveness, although this variable was not significant in all specifications. In contrast, per capita income level, weighted distance from partner countries, weighted GDP growth in partner countries and population come out as statistically insignificant although with the expected signs; this could be due to the fact that supply chain participation, which strongly depends on gravity variables, is partly capturing their impact on exports. We also used time dummies and dummy variable for the euro area crisis, which were found statistically insignificant. We did not use country-specific dummy variables as it prevents us from identifying structural factors that are important for export performance, as most structural variables move slowly over time. This may imply that the impact of structural variables is somewhat overestimated in our regression.8
NMS-6: Contribution of Structural Factors to Relative Export Performance in the EU Market
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
Source: Staff calculations using regression results in Table 1, column 2.10. Our empirical findings identify country-specific policy priorities. We look at the contributions of structural variables in explaining a country’s export performance in the EU market relative to its NMS peers. Czech Republic and Hungary show an above average export performance relative to the other NMS, while Poland, Bulgaria and Romania show a below average performance during 2003-11. A decomposition of contribution of structural factors based on regression results shown in Table 1, Column 2 yields the following observations (Figure 3):
For the Czech Republic, vocational training and skills upgrade, higher education, a favorable foreign investment environment and links with supply chains have all contributed positively during 2003-11, while labor market variables, both wage cost and incentives to work, have not been a source of competitiveness. For Hungary, wage competitiveness, strong links with supply chains and foreign investment environment have contributed positively, with contribution from foreign investment environment declining in recent years. Vocational training and higher education are, on the other hand, areas that have contributed negatively to Hungary’s export performance relative to the NMS.
For Poland, we have seen an improvement in export performance over time with the gap relative to the other NMS decreasing over time. Higher education and competitive wages have contributed positively, while a relatively lower degree of participation in supply chains has been a drag. Although foreign investment environment contributes negatively, in recent years Poland has seen improvement in foreign investment environment and a pick-up in off-shoring and outsourcing of business services (McKenzie 2013). These factors have boosted Poland’s exports to the EU since 2010. For Bulgaria and Romania, the below-average performance in exports has been persistent with the gap relative to the NMS average worsening over time. This highlights the need for broad-based reforms, particularly in the areas of human capital and foreign investment environment (Figure 4).
NMS-6: Export Quality and Room for Improvement
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
1/ Vertical axis shows a country’s place in the export quality distribution in the world.2/ Room for quality upgrade shows the gap between country’s export quality and the quality demand by trading partners.Sources: Staff calculations using Henn, Papageorgiou, and Spatafora (2013) database.C. Export Quality in NMS-6: Room for Growth
11. In NMS-6, export quality is generally high when compared to the rest of the world. We assess the quality of merchandise exports to the world using an index developed by Henn, Papageorgiou, and Spatafora (2013) based on an estimated relationship between export quality, export unit value, production cost, and the distance from importers.9
The overall exports quality for NMS-6 is above the 60th percentile when compared to all countries in the world (Figure 4). The Czech Republic leads with an overall quality level close to the 90th percentile and Romania, at 61st percentile, lags others. Our analysis of quality at a more disaggregated product level shows three tiers10: the Czech Republic in the highest tier where the quality of export goods ranges between 61st and 97th percentiles; Croatia, Hungary, and Poland in the next tier, where the quality ranges between 40th and 89th percentiles; and Bulgaria and Romania in the third tier, where the quality ranges between 24th and 80th percentiles. In other words, underneath an overall high quality, there seems to be a wide range even for countries like the Czech Republic.
Next, we calculate room for quality improvement, which looks at not only a country’s standing in the quality ladder relative to others, but also the average quality absorbed by its importers. A positive gap indicates the quality demanded by importers is larger than that provided by the exporting NMS. Our analysis reveals positive quality gaps for the NMS at the overall and product levels (Figure 4). A look at the SITC 4-digit products indicates significant room for quality improvement in the following areas: textile products (Bulgaria, Croatia, and Romania); wood products (Romania), paper products (Poland), beverages and tobacco products (Bulgaria and Poland), and in footwear products (Croatia) (Figure 4).
12. A comparison of export quality in the EU market using a different methodology draws somewhat different conclusions. Based on Di Comite, Thisse and Vandenbussche (2014), which uses firm-level cost data in a mark-up model to capture quality of exports in the EU market relative to other EU exporters, the NMS-6 show a quality distribution that is concentrated at the low (Bulgaria, Romania and Poland) and middle (Hungary and Czech Republic) part of the quality spectrum (see chart). The share of products where quality is below the 50th percentile relative to other EU countries ranges between 62 percent in Poland to 52 percent in Hungary. In other words, more than half of export products from the NMS are in the bottom half of the quality ladder showing significant room for improvement with respect to other exporters in the EU market. Relatively speaking, the Czech Republic and Hungary still come out at the top among the NMS just as they do with respect to the world market with export products concentrated in the mid-quality range.
NMS-6: Quality of Exports to the EU Market Distribution of Export Quality, 2011
(Percent of total exports values to EU15)
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
Source: Di Comite, Thisse and Vandenbussche (2014).Note: Quality ranks are normalized between zero and 1; “1” = highest.13. For the NMS, export quality is found to be positively correlated with export value in both EU and world markets (see figure below on Exports Value and Quality). This is not surprising as most NMS have experienced an improvement in quality of export producing since 2005 as exports also grew. Vandenbussche (2014) finds that the estimated price elasticity of quality is around 0.5, implying that quality upgrading would likely to result in a firm’s capacity to increase price, profits and market share. This shows causality from quality to higher exports. Going forward, this positive relationship between quality and exports is likely to strengthen. Globally speaking, the NMS are not countries where low labor costs or labor abundance could be a source of comparative advantage given ageing population, although this may be currently the case relative to advanced Europe. So improving quality has to be a part of the strategy to enhance exports and our analysis shows significant room for improvement particularly in the EU market.
NMS: Exports Value and Quality
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
Source: For weighted export quality, staff calculations using Henn, Papgeorgiou and Spatafora (2013) for world exports and Di Comite, Thisse and Vandenbussche (2014) for EU exports; For value added exports, staff calculations using world input output data.14. How can countries improve export quality over time?
A survey of literature shows that some of the structural reforms that explain differences in exports performance in our regression analysis are also the ones that tend to explain differences in export quality: human capital, institutional quality, and foreign investment (see for example Zhu et al, 2009, Henn et al, 2013 and Weldemicael, 2012). In addition, R&D expenditure is also important for quality improvement.
The EBRD 2014 Transition Report, which looks at innovation and knowledge-based growth in transition economies, finds that different factors matter in quality improvement at different levels of economic development and product quality. At a relatively low level of development and product quality, when countries are trying to access technology, openness and facilitation of foreign investment are important. The study also finds firms that are part of the global supply chains to be more innovative than non-linked firms. The capacity to absorb such technology and replicate depends on the quality of secondary and undergraduate education, and the effectiveness of on-the-job training. Thus, to innovate over learnt technology depends on postgraduate education, quality of scientists and engineers, quality of scientific research, flexibility of product and labor market, effective cooperation between science and industry, and availability of venture capital which become important for countries with mid-quality products trying to move up.
15. Based on this, the NMS-6 would have different policy priorities in terms of improving export quality. For Bulgaria and Romania, the focus should be on improving foreign investment regime, boosting secondary education, and linking with supply chains which would help with acquiring technology. For the Czech Republic and Hungary, policies need to focus on improving the environment for innovation. For these two countries, a comparison outside the region also points to the need for ramping up higher education and R&D spending (Box 1). Diversification of exports outside the EU and into new products are other ways to enhance exports for these two given that a significant part of the GDP is derived from value added exports to the EU.
D. Services Exports: Scope for Further Increase
16. Goods dominate over services in exports from NMS-6 to the EU. Although services sector contribute to two-thirds of the EU GDP and create 9 out of 10 jobs, its share in intra-EU trade is low. For the NMS-6, the share of services sector in value added exports is only a third and much less than that in gross exports (Figure 5). Croatia is the only country among the NMS-6 where services products, mostly related to the tourism sector, dominate exports to the EU. The lower share of services in exports, among other things, is explained by specific characteristics of service products: many services are traditionally non-tradable which can only be delivered at production location and hence not part of the cross-border trade. But we want to explore whether a lack of comparative advantage relative to other EU members and restrictive market access may be contributing to relatively low services exports. When it comes to services exports, the single market does not work quite as well as it does for goods. Countries face numerous restrictions in the form of authorization, economic needs test, licenses, territorial restrictions and restrictions on multidisciplinary activities. In this regard, we want to see to what extent the adoption of Services Directive (SD) in 2006 has helped reduce barriers to exports.
NMS-6: Services Exports and Services Directive
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
Note: Most liberalized sectors include travel agency, real estate agents, tourist guide, and hotels, which are the sectors with the top percentile percent changes on barriers after the implementation of the Services Directive.Source: Staff calculations using World Input Output Table, Eurostat and Haver Analytics.17. Most services exports from the NMS-6 fall under sectors covered by the SD.11 The weighted average share of SD sectors in total services exports from NMS-6 to the EU (70 percent for NMS-6, almost 90 percent for Croatia) is significantly higher than the NMS-EA (59 percent for NMS-EA) and other EU (62 percent) (Figure 5). According to the assessment by European Commission, the implementation of the SD has reduced average restrictions on services imports across products and countries by about 30 percent since 2006, although with considerable variations (Montiagudi et al, 2012). Exports from the NMS-6 benefited from the SD as services exports from these countries in sectors covered by the SD grew more (on average 8 percent annually) than from other EU members (Figure 5). The increase was even more pronounced when we look at sectors that were most liberalized after the SD.
18. However, significant barriers remain regarding services exports, particularly with regards to professional services. The SD was adopted in 2006 in order to promote competition and trade in services products. The intended implementation period was 2006-9 during which member countries were to review their respective regulatory framework for services in order to identify restrictions that can be removed. However, countries were given considerable leeway in the sense that pre-existing restrictions could be maintained if they were deemed necessary to protect public interest and as long as they were non-discriminatory, necessary and proportionate. Countries worked in clusters of 5 members each for mutual evaluation of abolition/amendment of restrictions. Given the broad coverage and the deference to member states for action, liberalization of services trade under the SD has fallen short of expectations (Corugedo and Ruiz, 2014). Specifically, among many advanced economies in the EU which are major absorbers of exports from the NMS-6, many barriers remain on professional and technical services after the implementation of the SD.
19. Our analysis of comparative advantage shows that the NMS-6 would greatly benefit from further liberalization of services imports by EU member states. Hungary, Bulgaria and Romania show comparative advantage in higher number of services products than goods products (Annex II). Croatia, where a RCA analysis based on value added exports was not possible due to data unavailability, would also likely fall into this group given the high share of services exports. The Czech Republic, Hungary, Poland and Romania hold comparative advantage in professional and technical services relative to other EU members (Table 2). The weighted average share of professional services in total SD exports range between 30–40 percent in these four countries. Further liberalization of services trade, particularly those in professional services, would greatly help NMS-6 increase exports to the EU.
RCA: Exports on Professional and Technical Services
RCA: Exports on Professional and Technical Services
| RCA | % of exports in sectors under SD | |
|---|---|---|
| Bulgaria | 0.6 | 16 |
| Croatia | 0.4 | 8 |
| Czech Republic | 1.1 | 30 |
| Hungary | 1.1 | 31 |
| Poland | 1.1 | 36 |
| Romania | 1.1 | 40 |
| Estonia | 0.8 | 27 |
| Latvia | 0.7 | 31 |
| Lithuania | 0.4 | 23 |
| Slovak Republic | 0.8 | 20 |
| Slovenia | 0.5 | 16 |
RCA: Exports on Professional and Technical Services
| RCA | % of exports in sectors under SD | |
|---|---|---|
| Bulgaria | 0.6 | 16 |
| Croatia | 0.4 | 8 |
| Czech Republic | 1.1 | 30 |
| Hungary | 1.1 | 31 |
| Poland | 1.1 | 36 |
| Romania | 1.1 | 40 |
| Estonia | 0.8 | 27 |
| Latvia | 0.7 | 31 |
| Lithuania | 0.4 | 23 |
| Slovak Republic | 0.8 | 20 |
| Slovenia | 0.5 | 16 |
E. Policy Implications
20. Structural reforms play a key role in maximizing benefits of unrestricted access to the EU single market. Our analysis shows that improving exports to the EU depends on a competitive economy underpinned by structural reforms, particularly in the areas of higher education, skills upgrade, wage structure’s ability to provide incentives to work, and foreign investment environment. Other institutional reforms that promote successful integration with supply chains are also helpful in enhancing export performance, not just to the EU but to destinations outside the EU.
21. Our analysis identifies some country-specific structural reform priorities that can help boost export performance. For Bulgaria and Romania, where export performance has been persistently weak relative to other the NMS, closing the distance with peers will require broad-based reforms, particularly improvement in skills, education attainment, and foreign investment regime. For Poland, where export performance has consistently improved over time to almost closing the gap with the NMS average, a more conducive foreign ownership regime and greater links in services-based supply chains will help. For Hungary, where the contribution of the operating environment for foreign investors has declined in recent years, it would be important to strengthen such an environment to further enhance its integration with the EU.
22. There is room for export quality improvement in all NMS-6. The analysis presented in this paper, which takes into account both export quality relative to other exporters and quality demanded by importers, shows that there is room for improvement for exports from the NMS-6, particularly with respect to other exporters to the EU market. The room for quality improvement is particularly large for Bulgaria and Romania. We also find a strong positive relationship between exports value and quality, suggesting that pursuing structural reforms, such as improving human capital, labor market and business environment, would help increase both exports and quality.
23. For quality improvement, structural reforms need to be mindful of a country’s existing quality level. For countries producing products at the lower end of quality spectrum such as Bulgaria and Romania, accessing technology through improving foreign investment environment and greater links with supply chains are key. Countries that are at the medium-level of quality spectrum such as the Czech Republic and Hungary, improving skills and higher education, and innovation through higher R&D spending are priorities.
24. For countries that are already highly integrated with the EU single market and produce mid-quality products, diversification in products and markets will prove useful. This applies to the Czech Republic and Hungary, countries that derive a high share of domestic output from demand in the EU market. Looking outside the EU may be useful for these countries. Given that export products from these countries are at a relatively high level when compared to other exporters from the world, increasing quality of existing products would require a significant boost in R&D expenditure and tertiary education. Diversification of exports into new products would be another option for these countries to stay on the export-led growth path.
25. Improved market access can significantly increase services exports to the EU. Our analysis shows that a large part of services exports from the NMS fall under sectors covered by the SD. A number of the NMS have comparative advantage in these products including in professional services which remain most restricted. Further dismantling of restrictions by EU members, both advanced and emerging economies, will help maximize benefits from the single market. In this regard, a renewed impetus to the SD through third-party review of principles of non-discrimination, necessity and proportionality to assess public interest may also help.
Czech Republic and Hungary: What can be learnt from Korea?
For countries such as the Czech Republic and Hungary, a comparison with countries outside transition economies may also be instructive despite differences in policy environment. We looked at the experience of the world’s most successful export-driven countries, such as Japan and Germany, and Korea—a country that has successfully pursued a sustained period of export-led growth kick-started by the Japanese supply chain (figure). We focused on the evolution of structural variables indentified in empirical literature as significant for quality upgrade over time. Since 1970, Japan and Germany have demonstrated a negative room for quality improvement with respect to what their importers have demanded. This means that they have provided a quality above and beyond the level demanded by importers helping them maintain market shares and stay as leaders. Korea joined this group in the early 2000s after a steady improvement in quality. Available time series data for R&D spending and tertiary education, variables that are identified in literature as important contributors to quality improvement, shows a ramping up by Korea in both aspects and surpassing the levels of Germany by early to mid-2000s. For Czech Republic and Hungary, to pursue a similar path of quality upgrade and movement up the value chain, there seems to be a need for significant improvement on these fronts (education, R&D expenditure and business environment).
Quality Improvement: Lessons from Korea
Citation: IMF Staff Country Reports 2015, 098; 10.5089/9781484321393.002.A004
1/ Doing business ranking as of 2014, a higher number indicates lower quality.2/ A higher number for use of talents suggests more efficient use. This index is a composite index of the following indices: pay and productivity; reliance on professional management; country capacity to retain and attract talent; and relative female participation in the labor force. The value indicates as of 2014.Source: The World Bank and World Economic Forum.Annex. Data Appendix and Robustness Check for Regression Analysis for Export Integration
The following table provides definition of variables, sources and statistical properties.
Summary Statistics and Data Sources
Inactivity trap is one minus the ratio of difference in net and gross wage, where the difference is between in-work and out-of work wage for an average worker.
Summary Statistics and Data Sources
| Variable | No. of obs. | Mean | Std. dev. | Min. | Max. | Source |
|---|---|---|---|---|---|---|
| Domestic value-added exports to EU (% of GDP) | 90 | 17.9 | 5.2 | 10.0 | 29.5 | World Input-Output Database; World Economic Outlook database, IMF; and IMF staff calculations |
| Upper secondary or tertiary educational attainment (% of population aged 20–24 years) | 90 | 85.6 | 5.5 | 75.0 | 94.1 | Eurostat |
| Participation in continuous vocational training and skills upgrade (% of total employed) | 77 | 27.4 | 14.7 | 13.3 | 61.0 | LAF database, European Commission |
| Inactivity trap 1/ | 86 | 71.6 | 13.6 | 42.0 | 90.0 | LAF database, European Commission |
| Relative minimum wage (% of gross average wages) | 80 | 40.3 | 10.0 | 23.5 | 62.4 | LAF database, European Commission |
| Foreign investment and ownership environment | 80 | 6.7 | 1.2 | 4.3 | 8.9 | Economic Freedom of the World 2013 Annual Report |
| Share of exports processed by supply chain (% of gross exports) | 90 | 76.9 | 3.8 | 67.7 | 82.7 | World Input-Output Database and IMF staff calculations |
| Weighted real GDP growth of trading partners | 90 | 0.2 | 4.1 | -11.6 | 12.2 | World Input-Output Database; World Economic Outlook database, IMF; and IMF staff calculations |
| Population | 90 | 10.2 | 11.0 | 1.3 | 38.5 | World Economic Outlook database, IMF |
| PPP GDP per capita | 90 | 17312.7 | 5183.8 | 7828.2 | 29402.8 | World Development Indicators database, World Bank |
| GDP weighted distance | 90 | 2197.4 | 2288.7 | 423.7 | 9176.1 | GeoDist database, CEPII; World Economic Outlook database, IMF; and IMF staff calculations |
Inactivity trap is one minus the ratio of difference in net and gross wage, where the difference is between in-work and out-of work wage for an average worker.
Summary Statistics and Data Sources
| Variable | No. of obs. | Mean | Std. dev. | Min. | Max. | Source |
|---|---|---|---|---|---|---|
| Domestic value-added exports to EU (% of GDP) | 90 | 17.9 | 5.2 | 10.0 | 29.5 | World Input-Output Database; World Economic Outlook database, IMF; and IMF staff calculations |
| Upper secondary or tertiary educational attainment (% of population aged 20–24 years) | 90 | 85.6 | 5.5 | 75.0 | 94.1 | Eurostat |
| Participation in continuous vocational training and skills upgrade (% of total employed) | 77 | 27.4 | 14.7 | 13.3 | 61.0 | LAF database, European Commission |
| Inactivity trap 1/ | 86 | 71.6 | 13.6 | 42.0 | 90.0 | LAF database, European Commission |
| Relative minimum wage (% of gross average wages) | 80 | 40.3 | 10.0 | 23.5 | 62.4 | LAF database, European Commission |
| Foreign investment and ownership environment | 80 | 6.7 | 1.2 | 4.3 | 8.9 | Economic Freedom of the World 2013 Annual Report |
| Share of exports processed by supply chain (% of gross exports) | 90 | 76.9 | 3.8 | 67.7 | 82.7 | World Input-Output Database and IMF staff calculations |
| Weighted real GDP growth of trading partners | 90 | 0.2 | 4.1 | -11.6 | 12.2 | World Input-Output Database; World Economic Outlook database, IMF; and IMF staff calculations |
| Population | 90 | 10.2 | 11.0 | 1.3 | 38.5 | World Economic Outlook database, IMF |
| PPP GDP per capita | 90 | 17312.7 | 5183.8 | 7828.2 | 29402.8 | World Development Indicators database, World Bank |
| GDP weighted distance | 90 | 2197.4 | 2288.7 | 423.7 | 9176.1 | GeoDist database, CEPII; World Economic Outlook database, IMF; and IMF staff calculations |
Inactivity trap is one minus the ratio of difference in net and gross wage, where the difference is between in-work and out-of work wage for an average worker.
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Prepared by Jesmin Rahman, Ara Stepanyan, Jessie Yang and Li Zeng. The authors are grateful to Marinela Petrova (Bulgaria Ministry of Finance) and Christian Buelens (European Central Bank) for discussing the paper at the New Member States (NMS) Policy Forum in Warsaw on December 12, 2014, to Hylke Vandenbussche of the European Commission for providing data on export quality and to the NMS-6 country representatives that reviewed the paper during bilateral visits in November 2014.
NMS-6 includes Bulgaria, Croatia, Czech Republic, Hungary, Poland and Romania. NMS includes these countries and NMS-EA consisting of Estonia, Latvia, Lithuania, Slovakia and Slovenia.
NMS includes 11 member states that joined the EU during 2004–13. They are split into two groups: NMS-6 and NMS-EA. NMS-6 includes Bulgaria, Croatia, Czech Republic, Hungary, Poland and Romania, and NMS-EA includes countries that have joined the euro area in recent years: Estonia, Latvia, Lithuania, Slovakia and Slovenia. The “Other EU” group used in charts and tables in this study refers to all other EU members.
While the single market provides an opportunity for all EU firms, there may be domestic factors that prevent firms from taking full advantage of this opportunity - such as preferential treatment of incumbents or excessive entry regulations. These obstacles that may narrow the scope of unrestricted access to the EU single market for goods are outside the scope of this paper.
We were not able to include Croatia in the regression analysis due to lack of data for value added exports and many of the structural variables.
For robustness check, we also use gross exports of goods and services to the EU and exports (both value added and gross) to the world as the dependent variable. See forthcoming working paper by Rahman and others for details.
The details on data sources can be found in Annex I.
The coefficients of structural variables are robust to alternative specifications (for details see Rahman and others (forthcoming).
The intuition behind this approach is that after controlling for the production costs and taking into account the fact that exports to more distant destinations tend to be tilted towards higher-priced goods (because of higher shipping costs), higher quality goods would have higher export unit values. This approach does not assume that international export markets are competitive where individual exporters can be price takers. This index defines export quality within a product group rather than across the entire spectrum of export products. This means that a county that exports low-end high-tech products (for example, auto parts) may show a lower level of overall export quality compared with a country that exports high-end low-tech products (for example, designer clothing). The overall export quality is aggregated using quality indices calculated at the SITC 4-digit product level and export weights.
For this product level exercise, we only looked at SITC 4-digit products where a country has a market share of at least ½ percent in the world.
These include travel, construction services, computer and information services, operational leasing, miscellaneous business services, royalties, education and other personal, and cultural and recreational services. The rest of the services exports fall under the category of “Regulated” sector which include the following six sectors: transportation, communication services, financial services, insurance services, health, and government services.
