Abstract
Republic of Poland: Selected Issues
Raising Productivity Growth in Poland: the Role of Structural Transformation1
Poland’s continued integration with global markets has benefited growth and income convergence. Over the past decade, sector-specific productivity gains, particularly in manufacturing, accounted for the bulk of aggregate productivity growth, while the remaining one-fifth came from labor reallocation toward higher-productivity sectors (structural transformation). Continued income convergence would require completing structural transformation, while securing further within-sector productivity improvements. Cross-country econometric estimations suggest that attracting greenfield investment, improving the business climate, addressing labor market rigidities, and tackling long-term unemployment are key factors facilitating positive structural change.
A. Introduction
1. Sustained productivity gains are a key driver of economic success. Countries that manage to sustain aggregate productivity gains enjoy strong and durable growth. Increases in aggregate labor productivity can originate from two main sources. First, aggregate labor productivity gains arise when labor and other resources relocate from low-productivity sectors (such as agriculture) toward modern economic activities. The speed with which this structural transformation occurs is one of the key factors that differentiate high-growing countries from low-growing ones (McMillan et al., 2014). Moreover, structural transformation has been found to result in lower aggregate output volatility (Moro, 2012). Second, increases in sector-specific productivity can also generate significant aggregate labor productivity gains, supporting economic convergence.
2. Understanding the relative importance of factors underlying aggregate labor productivity growth allows to better target economic policies to maximize productivity gains. For example, to facilitate structural transformation, policies would need to focus on removing obstacles to labor mobility across sectors, such as overly restrictive labor market regulations. On the other hand, improving within-sector productivity would require moving up the technological frontier. Policies incentivizing research and development and innovation would be helpful in this regard.
3. This paper decomposes aggregate labor productivity growth in Poland and its European peers. The decomposition gauges the relative contribution to aggregate productivity improvements from structural transformation and from within-sector productivity gains. The paper draws on the literature on the decomposition of aggregate labor productivity growth (Fabricant, 1942; McMillan et al., 2014; de Vries et al., 2013) using detailed sectoral-level data for a large set of European countries.2 An innovation compared to earlier literature (de Vries et al., 2013) is that the framework used here allows to distinguish between the contribution to productivity growth from the reallocation of workers to sectors with above average productivity levels (static reallocation effects or structural change) and the contribution from the reallocation to sectors with above average productivity growth (dynamic reallocation effects). As pointed out in de Vries et al. (2013), this distinction is crucial insofar as the traditional decomposition methods mix structural change (the static reallocation effect) with dynamic reallocation effects. The method is therefore better suited to obtain a more robust measure of the extent of structural transformation.
4. The decomposition shows that within-sector productivity gains have been a strong driver of aggregate labor productivity growth in Poland, although structural transformation has also played an important role. Over the past decade, within-sector productivity gains have accounted for the bulk of aggregate productivity growth, with the remaining one-fifth coming from structural transformation. The manufacturing sector, which has benefited from integration with Europe’s supply chains, has been one of the main sources of increased productivity at the sectoral level.
5. Going forward, sustaining aggregate productivity improvements would require completing structural transformation, while boosting within sector productivity. Scenario analyses suggest that continued structural transformation would need to go hand-in-hand with further improvements in within-sector productivity to achieve meaningful catch-up gains for Poland.
6. Completing structural transformation would require facilitating labor mobility across sectors and promoting economic diversification. While previous research has generally focused on identifying the drivers of aggregate labor productivity growth, very few studies (McMillan et al., 2014) examined the determinants of structural transformation. To the best of our knowledge, our paper is the first to examine the key drivers of structural transformation in Europe. The econometric results show that securing large greenfield investments, improving the business climate, addressing labor market duality, and tackling long-term unemployment are key factors associated with positive structural change. In Poland, this would require reducing labor market duality by better aligning temporary employment contracts with regular contracts and continuing business climate improvements to encourage greenfield investments.
7. Earlier research suggests that further within-sector productivity enhancement entails moving up the value-added chain. Poland’s participation in the European supply chains has led to substantial technological transfers (IMF, 2013). Export growth in knowledge-intensive sectors has picked up, and the sophistication of domestic value added embodied in overall exports has also increased rapidly. Further progress in these areas would positively contribute to productivity growth and enhance Poland’s external competitiveness.
B. Poland: Labor Market and Productivity Trends
8. Employment growth in Poland has been stronger than elsewhere in Europe, led by services and the public sector. Poland managed to secure high employment gains in the last decade compared to only modest growth in advanced EU economies and a decline in regional peers (Figure 1). Most of the employment growth occurred in the early years following the EU accession and can be attributed to the expanding services and public sector, while the importance of agriculture has diminished. Unlike in many other parts of Europe, construction and industry have also positively contributed to employment gains. As in other New Member States (NMS),3 Poland’s employment structure is more heavily skewed toward agriculture and industry, than in advanced European countries.
Employment Structure in Poland
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Source: Eurostat.9. Poland’s labor productivity has increased markedly over the past decade, helped by its participation in the German supply chain. Poland’s labor productivity increased from 49 percent of EU average in 2000 to nearly 70 percent of EU average in 2013. Productivity gains in the industry and manufacturing sectors were particularly large, helped by technological transfers through Poland’s participation in the German supply chain.
10. However, cross-country comparison suggests that a sizable productivity gap remains. While Poland’s is gradually catching up with EU average, the speed of productivity convergence has been slower than in regional peers with comparable initial productivity levels such as Slovakia and Lithuania (Figure 2). Nevertheless, wages in Poland increased less than productivity, boosting its competitiveness.
Labor Productivity and Wages
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Source: Eurostat.11. Earlier research suggests that further productivity improvements can be achieved by moving up the value added chain. Participation in European supply chains has been found to be positively associated with substantial technological transfers and productivity growth in the manufacturing in Eastern European countries (IMF, 2013). In these countries, export growth in knowledge-intensive sectors has picked up and the sophistication of domestic value added embodied in overall exports has also increased rapidly, with positive implications for productivity, external competitiveness, and growth.
12. Furthermore, analysis of Poland’s relative sectoral productivity suggests potential gains from relocating labor to more productive sectors. The text chart shows sectoral productivity relative to economy-wide average by economic sector and its share in total employment. While agriculture and the public sector combined employ a third of Poland’s labor force, productivity in these sectors is significantly below economy-wide average. At the same time, productivity in the services sectors (including wholesale and retail trade, professional and scientific activities, industry, information and communication, and finance and real estate)—which have high labor absorption capacity, currently employing 38 percent of the labor force—exceeds economy-wide average. This suggests that there is potential in Poland to increase aggregate labor productivity by relocating labor from less productive sectors (e.g., agriculture) to these more productive sectors, particularly in services.
Labor Productivity and Sector’s Size in Poland
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Source: IMF staff estimatesC. Decomposing Aggregate Labor Productivity Growth Using Sector-Level Data
Methodology
13. We decompose aggregate productivity improvements into within and between effects. The “within effect” captures productivity growth within sectors, whereas the “between effect” measures the productivity effect of labor reallocation across sectors.
14. Three methods are used to perform the decomposition. Each method has important ramifications for the measurement and interpretation of structural change (see Appendix II for a full description of the methods and the underlying analytical presentation).
The first method follows McMillan et al. (2014) and decomposes aggregate labor productivity into two terms: the within-effect which is positive when the weighted change in labor productivity levels in sectors is positive and the reallocation-effect, which measures the contribution to productivity growth of labor reallocation across sectors and is positive when labor moves to more productive sectors.
The second method adds a dynamic element to the labor reallocation component and helps to improve the measure of structural change. Building on the decomposition proposed by de Vries et al. (2013), the reallocation term of the first method is split into two components: the first component captures whether workers move to sectors with above-average productivity levels (static reallocation effect), whereas the second component measures whether workers are moving to sectors that are experiencing positive (negative) productivity growth (dynamic reallocation effect). This allows for more granular analysis of labor reallocation, while helping to obtain a cleaner measure of structural change.
The third method we use controls for the bias present in the other two methods—that all expanding sectors contribute positively to aggregate productivity, even when they have below average productivity levels or growth rates. The decomposition follows de Vries et al. (2013) and adjusts the static and dynamic reallocation effects of an expanding sector to take into account its relative productivity level and its relative productivity change. The adjusted decomposition does not affect aggregate contributions from the within-effects and structural change, but it allows to measure the contribution of each sector to aggregate labor productivity growth. For example, the decomposition allows to determine the contribution of the manufacturing sector to aggregate labor productivity growth while decomposing this contribution into within and between components.
Decomposition of aggregate labor productivity in Poland and in Europe
15. Across Europe, sources of aggregate labor productivity vary.
In the CE3 group (Czech, Poland, and Slovakia) aggregate productivity growth has been mainly powered by within-sector productivity gains, particularly in manufacturing.
In core Euro area, there is much more heterogeneity. In Germany, there is evidence of declining aggregate productivity driven by both negative within-sector productivity growth and negative structural change since the global financial crisis.4 In France, the negative contribution from structural change is notable but dampened by positive within-sector productivity gains in the real estate, public administration, and manufacturing sectors. In Italy, the recent decline in aggregate labor productivity is due to productivity losses in almost all sectors.
16. In Poland, labor productivity growth has been largely driven by within-sector gains, although structural transformation has also played an important role. Within-sector productivity growth accounted for the bulk of aggregate productivity growth during 2002–13, while the remaining one-fifth is explained by structural transformation. While this result is consistent across the three methods used in this study, it is likely only a lower bound estimate for the contribution of structural transformation. When using more granular sectoral data for Poland (which are not available for all countries in this study), structural transformation becomes much more important for overall productivity gains (Figure 3).5
Poland: Decomposing Aggregate Labor Productivity
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Source: IMF staff estimates.17. Most of the within-sector productivity gains came from manufacturing. The sector has benefited from successful integration in pan-European supply chains and the associated technological and capital transfers, which helped to boost its productivity (Table 1).
Decomposition of Aggregate Labor Productivity Growth in Poland
Decomposition of Aggregate Labor Productivity Growth in Poland
| Components due to: Within | Between | |||||||
|---|---|---|---|---|---|---|---|---|
| Labor productivity growth | Static | Dynamic | ||||||
| Sectors | 2001–07 | 2008–13 | 2001–07 | 2008–13 | 2001–07 | 2008–13 | 2001–07 | 2008–13 |
| Agriculture | 0.20 | 0.11 | 0.22 | 0.07 | 0.08 | 0.04 | ||
| Arts | 0.04 | 0.01 | 0.02 | 0.01 | 0.00 | 0.00 | ||
| Construction | 0.05 | 0.18 | 0.02 | 0.05 | 0.00 | 0.00 | ||
| Finance | 0.05 | −0.04 | 0.14 | 0.02 | 0.00 | 0.00 | ||
| Industry | 0.12 | 0.13 | −0.02 | 0.03 | 0.00 | 0.00 | ||
| Information and | ||||||||
| communication | 0.01 | 0.10 | 0.14 | 0.06 | −0.01 | 0.01 | ||
| Manufacturing | 1.09 | 1.08 | 0.00 | −0.07 | 0.01 | −0.04 | ||
| Professional and | ||||||||
| Scientific | ||||||||
| activities | −0.01 | 0.06 | 0.17 | 0.15 | −0.03 | −0.01 | ||
| Public | ||||||||
| administration | 0.25 | −0.01 | 0.00 | 0.08 | 0.00 | −0.03 | ||
| Real estate | 0.36 | 0.01 | −0.20 | 0.06 | −0.08 | 0.00 | ||
| Wholesale and | ||||||||
| retail | 0.33 | 0.46 | 0.20 | 0.01 | −0.02 | 0.00 | ||
| Total economy | 3.2 | 2.6 | 2.5 | 2.1 | 0.7 | 0.5 | −0.1 | 0.0 |
Decomposition of Aggregate Labor Productivity Growth in Poland
| Components due to: Within | Between | |||||||
|---|---|---|---|---|---|---|---|---|
| Labor productivity growth | Static | Dynamic | ||||||
| Sectors | 2001–07 | 2008–13 | 2001–07 | 2008–13 | 2001–07 | 2008–13 | 2001–07 | 2008–13 |
| Agriculture | 0.20 | 0.11 | 0.22 | 0.07 | 0.08 | 0.04 | ||
| Arts | 0.04 | 0.01 | 0.02 | 0.01 | 0.00 | 0.00 | ||
| Construction | 0.05 | 0.18 | 0.02 | 0.05 | 0.00 | 0.00 | ||
| Finance | 0.05 | −0.04 | 0.14 | 0.02 | 0.00 | 0.00 | ||
| Industry | 0.12 | 0.13 | −0.02 | 0.03 | 0.00 | 0.00 | ||
| Information and | ||||||||
| communication | 0.01 | 0.10 | 0.14 | 0.06 | −0.01 | 0.01 | ||
| Manufacturing | 1.09 | 1.08 | 0.00 | −0.07 | 0.01 | −0.04 | ||
| Professional and | ||||||||
| Scientific | ||||||||
| activities | −0.01 | 0.06 | 0.17 | 0.15 | −0.03 | −0.01 | ||
| Public | ||||||||
| administration | 0.25 | −0.01 | 0.00 | 0.08 | 0.00 | −0.03 | ||
| Real estate | 0.36 | 0.01 | −0.20 | 0.06 | −0.08 | 0.00 | ||
| Wholesale and | ||||||||
| retail | 0.33 | 0.46 | 0.20 | 0.01 | −0.02 | 0.00 | ||
| Total economy | 3.2 | 2.6 | 2.5 | 2.1 | 0.7 | 0.5 | −0.1 | 0.0 |
18. Structural transformation since the crisis has been led by the dynamic services sector. Notable structural transformation gains were recorded in real-estate, science and technology, and to a lesser extent in information and communication (Table 1). At the same time, contributions to structural transformation from traditional sectors, such as agriculture, were less significant than in the pre-crisis period.
19. Going forward, sustaining aggregate productivity improvements would require that structural transformation be accompanied by additional within-sector productivity enhancement. Our empirical findings suggest that while structural transformation is helpful, it should be accompanied by continued improvements in within-sector productivity to result in meaningful catch-up gains for Poland. The illustrative scenarios in the text-chart shows that simply aligning Poland’s employment structure with the EU average, while maintaining within-sector productivity at its current levels (“convergence to EU15 employment structure” bar), is not sufficient to generate significant productivity improvements. This is because matching Poland’s employment shares to the EU structure does not automatically imply reallocation of labor toward more productive sectors. For example, while reducing the share of agriculture in Poland toward the EU15 level would improve aggregate productivity (since agriculture is one of the least productive sectors in Poland), increasing the size of the public sector in Poland toward EU15 levels, while maintaining its current productivity level, would have negative productivity implications for Poland, given the current below-average productivity in this sector. Similarly, reducing the share of industry in Poland to the EU15 average would have an immediate negative impact on aggregate productivity in Poland, given that this sector currently exhibits above-average productivity in Poland. This suggests that to maximize aggregate labor productivity gains, structural transformation has to go hand-in-hand with continued enhancement of within-sector productivity.
Illustrative scenarios of productivity convergence
(Productivity decomposition; percent of productivity in EU15)
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Note: Convergence to EU15 employment structure assumes adopting EU15 employment structure in Poland with sectoral productivity levelsunchanged. Sectoral productivity convergence assumes increase of sectoral productivities to EU15 levels with employment structure unchanged.Sources: Eurostat and IMF staff calculations.Poland: Sectoral Productivity and Changes in Employment
(Size of the bubble indicates initial employment share in 2008)
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Sources: IMF staff calculations.D. Factors Facilitating Structural Transformation
20. Exploratory regressions are performed to identify the main determinants of structural transformation. While previous research has generally focused on identifying the drivers of aggregate labor productivity growth, few studies (McMillan et al., 2014) examined the determinants of structural transformation. To the best of our knowledge, our paper is the first to examine the key drivers of structural transformation in Europe. McMillan et al. (2014) provides a framework to model structural transformation. We use similar methodology to identify the main determinants of structural transformation in Europe. Structural transformation is measured by static reallocations (see equation (3) in Appendix II). We then regress this measure on a large set of control variables which includes:
Initial level of economic development. Since the pattern of structural transformation might evolve with economic development, we control for a quadratic term of lagged real GDP per capita in PPP terms.
The initial share of agriculture in total employment. We expect as in McMillan et al. (2014), a positive contribution of this variable to structural change. The wider the initial structural gaps the larger the room for growth-enhancing structural change. Data are from Eurostat and are measured at the beginning of each period.
The share of temporary employment. We expect the share of temporary employment to be negatively correlated with structural transformation. The high share of temporary contracts is in general associated with lower onsite investments in vocational training. Low quality of jobs is a key impediment to labor mobility from low-productivity to high-productivity sectors. Data on this variable are from the OECD.
The importance of long-term unemployment. Long-term unemployment has been shown to have an adverse effect on the quality of the labor force and on the likelihood of job seekers to find new higher-quality jobs. Long-term unemployment is also an indication of structural impediments in the labor market, such as skill mismatches, which ultimately would impede structural transformation. The Eurostat share of unemployed people for more than a year is used.
Globalization is measured by greenfield foreign direct investment (FDI). High integration into the global economy, characterized by large inflows of greenfield FDI, should facilitate the creation of new firms, bringing about economic diversification and structural change. We use the share of greenfield FDI in GDP from the United Nations Conference on Trade and Development (UNCTAD) database.
The quality of the business climate. Favorable business climate encourages the development of new industries and sectors and contributes to economic diversification and thereby supports structural transformation. We use the World Bank (Doing Business dataset) measure of the cost to enforce contracts as our main indicator of business climate quality.
The size of the public sector. A disproportionately large public sector could be associated with lower structural transformation if labor productivity in the public sector lags aggregate labor productivity in the economy. An unduly large government sector has also the potential of crowding-out private sector, and could impede competition and entrepreneurship through inefficient taxation or crowding-out effects in financial markets. We control in the specification for government expenses as a percentage of GDP.
Country specific random-effects are controlled for to take into account unobserved country characteristics. Random effects are preferred over fixed-effects given the narrow time range (maximum observations per country is 3 non-overlapping periods of 7 years). This also ensures that the main focus remains on between-country variations rather than within-country effects. We are interested in understanding why some countries experienced large and positive structural transformation while others did not.
We control for period dummies to account for common shocks affecting all countries in the sample.
21. The results are consistent with the intuition above—in particular a more welcoming business climate and a less rigid labor market contribute positively to structural transformation. Specifically, a country’s attractiveness to greenfield FDI is positively associated with structural change. On the other hand, an unwelcoming business climate (e.g., weak enforcement of contracts and excessive) has a negative effect on structural transformation. The high share of temporary employment and long-term unemployment (capturing various structural labor market rigidities) are found to be negatively correlated with productive labor reallocations (Table 2). These results are robust to alternative specifications in which we expanded the list of explanatory variables. We controlled for education level, exchange rate depreciations, inflation, but their respective effects on structural transformation are not statistically significant.
Correlates of Structural Transformation in Europe
Correlates of Structural Transformation in Europe
| Dependent variable: | ||||||
|---|---|---|---|---|---|---|
| Structural transformation | (1) | (2) | (3) | (4) | (5) | (6) |
| Initial GDP per capita, ln | 4.766 | 0.845 | 7.459* | 5.186 | 4.507 | 2.983 |
| (3.471) | (3.586) | (4.001) | (3.807) | (5.041) | (5.455) | |
| (Initial GDP per capita, ln)2 | −0.228 | −0.0432 | −0.344* | −0.253 | −0.206 | −0.133 |
| (0.169) | (0.174) | (0.192) | (0.186) | (0.243) | (0.262) | |
| Initial share of agriculture | 0.0571*** | 0.0426*** | 0.0580*** | 0.0579*** | ||
| (0.0148) | (0.0131) | (0.0147) | (0.0151) | |||
| Long-term unemployment rate | −0.00979** | −0.0104** | −0.00953** | |||
| (0.00407) | (0.00444) | (0.00428) | ||||
| Greenfield FDI-to-GDP | 0.0697*** | 0.0859*** | 0.0637*** | |||
| (0.0175) | (0.0145) | (0.0190) | ||||
| Constraints on business climate | −0.0140* | |||||
| (0.00797) | ||||||
| Share of temporary contracts | −0.0174* | −0.0168* | ||||
| (0.00934) | (0.00916) | |||||
| Government expenses-to-GDP | −0.000613 | |||||
| (0.00260) | ||||||
| Intercept | −24.55 | −3.639 | −40.13* | −26.12 | −24.38 | −16.44 |
| (17.87) | (18.55) | (20.94) | (19.54) | (26.05) | (28.27) | |
| Observations | 78 | 52 | 52 | 52 | 70 | 65 |
| Number of countries | 28 | 26 | 26 | 27 | 27 | 27 |
Correlates of Structural Transformation in Europe
| Dependent variable: | ||||||
|---|---|---|---|---|---|---|
| Structural transformation | (1) | (2) | (3) | (4) | (5) | (6) |
| Initial GDP per capita, ln | 4.766 | 0.845 | 7.459* | 5.186 | 4.507 | 2.983 |
| (3.471) | (3.586) | (4.001) | (3.807) | (5.041) | (5.455) | |
| (Initial GDP per capita, ln)2 | −0.228 | −0.0432 | −0.344* | −0.253 | −0.206 | −0.133 |
| (0.169) | (0.174) | (0.192) | (0.186) | (0.243) | (0.262) | |
| Initial share of agriculture | 0.0571*** | 0.0426*** | 0.0580*** | 0.0579*** | ||
| (0.0148) | (0.0131) | (0.0147) | (0.0151) | |||
| Long-term unemployment rate | −0.00979** | −0.0104** | −0.00953** | |||
| (0.00407) | (0.00444) | (0.00428) | ||||
| Greenfield FDI-to-GDP | 0.0697*** | 0.0859*** | 0.0637*** | |||
| (0.0175) | (0.0145) | (0.0190) | ||||
| Constraints on business climate | −0.0140* | |||||
| (0.00797) | ||||||
| Share of temporary contracts | −0.0174* | −0.0168* | ||||
| (0.00934) | (0.00916) | |||||
| Government expenses-to-GDP | −0.000613 | |||||
| (0.00260) | ||||||
| Intercept | −24.55 | −3.639 | −40.13* | −26.12 | −24.38 | −16.44 |
| (17.87) | (18.55) | (20.94) | (19.54) | (26.05) | (28.27) | |
| Observations | 78 | 52 | 52 | 52 | 70 | 65 |
| Number of countries | 28 | 26 | 26 | 27 | 27 | 27 |
E. Factors Facilitating Within-Sector Productivity
22. Recent cross-country research confirms the importance of investing in human capital and removing market frictions to boost sectoral productivity. Recent studies using macro-variables and focusing on within-sector productivity have identified credit and labor market frictions, such as wage rigidities and sectoral heterogeneity in the access to external financing, and insufficient investment in human capital as the primary obstacles to sectoral productivity growth (Nabar and Yan, 2013).
23. Vertical productivity spillovers and better business environment have also been found important. Studies adopting firm-level approaches emphasize the central role played by vertical productivity spillovers from multinational firms to local firms (Kinda, 2012). Recent IMF staff research confirms that participation in global supply chains is positively associated with substantial technological transfers and productivity growth in the manufacturing sector in Eastern European countries (IMF, 2013). Factors such as good management experience, agglomeration effects, and the provision of infrastructural services have also been found significant in explaining differences in productivity levels (Chaffai et al., 2012).
F. Implications for Poland
24. For Poland, continued economic convergence would require a concerted effort to complete structural transformation and further enhance within sector productivity. This would entail facilitating labor mobility across sectors and moving up the value-added chain (Figure 4).
Structural Developments in Poland
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Source: Haver analytics; OECD stat.; UNCTAD; European Commission; The World Bank.25. Structural transformation would require facilitating labor mobility. Sectoral labor mobility is held back by the high prevalence of temporary contracts, the high numbers of regulated professions,6 barriers to greenfield FDI, and barriers to internal migration. Temporary contracts reduce incentives to invest in worker’s human capital and training, making it less likely that they acquire new skills necessary to succeed in new jobs. More generally, there is a need to increase adult participation in lifelong learning to diversify skills and factilitate labor mobility. Mobility to higher-productivity sectors is also impeded by a large number of regulatory requirements. Geographical mobility of labor is deterred by overly regulated rental market, including the strong protection of tenants, which discourages investment in rental properties and drives up rents in cities and other dynamic regions that generate jobs. And attracting greenfield investment, which supports economic diversification, requires improvements to the business climate. Policies should therefore focus on:
Reducing labor market duality by better aligning temporary employment contracts (i.e., Civil Law Contracts) with regular contracts and limiting duration of consecutive short-term contracts. Temporary civil law contracts have often been used by employers to avoid paying employees’ social security contributions; they have also been shown to reduce incentives for employers to invest in employee training (World Bank, 2015).
Improving adult participation in lifelong learning to diversify skills.
Further streamlining the list of regulated professions to facilitate labor mobility to services sectors.
Further reducing regulatory hurdles in the rental market to ease geographical mobility.
Improving business climate to strengthen economic diversification and aid sectoral mobility.
Change in number of jobs*, 2000-13
(percent)
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Sources: Eurostat and IMF staff calculations.Score of Innovation, 2013
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Source: European Commission26. Enhancing within-sector productivity entails moving up the value-added chain. Poland’s integration with the German supply chain has resulted in welcome technology transfers and has increased the sophistication of domestic value added embodied in exports. Further advancement along the value-added chain would require continued innovation—an area where Poland still lags behind its peers. Increasing research and development (R&D) expenditures, including through FDI, could help spur innovation. Better targeted vocational training would facilitate the absorption of new production methods and technologies. Policies should therefore focus on:
Improving investment climate to attract new FDI and the associated new technologies and know-how, including by easing regulations for construction permits and strengthening contract enforcement.
Incentivizing R&D spending in the business sector, including through well-targeted tax incentive schemes, supported by further improvements in tax administration to reduce potential abuse of such schemes for tax avoidance.
Better aligning vocational training with employers’ needs.
Maintaining strong institutions and policies to increase the risk-adjusted returns to innovation.
Number of Regulated Professions in Europe
(In 2015)
Citation: IMF Staff Country Reports 2015, 183; 10.5089/9781513518589.002.A003
Sources: European CommissionReferences
Chaffai, M., Kinda, T., and Plane, P., 2012, “Textile Manufacturing in Eight Developing Countries: Does Business Environment Matter for Firm Technical Efficiency?” Journal of Development Studies, 48(10), pp. 1470–1488.
de Vries, G., Timmer, M., and de Vries, K., 2013, “Structural Transformation in Africa: Static Gains, Dynamic Losses,” GGDC Research Memorandum, No. 136, Groningen Growth and Development Centre, Groningen, www.ggdc.net/publications/memorandum/gd136.pdf.
Fabricant, S., 1942, Employment in Manufacturing, 1899–1939. NBER, New York.
IMF, 2013, “German-Central European Supply Chain—Cluster Report,” IMF Country Report No. 13/263, International Monetary Fund.
Kinda, T, 2012, “Foreign Ownership, Sales to Multinationals and Firm Efficiency: The Case of Brazil, Morocco, Pakistan, South Africa and Vietnam,” Applied Economics Letters, 19(6), pp. 551–555.
McMillan, M., Rodrik, D., and Verduzco-Gallo, I., 2014, “Globalization, Structural Change, and Productivity Growth, with an Update on Africa,” World Development, 63: pp. 11–32.
Moro, A., 2012, “The Structural Transformation between Manufacturing and Services and the Decline in the U.S. GDP Volatility,” Review of Economic Dynamics 15 (3):pp. 402–15.
Yan, K., Nabar, M., 2013, “Sector-Level Productivity, Structural Change and Rebalancing in China,” IMF Working Papers, 13/240, International Monetary Fund.
Timmer, M., 2000, The Dynamics of Asian Manufacturing. A Comparison in the late Twentieth Century, Edward Elgar Publishers, Cheltenham.
World Bank, 2015, “Balancing Flexibility and Worker Protection: Understanding Labor Market Duality in Poland,” Forthcoming Policy Note, World Bank.
Appendix I. List of Sectors
We use a 10-sector decomposition for each EU member country. Annual sectoral data published by the European Commission on real sectoral value added and employment under the Nomenclature of Economic Activities (NACE) revision II, are used for each country (ESA 2010 data). This provides a unified and comparable breakdown of national account data by sectors over the past two decades.
Sectors (NACE Rev. 2)
Sectors (NACE Rev. 2)
| Agriculture, forestry and fishing | Agri |
| Arts, entertainment and recreation; other service activities; activities of household and | |
| extra-territorial organizations and bodies | Arts |
| Construction | Constr |
| Financial and insurance activities | Fin |
| Industry (excludes manufacturing and construction) | Indus |
| Information and communication | IC |
| Manufacturing | Manuf |
| Professional, scientific and technical activities; administrative and support service | |
| activities | SciTech |
| Public administration, defence, education, human health and social work activities | Pub |
| Real estate activities | Resta |
| Wholesale and retail trade, transport, accommodation and food service activities | Wrt |
Sectors (NACE Rev. 2)
| Agriculture, forestry and fishing | Agri |
| Arts, entertainment and recreation; other service activities; activities of household and | |
| extra-territorial organizations and bodies | Arts |
| Construction | Constr |
| Financial and insurance activities | Fin |
| Industry (excludes manufacturing and construction) | Indus |
| Information and communication | IC |
| Manufacturing | Manuf |
| Professional, scientific and technical activities; administrative and support service | |
| activities | SciTech |
| Public administration, defence, education, human health and social work activities | Pub |
| Real estate activities | Resta |
| Wholesale and retail trade, transport, accommodation and food service activities | Wrt |
Appendix II. Methods to Decompose Aggregate Labor Productivity
We use a standard decomposition of aggregate labor productivity. Researchers typically use the framework from Fabricant (1942) which decomposes the change in aggregate productivity into a “within” and a “between” effects. The “within effect” captures productivity growth within sectors, whereas the “between effect” measures the productivity effect of labor reallocation across sectors. Three methods are used to perform the decomposition given that each method has important ramifications for the measurement and interpretation of structural change.
The first method uses initial employment shares and final period productivity levels. This follows closely McMillan et al. (2014). The change in aggregate labor productivity level (Pt) is decomposed as follows:
where i stands for the sector, L for the number of employed people, and t the period indicator. The second term on the right-hand side quantifies the degree of structural transformation. The change in aggregate productivity is decomposed into within-sector productivity changes (the first term on the right-hand side which we call the “within-effect” (also known as “intra-effect”), and the effect of changes in the sectoral allocation of labor which we call the “reallocation effect,” (the second term, also known as the “shift-effect” or “structural-change effect”). The within-effect is positive when the weighted change in labor productivity levels in sectors is positive. The reallocation-effect measures the contribution of labor reallocation across sectors, being positive when labor moves from less to more productive sectors. As discussed by de Vries et al. (2013), it is worth noting that the reallocation term presented in equation (1) is only a static measure of the structural change as it depends on differences in productivity levels across sectors, not on growth rates.
The second method combines static and dynamic reallocation effects. Building on the decomposition proposed by de Vries et al. (2013), the reallocation term of equation (1) is split into two terms: whether workers move to above-average productivity level sectors (static reallocation effect) and whether workers are moving to sectors that are experiencing positive (negative) productivity growth (dynamic reallocation effect). This method has an advantage of better measuring the reallocation effects by splitting it into a static and a dynamic part: the contribution from the reallocation of workers to sectors with above average productivity levels (static reallocation effects or structural change) and the contribution from the reallocation to sectors with above average productivity growth (dynamic reallocation effects). The decomposition looks as follows:
The third term in equation (2) represents the joint effect of changes in employment shares and sectoral productivity (Timmer, 2000).
The third method helps to assess sectoral contributions to labor productivity growth. The method controls for the bias—present in all previous methods—that all expanding sectors contribute positively to aggregate productivity, even when they have below average productivity levels or growth rates. The decomposition follows de Vries et al. (2013) and adjusts the static and dynamic reallocation effect of an expanding sector to take into account its relative productivity level and its relative productivity change. We divide sectors into expanding and shrinking ones based on changes in their employment shares and calculate the static between-effect relative to the average productivity level of the shrinking sectors and the dynamic between-effect relative to the average productivity change of the shrinking sectors. The modified decomposition is as follows:
where j is the set of expanding sectors, and k is the set of shrinking sectors, and average labor productivity of shrinking sectors at time t is given by:
While this adjusted decomposition does not affect aggregate contributions from the within and structural change, it allows gauging individual sectors’ contribution to aggregate productivity growth.
Prepared By Christian Ebeke, Krzysztof Krogulski, and Robert Sierhej.
The focus is on labor productivity given that detailed and comprehensive data on sectoral capital stock and deflators are not always available. Also, labor remains an important component of aggregate production.
NMS include: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Romania, Slovakia, and Slovenia.
Due to missing data for the manufacturing sector in Eurostat for Spain, we were not able to proceed to the full decomposition of aggregate labor productivity for the country.
This refers to the case of the economy divided into 64 branches instead of 10 sectors.
Given ongoing measures to substantially reduce the numbers of regulated professions in Poland, the number shown in the text might not take into account those recent measures.
