Abstract
Republic of Estonia: Selected Issues
How Worried Should We be about Fast ulc Growth in Estonia?1
In recent years, wages in Estonia have been rising rapidly despite stagnating labor productivity, pushing up unit labor costs (ULCs) over time and relative to peers. This is prima facie evidence for a loss of competitiveness, but because ULCs are a crude indicator that can be misleading, they need to be explored in conjunction with other metrics to get a clearer picture. While somewhat less alarming, they broadly corroborate that ULCs have now reached a level where further unmitigated wage growth risks doing material damage to competitiveness and income convergence prospects with Western Europe. This highlights the importance of making Estonia’s pro-productivity policies a success, but in the nearer term the onus is on mitigating excessive wage growth because this is where Estonia, together with the other Baltics, distinguishes itself from the rest of Central and Eastern Europe (CEE). Tax policy can provide short-term relief.
1. ULC developments in Estonia look alarming at first sight. ULCs, as well as the closely related concept of the real effective exchange rate, are key indicators in the assessment of countries’ external competitiveness. They are widely used in IMF country reports, the IMF’s internal vulnerability exercise, OECD economic surveys, and the EC’s macroeconomic imbalance procedure (European Commission, 2016). According to this metric, developments in Estonia are out of line with those in the EU overall and in the largest four economies in CEE, with ULCs double their 2002 levels and 17 percent higher than in 2013:Q2.2 For the EU as a whole, ULCs grew by only 2 percent—a 15 percent gap with Estonia, which exceeds the European Commission’s 9 percent threshold for ULC growth by a large margin and raises a red flag in the IMF’s vulnerability exercise for emerging market economies based on the assessment of the real effective exchange rate. Focusing more narrowly on the manufacturing sector, which is particularly exposed to foreign competition, shows a similar picture. How alarmed should we be?
Nominal Unit Labor Costs, 2003-2016:Q2
(Four-quarter moving average, in euros, 2002=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.CEE-4 comprises the Czech Republic, Poland, Slovakia, and Hungary.Nominal Unit Labor Costs, 2013:Q2-2016:Q2
(Four-quarter moving average, in euros, 2013:Q2=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.CEE-4 comprises the Czech Republic, Poland, Slovakia, and Hungary.2. Conceptual drawbacks of the ULC indicator may overstate the deterioration in Estonia’s external competitiveness. Those are mostly related to the poor measurement of productivity, which is defined simply as the gross value added per employed person and does not correctly account for capital deepening, product quality improvements, changing human capital, composition effects from shifts between economic activities, etc. These shortcomings could potentially bias ULC-based assessments against Estonia. Moreover, limitations to every competitiveness indicator make it necessary to evaluate a host of them and come to an overall assessment in an eclectic approach (Lipschitz and McDonald, 1992). Other indicators include real ULCs—essentially the complement to profit shares, enterprise profitability, export market shares, the current account, and survey results. They may paint a less worrisome picture of Estonia’s competitiveness.
3. The labor cost side of ULC developments deserves particular attention, because this is where Estonia differs most from the rest of CEE. Since 2002, labor costs in manufacturing grew by an extra 45 percent in Estonia relative to CEE, while the shortfall in productivity gains was a much smaller 6 percent. In the more recent period since mid-2003, labor costs also accounted for close to two thirds of Estonia’s ULC increase relative to that in CEE. Estonia broadly shares this feature with Latvia and Lithuania. What is so different in the Baltic labor markets?
Productivity and Compensation in Manufacturing, 2003-2016:Q2
(Relative to CEE-4, four-quarter moving average, 2002 = 100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.CEE-4 comprises the Czech Republic, Poland, Slovakia, and Hungary.Productivity and Compensation in Manufacturing, 2003-2016:Q2
(Relative to CEE-4, four-quarter moving average, 2002=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.CEE-4 comprises the Czech Republic, Poland, Slovakia, and Hungary.4. The rest of this chapter is organized as follows: The next section explores whether the various conceptual drawbacks of ULCs as competitiveness indicator bias results against Estonia and, if so, by how much. It finds that most do not, but that income convergence might explain a substantial part of Estonia’s rapid ULC growth relative to the EU. Attention shifts to real ULCs, a concept akin to the labor share of national income, in the section B. Here developments are not as alarming as in ULCs, but nonetheless are now entering the territory where they could do material damage to growth and income convergence prospects. Section C explores various other competitiveness indicators, including export market shares, enterprise profitability as per company accounts, survey results, and the current account. They broadly corroborate concerns about competitiveness developments. Section D goes on to delve deeper into why wage growth seems to have decoupled from productivity in Estonia, but not elsewhere in the region. It finds a significant role of government and minimum wage policies, and some evidence of unwarranted momentum, while Estonia’s particularly unfavorable demographics appear to be only of second order importance. Section E concludes. An appendix compares the explanatory powers of ULCs and real ULCs. If finds that real ULCs are more closely associated with export developments and therefore deserve heightened attention in competitiveness assessments.
A. Potential Bias in ULC-based Competitiveness Assessments
5. Drawbacks to ULCs as competitiveness indicators are undisputed, but the real question is whether they bias results against Estonia. Changes in the production structure, business cycle positions, capital deepening, the presence of a nontradable sector, non-cost competitiveness factors, demographics, and the speed of income convergence can all affect ULCs unrelated to competitiveness. But this will only overstate Estonia’s loss of competitiveness as measured by ULC developments relative to trading partners if these drawbacks push up Estonia’s ULCs more than those elsewhere. If so, it would mitigate concerns about Estonia’s relatively sharp ULC increase.
6. Shifts between economic activities had little bearing on ULC developments. When the structure of an economy shifts toward labor intensive activities, ULCs tend to rise, but this would not necessarily signal a deterioration in competitiveness. In the case of Estonia, using constant industry shares to calculate ULCs makes little difference. When using the average shares of 39 activities during 2003–14, or the activity weights of the EU, the cumulative ULC increase actually ends up being a marginal 5-10 percent higher. Hence, composition effects do not inflate Estonia’s ULC growth and do not exaggerate the competitiveness decline.
7. Estonia’s relative business cycle position has not been a driver of ULC growth either. ULCs are strongly influenced by the business cycle, as Estonia’s boom-bust cycle in the 2000s clearly demonstrates (Bakker and Klingen, 2012). In the boom phase until 2007, overheating pushed wages and ULCs up. The economic slump in 2008–09 led to further increase as labor productivity collapsed. But when companies subsequently reduced employment and wages started to fall, ULCs came down sharply. However, in the time periods analyzed here—2003 to 2016:Q2 and 2013:Q2–2016:Q2—Estonia’s output gap as estimated for the IMF’s World Economic Outlook changed little and by about the same as in the EU on average. For the longer period since 2003, the output gap widened by 0.5 percent of potential GDP for both, and in the more recent period it declined by 1.2 and 1.4 percent of GDP, respectively. Again, hardly enough of a difference to distort the ULC-based competitiveness assessment.
8. Capital deepening biased the ULC-based competitiveness assessment for Estonia only marginally. High investment and a high rate of capital accumulation tend to increase labor productivity and mitigate ULC growth without affecting competitiveness. Investment in Estonia has been much higher than in peers, averaging 33 percent of GDP during 2003–2016:H1 and 27 percent of GDP in the last three years, compared to 27 percent and 24 percent of GDP in CEE, respectively. Investment excl. construction, which is arguably more indicative of the buildup of productive capacity, was about the same in Estonia and in peers. Growth contributions from capital were accordingly very similar. Hence, peers’ ULCs were not biased down by less than in Estonia—if anything if was the other way around.
Investment Ratios, 2003-16
(Percent of GDP)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.Growth Contribution Non-construction Capital, 2003-161
(Ppts of GDP)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.1\ Assumes capital-output ratio of 110 percent of GDP in 2000, depreciation over 15 years, and a non-construction income share of capital of 20 percent for all countries.9. Economy-wide ULC developments overstate Estonia’s competitiveness loss relative to the EU average, but not relative to CEE. ULCs calculated from all economic activity also capture developments in the nontradable sector, which is largely irrelevant for competitiveness because it does not compete much externally. In catching-up economies productivity tends to grow more slowly in the nontradable sector than in the tradable sector while wages typically rise at about the same rate in both. As a result, the more relevant ULC growth in manufacturing is typically slower than economy-wide ULC growth, a phenomenon referred to as the Balassa-Samuelson effect. This effect is clearly visible in the case of Estonia over the long horizon since 2003, but also to a similar extent in CEE. Moreover, since mid-2013 the effect has largely disappeared and relative ULC developments were in line with those in the EU. To eliminate distortions introduced by the nontradable sector, one can also simply base the competitiveness assessment on ULC developments in the manufacturing sector, as a proxy for the nontradable sector. On this metric, Estonia’s increase still exceeds that of CEE and the EU by a very wide margin.
Manufacturing Relative to Total Economy ULCs, 2003-2016:Q2
(Cumulative difference between increases in ppts)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.Manufacturing Relative to Total Economy ULCs, 2013:Q2-2016:Q2
(Cumulative difference between increases in ppts)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.10. Omission of non-price aspects of competitiveness biases results somewhat against Estonia, though not in the more recent period. The most important element of non-price competitiveness are product quality improvements. In principle, they should be captured in real gross value added figures that enter ULC calculations, but in practice it rarely is because of the limited use of hedonic price indices. As a result, productivity growth is understated and ULCs growth overstated. To see whether this effect played more strongly in Estonia than in peers, and therefore distorted the comparison of ULC developments against Estonia, one can examine export prices—a commonly used proxy for quality.3 Over the long horizon of 2003–2016:H1, Estonia’s export prices indeed grew somewhat faster than those of CEE. But this would only potentially explain 15 ppts out of Estonia’s extra 65 percent increase of manufacturing ULCs over this period. Moreover, in the last three years, there was no material difference between export price developments in Estonia and CEE any more.
Export Prices, 2003-16:H1
(2002=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.Export Prices, 2013-16:H1
(2013=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.11. Demographic effects on ULC developments have been negligible. Demographics can influence ULCs through labor hording effects. If labor market entries are set to decline, as is the case in Estonia, as well as to some extent in CEE more generally, firms may be especially reluctant to lay off workers and especially eager to secure labor resources as soon as they become available. This behavior results in labor hording where employment is somewhat higher than currently warranted in anticipation of future needs. Labor productivity is accordingly lower and ULCs are higher. Labor hording should be reflected in hours worked per employee. They have been declining throughout Europe for a long time, with Estonia in line with this general trend. Over the last three years, Estonia’s decline was somewhat larger than in CEE, but at a differential of less than 2 ppts, this had no material impact on relative ULC developments.
12. Income convergence helps justify almost half of Estonia’s extra long-run ULC growth vis-à-vis the EU, but only a small fraction of the differential with CEE. The positive association of income convergence and ULC growth is an empirical regularity and a catch-all for the various particularities of catching-up economies such as Estonia. As such, it captures some of the factors already discussed above, e.g. those related to product quality improvements, as well as Balassa-Samuel effects for economy-wide ULCs, but goes potentially further. Focusing on manufacturing ULCs, Estonia has seen an increase of around 65 percent since 2003 with some 30 percent explained by income convergence according to estimates for a sample of European countries. However, the comparison with CEE is less benign. Since CEE converged by almost as much as Estonia, Estonia’s extra income convergence only explains 15 ppts of Estonia’s extra ULC growth. This happens to be on the same order of magnitude as the product quality effect. Moreover, because income convergence toward the EU average has been very muted for Estonia and CEE since mid-2013 it plays only a marginal role in explaining more recent relative ULC developments.
Income Convergence and Nominal ULC Growth
(Between 2002 and 2015)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat and IMF staff calculations.Income Convergence and Nominal ULC Growth
(Between 2012 and 2015)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.B. Assessment Based on Real Unit Labor Costs
13. Real ULCs are a crucial indicator for competitiveness assessments, as well as for the allocation of resources between the tradable and non-tradable sectors. Unlike the nominal ULCs discussed above, they relate labor costs per employee to nominal rather than real output per employed person. They broadly track the labor share of income, and therefore also its complement, the profit share of income. While less commonly used than nominal ULCs, they have strong explanatory power for export performance (Annex 1). In addition, changes in the ratio of real ULCs in manufacturing and economy-wide ULCs are closely associated with resource shifts between the tradable and non-tradable sectors. This could be another concern related to rapid real ULC growth: when it is driven by excessive wage developments, real ULCs in manufacturing could rise faster than economy-wide ULCs, because firms exposed to international competition are more constrained in passing higher costs through to prices, giving rise to incentives for moving economic activity to the nontradable sector, where the potential for productivity gains may be lower. Long-term growth and income convergence may weaken as a result.
14. Estonia’s real ULC growth over the past three years also looks rather worrisome. Since 2003, real ULCs in manufacturing have risen by a cumulative 15 percent, compared to declines of 10 percent in CEE and 6 percent for the EU. About two-thirds of the gap with CEE has emerged since mid-2013. While there are no established thresholds to gauge when real ULC growth becomes problematic, the examples of Korea and Italy are nonetheless instructive (Annex 1). Pro-rating the rise of real manufacturing ULCs in these countries to match the length of the 2003–2016:H1 period in Estonia, shows a comparable increase of 8 percent in Korea, which was unproblematic, and an increase of 19 percent in Italy, which was associated with the economy falling back. This suggests that Estonia’s 15 percent increase cannot be taken lightly.
Real Unit Labor Costs, 2003-2016:Q2
(Four-quarter moving average, 2002=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.Real Unit Labor Costs, 2013:Q2-2016:Q2
(Four-quarter moving average, 2013:Q2=100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.15. Another concern is the development of real ULCs in manufacturing relative to ULCs for all economic activities. With real ULCs moving inversely with profitability, a fall in relative manufacturing real ULCs generates incentives for firms to move into the nontradable sector, which may have less potential for productivity gains, thereby undermining long-term growth prospects. Over the period since 2003, manufacturing real ULCs grew significantly less in CEE, the EU and the other Baltic countries. But in Estonia they grew by about the same. This generated pull into the manufacturing sector in peers, but not in Estonia.
16. Income convergence is not a convincing mitigating factor in evaluating real ULC growth and neither is Estonia’s current level of real ULCs. Unlike nominal ULCs, real ULCs are not systematically associated with income convergence. Estonia’s particularly rapid real ULC growth can therefore not be partly attributed to especially strong income convergence. It can also not be excused by coming off a low base and therefore maybe not being problematic yet in terms of levels. Indeed, if anything, overall and manufacturing real ULCs are already at higher levels than one would expect for an economy of Estonia’s income level.4
Income Convergence and Real ULC Growth
(Between 2002 and 2015)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; and IMF staff calculations.Relative Income Positions and Real ULC Levels
(In 2015)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat and IMF staff calculations.C. Supplementary Competitiveness Indicators
17. Competitiveness assessments cannot rely on ULCs alone. Any single indicator has drawbacks and can send misleading signals. This section looks at supplementary metrics—export market shares, profitability, survey results, and the current account balance—to corroborate the ULC-based readings.
Export Market Share Developments
18. Export market share developments have become less favorable than in the past. Estonia’s export growth used to consistently outpace the expansion of global trade. The global financial crisis was a setback from which Estonia swiftly recovered. But after the rebound had run its course around 2013 and in contrast to developments in CEE, export market shares started to slip. Trade with Russia, which suffered from sanctions and ruble depreciation in the wake of the Russia-Ukraine conflict in the spring of 2014, played a role, but excluding it does not materially alter the picture. A more likely culprit is Estonia’s weak overall growth, with GDP growth falling behind global developments almost as much as export growth did. Considering that not all countries can constantly gain market share or become more open to trade faster than the rest of the world, the developments in Estonia since 2013 are not necessarily of serious concern. But they are still somewhat disappointing for an economy that seeks to catch up with living standards in Western Europe. It is too early to tell whether they reflect deteriorating competitiveness, especially as export market shares tend to be affected with a lag. Close monitoring is called for.
Export Market Shares: Goods and Services
(2003 = 100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: IMF, WEO; Statistics Estonia; and IMF staff calculations. 1/ Excludes exports to Russia from Estonian exports and Russian imports from global exports.Export Market to GDP Shares: Goods and Services
(2003 = 100)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: IMF, WEO; Statistics Estonia; and IMF staff calculations. 2/ Excludes exports to Russia from Estonian exports, Russian imports from global exports, and Russian GDP from global GDP.Enterprise Profitability
19. Enterprise data fully confirm pressures on profits in the tradable sector. Profit margins in the manufacturing sector are down to little more than half their historical average and nearing the all-time low seen at the height of the global financial crisis. In other parts of the tradable sector profitability is also down, but closer to historical norms. The big contrast is with the nontradable sector, where profit margins are in line with their long-term averages. This picture is consistent with diminished competitiveness, which typically manifests itself initially in compressed profit margins in sectors that compete externally before it starts affecting export market shares.
Profit-to-Turnover in Manufacturing, 2003-16
(Percent)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Statistics Estonia; and IMF staff calculations.Wholesale and Retail Trade, 2013-16
(Percent)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Statistics Estonia; and IMF staff calculations.Survey Results
20. European Commission industry surveys also show an erosion in the external competitive position. The balance of opinion has been declining for several years, falling below its long-term average in mid-2013. Its pattern closely mirrors that of profit margins in manufacturing, although in terms of levels the lows reached during the global financial crisis are still a long way off and there was a slight uptick in 2016:Q3. Again, this self-assessment is very different in CEE, where industrial firms report improving and above-average competitiveness. In the other Baltic economies and the EU as a whole industry’s assessments have been hovering around their long-term averages in the past few years.
Competitive Position, 2003:Q3-2016:Q3
(Balance of opinions in industry; average of extra and intro EU position)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: European Commission; and IMF staff calculations.The Current Account Balance
21. Estonia’s current account does not currently raise any red flags regarding external competiveness. While it had been in deficit for most of the period since reestablishment of independence and ballooned to a record of -15 percent of GDP at the height of the economic boom in 2007, it corrected very quickly in the ensuing recession and remained in broad balance through the recovery. In 2015 it recorded a surplus of 2.2 percent of GDP. This hardy suggests underperforming exports and domestic suppliers that are struggling to compete with imports.
22. However, developments of Estonia’s current account balance are dominated by movements in saving-investment balances rather than changes in competitiveness. Any gains or losses in competitiveness have been swamped by swings in investment. In 2015, the investment-to-GDP ratio was some 6 ppts below its long-term average. In addition, terms-of-trade gains since 2005 have had a favorable impact on the current account balance of an estimated 3.6 percent of GDP. EU funds and fiscal surpluses were further boons to the external position. If these items were to revert to previous norms, Estonia would need to improve its competitive position to ensure current account sustainability.
Investment Ratios, 1995-2016
(Percent of GDP)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: IMF, WEO; and IMF staff calculations.Current Account Balance, 1995-2015
(Percent of GDP)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Haver; and IMF staff calculations.D. The Root Causes of Estonia’s Rapid Wage Growth
23. The strength of the economy, along with public sector wage polices, are the key drivers of wage growth (Table 1). A panel regression for 21 European countries over the period 2000–15 explores the issue. Real wage growth is clearly associated with real GDP growth and the output gap—higher GDP growth and less slack in the economy mean faster growth of real wages. Government wages also play an important role. Broadly in line with other studies, a 1 percent increase of real government wages is associated with a 0.306 percent increase of overall real wages (IMF, 2016). Minimum wages also seem to pass through to general wages with a coefficient of 6.3 percent, again in line with findings in the literature (e.g., Raei, Sodsriwiboon, and Sour, 2016). There may also be an influence from migration, with more outward migration going together with higher wage growth, presumably because it reduces labor supply. Furthermore, there seems to be some evidence that inflation reduces real wage growth, at least in the manufacturing sector. Contrary to popular perception, the declining number of labor market entries, proxied as the population share of the 18–25 year olds or the change thereof, does not turns out to be a significant determinant of wage growth. The wage gap with the EU average seems not to play a role either, suggesting that there is no evidence of wage convergence over and above what is due to differentials in real GDP growth.
Real Wage Growth
Real Wage Growth
| Dependent Variable: Real Wage Growth in Manufacturing | Dependent Variable: Real Wage Growth All Economic Activity | |||||||
| Method: Panel Least Squares | Method: Panel Least Squares | |||||||
| Sample (adjusted): 2001 2015 | Sample (adjusted): 2001 2015 | |||||||
| Periods included: 15 | Periods included: 15 | |||||||
| Cross-sections included: 19 | Cross-sections included: 21 | |||||||
| Total panel (unbalanced) observations: 267 | Total panel (unbalanced) observations: 302 | |||||||
| Variable | Coefficient | Std. Error t-Statistic | Prob. | Variable | Coefficien’ Std. Error | t-Statistic | Prob. | |
| Constant | 2.644 | 0.504 5.248 | 0.000 | Constant | 0.695 | 0.228 | 3.044 | 0.003 |
| Output gap | 0.212 | 0.111 1.910 | 0.057 | Output gap | 0.130 | 0.046 | 2.843 | 0.005 |
| Real GDP growth | 0.366 | 0.101 3.632 | 0.000 | Real GDP growth | 0.187 | 0.039 | 4.867 | 0.000 |
| Real minimum wage growth | 0.175 | 0.062 2.812 | 0.005 | Labor market entries | 1.285 | 0.753 | 1.705 | 0.089 |
| Real public sector wage growth | 0.230 | 0.087 2.631 | 0.009 | Real minimum wage growth | 0.077 | 0.024 | 3.202 | 0.002 |
| Lagged real wage growth | −0.227 | 0.062 -3.643 | 0.000 | Real public sector wage growth | 0.292 | 0.030 | 9.606 | 0.000 |
| Effects Specification: Cross-section fixed (dummy variables) | Migration | −0.963 | 0.354 | −2.722 | 0.007 | |||
| Lagged real wage growth | 0.072 | 0.045 | 1.591 | 0.113 | ||||
| Effects Specification: Cross-section fixed (dummy variables) | ||||||||
Real Wage Growth
| Dependent Variable: Real Wage Growth in Manufacturing | Dependent Variable: Real Wage Growth All Economic Activity | |||||||
| Method: Panel Least Squares | Method: Panel Least Squares | |||||||
| Sample (adjusted): 2001 2015 | Sample (adjusted): 2001 2015 | |||||||
| Periods included: 15 | Periods included: 15 | |||||||
| Cross-sections included: 19 | Cross-sections included: 21 | |||||||
| Total panel (unbalanced) observations: 267 | Total panel (unbalanced) observations: 302 | |||||||
| Variable | Coefficient | Std. Error t-Statistic | Prob. | Variable | Coefficien’ Std. Error | t-Statistic | Prob. | |
| Constant | 2.644 | 0.504 5.248 | 0.000 | Constant | 0.695 | 0.228 | 3.044 | 0.003 |
| Output gap | 0.212 | 0.111 1.910 | 0.057 | Output gap | 0.130 | 0.046 | 2.843 | 0.005 |
| Real GDP growth | 0.366 | 0.101 3.632 | 0.000 | Real GDP growth | 0.187 | 0.039 | 4.867 | 0.000 |
| Real minimum wage growth | 0.175 | 0.062 2.812 | 0.005 | Labor market entries | 1.285 | 0.753 | 1.705 | 0.089 |
| Real public sector wage growth | 0.230 | 0.087 2.631 | 0.009 | Real minimum wage growth | 0.077 | 0.024 | 3.202 | 0.002 |
| Lagged real wage growth | −0.227 | 0.062 -3.643 | 0.000 | Real public sector wage growth | 0.292 | 0.030 | 9.606 | 0.000 |
| Effects Specification: Cross-section fixed (dummy variables) | Migration | −0.963 | 0.354 | −2.722 | 0.007 | |||
| Lagged real wage growth | 0.072 | 0.045 | 1.591 | 0.113 | ||||
| Effects Specification: Cross-section fixed (dummy variables) | ||||||||
24. Estonia’s strong wage growth in manufacturing reflects wage policies and other country idiosyncrasies. During 2013–15, manufacturing wages grew by 6.2 percent per year in real terms, significantly more than in CEE and the EU, although the other Baltic economies saw even larger increases. According to the estimates, public sector wages and minimum wages contributed 1 percentage point each. CEE had much lower such contributions. Estonia’s country fixed effect, which captures all other unspecified country idiosyncrasies, also adds 1 percentage point more than in CEE and 2 percentage points more than in the EU. Emigration and inflation did not play a quantitatively important role. Growth and output gap made very similar contributions to elsewhere.
Contributions to Real Wage Growth in Manufacturing, 2013-15
(Annual average in percent)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Source: IMF staff estimates.25. Evidence from sectoral data suggests that wage developments in Estonia are not entirely a reflection of labor market tightness and that other non-economic factors, such a wage policies and momentum, may be at play. In recent years, sectors that did very well often saw lower-than-average wage increases and sectors that did poorly saw above-average wage increases. For example, wages rose relatively slowly in wholesale and retail trade, despite very strong consumption growth. The ICT sector, where employers have a particularly hard time finding suitable workers, registered wage growth not much above the average. Conversely, real estate activities have done poorly as of late, yet wages in this sector grew the most. There is also no statistically significant association between sectoral wage growth and sectoral vacancy rates, or the change of sectoral vacancy rates.
Wage Growth by Sector: Deviations from Average, 2012:Q1-2016:Q1
(Percent, four-quarter moving average)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Statistics Estonia; and IMF staff calculations.Sectoral Wage Growth and Vacancies, 2012:Q1-2016:Q1
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Statistics Estonia; and IMF staff calculations.E. Conclusions and Policy Implications
26. ULC developments in Estonia raise important red flags about competitiveness. The divergence between rapid wage growth and stagnant productivity is certainly not sustainable. The pace of ULC growth relative to trading partners and the associated appreciation of the real effective exchange rate exceed conventional prudent thresholds. Unlike in the past, Estonia’s relative rise in ULCs is no longer accompanied by a rise in relative export prices, suggesting that non-cost competitiveness factors ceased to sufficiently offset declining cost competitiveness. Whether ULCs have already reached a level that materially compromises competitiveness is harder to tell, but overall the evidence suggests that ULCs are close to this point. Real ULCs, which emerge as a particularly good indicator, are not only growing rapidly, but are also high by standards of a country at Estonia’s income level. Company profitability in the tradable sector well below long-term averages and slipping export market shares are corroborating evidence.
27. Fast ULC growth is unlikely to become an issue for the external balance anytime soon, but risks undermining growth and income convergence. Estonia’s current account has been broadly in balance in recent years compared to sizable historical deficits, largely on account of lower investment. With Estonia’s investment ratio still considerably higher than in European peers, notwithstanding the decline in recent years, a return to large current account deficits is unlikely even if competitiveness continued to suffer. But unmitigated ULC growth would likely undermine the economy’s growth potential as the tradable sector struggles and as resources shift to the nontradable sector where the scope for productivity gains is likely more limited.
28. Policies should adopt a three-pronged approach to tackle excessive ULC growth. Raising productivity growth is the most attractive track, because it provides the underpinnings for higher living standards at the same time. But policies to boost productivity growth will take time to come to fruition and are unlikely to bring it all the way up to the current pace of real wage growth even in the medium term. Hence policies to cool excessive wage growth are highly relevant. As a third track, tax policy could provide immediate relief from wage pressures on profitability.
Boosting productivity. Estonia already has many commendable programs to promote innovation and upgrading human capital in place. But they could be scaled up and broadened to more applied innovations, incentives for their take-up could be strengthened, and their effectiveness could be lifted by high-profile coordination and oversight through a productivity unit housed in the Prime Ministry. Higher public investment to close infrastructure gaps would also be helpful and there is some room to further improve Estonia’s already favorable business environment.
Cooling excessive wage growth. Policies have contributed to rapid wage growth through five consecutive minimum wage hikes of 10 percent per year and government wage increases that averaged over 8 percent annually in the last three years. A more cautious approach going forward would be helpful. The authorities should also clearly communicate that current wage growth is unsustainable in order to lean against the growing detachment from economic fundamentals. Moreover, efforts to boost labor supply for the private sector should be intensified: the release of labor resources from Estonia’s relatively large government sector could be stepped up; tight limits on immigration from non-EU countries could be loosened; and there is room to further raise labor participation, especially for younger women.
Providing tax relief. A cut in social security contribution rates would provide immediate relief from pressures of wages on profitability. At over 30 percent, they are high and the previous government had planned to gradually reduce them. These plans could be reinstated and accelerated. Since social taxes are mostly employer-paid, cuts would benefit profits in the first instance. In the longer run and as wage contracts are renegotiated, the benefits from the cut would be shared by employers and employees.
References
Bakker, Bas and Christoph Klingen (eds.), 2012, How Emerging Europe Came Through the 2008/09 Crisis: An Account by the Staff of the IMF’s European Department. Available via: https://www.imf.org/external/pubs/cat/longres.aspx?sk=25765.0
Benkovskis, Konstantins and Ramune Rimgailaite, 2011, “The Quality and Variety of Exports from the New EU Member States—Evidence from Very Disaggregated Data,” Economics of Transition, Vol. 19, Issue 4.
European Commission, 2016, “The Macroeconomic Imbalance Procedure—Rationale, Process, Application: A Compendium,” Institutional Paper 039. Available via: http://ec.europa.eu/economy finance/publications/eeip/ip039 en.htm.
International Monetary Fund, 2016, “Managing Government Compensation and Employment—Institutions, Policies, and Reform Challenges,” IMF Board Paper. Available via: https://www.imf.org/external/np/pp/eng/2016/040816a.pdf.
Lipschitz, Leslie and Donogh McDonald, 1992, “Real Exchange Rates and Competitiveness: A Clarification of Concepts, and some Measurements for Europe,” Empirica–Austrian Economic Papers, Volume 19, No. 1, pages 37–69. Available via: http://link.springer.com/article/10.1007/BF00924805.
Raei, Faezeh, Piyaporn Sodsriwiboon, and Gabriel Srour, 2016, “Getting Minimum Wages Right in Central, Eastern and Southeastern Europe,” Cross-Country Report on Minimum Wages: Selected Issues No. 16/151. Available via: http://www.imf.org/external/pubs/cat/longres.aspx?sk=43961.0.
Annex I. Nominal vs. Real Exchange Rates
1. Nominal and real ULCs are conceptually very different even though the formulas to calculate them are almost identical.
Nominal ULCs are calculated as the ratio of labor compensation per employee and real value-added per employed person. They are a nominal index that increases over time as wages rise in an inflationary environment. When expressed in a common currency and compared to trading partners, they are equivalent to the real effective exchange rate. Because they compare labor costs adjusted for productivity differentials across country, they are routinely the starting point for an assessment of exchange rates and external price competitiveness.
Real unit labor costs are calculated as the ratio of labor compensation per employee and nominal value-added per employed person and are a unitless indicator that does not systematically change in an inflationary environment. If the split of the employed between employees and the self-employed remains roughly constant over time, it broadly tracks the labor share of income, the complement to the share of profits and mixed income in gross value added. Hence, rising real ULCs point to a compression of profits, which in turn points to a loss of competitiveness if it occurs in the tradable sector.
2. Nominal and real ULCs have their conceptual drawbacks, but there are more issues with nominal ULCs. Both are affected by developments unrelated to competitiveness, such as the business cycle position, capital deepening, and shifts between sectors with different labor intensity. But in addition, nominal ULCs suffer from the fundamental problem that their increase may be the cause as well as the result of changes in competitiveness: an increase due to an autonomous rise in wages is a loss of competitiveness, but an increase due to an autonomous increase in product quality, export prices, and wages is a competitiveness gain. Moreover, nominal ULCs are an index number without meaningful interpretation to their levels, making it difficult to say whether an increase has already compromised competitiveness because it might have come off a low base.
3. The relative performance of nominal and real ULCs is ultimately an empirical question. With regard to export performance, which indicator is more closely associated with gains and losses of market shares? With regard to growth in the tradable sector compared to the nontradable sector, which indicator has the better predictive power? With regards to income convergence, which indicator is more relevant? Regardless of the outcome of this performance comparison, a thorough competitiveness assessment should retain both indicators, but the exercise still conveys a sense of how much relative importance to attach to nominal and real ULCs in coming to an overall judgement.
4. Real ULCs appear more closely associated with export performance than nominal ULCs. A sample of 28 European countries shows a strong relationship between changes in real ULCs and changes in export market shares. Long periods of 12 years are considered for these changes to smooth out business cycle effects and other shocks. The association is even closer for the manufacturing sector, which is more exposed to trade. Export market shares for manufacturing goods are calculated from SITC Revision 4 data provided through the World Integrated Trade Solutions portal, which allow a close matching of exports by type of good to the associated manufacturing activity. In contrast, there is only a weak correlation between exports and nominal ULCs in manufacturing and none with economy-wide nominal ULCs.
Export Market Shares Gains and Real ULC Growth
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; WITS; IMF, WEO; and IMF staff calculations.Export Market Shares Gains and Nominal ULC Growth
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; WITS; IMF, WEO; and IMF staff calculations.5. Real ULCs developments are also better aligned with changes in internal resource allocation. When ULCs in the manufacturing sector, which is most active in exporting, rise relative to those for all activities, one would expect activity to shift away from exports, because in exporting it becomes relatively more difficult to make sales and profits. There is indeed a close association between changes in relative real ULCs and changes in the share of export-oriented activity in the economy. Relative profitability between activity in the tradable and nontradable sectors seems to matter for internal resource allocation. In contrast, relative nominal ULCs fail to show much relation to internal resource allocation.
Real ULCs and Internal Resource Reallocation
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; WfTS; IMF, WEO; and IMF staff calculations.Nominal ULCs and Internal Resource Reallocation
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: Eurostat; WfTS; IMF, WEO; and IMF staff calculations.6. A case study suggests that income convergence prospects are more strongly influenced by real rather than nominal ULC developments. Korea and Italy are a case in point. Korea exhibits a largely uninterrupted stretch of income convergence with the U.S. over many decades, measured as relative GDP per capita in purchasing power parity U.S. dollars. In contrast, Italy’s relative income level remained broadly constant until the late 1990s and subsequently declined substantially. Nominal ULCs relative to those in the U.S. exhibit large gyration, partly related to exchange rate movements: a large increase up until the oil crises in the late 1970s and early 1980s followed by a correction, a large increase in the run-up to the Asian crisis in the second half of the 1990s followed by a sharp decline thereafter, and again a pronounced boom-bust cycle around the global financial crisis in 2008. These outsized fluctuations make it difficult to interpret nominal ULCs, even if some of these episodes left a mark on income convergence. Real ULCs in manufacturing paint a clearer picture. Korea’s moderate and gentle increase by around 20 percent over the past three decades did not harm income convergence. But Italy’s 50 percent increase over the same period did, especially the sharp rise after the launch of the euro at the turn of the century.
Korea: ULCs and Convergence, 1970-2015
(Relative to US, ULCs indexed to 100 in 1970)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: EC, AMECO; US BEA; Bank of Korea; and IMF staff calculations.Italy: ULCs and Convergence, 1970-2015
(Relative to US, ULCs indexed to 100 in 1970)
Citation: IMF Staff Country Reports 2017, 010; 10.5089/9781475565911.002.A001
Sources: EC, AMECO; US BEA; Bank of Korea; and IMF staff calculations.Prepared by Christoph Klingen.
The four largest CEE economies comprise the Czech Republic, Poland, Slovakia, and Hungary. They are referred to as the CEE-4.
More sophisticated approaches to estimating product quality have been developed more recently. They introduce micro-foundations and estimate quality, and consumer taste from relative unit values and real market shares. However, an application to Latvia still shows an unrealistic surge in product quality in the boom years and a steep decline in the subsequent recession (Benkovskis and Rimgailaite, 2011).
The lack of an association between real ULC growth and income convergence on the one hand and the existence of an association between ULC levels and relative income levels on the other hand is not inconsistent. It arises because it was the richer Western European economies that started out with higher real ULCs and maintained that lead over the period 2003–15 when CEE narrowed the income gap.
References
Gal, Peter N., 2013, “Measuring Total Factor Productivity at the Firm Level using OECD-ORBIS,” No. 1049 (Paris: Organization of Economic Cooperation and Development).
Gilhooly, Bob, 2009, “Firm-level Estimates of Capital Stock and Productivity,” Economic and Labor Market Review, Vol. 3, No. 5, pp. 36–41.
Wooldridge, Jeffrey M., 2009, “On Estimating Firm-level Production Functions Using Proxy Variables to Control for Unobservables,” Economics Letters, Vol. 104, Issue 3, pp. 112–114 (Philadelphia: Elsevier B.V.).
Annex I. Data Sources and Regression Results
Estonia: Orbis Data Coverage
Estonia: Orbis Data Coverage
| Sector | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
|---|---|---|---|---|---|---|---|---|---|---|
| Percentage of Employment | ||||||||||
| Agriculture | 61.4 | 67.0 | 63.4 | 69.1 | 69.5 | 58.8 | 56.1 | 57.8 | 78.8 | 77.7 |
| Manufacturing | 43.4 | 45.5 | 47.0 | 48.9 | 46.6 | 50.0 | 51.8 | 48.4 | 53.2 | 52.4 |
| Construction | 32.7 | 36.2 | 37.3 | 33.2 | 30.0 | 36.4 | 40.5 | 44.5 | 45.2 | 41.6 |
| Trade | 47.9 | 57.2 | 60.1 | 55.4 | 47.5 | 50.6 | 49.8 | 50.0 | 51.1 | 49.0 |
| Market Services | 31.4 | 42.4 | 35.2 | 24.6 | 23.0 | 26.5 | 28.0 | 27.5 | 30.6 | 33.1 |
| Basic Services | 9.3 | 9.9 | 10.7 | 9.7 | 8.5 | 10.1 | 10.3 | 10.8 | 11.6 | 10.5 |
| Total | 33.1 | 40.7 | 38.4 | 32.7 | 29.8 | 33.0 | 35.0 | 35.1 | 38.2 | 37.7 |
| Percentage of Value Added | ||||||||||
| Agriculture | 91.6 | 90.4 | 78.9 | 95.5 | 91.0 | 82.0 | 83.9 | 85.0 | 98.3 | 107.4 |
| Manufacturing | 64.5 | 68.8 | 72.0 | 69.4 | 67.2 | 73.6 | 68.8 | 72.7 | 75.7 | 76.8 |
| Construction | 59.5 | 53.0 | 48.4 | 48.4 | 57.2 | 61.4 | 53.7 | 59.9 | 63.6 | 59.0 |
| Trade | 71.4 | 73.4 | 83.0 | 78.3 | 76.7 | 77.8 | 78.3 | 82.8 | 83.8 | 77.3 |
| Market Services | 58.9 | 67.5 | 66.0 | 64.1 | 59.5 | 68.3 | 68.9 | 64.2 | 66.5 | 66.4 |
| Basic Services | 12.9 | 15.0 | 16.8 | 18.0 | 15.0 | 15.4 | 16.1 | 15.6 | 15.9 | 15.5 |
| Total | 52.7 | 56.2 | 57.0 | 56.8 | 53.9 | 57.0 | 56.4 | 56.1 | 58.8 | 57.8 |
Estonia: Orbis Data Coverage
| Sector | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
|---|---|---|---|---|---|---|---|---|---|---|
| Percentage of Employment | ||||||||||
| Agriculture | 61.4 | 67.0 | 63.4 | 69.1 | 69.5 | 58.8 | 56.1 | 57.8 | 78.8 | 77.7 |
| Manufacturing | 43.4 | 45.5 | 47.0 | 48.9 | 46.6 | 50.0 | 51.8 | 48.4 | 53.2 | 52.4 |
| Construction | 32.7 | 36.2 | 37.3 | 33.2 | 30.0 | 36.4 | 40.5 | 44.5 | 45.2 | 41.6 |
| Trade | 47.9 | 57.2 | 60.1 | 55.4 | 47.5 | 50.6 | 49.8 | 50.0 | 51.1 | 49.0 |
| Market Services | 31.4 | 42.4 | 35.2 | 24.6 | 23.0 | 26.5 | 28.0 | 27.5 | 30.6 | 33.1 |
| Basic Services | 9.3 | 9.9 | 10.7 | 9.7 | 8.5 | 10.1 | 10.3 | 10.8 | 11.6 | 10.5 |
| Total | 33.1 | 40.7 | 38.4 | 32.7 | 29.8 | 33.0 | 35.0 | 35.1 | 38.2 | 37.7 |
| Percentage of Value Added | ||||||||||
| Agriculture | 91.6 | 90.4 | 78.9 | 95.5 | 91.0 | 82.0 | 83.9 | 85.0 | 98.3 | 107.4 |
| Manufacturing | 64.5 | 68.8 | 72.0 | 69.4 | 67.2 | 73.6 | 68.8 | 72.7 | 75.7 | 76.8 |
| Construction | 59.5 | 53.0 | 48.4 | 48.4 | 57.2 | 61.4 | 53.7 | 59.9 | 63.6 | 59.0 |
| Trade | 71.4 | 73.4 | 83.0 | 78.3 | 76.7 | 77.8 | 78.3 | 82.8 | 83.8 | 77.3 |
| Market Services | 58.9 | 67.5 | 66.0 | 64.1 | 59.5 | 68.3 | 68.9 | 64.2 | 66.5 | 66.4 |
| Basic Services | 12.9 | 15.0 | 16.8 | 18.0 | 15.0 | 15.4 | 16.1 | 15.6 | 15.9 | 15.5 |
| Total | 52.7 | 56.2 | 57.0 | 56.8 | 53.9 | 57.0 | 56.4 | 56.1 | 58.8 | 57.8 |
Orbis: Number of Estonian Firms with Useable Data
Orbis: Number of Estonian Firms with Useable Data
| Sector | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
|---|---|---|---|---|---|---|---|---|---|---|
| Number of firms with data on Employees | ||||||||||
| Agriculture | 2,115 | 2,284 | 2,521 | 2,809 | 2,751 | 3,393 | 3,673 | 4,016 | 4,254 | 4,200 |
| Manufacturing | 2,789 | 3,020 | 3,305 | 3,536 | 3,288 | 4,051 | 4,295 | 4,606 | 4,763 | 4,538 |
| Construction | 2,413 | 3,016 | 3,854 | 4,306 | 3,956 | 5,039 | 5,554 | 6,115 | 6,400 | 6,042 |
| Trade | 5,491 | 5,879 | 6,352 | 6,899 | 6,424 | 8,035 | 8,494 | 9,100 | 9,319 | 8,824 |
| Market Services | 9,436 | 10,559 | 11,848 | 13,305 | 12,596 | 15,911 | 17,179 | 18,891 | 19,836 | 19,036 |
| Basic Services | 1,091 | 1,215 | 1,423 | 1,595 | 1,498 | 2,006 | 2,193 | 2,343 | 2,420 | 2,208 |
| Total | 23,335 | 25,973 | 29,303 | 32,450 | 30,513 | 38,435 | 41,388 | 45,071 | 46,992 | 44,848 |
| Number of firms with data on Value Added and Labor Productivity | ||||||||||
| Agriculture | 1,566 | 1,673 | 1,838 | 2,037 | 1,977 | 2,369 | 2,553 | 2,820 | 2,973 | 2,954 |
| Manufacturing | 2,018 | 2,217 | 2,411 | 2,574 | 2,345 | 2,857 | 3,007 | 3,203 | 3,309 | 3,149 |
| Construction | 1,685 | 2,146 | 2,736 | 3,054 | 2,681 | 3,312 | 3,634 | 3,991 | 4,238 | 4,055 |
| Trade | 3,814 | 4,081 | 4,377 | 4,680 | 4,285 | 5,129 | 5,294 | 5,602 | 5,610 | 5,295 |
| Market Services | 6,796 | 7,500 | 8,370 | 9,037 | 8,391 | 10,350 | 10,854 | 11,831 | 12,331 | 11,829 |
| Basic Services | 786 | 872 | 1,016 | 1,119 | 997 | 1,285 | 1,384 | 1,478 | 1,526 | 1,430 |
| Total | 16,665 | 18,489 | 20,748 | 22,501 | 20,676 | 25,302 | 26,726 | 28,925 | 29,987 | 28,712 |
| Number of firms with data on Total Factor Productivity | ||||||||||
| Agriculture | 1,511 | 1,625 | 1,786 | 1,950 | 1,884 | 2,277 | 2,462 | 2,718 | 2,870 | 2,849 |
| Manufacturing | 1,963 | 2,164 | 2,345 | 2,480 | 2,212 | 2,715 | 2,891 | 3,066 | 3,166 | 3,023 |
| Construction | 1,644 | 2,095 | 2,640 | 2,869 | 2,358 | 2,965 | 3,417 | 3,756 | 4,008 | 3,820 |
| Trade | 3,654 | 3,937 | 4,167 | 4,337 | 3,820 | 4,663 | 4,905 | 5,204 | 5,256 | 4,955 |
| Market Services | 6,479 | 7,193 | 7,920 | 8,383 | 7,520 | 9,481 | 10,135 | 11,043 | 11,465 | 11,072 |
| Basic Services | 750 | 838 | 966 | 1,031 | 883 | 1,179 | 1,283 | 1,381 | 1,424 | 1,326 |
| Total | 16,001 | 17,852 | 19,824 | 21,050 | 18,677 | 23,280 | 25,093 | 27,168 | 28,189 | 27,045 |
Orbis: Number of Estonian Firms with Useable Data
| Sector | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
|---|---|---|---|---|---|---|---|---|---|---|
| Number of firms with data on Employees | ||||||||||
| Agriculture | 2,115 | 2,284 | 2,521 | 2,809 | 2,751 | 3,393 | 3,673 | 4,016 | 4,254 | 4,200 |
| Manufacturing | 2,789 | 3,020 | 3,305 | 3,536 | 3,288 | 4,051 | 4,295 | 4,606 | 4,763 | 4,538 |
| Construction | 2,413 | 3,016 | 3,854 | 4,306 | 3,956 | 5,039 | 5,554 | 6,115 | 6,400 | 6,042 |
| Trade | 5,491 | 5,879 | 6,352 | 6,899 | 6,424 | 8,035 | 8,494 | 9,100 | 9,319 | 8,824 |
| Market Services | 9,436 | 10,559 | 11,848 | 13,305 | 12,596 | 15,911 | 17,179 | 18,891 | 19,836 | 19,036 |
| Basic Services | 1,091 | 1,215 | 1,423 | 1,595 | 1,498 | 2,006 | 2,193 | 2,343 | 2,420 | 2,208 |
| Total | 23,335 | 25,973 | 29,303 | 32,450 | 30,513 | 38,435 | 41,388 | 45,071 | 46,992 | 44,848 |
| Number of firms with data on Value Added and Labor Productivity | ||||||||||
| Agriculture | 1,566 | 1,673 | 1,838 | 2,037 | 1,977 | 2,369 | 2,553 | 2,820 | 2,973 | 2,954 |
| Manufacturing | 2,018 | 2,217 | 2,411 | 2,574 | 2,345 | 2,857 | 3,007 | 3,203 | 3,309 | 3,149 |
| Construction | 1,685 | 2,146 | 2,736 | 3,054 | 2,681 | 3,312 | 3,634 | 3,991 | 4,238 | 4,055 |
| Trade | 3,814 | 4,081 | 4,377 | 4,680 | 4,285 | 5,129 | 5,294 | 5,602 | 5,610 | 5,295 |
| Market Services | 6,796 | 7,500 | 8,370 | 9,037 | 8,391 | 10,350 | 10,854 | 11,831 | 12,331 | 11,829 |
| Basic Services | 786 | 872 | 1,016 | 1,119 | 997 | 1,285 | 1,384 | 1,478 | 1,526 | 1,430 |
| Total | 16,665 | 18,489 | 20,748 | 22,501 | 20,676 | 25,302 | 26,726 | 28,925 | 29,987 | 28,712 |
| Number of firms with data on Total Factor Productivity | ||||||||||
| Agriculture | 1,511 | 1,625 | 1,786 | 1,950 | 1,884 | 2,277 | 2,462 | 2,718 | 2,870 | 2,849 |
| Manufacturing | 1,963 | 2,164 | 2,345 | 2,480 | 2,212 | 2,715 | 2,891 | 3,066 | 3,166 | 3,023 |
| Construction | 1,644 | 2,095 | 2,640 | 2,869 | 2,358 | 2,965 | 3,417 | 3,756 | 4,008 | 3,820 |
| Trade | 3,654 | 3,937 | 4,167 | 4,337 | 3,820 | 4,663 | 4,905 | 5,204 | 5,256 | 4,955 |
| Market Services | 6,479 | 7,193 | 7,920 | 8,383 | 7,520 | 9,481 | 10,135 | 11,043 | 11,465 | 11,072 |
| Basic Services | 750 | 838 | 966 | 1,031 | 883 | 1,179 | 1,283 | 1,381 | 1,424 | 1,326 |
| Total | 16,001 | 17,852 | 19,824 | 21,050 | 18,677 | 23,280 | 25,093 | 27,168 | 28,189 | 27,045 |
Estonia: Difference-in-Means Results: Change in TFP
Estonia: Difference-in-Means Results: Change in TFP
| Average Firm Characteristic | Change in Total Factor Productivity | |||||||
|---|---|---|---|---|---|---|---|---|
| 2010-2014 | 2005-2009 | |||||||
| Top third | Bottom third | Difference | t-stat | Top third | Bottom third | Difference | t-stat | |
| Total factor productivity | 2.54 | 3.41 | −0.87 | −37.89 | 2.64 | 3.48 | −0.83 | −32.89 |
| Labor productivity | 126.88 | 266.68 | −139.80 | −19.20 | 135.81 | 291.68 | −155.87 | −14.87 |
| Return on equity | −12.48 | 26.73 | −39.20 | −23.59 | 7.53 | 34.86 | −27.34 | −18.04 |
| Return on assets | −0.31 | 16.46 | −16.77 | −36.35 | 6.32 | 21.39 | −15.07 | −30.09 |
| Profit rargin | −0.96 | 12.18 | −13.14 | −35.96 | 4.04 | 13.82 | −9.78 | −25.13 |
| Size (Value Added) | 1245.56 | 1955.05 | −709.48 | −4.46 | 1715.02 | 3191.32 | −1476.30 | −5.90 |
| Size (Number of employees) | 8.77 | 7.31 | 1.45 | 2.63 | 13.37 | 12.92 | 0.45 | 0.32 |
| Assets per employee | 75.82 | 113.76 | −37.94 | −2.74 | 49.12 | 79.99 | −30.87 | −2.52 |
| Employee cost share | 24.31 | 21.81 | 2.50 | 6.93 | 23.08 | 18.02 | 5.06 | 12.90 |
| Firm age | 9.17 | 8.74 | 0.43 | 3.36 | 7.47 | 7.79 | −0.32 | −2.43 |
| Average cost of employees | 8.18 | 9.83 | −1.65 | −7.94 | 5.63 | 7.57 | −1.94 | −11.07 |
| Current ratio | 4.06 | 5.32 | −1.26 | −7.01 | 3.08 | 4.33 | −1.25 | −7.57 |
| Liquidity ratio | 3.42 | 4.62 | −1.20 | −7.10 | 2.51 | 3.69 | −1.18 | −7.33 |
| Solvency ratio | 51.28 | 60.73 | −9.45 | −15.14 | 48.47 | 59.68 | −11.21 | −16.80 |
| Gearing | 78.05 | 48.27 | 29.78 | 10.20 | 85.94 | 44.55 | 41.40 | 12.80 |
Estonia: Difference-in-Means Results: Change in TFP
| Average Firm Characteristic | Change in Total Factor Productivity | |||||||
|---|---|---|---|---|---|---|---|---|
| 2010-2014 | 2005-2009 | |||||||
| Top third | Bottom third | Difference | t-stat | Top third | Bottom third | Difference | t-stat | |
| Total factor productivity | 2.54 | 3.41 | −0.87 | −37.89 | 2.64 | 3.48 | −0.83 | −32.89 |
| Labor productivity | 126.88 | 266.68 | −139.80 | −19.20 | 135.81 | 291.68 | −155.87 | −14.87 |
| Return on equity | −12.48 | 26.73 | −39.20 | −23.59 | 7.53 | 34.86 | −27.34 | −18.04 |
| Return on assets | −0.31 | 16.46 | −16.77 | −36.35 | 6.32 | 21.39 | −15.07 | −30.09 |
| Profit rargin | −0.96 | 12.18 | −13.14 | −35.96 | 4.04 | 13.82 | −9.78 | −25.13 |
| Size (Value Added) | 1245.56 | 1955.05 | −709.48 | −4.46 | 1715.02 | 3191.32 | −1476.30 | −5.90 |
| Size (Number of employees) | 8.77 | 7.31 | 1.45 | 2.63 | 13.37 | 12.92 | 0.45 | 0.32 |
| Assets per employee | 75.82 | 113.76 | −37.94 | −2.74 | 49.12 | 79.99 | −30.87 | −2.52 |
| Employee cost share | 24.31 | 21.81 | 2.50 | 6.93 | 23.08 | 18.02 | 5.06 | 12.90 |
| Firm age | 9.17 | 8.74 | 0.43 | 3.36 | 7.47 | 7.79 | −0.32 | −2.43 |
| Average cost of employees | 8.18 | 9.83 | −1.65 | −7.94 | 5.63 | 7.57 | −1.94 | −11.07 |
| Current ratio | 4.06 | 5.32 | −1.26 | −7.01 | 3.08 | 4.33 | −1.25 | −7.57 |
| Liquidity ratio | 3.42 | 4.62 | −1.20 | −7.10 | 2.51 | 3.69 | −1.18 | −7.33 |
| Solvency ratio | 51.28 | 60.73 | −9.45 | −15.14 | 48.47 | 59.68 | −11.21 | −16.80 |
| Gearing | 78.05 | 48.27 | 29.78 | 10.20 | 85.94 | 44.55 | 41.40 | 12.80 |
Estonia: Difference-in-Means Results: Change in Employment
Estonia: Difference-in-Means Results: Change in Employment
| Average Firm Characteristic | Change in Employment | |||||||
|---|---|---|---|---|---|---|---|---|
| 2010-2014 | 2005-2009 | |||||||
| Top | Bottom | Difference | t-stat | Top | Bottom | Difference | t-stat | |
| Total factor productivity | 3.12 | 2.92 | 0.19 | 10.36 | 3.26 | 2.87 | 0.39 | 15.93 |
| Labor productivity | 210.27 | 150.40 | 59.87 | 10.51 | 270.64 | 146.47 | 124.17 | 12.32 |
| Return on equity | 11.10 | 0.29 | 10.81 | 9.23 | 27.33 | 11.46 | 15.87 | 11.24 |
| Return on assets | 9.06 | 5.31 | 3.76 | 11.15 | 15.30 | 9.36 | 5.93 | 13.55 |
| Profit rargin | 5.51 | 4.16 | 1.35 | 4.72 | 9.62 | 6.13 | 3.49 | 10.20 |
| Size (Value Added) | 2978.75 | 1190.75 | 1788.01 | 10.69 | 3428.06 | 2235.26 | 1192.80 | 4.47 |
| Size (Number of employees) | 13.34 | 7.85 | 5.49 | 6.31 | 16.50 | 15.46 | 1.03 | 0.83 |
| Assets per employee | 134.97 | 111.79 | 23.18 | 1.94 | 142.44 | 47.09 | 95.34 | 7.56 |
| Employee cost share | 20.32 | 26.51 | −6.19 | −21.97 | 18.27 | 23.05 | −4.78 | −13.09 |
| Firm age | 8.38 | 9.50 | −1.12 | −12.78 | 6.82 | 8.35 | −1.53 | −13.09 |
| Average cost of employees | 9.67 | 7.83 | 1.84 | 14.12 | 7.77 | 5.25 | 2.52 | 19.24 |
| Current ratio | 4.20 | 5.89 | −1.69 | −12.26 | 3.44 | 3.70 | −0.26 | −1.87 |
| Liquidity ratio | 3.48 | 5.07 | −1.59 | −12.20 | 2.77 | 2.91 | −0.14 | −1.09 |
| Solvency ratio | 51.83 | 59.53 | −7.70 | −18.15 | 50.58 | 52.13 | −1.56 | −2.76 |
| Gearing | 73.52 | 54.95 | 18.58 | 9.66 | 71.55 | 68.21 | 3.34 | 1.22 |
Estonia: Difference-in-Means Results: Change in Employment
| Average Firm Characteristic | Change in Employment | |||||||
|---|---|---|---|---|---|---|---|---|
| 2010-2014 | 2005-2009 | |||||||
| Top | Bottom | Difference | t-stat | Top | Bottom | Difference | t-stat | |
| Total factor productivity | 3.12 | 2.92 | 0.19 | 10.36 | 3.26 | 2.87 | 0.39 | 15.93 |
| Labor productivity | 210.27 | 150.40 | 59.87 | 10.51 | 270.64 | 146.47 | 124.17 | 12.32 |
| Return on equity | 11.10 | 0.29 | 10.81 | 9.23 | 27.33 | 11.46 | 15.87 | 11.24 |
| Return on assets | 9.06 | 5.31 | 3.76 | 11.15 | 15.30 | 9.36 | 5.93 | 13.55 |
| Profit rargin | 5.51 | 4.16 | 1.35 | 4.72 | 9.62 | 6.13 | 3.49 | 10.20 |
| Size (Value Added) | 2978.75 | 1190.75 | 1788.01 | 10.69 | 3428.06 | 2235.26 | 1192.80 | 4.47 |
| Size (Number of employees) | 13.34 | 7.85 | 5.49 | 6.31 | 16.50 | 15.46 | 1.03 | 0.83 |
| Assets per employee | 134.97 | 111.79 | 23.18 | 1.94 | 142.44 | 47.09 | 95.34 | 7.56 |
| Employee cost share | 20.32 | 26.51 | −6.19 | −21.97 | 18.27 | 23.05 | −4.78 | −13.09 |
| Firm age | 8.38 | 9.50 | −1.12 | −12.78 | 6.82 | 8.35 | −1.53 | −13.09 |
| Average cost of employees | 9.67 | 7.83 | 1.84 | 14.12 | 7.77 | 5.25 | 2.52 | 19.24 |
| Current ratio | 4.20 | 5.89 | −1.69 | −12.26 | 3.44 | 3.70 | −0.26 | −1.87 |
| Liquidity ratio | 3.48 | 5.07 | −1.59 | −12.20 | 2.77 | 2.91 | −0.14 | −1.09 |
| Solvency ratio | 51.83 | 59.53 | −7.70 | −18.15 | 50.58 | 52.13 | −1.56 | −2.76 |
| Gearing | 73.52 | 54.95 | 18.58 | 9.66 | 71.55 | 68.21 | 3.34 | 1.22 |
Estonia: Regression Results
Estonia: Regression Results
| TFP Growth | Change in Employment | |||
|---|---|---|---|---|
| 2010-2014 | 2005-2009 | 2010-2014 | 2005-2009 | |
| Univariate Regressions1 | ||||
| Total factor productivity | −0.3889*** | −0.4012*** | 0.0213*** | 0.0791*** |
| (0.0067) | (0.0091) | (0.0037) | (0.0054) | |
| Labor productivity | −0.0007*** | −0.0006*** | 0.0001*** | 0.0002*** |
| (0.0000) | (0.0000) | (0.0000) | (0.0000) | |
| Return on assets | −0.0146*** | −0.0166*** | 0.0018*** | 0.0027*** |
| (0.0004) | (0.0005) | (0.0001) | (0.0002) | |
| Return on equity | −0.0030*** | −0.0036*** | 0.0004*** | 0.0007*** |
| (0.0001) | (0.0002) | (0.0000) | (0.0001) | |
| Profit rargin | −0.0205*** | −0.0199*** | 0.0016*** | 0.0030*** |
| (0.0004) | (0.0006) | (0.0002) | (0.0003) | |
| Value Added | −0.0184** | −0.0402*** | 0.0004 | −0.0045 |
| (0.0072) | (0.0095) | (0.0039) | (0.0053) | |
| Number of employees | 0.0002 | 0.0003* | −0.0002*** | −0.0005*** |
| (0.0002) | (0.0002) | (0.0001) | (0.0001) | |
| Assets per employee | −0.0097*** | −0.0074*** | 0.0032*** | 0.0099*** |
| (0.0014) | (0.0025) | (0.0004) | (0.0009) | |
| Labor cost share | 0.0033*** | 0.0081*** | −0.0052*** | −0.0040*** |
| (0.0005) | (0.0006) | (0.0002) | (0.0003) | |
| Firm age | 0.0044*** | −0.0029* | −0.0125*** | −0.0123*** |
| (0.0012) | (0.0016) | (0.0005) | (0.0008) | |
| Average cost of employees | −0.0081*** | −0.0201*** | 0.0036*** | 0.0142*** |
| (0.0009) | (0.0015) | (0.0004) | (0.0008) | |
| Share of exports | 0.0931** | −0.0194 | 0.1194*** | 0.0662** |
| (0.0383) | (0.0547) | (0.0163) | (0.0268) | |
| High-Tech | −0.0021 | 0.0892*** | −0.0610*** | 0.0670*** |
| (0.0219) | (0.0283) | (0.0090) | (0.0138) | |
| Multivariate Regression | ||||
| Total factor productivity | −0.3506*** | −0.3331*** | −0.0134*** | 0.0339*** |
| (0.0078) | (0.0110) | (0.0043) | (0.0065) | |
| Return on assets | −0.0085*** | −0.0099*** | 0.0020*** | 0.0015*** |
| (0.0004) | (0.0005) | (0.0002) | (0.0003) | |
| Value Added | 0.0087 | 0.0073 | 0.0015 | −0.0113** |
| (0.0064) | (0.0088) | (0.0039) | (0.0057) | |
| Firm age | −0.0060*** | −0.0081*** | −0.0121*** | −0.0096*** |
| (0.0011) | (0.0015) | (0.0006) | (0.0009) | |
| Average labor cost | 0.0070*** | 0.0012 | 0.0050*** | 0.0121*** |
| (0.0009) | (0.0016) | (0.0005) | (0.0010) | |
| Assets per employee | −0.0089*** | −0.0155*** | 0.0050*** | 0.0166*** |
| (0.0013) | (0.0039) | (0.0008) | (0.0019) | |
| Share of exports | 0.2166*** | 0.0605 | 0.1060*** | 0.0115 |
| (0.0343) | (0.0495) | (0.0198) | (0.0309) | |
| High-Tech | 0.1542*** | 0.2719*** | −0.0954*** | 0.0047 |
| (0.0199) | (0.0263) | (0.0113) | (0.0162) | |
| Number of obs. | 14767 | 10543 | 16896 | 12321 |
| R-squared | 0.221 | 0.192 | 0.043 | 0.046 |
Estonia: Regression Results
| TFP Growth | Change in Employment | |||
|---|---|---|---|---|
| 2010-2014 | 2005-2009 | 2010-2014 | 2005-2009 | |
| Univariate Regressions1 | ||||
| Total factor productivity | −0.3889*** | −0.4012*** | 0.0213*** | 0.0791*** |
| (0.0067) | (0.0091) | (0.0037) | (0.0054) | |
| Labor productivity | −0.0007*** | −0.0006*** | 0.0001*** | 0.0002*** |
| (0.0000) | (0.0000) | (0.0000) | (0.0000) | |
| Return on assets | −0.0146*** | −0.0166*** | 0.0018*** | 0.0027*** |
| (0.0004) | (0.0005) | (0.0001) | (0.0002) | |
| Return on equity | −0.0030*** | −0.0036*** | 0.0004*** | 0.0007*** |
| (0.0001) | (0.0002) | (0.0000) | (0.0001) | |
| Profit rargin | −0.0205*** | −0.0199*** | 0.0016*** | 0.0030*** |
| (0.0004) | (0.0006) | (0.0002) | (0.0003) | |
| Value Added | −0.0184** | −0.0402*** | 0.0004 | −0.0045 |
| (0.0072) | (0.0095) | (0.0039) | (0.0053) | |
| Number of employees | 0.0002 | 0.0003* | −0.0002*** | −0.0005*** |
| (0.0002) | (0.0002) | (0.0001) | (0.0001) | |
| Assets per employee | −0.0097*** | −0.0074*** | 0.0032*** | 0.0099*** |
| (0.0014) | (0.0025) | (0.0004) | (0.0009) | |
| Labor cost share | 0.0033*** | 0.0081*** | −0.0052*** | −0.0040*** |
| (0.0005) | (0.0006) | (0.0002) | (0.0003) | |
| Firm age | 0.0044*** | −0.0029* | −0.0125*** | −0.0123*** |
| (0.0012) | (0.0016) | (0.0005) | (0.0008) | |
| Average cost of employees | −0.0081*** | −0.0201*** | 0.0036*** | 0.0142*** |
| (0.0009) | (0.0015) | (0.0004) | (0.0008) | |
| Share of exports | 0.0931** | −0.0194 | 0.1194*** | 0.0662** |
| (0.0383) | (0.0547) | (0.0163) | (0.0268) | |
| High-Tech | −0.0021 | 0.0892*** | −0.0610*** | 0.0670*** |
| (0.0219) | (0.0283) | (0.0090) | (0.0138) | |
| Multivariate Regression | ||||
| Total factor productivity | −0.3506*** | −0.3331*** | −0.0134*** | 0.0339*** |
| (0.0078) | (0.0110) | (0.0043) | (0.0065) | |
| Return on assets | −0.0085*** | −0.0099*** | 0.0020*** | 0.0015*** |
| (0.0004) | (0.0005) | (0.0002) | (0.0003) | |
| Value Added | 0.0087 | 0.0073 | 0.0015 | −0.0113** |
| (0.0064) | (0.0088) | (0.0039) | (0.0057) | |
| Firm age | −0.0060*** | −0.0081*** | −0.0121*** | −0.0096*** |
| (0.0011) | (0.0015) | (0.0006) | (0.0009) | |
| Average labor cost | 0.0070*** | 0.0012 | 0.0050*** | 0.0121*** |
| (0.0009) | (0.0016) | (0.0005) | (0.0010) | |
| Assets per employee | −0.0089*** | −0.0155*** | 0.0050*** | 0.0166*** |
| (0.0013) | (0.0039) | (0.0008) | (0.0019) | |
| Share of exports | 0.2166*** | 0.0605 | 0.1060*** | 0.0115 |
| (0.0343) | (0.0495) | (0.0198) | (0.0309) | |
| High-Tech | 0.1542*** | 0.2719*** | −0.0954*** | 0.0047 |
| (0.0199) | (0.0263) | (0.0113) | (0.0162) | |
| Number of obs. | 14767 | 10543 | 16896 | 12321 |
| R-squared | 0.221 | 0.192 | 0.043 | 0.046 |
Prepared by Pragyan Deb with contributions from Andreas Tudyka.
The unweighted average of labor productivity across firms declined over the period.
EU12 refers to the 12 countries that made up the European Union prior to the eastward expansion starting from2004.
Since Orbis data is in nominal terms, real values are obtained using industry level value added and investment deflators available from Eurostat. As a robustness check, the deflators were also de-trended using the Christiano-Fitzgerald time-series filter (at 2 years), which yielded very similar results.
Following Gal (2013) some of the variables are imputed when missing. Specifically, when data on value added is missing, it is imputed using EBITDA and cost of employees. In addition, total asset is used as a proxy when data on (tangible) fixed asset is not available.
Basic services cover public administration, education and health services, and other administrative and support services. Market services include transportation, accommodation, professional, ICT, and financial and real estate services.
When data is not available at the 4-digit NACE level, the 2-digit NACE is used as a fallback. Export orientation, while an improvement over the tradeable and non-tradeable breakdown, is still a crude proxy. A majority of the export orientation data was only available at the two-digit industry level and therefore does not pick up differences in export orientation within a particular (two-digit) industrial sector. Therefore, it is possible that within a particular sector, firms actually involved in exports performed better. In addition, this data does not capture the role of warehousing. A firm selling its products to a domestic warehousing company, which in turn exports the product, will be picked up as a domestic sale in our data. Therefore, some of the firms and sectors may be misclassified in the low export orientation category.
The multivariate specification addresses the question whether being classified as high-tech has an impact on productivity growth compared to all other firms in the sample, while the approach in section B investigates the effect of being high-tech within manufacturing and services.
