Euro Area Policies: Selected Issues

The European Union’s (EU) financial stability framework is being markedly strengthened. This is taking place on the heels of a severe financial crisis owing to weaknesses in the banking system interrelated with sovereign difficulties in the euro area periphery. Important progress has been made in designing an institutional framework to secure microeconomic and macroprudential supervision at the EU level, but this new set-up faces a number of challenges. Developments regarding the financial stability may assist in the continuing evolution of the European financial stability architecture.

Abstract

The European Union’s (EU) financial stability framework is being markedly strengthened. This is taking place on the heels of a severe financial crisis owing to weaknesses in the banking system interrelated with sovereign difficulties in the euro area periphery. Important progress has been made in designing an institutional framework to secure microeconomic and macroprudential supervision at the EU level, but this new set-up faces a number of challenges. Developments regarding the financial stability may assist in the continuing evolution of the European financial stability architecture.

V. Raising Potential Growth in Europe: Mind the Residual34

A. introduction

110. Higher potential growth is needed in Europe to underpin debt sustainability, keep up living standards and shoulder the costs of ageing. The challenge ahead is substantial, as even before the crisis potential growth in mature European economies hobbled around 2 percent, at a considerable distance from e.g. the U.S.

111. The bulk of the EU-U.S. growth gap is explained by Total Factor Productivity (TFP). It has become commonplace for growth accounting exercises to identify TFP as the main driver underlying the superior GDP growth in the U.S. since the mid-1990s (see, for instance, Jorgenson and others, 2008; van Ark and others, 2008), in stark contrast with previous decades, when TFP growth performance in Europe was well above that of the U.S.

112. As a residual measure TFP has multiple interpretations, but it reflects in some way the overall efficiency of the production process. This study is an attempt to link the growth residual to policy-relevant determinants such as levels of human and ICT capital, the regulatory environment, taxation and the degree of openness. Our focus is on ICT and market services industries, where U.S. TFP growth advantage was mostly concentrated.

113. The paper is organized as follows. Section B presents TFP patterns in 13 European countries and the U.S. This is followed by the description of the empirical setup and the data used in the analysis in Section C. Section D presents the main results and discusses the specific channels through which fundamentals may affect productivity. Section E uses the estimated model for policy simulation and Section F concludes.

B. TFP Growth in Europe and the United States

114. This section examines growth accounting stylized facts for the U.S. and 13 European countries. The sample includes Austria, Belgium, Denmark, Spain, Finland, France, Germany, Italy, Netherlands, Slovenia, Sweden, Ireland, the U.K. (henceforth referred to as EU13 or Europe for short), and the U.S. The analysis of other standard EU country groupings is prevented due to lack of data availability.

115. During the pre-crisis period 1995–07, EU13’s growth performance lagged by 1.2 percentage points relative to the U.S. As documented in previous studies a substantial part of this growth differential (0.9 percentage points) was explained by weaker TFP growth, with only 0.3 percentage points being attributable to the differential growth in factor quality and quantity (Table V.1). The shortfall in TFP growth vis-à-vis the U.S. represents a reversal compared with the period 1980–95, where productivity growth in Europe (at 0.9 percent) situated well above the U.S. (at 0.5 percent).

Table V.1.

Contribution to Growth of Real Output in the Market Economy, EU Economies and the U.S., 1995–07

(annual average growth rates, in percent)

article image
Source: KLEMS database. The market economy ICT production (manufacturing of electrical and optical equipment, post and telecommunication services), goods production (agriculture, mining, manufacturing excluding electrical machinery, construction and utilities), and market services (distribution services, financial and business

From 1995-06.

From 1996-06.

Data for European Union refer to 13 countries in the table.

116. TFP experiences within Europe are very diverse. While productivity growth in Finland, Austria, Ireland, Slovenia and Sweden outperformed the U.S. during 1995–07, TFP decreased sharply in Spain and, less markedly, in Italy and Denmark. Core economies in the region, such as Belgium and Germany, while registering positive growth rates, generally fared poorly compared with the U.S.

117. TFP growth strongly differs across industries too. The productivity growth gap between Europe and the U.S. is largely accounted for by market services and, to a lesser extent, ICT-producing sectors (Table V.2). In contrast, goods production seems to be more efficient in Europe. For instance, in Austria, Germany, Finland, Slovenia and France, manufacturing industries are still important sources of productivity growth. In Spain and Italy, lackluster performance overall is not only due to slow growth in market services, but also in manufacturing, where traditional labor-intensive sectors face increasing low-cost competition from China and Eastern Europe (see, e.g. Chen and others, 2011). It is also important to note that Europe’s lagging productivity is due more to a lack of dynamism within industries than to a bias towards low-productive industries. While positive, the TFP growth differential attributable to the allocation of resources towards lower-than-average productivity sectors is only about 20 percent of the total TFP growth gap (Table V.2, column 5). In what follows, we restrict our empirical analysis to productivity performance at the industry level.

Table V.2.

Major Sector Contribution to TFP Growth in the Market Economy, 1995–07

(annual average growth rates, in percent)

article image
Source: KLEMS database. ICT production includes manufacturing of electrical and optical equipment, post and telecommunication services. Goods production includes agriculture, mining, manufacturing (excluding electrical machinery), construction and utilities. Market services include distribution services, financial and business services, and personal services.

From 1995-06.

From 1996-06.

Data for EU13 refer to the 13 countries in the table.

118. Cross-Atlantic TFP growth differences are concentrated in a handful of sectors. Differences are found to be especially large in computers, electrical and optical equipment, and, for market services, in retail distribution, finance and business services, and real estate activities (Figure V.1). By contrast, Europe exhibits stronger TFP growth in network utilities, such as electricity, gas and water supply, and especially post and telecommunications.

Figure V.1.
Figure V.1.

EU13 TFP Growth Differential with the U.S.

(% Difference, Average, 1995-07)

Citation: IMF Staff Country Reports 2011, 186; 10.5089/9781462338542.002.A006

Source: EU KLEMS database.

C. Empirical Specification and Data Description

Empirical setup

119. Our empirical approach is policy oriented. Conceptually there are as many specifications for TFP as theories of endogenous growth. As our main focus is polices, we will adopt an eclectic approach focusing on two empirical regularities, namely technological catch-up with the leading industries and knowledge spillovers from the frontier economies to laggards (see Scarpetta and Tressel, 2002; Aghion and Howitt, 2005; and Griffith and others, 2006). The estimated equation is:

(1)TF^Pi,j,t=α+β1(TF^Pi,j,tLeader)+β2(lnTFPi,j,t1TFPLeader,i,j,t1)+γXi,j,t+σ1(TF^Pi,j,tLeader)Xi,j,t+σ2(lnTFPi,j,t1TFPLeader,i,j,t1)Xi,j,t+ρ1Di+ρ2Dj+ρ3Dt+ɛ

where the indices i, j, t denote countries, industries and years; Leader denotes the country exhibiting the highest TFP level in sector j in year t; the sign ^ indicates growth rates; Xi,j,t is a set of additional control factors that may affect TFP growth rates independently or interacted with explanatory factors in the baseline specification; and Di, Dj and Dt are country, industry and year fixed effects.

120. The specification allows for catching-up phenomena and knowledge spillovers. TFP growth in a given country and sector depends on its ability to keep pace with TFP growth in the country with the highest productivity level (the frontier economy), as captured by the coefficient β2. A negative coefficient indicates that the farther a sector is from the technology frontier the greater the scope for productivity improvements arising from technological catch-up. In addition TFP growth in the leader can have a direct impact on the followers’ productivity growth (β1)—whenever followers are involved in analogous breakthrough innovations or in the presence of knowledge spillovers, including through trade.

121. The economic environment is assumed to influence the capacity to catch up with state-of-the-art technologies and to benefit from knowledge spillovers. The variables we use to represent such framework conditions include human capital, the adoption of ICT technology, product market regulations, openness and corporate taxes. These variables enter both separately and interacted with the leader’s TFP growth and the laggard’s TFP distance from the frontier.

Data description

122. Our panel data have cross-country, cross-sector and time series dimensions. Our panel sample covers the 1980–07 period and includes 33 industries (Table V.3) located in the U.S. plus the EU13, namely all market services, the ICT-producing manufacturing sector and all industries in the economy that use ICT goods intensively. TFP growth rates are taken from the EU KLEMS (KLEMS henceforth) database, which contains a growth accounting exercise based on high-quality measures of factor inputs. KLEMS’ growth accounting framework distinguishes between labor with different skill levels, ICT and non-ICT capital stock. In this framework, innovations that are factor specific are embodied in the definition of capital or labor inputs. For instance, the direct effect of the ICT revolution appears as a change in the composition of capital services (from non-ICT to ICT capital services). Similarly, changes in human capital are embodied in the stock of labor services. This methodology thus allows assessing TFP developments excluding the impact of changes in the quality of both capital and labor inputs.

Table V.3.

Industry Coverage

article image
Source: IMF Staff.

The acronym nec stands for “not elsewhere classified.”

123. The technology gap term is constructed using the Groningen Growth and Development Center (GGDC) Productivity Level database. This databank (Inklaar and Timmer, 2009) contains PPP-adjusted TFP levels consistent with the KLEMS growth accounts. Anchoring TFP KLEMS growth rates to the 1997 levels, technological leaders and followers can be identified across all sample countries, industries and time periods. As a way of illustration, in 2007, Sweden and Netherlands were situated at the technological frontier in network utilities, while the U.S. dominated in ICT-producing industries and business services (Figure V.2). The TFP growth at the frontier (the other key factor in the baseline model) is represented by the TFP growth of the country with the highest TFP level in industry j, in year t.

Figure V.2.
Figure V.2.

Technological Leaders in 2007: Selected Services Sectors

(Productivity Levels relative to the U.S. US=1 in 1997)1/

Citation: IMF Staff Country Reports 2011, 186; 10.5089/9781462338542.002.A006

Sources: EU KLEMS database; GGDC Productivity Level database; and Staff Calculations.1/ For each country, the technology gap term is computed as the ratio between the productivity level in that country and the productivity level in the US. US productivity levels, set at 1 for all sectors in 1997, are backcast and forecast using EU KLEMS TFP growth rates.

124. Human capital is proxied by the ratio of high skilled labor to overall labor. Skilled labor is measured by the share of the labor force having completed tertiary education. The ratio of ICT capital to non-ICT capital is added to the regression to control for the role of ICT technologies in facilitating TFP growth. According to this measure, human capital is found to be most developed in U.S., Finland, Spain, Sweden, and Ireland, both in services and ICT sectors, while the proportion of ICT in non-ICT capital services is highest in Anglo-Saxon and Nordic countries (Figure V.3).

Figure V.3.
Figure V.3.

EU13 and the U.S.: TFP Growth Fundamentals Levels, 2007

Citation: IMF Staff Country Reports 2011, 186; 10.5089/9781462338542.002.A006

Source: EU KLEMS database; OECD Tax database; and WEO.

125. The regulatory stance in product markets is measured by the OECD Regulatory Impact indicator (RI). The RI indicators (developed by Conway and Nicoletti, 2006), measure the “knock on” effect on each industry arising from anti-competitive regulations in up-stream network industries (energy, transport and telecommunications), retail distribution and professional services. Besides reflecting the extent of anti-competitive regulation in non-manufacturing sectors, the RI indicators also capture their economic importance as supplier of intermediate inputs to other sectors—hence the importance of increasing competition in these sectors. Barring post and telecommunications, “knock on” effects in key ICT and services sectors in EU13 countries are generally well above the U.S. (Figure V.4).

Figure V.4.
Figure V.4.

EU12 and the U.S.: Impact of Regulations Network Industries, Distribution and Business Services1/

(OECD Regulatory Indicators, 2008)

Citation: IMF Staff Country Reports 2011, 186; 10.5089/9781462338542.002.A006

Source: OECD.1/ The OECD Regulatory Impact Indicators measure the “knock on” effect of each industry arising from anti-competitive regulations in network industries (energy, transport and commnications), retail distribution and professional services.

126. Gauging the impact of corporate taxes on sectoral TFP is not straightforward, as those tax indicators are not differentiated by industries. An indirect way to test for these effects is to see whether the impact of corporate taxes on TFP growth is shaped by industry-specific characteristics, in particular, profitability rates (Vartia, 2008). To implement this, the OECD statutory corporate tax levied on corporate profits at a flat rate (and applying to the majority of the corporations) is linked to profitability, as determined by the ratio of operating profits over value added (both obtained from the OECD STAN database)35. Firms’ profits are taxed at the highest rates in core Europe and the U.S. while Ireland, Austria, Denmark, and the Netherlands stand out as the countries with the lowest tax burden on profits (Figure V.3).

127. We enter the degree of openness in the regression separately and interacted with both the technological gap and TFP growth of the leader country. This approach is consistent with previous findings in the economic growth literature pointing to a strong link between openness, innovation spillovers and the technological catch up (see, for instance, Rivera-Batiz and Romer, 2006). In order to obtain industry-specific measures of openness, we express for each sector the sum of imports and exports (as compiled in the OECD STAN database) as a share of sectoral value added. The small European economies of Belgium, Ireland and the Netherlands are predominantly open, while the U.S., U.K., France, Italy, and Spain are comparatively closed (Figure V.3).

D. Results

128. Confirming past studies, our results suggest that, across the whole sample of industries, TFP growth benefits from the innovations carried out in the leading economy. In addition, the coefficient of the productivity gap is negative36 and significant, indicating the importance of international diffusion of new-vintage technologies. Broadly, a 1 percent increase in TFP growth in the frontier economy results in a 0.1 percentage point increase in TFP growth, while a 1 percent larger TFP gap one period earlier results in almost 0.4 percentage point increase in TFP growth. However, given the size of the average TFP gap and the average TFP growth rate of the frontier economy, the contribution of the leader’s productivity improvements is, on average, about six times larger.

129. Both human and ICT capital appear to have a significant explanatory power. Our estimates suggest that human capital has a positive coefficient on its own (even though our measure of TFP controls for the composition effects of labor input) and when interacted with TFP growth at the frontier, pointing to the importance of a highly educated workforce for innovation and knowledge spillovers. In line with past studies investigating the role of human capital in determining the pace of convergence with frontier innovation (see, for instance, Vandenbusche and others, 2006), the positive coefficient of the interaction with the catch up effect implies a stronger TFP impact of human capital the closer a sector is from the frontier. As with human capital, a higher proportion of ICT capital appears to be a facilitator of technology spillover effects.

130. In keeping with previous studies, we find a direct negative impact of RI on TFP growth. The literature pointing to a negative relationship between product market regulation, entrepreneurship and productivity is voluminous (see for example Scarpetta and Tressel, 2002; Brandt, 2004; Conway and others, 2006; Crafts, 2006; and their extensive reference lists). Product market rigidities can impair productivity by reducing incentives to invest, adopt frontier technologies, or innovate, not least because overregulated markets prevent the entry of high-productivity firms and the exit of inefficient competitors (Fonseca and others, 2001, Barseghyan, 2008, and Nicoleti and Scarpetta, 2003). We find that product market regulations reduce TFP growth more markedly the closer are industries from the frontier (as indicated by the negative interaction with the technological gap variable), where productivity growth is more strongly based on innovation rather than the adoption of existing technologies. As revealed by the negative significant interaction with TFP growth in the frontier economy, our results support the view that innovation incentives are increased by competitive pressures (Aghion and others, 2006).

131. Corporate taxes appear to hamper TFP growth. Our estimation approach assumes that corporate taxes affect TFP through industry-specific profitability rates. In line with Vartia (2008), the estimated coefficient of the product of corporate tax and profitability rates is negative. This can be interpreted as an adverse effect of corporate tax rates on TFP, with this effect being larger in industries that are inherently characterized by a high return. By investing in R&D activities relatively more than the average firm in the economy, high-profitable industries may be also more vulnerable to tax increases insofar as they shrink the remuneration of factors associated with high-risk projects. Besides reducing incentives to innovate, corporate taxes may distort relative factor prices leading to inefficient factor input combinations which may lower TFP growth (Auerback and Hines, 2002). Corporate taxes may also reduce FDI and hinder knowledge spillovers to domestic firms, arguably more so in those industries characterized by the highest returns.

132. Openness is key to knowledge creation and assimilation. To gauge the potential of trade flows in improving TFP growth, we enter openness separately and interacted with the catch-up factor. To our knowledge, the influence of openness was not considered in previous analysis. We find a positive impact of openness on economic efficiency, both directly and by improving the absorption capacity of existing technologies.

133. We ascertain the relative importance of each explanatory factor taking into account both direct and indirect effects. Using the estimated coefficients provided in Table V.4, we compute the response of TFP growth (averaged across both industries and countries) to a one-standard-deviation increase in each variable, independently or interacted with knowledge spillovers and/or the catch-up term. The results (Table V.5) point to regulatory impact and corporate taxes as the variables having the highest influence on productivity growth, followed by openness and human capital. However, this policy ranking is more apparent than real, as the changes associated with one-standard-deviation increases in the first two variables are huge. Thus we turn in the following section to the analysis of more meaningful policy experiments.

Table V.4.

TFP Growth Determiants: EU13 and the U.S. (1980–07), ICT Industries and Market Services

article image
Note: Panel regression with country, industry and fixed effects. Robust standard errors are reported in the parentheses. *, ** and *** denotes significant at 10, 5 and 1 percent levels.
Table V.5.

Change in TFP Growth from a 1 Standard Deviation Increase in Model Variables

article image

Evaluated at sample mean.

E. Can Policies Help?

134. This section presents some illustrative simulation experiments. To this aim, we quantify the differential (on average over the period 1995–07) between each country’s TFP growth, as estimated by the model, and the TFP growth that would prevail if all sectors in each country were to see i) regulation reduced to the lowest sample levels; ii) ICT/non-ICT capital ratio increased to the highest sample levels; iii) human capital augmented to mimic best sample practices; iv) openness increased by 20 percentage points; v) corporate taxes cut down to 22 percent37; and vi) the suggested changes undertaken altogether. Rather than as a literal description of what every country ought to do, the simulation presented here is meant to illustrate the scope for TFP gains and to generate a benchmark against which to make cross-country comparisons. Clearly, countries need not to adopt such reform package in full, but may target specific policy areas and decide on the speed of reform that best suits their needs.

135. Simulated scenarios imply that best to improve TFP growth in most countries is to open markets to domestic and foreign competition. Belgium, Italy, Austria, France, Germany, and Spain have to gain the most from lightening their regulatory environment. Though less powerful than a shock to the regulatory impact, higher exposure to international competition also unlocks considerable TFP growth returns, most markedly in the relatively closed economies of the U.S., Spain, Italy, and France. Bolstering human capital is found to deliver sizable productivity increases too, especially in Ireland, Sweden, Denmark, Sweden, Netherlands, and Belgium. Corporate tax cuts would trigger substantial TFP improvements in Germany and Italy. Intriguingly enough, there is no visible TFP effect to incorporating higher levels of ICT capital as a share of overall capital. For all countries identified above, each of the simulated scenarios would add at least 0.4 percentage points to TFP growth.

136. The results are indicative of a substantial scope for reform. The combined impact of those changes would yield large TFP growth returns in all countries, with Germany, Belgium, Italy, France, Austria, and Denmark benefiting the most from the implementation of such an ambitious policy package (Figure V.5). Of course, actual growth impact will vary with the ambition of the reform agenda, the speed of its implementation, and the time needed for these reforms to take hold.

Figure V.5.
Figure V.5.

Reform Impact on Annual Aggregate TFP Growth1/

(percentage points deviation from baseline, average 1995–07)

Citation: IMF Staff Country Reports 2011, 186; 10.5089/9781462338542.002.A006

Source: KLEMS data and Staff Estimates.1/ In the simulated scenario RI is reduced to the lowest sample levels; ICT to non-ICT capital ratio is increased to the highest sample levels; human capital is augmented to mimic best practices; openne is aligned with the most exposed country for each sector; and corporate taxes are cut down to 22 percentage points across the board.

137. The sectoral approach allows us to identify the industries contributing the most to unleash TFP growth potential. Some regularities are revealed by the data upon the adoption of the policy package described under point vi) above (and represented in Figure V.6 for aggregate TFP in each country). Electrical and optical equipment stands out as the sector with the highest TFP growth potential in 10 countries, followed by renting of machinery, equipment and business activities, and other community, social and personal services (7 countries each). Printing and publishing, machinery, nec38, and wholesale and retail trade seem to lock substantial productivity gains in 6 countries. In contrast, the TFP improvements arising from network industries (post and telecommunication, transport and storage and utilities) are comparatively more modest. This may point to the effectiveness of the Single Market Program in liberalizing traditionally monopolistic sectors, compared with its capability to open up professional services in general. Quite clearly, there is no one-size-fits-all approach to fostering TFP growth but there is room for focusing policy initiatives on those industries where TFP gains are concentrated in most countries.

Figure V.6.
Figure V.6.

Reform Impact on Sectoral TFP Growth

(Percentage points deviation from baseline, average 1995–07)

Citation: IMF Staff Country Reports 2011, 186; 10.5089/9781462338542.002.A006

Sources: KLEMS; and IMF staff estimates.1/ The acronym nec stands for “not elsewhere classified.”

F. Conclusion

138. Europe needs to seek growth by opening up markets to domestic and foreign competition, enhancing human capital and, to a lesser extent, easing corporate taxation. Continued efforts in this direction should improve Europe’s capacity to sell goods and services abroad and become an attractive destination for investors. And to the extent that our findings are relevant, more openness—a powerful innovation generator and transmitter of existing technologies—should bring about more growth for Europe.

139. More competition in services would spur innovation and accelerate convergence with frontier technologies. The Single Market Program has been more effective in liberalizing monopolistic sectors such as energy and telecommunications than in removing obstacles to competition in professional services. Many services could benefit from a truly single market across Europe. An ambitious implementation of the Services Directive (i.e. reducing to the minimum the list of justified restrictions for proportionality reasons by alignment to best practices under the Mutual Evaluation Process) should unleash the potential to increase productivity growth across Europe.

140. Improving education is a must for keeping up with rapid technological change and for continuing innovation. Beyond increases in spending on education and training, the quality of this spending is crucial. Experience shows that evaluation and targeting of training are important to maximize its impact.

141. Our results provide some support to the view that corporate taxes are harmful for growth insofar as they discourage innovation in the most dynamic and profitable firms. Previous studies have also pointed to the relationship between lower corporate taxes, FDI, and productivity gains for domestic firms. Although reducing the tax burden on corporates’ profits might, in the short run, may conflict with the need to ensure fiscal sustainability, this does not imply that government should not start consider avenues for fiscally neutral shifts.

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34

Prepared by Nandaka Molagoda and Esther Perez with statistical assistance from Xiaobo Shao.

35

There are many ways to combine profitability and tax rates but in the empirical specification discussed in section IV the product of both variables proved to be particularly relevant.

36

The technological gap measures the distance of each sector from the leader country, thus it always takes negative values (see equation (1)). A negative coefficient for the technological gap thus implies a positive impact on TFP growth.

37

This is the average value of statutory tax rates in Ireland over the period 1995-07.

38

Not classified elsewhere

Euro Area Policies: 2011 Article IV Consultation—Lessons from the European Financial Stability Framework Exercise; and Selected Issues Paper
Author: International Monetary Fund
  • View in gallery

    EU13 TFP Growth Differential with the U.S.

    (% Difference, Average, 1995-07)

  • View in gallery

    Technological Leaders in 2007: Selected Services Sectors

    (Productivity Levels relative to the U.S. US=1 in 1997)1/

  • View in gallery

    EU13 and the U.S.: TFP Growth Fundamentals Levels, 2007

  • View in gallery

    EU12 and the U.S.: Impact of Regulations Network Industries, Distribution and Business Services1/

    (OECD Regulatory Indicators, 2008)

  • View in gallery

    Reform Impact on Annual Aggregate TFP Growth1/

    (percentage points deviation from baseline, average 1995–07)

  • View in gallery

    Reform Impact on Sectoral TFP Growth

    (Percentage points deviation from baseline, average 1995–07)