Veronique Genre
email: veronique.genre@ecb.int.

Karsten Kohn
email: karsten.kohn@kfw.de.

Daphne Momferatou
email: daphne.momferatou@ecb.int.

Véronique Genre

Understanding inter-industry wage structure in the euro area

Keywords: J31 -Wage Level, Structure; Differentials by Skill, Occupation, etc. < J3 -Wages, Compensation, and Labor Costs < inter-industry wage differentials, cross-country sector-level data, panel estimation, euro area

This paper focuses on the euro area wage structure and its potential determinants from a sectoral viewpoint. Merging information from the OECD Structural Analysis database with data from the EU Labour Force Survey, we construct a cross-country panel of 22 industries in 8 euro area countries for 1991-2002. Data inspection confirms the existence of a fairly stable inter-industry wage structure that is similar across countries. We then apply panel data techniques to identify factors explaining inter-industry wage differentials in the euro area. Both workforce characteristics (e.g., human capital variables) and firm-related characteristics (e.g., capital intensity, productivity) contribute significantly. However, considerable wage heterogeneity across sectors remains. Idiosyncratic sector and country specifics, reflecting different socio-cultural and institutional backgrounds, appear to bear a major role. While our empirical analysis only uses direct evidence from workforce and firm-related characteristics, we also relate remaining heterogeneity to institutional characteristics, based on related literature.

Introduction

According to the Optimal Currency Area theory, a number of criteria need to be fulfilled in order to maximize economic efficiency among countries sharing the same currency. Among these, flexible labour markets are needed to enhance the ability of individual countries to respond to specific circumstances and economic shocks. Wages in particular may need to adapt adequately and adjustments may need to reflect closely regional or sectoral productivity differences.

Hence, understanding sectoral wage differentiation in the euro area is an important issue for policy makers. A number of determining factors of inter-industry wage differentials have been identified in the literature. Our paper extends upon the existing literature by examining the inter-industry wage structure in the euro area between the early 1990s and the early 2000s, using a panel of 22 industries in eight euro area countries. Our approach is consistent with the fact that euro area countries share some common structural features, while also allowing for potential heterogeneity across countries and/or sectors which may be due to different institutional structures, such as wage bargaining systems or degrees of job protection.

The paper is organized as follows. 1 Section 2 briefly reviews theoretical rationales for inter-industry wage differentials and summarises related empirical evidence. Section 3 presents our data and stylised facts for the euro area, as well as the methodology for and the results of our econometric analysis. Section 4 concludes.

Inter-industry wage differentials: theory and empirical evidence

Although a textbook competitive labour market model would argue that wages should be the same for equivalent workers working in equivalent jobs, it has long been noted that there are substantial differences in wages across industries for workers with similar characteristics doing apparently similar jobs [START_REF] Slichter | Notes on the Structure of Wages[END_REF]. Empirically, substantial wage differentials have been found in many countries and they are surprisingly persistent over time. A number of arguments have been put forward to explain this phenomenon.

2.1

The theoretical rationale for inter-industry wage differentials

On the one hand, standard competitive theories argue that wage differentials reflect labour productivity differences among the workforce resulting from differences in individual human capital endowments [START_REF] Becker | Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education[END_REF][START_REF] Mincer | Schooling, Experience and Earnings[END_REF]. Workers' characteristics such as educational attainment, professional experience or age are thus crucial to explain individual wage levels. As employees working in the same industry may require similar skills, these differences may largely translate into industry differences. Alternatively, differences in wage levels might also compensate for non-pecuniary aspects of work that directly affect a worker's utility, such as available social benefits or an atypical work environment (see [START_REF] Purse | Work-related fatality risks and neoclassical compensating wage differentials[END_REF]).

Another explanation put forward by tenants of competitive theories is that wage differentials may just reflect a temporary disequilibrium in response to shifts in labour demand or supply wherever labour is imperfectly mobile, notably in the short run. In this case, inter-industry reallocations are hampered by frictions. Search and matching models (see [START_REF] Mortensen | New Developments in Models of Search in the Labor Market[END_REF] extend upon the concept of frictions. In case there are informational asymmetries and job search is costly, wages fall short of marginal productivity. In models with on-the-job search, firms can follow different equilibrium strategies: either they pay low wages invoking a high employee turnover or they pay higher wages resulting in lower turnover. Again, this may translate to the sectoral level as firms in the same industry can be expected to face similar frictions. In summation, competitive 1

For an extended version of this article we refer to the discussion paper version, [START_REF] Genre | Understanding Inter-Industry Wage Structures in the Euro Area[END_REF]. On the other hand, tenants of new wage determination theories focus on reasons why firms may find it more profitable to pay higher wages than those suggested by the equilibrium level resulting from market forces at play. These models rely on the major assumption that there is imperfect market competition. Consequently, firms would be able to extract rents from their product markets and pay higher wages.

Rent-sharing may be motivated by a number of reasons. Much attention has been given to "efficiency wage" models that attempt to explain why firms may pay more than the reservation wage to basically select the most efficient workers [START_REF] Yellen | Efficiency Wage Models of Unemployment[END_REF]Akerlof and Yellen, 1986). Several different versions of the efficiency wage model have been proposed

in the literature: 2

• According to "turnover models", firms may wish to pay higher wages to reduce quits and turnover (see [START_REF] Salop | A Model of the Natural Rate of Unemployment[END_REF][START_REF] Stiglitz | Wage Determination and Unemployment in L.D.C.s: The Labor Turnover Model[END_REF][START_REF] Stiglitz | Equilibrium Wage Distribution[END_REF]. Higher wage rates make jobs more attractive and workers less likely to switch to other jobs. These models predict that high wage industries are those in which turnover costs are highest.

• In "shirking models" [START_REF] Shapiro | Equilibrium Unemployment as a Worker Discipline Device[END_REF], firms engage in some monitoring of their employees and fire those workers caught shirking. By paying above market wages, firms decrease the incentive to shirk, as the risk of unemployment makes dismissals costly to the employee. According to these models, high wage industries are those with high monitoring costs or those which bear a relatively high cost of employee shirking.

2 See [START_REF] Groshen | Five Reasons Why Wages Vary Among Employers[END_REF] for a survey including a categorisation of different approaches. • "Adverse selection models" (Stiglitz, 1976;[START_REF] Weiss | Job Queues and Layoffs in Labor Markets with Flexible Wages[END_REF][START_REF] Greenwald | Adverse Selection in the Labor Market[END_REF] assume that the average quality of the pool of job applicants increases together with the wage rate. In these models, industries which are more sensitive to labour quality differences or face higher costs of measuring labour quality will offer higher wages in order to raise the average quality of the workforce.

• Finally, so-called "fair wage models" [START_REF] Aidt | Unions and Collective Bargaining-Economic Effects in a Global Environment, The World Bank[END_REF][START_REF] Akerlof | Gift Exchange and Efficiency Wages: Four Views[END_REF][START_REF] Akerlof | The Fair Wage-Effort Hypothesis and Unemployment[END_REF], suggest that employees will exert more effort if they think they are paid fairly. These models predict that high wage industries are those where teamwork and workers' cooperation are particularly important. Along this line of reasoning also, industries with high profits should be those paying higher wages.

From a sectoral perspective, firms within the same industry are likely to face similar product market conditions and hence share similar characteristics that may influence the average wage level and differentiate it from the average wage level in different industries.

Specific production technologies or the concentration of large firms which make employees' productivity more difficult to monitor, may contribute to increasing the average wage level in a specific industry. Other sectoral characteristics, such as the exposure to international competition, are also likely to influence the amount of product market rents and therefore the capability to pay higher wages.

Finally, the actual outcome of rent sharing between employers and employees crucially depends on the relative bargaining power of the involved parties, as well as on institutional settings. The presence of strong and coordinated trade unions is likely to induce higher wages for the represented market segments [START_REF] Naylor | Economic models of union behaviour[END_REF]. Unions are traditionally strong in manufacturing industries and in public sector services, whereas they are less prevalent in private service industries. In "insider-outsider models" [START_REF] Lindbeck | The Insider-Outsider Theory of Employment and Unemployment[END_REF], firms may find it more profitable to pay more than competitive wages to insiders in order to avoid strikes or an increase in unionisation, and to maintain industrial peace. Moreover, insiders, who have gained firm-specific skills, are more productive than outsiders and thus in a better position to claim and obtain higher wages. These models based on bargaining power imply that the wage level will depend on a number of different factors: the nature of jobs (e.g., the proportion of skilled occupations in an industry), the size of firms (which affects employers' ability to replace numerous wage claimers at the same time) and the firms' ability to pay (i.e., market power). In addition, labour market institutions such as wage setting regimes or legal labour market requirements affect the speed of wage adjustments [START_REF] Blau | Institutions and Laws in the Labor Market[END_REF]. Beyond firm or sector specific effects, different institutional frameworks should be visible at the country level.

Evidence from the empirical literature

Empirical evidence shows the existence of substantial and persistent inter-industry wage differentials in various countries. Starting with [START_REF] Slichter | Notes on the Structure of Wages[END_REF], a large body of literature estimated wage differentials using individual-level data, which allow controlling for a number of individual and match-specific determinants of wage differences. In a seminal paper, [START_REF] Krueger | Efficiency Wages and the Inter-Industry Wage Structure[END_REF] concluded that considerable differences between US industries remained even after accounting for observed as well as unobserved individual heterogeneity. [START_REF] Katz | Industry Rents: Evidence and Implications[END_REF] introduced a two-step approach. In a first step, they used individual micro data to estimate inter-industry wage differentials net of observable individual characteristics. In a second step, they related these estimates to industry-specific determinants such as average establishment size or composition of the workforce at the two-digit industry level. Their findings also confirm the existence of substantial wage differentials across sectors. [START_REF] Abowd | High Wage Workers and High Wage Firms[END_REF] used linked employeremployee data and controlled for individual as well as firm heterogeneity (both observed and unobserved). Again, even though the addition of firm heterogeneity considerably reduces inter-industry differentials, sizeable differences in wage levels across sectors remain. Industry-level analyses can benefit from internationally comparable data sets and thus provide the possibility of cross-country comparisons. There are a few studies which focus on cross-country comparisons of inter-industry wage differentials. 3 [START_REF] Gittleman | International Comparison of Inter-Industry Wage Differentials[END_REF] collect evidence for some OECD countries for different years between 1970 and 1985. As a main result, they note that the rank order of industry differences remained fairly stable over time. [START_REF] Albaek | Inter-Industry Wage Differentials in the Nordic Countries[END_REF] analyse wage differentials in the Nordic countries.

Their two main conclusions are that dispersion between countries is smaller than the dispersion between industries, and that although controlling for individual characteristics considerably reduces variability, it leaves the general pattern of differentials unchanged. [START_REF] Erdil | A Comparative Analysis of Inter-Industry Wage Differentials: Industrialized versus Developing Countries[END_REF] focus on differences in wage structures between industrialized and developing countries. Looking at rank correlations, they find rather small differences between the two groups of countries. Yet their attempt to regress wage differentials on possible determinants uses pooled data without controlling for worker characteristics or industry-specific effects. [START_REF] Jean | Product Market Regulation and Wage Premia in Europe and North America: An Empirical Investigation[END_REF] examine the impact of product market regulation on industry wage premia in European and North American countries. Following the two-step approach of [START_REF] Katz | Industry Rents: Evidence and Implications[END_REF] and using a cross-section of data from different OECD databases in 1996, they estimate positive impacts of different measures for product market regulation on industry-level wages. However, their approach which controls for the impact of human capital variables already at the first stage does not allow the authors to investigate additional determinants of wage differences between sectors.

As a first step towards investigating inter-industry wage structures for the euro area as a whole, [START_REF] Genre | Wage Diversity in the Euro Area -An Overview of Labour Cost Differentials across Industries[END_REF] provide descriptive evidence on the magnitude and the development of wage differentials since the early 1980s and motivate possible determinants by means of bivariate correlation measures. They find substantial differences 3 See [START_REF] Dickens | Inter-Industry Wage Differences and Industry Characteristics[END_REF] and [START_REF] Krueger | Reflections on the Inter-Industry Wage Structure[END_REF] for surveys of sector-level studies for single countries. in wage levels between various sectors which are largely similar across euro area countries, the UK and the US. Moreover, the inter-industry structure in the euro area economy is found to be fairly stable throughout the 1980s and 1990s, with only a small increase in overall wage dispersion across industries during this period.

Empirical Analysis

Data and stylised facts for the euro area

Our The STAN database allows us to calculate the average wage in sector i in country j at time t, w ijt , as the ratio of total compensation to the number of employees. Wage differentials y ijt are then defined as the relative deviation of sector i's wage w ijt from the overall country average w jt :

) / ln( jt ijt ijt w w y ≡ . ( 1
)
Descriptive evidence confirms the existence of substantial and persistent differences in average wage levels in the various sectors of the euro area. As shown in figure 1, there is a great degree of wage dispersion across sectors, with strongly negative wage differentials (more than 50% in agriculture, but also quite significant ones in textile industries, hotels and restaurants, and in social and personal services) and strongly positive ones (up to nearly 50% in utilities and financial intermediation). While the same overall pattern is visible for all countries, the level of variation differs across countries to some degree (figure 2). For example, Portugal shows the most pronounced differentials. Moreover, there still exists some degree of relative variation across countries. In general, there are higher differences across countries in service sectors than in manufacturing, where the wage structure tends to be more similar. This result may to some extent be explained by the greater exposure of manufacturing sectors to global forces and competition, compared with the relatively more insulated service sectors. In addition, the role of the public sector, which would be especially relevant for services, may also contribute to heterogeneity across countries.

INSERT FIGURE 1 ABOUT HERE

INSERT FIGURE 2 ABOUT HERE

Changes of inter-industry wage differentials between the years 1991 and 2002 are illustrated in figure 3. The average euro area wage structure remained remarkably stable over this period and the sectors which paid relatively well at the beginning of the 1990s continued to do so in 2002. However, while the overall structure remained nearly unchanged, there has been some visible increase in the degree of dispersion over time.

INSERT FIGURE 3 ABOUT HERE

Figure 4 displays the evolution of weighted within-country standard deviation of wage differentials

∑ = - ≡ N i ijt i jt y g N 1 2 1 σ , ( 2
)
where g i denotes employment weights of sectors i. Increasing dispersion is common to most euro area countries with the notable exceptions of Spain, where wage differentials tended to narrow, and of Italy and Austria, where the overall wage dispersion did not change much between 1991 and 2002. The increase in average euro area wage dispersion during the 1990s thus appears to be mainly driven by three of the largest euro area countries, namely Germany, France, and The Netherlands. In addition to offering data required to compute wage differentials, the STAN database also allows for the construction of important indicators such as export intensity and import penetration ratios, apparent labour productivity, or capital intensity. Moreover, we merge the STAN data with information from the European Union Labour Force Survey (LFS) provided by Eurostat. In particular, the LFS offers sectoral indicators regarding the structural composition of the workforce (see the appendix for details).

INSERT FIGURE 4 ABOUT HERE

Estimation approach

In order to disentangle possible determinants of the euro area wage structure and to derive conditional inter-industry differences, we take advantage of the panel nature of the data.

Using data for sectors i=1,…,N in countries j=1,…,J at time (years) t=1,…,T, we consider the basic error components wage regression ln( / )

ijt jt ijt ijt w P X u β = + , (3)
where ln( / )

ijt jt w P denotes log real wages; X is a vector of observed covariates and β is a coefficient vector to be estimated. The set of covariates X includes workforce (or employee) characteristics as well as firm (or employer) characteristics. More precisely, employee-related variables include information on age and skill structure of the workforce 4 , on its composition in terms of occupations, on the share of employees working part-time 5 , the share of female employees 6 , the share of temporary employment 7 , the degree of self-employment, 8 and on average hours worked per week. Employer-related characteristics include variables such as real capital intensity and apparent labour 4 See [START_REF] Becker | Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education[END_REF] and [START_REF] Mincer | Schooling, Experience and Earnings[END_REF] for the traditional human capital argument.

5

The extent of part-time work has been rising over the past decades (OECD, 2006), and hourly wages have been proven to be lower for part-timers compared with full-timers (OECD, 1999).

6

The existence of gender wage gaps has been extensively documented in the literature (see [START_REF] Altonji | Race and Gender in the Labor Market[END_REF].

7

Ceteris paribus, workers with temporary contracts receive lower wages than those with permanent contracts [START_REF] Booth | Temporary Jobs: Stepping Stones or Dead Ends?[END_REF]. µ , industry-fixed effects i µ , time effects t µ , and an idiosyncratic term ijt ε :

ijt t j i ijt u µ µ µ ε = + + + . ( 4
)
The set of time effects t µ takes account of euro area-wide business cycle effects in a flexible way. The country and sector-specific effects, j µ and i µ , capture general economic conditions or socio-cultural and institutional backgrounds-such as the extent of unionisation or the degree of centralisation and coordination of collective bargaining. 12 In appendix B of the working paper version [START_REF] Genre | Understanding Inter-Industry Wage Structures in the Euro Area[END_REF] we show that the full set of sector effects i µ , which yields the conditional inter-industry structure net of all observed impacts, can be estimated by means of orthogonalised weighted industry dummies. The conditional inter-industry wage structure can then be compared with unconditional wage differentials ijt y .

An alternative specification of (4) would include sector and country time trends:

ijt j i j i ijt u t t δ δ µ µ ε = + + + + . ( 5
)
The country-by-sector dimensioning of the data provides a large cross-section, but there is only a limited number of periods available (at maximum 12 years). Moreover, the 8 See [START_REF] Hamilton | Does Entrepreneurship Pay? An Empirical Analysis of the Returns to Self-Employment[END_REF] for wage effects of self-employment. 9

See [START_REF] Abowd | High Wage Workers and High Wage Firms[END_REF] on productivity differences. [START_REF] Blanchflower | Wages, Profits, and Rent-Sharing[END_REF] note that wage increases follow preceding movements in profits. Under standard assumptions, pooled OLS (POLS) estimation of (3) provides consistent results. However, as evidence on the persistence of wage differences suggests, some autocorrelation is likely to be present and thus needs to be accounted for. One option to do so is to compute robust standard errors [START_REF] Newey | A Simple, Positive Semi-Definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix[END_REF]. A more efficient alternative, however, is to run panel GLS estimation that uses the autocorrelation structure for weighting. Finally, a third approach would be to add lagged log wages

= + + . ( 6
)
Consistency of POLS then hinges on the prerequisite that ijt ε exhibits no first-order autocorrelation.

A more complex specification of the error term allows for an interaction of country and sector-specific unobserved effects ij µ :

ijt t ij ijt u µ µ ε = + + . (7)
In this case, first differencing (FD) or quasi-differencing (estimation with fixed effects, FE) of our basic specification would remove the time-invariant ij µ . Country and sector effects in the differenced equation would then correspond to country and sector trends in specification (5). In case of the dynamic specification (6), results from FE or FD estimations would be biased [START_REF] Nickell | Biases in Dynamic Models with Fixed Effects[END_REF]. However, consistency can be achieved by using the generalized methods of moments (GMM) estimation following [START_REF] Anderson | Formulation and Estimation of Dynamic Models Using Panel Data[END_REF] and [START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF].

Results

Table 1 summarises our main results, based on panel GLS estimations of equation ( 3), including time, country, and industry-specific effects and allowing for first-order autocorrelation. Specification (A) includes workforce characteristics only. The estimated determinants are in line with a priori expectations. 13 In particular, a high share of young or low-skilled employees in an industry ceteris paribus comes along with lower real wages, while industries with a high share of older employees pay higher wages. Moreover, significantly lower wages are paid in sectors with high shares of women or part-timers.

INSERT TABLE 1 ABOUT HERE

Specification (B) exclusively focuses on firm characteristics. This substantially limits the number of available observations, since some of the variables are only available for manufacturing industries. Again, the results are broadly in line with a priori expectations.

In particular, high apparent labour productivity brings about significantly higher wages.

Since apparent productivity is defined as the ratio of real value added to the number of employees, it does not only measure labour productivity but also gives an indication on the size of rents which can be distributed between workers and capital owners. Consequently, the estimated coefficient would be in line with a positive direct effect of labour productivity as well as with the argument of profit sharing. Capital intensity also enters positively and significantly. High capital intensity, reflecting, for example, the use of specialised machinery, increases workers' productivity and thus leads to a higher average wage. Moreover, capital-intensive industries are likely to be characterised by a high degree of firm concentration and are thus most likely to extract product market rents. Again in line with the literature, larger firms, and in particular those with more than 50 employees, pay higher wages than smaller ones. Finally, although export orientation turns out insignificant 13 in this specification, the import penetration ratio of an industry shows a significant and positive coefficient-firms which are more internationally integrated are more likely to extract gains from trade, which can then be distributed between employers and employees.

Specification (C) brings together all workforce and firms characteristics. Interestingly, the partial effects of most workforce characteristics become insignificant. This finding could be due to two effects. First, the sample must cover manufacturing industries only and exhibits less variation in sector specifics, as compared to the full sample. Second, the extent of collinearity between workers' and firms' characteristics is likely to be larger in manufacturing than in the rest of the economy. For example, bigger firms in manufacturing may tend to systematically employ more high-skilled workers due to the use of complex machineries and equipment in their production technology.

Hence, our preferred specification (D) contains workforce characteristics and a slightly reduced set of firm characteristics, thus circumventing the problem of sample reduction.

By and large, the joint inclusion of worker and employer characteristics confirms the coefficient estimates of specifications (A) and (B), suggesting that multicollinearity is a minor issue. Yet some worker characteristics reveal slightly smaller partial effects. For example, the coefficient of the share of female employees is lower by 9 percentage points.

This finding hints towards some selectivity bias in specification (A), in which the effect of the omitted firm characteristics is taken up by the workforce variables. In addition, a few additional variables are significant in the preferred specification. For example, the share of older workers has a significantly positive impact, which would be in line with human capital theory or seniority-based remuneration schemes. The share of self-employment in an industry now shows a significantly negative coefficient. To the extent that selfemployed workers are in direct competition with wage-earners, the presence of self-

Sensitivity Checks

Sensitivity checks with respect to the specification of the error component and to the choice of the estimation strategy are undertaken in tables 2 and 3. restricts the sample to manufacturing industries (as in column A). Estimated coefficients are rather similar to those of the benchmarks. More specifically, they lie within the range spanned by the respective estimates in columns (A) and (B), but most of the worker characteristics are insignificant. This result suggests that in fact both the higher degree of similarity among the workforce and the larger extent of collinearity between worker and firm characteristics inclined by the restriction of the sample contribute to the deviation of the full specification from our preferred one.

INSERT TABLE 2 ABOUT HERE

Specifications (D) and (E) include country and sector-specific time trends instead of the full sets of country, sector, and year effects. While allowing for differences in unobserved effects across countries and across sectors, this approach comes at the price of reduced flexibility regarding business cycle effects. The coefficient estimates remain broadly the same, but again the impact of some worker characteristics is estimated less precisely. This finding supports the understanding that, for example, trends towards skill upgrading or which is highly correlated with the part-time indicator. Finally, columns (F) and (G) estimate wage differentials ijt y rather than log wage equations. Just as in the benchmark specifications, unobserved industry effects are accounted for, and again the results broadly match those of the benchmark. 14 However, the impact of human capital variables is estimated less reliably. Subtracting country-by-time averages presumably takes away too much variation and thus renders the estimation of coefficients more problematic.

Table 3 investigates the sensitivity of our results according to different estimation techniques. Compared with the results of the GLS benchmark reported in column (A), POLS estimates in column (B) show the same sign, but in most cases are larger in absolute value. We consider these results less reliable because-even though being consistent-the estimation does not take advantage of the autocorrelation structure in the data. Column (C) reports POLS estimates using the lagged endogenous variable as a regressor. Again the (short-run) ceteris paribus effects are of the same sign as the benchmark coefficients, but the large persistency parameter renders most of them insignificant. Moreover, as the persistency parameter does not provide additional information regarding the economic determinants of wage differences, the approach is judged inferior to the benchmark specification.

INSERT TABLE 3 ABOUT HERE

The last three specifications in table 3 allow for interactions of country and sector-specific unobserved effects as defined in equation ( 7). These fixed effects are removed by means of FD (column D), FE (column E), or GMM estimation following Arellano and Bond (1991, column F). The effects of worker characteristics turn out to be insignificant in all three 14 Only the coefficient of the share of low-skilled workers changes its sign. specifications. Since all three of these regressions remove any between-cell variation, the result reflects the small degree of variation in wages over time.

Conditional Inter-Industry Differentials

The benchmark specification in column (D) of table 1 thus remains our preferred one. This specification is estimated using a full set of orthogonalised weighted industry dummies.

The resulting conditional inter-industry wage structure, illustrating "pure" sector effects, is compared with the observed wage structure for the euro area in the year 2002 in figure 5.

INSERT FIGURE 5 HERE

Overall, our model achieves a noticeable reduction in the variability of wage differentials.

Once the impact of worker and firm characteristics has been taken into consideration, the remaining employment-weighted standard deviation in wage levels across sectors is 0.183.

This corresponds to about two thirds of the standard deviation given by the observed differentials (0.240). This result, however, also means that a significant part of sectoral wage differentials remains after controlling for worker and firm characteristics. In line with the results provided by the literature, the general pattern of differentials across sectors remains the same. Interestingly, the model captures the euro area wage structure for some sectors better than for others. For example, the worker and firm variables included in the model explain a large part of the positive spike for the utilities sector (12) or of the negative spike for the textile industry (3). For other sectors, such as agriculture (1), we detect a higher impact of idiosyncratic industry specifics which are not captured by any of the observed variables. In some cases unconditional and conditional differentials even have opposite signs. In these sectors the idiosyncratic industry specifics are overcompensated by other determinants of the wage structure. In case of the health and social work sector, for example, the base wage tends to be above average, but the composition of the workforce,

Conclusion

This paper analyses the euro area wage structure from a sectoral point of view. Previous theoretical and empirical studies on inter-industry wage differentials at the country level provide us with a number of possible determinants of observed differences in wages across industrial sectors. Our analysis extends upon this literature by using a large cross-country data set for the euro area as a whole, combining OECD's STAN database with data from the EU Labour Force Survey, and by using panel data estimation techniques.

Our results confirm the existence of large and persistent wage differentials across sectors of the euro area economy. Traditional determinants discussed in the literature explain a significant part of these differences, as well as of the slight increase in sectoral wage dispersion during the 1990s. In particular, characteristics of the workforce such as the importance of part-time work, the shares of young, older, and female workers, or the share of self-employment in a sector are relevant variables for explaining differences in average wages across sectors. Firm characteristics such as capital intensity and apparent labour productivity in a sector also have significant impacts. However, while our preferred model captures reasonably well the overall wage structure, it also reveals the non-negligible importance of idiosyncratic factors. Unobservable industry-specific factors may, on occasions, exert pressure on wages that counterbalances and, at times, overcompensate for the influence of traditional observable determinants.

As a first step towards understanding the determinants of the inter-industry wage structure in the euro area, our analysis opens several avenues for further research. For example, a comparison with the US, using a similar dataset, would put the extent and the evolution of 15 See the working paper version [START_REF] Genre | Understanding Inter-Industry Wage Structures in the Euro Area[END_REF] for additional analyses of within-country dispersion. Definitions of variables taken or calculated from STAN are as follows:

• Compensation of employees comprises wages and salaries of employees paid by producers as well as supplements such as contributions to social security, private pensions, health insurance, life insurance and similar schemes.

• The number of employees includes all persons in employment, disregarding selfemployed and unpaid family workers. In case of missing values for the number of employees in single sectors and years, numbers were backcast based on growth rates of corresponding employment numbers which include self-employed.

• Export intensity and import penetration are calculated as the respective ratios of exports or imports to total output (available for manufacturing industries only).

• Apparent productivity is measured by real value added (base year 2000) per employee.

• Capital intensity is calculated as real gross fixed capital formation (base year 2000) per employee.

Eurostat further supplied cell-level information from the European Union Labour Force Survey (LFS). The LFS is a quarterly 16 household survey administered by the national statistical institutes of European Union (EU) and Candidate Countries in accordance with International Labour Organisation (ILO) guidelines. The EU LFS micro data collection 16 Data have traditionally been reported for one reference quarter per year. Between 1996 and 2005 the LFS has gone through a transition towards a continuous quarterly survey. We chose the second quarter as reference when multiple quarters were available. • The extent of self-employment is captured by the ratio of self-employed to the number of employees.

• Establishment size reports the shares of employees in four size brackets (up to 10 employees, 11-19, 20-49, and 50 or more employees).

• Hours worked are defined as the average number of hours usually worked per week.

Finally, we calculate country-specific price deflators using harmonized indices of consumer prices (HICP, base year 2000) provided by Eurostat. . 35 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 year Weighted standard deviation of wage differentials. Data source: OECD STAN.

  in the long-run and beyond temporary frictions, sectoral wage differentials should largely reflect individual differences that translate into sector-specific differences.

  study extends upon the empirical literature by analyzing inter-industry wage differentials for the euro area by means of panel data techniques. Using data from the Structural Analysis (STAN) database of the OECD and from the European Union Labour Force Survey (LFS), we construct various indicators for a panel of 22 branches of economic activity in eight euro area countries between 1991 and 2003 (see the appendix).

  firm size 10 and the exposition to foreign trade measured by import penetration ratios and export orientation 11 . The error term ijt u contains country-specific effects j

  revealed rather little variation of the industry structure across time. Estimation thus relies on fixed-T asymptotics.

  sector lowers the bargaining power of employees. In addition, sectors with a high share of self-employed are typically labour-intensive. Ceteris paribus, this would contribute to a lower average wage.

  force participation show different patterns across countries and sectors of the economy.

  intensity, and apparent productivity in this specific sector contribute to the observed negative differential.15

  the euro area into perspective. Exploring sectoral differences based on micro-level data such as the linked employer-employee data provided by the European Structure of Earnings Survey would be another promising route to follow. This approach could possibly yield additional insights on those sector-specific factors which have so far remained unobserved. from different sources. The Structural Analysis database (STAN) of the Organisation for Economic Co-operation and Development (OECD) provides annual data for OECD countries. It is primarily based on national accounts data and uses data from other sources such as industrial surveys or censuses to estimate missing details. On principle, data are available for the period 1970-2003. Due to missing data, however, we restrict our sample to the years 1991-2002. The euro area aggregation used in this paper consists of the eight countries Germany, Spain, France, Italy, The Netherlands, Austria, Portugal, and Finland, which together cover more than 90% of the euro area in terms of GDP. Only insufficient data were available for Belgium, Greece, Ireland, and Luxemburg.Table A.1 in the discussion paper version (Genre et al. 2009) displays our classification of 22 industries as well as respective weights based on employment shares in the euro area.

  , but as the survey has not been mandatory until the early 1990's, data are mainly available from 1993 onwards only. However, several country-series start in later years only, as observations with inconsistencies between the LFS waves had to be excluded. The definition of variables taken from LFS statistics is as follows:• The number of employees includes all persons in employment, disregarding selfemployed and family workers. It is broken down by several dimensions, including three age groups (below 25 years, 25-54 years, and above 55 years); three skill classes in accordance with the International Standard Classification of Education (ISCED: low-skilled workers with at most a lower secondary degree, medium-skilled with an upper secondary degree, and high-skilled employees holding a tertiary degree); three occupation groups in accordance with the International Standard of Occupations, ISCO (management and professional workers ISCO 1-2, base category ISCO 3-8, elementary occupations ISCO 9); gender (share of female employees); the share of part-time employees; and the share of employees holding a temporary work contract.

Figure 1 :Figure 4 :

 14 Figure 1: Inter-industry wage differentials in the euro area, average 1991-2002

  (GLS) Estimation by panel GLS, allowing for first-order autocorrelation. Additional controls: country, sector, and year effects. (POLS) Estimation by pooled OLS, standard errors allowing for heteroscedasticity and first-order autocorrelation. Additional controls: country, sector, and year effects. (POLS_Lag) Estimation by pooled OLS, standard errors allowing for heteroscedasticity. Additional controls: country, sector, and year effects. Test for autocorrelation: p-value = 0.951. (FD) Estimation in first differences, standard errors allowing for heteroscedasticity. Additional controls: country and sector effects. Test for autocorrelation: p-value = 0.403. (FE) Estimation with (sector X country) fixed effects, allowing for first-order autocorrelation. Additional controls: year effects. (GMM) Estimation by two-stage GMM, instrumentation a la[START_REF] Arellano | Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations[END_REF]. Additional controls: year effects. Test for second-order autocorrelation: p-value 0.506.

Figure 5 :

 5 Figure 5: Pure sector effects versus observed inter-industry wage differentials, euro area 2002

Table 2

 2

	confirms the

Table 1

 1

	: Preferred specifications: Panel GLS
		(A)	(B)	(C)	(D)
		ln(W/P)	ln(W/P)	ln(W/P)	ln(W/P)
	SHAREYOUNG	-0.130***		-0.025	-0.084**
		(0.046)		(0.062)	(0.041)
	SHAREOLD	0.109*		0.049	0.117**
		(0.065)		(0.087)	(0.059)
	SHARELOWSKILL	-0.127***		-0.084	-0.095***
		(0.035)		(0.051)	(0.031)
	SHAREHIGHSKILL	0.029		-0.104	-0.032
		(0.044)		(0.065)	(0.039)
	SHAREPARTTIME	-0.368***		-0.117	-0.259***
		(0.050)		(0.073)	(0.044)
	SHARETEMP	0.033		0.015	-0.005
		(0.041)		(0.056)	(0.037)
	SHAREFEMALE	-0.159***		-0.039	-0.073**
		(0.038)		(0.052)	(0.034)
	SHARESELF	-0.012		-0.032***	-0.045***
		(0.010)		(0.009)	(0.009)
	LOGCAPINT		0.029***	0.048***	0.025***
			(0.007)	(0.009)	(0.006)
	LOGPROD		0.225***	0.220***	0.260***
			(0.018)	(0.020)	(0.013)
	SHARESIZE19		0.071	0.139*
			(0.061)	(0.073)
	SHARESIZE49		0.037	0.116**
			(0.049)	(0.059)
	SHARESIZE50+		0.063*	0.119***
			(0.036)	(0.042)
	EXPINT		-0.033	-0.072**
			(0.028)	(0.028)
	IMPPEN		0.060***	0.052**
			(0.022)	(0.022)
	Observations	1613	769	656	1543
	RHO	0.86	0.79	0.73	0.86
	Estimation by panel GLS, allowing for first-order autocorrelation (RHO).
	All specifications additionally include country, sector, and year specific effects.
	Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

Table 2 :

 2 Sensitivity analyses I: Different covariates, sample restriction Estimation by panel GLS, allowing for first-order autocorrelation (RHO).Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

		(A)	(B)	(C)	(D)	(E)	(F)	(G)
		LOG(W/P)	LOG(W/P)	LOG(W/P)	LOG(W/P)	LOG(W/P)	Y	Y
	SHAREYOUNG	-0.025	-0.084**	-0.031	-0.005	-0.047	0.047	-0.022
		(0.062)	(0.041)	(0.046)	(0.066)	(0.040)	(0.052)	(0.037)
	SHAREOLD	0.049	0.117**	0.078	0.069	0.085	0.054	0.091*
		(0.087)	(0.059)	(0.065)	(0.091)	(0.057)	(0.067)	(0.051)
	SHARELOWSKILL	-0.084	-0.095***	0.014	-0.127**	-0.113***	-0.015	0.034*
		(0.051)	(0.031)	(0.039)	(0.052)	(0.030)	(0.028)	(0.021)
	SHAREHIGHSKILL	-0.104	-0.032	-0.082*	-0.096	-0.014	0.063	0.106***
		(0.065)	(0.039)	(0.049)	(0.066)	(0.038)	(0.046)	(0.031)
	SHAREPARTTIME	-0.117	-0.259***	-0.078	-0.130*	-0.277***	0.013	-0.160***
		(0.073)	(0.044)	(0.058)	(0.076)	(0.043)	(0.054)	(0.032)
	SHARETEMP	0.015	-0.005	-0.069	-0.067	-0.081**	-0.003	-0.004
		(0.056)	(0.037)	(0.044)	(0.057)	(0.035)	(0.041)	(0.029)
	SHAREFEMALE	-0.039	-0.073**	0.003	-0.031	-0.053	-0.049	-0.097***
		(0.052)	(0.034)	(0.039)	(0.053)	(0.033)	(0.041)	(0.030)
	SHARESELF	-0.032***	-0.045***	-0.026***	-0.040***	-0.053***	-0.015*	-0.021**
		(0.009)	(0.009)	(0.008)	(0.009)	(0.008)	(0.009)	(0.009)
	LOGCAPINT	0.048***	0.025***	0.033***	0.051***	0.029***	0.017**	0.014***
		(0.009)	(0.006)	(0.006)	(0.009)	(0.005)	(0.007)	(0.005)
	LOGPROD	0.220***	0.260***	0.225***	0.234***	0.280***	0.134***	0.161***
		(0.020)	(0.013)	(0.016)	(0.020)	(0.013)	(0.017)	(0.011)
	SHARESIZE19	0.139*			0.158**		0.117**
		(0.073)			(0.075)		(0.059)
	SHARESIZE49	0.116**			0.119**		-0.006
		(0.059)			(0.060)		(0.048)
	SHARESIZE50+	0.119***			0.138***		0.153***
		(0.042)			(0.042)		(0.032)
	EXPINT	-0.072**			-0.069**		-0.044
		(0.028)			(0.028)		(0.028)
	IMPPEN	0.052**			0.050**		0.058**
		(0.022)			(0.021)		(0.023)
	COUNTRY EFFECTS	yes	yes	yes
	SECTOR EFFECTS	yes	yes	yes			yes	yes
	YEAR EFFECTS	yes	yes	yes
	COUNTRY TRENDS				yes	yes
	SECTOR TRENDS				yes	yes
	Observations	656	1543	906	656	1543	656	1541
	RHO	0.73	0.86	0.83	0.69	0.86	0.84	0.89

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

See Brown and Medoff (1989) and[START_REF] Oi | Firm Size and Wages[END_REF] for employer-size wage effects.

See Johnson and Stafford (1999) andIMF (2006) for the impact of international trade and globalization on industry-level price and wage formation. Both import and export variables are available for manufacturing industries only.

See the synopses in[START_REF] Aidt | Unions and Collective Bargaining-Economic Effects in a Global Environment, The World Bank[END_REF],[START_REF] Flanagan | Macroeconomic Performance and Collective Bargaining: An International Perspective[END_REF],OECD (1997[START_REF] Purse | Work-related fatality risks and neoclassical compensating wage differentials[END_REF][START_REF] Rowthorn | Centralisation, Employment and Wage Dispersion[END_REF].

Note that the set of workforce characteristics does not include occupation variables since occupation and skill categories turn out to be highly correlated. The same reasoning applies to average hours worked,

This paper was written while Karsten Kohn was working at the European Central Bank. Views expressed do not necessarily reflect those of the European Central Bank, the European System of Central Banks or KfW.


Cornelius Von Wilmowsky
email: c.wilmowsky@web.de

Philipp Stockmann

Igor Alexander Harsch

Kerstin Amann

Philipp Metzler

Rainer Lutz

Tobias Moest

Friedrich Wilhelm Neukam

Karl Andreas Schlegel

Diabetes mellitus negatively affects peri-implant bone formation in the diabetic domestic pig

Keywords: Immunohistochemistry, Periodontal disease Main Methodology: Animal Model Diabetes, Bone, Implants, Osseointegration, Pig

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Diabetes mellitus is one of the most prevalent endocrine diseases and the third most common cause of disability and morbidity in the western world. In the year 2000, more than 171 million people were suffering from diabetes, and the number of diabetics has been predicted to increase to 366 million worldwide by the year 2030 [START_REF] Wild | Global prevalence of diabetes: estimates for the year 2000 and projections for 2030[END_REF]). Thus, the number of diabetic patients indicated for dental implants will grow steadily.

Diabetes is associated with an increased prevalence and extent of periodontitis and tooth loss compared to non-diabetic patients [START_REF] Ferreira | Prevalence and risk variables for peri-implant disease in Brazilian subjects[END_REF][START_REF] Kaur | Association between type 1 and type 2 diabetes with periodontal disease and tooth loss[END_REF]. A direct correlation has been shown between alterations in implant stability and impaired implant integration and hyperglycaemic conditions [START_REF] Oates | Glycemic control and implant stabilization in type 2 diabetes mellitus[END_REF]). Diabetes mellitus is classified as a risk factor for implant treatment, and severe or poorly controlled diabetes mellitus has been suggested to be a contraindication for treatment with dental implants [START_REF] Buser | Basic surgical principles with ITI implants[END_REF], Neukam & Esser 2000). On the other hand, [START_REF] Tawil | Conventional and advanced implant treatment in the type II diabetic patient: surgical protocol and long-term clinical results[END_REF] found that individuals with well controlled diabetes have implant survival rates similar to those of controls without diabetes.

Even though several clinical studies have analysed the success and failure of implants in diabetic patients, only experimental animal studies allow a systematic evaluation of the biological and pathological effects of diabetes on the osseointegration of implants and medical devices [START_REF] Mellado-Valero | Effects of diabetes on the osseointegration of dental implants[END_REF][START_REF] Oates | Glycemic control and implant stabilization in type 2 diabetes mellitus[END_REF]. In an experimental study in diabetic rabbits, [START_REF] Margonar | The influence of diabetes mellitus and insulin therapy on biomechanical retention around dental implants: a study in rabbits[END_REF] reported that diabetes mellitus negatively influences the mechanical retention of implants placed in the tibia, regardless of whether insulin therapy was applied. Giglio et al. (2000) investigated the osseointegration of dental implants placed in the tibiae of diabetic rats; newly formed bone around implants was immature and less organized in uncontrolled diabetic rats compared to non-diabetic controls. Another study reported a bone-implant-contact of less than 50% in alloxan-induced diabetic rats compared to healthy controls [START_REF] Siqueira | Bone formation around titanium implants in the rat tibia: role of insulin[END_REF]. However, [START_REF] Gerritsen | Wound healing around bone-anchored percutaneous devices in experimental diabetes mellitus[END_REF] demonstrated that diabetes mellitus has no adverse effect on the clinical performance of percutaneous implants in rabbits, claiming that impaired wound healing in diabetes is not a contraindication for the anchorage of percutaneous implants in cortical bone. Thus, clinical trials and experimental animal studies evaluating the effect of diabetes on implant failure are still controversial. Furthermore, the coherence between diabetes and wound healing is complex and not yet fully understood, particularly in regards to the oral cavity [START_REF] Taylor | Diabetes, periodontal diseases, dental caries, and tooth loss: a review of the literature[END_REF]. Even though experimental investigations with diabetic animals are gaining popularity, these studies deal with one fundamental problem -the choice of animal model [START_REF] Kotsovilis | A comprehensive and critical review of dental implant placement in diabetic animals and patients[END_REF]. Rats, mice, and rabbits have very permissive bone healing and high metabolism rate, making their results difficult to evaluate and transfer to humans [START_REF] Nunamaker | Experimental models of fracture repair[END_REF][START_REF] Pearce | Animal models for implant biomaterial research in bone: a review[END_REF]. Therefore, a species anatomically and metabolically similar to humans and with similar bone remodelling as humans is necessary for studies investigating bone-implant interactions and a crucial factor when later extrapolating the results to the clinical situation.

Our research group previously established a diabetic domestic pig, which is well suited for investigating peri-implant bone formation in diabetes mellitus due to its advantages in comparative experimental medicine [START_REF] Von Wilmowsky | Establishment of a streptozotocin-induced diabetic domestic pig model and a systematic evaluation of pathological changes in the hard and soft tissue over a 12-month period[END_REF][START_REF] Marshall | Early micro-and macro-angiopathy in the streptozotocin diabetic minipig[END_REF][START_REF] Schlegel | The monocortical critical size bone defect as an alternative experimental model in testing bone substitute materials[END_REF]. The pig has been recognized as a valuable model in biomedical research because of its anatomical, physiological, and metabolic similarities with the human organism [START_REF] Swindle | Swine as models in experimental surgery[END_REF][START_REF] Laiblin | Clinical chemistry examinations of bone and muscle metabolism under stress in the Gottingen miniature pig-an experimental study[END_REF].

The aim of this study was to investigate peri-implant bone formation in the diabetic domestic pig.

Pathological changes in the hard and soft tissues of the diabetic pigs before placement of the implants was important; therefore, we used biological conditions that resemble the clinical situation.

Materials and Methods

Animals and surgical procedure

Twenty-five domestic pigs (10 healthy controls and 15 diabetic) were used in this study. Housing and feeding were according to standard animal care protocols. The animals were under sequential veterinary control during the entire experimental period. The experimental protocol was approved by the Committee for Animal Research, Government of Midfranconia, Ansbach, Germany (Approval No. 54-2531-25/07). All surgical procedures and the induction of diabetes were performed using intubation anaesthesia. Streptomycin (0.5 g/day; Gruenenthal, Stolberg, Germany) was applied intramuscularly 1 h before the operation and 2 days postoperatively. For postoperative pain control, each animal received buprenorphin every 12 h for 3 days at a dose of 0.1 mg/kg body weight (BW).

Induction of diabetes mellitus

The animals were 12 weeks old (33 ± 3.4 kg) at the beginning of the study. For the induction of diabetes, streptozotocin (STZ) (90 mg/kg BW, Zanosar, Pharmacia, Pfizer, New York, USA) was In order to define diabetes and meet the criteria set by the World Health Organisation (WHO) and American Diabetes Association (ADA) for diabetes, the blood glucose level and BW were measured on a regular basis and an intravenous glucose tolerance test (IvGTT) carried out as described previously (WHO 2006, ADA 2010[START_REF] Von Wilmowsky | Establishment of a streptozotocin-induced diabetic domestic pig model and a systematic evaluation of pathological changes in the hard and soft tissue over a 12-month period[END_REF]) (Table 1).

Implant surgery

The implants were placed after 15 months after the induction of diabetes (Fig. 1). This time frame was chosen to guarantee that the diabetes was present long enough to induce significant pathological changes in the hard and soft tissues. Thus, we aimed to guarantee that the implants were placed in a compromised wound healing situation.

Two test groups were formed and examined at two different time points. Five healthy controls and eight diabetic animals were examined 4 weeks after implant placement (16 months after the induction of diabetes), and the other five healthy and seven diabetic animals were examined at 12 weeks (18 months after the induction of diabetes). Five implants (Straumann SLA, 4.1 mm diameter, 10 mm length; Straumann GmbH, Freiburg, Germany) were placed in the frontal skull of each animal. A sagittal incision was made, the soft tissue and periosteum mobilised, and the implants inserted into the bone. The periosteum and skin were closed over the defects using absorbable Vicryl sutures (Vicryl® 3.0; Vicryl® 1.0; Ethicon GmbH & Co. KG, Norderstedt, Germany).

To evaluate diabetes-induced histopathological changes in the hard and soft tissues, biopsies were taken from the left ear and frontal skull before the induction of diabetes, and 6 and 12 months after the administration of STZ. The biopsies were evaluated quantitatively and qualitatively using microradiography, light microscopy, and scanning electronic microscopy as described previously [START_REF] Von Wilmowsky | Establishment of a streptozotocin-induced diabetic domestic pig model and a systematic evaluation of pathological changes in the hard and soft tissue over a 12-month period[END_REF]. Biopsies were also taken after the scarification of the animals at 16 and 18 months to measure the bone mineral density (BMD) and further analyse the hard and soft tissue qualitatively (Fig. 1).

Histological preparation

In order to obtain the samples for histology, the animals were sacrificed 4 and 12 weeks after implant insertion. The animals were given an intramuscular injection of azaperone (1 mg/kg) and midazolam (1 mg/kg) in the neck, and then euthanized by an intravascular injection of 20% pentobarbital solution until cardiac arrest occurred. The skull cap was removed and immediately frozen at -80°C. Samples were immersed in 1.4% paraformaldehyde at 4°C in order to remove insoluble organic matrix constituents. The samples were subsequently dehydrated in an ascending alcohol series at room temperature in a dehydration unit (Shandon Citadel 1000, Shandon GmbH, Frankfurt, Germany). The explants were embedded using Technovit 9100 (Heraeus Kulzer, Kulzer Division, Werheim, Germany), which is suitable for the cutting and grinding technique suggested by [START_REF] Donath | A method for the study of undecalcified bones and teeth with attached soft tissues. The Säge-Schliff (sawing and grinding) technique[END_REF]. To produce microradiographs, the embedded bone specimens were reduced to 120-µm sections using a precision saw and grinding machine (Exakt Gerätebau, Norderstedt, Germany) (Fig. 2a). The bone specimens were reduced to 30 µm to apply toluidine blue O solution for the evaluation of boneimplant-contact (BIC). The samples were polished and treated in a continuously stirred 10% H 2 O 2 solution for 5 min. The polished sampled were rinsed with water, dried, and stained in toluidine blue O solution for 15 min. The harvested soft tissue biopsies were qualitatively assessed for angiopathies by the evaluation of irregularities in the endothelium and examined with a scanning electron microscope (SEM; Fig. 2b) (FE-SEM, S-4800, Hitachi, Japan).

To qualitatively evaluate the bone samples, they were fixed in formalin, decalcified, embedded in paraffin, and stained using the Masson Goldner Trichrome (MG) dying method. Examination of the slides consisted of descriptive histology using light and polarization microscopy analysis (Axioskop, Zeiss, Oberkochen, Germany; 20x magnification, Fig. 3) [START_REF] Saino | Evidence for an extensive collagen type III proximal domain in the rat femur. II. Expansion with exercise[END_REF].

Microradiography and bone mineral density

To produce microradiographs, the resin-embedded bone sections were irradiated in a cabinet X-ray system (Faxitron X-ray, Lincolnshire, USA) using a tube voltage of 13 kV and 0.3 mA for 2.5 min. The developed radiographs (Kodak, Stuttgart, Germany) were scanned into tiff format (Epson perfection 4990 Photo, Seiko Epson Corp, Nagano, Japan). The mineral density was measured from the microradiograph as described previously [START_REF] Boivin | The degree of mineralization of bone tissue measured by computerized quantitative contact microradiography[END_REF] using Bioquant Osteo software V7.10.10 (Bioquant, Nashville, USA) to analyse the samples at four different spots (two on each side; 10x magnification; Fig. 2a). The percentage of colour scaling on a grey scale image can be determined with this program. The area where mineralized tissue was detected and calculated as a percentage of the total area was determined to be the mineral density.

Bone-implant-contact

Stained specimens were examined at four spots (two on each side of the implant; Fig. 2b) under a light microscope (Axio Imager A1, Zeiss, Jena, Germany; 10x magnification) and subsequently fed into a computer with an attached video camera (QICAM FAST 1394, Qimaging, Canada). Bioquant Osteo software V7.10.10 was used to determine the percentage of direct contact between mineralized bone and the implant surface, the BIC.

Immunohistochemistry

For immunohistochemical analysis, the samples were cut in 5-µm-thick slices using a microtome saw (Leica, Wetzlar, Germany) and dyed using antibodies for collagen type-I (Novocastra, Berlin, Germany) and osteocalcin (Takara Biomedicals Europe, Saint Beauzire, France) (Fig. 2c). Histological sections were partly pre-treated in citrate buffer for collagen type-I staining. Endogenous peroxidase was blocked by incubation in 3% H 2 O 2 solution for 15 min. The surface of the sample was subsequently blocked with protein-block serum free (Dako Diagnostics GmbH, Berlin, Germany) to prevent nonspecific staining. Next, the primary antibodies against collagen type-I (1:10) and osteocalcin (1:1500) were added. To enable a coloured presentation, secondary antibody (Dako Diagnostics) was added. Finally, the addition of StreptAB/HRP (Dako Diagnostics) enabled the binding of the actual dye, AEC1 (Dako Diagnostics). The procedure was completed by haematoxylin-eosin counterstaining. All samples were accompanied by a negative control. Immunohistochemical analysis was performed at four different regions of interest (ROI) with 20-fold magnification (Fig. 2c). Stained areas of the ROI were digitally marked, and the percentage of stained area determined using the Bioquant Osteo software V7.10.10.

Statistical analysis

All measurements were performed by one examiner who was blinded to the identity of the specimens

Results

The animals did not show signs of a physical impairment and were in stable condition throughout the study. The glucose metabolism of the insulin-deficient diabetes was in a stable condition, even in the absence of an insulin therapy.

Qualitative evaluation of the hard and soft tissues

After 18 months, SEM examination of the vasculature of the ear lobe skin in the healthy pigs (n = 5 samples) revealed regularly shaped endothelial cells with no signs of increased size or disconnection.

The endothelium was smooth and in regular condition (Fig. 4a andb). In contrast, examination of the skin of the diabetic group (n = 7 samples) showed mixed parietal microthrombi consisting of platelets and fibrin on the vascular endothelium. The endothelium usually showed changes in the form of an irregular, fissured surface and detachment of endothelial cells. (Fig. 4c andd).

Qualitative evaluation of the hard tissue biopsies of the control animals (n = 5 samples) found regularly shaped bone after 18 months. The MG staining revealed inconspicuous signs of bone remodelling that seemed to be physiological (Fig. 3a). Polarisation microscopy of the same samples showed physiological mineralization areas (Fig. 3b). In contrast, the bone samples from the diabetic group (n = 7 samples) were irregularly shaped and showed wider bone trabeculae with a compressed bone marrow space (Fig. 3c). The mineralization zones were expanded compared to the healthy control group, which was verified by polarisation microscopy (Fig. 3d).

Even though the degree of distinctiveness of the microangiopathie and bone morphology showed variances within the animals of the diabetic group, pathological differences were apparent in all animals compared to the samples of the healthy control animals.

Bone mineral density

The BMD results are given in Table 2. Examination of the peri-implant BMD 4 weeks after implant placement revealed significantly greater bone mineralization in the healthy control group (n = 25 implants) compared to the diabetic group (n = 40 implants; Fig. 5). Twelve weeks after implant placement, a significantly higher peri-implant BMD was measured in the control group (n = 25 implants) compared to the diabetic group (n = 35 implants; Fig. 5).

Regarding time differences no significance could be found between 4 and 12 weeks, neither in the control group (p = 0.981) nor in the diabetic group (p = 0.246).

Bone-implant-contact

The results of the evaluation of bone in direct contact with the implants are given in Table 2. Four weeks after implant placement, BIC was significantly increased in the control group (n = 25 implants) compared to the diabetic group (n = 40 implants; Fig. 5). Twelve weeks after implant placement, BIC decreased in both groups (n = 25 implants in control vs. n = 35 implants in diabetics), and the difference remained significant (Fig. 5).

The comparison of both points in time showed no significant difference in the control group (p = 0.876) and in the diabetic group (p = 0.397).

Immunohistochemistry

The protein expression measured for collagen type-1 and osteocalcin as a percentage of stained area is given in Table 2. Collagen type-I expression was lower in the control group (n = 25 implants) compared to the diabetic group (n = 40 implants) at 4 weeks, but the difference was not significant (Fig. 6). Twelve weeks after implant placement, collagen type-I expression decreased in both group (n = 25 implants in control vs. n = 35 implants for diabetic), and the difference remained non-significant (Fig. 6). Between 4 and 12 weeks no significance could be found in the protein expression in the control group (p = 0.341). The protein expression in the diabetic group did significantly decrease after 4 weeks (p = 0.022).

Osteocalcin expression at 4 weeks was lower in the diabetic group (n = 40 implants) compared to the control group (n = 25 implants), but the difference was not significant (Fig. 6). After 12 weeks, the expression of osteocalcin increased in both groups (n = 35 implants in diabetic group vs. n = 25 implants in the control), and the difference remained non-significant (Fig. 6). The increase of protein expression was significantly higher in both groups between 4 and 12 weeks (control p = 0.015; diabetic p = 0.045).

Discussion

Compared with the general population, a higher implant failure rate is seen in diabetic patients. Therefore, diabetes is currently classified as a relative contraindication for implant therapy [START_REF] Morris | Implant survival in patients with type 2 diabetes: placement to 36 months[END_REF]. Animal models are essential for understanding of biological pathways and regulation leading to diminished osseointegration, and thus understanding the mechanisms leading to increased implant Even though many experimental studies have been performed with diabetic animals, they have dealt with two fundamental problems: the choice of the animal model and the time point chosen for the placement of implants. Regarding the animal model, the selection of an appropriate experimental model with bone repair analogous to that of humans is a prerequisite for the transferability of the experimental results to clinical applications [START_REF] Bosetti | Type I collagen production by osteoblast-like cells cultured in contact with different bioactive glasses[END_REF].

Although the rat is one of the most commonly used species in diabetes and medical research, it has notable dissimilarities with humans regarding bone micro-and macrostructure, as well as bone composition. Furthermore, the limitations of size make rats unsuitable for testing multiple implants simultaneously [START_REF] Pearce | Animal models for implant biomaterial research in bone: a review[END_REF]). Studies dealing with rabbits face the same problems. The rabbit reaches skeletal maturity at around 6 months of age, and the architecture and composition of its bone is remarkably different from that of humans [START_REF] Gilsanz | Effect of sex steroids on peak bone density of growing rabbits[END_REF]). However, rabbits are useful and necessary for screening implant materials prior to testing in larger animal models [START_REF] Pearce | Animal models for implant biomaterial research in bone: a review[END_REF]).

In addition, rats and rabbits have very permissive bone healing and a higher metabolism rate with faster skeletal changes and bone turnover than humans, making their results difficult to evaluate and apply to humans [START_REF] Nunamaker | Experimental models of fracture repair[END_REF][START_REF] Pearce | Animal models for implant biomaterial research in bone: a review[END_REF].

With regard to bone anatomy, morphology, healing, and remodelling, the adult domestic pig was our animal of choice. Tissue blood circulation, circulatory processes, fracture healing, and the rate of new bone formation in the pig correlate with those of humans [START_REF] Borden | Structural and human cellular assessment of a novel microsphere-based tissue engineered scaffold for bone repair[END_REF][START_REF] Schlegel | Preclinical animal model for de novo bone formation in human maxillary sinus[END_REF]). Thus, we previously established a streptozotocin-induced diabetic domestic pig model to evaluate bone formation around dental implants [START_REF] Von Wilmowsky | Establishment of a streptozotocin-induced diabetic domestic pig model and a systematic evaluation of pathological changes in the hard and soft tissue over a 12-month period[END_REF]. To ensure that the internal clinical parameters of diabetes mellitus were fulfilled prior to implantation, the soft and hard tissues were systematically evaluated over a 12-month period for pathological changes. The clinical parameters (blood glucose level, BW, IvGTT), determined by the ADA (2010) and WHO (2006) for the definition of diabetes mellitus were fulfilled. Pathological changes were already visible in the skin vasculature after 6 months, with significant arterial wall thickening in the diabetic group which is in accordance with the literature [START_REF] Marshall | Early micro-and macro-angiopathy in the streptozotocin diabetic minipig[END_REF]. The BMD was lower in the diabetic group after 6 months, with a significant difference compared to control pigs after 12 months [START_REF] Von Wilmowsky | Establishment of a streptozotocin-induced diabetic domestic pig model and a systematic evaluation of pathological changes in the hard and soft tissue over a 12-month period[END_REF]). After validating these pathological changes, the implants were inserted. Animal studies lasting for only a short time period might not resemble the compromised bone healing seen clinically in humans. As we found, it takes 12 months to develop significant pathological changes in the hard tissue [START_REF] Von Wilmowsky | Establishment of a streptozotocin-induced diabetic domestic pig model and a systematic evaluation of pathological changes in the hard and soft tissue over a 12-month period[END_REF].

The evaluation of BIC in our study revealed significantly less BIC in the diabetic group compared to the healthy control animals after 15 and 18 months. Other experimental diabetic animal studies have shown that newly formed bone around implants is immature and less organized in diabetic animals compared to healthy control animals, suggesting qualitative differences in the newly formed bone [START_REF] Nevins | Wound healing around endosseous implants in experimental diabetes[END_REF]). In addition, bone healing around implants after 30 days was delayed in diabetic animals compared to healthy controls (Giglio et al. 2000).

Diabetes seems to have a substantial effect on successful implant osseointegration. Diabetes has been shown to negatively affect bone formation, as well as bone mineralization and bone density [START_REF] Devlin | Healing of tooth extraction sockets in experimental diabetes mellitus[END_REF][START_REF] He | Diabetes causes decreased osteoclastogenesis, reduced bone formation, and enhanced apoptosis of osteoblastic cells in bacteria stimulated bone loss[END_REF], Goodmann & Hori 1984). Prerequisite and crucial for proper and regular bone healing and bone mineralization is early vascularisation (Burhardt et al. 1987[START_REF] Glowacki | Angiogenesis in fracture repair[END_REF]. Angiopathies are the most frequent secondary complication in diabetes, and the microcirculation is decreased [START_REF] Martin | Abnormal angiogenesis in diabetes mellitus[END_REF]. Thus, impaired wound healing and wound infections are the main complications of the metabolic context of diabetes [START_REF] Hirsch | Enhanced susceptibility to infections in a diabetic wound healing model[END_REF]. Therefore, we systematically investigated the development of angiopathies in the diabetic domestic pig. SEM evaluation of the skin vasculature 18 months after the induction of diabetes revealed pathological changes visible as altered, swollen, and thickened endothelium with mixed parietal microthrombi, and changes were already visible after 6 months. These angiopathies most likely contribute to endothelial dysfunction and diminished microcirculation, leading to impaired peri-implant bone formation as documented in our study. Thereby advanced glycolation endproducts (AGEs) seem to play a major role in the development of angiopathies among people with diabetes. A constant hyperglycaemia, as it was present in our animals, leads to an increase of AGEs contributing to vascular complications (Ahmed & Thornalley 2007[START_REF] Jakus | Advanced glycation end-products and the progress of diabetic vascular complications[END_REF]. It has been shown that the pharmacological [START_REF] Schalkwijk | Early-and advanced non-enzymatic glycation in diabetic vascular complications: the search for therapeutics[END_REF]. Furtermore the formation of AGEs in high glycaemic conditions, such as diabetes, contribute to a slower rate of osseointegration by influencing the expression of bone matrix proteins and thus negatively affect implant stability [START_REF] Quintero | Advanced glycation endproducts and rat dental implant osseointegration[END_REF][START_REF] Retzepi | The effect of diabetes mellitus on osseous healing[END_REF].

The hyperglycaemia present in diabetic animals may also contribute to less BIC and lower BMD in our study. Diabetes-related hyperglycaemia can modulate osteoblast signalling pathways and suppress the expression of genes associated with osteoblast maturation. This context leads to decreased calcium uptake in osteoblasts, contributing to less bone mineralization, which was seen in our study [START_REF] Balint | Glucose-induced inhibition of in vitro bone mineralization[END_REF][START_REF] Zayzafoon | Extracellular glucose influences osteoblast differentiation and c-Jun expression[END_REF]. Qualitative and quantitative evaluations of the hard tissue showed less mineralized bone and unorganized bone with diminished quality in the diabetic animals compared to the healthy controls.

Increased glucose metabolism has been suggested to lead to an altered energy status and increased lactate acid synthesis. Acid-sensitive channels expressed on osteoblasts respond to low pH conditions by decreasing mineralization and gene expression [START_REF] Jahr | Identification of acidsensing ion channels in bone[END_REF][START_REF] Brandao-Burch | Acidosis inhibits bone formation by osteoblasts in vitro by preventing mineralization[END_REF].

These findings might explain why, in studies with insulin-treated animals, insulin has the capacity to regulate and reduce the effects of diabetes on bone healing, and why no difference was found in the BIC between healthy control and insulin-treated diabetic animals [START_REF] Siqueira | Bone formation around titanium implants in the rat tibia: role of insulin[END_REF][START_REF] Kwon | Maintenance of osseointegration utilizing insulin therapy in a diabetic rat model[END_REF]. These findings might also explain those of clinical studies investigating the effects of sufficiently controlled diabetes on the osseointegration of dental implants. In a review by [START_REF] Javed | Impact of diabetes mellitus and glycemic control on the osseointegration of dental implants: a systematic literature review[END_REF], no evidence was reported for diminished clinical success or significant early healing complications associated with implant therapy based on the glycaemic control levels in diabetic patients. Therefore, implant placement is not contraindicated in diabetic patients with good metabolic control [START_REF] Mombelli | Systemic diseases affecting osseointegration therapy[END_REF].

Regarding time effects we could not find any significant differences in both groups between 4 and 12 weeks, neither in the BIC nor in the BMD. A possible explanation for this finding could be the fact, that the implants were placed in spongious bone. It is known that this bone structure has a faster healing time and remodelling in comparison to cortical bone structure (Davies 2003). It can be suggested that the majority of the healing process had already taken place during the first 30 days. On the other hand other studies have shown differences of the amount of BIC during the observation period of 12 weeks.

Several factors are influencing the BIC like implant design and surface, insertion technique as well as an immediate or delayed loading of implants. Furthermore the differences in bone architecture of different study models might be a parameter influencing bone formation around implants. As stated above the pig was our animal of choice even tough it has been found out that the bone macrostructure of pigs does have a denser trabecular network compared to humans [START_REF] Mosekilde | Calciumrestricted ovariectomized Sinclair S-1 minipigs: an animal model of osteopenia and trabecular plate perforation[END_REF]. Still a missing difference between the BIC after 4 and 12 weeks remains unclear.

A deficit in the recruitment of mesenchymal stem cells (MSCs) of the osteoblastic lineage might contribute to the impaired osteoid matrix production and suppressed osteoblast cell activity observed during the early osseous healing period in diabetes [START_REF] Mccabe | Understanding the pathology and mechanisms of type I diabetic bone loss[END_REF]. Diabetic animals have been reported to produce sufficient amounts of immature mesenchymal tissue, but they fail to adequately express genes that regulate osteoblast differentiation [START_REF] Lu | Diabetes interferes with the bone formation by affecting the expression of transcription factors that regulate osteoblast differentiation[END_REF]. This finding suggests that a difference in the expression of these markers has to be present in the peri-implant bone in both groups because BMD and BIC were significantly reduced in the diabetic group in our study.

Higher levels of collagen type-I were observed in the diabetic group after 4 and 12 weeks. This finding can be explained by the reduced bone mineralization in the diabetic animals. In diabetes, the bone is not adequately mineralized, leaving a greater amount of collagenous, non-mineralized area on bone biopsies. The zones of insufficient mineralized bone were expanded compared to the mineralization zones of the healthy controls. The decreased collagen protein expression between 4 and 12 weeks is feasible as it is considered to be a basic initial bone matrix protein in bone formation, and its expression is an early indicator of de novo bone formation. Furthermore it might be possible that a higher collagen type-I expression could also influence bone density. This has to be quantitatively evaluated in further studies.

In order to gain further information about the process of bone mineralization, we investigated the expression of osteocalcin. The appearance of osteocalcin in bone maturation is known to be delayed from the appearance of collagen type-I, and it indicates the mineralization process in bone formation implemented by the calcification of osteocytes [START_REF] Thorwarth | Expression of bone matrix proteins during de novo bone formation using a bovine collagen and platelet-rich plasma (PRP)-An immunohistochemical analysis[END_REF].

Osteocalcin was diminished in the diabetic group compared to healthy controls after 4 and 12 weeks.

Because osteocalcin acts as a calcium binder, diminished expression might contribute to a reduced mineralization potential and rate in the diabetic bone, as that seen in our study (Liebermann et al. 2002). These findings correlate with findings in diabetic patients. Higher serum-osteocalcin levels have been shown to correlate with increased bone mineral density [START_REF] Kanazawa | Serum osteocalcin/bone-specific alkaline phosphatase ratio is a predictor for the presence of vertebral fractures in men with type 2 diabetes[END_REF]. Furthermore, decreased osteocalcin mRNA in diabetic bone is assumed to lead to a decreased rate of mineral apposition due to inhibited calcium uptake [START_REF] Balint | Glucose-induced inhibition of in vitro bone mineralization[END_REF], Verhaege et al. 1990 [START_REF] Botolin | Increased bone adiposity and peroxisomal proliferator-activated receptor-gamma2 expression in type I diabetic mice[END_REF]. Thus, reduced osteocalcin expression reduces the possibility of binding calcium and incorporating it into the matrix, which is necessary for adequate bone matrix mineralization. The statistically significant increase between 4 and 12 weeks might be explained by its delayed appearance in bone formation.

A drawback of the immunohistochemical analysis is the investigation of only two bone matrix proteins at two points of time. Regarding the complexity of the biological pathways of bone metabolism, we are only able to get a picture at a given instant. Lu and colleagues found differences in the expression of osteocalcin and collagen type-I in diabetic mice compared to healthy control animals on day 4 and 6 after marrow ablation [START_REF] Lu | Diabetes interferes with the bone formation by affecting the expression of transcription factors that regulate osteoblast differentiation[END_REF]. Earlier time frames for investigation might be necessary to find significant differences in the protein expression pattern. On the other hand it might be suggested that osteocalcin and collagen type-I are important proteins in bone metabolism but do not play a significant role in a pathological, hyperglycaemic situation. Comparisons with human studies regarding the expression of osteoclacin and collagen type-I in a hyperglycaemic situation are not possible since these proteins were mainly evaluated in the serum.

However, the regulation of proteins in the hard and soft tissues of diabetic organisms and the causes of pathological changes in the interaction between molecular pathways remains complex and not fully understood. Even though the results of our immunohistochemical analysis were not significant and the missing statistical differences remain unclear, we believe that these results might be of interest. These findings might be a hint about differences in protein expression and may point to additional parameters that will be important for investigations into the effect of diabetes on peri-implant bone formation.

Even though the diabetic domestic pig is well suited for these kinds of investigations, this animal model and this study have limitations. Due to its size and excessive final body weight, keeping pigs is sometimes intricate and long lasting studies are expensive. Moreover, the implants were placed in the frontal skull. Previous studies have shown that the mineralization rate is not significantly different between the frontal skull of the domestic pig and the maxillary bone of humans [START_REF] Schlegel | Preclinical animal model for de novo bone formation in human maxillary sinus[END_REF]), but this fact has not been validated for the bone structure of the lower jaw. Therefore, the results of this study are valid for spongious bone as it can be found in the upper jaw of humans. In addition the pig does have a denser trabecular network compared to humans which might lead to a higher BIC than it would be evident in humans [START_REF] Mosekilde | Calciumrestricted ovariectomized Sinclair S-1 minipigs: an animal model of osteopenia and trabecular plate perforation[END_REF]). Further investigations should address periimplant bone formation on the macroscopic and microscopic level and molecular level in the cortical bone.

Conclusions

Our study is the first to evaluate the impact of diabetes on dental implants using a pig model, which is known to have a high degree of similarity with the human organism. We demonstrated that diabetes negatively affects bone mineralization and bone formation in the area surrounding dental implants. These results should be taken into consideration in the clinical setting with patients who have poorly controlled diabetes and an indication for dental implants. Table 2: The results of the bone mineral density (BMD), the bone-implant-contact (BIC) as well as the protein expression of collagen type-I and osteocalcin are shown. The asterix (*) indicates statistical differences between the two groups under investigation (p < 0.05).

  (1 g/10 ml) and administered to the pigs intravenously via an ear vein with continuous infusion over 15 minutes.

  being evaluated. All values are given as means and standard deviation. The software program SPSS (version 14.0 for Windows) was used to analyse all data. Differences among groups and days were evaluated by the Mann-Whitney U-test. Multiple testing has not been performed due to a comparison of only two groups: control vs. diabetic. Values of p < 0.05 were considered significant.

  certain transferability of the results to humans might be possible if the animal model is chosen carefully for the aspects and questions being investigated[START_REF] Held | Appropriate animal models[END_REF].

  had a pathological situation that is often found in diabetic patients, as 50% of type 1 diabetes patients have significant histopathological changes in the bone macrostructure compared to healthy age-matched subjects, and almost 20% of patients aged 20 -56 years meet the criteria for osteoporosis. Therefore, a large proportion of diabetic patients with an indication for implant therapy are suffering from diminished bone quality[START_REF] Mccabe | Understanding the pathology and mechanisms of type I diabetic bone loss[END_REF][START_REF] Adami | Bone health in diabetes: considerations for clinical management[END_REF]).

Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :

 23456 Figure 1: Chronological sequence of the study. 157x30mm (600 x 600 DPI)

  , McCabe 2007,

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Table 1 :

 1 In this table the blood glucose level and the body weight of the diabetic and healthy control animals are shown over the study period of 18 months. The results of the intravenous glucose tolerance test (IvGTT) are pictured on the right side of the table. These tests were performed four weeks after the induction of the diabetes. The asterix (*) indicates statistical differences between the diabetic and the healthy control animals (p < 0.05).

	Time	BMD	BIC	Collagen type-I	Osteocalcin
	[weeks]		[%]	[%]	[%]		[%]
		control	diabetic	control	diabetic	control	diabetic	control	diabetic
	4	57.59 ± 7.35	60.08 ± 10.16*	86.05 ± 10.15	65.53 ± 17.98*	25.62 ± 11.43	29.62 ± 7.46	16.48 ± 7.75	14.51 ± 7.99
	12	46.35 ± 9.44	44.07 ± 13.58*	83.95 ± 14.81	60.95 ± 17.45*	20.81 ± 7.07	24.04 ± 7.57	24.54 ± 11.06	22.58 ± 12.41
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Journal of Clinical Periodontology -PROOFJournal of Clinical Periodontology -PROOF

Journal of Clinical Periodontology -PROOF

Acknowledgements

This project was funded by the ELAN-fonds of the University of Erlangen (ELAN-55410013) and the ITI Foundation/Research Committee (ITI-5052007). The work of Andrea Krautheim-Zenk, Susanne Schönherr, and Heidemarie Heider is greatly appreciated. We would also like to thank Elfriede Tissera of the Department of Manufacturing Technology for the electronic scanning pictures. The Authors state that there are no conflicts of interest.