Labour productivity growth in the euro area has been lacklustre for some time. This is only partially explained by cyclical factors. Structural developments, including sectoral structural change, are acting as an additional drag. Sectoral structural change influences the extent to which efficiency gains and losses in individual sectors are transferred to the wider economy. A widely used measure of efficiency is total factor productivity (TFP). Its path captures the portion of output growth that is not explained by input factors such as labour or the capital stock. There is solid evidence from numerous advanced economies that industry (and especially manufacturing) has typically recorded stronger TFP gains in the past than the services sectors. This suggests that the euro area’s long-standing trend towards a service economy has contributed to its weak aggregate productivity growth.

However, the shift in sectoral shares is not the only factor to play a role. Cross-sectoral input-output linkages have also evolved in the wake of structural change. These production linkages are instrumental to the way in which efficiency gains or losses across sectors affect the economy as a whole. Therefore, the consideration of cross-sectoral input-output linkages is essential in order to understand the effects of structural change.

Manufacturing TFP growth has slowed in the four largest euro area countries over the past two decades. With the macroeconomic impact of these efficiency gains also diminishing as a result of structural change, manufacturing’s contribution to aggregate productivity growth has considerably weakened. In the services sectors, meanwhile, efficiency has actually fallen in three of these four countries. Given the greater economic weight of services today, such efficiency losses are now weighing more heavily on aggregate productivity than in previous decades.

The global trend towards a more service-oriented economy is neither an economic policy problem nor a phenomenon amenable to policy intervention. Efficiency trends within sectors, however, are a different matter. Strengthening the efficiency of the services sector in particular will be pivotal given its now much greater macroeconomic significance. A comparison with the United States suggests that there is considerable untapped potential in the euro area. Fully exploiting this potential – especially with regard to technological innovation – will, however, require a favourable economic and regulatory framework as well as structural reforms.

Labour productivity growth in the euro area has been faltering for some time. Cyclical factors have likely contributed to this. Severe recessions – as seen during the global financial and economic crisis or the coronavirus pandemic – can cause lasting damage to productivity growth by weakening the development and diffusion of innovations or by hampering efficient allocation of production factors. 1 Taking a longer-term view, however, we can see that the slowdown in productivity growth in the four largest euro area countries goes back several decades. This points to structural causes being partly behind the recent weakness in productivity performance.

Sectoral structural change is a possible factor behind the slowdown in aggregate productivity growth. In general terms, sectoral structural change refers to shifts in economic activity between economic sectors over time. 2 Alongside various other factors (enterprises’ reduced capacity to innovate and adapt, weakening business dynamism and regulatory barriers in labour and product markets), it is regarded as a possible structural explanation for the decline in the euro area’s labour productivity growth. 3 Sectoral structural change may, for example, cause relatively productive parts of the economy to wane in importance in macroeconomic terms, while at the same time increasing the economic weight of sectors with comparatively weak growth in TFP (total factor productivity – a common measure of efficiency). In addition, sectoral structural change can significantly alter the linkages between economic sectors. Recent studies show that intermediate inputs and their cross-sectoral use play an important role in the diffusion of efficiency gains. 4 As a result, sectoral developments do not unfold in isolation but feed through to other areas of the economy, for example via production networks. 5  

Sectoral structural change is reflected in the changing shares of economic sectors in aggregate economic activity. From a statistical perspective, the starting point for gathering an accurate picture of structural change is to break the economy down into meaningful sectors and measure economic activity within them using a consistent methodology. A commonly used basic classification divides the economy into the primary (agriculture), secondary (industry including manufacturing) and tertiary (services) sectors. Typical measures used to quantify economic activity are labour input and gross value added. 6 Sectoral structural change is usually examined in the context of a long-term economic development process. 7 The latter can be described using GDP per capita. Our descriptive analysis focuses on advanced economies, including the four largest euro area countries and the United States. 8

The process of economic development is accompanied by pronounced shifts in relative sectoral labour input. In the advanced economies examined here, the proportion of total hours worked accounted for by agriculture, industry and services follows a characteristic trajectory. As GDP per capita rises, the share of hours worked in agriculture declines steadily. In industry, the hump-shaped path widely documented in the literature is evident, with an initially increasing and later declining share of total hours worked (though the increased share of labour input typically observed early in the economic development process is only partially captured here given data limitations). 9 By contrast, the share of hours worked in the services sector grows steadily with rising GDP per capita. 10  

Similar patterns are observed for the sectoral shares of aggregate gross value added. The data show that the services sector accounts for a considerable portion of gross value added even at a relatively low level of GDP per capita and that this portion increases substantially as economies shift further toward services. 11 This development is relatively advanced in the United States. In 2020, industry accounted for only about 18 % of total gross value added, placing its share at the lower end among the countries considered here. By contrast, the share of services was the highest, at around 81 %. 

These sectoral shifts are also evident for major expenditure components. Over time, the sectoral shares of industry and services in private consumption and investment broadly follow the patterns seen for gross value added, though they differ markedly in magnitude across the expenditure components. 

The patterns observed are consistent with common explanations of sectoral structural change. For example, a number of studies suggest that employment shares are likely to decline, especially in those sectors of the economy with the greatest efficiency gains. Accordingly, averaged over time, the highest growth in TFP for the four largest euro area countries and the United States has been in agriculture, followed by industry and services. 12 The sectoral shifts in shares of gross value added can also be explained by the diverging productivity developments between economic sectors. In addition to this, studies show that long-run income effects are likely to play a key role in the emergence of these characteristic patterns. As incomes rise, demand shifts across sectors, which can explain the declining importance of agriculture as well as the initially increasing, then decreasing, relative importance of industry over the course of economic development (an overview of the key drivers of sectoral structural change is provided in the supplementary information entitled “Key drivers of sectoral structural change”). 13

Sectoral structural change also affects the use of intermediate inputs across sectors. In advanced economies, the pronounced division of labour implies that intermediate inputs typically account for a substantial share of total expenditure on production inputs. 14 Structural changes are thus also reflected in input-output linkages. 15  

Data from the Long-run World Input-Output Database and the OECD Inter-Country Input-Output Database provide insights into the use of intermediate inputs over the course of economic development. These data allow us to measure the sectoral shares of the total intermediate inputs used in the economy. In light of their macroeconomic importance, we focus here on the manufacturing sector and a group of selected services sectors, including professional, scientific and technical activities as well as information and communication. 16

Sector-specific use of intermediate inputs also follows pronounced patterns over the course of economic development. In manufacturing, a hump-shaped pattern emerges once again: the sector’s share in total economy-wide intermediate input use initially increases before declining as development progresses. By contrast, the services sectors’ share of total intermediate input use rises markedly. However, the scale and timing of these shifts vary across countries. Between 1970 and 2020, the share of intermediate inputs used in the services sectors grew by 20 percentage points to approximately 33 % in the United States, for example, whereas in Italy it rose by only 10 percentage points to around 17 %. The opposite pattern is observed for manufacturing: in the United States, its share fell from around 47 % to 21 %, while in Italy it only declined from 49 % to 39 %. 17

Developments in aggregate labour productivity depend to a large extent on how sectoral efficiency changes propagate through the economy. Efficiency gains, for example due to technological progress, are typically measured by growth in TFP. 18 Alongside capital deepening, TFP growth is a key driver of labour productivity. The macroeconomic effects depend not only on the magnitude and direction of sectoral efficiency improvements. Sectoral interlinkages are also crucial, as they determine the channels through which TFP changes can unfold. 19  

We examine the significance of sectoral structural change for aggregate labour productivity growth in two steps. First, we use a macroeconomic model to analyse how the aggregate impact of a given sectoral TFP shock evolves with structural change. The implications for observed aggregate labour productivity growth only become apparent, however, once sectoral TFP developments are taken into account. These are examined in the second step.

A suitable analytical framework is needed to assess the impact of sectoral structural change on aggregate labour productivity growth. Empirical evidence suggests that sectoral structural change has led to significant changes on the production and demand side. The implications of these developments for aggregate labour productivity growth cannot be directly inferred from the data, particularly in light of evolving input-output linkages. This calls for an analytical framework that explicitly captures the relationships between economic sectors and accounts for sectoral feedback effects. 

The Bundesbank’s multi-sector model – MuSe – provides a framework to analyse structural change, including the evolving cross-sectoral role of intermediate inputs. MuSe is a multi-sector macroeconomic model belonging to the class of dynamic stochastic general equilibrium (DSGE) models 20 . It represents the economy as a set of interconnected economic sectors. In the model, a sector’s output is used not only for consumption or investment purposes but also as intermediate input in other economic sectors’ production processes. The model takes into account the limited substitutability of intermediate inputs and the differences in the composition of intermediate input bundles across sectors. This allows us to analyse sectoral production linkages and their role in the transmission of exogenous shocks. The model specification used here distinguishes eight sectors. 21 Compared with the more aggregated descriptive analysis, this finer sectoral breakdown enables a more granular assessment of heterogeneities in the wake of structural change. 22 While the focus is on the four largest euro area economies, the model has additionally been calibrated for the United States.

The MuSe model can be used to examine how structural change affects the impact of sectoral TFP shocks on aggregate labour productivity. The key metric here is the sectoral labour productivity multiplier. It measures how strongly a permanent, unexpected 1 % increase in TFP in a given sector of the model affects aggregate labour productivity in the long run. 23 The analysis focuses on changes in the labour productivity multiplier in the manufacturing sector and a group of selected services sectors associated with sectoral structural change. 24  

We compare three scenarios to highlight the importance of changes in input-output linkages resulting from sectoral structural change. The starting point is a baseline scenario in which the model parameters correspond to the data for 1970. 25 In a second, counterfactual scenario, only the input-output linkages in the model are updated to the year 2020. This allows us to isolate the influence of changes in input-output linkages over time on the transmission of sectoral TFP shocks. In the third scenario, the remaining model parameters are also adjusted to their 2020 values. 26 Comparing this scenario with the second reveals which aspects of structural change, beyond input-output linkages, affect the macroeconomic transmission of a TFP shock. These may include changes in the structure of consumption and investment.

Sectoral structural change has reduced the aggregate productivity effects of efficiency gains in the manufacturing sector, not least through changes in input-output linkages. The simulation results show that the long-term effects of a permanent 1 % increase in TFP in the manufacturing sector are dependent crucially on the structure of sectoral interlinkages. When the model explicitly accounts for changes in input-output linkages observed over time (scenario II), the aggregate productivity effect in euro area countries is consistently smaller than in the baseline scenario. This reflects the decline, driven by structural change, in the share of intermediate inputs sourced from the manufacturing sector, which weakens the transmission of a TFP shock from this sector to the overall economy. When the model parameters are fully calibrated to 2020, now capturing the structural change comprehensively (scenario III), the multiplier effect of a permanent TFP increase on aggregate labour productivity is further reduced in four of the five countries examined. 27 This is due, first, to additional structural changes on the supply side not captured in the other scenarios. Second, the relative demand for manufacturing output used for consumption and investment has declined over the course of economic development, further weakening the aggregate effects of TFP changes in this sector.

In the services sectors examined, by contrast, sectoral structural change significantly amplifies the aggregate productivity effects of TFP changes. 28 The simulation results for services differ from those for manufacturing in several respects. First, in the baseline scenario, the long-run gains in aggregate labour productivity resulting from a permanent TFP increase in these sectors are comparatively small in all the countries examined. Second, when changes in the input-output linkages over time are taken into account (scenario II), the aggregate productivity effect increases markedly in all countries. When all structural changes between 1970 (1980 for the United States) and 2020 are incorporated in the model calibration (scenario III), the multiplier effect of a permanent TFP increase is typically amplified again considerably. This mainly reflects changes in the composition of consumption and investment baskets resulting from sectoral structural change, with services gaining substantially in importance over time. Compared with the corresponding simulation results for manufacturing, changes in input-output linkages thus play a smaller role.

To assess the macroeconomic significance of the changes in transmission channels resulting from structural change, it is necessary to account for the sectoral efficiency gains or losses observed in the data. The simulation results show that the impact of structural change – measured by the change in the labour productivity multiplier – differs considerably between the manufacturing sector and the services sectors examined. In the euro area countries considered, the effect of a change in manufacturing efficiency on aggregate labour productivity in 2020 was markedly weaker than 50 years earlier. By contrast, an equivalent change in TFP in services generally had a stronger effect in 2020 than in 1970. Similar patterns can be observed for the United States over the period from 1980 to 2020. 29 However, these results alone do not allow us to quantify the contribution of sectoral structural change to the evolution of aggregate labour productivity growth. To this end, we also have to consider the sectoral TFP gains or losses observed in the data. 

Sectoral TFP changes are not directly observable and therefore need to be estimated. We estimate TFP growth rates in the individual sectors using a standard growth accounting decomposition of sectoral gross output growth. 30 This allows us to explicitly account for intermediate inputs used in the production process. 31 Owing to data availability, the analysis of TFP growth in the five advanced economies considered is limited to the period from 1980 to 2019. In order to capture changes over time, we distinguish between two sub-periods: 1980 to 1998 and 1999 to 2019.

TFP growth in the services sectors lags behind that in manufacturing throughout the observation period. While average TFP growth rates in manufacturing are consistently positive across the countries considered, the services sectors in some cases even exhibit substantial efficiency losses. 32 Such efficiency losses were particularly pronounced in the sub-period from 1980 to 1998. However, the figures for average TFP growth in several countries point to efficiency losses in services over the period from 1999 to 2019, too.

A comparison of the two sub-periods reveals a broadly consistent pattern in manufacturing across the countries considered. Average TFP growth in manufacturing declined over time, both in the large euro area countries and in the United States. This slowdown was particularly pronounced in Italy, where average TFP growth in the 1999–2019 period was less than a quarter of that in the preceding sub-period. The decline in the growth rate was smallest in Germany.

For the services sectors considered, the picture is mixed and highly country-specific across both sub-periods. Among the euro area countries, average TFP growth in Germany and France weakened over time. The decline was particularly pronounced in Germany: while average TFP growth rates remained clearly positive between 1980 and 1998, efficiency losses are observed on average over the period from 1999 to 2019. The pattern differs for Italy and Spain. Although services sectors in both countries experienced efficiency losses in each sub-period, these diminished considerably over time in some cases. A notable development is observed in the United States. Following pronounced efficiency losses between 1980 and 1998, estimates for the period from 1999 to 2019 indicate by far the highest TFP growth rates here among the countries considered. A key driver of this development was the exceptionally strong TFP growth in the information and communication subsector from the mid-1990s onwards. 33

The interplay between sectoral TFP developments and changes in transmission mechanisms provides an explanation for the weak productivity growth in the euro area. In the manufacturing sector, TFP growth declined markedly in the four largest euro area countries. At the same time, the positive impact of these efficiency gains on aggregate labour productivity diminished significantly as a result of sectoral structural change. In the services sectors, structural change amplified the macroeconomic impact of sectoral efficiency gains or losses. However, average TFP growth in these sectors has been negative in three of the four largest euro area countries over recent decades. Owing to the higher multiplier effect associated with structural change, such efficiency losses are weighing on the euro area’s aggregate labour productivity more heavily today than was the case some 50 years ago. France appears to be the only exception where a positive effect may be conceivable. However, TFP growth in services has also weakened over time, suggesting that the increased multiplier effect may at times have merely offset these developments.

Sectoral structural change affects aggregate productivity growth through various channels, including changes in input-output linkages. As a result of structural change, TFP gains or losses in the manufacturing sector have a markedly smaller impact on aggregate labour productivity today than the they did around 50 years ago. By contrast, efficiency developments in the services sectors now play a more prominent role in shaping aggregate labour productivity. A key factor behind this is the evolution of input-output linkages across sectors. Together with sectoral TFP developments, these linkages determine aggregate productivity developments.

In addition to the slowing TFP growth rates in manufacturing, efficiency losses in the services sectors are key to why structural change is subduing labour productivity growth in the euro area and point to potential economic policy levers. The shift towards a service-based economy and the associated long-term transformation of the economic structure can only be influenced to a limited extent by economic policy – and generally should not be. The picture is different for sectoral efficiency developments. As services’ share of the economy has grown over time, bolstering TFP growth in this sector is a particularly important objective. The comparison with the United States points to considerable potential in this regard. Over the past two decades, average TFP growth in the services sector has been comparatively strong there, not least due to the strong momentum in the information and communication sector.

The rapid and broad diffusion of technological innovations, including applications for artificial intelligence (AI), has a central role to play in driving efficiency in the services sectors. Bundesbank analyses for the major euro area countries and the United States show that the productivity-enhancing impact of digitalisation depends, to a large extent, on how widely digital intermediate inputs are used in the various sectors of the economy. 34 Technologies that can be deployed as intermediate inputs across a broad range of sectors exhibit particularly strong multiplier effects. Generative AI could represent such a key technology. Recent Bundesbank surveys of German enterprises indicate that the use of generative AI has increased markedly in recent years (see Supplementary information “Generative AI in German enterprises: adoption, costs and expected economic impact”). Adoption is particularly widespread in the services sectors. Here, the majority of firms surveyed expect noticeable productivity gains, underscoring the potential of a broad-based application of this technology.

A favourable economic and regulatory framework is needed to exploit potential efficiency gains. Efficient market structures, strong competition, open and integrated goods and services markets, access to financing and a reduction in regulatory fragmentation are fundamental to aggregate productivity growth, not only in terms of the rapid diffusion of new technologies. 35 Against this backdrop, further deepening and harmonising the European single market is a key economic policy objective and one that may help to realise economies of scale and strengthen innovation incentives. 36 Bundesbank studies also demonstrate the importance of smooth market entry and exit for firms for aggregate labour productivity. Many advanced economies, including in the euro area, have seen business dynamism decline in recent decades, however. 37

In addition, investment in digital infrastructure, data access and training is crucial to effectively integrating technological innovations into existing production networks. 38 Only with the interplay of technological innovation, well-functioning markets and cross-sectoral linkages will it be possible to leverage structural change and its impact on transmission mechanisms for the purpose of achieving lasting improvements in aggregate productivity growth.

The analysis of sectoral structural change and its implications for labour productivity growth draws on several complementary international datasets. Taken together, they allow us to consistently capture long-run changes in the structure of the economy, including input-output linkages, as well as sectoral dynamics in TFP. The data are used to both inform the descriptive analysis and to parametrise the multi-sector DSGE model MuSe.

We use data from the EU KLEMS database, the Long-run World Input-Output Database (LR-WIOD) and the OECD Inter-Country Input-Output (ICIO) database. The EU KLEMS database contains sectoral data on gross output and the use of labour, capital and intermediate inputs in nominal and real terms. It enables the computation of factor intensities and the intermediate input intensity of gross output, which we use to calibrate the MuSe model and for the descriptive analysis. For this study, we splice the EU KLEMS datasets from 2009 and 2023 to cover the period from 1970 to 2020. 39 LR-WIOD data for the period from 1970 to 1995 and OECD-ICIO data for the years from 1996 to 2020 are used for the parametrisation of production linkages and final demand. 40 Combining these datasets enables us to capture changes in sectoral production networks as well as in consumption and investment demand over five decades. In addition, these sources are used to compile descriptive statistics on gross value added, the structure of consumption and investment, and input-output linkages.

To bridge methodological discontinuities between the datasets, we splice the data using growth rates. 41 Combining absolute figures from different datasets can create discontinuities in the time series. We therefore use the growth rates from the LR-WIOD database to extrapolate the time series backwards, starting from the absolute figures in the OECD ICIO data for 1996. 42 Combining different data sources can disrupt the accounting identity between gross output, intermediate inputs and value added. The input-output tables are therefore adjusted so that a sector’s gross output corresponds to the sum of inputs used and gross value added. This is achieved using a proportional scaling procedure (“one-sided RAS”). 43 Within each sector’s intermediate input use column, all entries are scaled proportionally by a common factor so that the sum of the column matches total intermediate inputs used, while the relative shares of the individual intermediate goods in the sector’s total inputs remain unchanged.

The underlying data sources use different industry classifications. NACE (Nomenclature générale des activités économiques dans les Communautés européennes) is the European standard classification of productive economic activities. It provides a systematic framework for classifying economic activities and is used to assign statistical units to industries based on their principal activity. Between 2000 and 2007, the NACE classification was fundamentally overhauled. The version resulting from this (NACE Rev. 2) replaced NACE Rev. 1.1 and applied from 2008 to 2024. Concordance tables are used to align the data based on NACE Rev. 1.1 with the NACE Rev. 2 classification. 44  

The maximum level of sectoral detail that can be represented in the MuSe DSGE model is determined by the LR-WIOD database, which in some cases reports only aggregated sector groups rather than individual industries. Accordingly, MuSe distinguishes eight economic sectors: 1) agriculture, forestry and fishing, 2) mining and quarrying, 3) manufacturing, 4) electricity, gas, steam and air-conditioning supply, water supply, sewerage, waste management and remediation, 5) construction, 6) wholesale and retail trade, repair of motor vehicles and motorcycles, transportation and storage, and accommodation and food service activities, 7) information and communication, financial and insurance activities, real estate activities, professional, scientific, technical, administration and support service activities, and 8) public administration, defence, compulsory social security, education, human health and social work activities as well as other services. 45 Relative to the descriptive analysis, this finer sectoral resolution allows the model to capture sector-specific developments more precisely. 46 The extent of heterogeneity in structural change within services becomes apparent, for example, in the shifts in sectors’ shares of total hours worked between 1970 and 2020. Both quantitative and qualitative differences between the individual sector groups are evident in some cases. Ignoring such heterogeneity can distort the interpretation of macroeconomic developments. 47

The model-based analysis is restricted to the four largest euro area economies and the United States, whereas the descriptive analysis also covers additional advanced economies. In addition to the four largest euro area countries and the United States, input-output datasets (LR-WIOD and OECD-ICIO) include Australia, Austria, Belgium, Canada, Denmark, Finland, Japan, Korea, the Netherlands, Portugal, Sweden and the United Kingdom. Alongside the four largest euro area economies and the United States, the EU KLEMS database provides data for Austria, Belgium, Denmark, Finland, Japan, the Netherlands, Portugal, Sweden and the United Kingdom. 

EU KLEMS data are also used to estimate sectoral TFP trajectories. We infer sector-level TFP using standard growth accounting of gross output growth. 48 It should be noted that the resulting sectoral TFP dynamics may differ from gross value added-based approaches, as illustrated by the two series for Germany. 49 Whereas the value added perspective does not take into account intermediate inputs as a separate factor, the gross output perspective used here explicitly includes the use of intermediate inputs as a factor of production. Under standard assumptions, 50 there is a mechanical relationship between the two measures: The growth rate of gross value added-based TFP approximates the output-based TFP, scaled by the factor \( 1/(1-S_M) \), where \( S_M \) denotes the share of intermediate inputs in gross output. The greater the intermediate input intensity, the more the two TFP measures may differ. Since the scaling factor is greater than one, changes in gross value added-based TFP are typically more pronounced than those in gross output-based approaches. This means that gross value added-based time series often exhibit larger short-term fluctuations. Changes in the share of intermediate inputs – for example, due to increased division of labour, outsourcing or technology-driven shifts in the production structure – directly affect measured sectoral efficiency in gross value added-based approaches. In the gross output perspective, meanwhile, they are explicitly reported as factor contributions. The gross output perspective is particularly well suited to analysing intersectoral production linkages, as it attributes changes in the use of intermediate inputs not to TFP but to the corresponding input contributions. The choice of output definition thus systematically influences both the level and dynamics of sectoral TFP time series.

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