Chapter 1. Assessing the links between business dynamics and policy settings

This chapter presents an overview of the chapters included in this volume and relates them with the cross-country evidence gathered by the OECD Dynemp project, highlighting common trends and differences. Based on analyses of data from Belgium, Brazil, Canada, Costa Rica, Japan, the United Kingdom, Norway and New Zealand, the studies illustrate how firm characteristics (age, size, sector) and economic conditions (market conditions, stage in the business cycle and in economic development) affect employment growth, firm performance, resource allocation and productivity growth. The results shed new light on the important role played by the recent global financial crisis on OECD countries and emerging economies.



Business dynamics is an important driver of job creation and productivity growth. A growing body of evidence shows large differences in business dynamics across countries and over time, in particular over the business cycle. However, our understanding of these differences remains limited, adversely affecting efficient policy design. The purpose of this book is to fill this gap by providing new evidence from countries at different stages of development and of different sizes. It also tries to provide evidence of firms’ heterogeneous responses to shocks and to investigate the impact of one of the biggest shocks that firms have had to face in the last decade: the global financial crisis. The hope is that the evidence collected in this volume will help readers to better understand business dynamics and the heterogeneous impact of policies and framework conditions across different firms and countries and help policy makers to design better policies that can foster both employment and productivity growth.

The book is part of a larger effort by the OECD Directorate for Science, Technology and Innovation to provide new evidence on employment dynamics and productivity across countries exploiting firm-level data. As part of the same strand of work, the OECD is leading two projects, DynEmp and MultiProd, that have relied on countries’ confidential micro-data to conduct comparable cross-country analysis on employment dynamics and productivity, respectively (see also and The two OECD projects collect and analyse harmonised cross-country micro-aggregated data from administrative data or official representative surveys, such as business registers, social security and corporate tax records or national statistics offices’ surveys of production. Both projects rely on the active participation of a network of national experts who have access to the relevant micro-data sources in the respective countries.

The value of such exercises rests on the ability to assess the effects of different policy settings on firm-level outcomes. On the one hand, country-specific studies are often constrained by the relatively limited variance of policy settings (except in limited cases such as in federal systems). On the other hand cross-country studies which focus on outcomes at higher levels of aggregation cannot capture the heterogeneity of responses of different actors to the same policy settings. The OECD has a particularly important role to play in helping to bridge this gap. This approach offers a unique opportunity for creating longitudinal data that go beyond cross-sectional cross-country comparisons or industry-level statistics. The projects can generate data designed to answer specific policy questions and at different sectoral or geographical levels. The program codes are modular and can easily be modified to allow for additional categorisation of the micro-data according to firm characteristics not considered in previous analysis (e.g. for ownership or trade status).

In fact, while considerable progress has been made in recent years in providing researchers with secure access to official micro-data on firms at country level, significant obstacles remain in terms of transnational access. The challenges of transnational access are many, starting from locating and documenting information on available sources and their content (i.e. coverage, variables, classifications, etc.) and on accreditation procedures (i.e. eligibility, rules, costs and timing). There are language barriers, as translated versions of information on data and accreditation procedures seldom exist or are incomplete. In addition, completing country-specific application forms for accreditation procedures is often demanding and different procedures exist for data held by different agencies even within the same country. Finally, data access systems differ across countries, implying that while remote access or execution could be possible in some countries, in others it is only possible to access on site, requiring researchers to travel to the location in question. These are just some of the challenges related to accessing data, before researchers can even begin confronting differences in the content and structure of micro-data themselves, and the time and human capital investment required to become acquainted with the “nitty gritty” of each database.

As a result, multi-country studies requiring the exploitation of micro-data are very difficult to conduct, and often rely on the formation and co-ordination of networks of national researchers, with each team having access to their respective national micro-data. The comparability of the country level results needs therefore to be insured via the use of a common protocol for data collection and aggregation and a common model specification for the econometric analysis.

This methodology followed in the DynEmp and MultiProd project is called distributed micro-data analysis, which involves writing a computer code by OECD and then running this code in a decentralised manner by representatives in national statistical agencies or experts in public institutions who have access to the national micro-level data, who have access to the national micro-level data. At this stage, micro-aggregated data is generated by the centrally designed, but locally executed, program codes, which are then sent back for comparative cross-country analysis to the OECD. These data reduce confidentiality concerns as they aggregate information at a sufficiently high level, and achieve a high degree of harmonisation as the definition of the extracted information is the same, ensured by the centrally written computer routine.

Despite a few instances when a similar approach has been used in the past – in academic circles as well as within the OECD, the World Bank and more recently the European Central Bank – this procedure is still not widely applied today when collecting statistical information. This may have to do with the amount of time needed to set up and manage the network as well as developing a well-functioning, “error-free” program code which is able to both accommodate potential differences across national micro-level databases and minimise the burden on those who have access to the data and run the code.

The DynEmp project is based on a distributed data collection exercise aimed at creating a harmonised cross-country micro-aggregated database on employment dynamics from confidential micro-level sources. The primary sources of firm and establishment data are national business registers and for some countries, such as Brazil, social security records. The first phase of the project was implemented in the first half of 2013 and was called “DynEmp Express”. This first phase was based on a simplified statistical routine which led to the collection of a database covering 18 countries. The second phase of the project, called DynEmp v.2, aims at building a database which contains more detailed data on the within-sector contribution of start-ups and young firms to employment growth, with the aim of analysing the role played by national policies and framework conditions for employment growth (see e.g. Calvino, Criscuolo, and Menon 2015). At the time of writing, 22 countries have been successfully included in the DynEmp v.2 database (Australia, Austria, Belgium, Brazil, Canada, Costa Rica, Denmark, Finland, France, Hungary, Italy, Japan, Luxembourg, the Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, Turkey, the United Kingdom and the United States).

The advantages of using harmonised micro-aggregated data from business registers for the study of business dynamics and from official surveys for productivity analysis are manifold. First of all, for the study of business dynamics the cross-country use of business registers allows separate identification of the different channels of employment growth, distinguishing between gross job creation and gross job destruction, and between the extensive (firm entry and exit) and the intensive (post-entry growth) margins. Furthermore, the role of firm age and size can be examined separately and jointly. Finally, each of these elements can be compared across countries, sectors and over time. Similarly, when analysing productivity, being able to use official survey data that cover an (often stratified) random sample of firms over time and can be reweighted using business registers in order to be made representative allows for the reliable and comparable analysis of productivity distributions; the description of trends in productivity dispersion over time, etc. and the estimation of entry and exit contribution to growth, and so on.

However, the DynEmp and MultiProd projects, by their very nature and in order to ensure comparability, rely on an approach based on a minimum common denominator where both the policy questions are of interest and the data needed to answer those questions are available in the large majority of participating countries. For this reason, this volume leverages on the great expertise of the network members for the chapters in the book to push the boundaries that contain the DynEmp project in three directions: policy questions; methodology and data needs. The rest of this introduction will therefore provide an overview of the different contributions, highlighting where they confirm evidence found in the cross-country analysis and where they are novel in terms of methodology and/or findings. This introduction will, hopefully, provide a good account of how each of the chapters of the book contributes to building a solid evidence base for policy making.

Going beyond the average firm paradigm

The importance of the process of creative destruction and of post-entry growth and the ability to document these processes are pointing to the paramount importance of going beyond the average firm paradigm and embracing firm heterogeneity in the analysis of business dynamics. All chapters in the book are examples of how looking at firms characteristics, such as size, age, ownership and trade status are important in shedding further light on the process of job creation and destruction in the economy.

Chapter 2, by Danilo Coelho, Carlos Henrique Corseuil and Miguel Nathan Foguel from the Instituto de Pesquisa Econômica Aplicada (IPEA), analyses the statistical patterns of employment dynamics across establishments with different characteristics (e.g. size) and at different points in their life cycle.

The chapter studies employment dynamics in the Brazilian formal sector using Relação Anual de Informações Sociais (RAIS) data, which is a survey of all formal establishments in Brazil collected by the labour ministry, Ministério do Trabalho e Emprego (MTE) containing information on wages, workers and employers’ characteristics, from 1995 to 2013. The analysis provides new empirical evidence on two main issues. Firstly, the authors examine in detail the firm-level employment distribution by age and size class separating age, cohort and macro effects and the role of exit thanks to a decomposition method first proposed by Deaton and Paxson (1994). Secondly, the authors assess the extent to which the Brazilian formal sector suffers from a “missing middle” issue, i.e. to what extent it is characterised by lower employment shares in the middle part of the firm and employment distribution. The evidence confirms the existence of a “missing middle” problem in the Brazilian manufacturing sector, and its magnitude appears larger than in other countries in Latin America and Asia for which comparison is possible.

In order to provide evidence on this issue the authors focus on the growth performance of small establishments at entry: the results confirm evidence found for other countries in the DynEmp project, as outlined in Figures 1.1and 1.2, respectively: younger businesses are characterised by both disproportionally high employment growth rates and higher exit rates (particularly within the first three years of their lives).

Figure 1.1. Young firms have higher employment growth rates

Notes: Notes: Figures report the average employment growth index by country and age class. Units with missing age classes are not reported. Figures report yearly averages conditional on the availability of data. The sectors covered are: manufacturing, construction, and non-financial business services. Owing to methodological differences, figures may deviate from officially published national statistics.

Source: Source: OECD (2016b), DynEmp v.2 database, Data for some countries are still preliminary.

Figure 1.2. Average employment level by firm age

Notes: Notes: The graph reports the age coefficients of the exit “distributed regression” (which has as response variable an exit dummy and as explanatory variables age, size, three-digit sector and year dummies). Regression coefficients are normalised by country (subtracting the country minimum value and dividing by the country maximum value) and then averaged out across available countries. Norway has been excluded due to ongoing checks on unusual dynamics in the underlying data. Exit regressions are not available for the United Kingdom. Firm age is reported on the horizontal axis (1 to 8 years old). Owing to methodological differences, figures may deviate from officially published national statistics.

Source: Source: Calvino, Criscuolo and Menon (2016a), “No country for young firms?: Start-up dynamics and national policies”,, based on the OECD DynEmp v.2 database, Data for some countries are still preliminary.

The results for Brazilian businesses show that employment dynamics are mainly driven by pure age effects, with cohort and macro effects playing a limited role. Small establishments are born very small, and are able to exhibit high growth rates at the beginning of their activity, but they do not grow enough to increase their scale to that of mid-sized establishments and tend to die early.

This evidence is in line with cross-country evidence from the DynEmp project showing that average size at entry in most countries is not higher than six (See Figure 1.3). Also most micro-firms (i.e. firms that start with less than ten employees) remain micro even after five years after entry, while a very tiny proportion grows beyond the ten employee threshold and account for most of the job creation from this group of firms (Figure 1.4).

Figure 1.3. Across countries the average start-up employs less than ten employees

Notes: Notes: The graph shows the average size of surviving entrants expressed as total employment of surviving entrants over number of surviving entrants. Figures report the average for different time periods t = 2001, 2004 and 2007, conditional on the availability of data. Sectors covered are: manufacturing, construction, and non-financial business services. Owing to methodological differences, figures may deviate from officially published national statistics.

Sources: Sources: Calvino, Criscuolo and Menon (2016), “No country for young firms?: Start-up dynamics and national policies”,; OECD (2016b), DynEmp v.2 database,

Figure 1.4. Most micro start-ups remain micro-firms five years after entry

Notes: Notes: Figures report the average for different time periods t = 2001, 2004 and 2007, conditional on the availability of data. Panel A represents the share (in terms of number of units) of micro (0-9 employees) entrants at time t by their size class at time t + 5. Panel B represents the net contribution to aggregate flows (defined as net job creation by the group over the sum of the net job variations by all groups in absolute values) for micro (0-9 employees) entrants at time t by size class at time t + 5. Size classes are aggregated as follows: stable (0-9 employees), growing (10 or more employees) and exiting units. Firms with missing employment at the beginning or at the end of the period are excluded. The sectors covered are: manufacturing, construction, and non-financial business services. Owing to methodological differences, figures may deviate from officially published national statistics.

Source: Source: OECD (2016a), “No country for young firms?”, based on the OECD DynEmp v.2 database,

Chapter 2 shows that these patterns lead to a “missing middle” in the plant size distribution in Brazil. The problem can be the result of a large set of factors such as entry costs, the tax system, the level of development of financial markets, the regulatory environment, and the scale and composition of market demand.

The chapter provides two very interesting methodological contributions: the first is the use of the Deaton Paxson decomposition (Deaton and Paxson, 1994) to isolate pure age effects from period-specific shocks and birth cohort idiosyncratic characteristics. The methodology also corrects for a composition effect towards larger establishments arising over time due to the higher exit probability of start-ups. Secondly the authors follow the methodology first proposed by Tybout (2014) to uncover the presence of a missing middle in the size distribution of plants, based on a comparison of the observable plant size distribution with a Pareto one, i.e. analysing differences between empirical and theoretical distributions. These could be interesting avenues for methodological extensions of the next phase of the DynEmp project.

As in many other countries, the segment of micro and small establishments has received a great deal of attention from public policy in Brazil which has a large and diversified institutional framework to support this type of establishment: programmes that provide credit at low interest rates and credit guarantees to micro and small establishments, tax subsidies for establishments whose revenues are below a defined threshold, a large programme of government procurement targeted at micro and small establishments, and training courses and technical assistance dedicated to helping potential entrepreneurs and already established small businesses to improve their operations. The chapter draws attention to the fact, as often happens, that the programmes have not been evaluated, and so their effectiveness and the optimality of their design remains unknown.

Organic versus non-organic growth

For policy makers it is important to understand how firms grow; i.e. whether they grow through the creation of new jobs within the firm (organic growth) or whether their expansion relies on acquisition of, or merger with, existing firms (mergers and acquisitions [M&As] non-organic growth). The two types of growth strategies at the micro level translate to very different outcomes at the macro level: while organic growth translates into an increase of aggregate employment figures, non-organic growth might translate, at best, into no change and, in the case of consolidation between firms, in a decrease in aggregate employment growth. Policy concerns have been raised especially over the potential negative impact of foreign acquisitions on domestic employment in host countries. In addition, if the M&A activity results in an increase in market power, rents and super-normal returns on capital, M&As might have a negative impact on aggregate economic growth, and possibly, to rising inequality.

Unfortunately, data that allow for the distinction between organic and non-organic growth are rarely available. Thus, the contribution of Chapter 3 is very important for building the evidence base in this area. Contributed by Michel Dumont, Chantal Kegels, Hilde Spinnewyn and Dirk Verwerft from the Federal Planning Bureau, it assesses the role of M&As in employment dynamics in Belgium and investigates whether the distinction between organic versus non-organic growth matters for analyses of job creation using data from Zephyr (Bureau van Dijk) between 2001 and 2014 linked with additional sources of data on employment and other firms’ balance sheet information.

The results suggest that concerns about the negative employment effects of foreign acquisitions are not warranted for Belgium. Domestic acquisitions and intra-industry acquisitions are found to have negative effects on employment in target firms, which are partially offset by job creation in Belgian acquiring firms. But the acquisition of a Belgian target by a foreign firm actually appears to have a positive impact on employment in the Belgian target in the case of inter-industry acquisitions, i.e. when the foreign acquirer does not belong to the same industry as the Belgian target firm.

Thus, for the small group of Belgian firms that are active in M&A deals, acquisitions seem instrumental in achieving high growth, but young Belgian firms appear to be less inclined to acquire other firms than young foreign firms to acquire domestic firms. An interesting policy research agenda is improving our understanding of the causes of the low involvement of Belgian firms in M&A deals, and in particular to what extent financing constraints or share ownership and corporate governance rules play a role in explaining these patterns.

The impact of the crisis on employment stocks, flows and business dynamics

The DynEmp project provides some interesting evidence of business dynamics over the cycle and in particular during the crisis. As shown in Figure 1.5, prior to the crisis, the gap between job creation and job destruction was comparatively small, reflecting a Schumpeterian process of creative destruction which reallocates employment from firms destroying jobs to firms creating jobs. The 2008 economic crisis had a significant impact on this process, resulting in a sharp increase in gross job destruction and a drop in gross job creation. This gap contracted only partially over the 2009-10 biennium, with creation and destruction rates eventually aligning to pre-crisis levels during the following two-year period. Evidence from Blanchenay et al. (forthcoming) shows that only a few countries have shown a strong resilience to the financial crisis and a cohort-level analysis suggests that the effects of the crisis on start-ups are probably long-lasting: entrants and small firms of the “crisis” (year 2007) cohorts are still situated on a lower growth path five years later relative to those of the “boom” (year 2001) cohort.

Figure 1.5. Job creation, job destruction and churning rate

Note: Note: Notes available at

Source: Source: OECD (2015), OECD Science, Technology and Industry Scoreboard 2015: Innovation for growth and society,, based on the OECD DynEmp v.2 database,

Chapter 4, by Michael Anyadike-Danes and Mark Hart, investigates employment dynamics in the United Kingdom, using a firm-level longitudinal database that covers the whole private sector, based on the United Kingdom Business Register from 1998 to 2014. Unlike most of the existing literature, this analysis focuses on the dynamics of the stock of firms and jobs – taking advantage of a simple but effective decomposition framework – separately focusing on births, deaths and “continuing” firms. The analysis highlights the important role of variation in firm births, especially over the Great Recession period, and the relatively limited variation in average jobs per firm. In fact the paper highlights that the impact of variations in births on job dynamics is not only immediate but has an impact which persists over many years. The global financial crisis’ (GFC) distinctive feature was a collapse in business entries, which fell by about 25% between 2008 and 2009, causing a “loss” of a quarter of a million jobs. This drop in births affected the stock of continuing firms in the following year leading to a drop in the stock of firms (60 000 firms) and a loss of 170 000 jobs. Thus the drop in entry during the GFC was evident in the following years with additional job losses until 2012 when the recovery started.

The main policy message of this chapter is that “today’s start-ups are tomorrow’s continuing firms”. This message becomes evident thanks to the use of the new approach based on stocks of jobs and firms rather than flows. This approach can lead policy makers and researchers to look at policies from a different perspective, especially in focusing on interventions which improve firms’ chances of survival and their prospects for growth, particularly when targeted at young firms.

Chapter 5, by Richard Fabling and David Maré analyses the dynamics of employment adjustments during the 2000s, with a special focus on the aftermath of the global financial crisis, which was initially less severe in New Zealand but was more prolonged than elsewhere. The authors use a comprehensive firm-level database that spans from 1999 to 2010. The data show that the country’s labour market is characterised by considerable heterogeneity across firms, both before and after the crisis, in the size of output shocks, the amount of employment adjustment in response to any given output shock faced by firms, and in the size of worker flows resulting from the adjustment.

The analysis highlights that the crisis affected the distribution of output shocks and altered the relationship between output shocks. It also caused changes in job and worker flows and levels of employment and wages. For any given output shock and change in net employment growth, worker, job turnover rates and wage growth were lower during the crisis. This could suggest that workers might have been willing to accept lower wages and might not have changed jobs during the crisis as they were worried about not finding suitable alternatives. This in turn might have worsened the outlook for those who were out of the job market or had lost their jobs. The authors argue that this might point to the importance of using active labour market policies for the latter group of workers, (e.g. young workers or workers temporarily out of work) or increased generosity of unemployment benefit levels during recessions to help to fund extended job searches.

A particularly interesting point raised by the chapter is the link between labour market policy settings and the level of resilience to crises of different intensities and natures, e.g. cyclical shocks versus shocks that require a reallocation of employment across industries, occupations, or regions. A labour market that is resilient to cyclical shocks, thanks to smoothing policies, such as long-term contracts, unemployment insurance and benefits, or active labour market policies, may not be suited to respond to shocks that require significant reallocation of employment, which would rather require a policy mix that facilitates retraining, job turnover and reallocation, and geographic and industry mobility.

New Zealand’s labour market institutions favour flexibility and work incentives, and offer relatively light levels of support for those out of work. Thus, the financial crisis should have been characterised by lower worker flows (which is evident in the data), and higher firm exit rates, which is not supported by the data, perhaps because this margin of adjustment is typical of very small firms (less than three employees and working proprietors only) that are excluded from the analysis.

In future work the analysis already conducted using the DynEmp data – e.g. in Blanchenay et al. (2016) – could be expanded to analyse the role of different labour market policy mixes in explaining different labour markets’ responses to the GFC across countries and further investigate the existence of cleansing or scarring effects following the GFC across different countries.

The role of sectors, ownership and trade status for job creation and destruction and business dynamics

Similarly to Chapters 4and 5, Chapter 6 – by Catalina Sandoval, Francisco Monge, Tayutic Mena, Arlina Gómez and David Mora from the Ministry of Foreign Trade – looks at adjustments of the labour market after the GFC in Costa Rica, where the structural unemployment rate experienced a significant upward shift of three percentage points following the crisis.

Increased unemployment probably reflects structural changes in the economy and a mismatch between labour supply and demand. Unlike other countries considered in this volume and in the DynEmp project, Costa Rica is still characterised by high shares of employment in manufacturing and agriculture. At the same time, the country is increasingly transforming into a service-oriented economy, is integrating in global markets via international trade and foreign direct investment, and labour demand is growing in higher value-added services and advanced manufacturing. Agriculture is slowly reducing its absorption capacity of low-skilled labour, which still represents a large share of employment in this sector.

Chapter 3 already raised the importance of distinguishing firms not only according to their age and size, but also according to their ownership. Chapter 6 extends the analysis to Costa Rica to differentiate across firms according not only to their ownership but also to their different trade status and their sector of activity. In fact business dynamics for firms of different ages and sizes differ significantly whether the focus is on manufacturing, services or agriculture, and whether these firms are engaged in international trade or not. Micro, small and medium-sized enterprises (MSMEs) that export are growing faster than those that focus on the domestic market; and those operating within the Free Trade Zone regime are also more likely to experience positive growth than firms outside the regime. But 93% of exporting MSMEs in the services sector increased employment, while in agriculture and manufacturing, this share was only 35%.

Business dynamics, reallocation and productivity

Business dynamics is at the heart of the policy debates because it often reflects a healthy process of Schumpeterian creative destruction which is conducive to long-term growth. The idea is that business entry and exit and job churning mirror the economy’s ability to reallocate resources from less to more productive firms, and policies should aim at fostering this reallocation process or at least not to interfere with it while at the same time providing safety nets and compensatory measures that alleviate the costs for those who lose during the reallocation process.

Chapters 7,  8and 9 extend the analysis of DynEmp in two directions.

Chapter 7, by Jay Dixon, looks at which measures of growth are more likely to be correlated with subsequent growth. DynEmp, like many other studies, is limited by data availability and can focus for most countries only on one dimension of growth, i.e. in terms of employment, even though for a few countries the analysis could be extended to sales, and sales per employee. The work of Dixon highlights the importance of looking at several dimensions of growth at the same time, in employment, sales, profits and productivity, to capture the dynamic and complex process of firm growth, and understand firms’ future growth prospects.

The authors focus on continuing firms in three aggregate industry sectors (construction, manufacturing and services) for 2000-13 and on the correlations at leads and lags between the growth rates of employment, assets, sales, profits and labour productivity relying on quantile regression, and comparing firms at the median, at the 10th and 90th percentiles, to investigate asymmetries in the growth distribution. The authors also focus on growth patterns across firms of different sizes and ages to provide a more comprehensive picture of the growth process in Canada to policy makers.

One nice feature of the Canadian analysis is that organic growth can be identified, thanks to a unique database called LEAF developed by Statistics Canada. These data allow the authors to show that there is very little organic growth for the median firm according to all outcome variables considered. High growth in employment, profits and labour productivity does not seem to be a persistent feature of a firm: above-median growth performance (i.e. positive growth) of employment, profits and labour productivity in one year is likely to follow or come before periods of negative growth.

However, growth in sales appears to drive subsequent growth not only in sales but also in all other variables, such as employment; this pattern suggests that demand shocks for firms’ products play a significant role in firm’s decision and capability to grow. Profits present a similar pattern but at a much lower level. When focusing on different firm size and age categories profits seem to have a comparatively greater effect on growth for smaller and younger firms, although the effect is still small.

The importance of increases in sales for subsequent growth suggests that opening markets and improving access to consumers may be important goals for policy makers seeking to foster firm growth and increase productivity growth, especially for small firms.

Chapter 8, by Arvid Raknerund and Diana-Cristina Iancu from Statistics Norway proposes a new decomposition of employment and labour productivity growth, that extends standard methods to identify three main sources of productivity growth: within-firm productivity growth, between-firm reallocation effects and entry/exit dynamics. The analysis is based on Norwegian firm-level registry data covering all incorporated firms in the Norwegian mainland economy from 1996 to 2014.

The decomposition allows for quantifying the contributions to employment and labour productivity growth from different sectors and from different firms (e.g. small and medium-sized enterprises (SMEs) versus large firms and exporters versus non-exporters).

The data uncovers a strong downward trend in labour productivity during the last decade: with the growth in real value-added per employee dropping from an annual 3.2% during 1996-2004 to an annual 1.3% during 2005-14, confirming a downward trend in Norwegian labour productivity also found in other OECD countries (OECD, 2015).

The authors find that the decline in productivity growth observed at the beginning of the 2000s (2002-07), might be partly related to the strong employment growth via entry of new firms in some services sectors with low initial productivity levels, such as administrative and support services of new firms, that are on average less productive than incumbents. While the results can only partially explain the strong decline in productivity growth observed in Norway in the last decade they highlight an important channel through which employment dynamics can affect productivity growth of a country. The results presented in the chapter suggest that a possible explanation for the productivity slowdown is to be sought in the diminishing role of manufacturing and the growing role of wholesale and retail trade and information and communication as drivers of productivity growth in the mainland Norwegian economy.

The chapter also uncovers some other interesting differences with regard to in the contribution to the sources of productivity and employment growth: e.g. large continuing firms (> 50 employees) are the main contributors to labour productivity growth, but they contribute little, and even sometimes negatively, to employment growth, with new jobs being created mainly by entering firms and SMEs. The latter result confirms that the results of the DynEmp project are robust across many countries. Similarly notable differences also arise when comparing the contribution of exporting and non-exporting firms to productivity and employment growth: exporters contributed during 2002-10 at least as much to aggregate productivity growth (in percentage points) as to employment growth in all periods, whereas non-exporting firms tended to contribute gradually less to productivity growth but remain the largest contributors in terms of employment growth.

Finally, Chapter 9, by Kenta Ikeuchi addresses the empirical question on the cleansing effects of economic crises focusing on the last 20 years of the Japanese economic cycle. The chapter provides a very interesting overview of the relevant literature focusing on the debate related to the impact of economic crises on business dynamics, reallocation and productivity and highlighting the different empirical results from this strand of research, focusing on existing evidence of the Japanese experience.

Having put his analysis into a broader context, the author then considers the effect of four crisis periods in Japan over the 1980-2012 period on the labour market and on productivity. The evidence uncovered in the chapter shows that during the crises, labour inputs and productivity decreased sharply; in the economic recovery periods following the crises, while productivity increased, labour inputs did not increase. In fact, the labour input composition remained strongly affected by the downturn, with demand for self-employed and regular workers diminished by the crises and demand for part-time workers increased.

The results go further in analysing the effect of the crisis on firm-level within-industry reallocation effects exploiting a comprehensive panel dataset of Japanese listed companies. During the period from 1980-2012, the reallocation of labour inputs was productivity-enhancing in Japan. In line with evidence from the United States, the results show that only in the case of the global financial crisis the productivity-enhancing reallocation mechanism was not strengthened by the downturn. All previous economic crises had reinforced the productivity-enhancing reallocation mechanism, in both the manufacturing and non-manufacturing sectors.

Interestingly, the author points to the global nature of the global financial crisis (GFC) as a possible explanation for this result for Japan. The GFC resulted in a fluctuating global financial market and ultimately in a sharp decline of net Japanese exports. This disproportionally affected more internationalised firms, which are also the most productive in the economy. The chapter concludes with a very interesting suggestion for future research in this area: a comparably rich international dataset that is, for employment and productivity dynamics, linked to global value-chain data. This could be an interesting avenue to pursue in the next phases of the DynEmp and MultiProd projects.


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