Smart Public Intangibles
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Grant agreement ID: 612774
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INSTITUTO VALENCIANO DE INVESTIGACIONES ECONOMICAS, S.A.
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Identifying the ingredients of economic growth
Grant agreement ID: 612774
1 December 2013
30 November 2016
€ 3 260 536,40
€ 2 497 762
INSTITUTO VALENCIANO DE INVESTIGACIONES ECONOMICAS, S.A.
This project is featured in...
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Final Report Summary - SPINTAN (Smart Public Intangibles)
The SPINTAN project aimed at discovering the theoretical and empirical underpins of public intangible policies complementing an ever growing literature on intangibles in the market sector. The project had three strands – measurement and conceptual issues; construction of a set of database around public intangibles; and analytical research on various aspects of that performance. The overall research purpose of the project has been to identify and measure public sector intangible investment and capital services, to evaluate its role as a driver of firm-industry-country economic growth and to provide new insights to the innovation policy agenda about the key role of public sector knowledge creation.
Measuring intangible capital in the public sector, edited by Corrado, Jäger and Jona-Lasinio (the Manual from now on) sets out a number of conceptual issues on how to measure intangibles in the public sector. It extends the private sector classification due to Corrado, Hulten and Sichel (2005) to the public sector and discusses a measurement framework within the scope of GDP that enables new empirics on the evolution of productivity and living standards, as well as the analysis of policies supporting economic growth through public intangible investments. The aim is to construct a database that:
(a) disaggregates industries of interest by institutional sector,
(b) includes data on external funding of R&D performed by private enterprises,
(c) imputes a net return to government capital and
(d) uses industry capital compensation measured to include all public payments
It has been constructed a database for public intangible investment and capital services for 22 European countries, the US, China, India and Brazil over the period 1995-2013. The estimates are harmonized cross-country measures coherent with National Accounts Principles (System of National Accounts (2008 SNA). Its availability allows completing the coverage of intangible investment by industry making possible the analysis of productivity for the total economy based on a complete accounting of intangible capital inputs.
The project has addressed other important issues with implications from the political standpoint. For example, the results corroborate that the recent 2008/2009 financial and economic crisis had a severe and still lasting impact on the ability of governments to invest into R&D, increasing heterogeneity within the EU. The results also show that public investment should not only focus on the classical tangible assets, but also on intangible capital. An excessive focus on one specific input category, say tangible capital, will not lead to the expected results because the other inputs are also required, such as intangibles, in order to achieve the maximum output possible. Another interesting result is that there are substantial efficiency differences in research activity across countries. The results suggest that there is a wide margin for the EU to substantially increase research output of the European HEIs without having to assign additional resources, lower the quality or change the field of science. These are just a few examples of a set of interesting contributions developed within the SPINTAN project.
(1) Available at http://www.spintan.net/manual-and-reports/
Project Context and Objectives:
The point of departure of the project was the recognition that intangible investments are a major determinant of innovation, growth and employment in the “knowledge economy”. Endogenous growth models had already emphasized knowledge and skills as important intangible assets and stressed the role of intangibles in generating persistent growth. The importance of R&D and innovation is also explicitly recognized in the “Lisbon process”, including the current Europe 2020 agenda, aimed at improving the growth and employment performance of the EU. However, our understanding of the contribution of intangibles assets to economic performance remained incomplete since the vast majority of the research undertaken previous to SPINTAN was confined to the business sector of the economy, ignoring the potentially important role played by public sector intangibles.
Our reference is the work of Corrado, Hulten and Sichel (2005, 2009) CHS from now on. They cut through the conceptual problem of defining intangible assets by referring to a standard inter-temporal framework that leads to the conclusion that “any use of resources than reduces current consumption in order to increase it in the future [...] qualifies as investment”. Then, all types of capital should be treated symmetrically, for example “investment in knowledge capital should be placed on the same footing as that of investment in plants and equipment”.
A convenient consequence of the CHS approach and its emphasis on the symmetric treatment of all assets is also that one does not have to worry too much about defining “intangibles” by way of specific characteristics. It is more important to reason in terms of capital goods and to check whether spending activity meets the test of being a current outlay that enhances future consumption. Additionally, the CHS approach is grounded in theory and thus provides guidance on measurement that makes it a useful approach from the perspective of monitoring intangibles as part of a periodic measurement program carried out by a statistical office.
COINVEST, INNODRIVE and INDICSER are the main initiatives undertaken at the European level to measure the importance of intangible assets and their impact on economic performance and growth. Both of these EU-funded research projects follow the CHS framework to estimate the amount of investment in intangible assets for EU countries, but the estimates differ in terms of underlying data series and the proxies used for different expenditures that account as intangible investment. The INTAN-Invest initiative has put the effort on harmonizing these initial sets of measures providing estimates of business sector intangibles for EU27 member countries, plus Norway and the US.
The SPINTAN project proposes to extend both, the theoretical and the empirical, approach introduced by CHS including the non-market sector intangibles in their analytical framework in different complementary directions that can be summarized in the following three objectives:
1. Develop the conceptual framework for non-market sector intangibles. Design the methodology to be followed and its implementation according to the available statistical information.
2. Building up a non-market intangibles database for a wide set of EU countries supplemented by some big non-EU countries.
3. Analyse the impact of non-market intangibles on innovation, well-being and “smart” growth (including education, research and innovation and the creation of a digital society). A special attention has been paid to the consequences of austerity policies implemented during the recent crisis.
The project has been divided into six main work areas corresponding to the following work packages and objectives.
WP 1 addresses several methodological issues, giving a prominent role to the definition of intangible asset boundaries in the Public Sector. The CHS approach was originally thought to identify intangible assets in the market sector. Thus, the first task has been to specify the boundaries between public and private, or market and non-market sectors. The second, to revise the CHS asset classification from the non-market perspective. And third, to identify the industries with a higher participation of the public sector. Additionally, the project has addressed other crucial conceptual issues such as the rate of return on public intangibles. In the current National Accounts framework the basic assumption is that government capital has a net return of zero, which is clearly unsatisfactory from both analytical and policy perspectives.
WP 2 had the objective of constructing a database on intangible investment in the non-market sector. The aim has been to produce a cross-country harmonized database of non-market sector intangibles coherent with the market sector estimates of intangibles developed by INTAN-Invest for the EU and some additional countries. The availability of both, the non-market and market sectors estimates of intangible investment will make a considerable contribution to our understanding of knowledge-driven economies and related policies.
WP 3 concentrates on three specific areas that are important for analysing the nature and structure of public sector investment in intangibles: health, education and R&D. It investigates the effects of these investments on competitiveness, long-term smart growth and societal challenges. Health analysis takes a twofold -macro and micro- perspective. The impact of education, and especially higher level education, and R&D is analysed from four different perspectives: (1) investigating the link between spending on intangibles in the educational sector and different performance indicators across countries; (2) analysing the impact of intangibles on school performance in one country (UK) making use of a newly released database; (3) estimating the contribution of university investment to the improvement of a set of socio-economic variables using a methodology already tested for Spanish universities; and (4) measuring the productivity of academic institutions by looking at the quality-adjusted output in terms of scientific publications and patents.
WP 4 analyses the existing synergies between public intangibles and market sector productivity performance and its impact on economic growth. The main question addressed is what, if any, are the spillovers from public sector investments? The public sector is a major investor in intangible assets, especially human and scientific knowledge capital via its public investments in education and R&D. The public sector also is a major investor in tangible assets such as transportation and telecommunications infrastructures. Investments in these assets, both tangible and intangible, are believed to exert positive macroeconomic effects in the long run. Spillovers from public sector R&D are but one dimension of possible spillovers from investments in knowledge/intangible assets. Complementarities between private ICT and intangible investments have been demonstrated in prior work, and are also addressed in this WP and also in WP 5. Additionally, WP 4 included two cases study. Making use of the Macro econometric model (MeMo-It) developed by Istat (Istat, 2012) for Italy, it analyses the relationship between fiscal stimulus and business cycle taking into account the role of public sector intangible capital. It also provides a case study for Spain placing public capital accumulation on infrastructures directly into the picture. It analyses public tangible (six different types of infrastructures) and intangible capital as well as their spillovers over the market sector performance using an econometric approach.
The main objective of WP 5 is to analyse the impact of budgetary austerity on economic performance. More specifically it addresses the following nine research topics: (1) defines a blueprint of a growth-friendly consolidation strategy; (2) analyses the impact of austerity policies through their impact on intangible assets on income distribution and on the well-being of the European citizens; (3) investigates the effects of fiscal consolidation on education and growth; (4) measures the impact of austerity policies when complementarities between tangibles and intangibles are present; (5) revises the impact of the economic crisis on public investment in intangibles (notably R&D, education, and knowledge infrastructures); (6) analyses international linkages during the last economic cycle: expansion and crisis; (7) reviews government spending in the New Member States; (8) studies the reaction of public ICT investment to economic crisis; and (9) develops a real time database.
WP 6 provides a general diagnosis of the state of the EU countries from the intangibles perspective. It also includes a policy note on the relation between business and government on how to leverage the benefits from public and private intangibles.
The outputs generated within the SPINTAN project can be summarized around three contributions:
1. Conceptual and measurement issues
2. Development of databases
3. Analytical research
This report considers each of these three areas in turn.
1. Conceptual and measurement issues
The general framework is addressed in WP1 and it has materialized in the elaboration of the Manual Measuring Intangible Capital in the Public Sector edited by Carol Corrado, Kirsten Jäger and Cecilia Jona-Lasinio already cited. Here we will concentrate in three topics which summarize the SPINTAN framework from the measurement perspective. (1) the industry coverage; (2) the definition of the non-market sector; and (3) the type of intangible assets considered as well as its relation with CHS (2005, 2009) classification for the market sector.
I.1. Industry Coverage
SPINTAN estimates the intangible capital of non-market industries. Non-market industries consist of the following NACE Rev. 2 sections: 1. Public Administration and Defence; 2. Education; and 3. Human Health and Social Work activities . To this list we add 4. Scientific Research and Development and 5. Arts, Entertainment and Recreation because these industries contain significant non-market production (e.g. federally-run research laboratories, public parks and museums) in many countries. The use of “market” vs. “non-market” groupings of industries is not precise because an industry can reflect activity carried out by a mix of producers, as is evident with NACE Section R and the larger section of which NACE Section MB is a part.
There are industries with significant government or non-market production in some countries besides those mentioned above. These tend to be industries that engage in activities not germane to our topic areas, e.g. transportation and homebuilding. On the other hand, there are industries of interest to our work in SPINTAN that are not listed, e.g. those receiving government R&D subsidies, but such industries tend to have little non-market production other than their own-produced intangible assets for which we have already accounted.
I.2. Industries vs. Institutional Sector
National accountants classify economic activity according to institutional sectors, or industries and differentiate between private/public versus market/non-market sectors. The national accounts non-market sector consists of general government (public) and non-profit institutions serving households (NPISH; private). The public sector consists of general governments (non-market) and government sponsored enterprises (GSEs; market).
Investment activities of the general government and non-profit institutions are the focus of SPINTAN. It is important to recognize that many non-profit institutions are considered market producers according to the System of National Accounts (SNA) because they are able to charge “economically significant” prices . In other words, such institutions are not NPISH but rather are NPIPP (non-profit institutions with pricing power) where NPI=NPISH+NPIPP. Educational institutions, for example, can be public or private, and among the latter, while most are non-profit institutions, some are classified as market producers. The Arts and Entertainment industry is equally diverse in terms of its institutional composition, as is Health and Social Services in certain countries. All told, all but one of the industries that we work with (NACE 84, Public Administration and Defence) consists of a mix of institutions: business (whether for-profit or non-profit), non-profit institutions serving households, and general government. Finally, we note that because of the societal focus of our topic areas, in SPINTAN we do not concern ourselves with GSEs even though these tend to be companies traditionally associated with public infrastructure investment, e.g. rail and power companies. And to be perfectly clear, we also do not concern ourselves with segments of non-profits outside our topic areas, e.g. religious organizations, or membership organizations serving business.
I.3. CHS type of asset: market vs non-market
The list of the traditional CHS intangibles assets for the market sector has been mapped to the public or non-market sector. We introduce two broad categories of public intangible assets: information, scientific, and cultural assets, and societal competencies. The asset boundary is slightly different depending on the market-non-market nature of the sector. But before we discuss what’s different between the market sector and the non-market sector, let us make a few points about the similarities. First, while the character of some assets are rather different when produced by public institutions, e.g. R&D, organizational, and mineral exploration, one may still draw a correspondence between these assets across sectors. For example, Jarboe (2009) defines public investments in brand as expenditures for export promotion, tourism promotion, and consumer product and food and drug safety (i.e. investments in product reputation). The correspondence for computer software, purchased investments in organizational capital, and function-specific worker capital (employer-provided training) is of course far closer.
Information assets, cultural assets, and organisational capital are rather different in a public sector context. Open data refers to information assets in the form of publicly collected data issued and curated for public use. This runs the gamut from patent records to demographic statistics and national accounts to geographic information and local birth/death records. After asking the question, “What are public sector intangible assets in the United Kingdom?” Blaug and Lekhi (2009, p. 53) concluded that “perhaps the most important... is information assets.” Jarboe (2009) includes government information creation as a high-level category in his estimates of U.S. federal government intangible investments. The category includes spending on statistical agencies, the weather service, federal libraries, nonpartisan reporting and accounting offices, and the patent office, which suggests information assets loom large in the United States as well. Indeed, it has long been held that the U.S. Census Bureau’s release of its TIGER (Topologically Integrated Geographic Encoding and Referencing) dataset—in 1991—bootstrapped the country’s booming geospatial industry. Cultural assets are public intangible assets whose services are used in production in cultural domains dominated or influenced by the public and non-market sectors. The capital used in many cultural domains is included in existing estimates of private capital (tangible and intangible), but public investments (or funding) for new asset creation needs to identified and newly capitalized. Note that cultural assets are notionally grouped with public architectural and engineering design, on the grounds that the British Museum’s tessellated glass ceiling or the Louvre Pyramid are as valuable (and as incalculable) as the museums’ contents although of course their correspondence to private counterparts is apparent.
2. Development of databases
The SPINTAN project has produced three databases:
i) Intangibles in the Public Sector (1955-2012) which is complemented by
ii) Real time data (2013-2015)
iii) Task based estimates of organizational capital (2000-2013)
i) Intangibles in the Public Sector (1955-2012)
The activity of WP2 has been centered on the analysis of the existing data sources, the collection of official data and the construction of a database containing harmonized estimates of intangible investment generated following the conceptual framework outlined in WP1. The SPINTAN database contains measures of intangible investment for the Corrado, Hulten and Sichel (2005) list of intangible assets in the nonmarket The data cover 22 European economies and the U.S. over the period 1995-2015, China from 1995 to 2013 and Brazil from 2004 to 2014 .
Estimates of harmonized intangible investment have been generated adopting an expenditure-based approach taking into account the following methodological criteria: 1) Exhaustiveness: a comprehensive measure of intangible investment incorporates the estimate of two components: own-account and purchased intangible investments. 2) Consistency with national accounts (NA): guaranteed by the use of NA as the main data source of information. 3) Reproducibility and international comparability assured by the adoption of official data sources homogeneous across countries.
The estimates of intangible investment follow two different approaches depending on the inclusion/exclusion of the asset in the NA boundaries. If the assets are already included in the NA they are identified as “National Account Intangible Asset” (NAIA) if they are excluded they are labelled as “New Intangible Assets” (NewIA).
Measures of intangible assets (R&D, Computer software and databases, Mineral exploration and Artistic originals) already included in the NA (NAIA) are gathered directly from the NA since they include both the own account and the purchased component of intangible investment. However, very often NA data cross-classified by asset, industry and institutional sector are not available, so that there are two different possibilities: National Statistical Institutes (NSI) provide GFCF by industry and by sector but not the cross classification (industry-sector), or alternatively, NSI release only GFCF data by industry without any detail by institutional sector.
The basic information about expenditures performed to purchase NIA are classified as intermediate costs in NA. Thus the estimate of the purchased component of intangibles is rather straightforward involving the following steps: i) the estimate of the total expenditure in intangible assets; ii) the identification of a capitalization factor to compute the share of total expenditure that can be classified as gross fixed capital formation. The estimate of the own account component has been generated only for organizational capital by means of employment and labour cost data gathered from the Labor Force Surveys and the Structure of Earning Surveys (Eurostat).
The SPINTAN Database has been constructed resorting to the R integrated suite of software facilities for data manipulation, calculation and graphical display (Free Software Foundation’s GNU General Public License). The SPINTAN database has been designed as a flexible environment where it is possible to explore quickly the data along several dimensions, such as the category of intangible assets or the number of industries, and to update them on a rolling basis. The structure of the DB mirrors the multiple relationships existing between the elementary entities (sources, variables, asset characteristics)
ii) Real time data (2013-2015)
The database described above has been complemented by a Real time database for the last three years, 2013-2015. Usually most recent data for intangible assets are available when data for use table are released. That is, at least for European statistical system, more than 2 years from the reference period. Nevertheless, policy makers need updated pictures for a fine tuning of their political proposal. A methodology for real time estimation is proposed to fill this gap . The method takes explicitly into account the heterogeneity of the intangible assets. Particularly for the assets different from software and R&D, which are already considered in the National Accounts, a three step procedure is proposed that uses all the most recent information available from National Accounts and in the Short-Term Statistics domain. The procedure has been developed using Italy as the case study, obtaining a provisional picture of the nominal investment in intangible that can be timelier. Afterwards the method has been applied to the European countries and an updated database with data until 2015 is now available.
iii) Task based estimates of organizational capital (2000-2013)
This database offers a complementary perspective for measuring investment in Organizational Capital . The aim is investigating the measurement of public organizational capital from survey evidence on tasks performed. It discusses the concept of organisational capital and its measurement, particularly on its role in the public sector. Not only the definition of organisational capital and its measurement is elaborated but also the question who generates organizational capital – especially in the public sector. A conservative approach has been adopted. First, costs of managers and leaders are considered, but scaled by the fraction of their time -as best it can be estimated on organisational activities. Second, given the difficulties of translating this into firm-owned assets, a conservative perspective is taken by depreciating organisational capital very quickly. The main conclusion from this analysis is that there is a need to take account of professional employees as well as managers in using the labour cost approach to estimate organisational capital in the public sector. However, unlike managers, there are arguments in favour of only allocating a proportion of the time of professionals in the calculations.
3. Analytical results
The SPINTAN project has produced a number of pieces of research around the general topic of public intangibles which can be revised in the Working Papers’ and Policy Briefs’ section of the project´s website . Some of them make use of the SPINTAN database, while others take advantage of additional sources of information providing a complementary view of the role played by intangible assets. The analytical results generated by the project are summarized below. They have been grouped around the 6 Working Packages in which SPINTAN was divided. Due to its special characteristics, the main contributions in work packages 1 and 2 are summarized in terms of the tasks performed.
WP 1. Public sector intangibles. Methodological and measurement issues
Task 1 -Theoretical Framework and Guidelines- aims at analysing the key issues with regard to the boundaries of public intangibles and possible double counting between different public and private categories and to offer a framework that facilitates the estimation and analysis of public sector activity consistently across countries. For this reason, a framework has been developed for the analysis of public investments, tangible and intangible, at the level of detail needed for the economic analysis of impacts of public policies influencing economic growth. The existing intangibles framework has been expanded by considering nonmarket production by public and non-profit institutions. Investment activities of the general government and non-profit institutions (NPI) are the focus of SPINTAN. The list of intangible assets for the market sector is not suitable for the nonmarket sector and so two broad categories of intangible assets: information, scientific, and cultural assets, and societal competencies have also been considered.
Task 2 -Measurement Methods- develops the theoretical framework and guideline piece from task 1 further and is concerned about the main methodological steps to generate measures of public intangible investments. It demonstrates the main challenges to obtain nominal and real estimates of intangible investments and capital stocks coherently with National Account principles. Many challenges are encountered when estimating the value of public investments. First, we introduce measurement methods for all intangible assets of the nonmarket sector and thereby distinguish between information, scientific, and cultural assets, and societal competencies. Second, we propose a method of integrating the Jorgenson-Fraumeni lifetime income approach to measuring human capital with the treatment of education as social infrastructure. Third, conceptual issues in measurement, including the estimation of nominal investment flows, net stocks, and the rate of return to nonmarket assets are elaborated.
Task 3 -Capital Measurement: net stocks and rates of return- has two distinct sub-tasks. The first sub-task is to estimate service lives based on a comprehensive assessment of alternative sources and investigate alternatives to the standard perpetual inventory model with geometric depreciation for estimating net stocks from service lives. The second sub-task is to assess alternative choices of determining an exogenous rate of return to capital on public sector assets.
The first task deals with the choice of the methodology to assess appropriate values for depreciation for intangible assets, which are in line with the National Accounts conventions as suggested by ESA 2010 in the first section. Following ESA, service lives of assets are a prerequisite to determine depreciation. This is independent from the methodology applied to estimate the level of net stocks. The question whether one has to assume different service lives for public intangibles in comparison with the service life assumption of private intangibles lastly is a question of the degree of breakdown by type of asset.
The second task reviews the leading options for imputing a return to public and non-market capital; Why imputing a return to public and non-market assets matters for productivity calculations; and why many typical calculations of the ex post rate of return for many industry sectors are likely to be inaccurate. It starts presenting the main methodological issues concerning the measurement of both tangible and intangible capital. It continues considering the social rate of time preference (SRTP) as a coherent solution for the imputation. The SRTP is the rate at which a society abstains from current consumption. Its theoretical underpinnings are reviewed and results presented.
Task 4 -Organisational Capital of Public Institutions - already revised in section 2.iii above.
Task 5 -Alternative perspectives: global perspective- takes an international comparative look at resources devoted to the provision of social services, focusing especially on education and health care services. Although spending more resources does not necessarily imply that more real services are provided, a comparative analysis of spending and investment levels across countries is a necessary first step toward understanding differences in outcomes and factors that drive social expenditure efficacy. For global comparisons of social policies, one needs to have a measure of the “true” level of social expenditure in country. We found that value added in social activities measured using data classified by kind-of-activity (i.e. by industry) was a reasonable proxy for the “true” level of social expenditure defined by fiscal policy analysts who work with tax revenue, tax expenditure, and government expenditure data to determine “net” effects. Also, market sector intangible investment was relatively resilient compared with tangible investment during the financial crisis, and that intangibles have continued to grow in the recovery (albeit at a relative weak rate by historical standards). Third, a comparative sources of growth analysis revealed intangible capital to be a driver of measured real growth in social activities in both Europe and the United States. Relative to tangible capital, however, intangibles loom more important only in the United States.
Additionally, it was produced within WP1 two Working Papers. The first is an extension of the five tasks that are laid out in the project’s description of work. The starting point for this paper is that society's consumption of education services is the acquisition of schooling knowledge assets whose change in value should be included in saving and net investment. We estimate the nominal value of education services produced by the public sector by using the Jorgenson-Fraumeni lifetime income approach. The model is estimated using data for the UK under a range of assumptions. The ratio of our preferred measure to education expenditures suggests that society obtains a very high economic benefit from education.
The second extension develops a methodology to disentangle public R&D expenditure (GBARD) by NACE industries and to identify the part devoted to ICT assets within each sector. We start from the methodology developed by Stančik (2012). Essentially, our methodology is based on the definition of a NACE-NABS correspondence and the construction of proper weights to assign GBARD by NABS into each NACE industry. The weights are based on the assumption that R&D intensity in each industry is related to the share of labour costs of employees with higher education over total labour costs. Finally, to compute the part of R&D expenditure devoted to ICT assets in each NACE industry, we assume that it is proportional to the share of labour costs of employees with higher education performing ICT occupations. The methodology is applied to the European Union and the Member States since 2006. The database comprises 37 NACE Rev. 2 industries.
Work package 2. Harmonized cross-country estimates of intangible investment in the public sector.
The main analytical results from WP can be summarized as follow.
Task 1 -Screening of available data sources on public sector expenditure on intangibles- aims to screen what information is available from official data sources, particularly National Accounts, to generate measures of public intangible investment. A comprehensive measure of intangible investment requires the estimate of two main components: own-account and purchased intangible investments. The main identified reference source for the purchased component of the NIA is the Use table at current prices (NACE Rev. 2). The USE table contains the intermediate costs of each industry for the following products: Advertising and Market Research Services (CPA M73), Architectural and engineering services, technical testing and analysis services (CPA M71) and Legal and accounting services, services of head offices and management consulting services (CPA M69 and M70), Education Services (CPA P85). However, the USE tables are usually available by industry and not by institutional sector. In this latter case, NA information has to be merged with Structural Business Statistics data to identify the share of nonmarket production for each industry of interest . The main data source for the own account component the Labor Force Surveys and the Structure of Earning Surveys (Eurostat).
Task 2 -Harmonized measures of intangible investment in the public sector for 22 European economies and the US- provides estimates of intangible investment and capital stock cross classified by industry and institutional sector for 22 European countries and the US over the period 1995-2015, both at current and chained linked prices. Generated data suggest that the GDP share of intangible investment in the nonmarket sector increased in most of the EU countries over the time span accounting on average for 1 percent of GDP in 2010 (from 0.5 on average in 2000). The relative importance of each asset category varies considerably across countries with innovative property being the largest in Denmark, Austria and Spain and Economic competencies accounting for the largest share of nonmarket intangible investment in Ireland and UK. Interestingly, over the financial crisis eight countries out of twenty-two recorded a significant decline of intangible investments. In the remaining EU economies, the dynamics of nonmarket intangibles has been somewhat counter cyclical.
Task 3 – Measures of public intangible investment for China, India and Brazil. Intangible investment measures for the nonmarket sector for the industries of interest data have been generated for China for the period 1995-2013 while for Brazil the estimates refer to the nonmarket sector as a whole over the years 2004-2014. Estimates for India have not been generated because of lack of available information sources. The estimate of China public spending on intangibles is based on Hao and Wu (2015) which revised the national estimates of Hulten and Hao (2012) with newly found data sets and break down intangible investment of the whole economy into 37 industries. They estimate that the whole economy of China spent7.3% of GDP on intangible assets in 2010. Here are their estimates of intangible spending in SPINTAN-related industries. The industry of public administration and defence spent 0.19% of GDP on intangibles, the education industry, 0.32%, and health and social security services, 0.12%.
Brazil invested on average 0.33 percent of GDP in public intangibles from 2004 to 2014. Intangible investment grew at an average annual growth rate of 6 percent from 0.23 percent to 0.43 percent in this period. Public investment did not decline and continued to grow during the financial crisis like in the United States and most European countries. Interestingly, investment dropped in the United States and Brazil after 2010. Brazil’s investment rose again from 2012 onwards and continued to decline in the United States until 2013.
Task 4 – Task based approach to measure organizational capital - aims to generate estimates of organizational capital adopting an alternative method compared to the approach suggested by CHS (2005) . The task-based methodology allows identifying that part of the workforce contributing to the long term functioning of an organisation and deals with: the organisation, planning and prioritisation of work; the assignment of employees to teams and to tasks; the provision of training; and the supervision and co-ordination of activities and communication across and within groups. Total investment in Organizational Capital (OC) for 2012 is found to range from 1.4% of value added in the Czech Republic to 3.7% in the United Kingdom, with an average 2.2% across all countries. Managers appear to account for less than half of total employment and investment in OC, with total investment in OC is higher in services than in manufacturing. Extending the methodology to estimate a panel of industry-level investment for 20 EU countries and the U.S. yields similar patterns. Experimental figures of OC investment.
Workpackage 3. Education, health and R&D: Impacts on smart growth
Workpackage 3 considered a number of aspects of performance in the education and health sectors. It investigated the links between intangible organizational capital and performance at the producing unit level, concentrating on health and education. It also investigated links between person’s willingness to invest in education and health. A strand of the research considered performance more generally in higher education institutions (HEIs) both in terms of breaking down the reasons for differential productivity across EU countries and looking at the impact of HEI on society more generally.
The work on hospital performance and intangible organisational capital used a common methodology for three countries: Germany, Hungary and England. The research teams in each country, as much as possible, attempted to use common data definitions and statistical techniques. Therefore each country measured investments in organisational capital in terms of expenditures on personnel who generated this asset, following the approach to measuring this asset in the main SPINTAN database. Hospital performance was measured as an index of overall output based on clinical activities and medical procedures undertaken by general multi-function hospitals, so excluding specialist hospitals. Each country estimated a hospital production function including inputs and a number of common control variables. The studies differed, however, in terms of data sources and details of the analysis .
The regression results presented in the studies show a positive impact of organisational capital on hospital outputs when broad measures are employed that include both general managers and clinical practitioners with some managerial responsibilities. This is consistent with the more general research on organisational capital carried out by researchers at the OECD and reported in SPINTAN working paper no. 21 “Investment in organisational capital: methodology and panel estimates” by Marie Le Mouel, Luca Marcolin, Mariagrazia Squicciarini. The results using this broad measure are strongest and most robust for Germany as the data for that country included a much greater sample of hospitals and time periods. Both the Hungary and England studies were more constrained by data availability on the main variables of interest and by their more concentrated delivery of health services, leading to a smaller number of producing units. In both Hungary and England, this broad measure of organisational capital was contrasted with more narrow measures that excluded clinical personnel. The results suggest that links between organisational capital and hospital performance as measured by a cost weighted activity index or by changes in mortality rates are not statistically observable when just employing a narrow measure. This is not to say that general managers do not affect hospital performance as the outputs from their specific tasks are difficult to measure. Some attempt to do so was carried out for England, looking at reductions in waiting times, but this did not give significant results.
A similar approach was used to investigate the links between organisational capital and education in a case study for England . Using administrative data on the workforce in each English secondary school the authors measure schools’ organisational capital generated by the leadership group, as well as that created by other staff, such as classroom teachers, who also hold leadership roles. This study makes use of three separate administrative databases, the School Workforce Census, the School Census, and school-level attainment at age 16 from the National Pupil Database. By linking data on the composition of the schools workforce with measures of pupil attainment, and using both descriptive and multivariate methods that control for a range of school characteristics, they could assess the importance of those with leadership and managerial responsibilities on school performance.
The results indicate a positive and statistically significant association between organisational capital and school performance. This relationship is evident not only for organisational capital embodied within senior leaders, but also among the wider workforce contributing to leadership and management within schools. The authors also explore changes in leadership in response to school inspections. They find some evidence that schools instigate organisational changes following inspections. Schools rated satisfactory show an increase in the relative size of their broader leadership group following inspection. The same did not apply for schools rated good or outstanding (who may see no need for change) or for schools rated inadequate (who may be constrained in their ability to implement changes due to restrictions placed on schools receiving this rating).
The link between education and health is investigated by Martin Weale . The basic structure underlying the study is provided by the lifecycle framework, where education will be pursued when the return in terms of future earnings and any other welfare gains exceeds the current costs of devoting time to education which could have been spent working. Declining mortality rates therefore increase the benefit of education, because the expected benefit of higher wages is increased. Generally rising wages, on the other hand, are likely to make leisure more attractive, making retirement earlier and thus reducing the incentive for education. There is some evidence that education itself affects mortality rates, and that may also affect the take-up of education.
The results draw attention to the way in which retirement decisions are a balance between a desire for leisure, which increases as wages rise, and the effects of rising longevity, which lead to delayed retirement; at present the longevity effects are probably stronger than those of wage growth. They show that on their own variations in lifespan have relatively little effect on the demand for education. If however education has effects in addition to those on earnings, more powerful effects can be found. For example, a direct influence on survival makes it possible to explain the majority of the increased length of education in the UK, comparing men born in 1906 with those born in 1982. Mortality rates are, however, now so low except in old age that this effect is unlikely to have much more influence. The effect of discounting is such that, even if there is a substantial impact on mortality rates late in life, the benefit seen from the perspective of someone close to the age at which education is completed is small.
An analysis of the research output of the EU HEIs develops a methodology which allows to break down the differences in scientific output per researcher among the HEIs of each country in terms of a) differences in efficiency within each field of science (FOS), b) differences in FOS specialisation of the HEIs in each country, c) differences in quality and d) differences in allocation of resources per researcher. This uses a 5-step frontier approach based on a DEA nonparametric methodology, which allows estimation of comparative levels of productivity in scientific research across European university systems.
The results suggest that, on average, given the actual use of inputs and taking into account quality, the research output of the HEIs in the EU could increase by around 18% if all the inefficiencies were removed, with output capable of doubling or more in some countries. Inefficiencies within each specific field of science account for about 88% of the total with remaining much smaller share due to the inefficiency associated with the field of science composition. Inefficiency is a particular problem in some countries, but much lower in countries like the United Kingdom, Sweden, Netherlands or Germany. Inefficiencies is only one side of the story, however, - the amount of resources is also important. The results confirm that in the case of the EU countries research output per capita tends to increase with the volume of resources per researcher. A large part of the differences in research output per capita across EU countries is associated with differences in this area and would persist even if all the countries were capable of completely removing their inefficiency.
The activities of universities generate many benefits to society including that the R&D and teaching activities are mainly devoted to the generation of intangible assets such as innovation, human capital, education and culture. These intangible university assets contribute not only to the economic growth of their societies, but also to other numerous social variables related to well-being, such as labour participation and unemployment. The contribution of the Higher Education Institutions (HEIs) to the socioeconomic development of the EU and each of its 28 member countries is analysed over the period 2000-2014 . This paper takes account of the direct effects of higher education on the human capital of individuals, as well as its indirect effect through employment probabilities. In addition the contribution of HEIs to the stock of technological capital is considered taking into account their R&D activity. This applies probit models to microdata from the EU-LFS. Using those results and data on R&D expenditure by institution counterfactual scenarios in which HEIs are assumed not to exist are estimated for each country and then compared with the actual situation.
The estimates obtained show that technological capital stocks, per capita human capital of workers, participation rates and employment rates are higher in all EU countries than in the hypothetical scenario without HEIs. The results obtained by applying growth accounting methods indicate that HEIs are an important source of growth in the EU countries, contributing also to alleviate the adverse effects during periods of crisis. For the EU as a whole, the estimates indicate that GDP per capita would now be a fifth lower without HEIs. The results also show the existence of GDP per capita differences between EU countries of up to a 15% associated with the activities of HEIs. The relative effect is bigger in Luxembourg, Bulgaria and Belgium and lower, although still positive and sizeable, in Portugal, Slovakia and Italy.
Workpackage 4: Spillovers from public sector intangibles
WP4 has built on the previous work of the project by using the database to examine econometrically spillovers from public and private intangibles. The methodological paper “A note on estimating spillovers from Public Intangibles” sets out the methodological contributions.
The background is as follows. What, if any, are the spillovers from public sector investments? The public sector is a major investor in intangible assets, especially human and scientific knowledge capital via its public investments in education and R&D. The public sector also is a major investor in tangible assets such as transportation and telecommunications infrastructures. Investments in these assets, both tangible and intangible, are believed to exert positive macroeconomic effects in the long run.
Regarding intangibles, the analysis of public sector spillovers in OECD countries typically looks (in isolation) at R&D and education. Spillovers from publicly performed R&D to market sector productivity were studied by, e.g. Guellec and Van Pottelsberghe de la Potterie (2002, 2004), who found strongly positive effects in their cross-country work. Spillovers from education to economic growth have also been studied extensively, with very mixed results.
What do we do that is new in regard to SPINTAN?
• Spillovers from public sector R&D are but one dimension of possible spillovers from investments in knowledge/intangible assets. For example, O'Mahony and Riley (2012) examine whether employer-provided training may facilitate the generation of spillovers from education. Their results support the assumption that spillovers from education within broad sectors are stronger when employers engage in training and suggest the need to examine the possibility of multiple channels and also interactive mechanisms whereby intangible assets might impact growth. Complementarities between private ICT and intangible investments have been demonstrated in prior work (Brynjolfsson, Hitt, and Yang, 2002; Corrado, Haskel, and Jona-Lasinio, 2016).
• Besides the well documented spillovers from the conduct of corporate R&D, there might be pure spillovers from business investments in non-R&D intangibles
We start in this methodological notes setting out some of the nuances in this literature. For example, many papers don’t use consistent National Accounts data when looking at the effects of R&D etc. meaning one has to be very careful in the interpretation of TFP/intangible capital correlations in relation to inferring rates of return. Furthermore, the calculation of TFP may needs capitalization and ascribing a rate of return to public capital which most studies do not do. A discussion of this is also set out in Haskel and Westlake’s draft book.
The WP4 paper “Are there spillovers to public R&D?” starts with the observation that business investments in non-R&D intangibles grew dramatically in relative importance in the United States from the late 1970s to the mid-2000s. Indeed, after 2007, the divergent paths of tangible and intangible investment in both the US and EU are especially striking, with major slowdowns in tangible and intangibles investment in the financial crisis, but then a relative recovery in intangible investment after that. Furthermore, there are large changes in public R&D support over the period.
The paper uses the framework set out in methodological note cited above and then introduces data to look at the correlation between various measures of TFP and market and non-market -sector R&D and other intangible investments. It starts with a whole economy database (quite a total economy dataset because it drops agriculture, real estate and some other small industries). It then looks at a market and non-market sector database finally at all industries. It argues that there is evidence for both private spillovers of R&D and intangibles, and the spillovers from public sector R&D to market sector productivity. The findings suggest rates of return between 10 and 45%.
Deliverable 4.3: ‘Detailed cases of intangibles in particular models and contexts’ is composed of two different tasks presenting two case studies for Italy and Spain. For Italy it is developed a model for the dynamics of public and private investment in the framework of the macro-econometric model MeMo.it for the Italian economy that takes into account asset specific characteristics potentially affecting the reactivity of aggregate and disaggregate capital accumulation over the business cycle. The analysis is focused on the behaviour of intellectual property investment in the market and nonmarket sectors and on their synergies. The main findings support the assumption of relevant positive interactions between market and nonmarket intellectual property investment in the Italian economy, and of strong positive correlation between market and nonmarket R&D. These results suggest that nonmarket intangible expenditure can be a key policy instrument to foster market investment in intangible assets. The model developed under WP4 activity is the first attempt to extend traditional macro-econometric models to account for the dynamics of investment by assets and by institutional sectors.
The second case study takes Spain as the reference . It analyses two alternative ways by which different types of capital influence Spanish economic growth focusing on the role of intangibles (both public –SPINTAN data– and private) and infrastructures. First, we estimate production functions to assess their contribution and the possible complementarities between IT capital and private and public intangibles. Second, we explore the existence of spillovers associated with intangibles and public capital. The evidence is based on the Spanish economy using both aggregated and industry data (24 industries) for the period 1995-2014. Some weak evidence is found for the effects of private intangible capital on labour productivity growth, although the direct effect cannot be precisely identified. We find no robust evidence of complementarity between intangibles and IT capital or of the role of public capital. The capacity of intangibles and public capital to generate spillovers is also not clear cut. The results are explained by both the high correlation between all types of capital due to the over-investment process in the years of expansion previous to the crisis, and to the counter-cyclical profile of total factor productivity in Spain.
Workpackage 5: Austerity and recovery
The main objective of WP5 has been to analyse the impact of the public sector’s reaction to the crisis and budgetary austerity on economic performance through. It has focused on its effect on the investment on intangibles and the expected long-run consequences for the post-crisis recovery. The effects go through R&D, human capital accumulation and public IT spending; the role of the complementarity or substitutability between investment in tangibles and intangibles and the case of the New State Members. In order to achieve those aims WP5 was organized along eight different tasks. The main analytical results are contained in the corresponding deliverables and the subsequent nine working papers derived from them. Furthermore, eight policy briefs were elaborated focusing on the main results and its relevance for social agents and European policies.
A novel method for the analysis of the long-run effect of fiscal consolidation on economic growth is developed and applied to the European experience by Kleis and Moessinger . The method is based on quantitative case studies and relies on a qualitative (narrative) definition of fiscal consolidations based on an examination of historical policy documents and the use of the synthetic control method (SCM), which offers the chance to investigate the hypothetical post-consolidation economic growth trajectory in the consolidating country in the absence of a fiscal consolidation. This approach is applied to the evolution of post-consolidation trajectories of economic growth in six case studies of OECD countries (Austria 1996, Belgium 1992, Portugal 1983, Spain 1994, Sweden 1984, UK 1994). The aim is to analyse whether fiscal consolidations depress economic growth or contribute to an expansionary (so called non-Keynesian) effect. The results do not offer clear-cut evidence on the long-run effect of fiscal consolidation on economic growth, in contrast to recent studies that reject the hypothesis of non-Keynesian effects.
The distributional effects of recent fiscal consolidation policies are analysed for 16 EU countries by looking at their income distributions during the recent global financial and economic crisis, and the subsequent period of fiscal consolidation . Data from the EU Survey of Income and Living Conditions (EU-SILC) are used to track the distributional effects of public interventions in the economy of money transfer and taxes and also of in-kind transfers –mainly education and health– which are of great and increasing importance in developed economies. The results point to the absence of relative inequality effects of fiscal consolidations in those countries where public sector cuts have been deeper. This fact is due to the emphasis on measuring inequality in relative terms, given that primary incomes have fallen by more than the cuts in public services provided to citizens. Individual national experiences vary, but in-kind public transfers have been highly redistributive also during the crisis despite fiscal consolidation policies.
Fiscal consolidation may have long-run supply-side effects on output through its effect on the education sector and human capital accumulation. The effect of crisis and public expenditure consolidation is analysed for the EU as a whole and also for some groups of countries and specific countries especially affected by fiscal consolidation . The analysis takes into account the effect of both fiscal consolidation and crisis on European dropout rates and analyses the effects of educational attainment on participation in the labour market, employability and labour productivity using EU-LFS and EU-SILC microdata. Some estimates of long-run impacts on output based on those results are provided for different scenarios. The results show that fiscal consolidation might affect negatively educational attainment when public expenditure on education is reduced. However, also as a result of the economic crisis, job opportunities for young people drastically decrease in the fiscal consolidation countries. This reduces the opportunity cost of studying, extending schooling, reducing dropout rates and fostering human capital accumulation. All in all, the latter effect seems to dominate any negative long-run supply-side effect from a lower level of public spending on education in the case of the EU countries.
The question of whether intangible capital is a substitute or, to some degree, a complement to standard inputs in the production of public goods has been analysed for the EU countries using SPINTAN data . The results show that intangible capital is just weakly substitutable with other inputs, inter alia, tangible capital and intangible capital are weak substitutes for each other. Therefore public investment in the public sectors should not only focus on the classical tangible assets, but also on intangible capital. This will not only increase the output through the positive effect of intangible capital, but it is also required because intangibles and other inputs are just weakly substitutable. An excessive focus on one specific input category, say tangible capital, will not lead to the expected results because the other inputs are also required, such as intangibles, in order to achieve the maximum output possible.
The way public R&D expenditure changes over the business cycle is investigated for different types of government R&D expenditure, using panel data from 26 OECD countries over the period 1995 to 2015 . The results show evidence for a strong pro-cyclical effect on public R&D investments on average, but with a significant degree of country heterogeneity. Whereas European innovation leaders and non-EU countries pursue a counter-cyclical strategy, innovation followers and moderate innovators behave pro-cyclical. This leads to an increasing innovation gap in Europe. These results corroborate that the recent 2008/2009 financial and economic crisis and the sluggish recovery in many countries had a severe and still lasting impact on the ability of governments to invest into R&D, increasing heterogeneity within the EU.
The changes in public spending structures in the EU Member States over the period 1995 to 2013 are analysed based on data on government expenditures by function (COFOG) with a focus on social expenditure categories health, education and social protection spending expressed in per capita terms in PPPs at constant prices . The analysis shows that expenditures increased in general in real terms, while large differences in spending levels are observed across countries. In EU countries which have been hit hard by the economic crisis cuts have been enacted. Furthermore the paper analyses the levels and changes of individual expenditures on health and education based on COICOP data (Classification of Individual Consumption by Purpose) across EU Member States. The effects of public and private expenditures on public health and other social outcomes are also examined using an econometric analysis. The results show that higher levels of public expenditures and lower levels of economic poverty are significantly correlated with superior population health and public welfare. Reductions in public expenditure would be detrimental to progress in public health, the participation of young people in education and employment, but also to lowering crime rates.
The factors underlying the growing size and the change in the structure of public expenditures in the European Union since 2007 are both analysed, with special regard to the New Member States (NMS) . The increase of the expenditure ratio-to-GDP is separated into the impact of the change in GDP and the effect of the change of actual public expenditures. The results of this decomposition method show that the fall of GDP was more important in shaping the fiscal trends in the year of the acute crisis than the demand-boosting actions. Considering the entire period since 2008, the higher expenditure ratio in 2014 can be fully explained by the effect of higher actual expenditures. The analysis over time shows a great variety both in the old and in the new member states. Concerning the structure of raising expenditure ratio the main characteristic of the changes can be summarized by the growing share of expenditures on social protection and health since 2008 for the EU28 average. In NMS, however, the share of expenditures on social protection decreased since the global crisis.
The general question of which kind of government spending was undertaken in reaction to the economic crisis in 2007-2008 and its effects on the creation of intangible assets in the public sector is analysed using the case method . The German public IT-spending programme 2009-2011 adopted after the crisis is considered in terms of its tangible vs. intangible output. This relatively well-described programme is adopted as a use case for categorizing IT-related intangibles in government beyond software (including e.g. IT-training, innovation in e-services), allowing to investigate how to form insightful aggregates of intangible IT-related investment from project level data and, in comparison, from the regular public budget in Germany. The results, based on quantitative information, qualitative information and approximations, show that about half of the total spending was on intangibles, of which again about half went into software and a quarter into consulting. This research shows that the classification of intangibles can be applied via key word search to the anti-crises programme and proves a very useful framework for classification also for project-level data in the public sector. In contrast to budget data, project-level data more frequently allows the identification of outputs rather than inputs of intangible investment.
2 The usual grouping of non-market industries also includes real estate, which is not discussed here.
3 The SNA instructs that producers be classified as businesses if they are able to charge economically significant prices, e.g. schools, colleges, universities, hospitals constituted as non-profit institutions are to be classified as market producers when they charge fees that are based on their production costs and that are sufficiently high to have a significant influence on the demand for their services (European Commission et al., 2009; para 4.88). In practice, for European countries, the European System of National and Regional Accounts (ESA) implement this as a quantitative criterion, considering economically insignificant prices to be those that cover less than half the cost of production.
4 Note this assumes National Statistical Offices have not already done so as part of their efforts to capitalize Artistic and Entertainment originals. Unfortunately, this is difficult to ascertain because the published investment by asset type data for most European countries include a category called “other intangible assets” that (a) is defined as mineral exploration + artistic and entertainment originals, (b) is usually very small in magnitude, and (c) implies little or no public investment. An exception to (c) is mineral exploration in Norway. An exception to (a) and (b) is the United States where these assets are separately shown yet (c) holds true. It appears then that public cultural assets are in practice distinct from artistic and entertainment originals and investments in them need to be capitalized as public intangibles.
5 SPINTAN Working Paper n 11; Estimates of intangible investment in the public sector: EU, US, China and Brazil; Fabio Bacchini, Carol Corrado, Janet Hao, Jonathan Haskel, Roberto Iannaccone, Massimiliano Iommi, Kirsten Jagër, Cecilia Jona-Lasinio.
6 SPINTAN Working Paper No. 22; Real time estimation for policy analysis; Fabio Bacchini and Roberto Iannaccone
7 SPINTAN Working Paper nº 21; Investment in organizational capital: methodology and panel estimates; Marie Le Mouel, Luca Marcolin, Mariagrazi Squicciarini.
8 So far, the project has generated 23 Working Papers and 16 Policy Briefs, available at www.spintan.net.
9 SPINTAN Working Paper nº 21; Investment in organizational capital: methodology and panel estimates; Marie Le Mouel, Luca Marcolin, Mariagrazi Squicciarini.
10 SPINTAN Working Paper No. 19; Measuring education services as intangible social infrastructure; Carol Corrado and Mary O´Mahony.
11 SPINTAN Working Paper nº 23; A proposal for disentangling funded R&D (GBARD) by industry; Matilde Mas, Eva Benages, Juan Fernández de Guevara and Laura Hernández.
12 SPINTAN Working Paper nº11; Estimates of intangible investment in the public sector: EU, US, China and Brazil; F. Bacchini, C. Corrado, J.Hao J.Haskel R. Iannaccone, M. Iommi, K. Jager, C. Jona Lasinio.
13 SPINTAN Working Paper nº 21; Investment in organizational capital: methodology and panel estimates; Marie Le Mouel, Luca Marcolin, Mariagrazi Squicciarini
14 See SPINTAN Working paper nº 9; Hospital performance and intangible investments: the impact of own account organizational capital by Erika Schulz and Laura Beckmann for Germany; SPINTAN Working paper nº 12, Organisational capital and hospital performance in England by Mary O’Mahony, Silvia Beghelli and Lucy Stokes; and SPINTAN Working paper nº13 Organisational capital and hospital performance in Hungary by Antónia Hüttl and Ágnes Nagy.
15 SPINTAN working paper nº 20, The role of intangibles in school performance: a case study for England by Lucy Stokes, David Wilkinson and Alex Bryson.
16 SPINTAN working paper nº 4 ; The effects of survival rates on education in a simple life-cycle model; Martin Weale
17 SPINTAN working paper no. 5 “The research output of universities and its determinants: quality, intangible investments, specialisation and inefficiencies, by José Manuel Pastor and Lorenzo Serrano
18 SPINTAN Working Paper nº 18; A Valuation of the Long-Term Socioeconomic Contributions of the European Higher Education Institutions by José Manuel Pastor, Lorenzo Serrano and Ángel Soler.
19 SPINTAN –Deliverable 4.3a: Modeling private and public intangible investments in a macro-econometric framework; Fabio Bacchini and Cecilia Jona-Lasinio.
20 SPINTAN Deliverable 4.3b; Intangible capital and infrastructures in Spanish economic growth: direct contribution and spillovers; Juan Fernández de Guevara and Matilde Mas
21 SPINTAN Working Paper No. 6; The long-run effect of fiscal consolidation on economic growth: evidence from quantitative case studies; Mischa Kleis and Marc-Daniel Moessinger
22 SPINTAN Working Paper No. 7; Fiscal consolidation and income distribution; Francisco J. Goerlich and Laura Hernández
23 SPINTAN Working Paper No. 10; Fiscal consolidation and crisis in the EU: exploring long-run supply-side effects through education; Lorenzo Serrano, Ángel Soler and Laura Hernández
24 SPINTAN Working Paper No. 15; Intangible capital: Complement or substitute in the creation of public goods?; Alexander Schiersch and Martin Gornig
25 SPINTAN Working Paper No. 16; Public Investment in R&D in reaction to economic crises – a longitudinal study for OECD countries; Maikel Pellens, Bettina Peters, Christian Rammer and Georg Licht
SPINTAN Working Paper No. 17; Public ICT investment in reaction to the economic crises – a case study on measuring IT-related intangibles in the Public Sector; Marianne Saam, Laura Weinhardt and Lukas Trottner
26 SPINTAN Working Paper No. 8; Development of public spending structures in the EU member states: social investment and its impact on social outcomes; Sebastian Leitner and Robert Stehrer
27 SPINTAN Working Paper No. 14; Structural changes in Public Expenditures in the European Union since 2008 – with special regard to new member states; Éva Palócz, Zoltán Matheika and Péter Vakhal
28 SPINTAN Working Paper No. 17; Public ICT investment in reaction to the economic crises – a case study on measuring IT-related intangibles in the Public Sector; Marianne Saam, Laura Weinhardt and Lukas Trottner
Bacchini, F., and R. Iannaccone (2016). Real time estimation for policy analysis. SPINTAN Working Paper No. 22.
Bacchini,F., C. Corrado, J.Hao J.Haskel R. Iannaccone, M. Iommi, K. Jager, C. Jona Lasinio (2016). Estimates of intangible investment in the public sector: EU, US, China and Brazil. SPINTAN Working Paper No. 11.
Blaug, R. and R. Lekhi (2009). Accounting for intangibles: Financing reporting and value creation in the knowledge economy. Research report, The Work Foundation.
Brynjolfsson, E., L.M. Hitt and S. Yang (2002). Intangible assets: Computers and organiza-tional capital. Brookings Papers on Economic Activity 1,137.
Corrado, C., C. Hulten and D. Sichel (2005). Measuring capital and technology: An expanded framework.
Corrado, C., C. Hulten and D. Sichel (2009). Intangible capital and U.S. economic growth, Review of Income and Wealth 55(3).
Corrado, C., M. O’Mahony and L. Samek (2016). Measuring Education Services as Intangible Social Infrastructure. SPINTAN Working Paper No. 19.
Corrado, Jäger, Jona-Lasinio (2016): Spintan Manual: Measuring intangible capital in the public sector: A manual. Valencia: Ivie.
European Commission, International Monetary Fund, Organization for Economic Cooperation and Development, United Nations and World Bank (2009). System of National Accounts 2008. NewYork, N.Y.: United Nations.
Goerlich, F.J. and L. Hernández (2016). Fiscal consolidation and income distribution. SPINTAN Working Paper No. 7.
Guellec, D. and De La Potterie, B. V. P. (2002). R&D and productivity growth. OECD Economic Studies, 2001(2), 103-126.
Guellec, D., and De La Potterie, B. V. P. (2004). From R&D to productivity growth: Do the institutional settings and the source of funds of R&D matter?. Oxford Bulletin of Economics and Statistics, 66(3), 353-378.
Hulten, C. and J. Hao (2012). «Intangible Investment in China». Deliverable to the project of World Input Output Database.
Hüttl, A. and A. Nagy (2016). Organisational capital and hospital performance in Hungary SPINTAN Working Paper No. 13.
Jarboe, K. (2009). US policies for fostering intangibles. Presentation, 6th Summiton Monetizing & Maximizing IP, New York, 5 March.
Kleis, M. and M.D. Moessinger (2016). The long-run effect of fiscal consolidation on economic growth: evidence from quantitative case studies. SPINTAN Working Paper No. 6.
Le Mouel, M., L. Marcolin and M. Squicciarini (2016). Investment in organisational capital: methodology and panel estimates. SPINTAN Working Paper No. 21.
Leitner, S. and R. Stehrer (2016). Development of public spending structures in the EU member states: social investment and its impact on social outcomes. SPINTAN Working Paper No. 8.
Mas, M., E. Benages, J. Fernández de Guevara, L. Hernández (2016). A proposal for disentangling funded R&D (GBARD) by industry SPINTAN Working Paper No. 23.
O’Mahony, M., S. Beghelli and L. Stokes (2016). Organisational capital and hospital performance in England. SPINTAN Working Paper No. 12.
Palócz, E., Z. Matheika and P. Vakhal (2016). Structural changes in Public Expenditures in the European Union since 2008 – with special regard to new member states. SPINTAN Working Paper No. 14.
Pastor, J.M. and L. Serrano (2016). The research output of universities and its determinants: quality, intangible investments, specialisation and inefficiencies. SPINTAN Working Paper No. 5.
Pastor, J.M. L. Serrano and A. Soler (2016). A Valuation of the Long-Term Socioeconomic Contributions of the European Higher Education Institutions. SPINTAN Working Paper No. 18.
Pellens, M., B. Peters, C. Rammer and G. Licht (2016).
Public Investment in R&D in reaction to economic crises – a longitudinal study for OECD countries. SPINTAN Working Paper No. 16.
Saam, M., L. Weinhardt and L. Trottner (2016). Public ICT investment in reaction to the economic crises – a case study on measuring IT-related intangibles in the Public Sector. SPINTAN Working Paper No. 17.
Schiersch, A. and M. Gornig (2016). Intangible capital: Complement or substitute in the creation of public goods?. SPINTAN Working Paper No. 15.
Schulz, E. and L. Beckmann (2016). Hospital performance and intangible investments: the impact of own account organizational capital. SPINTAN Working Paper No. 9.
Serrano, L., A. Soler and L. Hernández (2016). Fiscal consolidation and crisis in the EU: exploring long-run supply-side effects through education. SPINTAN Working Paper No. 10.
Stančik, J. (2012). “A Methodology for Estimating Public ICT R&D Expenditures in the EU”. JRC Science and Policy Report No.25433. Institute for Prospective Technological Studies, Seville. http://ipts.jrc.ec.europa.eu/publications/pub.cfm?id=5119
Stokes, L., D. Wilkinson and A. Bryson (2016). The role of intangibles in school performance: a case study for England. SPINTAN Working Paper No. 20.
Weale, M. (2015). The effects of survival rates on education in a simple life-cycle model. SPINTAN Working Paper No. 4.
The three main impacts expected from this type of projects are
1. Use of the output of the project by academics and the policy community
2. Insights into measurement and conceptual issues and data deficiencies that inform national and international statistical agencies, academics and the policy community who use the data
3. Analytical results that inform policy
Based on the very interested reaction from different institutions and international organizations to date, the expected impact of the project is high. The identification of the methods to measure public intangible investment and the provision of harmonized data on intangibles for 22 EU countries, the US, China and Brazil is a significant advancement for our understanding of the role of knowledge based capital as a driver of growth in modern economies. The database constructed on non-market intangibles has the very interesting property that it can be combined with other well-known and widely used databases such as EU KLEMS, INTAN-Invest and WIOD. The project team has already used these combined datasets in the analytical research as reported above.
The database described above has been complemented by a real time database for the last three years, 2013-2015. Usually the most recent data for intangible assets are available when data for use tables are released. At least for the European statistical system, this is often more than 2 years from the reference period. Nevertheless, policy makers need updated pictures for a fine tuning of their political proposals. A methodology for real time estimation has been proposed to fill this gap.
The effort devoted to the construction of the non-market database has been complemented by the development of a new methodology -as well as its practical implementation- for one of the most important intangibles assets, organizational capital (OC) from survey evidence on tasks performed. The main conclusion is that in measuring OC, it is necessary to take into account professional employees as well as managers. This finding was confirmed by case studies of performance in hospitals and schools.
The research team has publicised the results of the project in the various academic and policy conferences listed below. Worth mentioning are the presentations in the World Conference on Intellectual Capital for Communities 2014 and 2015 (Paris); World Klems 2014, 2016 (Tokyo, Madrid); Interamerican Development Bank 2016 (Washington); IARIW Conference 2014, 2015, 2016 (Rotterdam, Paris, Dresden); ASSA Conference 2015 (Boston); SEM Conference 2015, 2016 (Paris, Thessaloniki); OECD WPIA Conference 2015, 2016 (Paris); KIIS Conference 2015, 2016 (Valencia); Franco-German Conference CEPII-CESifo-DIW-OFCE 2016 (Paris) and OECD Blue Sky Forum 2016 (Ghent) among others.
The research team sees the database as a dynamic resource which will be extended and updated beyond the life of the project. In fact, the ambition of the team is to generate a comprehensive database incorporating the institutional breakdown in SPINTAN into a revised and extended EU KLEMS, with the market intangibles data provided by INTAN-Invest, and the World Input-Output Database (WIOD). This database should be the methodological reference for a new set of measures for productivity growth analysis. The implementation of SNA 2008 in (almost) all developed countries offers a great opportunity for this type of initiative.
An important by-product of these types of projects is that the process of constructing indicators throws up conceptual and measurement issues that inform research in these areas. Equally important is that the attempt to match data to the theoretical measurement frameworks highlight deficiencies in the available information and can produce recommendations for future data gathering exercises. In this respect, the project has produced a Manual containing a thorough review of the main conceptual and measurement problems. This represented a joint effort by the consortium members. It consists of five chapters and six appendices. This Manual is the first on the measurement of intangible assets to be produced, so it is expected to be the main reference in this field.
The complexity of advanced economies’ welfare states can mask the true resources devoted to social activities. The emphasis on direct government social expenditure in political debates, for example, overlooks the consequences of political tools such as investment grants (used across and within EU countries) and tax expenditures (used heavily in the United States) that can influence the public/private mix of social expenditures in a country.
Studying the efficacy of social spending and determining spillovers to intangibles (R&D and non-R&D) and education is on the cutting edge of economic policy and economic measurement research. The framework and results reviewed in this project suggested the utility of continuing to build and investigate the links between intangible investments, productivity, and public policy in major sectors and industry-based production and expenditure accounts. Such accounts need to recognize the full range of intangibles and model education services as social asset accumulation.
For global comparisons of social policies, one needs to have a measure of the “true” level of social expenditure in a country. We found that value added in social activities measured using data classified by kind-of-activity (i.e. by industry) was a reasonable proxy for the “true” level of social expenditure defined by fiscal policy analysts who work with tax revenue, tax expenditure, and government expenditure data to determine “net” effects. This suggests that productivity analysis (appropriately designed as indicated above) merits more use in fiscal policy analysis.
A very interesting result is that market sector intangible investment was relatively resilient compared with tangible investment during the financial crisis, and that intangibles have continued to grow in the recovery (albeit at a relatively weak rate by historical standards). Among intangibles, R&D was especially well maintained during the recessionary period. Although this is typical cyclical behaviour (based on evidence for the United States), steady and consistent R&D policies may have played a role. The rate of public R&D investment was well maintained during both the recessionary period and the European austerity that followed.
Intangible capital is a driver of measured real growth in social activities in both Europe and the United States. Relative to tangible capital, however, intangibles loom more important only in the United States. Productivity spillovers from public R&D likely contributed positively to productivity change in the market sector since the global financial crisis.
Existing empirical literature shows that intangible capital accounts for one-fifth to one-third of labour productivity growth in the market sector of the US and EU economies. Intangible investments are growth promoting thus being a fundamental variable for policy making. However, National Account capital estimates do not include all intangible assets in the asset boundary thus understating their growth contribution. The results from this project indicates that policymakers need an expanded framework and better data to design effective growth and innovation policies. Statistical agencies are likely to benefit from the methodological effort conducted under the SPINTAN project.
The case studies for hospitals and schools, together with the more general findings on the task based approach to measuring organisational capital, highlight the important role that professionals such as doctors and teachers have in driving change. This suggests that, at least for public service provision, it is important to have a leadership structure that facilitates cooperation between general managers and senior professionals. Responsibility for initiating change lies with both types and general managers lack the specific knowledge to be able to take on the necessary leadership roles on their own. Therefore policies that favour a top down leadership approach may not be the best for enhancing service provision for some public services.
The analysis of the role of Higher Education Institutions (HEIs) indicates that HEIs are an important source of growth in the EU countries, also contributing to alleviate the adverse effects during the crisis. This suggests that any EU policy aiming at achieving long term "smart" growth should benefit from taking into account and fostering the contribution of the investment in intangibles associated to HEIs activities. The results show that there is a wide margin to substantially increase research output in the EU without using more resources, suggesting at the same time that any evaluation of this activity should take into account the different dimensions affecting research outcomes: research resources per capita, research quality and specialization.
Many policy-makers find the estimation of rates of return to public R&D to be potentially a very useful figure in better understanding how a direct policy lever might have benefits. Data for such rates of return, which this framework is capable of calculating, are quoted extensively by, for example, UK ministers.
The inclusion of public intangibles in a macro-econometric model is a first attempt to provide policymakers with a flexible framework to evaluate the investment policies taking explicitly into account public and private investment in R&D.
The results from the analytical papers can be used to inform policy and may highlight important policy relevant issues as discussed above. Some the research done offer useful results about the impact of the public sector’s reaction to the crisis and budgetary austerity on economic performance through its effect on the investment on intangibles and the expected long-run consequences for the post-crisis recovery. In particular, about the effects through R&D, human capital accumulation and public IT spending; the role of the complementarity or substitutability between investment in tangibles and intangibles and the case of the New State Members. The specifics on the new knowledge supplied and its relevance for social agents and European policies are to be found in the sixteen policy briefs elaborated
Dissemination activities undertaken in SPINTAN included the following
Project Website: A project website was created at the beginning of the project. The aim of the website was -and still is since it is intended to be kept alive for at least two more years- to provide information on both the researchers and the research produced by the SPINTAN project. The website address is www.spintan.net.
Working Papers: The website contains over 20 working papers covering conceptual and measurement issues as well as analytical papers. The general rule approved by the Steering Committee was that each task should produce at least a working paper. Some of these papers subsequently were published in academic journals. They are posted in the Working Papers section of the website.
Policy briefs: The Steering Committee also approved that, when advisable, the working papers should be accompanied by a corresponding policy brief, and announcement through the project´s twiter account. A total of 16 policy briefs have been published so far. They are available at the Policy Briefs section of the webpage.
Database: The project has generated three databases as outlined above. Thses have been posted in the website for public use. All data are in easily downloadable excel format, with detailed description of variables and sources in both the excel files and, where necessary, in separate explanatory documents. The datasets and corresponding documentation are posted on the SPINTAN data section of the website.
Social Media: The SPINTAN project has taken advantage of the new technologies to disseminate its research. Project policy briefs and working papers have been made available to the general public through the project’s website, www.spintan.net. In addition, information on the activities, presentations, conferences and public events related to the research project is gathered under the website’s events and news sections. The creation of the project’s own twitter profile has also facilitated the access to an audience interested in the value of intangible assets.
– Kick off meeting: January 16-17, 2014. Venue: Fundación Adeit, Valencia (Spain).
– SPINTAN – WP5 meeting. Venue: NIESR, London. October 17, 2014.
– Milestone 2. Workshop to disseminate results of reviews of concepts and measurement. 9-10, December 2014. Venue: The Conference Board Europe. Brussels. Invited keynote participants were: Daniel Ker (UK ONS) and Eric Bartelsman (Vrije Universiteit Amsterdam).
– Milestone 3. Midterm conference and consortium research co-ordination meeting with representation from the EC. April, 23-24, 2015. Invited keynote speakers were John Van Reenen (London School of Economics, Centre for Economic Performance) and Ahmed Bounfour (University of Paris Sud).
– Milestone 5. Workshop to disseminate research results for WP 3-6. November, 12 - 13th, 2015.
– Milestone 9. Workshop and consortium research Co-ordination meeting. Valencia, October, 2016. This meeting took place in Valencia (Mary O’Mahony, Matilde Mas, Juan Fernández de Guevara, Cecilia Jona-Lasinio –video conference-, Lorenzo Serrano) to evaluate the situation of the project and if there was any problem. As all the partners had already informed that all the deliverables would be submitted before the end of the Project, the meeting consisted in reviewing all the tasks and reports that would be need to be fulfilled once the Project was over.
– Milestone 10. Consortium Final Conference with representation from the EC. September, 12 - 13th, 2016. Keynote speakers: Salvatore Rossi (Senior Deputy Governor, Bank of Italy), Pierre Mohnen (Maastricht University), Giorgio Alleva (ISTAT), Valentina Meliciani (LUISS University), Antonella Baldino (Cassa Depositi e Prestiti). Venue: LUISS Guido Carli University, Rome.
Steering Committee meetings
– Thursday, November 12th, 2015 (12:00-13:00). Valencia. The meeting focused on the definition of the strategy both to launch the Working Paper Series, and to disseminate the results of the project. The feasibility of organizing a policy meeting in Brussels was also discussed.
– Tuesday, September 13th, 2016. Rome 13:00-14:15. The meeting was concentrated on the organization of the Policy Meeting in Brussels.
– Smart Public Intangibles – The SPINTAN Project. Main findings and future challenges. November 22nd 2016. The European Commission. ORBN 6/A066, Square Frere Orban, 8, 1040 Brussels. 13:30 – 15:30. The meeting was organized around two main topics: 1) Methods, data and empirical findings and 2) Public intangibles matter: productivity analysis and policy challenges.
29 SPINTAN Manual; Measuring Intangible Capital in the Public Sector: A Manual
Edited by: Carol Corrado, Kirsten Jager and Cecilia Jona-Lasinio
List of Websites:
Professor Matilde Mas Ivars, Scientific Coordinator
Grant agreement ID: 612774
1 December 2013
30 November 2016
€ 3 260 536,40
€ 2 497 762
INSTITUTO VALENCIANO DE INVESTIGACIONES ECONOMICAS, S.A.
This project is featured in...
Deliverables not available
Grant agreement ID: 612774
1 December 2013
30 November 2016
€ 3 260 536,40
€ 2 497 762
INSTITUTO VALENCIANO DE INVESTIGACIONES ECONOMICAS, S.A.
This project is featured in...
Grant agreement ID: 612774
1 December 2013
30 November 2016
€ 3 260 536,40
€ 2 497 762
INSTITUTO VALENCIANO DE INVESTIGACIONES ECONOMICAS, S.A.