#  1 Introduction

The main contribution of D7.4 is to introduce the changes undergone in Work
Package 5 which now relies on various data sources to achieve the new
objectives described in the Grant amendment: on the one hand, providing new
Total Productivity Estimates for European countries, and on the other hand,
determining the respective roles of drivers of unemployment in explaining why
some European countries recovered faster than others after the Great
Recession. To do so, Work Package 5 now involves different empirical
estimations. Consequently, two Work Packages are currently using data. The
report is organized in two main sections related to Work Packages 5 and 6
respectively. The conclusion summarizes the key implications regarding the
replicability of the project findings.

#  2 Work Package 6: estimations of the R&D and Technologies adoption
parameters

The first set of estimations relates to R&D parameters. P1-P3 are estimated
based on patent data and OECD data related to Research and Development (R&D)
expenditures. P4 and P5 are based on previous work developed by Comin and
Mestieri (2014), known as the CHAT dataset. Finally, P6 is estimated by
aggregating and merging two sources of data: micro-data coming from ZEW and
the confidential dataset summarizing all agreements between firms and the
Fraunhofer institutes.

##  2.1 Data description: micro datasets

###  2.1.1 Mannheim Innovation Panel (MIP)

The MIP represents an annual survey conducted by ZEW on the behalf of the
German Ministry of Research and Education. The MIP provides information about
the introduction of new products, services and processes, the expenditures for
innovations and how the economic success achieved with new products, new
services and improved processes. In addition, the survey gives information
about the factors which promote and also hinder innovation activities of
enterprises. The innovation survey from ZEW lays an important basis for
evaluating Germany’s technological performance. It is also the basis for the
German Community Innovation Survey (CIS), which constitutes the EU science and
technology statistics surveys carried out each two years by EU member states
and number of ESS member countries.

###  2.1.2 PATSTAT

PATSTAT contains bibliographical and legal status patent data from leading
industrialised and developing countries. This is extracted from the EPO’s
databases and is provided as raw data or online. The PATSTAT product line
forms a unique basis for conducting sophisticated analyses of bibliographic
and legal status data. It has become a standard in the field of patent
intelligence and statistics. The PATSTAT product line consists of three
individual databases. They are available in raw data format or via PATSTAT
Online, a web-based interface to the databases. For the conditions related to
different access modalities, see  https://www.epo.org/searchingfor-
patents/business/patstat.html#tab-2.

##  2.2 Data description: macro datasets

###  2.2.1 GERD dataset - OECD

The OECD data is available online and covers a wide range of OECD and non-OECD
countries. Data is available from 1981 onwards. RD data reported in this
dataset have been collected according to 2002 guidelines of the Frascati
Manual, which have been now superseded by the Manual’s 2015 edition. Revised
definitions are not expected to revise significantly the major indicators.
Data are provided in million national currency (for the euro zone, pre-EMU
euro or EUR), million current PPP USD and million constant USD (2010 prices
and PPPs). This table contains research and development (R&D) expenditure
statistics. Data includes gross domestic R&D expenditures by sector of
performance (business enterprise, government, higher education, private non-
profit, and total intramural) and by source of funds (business enterprise,
government - including public general university funds -, higher education,
private non-profit and funds from abroad - including funds from enterprises
and other funds from abroad).

###  2.2.2 CHAT dataset

The Cross-country Historical Adoption of Technology (CHAT) dataset is an
unbalanced panel with information on the adoption of over 100 technologies in
more than 150 countries since 1800.It contains information on the diffusion of
about 104 technologies in 161 countries during the last 200 years. It extends
the data used in Comin and Hobijn (2004) and Comin, Hobijn, and Rovito (2006).
Almost all of our source data is only available at an annual frequency. For
some of the older technologies, like steamships, data go back until the early
Nineteenth Century. The last year in the sample is 2003. Some data, especially
in the earlier part of the sample, is not available at an annual frequency.
The technology measures in CHAT capture a similar intuition. They are either:
(i) the number of capital goods specifically related to accomplishing
particular tasks, (ii) the amounts of particular tasks that have been
accomplished, (iii) the number of users of a particular manner to accomplish a
task. For more details about CHAT, see  http://www.nber.org/papers/w15319.pdf.

##  2.3 Access to the datasets

###  2.3.1 Micro-data: replication of results vs confidentiality

The MIP data is composed of micro-data (German firms) and their use for
scientific purposes is free of charge. The ZEW places value on the fact that
the whole scientific community benefits from the MIP. The data is placed at
the disposal of external users in an anonymous form (Scientific Use File /
Education Use File) for scientific, non-commercial purposes. At the moment
more than 100 scientists use the Scientific Use files, and over a dozen of
researchers visit ZEW’s Research Data Centre every year. For more information
about the Data Centre, please see  https://kooperationen.zew.de/en/zew-
fdz/home.html.

PatStat is widely used in the academic community to extract patent data. Even
if the latter is private, most of universities with faculties in Economics and
Economics of Innovation acquires licenses for its use. The scripts linked to
the patent extraction can be found on the GitHub depository in order to
replicate the results for each sector and country over time. The only
exception to an opened access to the data concerns the micro-data about the
confidential agreements between the Fraunhofer institutes and German firms to
license, or to perform R&D. The FRAME project could benefit from a specific
allowance to use the data within the FRAME project only (see Appendix A for
more details).

###  2.3.2 Aggregated data: replication and public sources

Besides the limitations previously mentioned, the other data sources are
already publicly available: the GERD dataset is available online  1  , CHAT
dataset  2  . To ease the replication of the research results across Work
Packages, the detailled calibrated estimates will be summarized on the project
website. Moreover, the scripts used in extracting patent data are already
available on the GitHub depository  3  . Doing so allows people with licences
to PatStat to replicate the queries and estimations of P1-P3.

#  3 Work Package 5

The reference method developed by Basu, Fernald, and Kimball (2006) cannot be
easily applied in the European context. The lack of available quaterly data
does not allow a direct application of their methodology to the European case.
However, the EU-KLEMS database provides an alternative by providing series of
annual TFP measures. The main drawback is linked to the assumption behind the
estimation: constant returns to scale and do not adjust for changes in factor
utilization. Work Package 5 proposes to extend these estimations by relying
this assumption and uses EU-KLEMS as a starting point.

##  3.1 TFP estimation: EU countries vs the USA

###  3.1.1 Main datasets: description and access

Work Package 5 uses outputs and inputs measures at the industry level (for
further details, see O’Mahony and Timmer (2009) and Jäger (2017)). Two main
sources of data are used to evaluate the added-value of the developed
methodology. First, the EU KLEMS is used for EU countries to provide annual
industry-level growth accounting data. Second, the World KLEMS data has been
used to assess the relevancy of the methodology to estimate the TFP for the
USA. EU-KLEMS is maintained and updated on the official website of the
initiative:  http: //www.euklems.net/.  Therefore, data is freely available
and recently updated. The EU KLEMS updates in the new ISIC Rev. 4 industry
classification are provided on a country by country basis. Similarly, the use
of capacity utilization is also available on the European Commission website
and freely available  https://ec.europa.eu/info/business-economy-
euro/indicatorsstatistics/economic-databases/business-and-consumer-
surveys/download-business-a  nd  consumer-survey-data_en.  Regarding the USA,
the industry measures used to estimate the TFP are coming from another
publicly dataset, available online  http://www.worldklems.net/ data.htm.  The
US capacity utilization data comes from the Federal Reserve Board’s monthly
reports on Industrial Production and Capacity Utilization (G.17)  4  The data
is constructed by the Federal Reserve on the basis of an underlying Census
Bureau survey of manufacturing firms, the Census Bureau’s Quarterly Survey of
Plant Capacity (QSPC).

In order to tackle endogeneity, Work Package 5 relies on different variables
to instrument the relationship between the capacity utilization and TFP.
Therefore, secondary datasets are also involved.

###  3.1.2 Secondary datasets: description and access

Work Package 5 relies on different sources of shocks to tackle endogeneity:
shock in oil prices, monetary policy shocks, and fiscal policy shocks. Oil
prices are computed by deflating the Brent Europe price of oil with each
country’s GDP deflator for European country. The latter relies on two public
available data sources: the Brent Europe price (COILBRENTEU) from the

World Bank  5  and the OECD GDP data see  https://data.oecd.org/gdp/gross-
domesticproduct-gdp.htm.  For the USA, oil prices are retrieved from FRED
(Federal Reserve Bank of St. Louis) available online at
https://fred.stlouisfed.org/series/WPU114112153.

The variable linked to uncertainty has been computed based on the number of
journal articles per year and country, and known as the Economic Policy
Uncertainty (EPU). This data source is already widely used in the economic and
finances academic communities  6  . The EPU database is maintained and
available by various US universities, its use is also free of charge and
available online:  www.policyuncertainty.com.

The variable based on monetary shocks comes from the ECB policy announcements
which are used in Jarocinski and Karadi (2018) but is not yet publicly
available. For the moment, the study is under review and the summary of the
monetary shocks has been directly communicated to the Work Package 5 team by
Jarocinski and Karadi. When the study will be published, it will be possible
to replicate the estimations with the specified monetary shocks. For the
United States, the WP5 team uses the series of narratively identified monetary
policy shocks from the seminal work of Romer and Romer (2004), as updated in
Wieland and Yang (2016) and provided at an annual frequency in the latter
paper  7  .

For fiscal shocks, WP5 mainly relies on a database on fiscal consolidation
shocks compiled by Alesina et al.(2015), which identifies changes in taxes and
government spending motivated by debt and deficit reduction concerns, and
therefore arguably unrelated to productivity shocks. Their database, which
builds on earlier efforts by Pescatori et al. (2011), is available at the
annual level for all countries in our sample between 1978 and 2014  8  . For
the United States, a measure of exogeneous tax changes developed by Romer and
Romer (2010)  9  is used and available at the quarterly level for the period
1945-2007.

##  3.2 Drivers of unemployment

The aim is to evaluate how much discount factors can explain of the actual
dynamics of unemployment by comparing the unemployment rate predicted by the
model to the actual data for each of the four countries we consider. To
evaluate the extent to which variation in discounts can explain unemployment
variability across EU countries, WP5 relies on realized country specific
returns on stock market data. Data is provided by WRDS.The estimations are
also compared with the Leading Economic Indicators from OECD, as these
measures have been shown to predict stock maret returns (see Zhu and Zhu, 2014
on Financial Research Letters)  10  . Before doing that, WP5 explores the
qualitative predictions of the model by discussing the Impulse-Response
Functions (IRFs) which involves different datasets due to the calibration
exercises.

###  3.2.1 Data description

SDF are estimated with data collected onstock market returns as a measure of
risky return in each country. WRDS offers measures of national stock market
returns and are computed (by WRDS) as weighted averages of price variations of
each stock in each stock exchange. The discussion of the IRF involves
different data sources to calibrate the model. First, to benchmark the model,
the project team of WP5 tries to mimic the results of Hall (2017) by using US
data. The latter is about the stock market prices and dividends. The data has
been collected by Professor Robert Shiller. Secondly, the authors discuss the
results in the light of previous contributions for EU countries, namely Elsby
et al.(2013). The WP5 project team does not follow the same methodology as
Elsby et al. (2013) but computes the productivity shocks by dividing the GDP
of various countries by the total number of workers in these countries. D5.1
and D5.2 use the calibration existing in the literature but is rather US-
biased. In order to provide relevant estimates for the EU countries, the
authors have calibrated the model with OECD data to characterize the labour
market dynamics with the: NRR (net replacement rates, for the new
calibration), DUR_D (unemployed persons by duration, to calibrate job-finding
and separation rates) and MEI (for quarterly measures of unemployment).

###  3.2.2 Access to the datasets

The market returns are estimated based on the computation of a private
database provider, Wharton Research Data Services (WRDS). WRDS offers a
dataset called “World Indices by WRDS”. WRDS computes of stock market returns
at the country level, so what WRDS does her is to aggregate the stock market
returns of listed companies from the security issue level for a given country.
The access to the data requires a license and can be accessed online:
https:// wrds-www.wharton.upenn.edu/pages/support/manuals-and-overviews/wrds-
world-index/.  The Euro OverNight Index Average (EONIA) as a measure of the
net risk-free return. The data is provided by the Statistical Data Warehouse
of the European Central Bank. All series are expressed in percent per annum
and available at monthly frequency. The data collected by Robert

Shiller is available publicly at
http://www.econ.yale.edu/shiller/data/ie_data.xls.  Such dataset has monthly
observations about prices Pt and dividends dt, which have been provided by
Standard and Poor’s. Finally, the productivity shocks are also based on
another public source of data, see
http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10_ gdp&lang=en
and  http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=namq_10_
pe&lang=en.  Finally, the calibration of the labour market parameters is also
by definition available for replication and for different countries to fit the
researchers’ needs for replicability purposes as well. For more information
about the data, please see  https://stats.oecd.org/
Index.aspx?DataSetCode=NRR.

#  4 Conclusion

Broadly speaking, the FRAME project will ease the access to the data and
scripts for replication within the scientific community. Most of the sources
of data is publicly available, either by being in opened access already, or by
belonging to national statistical offices. Consequently, the use, archiving
and, preservation of these datasets follow the rules of the respective
institutions. However, due to confidential agreement and copyrights, two micro
sources of information will not be available (i.e. PatStat and Fraunhofer
Institutes licensing agreements). The confidentiality linked to the data does
not impact the replication of the macro-findings across Work Packages because
the aggregated estimations to define the calibration of the model will be
provided. Only the micro-estimations from Work Package 6 will not be possible
to replicate.

The archiving policy of the data and scripts involved in the FRAME project
will follow the ZEW Data Protection Policy. Consequently, the latter will be
available for replication and scientific purposes for the next 10 years at the
ZEW data center  11  . The upload of the different scripts linked to each type
of modelling will be achieved after receiving the acceptance of scientific
journals of the related studies. Doing so will ensure the publication of the
FRAME results and their replication. The access to the different datasets
involved within the project is centralized on the website  12  to cover the
largest spectrum of users. The project website links to the official sources
of the data but the datasets are not associated to official PID. It is
therefore likely that the url used to cross-link the datasources might be
affected.

The Data Management Plan has been designed to be the most user friendly as
possible by providing access to most of the data involved in the modelling
approaches but also in calibrating the models. The depository will be updated
over the submission process of the different deliverables. The deliverables
will be available on the project website after receiving the acceptance of the
deliverables by the European Commission. The scripts linked to the different
models will be made available for download when the project members will
receive the acceptance of their related articles into scientific journals. The
scripts linked to the modelling approaches are based on the Dynare software,
which is available for free.

#  A Agreement for the exploitation of the Fraunhofer dataset

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