This paper examines whether there are spillover effects from public intangibles like research and development (R&D) to private sector productivity. The authors constructed a new dataset covering 12 countries, 20 industries, and 4 time periods to analyze the relationship between non-market R&D spending and market sector productivity growth. Statistical analysis found a positive correlation, with countries that had higher lagged non-market R&D exhibiting higher subsequent market sector productivity growth. Regression results also suggested spillover effects from non-market R&D to market productivity, with estimated rates of return on public R&D found to be very high. The findings provide evidence that public investment in intangibles like R&D can boost private sector

1. Spillovers from public intangibles
C. Corrado, (The Conference Board), New York
J. Haskel, (Imperial College Business School, CEPR and IZA), London
C. Jona-Lasinio, (LUISS Lab and ISTAT), Rome
OECD Blue Skies Conference, September 2016. All papers referred to at
www.spintan.net
www.spintan.net has received funding from the European Union's Seventh Framework
Programme for research, technological development and demonstration under grant agreement
No. 612774
Corrado, Haskel, Jona-Lasinio SPINTAN 1 / 12

2. Background and non-technical summary
• The public sector is a major investor in many tangible asset (computers, machines,
roads etc.) and intangible assets (e.g. education, training and R&D)
• Major policy question: are there spillovers from public intangibles to the private
sector?
• Evidence for spillovers from public R&D to the market sector growth exists
e.g. Guellec and van Pottelsberghe (2002, and 2004), Salter and Martin
(2002) and Park (1995).
• Evidence for spillovers from general education to growth harder to detect
(Krueger and Lindahl, 2001) i.e. most benets go to workers
• This paper uses new data to look for eects of public sector RD on private sector
productivity ( spillovers), and estimates the public RD rate of return. This is
hard because:
• Eects are hard to detect since public sector RD often is free, so no
observable market transaction to proxy rate of return (e.g. a licence payment)
• The private sector also invests in tangible and intangibles, so this has to be
controlled for.
• We can summarise our ndings in one picture...
Corrado, Haskel, Jona-Lasinio SPINTAN 2 / 12

3. TFP growth and Non-market RD
• Roughly, countries with high (lagged) non-market RD have higher market-sector
(total factor) productivity growth
• This is conrmed in statistical analysis with more controls etc.
ATDE
ES
FI
FR
IT
NL
SE
UK
US
AT
DE
ES
FI
FR
IT
NL
SE
UK
US
AT
DE
ES
FI
FR
IT
NL
SE
UK
US
-.010.01.02.03
-.06-.04-.020
-.0050.005.01.015
0 .005 .01 .015
0 .005 .01 .015
2004-07 2008-09
2010-13
DlnTFP,marketsector
(Non-market RD/PqQ) in previous period
Graphs by period
Market sector TFP growth and non-market RD
Corrado, Haskel, Jona-Lasinio SPINTAN 3 / 12

4. Exploring spillovers, some details
• We wish to study ∆lnTFP in the market sector and RD and intangibles in the
market and non-market sector
• Thus we need to build a data set with ∆lnTFP in the market sector and intangibles
in the non-market sector
• We need to do this consistently since
• National Accounts now capitalises RD so the interpretation of rates of
return has changed
• If we are to study spillovers from intangibles, we need to build in intangibles,
which means adjusting outputs, inputs and all rates of return
• Public RD spend does not vary much within countries over time, much more
across countries and hence we wish to look across countries
• Thus we construct a consisent database with outputs and inputs with the following
dimensions
• Country (12 countries, US, Northern Europe: DE, FR, UK; Scandinavian: DK
FI, SE; Small Europe: AT, CZ, NL; Mediterranean: ES, IT)
• Industry: 20 industries (A-U Nace Rev 2)
• Institutional sector: Market and non-market within each of the 20 industries
• Asset: Tangible and intangible assets (NA, INTAN Invest and SPINTAN)
• Methodology is discussed in Corrado, Haskel, Jona-Lasinio (2014), SPINTAN
Working Paper N.1
Corrado, Haskel, Jona-Lasinio SPINTAN 4 / 12

5. Exploring spillovers, some details, contd
• To do this we
• Collect output and investment data by year-country-industry-institutional
sector-asset from
national accounts (for output and tangibles) and
own-calculations (for non-national accounts intangibles plus splits by
sector)
• Build capital stocks and consistent capital rental prices
• Link with EU-KLEMS labour composition data
• Calculate growth accounts
• Look at ∆lnTFP in the market sector and RD and intangibles in the market and
non-market sector
Corrado, Haskel, Jona-Lasinio SPINTAN 5 / 12

6. Tangible and intangible investment:
(adjusted) value added shares (2013)
There are substantial dierences in intangible spending between countries...
Corrado, Haskel, Jona-Lasinio SPINTAN 6 / 12

7. Tangible and intangible investment before and after the crisis:
EU vs US
..and intangible spend varies across the cycle.
.6.811.2
1995 2000 2005 2010 20151995 2000 2005 2010 2015
EU US
Tangible Intangible
Year
Graphs by Country_group
Summed using PPPs, 2007=1
Tangible and intangible real investment, EU v US
Corrado, Haskel, Jona-Lasinio SPINTAN 7 / 12

8. Theory and model
Consider a production possibility frontier for country c, industry i, time t relating
value added Q to services of: labour, L, tangible capital K and intangible captial R:
Qc,i,t = Ac,i,tFc,i (Lc,i,t, Kc,i,t, Rc,i,t) (1)
Log dierentiating equation gives:
∆lnQc,i,t = L
c,i,t∆lnLc,i,t + K
c,i,t∆lnKc,i,t + R
c,i,t∆lnRc,i,t + ∆lnAc,i,t (2)
where X
denotes the output elasticity of an input X. Optimisation implies:
X
c,i,t = sX
c,i,t + dX
c,i,t, X = L, K, R (3)
i.e. output elasticities deviate from factor shares by d, where d is due to e.g.,
spillovers. Thus to examine spillovers regress
∆lnTFPQ
c,i,t = dL
c,i,t∆lnLc,i,t + dK
c,i,t∆lnKc,i,t + dR
c,i,t∆lnRc,i,t + ∆lnAc,i,t (4)
where ∆lnTFPQ
c,i,t is calculated as
∆lnTFPQ
c,i,t = ∆lnQc,i,t − sL
c,i,t∆lnLc,i,t − sK
c,i,t∆lnKc,i,t − sR
c,i,t∆lnRc,i,t (5)
Corrado, Haskel, Jona-Lasinio SPINTAN 8 / 12

9. Theory and model, contd
Consider market and non-market
∆lnTFPQ,MKT
c,t = ac + at + dL
∆lnLMKT
c,t + dK
∆lnKMKT
c,t + dR
∆lnRMKT
c,t (6)
+ dR,NonMKT
∆lnRNonMKT
c,t + vc,t
which we write
∆lnTFPQ,MKT
c,t = ac + at + dL
∆lnLMKT
c,t + dK
∆lnKMKT
c,t + dR
∆lnRMKT
c,t (7)
+ ρ(RDNonMKT
/QMKT
)c,t + vc,t
Corrado, Haskel, Jona-Lasinio SPINTAN 9 / 12

10. Theory and model, contd
∆lnTFPQ,MKT
c,t = ac + at + dL
∆lnLMKT
c,t + dK
∆lnKMKT
c,t + dR
∆lnRMKT
c,t (8)
+ ρ(RDNonMKT
/QMKT
)c,t + vc,t
How best to estimate this?
• We worry about measurement error, endogeneity, unobservables, recession,
unobserved cyclicality etc.
• To make things transparent, we simply collapse the data into
• market and non-market sector
• 10 countries
• 4 time periods, 1999-03, 2004-07, 2008-09, 2010-13
• And estimate by random eects
∆lnTFPQ,MKT
c,t = ac + at + dR
∆lnRMKT
c,t−1 (9)
+ ρ(RDNonMKT
/QMKT
)c,t−1 + dL
∆lnLMKT
c,t−1 + vc,t
Corrado, Haskel, Jona-Lasinio SPINTAN 10 / 12

11. Results consistent with spillovers from public RD with high return
Table: Dependent variable: ∆lnTFPMKT . Method: random eects with period dummies,
all variables lagged one period. T stats in brackets
(1) (2) (3) (4) (5) (6)
DlnK_rd_MKT 0.07 0.08 0.07
(1.31) (1.24) (1.11)
N(NonMKT)/PqQ 0.53 0.56 0.56 0.54 0.53
(2.39) (1.80) (1.89) (2.31) (2.55)
DlnK_intan_xrd_MKT 0.10
(1.95)
DlnK_intan_MKT 0.18 0.20
(1.75) (1.61)
DlnL_MKT 0.50
(0.42)
Observations 30 30 30 30 30 30
Number of ctry 10 10 10 10 10 10
r2_o 0.722 0.726 0.732 0.736 0.736 0.740
Corrado, Haskel, Jona-Lasinio SPINTAN 11 / 12

12. Conclusion
• New dataset to look at spillovers from all public intangibles to private sector
• This paper looks for spillovers from public RD using cross-country data,
pooled over 4 year time periods
• Spillovers from market intangibles to market productivity, in line with other
results
• Spillovers from non-market RD to market productivity, rates of return are
very high!
• Spillovers take time
• Broad thrust of work backed up by time series work
• Further work will make data available and look for more eects and rates of
return
Corrado, Haskel, Jona-Lasinio SPINTAN 12 / 12