Download TFP, intangible assets and spatial dependence in the European

TFP, intangible assets and spatial dependence
in the European regions
Barbara Dettori, Emanuela Marrocu, Raffaele Paci
University of Cagliari and CRENoS, Italy
EU FP7 SSH Project "Intangible Assets and Regional Economic Growth"
Liverpool, ERSA Conference, 2008
General aim of the paper
To provide a measure of the Total Factor Productivity
in the European regions through the estimation of a
production function with the inclusion of traditional
inputs (physical capital, labour) and to asses the role
of the “intangible assets” (human capital, social
capital, technology) within a dynamic spatial model
Motivation
Since the Lisbon declaration in 2000 there is a growing
attention in Europe to the “intangible” assets as they play
a key role in the development of a “knowledge economy”
and thus in determining the economic performances of
countries and regions.
In the industrialized economies the ability to compete in the
open markets is more and more based on production
factors like the quality of labour (human capital), the degree
of cohesion and trust in the society (social capital), the level
of innovation (technology).
However there is a lack of systematic studies on the
simultaneous effects of intangible assets on the economic
performance at the regional level.
Our contribution
In our study we estimate a Cobb-Douglas production function in
order to measure the effects of various inputs on the level of
production.
We consider 200 regions of 17 countries in Europe, the 15
members of the EU15 plus Switzerland and Norway and compute
a measure of TFP for those regions.
We augment the production function including intangible assets
(human capital, social capital, technology) to assess their
simultaneous role in the determination of the production level.
We control for the presence of spatial association by estimating
dynamic spatial models: spatial lag dependent variable and error
models.
Outline of the presentation
1. Estimation of the production function with only traditional
inputs for a long time period 1985-2006 and measure of TFP
at the regional level
•
•
•
•
Data
Estimation methods
Econometric results
TFP
2. Estimation of the production function with the inclusion of
intangible assets for the period 2002-2004
•
•
•
Data
Estimation methods
Econometric results
The European regions considered
Appendix 1. Regions and NUTS level
Country
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Luxembourg
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
N. regions
9
3
1
6
22
40
13
2
20
1
4
7
5
15
8
7
37
NUTS
2
1
0
2
2
2
2
2
2
0
1
2
2
2
2
2
2
Data sources
Cambridge Econometrics, 2008
• Value Added: constant price
• Labour: unit of labour (full time equivalent)
• Hours: total number of hours worked in the year
• Investment: gross fixed investment, constant price
The calculation of the physical capital stock
Methodology
For the period 1985-2006, the stock of physical capital Kit for
each region i and time t is constructed from the flow of gross
investment I by using the perpetual inventory method and
assuming an annual depreciation rate d equal to 10% and
constant over time and region:
Kit = (1 - d) Kit-1 + Iit-1
The capital stock value for the initial year 1984 has been
assumed equal to the cumulative sum of discounted investment
flows over the ten-years period 1975-1984.
Map 1. Physical capital stock (pc average values 1985-2006)
Map 2. Value Added (pc average values 1985-2006)
Map 3. Units of labour (pc average values 1985-2006)
Production function estimation with traditional inputs
Yit = Ai K itα Lβit
Cobb-Douglas production function with pooled data where:
Y = value added at base prices
K = stock of capital
L = labour units (or total number of worked hours per year)
i =1..., 200 regions
t =1985, ... 2006 time period
All variables are normalised to population in order to control for
different size of regions.
Univariate time series properties of the data
According to a wide battery of panel unit root tests, (Levin,
Lin & Chu t*; Breitung t-stat ; Im, Pesaran and Shin Wstat; ADF - Fisher Chi-square; PP - Fisher Chi-square; Hadri
Z-stat) the time series variables of value added, labour
units and capital stock exhibit a nonstationary kind of
behaviour over the sample period 1985-2006.
The results are robust with respect to different specification
of the lag structure and of the deterministic components
included in the test regressions (individual effects,
individual effects and individual trends).
Long run properties of the data
Both panel and group cointegration tests (Pedroni tests and Kao test)
provide evidence that the estimated production function is a nonspurious long run relationship.
Estimation of error correction models allows to test for weak
exogeneity of the variables included in the production function
regression. The test is a t-statistic for the null hypothesis that the
coefficient λi associated with the previous period disequilibrium term
is not significantly different from zero.
The ECM specification for value added is in the form (similar models
are estimated for the two production inputs):
p
p
p
j =1
j =1
j =1
∆y it = c1 + λ1εˆi ,t −1 + ∑ β 11, j ∆y i ,t − j + ∑ β 12, j ∆k i ,t − j + ∑ β 13, j ∆l i ,t − j + u1it
Results: only the labour unit variable can be considered weakly
exogenous with respect to value added and capital.
Spatial models
We detect the presence of spatial autocorrelation and
therefore spatial models has been estimated:
- lag dependent variable
- error term
Results from the two models are similar,
here only the lag dependent variable is presented.
Weight matrix (W): inverse of distance in km.
We have also used the contiguity matrix, results are similar
Estimation procedures
Given the previous results on endogeneity, the production
function has been also estimated with instrumental variables
where the instruments are the values of the variables with one
year lag.
In most regressions Time and Regional fixed effects have been
included.
The econometric model:
yit = ai + αkit + β lit + δWyit + TFE + RFE + ε it
TFP for the European regions. Average 1986-2006 (reg.4)
TFP for the European regions
(computed from regional fixed effect of regr. 4)
The role of intangible assets /1
We include in the production function also the intangible assets
whose positive role on the economic performance is becoming
increasingly important in the industrialized economies.
Human capital. The literature has emphasized its the positive
role on productivity level and growth (Mankiw et al., 1992). At the
regional level a higher availability of well educated labour forces
represents an advantage for the localization of innovative firms
thus promoting local productivity (Rauch, 1993).
Social capital. A high social capital in a certain area correspond
to an increase of trust among agents, a reduction of the
transaction costs for both firms and consumers (Diani, 2004) and
a wider diffusion of knowledge (Helliwell-Putnam, 1995).
All these effects enhance the economic performances (Coleman,
1990; Temple and Johnson 1998, Guiso et al 2007).
The role of intangible assets /2
Technology. The inclusion in the production function of a direct
measure of technology has been originally suggested by Griliches
(1979) and afterwards it has been used at the firms, industries
and aggregate levels. The idea is that technology is partly a
public goods and therefore a higher degree of knowledge capital
available in a certain area benefits firms and thus it improves
aggregate productivity. (Fischer et al 2008 for EU; Robbins
2006 for US).
In general, these “intangible” inputs reinforce together and
improve the level of production because they create a more
favourable economic environment to the firms (see for Italian
regions Marrocu, Paci 2008 AE)
Data sources for intangible assets
• Human capital :
population that has attained at least a university degree (ISCED
5-6) over total population
(source: Eurostat).
• Social capital (social participation):
population that have taken part at least once in the last 12
months in social activities such as voluntary service, unions and
cultural associations meetings over total population
(source: European Social Survey, Round 1 and Round 2).
• Technology :
Patent applications for 1000 inhabitants
(source: OECD, Patent Cooperation Treaty)
also used R&D expenditure but we have data for only one period
(correlation coefficient between patent and R&D =0.82).
Only two time observations are available: 2002 and 2004
Map 5. Human capital (pc average values 2002, 2004)
Map 6. Social capital (pc average values 2002, 2004)
Map 7. Technology (pc average values 2002, 2004)
Production function estimation with intangible inputs
Yit = Ai K itα Lβit HK itγ 1 SK itγ 2Titγ 3
Y = value added at base prices, L = labour units, K = stock of
capital; HK = human capital, SK = social capital, T = technology,
i=1.. 199 regions (Berlin excl.)
t = 2002, 2004 time period.
All variables are normalised to population in order to control for
different size of regions.
The econometric model with spatial lag dependent variable:
yit = ai + αkit + β lit + γ 1hkit + γ 2 skit + γ 3tit + δWyit + ε it
General Results
There is a strong evidence for the presence of spatial
autocorrelation in the production levels across the European
regions.
The coefficient of the spatial lag production level is always
positive and significant and it shows a high elasticity.
The geographical distribution of the estimated TFP confirms a
centre-periphery pattern with low values for the southern
regions in Spain, Italy, Greece and Portugal.
The intangible assets (human capital, social capital and
technology) prove to play a positive and significant role in
determining the production levels.