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Discussion Paper 05-2017
Automation and demographic change
Ana Abeliansky, Klaus Prettner
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Automation and demographic change
Ana Abelianskya and Klaus Prettnerb
a) University of Göttingen
Department of Economics
Platz der Göttinger Sieben 3
37073, Göttingen, Germany
email: [email protected]
b) University of Hohenheim
Faculty of Economics and Social Sciences
Schloss, Osthof-West
70593 Stuttgart, Germany
email: [email protected]
Abstract
We analyze the effects of declining population growth on the adoption of automation technology. A standard theoretical framework of the accumulation of traditional
physical capital and of automation capital predicts that countries with a lower population growth rate are the ones that innovate and/or adopt new automation technologies
faster. We test the theoretical prediction by means of panel data for 60 countries over
the time span from 1993 to 2013. Regression estimates provide empirical support for
the theoretical prediction and suggest that a 1% increase in population growth is associated with approximately a 2% reduction in the growth rate of robot density. Our
results are robust to the inclusion of standard control variables, the use of different
estimation methods, the consideration of a dynamic framework with the lagged dependent variable as regressor, and changing the measurement of the stock of robots.
JEL classification: J11, O14, O33, O40.
Keywords: Automation, Industrial Robots, Demographic Change, Declining Population Growth, Economic Growth.
1
1
Introduction
Industrialized countries have experienced substantial declines in fertility and in birth rates
over the last few decades. For example, in the United States, the total fertility rate (TFR)
fell from 3.33 children per woman in the period 1950-1955 to 1.89 children per woman in
the period 2010-2015. Over the same time span, the crude birth rate (CBR) decreased
from 24.4 children per 1000 inhabitants to 12.6 children per 1000 inhabitants (see The
United Nations, 2015, and Table 1 in which we depict the situation in the G7 countries).
These demographic changes have already had a pronounced effect on the evolution of the
labor force. Furthermore, the relatively larger cohorts that entered the labor markets of
these countries in the 1960s and 1970s are now starting to reach the retirement age such
that a substantial decline in the working-age population is most likely to prevail in the
coming decades.
There are many concerns among economists regarding the long-run consequences of
the mentioned demographic developments. For example, it is often argued that social security systems and retirement schemes would need to be reformed to ensure that they are
accurately financed in the future when fewer and fewer workers will have to support ever
more retirees (see Gruber and Wise, 1998; Bloom et al., 2010; The Economist, 2011), there
are concerns that investment rates will decline when the retiring cohorts run down their
assets (Mankiw and Weil, 1989; Schich, 2008), and some are afraid that the innovative
capacity of aging societies will decline (see, for example, Canton et al., 2002; Borghans
and ter Weel, 2002; Irmen and Litina, 2016; Gehringer and Prettner, 2017). Some commentators have even gone so far as to argue that aging is a “threat more grave and certain
than those posed by chemical weapons, nuclear proliferation, or ethnic strife” (Peterson,
1999).
Table 1: TFR in the G7 countries 1950-1955 and 2010-2015 (United Nations, 2015)
Country
Canada
France
Germany
Italy
Japan
U.K.
USA
TFR
1950-1955
3.65
2.75
2.13
2.36
3.00
2.18
3.33
TFR
2010-2015
1.61
2.00
1.39
1.43
1.40
1.92
1.89
CBR
1950-1955
27.4
19.1
15.6
18.2
23.8
15.1
24.4
CBR
2010-2015
10.9
12.4
8.3
8.6
8.3
12.6
12.6
As far as the expected labor shortages due to population aging are concerned, there
is a silver lining on the horizon. In recent years, robots have started to take over many
tasks that have previously been regarded as non-automatable and it is expected that
this trend will continue in the future (see Frey and Osborne, 2013; Arntz et al., 2016;
Acemoglu and Restrepo, 2017, for different views on the extent of this development and
for a discussion on how automation could alleviate the burden of population aging). Very
2
prominent examples that have received an extensive media coverage in recent years are
autonomous cars and lorries that could soon transport passengers and goods without the
need to rely on the (usually highly imperfect) driving skills of humans (fully automated
food deliveries are already present in some cities, see El Pais, 2016); 3D printers are starting
to produce highly specialized products – that could not be mass-manufactured before and
which therefore required a lot of specialized human labor input – at a large scale; software
based on machine learning is now already able to more reliably diagnose diseases than
doctors; and even the skills of authors become more and more obsolete as algorithms are
able to write newsflashes, reports, and even novels on their own.1 Admittedly, it is still
a much bigger pleasure to read “Anna Karenina” than “True Love” (a novel written by
an algorithm programmed to rewrite Anna Karenina in the style of the Japanese author
Haruki Murakami; see Barrie, 2014). However, things might change quite fast and maybe
we will some day find out how “The Castle” or “The Man Without Qualities” could have
come to an end – hopefully in the style of Kafka and Musil, respectively.
The paper is structured as follows. In Section 2, we provide some theoretical considerations on the potential effects of automation in the face of the demographic changes
outlined in the introduction and we assess for which countries with a given demographic
structure the adoption of robots is most likely to occur at a fast rate. In Section 3 we test
the theoretical predictions empirically and in Section 4 we discuss our results and draw
some policy conclusions.
2
Declining population growth and automation: Theoretical considerations
2.1
Basic assumptions
Consider an economy with three production factors, human labor, traditional capital (machines, assembly lines, production halls, office buildings, etc.), and automation capital
(robots, 3D printers, driverless cars, devices based on machine learning, etc). Time t
evolves discretely such that one time step corresponds to approximately 25 years and
the population grows at rate n between time t and time t + 1. Traditional capital and
automation capital can be accumulated and they fully depreciate over the course of one
generation. Human labor and traditional physical capital are imperfect substitutes, while
automation capital is – by its definition – a perfect substitute for labor. Note the special
and non-standard role that automation capital plays in such a setting: on the one hand, it
performs the tasks of human labor and therefore constitutes a perfect substitute for this
production factor; on the other hand, its accumulation resembles the process of standard
physical capital accumulation and the income stream that automation capital generates
1
See, for example, The Economist (2014), Abeliansky et al. (2015), Lanchester (2015), Graetz and
Michaels (2015), Brynjolfsson and McAfee (2016), and Prettner (2017) on different aspects of automation
and on new developments
3
flows to the capital owners/savers of an economy. Overall, we follow the simplified exposition of Solow (1956) and assume that households save a constant fraction s ∈ (0, 1) of
their total income. The savings rate could also be endogenized along the lines of Ramsey
(1928), Cass (1965), and Koopmans (1965) but this would mainly complicate the exposition without adding substantially new insights regarding the effect of demographic change
on automation. In a very interesting contribution, Steigum (2011) analyzes the effects
of automation capital that is a (potentially imperfect) substitute for human labor, on
long-run economic growth.2
2.2
Households and population growth
The population size is given by Nt and its evolution is governed by the difference equation
Nt+1 = (1 + n)Nt ,
where n is the population growth rate. This rate is expected to fall in the future because
of the demographic changes outlined in the introduction. We assume that there is inelastic
labor supply of households and full employment such that the labor force at time t is also
given by Lt ≡ Nt . Consequently, a reduction in the population growth rate translates into
a reduction in the growth rate of the work-force which is realistic, although, of course, it
typically requires a certain amount of time. We abstract from this delay and assume that
the decline of the population growth rate also represents the decline in the work-force.
Aggregate savings are given by St+1 = sNt and there are two savings vehicles, traditional physical capital and automation capital. As a consequence, there is a no-arbitrage
condition that has to hold in any equilibrium in which individuals are investing in both
types of assets. This condition states that the rates of return on traditional physical
capital and on automation capital have to be equal.
2.3
Production and automation
We follow Prettner (2017) and assume that the production function has a Cobb-Douglas
structure with respect to human labor and traditional physical capital. However, the
additional non-standard production factor “automation capital” is a perfect substitute for
labor such that aggregate output is given by
Yt = Ktα [Lt + Pt ]1−α ,
where Kt refers to traditional physical capital, Pt denotes automation capital, and α ∈
(0, 1) is the elasticity of output with respect to traditional physical capital. We abstract
2
Endogenizing the savings rate along the lines of Diamond (1965), however, runs into problems because
the accumulation of automation capital reduces the wages of households in the first period of their lives
and therefore reduces their savings capacity, which leads to a stagnation equilibrium (Sachs and Kotlikoff,
2012; Benzell et al., 2015; Sachs et al., 2015; Gasteiger and Prettner, 2017). However, it is more realistic
that savings are not only made out of wage income but also out of capital income.
4
from factor-augmenting technological progress that would only act as an additional source
of economic growth but it would not alter the crucial mechanisms in our framework.3 We
assume that there is perfect competition on factor markets such that production factors
are paid their marginal value product. Normalizing the price of final output to 1, the wage
rate and the rates of return on the two types of capital are given by
α
Kt
= (1 − α)
,
Lt + Pt
α
Kt
,
= (1 − α)
Lt + Pt
Lt + Pt 1−α
= α
,
Kt
wt
autom
Rt+1
= wt
trad
Rt+1
(1)
(2)
(3)
autom is the gross interest rate paid on automation capital, which is equal to the
where Rt+1
trad is the gross interest rate paid on traditional physical capital. While
wage rate, and Rt+1
the effects of Kt and Lt on wages and on the rate of return on traditional physical capital
are straightforward, we have a non-standard effect of the accumulation of automation capital: As Pt increases, the wage rate decreases because workers compete with automation
capital, whereas the rate of return on traditional physical capital increases because automation capital substitutes for workers and therefore raises the productivity of traditional
physical capital. Together with the fact that the income stream earned by automation
capital flows to the capital owners this mechanism has the potential to explain the decrease
in the labor income share that we have observed over the last few decades (Steigum, 2011;
Prettner, 2017). It is important to note at this point, that, while automation reduces the
marginal value product of labor and thereby the wage rate, labor productivity as measured
by output per worker increases in the wake of automation.
The no-arbitrage condition states that investments in both types of physical capital
autom = Rtrad . Setting Equations
have to yield the same rate of return, i.e., it holds that Rt+1
t+1
(2) and (3) equal to each other and solving for Kt and Pt , respectively, yields
Pt =
1−α
Kt − Lt
α
⇔
Kt =
α
(Pt + Lt ).
1−α
(4)
Plugging the expression for traditional physical capital from Equation (4) into the
aggregate production function provides
Yt =
α
1−α
[Lt + Pt ],
(5)
where it is immediately clear that the standard convergence process to a stationary equi3
For an R&D-based endogenous growth model along the lines of Romer (1990) in which firms can
invest in robot technology, see Hémous and Olsen (2016). In Acemoglu and Restrepo (2015) new tasks are
constantly created by R&D, while, at the same time, old tasks are automated. Both of these papers focus
on a different aspect of the rise of machines than the implications of demographic change for investments
in (and the adoption of) automation technology.
5
librium with no long-run growth that we know from the Solow (1956) model without
technological progress does not hold anymore. Instead, the production function has the
potential to lead to long-run growth if the savings rate is large enough such that a positive
accumulation rate of automation capital can be sustained (cf. Steigum, 2011; Prettner,
2017). Note that Equation (5) resembles the properties of an AK type of production
structure. However, in contrast to standard AK type of growth models this is not due
to an assumption that removes the diminishing marginal product with respect to physical
capital but due to the structure of the production process as derived in the presence of
automation capital.4 Allowing for a different rate of depreciation for traditional physical
capital and for automation capital would leave our central results unchanged. The only
difference would be that an additional constant term (the difference between the rates
of depreciation between the two forms of capital) appeared in Equation (4) and in the
derivations that are based on this equation.
From Equation (5) it follows that per capita GDP is given by
yt =
α
1−α
(pt + 1),
where pt is the automation density in terms of automation capital per capita. We immediately see that the prosperity of a country is positively linked to its automation density. The
intuitive explanation for this is clear. For a given population size, automation overcomes
the diminishing marginal product of traditional physical capital that acts as a boundary
for long-run economic growth in the standard Solow (1956) model (see Prettner, 2017,
for the analysis of the implications of automation for long-run economic growth in such a
setting). Once the tasks which could previously only be carried out by human labor are
automated, the stock of labor becomes, essentially, a reproducible production factor. At
the aggregate level, this implies that there are constant returns to scale with respect to all
reproducible production factors such that automation creates the potential for long-run
growth without additional factor-augmenting technological progress. Next, we analyze
how the automation density itself depends on the demographic setting, which is our main
question of interest that we analyze empirically in Section 3.
2.4
The effect of demographic change on automation density
Since households save a constant fraction s ∈ (0, 1) of their total income Yt and the
economy is closed, aggregate investment is It = sYt such that
Kt+1 + Pt+1 = sYt .
4
Peretto and Saeter (2013) construct a long-run economic growth model in which firms can invest in
technological progress that raises the productivity of physical capital by increasing the elasticity of final
output with respect to physical capital. In the long-run limit, their aggregate production function converges
to an AK type of production function and economic growth is perpetual.
6
Substituting for Kt+1 by the no-arbitrage relationship (4), for Yt by Equation (5), and
dividing by the population size Nt+1 provides the following expression
α(pt+1 + 1)
+ pt+1 = s
1−α
α
1−α
α
1 + pt
.
1+n
Solving this equation for the automation density in period t + 1 as a function of the
automation density in period t and the parameter values of the model yields
pt+1 = s(1 − α)
α
1−α
α
1 + pt
− α.
1+n
(6)
From this equation it follows immediately that a country with a lower population growth
rate will have a higher density of automation technology. We summarize this in the
following proposition.
Proposition 1. Consider a country in which the production structure is described by an
aggregate production function of the form of Equation (5). Households save a constant
fraction s ∈ (0, 1) of their total income (labor income and capital income in the form of
traditional physical capital and automation capital), and the no-arbitrage condition (4)
holds for both types of investments. In this case a country will have a higher density of
automation capital if it has a lower population growth rate (n).
Proof. Taking the derivative of Equation (6) with respect to n we get
∂pt+1
= −s(1 − α)
∂n
α
1−α
α
1 + pt
< 0.
(1 + n)2
Not that this expression is, in general, not equal to -1 such that our result is not just due
to the fact that automation density is defined as the aggregate stock of robots divided by
the population size.
The intuition for this finding is the following: A country in which the population –
and with it the labor force – grows fast, exhibits a comparably high rate of return on
traditional physical capital and there is no need to invest in automation capital. In fact,
in such a country, the rate of return on investment in automation capital tends to be rather
low (think, for example, of African countries with a very fast population growth rate such
as Mali and Niger: investing in automation would not be an attractive business strategy
in these countries). By contrast, in a country in which the population – and with it the
labor force – stagnates or even decreases, the rate of return on investment in automation
is high and the rate of return on investment in traditional physical capital is rather low
(think, for example, of aging European countries such as Germany and Italy and aging
East Asian countries such as Japan and South Korea). Consequently, our theory predicts
that the automation density is high in countries in which the growth rate of the population
is low or even negative.
7
Table 2: Robot density and population growth in the top 10 countries with the highest
robot density (International Federation of Robotics, 2015; United Nations, 2015)
Country
South Korea
Japan
Germany
Italy
Sweden
Denmark
United States
Spain
Finland
Taiwan
robot density per 10,000 employees
in manufacturing
347
339
261
159
157
145
135
131
130
129
average population growth
between 2010 and 2015
0.48%
-0.12%
0.16%
0.07%
0.83%
0.42%
0.75%
-0.21%
0.50%
N/A
Note: The robot density is measured in terms of robots per 10,000 employees in manufacturing in
2015. The population growth rate is calculated as the average population growth rate from 2010 to
2015. The data sources are (International Federation of Robotics, 2015; United Nations, 2015).
A first glimpse on whether this is true is provided by Table 2 that depicts the robot
density as of 2015 together with the average population growth rate in the preceding 5-year
interval from 2010 to 2015 for the ten countries with the highest robot density. In general,
we observe that the population growth rate in these countries is rather low and in some
of them even negative. However, this could just be due to the fact that these countries
are richer, implying that they have a lower fertility rate and that they are, at the same
time, able to invest more in automation technology. In the next section we therefore test
whether our theoretical implication is borne out by the data in a more thorough way.
3
Declining population growth and automation: Empirical
results
In this section we introduce the data, then we test Proposition 1 empirically, and finally
we provide a number of robustness checks.
3.1
Data description
The only available data-set so far to study the adoption of robots is the one collected by
the International Federation of Robotics (IFR). The IFR reports the yearly delivery of
“multipurpose manipulating industrial robots” as defined by the International Organization for Standardization5 for several countries, starting from 1993. We use the data until
2013 because the data for the year 2014 seem to be unreliable: There are several zeroes
that look like reporting errors in comparison to previous values from the data series. In
5
This refers to “Manipulating industrial robot as defined by ISO 8373: An automatically controlled,
reprogrammable, multipurpose manipulator programmable in three or more axes, which may be either fixed
in place or mobile for use in industrial automation applications” (Graetz and Michaels, 2015; International
Federation of Robotics, 2012).
8
the baseline specification we use 3 year averages of the data which provides us with 7
time periods. The sample includes 60 countries for which the data are available (for the
list of countries see Table 9 in the Appendix). We had to combine the NAFTA countries
(Canada, the United States, and Mexico) into one country because they report the values
jointly until 2011.
The IFR also reports the deliveries and stock of robots at the industry level. They
consider that robots have a life-time horizon of 12 years, after which they are deployed
(International Federation of Robotics, 2016). Following Graetz and Michaels (2015), we
use an alternative way to calculate the stock of robots (for all robots and for the manufacturing industry) that relies on the perpetual inventory method under the assumption of a
depreciation rate of 10%. Similar to Graetz and Michaels (2015), we prefer this method
over the one used by the IFR because it is more in line with the standard economics
literature. Since the IFR reports the stock of robots in 1993, this is our first value for the
constructed series. Although all countries report the total stock of robots, not all of them
report the stock nor the deliveries disaggregated at the industry level on a yearly basis.
Given that we are mainly interested in the robots used in the manufacturing sector, we
follow Graetz and Michaels (2015) and take the average share of deliveries of manufacturing robots over the total deliveries of robots (when the data are available), construct
an average share, and impute the values for deliveries of manufacturing robots, as well as
for the initial stock of robots (when the corresponding data were not available). In Table
8 in the Appendix we show the first reported year of robots’ data disaggregated by the
industry level for the countries for which there were gaps in the reported data.
In the following figures we show how the robot density has evolved between the first
period of the sample (1993-1995) and the last one (2011-2013). We discriminate between
percentiles with Figure 1 (covering the period 1993-1995) reporting in the lightest shade
of blue for the 75th percentile, proceeding with the 90th percentile, the 95th percentile,
and finally the last 5% of the distribution (there are a lot of zeroes in this period which is
why we use the 75th percentile as the first cutoff). For comparison, in Figure 2 (covering
the period 2011-2013) we use the same cutoffs as in the previous figure. We observe a
strong increase in robot density, especially in Europe and South Asia. Similar figures but
only for robots used in the manufacturing sector are displayed in the Appendix (Figures
3 and 4).
We also collected information from the International Monetary Fund (IMF) on the
investment share in terms of GDP. We constructed our investment variable summing the
reported values of private investment, public investment, and joint ventures between the
state and the private sector. As for other control variables, we included GDP per capita
measured in constant US$ with a base year of 2010 from the World Development Indicators,
openness measured as exports and imports over GDP, and the gross enrollment ratio in
secondary school as in Busse and Spielmann (2006)6 .
6
The natural choice of a proxy variable for education would have been the mean years of schooling as
reported by (Barro and Lee, 2013). However, this variable is only available in 5 year intervals. Another
9
Figure 1: Average robot density for the period 1993-1995
[0,.00012]
(.00012,.0003]
(.0003,.0005]
(.0005,.003]
No data
Source: IFR and World Development Indicators. Note: The USA, Canada and Mexico
have the same values because of the joint reporting.
Figure 2: Average robot density in the period 2011-2013
[0,.00012]
(.00012,.0003]
(.0003,.0005]
(.0005,.003]
No data
Source: IFR and World Development Indicators. Note: The USA, Canada and Mexico
have the same values because of the joint reporting.
10
3.2
Empirical estimates
Based on Proposition 1, we estimate the relationship between robots adoption and population growth by means of the following equation:
ln(p̂i,t ) = c + α ln(ni,t−1 ) + β ln(si,t−1 ) + γ ln(xi,t−1 ) + dt + i,t ,
(7)
where p̂i,t is the growth rate of the stock of robots (either manufacturing robots, or the
total amount of robots), ni,t−1 is the population growth rate between period t-1 and t-2,
si,t−1 is the investment rate in period t − 1, xi,t−1 is a vector of further control variables
that will be used in the robustness analysis (i.e. GDP per capita), and dt are time-specific
effects to control for events and trends that affect all countries in the same manner, for
example, the global economic and financial crisis that started in 2007/2008. Since we have
zeroes and negative values in the dependent variable and in the population growth rate,
we employed the Box-Cox transformation (Box and Cox, 1964) instead of simply applying
logarithms.7 We apply the logarithmic transformation because this alleviates concerns
regarding heteroskedasticity and non-linearities in the non-transformed variables. We
relied on 3-year averages to alleviate problems regarding measurement errors and businesscycle effects (while the economic growth literature usually relies on 5 year averages, we
would only have 2 consecutive time periods left for estimation in this case).
We first estimate Equation (7) using pooled OLS (POLS) and then proceed with a
random-effects (RE) and a fixed-effects (FE) specification. Finally, we take the potential
dynamics into account by including the lagged dependent variable in the regressions and
applying various corrected fixed effects estimators (CorrFE) following Bruno (2005a,b),
and the system GMM estimator [GMM (sys)] of Blundell and Bond (1998). Note that both
of these types of estimators are seen as remedies for the Nickell (1981) bias in a dynamic
panel data setting. We report the results for the total amount of robots and then also
separately for the subset of manufacturing robots. Moreover, we assess the robustness of
our results by adding a proxy for education, a proxy for GDP per capita, and a proxy for
openness. In other robustness checks reported in the Appendix, we also consider different
depreciation rates in the construction of the robot data series (5% and 15% instead of
10%), and a different transformation of the robot adoption and population growth rates
[a neglog transformation as employed by Whittaker et al. (2005)].
Based on the theoretical considerations we expect to find a negative coefficient for the
population growth rate that is smaller than -1 and a positive sign for the investment rate.
When we include the controls, we expect a positive coefficient for GDP per capita because
higher income implies a lower return to traditional capital accumulation and therefore
alternative would have been to use the literacy rate but this is close to one in most countries of the sample
and there are also a lot of missing values for this variable, so we decided to stick with the gross enrollment
ratio (Busse and Spielmann, 2006, also uses this variable).
7
We created a new variable in the following manner: z = ln(growth rate − k), choosing k such that
the skewness of z is zero. The correlation between the non-transformed variables and the variables in
logarithms (naturally omitting the zeroes and the negative values) is 0.89.
11
a higher incentive to employ robots. Furthermore, a better educated population might
be more inclined to invest in (or adapt to) robots such that he coefficient of education
should also be positive. However, we have no a priori expectation regarding the sign of
the coefficient for openness – on the one hand, as countries become more open, they could
need fewer robots because domestic production could easier be substituted by imports;
on the other hand, open economies are also subject to stronger international competition
such that there is an incentive to automatize the production in search of efficiency gains.
3.2.1
Main Results
Table 3 contains the regression outputs from a baseline specification of Equation (7).
As regressors we include the two crucial variables that are suggested by our theoretical
considerations in Equation (6), the population growth rate and the investment rate. We
observe that there is a negative relationship between population growth and the growth
rate of the robot density in all specifications and it is statistically significant in the majority
of the cases. Only in column (1), which reports the POLS regression, we find the coefficient
not to be statistically significant which is most likely due to the lack of accounting of
country-level heterogeneity. Our results are robust to the dynamic specifications using
the corrected fixed effects estimators, as well as the system GMM estimator. For the
choice between corrected fixed effects and system GMM we prefer the corrected fixed
effects specifications because Judson and Owen (1999) report that this estimator performs
better when the amount of time periods is smaller than 10, which is the case in our
sample. Although the lagged dependent variable is statistically significant, the size of the
coefficient does not suggest strong evidence for the use of a dynamic specification. Our
preferred specification is therefore the fixed effects regression because the Hausman test
suggests that the results from the random effects specification are inconsistent and that
we therefore the need to control for unobserved heterogeneity. The coefficient estimate
for the population growth rate in case of the fixed-effects specification suggests that when
population growth increases by 1%, the robot density growth will decrease by 2%. As
far as the main control variable (the investment share) is concerned, we find the expected
positive relationship, although it is not statistically significant.
Table 4 shows the results for the growth rate of the manufacturing robot density.
As in the previous case, we document a positive correlation, although not statistically
significant, between the investment rate and the growth rate of the manufacturing robots
density. We again find a negative association between the population growth rate and
the robots density growth rate with the size of the coefficients being similar to the those
reported in Table 3. In this case, there is even less evidence than before for the need of a
dynamic specification because the coefficients of the lagged dependent variable are smaller
in size and not even statistically significant in the case of the system GMM estimator.
12
Table 3: The relation between total robots growth and population growth
(1)
POLS
(2)
RE
(3)
FE
(4)
CorrFE (bb)
(5)
CorrFE (ab)
(6)
CorrFE (ah)
(7)
GMM (sys)
0.259***
(0.090)
-1.803***
(0.562)
0.324
(0.340)
0.245**
(0.0987)
-1.828***
(0.557)
0.335
(0.341)
0.226**
(0.111)
-3.515***
(1.205)
0.115
(0.473)
yes
0.922
0.623
60
300
p̂t−1
nt−1
st−1
Country FE
Time FE
AR(2) test
Hansen test
Countries
Observations
-0.539
(0.328)
0.063
(0.119)
-0.694*
(0.354)
0.090
(0.129)
-2.030**
(0.894)
0.419
(0.495)
0.316***
(0.779)
-1.690***
(0.597)
0.304
(0.357)
no
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
60
300
60
300
60
300
60
300
60
300
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
Table 4: The relation between manufacturing robots growth and population growth
(1)
POLS
(2)
RE
(3)
FE
(4)
CorrFE (bb)
(5)
CorrFE (ab)
(6)
CorrFE (ah)
(7)
GMM (sys)
0.197**
(0.086)
-2.055***
(0.570)
0.146
(0.343)
0.180**
(0.0914)
-2.078***
(0.566)
0.155
(0.343)
0.120
(0.120)
-3.908***
(1.237)
0.311
(0.401)
yes
0.623
0.506
60
300
p̂t−1
nt−1
st−1
Country FE
Time FE
AR(2) test
Hansen test
Countries
Observations
-0.457
(0.336)
0.026
(0.095)
-0.632*
(0.368)
0.043
(0.101)
-2.185**
(0.973)
0.175
(0.490)
0.264***
(0.077)
-1.950***
(0.613)
0.132
(0.365)
no
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
60
300
60
300
60
300
60
300
60
300
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
13
3.2.2
Robustness Analysis
As a first robustness check we control for three potential omitted variables: GDP per
capita, openness of the economy, and secondary school enrollment. Omitting these variables could be a source of bias because GDP per capita might be correlated with the
population growth rate due to the fact that richer countries are more able to invest in new
technologies and they are also the ones that are disproportionally affected by declining
fertility as outlined in Section 1; an open economy might be under more pressure to stay
competitive, and, at the same time, smaller economies by means of the population size
tend to be more open; and education is highly correlated with GDP per capita, while, at
the same time, a better educated population might be more inclined to invest in (or adapt
to) robots.
Table 5: Total robots growth including controls
(1)
POLS
(2)
RE
(3)
FE
(4)
CorrFE (bb)
(5)
CorrFE (ab)
(6)
CorrFE (ah)
(7)
GMM (sys)
0.137
(0.085)
-1.494**
(0.704)
-0.337
(0.443)
2.280***
(0.784)
0.111
(0.171)
-0.136
(0.503)
0.140
(0.088)
-1.485**
(0.708)
-0.336
(0.445)
2.283***
(0.787)
0.111
(0.171)
-0.139
(0.506)
0.279
(0.202)
-3.247*
(1.879)
-0.316
(0.485)
-0.080
(0.421)
0.334
(0.244)
-0.144
(0.795)
yes
0.979
0.156
57
262
p̂t−1
nt−1
st−1
yt−1
et−1
opent−1
Country FE
Time FE
AR(2) test
Hansen test
Countries
Observations
-0.565
(0.379)
0.092
(0.130)
-0.172**
(0.073)
0.148
(0.180)
0.040
(0.142)
-0.731*
(0.422)
0.107
(0.134)
-0.151**
(0.073)
0.133
(0.176)
0.034
(0.155)
-1.554**
(0.689)
-0.416
(0.556)
2.535***
(0.911)
0.112
(0.192)
-0.088
(0.519)
0.210**
(0.082)
-1.377*
(0.754)
-0.377
(0.486)
2.316***
(0.883)
0.106
(0.185)
-0.149
(0.552)
no
yes
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
262
57
262
57
262
57
262
57
262
57
262
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
Table 5, which includes the mentioned control variables, shows again a negative correlation between robot density growth and population growth. The magnitude of the
coefficients in the different specifications are marginally smaller than in the previous tables. However, except for the pooled OLS specification, they are statistically significant
at the 5% or at the 10% level. One reason for this could be that we had to accept a
reduction in the sample size because of several missing observations for the openness and
the secondary enrollment variables. The coefficient estimate of the investment rate is still
14
Table 6: Manufacturing robots growth including controls
(1)
POLS
(2)
RE
(3)
FE
(4)
CorrFE (bb)
(5)
CorrFE (ab)
(6)
CorrFE (ah)
(7)
GMM (sys)
0.064
(0.079)
-1.700**
(0.703)
-0.567
(0.441)
2.551***
(0.785)
0.174
(0.171)
-0.033
(0.504)
0.60
(0.081)
-1.697**
(0.706)
-0.570
(0.442)
2.580***
(0.787)
0.173
(0.171)
-0.036
(0.507)
0.043
(0.131)
-1.833
(1.218)
-0.241
(0.349)
-0.523***
(0.169)
0.352*
(0.180)
-0.392
(0.659)
yes
yes
57
262
yes
yes
57
262
yes
0.720
0.234
57
262
p̂t−1
nt−1
st−1
yt−1
et−1
opent−1
Country FE
Time FE
AR(2) test
Hansen test
Countries
Observations
-0.472
(0.382)
0.061
(0.109)
-0.197***
(0.068)
0.187
(0.175)
0.024
(0.148)
-0.636
(0.422)
0.067
(0.108)
-0.181***
(0.067)
0.182
(0.166)
0.021
(0.158)
-1.726**
(0.702)
-0.646
(0.558)
2.617***
(0.841)
0.174
(0.174)
0.000
(0.515)
0.148*
(0.078)
-1.599**
(0.771)
-0.586
(0.496)
2.531***
(0.899)
0.171
(0.189)
-0.059
(0.566)
no
yes
57
262
no
yes
57
262
yes
yes
57
262
yes
yes
57
262
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
not statistically significant across the specifications, as in the previous case. In columns
(1) and (2), GDP per capita has a negative sign, which is surprising given that we expect
that richer countries would be able to invest more in new technologies. However, GDP
per capita reverts its sign from column (3) onwards. We believe that the reason for this is
the presence of unobserved heterogeneity, as also the Hausman test indicates. Secondary
enrollment has the predicted sign, although it is not statistically significant. Finally, openness has a negative sign in most of the specifications although none of the coefficients is
statistically significant. Finally, the coefficient size of the lagged dependent variable shows
that there is no pressing need to take the dynamics into account in the regression.
Turning to the manufacturing robots (Table 6), we observe a similar pattern as for the
case of total robots. All specifications show a negative correlation between manufacturing
robots growth and population growth. In contrast to the previous results, we find no
statistical significance in column (7). However, this could be related to the fact that we do
not need a dynamic specification and that the system GMM estimator is known to be very
inefficient in case of a small time dimension. As in the previous tables we find no evidence
of the importance of investment or secondary schooling for robots adoption. Similar to the
case of total robots, we find a positive relationship between GDP per capita and the growth
rate of manufacturing robots density. A puzzling result is the change in the sign of per
capita GDP in case of the system GMM estimator. However, the estimations performed
15
with the corrected fixed effects estimators still exhibit the significantly positive result.
In Tables 10 and 11 in the Appendix we report the same specification but omitting the
controls that were not statistically significant (i.e., secondary enrollment and openness).
The results do not change dramatically but the significance of the puzzling negative sign
of per capita GDP in case of the system GMM estimator vanishes.
As further robustness checks, we used 2-year averages instead of averaging the data over
3 years. Tables 12 and 13 in the Appendix show the corresponding results. As before, we
observe a statistically significant negative correlation of the population growth rate with
the growth of robot density (either of the total stock of robots or the ones employed in the
manufacturing sector). However, the magnitude of the correlation is smaller in absolute
value. The investment rate coefficient continues to be statistically insignificant in both
tables, having a positive sign in most of the cases. Only in column (7) of Table 13 the
coefficient of the investment rate is negative, although this estimate should be considered
with caution because the AR(2) test cannot rule out remaining autocorrelation of the
residuals at the 10% significance level. Moreover, we also constructed two alternative
robot stocks using 5% and 15% as alternative depreciation rates. The estimates for the
baseline model can be seen in Tables 14 and 16 (for total robots) and Tables 15 and 16
(for manufacturing robots) in the Appendix. We find no substantial differences with our
previous estimates. As the two final robustness checks we use the neglog transformation for
both the population growth rate and the robot density growth rate. This transformation
involves making the following adjustments to the variable (which we call x for simplicity).
If x <= 0, then we use − ln(−x+1) instead and if x > 0, then we use ln(x+1) instead. The
results are shown in Tables 18 and 19 of the Appendix. Again, the results remain similar
in terms of the sign and the statistical significance, although the size of the coefficients
is higher. The results show some sensitivity with relation to the transformation of the
variables that can take the value of 0 or negative values.
4
Conclusions
We propose a theoretical framework of production in the age of automation for countries
that are subject to declining population growth and population aging. In so doing we
introduce a new production factor that resembles the properties of labor in the production process, i.e., it is a perfect substitute for labor, while it resembles the properties of
traditional physical capital in the accumulation process, i.e., it is accumulated in the same
manner as physical capital due to the savings and investment behavior of households.
For this case the standard Solow (1956) framework predicts, under certain circumstances,
perpetual growth even without technological progress and a declining labor income share
(Prettner, 2017). In our contribution we show that countries with a lower population
growth rate have a stronger incentive to invest in the adoption of automation. The empirical estimates that we present, subject to several robustness tests, support this theoretical
prediction.
16
As far as the policy implications are concerned, our theoretical and empirical findings
suggest that countries, which are subject to larger demographic challenges, will be the
first to adopt and/or invent new automation technologies. This in turn might help them
to overcome some of the negative effects that declining population growth and population
aging imply for long-run economic prosperity, issues that also the media is nowadays
heavily concerned with (see, for example, The Washington Post, 2016). Of course, the
transition to automation technologies might not be all that smooth because automation
capital competes with labor and therefore could act so as to depress wages. If this concern
is valid and widespread, it might lead to resistance against automation from labor unions
and the population at large. Altogether, it might therefore be in everybody’s interest
if governments enact policies that alleviate the burden of those who suffer because of
automation. Potential policies along these lines could include education subsidies and
re-training programs for those who loose their jobs because of automation, making sure
that unemployment insurance is widely available, etc. Furthermore, it would at some
point be necessary to rethink how social security systems are financed because the main
contribution is now made by the production factor labor. If labor income becomes a
smaller and smaller share of total income, however, alternatives would need to be found.
One remedy that is often suggested would be to make sure that everybody owns some
part of the automation capital of an economy, for example, a driverless car that earns an
income stream for him or her (Pratt, 2015; The Economist, 2017).
Of course we have to admit that our framework stayed deliberately simple and the
results that we present are meant as a first step in the direction of analyzing the interrelations between demography and automation. In reality, there are different skill groups
in the population and the tasks that are performed by the different skill groups might be
more or less suited to automation and they might even change over time (cf. Acemoglu
and Restrepo, 2015). A more detailed framework should be able to take this into account
and to empirically distinguish between the education level of different types of workers,
and also the heterogeneity of tasks that workers perform. However, this crucially hinges
on the data for automation in general, and robots, in particular, to become more widely
available. Furthermore, a more detailed modeling of demographic change is called for that
takes survival to old age and changing life expectancy into account. First steps in this
direction have been undertaken by Gasteiger and Prettner (2017).
Acknowledgments
We would like to thank Matthias Beulmann, Georg Graetz, Volker Meier, and the participants at the annual meeting of the field committee for population economics of the
German Economic Association in Passau 2017 for valuable comments and suggestions and
David Höhle for excellent research assistance. We are grateful for the funding provided by
the Faculty of Economics and Social Sciences at the University of Hohenheim within its
research focus “Inequality and Economic Policy Analysis (INEPA)”.
17
5
5.1
Appendix
Summary Statistics
Table 7: Summary statistics
Variable (in logs)
p̂t−1
nt−1
si;t−1
yt−1
et−1
opent−1
5.2
Observations
300
300
300
300
267
295
Mean
4.300
-2.057
2.879
9.351
4.368
4.262
Std. Dev.
0.909
0.239
0.609
1.262
0.540
0.523
Minimum
-2.126
-2.788
-1.697
6.539
1.616
2.789
Maximum
8.249
-1.179
3.815
11.408
5.065
6.033
Countries included
Table 8: Countries with adjusted values to create manufacturing stock
Country
Argentina
Australia
Austria
Belgium
Brazil
Bulgaria
Canada
Chile
China
Denmark
Greece
Hungary
Iceland
Malta
Peru
India
Indonesia
Ireland
Israel
Japan
Year
2004
2006
2003
2004
2004
2006
2011
2005
2006
1996
2006
2004
2006
2006
2006
2006
2006
2006
2005
1996
Country
South Korea
Malaysia
Mexico
Netherlands
New Zealand
Philippines
Poland
Portugal
Romania
Russia
Singapore
Slovakia
Slovenia
South Africa
Switzerland
Thailand
Turkey
USA
Vietnam
Year
2001 (gap in 2002)
2006
2011
2004
2006
2006
2004
2004
2004
2004
2005
2004
2005
2005
2004
2005
2005
2004
2005
Note: The year indicates the first time that the country reported disaggregated deliveries of robots at the industry level.
18
Table 9: Countries included in the sample
Argentina
Australia
Austria
Belgium
Brazil
Bulgaria
Chile
China
Colombia
Croatia
Czech Republic
Denmark
Egypt
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
India
Indonesia
Ireland
Israel
Italy
Japan
South Korea
Kuwait
Lithuania
Malaysia
Moldova
Morocco
NAFTA
Netherlands
New Zealand
Norway
Oman
Pakistan
Peru
Philippines
Poland
Portugal
Romania
Russia
Saudi Arabia
19
Serbia
Singapore
Slovakia
South Africa
Spain
Sweden
Switzerland
Thailand
Tunisia
Turkey
Ukraine
United Kingdom
Uzbekistan
Venezuela
Vietnam
5.3
Distribution of the manufacturing stock of robots
Figure 3: Average manufacturing robot density for the period 1993-1995
[0,.0001021]
(.0001021,.0002529]
(.0002529,.0004311]
(.0004311,.003]
No data
Source: IFR and World Development Indicators. Note: The USA, Canada and Mexico
have the same values because of the joint reporting.
Figure 4: Average manufacturing robot density in the period 2011-2013
[0,.0001021]
(.0001021,.0002529]
(.0002529,.0004311]
(.0004311,.003]
No data
Source: IFR and World Development Indicators. Note: The USA, Canada and Mexico
have the same values because of the joint reporting.
20
5.4
Estimates with only GDP per capita as control
Table 10: Total robots growth including GDP per capita
(1)
POLS
(2)
RE
(3)
FE
(4)
CorrFE (bb)
(5)
CorrFE (ab)
(6)
CorrFE (ah)
(7)
GMM (sys)
0.197**
(0.092)
-1.430**
(0.611)
0.053
(0.374)
1.855**
(0.737)
0.197**
(0.100)
-1.421**
(0.607)
0.052
(0.374)
1.872**
(0.735)
0.119
(0.163)
0.565
(8.093)
0.003
(0.420)
-0.554
(1.130)
yes
yes
60
300
yes
yes
60
300
yes
0.438
0.591
60
300
p̂t−1
nt−1 h
st−1
yt−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.601*
(0.320)
0.102
(0.143)
-0.137***
(0.049)
-0.732**
(0.345)
0.123
(0.148)
-0.131***
(0.048)
-1.444*
(0.758)
0.003
(0.557)
2.195***
(0.817)
0.250***
(0.079)
-1.283*
(0.659)
-0.006
(0.400)
1.944**
(0.800)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent level,
respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while the ones from (4) to
(6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and an orthogonal transformation. All of the
variables are in logarithms, while population growth and robots growth were transformed with the Box-Cox transformation.
CorrFE refers to the corrected fixed effects with “bb” indicating initialization by the Blundell and Bond (1998) estimator,
“ab” initialization by the Arellano and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982)
estimator.
21
Table 11: Manufacturing robots growth including GDP per capita
(1)
POLS
(2)
RE
(3)
FE
(4)
CorrFE (bb)
(5)
CorrFE (ab)
(6)
CorrFE (ah)
(7)
GMM (sys)
0.124
(0.087)
-1.587***
(0.614)
-0.191
(0.376)
2.174***
(0.740)
0.119
(0.091)
-1.577***
(0.612)
-0.197
(0.376)
2.215***
(0.739)
0.005
(0.082)
0.466
(4.403)
0.020
(0.476)
-0.626
(0.511)
yes
yes
60
300
yes
yes
60
300
yes
0.250
0.427
60
300
p̂t−1
nt−1
st−1
yt−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.525
(0.326)
0.069
(0.119)
-0.152***
(0.046)
-0.667*
(0.355)
0.080
(0.120)
-0.145***
(0.046)
-1.554*
(0.806)
-0.272
(0.533)
2.365***
(0.717)
0.186**
(0.078)
-1.468**
(0.674)
-0.229
(0.409)
2.221***
(0.815)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while the
ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and an orthogonal
transformation. All of the variables are in logarithms, while population growth and robots growth were transformed
with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with “bb” indicating initialization by
the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano and Bond (1991) estimator, and “ah”
initialization by the Anderson and Hsiao (1982) estimator.
22
5.5
Baseline estimates using 2-year averages instead of 3-year averages
Table 12: Total robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.351***
(0.050)
-0.736**
(0.343)
0.247
(0.196)
0.393***
(0.051)
-0.706*
(0.370)
0.257
(0.208)
0.291***
(0.071)
-1.415*
(0.760)
0.091
(0.155)
yes
yes
60
539
yes
yes
60
539
yes
0.143
0.276
60
539
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.435
(0.294)
0.093
(0.099)
-0.606*
(0.344)
0.135
(0.108)
-1.160*
(0.594)
0.380
(0.326)
0.366***
(0.049)
-0.717**
(0.359)
0.230
(0.214)
no
yes
60
539
no
yes
60
539
yes
yes
60
539
yes
yes
60
539
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
23
Table 13: Manufacturing robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.316***
(0.050)
-0.790**
(0.346)
0.148
(0.199)
0.369***
(0.051)
-0.754**
(0.376)
0.169
(0.213)
0.297***
(0.083)
-1.398*
(0.780)
-0.033
(0.195)
yes
yes
60
539
yes
yes
60
539
yes
0.055
0.155
60
539
p̂t−1
nt−1
st−1
Country FE
Year Dummies
AR(2) test
Hansen test
Countries
Observations
-0.336
(0.292)
0.058
(0.074)
-0.519
(0.347)
0.088
(0.079)
-1.142*
(0.604)
0.247
(0.316)
0.341***
(0.049)
-0.775**
(0.364)
0.132
(0.219)
no
yes
60
539
no
yes
60
539
yes
yes
60
539
yes
yes
60
539
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
24
5.6
Baseline estimates using 5% depreciation rate
Table 14: Total robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.294***
(0.088)
-1.835***
(0.612)
0.405
(0.371)
0.280***
(0.099)
-1.862***
(0.608)
0.419
(0.374)
0.299**
(0.137)
-2.687**
(1.291)
-0.146
(0.622)
yes
yes
60
300
yes
yes
60
300
yes
0.783
0.177
60
300
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.591*
(0.332)
0.077
(0.125)
-0.718**
(0.353)
0.103
(0.136)
-2.151**
(0.937)
0.545
(0.519)
0.339***
(0.077)
-1.731***
(0.645)
0.385
(0.387)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
Table 15: Manufacturing robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.246***
(0.085)
-2.116***
(0.623)
0.244
(0.376)
0.227**
(0.093)
-2.147***
(0.618)
0.258
(0.376)
0.200
(0.128)
-3.024***
(1.117)
0.094
(0.458)
yes
yes
60
300
yes
yes
60
300
yes
0.884
0.119
60
300
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.526
(0.345)
0.051
(0.111)
-0.673*
(0.370)
0.070
(0.117)
-2.332**
(1.018)
0.318
(0.517)
0.300***
(0.076)
-2.018***
(0.662)
0.229
(0.395)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
25
5.7
Baseline estimates using 15% depreciation rate
Table 16: Total robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.240***
(0.092)
-1.763***
(0.528)
0.266
(0.319)
0.227**
(0.098)
-1.782***
(0.523)
0.272
(0.319)
0.174
(0.106)
-4.050***
(1.377)
0.291
(0.542)
yes
yes
60
300
yes
yes
60
300
yes
0.790
0.891
60
300
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.515
(0.323)
0.055
(0.118)
-0.683*
(0.353)
0.081
(0.126)
-1.945**
(0.858)
0.337
(0.477)
0.301***
(0.079)
-1.658***
(0.562)
0.247
(0.335)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
Table 17: Manufacturing robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.162*
(0.085)
-1.998***
(0.534)
0.071
(0.321)
0.149*
(0.090)
-2.012***
(0.531)
0.075
(0.320)
0.071
(0.123)
-4.411***
(1.430)
0.290
(0.491)
yes
yes
60
300
yes
yes
60
300
yes
0.481
0.813
60
300
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-0.419
(0.328)
0.009
(0.086)
-0.605*
(0.365)
0.022
(0.091)
-2.079**
(0.938)
0.072
(0.469)
0.233***
(0.078)
-1.901***
(0.575)
0.059
(0.342)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
26
5.8
Baseline estimates using the neglog transformation
Table 18: Total robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.477***
(0.081)
-21.720**
(10.138)
1.327
(0.916)
0.456***
(0.092)
-22.657**
(9.892)
1.383
(0.921)
0.473***
(0.105)
-40.401***
(14.349)
-0.575
(1.030)
yes
yes
60
300
yes
yes
60
300
yes
0.100
0.186
60
300
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-12.135*
(6.436)
0.321
(0.430)
-15.798**
(6.399)
0.499
(0.475)
-35.286*
(18.480)
2.409**
(0.957)
0.496***
(0.068)
-20.726**
(10.299)
1.275
(0.909)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
Table 19: Manufacturing robots
(1)
POLS
(2)
RE
(3)
FE
(4)
corrFE (bb)
(5)
corrFE (ab)
(6)
corrFE (ah)
(7)
GMM (sys)
0.192**
(0.086)
-15.714***
(3.880)
0.237
(0.347)
0.174*
(0.918)
-15.892***
(3.846)
0.247
(0.347)
0.186*
(0.110)
-23.165***
(8.161)
0.152
(0.355)
yes
yes
60
300
yes
yes
60
300
yes
0.798
0.219
60
300
p̂t−1
nt−1
st−1
Country FE
Year FE
AR(2) test
Hansen test
Countries
Observations
-4.084
(2.791)
0.030
(0.094)
-5.570*
(3.069)
0.049
(0.100)
-16.691**
(7.375)
0.266
(0.469)
0.257***
(0.077)
-14.854***
(4.150)
0.219
(0.369)
no
yes
60
300
no
yes
60
300
yes
yes
60
300
yes
yes
60
300
Note: We report standard errors in parenthesis. ***, **, and * indicate significance at the 1, 5, and 10 percent
level, respectively. The standard errors of columns (1), (2), and (3) are clustered at the country level, while
the ones from (4) to (6) are bootstrapped with 50 iterations. Column (7) uses collapsed instruments and
an orthogonal transformation. All of the variables are in logarithms, while population growth and robots
growth were transformed with the Box-Cox transformation. CorrFE refers to the corrected fixed effects with
“bb” indicating initialization by the Blundell and Bond (1998) estimator, “ab” initialization by the Arellano
and Bond (1991) estimator, and “ah” initialization by the Anderson and Hsiao (1982) estimator.
27
References
Abeliansky, A., Martinez-Zarzoso, I., and Prettner, K. (2015). The Impact of 3D Printing
on Trade and FDI. cege Discussion Paper 262.
Acemoglu, D. and Restrepo, P. (2015). The Race Between Man and Machine: Implications
of Technology for Growth, Factor Shares and Employment. Mimeo.
Acemoglu, D. and Restrepo, P. (2017). Secular Stagnation? The Effect of Aging on
Economic Growth in the Age of Automation. NBER Working Paper 23077.
Anderson, T. W. and Hsiao, C. (1982). Formulation and estimation of dynamic models
using panel data. Journal of Econometrics, Vol 18:47–82.
Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo
evidence and an application to employment equations. Review of Economic Studies,
Vol. 58:277–297.
Arntz, M., Gregory, T., and Zierahn, U. (2016). The Risk of Automation for Jobs in
OECD Countries. A Comparative Analysis. OECD Social, Employment and Migration
Working Papers, No. 189, OECD Publishing, Paris.
Barrie, J. (2014). Computers Are Writing Novels: Read A Few Samples Here. Available at
http://www.businessinsider.com/novels-written-by-computers-2014-11?IR=T
[Accessed at January 22, 2017].
Barro, R. J. and Lee, J.-W. (2013). A New Data Set of Educational Attainment in the
World, 1950-2010. Journal of Development Economics, Vol. 104:184–198.
Benzell, S. G., Kotlikoff, L. J., LaGardia, G., and Sachs, J. D. (2015). Robots are us:
Some Economics of human replacement. NBER Working Paper 20941.
Bloom, D. E., Canning, D., and Fink, G. (2010). Implications of Population Ageing for
Economic Growth. Oxford Review of Economic Policy, Vol. 26(No. 4):583–612.
Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic
panel data methods. Jornal of Econometrics, Vol. 87:115–143.
Borghans, L. and ter Weel, B. (2002). Do older workers have more trouble using a computer
than younger workers? The Economics of Skills Obsolescence, Vol. 21:139–173.
Box, G. E. and Cox, D. R. (1964). An analysis of transformations. Journal of the Royal
Statistical Society. Series B (Methodological), pages 211–252.
Bruno, G. S. F. (2005a). Approximating the bias of the LSDV estimator for dynamic
unbalanced panel data models. Economics Letters, Vol. 87:361–366.
28
Bruno, G. S. F. (2005b). Estimation and inference in dynamic unbalanced panel-data
models with a small number of individuals. The Stata Journal, Vol. 5(No. 4):473–500.
Brynjolfsson, E. and McAfee, A. (2016). The Second Machine Age: Work, Progress, and
Prosperity in a Time of Brilliant Technologies. Norton & Company.
Busse, M. and Spielmann, C. (2006). Gender inequality and trade. Review of International
Economics, 14(3):362–379.
Canton, E., de Groot, H., and Hahuis, R. (2002). Vested interests, population ageing and
technology adoption. European Journal of Political Economy, Vol. 18(No. 4):631–652.
Cass, D. (1965). Optimum Growth in an Aggregative Model of Capital Accumulation.
The Review of Economic Studies, Vol. 32(No. 3):233–240.
Diamond, P. A. (1965). National Debt in a Neoclassical Growth Model. American Economic Review, Vol. 55(No. 5):1126–1150.
El Pais (2016). Los robots de reparto llegan a las calles de Europa y Estados Unidos. El
Pais, December 18th 2016.
Frey, C. B. and Osborne, M. A. (2013). The Future of Employment: How Susceptible are Jobs to Computerisation?
Available at https://web.archive.org/web/
20150109185039/http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_
Future_of_Employment.pdf.
Gasteiger, E. and Prettner, K. (2017). The effect of automation in an OLG economy.
Mimeo.
Gehringer, A. and Prettner, K. (2017). Longevity and technological change. Macreoconomic Dynamics. (forthcoming).
Graetz, G. and Michaels, G. (2015). Robots at Work. CEPR Discussion Paper 10477.
Gruber, J. and Wise, D. (1998). Social security and retirement: an international comparison. American Economic Review, Vol. 88:158–163.
Hémous, D. and Olsen, M. (2016). The Rise of the Machines: Automation, Horizontal
Innovation and Income Inequality. Mimeo.
International Federation of Robotics (2012). World Robotics Industrial Robots 2012.
International Federation of Robotics (2015).
Robot density: Top 10 countries with
most industrial robots for every 10,000 people employed in manufacturing. Available
at https://roboticsandautomationnews.com/2015/07/17 [Accessed at January 22,
2017].
International Federation of Robotics (2016). World Robotics Industrial Robots 2016.
29
Irmen, A. and Litina, A. (2016). Population Aging and Inventive Activity. CESifo Working
Paper No. 5841.
Judson, R. A. and Owen, A. L. (1999). Estimating dynamic panel data models: a guide
for macroeconomists. Economics Letters, Vol. 65:9–15.
Koopmans, T. C. (1965). On the Concept of Optimal Economic Growth. In The Econometric Approach to Development Planning. Amsterdam: North Holland.
Lanchester, J. (2015). The Robots Are Coming. London Review of Books, Vol. 37(No.
5):3–8.
Mankiw, G. N. and Weil, D. N. (1989). The Baby-Boom, the Baby-Bust and the Housing
Market. Regional Science and Urban Economics, Vol. 19:235–258.
Nickell, S. S. (1981). Biases in dynamic models with fixed effects. Econometrica, Vol.
49:1117–1126.
Peretto, P. F. and Saeter, J. J. (2013). Factor-eliminating technical change. Journal of
Monetary Economics, Vol. 60(No. 4):459–473.
Peterson, P. G. (1999). Gray Dawn: The Global Aging Crisis. Foreign Affairs, (January/February).
Pratt, G. A. (2015). Is a Cambrian Explosion Coming for Robotics? Journal of Economic
Perspectives, Vol. 29(No. 3):51–60.
Prettner, K. (2017). A note on the implications of automation for economic growth and
the labor share. Macroeconomic Dynamics. (forthcoming).
Ramsey, F. P. (1928). A Mathematical Theory of Saving. The Economic Journal, Vol.
38(No. 152):543–559.
Romer, P. (1990). Endogenous Technological Change. Journal of Political Economy, Vol.
98(No. 5):71–102.
Sachs, J. D., Benzell, S. G., and LaGardia, G. (2015). Robots: Curse or blessing? A basic
framework. NBER Working Paper 21091.
Sachs, J. D. and Kotlikoff, L. J. (2012). Smart machines and long-term misery. NBER
Working Paper 18629.
Schich, S. (2008). Revistiting the asset-meltdown hypothesis. OECD Journal: Financial
Market Trends, Vol. 2008(No. 2):209–222.
Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly
Journal of Economics, Vol. 70(No. 1):65–94.
30
Steigum, E. (2011). Frontiers of Economics and Globalization: Economic Growth and
Development, chapter 21: Robotics and Growth, pages 543–557. Emerald Group.
The Economist (2011). 70 or bust! Why the retirement age must go up. A special report
on pensions. The Economist, April 7th 2011.
The Economist (2014). Immigrants from the future. A special report on robots. The
Economist, March 27th 2014.
The Economist (2017). Why taxing robots is not a good idea. february 25th 2017.
The Washington Post (2016). Robots wont kill the workforce. Theyll save the global
economy. The Washington Post, December 2nd 2016.
United Nations (2015). World Population Prospects: The 2015 Revision. United Nations,
Department of Economic and Social Affairs. Population Division, Population Estimates
Section.
Whittaker, J., Whitehead, C., and Somers, M. (2005). The neglog transformation and
quantile regression for the analysis of a large credit scoring database. Journal of the
Royal Statistical Society: Series C (Applied Statistics), 54(5):863–878.
31
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520
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520
01-2016
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520
02-2016
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520
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FOOD INSECURITY AMONG OLDER EUROPEANS:
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530
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Inst
07-2016
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HOW IMPORTANT IS PRECAUTIONARY LABOR
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520
08-2016
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Jianhong Xue
FUEL FOR LIFE: DOMESTIC COOKING FUELS AND
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530
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Seung Kyu-Yi,
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THE CO-EVOLUTION OF INNOVATION NETWORKS:
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520
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CONTINUITY UNDER A DIFFERENT NAME.
THE OUTCOME OF PRIVATISATION IN SERBIA
520
11-2016
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Michael Kuhn
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THE CONTRIBUTION OF FEMALE HEALTH TO
ECONOMIC DEVELOPMENT
520
12-2016
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THE QUEST FOR STATUS AND R&D-BASED
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520
13-2016
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STRUCTURAL SHIFT AND INCREASING VARIETY
IN KOREA, 1960–2010: EMPIRICAL EVIDENCE OF
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CREATION OF NEW SECTORS
520
14-2016
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THE EFFECT OF TEENAGE EMPLOYMENT ON
CHARACTER SKILLS, EXPECTATIONS AND
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520
15-2016
Seung-Kyu Yi
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HAS THE GERMAN REUNIFICATION
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520
16-2016
Gregor Pfeifer
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LIGHTS EVIDENCE FROM SOUTH AFRICA
520
17-2016
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CELEBRATING 30 YEARS OF INNOVATION
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570
18-2016
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THE IMPLICATIONS OF AUTOMATION FOR
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520
19-2016
Klaus Prettner
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HIGHER EDUCATION AND THE FALL AND RISE OF
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520
20-2016
Vadim Kufenko
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520
No.
Author
Title
Inst
01-2017
Annarita Baldanzi
Alberto Bucci
Klaus Prettner
CHILDRENS HEALTH, HUMAN CAPITAL
ACCUMULATION, AND R&D-BASED ECONOMIC
GROWTH
INEPA
02-2017
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Marie Lambert
Hans-Peter Burghof
MORAL HAZARD IN VC-FINANCE: MORE
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INEF
03-2017
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Nguyen Minh Duc
Antonia Rehl
Ute Siepmann
Pham Van Dinh
LABOUR AS A UTILITY MEASURE RECONSIDERED
520
04-2017
Bohdan Kukharskyy
Sebastian Seiffert
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CAUSES
520
05-2017
Ana Abeliansky
Klaus Prettner
AUTOMATION AND DEMOGRAPHIC CHANGE
520
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Nr.
Autor
Titel
CC
01-2009
Julian P. Christ
NEW ECONOMIC GEOGRAPHY RELOADED:
Localized Knowledge Spillovers and the Geography of Innovation
IK
02-2009
André P. Slowak
MARKET FIELD STRUCTURE & DYNAMICS IN INDUSTRIAL
AUTOMATION
IK
03-2009
Pier Paolo Saviotti,
Andreas Pyka
GENERALIZED BARRIERS TO ENTRY AND ECONOMIC
DEVELOPMENT
IK
04-2009
Uwe Focht, Andreas
Richter and Jörg
Schiller
INTERMEDIATION AND MATCHING IN INSURANCE MARKETS
05-2009
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WHY BLU-RAY VS. HD-DVD IS NOT VHS VS. BETAMAX:
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06-2009
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Mario Larch and
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UNEMPLOYMENT IN AN INTERDEPENDENT WORLD
ECO
07-2009
Steffen Otterbach
MISMATCHES BETWEEN ACTUAL AND PREFERRED WORK
TIME: Empirical Evidence of Hours Constraints in 21 Countries
HCM
08-2009
Sven Wydra
PRODUCTION AND EMPLOYMENT IMPACTS OF NEW
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IK
09-2009
Ralf Richter,
Jochen Streb
CATCHING-UP AND FALLING BEHIND
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TO GERMAN MACHINE TOOL MAKERS
IK
HCM
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Nr.
Autor
Titel
CC
10-2010
Rahel Aichele,
Gabriel Felbermayr
KYOTO AND THE CARBON CONTENT OF TRADE
ECO
11-2010
David E. Bloom,
Alfonso Sousa-Poza
ECONOMIC CONSEQUENCES OF LOW FERTILITY IN EUROPE
HCM
12-2010
Michael Ahlheim,
Oliver Frör
DRINKING AND PROTECTING – A MARKET APPROACH TO THE
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ECO
13-2010
Michael Ahlheim,
Oliver Frör,
Antonia Heinke,
Nguyen Minh Duc,
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LABOUR AS A UTILITY MEASURE IN CONTINGENT VALUATION
STUDIES – HOW GOOD IS IT REALLY?
14-2010
Julian P. Christ
THE GEOGRAPHY AND CO-LOCATION OF EUROPEAN
TECHNOLOGY-SPECIFIC CO-INVENTORSHIP NETWORKS
IK
15-2010
Harald Degner
WINDOWS OF TECHNOLOGICAL OPPORTUNITY
DO TECHNOLOGICAL BOOMS INFLUENCE THE RELATIONSHIP
BETWEEN FIRM SIZE AND INNOVATIVENESS?
IK
16-2010
Tobias A. Jopp
THE WELFARE STATE EVOLVES:
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17-2010
Stefan Kirn (Ed.)
PROCESS OF CHANGE IN ORGANISATIONS THROUGH
eHEALTH
18-2010
Jörg Schiller
ÖKONOMISCHE ASPEKTE DER ENTLOHNUNG
UND REGULIERUNG UNABHÄNGIGER
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19-2010
Frauke Lammers,
Jörg Schiller
CONTRACT DESIGN AND INSURANCE FRAUD: AN
EXPERIMENTAL INVESTIGATION
HCM
20-2010
Martyna Marczak,
Thomas Beissinger
REAL WAGES AND THE BUSINESS CYCLE IN GERMANY
ECO
21-2010
Harald Degner,
Jochen Streb
FOREIGN PATENTING IN GERMANY, 1877-1932
22-2010
Heiko Stüber,
Thomas Beissinger
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ECO
23-2010
Mark Spoerer,
Jochen Streb
GUNS AND BUTTER – BUT NO MARGARINE: THE IMPACT OF
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ECO
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HCM
ICT
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Nr.
Autor
Titel
CC
24-2011
Dhammika
Dharmapala,
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EARNINGS SHOCKS AND TAX-MOTIVATED INCOME-SHIFTING:
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25-2011
Michael Schuele,
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QUALITATIVES, RÄUMLICHES SCHLIEßEN ZUR
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AUTONOMER BDI-AGENTEN
ICT
26-2011
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VERHALTENSMODELLE FÜR SOFTWAREAGENTEN IM
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ICT
27-2011
Monnet Benoit,
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ENGEL CURVES, SPATIAL VARIATION IN PRICES AND
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28-2011
Nadine Riedel,
Hannah SchildbergHörisch
ASYMMETRIC OBLIGATIONS
ECO
29-2011
Nicole Waidlein
CAUSES OF PERSISTENT PRODUCTIVITY DIFFERENCES IN
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1990
IK
30-2011
Dominik Hartmann,
Atilio Arata
MEASURING SOCIAL CAPITAL AND INNOVATION IN POOR
AGRICULTURAL COMMUNITIES. THE CASE OF CHÁPARRA PERU
IK
31-2011
Peter Spahn
DIE WÄHRUNGSKRISENUNION
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32-2011
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Stefan Kirn
ANWENDUNG DES ÖFFENTLICHEN VERGABERECHTS AUF
MODERNE IT SOFTWAREENTWICKLUNGSVERFAHREN
35-2011
Andreas Pyka
AVOIDING EVOLUTIONARY INEFFICIENCIES
IN INNOVATION NETWORKS
IK
36-2011
David Bell, Steffen
Otterbach and
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ECO
IK
ICT
IK
Nr.
Autor
Titel
CC
39-2011
Michael Ahlheim,
Tobias Börger and
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RESPONDENT INCENTIVES IN CONTINGENT VALUATION: THE
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ECO
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Tobias Börger
A DIRECT TEST OF SOCIALLY DESIRABLE RESPONDING IN
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41-2011
Ralf Rukwid,
Julian P. Christ
QUANTITATIVE CLUSTERIDENTIFIKATION AUF EBENE
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IK
Nr.
Autor
Titel
CC
42-2012
Benjamin Schön,
Andreas Pyka
A TAXONOMY OF INNOVATION NETWORKS
43-2012
Dirk Foremny,
Nadine Riedel
BUSINESS TAXES AND THE ELECTORAL CYCLE
44-2012
Gisela Di Meglio,
Andreas Pyka and
Luis Rubalcaba
VARIETIES OF SERVICE ECONOMIES IN EUROPE
IK
45-2012
Ralf Rukwid,
Julian P. Christ
INNOVATIONSPOTENTIALE IN BADEN-WÜRTTEMBERG:
PRODUKTIONSCLUSTER IM BEREICH „METALL, ELEKTRO, IKT“
UND REGIONALE VERFÜGBARKEIT AKADEMISCHER
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Julian P. Christ,
Ralf Rukwid
INNOVATIONSPOTENTIALE IN BADEN-WÜRTTEMBERG:
BRANCHENSPEZIFISCHE FORSCHUNGS- UND
ENTWICKLUNGSAKTIVITÄT, REGIONALES
PATENTAUFKOMMEN UND BESCHÄFTIGUNGSSTRUKTUR
IK
47-2012
Oliver Sauter
ASSESSING UNCERTAINTY IN EUROPE AND THE
US - IS THERE A COMMON FACTOR?
48-2012
Dominik Hartmann
SEN MEETS SCHUMPETER. INTRODUCING STRUCTURAL AND
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IK
49-2012
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Andreas Pyka
DISTAL EMBEDDING AS A TECHNOLOGY INNOVATION
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IK
50-2012
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Víctor Gómez
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NEW INSIGHTS FROM WAVELET ANALYSIS
51-2012
André P. Slowak
DIE DURCHSETZUNG VON SCHNITTSTELLEN
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52-2012
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WHY IT MATTERS WHAT PEOPLE THINK - BELIEFS, LEGAL
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53-2012
Dominik Hartmann,
Micha Kaiser
STATISTISCHER ÜBERBLICK DER TÜRKISCHEN MIGRATION IN
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IK
54-2012
Dominik Hartmann,
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Erginkoç
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INNOVATIONSNETZWERKE. ERSTE ERGEBNISSE DES TGINPROJEKTES
IK
55-2012
Michael Ahlheim,
Tobias Börger and
Oliver Frör
THE ECOLOGICAL PRICE OF GETTING RICH IN A GREEN
DESERT: A CONTINGENT VALUATION STUDY IN RURAL
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IK
ECO
ECO
ECO
IK
ECO
ECO
Nr.
Autor
Titel
CC
56-2012
Matthias Strifler
Thomas Beissinger
FAIRNESS CONSIDERATIONS IN LABOR UNION WAGE
SETTING – A THEORETICAL ANALYSIS
ECO
57-2012
Peter Spahn
INTEGRATION DURCH WÄHRUNGSUNION?
DER FALL DER EURO-ZONE
ECO
58-2012
Sibylle H. Lehmann
TAKING FIRMS TO THE STOCK MARKET:
IPOS AND THE IMPORTANCE OF LARGE BANKS IN IMPERIAL
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ECO
59-2012
Sibylle H. Lehmann,
Philipp Hauber and
Alexander Opitz
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60-2012
Martyna Marczak,
Víctor Gómez
SPECTRAN, A SET OF MATLAB PROGRAMS FOR SPECTRAL
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61-2012
Theresa Lohse,
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THE IMPACT OF TRANSFER PRICING REGULATIONS ON
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ECO
Nr.
Autor
Titel
CC
62-2013
Heiko Stüber
REAL WAGE CYCLICALITY OF NEWLY HIRED WORKERS
ECO
63-2013
David E. Bloom,
Alfonso Sousa-Poza
AGEING AND PRODUCTIVITY
HCM
64-2013
Martyna Marczak,
Víctor Gómez
MONTHLY US BUSINESS CYCLE INDICATORS:
A NEW MULTIVARIATE APPROACH BASED ON A BAND-PASS
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65-2013
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Andreas Pyka
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66-2013
Christof Ernst,
Katharina Richter and
Nadine Riedel
CORPORATE TAXATION AND THE QUALITY OF RESEARCH
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ECO
67-2013
Michael Ahlheim,
Oliver Frör, Jiang
Tong, Luo Jing and
Sonna Pelz
NONUSE VALUES OF CLIMATE POLICY - AN EMPIRICAL STUDY
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68-2013
Michael Ahlheim,
Friedrich Schneider
CONSIDERING HOUSEHOLD SIZE IN CONTINGENT VALUATION
STUDIES
ECO
69-2013
Fabio Bertoni,
Tereza Tykvová
WHICH FORM OF VENTURE CAPITAL IS MOST SUPPORTIVE
OF INNOVATION?
EVIDENCE FROM EUROPEAN BIOTECHNOLOGY COMPANIES
70-2013
Tobias Buchmann,
Andreas Pyka
THE EVOLUTION OF INNOVATION NETWORKS:
THE CASE OF A GERMAN AUTOMOTIVE NETWORK
IK
71-2013
B. Vermeulen, A.
Pyka, J. A. La Poutré
and A. G. de Kok
CAPABILITY-BASED GOVERNANCE PATTERNS OVER THE
PRODUCT LIFE-CYCLE
IK
72-2013
Beatriz Fabiola López
Ulloa, Valerie Møller
and Alfonso SousaPoza
HOW DOES SUBJECTIVE WELL-BEING EVOLVE WITH AGE?
A LITERATURE REVIEW
HCM
73-2013
Wencke Gwozdz,
Alfonso Sousa-Poza,
Lucia A. Reisch,
Wolfgang Ahrens,
Stefaan De Henauw,
Gabriele Eiben, Juan
M. Fernández-Alvira,
Charalampos
Hadjigeorgiou, Eva
Kovács, Fabio Lauria,
Toomas Veidebaum,
Garrath Williams,
Karin Bammann
MATERNAL EMPLOYMENT AND CHILDHOOD OBESITY –
A EUROPEAN PERSPECTIVE
HCM
IK
CFRM
Nr.
Autor
Titel
CC
74-2013
Andreas Haas,
Annette Hofmann
RISIKEN AUS CLOUD-COMPUTING-SERVICES:
FRAGEN DES RISIKOMANAGEMENTS UND ASPEKTE DER
VERSICHERBARKEIT
75-2013
Yin Krogmann,
Nadine Riedel and
Ulrich Schwalbe
INTER-FIRM R&D NETWORKS IN PHARMACEUTICAL
BIOTECHNOLOGY: WHAT DETERMINES FIRM’S
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76-2013
Peter Spahn
MACROECONOMIC STABILISATION AND BANK LENDING:
A SIMPLE WORKHORSE MODEL
77-2013
Sheida Rashidi,
Andreas Pyka
MIGRATION AND INNOVATION – A SURVEY
IK
78-2013
Benjamin Schön,
Andreas Pyka
THE SUCCESS FACTORS OF TECHNOLOGY-SOURCING
THROUGH MERGERS & ACQUISITIONS – AN INTUITIVE METAANALYSIS
IK
79-2013
Irene Prostolupow,
Andreas Pyka and
Barbara Heller-Schuh
TURKISH-GERMAN INNOVATION NETWORKS IN THE
EUROPEAN RESEARCH LANDSCAPE
IK
80-2013
Eva Schlenker,
Kai D. Schmid
CAPITAL INCOME SHARES AND INCOME
INEQUALITY IN THE EUROPEAN UNION
ECO
81-2013
Michael Ahlheim,
Tobias Börger and
Oliver Frör
THE INFLUENCE OF ETHNICITY AND CULTURE ON THE
VALUATION OF ENVIRONMENTAL IMPROVEMENTS
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ECO
82-2013
Fabian Wahl
DOES MEDIEVAL TRADE STILL MATTER? HISTORICAL TRADE
CENTERS, AGGLOMERATION AND CONTEMPORARY
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ECO
83-2013
Peter Spahn
SUBPRIME AND EURO CRISIS: SHOULD WE BLAME THE
ECONOMISTS?
ECO
84-2013
Daniel Guffarth,
Michael J. Barber
THE EUROPEAN AEROSPACE R&D COLLABORATION
NETWORK
IK
85-2013
Athanasios Saitis
KARTELLBEKÄMPFUNG UND INTERNE KARTELLSTRUKTUREN:
EIN NETZWERKTHEORETISCHER ANSATZ
IK
HCM
ECO, IK
ECO
Nr.
Autor
Titel
CC
86-2014
Stefan Kirn, Claus D.
Müller-Hengstenberg
INTELLIGENTE (SOFTWARE-)AGENTEN: EINE NEUE
HERAUSFORDERUNG FÜR DIE GESELLSCHAFT UND UNSER
RECHTSSYSTEM?
ICT
87-2014
Peng Nie, Alfonso
Sousa-Poza
MATERNAL EMPLOYMENT AND CHILDHOOD OBESITY IN
CHINA: EVIDENCE FROM THE CHINA HEALTH AND NUTRITION
SURVEY
HCM
88-2014
Steffen Otterbach,
Alfonso Sousa-Poza
JOB INSECURITY, EMPLOYABILITY, AND HEALTH:
AN ANALYSIS FOR GERMANY ACROSS GENERATIONS
HCM
89-2014
Carsten Burhop,
Sibylle H. LehmannHasemeyer
THE GEOGRAPHY OF STOCK EXCHANGES IN IMPERIAL
GERMANY
ECO
90-2014
Martyna Marczak,
Tommaso Proietti
OUTLIER DETECTION IN STRUCTURAL TIME SERIES
MODELS: THE INDICATOR SATURATION APPROACH
ECO
91-2014
Sophie Urmetzer,
Andreas Pyka
VARIETIES OF KNOWLEDGE-BASED BIOECONOMIES
IK
92-2014
Bogang Jun,
Joongho Lee
THE TRADEOFF BETWEEN FERTILITY AND EDUCATION:
EVIDENCE FROM THE KOREAN DEVELOPMENT PATH
IK
93-2014
Bogang Jun,
Tai-Yoo Kim
NON-FINANCIAL HURDLES FOR HUMAN CAPITAL
ACCUMULATION: LANDOWNERSHIP IN KOREA UNDER
JAPANESE RULE
IK
94-2014
Michael Ahlheim,
Oliver Frör,
Gerhard
Langenberger and
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CHINESE URBANITES AND THE PRESERVATION OF RARE
SPECIES IN REMOTE PARTS OF THE COUNTRY – THE
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95-2014
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Andreas Pyka,
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Monteiro Gomes
RANKING THE PERFORMANCE OF NATIONAL INNOVATION
SYSTEMS IN THE IBERIAN PENINSULA AND LATIN AMERICA
FROM A NEO-SCHUMPETERIAN ECONOMICS PERSPECTIVE
IK
96-2014
Daniel Guffarth,
Michael J. Barber
NETWORK EVOLUTION, SUCCESS, AND REGIONAL
DEVELOPMENT IN THE EUROPEAN AEROSPACE INDUSTRY
IK
ECO
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