Download Population, Economic Growth, and TFP in Developed Countries

Chapter 2
Population, Economic Growth, and TFP
in Developed Countries
Abstract In this chapter, we investigate the relationship between population size
and economic growth using growth accounting and empirical studies. We begin
with an explanation of the production function and growth theory. Although economic growth can be achieved by increasing the amounts of either labor or capital
in the production function, it can also be realized through increased efficiency or, in
other words, by improving how factors are used together. This improved efficiency,
which contributes to an increase in GDP, is closely related to technological progress. Since it is generally difficult to estimate technological progress in the
macroeconomy, we need to identify indicators that capture technological progress
for our empirical research. We will demonstrate the significance of total factor
productivity (TFP), which is a proxy variable for technological progress, by using
the results of growth accounting in the OECD countries. The OECD (2013) has
published growth accounting data for selected countries from 1985 to 2010. From
this report, we find that the average economic growth rate was 2.58 %, and the
average contribution ratio of multifactor productivity (MFP) was 45.5 %. In other
words, almost half the economic growth came from the contribution of MFP. After
reviewing the traditional growth theory, we present our empirical results on the
relationship between population growth and economic growth. Our empirical tests
confirm that the relationship between economic growth and population growth is
negative, as proposed in the Solow growth model. However, theoretically, population growth should spur technological progress, as discussed in the previous
chapter. We therefore conduct more direct empirical tests on the relationship
between population growth and technological progress in the next chapter.
The previous chapter summarized historical views of the relationship between
population and technological progress and concluded that technological progress
has resulted in rapid economic growth. In this chapter, we investigate the relationship between population and economic growth using growth accounting and
empirical studies. We begin with an explanation of the production function and
growth theory. Further, we will demonstrate the significance of TFP, which is a
proxy variable for technological progress, utilizing the results of growth accounting
in Organization for Economic Cooperation and Development (OECD) countries.
© The Author(s) 2016
H. Kato, An Empirical Analysis of Population and Technological Progress,
Population Studies of Japan, DOI 10.1007/978-4-431-54959-8_2
21
22
2 Population, Economic Growth, and TFP in Developed Countries
Finally, we will review the traditional growth theory and present the empirical
results of the relationship between population and economic growth.
2.1
2.1.1
Economic Growth and Growth Accounting
Economic Growth and Production Factors
Economic growth is defined as the increase in value added to an economy in a year,
in a quarter, or in a certain period. In the System of National Accounts (SNA), the
economic growth rate is measured by the growth rate of gross domestic product
(GDP), which is the key economic indicator for the total economy. From the point
of view estimating GDP, three approaches from the demand side, supply side, and
income are used. The demand-side approach defines GDP as the total expenditure
on final goods and services consumed or invested by economic agents in the
economy. On the other hand, the supply-side approach uses the level of production
by the industries and the government. From a theoretical viewpoint, GDP estimations using the demand-side and supply-side approaches are balanced in an equilibrium economy.
The economic growth rate is defined as the percentage rate of increase in
GDP, or:
GDP growth rate at the current period = increase of GDP at the current period/GDP
at the last period (%).
Because modern economic activity is often accompanied by a business cycle that
involves short-term changes in expenditures, the supply-side approach is used for
long-term observation of economic growth or to establish a GDP trend. The
supply-side approach generally uses the concept of the production function. The
production function will be explained in greater detail below.
For simplicity, the basic production process will be discussed. In that regard, we
assume that there is only one kind of output produced using different types of
production factors. Since GDP is an indicator of output, we can translate this to the
economy as a whole, and we also assume two factors of production: capital and
labor. Then, the production process can be shown by this simple equation (2.1):
GDP ¼ FðCapital; LaborÞ
or
Y ¼ FðK; LÞ;
ð2:1Þ
where Y is GDP, K is capital, and L is labor.
Though economic growth can be achieved by increasing either the amount of
labor or the capital in the production function, there is a limit to the contribution of
a single factor for an increase in GDP, due to the law of diminishing marginal
productivity. Marginal productivity refers to the additional output gained by adding
one unit of factor of production when other factors are held constant, and it gradually decreases as the amount of that factor increases.
2.1 Economic Growth and Growth Accounting
23
In reality, it is not ordinary for only one factor to increase when other factors do
not change; we therefore need to analyze economic growth with all factor changes
taken into account. GDP can be increased by increasing the labor and capital inputs
used in production; however, economic growth can also be realized through
increased efficiency, in other words, by improving how factors are used together.
This improving efficiency, which contributes to an increase in GDP, is referred to as
total factor productivity (TFP), also called multifactor productivity (MFP), and is
closely related to technological progress. Thus, we often allow TFP to act as a
proxy variable for technological progress. Because it is difficult to measure efficiency gains or TFP directly from observation data, we estimate TFP as a residual
that is the part of economic growth that cannot be explained through capital
increases or labor increases. Considering TFP, we can write the production function
as follows:
Y ¼ AFðK; LÞ;
ð2:2Þ
where A denotes TFP or the level of technology. Note, in economics, “technology”
is used in a broad sense and is not limited to engineering technology. As mentioned
above, technology means general gains in efficiency or productivity, and it is this
that acts as the engine of long-term economic development.
2.1.2
The Cobb–Douglas Production Function and Growth
Accounting
We illustrate the discussion above using the Cobb–Douglas production function.
The Cobb–Douglas production function is formulated as follows:
YðtÞ ¼ AðtÞKðtÞa LðtÞ1a :
ð2:3Þ
All variables are a function of time t. The parameter a is the share of income
received by the owners of capital, and the share of the income received by labor and
the share is 1 a. We can call this parameter a as the share of capital cost, because
it is the share of cost paid to capital in producing GDP.
Taking the logarithm of both sides, we then have
ln YðtÞ ¼ ln AðtÞ þ a ln KðtÞ þ ð1 aÞ ln LðtÞ;
and differentiating the above equation by time t,
DYðtÞ DAðtÞ
DKðtÞ
DLðtÞ
¼
þa
þ ð1 aÞ
:
YðtÞ
AðtÞ
KðtÞ
LðtÞ
ð2:4Þ
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2 Population, Economic Growth, and TFP in Developed Countries
This is the basic equation of growth accounting. In other words, this equation
shows that the GDP growth rate is decomposed to the rate of change of capital and
labor and to the rate of TFP growth, which is computed as residual. The contribution of capital (labor) to GDP growth is the speed of growth of capital (labor)
multiplied by the share of capital (labor) in GDP.
Though the parameter value a is stable and is similar in developed countries
(known as stylized facts in the field of economic growth theory), we can more
accurately calculate the share of capital cost using SNA data or other data sources.
In addition, many researchers or institutes try to calculate growth accounting. Next,
the growth accounting calculation employed by the OECD will be described.
2.2
2.2.1
Growth Accounting in OECD Countries
Data of Technological Progress by OECD
Since it is generally difficult to estimate technological progress in the macroeconomy, we need to identify indicators that capture technological progress for our
empirical research. In addition, for the most part, the indicators should be common
across developed countries in order to make a comparison. One indicator is published by the OECD as MFP. Briefly, MFP growth represents the unexplained
portion of an increase in GDP growth which cannot be accounted for through
growth in labor or capital input. The OECD (2001) stated that MFP growth is a
proxy indicator of technological progress, and it is the increase in GDP growth that
is not embodied in the amounts of either capital or labor. However, it is interpreted
in a somewhat larger sense. The OECD (2008) said “MFP growth comes from more
efficient use of labor and capital inputs, for example through improvements in the
management of production processes, organizational change or more generally,
innovation” (OECD 2008, p. 24). In addition, resource constraints of MFP data
hamper efforts to precisely measure labor and capital input and this in turn affects
MFP.
The OECD published an online MFP database that can be downloaded from
“Growth in GDP per capita, productivity and ULC.”1 This database contains the
results of measurements of the MFP growth rate for 20 OECD countries, from 1995
to 2012.2 It should be noted that some fiscal years are missing from the data, and
some new countries became OECD members during the period covered by the data;
for these reasons, the data do not represent a balanced panel data set for all
20 countries.
1
http://stats.oecd.org/Index.aspx?QueryId=54566.
The level of MFP is standardized as 100 in 2005. An old database contained MFP growth rates
for 19 OECD countries for the period 1985–2007. In addition, MFP is formulated for the purpose
of international comparison, and OECD statistical data cannot necessarily be considered the
optimal basis for calculating MFP for individual nations.
2
2.2 Growth Accounting in OECD Countries
25
1.4
1.2
1.2
1.2
1.0
1.0
0.9
0.8
0.8
0.6
0.7
0.7
0.6
0.4
0.2
0.1
0.0
Australia Canada
France Germany
-0.2
Italy
- 0.1
Japan
Spain
Sweden
United
Kingdom
United
States
Fig. 2.1 Average growth rate of MFP (1995–2012, %). Source OECD database “Growth in GDP
per capita, productivity and ULC”
With regard to calculating MFP, it is defined as the difference between the rate of
change in output (Q) and the rate of change in input (X). Equation (2.5) provides the
definition as follows:
MFPt
ln
MFPt1
Qt
Xt
¼ ln
ln
:
Qt1
Xt1
ð2:5Þ
Note, output (Q) is the real gross domestic product in the OECD National
Accounts, and input (X) is labor and seven categories of capital stock. The difference between MFP and standard TFP lies in this use of seven categories of capital
stock.3
Figure 2.1 shows MFP growth, on average, from 1995 to 2012 for 10 major
nations (Australia, Canada, France, Germany, Italy, Japan, Spain, Sweden, the UK,
and the USA). It reveals that Sweden displayed the highest rate of increase in MFP
at 1.2 %, followed by the USA at 1.2 %, almost the same as Sweden, the UK at
1.0 %, and Germany at 0.9 %. In contrast to those countries, Italy had the lowest
rate of MFP increase at −0.1 %, and Spain’s rate of increase was 0.1 %. With
respect to Japan, the MFP growth rate was 0.7 %, on average, from 1995 to 2012.
2.2.2
Growth Accounting in OECD Countries
Technological progress is the most fundamental source of human progress. In
addition, it is also the most important engine of economic growth. In the previous
3
See Schreyer (2003) or Wölfl and Hajkova (2007) for more details.
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2 Population, Economic Growth, and TFP in Developed Countries
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
DEU ITA
CHE FRA JPN SWE DNK FIN
NZL CAN USA AUT ESP NLD GBR AUS
IRL KOR
-1.00
Labor input
Capital input
MFP
Fig. 2.2 Growth accounting in OECD countries (1985–2010). Data OECD (2013)
section, the growth accounting method was explained. Considering TFP (or MFP)
is the proxy variable of technological progress, we will now examine how TFP
contributed to economic growth in developed countries, using growth accounting
by the OECD (2013).
The OECD (2013) has published growth accounting data for selected countries
from 1985 to 2010. In these calculations, the contribution of labor (capital) to GDP
growth is measured as the growth of labor (capital) input, multiplied by the share of
labor (capital) in GDP. Figure 2.2 shows the contribution degree of capital, labor,
and MFP to GDP growth rate.4 Eighteen OECD countries were selected.5
From the figure, we can see the importance of MFP and capital to the GDP
growth rate in almost all of the countries; however, labor input was important for
only a few countries from 1985 to 2010. For example, Japan, Finland, and Germany
experienced negative GDP contributions from labor inputs.
South Korea recorded the highest economic growth in this period among the
18 countries, with an average economic growth rate of 6.10 %. The contribution
4
Although, in the original OECD (2013) publication, the contribution of capital to GDP is broken
down into Information and Communication Technologies (ICT) capital and non-ICT capital, we
combined the two kinds of capital for convenience.
5
Germany, Italy, Czech Republic, France, Japan, Sweden, Denmark, Finland, New Zealand,
Canada, USA, Austria, Spain, Netherland, United Kingdom, Australia, Ireland, and South Korea.
2.2 Growth Accounting in OECD Countries
27
ratio of MFP to GDP growth was 62.8 % in Korea; meanwhile, the contribution
ratio of labor input was only 9.8 %. On the contrary, Germany’s economic growth
rate from 1985 to 2010 was the lowest of the 18 countries, with an average growth
rate of only 1.22 %. Interestingly, South Korea’s contribution of MFP to GDP
growth is larger than capital and labor input. The contribution ratio of MFP was
72.6 %, the second-highest value among the 18 countries.
For all countries, we find that the average economic growth rate was 2.58 %, and
the average contribution ratio of MFP was 45.5 %. In other words, almost half of
the economic growth came from the contribution of MFP. Among the 18 countries,
Finland had the highest contribution ratio of MFP, 80.2 %, because its GDP growth
rate was 2.08 % and the growth rate of MFP was 1.67 %. Conversely, the contribution ratio of MFP to GDP growth was low in both Spain and Canada, with
ratios of 13.1 and 14.1 %, respectively.
As for Japan, the average GDP growth rate was 1.91 %, making it the fifthlowest country, and the growth rate of labor was −0.32 %. This was because of the
decreased size of the labor force in the mid-1990s. The growth rate of MFP was
1.36 % from 1985 to 2010, and the contribution ratio to GDP growth was 71.0 %,
the third-highest value of the 18 countries.
2.2.3
Some Problems Related to Technological Progress
Indicators
(1) Productivity and TFP
Based on current research, it can be said that the contribution of MFP or
technological progress to economic growth, through the analysis of growth
accounting, is large. However, there are other relevant indicators. Productivity
also means the degree of efficiency of production; therefore, it may also be an
indicator of technological progress.
The term “productivity” is used frequently in the context of economics; however, it is difficult to define because its significance varies. Generally, productivity
is defined as the ratio of an amount of output or GDP to one unit of input. In
addition, a growth rate of labor productivity is calculated as:
Growth rate of Labor Productivity ¼ Growth Rate of ðGDP=Labor inputÞ:
In general, labor productivity is interpreted as single-factor productivity (SFP),
and TFP is defined as the total factor or multifactor productivity. Hence, if labor is
selected as an input production factor, we can then measure labor productivity. If
we choose capital as an input factor, then capital productivity is calculated.
Furthermore, MFP is defined as the productivity of combined input factors, which
are labor, capital, and intermediate inputs (e.g., raw materials, energy). Thus, we
can say that a variety of definitions of productivity are possible, depending on what
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2 Population, Economic Growth, and TFP in Developed Countries
Table 2.1 Overview of main productivity measures
Type of
output
measure
Type of input measure
Labor
Capital
Gross
output
Labor productivity Capital productivity
(based on gross
(based on gross
output)
output)
Labor productivity Capital productivity
(based on value
(based on value
added)
added)
Single-factor productivity measures
Value
added
Capital and labor
Capital, labor, and
intermediate inputs
(energy, materials,
services)
Capital–labor
KLEMS
MFP (based on
multifactor
gross output)
productivity
Capital–labor
–
MFP (based on
value added)
Multifactor productivity (MFP)
measures
Source OECD (2001, p. 13)
inputs are used as the indices for productivity measurements. Incidentally, labor
productivity is the most representative index.
Note, the definition of productivity will differ depending on what type of output
we choose (e.g., a value-added basis of GDP or a production basis which incorporates intermediate goods and services).
Table 2.1 depicts the main productivity measures from the OECD (2001).
The OECD (2001) offered an explanation of the productivity definition. As for
labor productivity, based on value added, it is defined as the quantity index of value
added, divided by the quantity index of labor input. Capital–labor MFP, based on
value added, is defined as the quantity index of value added divided by the quantity
index of combined labor and capital input, and so on. A more detailed definition of
MFP will be explained below.6
(2) Independence between TFP and Labor
From the above discussions, it can be said that TFP or MFP is a proxy index of
technological progress and has an important role in growth accounting. As
such, measuring TFP is an indispensable procedure for the analysis of economic growth. On the other hand, both labor and capital are also essential in
establishing growth factors. In this case, how are capital and labor related to
technological progress? Assuming the production function, described as Eq. 1.
1, we presume that technological progress is independent of the labor force
(and capital). Mainly, because TFP is defined as the efficiency gain of production in total, technological progress should be interpreted as an exogenous
shock. However, is there actually an independent relationship between labor
(capital) input and TFP in the economy?
The OECD (2001) said that “conceptually, capital-labor productivity is not, in general, an
accurate measure of technical change…” and MFP “reflects the combined effects of disembodied
technical change, economies of scale, efficiency change, variations in capacity utilization and
measurement errors” (p. 16).
6
2.2 Growth Accounting in OECD Countries
29
Examining this point, two possibilities can be considered. Firstly, a
labor-augmenting technological progress exists. Using the production function
form, we can describe this as follows:
Y ¼ FðK; ALÞ:
This labor-augmenting technological progress serves to enhance the efficiency of
labor, and in this case, it is difficult to calculate the pure contribution of technological progress to GDP growth.
Secondly, there is a fundamental theory that some significant relationship exists
between technological progress and labor input. This is the main theme of this
study. The purpose of this study is to explore the relationship between TFP and
population. If the population size (or labor input) is one of the causes of the speed
of technological progress, then both factors are not independent in the production
function. This study will examine this relationship in detail below.
2.3
2.3.1
Population and Economic Growth
Population and Economic Growth
As described above, technological progress is the most important factor for economic growth. However, we cannot directly measure technological progress
through the use of indicators; therefore, TFP is utilized as a proxy variable. An
advantage of using TFP as a proxy variable is that it is easy to decompose its
contribution by growth accounting. Firstly, we will review the Solow growth
model, which is the simplest theory used to describe economic growth.
Assuming the specific production function of homogeneous of degree one as
follows:
Y ¼ FðK; LÞ ) Y ¼ K a L1a ;
then it transforms per capita.
Y
¼
L
a 1a a
K
L
K
¼
;
L
L
L
where Y is GDP, K is capital stock, and L is labor or population. Also it defines the
capital–labor ratio as k ¼ KL and per capita GDP as y ¼ YL . We can describe the
production function in terms of per capita.
y ¼ ka
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2 Population, Economic Growth, and TFP in Developed Countries
The transition equation of capital stock is as follows:
Kt Kt1 ¼ It
I is investment, and in equilibrium in economy, S is defined as saving, as follows:
S ¼ I:
We can rewrite this as follows:
S ¼ sY:
Next, considering the dynamic change of the capital–labor ratio, we can show that
k_ K_ L_
¼ ;
k K L
and assume the labor growth rate is constant n. The next equation is derived as
follows:
Y=L
y
k_ sY
n¼s
n ¼ s n ! k_ ¼ sy nk
¼
K
K=L
k
k
This equation shows the Solow growth model and implicates the relationship
between GDP and labor (or population). In a steady state, by considering the
production function in terms of per capita, it is easy to derive Eq. (2.6). Note, *
means the value in a steady state.
y ¼
a
h s i1a
n
ð2:6Þ
Equation (2.6) shows that the level of GDP per capita decreases as the rate of
population growth increases. From this logic, it is concluded that an increased
population generates lower levels of per capita income in a steady state. Figure 2.3
illustrates this conclusion.
2.3.2
The Results of Empirical Studies (1)
The relationship between economic growth and population will now be examined,
based on empirical studies. Firstly, we will explore the relationship between the
population growth rate and the economic growth rate for countries in the long term.
To collect as much data as possible, for both developed and developing
countries, we utilized the “World Bank Open Data.” The selected variables are real
GDP in domestic currencies and population figures from 1960, 1985, and 2010.
2.3 Population and Economic Growth
31
Fig. 2.3 The Solow growth
model
y
n'k
n'>n
nk
y*
y'*
k
k'*
k*
From these data, the average growth rate can be easily calculated. Therefore, we
prepared three different average growth rates, namely from 1960 to 1985, 1985 to
2010, and 1960 to 2010. The data included 91 countries.
Figure 2.4 shows the simple relationship between the population growth rate
and the economic growth rate. The X-axis denotes the population growth rate and
the Y-axis denotes the economic growth rate from 1960 to 2010. The correlation
coefficient of the two variables was −0.207, a weak negative relation was observed,
and we could not obtain strong evidence supporting the conclusion of the Solow
growth model.
The conclusion of the Solow growth model is derived using a per capita variable.
Therefore, a per capita growth rate should be used instead of a macroeconomic
12.0%
Economic Growth rate
Number of Countries: 91
10.0%
8.0%
Correlation
coeficient = -0.207
6.0%
4.0%
2.0%
Population Growth rate
0.0%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
Fig. 2.4 Economic growth and population increase: 1960–2010. Data from The World Bank’s
Open Data
32
2 Population, Economic Growth, and TFP in Developed Countries
growth rate. Per capita growth rate is calculated using the figure obtained from the
macroeconomic growth rate minus the population growth rate.
Figure 2.5a shows the relationship between the per capita growth rate (measured
at the X-axis) and the population growth rate (measured at the Y-axis) from 1960 to
2010.
(a)
8.0%
Economic Growth rate per capita
Number of Countries: 91
6.0%
Correlation
coeficient =-0.359
4.0%
2.0%
Population Growth rate
0.0%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
-2.0%
-4.0%
(b) 10.0%
Economic Growth rate per capita
Number of Countries: 91
8.0%
6.0%
Correlation
coeficient =-0.168
4.0%
2.0%
Population Growth rate
0.0%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
-2.0%
(c) 10.0%
8.0%
Economic Growth rate per capita
Number of Countries: 91
6.0%
Correlation
coeficient = -0.357
4.0%
2.0%
Population Growth rate
0.0%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
-2.0%
-4.0%
-6.0%
Fig. 2.5 Economic growth per capita and population increase. a 1960–2010, b 1960–1985,
c 1985–2010. Data from The World Bank’s Open Data
2.3 Population and Economic Growth
33
The correlation coefficient of the data in Fig. 2.5a is estimated as −0.359, which
is slightly stronger than the data in Fig. 2.4. Furthermore, we divided the sample
data period into two periods, from 1960 to 1985 and 1985 to 2010, and again
estimated the correlation coefficient between them. Figure 2.5b is the result of the
first half of the period (1960–1985), and a somewhat weaker relationship seems to
exist between the variables during this period than during the period as a whole
(1960–2010). The correlation coefficient is −0.168, which is almost half of the
estimated value in the total period. However, in the latter half of the period
(1985–2010), the correlation coefficient between the two growth rates is stronger
than during the first half. The coefficient is −0.357.
These empirical studies confirm that the relationship between economic growth
and population growth is negative, as proposed in the Solow growth model.
However, the statistical significance of the relationship is somewhat ambiguous
because the estimated correlation coefficients of absolute value are small.
One of the reasons for this weak relationship is that it included countries at
different stages of development. It may be necessary to separate developed and
developing countries. Therefore, in the next section, only developed countries are
analyzed to verify the relationship between population and economic growth.
2.3.3
The Results of Empirical Studies (2)
Before investigating the relationship between population and economic growth, it is
helpful to review the study conducted by Beaudry and Collard (2003). They
attempted to estimate the relationship using three different types of economic
performance, specifically growth in output per adult, growth in output per worker,
and the change in employment per adult. Their data were from the period
1960–1997, encompassing 18 developed countries. They observed that around the
first half of the 1970s the relationship between economic growth and population
growth changed drastically. Beaudry and Collard (2003) set the growth rates in
GDP per capita (or per adult, an adult defined as being between 15 and 64 years of
age) as dependent variables, and these were regressed on the growth rate of population and the initial (log) level of GDP per capita in the initial year, which
represented a convergence hypothesis in economic growth.
Beaudry and Collard (2003) discovered that population or the adult growth rate
exerted only a small and insignificant effect on economic performance over the
period 1960–1974. Additionally, they found strong evidence of convergence, which
is consistent with the standard economic growth theory. On the other hand, the
effect of population growth had a stronger negative effect on economic growth
during the period 1975–1997. By referring to Beaudry and Collard’s (2003) work,
we will investigate an extended period of data for OECD countries.
Using OECD data for 24 countries from 1970 to 2010, we estimated regression
equations to verify the relationship between population and economic growth.
Dependent variables are the economic growth rate per capita, denoted as %Δ(Y/P),
34
2 Population, Economic Growth, and TFP in Developed Countries
and per adult (15–64-year-old), denoted as %Δ(Y/A), respectively. As for the
independent variables, we prepared a population growth rate (Pop Gr), an adult
growth rate (Adult Gr), an initial log level of GDP per capita in the initial year
[Initial(Y/P)], and an aging ratio (AGE). Note the aging ratio selected data in the
mid-year of each period. Furthermore, we divided the sample period into two terms,
from 1970 to 1990 and from 1990 to 2010; we therefore prepared three different
sample periods, including the total period.
Table 2.2 shows the results of the above regressions. Panel A is the result of the
full sample period, and estimated parameters of both the population growth rate and
the adult growth rate are negative, but not significant to economic growth. It should
Table 2.2 Population and economic growth in OECD countries
Dep. var.
%Δ(Y/P)
Panel A: 1970–2010
Pop Gr
−0.355
(0.328)
Adult Gr
Initial (Y/P)
−0.015
(0.003)***
Age
0.523
R2
Panel B: 1970–1990
Pop Gr
−0.882
(0.326)**
Adult Gr
Initial (Y/P)
−0.022
(0.004)***
Age
0.579
R2
Panel C: 1990–2010
Pop Gr
0.221
(0.412)
Adult Gr
Initial (Y/P)
Age
−0.009
(0.004)*
%Δ(Y/P)
%Δ(Y/A)
%Δ(Y/A)
−0.508
(0.251)**
−0.014
(0.003)***
−0.955
(0.592)
−0.012
(0.004)***
−0.118
(0.141)
0.471
−0.882
(0.740)
−0.012
(0.004)***
−0.109
(0.137)
0.538
0.453
−2.718
(0.694)***
−0.010
(0.006)*
−0.507
(0.175)***
0.703
−0.748
(0.270)**
−0.020
(0.005)***
0.486
−1.591
(0.629)**
−0.016
(0.005)***
−0.282
(0.191)
0.536
−0.756
(0.609)
0.002
(0.007)
−0.209
(0.101)*
0.316
0.068
(0.300)
−0.005
(0.005)
−0.537
(0.508)
0.002
(0.006)
−0.162
(0.111)
0.152
0.171
0.062
R2
Data OECD “OECD Data Base”
Calculation by author
* means significant at 10%, ** means significant at 5%, and *** means singnificant at 1%
2.3 Population and Economic Growth
35
be concluded that population growth has not affected economic growth in the past
40 years. For reference, the initial level of GDP per capita strongly affects economic
growth negatively, so it could be referred to as the hypothesis of convergence which
standard economic growth theory described.
From the results of Panel B, in contrast to panels A or C, the population growth
rate had a negative and significant effect on economic growth, and the aging ratio
also negatively affected economic growth. However, in the more recent sample,
Panel C, there was no significant relationship between the dependent and the
independent variables. It is interesting that the results of these regressions are not
consistent with the results of Beaudry and Collard (2003).
After reviewing the traditional growth theory, we present our empirical results
on the relationship between population growth and economic growth. Our empirical
tests confirm that the relationship between economic growth and population growth
is negative, as proposed in the Solow growth model. However, theoretically,
population growth should spur technological progress, as discussed in the previous
chapter. We therefore conduct more direct empirical tests on the relationship
between population growth and technological progress in the next chapter.
References
Beaudry, P., & Collard, F. (2003). Recent technological and economic change among industrialized
countries: insights from population growth. Scandinavian Journal of Economics 105(3),
441–463.
OECD. (2001). Measuring productivity OECD manual.
OECD. (2008). Compendium of productivity indicators 2008.
OECD. (2013). OECD Factbook 2013.
Schreyer, P. (2003). Capital stocks, capital services and multi-factor productivity measures. OECD
Economic Studies 37.
Wölfl, A., & Hajkova, D. (2007). Measuring multifactor productivity growth. OECD Science,
Technology and Industry Working Papers, 2007/05.
World Bank. (2015). World Bank Open Data. http://data.worldbank.org/ (Visit 2015.4.30).
http://www.springer.com/978-4-431-54958-1