How different are demographic impacts on trade openness bygeographic region? : Findings from Europe, Asia, America,and Africa
Yukio Fukumoto
Tomoko Kinugasa
November 2019
Discussion Paper No.1912

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANHow different are demographic impacts on trade openness by geographic
region? : Findings from Europe, Asia, America, and Africa

Yukio Fukumoto
Department of Economics, Osaka University of Economics
2-2-8 Osumi, Higashiyodogawa-ku, Osaka 533-8533, Japan
Email: fukumoto@osaka-ue.ac.jp
Tomoko Kinugasa *Graduate School of Economics, Kobe University
2-1, Rokkodai-cho, Nada-ku Kobe, 657-8501, Japan
Email: kinugasa@econ.kobe-u.ac.jp
Abstract
Trade openness and the share of the working-age population vary in different geographic
regions of the world, especially, they tend to be high in Europe. Under the hypothesis that theshare of the working-age population has a positive effect on trade openness, we clarify thedifference of trade openness by region caused by age structure using the panel data for thefollowing four regions in the world: Europe, Asia, America, and Africa. We estimate equations
including trade openness as the dependent variable and the share of the working-age population
as one of the independent variables based on fixed-effects models and conduct the BlinderOaxaca decomposition by region. Our empirical results show that the share of the working-age
population has significantly positive effects on trade openness in three regions except Africa.
Moreover, high trade openness in Europe compared with Asia or America can be explained by
endowment effect of age structure and that compared with Africa can be explained by both
endowment and coefficient effects of age structure. Therefore, trade openness is greatly
influenced by age structure in Europe, but hardly in Africa.

*

Corresponding author.

11. Introduction
Many demographers have found that age structure greatly influences macroeconomy
through labor force and saving. Studies such as Bloom and Williamson (1998) , Bloom et al.
(2000), and Kelly and Schmidt (2005) and Bloom et al. (2017) insist that age structure affecteconomic growth because population in different age have different contribution to economy
through different labor force participation by age. Many studies based on life cycle hypothesis
conclude age structure is an important determinant of national saving and consumption becauseof different saving behavior in different generation (Modigliani and Brumberg, 1954; Fry and
Mason,1982; Mason,1987, 1988; Fair and Dominguez, 1991; Kinugasa and Mason, 2007;
Mason and Kinugasa, 2008; Dramani and Oga, 2017). Extending their research to open
economy macroeconomics, the relationship between age structure and current account balanceor capital flow is investigated (Taylor and Williamson, 1994; Taylor,1995; Higgins andWilliamson, 1997; Higgins,1998; Hassan et al., 2011).
The main interest of this research is trade openness, which is defined as the ratio of trade
to GDP here, has been historically high at the global level and has attracted attention ofinternational economists. 1 Trade openness is one of the key determinants of economic growthand it is important to research what influences trade openness. 2 As for demographic effect on

1

See Feenstra and Taylor 2014, Chapter 1; Krugman et al. 2014, Chapter 1.

2

For details, see Barro and Sala-i-Martin (1995), Frankel and Romer (1999) and Yanikkaya(2003). We also need to note that growing trade openness under the unbalanced situation of the2

trade openness, Alesina and Wacziarg (1998) indicate that an increase in country size induced
by an increase in population can decrease trade openness because welfare of a country withsmall population increases by exporting limited goods and importing many kinds of goods
from other countries, but they do not analyze the effect of age structure. The point of Braude(2000) has important implication for the effect of age structure on international trade. Braude(2000) states that a change in age structure can influence consumption structure, although theobject of empirical analysis is real exchange rate. Education, nursing care, and medical
services are mostly non-tradable and the share of consumption for non-tradable goods is likely
to be high in dependent age. Fukumoto and Kinugasa (2017) is the first study that analyze theeffect of age structure on trade openness. The authors apply the aforementioned work of
Braude (2000) and point that higher share of working-age population in total population leads
higher trade openness. This is because working-age individuals have higher share of tradable
goods in total demand than dependent individuals.
This research focuses attention on difference of age structure and trade openness byregion. Glancing over the world, age structure is quite different among geographic regions.
Wilson (2011; pp. 384–385) contends that the geographic regions differ in regard to the timing
and tempo of the fertility transition. 3 In Europe, the share of working-age population in totalexport and the import has caused serious trade friction and has been regarded as political andeconomic problems.
3

For details of demographic transition, see Kirk (1996), van de Kaa (2002) and Lee (2015). As

Reher (2011, p. 14) discusses, the changes in fertility affect the base of the population pyramid.
3

population is high and the share of young dependent population is high in Africa. Trade
openness also differs substantiall by region. Regionalism could be one important reason.
Mansfield and Milner (1999; p. 589) insist that much of the existing research on regionalism
centers on international trade. Regionalism promotes intra-regional trade integration, and will
influence trade openness in each region. According to the data on the share of intra-regional
trade, nearby countries are the main trade partners. 4 Engel and Rogers (1998), Frankel et al.
(1998) and Fukumoto (2011) conclude that goods markets have been regionalized. 5 In
contrast, nobody has pointed that difference of trade openness among region is influenced byage structure. In fact, trade openness and the share of working-age population in Europe arehigher than any other region.
This research hypothesize that the difference of age structure causes difference of trade
openness by region. Moreover, we suspect that the effect of age structure will be different byregion. We investigate formally using statistical method whether the estimated coefficient forage structure varies by region. In our empirical analysis, the world is divided into the following
four regions: Europe, Asia, America, and Africa. First, we estimate trade-openness equations in

4

The World Trade Organization (WTO, hereafter) reports the intra- and inter-regionalmerchandise trade, 2014 (Table I.4) in International Trade Statistics 2015(https://www.wto.org/english/res_e/statis_e/its2015_e/its15_world_trade_dev_e.htm).
5

Anderson and Norheim (1993) discuss the regionalization of world trade from 1830 to 1990

in Europe, and from 1928 to 1990 in America, Asia, Africa, and the Middle East. See also
Norheim et al. (1993).
4

which the dependent variable is trade openness and the independent variables include the shareof the working-age population, based on fixed-effects models by region, and analyze thecoefficients of the share of the working-age population. Next, we use the Blinder-Oaxaca
decomposition for the trade-openness equations of two regions to identify endowment andcoefficient effects of age structure. The endowment effect of age structure is based on the
differences of the share of the working-age population between two regions. If the share of theworking-age population is greatly different by region, the endowment effect will have an
important contribution on the difference of trade openness by region. The coefficient effect ofage structure is based on the difference of the coefficients of the share of the working-age
population in trade-openness equations between two regions. Therefore, the coefficient effectmight play an important role in the difference of trade openness by region even if the shares of
the working-age population of two regions are equal.
The remainder of this paper is organized as follows. In Section 2, we discuss factors that
influence trade openness and introduce variables used in the empirical analysis. In Section 3,
we describe the empirical results of fixed-effects models by region. In Section 4, we describe
the empirical results of the Blinder-Oaxaca decomposition. Finally, in Section 5, we draw
conclusions.

2. Model and Data
Our study is based on that the share of tradable goods in aggregate demand is higher for
the working-age individuals than for the dependent individuals. Braude (2000; Section 3.1)
initiated this line of research, focusing attention on the tendency of dependent individuals to5consume more non-tradable goods, such as childcare, education, nursing care, and medical
services, than the working-age individuals. Braude (2000), Andersson and Österholm (2005,
2006), Rose et al. (2009) and Hassan et al. (2015) suggest that age structure affects the realexchange rate. We hypothesize that an increase in the share of the working-age population
would increase trade openness under intergenerational differences in demand for tradable andnon-tradable goods, following Fukumoto and Kinugasa (2017). A higher share of tradable
goods in aggregate demand will lead to a higher share of tradable goods in aggregate supply. If
the share of tradable goods has a positive impact on trade openness, as suggested by Aizenman(1985), increases in the share of tradable goods in aggregate demand and supply caused by anincrease in the share of the working-age population will increase trade openness. 6
We discuss the effect of age structure on difference of trade openness by region,
distinguishing the following two kinds of effects: endowment and coefficient effects. The
endowment effect refers to the effect of the difference of age structure by region on trade

6

Hill (1989) and Herbertsson and Zoega (1999) point out that an increase in the share of theworking-age population increases net export. Therefore, an increase in the share of theworking-age population can increase GDP, because net export is a part of GDP. However, thiscontribution to GDP will be limited, because an increase in the share of the working-age
population increases both import and export. That is to say, a higher share of the working-age
population will increase trade volume more than GDP. As a result, a higher share of theworking-age population would have a positive effect on not only trade volume, but also trade
openness.
6

openness. This is clear from the findings of previous studies that an increase in the share of theworking-age population would increase trade openness. That is, trade openness would tend tobe high in the region with high share of the working-age population even if factors other thanage structure were equal in each region. The coefficient effect is based on the fact that theeffects of 1% point increase of the share of the working-age population on trade openness aredifferent among regions. The share of demand of tradable goods of different generation might
greatly different in each region. In fact, even if dependent individuals consume more nontradable goods than the working-age individuals, their weights will not necessarily be equal byregions. In addition, the share of trade of tradable goods will not necessarily be equal among
all regions. Previous studies did not conduct empirical analysis by region, so they did notconsider coefficient effect. 7
We investigate whether age structure influences trade openness with consideration toseveral factors suggested by previous studies to influence trade openness. We estimate tradeopenness equations based on fixed-effects models by region, and then conduct the BlinderOaxaca decomposition for two regions. The following variables are considered in our abovementioned analyses. The dependent variable is Trade openness, which is defined as the ratio of7

It has already been observed that coefficients representing the effect of age structure on othermacroeconomic fundamentals are sensitive to regional diversity. For example, see Doshi(1994) about relationship between age structure and the saving rate.
7

the sum of exports and imports to GDP. The independent variables are Age structure,
Government size, Country size, Economic development, and Period dummies. 8
We use the share of the working-age population as age-structure variable. It is defined as
the ratio of the working-age population (aged 15–64) to the total population. The share of theworking-age population would have a positive effect on trade openness. Tian et al. (2011) andFukumoto and Kinugasa (2017) determined the positive effect of share of the working-age
population on international trade, but they did not examine the effect in individual regions. 9
The government consumption share of GDP is defined as the variable of government size.
Benarroch and Pandey (2008; Table 2) found that there is a negative causality fromgovernment size to trade openness. 10 Population is considered to be representative of thecountry size. Alesina and Wacziarg (1998; Tables 2 and 6) found that larger country size had anegative effect on trade openness. Real GDP per capita is used as the variable representing
economic development. Feenstra (1998; p. 33) contends that increases in demand for luxury
non-tradable goods due to economic development would reduce trade openness. Ram (2009;
Tables 2, 5 and 6) used real GDP per capita as a control variable to test the negative effect of8

Due to the limitation of data with respect to the sample period and sample countries, it isdifficult to include other variables such as tariff rates.
9

Tian et al. (2011) found a positive effect for the share of working-age population on tradevolume in terms of the labor supply and labor income.
10

The relationship between trade openness and government size is a topic that has attracted

the attention of international economists. See, for example, Alesina and Wacziarg (1998).
8

country size on trade openness. We use period dummies as proxy variables for the tradebarriers and foreign outsourcing. Declining trade barriers and increasing foreign outsourcing
could be important reasons for the upward trend of trade openness over the last several
decades. Intra-regional trade integration also comprises a part of the period dummy variables.
These would influence trade openness for many countries in the same region during the same
period. Thus, it is possible that the effects of period dummies on trade openness differ greatly
by region.
Our data sources are as follows. The data for variables other than age structure are
obtained from Penn World Table 8.1. Trade openness is calculated as the sum of the absolutevalues of the shares of merchandise exports and imports in GDP (Indicator Codes: csh_x and
csh_m, respectively). Government size is represented as the share of government consumption
in GDP (Indicator Code: csh_g). We obtain real GDP per capita by dividing real GDP(Indicator Code: rgdpo) by population (Indicator Code: pop). The variables for age structure
are obtained from World Population Prospects: The 2015 Revision by the United Nations,
Department of Economic and Social Affairs, Population Division. 11
We include the data for 92 countries from 1951 to 2010. We choose countries whose data
are available from both Penn World Table 8.1 and World Population Prospects: The 2015
Revision. We excluded countries in which the population is extremely low, trade openness isextremely large or small, and oil industries have a substantial share of GDP, as well as those

11

File POP/15-1: Annual total population (both sexes combined) by five-year age group,

major area, region and country, 1950–2100 (thousands).
9

classified as Least Developed Countries (LDCs, hereafter). Doing so reduces the probability
that the estimated coefficients of independent variables obtained from statistical inferences will
be dominated by large outliers. The 92 countries are categorized into the following four
regions: 33 European countries; 28 Asian countries; 18 American countries; and 13 Africancountries. 12 We calculate the five-year average data from 1951 to 2010, and create panel datafor 12 periods: 1951–1955, 1956–1960, 1961–1965, 1966–1970, 1971–1975, 1976–1980,
1981–1985, 1986–1990, 1991–1995, 1996–2000, 2001–2005, and 2006–2010. 13
Here, we discuss whether trade openness and age structure are related, based on data for
the four geographic regions. Figure 1 presents line graphs depicting trade openness and theshare of the working-age population, and shows that both have upward trends. Glancing
through the sample period, we can see that trade openness was higher in the following order:
Europe > Asia > Africa > America. In the same way, the share of the working-age population
was higher in the following order: Europe > Asia > America > Africa. The orders for trade
openness and the share of the working-age population are consistent for Europe and Asia.

< Insert Figure 1 >12

The countries in the sample are listed in Appendix Table.

13

If there is a missing value in any period, we do not include that period in the sample. For

example, if data is available after 1958 for a country, we include that country for the period of
1961–1965 and afterwards.
10

Figure 2 presents scatter plots of trade openness and the share of the working-age
population. The distributions in Asia and America indicate that these two variables are
positively correlated. Figure 3 presents scatter plots of logged trade openness and the share of
the working-age population. Trade openness is natural-logged because trade openness in many
countries is less than 1 but not a few countries in Europe and Asia have trade openness muchhigher than 1. In addition, Figure 2 suggests that trade openness increases along with anincrease in the share of the working-age population more rapidly than a linear relationship.
According to Figure 3, the relationship is closer to linear in Europe and Asia. In Figures 2 and3, the distributions in Europe and Africa are dense, and they do not seem to be correlated
strongly. However, this might be because Europe and Africa have stronger intra-regional ties
than the other two geographic regions. That is, trade openness may be similar within Europe
and Africa because the share of the working-age population is similar within each region. 14< Insert Figures 2 and 3 >Table 1 presents descriptive statistics of the variables used in our empirical analysis.
Whether trade openness should be logged or not is not necessarily based on economic theory
and neither possibility can be denied according to Figure 2 and 3. Therefore, we conduct

14

There seems to be several outliers in the bottom of Figure 3 (a), and these are Albania andRomania. In the empirical analyses in Section 3, these two countries are included, but ourconclusion does not change even if we conduct empirical analyses excluding these countries.
11empirical analyses for both cases of non-logged and logged trade openness. Share of theworking-age population and government consumption share of GDP are not logged andpopulation and GDP per capita are natural-logged.

< Insert Table 1 >3. Empirical Results of Fixed-Effects Models
We estimate the trade-openness equations that include the country dummies based on the
fixed-effects models. The country dummies represent country-specific characteristics such as
tradition, language, culture, and natural enviroment. 15 We consider the following equations bygeographic region:

OpennessRit = αRi + βR1 WorkingRit + βR2 GovRit + βR3 PopRit + βR4 GDP/Pop Rit+ βR5 D(1956–1960)t + βR6 D(1961–1965)t + βR7 D(1966–1970)t+ βR8 D(1971–1975)t + βR9 D(1976–1980)t + βR10D(1981–1985)t+ βR11 D(1986–1990)t + βR12 D(1991–1995)t + βR13D(1996–2000)t+ βR14 D(2001–2005)t + βR15 D(2006–2010)t + εRit

15

(1)

The random-effects models assume that unobserved country fixed-effects do not correlatewith the independent variables. Such an assumption would not be sound for the informationconsidered in our analysis.
12

The superscripts R denote region ( R = Europe, Asia, America, Africa), and the subscripts i andt denote country and period, respectively. The dependent variable OpennessRit represents trade
openness. αRi is a country dummy variable. Let WorkingRit be the share of the working-age
population. GovRit denotes the government consumption share of GDP. Popit denotes
population, and GDP/PopRit is the real GDP per capita. D(1956–1960)t, D(1961–1965)t,D(1966–1970)t, D(1971–1975)t, D(1976–1980)t, D(1981–1985)t, D(1986–1990)t, D(1991–
1995)t, D(1996–2000)t, D(2001–2005)t, and D(2006–2010)t are period dummies for 1956–
1960, 1961–1965, 1966–1970, 1971–1975, 1976–1980, 1981–1985, 1986–1990, 1991–1995,
1996–2000, 2001–2005, and 2006–2010, respectively. εRit denotes the disturbance term.
Equation (1) is almost the same as Equation (3) in Table 5 in Ram (2009) except an agestructure variable.
Tables 2 and 3 present empirical results with non-logged and logged dependent variables,
respectively. Standard error is calculated based on Cameron and Miller (2015). The signs of theestimated coefficient of the share of the working-age population are positive in Tables 2 and 3.
In addition, these coefficients are statistically significant, except Africa in Tables 2 and 3, andexcept America in Table 3. The estimated coefficients of the share of government consumption
in GDP are negative, except Europe in Tables 2 and 3, and all of the coefficients are
insignificant. Thus, the hypothesis of Benarroch and Pandey (2008) does not seem to besupported. The signs of the estimated coefficient of population are negative, except Asia in
Table 2, and most coefficients are significantly negative in Tables 2 and 3. Overall, thehypothesis of Alesina and Wacziarg (1998) seems to be supported. The signs of the estimatedcoefficient of GDP per capita are negative, except Asia in Table 2, and the coefficients are13

significantly negative in America and Africa. The empirical results are consistent with the
hypothesis of Feenstra (1998) in some regions. In Tables 2 and 3, most period dummies are
significantly positive, and the coefficients for later periods are larger than those for earlier
periods, except Asia in Table 2. This indicates that the degree of development of intra-regional
trade integration differs among regions. 16 As observed above, the results of Tables 2 and 3 aredifferent in terms of significance for some variables. Nevertheless, the signs of the coefficientsare not significantly opposite from the hypothesis discussed in Section 2 in any estimations.

< Insert Tables 2 and 3 >Based on the results in Tables 2 and 3, the effects of age structure on trade openness byregion can be discussed as follows. In Table 2, the coefficients of age structure are higher in thefollowing order: Europe > Asia > America > Africa. In Table 3, the coefficients of age structure
are higher in the following order: Asia > Europe > America > Africa. In Europe, the similarityof age structure may influence the similarity of trade openness, because the dispersion of trade
openness and the share of the working-age population are small in Figure 3. In case of Asia, therelationship between trade openness and age structure is stronger when trade openness islogged. Meanwhile, significant relationship between these variables is not observed for

16

Since the 1990s, many Regional Trade Agreements have been concluded. Detailed

information is available from the website of the WTO (https://www.wto.org/ english/
tratop_e/region_e/region_e.htm).
14

America when trade openness is logged. Therefore, the results might be different depending on
model specification.
The effects of age structure on trade openness in America and Africa seem to be smaller
than those in Europe and Asia. Trade openness might be less sensitive to age structure in
America because trade policies based on the infant industry argument were predominant inSouth American countries, compared to Asian countries (Krugman et al. 2014; Chapter 11).
Manufacturing protection under import-substituting industrialization could decrease the shareof international trade of tradable goods. It is possible that even countries not classified as LDCs
do not prepare goods and labor markets in Africa, compared to the other three regions. 17 In
such circumstances, the supply of non-tradable goods that require time-consuming skills andtraining, especially education and medical services, will be insufficient for any generation. The
consumption structure of tradable and non-tradable goods could then be less different among
generations, so that the age structure might not influence trade openness.

4. Empirical Results of the Blinder-Oaxaca Decomposition
In examining the influence of age structure on difference in trade openness by region, we
focus attention on which is more important, endowment effect or coefficient effect. In case theendowment effect works, even if the shares of tradeable goods and non-tradable goods in total
demand for the working-age individuals are not different by region, trade openness could be

17

Bloom et al. (1998; p. 211) state that Africa’s economic performance is further impeded by

its extraordinarily disadvantageous geography and demographic circumstances.
15different because of the difference of the share of working age population by region. In casethe coefficient effect works, even if the share of the working-age population is not different byregion, trade openness could be different because of the difference of the share of the demandof tradable goods and non-tradable goods for the working-age individuals by region. In Europe,
the share of the working-age population is higher than any other region (Figure 1 and Table 1),
so endowment effect would definitely be important. Moreover, the coefficient of the share ofworking-age population is higher than the other regions except one case (Tables 2 and 3), thus
it is possible that coefficient effect is important.
The Blinder-Oaxaca decomposition would be effective in distinguishing the two kinds ofeffects of age structure on difference of trade openness in Europe and other regions. 18 Basedon Equation (1), the difference of the average trade openness between Europe and other region
is expressed in Equation (2). Superscripts R denote regions other than Europe ( R = Asia,
America, Africa).

OpennessEurope - OpennessR = βEurope1 ( WorkingEurope - WorkingR )18

The Blinder-Oaxaca decomposition was suggested by Blinder (1973) and Oaxaca (1973) as

the method to decompose the effects of sex and race on wage gap mainly in the field of labor
economics. For example, the method is used in order to distinguish whether wage gap between
men and women exists due to different education level or due to different evaluation for men
and women even under the same education level. See also Weichselbaumer and Winter-Ebmer(2005) and Carson (2017).
16

+(βEurope1 – βR1 ) WorkingR + Ω(2)

where the bar on the variable means the average in each region and Ω represents the factors
other than age structure. In Equation (2), the left hand side is the difference of trade openness
between the two regions. In the right hand side, βEurope1 ( WorkingEurope - WorkingR ) isendowment effect and explains the difference of the share of the working-age population
between two regions and (βEurope1 – βR1 ) WorkingR is coefficient effect and explains thedifference of the coefficients for the two regions. 19
Tables 4 and 5 present empirical results with non-logged and logged dependent variables,
respectively. In the first columns from the left of these tables, regions to compare with Europe
are indicated. These table show the difference of trade openness, and endowment andcoefficient effects of age structure. Tables 4 and 5 have many common characteristics. First,
trade openness in Europe is higher than any other region, and significantly different from other19

Equation (2) can also be expressed as follows.

OpennessEurope - OpennessR=βR1 ( WorkingEurope - WorkingR )+ (βEurope1 – βR1 ) WorkingEurope + Ω(2’)

We suggest that Equation (2) can provide more suitable endowment effect of age structure thanEquation (2’). In Europe, GDP per capita is higher and free trade has developed more than any
other region, so the deviation from the appropriate consumption structure of tradable and nontradable goods in each generation may be small. Therefore, we use βEurope1 , not βR1, ascomponents of endowment effect of age structure.
17

regions. Next, the endowment effect is significant for all three regions and it is indicated thatthe share of the working-age population in Europe is higher than any other region. On the other
hand, coefficient effect for only between Europe and Africa is significant.

< Insert Tables 4 and 5 >The results indicate that the endowment effect of age structure plays an important role indifference of trade openness between Europe and Asia and between Europe and America. 20 In
Table 4, endowment effect of age structure (0.111) can explain most of the difference of trade
openness between Europe and Asia (0.111). Moreover, the endowment effect of age structure(0.166) can explain a large part of the difference of trade openness between Europe andAmerica (0.249). That is, effects of various factors other than age structure offset each other,and the difference of trade openness can be explained mostly by the share of the working-age
population. The difference of trade openness between Europe and Africa is influenced by not
only endowment effect but also coefficient effect of age structure. In Table 4, the sum of
endowment and coefficient effects of age structure (1.356) are much more than the differenceof trade openness between Europe and Africa (0.174). It, therefore, seems that factors otherthan age structure increases trade openness more in Africa than Europe. This implies that thedifference of trade openness between Europe and Africa will not decrease even if the share ofworking age population increases in Africa and age structure in Africa become similar to that

20

Results of Table 5 are similar to Table 4, so we only discuss Table 4.
18

in Europe. According to Tables 4 and 5, whether or not trade openness is logged influences theresults of the Blinder-Oaxaca decomposition. Nevertheless, significance is not different, so age
structure would definitely contribute to high trade openness in Europe.

5. Conclusions
In this research, we focus attention on age structure as a reason why trade openness ishigher in Europe than Asia, America, or Africa. Our empirical results by region based on fixedeffects models showed that the share of the working-age population has a significantly positiveeffect on trade openness for the three regions of Europe, Asia and America, except Africa. In
addition, we investigated the difference of trade openness in Europe and other regions based on
the Blinder-Oaxaca decomposition. According to the result, only endowment effect of age
structure is significant for Asia and America, and both endowment and coefficient effects ofage structure are significant for Africa.
Our empirical results lead to the following conclusions. If the share of the working-age
population were equal in the four regions, the influence of age structure on the difference intrade openness would not be different in Europe, Asia or America. In reality, the share of theworking-age population is high in Europe, which may cause high trade openness in the region.
On the other hand, trade openness may not increase in Africa even if increases in the share of
the working-age population raise trade openness in other regions. In Africa, the fact that therelationship between age structure and trade openness is weak could be a factor for theempirical result that the coefficient effect of age structure is significant in Blinder-Oaxaca
decomposition for Europe and Africa.
19

Acknowledgements
We appreciate Professors Andrew Mason, Mitoshi Yamaguchi, Ryozo Miyao, Taro Esaka,
Hidehiko Kamiya, Machiko Watanabe, Makoto Shimizu, Adam M. Majoe, Adam Bestenbostel,
Tomokazu Nomura and Takayuki Ogawa for their valuable comments and this paper has been
improved greatly thanks to their comments. This work was supported by JSPS KAKENHI Grant
Numbers JP26292118 and JP24310031, and the OUE travel grant for international conferences.

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25

Figure 1. Line graphs of trade openness and share of working-age population(a) Trade openness
1.00
0.80
0.60
0.40
0.20

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
EuropeAsia

America

Africa(b) Share of working-age population
0.70
0.65
0.60
0.55
0.50

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
EuropeAsia

America26

AfricaFigure 2. Scatter plots of trade openness and share of working-age population(a) Europe(b) Asia

3

5
4

2

3
2

1

1
0

0.5

0.6

0.7

0

0.8

0.4

(c) America(d) Africa

1.6

1.6

1.2

1.2

0.8

0.8

0.4

0.4

0

0.4

0.5

0.6

0.7

0

0.8

0.4

0.5

0.6

0.6

0.7

0.8

0.8

Note: The vertical axis is trade openness and the horizontal axis is the share of working-age
population.
27

Figure 3. Scatter plots of logged trade openness and share of working-age population(a) Europe(b) Asia

2

3
1

0

-1

-2

-3
-4
-6

-5
0.5

0.6

0.7

0.8

-7

(c) America

0.4

1

0

0

-1

-1

-2

-2

0.4

0.6

0.7

0.8

0.5

0.6

0.7

0.8

(d) Africa

1

-3

0.5

0.5

0.6

0.7

0.8

-3

0.4

Note: The vertical axis is logged trade openness and the horizontal axis is the share of workingage population.
28

Table 1. Descriptive statistics(a) Europe
Variable
Trade opennessln (Trade openness)
Share of the working-age population
Government consumption share of GDP
ln (Population)ln (GDP per capita)(b) Asia
Variable
Trade opennessln (Trade openness)
Share of the working-age population
Government consumption share of GDP
ln (Population)ln (GDP per capita)(c) America
Variable
Trade opennessln (Trade openness)
Share of the working-age population
Government consumption share of GDP
ln (Population)ln (GDP per capita)

Obs.

Mean

S. D.

Min

Max

274
274
274
274
274
274

0.589
-0.808
0.659
0.195
2.414
9.367

0.424
0.833
0.027
0.070
1.062
0.682

0.012
-4.405
0.551
0.052
0.294
7.108

2.698
0.993
0.727
0.526
4.412
10.697

Obs.

Mean

S. D.

Min

Max

250
250
250
250
250
250

0.478
-1.362
0.605
0.202
3.046
8.454

0.683
1.163
0.061
0.112
1.773
1.020

0.002
-6.1420.482
0.042-0.144
6.651

4.386
1.478
0.749
0.606
7.174
10.774

Obs.

Mean

S. D.

Min

Max

215
215
215
215
215
215

0.340
-1.262
0.578
0.141
2.254
8.579

0.213
0.616
0.057
0.054
1.524
0.765

0.068
-2.6910.480
0.034-0.370
7.288

1.229
0.207
0.711
0.413
5.720
10.655

Mean

S. D.

Min

Max

0.416
-1.040
0.547
0.191
1.818
8.022

0.252
0.575
0.046
0.094
1.451
0.730

0.068
-2.6870.470
0.040-0.744
6.210

1.386
0.327
0.697
0.501
4.361
9.678

(d) Africa
VariableObs.
139
Trade openness139ln (Trade openness)
139
Share of the working-age population
139
Government consumption share of GDP
139
ln (Population)
139
ln (GDP per capita)
Note: ln (X) means that variable X is natural logged.

29

Table 2. Empirical results for non-logged trade openness based on fixed-effects models
Independent variable
EuropeAsia
America
AfricaWorking
Coef.
2.060 *
1.448 **
1.401 **
0.003
S. E.
1.032
0.657
0.657
0.765
Gov
Coef.
0.128
-0.171-0.005-0.599
S. E.
0.393
0.218
0.244
0.519
Pop
Coef.
-0.806 **0.007-0.359 ** -0.567 *
S. E.
0.396
0.113
0.133
0.261
GDP/PopCoef.
-0.1040.061-0.162 ** -0.244 **
S. E.
0.101
0.099
0.064
0.094
D(1956-1960) Coef.
0.087 ***
-0.0320.044 **
0.079 *
S. E.
0.031
0.044
0.020
0.040
D(1961-1965) Coef.
0.208 ***
-0.0350.105 **
0.129
S. E.
0.065
0.090
0.038
0.077
D(1966-1970) Coef.
0.298 ***
-0.1400.172 *** 0.222 *
S. E.
0.094
0.090
0.053
0.116
D(1971-1975) Coef.
0.419 ***
-0.1450.249 *** 0.379 **
S. E.
0.120
0.102
0.074
0.172
D(1976-1980) Coef.
0.530 ***
-0.0770.321 *** 0.557 **
S. E.
0.146
0.113
0.089
0.235
D(1981-1985) Coef.
0.583 ***
-0.0590.300 *** 0.672 **
S. E.
0.165
0.122
0.093
0.296
D(1986-1990) Coef.
0.668 ***
-0.1110.267 **
0.609 *
S. E.
0.191
0.138
0.094
0.292
D(1991-1995) Coef.
0.757 ***
0.015
0.355 *** 0.682 *
S. E.
0.218
0.173
0.107
0.329
D(1996-2000) Coef.
0.837 ***
0.047
0.484 *** 0.785 *
S. E.
0.229
0.165
0.124
0.367
D(2001-2005) Coef.
1.003 ***
0.045
0.547 *** 0.942 **
S. E.
0.251
0.153
0.136
0.431
D(2006-2010) Coef.
1.205 ***
0.038
0.671 *** 1.074 **
S. E.
0.283
0.164
0.147
0.432
Adjusted R-squered
0.751
0.309
0.502
0.209
Number of observations
274
250
215
139
Number of countries
33
28
18
13
Note: The dependent variable is trade openness. The estimates of country dummies are notreported. ***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively.
30

Table 3. Empirical results for logged trade openness based on fixed-effects models
EuropeAsia
America
AfricaWorking
Coef.
5.447 *
7.135 **
2.774
1.675
S. E.
3.011
2.786
1.783
1.886
Gov
Coef.
0.496
-0.585-0.782-0.205
S. E.
0.902
0.812
0.733
0.987
Pop
Coef.
-0.939-0.496-0.917 ***-1.130
S. E.
1.048
0.338
0.311
0.654
GDP/PopCoef.
-0.044-0.169-0.325 **-0.486 **
S. E.
0.397
0.182
0.137
0.169
D(1956-1960) Coef.
0.180 *
-0.156
0.083
0.134
S. E.
0.091
0.139
0.058
0.086
D(1961-1965) Coef.
0.281
0.085
0.216 *
0.140
S. E.
0.255
0.192
0.110
0.195
D(1966-1970) Coef.
0.441
0.098
0.362 **
0.282
S. E.
0.328
0.226
0.158
0.286
D(1971-1975) Coef.
0.595 *
0.053
0.568 ***
0.613
S. E.
0.323
0.326
0.196
0.400
D(1976-1980) Coef.
0.867 **
0.303
0.818 ***
0.929
S. E.
0.373
0.343
0.225
0.535
D(1981-1985) Coef.
0.931 **
0.392
0.795 ***
1.056
S. E.
0.394
0.388
0.232
0.670
D(1986-1990) Coef.
0.996 **
0.296
0.664 **
0.960
S. E.
0.441
0.453
0.249
0.695
D(1991-1995) Coef.
1.162 **
0.508
0.965 ***
1.183
S. E.
0.496
0.492
0.269
0.751
D(1996-2000) Coef.
1.329 **
0.853
1.311 ***
1.398
S. E.
0.545
0.514
0.292
0.825
D(2001-2005) Coef.
1.581 **
0.944 *
1.454 ***
1.655 *
S. E.
0.601
0.553
0.314
0.917
D(2006-2010) Coef.
1.796 **
1.034
1.748 ***
1.960 *
S. E.
0.672
0.609
0.361
0.993
Adjusted R-squered
0.725
0.477
0.481
0.291
Number of observations
274
250
215
139
Number of countries
33
28
18
13
Note: The dependent variable is logged trade openness. The estimates of country dummies are
not reported. ***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively.
31

Table 4. Empirical results for Blinder-Oaxaca decomposition in case of non-logged trade
openness

Geographic region

Asia
America
Africa

Coef.
S. E.
Coef.
S. E.
Coef.
S. E.

Difference in trade
openness
0.111 ***
0.051
0.249 ***
0.030
0.174 ***
0.034

Contribution of age structure
Endowment effectCoefficient effect

0.111 ***
0.037
0.166 ***
0.055
0.230 ***
0.076

0.370
0.563
0.381
0.434
1.126 **
0.474

Note: The results other than the share of the working-age population are not reported. ***,**, and * indicate significance at the 1, 5, and 10 percent levels, respectively. Europe is set as abenchmark.

32

Table 5. Empirical results for Blinder-Oaxaca decomposition in case of logged trade
openness

Geographic region
Asia
America
Africa

Coef.
S. E.
Coef.
S. E.
Coef.
S. E.

Difference in trade
openness
0.554 ***
0.090
0.453 ***
0.066
0.232 ***
0.071

Note: See note of Table 4.

33

Contribution of age structure
Endowment effectCoefficient effect0.293 ***
0.079
0.440 ***
0.116
0.608 ***
0.158

-1.021
1.215
1.545
0.962
2.064 **
0.969

Appendix Table. List of countries
Countries

PeriodEurope: 33 countries
Albania
Austria
BelarusBelgium
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
IrelandItaly
LatviaLithuania
Netherlands
PolandPortugal
Republic of Moldova
Romania
SerbiaSlovakia
Slovenia
Spain
SwedenSwitzerland
TFYR Macedonia
UkraineUnited Kingdom

1971-2010
1951-2010
1991-2010
1951-2010
1991-2010
1971-2010
1991-2010
1991-2010
1951-2010
1991-2010
1951-2010
1951-2010
1951-2010
1951-2010
1971-2010
1951-2010
1951-2010
1991-2010
1991-2010
1951-2010
1971-2010
1951-2010
1991-2010
1961-2010
1991-2010
1991-2010
1991-2010
1951-2010
1951-2010
1951-2010
1991-2010
1991-2010
1951-2010

Asia: 28 countries
Armenia
Australia
Azerbaijan

1991-2010
1951-2010
1991-2010

Countries

34

PeriodAsia: continued
Malaysia
Mongolia
New ZealandPakistan
Philippines
Republic of Korea
Singapore
Sri Lanka
Tajikistan
ThailandTurkeyTurkmenistan
Uzbekistan
Viet Nam

1956-2010
1971-2010
1951-2010
1951-2010
1951-2010
1956-2010
1961-2010
1951-2010
1991-2010
1951-2010
1951-2010
1991-2010
1991-2010
1971-2010

America: 18 countries
Argentina
Bolivia
BrazilCanadaChile
Colombia
Costa Rica
Dominican Republic
GuatemalaHonduras
Jamaica
MexicoPanamaParaguay
Peru
Trinidad and Tobago
United States
Uruguay

1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1956-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010
1951-2010

Africa: 13 countries
CameroonCongo

1961-2010
1961-2010

ChinaGeorgia
Hong Kong
India
Indonesia
IsraelJapan
JordanKazakhstan
Kyrgyzstan
Lebanon1956-2010
1991-2010
1961-2010
1951-2010
1961-2010
1951-2010
1951-2010
1956-2010
1991-2010
1991-2010
1971-2010

Côte d'Ivoire
Egypt
Gabon
Ghana
Kenya
Mauritius
MoroccoNamibiaSouth Africa
Swaziland
Tunisia

1961-2010
1951-2010
1961-2010
1956-2010
1951-2010
1951-2010
1951-2010
1961-2010
1951-2010
1971-2010
1961-2010

Note: This table lists sample countries and periods. Oceania countries that can be included in
the sample are only Australia and New Zealand. These two countries are included in Asia in
our analysis because they are close to Asia compared with other region and have strong
connection with Asia in terms of trade. Small countries, highly oil-dependent countries,
countries with extremely high or low trade openness, and countries classified as LDCs are notincluded, as discussed in Section 2. We regard countries with population less than 1,000,000 in
2005–2010 as small countries. Bahrain and Norway are also considered to be highly oildependent countries. Trade openness and share of government consumption in GDP of El
Salvador are extremely large in some periods, and the trade openness of Zimbabwe isextremely small in 1956–60; they seemed to be an outlier. The United Nations, Department of
Economic and Social Affairs, Committee for Development Policy (2015) lists 48 countries as
LDCs. We also exclude Botswana, which was classified as an LDCs. Finally, the following 71
countries are excluded from the sample: Angola, Antigua and Barbuda, Bahamas, Bahrain,
Bangladesh, Barbados, Belize, Benin, Bhutan, Botswana, Brunei Darussalam, Burkina Faso,
Burundi, Cabo Verde, Cambodia, Central African Republic, Chad, Comoros, Cyprus,
Democratic Republic of the Congo, Djibouti, Ecuador, El Salvador, Equatorial Guinea,
Ethiopia, Fiji, Gambia, Grenada, Guinea, Guinea-Bissau, Iceland, Iran, Iraq, Kuwait, Lao
People's Democratic Republic, Lesotho, Liberia, Luxembourg, Macao, Madagascar, Malawi,
Maldives, Mali, Malta, Mauritania, Montenegro, Mozambique, Nepal, Niger, Nigeria, Norway,
Oman, Qatar, Russian Federation, Rwanda, Saint Lucia, Saint Vincent and the Grenadines, Sao
Tome and Principe, Saudi Arabia, Senegal, Sierra Leone, Sudan, Suriname, Syrian Arab
Republic, Togo, Uganda, United Republic of Tanzania, Venezuela, Yemen, Zambia, andZimbabwe.

35