Download Does convergence cause trade, or does trade cause convergence?

J. Int. Trade & Economic Development 13:4 397–418
Does convergence cause trade, or does
trade cause convergence?
Teresa Cyrus
Department of Economics, Dalhousie University, Halifax,
Nova Scotia, Canada
Abstract
This paper examines the direction of causality between international trade and
cross-country income differences in several ways. First, instruments for income
are used in pooled gravity regressions to determine the effect of income differences
on bilateral trade, and instruments for trade are used in regressions to determine
the causes of income dispersion. Results of these cross-country estimations show
that more similar countries trade more, while trade appears to increase dispersion.
However, fixed-effects regression, random-effects regression, and Granger
causality tests show that trade reduces income differences over time. Thus, while
the postwar era has seen increasing trade and conditional convergence, the
causality is bi-directional: convergence causes trade, and trade causes convergence.
Keywords
Trade, convergence, income, gravity, causality, instruments.
1. INTRODUCTION
Over the postwar period, the world has become increasingly integrated
through international trade. At the same time, the countries of the world
have experienced convergence in income per capita, holding capital
accumulation and population growth constant. Has convergence caused
trade to increase? Has trade caused convergence? Is some other factor
causing both? This paper explores the direction of causality between trade
and income differences.
From 1965 to 2000, the average share of trade (exports plus imports) in
income has risen fairly consistently for the 56 countries considered in this
paper, from 43.94 per cent in 1965 to 81.18 per cent in 2000.1 For the same
group of countries, convergence conditional on country-specific attributes
(saving rates and population growth rates) has occurred.2
Address for Correspondence
Teresa Cyrus, Department of Economics, Dalhousie University, Halifax, Nova Scotia,
Canada, B3H 3J5. E-mail: [email protected]
The Journal of International Trade & Economic Development
ISSN 0963-8199 print/ISSN 1469-9559 online ª 2004 Taylor & Francis Ltd
http://www.tandf.co.uk/journals DOI: 10.1080/0963819042000300573
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The Journal of International Trade & Economic Development
How might convergence affect trade? Both old- and new-style trade
theories give reasons for links from income to trade. Heckscher – Ohlin –
Samuelson trade theory states that trade is based on differences in factor
proportions; differences in capital – labour ratios will increase trade. By
contrast, the new trade theory, due predominantly to Krugman (1979) and
Helpman (1981), based on monopolistic competition and economies of
scale, implies that similarities in income between countries will increase
trade.
How might trade affect convergence? The literature on knowledge
dissemination and technology transfer shows how trade may lessen
income differences between countries.3 One possibility, illustrated in
Grossman and Helpman (1991), is that goods may have ideas or
knowledge embedded in them. Trade allows these ideas to flow across
borders, thereby allowing follower countries to catch up to those more
advanced. Another possibility is that exposure to international trade
may increase competitive pressures, which force domestic firms to
increase their productivity, resulting in higher income. In contrast, other
models show that trade may in fact increase income differences between
countries. For example, as in Young (1991), poor countries may become
trapped in producing goods that offer little scope for technology growth
or development.
Thus, the trade literature provides reasons why income differences
will affect trade, but the sign of the effect is unclear. Similarly, the
convergence literature provides reasons why trade will affect income
differences, but it is not clear whether trade causes income differences to
rise or to fall. Obviously, there is a question of causality here. If we
believe that income dispersion affects trade, and that trade affects
dispersion, then it may be difficult to know the true sign and magnitude
of these effects.
A few others have tried empirically to link trade and income convergence,
but have not established causality. Ben-David (1993) finds a relationship
between the timing of trade liberalization and income convergence in the
European Community. Helliwell and Chung (1990) examine 19 industrial
countries, and find evidence that countries that have increased their
openness to international trade experience faster convergence. Helpman
(1987) shows that, for a sample of 14 OECD countries over the postwar
period, dispersion has fallen as the trade – income ratio within the group has
risen. In contrast, Slaughter (2001) uses a differences-in-differences
approach to test whether trade-liberalizing countries experience a greater
change in convergence than countries that have not liberalized; he finds no
strong evidence that this is the case. None of these papers addresses
causality, however.
This paper uses data on bilateral trade, while most authors who
examine trade use the ratio of exports or total trade to GDP. Using
Does convergence cause trade or does trade cause convergence?
399
bilateral trade provides for a richer dataset; it allows for many more
observations to be used in estimation and, more importantly, if we
believe that convergence happens because countries learn from their
trading partners, then including information on those trading partners
is essential. It should be noted that the word ‘convergence’ here refers
to a fall in cross-country income differences, not beta or sigma
convergence as described by, for example, Barro and Sala-i-Martin
(1991).
I address the dilemma of causality in three ways. First, I estimate
two equations, one for trade and one for income differences, using
instruments for these two variables. In the bilateral trade equation, the
instruments I use for per-capita income differences include differences in
initial income per capita and in factor accumulation rates (physical and
human capital accumulation rates and the population growth rate). In
the income-differences equation, the instruments I use for bilateral trade
are distance and adjacency, which are standard gravity variables;4
dummy variables representing whether the two countries speak the same
language or belong to the same trading bloc; and the sum of the two
countries’ populations.
Second, similar regressions will be run using the fixed-effects methodology. This has the advantage of taking into account country-specific fixed
effects. However, since many of the variables do not vary over time, they
cannot be included in these regressions, so the equations differ somewhat
from the OLS and instrumental variables equations. While the randomeffects estimator is most likely inconsistent due to correlation between the
explanatory variables and the unobserved effects, the results of randomeffects regressions are reported for the sake of comparison.
Finally, Granger causality tests will be performed to determine whether
past values of income differences are useful in predicting current trade flows,
and whether past values of bilateral trade are useful in predicting current
levels of income differences.
This paper contributes to this literature by seeking to determine the
direction of causality between income convergence and trade. The results
robustly show that income differences lower trade. However, the results
are mixed as to whether trade raises or lowers income differences; crosssectional instrumental variables regressions show that trade raises
differences, but the time-series results show the opposite. At any given
point in time then, increased trade is associated with higher income
differences, but over time, trade causes income differences to contract.
In Section 2, the theoretical links between trade and income
differences are presented. Section 3 presents the equations to be
estimated and describes the data. The empirical results are presented
in Section 4, and Section 5 considers robustness and specification tests.
Section 6 concludes.
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2. THEORETICAL CONSIDERATIONS
2.1 The impact of income differences on trade
How do income differences affect trade? The factor-proportions theory
originated by Heckscher (1919) and Ohlin (1924), and refined by Samuelson
(1948, 1949) and Jones (1956), among others, makes predictions different
from the new trade theory of Krugman (1979) and Helpman (1981).
The seminal work of Heckscher and Ohlin, like the earlier theory of
Ricardo, states that differences between countries drive trade. The factorproportions theory links a country’s relative factor endowments and the
factor content of its trade flows, stating that a country will export the
commodities that abundantly use its intensive factors; countries will engage
in trade only if their capital – labour ratios differ. Since there is a high
correlation between capital – labor ratios and output per capita,5 the factorproportions theory then implies that countries will engage in trade only if
their levels of output per person differ.
The factor-proportions theory does not explain trade between similar
countries, and yet, in the postwar era, a large proportion of trade has been
between industrial countries. In addition, much trade has been in
differentiated products. Krugman (1979, 1980, 1981) and Helpman (1981)
explain these empirical regularities with models of monopolistic competition, economies of scale, and intra-industry trade, showing that even
countries with identical factor endowments will engage in international
trade. In the factor-proportions model, of course, identical factor
endowments in two countries imply no bilateral trade whatsoever.
Both Krugman and Helpman mention the similarity of their conclusions
to those of Linder (1961), who posits that countries that have more similar
demand structures will trade more. Linder points out that the level of
average income is the most important factor affecting the demand structure,
stating that ‘per-capita income differences are a potential obstacle to trade’
(p. 98); thus, countries with more similar levels of income per capita should
trade more.6
Including per-capita differences in income as an explanatory variable in a
gravity regression will allow us to see the impact of dispersion on trade. If
differences increase trade, then the factor-proportions theory is sufficient to
explain trade. If, on the other hand, differences reduce trade, then the new
trade theory is supported.
2.2 The impact of trade on income differences
Trade may affect the differences in per-capita income between countries, but
it is not clear whether trade causes dispersion to fall over time, or to rise.
While many recent papers have explored the theoretical relationship
Does convergence cause trade or does trade cause convergence?
401
between trade and convergence, links from trade to both convergence and
divergence have been found.
Those who claim that trade lessens income differences often assert a link
through technology. This is due, for example, to international transfers in
knowledge, where knowledge is embedded in goods. Countries at a lower
level of development may be able to increase their productivity by importing
goods from richer countries and making use of the technology and ideas
contained therein. Poor countries can thus close the gap between their low
levels of technology and the higher levels in other countries.
Grossman and Helpman (1991), in an analysis similar to that of RiveraBatiz and Romer (1991), assert that trade allows countries to learn about
innovation in products and techniques. They produce a theory in which
knowledge diffusion, once channels of communication have been opened
between countries, increases the rates of innovation and growth in each
country. When they allow for the integration of product markets, they find
that trade has both level and growth effects through ‘the elimination of
duplicative research’ (p. 245).
Ben-David and Loewy (1996) construct a model in which technology
grows at a fixed rate in a closed economy but, in an open economy, grows
instead at an endogenous rate that depends on trade. Their analysis is
focused on the trade liberalization process, but Ben-David (1996b) amends
the Ben-David – Loewy model to focus on bilateral trade volumes. BenDavid (1996a and 1996b) find convergence in TFP and output per worker
when countries are grouped together based on trade flows; random
groupings show no convergence.
Another group of papers finds that trade may cause income differences to
rise, not fall, over time. While Grossman and Helpman (1991) find that, in
general, growth rates will be faster as a result of trade, there are three cases
in which trade may increase differences between countries by dampening
incentives for research. First, a small country facing increased competition
from abroad may find that the profitability of investments in knowledge has
fallen. Second, a country that begins with a disadvantage in research
productivity and then enters into international competition with a more
advanced country will find a slowing of innovation and growth. Third, a
country highly endowed with unskilled labour may specialize in traditional
manufacturing and may experience a fall in its overall growth rate of
manufactured output.
Grossman and Helpman (1995), Helpman (1984), and Ethier (1982) show
that, if there are increasing returns to scale on the national level, a country
that specializes in the constant-returns-to-scale sector may lose from trade.
Feenstra (1990) presents a model with two countries of different sizes and no
knowledge spillovers, in which trade causes a slowing in the rates of product
creation and of growth in the smaller country. In Young’s (1991) model of
learning by doing, poorer countries are forced by competition with richer
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countries into producing goods in which learning by doing has been
exhausted; thus, poorer countries will experience slower rates of technical
progress and GDP under free trade than under autarchy.
Including trade as an explanatory variable for income differences will
allow the impact of trade on income differences to be seen. If the knowledge
diffusion that accompanies trade in goods pulls poorer countries toward
richer countries, then we will see that trade lowers dispersion. On the other
hand, if poorer countries find themselves worse off as a result of trade, then
we will find that trade increases dispersion.
3. SPECIFICATIONS AND DATA
3.1 Trade and income differences equations
To determine the impact of income differences on trade, and the impact of
trade on income differences, two equations will be estimated: a gravity
regression that allows differences in income per capita to affect bilateral
trade, and a dispersion regression that allows trade to affect differences in
income per capita.
The bilateral trade equation is as follows:
ln Tradeij ¼ b0 þ b1 ðln GDPi þ ln GDPj Þ þ b2 jðln GDPi =Popi -ln GDPj =Popj Þj
þ b3 ðln Distanceij Þ þ b7 ðln Remotenessi þ ln Remotenessj Þ
þ b4 ðAdjacencyij Þ þ b5 ðLanguageij Þ þ b6 ðTrading Blocij Þ þ eij
ð1Þ
The dependent variable is the log of bilateral trade (exports plus imports).
The independent variables are the sum of the log of country i’s total GDP
and the log of country j’s total GDP; the absolute value of the difference
between the log of country i’s income per capita and the log of country j’s
income per capita; the log of the great-circle distance between the two
countries; the sum of the log of the two countries’ remoteness terms, which,
for each country, is a GDP-weighted average of its distance from all trading
partners; a dummy variable for whether the two countries share a common
border; a dummy variable representing whether the same language is spoken
in the two countries;7 and a dummy variable for whether the two countries
are members of the same trading bloc.8 Note that if b2 is negative, then the
new trade theory is supported, since countries with more different incomes
trade less than countries with more similar incomes. The factor-proportions
model would predict the opposite, that trade is based on differences, not
similarities; in this case, b2 would be positive.
The presence of ln GDPi + ln GDPj in the regression is intended to
normalize for size. The concern here is not with the effects of being a large or
a small country per se, but instead to isolate the effects of differences or
Does convergence cause trade or does trade cause convergence?
403
similarities in per-capita GDP. Obviously, holding distance constant, a
hypothetical country’s trade with a smaller country like Belgium will be less
than its trade with a larger country like Germany, simply because Belgium
has a lower GDP and thus has less to trade.
The income differences equation is as follows:
jðln GDPi =Popi ln GDPj =Popj Þj ¼ b0 þ b1 ðln Tradeij Þ
þ b2 jðln GDP60;i =Pop60;i -ln GDP60;j =Pop60;j Þj
þ b3 jðln ski -ln skj Þj þ b4 jðln shi -ln shj Þj
ð2Þ
þ b5 j½ln ðni þ g þ dÞ-ln ðnj þ g þ dÞj þ mij
Here, the dependent variable is the absolute value of the difference
between the log of country i’s income per capita and the log of country j’s
income per capita. The independent variables are the log of bilateral trade;
the absolute value of the difference in the two countries’ log GDPs per
capita in 1960; the absolute value of the difference in their log investment
shares (sk); the absolute value of the difference in their log secondary-school
enrolment ratios (sh); and the absolute value of the difference in the log
growth in their populations (n, plus 0.05, to account for technological
progress and depreciation). Note that if b1 is negative, then trade causes
convergence (by lowering income differences); if b1 is positive, then trade
causes divergence.
Data on bilateral trade, income, and the other variables are available for
1965, 1970, 1975, 1980, 1985, 1990, 1995 and 2000, for the 56 countries listed
in Appendix B. Data sources are found in Appendix A.
3.2 Ordinary least squares and instrumental variables
The first test of the trade – income differences relationship involves a careful
estimation of equations (1) and (2). Uncertainty as to the direction of
causality between trade and income differences tells us that ordinary least
squares is not the correct estimator. For example, if higher trade causes
smaller income differences, then income differences may be significant in a
trade regression, even if this is not the correct direction of causality. More
generally, it may be that a third factor – similar free-market government
policies in two countries, for example – causes both high bilateral trade and
low income differences. To solve this problem, instrumental variables will be
used for both income differences and for trade. These two equations
represent a system, so that the variables in the trade regression other than
income may be used as instruments for trade in the income – differences
equation, and the variables in the income – differences regression other than
trade may be used as instruments for income in the trade equation.
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Good instruments for income differences in a trade regression must be
correlated with dispersion and uncorrelated with the error term. The
instruments used here are the factor accumulation variables from Mankiw et
al. (1992). Since these variables are instrumenting for income differences, the
form of the instruments used here will be the absolute value of the difference
in each variable between country i and country j.
In their augmented Solow model, Mankiw et al. show that factor
accumulation rates over the period 1960 to 1985 do a good job of explaining
GDP per worker in 1985. The equation estimated is:
ln yi ¼ b0 þ b1 ðln ski Þ þ b2 ðln shi Þ þ b3 ½ln ðn1 þ g þ dÞ þ eij
ð3Þ
where sk is the physical capital accumulation rate (the average share of
investment in GDP), sh is the human capital accumulation rate (the average
share of the working-age population in secondary school), and n is the
population growth rate (the growth rate of the working-age population);
and g + d accounts for (worldwide) technological progress and depreciation, set equal to 0.05.
Mankiw et al. show that factor accumulation rates are highly correlated
with income, but are they uncorrelated with the error term in a trade
equation? To be correlated with the error term, these variables must affect
trade in some way other than through income (or through the other righthand side variables). It is difficult to imagine how differences in the share of
investment in GDP, or in the secondary-school enrolment ratio, or in the
growth of the working-age population, could affect trade other than by
affecting income differences.
Good instruments for trade in an income-differences regression must be
correlated with trade and uncorrelated with the error term. The instruments
used here include geographic, cultural, and size variables. The geographic
variables, the log of distance (measured as the great-circle distance between
main cities) and a dummy for adjacency, have been shown in standard
gravity regressions to be highly correlated with trade.9 The cultural variables
include dummy variables representing whether the two countries speak the
same language, and whether the two countries belong to the same trading
bloc.
All of these variables are highly correlated with trade; however, it is
possible to argue that they are not uncorrelated with the error term in an
income-differences regression. For example, while adjacent countries
undoubtedly trade more, they also probably have similar incomes. Similarly,
countries that speak the same language may share a common colonial
history, and may thus have similar incomes. The correlation of these
variables with the error term in an income-differences regression is not likely
to be large, however. In fact, regressing the residual of the income-
Does convergence cause trade or does trade cause convergence?
405
dispersion equation on these variables shows that they are not significant in
explaining the residual (this is an over-identifying restrictions test, which
will be described in more detail later).
Finally, the size variable is the sum of the two countries’ log populations.
This is intended as an instrument for the sum of the countries’ total GDPs,
since the sum of total GDPs is plagued by the same endogeneity problems as
the difference in per-capita GDPs. It is likely that population will affect
income differences only by affecting trade.
3.3 Fixed and random effects
Since the available data constitute a panel across countries and over time,
panel data techniques can also be used to examine the relationship between
bilateral trade and income differences. However, since the fixed-effects
technique does not allow for any time-invariant variables to be included,
many variables must be left out of such a regression: distance, remoteness,
adjacency, common language, and common trading bloc in the trade
equation; and initial per-capita income differences in the income equation.
The fixed effects regressions thus do not exactly test the joint trade – income
differences relationship as presented here. However, this technique does
allow for country-pair heterogeneity and gives each country-pair its own
intercept; the removal of these country-specific fixed effects from consideration allows for a close look at the relationship between trade and
convergence.
It is possible to use instrumental variables in a fixed-effects regression,
but again, the problem here is that many of the planned instruments do not
vary over time and so cannot be used. A possible solution is to use a lagged
value of the endogenous variable as its instrument. Thus, in the fixed-effects
trade regression, lagged income differences are used as an instrument for
current income differences; in the fixed-effects income differences regression,
lagged trade is used as an instrument for current trade.
A Hausman test for equality of the fixed effects and random effects
coefficients shows that a random-effects technique produces inconsistent
estimates; still, the random-effects coefficients are reported for the sake of
comparison. The advantage to using the random-effects technique is that
both time-series and cross-sectional variation are used, so time-invariant
variables can be included.
3.4 Causality tests
Another way to examine this relationship is to use Granger causality tests.
One variable can be said to Granger-cause another if the first variable is
useful in predicting future values of the second variable. Like the fixed- and
random-effects regressions, this kind of test makes good use of the time-
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The Journal of International Trade & Economic Development
series properties of this dataset. For the trade equation, a regression can be
run of current trade on past trade and past income differences. If the lagged
income differences are significant in explaining current trade, then income
differences Granger-cause trade. Similarly, if lagged trade is significant in
explaining current income differences, then trade Granger-causes income
differences.
4. EMPIRICAL RESULTS
4.1 The impact of income differences on trade
Table 1 presents the effect of income differences on bilateral trade. Data
from 1965 to 2000, at five-year intervals, are used.10 Dummy variables
representing each year were included, but are not reported in order to save
space. Columns 1 and 2 present the results for pooled ordinary least squares
(OLS) and instrumental variables (IV) regressions; the fixed-effects (FE) and
FE-IV results are in columns 3 and 4; and the random-effects (RE) results
are in column 5. The OLS and IV results show that total GDP, differences in
GDP per capita, distance, remoteness, a common border, a common
language, and membership in the same trading bloc are highly significant in
explaining bilateral trade.
The instrumental variables equation shows that a 1 per cent increase in
the difference of two countries’ GDP per capita lowers trade by 0.15 per
cent. Of course, a change in a country’s GDP affects both the sum of total
GDPs as well as the difference in per-capita GDPs. The total effect on trade
depends on whether the richer or poorer country (as measured by per-capita
GDP) experiences a rise in income. If the richer country’s GDP increases by
1 per cent, then trade rises by just 0.43 per cent; the increase in GDP raises
trade by 0.58 per cent, reflecting the fact that the country is now producing
more goods to trade, but lowers trade by 0.15 per cent, since the country’s
income per capita becomes more different from that of its trading partners.
However, if it is the poorer country that receives a 1 per cent boost to GDP,
trade rises by 0.73 per cent (0.58 per cent + 0.15 per cent); the country has
more goods to trade, and its GDP per capita is now more similar to that of
its trading partner.
All of the other explanatory variables are highly significant and have the
expected signs. Distance and remoteness enter negatively; adjacency, sharing
a common language, and membership in a common trading bloc have a
positive effect on bilateral trade. For the last three variables, which are
dummy variables, the coefficients should be exponentiated; for example,
belonging to the same trading bloc more than doubles bilateral trade, since
e0.79 = 2.20.
The fixed effects regression, in column 3, includes only the sum of the two
countries’ total incomes and the difference in their per-capita incomes as
explanatory variables (as well as year dummy variables, which are not
0.78**
(0.0077)
7 0.22**
(0.019)
7 0.61**
(0.027)
7 0.78**
(0.061)
0.061
(0.084)
0.60**
(0.039)
0.73**
(0.059)
7 6.44**
(1.12)
0.68
9642
0.58**
(0.010)
7 0.15**
(0.023)
7 0.49**
(0.028)
7 1.20**
(0.065)
0.33**
(0.084)
0.69**
(0.041)
0.79**
(0.062)
7.25**
(1.23)
0.66
9642
IV
7 32.70**
(0.93)
0.78
9642
0.99**
(0.025)
7 0.48**
(0.029)
FE
7 32.66**
(1.12)
0.75
7499
0.98**
(0.030)
7 0.48**
(0.041)
FE-IV
0.87**
(0.015)
7 0.40**
(0.025)
7 0.67**
(0.060)
7 0.86**
(0.13)
7 0.096
(0.20)
0.69**
(0.086)
0.63**
(0.14)
7 7.78**
(2.40)
0.68
9642
RE
Notes: The dependent variable is the log of bilateral trade in 1965, 1970, 1975, 1980, 1985, 1990, 1995, and 2000. OLS refers to ordinary least squares, IV to
instrumental variables, FE to fixed effects, and RE to random effects. Time dummy variables are included but not reported here. Standard errors are in
parentheses (robust standard errors for the OLS and IV equations). **, * and # denote significance at the 99%, 95%, and 90% levels, respectively. The R2
reported is the adjusted R2 for the OLS and IV equations, the ‘within’ R2 for the FE regression, and the ‘overall’ R2 for the RE regression. The instruments
are described in the text.
R2
N
Constant
Trading Blocij
Languageij
ln Remotenessi
+ ln Remotenessj
Adjacencyi
ln Distanceij
jln GDPi/Popi - ln GDPj/Popjj
ln GDPi + ln GDPj
OLS
Table 1 The impact of income differences on trade
Does convergence cause trade or does trade cause convergence?
407
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The Journal of International Trade & Economic Development
reported), since the other gravity variables do not change over time. Here,
the coefficients become larger in size but remain strongly significant. The
random-effects coefficients, in column 4, are also similar, although
adjacency is no longer significant.
It should be noted that, in all cases, the coefficient on per-capita income
differences is negative and significantly different from zero. Similarities, not
differences, increase trade; convergence, then, does cause trade.
Results specific to OECD countries are found in Table 2. For the OLS
and IV equations, I include a dummy variable indicating that both of the
trading countries are members of the OECD. To allow the coefficients to
vary for OECD countries, I also include interaction terms of all of the
variables with the OECD dummy. The first two columns show the OLS
results; the second two show the IV results. For each specification, the first
column shows the coefficients and standard errors on the variable other than
the OECD dummy and its interactions; these are shown in the second
column. The effects of each variable on trade between OECD countries can
thus be seen as the sum of the usual coefficient and the OECD interaction
coefficient.
For countries in the OECD, total GDP has a smaller effect on trade than
for non-OECD countries, but it is still significant. Interestingly, differences
in GDP per capita now enter positively for non-OECD countries, but the
coefficient is insignificant; however, income differences for OECD countries
become even more important. In fact, the magnitude of the coefficient on the
difference in per-capita income exceeds that of the coefficient on total
income. Thus, a 1 per cent increase in the richer country’s GDP actually
causes trade to fall, not rise, by 0.24 per cent (0.30 per cent - 0.54 per cent);
an increase in the poorer country’s GDP causes trade to rise by 0.84 per
cent. The effects of distance, adjacency, and speaking the same language are
stronger in the OECD, as evidenced by coefficients on the interaction terms
with the same positive sign as the overall coefficients. However, remoteness
now raises trade for OECD countries, and the effect of belonging to the
same trading bloc is now very small. This may be due to the fact that many
of the 22 OECD countries in this sample belong to such a bloc, and thus
there is little variation in the sample.
The final column shows the fixed-effects results. Using an OECD dummy,
which remains constant over time, would not be possible here, so instead I
run a fixed-effects regression on only the OECD countries. The results are
similar to the overall FE regression in Table 1.
4.2 The impact of trade on income differences
The results in Table 3 show that differences in initial income per capita and
in factor accumulation rates are significant in explaining differences in
current income per capita. In the OLS specification, bilateral trade is
0.044**
(0.013)
7 0.79**
(0.071)
7 0.050**
(0.048)
7 0.13**
(0.043)
0.21
(0.15)
0.37**
(0.078)
0.20**
(0.061)
0.15
(0.095)
0.75**
(0.0091)
7 0.090**
(0.023)
7 0.68**
(0.030)
7 0.53**
(0.067)
7 0.040
(0.12)
0.54**
(0.043)
0.40**
(0.084)
7 9.38**
(1.19)
0.69
9642
0.67**
(0.026)
0.032
(0.027)
7 0.66**
(0.034)
7 0.73**
(0.070)
0.29*
(0.12)
0.62**
(0.044)
7 0.074
(0.14)
7 3.20*
(1.46)
0.65
9642
Variable
IV
1.25**
(0.068)
7 0.30**
(0.094)
7 0.37**
(0.10)
7 0.57**
(0.14)
7 0.055
(0.076)
0.86**
(0.25)
0.27
(0.19)
0.48**
(0.10)
0.11
(0.095)
0.26#
(0.13)
7 42.06**
(2.53)
0.92
1797
FE
OECD
Notes: The dependent variable is the log of bilateral trade in 1965, 1970, 1975, 1980, 1985, 1990, 1995 and 2000. OLS refers to ordinary least squares, IV to
instrumental variables, and FE to fixed effects. Time dummy variables are included but not reported here. Standard errors are in parentheses (robust
standard errors for the OLS and IV equations). **, * and # denote significance at the 99%, 95% and 90% levels, respectively. The R2 reported is the adjusted
R2 for the OLS and IV equations, and the ‘within’ R2 for the FE regression. The instruments are described in the text The total effect of each variable for
OECD countries is the sum of the ‘variable’ and ‘OECD’ coefficients.
R2
N
constant
Trading Blocij
Languageij
ln Remotenessi
+ ln Remotenessj
Adjacencyij
ln Distanceij
jln GDPi/Popi - ln GDPj/Popjj
ln GDPi + ln GDPj
OECD
Variable
OLS
Table 2 The impact of income differences on trade: OECD
Does convergence cause trade or does trade cause convergence?
409
7 0.019**
(0.0021)
0.72**
(0.0072)
0.51**
(0.013)
0.20**
(0.011)
0.094**
(0.0052)
7 0.13**
(0.013)
0.70
9642
0.028**
(0.0035)
0.72**
(0.0073)
0.56**
(0.013)
0.22**
(0.012)
0.082**
(0.0054)
7 0.30**
(0.017)
0.69
9642
IV
0.12**
(0.010)
7 0.19**
(0.011)
0.017**
(0.0050)
1.28**
(0.016)
0.11
9642
7 0.069**
(0.0036)
FE
0.061**
(0.013)
7 0.15**
(0.014)
0.011#
(0.0058)
1.69**
(0.035)
0.01
7499
7 0.19**
(0.011)
FE-IV
7 0.048**
(0.0028)
0.81**
(0.013)
0.21**
(0.010)
7 0.12**
(0.011)
0.038**
(0.0049)
0.27**
(0.020)
0.65
9642
RE
Notes: The dependent variable is the absolute value of the difference between country i and country j’s log GDP per capita in 1965, 1970, 1975, 1980, 1985,
1990, 1995 and 2000. OLS refers to ordinary least squares, IV to instrumental variables, FE to fixed effects, and RE to random effects. Time dummy
variables are included but not reported here. Standard errors are in parentheses (robust standard errors for the OLS and IV equations). **, * and # denote
significance at the 99%, 95% and 90% levels, respectively. The R2 reported is the adjusted R2 for the OLS and IV equations, the ‘within’ R2 for the FE
regression, and the ‘overall’ R2 for the RE regression. The instruments are described in the text.
R2
N
Constant
jln (ni + g + d) - ln (nj + g + d)j
jln shi - ln shjj
jln GDP60,i /Pop60,i
– ln GDP60,j/Pop60,jj
jln ski - ln skjj
ln(Tradeij)
OLS
Table 3 The impact of trade on income differences
410
The Journal of International Trade & Economic Development
Does convergence cause trade or does trade cause convergence?
411
significant in reducing income differences; but when instruments are
provided for trade, trade increases income differences. How can this be
reconciled with the common result of cross-country growth regressions
(such as Frankel and Romer, 1999) that a higher trade share increases
income? The answer could be that trade does increase income, but it
increases rich countries’ income more than poor countries’. Interestingly,
the fixed-effects and random-effects regressions provide the same answer as
OLS: trade reduces income differences. Initial income differences and all of
the factor accumulation variables are significant in all of the specifications.
Table 4 presents results for the OECD. In both the OLS and the IV
equations, differences in initial income, physical capital accumulation rates,
and population growth rates are less important for OECD than non-OECD
countries, while differences in human capital accumulation rates are more
important for OECD members. The OECD dummy is negative in the IV
specification, indicating that OECD countries have smaller income
differences, all else held constant, than non-OECD countries. As in the
standard regressions, trade lowers income differences in the OLS specification but raises them once instruments are used for trade. Still, the negative
coefficient on the interaction term with trade shows that the divergent
aspects of trade are smaller for OECD countries than others. Again, the
fixed-effects regression, which is run only for OECD countries, shows that
trade reduces income differences.
4.3 Causality tests
To determine whether income differences Granger-cause trade, it is
necessary to regress trade on lagged trade and on lagged income differences;
if the coefficient on lagged income differences is significant, then income
differences Granger-cause trade. Similarly, if lagged trade is significant in an
income-differences regression, then trade Granger-causes income differences. An important part of the Granger test is determining how many lags
to include. Usually, with data that are yearly or less frequent, only one or
two lags are appropriate. The Akaike Information Criterion (AIC)
confirmed that the correct lag length is one.
Before testing for Granger causality, it is important to determine whether
the variables in question have unit roots, as this would affect the validity of
the results. Therefore, the Im – Pesaran – Shin (2003) test for unit roots in
the context of panel data was undertaken for both trade and income
differences. The finding was that trade is stationary, but income differences
are integrated of order 1. The Granger causality tests must then be amended
in the manner of Toda and Yamamoto (1995), with one additional lag
added due to the fact that one of the variables was found to be I(1). A
standard Wald test is applied, but only to the first lag, not the additional lag;
the Wald statistic then has an asymptotic w2 distribution.
7 0.015**
(0.0032)
7 0.14**
(0.019)
7 0.18**
(0.036)
0.094*
(0.026)
7 0.030*
(0.0091)
0.015
(0.019)
7 0.0055*
(0.0027)
0.70**
(0.0078)
0.48**
(0.014)
0.17**
(0.012)
0.098**
(0.0061)
7 0.083**
(0.014)
0.71
9642
0.062**
(0.0051)
0.68**
(0.0084)
0.53**
(0.015)
0.19**
(0.013)
0.079**
(0.0065)
7 0.25**
(0.019)
0.68
9642
Variable
IV
7 0.013*
(0.0053)
7 0.15**
(0.022)
7 0.22**
(0.040)
0.11**
(0.029)
7 0.16
(0.010)
7 0.14**
(0.026)
OECD
0.0027
(0.018)
7 0.037*
(0.016)
0.0056
(0.0045)
0.55**
(0.030)
0.20
1797
7 0.022**
(0.0063)
FE
Notes: The dependent variable is the absolute value of the difference between country i and country j’s log GDP per capita in 1965, 1970, 1975, 1980, 1985,
1990, 1995 and 2000. OLS refers to ordinary least squares, IV to instrumental variables, and FE to fixed effects. Time dummy variables are included but not
reported here. Standard errors are in parentheses (robust standard errors for the OLS and IV regressions). **, * and # denote significance at the 99%, 95%
and 90% levels, respectively. The R2 reported is the adjusted R2 for the OLS and IV equations, and the ‘within’ R2 for the FE regression. The instruments
are described in the text. The total effect of each variable for OECD countries is the sum of the ‘variable’ and ‘OECD’ coefficients.
R2
N
Constant
jln (ni + g + d) - ln (nj + g + d)j
jln shi - ln shjj
jln GDP60,i /Pop60,i
– ln GDP60,j/Pop60,jj
jln ski - ln skjj
ln(Tradeij)
OECD
Variable
OLS
Table 4 The impact of trade on income differences: OECD
412
The Journal of International Trade & Economic Development
Does convergence cause trade or does trade cause convergence?
413
Results from the Granger causality tests are presented in Table 5. Part (a)
shows the results from regressing bilateral trade on lagged values of trade
(five and ten years in the past) and lagged income differences. It can be seen
that past income differences are significant in explaining current bilateral
trade flows. The w2 statistic shown is for the significance of the first lagged
income differences variable, and we reject the null hypothesis that income
differences cannot be used to predict current trade flows. Income differences
thus Granger-cause trade.
Part (b) of the table shows the results from regressing bilateral income
differences on lagged values of income differences and lagged values of
trade. Again, we reject the null hypothesis of Granger non-causality, and we
see that trade Granger-causes income differences with a negative sign,
showing that trade leads to lower income differences.
5. ROBUSTNESS AND SPECIFICATION TESTS
5.1 Robustness tests
The OLS and IV equations test for contemporaneous correlation. Another
way to perform a similar test is to use a three-stage least squares system of
Table 5 Granger causality
(a) Do income differences granger-cause bilateral trade?
ln(Tradeij)
jln GDPi /Popi – ln GDPj/Popjj
Adj. R2
w2-statistic
Prob 4 w2
(b) Does bilateral trade granger-cause income differences?
jln GDPi /Popi – ln GDPj/Popjj
ln(Tradeij)
Adj. R2
w2-statistic
Prob 4 w2
Lag 1
Lag 2
0.70**
(0.013)
7 0.23**
(0.063)
0.87
13.96
0.0002
0.22**
(0.012)
0.17**
(0.063)
1.30**
(0.012)
7 0.011**
(0.0024)
0.96
19.01
0.0000
7 0.31**
(0.012)
0.0090**
(0.0024)
Notes: In (a), the dependent variable is the log of bilateral trade. In (b), the dependent variable
is the absolute value of the difference between country i and country j’s log GDP per capita. **,
* and # denote significance at the 99%, 95% and 90% levels, respectively.
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The Journal of International Trade & Economic Development
equations. When this kind of test was performed, results were very similar to
the IV regressions: income differences were shown to lower trade, and trade
was found to raise income differences.
Tests were performed to check whether a single instrument was driving
the results. For each instrumental variables regression, additional regressions were performed which excluded each instrument. The results were
robust to these tests. The coefficients on income differences and on trade (as
well as the other variables) varied only slightly in each regression, and the
significance levels were unchanged.
5.2 Specification tests
Hausman (1978) tests were performed for all regressions. The Hausman test
is a test of equality between the OLS estimates and the IV estimates. The
purpose of the test is to determine whether OLS provides consistent and
efficient estimates, which will be the case if there is no correlation between
the regressors and the error term. If this is true, then the IV estimates should
simply equal the OLS estimates plus noise. The null hypothesis of the
Hausman test, then, is that there is no endogeneity and so OLS is the correct
specification. For both the trade equation and the income-differences
equation, the null hypothesis that the OLS and IV coefficients are the same
is soundly rejected at the 99 per cent level.
The Hausman tests indicate a need for instruments, but can the instruments
that have been chosen be considered valid? To determine this, tests of overidentifying restrictions were performed to discover whether the chosen
instruments are indeed uncorrelated with the error term. In an overidentifying restrictions test, the residual of the OLS equation is regressed on
the proposed instruments. The R2 of this regression is multiplied by the
number of observations to obtain a w2 statistic with degrees of freedom equal
to the number of endogenous variables less the number of instruments.
In all cases, the w2 statistic is significant at a 90 per cent level or higher,
showing that there is at least some correlation of the instruments with the
error term, and thus that they are not perfect instruments. However, it
should be noted that even almost perfect instruments may have difficulty in
passing this test, since the high number of observations in these regressions
pushes up the w2 statistic. Any correlation of the instruments with the error,
no matter how small, will cause the instruments to fail the test if the sample
size is sufficiently large. It may instead be useful to examine the R2 of the
equations that regress the OLS residual on the instruments. If the R2 is low,
then it may be inferred that, even if particular instruments are significant,
together they have little overall power to explain the error term. The R2 in
these regressions is 0.015 for the trade instruments and 0.036 for the income
differences instruments, showing that, in general, the instruments explain
little of the error term.
Does convergence cause trade or does trade cause convergence?
415
6. CONCLUSION
This paper provides several ways of estimating the relationship between
trade and convergence. First, instruments for income differences and for
trade are provided in order to determine their true contemporaneous
relationship. Using initial income and factor accumulation variables as
instruments for income, it is shown that countries with more similar incomes
per capita trade more, thus supporting the new trade theory over theories of
trade based solely on factor proportions. Trade is instrumented by gravity
and cultural variables and seems to increase income differences. However, in
tests utilizing the time-series nature of the dataset, trade is found to close the
gap between rich and poor countries. Fixed- and random-effects regressions
show that income differences reduce trade and that trade reduces income
differences. Granger causality tests reinforce this result and show that the
causality is bi-directional. At a particular point in time, trade is associated
with greater differences in per-capita income, but over time, trade causes
those differences to diminish.
OECD countries are examined in some detail; since this group has seen
the greatest amount of both trade and convergence, perhaps the process
works differently for them. The results for OECD countries differ in some
small ways, but the fundamental linkages are basically the same as those of
non-OECD countries. There does not appear to be a ‘convergence club’ at
work here.
NOTES
1
2
3
4
5
6
Data are from the Penn World Table. The 101 countries in the Penn World
Table for which data are available have experienced a similar rise in the average
trade share.
See Mankiw et al. (1992) for a discussion of conditional convergence.
See Grossman and Helpman (1995) for a survey.
The gravity equation of bilateral trade, analogous to Newton’s law regarding
the gravitational force between two objects, states that two countries’ trade is
directly related to the size, or GDP, of the two countries, and inversely related to
the distance between them.
Data from an earlier version of the Penn World Table show that, for the 56
developing and industrial countries in this sample in 1990, the correlation
between capital per worker and output per capita is 86 per cent.
The predictions of Helpman and Krugman and Linder are similar, but are not
quite the same. To be specific, Helpman and Krugman predict that the product
of two countries’ GDPs will have a positive effect on their trade, whereas Linder
predicts that the difference of two countries’ per-capita GDPs will have a
negative effect. To be able to choose between these predictions, a gravity
regression was run which included income and income per capita for both
trading countries. It was found that both predictions are upheld. The sum of the
logs of the two countries’ GDPs raises their trade, and the difference between
their per-capita GDPs lowers trade.
416
7
8
9
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The Journal of International Trade & Economic Development
The languages included are Arabic, Chinese, Dutch, English, French, German,
Japanese, Portuguese and Spanish.
The trading blocs included are EU/EFTA, NAFTA, ASEAN, Andean Pact,
Mercosur, US-Israel FTA, and Australia-New Zealand FTA. Following
Frankel (1997), the trading bloc dummy extends back to the beginning of the
sample, even for countries that formed trading blocs later. The rationale is that
the ties that cause the formation of a trading bloc tend to exist long before the
bloc formally comes into being.
See, for example, Frankel et al. (1995).
Although 12,320 observations are possible for the 1540 country pairs (56 6 55 /
2) over 8 time periods, only 9642 observations are used. This is because the
remaining observations are instances of zero bilateral trade, which drop out
when logs are taken. Common ways to include the zero-trade observations,
including the use of tobit and of re-coding zero trade to a small number, resulted
in very similar results.
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APPENDIX A. DATA SOURCES
The trade data are from the IMF Direction of Trade Statistics. The
adjacency and common language data were constructed by Shang-Jin Wei
(www.nber.org/*wei) and were provided by Jeffrey Frankel and Shang-Jin
Wei. The distance and remoteness variables were calculated using the greatcircle method. The GDP, GDP per capita, physical capital accumulation,
and population growth data were obtained from the Penn World Table 6.1
(pwt.econ.upenn.edu). The human capital accumulation data were obtained
from the UNESCO Statistical Yearbook and the World Bank’s World
Development Indicators. The common trading-bloc dummy variable was
constructed by the author.
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The Journal of International Trade & Economic Development
APPENDIX B. LIST OF COUNTRIES USED IN REGRESSIONS
OECD:
Asia:
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Italy
Iceland
Ireland
Japan
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Africa:
Algeria
Egypt
Ethiopia
Ghana
Kenya
Morocco
Nigeria
South Africa
Tunisia
China
Hong Kong
India
Indonesia
Iran
Israel
South Korea
Malaysia
Pakistan
Philippines
Singapore
Taiwan
Thailand
Latin America:
Argentina
Bolivia
Brazil
Chile
Colombia
Ecuador
Mexico
Paraguay
Peru
Uruguay
Venezuela