WPS  &)otoi
PoItIcY RESEARCH WORKING PAPER    2001
Measuring the Dynamic                                                       Empirical analysis confirms
that a policy of trade
Gains from  Trade                                                          openness has a strong
positive impact on economic
Rofinalnl  AA`4zcz1'M  1growth. The  accelerated
accumulation of physical
capital accounts for more
than half this growth.
Enhanced technological
transmissions and
improvements in the quality
of macroeconomic policy
each account for about 20
percent of the effect of
openness on growth.
[Fie WXorld Bank
DevelopmIenr  Econionsiics
Development Prospects Group                                                          U
1Novenhe11r 1'3998



I POLICY RESEARCH WORKING PAPER 2001-
Summary findings
Wacziarg investigates the links between trade policy and     quality of macroeconomic policy each account for about
economic growth using data from a panel of 57 countries    20 percent of the impact of trade openness on growth.
from 1970--89. This is the first attempt to empirically        This decomposition is robust to alternative
evaluate, in a cross-country context, the respective roles   specifications and time periods. Wacziarg also
of various theories of dynamic gains from trade in           successfully tests whether the empirical methodology
explaining the observed positive impact of trade             captures all or most of the effects of trade policy on
openness on economic growth.                                 growth.
Wacziarg uses a new measure of trade openness, based         The lack of statistically significant results concerning
on the effective policy component of trade shares, in a      several other channels may be due to measurement
simultaneous equations system aimed at identifying the       problems. The black market premium may be a weak
effect of trade policy on several determinants of growth.    proxy for the efficiency of the price system. Moreover,
The results suggest that a policy of trade openness has a    international technological transmissions are very hard to
strong positive impact on economic growth.                   measure, so there may be a downward bias in the
The accelerated accumulation of physical capital           estimates based on the manufactured exports channel,
accounts for more than half this effect. Enhanced            and a corresponding overstatement of other channels.
technological transmissions and improvements in the
This paper - a product of the Development Prospects Group, Development Economics - is part of a larger effort in the
Bank to analyze the relationship between openness and economic growth. Copies of the paper are available free from the
World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sarah Crow, room MC4-706, telephone 202-473 -
0763, fax 202-522-2578, Internet address scrow@worldbank.org. The author may be contacted  at
wacziarg@gsb.stanford.edu. November 1998. (52 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should he cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center



Measuring the Dynamic Gains from Trade
Romain Wacziarg*
Stanford University
*Graduate School of Busincss, Stanford University, Stanford, CA, 94305. This paper was writtcn
while I was visiting the World Bank's International Economics Department, Analysis and Prospects Di-
vision. I thank the staff of IECAP for their help and suggestions. Special thanks to Milan Brahmbhatt,
Francesco Caselli, Uri Dadush, David Dollar, Jean Imbs, Norman Loayza, Jean-Frang.ois Ruhashyankiko,
Jose Tavares, Athanasios Vamvakidis and Alan Winters for helpful comments and discussions. Please refer
any comments to wacziarg@gsb.stanford.edu.



1 Introduction
The positive empirical association between trade openness and economic growth is a topic
of little disagreement among economists.1 Although theories promoting inward-oriented
development strategies flourished in the fifties and sixties, the unsustainable and often
destructive effects of import-substitution policies have, by and large, discredited the idea
that the costs of an open trade regime may outweigh its potential benefits. Even relatively
recent theories of imperfect competition applied to international trade, although they often
overturn the results of more conventional approaches, have led to notoriously cautious
policy prescriptions as far as protection is concerned.
However, it is unclear whether economists have a clear empirical understanding of the
sources of these gains from trade, especially in a dynamic framework. Theory points to
a number of possible costs and benefits of trade openness, not mutually exclusive in gen-
eral. Some of these theories stress the role of technological spillovers and the international
transmission of knowledge as a source of growth for open economies2. More traditional,
static theories involve the role of allocative efficiency, which can be achieved more easily
with an open trade regime even when factors of production are assumed to be immobile.
Higher levels of output are attained when countries specialize according to their compar-
ative advantage, so growth rates can be expected to increase in the transition that follows
a liberalization episode. The increased degree of market competition resulting from a
wider scale of market interactions yields further gains in efficiency.3 More generally, by
increasing the size of the market, trade openness allows economies to better capture the
potential benefits of increasing returns to scale. Yet another set of theories points to the
complementary aspects of virtuous policies: Trade policy openness may create incentives
for governments to adopt less distortionary domestic policies and more disciplined types
of macroeconomic management.
There has been very little empirical work trying to determine the relative roles of these
different factors in explaining the observed positive impact of trade openness on growth.
One tends to interpret the finding that trade openness spurs growth according to one's
preferred theory, and to disregard two important possibilities: several of these forces may
be operating simultaneously; and trade openness may also involve some dynamic costs,
even if these are outweighed by the benefits. This becomes especially important in the
context of increasing integration: by determining the source of the costs and benefits of
trade liberalization, policy makers can hope to maximize the latter and to minimize the
former.
This paper employs a fully specified empirical model to evaluate the channels whereby
trade policy may affect growth. It starts with the specification of equations describing
the incidence of trade policy on several growth determining variables. These equations
are meant to capture different theoretical arguments used to characterize the potential
costs and benefits of trade policy openness. The next step involves including the various
channel variables in a growth regression. By multiplying the effects of trade policy on
'See, for instance, Sachs and Warner (1995a), Vamvakidis (1996), Edwards (1992), Frankel (1996)
among many other studies.
2See, for instance, Grossman and Helpman (1991).
3For instance, in Wacziarg (1997).
1



the channel and the effect of the channel on growth, one is able to identify the effect of
trade policy on growth through that specific mechanism. The results of this paper suggest
a strong positive effect of trade policy openness on economic growth, with accelerated
accumulation of physical capital accounting for more than one half of this total effect.
The paper is organized as follows: Section 2 analyzes the theoretical basis for the
six channels, discusses measurement issues and provides preliminary evidence concerning
trade policy and growth. Section 3 describes the empirical methodology, based on a
random effects, instrumental variables, efficient estimator. Section 4 provides parameter
estimates for the various equations in the model. Section 5 contains a summary of the
channel effects and addresses issues of robustness and exhaustiveness. Section 6 concludes.
2 Theory, Measurement and Preliminary Evidence
2.1 The Six Channels in Economic Theory
Six linkages between trade policy and economic growth are considered in our empirical
model.4 These are meant to capture the dominant theories concerning dynamic gains (or
possibly losses) from trade. The underlying assumption is that these six channels, taken
together, adequately capture most or all of the total effect of trade policy on growth.
We can classify them according to three broad categories: government policy, domestic
allocation and distribution, and technological transmissions.
2.1.1 Government Policy
The first possibility is that trade openness creates incentives for policy makers to pursue
virtuous macroeconomic policies, either because they face the threat of capital flight or
because they have bound themselves in international agreements, implicit or explicit, that
provide a check on policy. The requirement to maintain a competitive environment for
domestic firms engaged in foreign transactions may also require the maintenance of a stable
macroeconomic context. In turn, the quality of macroeconomic policy is likely to have
favorable effects on growth (Fischer (1993)). Indeed, macroeconomic stability may reduce
the level of price uncertainty; furthermore, moderate levels of public deficit and public
debt reduce the extent of crowding out as well as the likelihood of future tax increases,
furthering the ability of domestic firms to compete on global markets.
Another way to capture the effects of trade openness on governmental activity is to
consider its effect on the size of government. If more open economies are subject to larger
exogenous supply and demand shocks, a larger government may be better able to provide
insurance or consumption smoothing through redistribution or other forms of social pro-
grams (Rodrik (1996)). On the other hand, open economies may tend to subscribe more
widely to laissez-faire arguments, and to limit the extent of taxation in order to preserve
the economy's price competitiveness and attractiveness to foreign investors. The effect of
trade policy openness on government size, measured by the public consumption of goods
and services, is therefore theoretically ambiguous. On the other hand, although theory
points to the existence of a positive growth-maximizing size of government resulting from a
4Other, possibly omitted channels arc discussed in Section 5.
2



trade-off between the productive function of public activities and the distortionary nature
of taxation (Barro and Sala-i-Martin (1992)), the negative impact of a larger government
on growth in a cross-section of countries seems to be an established empirical fact (Barro
(1991)).
2.1.2 Allocation and Distribution
Open economies are less likely to have tradable goods prices that differ substantially from
those prevailing on world markets, because free trade should lead to an equalization of
the prices of traded goods across countries. Once the effect of non-tradable goods on
deviations from purchasing power parity has been eliminated, one should expect countries
with open trade policies to have lower overall price levels (relative to some benchmark
country like the United States) than closed economies (Dollar (1992)). Such a result
stems from the fact that open countries tend to specialize according to their comparative
advantage. Hence, theory points to a lower degree of price distortions in open economies.
In turn, price distortions have been shown to adversely affect accumulation and growth
(Easterly (1989) and (1993)). This is just one aspect of the allocation effects of free trade,
having to do with a more efficient price system in open economies.
Factor accumulation may also be of crucial importance. Much of the effect of trade
policy on growth may well work through the domestic rate of physical investment, which is
a determinant of economic growth in a nearly tautological sense (Levine and Renelt (1992),
Baldwin and Seghezza (1996)). The investment channel may capture several types of
theories. Firstly, countries that are relatively labor abundant, when they adopt open trade
policies, are likely to experience an increase in the wage-rental ratio, because tendencies
towards factor price equalization lead to upward pressures on the wage rate and downward
pressures on the price of investment goods. Translated into a dynamic context, this
should lead to a greater level of investment relative to GDP. The growth benefits from
this effects should fade out as more and more countries become open. Although this
type of theoretical argument can only apply to relatively labor abundant economies, most
protectionist countries tend to be more labor abundant, so that the benefits of openness
in terms of growth may be greatest precisely for those countries that are still closed.5
Secondly, and perhaps more importantly, investment may respond to openness through
a size of the market effect.6 As first stressed by Adam Smith, market size imposes a
constraint on the division of labor, so that more open countries are better able to exploit
increasing returns to scale. Trade liberalization may thus provide the type of 'big push'
effect on capital accumulation which Murphy, Shleifer and Vishny (1989) argued was
required in order for less developed countries to move from a low growth equilibrium
51However, the scope of this argument is somewhat limited. Since currently 'open' economies tend to
be relatively capital abundant, we would be left with the task of explaining why their investment rates
tend to be higher than in 'closed' countries, once other determinants of investment are kept constant.
Indeed, openness for capital abundant countries is associated with a lower wage-rental ratio under free
trade compared to autarky, hence presumably with lower investment rates under free trade. Hence, this
type of theory helps make a normative case in favor of liberalization, but does not really explain the
currently observed positive impact of trade on investment.
6We need to explain why lower restrictions on imports should lead to a larger market for exports: since
economies face an intertemporal budget constraint, balanced trade must hold at least in the long-run. In
this case, removing restrictions to imports is equivalent to allowing a greater volume of exports.
3



to a path of sustained industrialization. Preliminary empirical evidence showing that
the extent of the market raises growth largely through an increase in the rate of capital
accumulation was provided by Ades and Glaeser (1994), thus lending support to 'Big
Push' theories. Using a related argument, Wacziarg (1997) argues that the extent of the
market is an important determinant of the degree of product market competition. The
entry of new firms on export markets, after an episode of liberalization, may well entail
large fixed investments. This points to the rate of investment as a potentially important
channel linking trade policy openness and growth.
Thirdly, trade liberalization may simply allow domestic agents to import capital goods
that were unavailable previously (or produced locally but at higher costs), thus removing
structural constraints on investment. These imports of capital goods, which make up
sizable proportions of the imports of many recently liberalized developing countries, also
embody more recent technologies, a further source of growth.
2.1.3 Technological Transmission
The last channels that we consider stem from the recent literature on endogenous growth: if
knowledge spillovers are a driving force for sustained, long-run growth, and open economies
are more exposed to a worldwide stock of productivity enhancing knowledge, then techno-
logical transmissions can be a channel through which trade openness affects growth and
convergence (Barro and Sala-i-Martin (1997), Grossman and Helpman (1991)). There are
two potential ways by which openness may increase the exposure of the domestic economy
to technological transmissions.
Firstly, more frequent and sustained international trade interactions may make it easier
for domestic producers to imitate foreign technologies and to incorporate this knowledge
in their own productive processes (Edwards (1992)). This increased exposure can stem
from direct imports of high technology goods or from greater interaction with the sources
of innovation (through enhanced international communication and mobility brought forth
by economic integration). This should translate into a higher capacity to compete with
more advanced economies on world markets. Such a pattern was certainly part of the East
Asian growth miracle, characterized by broad transformations in the product composition
of output and exports from agriculture to heavy industry and finally to high technology
goods, via the imitation of technology originating in Europe and the United States.
Secondly, foreign direct investment, whether or not it is associated with joint ventures,
often leads to the direct international transmission of advanced types of technology, ei-
ther through capital goods imports which are later imitated, or through the diffusion of
knowhow and expertise. However, it is unclear, a priori, that trade openness is associated
with greater levels of foreign direct investment. On the one hand, FDI may act as a substi-
tute for trade, as foreign investment is used to set up plants producing goods that cannot
be imported due to trade restrictions ("tariff-hopping"). On the other hand, investors may
view trade openness as a signal that a country is committed to stable and market oriented
economic policies; in addition, trade openness allows them to import the intermediate
goods that are required to initiate the projects, to expect repatriation of some profits and
to export the goods that they produce. Falling transport costs may allow a 'slicing up the
value added chain', whereby firms can "produce a good in a number of stages in a number
4



of locations, adding a little bit of value at each stage" (Krugman (1995)). Hence, one
can plausibly argue that FDI acts as a complement, not a substitute, to trade openness.
Indeed, existing evidence suggests that open economics tend to attract more foreign direct
investment than closed economies (Harrison and Revenga (1995)).
In turn, FDI is likely to spur growth. In fact, since the share of FDI in GDP is typically
small (on the order of 1% of GDP on average), it is hard to argue that FDI spurs growth via
traditional physical capital formation. It is likely that, if there is any significant dynamic
effect of FDI, it captures the incidence of a certain type of technological transmissions.
This, indeed, is the interpretation that we shall favor for the FDI channel.
2.2 Characteristics of the Data
2.2.1 Construction of the Trade Policy Openness Index
Measuring the nature of trade regimes constitutes a major challenge for any study involving
the analysis of trade policy. Indeed, measures of protection are not readily available for a
vast number of countries and time periods. It is worth spending some time assessing the
existing measures of trade openness, of which there are three broad categories:
Outcome measures describe the volume of existing trade, or its components. This type
of indicator is most subject to endogeneity problems with respect to growth (Frankel and
Romer (1995)), but measures actual exposure to trade interactions and hence may account
quite well for the effective level of integration. On the other hand, it may correlate only
imperfectly with attitudes or institutions relating to openness. The tendency to confuse
outcome measures with policy attitudes (which are presumed to partly determine the
outcome) has been a feature of past research, largely because precise measures of actual
trade policies are not widely available.
Policy indicators, such as tariff rates, non tariff barriers, tariff revenues, etc., describe
the institutional features of a country's attitude towards the rest of the world, as far
as trade and factor flows are concerned. As such, they are likely to be an important
determinant of the outcome measures. However, endogeneity problems in their relationship
with growth are not absent, and their availability tends to be limited. Furthermore, they
may not directly reflect the degree of effective protection faced by domestic agents, but
only the legal framework to which they are confronted.
Lastly, we can consider measures of effective protection based on deviations from the
predicted free trade volume of trade. Factor endowment and gravity models of trade
generate predictions about a country's propensity to trade internationally. For instance,
country size, distance from major trading partners, negative terms of trade shocks can
be thought to affect trade volumes negatively. Similarly, relative endowments of skilled
labor, unskilled labor, capital and land (or natural resources) may have an impact on
overall trade volumes, as well as, perhaps more obviously, their composition. Using this
type of variables only, one can attempt to predict a country's potential free trade volume
of international commercial transactions. Deviations of the observed trade volume from
this potential volume provide a measure of how restrictive the trade regime really is.
Given these three alternatives, which one should we choose ? Because most theories
about dynamic gains from trade have to do with policy measures, in the sense that the
5



relevant comparisons generally involve contrasting free trade to restricted trade or autarky,
our objective must be to construct an index of trade policy that adequately captures the
nature of the policy regime vis-a-vis international trade.7 The use of outcome measures
seems undesirable on these grounds. We are left with a choice between direct policy
indicators and effective protection measures. In fact, this paper employs a (presumably
optimal) combination of both.
There are three drawbacks to using effective protection measures. First, there is no
guarantee that the predicted level of trade adequately measures the volume of commercial
transactions that would prevail under complete free trade, because determinants of po-
tential trade may have been omitted. Second, some gravity or endowment determinants
of potential trade may be highly correlated with policy attitudes. For instance, large
countries tend to have more restrictive trade policies, and so do relatively labor abundant
countries. If this is the case, the deviation of observed from potential trade may exclude
some valid information about policy (all the variation in policy due to size effects and labor
abundance has been removed). Lastly, as long as the observed volume of trade contains a
white noise disturbance term, deviations from predicted volumes will also contain a white
noise disturbance (whose share of the variance in the total variance of the measure has
increased due to the differencing), and any use of such a variable as a regressor will induce
downward bias associated with measurement error. The most serious problem is probably
the second one, because gravity-type variables can be shown empirically to be important
determinants of policy itself (we shall return to this issue in Section 4).
The major drawback of direct policy attitude measures is that they may not capture
effective levels of protection. The approach in this paper constitutes an attempt to avoid
this problem as well as those associated with effective protection measures. Outcome
measures can be viewed as resulting from a series of factors: gravity determinants, factor
endowments and policy variables. Appendix IV examines a regression of trade volumes
on several openness-determining variables. The objective is to largely explain the extent
of observed trade interactions. This can then be broken down into several components:
the policy component of observed trade shares is obtained as the weighted sum of the
policy measures included in the regression, where the weights are the estimated coefficients
from the trade volume regression. This measure can then be used as an index of trade
policy openness, which can be interpreted as the portion of observed trade shares that is
due to the effective impact of trade policy. This procedure avoids both the problem of
measurement error due to the construction of the difference between observed and potential
trade volumes, and the problem of collinearity between gravity/endowment factors and
policy factors. It also limits the potential effect of omitted variables in the equation that
determines trade volumes, insofar as these omitted factors can be assumed to bear a weak
correlation with the policy determinants that are included in the regression.
Our main concern is to obtain a measure that applies to a broad range of countries over
the period 1970-1989, and that adequately accounts for several aspects of trade policy:
tariff barriers, non-tariff barriers and other forms of attitudes towards international trade
which capture whether the trade policy regime is outward-oriented or not. These consid-
7Appendix IV presents empirical evidenec in favor of this choice: thc growth effects of trade openness
are due mostly to the trade policy regime, rather than to the gravity component of trade shares.
6



erations inspired the choice of the policy indicators chosen to construct the index.8 First,
tariff rates were available for the period 1980-1993 only, and for approximately 50 coun-
tries. To capture the effects of tariff barriers, we used the share of import duty revenues in
total imports (from the IMF's government finance statistics), available for more countries
and a wider time span. This has two advantages. First, it better captures the effective
degree of tariff restrictions. Direct overall measures of tariff protection obtained from
UNCTAD are unweighted averages of goods-specific tariff rates. However, duty revenues
are by construction weighted by the composition of imports. Furthermore, there may be
a weak relationship between officially declared tariff rates and those that are effectively
implemented. Duty revenues once again avoid this problem by measuring the amount of
tariff revenue actually collected. One potential limitation of the use of tariff revenues is
that prohibitive tariff rates will tend to reduce revenues through a "Laffer curve" effect
applied to imports. Hence, the use of revenues may lead to underestimate the true level
of tariff barriers. However, we are considering duty revenues as a share of total imports,
which may greatly limit the incidence of this problem (high tariff rates work to reduce
revenues by deterring imports, so the ratio of the two should roughly reflect effective tariff
rates). Table I contains correlations between tariff revenues and tariff rates, for the dates
and countries available for both measures. The correlations are very high, suggesting that
the choice between the two measures may not be a crucial issue.
Table I. Correlations Between Duty Revenues and Unweighted Tariff Rates
[Import Duties  Import Duties  Import Duties 1
1980-84    l   1985-89    l 1990-94
Tariff rate 1980-84     0.67           0.74            0.73
Tariff rate 1985-89     0.64           0.75            0.72
Tariff rate 1990-94     0.80           0.84            0.83
Number of countries: 50.
Non-tariff barriers constitute the second component of our trade policy index. Insofar
as policy-makers employ a diverse set of tools to attain certain policy objectives, and the
mix varies across countries, NTBs may actually capture much of the effective degree of
protection. However, measures of NTBs are highly imperfect. Available data concern the
coverage rate of NTBs, i.e. the percentage of goods affected by quotas, voluntary export
restraints, etc., but not the extent to which these constraints are binding. Furthermore,
time series data for NTBs have yet to be assembled. We use an unweighted coverage ratio
for the pre-Uruguay Round time period, published by UNCTAD. Presumably, the extent
of NTBs has varied somewhat across time although, as with tariffs, it is likely to be highly
autocorrelated within countries. We are unable to account for this time-series variation,
since we only have one observation for the 23 years under consideration. Presumably, this
type of measurement error should weaken the relationship of NTBs with trade volumes,
and correspondingly reduce the weight of this indicator in the overall index.
We try to capture the overall attitude of policy makers using a third component for
the index of trade policy. Sachs and Warner (1995a) have compiled a list of dates of
trade liberalization, including episodes of temporary liberalization, for a large sample of
8Appendix III describes in more detail the procedure used to construct this index of trade policy.
7



countries. These dates were constructed by examining trade policy data and by conducting
a systematic analysis of the literature concerning the trade regimes of specific countries
(the results of this search are reported, for each country, in the appendix to their paper).
We constructed dummy variables for a country's liberalization status, for each year. These
were then averaged over the time periods under study (1970-74, 1975-79, 1980-84, 1985-
89). Liberalization status is highly correlated with other components of trade policy, and
is meant to capture the prevailing policy attitude towards foreign trade. Insofar as this
indicator receives some weight in the index, it captures factors other than just tariffs
barriers and NTBs; in particular, it may help account for the effect of time variations in
NTBs which we cannot explicitly account for, due to data unavailability.9
Correlating the trade policy index with its three components (Appendix III, Table A-
III-II) can give an idea of the relative weights attached to each of these. All the components
bear correlations with the overall index that are larger than 0.4 in absolute value but the
duty revenue component dominates with a correlation ranging from 0.72 to 0.77, depending
on the time period under consideration. The non-tariff barriers component received the
smallest weight.
We can obtain preliminary insights into the relationship between growth and trade
policy by examining summary statistics for the two variables. Tables II and III display
first and second moments for per capita GDP growth and the policy index for five-year
averages, over the 1970-89 period.
Table II. Summary statistics for Growth and the Trade Policy Index
|_____________   Mean  I Std. Dev. [ Minimum  I Maximum-]
Growth 70-74          3.990         2.520      -0.499       12.351
Growth 75-79         2.333          2.845      -6.688       10.433
Growth 80-84         0.380          2.740      -8.277        6.018
Growth 85-89          1.974         2.455      -3.063        8.770
Trade Policy 70-74   -1.305         8.496     -17.840       10.438
Trade Policy 75-79    -0.937        8.460     -18.716       10.781
Trade Policy 80-84    -0.712        8.663     -19.358       10.784
Trade Policy 85-89    -0.326        9.425     -26.000       10.781
Number of Observations: 57
Table III indicates that trade policy tends to be much more persistent over time than
growth rates. The simple contemporaneous correlations between growth and openness are
positive but their magnitudes are somewhat small, especially for the 1975-79 period during
which the oil shock may have affected the relationship between openness and growth in a
negative way. Overall these simple correlation suggest that the relationship between trade
policy openness and growth may be conditional on other growth determinants rather than
absolute.
9The exclusion of this indicator from the trade policy index reduced the precision of the estimates
prcsented below, but did not changecthe qualitative nature of the results.
8



Table III. Correlation Matrix for Growth and the Trade Policy Index
I Growth  Growth  Growth  Growth  Trade  Trade  Trade
l                l______ _   70-74    75-79    80-84    85-89   70-74  75-79  80-84
Growth 75-79        0.283    1.000
Growth 80-84        0.249    0.397    1.000
Growth 85-89        0.264    0.361    0.391      1.000
Trade Pol. 70-74    0.242    0.168    0.259    0.286   1.000
Trade Pol. 75-79    0.241    0.168    0.270    0.284   0.991   1.000
Trade Pol. 80-84    0.267    0.177    0.285    0.294   0.967   0.982   1.000
Trade Pol. 85-89    0.325    0.101    0.118    0.223   0.908   0.919   0.930
Number of Observations: 57
2.2.2 Measurement of the Channel Variables
Some of the channel variables considered in Section 2.1 can be readily measured. Such is
the case for foreign direct investment inflows as a share of GDP, government consumption
of goods and services as a share of GDP and the domestic investment rate. So three of
our six channels can be captured in fairly uncontroversial ways as far as measurement is
concerned.
The other three channels are captured by composite indices or approximated using
available data.10 The quality of macroeconomic policy is captured by an index that gives
equal weight to each of three decile rankings of policy characteristics for each country.
Specifically, for each time period, each country is ranked on a scale of 1 to 10 according
to its decile position for the level of the public debt as a percentage of GDP, the level
of the government deficit as a share of GDP, and the growth of M2 net of total real
output growth (higher numbers signial better policies). The rankings are then averaged
to obtain an index of overall macroeconomic policy quality, which reflects a country's
position relative to others. This avoids the problem of having to characterize a 'good'
macroeconomic policy in absolute terms.
The extent of technological transmissions is approximated by the share of manufactured
exports in total merchandise exports, admittedly an imperfect proxy for technological
transmissions."1 The main rationale for this measure is that countries able to compete
effcctively on world markets for manufactured goods and to produce at world standards
are likely to incorporate more of the existing modern technologies in their productive
processes. Other suggestions for the measurement of technological transmissions include
the share of manufactured imports in merchandise imports, but this measure suffers a
major drawback: imports of manufactures may act as a substitute rather than a proxy for
technological transmissions.12 On the other hand, if a country is able to produce at world
�0Appendix III describes the construction of these indices and proxies in more detail.
"The share of manufactures in merchandise exports was used as a proxy for technological transmissions
in the World Bank's Global Economic Prospects, 1996.
'2XVe tried to employ the share of manufactured imports to total merchandise imports as a proxy
for technologica.l transmissions, instead of the share of manufactured exports. We could determine no
statistically significant relationship between this variable and growth on the one hand, and with trade
policy openness on the other, even when controlling for a diverse set of variables.
9



standards, the likelihood of it absorbing relatively modern technologies is higher. The
crucial point is that technological advances and knowledge embodied in existing goods
must make their way into production processes in order to truly qualify as technological
transmissions. More direct measures of technological absorption, such as patent licensing
agreements, are extremely difficult to assemble for a wide array of countries.
Lastly, we need a measure of price distortions prevailing within the economy, in order
to capture the effect of trade policy on the efficiency of the price system. Appendix
III-3 describes a direct way to measure price distortions originating from trade policy
or domestic sources such as taxation, subsidies and imperfectly competitive pricing.13
However, our analysis employs a less direct approach. The black market premium on
the official exchange rate is widely used in cross-country analyses, to approximate the
implementation of distortionary policies. As argued in Barro (1995), "the black market
premium on foreign exchange is a widely available and apparently accurate measure of
a particular price distortion. The premium likely serves as a proxy for governmental
distortions of markets more generally".
It is useful to examine simple statistics for the channels variables, openness and growth
averaged over the period under consideration (Tables IV and V). This might provide some
preliminary evidence about the relevance of our choice of channels. Table IV provides
information about the means and standard deviations of the main variables, which may
prove useful when interpreting the regression results.
Table IV. Summary Statistics for the main variables.
|________________ |Mean    Std. Dev. I Minimum  [ Maximum|
Growth                      2.169         1.858        -1.798        7.513
Trade Policy Openness      -0.820         8.588       -19.511       10.696
Macro Policy Quality        5.203         1.711        1.750         8.833
Black Market Premium       42.417        83.247        -0.471      437.182
Government Consumption    15.591          6.681        7.731        33.962
Manufactured Exports       36.933        25.138        0.421        83.664
Investment Share           19.381         7.745        1.320        36.135
Foreign Direct Investment    0.871        1.217        -0.761        7.876
Human Capital               1.515         1.163        0.084         5.343
Log Income Per Capita       8.159         0.993        6.154         9.586
Number of Observations: 57
Table V displays correlations between the main variables. The most interesting columns
to examine for our purposes are the first and second. The first column shows the uncon-
ditional relationship between channel variables and growth, while the second one contains
the correlations of trade policy with the channels. Multiplying the numbers in each col-
umn gives a rough idea of what to expect in terms of channels. In particular, simple
correlations suggest that all of the channels involve a positive effect of trade on economic
growth. The largest correlations appear to be in the investment and manufactured ex-
ports channels. Overall, these correlations show that the trade policy index is positively
13Appendix III-3 also explains why this index was not used in the analysis.
10



related to FDI as a share of GDP, macroeconomic policy quality, manufactured exports as
a share of merchandise exports and the domestic investment ratio. In turn, each of these
are positively related to growth. Trade policy openness is negatively related to the black
market premium and government size. In turn, each of these is negatively associated with
growth.
Table V. Correlation matrix for the main variables
1 Growth 1 Trade  Macro  BMP I Govt.  Manuf Inves.  FDI  Hum. 1
l__________ l_____ |Policy  Policy |_      |_Cons.  Exp.  Share |_ |_Cap.
Trade Pol.     0.331   1.000 l
Macro Pol.    0.384  0.420   1.000
BMP           -0.408  -0.404  -0.304  1.000
Govt. Cons.   -0.421  -0.265  -0.594  0.390  1.000
Manuf. Exp.   0.387  0.602   0.393  -0.484  -0.268   1.000
Invest. Sh.    0.483   0.674   0.441  -0.498  -0.428   0.556  1.000
FDI            0.503   0.263   0.155  -0.255  -0.296  -0.012  0.342  1.000
Human Cap.   0.185   0.554   0.361  -0.357  -0.334   0.487  0.522  0.116  1.000
Log Income     0.266   0.743   0.469  -0.530  -0.504   0.648  0.754  0.188  0.750
Number of Observations: 57
3 Estimation Framework
This section briefly reviews the technical aspects of the estimation method employed in
this paper. The method was first developed and employed in a cross-country growth
context by Tavares and Wacziarg (1998), to analyze the effects of democracy on growth.
The underlying econometric theory is an extension of Zellner and Theil (1962) to the case
of panel data.
3.1 The Structural Model
The basic framework for the cross-sectional analysis consists of a simultaneous equations
model aimed at identifying the various effects of trade policy on growth. The model con-
sists of a growth equation, an equation determining the nature of trade policy, and a series
of channel equations describing the effects of trade policy on several growth determining
variables. This series of equations constitutes the structural model, derived from economic
theory: the channel variables are included in the growth regression, but the measure of
trade policy openness only appears in the channel relationships. The hope is that the
specification of the channels fully exhausts the potential ways in which openness affects
growth (some formal evidence concerning this issue will be provided in Section 5). The
equation describing the determinants of trade policy openness only appears in order to
make explicit endogeneity issues, having to do with the simultaneous determination of
trade policy, growth and the channel variables. In particular, several channel variables
may appear on the right-hand side of the trade policy equation. But this relationship
could be removed altogether with no implication on the estimation of the channel effects.
11



3.2 Estimation
The parameters of the structural model are estimated jointly using three-stage least
squares. This method achieves consistency by appropriate instrumenting, and efficiency
through optimal weighting. It combines features of instrumental variables, random effects
and generalized least squares models.
Each equation in the structural model is formulated for the four time periods under
scrutiny (1970-74, 1975-79, 1980-84, 1985-89).14  Joint estimation allows the derivation
of a large covariance matrix for the error terms of all 32 equations. Hence, both cross-
period and cross-equation error correlations are brought into the picture. This ensures
the efficiency of the estimates. The fact that cross-period error correlations are taken
into account is akin to assuming that the error terms contain country-specific effects that
are uncorrelated with the right-hand side variables. The flexibility of the error covariance
matrix means that we are able to obtain substantial efficiency gains compared to estimating
each equation separately.
Since several endogenous variables appear on the right-hand side of the structural
equations, endogeneity bias must be a major concern. To achieve consistency, we need to
instrument for every endogenous variable appearing as a regressor. This is done by first
writing the model's reduced form, in which every endogenous variable is rewritten as a
function of all the exogenous variables in the system. The fitted values of each endogenous
variables from OLS estimation of the reduced form will provide suitable instruments for
each corresponding endogenous variables in the structural form.15  Constructing these
fitted values constitutes the first stage of the 3SLS procedure. The second stage consists
of estimating each equation in the structural model separately via instrumental variables
(or two-stage least squares), using the instruments constructed in the first stage. This
allows the derivation of a consistent covariance matrix for the error terms of the model.
Lastly, the third stage involves employing this covariance matrix as a weighting matrix as
well as the instruments .derived in the first stage, to jointly estimate the equations in the
structural model using instrumental variables-generalized least squares. Instrumenting
ensures consistency, while joint estimation ensures asymptotic efficiency.
3.3 Identification and Restrictions
As far as specification is concerned, some assumptions are required for this methodology
to carry through. Enough instruments must be validly excludable from each equation for
the order condition to be met. For each equation, the order condition for identification
states that at least as many exogenous variables must be excluded as regressors as there
are endogenous variables included on the right-hand side: enough exogenous variables
"'In addition, we present results including the 1990-92 period, although this leads to a loss in degrees
of freedom. For this resaon, the baseline model only extends until 1989.
1'5Given the above specification of the baseline model, the instruments are: male and female human
capital, the island dummy, the log of population, the democracy index, the log of area, terms of trade
shocks, population density, the secondary school completion rate, the share of population over 65, the
share of population under 15, ethnolinguistic fractionalization, postwar independence status, each taken
at every time period when applicable. Reflecting concerns for the endogeneity of per capita income levels,
this variable was excluded from the instrument list (see Caselli et al., (1996)).
12



must be validly left out of each equation for the system as a whole to be identified.'6
The chosen specification is based on existing empirical work on the determinants of
the various endogenous variables under study. For instance, the growth and investment
equations are based on common specifications used in the cross-country growth literature
(Barro and Sala-i-Martin (1995)). Similarly, the specification of the government size equa-
tion is based on Rodrik (1996) and Alesina and Wacziarg (1997). For other channels, such
as the macroeconomic policy quality channel, we relied on theoretical priors to determine
the set of exclusions.17 The specification of each equation is given in Section 4, which
contains the results for the parameter estimates of each equation in the system.
In order to assess the long-run effects of trade policy on growth in a unified manner, we
impose cross-period parameter equality restrictions: none of the estimates of the parame-
ters in the structural model are allowed to vary across time. This allows efficiency gains via
higher degrees of freedom, as the number of estimated parameters in the system is divided
by four. To examine whether these restrictions are justified, there are two alternatives.
The first one is to run the system without the restrictions and to test the hypothesis that
the parameters are jointly equal between the two models. However, the loss in degrees
of freedom is such, that it is unclear whether the difference in parameters is due to the
imprecision of the estimates in the unrestricted model, or to the time varying nature of
the processes being modeled. The second, preferred alternative is to examine whether the
results are sensitive to the inclusion of any given period. This is done is Section 5.
4 Parameter Estimates
This section presents, for each equation in the system, the results of the estimation pro-
cedure applied to five variants of the same model. Model I is the baseline model for this
paper, for the period 1970-89. Model II includes the 1990-92 period into the analysis,
with a corresponding loss of 8 observations. Model III restricts the sample to developing
countries. Model IV examines the robustness of the model to the estimation method, by
employing the Seemingly Unrelated Regression estimator. This estimator, while inconsis-
tent (no instruments are used), is characterized by greater efficiency and may provide some
indication of the model's robustness. Lastly, in model V, regional dummy variables were
added to every equation in the system, to account for time invariant region specific effects.
We should expect this inclusion to reduce the overall effect of trade policy on growth, as
much of the between-country variation in the endogenous variables is now accounted for
by the regional dummies.
4.1 Growth equation
The results for the growth equation closely match existing findings in the cross-country
empirical growth literature (see, for example, Barro (1991)). The rate of conditional
convergence in our sample (equal to the estimated coefficient of the log of initial income),
1.67%, is in line with common analyses of convergence in a cross-sectional framework.
16We do not check the rank condition for identification, which can be safely assumed to hold for a system
of this size.
'7Tavares and Wacziarg (1998) discuss in more detail the issue of specification search for the type of
system that wc are considering.
13



Table VI: Growth Equation
Dep. Var:  | Baseline   1970-92       Devel.  |  SUR        Regional
Growth       1970-89             [Countries _              Dummies
Intercept         10.598       7.815       5.543       9.006       7.113
(4.70)     (6.74)      (3.55)      (4.59)      (2.99)
Log Initial       -1.672     -1.132       -1.106      -1.390      -0.740
Income            (-5.81)    (-7.66)      (-5.45)     (-5.17)    (-2.24)
BMP               -0.007      -0.005      -0.005      -0.005      -0.007
(-9.08)    (-21.81)   (-13.09)     (-8.85)     (-9.14)
Government        -0.042     -0.055       -0.025      -0.043      -0.043
Consumption       (-1.57)    (-5.76)     (-1.84)     (-2.20)     (-2.13)
Manufactured       0.004       0.002       0.006       0.007      -0.004
Exports           (0.45)      (0.53)      (1.01)      (1.14)     (-0.72)
Investment         0.143       0.132       0.146       0.143       0.109
Rate              (6.86)    (12.10)       (7.27)      (7.99)      (5.06)
FDI                0.320       0.249       0.271       0.355       0.178
(4.68)     (8.44)      (4.79)      (4.83)      (2.75)
Macro Policy       0.489       0.290       0.505       0.333       0.280
Quality           (4.22)      (8.62)      (8.70)      (5.03)      (3.27)
Male Human         0.481       0.732       1.351       0.448      -0.136
Capital            (1.59)     (4.24)      (5.47)      (1.57)     (-0.42)
Female Human      -0.387      -0.862      -1.284      -0.429       0.005
Capital           (-1.39)    (-5.65)      (-5.30)    (-1.58)      (0.02)
Latin America                                         -           -2.291
Dummy                                                            (-6.32)
South East                     _ -                    -           0.047
Asia Dummy                                                        (0.06)
Sub-Saharan                                           -           -2.126
Africa Dummy                                                     (-4.39)
OECD                                -                 -           -1.466
Dummy                                                            (-3.15)
R-squared       .25 .29   .24 .26 .46   .34 .41    .27 .28     .23 .41
.41 .31    .39 .18    .54 .37     .45 .32     .52 .30
Obs. (periods)    57(4)     49(5)       36(4)       57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
Most of the other estimates reflect the current "Washington consensus" on the de-
terminants of growth: Table VI contains evidence pointing to the positive effects of the
domestic investment rate, male human capital, macroeconomic policy quality and FDI on
growth. Negative factors include the black market premium, female human capital and
government consumption of goods and services, while manufactured exports seem largely
unrelated to economic growth in most specifications. The pattern of human capital coef-
ficients is in line with results by Barro (1991), and can be interpreted as resulting from
conditional convergence.18
18A larger gap between male and female human capital signals a lower level of per capita income.
Conditional on steady-state determining variables, this gap should be negatively associated with growth.
14



These results do not seem sensitive to changes in the spccification. Both the signs and
orders of magnitude of the coefficients are preserved in most cases. In particular, the signs
and magnitudes of all of the channel variables are maintained.
4.2 Openness equation
The equation accounting for the degree of trade policy openness (Table VII) is considered
solely to capture various endogeneity issues. Its inclusion in the model should not affect
the estimates in the other equations, except insofar as efficiency gains are concerned.
The growth rate of per capita GDP is included to control for endogeneity in the growth-
openness relationship. A one percentage point increase in growth is shown to trigger a
.32 percentage point increase in the policy component of the trade ratio. While highly
significant statistically, this effect is very small economically.
Table VII. Openness Equation
Dep. Var:    Baseline   1970-92    Devel.           SUR    [ Regional
Trade Policy   1970-89               [ Countries |            |Dummies
Intercept          -53.851    -49.902     -23.667      -53.115     -46.642
(-16.55)   (-21.34)     (-6.56)    (-17.66)      (-9.77)
Log Initial          6.548      6.559        3.468       6.422       5.528
Income             (17.55)    (30.36)      (10.07)     (18.57)      (12.96)
Island Dummy        -3.049     -3.483       -2.124      -3.177       -3.848
(-2.37)    (-5.08)     (-1.83)      (-2.58)     (-2.96)
Log Area            -0.888     -0.653       -0.005      -0.866      -0.718
(-2.20)    (-3.73)     (-0.02)      (-2.35)     (-2.35)
Terms of Trade      -7.148    -13.690       -1.480      -6.877      -5.014
Shocks              (-4.97)   (-23.73)     (-1.56)     (-4.63)      (-4.01)
Growth               0.321      0.228        0.385       0.377       0.230
(10.44)    (20.31)     (30.24)      (12.03)      (8.13)
Log Population       0.420     -0.044       -0.973       0.432       -0.177
(0.79)    (-0.19)      (-2.19)      (0.90)     (-0.40)
Latin America        -          -           -           -            3.570
Dummy                                                                (2.41)
South East Asia      -          -           -           -           12.173
Dummy                                                                (6.99)
Sub-Saharan          -          -           -           -            6.597
Africa Dummy                                                         (4.16)
OECD                 -          -           -           -            8.950
Dummy                                                                (5.65)
R-squared         .55 .53   .54 .52.58    .26 .28     .55 .53     .67 .66
.60 .54    .53.45     .37 .32      .60 .54     .74 .72
Obs. (periods)     57(4)      49(5)       36(4)       57(4)        57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
If, in addition to this, the average lcvel of human capital (male and female) has a positive effect on the
steady-state income level, we obtain the observed pattern of male and female human capital coefficients.
15



Measuring country size using the log of area, we find that larger countries have more
restrictive trade policies, reflecting several possible theoretical explanations. Firstly, under
any model with increasing returns, larger countries should experience smaller losses from
protection than smaller ones, prompting them to a greater vulnerability to protectionist
arguments. Secondly, in the neoclassical trade theory, the optimal trade policy for a large
country is not complete free trade. Because they can affect their terms of trade, large
countries should implement an optimal tariff in order to reach allocative efficiency, and
this incentive may be partly reflected in the estimated effect of land area (note however
that the coefficient country size measured by the log of population is not significantly
different from zero).19 At any rate, the significance of the area variable and of the island
dummy indicate that 'gravity' variables do bear some relationship with trade policy, and
provide further justification for the method used to construct the trade policy openness
index.
4.3 Government Policy
4.3.1 Macroeconomic Policy Quality
The policy quality equation brings out the positive effects of democracy and trade openness
on the quality of macroeconomic management (Table VIII). In the baseline model, a 10
percentage point difference in trade policy openness, which corresponds to one standard
deviation of the index, is associated with a 0.27 increase in the index of macroeconomic
policy quality, which ranges from 1 to 10. This estimate remains statistically significant
in four of the five models, and increases in magnitude when the sample is restricted to
developing countries.
The effect of initial per capita income on the quality of macroeconomic policy is gen-
erally positive, but not significant at the 5% level in the baseline model. Countries with a
larger share of government consumption and a high black market premium also have worse
macroeconomic policies, indicating that bad policies tend to go together. The negative
coefficient on the terms of trade shocks may reflect the fiscal response to economic shocks.
t'Sce also the discussion in Alesina and Wacziarg (1997) and Alesina, Spolaore and Wacziarg (1997) for
more on thc relationship between country sizc and trade openness.
16



Table VIII: Macroeconomic policy quality channel
Dep. Var:    Baseline   1970-92         Devel.        SUR       Regional
Macro Policy   1970-89                 1 Countries                Dummies
Intercept             5.980       5.695       11.534       6.647        4.371
(5.14)      (6.49)      (8.49)      (5.14)       (2.81)
Log Initial           0.187       0.203       -0.501       0.093       0.393
Income               (1.42)      (2.13)       (2.99)      (0.65)       (2.00)
Trade Policy         0.027       0.038        0.033       0.048        0.014
Openness            (2.19)      (5.57)       (3.81)       (4.07)      (1.28)
BMP                  -0.002      -0.004       -0.001      -0.001      0.0002
(-1.90)     (-7.92)     (-3.42)     (-1.16)      (-0.20)
Government           -0.126      -0.126       -0.124      -0.122      -0.130
Consumption         (-8.25)    (-11.67)     (-12.09)    (-10.44)      (-8.57)
Ethnolinguistic      -0.006       0.001       -0.014      -0.005      -0.005
Fractionalization    (-1.45)     (-0.16)     (-5.37)      (-1.21)     (-0.96)
Terms of Trade       -1.318       0.213       -1.091      -1.475      -1.252
Shocks              (-1.86)     (-0.54)      (-2.03)      (-2.35)     (-1.95)
Latin America         -          -            -           -            -0.310
Dummy                                                                 (-0.87)
South East Asia       -          -            -           -            0.631
Dummy                                                                 (-1.51)
Sub-Saharan           -          -            -           -            -0.176
Africa Dummy                                                          (-0.45)
OECD                  -          -            -           -            -0.147
Dummy                                                                 (-0.32)
R-squared          .36 .28   .35 .36 .45    .34 .37     .37 .28     .34 .29
.34 .36    .42 .35     .42 .34      .35 .36      .38 .37
Obs. (periods)      57(4)      49(5)        36(4)       57(4)        57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
4.3.2 Government Size Equation
Trade policy has a positive impact on government size (Table IX) in the baseline regression.
This provides some support to results by Rodrik (1996), who also reported a significantly
positive impact of trade shares on government size, although the result disappears when
the sample is restricted to developing countries.20 Taken together with the results of the
growth regression, this suggests that government size may be a channel whereby openness
works negatively for growth.
Other determinants of government size are included in the regression, following Ro-
drik's specification. The log of initial per capita income is negatively related to government
consumption. Its inclusion into the regression drives much of the positive effect of trade
20However, Alesina and Wacziarg (1997), using a wider sample of countries, have cast some doubt on
Rodrik's results, by showing that they are sensitive to the chosen specification and to the inclusion of
country size in the regression.
17



policy (the sign of this variable is reversed when initial income is excluded from the re-
gression). The role of a large population in limiting the size of govcrnmcnt can be viewed
as the result of increasing returns in the provision of public goods (Alesina and Wacziarg,
(1997)). These may result from the partly nonrival character of many such goods, such as
defense, diplomacy and the maintenance of law and order. The signs of most of the other
determinants of government size arc as expected: population density is associated with a
smaller government, perhaps capturing another type of scale effect. Dependency rates are
associated with larger governments, in line with the idea that government consumption is
likely to respond positively to increased schooling and retirement needs.
Table IX: Size of Government Channel
Dep. Var.:    Baseline | 1970-92    Devel.         SUR       Regional
Govt. Cons.  | 1970-89 |             Countries |             Dummies
Intercept           57.718     37.621      31.387      33.873     40.759
(10.58)    (22.73)      (8.14)      (8.50)      (7.88)
Log Initial         -4.439     -2.848      -0.875      -2.332      -2.463
Income             (-9.58)    (-32.57)     (-2.84)     (-5.93)    (-5.34)
Trade Policy        0.154      0.121       0.034       0.102       0.249
Openness           (3.73)    (43.28)       (1.34)     (2.50)      (5.73)
BMP                 0.008       0.004       0.006       0.006       0.007
(20.19)    (30.77)     (24.17)     (15.55)     (20.57)
Log Population      -0.911     -0.900      -1.856      -0.977      -0.726
(-4.52)    (-8.08)     (-7.45)     (-4.82)    (-3.25)
Population          -0.003     -0.003      -0.005      -0.004      -0.004
Density            (-5.87)    (-16.65)    (-8.59)      (-6.60)    (-6.47)
Population          16.262     32.549     -10.267      26.215      14.491
over 65             (1.54)     (4.94)      (0.79)      (2.85)      (1.39)
Population           1.653     18.525      14.574      18.595      12.093
under 15            (0.29)     (7.76)      (4.07)      (3.80)      (2.48)
Ethnolinguistic     0.038       0.039       0.107       0.056      0.032
Fractionalization   (3.23)     (5.26)     (12.21)      (4.66)      (1.97)
Latin America       -          -           -           -           -6.095
Dummy                                                             (-4.51)
South East Asia     -          -           -           -           -4.565
Dummy                                                             (-3.18)
Sub-Saharan         -          -           -           -           -2.003
Africa Dummy                                                      (-1.30)
OECD Dummy          -          -           --5.846
(-3.91)
R-squared         .28 .28   .21 .29 .47   .25 .33   .29 .33     .35 .36
.42 .53    .55 .55    .47 .48     .46 .52    .48 .59
Obs. (periods)     57(4)     49(5)       36(4)       57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (Whitc-Robust) standard errors, in parentheses)
18



4.4 Allocation effects: Distortions and Capital Accumulation
4.4.1 Distortions channel
The baseline model displays a negative but insignificant effect of trade policy on price
distortions, proxied by the level of the black market premium, once other determinants of
distortions are held constant (Table X). A 10 point increase in the trade policy index is
associated with a 3.4 percentage point reduction in the black market premium, although
the slope parameter is estimated very imprecisely. However, this effect becomes significant
at the 90% level in all other specifications. In particular, the estimated coefficient become
large economically when OECD countries are excluded from the sample, as we find that
a 10 point increase in trade policy openness reduces the black market premium by 18
percentage points.
Table X: Distortions Channel
Dep. Var:   Baseline   1970-92        Devel.  |  SUR         Regional
BMP       J 1970-89 |             Countries l             Dummies
Intercept          39.720     80.849      168.293     124.906     104.804
(0.83)     (8.48)      (3.66)      (2.91)       (1.81)
Log Initial        -2.535     -5.314     -17.208      -11.666     -13.617
Income            (-0.43)     (-4.49)     (-2.78)     (-2.18)     (-1.90)
Trade Policy      -0.344      -0.855      -1.826      -0.900      -1.092
Openness          (-0.63)    (-7.56)     (-2.45)     (-1.77)      (-1.69)
Government          3.821      1.493        2.407      2.452        3.688
Consumption        (8.13)    (14.62)       (8.44)      (6.49)      (8.28)
Democracy         -51.987    -42.665      -46.554     -35.274     -56.272
(-4.69)    (-15.57)    (-3.65)     (-3.62)     (-4.74)
Population         -0.025     -0.012      -0.016       -0.027     -0.0004
Density           (-3.37)    (-10.16)     (-1.50)     (-3.77)     (-0.03)
Terms of Trade     71.589    -36.730       47.780      57.464      76.925
Shocks             (2.87)      (2.77)      (1.73)      (2.29)      (2.87)
Latin America            -           -                 -           44.517
Dummy                                                              (3.55)
South East               -           -                 -          -11.335
Asia Dummy                                                        (-0.64)
Sub-Saharan              -           -                 -           11.584
Africa Dummy                                                       (0.89)
OECD                     -           -                 -          42.212
Dummy                                                              (3.24)
R-squared        .19 .23   .17 .18 .06    .15 .28   .24 .29     .20 .27
.10 .27    .18 .23    .09 .17     .12 .27     .13 .33
Obs. (periods)    57(4)      49(5)       36(4)       57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
The inclusion of government size, which enters with a positive sign, provides further
evidence of the complementarity between maintaining a small level of public spending and
policies aimed at ensuring the efficiency of the price system. Democracy, measured by an
19



objective index compiled by Gastil and his followers for the yearly Freedom in the World
reports, is associated with lower distortions, even when controlling for initial income. This
may reflect the ability of democracy to provide a check on the abuses of policy-makers,
as argued in Tavares and Wacziarg (1998). Finally, and as expected, a higher level of per
capita income is associated with reduced distortions.
4.4.2 Investment channel
Trade policy bears a strong and robust poSitivc relationship with the share of investment
in GDP (Table XI). This constitutes one of the main findings of this paper. Estimates
from the baseline model suggest that a one standard deviation difference in the trade policy
index is directly associated with a 3.2 percentage point increase in the ratio of domestic
investment to GDP. This effect is robust with respect to alternative models, although its
magnitude is reduced when the 1990-92 period is brought into the picture.
Table XI: Investment Channel
Dep. Var:   Baseline   1970-92 [  Devel.  |  SUR    [ Regional
Inves. Rate  1 1970-89 |            [ Countries             [Dummies
Intercept         27.493      12.459       8.243      15.498      25.778
(3.72)     (2.82)      (1.27)      (2.41)       (3.46)
Log Initial         1.003      2.609       2.746       2.414       1.277
Income             (1.56)      (6.59)      (5.38)      (4.25)      (1.98)
Trade Policy       0.317      0.161        0.270       0.228       0.204
Openness          (6.72)      (9.77)      (7.04)      (5.40)      (4.40)
BMP                -0.010     -0.010      -0.006       -0.007      -0.007
(-7.15)    (-20.13)   (-18.60)     (-8.97)     (-5.70)
Macro Policy       1.027       0.609       0.381       0.390       0.250
Index              (6.97)      (9.79)      (5.31)      (3.16)      (1.88)
Population        -38.321    -33.230     -24.285      -30.457     -30.237
under 15          (-5.16)     (-7.54)     (-4.27)     (-4.12)     (-4.06)
Population        -88.353    -65.596     -67.586      -73.547     -88.871
over 65           (-5.45)     (-7.58)     (-2.66)     (-4.33)     (-5.90)
Ethnolinguistic    -0.047     -0.036      -0.014       -0.051      -0.058
Fractionaliz.     (-3.02)     (-4.37)     (-0.85)     (-3.43)     (-3.38)
Latin America      -           -           -           -           -1.809
Dummy                                                             (-1.35)
South East         -           -           -           -           3.778
Asia Dummy                                                         (2.16)
Sub-Saharan        -           -           -           -           -2.227
Africa Dummy                                                      (-1.57)
OECD               -           -           -           -           3.520
Dummy                                                              (2.30)
R-squared       .44 .56   .49 .60 .61   .21 .52     .49 .62     .53 .67
.61 .62    .73 .58    .57 .50     .62 .65     .69 .70
Obs. (periods)    57(4)      49(5)       36(4)       57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in pa.rentheses)
20



Other determinants of domestic investment include life cycle variables (dependency
ratios), ethnolinguistic fractionalization and initial income. Contrary to what conditional
convergence would imply, the share of investment in GDP is larger for richer countries when
other determinants of investment are held constant. This suggests that the forces behind
conditional convergence may have little to do with the traditional assumption of dimin-
ishing marginal product of capital, but perhaps with some form of convergence-inducing
technological transfers (Barro and Sala-i-Martin, 1995). Furthermore, as expected, a low
level of distortions and a high quality of macroeconomic management appear conducive
to physical capital investment.
4.5 Technological Transmissions
4.5.1 Manufactured exports channel
The transmission of technology, proxied by the ratio of manufactured exports in total
merchandise exports, is strongly influenced by trade policy (Table XII). In the baseline
model, a 10 percentage point increase in the policy component of trade shares is associated
with a 6.35 percentage point rise in the manufactures to merchandise exports ratio. Both
the magnitude and the precision of the estimates are robust in four out of five specifications
of the model.
Other regressors included in this equation bear the expected signs: population density,
which proxies for the labor/land ratio, is positively associated with the export share of
manufactures (presumed to be relatively labor intensive rather than land intensive); human
capital, measured by the proportion of the adult population having completed secondary
school, captures the ratio of skilled to unskilled labor, which is also expected to bear
a positive relationship with the share of manufactures in merchandise exports. Initial
income displays a positive and significant estimated coefficient. All of these conditioning
variables can be interpreted as relative endowments, which are obvious determinants of
the composition of exports.
21



Table XII: Manufactured Exports Channel
Dep. Var:  | Baseline 1 1970-92        Devel.        SUR       Regional 1
Manuf. Exp.  1970-89 |                | Countries _             Dummies
Intercept         -75.796     -82.105      -46.472      -73.793     -38.292
(-6.94)    (-12.76)     (-5.15)      (-8.23)     (-2.77)
Log Initial         7.289       8.482        3.718        7.497       5.310
Income              (5.18)     (11.15)       (4.32)      (6.99)      (3.18)
Trade Policy        0.635      0.567       -0.369        0.676       0.619
Openness           (4.59)    (10.15)       (-4.82)      (6.87)       (4.32)
BMP                -0.013      -0.024       -0.024       -0.020      -0.019
(-5.49)    (-22.95)    (-19.90)    (-14.81)      (-7.69)
Secondary Sch.      0.291       0.205        1.743       0.232        0.164
Completion          (3.09)      (2.41)     (19.75)       (2.57)      (2.05)
Log Population      5.215       5.216        3.451        4.964       4.219
(5.68)      (8.76)      (4.92)      (5.53)       (5.18)
Population          0.019       0.014        0.015       0.017        0.020
Density            (5.22)       (9.10)       (7.33)      (4.91)      (6.05)
Latin America                                            -          -20.070
Dummy                                                                (-5.17)
South East                                               -          -18.118
Asia Dummy                                                           (-3.97)
Sub-Saharan                                              -          -18.010
Africa Dummy                                                         (-4.65)
OECD                            -           -            -           -5.017
Dummy                                                                (-1.15)
R-squared         .50 .52   .48 .48 .43    .21 .38    .51 .53     .55 .59
.49 .53    .51 .50     .34 .53      .49 .53     .54 .62
Obs. (periods)    57(4)       49(5)       36(4)        57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
4.5.2 Foreign Direct Investment Channel
Foreign direct investment appears to be a complement, rather than as substitute to trade
policy openness (Table XIII). A 10 points change in the trade policy index is associated
with a 0.46% direct increase in the FDI to GDP ratio, which represents about 50% of
this variable's mean, a large effect indeed. Countries with lower distortions, which in turn
attracts more FDI. A similar effect holds for countries with relatively smaller governments.
Non-distortionary policies, a commitment to non-interventionist policies and free trade all
appear conducive to attracting foreign capital. In turn, the effect of FDI on growth can
be interpreted as a technological transmission mechanism, since FDI represents too small
an effect on the growth of the domestic capital stock to represent a direct accumulation
effect.2' The estimates from the FDI channel equation are robust across the five variants
21Wc tried to use the investment rate net of the foreign direct investment rate in the investment channel,
to better separate the two effects. The results for both equations were similar. However, the precision of
the parameter estimate for the trade policy coefficient in the investment equation decreased somewhat. At
22



of the baseline model. Furthermore, isolating developing countries lcads to a doubling of
the trade policy coefficient.
Among the other determinants of FDI, former colonies having gained independence
after the Second World War tend to attract more FDI, other things equal. This may
reflect privileged economic ties between certain countrics and their former colonizers.
Table XIII: Foreign Direct Investment Channel
Dep. Var.:   Baseline   1970-92       Devel.       SUR       Regional
FDI share    1970-89                Countries                Dummies
Intercept          1.177       1.124       1.805       1.149       1.679
(5.73)     (8.52)     (13.55)      (7.24)       (4.90)
Trade Policy      0.045       0.059       0.085       0.036       0.057
Openness          (4.01)     (9.62)      (13.82)      (3.41)      (4.29)
BMP               -0.001      -0.001      -0.001      -0.001     -0.0002
(-3.60)    (-20.54)    (-3.48)     (-3.09)     (-0.92)
Government        -0.054      -0.048      -0.047      -0.051      -0.060
Consumption       (-4.15)    (-11.70)     (-6.67)    (-4.36)      (-5.82)
Postwar            0.928       1.009       0.329       0.787       0.634
Dummy              (3.96)     (6.17)      (1.92)       (3.41)      (3.23)
Island Dummy       0.988       1.192       1.239       1.076       0.943
____ _  (4.74)  (6.07)     (13.17)      (4.87)      (4.29)
Latin America      -          -           -           -            0.086
Dummy         l_l_l_(0.31)
South East         -          -           -           -            0.150
Asia Dummy                                       __________       (0.36)
Sub-Saharan        -          -           -           -           -0.261
Africa Dummy              _                                       (-1.34)
OECD               -          -           -           -           -0.748
Dummy                                        _                    (-1.96)
R-squared       .33 .36   .22 .32 .31   .45 .50    .34 .35      .37 .39
.28 .23    .24 .26    .40 .29     .28 .23     .35 .24
Obs. (periods)    57(4)     49(5)       36(4)        57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
any rate, FDI represents a very small fraction of total domestic capital forma.tion in our sample.
23



5 Summary of the Channel Effects and Robustness Analysis
5.1 Analyzing the channel effects in the baseline model
The summary of the channel effect of trade policy on growth, based on the baseline model,
is given in Table XIV, which reports the effects of each channel on growth and the effect of
trade policy on each channel. The last column displays the product of the two coefficients.
The t-statistics for the channel effects are obtained by computing linear approximations
of the products of the parameters around the estimated parameter values, and applying
the usual formula for the variance of linear functions of random variables to this linear
approximation. Computing these standard errors is possible thanks to the joint estimation
of all the equations in the system, which allows the derivation of the covariance matrix for
all of the estimated parameters. In the baseline model, three of the six channels involve
statistically significant effects of trade policy openness on growth at the 90% level. The
overall effect, once all the channels have been added, is significant at the 99% level.
Table XIV: Summary of Channel Effects (Baseline Model)
Channel         Effect of the       Effect of Trade     Effect of Jrade
| Channel on Growth  Policy on Channel j Policy on Growth
Distortions                  -0.007               -0.344              0.002
(-9.08)             (-0.63)              (0.63)
Government                   -0.042               0.154               -0.007
Consumption                  (-1.57)              (3.73)             (-1.52)
Manufactured                  0.004               0.635               0.002
Exports                      (0.45)               (4.59)              (0.45)
Investment                    0.143               0.317               0.045
Rate                         (6.86)               (6.72)              (5.12)
Foreign Direct                0.320               0.045               0.014
Investment                   (4.68)               (4.01)              (3.79)
Macro Policy                  0.489               0.027               0.013
Quality                      (4.22)               (2.19)              (1.90)
Total Effect                                                          0.071
(5.94)
(t-ststistics based on heteroskedastic-consistent (White-Robust) standard errors in parentheses)
According to Table XIV, trade policy openness works positively for growth through
five out of six of the channels. Some channels are weak in magnitude: reduced distortions
account for roughly 3% of the net effect of open trade policy openness on growth, and is
statistically insignificant. This is a surprising result in light of the importance that alloca-
tive efficiency has received in the arguments about static and dynamic gains from trade.
The same holds for the manufactured exports channel, meant to capture technological
transmissions. The government size channel works negatively for growth, although the
effect is weak both in terms of magnitude and in terms of statistical significance. Differ-
ences in the quality of macroeconomic policy and in the ratio of FDI to GDP appear to
be relatively important channels, each accounting for roughly 20% of the total effects of
trade policy on growth.
24



The most important channel by far seems to be the investment rate. It accounts for
close to 63% of the total effect of trade policy on growth, a somewhat unexpected result.
Several theoretical arguments point to the potential direct impact of trade policy openness
on investment, such as those outlined in section 2.1.2. However, dominant theories about
dynamic gains from trade generally do not put physical capital accumulation directly at
the center of their logic, although the returns to capital are predicted to increase as a
result of openness in most of these theories.
Furthermore, theories that stress the favorable effects of trade openness on capital
accumulation are often of a static nature. Either through its pro-competitive effects of
through enhanced efficiency in the sectoral composition of output, openness raises the
steady state capital-labor ratio, which requires more investment in the transition to the
steady state. Common estimates of the speed of convergence to the steady state (2%)
suggest that this convergence might be rather slow, implying that a country that liberalizes
might experience a rather lasting surge in its investment ratio, before the marginal product
of capital falls back to its steady state level. Since many of the countries in our sample
liberalized their trade regimes either during the period under consideration, or just before,
our estimate of the investment effects of trade policy openness might well be capturing
this transitional effect.
Long-run effects of trade openness on growth are also theoretically possible. In the en-
dogenous growth literature, any mechanism that prevents the marginal product of capital
from falling to zero spurs growth by preventing a fall in the rate of investment. Tech-
nological transmissions, improved policy quality and allocative efficiency are thought to
work mainly by raising the productivity of factors, and generate long-run growth through
endogenous mechanisms. However, given our methodology, such an effect should show up
through the technological transmissions, distortions or policy quality channels.
Another possible explanations for the results is that measurement error in some of the
channel variables leads us to overstate the effect of trade policy via investment. Indeed,
if investment is positively correlated with technological transmissions, and the share of
manufactured exports in total merchandise exports is a weak proxy for the extent of
technological transmissions, then part of this effect will be accounted for by the investment
channel. This again, seems to point to the logical complementarity between physical
capital accumulation and the overall improvement in the productivity of existing factors.
A similar argument could be made concerning price distortions. However, the scope of
this argument is somewhat limited by the fact that we are using instruments for all of
the channel variables: if the measurement errors in the instruments are uncorrelated with
measurement errors in the channel variables, the incidence of attenuation bias will be
greatly reduced.
To summarize, this model provides strong evidence in favor of the beneficial total effect
of trade policy on growth. A 10 percentage point increase in the trade policy measure,
which corresponds roughly to one standard deviation, is associated with a 0.71 percentage
point increase in the annual growth rate once all of the channels of influence are brought
into the picture. This effect is estimated with great precision. The most important
channels by far seem to be through investment (63% of the total effect). Technological
transmissions, according to our accounting framework, explain 22.5% of the overall positive
effect of trade on growth, and macroeconomic policy quality accounts for 18% of this effect.
25



5.2 Robustness analysis
5.2.1 Robustness to the Specification
We now turn to the analysis of sensitivity for our model. Table XV contains the channel
decomposition of the impact of trade on growth in the five different specifications of the
model. In addition to the t-statistics, this channel also contains Wald tests for the signifi-
cance of the products of coefficients. These Wald statistics are asymptotically distributed
as x2 variables with 1 degree of freedom. As the table shows, the p-values implied by the
t-tests and those obtained from the Wald tests are very similar. Figure 1 displays the six
channels graphically.
Table XV. Channel Effects under Alternative Models
I          II         III        IV          V           VI
I Baseline   1970-92    Devel.       SUR       Regional
1970-89               Countries              Dummies
Distortions      0.002      0.005       0.009       0.005        0.007
(0.63)    (7.28)       (2.51)     (1.73)       (1.71)
Wald Test        0.399     53.042       6.315       2.983        2.924
p-value         (0.53)      (0.00)      (0.01)     (0.08)       (0.09)
Govt.           -0.007     -0.007      -0.001      -0.004       -0.011
Consump.        (-1.52)    (-5.85)     (-1.14)     (-1.57)     (-1.93)
Wald Test        2.309      34.184      1.291       2.477        3.709
p-value         (0.13)      (0.00)      (0.26)     (0.12)       (0.05)
Manuf.           0.002      0.001       -0.002      0.005       -0.003
Exports         (0.45)      (0.53)     (-1.00)     (1.11)      (-0.70)
Wald Test        0.201      0.282       0.994       1.228        0.490
p-value         (0.65)      (0.60)      (0.32)     (0.27)       (0.48)
Investment       0.045      0.021       0.039       0.033        0.022
Rate            (5.12) l    (7.98)      (5.20)     (4.37) l     (3.54)
Wald Test       26.199 l   63.639      27.076      19.075 l     12.567
p-value     |   (0.00) |    (0.00)      (0.00)     (0.00) |     (0.00)
Foreign Dir.     0.014      0.015       0.023       0.013 l      0.010
Investment  1   (3.79) |    (6.02)      (4.90)     (3.46) |     (2.37)
Wald Test       14.385 |   36.236      24.058      11.967J       5.637
p-value     |   (0.00) |    (0.00)      (0.00)     (0.00) |     (0.02)
| Macro Policy    0.013       0.011 [    0.017       0.016        0.004
Quality          (1.90) 1    (4.24)     (3.36)     (2.84) |     (1.18)
Wald Test        3.609 }    17.980 f    11.293      8.078        1.402
p-value         (0.06) l    (0.00)      (0.00)     (0.00) |     (0.24)
Total Effect    0.071 T    0.046        0.085      0.067        0.030
l            l  (5.94)    (11.71)      (7.85)      (5.73)       (2.38)
Wald Test       35.332 |  137.215      61.624      32.888        5.688
p-value          (0.00) |    (0.00)     (0.00)     (0.00) |     (0.02)
26



Column III shows that, when adding the 1990-92 time period, most of the previously
insignificant effects become significant. Although the addition of this time period reduces
the number of observations, it raises by 20% the amount of data used to estimate each
parameter compared to the case where only four time periods are used. The signs and
relative magnitudes of most of the effects arc maintained. The reduction in the overall
effect, from 0.71 to 0.46, is almost entirely due to a reduction in the investment chan-
nel. Distortions and government size become statistically significant channels, although
relatively small in magnitude.
Column IV shows that the effect of trade policy on economic growth is actually in-
creased when the sample is restricted to developing countries. This is due to the fact that
the distortions channel is now significant, and represents roughly 10% of the overall effect.
The other channels are preserved. Column V shows that changing the estimator used for
the analysis does not greatly affect the sign and magnitude of the estimated effects. In
fact, the overall effect of trade policy is roughly preserved compared to the baseline model.
Our estimator does not allow for country specific fixed effects that can covary with the
right-hand side variables in the various equations. Accounting for country specific fixed
effects would involve rewriting the econometric theory underlying the estimation proce-
dure, a task that is left for future research. However, in order to account for the possibility
that regional specificities might be the driving force of the results, regional dummies for
Latin America, Sub-Saharan Africa, South East Asia and the OECD countries were added
to each of the channel equations, as well as to the list of instruments (Column VI). Since
accounting for fixed effects tends to wipe out much of the cross-sectional variation (the
fixed effects estimator uses only the variation within regions across time, discarding the
between-country variation), we should expect the inclusion of these variables to lower the
estimated effects of trade policy. This is indeed the case, as shown in Figure 1. The
total effect of trade policy is reduced by the inclusion of region specific dummies, but the
respective shares of each channel are roughly preserved. In particular, the dominant role
of physical capital formation is maintained.
To summarize, the main message of this paper, namely that trade policy openness
works mostly through the rate of physical capital investment, appears robust to a variety
of modifications of the baseline model.
5.2.2 Robustness to the Time Coverage
In order to examine the robustness of the model with respect to its time coverage, and
therefore with respect to the cross-equation parameter equality restrictions, we excluded
each time period from the baseline model one at a time. Furthermore, the exogenous
variables corresponding to the excluded period were removed from the list of instruments.
The resulting channel effects are presented in Table XVI. We should expect the precision
of the parameter estimates to be greatly reduced, as we are now throwing out 25% of the
data in each case. This is indeed the case, as the t-statistics on most of the channel effects
are considerably lower when only three time periods are used for estimation. For exam-
ple, the macroeconomic policy and government size channels no longer appear significant
statistically. However, both the signs and magnitudes of the estimates are remarkably
close to those of the baseline model. In particular, the investment effect is preserved in
27



all specifications, and in all but one case the overall effect of trade policy remains of the
same magnitude. This provides evidence that the estimates are robust with respect to the
time period coverage.
Table XVI - Sensitivity to the Time Period Coverage
[___________ |excl. 1970-84  excl. 1975-79 | excl. 1980-84  excl. 1985-89
Distortions           -0.007         -0.002          0.013          0.001
(-1.15)        (-0.37)        (1.30)          (0.07)
Wald Test             1.315           0.133          1.679          0.005
p-value               (0.25)         (0.72)         (0.20)         (0.94)
Government            -0.006          0.002          0.005         -0.010
Consumption          (-1.09)         (0.19)         (0.62)         (-1.53)
Wald Test             1.196           0.035          0.391          2.351
p-value               (0.27)         (0.85)         (0.53)         (0.13)
Manufactured          0.013           0.009          0.004          0.010
Exports               (1.83)         (0.89)         (0.54)         (0.70)
Wald Test             3.357           0.792          0.294          0.494
p-value               (0.07)         (0.37)         (0.59)         (0.48)
Investment            0.032           0.093          0.021          0.035
Rate                  (2.62)         (5.05)         (1.80)         (2.07)
Wald Test             6.863          25.508          3.229          4.281
p-value               (0.01)         (0.00)         (0.07)         (0.04)
Foreign Dir.          0.021           0.004          0.012          0.016
Investment            (4.09)         (1.17)         (2.43)         (2.41)
Wald Test            16.705           1.368          5.924          5.830
p-value               (0.00)         (0.24)         (0.01)         (0.02)
Macro                 -0.026          0.001          0.008          0.009
Policy               (-1.98)         (0.11)         (0.78)         (1.08)
Wald Test             3.906           0.013          0.610          1.163
p-value               (0.05)         (0.91)         (0.43)         (0.28)
Total Effect          0.027          0.108          0.061          0.060
l___________ l(1.48)                 (3.87)          (3.62)         (2.53)
Wald Test             2.203          15.008         13.140          6.399
p-value               (0.14)         (0.00)         (0.00)         (0.01)
5.3 Exhaustiveness of the model
The last concern that we address is that the six channels considered above may not fully
capture the total effect of trade policy on growth. In particular, we may have omitted
one channel or more, leading both to an incomplete characterization of the effects of trade
policy and to potential biases in the estimates of the included channels (insofar as the
omitted channels covary with the included ones in the growth regression).
28



5.3.1 Other possible channels
We start with a brief discussion of other possiblc linkages which may have becn omitted
from the system. Firstly, the accumulation of human capital might be onc of the channels
linking trade policy and economic growth. Indeed, if trade openness modifies the relative
returns to factors, then it may create greater incentives to accumulatc human capital.
For instance, if an open trade policy spurs technological transmissions, and if technology
and skills are complements, then trade openness will increase the returns to accumulating
human capital. However, specifying a human capital channel led to no significant linkage
effect: the coefficient on the trade policy variable was essentially zero oncc other determi-
nants of human capital formation, such as per capita income, were held constant. This was
robust with respect to the inclusion of a diverse set of controls. Furthermore, the effects
of human capital on growth are not robust in our growth specification, a problem which
is compounded by the opposite signs of male and female human capital. Hence, human
capital does not appear to be an important channel linking trade policy and growth.
We carried out a similar exercise for income inequality. Neoclassical trade theory
provides several tools for the analysis of income distribution in relation to trade openness.
For example, the simple factor endowments theory of Hecksher-Ohlin-Samuelson predicts
that when a relatively unskilled labor abundant country moves from autarky to free trade,
returns to unskilled labor should increase in relative terms, with presumed positive effects
on income distribution. In turn, there are reasons to believe that inequality has an effect
on growth, although the direction of this effect appears a priori ambiguous. Alesina and
Perotti (1993), among others, have studied the issue of distribution and growth. They
argue that when the poor have a larger weight in the political decision making process, they
tend to vote for transfer schemes that involve distortive (i.e. growth reducing) taxation.
Empirically, they report that more unequal societies tend to display lower growth rates,
once other determinants of growth are held constant. However, including a measure of
income inequality (the Gini coefficient) in the basic growth regression gave rise to an
insignificant effect. Furthermore, the effect of trade policy on income inequality, once
controlling for the level of per capita income, was found to be essentially zero. Hence, the
income inequality channel does not appear to operate either, although the poor quality of
cross-country inequality data may be the source of this result.22
5.3.2 Unconditional effect of trade policy openness
The unconditional effect of trade policy on growth can be calculated by removing all of
the channel variables from the growth regression, and including the trade policy index in
their place (Table XVII). The resulting estimate suggests a strong association between
the trade regime and growth: A 10 percentage point increase in the trade policy index is
associated with a 0.66 percentage point increase in the annual growth rate in the baseline
model.
With the exclusion of many variables from the growth equation, the trade policy index
now captures much of the portion of their effect on growth that is not necessarily linked
to trade policy. However, this coefficient is useful in that it provides us with a rough
22Results for the income inequality and human capital channels are available from the author upon
request.
29



order of magnitude against which to compare the total effect of trade policy computed
above. Indeed, in all five models, the unconditional effect of trade policy (where we take
'unconditional' to mean that we are not conditioning on the channel variables) is roughly
of the same magnitude as the total effect of trade policy computed in Table XV. This
increases our confidence that no major channel has been omitted.23
Table XVII. Unconditional Effect of Trade Policy in the Growth Regression
Baseline [ 1970-92        Devel.  [  SUR    [ Regional
l____________  1970-89 [                [ Countries               [Dummies l
Intercept            2.666       1.744         1.686       4.159        4.780
(1.42)      (2.24)       (1.13)       (2.34)       (1.61)
Log Initial         -0.078       0.037        0.006        -0.259       -0.086
Income              (-0.32)      (0.38)       (0.03)      (-1.12)      (-0.23)
Male Human           0.725       0.948         1.893       0.671        -0.285
Capital             (2.11)       (5.30)      (13.54)       (2.18)      (-0.92)
Female Human        -0.926      -1.265        -1.840       -0.837       0.019
Capital            (-3.04)      (-8.02)      (-7.48)      (-2.99)       (0.06)
Trade Policy        0.066       0.061         0.095        0.091        0.073
Openness           (3.00)       (7.18)       (5.97)       (4.44)       (2.93)
Latin America        -           -            -                         -2.198
Dummy                                                                  (-6.74)
South East           -           -            -                         0.970
Asia Dummy                                                              (1.77)
Sub-Saharan          -           -            -                        -3.090
Africa Dummy                                                           (-5.70)
OECD                 -           -            -                         -1.438
Dummy                                                                  (-3.71)
R-squared         .12 .06   .12 .09 .09    .23 .20      .12 .06      .11 .31
.09 .03     .04 .11     .22 .02      .08 .03      .45 .11
Obs (Periods)      57(4)       49(5)        36(4)        57(4)        57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
5.3.3 Tests based on the residuals from the growth equation
A perhaps more formal test of exhaustiveness can be carried out by regressing the residual
vector from the growth regression on the index of trade policy. If any significant channel
has been left out of the growth regression, this should generate some correlation between
the estimated residual and the measure of trade openness. The results presented in Table
XVIII, based on a seemingly unrelated regression estimator, show that this is not the
case.24 In most of the models, the residual effect of trade policy is generally positive, but
23A remaining possibility is that we have omitted an important negative channel and an offsetting
positive channel, although this would be an unlikely coincidence.
24Again, this should not be taken as an absolute proof of exhaustiveness. To the extent that potentially
omitted channels covary with the included ones, then the latter will pick up the effects of trade policy that
should be accounted for by the missing channels; this would be reflected by a lower correlation between
the growth residual and trade policy openness. However, this test provides yet another indication that no
major channel has been omitted.
30



not significantly different from zero at any reasonable level of significance. This, again,
reinforces our confidence in the exhaustiveness of the model. The fact that the estimates
are generally positive shows that, if anything, our channel methodology has uncovered a
lower bound on the total effect of trade openness. In the only case where the estimate is
negative, the effect is very small in magnitude.
Table XVIII. Regression of the residuals from the growth equation on the
trade policy index
1 Baseline   1970-92      Devel.       SUR        Regional
|__________ |1970-89 |                Countries |              Dummies
Intercept          0.033       0.042      -0.183        0.048      -0.138
(0.18)      (0.24)     (-0.81)       (0.30)     (-0.94)
Trade Policy      0.013       0.019      -0.004        0.010       0.019
Openness         (0.83)       (1.20)     (-0.25)       (0.64)      (1.36)
R-squared      .0009 .01  .00003 .007    .07 .07   .000004 .006   .002 .01
.02 .0002  .02 .005 .02   .01 .04    .02 .00003    .03 .002
Obs. (periods)   57(4)      49(5)       36(4)        57(4)       57(4)
(t-statistics based on heteroskedastic-consistent (White-Robust) standard errors, in parentheses)
6 Conclusion
This paper constitutes the first attempt to empirically evaluate, in a cross-country context,
the respective roles of various theories of dynamic gains from trade in explaining the
observed positive impact of trade openness on economic growth. Trade openness affects
growth mainly by raising the ratio of domestic investment to GDP. Depending on the
specification, the rate of physical capital accumulation explains between 46% and 63%
of the impact of trade policy on economic growth. Foreign Direct Investment, used as
a proxy for technological transmissions, and the quality of macroeconomic policies each
account for roughly 20% of the overall effect. Lastly, we found weak evidence that the
size of government, measured by the ratio of public consumption to GDP, constitutes a
channel whereby trade policy affects economic growth negatively.
The lack of statistically significant results concerning manufactured exports and dis-
tortions may be due to measurement problems. These are the two channels for which
measurement, although improving on past attempts, is still subject to considerable short-
comings. The black market premium may be a weak proxy for the overall efficiency of the
price system. International technological transmissions are extremely hard to measure as
well, resulting perhaps in a downward bias in the estimates corresponding to this channel,
and a concurrent overstatement of the other channels. Future research should seek to
improve upon the measures used in this study.
The important role of investment in physical capital poses a serious theoretical chal-
lenge. While some theories about gains from trade do predict positive effects of openness
on the rate of return to capital, these effects should be captured either by the distortions or
the technological transmissions channels. Furthermore, theories based on dynamic gains
from technological transmissions and efficiency improvements center on the improvement
31



of the overall productivity of factors, rather than on the acceleration of their accumulation.
If specialization is limited by the extent of the market, under increasing returns to scale
theories, trade openness should allow entrepreneurs to undertake previously unprofitable
investments. Theories based on such a 'Big Push' may provide useful insights into the
nature of dynamic gains from trade.25 Further theoretical investigations into the interplay
between investment rates, trade openness and growth seem called for.
2oRcsults presented in appendix V, howevcr, suggests that such theories may not provide the full picture.
32



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34



Appendix I. List of countries
OECD              Asia        | Latin America        Africa
Australia       Cyprus             Argentina           Ghana
Austria         India              Barbados           Kenya
Belgium         Israel             Brazil              Malawi
Canada          Jordan             Colombia            Mauritius
Finland         Korea              Costa Rica          Sierra Leone
France          Malaysia           Dominican Republic  South Africa
Germany, West   Myanmar (Burma)  El Salvador           Tanzania
Greece          Pakistan           Guyana              The Gambia
Ireland         Philippines        Mexico              Tunisia
Italy           Singapore          Paraguay            Zaire
Japan           Sri Lanka          Peru                Zambia
Netherlands     Syria              Venezuela
New Zealand     Thailand
Norway
Portugal
Spain
Sweden
Switzerland
Turkey
U.S.A.
United Kingdom                                 .
35



Appendix II: Data Sources and Description
Variable Name: Growth
Source: Summers-Heston v. 5.6
Unit: % points
Description: Growth rate of PPP adjusted Gross Domestic Product
Variable Name: Import duties as a % of total imports
Source: IMF-IFS and IMF-GFS
Unit: % points
Description: Import duties in local currency as a percentage of total imports in local
currency.
Variable Name: Pre-Uruguay Round NTB coverage
Source: UNCTAD/World Bank
Unit: % points
Description: Coverage rate of non-tariff barriers pre-Uruguay Round
Variable Name: Sachs and Warner Liberalization Status
Source: Sachs-Warner (1995)
Unit: Values ranging from 0 to 1.
Description: For each year, a dummy variable was constructed based on the years of
liberalization in Sachs and Warner (1995). Liberalized countries took a value of 1, closed
countries took a value of zero. The data were averaged over the relevant 5 year sub-periods.
Variable Name: Manufactured Exports Share
Source: World Bank
Unit: % points
Description: Share of manufactured goods in merchandise exports
Variable Name: FDI ratio
Source: IMF
Unit: % points
Description: Ratio of gross Foreign Direct Investment inflows to GDP.
Variable Name: Democracy
Source: Gastil (Freedom In the World Reports, various issues)
Unit: Takes values from 0 (non-democracy) to 1 (country with fully developed democratic
institutions)
Description: Index of how democratic institutions are (regular elections, broad franchise,
wide access to office and relevance of elected officials).
Variable Name: Initial Income
Source: Summers-Heston v. 5.6
Unit: Log of per capita GDP in Dollars
Description: Real Gross Domestic Product per capita in a given year (PPP adjusted)
36



Variable Name: Human Capital
Source: Barro-Leo
Unit: Years
Description: Average years of secondary and higher education in the total population over
age 25.
Variable Name: Secondary School Completion Rate
Source: Barro-Lee
Unit: %
Description: Percentage of "secondary school complete" in the total population.
Variable Name: Macroeconomic Policy Quality
Source: Wacziarg / World Bank / IMF
Unit: index
Description: Index of macroeconomic policy quality. Constructed by ranking countries
according to the public debt to GDP ratio, deficit to GDP ratio and growth of MI net of
total output growth and assigning values from 1 to 10 to each decile, then averaging the
three resulting indicators. Index also ranges from 1 to 10. Higher numbers signal better
policies.
Variable Name: Black Market Premium
Source: Tavares-Wacziarg data set, initially World Currency Yearbook and IMF.
Unit: (Black market rate-official rate)/official rate. %
Description: Black market premium on the official exchange rate.
Variable Name: Public Consumption
Source: Summers-Heston v. 5.
Unit: %
Description: Share of government consumption of goods and services in GDP, excluding
transfers and public investment.
Variable Name: Population over 65
Source: Barro-Lee
Unit: %
Description: Share of population aged over 65 in the total population
Variable Name: Population over 15
Source: Barro-Lee
Unit: %
Description: Share of population aged over 15 in the total population
Variable Name: Terms of Trade Shocks
Source: Tavares-Wacziarg, initially from the World Bank.
Unit: %. A positive value means terms of trade move favorably, a negative value the
opposite.
Description: Growth rate of manufactured export priccs minus growth rate of manufac-
tured import price
37



Variable Name: Population
Source: Barro-Lee
Unit: Logarithm of population.
Description: Country population
Variable Name: Population Density
Source: Barro-Lee
Unit: 1000 population per million square km
Description: Population density
Variable Name: Ethnolinguistic fractionalization
Source: Mauro (1994)
Unit: Probability.
Description: Probability that two randomly selected persons from a given country will
not belong to the same ethnolinguistic group.
Variable Name: Postwar Independence
Source: Barro-Lee
Unit: Dummy variable
Description: Takes on a value of 1 if the country gained independence after the Second
World War.
38



Appendix III. Issues in Measurement
A-III-1. The Trade Policy Index.
Section 2.2.1 discusses the conceptual basis of the trade policy index used throughout
the paper. This part of the appendix describes the actual computation of the index in
more detail. Table A-III-I displays the results of the regression used to construct the
weights on the three components of trade policy, namely import duties as a share of total
imports, the per-Uruguay round NTB coverage ratio and the Sachs-Warner liberalization
status indicator (averaged over the relevant five-year time periods). The regression also
features gravity components such as land area and the log of population, as well as the
growth rate (Appendix IV provides evidence of reverse causation from growth to trade
shares).
Table A-III-I. Trade Volumes Regression
L     Dependent Variable:         l   3SLS*
Imports + Exports / GDP   |                   I
Constant                                182.561
(9.70)
Growth of per capita income               0.322
(1.12)
Land Area                                 -8.029
(-3.69)
Log of Population                         -9.121
(-3.42)
Import duties over total imports         -34.733
(-1.16)
Pre-Uruguay Round NTB coverage            -0.217
(-0.73)
Sachs/Warner liberalization status      11.2622
(2.12)
Adj. R-squared                    .60 .55 .53 .49
# of obs. (# of periods)              71 (4)
(t-statistics in parentheses)
* The instruments used were: Initial income, population density, religious dummies, oil producer
dummy, postwar independence dummy, log of population, share of population over 65, log of area.
As expected, the share of import duties in total imports and the NTB coverage ratio
receive a negative weight in the index, while the liberalization status receives a positive
weight. The lack of precision of the estimates, largely due to collinearity between the
policy measures, is not really a source of concern since the objective is only to generate
weights that provide a rough notion of how the three components effectively impact trade
volumes. Minor variations in these weights are not likely to affect the final results.26
26In fact, the results for the channels model are not very sensitive to the inclusion of NTBs in the index.
39



For each period, the trade policy openness index was computed as:
Trade Policy = -34.73*(Import Duty Share) - 0.217*(NTB) + 11.262*(Liber. Status)
Table A-III-II contains correlations betwcen the resulting trade policy index and its
various components for the time periods under consideration. This shows that the liber-
alization status and the duty ratio receive the greatest weight in the index, although the
correlation of NTBs with the overall index is substantial.
Table A-III-II. Correlations between the Components of the Index and the
Index Itself
Index   Index   Index   Index
1970-74  1975-79  1980-84  1985-90
Duty 70-74            -0.72    -0.70    -0.67    -0.64
Duty 75-79            -0.72    -0.75    -0.72    -0.69
Duty 80-84            -0.66    -0.68    -0.73    -0.71
Duty 85-90            -0.63    -0.64    -0.70    -0.77
NTB                   -0.47    -0.48    -0.45    -0.50
Liberalization 70-74   0.88     0.87    0.85     0.75
Liberalization 75-79  0.87     0.87     0.85     0.73
Liberalization 80-84  0.83     0.83     0.86     0.73
Liberalization 85-90  0.79     0.79     0.79     0.84
(Number of observations: 71)
The correlations between the underlying components of the trade policy indicator are
displayed in Table A-III-III. The signs of the correlations are as expected. The NTB
measure is weakly correlated with the other indicators, suggesting that its inclusion may
provide useful information about trade policy. However, the NTB coverage ratio receives
the smallest weight in the overall index.
Table A-III-III. Correlations between the Underlying Components of the
Index
Duty    Duty DDuty    Duty   NTB   Liber.    Liber.   Liber.
1970-74  1975-79  1980-84  1985-90        1970-74  1975-79  1980-84
Duty 70-74      1.00 l                                          ___lll    
Duty 75-79      0.94     1.00 |       l        l      l                 l 
Duty 80-84      0.84     0.89     1.00        r  __r 
Duty 85-90      0.74     0.78     0.92     1.00 l      l       l        l        l
NTB             0.07     0.14     0.11     0.17   1.00 _        l       l        l
Liber. 70-74    -0.52    -0.52    -0.50    -0.46  -0.13   1.00 1        1        1
Liber. 75-79    -0.53    -0.53    -0.50    -0.46  -0.10   1.00     1.00
Liber. 80-84    -0.51    -0.51    -0.48    -0.44  -0.07   0.95     0.97     1.00
Liber. 85-90    -0.49    -0.52    -0.46    -0.47  -0.15   0.87     0.86 r   0.87
(Number of observations: 71)
40



A-III-2. The Macroeconomic Policy Quality Index.
The index of macroeconomic policy quality used in this paper is based on three un-
derlying components: The ratio of government deficit to GDP, the ratio of governmcnt
debt to GDP and a measure of excessive monetary creation, equal to the difference be-
tween the growth rate of M2 and the growth rate of real GDP (this is based on the fact
that a growing economy needs to be supplied with liquidity; any excessive money growth
sustained for a long time is likely to result in nothing more thlan inflation). Each country
is first ranked according to each component. Each decile is then given a number from 1
to 10, with higher numbers signaling better policies (low excess money growth, low deficit
ratio, low debt ratio), and these rankings are simply summed up. The use of quantiles
avoids having to decide what a good policy is in absolute terms, and defines the quality
of macroeconomic policy relative to the policies adopted in the rest of the world. It also
avoids having an index that increases systematically through time due to the accumulation
of the public debt. Lastly, it increases the spread of the index compared to an index based
on scaled values of the underlying data, which provides more within- and cross-country
variation.
Summary statistics for the macroeconomic policy index are contained in Tables A-Ill-
IV and A-III-V. The correlations suggest that the excessive growth of money plays the
least part in the variation of the index of macroeconomic policy.
Table A-III-IV. Summary Statistics for the Macroeconomic Policy Index and
its Components
Variable        JMean IStd. Dev. Minimum   Maximum
Macro Index 70-74            5.26        1.84       1.33         9.33
Macro Index 75-79            5.13        1.84       1.33         8.67
Macro Index 80-84            5.20        1.99       1.00         9.33
Macro Index 85-89            5.47        1.86       1.00         9.00
Deficit ratio 70-74         -2.61        3.20      -19.44        3.60
Deficit ratio 75-79         -4.72        4.49      -16.53        5.29
Deficit ratio 80-84         -6.34        6.18      -43.62        2.43
Deficit ratio 85-89         -5.30        6.19      -47.02        3.64
Public Debt Ratio 70-74     25.15       18.74       0.00       118.67
Public Debt Ratio 75-79     33.51       26.33       0.00       174.86
Public Debt Ratio 80-84     52.29       50.74       0.00       332.28
Public Debt Ratio 85-89     74.08       67.87       0.00       436.85
Excess Money Growth 70-74    13.99      15.59       -0.33      143.14
Excess Money Growth 75-79   17.06       20.08       3.15       158.29
Excess Money Growth 80-84   22.37       36.75       3.49       233.15
Excess Money Growth 85-90   34.97      106.83       -7.01      853.53
Number of Observations: 88
41



Table A-III-V. Correlations of the Macroeconomic Policy Index with its
Components
1 Macro Index  Macro Index  Macro Index  Macro Index
l_________________________ l1970-74         1975-79       1980-84       1985-89
Deficit ratio 70-74            0.75          0.65          0.50          0.49
Deficit ratio 75-79            0.53          0.76          0.56          0.49
Deficit ratio 80-84            0.46          0.52          0.73          0.63
Deficit ratio 85-89             0.37         0.39          0.49          0.64
Public Debt Ratio 70-74        -0.65         -0.62         -0.46        -0.42
Public Debt Ratio 75-79        -0.64         -0.72         -0.63        -0.52
Public Debt Ratio 80-84        -0.48         -0.55         -0.67        -0.54
Public Debt Ratio 85-89        -0.41         -0.47         -0.65        -0.66
Excess Money Growth 70-74      -0.33         -0.07         0.04         -0.06
Excess Money Growth 75-79      -0.30         -0.26         -0.12        -0.23
Excess Money Growth 80-84      -0.35         -0.34         -0.40        -0.40
Excess Moncy Growth 85-90      -0.19         -0.22         -0.24        -0.38
Number of Observations: 88
A-III-3. An Alternative Measure of Price Distortions.
Dollar (1992) proposed a measure of outward orientation (or more generally, of distor-
tions) based on an internationally comparable consumer price index compiled by Summers
and Heston (1994). This index is constructed by pricing the same basket of goods across
countries, taking the US price basket as a numeraire. In the absence of nontradable goods,
trade-induced price distortions and domestic price distortions brought forth by taxes, sub-
sidies, and imperfectly competitive pricing (the extent of which can be expected to vary
systematically from country to country), full purchasing power parity ought to hold, and
the value of the index should be equal across countries. Hence, if one could somehow elim-
inate price level differences due to the existence of non-tradable goods, one could obtain
an index of price distortions.
Systematic price level differences due to the existence of non-tradable are related to
differences in factor endowments. Hence, the residual from a regression of the price level
on country factor endowments should yield a measure of distortions. However, it is not
clear that distortions themselves are unrelated to endowments, so that the residual may
be leaving out important variation in price distortions. Furthermore, measures of fac-
tor endowments are missing for many countries, especially as far as the capital stock is
concerned.
Estimates of price distortions obtained using this methodology did not give very con-
vincing results.27 OECD countries displayed abnormally high distortions levels, similar
to those of Africa. Both Latin America and South East Asia displayed relatively low dis-
tortions. This does not accord with our priors concerning the efficiency of price systems
across countries. Further research into a measure of distortions based on overall price
levels seems warranted.
27Results arc available from the author upon request.
42



Appendix IV. Trade Policy Matters for Growth
This appendix investigates which component of the trade shares, policy or gravity,
affects growth mostly. The objective is also to examine the issue of reverse causality
between trade shares and growth. Unlike in the text, the channel relationships are absent
from this Appendix. The system, made up of two sets of equations (the four growth
equations for periods 1970-74, 1975-79, 1980-84 and 1985-1989, and the four openness
equations for the same periods), is estimated jointly using three-stage least squares.28
The estimator used is the same as the one discussed in the text. Note that the measure
of openness now consists of the ratio of imports plus exports over GDP, which appears
directly in the growth equation.
A-IV-1. Reverse Causation
We first consider the effects of a higher trade to GDP ratio on the growth rate, as
well as the possibility of reverse causation whereby growth might affect the degree of
openness rather than the opposite. The regressors that appear in the growth equation
are: the log of initial GDP, the level of human capital (measured by the average number
of years of schooling in the total population over age 25), the black market premium on
the exchange rate, the investment share in GDP, the measure of trade openness (imports
plus exports over GDP) and the share of government consumption in GDP. The regressors
included in the openness equation are: the growth rate of GD'P (to assess the magnitude
of endogeneity), the log of the country's area, a measure of terms of trade shocks, the
log of population, and the Sachs and Warner dummy for liberalization status (averaged
over 5-year time periods), and a measure of the country's distance from the capitals of the
world's 20 major exporters. The results for this procedure are reported in Table A-IV-I.
The coefficient on the trade to GDP ratio is positive and significant at the 90% confi-
dence level. The magnitude of the coefficient suggests that a 10 percentage point increase
in the trade to GDP ratio leads to a 0.17 percentage point increase in the annual growth
rate of the economy. Although this is admittedly a small effect, it might be important
intertemporally (if the US had grown just 1 percentage point slower per annum since
1870, its per capita income would be that of today's Hungary or Mexico, see Barro and
Sala-i-Martin (1995), p.1).
We reestimated this growth relationship without controlling for the endogeneity of the
openness variable. Specifically, the growth regression above was reestimated in isolation
of the openness equation, and the openness variable was added to the list of instruments
(this is equivalent to not instrumenting for openness). The coefficient on the trade ratio
decreased to 0.004 and became insignificant at any reasonable confidence level (t=.52).
28The instruments used are: the log of initial income for all periods, population density for all periods,
a dummy for major religions (Muslim, Confucian/Buddhist, Catholic, Other Christians), a dummy for oil
exporting countries, the number of years the country was involved in an external war during the period
1960-1985, a dummy for whether the country obtained independence after the Second World War, the
log of population for all periods, the share of population over 65 for all periods, the log of the country's
land area, the log of the distance measure, and the measure of terms of trade shocks for all periods. The
panel data used throughout this Appendix contain 61 countries for 4 time periods (averages over 1970-74,
1975-79, 1980-84, 1985-89).
43



This broadly confirms previous results by Frankel (1996), showing that the effect of trade
openness on growth increases when controlling for endogeneity.
Table A-IV-I: Openness and Growth
Dep.= Growth (%)          3SLS         Dep.= Trade ratio (%)         3SLS
Constant                      14.041    Constant                        150.545
(4.88)                                    (10.50)
Log of Initial                -1.865    Growth rate (%)                   1.092
Income                       (-4.89)                                     (5.60)
Trade to GDP                   0.017    Log of land area                 -3.628
ratio (% GDP)                 (1.91)                                     (-2.40)
Years of schooling              2.03    Terms of trade                   20.972
(male)                        (3.33)    shocks                           (3.55)
Years of schooling             -1.82    Log of population                -7.464
(female)                     (-2.79)                                     (-4.37)
Black Market                  -0.839    Sachs-Warner                       7.54
Premium                      (-5.08)    dummy (averaged)                  (1.82)
Investment                     0.155    Log of distance                  -8.282
share (% GDP)                 (4.53)                                     (-2.05)
Government                   -0.119    R-squared                  .51 .57 .56 .55
consumption (% GDP)          (-2.77)
R-Squared              .31 .22 .22 .28
(t-statistics in parentheses)
The trade openness equation also displays common patterns: country size, as measured
by land area or population, has a significantly negative effect on trade openness. The
distance from the world's main trading nations also has a negative impact on the trade
ratio. Positive terms of trade shocks potentially lead to both more exports and more
imports, hence a positive impact on the trade ratio. The Sachs and Warner measure of an
open trade policy also has the expected sign, and is large in magnitude (economies with
open trade policies have trade to GDP ratios 7.54 percentage points higher than those with
policies that discourage trade, all other things equal). Lastly, the contemporaneous growth
rate has a positive and significant effect on the trade to GDP ratio. The magnitude of this
coefficient is rather small: a 1 percentage point increase in the growth rate leads to a 1.1
percentage point increase in the trade to GDP ratio. But we do find statistically significant
evidence of reverse causation: growth positively affects the trade to GDP ratio, even if the
effect may be considered small (especially compared to the effects of, for instance, country
size or the trade policy regime).
A-IV-2. Separating the impact of gravity effects from policy effects.
We now attempt to separate the effects of gravity-type variables on growth (country
land area and population, terms of trade shocks) from policy effects. In order to do this,
we first run a regression of trade openness, measured by the ratio of imports and exports
to GDP, on land area, distance, growth, terms of trade shocks and the log of population.
The fitted value from this regression is a country's "potential degree of openness". The
deviation of this fitted value from the observed measure of openness is interpreted as
44



the effect of policy on openness.29 The smaller the deviation, the more distortionary the
policy (negative deviations signal a policy that reduces the effective trade to GDP ratio
through protection, while positive deviations signal policies that favor international trade
integration). Results from this regression are as follows:
Table A-IV-II. Gravity Equation
[Dep= Trade ratio (%) [    3SLS
Constant                         157.131
(10.72)
Growth rate (%)                    0.943
(4.68)
Log of land area                  -3.719
(-2.39)
Terms of trade shocks             23.503
(3.76)
Log of population                 -7.048
(^4.12)
Log of distance                  -12.031
(-3.26)
R-squared                  .51 .56 .52 .55
(t-statistics in parentheses)
Following are summary statistics for the "potential openness component" and for the
"policy attitude component":
Table A-IV-III. Summary Statistics (Openness Decomposition)
|__________________ |Mean I Std dev.  Minimum   Maxiimum  |
Gravity component 1970-74   53.23        15.87          7.81        81.34
Gravity component 1975-79   53.42        16.35          9.25        81.45
Gravity component 1980-84   51.28        16.34          8.93        77.98
Gravity component 1985-89   48.44        16.30         11.15        77.45
Policy component 1970-74   -4.66        15.76       -44.51         33.53
Policy component 1975-79    1.44        16.82       -33.23         36.53
Policy component 1980-84    5.11        20.28       -35.07         65.03
Policy component 1985-89    5.93        17.89       -31.32         65.63
These two measures are then included in the growth regression instead of the observed
trade to GDP ratio. The results from this growth regression appear in table A-IV-IV.30
The estimates suggest that it is the "trade policy" component of openness that matters
29This is a much less reliable measure of trade policy openness than the one used in the text. Indeed,
the residual from the gravity equation may contain more than policy effects. Other potential defects of
this deviation approach are discussed in section 2.2.1 of the text. However, by purging out the gravity
component, we can gain some insight into the relative role of gravity and other components.
30The growth model was, once again, estimated jointly with a trade openness equation in which the
observed trade ratio was the dependent variable. The results for that equation are essentially the same as
those presented in the first part of this note.
45



most for growth. The gravity component alone is not statistically different from zero.
Once geographical and environmental factors have been "purged" out of the openness
measure, the effect of openness on growth increases by 60% and becomes significant at the
5% level.
Table A-IV-IV. Growth Regression with Decomposed Trade Effects
Dep = Growth rate (%) [   3SLS
Constant                         13.503
(4.56)
Log of Initial income            -1.690
(-4.13)
"Trade policy component"          0.027
(2.08)
"Gravity component"              0.0036
(0.28)
Years of schooling (male)         1.884
(3.01)
Years of schooling (female)      -1.806
(-2.72)
Black Market Premium             -0.834
(-5.01)
Investment share                  0.155
(4.48)
Government consumption           -0.118
(-2.69)
R-squared                 .33 .22 .22 .28
(t-statistics in parentheses)
A-IV-3. Conclusion
1. There is some evidence of reverse causation, although the magnitude of the effect
of growth on openness is rather small. A 1 percentage point increase in a country's
growth rate leads to a 1.1 percentage point increase in its trade to GDP ratio, other
things equal. Once endogeneity is controlled for, the estimated effect of trade openness
on growth increases and becomes statistically significant.
2. Once trade ratios have been "purged" of their gravity component, their effect on
growth becomes larger and even more significant. This can be considered evidence that
what matters most for growth is not the trade to GDP ratio per se, but the prevailing
trade policy. This provides justification the focus on trade policy openness throughout
this paper.
46



Appendix V. Increasing Returns, the Size of the Market and Growth:
A Replication Exercise
This appendix replicates and checks the robustness of the findings in a paper by Ades
and Glaeser (1994, henceforth AG), in which the authors document the fact that, in two
sets of economies (US States in the 19th century and developing countries since 1960),
increasing returns operate by expanding the extent of the market. Their evidence shows
that, in samples that display absolute divergence (which is possible only under increasing
returns technologies), countries with larger internal markets benefit less from openness in
terms of growth. Put differently, more open countries tend to display a smaller correlation
of growth with initial income than closed countries, with openness measured as the ratio of
exports plus imports to GDP. This is the case because openness eliminates the constraint
imposed on growth by the size of the internal market. Under increasing returns-size of the
market theories, what matters for growth is how much effective demand can be directed
towards the productive sector. Greater effective demand originates either from a larger
internal market of .from foreign markets, but only open countries can benefit from the
latter.
A-V-I. Divergence and the Extent of the Market.
The basic test of divergence involves regressing the growth rate (averaged between 1960
and 1985) on initial income in 1960 measured in PPP adjusted dollars. To evaluate the
role of openness and market size, two other regressors are added: openness (trade to GDP
ratio, averaged over 1960-1985) and an interaction term between openness and the initial
level of GDP.3" To examine robustness, other variables such as regional dummies and an
education variable are added. Table A-V-Il below replicates table 3 in AG (appendix).
The only difference compared to their set up is that the openness variable here is taken
from the latest version of the Summers-Heston data set, whereas AG used World Bank
data. Income and growth are from version 4.0 of Summers-Heston, as in AG. Results
based on the latest version of the Penn World tables (version 5.6) display no significant
differences compared to the ones presented in table A-V-IL.
In addition to the developing countries sample chosen by AG, in which they selected
countries with per capita income lower than $1,500 (in 1980 constant dollars), we run the
same regressions for an extended sample, which includes the OECD as well as a broader
range of developing economies. AG only sought to examine how increasing returns oper-
ated, so they selected a sample of countries in which they knew unconditional divergence
did hold. However, in order to analyze the effect of market size on growth in a broader
framework, and to check the robustness of the AG results, we extended the sample. An
increased market size should not be a channel whereby openness spurs growth in a sample
where increasing returns does not operate.
3'Note that there is no control for the endogencity of the openness measure. When replication the esti-
mations using an instrumental variables estimator, neither the sign nor the magnitude of the coefficients
were affected. The precision of the estimates, however, decreased significantly. This is a natural conse-
quence of using IV. See appendix IV for a more thorough investigation of this aspect of the growth-openness
relationship.
47



Table A-V-I. Summary Statistics for the Main Variables
_________________________I Mean   Std. Dev.  Mean  Std. Dev.]
Summers Heston v.4.0
Initial GDP 1960 (thsds of US$)    0.739       0.366      1.79       1.753
Growth 1960-1980 (annual)         0.0187       0.018    0.020        0.019
Number of countries               64*         64*       113        113
Summers Heston v.5.6
Openness 1960-85 (share)           0.563       0.282    0.615        0.391
Initial GDP 1960 (thsds of US$)    0.747       0.364    1.800        1.755
Growth 1960-1980 (annual)         0.0187       0.018    0.020        0.018
Number of countries               63*         63*       112        112
(* All part of the AG 65 country sample)
Table A-V-II. OLS Estimates of the AG regressions
I AG dev.  countries  sample         Full  sample           l
[                IIL______ _   __IF -(I)  1  (2) _    _ _(_3)   _(4)  l (5)   _ _(6)_
Constant           0.00704       -0.014  -0.0096    0.022   0.0015   0.0021
(1.39)     (-1.29)   (-1.02)  (10.63)    (0.34)   (0.43)
Initial income        0.015       0.033    0.016   -0.001    0.0038  -0.0021
(2.47)      (2.63)    (1.40)  (-2.63)    (2.03)  (-1.37)
Openness                         0.041     0.045              0.028   0.0201
(2.28)    (2.80)            (4.13)   (3.83)
Openness*initial  _              -0.035    -0.038           -0.0044  -0.0034
income                         (-1.813)   (-2.42)            (-1.64)  (-1.69)
Primary school                             0.031                       0.033
enrollment                                (4.12)                       (5.57)
Latin America                             -0.016                      -0.019
(-3.06)                    (-5.50)
Sub-Saharan                      -        -0.015               -      -0.017
Africa                                    (-2.63)                     (-4.59)
R-Squared           .088        .165      .505      .049     .186      .57
Obs.                 65         64         63        115      113      111
(t-statistics in parentheses)
Data is from Penn World Tables v.4.0, except openness measure, from Penn World Tables v.5.6
The results of the replication exercise are satisfactory. Both in terms of orders of mag-
nitude and in terms of the signs of the coefficients, regressions (1)-(3) closely track those
reported in AG. In regression (1), initial income bears a positive and significant coefficient,
suggesting absolute divergence in our sample. In regression (2), the inclusion of openness
and the interaction term also confirms the results in AG: in more open countries, the size
of the internal market, proxied by per capita income, has a lower effect on subsequent
growth.32 Put differently, the effect of openness on growth is lower for countries with a
larger internal market. This strongly suggests that one channel through which openness
32The relevance of this approximation is debatable. After all, total GDP is arguably a better proxy for
48



may matter for growth is the market size channel. The rcsults do not seem sensitive to the
inclusion of regional dummies and the primary school enrollment rate. In all regressions,
openness in isolation of the interaction term has a strong positive impact on growth (a 10
percentage point increase in the trade to GDP ratio rises the growth rate by 0.4 percentage
points per year, which is in line with the results in note #3).
Much of this breaks down when we consider a larger sample of countries. The uncon-
ditional regression (4) displays evidence of very weak absolute convergence (the coefficient
is very small: a 1000 dollar difference in per capita initial income (1980 base) entails a
0.1 percentage point difference in growth rates across countries).33  Although openness
retains its strong positive influence on growth, the interaction term is now insignificant
and ten times smaller than for the restricted sample. In a sample that includes countries
that do not seem to display increasing returns, there are no substantial gains to having a
larger market in terms of growth. This, admittedly, may not be considered very useful: we
know that, in theory, size cannot matter unless there are increasing returns. However, this
result suggests that, although the extent of the market channel may operate for certain
countries (mainly the poorest ones), it is certainly not the only channel whereby openness
spurs growth. Indeed, with this extended sample, openness still has a positive impact on
growth despite that fact that the increasing returns/size of the market story breaks down.
A-V-IL. Growth Decomposition
We continue the replication of the AG results by considering a decomposition of growth
rates according to a factorial analysis akin to growth accounting. Specifically, we start
with a simple production function in which output per capita is a function of technology,
of per capita physical capital stock and of per capita human capital:
Yit= F(A,Kit,Hit) = AeOitK:Hi                                (1)
or:
logYit = logA + -IlogKit + alogHit + Oit                        (2)
where A grows at a constant rate Oi and the coefficients a and 3 are assumed to be time
and country invariant.
Taking first differences of (2) yields:
o Yit    Klog           +alog  Ht                               (3)
g Yit-=          Kit-l          Ht-l
This is just a growth regression in which the right hand side variables represent the change
in factor inputs for a given country over time, and the residual qi captures the contribution
the size of the market than per capita GDP. Countries with a relatively high per capita GDP may still
display a small internal market if they are sparsely populated. In this ease, the size of their internal market,
if they are closed to trade, is imposing a constraint on their growth rates, as indivisibilities prevent certain
investrnents from being profitable unless the market for the corresponding products becomes larger. See
Alesina, Spolaore and Wacziarg (1997) for a discussion of this issue.
33The fact that increasing returns seem to hold for poorest countries (as in the AG sample) but no longer
when richer economies are brought into the picture may suggest interesting paths for future research: Why
is it that initial income has a positive impact on growth for poorer countries but not for richer ones ? The
study of the endogenous change in market structure is largely absent from economics.
49



of technological progress to growth (akin to Total Factor Productivity). For each country,
we can use (3) to determine the respective contributions of physical capital accumulation,
human capital accumulation, and technological change, to the overall observed growth
rate.
To estimate the parameters of (3), we can either estimate (2) using a country-specific
fixed effects estimator, and then proceed with the appropriate algebraic manipulation to
obtain (3), or we can directly estimate equation (3) for two dates (1960 and 1985), as in
table A-V-III below.34 The two solutions should yield algebraically the samc estimates.
Table A-V-ILI. Estimates of the Parameters in Equation (3)35
|  OLS|
a (human capital)    -0.045
(-1.00)
,i ( physical capital)    0.614
(10.27)
Number of Obs.          43
R-squared               .55
(t-statistics in parentheses)
We then construct a measure of the extent of the market based on a weighted average
of initial GDP, openness and the interaction between the two. As in AG, the weights are
obtained by running the basic growth regression (Table A-V-IH) and using the respective
estimates as the weights in the construction of the extent variable. The growth in per
capita human capital, the growth in per capita capital stock and the estimated residual
from equation (3) are then regressed on a constant and the extent of the market variable
to determine the magnitude of each channel (Table A-V-IV).
Table A-V-IV - Extent of the Market and the Sources of Growth
Dep. var.:    Growth of per cap. 1 Growth of human 1 Residual from  1
|____________ |capital (1960-85)  | capital (1960-85) |  equation (3)
Constant                          0.177                  0.521               0.132
(0.45)                (0.91)              (0.28)
Extent of the                     1.208                  0.970               1.783
Market                            (1.67)                (0.86)               (2.06)
Number of Obs.             42                      42                   42
R-squared                  .06                   0.018                  .10
(t-statistics in parentheses)
The total effect of the extent of the market on growth, through each of the channels,
is given in Table A-V-V:
34AG prefer a third method. They stack the initial income data for 1960 and 1985, and run a regression
of this stacked initia.l income vector on time specific dummies (one for 1960 and one for 1985), the log of
human capital in 1960 and the log of capital stock per capita in 1960. It is not clear what connection there
is between this specification and the relationship derived from theory a.s above. Additionally, I ha.ve not
been able to reproduce their results using their specification.
35Data for the capital stock are from Dhareshwar and Nehru (1994), population data and the human
capital measure (percentage of the population having completed secondary schooling) are from Barro-Lee.
50



Table A-V-V- Channel Effects of the Extent of the Market
Channel                   [ Estimated effectl
Via Human Capital _                                      -0.044
Via Physical Capital                                     0.742
Via the unexplained residual (productivity gains)         1.783
Total Effect                                             2.481
The results broadly confirm the AG findings (their tablc 8b): 36 The most important
channel appears to be the unexplained increase in productivity, which accounts for two
thirds of the effect of the extent of the market on growth. Physical capital accumulation
accounts for the remaining third, while the growth in human capital accounts for virtu-
ally nothing (mainly because it does not affect the growth performance in this sample).
The effect of a larger market on growth is thus twofold: Firstly, the level of investment
in physical capital is raised by a larger market. This is in line with theories that stress
the importance of demand spillovers and backward linkages (Rosenstein-Rodan, Murphy-
Shleifer-Vishny). Secondly, market size works by increasing the speed of technological
progress, embodied in the residual from regression (3). Several hypotheses can be formu-
lated to explain this. By allowing a greater degree of division of labor, a larger market size
may allow a shift towards the production of goods that embody more technology (this goes
hand in hand with an expansion of the variety of goods). Secondly, the technology effect
may simply be capturing the fact that more open economies tend to be more exposed to
foreign technology. An accelerated transmission of technology may well be an important
channel whereby openness spurs growth. These results tend to lend support to this type
of explanation.
A-V-III. Conclusion.
This appendix has explored the relationship between the extent of the market and
growth, using the methodology in Ades and Glaeser (1994). In a sample of the poorest
developing countries, which exhibits increasing returns to scale (unconditional divergence),
openness and initial income have a positive impact on growth. The interaction between
the two has a negative effect on accumulation. The effect of these 'extent of the market'
variables works mainly through growth enhancing technological improvements and the
accumulation of physical capital. Possible interpretations of these results are the following:
(1). The size of the internal market is an important constraint on growth. By inte-
grating in the world economy, many poor and small countries are likely to be better able
to exploit dynamic increasing returns and grow faster. However, this channel is by far not
the only channel whereby openness improves growth.
(2). Access to larger markets works in two ways: it makes previously unprofitable
investments worth undertaking, thus solving a coordination problem within the economy.
Furthermore, it allows technological improvements to take place, either through direct
technological transmissions, or through a shift in the product mix towards goods that
embody more sophisticated technology.
36 The magnitudes of the estimates are not directly comparable due to differences in units between the
data in AG and the data used herein. However, the estimated contributions of the factors can be compared.
51



Figure I - Graphical View of the Channel Effects
0.1 
0.08 
-5  0.06-
2
0.0 
0.04 
0 -
-0.02 -
o         o
o~~~~~~
O Macro Policy Quality
*FDI
O Investment
0 Manufactured Exports
0 Public Consumption
0111 Distortions



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Lessons from Recent Financial                                                38526
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WPS1994 Estimating Wealth Effects without   Deon Filmer            October 1998        S. Fallon
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