*This paper is part of a research project undertaken by the OECD Development Center on. External Trade and Income Distribution. We thank two anonymous ...
European Economic Review 34 (1990) 1113-l 132. North-Holland
INCOME
DISTRIBUTION,
DEVELOPMENT TRADE
AND FOREIGN
A Cross-Sectional Analysis* F. BOURGUIGNON DELTA
(joinr research unit: CNRS.
ENS and EHESS),
75014 Paris, France
C. MORRISSON OECD Development
Center, Paris, France
Received April 1989, final version received December 1989 This paper analyses cross-sectional evidence on income inequality in developing countries within a consistent theoretical framework where the major explanatory variables are factor endowments, their ownership structure and foreign trade distortions. The resulting explanation of cross-country differences in income distribution is considerably better than what is found in the existing literature. Endowments in mineral resources, land concentration in agricultural exports, trade protection and secondary schooling are shown to be major determinants of differences in income inequality across developing countries.
1. Introduction Cross-sectional studies of the relationship between income inequality and the level of development along the line of Kuznets’ well-known ‘inverted-U curve’ hypothesis now have a long history. Numerous authors have tried to check that, indeed, inequality tends to be low in poor countries, increases in middle income countries and decreases again in richer countries. Crosssectional data seem to confirm that hypothesis. Yet, the evidence is far from conclusive. On one hand, there is a considerable variance around the inverted-U shape, even when other variables in addition to GDP per capita are included in the analysis, so that ordinary econometric estimates often lack robustness. On the other hand, even if this evidence were not disputed, there would be no reason to believe a priori that a cross-sectional statistical relationship should necessarily transform into a longitudinal relationship, *This paper is part of a research project undertaken by the OECD Development Center on External Trade and Income Distribution. We thank two anonymous referees for useful comments. 0014-2921/9O/.SO3.500 1SElsevier
Science Publishers B.V. (North-Holland)
1114
F. Bourguignon and C. Morrison,
Income distribution,
development
and foreign trade
implying some kind of ‘iron-law’, relevant for any country, at any period and under any circumstances.’ Although there certainly is much truth in the latter critique, the present paper is in the spirit of cross-sectional analyses. We also estimate regressions across countries with various indicators of inequality as the dependent variables and several national characteristics as right-hand side variables on a restricted sample of countries, and we also come up with ‘significant’ coefftcients. However, there are several important differences between the present study and the previous literature. First, unlike in previous studies, we start here from a rigorous theoretical framework which leads to a reduced form model where income distribution logically appears as a function of relative factor endowments, their ownership structure in the population, and possible distortions in national prices due to trade protection. The second originality lies precisely in the emphasis placed upon external trade factors, which have received little attention in the Kuznets curve literature despite their obvious relevance for income distribution. Third, we come up with surprisingly good results: the share of international differences in income inequality that we are able to explain in a sample of only developing countries is much larger than in any other comparable study we are aware of in the Kuznets curve tradition. Fourth, we find that, in accordance with the theory, trade variables play a prominent role in shaping cross-country differences in income distribution. Fifth, our estimates prove to be fairly robust. Of course, these results do not rule out the major critique to the crosssectional Kuznets curve literature, which is that all countries may enjoy a considerable freedom in shaping their income distribution through several direct redistribution policies and that any country may follow a distribution path in the course of its development process quite different from what comes out of a comparison with the present state of the distribution in presently more advanced countries. Yet, the empirical fact that approximately half the difference in income distribution across a sample of mediumsized developing countries may be explained by a restricted set of exogenous variables including those which define the openness of the economy seems by itself a quite interesting result. 2. The theoretical framework Consider
a small open economy
with n individuals,
m factors, and p
‘The hypothesis that an inverted ‘U-shaped’ curve is relevant in representing the secular trend in income distribution within and across countries has been put forward by Ahluwalia (1976). This conclusion, while initially finding a broad measure of acceptance [cf. Robinson (1976). Stewart (1978)] has recently been contested by Anand and Kanbur (1984). Saith (1983) and Ram (1988).
F. Bourguignon and C. Morrison,
Income distribution,
development and foreign trade
11 I5
sectors of production. Let cij be the quantity of factor j owned by individual i, F, VW..., L,,) the production function in sector k, where L, is the quantity of factor j used in that sector, Wj the remuneration rate of factor j and pk the price of output k. Assuming perfect competition in factors’ and products’ markets, the income distribution Y = (yi, y,, . . . , y.) is defined by the following set of equations:
Fcj(L~,,L~, ,...,
f
Lkj= i
k=l
L~,)=w,/plr,
aij=Ep
j=l,2
k=l,2,...,~,
,...,
j=l,2,...,m,
(2)
m,
1=1
where Fk,( -) is the marginal product of factor j in sector k and E, the total endowment in factor j. If it is assumed that all goods are tradable, the domestic prices pk are given by the foreign prices, pt, corrected by the effects of protection. Denoting by tk the tariff - or the implicit tariff in case of quantitative restrictions - on good k:
The equilibrium conditions (2)-(3) for factors’ markets define the factors’ rewards, wj, as a function of the vector of endowments, E=(E,), that of foreign prices, p* =(p:), and the vector of tariffs, t =(tk):
wj=g,(E; P*; t).
(4)
The distribution of income is then determined by eq. (l), which leads to the reduced form model: Y=h(E;
p*;
t; A),
(5)
where A is the matrix of individual shares in the various factor endowments:
(6) To complete the model, we note that the demand side determines the volume of trade. Denoting by c&i; p) the demand for domestic good k by the class
1116 F. Bourguignon and C. Morrison,
Income distribution, development and/ore@
trade
i of households and assuming, as in the above model that all incomes are distributed to households, net exports are given by:
xk=4k(PiQ- i ck (Yi; PI
(7)
i=l
where qk( *) is the domestic supply of good k as determined by the solution of eqs. (2) and (3). It thus appears that the volume of trade is endogenous with respect to the income distribution. Its reduced form depends on the same set of exogenous variables as Y in (5) plus the set of preferences parameters, P, behind the consumption functions C,( a).
x, = x(E;
p*; t; A;
P).
(8)
The preceding argument generalizes to the case of an economy with non-tradable goods. However, the equilibrium on factors’ markets now depends on the demand side of the economy, since prices are not exogenous for non-tradable goods. It follows that the reduced form for the income distribution also includes the set of preferences parameters, P. Y = h’(E; p*; t; A; P).
(9)
Note, though, that, in a cross-sectional framework, differences in P are likely to be unimportant or highly correlated with other variables in the preceding expression, the relative factor endowments - e.g., capital per capita - in particular.* This simple conventional model for a small economy has several implications for a cross-sectional analysis of the relationship between income distribution and development. First, at a given point of time, the vector of world prices, p*, may be assumed to be the same for all (small) countries, so that, in the pure tradable goods case [eq. (5)], cross-sectional differences in income distribution may be assumed to depend essentially on differences in: (i) factor endowments (E), (ii) the distribution of factors’ ownership (A), (iii) the structure of protection (t). Second, we remark that the relationship between income distribution and foreign trade is captured through the structure of protection and not the volume - and the structure - of foreign trade, which logically is an *In theory all distortionary policy instruments - indirect taxes, in particular - should also be present among the arguments in (9). Given the dilliculty of observing cross-country differences in those instruments, we shall ignore them in what follows.
F. Bourguignon and C. Morrisson, Income
distribution,
development
and
foreign
trade
1Ill
endogenous variable. Regressing distributional characteristics on the export/ GDP ratio, for instance, would lead to simultaneity biases.3 Third, it must be clear that the function h( *) - or h’(a) in (9) - may be fairly complex because specialization is likely to result from the solution of (2) and (3) in most developing countries. So, h( *) may differ across countries with distinct factor endowments. As it clearly is impossible to account for all specific factors which determine the productive structure of an economy, any attempt to quantify the relationship h(a) necessarily is an extremely crude exercise. The estimation work that follows must be considered as such an attempt at testing the relevance of the general theoretical relationship between income distribution, factor endowments, the distribution of factors’ ownership, and trade protection. Because of the obvious empirical limitations of that exercise, we focus here on the simple case of pure tradable goods, thus, disregarding the incidence of state interventions in the domestic economy other than those linked to import and export taxes (or subsidies). 3. The empirical framework As mentioned above, the difficulty in building an empirical cross-sectional test of the preceding model lies in the multiplicity of productive factors which may determine the actual structure of an economy. Our empirical work is based upon the following factors: (1) labor (unskilled)
(2) (3) (4) (5) (6)
skilled labor (human capital) capital export-specific land non-export-specific land mineral resources
(N), (U (0 (T,), (W,
(W.
By combining these factors in varying proportions, five fundamental types of goods or services may be obtained, which may satisfactorily represent the major structural differences across developing countries. They are the following: primary agricultural and mineral exports, domestic food crops, manufactured goods and services. On the other hand, as the theoretical mode1 above refers to the case of open economies, we have restricted the sample to small or medium-sized economies in which the influence of foreign prices is likely to be predominant, or at least, stronger than in the few large developing economies like India or Brazil. Given the above list of productive factors, the problem now is to quantify ‘Note that the same problem arises in empirical studies which seek to explain GDP growth rates as a function of variables like export growth [see Heller and Porter (1978), Michaely (1977, 1979)].
1I18
F. Bourguignon and C. Morrisson, Income distribution, dwelopnum
and foreign
trade
them and to get some information about their distribution in the population. Several crude approximations proved necessary. First, note that under the plausible assumption of constant returns to scale in all sectors, the function h( *) in (4) must be homogeneous of degree zero with respect to factor endowments. So, only relative factor endowments, that is four variables, are to be considered. Concerning the first three factors in the above list, no specific problem should arise a priori. In a cross-section, the capital-labor ratio (K/N) may reasonably be approximated by GDP per capita. We shall see, however, that this variable proved correlated with other ones in the model and was ultimately dropped.4 The relative ‘humancapital’ endowment may be measured by the share of workers with schooling above some minimum level, - say primary school - in the labor force, or, equivalently, the secondary schooling rate (S) lagged a suficient number of years.’ Things are more delicate for the ratio of export-specific factors and land to other factors since no direct measure of the former is available. Approximating the relative endowments T,/Kand R/K by the shares of the corresponding exports in GDP is clearly incorrect since, as shown above, exports are endogenous variables at equilibrium. However, it is true that a ratio of those factor-specific exports over GDP, or a share in total exports, significantly different from zero reveals the presence of the corresponding factors. We, thus, replaced the relative export-specific factor endowment by two dummy variables, D,, and DR, equal to unity if, respectively, agricultural and mineral exports accounted for more than 5% of GDP and zero otherwise. Practically, these dummy variables would have been the same if they had been defined by a minimum share (3040%) of agricultural and mineral primary commodities in total exports. The exogeneity of these variables, which practically are equivalent to acknowledge that, e.g., Venezuela is primarily endowed with oil resources whereas Ivory Coast is primarily endowed with land and climate for coffee and cocoa, leaves little doubt. Concerning non-export-specific land, finally, we assumed that there was no significant difference, on a per capita basis, across the countries in our sample. This certainly is a simplification. Ideally, data on cultivable land per capita, corrected by the dummy variable D,,should have been used.6 We now consider the ownership structure of the preceding factors. By definition, the distribution of unskilled labor in the active population is egalitarian. The inequality of the distribution of human capital may be ‘The same phenomenon occurred with other proxies of capital like energy consumption or installed horse-power. 5Practically, the average schooling rate on the 30-year previous period was used, so that S is a good proxy for the share of individuals with secondary education in the total active population. 6Even such a variable would not be satisfactory because of the necessity to correct land by some quality index.
F. Bourguignon and C. Morrisson, Income distribution, development and foreign trade
I1 19
approximated by the variance of the dichotomic variable indicating the level of schooling of individuals in the labor-force. If S is the mean of that variable - i.e., the share of workers with secondary education or more - its variance is V,=S(l-S). In all countries, physical capital as well as mineral resources are extremely concentrated, so that it would make little sense trying to control crosssectional differences in income distribution by differences in the concentration of these factors. It is true that mineral or oil resources are publicly owned in a number of countries and also that the share of state-owned companies in the domestically-oriented modern sector of the economy differs widely across countries. The State may redistribute the corresponding income in a more or less egalitarian way, through public services and public investments for the whole population, but it may also favor a small class of top civil-servants and executives in publicly-owned companies, thus, contributing directly to income inequality. Without information about public choices in this matter, we simply ignored public ownership in our analysis although it may sometimes be a source of differences in income distribution across countries. Data on the concentration of cultivable land are available in most countries. The problem we face here is that, in general, those data are not disaggregated by types of crops, so that it is diffcult to distinguish the concentration of export-specific land and that devoted to domestic food crops. In what follows, three variables are used. The first one (V,) is the share of small- and medium-sized farms in the production of primary agricultural exports. The second one ( VD) is the share of small- and medium-sized farms in the production for domestic markets. The variable V, is the same share defined on the total agricultural production. It thus combines both the ownership structure of export-specific land (V,) and that of the land devoted to domestic food crops (V,).’ Note that it may not be unreasonable to assume that cross-country differences in V, are largely due to differences in v,. Concerning the vector of tariffs, t, we only considered manufactured goods. This choice is justified by the (limited) availability of data on effective protection for these goods and, again, by the fact that cross-national differences in protection are likely to concern mostly the manufactured sector. However, it must be kept in mind that, even though it might be weaker on average, effective protection on domestic foodstuff may also have powerful effects upon income distribution. In any case, we have been able to find data on effective protection in manufacturing for only 20 countries out of the 35 countries in our sample. a Another natural proxy for protection ‘Detail on how these variables were measured, or proxied is given in the appendix. sAs a matter of fact, the sample comprises 36 countries because of the special case of Togo (see the data appendix).
1120
F. Bourguignon and Cc-Morrison.
Income distribution, development andforeign
trade
which could have permitted to cover the whole sample is the foreign share of the domestic market for manufactured products. As discussed above, however, this variable, which has been used in several empirical studies about the effects of protection by B. Balassa, cannot be considered as truly exogenous, especially in relation with income distribution.g A more detailed description of the data used in this paper is given in the appendix. It is clear that most of them approximate in a crude way the variables entering the simple theoretical model discussed in the previous section. This makes the significant results reported in the next section all the more surprising and would justify, a posteriori, that more work should be done to improve the data base. The results that follow are based upon a sample of 36 small- and mediumsized developing economies. Excluding developed countries, avoids identifying significant income distribution determinants solely on the basis of the difference between developed and developing countries, certainly a debatable method - see on that point the convincing critiques by Anand and Kanbur (1984) and Saith (1983) of traditional econometric tests of Kuznets’ hypothesis. Income distribution data have been made homogeneous, as much as possible, across countries. The income recipient unit is the active individual. This choice is consistent with the theoretical justification of the models that are estimated. Redistribution in particular is ignored. Though, note that, in a cross-section which only comprises developing countries, differences in the distribution of income per active individual bear very much resemblance to the distribution of income or expenditures per family. For all countries in the sample the same consistency checks on available distributional data have been performed, leading to satisfactory results (see appendix).
4. Regression results The reduced form model presented in the theoretical section above has been estimated under a simple linear specification on the basis of the preceding exogenous variables. Various dependent variables have been used to describe the distribution of income - i.e., the share of bottom 40 and 60% of the active population, the share of the top 20% and the Gini coefficient but they all point to the same results. Of course, the linearity assumption is again a crude approximation of what should be the reiationship between income inequality, factor endowments, factors’ concentration and foreign trade. In the tradition of the Kuznets’ curve, the square of GDP per capita has been introduced as an additional regressor, but other explanatory 9Experiments with this variable reported in table 2.
have
not led to results
signilicantly
different
from those
F. Bourguignon am%.
Morrisson, Income distribution, development and foreign trade
1121
variables enter the various regressions linearly and independently. In theory, some transformation should have been performed upon the dependent variables since they all belong to the (0,l) interval and are inconsistent with the usual assumption behind OLS [see Anand and Kanbur (1984)]. However, their range of variation across the sample is quite limited so that this should not be a practical problem (see the data in the appendix). The regressions reported on table 1 refer to the complete sample of 35 countries, including some ‘semi-developed’ countries like Argentina or Spain in 1970. The protection variable is absent from these regressions because, as mentioned above, it is available only for a sub-sample of 20 countries. Note also that the variable V,, describing the concentration of human capital - i.e., schooling - has also been omitted because it proved strongly collinear with the schooling variable S. This is natural for a sample of countries where the level of secondary schooling is almost always below 50%. In that range V, =S( 1 -S) clearly is a monotonically increasing function of S. The first regression in table 1 - for each of the three income shares used to summarize the income distribution - corresponds to the usual Kuznets curve formulation. Inequality appears as a quadratic function of GDP per capita, which in the present framework is taken to represent the capital-labor endowment ratio. The explanatory power of that equation is very low and the coefficients of the independent variables seldom are significant. There is nothing surprising here. Several authors have already pointed out that Kuznets-curve estimates were hardly significant when developed countries were left out of the data sample [see Ram (1988)]. Following our theoretical argument, we now introduce the other endowment variables, S, D, and D, and ownership distribution variables. As may be seen from regressions (3) in table 1, these additional variables considerably modify the picture. First, the explanatory power of the model increases quite substantially as may be seen from the adjusted R2-statistic. It is close to 60% for the share of the bottom 60% and the share of the top 20x, an order of magnitude seldom reached in such cross-sectional analyses based on developing countries only - the comparison between regression (2) and (3) show that, although very significant, the schooling variables S is not the only cause for this rise in R2.io Second, the coefficients of the GDP per capita variables are substantially reduced and lose all significance, whereas the coefficients of the dummy variable DR is sizeable and strongly significant. At first sight, the preceding results seem to suggest some correlation between GDP per capita and the various explanatory variables subsequently introduced in the model. It may be checked that the main single variable “‘For a detailed analysis of the incidence of education on income distribution, see Alhuwalia (1976). where it was shown that the secondary schooling rate were significantly increasing the income-shares of the three intermediate quintiles in the income distribution, at the expense of the top quintile.
1122
F. Bourguignon
and C.+forrisson,
Income distribution,
1. .
1
* ’
3
0 I
development
and foreign
trade
T-statistics
(5)
in parentheses.
z, 60 (14)
62 (9)
(3)
(4)
58
55
(2)
Income share of the top 2Oy< (1)
0.009 (0.8) 0.015 (1.9) 0.005 (0.6)
o.5x1o-4 (1) -0.5x 1o-4 (1.4) -2.5 x lO-6 (0.7) -0.34 (4.8) -0.24 (3.8) -0.24 (4.8) _
-
-0.05 (1.2) -0.05 (1.5) _
-
-
-0.02 (0.5)
-
-
-
_
6.3 (3.4)
c&j (E,
_
‘E’ (1:4) (P:,
2.9
_
- 0.05 (1.2)
-
-
_
0.66
0.65
0.66
0.46
0.06
0.55
0.56
0.53
0.42
0.05
1124
F. Bourguignon
and C. Morrison,
Income distribution, development
andfireign
trade
responsible for the drop in the coefhcients of the GDP variables is the dummy Da which indicates the presence of mineral resources. The correlation between that variable and GDP per capita is only 0.36, a figure that permits to rule out that the estimates in regression (3) are plagued with colinearity biases. We note, however, that excluding GDP variables - regressions (4) maintains the R*-statistic at its previous level and makes practically all variables significant. The conclusion these results point to is quite interesting. In our sample of 35 medium- and small-sized developing countries, the reason why GDP per capita, and its square, appear to be a loosely significant determinant of income inequality, when no other variable is entered into the analysis, is partly due to the fact that GDP per capita is significantly correlated with the presence of exportable mineral resources. Our results suggest that it is that variable, rather than GDP, or the variables it is supposed to approximate, that is an important determinant of the income distribution. By itself, a sizeable share of mineral exports in GDP implies on average a loss of 4 to 6 percentage points in the share of the bottom 40 or 60% of the population, and an equivalent gain for the top 20%. This is a major influence which has certainly very little to do with the traditional hypothesis behind the Kuznets curve. The reasons for such an influence of mineral exports upon the income distribution have been discussed above. Of course, natural resources of this type have always been concentrated in very few hands in the past history of those countries where exports of mineral commodities played an import role. Nowadays, however, those resources are most often publicly appropriated this is the case for the 9 countries in our sample (see the data in the appendix) - so that one would not expect such a sizeable effect upon income inequality as that put into evidence by our regressions. A partial explanation is that the distribution of income is in fact changing very slowly over time because inequalities are in some sense bequested from a generation to another. It thus, is possible that the strong dependence of inequality upon the presence of mineral exports today still reflects the distributional consequences of those activities at the time they were privately appropriated. The effect could be quite different in a country where some natural resources would have just been discovered and would have been managed since the beginning by the public sector, although Algeria is a clear counter-example of that hypothesis. Indeed, that example shows that, even though nationalized, the mineral export sector may have two direct negative effects on income equality: the extremely high wages and salaries observed in that sector, and the high salaries - and in-kind payments - of top civil-servants financed by the oil rent. Agricultural exports also prove to have a negative influence on the distribution of income. Other things being the same, regressions (3) or (4)
F. Bourguignon and C. Morrisson, Income distribution, development and foreign trade
1125
show that being endowed with a comparative advantage in primary agricultural commodities means for a country that the bottom 40 or 60% of its population lose 2 percentage points of total income. However, the ‘ceteris paribus’ condition may be quite misleading here, since a comparative advantage in agriculture cannot be considered separately from the land ownership structure of the country. So, part of the effect of the V, variable in regressions (3) is to be associated with the presence of agricultural exports. In regression (5), we distinguish the concentration of agricultural production oriented towards the local market (Vn) from that of the production that is exported (V,). The results show that, as could be expected, agricultural exports have a negative effect upon income equality only inasmuch as they are strongly concentrated among producers. If all exports were originating in small- and medium-sized farms, then it may be seen in table 1 that the income share of the bottom 60% would drop only by a little more than 1 percentage point on average, whereas the drop would be close to 4 to 5 percentage points, if exported crops were only produced in large farms. Note, though, that the coefhcient of the product DaVx is not significant, so that there is some imprecision in the preceding calculations. Even so, however, it remains the case that the presence of agricultural exports concentrated in large farms undoubtedly increases the level of inequality. A general problem with the kind of cross-sectional analysis used in the present paper is that some apparently significant results may be due to the presence of a few atypical countries in the data sample. We have tested the robustness of the preceding results by taking out the most obvious outliers in the sample and repeating the regressions. All the results remained significant. Yet, it must be stressed that the five most egalitarian countries in the sample are at the same time countries for which both dummy variables, D, and D,, are nil, that is countries with no sizeable share of agricultural or mineral exports. These countries are Hong Kong, South Korea, Spain, Taiwan, and Yugoslavia. Indeed, they may be seen as atypical; South-East Asian countries have gone through a successful industrialization process, Spain may be considered closer to the developed than the developing world and income distribution in Yugoslavia is certainly affected - although less than in other East European countries - by the socialist organization of society. This is true, but, in the absence of continuous exogenous variable that might replace the dummy variables D, and DA, not much can be done about it. Note, on the other hand, that all data refer to 1970, that is a time at which today’s South-Eastern Asian NIC’s were just starting their accelerated development process. As may be seen from the data in the appendix, GDP per capita in Korea was then below what could be observed in Zambia or Colombia, and Taiwan was below Chile or Gabon. Likewise, GDP per capita in Spain was below that in Argentina, Chile and Uruguay. So, these five countries, which are largely responsible for the results we found, were not all, in 1970, at the
1126 F. Bourguignon and C. Morrisson, Income distribution, development and foreign trade Table 2 Regression results on the sample of 20 countries. Independent variables Dependent variable Income share of the bottom WA (1)
R2
Cst
s
Vr
D,
DA
14 (3.4) 12 (3.1)
0.005 (0.1) 0.03 (0.8)
0.015 (0.5) 0.03 (1.3)
-4.3 (2.9) -3.1 (2.2)
-3.2 (2) -2 (1.3)
23 (3.9) 21 (3.6)
0.07 (1.1) 0.11 (1.8)
0.04 (1) 0.07 (1.6)
-6.1 (2.7) -4.5 (2)
-4
60 (8.4) (2) 63 (9) T-statistics in parentheses.
-0.12 (1.6) -0.16 (2)
- 0.08 (1.6) -0.11 (2.1)
(2)
Income share of the bottom W/. (1) (2)
Income share of the top 20”/, (1)
_w;) (1)
(E)
(E)
(C2)
(:;
P
R*
adjusted
-
0.53
0.40
-2.5 (2.2)
0.65
0.46
-
0.58
0.44
-3.3 (2)
0.67
0.47
0.64
0.48
0.69
0.49
3.2 (1.5)
upper end of the development scale in our sample. They cannot be dropped from it only because they already had in 1970 a fairly developed manufacturing export sector. It may also be stressed that a large part of the difference between these countries and the rest of the sample is already picked up in the schooling variable. We now consider the role of the protection variable in shaping the income distribution in a country. As mentioned above, this variable has been collected for a sub-sample of only 20 countries and, as some of the previous variables, it has the inconvenience of being defined in a dichotomic way. The variable P in table 2 takes the value 1 for all countries where effective protection in the manufacturing sector exceeds 30% and 0 in the other cases (see data appendix). We first note from regressions (1) in table 2 that restricting the sample has not modified the results obtained with the more complete sample in table 1. As they were not significant, the GDP variables have been dropped. Although they are not as significant as before - probably because of fewer observations - the coefficient of the remaining variables are comparable to those shown in table 1, except for the coefficient of the schooling variable which has fallen - this may be explained by the fact that some of the
F. Bourguignon an&C. Morrisson, Income distribution, development andjoreign trade
1127
countries with the lowest level of schooling have been dropped from the sample. This conformity illustrates the robustness of the first set of results. The introduction of the protection variable, P, does not modify the results obtained previously either. The coefficient of the protection variable, itself, is significant, and the overall explanatory power is greatly enhanced, except for the income-share of the top 20%. For the other two dependent variables used in the analysis, the R2 statistic (unadjusted) is above 65x, a record in this type of study. According to the estimated coefficients, trade protection has a large potential for worsening the income distribution. The income-share of the bottom 40 or 60% of the population is on average 3% lower in a highly protected economy, whereas the gain is equivalent - but not statistically significant - for the top 20%. Of course, this is in accordance with standard theory. Protection lowers the reward to the relatively most abundant factors - presumably the most equally distributed - and raises that of the relatively scarce factors - presumably the most concentrated. As a matter of fact, protection is likely to modify the whole price system, so that one would expect that the introduction of that variable modifies the contribution to inequality of the phenomena that have been previously identified. Although barely significant, something of that sort is noticeable in table 2. Both the coefficients of schooling and of the land distribution are larger - in absolute value - whereas those of the export dummies DR and D, are smaller. Robustness tests are less easy to perform on this reduced sample because degrees of freedom are limited. Note, though, that it now is difficult to believe that the effect of the protection variable is again identified only through the inclusion of South-Eastern Asian countries and Spain in the sample. Other countries have relatively low levels of protection - e.g., Ivory Coast, Morocco, Argentina, Malaysia. In the present case, it is also likely that the use of a dummy variable is an advantage. Using a continuous index of trade protection might again have reinforced the role of East-Asian countries since they would probably have all rank at the bottom of the protection scale. 5. Summary
and conclusion
The present paper has shown that, within a consistent theoretical framework, a cross-sectional analysis among small- and medium-sized developing countries could explain quite a significant share of differences in income distribution, despite the very approximate nature of the data that could be gathered. This is in sharp contrast with the previous literature on this subject, and in particular all the attempts at checking Kuznets’ hypothesis. The results obtained in this paper point to a significant and large effect of comparative advantages and the foreign trade structure upon income inequality. Developing countries which are comparatively well endowed with
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F. Bourguignon and C. Moirisson, Income distribution, development and foreign trade
mineral resources and land (or climate) tend to be less ega!itarian than others, although the effect of the agricultural comparative advantage may be offset by the distribution of land. On the other hand, trade protection has also been shown to be a major determinant of income distribution. These results do not have the same policy implications. In a cross-section the inequalizing effect of mineral, and, in some cases, agricultural resources, may simply reflect some historical heritage which can hardly be controlled by policy. This is not the case of trade protection which is unequalizing and of schooling which has the opposite effect. Note, finally, that the results obtained in this paper do not totally rule out the U-curve hypothesis. When the original control variables introduced in our own analysis are removed, something like a U-curve appears to survive, even though the estimated coefficients of GDP per capita variables are barely significant - see regressions 1 in table 1. Our point is that this relationship is in fact an extremely indirect one which appears only through weak correlations between GDP per capita and more fundamental explanatory variables of income distribution. Appendix I Data on income distribution by economically active persons (columns 1, 2 and 3).
When they were available, we took the estimations referring to the distribution by active persons. Unfortunately in most countries only estimations on income distribution by individuals or by households exist. These have been corrected in the following way. First, we assumed that the income distribution by households and by individuals were similar. [See J. Lecaillon et al. (1984, Ch. 2)]. Second, we corrected household distribution data into active persons distribution data using the average difference we observed in a sample of 10 countries where both distributions were available (Argentina, Brazil, Colombia, Korea, India, Senegal, Sri Lanka, Tanzania, Venezuela, Zimbabwe). The sources for distribution data are: (a) Algeria, Morocco: estimation from J.B. Bellon’s thesis (1982). (b) Egypt, Sierra Leone, Spain, Sudan from W. Van Ginneken and J. Park (1984). (c) Yugoslavia from C. Morrisson (1984). (d) Other countries from J. Lecaillon et al. (1983). Data on per capita GDP (column 4)
We refer to the year 1970 because the estimations on income distribution
F. Bourguignon
and C. Morrison,
Income
distribution,
development
and
foreign
trade
1129
Table A.1 Statistical data. 1
2
3
4
6
7
8
9
10
11
Algeria Congo Egypt Gabon Ivory Coast Kenya
10.0 15.0 14.4 8.8 10.1 10.0
20.5 28.0 29.2 16.7 21.9 18.5
58.5 56.0 50.1 67.5 57.2 68.0
749 100 540 50 503 90 1,148 IO 601 80 343 40
5
100 100 loo 100 100 loo
100 69 98 49 84 85
1 0 0 1 0 0
0
I 1 1 1 1
11 19 32 15 11 9
1 0 1
Malawi Morocco Senegal Sierra Leone South Africa
13.9 10.0 13.0 13.2 4.2
26.2 19.5 22.4 25.9 14.3
55.2 63.0 64.4 54.5 60.0
I84 70 543 30 448 100 406 100 1,296 10
100 33 100 IO0 33
93 33 100 100 18
0 0 0 0 1
I I 1 0 1
2 13 10 9 I8
Sudan Tanzania Togo Zambia Zimbabwe
11.0 13.0 16.0 7.3 8.0
26.0 24.0 31.0 15.8 16.3
51.5 61.0 46.0 68.7 68.0
304 75 243 70 327 100 833 100 482 10
loo 100 100 100 75
93 92 100 100 50
0 0 0 1 0
1 I I 0 1
7 3 7 12 7
Argentina Chile Colombia Costa Rica Honduras
13.5 10.0 8.9 12.5 6.4
26.6 23.0 19.1 24.5 15.6
55.1 59.5 62.6 57.1 64.9
2,006 1,715 788 1,062 571
20 50 50 70 30
33 75 75 100 33
28 73 69 82 32
0 1 0 0 0
1 0 1 1 I
37 39 23 28 12
Panama Peru El Salvador Uruguay Venezuela
8.2 8.0 12.0 10.5 9.5
20.5 18.2 20.8 24.8 20.5
60.6 64.4 61.4 52.5 59.5
1,305 1,067 703 1,751 1,750
20 10 IO 30 50
33 75 33 33 33
28 57 22 32 34
1 1 0 0 I
1 I 1 I 0
40 30 22 57 37
Rep. of Korea Hong Kong Iran Malaysia Philippines
16.0 13.6 7.9 9.0 9.4
30.5 27.0 18.1 20.9 22.1
48.0 53.0 64.8 59.8 56.7
648 100 1,308 100 817 50 754 50 446 60
100 100 100 100 IOU
100 100 98 69 89
0 0 1 I 0
0 0 0 1 1
43 41 26 34 50
0 1
Sri Lanka Taiwan Thailand Spain Yugoslavia
13.5 20.0 8.7 15.3 15.3
28.5 36.3 20.5 31.1 31.8
48.0 41.4 58.9 46.0 43.7
260 70 910 100 452 90 1,671 70 1,404 100
100 loo 100 75 100
89 100 98 75 100
0 0 0 0 0
1 0 1 0 0
51 47 18 56 46
0 1 0 1
0
0
0
0 1 1 1
1 1 0 0
Note: Reference year is 1970. The sample of 35 countries includes all countries except Togo, and the sample of 20 countries includes countries where P is available. 1. Income share of poorest 40%. 2. Income share of poorest WA. 3. Income share of richest 20%. 4. GDP/capita. 5. Vx: share of small and medium-sized farms in export crops. 6. Vo: share of small and medium-sized farms in food crops. 7. VT: share of small and medium-sized farms in agricultural output. 8. D,: mineral exports/GDP (dummy= 1 if mineral exports/GDP=O.OS). 9. D,: agricultural exports/GDP (dummy= 1 if agricultural exports/GDP=0.05). IO. 5: rate of secondary school enrolment in 1960. 11. P: dummy= I in countries where the mean rate of effective protection exceeds 30%.
1130
F. Bourguignon
and C. Morrison,
Income distribution,
development
and foreign
trade
refer, in average, to that date. Figures reported are per capita GDP in U.S. dollars corrected on the basis of purchasing power parity using I. Kravis’s methods. All these figures come from the series used in A. Berry, F. Bourguignon and C. Morrisson (1983) in order to estimate the world distribution of income.
Data on education (column 10)
Rate of secondary school enrolment in 1960 from World Tables (1980).
Data on foreign trade (columns 8 and 9)
Source: United Nations Statistical Office (Geneva). They refer to the year 1970 (except for Congo and for Zimbabwe where the year chosen is 1963 and 1964).
Data on Agrarian structure (columns 5, 6 and 7)
No foreign trade statistics are available which break down exports of agricultural and forestry products according to the size of the enterprises concerned. In the case of timber exports (which account for only an insignificant share in most of the countries) it was assumed that these originated in large operations (based on concessions rather than ownership), as this is a capital-intensive industry conducted by modern corporations with access to substantial capital even if the number of their employees is small, except for the Philippines where small enterprises play an important role and where statistics on their output are available. For exports of agricultural and food products, the investigation was conducted country by country on the basis of exports data broken down by product. For each product, the available information was used to assess the share in output accounted for by small- and medium-size operations. In some countries the distinction is easily made. In Panama bananas and sugar (83% of agricultural exports) are produced almost exclusively by large plantations covering several thousand hectares. In Zimbabwe, agricultural exports in 1970 originated essentially in large operations by European farmers. Against this, the drastic Agrarian reforms which have eliminated all large-scale operations in Korea and Taiwan indicate that all their, very modest, agricultural exports can be ascribed to small- and medium-sized operations. While it is true that in some countries the share of small- and medium-sized operations is less easy to assess, an analysis of the sensitivity of
F. Bourguignon and E. Morrison,
Income distribution, development and foreign :rade
1131
the results against the margins of error related to V, shows that these remain practically unaffected. The estimation of the share of small- and medium-sized operations in food crops V,, is more difficult: because the data are not precise enough, we divided the countries into three groups according to the value of that share, 33, 75 or 100%. These figures indicate merely orders of magnitude. The measure of V, based on the estimations of V, and I/b is obtained by aggregating these two figures, each figure being weighted by the respective shares in agricultural output of food crops and export crops. The advantage of this method is that the agrarian structures in these two sectors are weighted according to output value instead of taking a general view of these structures without regard to the disparity between the two sectors in output value per hectare.
Data on protectionism
(column II)
Estimating the effective level of protection is also diflkult. Even for the countries studied, we do not always have a mean rate (either a simple mean or a mean value weighted by the value of exports) but only estimated rates for ten or twenty products. Whenever possible, we chose the effective protection rate and not the nominal protection rate. When necessary we also used a rate corrected for the overestimation of exchange rate. Customs tariffs are not the only barrier to trade. Depending on the country, quotas, import licenses and embargoes on certain products supplement the tariff system. Because of these other measures, the mean protection rate is not always significant, and, by implementing such measures, one country may in fact operate a more protectionist policy than another even though its mean rate is lower. In reality, a threshold effect operates here, as these arrangements only appear if the mean rate exceeds a minimum value. It therefore seemed preferable to adopt a method compatible with this situation involving the use of a dummy variable, P, having a value of 1 for all countries where the mean rate exceeds 30% and 0 for the rest. Most of the information on protection rates is taken from B. Balassa (1971), B. Balassa (1982), J. Donges (1976) and A. Krueger (198 1).
References Ahluwalia, M.. 1976, Inequality, poverty and development, Journal of Development Economics, 307-342. Anand, J. and S. Kanbur, 1984, Inequality and development: A reconsideration, in: H.P. Nissen, cd., Towards income distribution policies (EADI). Balassa, B. et al., 1971, The structure of protection in developing countries (John Hopkins Press, Baltimore).
1132 F. Bourguignon
and G Morrisson, Income distribution, development and foreign
trade
Balassa, B. and associates, 1982, Development strategies in semi-industrial economies (John Hopkins Press, Baltimore). Bellon, J., 1982, La repartition des revenus en Algerie et au Maroc. These 3tme cycle, Universite de Paris I. Berry, A., F. Bourguignon and C. Morrisson, 1983, The world distribution of income between 1950 and 1977, The Economic Journal, 331-350. Donges, J., 1976, A comparative survey of industrialization policies in fifteen semi-industrial countries, Weltwirtschaftliches Archiv, 627659. Heller. P. and R. Porter, 1978, Exports and growth: An empirical reinvestigation, Journal of Development Economics, 191-193. Krueger, A., H. Lary, T. Monson and N. Akrasanee, 1981, Trade and employment in developing _ _ countries, 1 (University of Chicago Press, Chicago). Kuznets. S., 1955. Economic growth and income ineaualitv. American Economic Review. l-28. Lecaillon, J.. F.. Paukert, C. Morrisson and D. ‘Germidis, 1984, Income distribution and economic development (ILO, Geneva). Michaely, M., 1977, Exports and growth: An empirical investigation, Journal of Development Economics, 49-53. Michaely, M., 1979, Exports and growth: A reply, Journal of Development Economics, 141-143. Morrisson, C., 1984, Income distribution in east European and western countries, Journal of Comparative Economics, 121-138. Ram, R., 1988, Economic development and income inequality: Further evidence on the U-curve hypothesis, World Development, 1371-1376. Robinson, S., 1976, A note on the U-hypothesis relating income inequality and economic development, American Economic Review, 437-440. Saith. A., 1983, Development and distribution, a critique of the cross-country U-hypothesis, __ _ Journal of Development Economics, 367-382. Stewart. F.. 1978. Ineaualitv. technolonv and oavment svstems. World Develooment. 275-293. Stolper,. Wl and ‘P. Samueison, 1941,~Pro&ion and real wages, Review of’Economic Studies, 58-73. Van Ginneken, W. and J. Park, 1984, Generating internationally comparable income distribution estimates, ILO, Geneva. World Tables, Second edition 1980, Third edition 1984.
