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Problemas del desarrollo

Print version ISSN 0301-7036

Prob. Des vol.53 n.209 Ciudad de México Apr./Jun. 2022  Epub Sep 19, 2022

https://doi.org/10.22201/iiec.20078951e.2022.209.69819 

Articles

Exports versus capital accumulation in Argentina, Brazil, Chile, and Mexico

Juan Alberto Vázquez Muñoza 

Josué Zavaleta Gonzáleza 

aBenemérita Universidad Autónoma de Puebla, Mexico. Email addresses: juan.vazquez@correo.buap.mx and josue.zavaleta@correo.buap.mx, respectively.


Abstract

This article analyzes the relative importance of exports and capital accumulation for growth rates in Argentina, Chile, and Mexico for the period 1961-2019, and Brazil for the period 1971-2019. This article finds evidence that capital accumulation has a significant positive effect on the growth rate, with exports also having a significant, although lesser, impact. Likewise, the effect of exports on reducing the external constraint to growth is insignificant compared to that of capital accumulation. The article concludes by suggesting that growth strategies should privilege capital accumulation, rather than exports.

Key Words: capital accumulation; exports; economic growth; trade balance; panel data

Resumen

En el presente artículo se realiza un análisis de la importancia relativa de las exportaciones y la acumulación de capital en el comportamiento de la tasa de crecimiento de Argentina, Chile y México para el periodo 1961-2019, y de Brasil para el periodo 1971-2019. Se ofrece evidencia de que la acumulación de capital tiene un efecto positivo y significativo en la tasa de crecimiento, mientras que el correspondiente a las exportaciones es significativo, aunque de baja magnitud. Asimismo, las exportaciones reducen la restricción externa al crecimiento en una medida poco relevante en comparación con la acumulación de capital. Se postula entonces la necesidad de cambiar el modelo de crecimiento exportador por uno centrado en la acumulación de capital.

Palabras clave: acumulación de capital; exportaciones; crecimiento económico; balanza comercial; panel de datos

Clasificación JEL: E22; O40; F43; C01; O54

1. INTRODUCTION

The Covid-19 pandemic caused the current economic crisis which in turn has slowed down most of the world economies’ Gross Domestic Product (GDP) growth rates. This rekindled the debate on whether strategies can positively contribute to reviving economic activity in the short and long term, as well as stimulate processes of sustained economic growth.

Currently, within the main Latin American economies, an export-led growth strategy dominates (see Perrotini et al. 2008; Kristjanpoller and Olson, 2014).1 One option borne from the economic crisis instigated by the Covid-19 pandemic is to continue with this model; however, the first signs of recovery seem to indicate that this policy will not be enough to reach the previous levels of economic activity, let alone surpass them (see Gamba, 2021; Werner et al., 2021).2

With regard to the export growth model, the strategy itself is not considered to be the problemper se, but rather that the trade liberalization processes were not those required to stimulate the export growth rate nor its productive linkage with the rest of the sectors.3One can also argue that the region’s sluggish growth is the result of a dramatic drop in the rate of capital accumulation (cf. Perrotini et al., 2008), so maybe the time is right for a change in the growth model, for a capital accumulation-led strategy to rise as an alternative capable of better results.

On the one hand, it is important to keep in mind that the liberalization of international trade can be justified, from the orthodox approach, as a measure to better position a country’s productive resources (cf. Feder, 1983) and to solve the shortage of capital goods needed to increase aggregate productivity (cf. Awokuse, 2008). However, it can also be supported by the heterodox approach as a way to relax restrictions on effective demand by complementing the domestic market with the external market (cf. Kaldor, 1970; Thirlwall, 1979). Likewise, fostering capital accumulation can be brought about by stimulating savings (cf. Solow, 1956) or through a direct policy to drive investment, derived from post-Keynesian growth models (cf. Kurz and Salvadori, 2010). In this regard, this article’s argument is housed within the heterodox approach even though it follows a causality flow framed by an open economy which employs Harrod's postulate on the dual-role of investment (see Moudud, 2000) as a source of demand and, therefore, potentially of a demand for imports and as a mechanism to replace imports and expand the domestic market given its ability to generate productive capacity.

Thus, the objective of this work is to show that investment, not exports, is the engine of growth in Latin America, both before and after the external debt crisis of the 1980s. We also seek to show that the reduction in capital accumulation seen in the region fundamentally explains the reduction in growth. To this end, we analyze four of the largest economies in the region: Argentina, Chile and Mexico during 1961-2019, and Brazil during 1971-2019.

This article is divided into four sections, including this introduction. The second section provides a brief exposition of Kaldor’s (1970) and Thirlwall's (1979) export growth models, and a part that integrates in Thirwall's model the role capital accumulation has in determining the growth rate. The third section presents an empirical analysis showing that capital accumulation is more relevant than export growth rate in determining the study’s sample economies’ growth rate. Finally, the fourth section presents some final thoughts on the subject.

2. EXPORT GROWTH MODELS AND CAPITAL ACCUMULATION

Economic growth theories provide various explanations for the differences seen in international growth rates, both between countries and between different time periods. Among the spectrum of explanations we find at one end those based on supply, that is the accumulation of productive factors through growth models both exogenous (cf. Solow, 1956; Mankiw et al., 1992) and endogenous (cf. Romer, 1986 and 1990; Lucas, 1988; Barro, 1990; Rebelo, 1991); at the other end are those based on demand (cf. Thirlwall, 1979; Clavijo and Ros, 2015).

Kaldor (1957), following a Keynesian approach at the demand end of growth theory, developed a model in which, in the short term, economic growth rates depend on capital accumulation. However, in the long run, growth depends on purely technological factors. In this case, differences seen in growth can be divided into two groups: short-term and long-term.

Thus, in the short term, different growth rates are explained by the differences in how the profits generated by income are distributed, with those economies which have the smallest share of profits growing the least. In the long run, growth rates depend on product’s elasticity in regards to capital and on the flow of new ideas and economies’ willingness to adopt them. However, regardless of its Keynesian nature, Kaldor’s model (1957) implies that in the long run growth is restricted by the accumulation of productive resources.

The idea that the long-term growth rate can be determined by demand and not by the accumulation of productive factors can be traced back to Kaldor's (1970) export growth model,4 which was formalized by Thirlwall (2003). This model is based on the “Verdoorn Law” and on the belief that exports are the only truly autonomous component of demand (cf. Thirlwall, 2003), thereby producing a virtuous cycle of more exports, more growth, more productivity, and more exports. The process described can be expressed in the following manner, assuming that the growth rate of product (g) depends on the growth rate of exports (x):

g=yx (1)

where γ is output elasticity in regards to exports; while assuming thatxdepends on the percentage variation rate of the real exchange rate (e+ π*- π) and the foreign product growth rate (z):

x=εxe+π*+Ψ*z     εx,Ψ*>0 (2)

whereeis the nominal depreciation/appreciation rate, π* and π are the external and domestic inflation rates respectively, ε x is the price elasticity of export demand and Ψ* is the income elasticity of the country’s export demand with regards to the world or relevant trading partners. In turn, π depends on the change percentage in nominal wages (w), labor productivity (l) and profit margin (τ):

π=τ+w-l (3)

The importance of the Verdoorn coefficient lies in the idea thatlis endogenous tog:

l=l0+λg       0<λ<1 (4)

Wherel 0 is the autonomous growth rate of labor productivity. The equilibrium growth rate is obtained by substituting equation (4) in (3), the result in equation (2) and the new result in equation (1), from which the long-term growth rate (ց LP ) is obtained:

gLP=γεxe+π*-τ-w+l0+Ψ*z1-γεxλ      γεxλ<1 (5)

Equation (5) shows that when the Verdoorn coefficient is equal to zero, ց LP is less than when it is between zero and one; therefore, equation (5) also shows a positive relationship between domestic growth rate and foreign growth rate, or in other words, between domestic growth rate and export growth rate.

According to Thirlwall (1979), Kaldor's (1970) export growth model suffers from omitting imports and their effect on the trade balance in the economic growth process. In this regard, Thirlwall (1979) argues that while long-term growth depends onx, the trade balance’s dynamic equilibrium is the main constraint to economies’ growth, which he demonstrates based on Harrod's (1933) theory of an external multiplier. Thirlwall’s model (1979) can be formalized on the basis that economies must maintain trade balance’s dynamic equilibrium in the long term; this condition, in terms of domestic goods, can be expressed as follows:

x=θ+m (6)

whereθis the variation percentage in the real exchange rate (e+ π*- π) andmis the import growth rate. We likewise assume that the behavior ofxcan be expressed by equation (2) and that, symmetrically,mdepends onθand on ց:

m=εmπ-π*-e+Ψg     εx,Ψ>0 (7)

where ε m is the price elasticity of import demand and Ψ is the income elasticity of the domestic country's import demand. Substituting equations (2) and (7) in (6) and solving forgyields the long-term growth rate consistent with the trade balance’s dynamic equilibrium (ց tb ):

gtb=εx+εm-1θ+Ψ*zΨ (8)

In equation (8) we see that ց tb has a positive relationship withθandz. Nevertheless, Thirlwall (1979) emphasizes the fact that even if the Marshall-Lerner condition is met (ε x + ε m - 1 > 0), it is impossible to use the real exchange rate as a permanent method for increasing ց tb To this we add the fact that it is normal for price elasticities to be very low and/or for relative prices to be more or less constant,5with which Thirlwall (1979) discards the effect of θ on ց tb . On the other hand, given that Ψ *z represents x, equation (8) can be restated as what is called the weak version of Thirlwall's Law:

gtb1=xΨ (9)

so, according to equation (9), givenΨ,long-term growth depends onx. Now, according to Vázquez-Muñoz (2018), the long-term predictions provided by the weak version of Thirlwall's Law are not plausible as they imply that whenΨis greater than one, the economy tends to only produce for the external market and, when it is less than one, tends to become a closed economy, which is not feasible in the real world.

Likewise, according to Vázquez-Muñoz (2018) and Ibarra (2015), the traditional specifications of the equations ofxandmyield biased estimates ofΨandΨ*, given that they do not take into consideration the effects of capital accumulation, such as whether an expansion into the foreign market is accompanied by domestic capital accumulation or not. In the former, the increase in export demand will be accompanied by a greater supply of domestic goods, while in the latter it will occur without a response from domestic production. This means that in the first case the estimate ofΨ* is greater than in the second case. Likewise, if domestic expansion occurs without capital accumulation there will be an increase inmwhile, if it happens in the presence of capital accumulation, the response of imports will be low, meaning that in the first case the estimate ofΨwill be higher than that in the second case.

Thus, Vázquez-Muñoz (2018) expands Thirlwall’s model (1979) in order to include the role of capital accumulation in import demand. According to Vázquez-Muñoz (2018), capital accumulation has two effects onm, one direct and positive stemming from the need to import capital goods, and another negative and indirect resulting from the possibility of replacing imported goods with domestically produced goods. The function of the import growth rate is then respecified as:

m=ΨIIK+Ψg-ce (10)

whereΨ I is import demand’s gross capital stock elasticity,Iis gross investment andceis the growth rate of economic capacity, understood as the desired level of production, given capital stock (K) (cf. Shaikh, 2016). That is,ceis a variable which depends on I/K:

ce=a+IK-δ (11)

whereais the growth rate of capital productivity. We likewise omit in equation (10) the effect of the real exchange rate on import demand. Therefore, assuming thatθis equal to one and remains constant, and considering thatΨ*zis equal tox, substituting equations (10) and (11) in (6), one obtains the long-term growth rate consistent with trade balance’s dynamic equilibrium which considers the role of capital accumulation (ց tbI ):

gtbI=xΨ+Ψ-ΨIΨIK+a-δ (12)

according to equation (12), the long-term growth rate in a model constrained by the trade balance’s dynamic equilibrium not only depends onx, but also onI/K.

Furthermore, we must add that according to Perrotiniet al.(2019), given that the adjustment variable in the model isg, it is important to differentiate between its components, that is between the growth of domestic demand for domestic goods (id) andx, as the adjustment will be verified in the former. This distinction is important because, contrary to the traditional postulate thatximproves trade balance, it can happen thatxworsens it due to the multiplier effect on income, givenΨ,and thatxtherefore negatively affectsid.

Next, we present empirical evidence on the importance ofxandI/Kin determining growth in economies constrained by the trade balance.

3. CAPITAL ACCUMULATION AND EXTERNAL CONSTRAINT, THE CASES OF ARGENTINA, BRAZIL, CHILE AND MEXICO

In this section we analyze the role of capital accumulation in determining GDP growth rate and in variations in the trade balance, as a proportion of GDP, in the cases of Argentina, Brazil, Chile and Mexico. The data used for the analysis was obtained from databases from CEPALSTAT,6 World Penn Table version 10.0 and the World Bank’s World Development Indicators. The period analyzed was that of 1961-2019 for the cases of Argentina, Chile and Mexico, and 1971-2019 for Brazil; all series used were measured in domestic currency at constant prices.

It is important to begin the analysis by highlighting that while Thirlwall (1979) argues that the main constraint to economic growth is the trade balance’s dynamic equilibrium, Moreno-Brid (1998-1999) argues that the real constraint lies in maintaining the trade balance constant in proportion to GDP. In this regard, and taking into account the external debt crisis which affected the Latin American countries in the early 1980s, Figure 1 shows the relationship between the annual variation in the trade balance as a percentage of the GDP (xmy) and the GDP growth rate (g) for the four economies in the sample before and after their external debt crisis.

Figure 1 shows, on the one hand, that the four economies exhibit a negative relationship betweenxmyandg, and, on the other hand, that after the external debt crisis, every case exhibited a decrease in the growth rate that keeps the trade balance constant as a proportion of the GDP.7 In the case of Argentina it went from 3.3 to 2.1%, in Brazil it went from 8.6 to 2.3%, Chile from 4.2 to 4.1%, and Mexico from 6.8 to 2.4%. This intrinsically means a tightening of the external constraint on economic growth8 and, according to what we have shown in the previous section, this phenomenon can be attributed to a drop in the export growth rate or a slowdown in capital accumulation.

Note: the line indicates g’s estimated values with regards toxmy using a linear relationship acquired using the Ordinary Least Squares method. Source: created by the authors using the CEPALSTAT’s and the World Bank’s World Development Indicators databases.

Figure 1 Annual variation in trade balance as a percentage of GDP (horizontal axis:xmy) and GDP growth rate (vertical axis:g

In order to analyze the effect capital accumulation has on the study’s sample economies’ growth rate, we first calculate the gross capital stock using the perpetual inventory method, for which in accordance with Berlemann and Wesselhöft (2014) we estimate the trend growth rate of the net capital stock as corresponding to that of gross investment (I) using the following equation:

lnI=Θ0+Θ1t+uI (13)

wherelndenotes the natural logarithmic operator,tis a trend variable,uIare the estimation errors,Θ i are the parameters to be estimated and, in particular,Θ 1 is the estimated value of the trend growth rate ofI.9

The gross investment trend growth rates are assumed to be equal to the trend growth rates of their net capital stocks. Once these were obtained for each country in the study’s sample, the initial net capital stock for each country was calculated as follows:

K0=I1Θ1+δ1 (14)

whereI 1 andδ 1 are the initial values of gross investment and the capital stock’s depreciation rate respectively. Then we obtained the series of net capital stock10 for each of the countries in the sample using the perpetual inventory method:

Kt=It+Kt-1-δtKt-1 (15)

Using the net capital stock series, Table 1 shows a closer relationship between GDP growth rates and gross capital accumulation rates than between GDP growth rates and export growth rates, both before and after the external debt crisis. In fact, according to what was described regarding the relationship betweenxmyandg, with the exception of Chile, all economies exhibited a reduction in their gross capital accumulation rate. Furthermore, it is worth pointing out that export growth rates fluctuated to a smaller degree than capital accumulation rates.

Table 1 Gross capital accumulation rate, export growth rate and GDP growth rate 

ց x I/K ց x I/K ց x I/K
1961-1980 1981-1990 1981-2019
Argentina 3.4 6.2 7.2 -0.7 5.5 4.3 2.8 4.3 5.6
Chile 3.7 7.4 3.2 3.1 6.4 3.8 4.6 5.0 8.7
Mexico 6.8 11.1 15.6 1.9 7.5 8.2 2.5 6.0 6.7
1971-1980 1981-1990 1981-2019
Brazil 8.5 10.2 15.2 1.8 7.7 8.6 2.4 5.5 7.4

Source: created by the authors using the databases from CEPALSTAT, the World Bank´s World Development Indicators databases and World Penn Table, version 10.0.

Based on the descriptive analysis of the data, we apply a more rigorous analysis of the relationship between GDP growth rate and export growth rate and the capital accumulation rate. To this end, and based on the theory for determining growth in an economy constrained by trade balance developed in the previous section, we calculate the following equation, based on equation (12):

g=Θ2+Θ3IK+Θ4x+ug (16)

whereΘ i are the parameters to be estimated andu g is an error term. The estimation of equation (16) is based on the panel data constructed with the available data for the sample countries. In this regard, this data allowed the construction of two panels, one balanced for the period of 1971-2019 and an unbalanced one covering the period of 1961-2019.

Table 2 presents the unit root tests for the series used. As one can see, all are stationary, allowing us to avoid the problem of spurious regression.

Table 2 Unit root tests for the I/K, id, x, ց series. 

Balanced panel
Test Levin-Lin-Chu Harris-Tzavalis Breitung Im-Pesaran-Shin Fisher-type ADF
I/K -1.72** -3.78*** -1.88** -1.43* 24.71***
id -8.63*** -21.71*** -4.84*** -8.21*** 90.79***
x -9.08*** -24.42*** -5.76*** -9.22*** 112.36***
ց -8.44*** -20.90*** -4.78*** -8.02*** 87.67***
Unbalanced panel
i/k -1.69** 26.48***
id -8.53*** 104.52***
x -11.63*** 154.09***
ց -8.91*** 102.19***

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. *; **; *** imply statistical significance at 10%, 5% y 1%, respectively. The null hypothesis of each test may vary. However, they all refer to the stationarity of the respective series.

Table 3 shows the results of the estimation of equation (16) for the methods of Fixed Effects, Random Effects, Feasible Generalized Least Squares and Panel Corrected Standard Errors. The use of four estimation models is due to two important reasons. First, we seek to obtain a robust estimate of equation (16) which guarantees the consistency of the estimated parameters in their statistical significance, whether the effect of dependent and independent variables is positive or negative in value and the magnitude of the estimated coefficient. In other words, we seek to demonstrate that even when changing the estimation technique, the estimations of the effect had by exports and capital accumulation are consistent. Second, estimation using Feasible Generalized Least Squares and Panel Corrected Standard Errors models allows one to control the estimation considering the panel type data models’ problems of autocorrelation, contemporaneous correlation and heteroscedasticity (Beck and Katz, 1995).

Table 3 Estimation of the GDP's growth rate (g) equation 

Independent variable Fixed effects Random effects Panel Corrected Standard Errors Feasible Generalized Least Squares
Balanced panel 1971-2019
x 0.086 0.085 0.078** 0.061**
(0.057) (0.056) (0.036) (0.031)
I/K 0.648*** 0.598*** 0.623*** 0.631***
(0.111) (0.105) (0.107) (0.096)
Constant -2.190** -1.806** -1.940** -2.323***
(0.491) (0.902) (1.007) (0.888)
Observations 196 196 196 196
Groups 4 4 4 4
F Statistic 294.47***
Wald chi2 Statistic R2 706.43*** 41.19*** 50.37***
R2 0.1745 0.1748 0.1578
Unbalanced panel 1961-2019
x 0.046 0.045 0.039
(0.052) (0.051) (0.031)
I/K 0.523** 0.424*** 0.464***
(0.120) (0.119) (0.077)
Constant -0.891 -0.121 -0.387
(0.724) (1.042) (0.807)
Observations 226 226 226
Groups 4 4 4
F Statistic 25.92***
Wald Chi2 Statistic 74.19*** 41.83***
R2 0.1421 0.1424 0.1217

Note: the results obtained are maintained when estimating for the subperiods of 1961-1980 and 1981-2019 (see Table Al).

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators

and World Penn Table, version 10.0. *; **; *** imply statistical significance ot 10%, 5% and 1%, respectively. Standard erors

are shown in parentheses ond are robust in all estimates.

According to the results acquired, there is a positive and significant relationship between the GDP growth rate and the capital accumulation rate; in contrast, the export growth rate does not seem to affect it, or to do so positively by a fairly small magnitude, as it was only statistically significant in the Pane Corrected Standard Errors and Feasible Generalized Least Squares models in the balanced panel, which implies exports have a reduced but positive effect on product growth.

Now, in order to present even more refined evidence of the comparative importance of capital accumulation versus export growth, we proceeded to substitute product growth rates as a dependent variable in the estimates with the growth of domestic demand for domestic goods.11 Therefore, the next equation for estimation is specified below:

id=Θ5+Θ6IK+Θ6x+uid (17)

whereΘ i are the parameters to be estimated andu id is an error term. In Table 2 we find the unit root tests for theidseries which is shown to be stationary. Meanwhile, Table 4 shows the results of equation (17)’s estimation; as one can see,I/Kconsistently exhibits a positive and significant relationship withid; the estimated coefficient is highly significant in all models and its size barely changes. Likewise, x is not found to have a consistent relationship withidas it is only significant for the unbalanced panel’s estimation with Panel Corrected Standard Corrected Errors, the estimated coefficient is very small however.

Table 4 Estimation of the equation for domestic demand for domestic goods (id) growth rate 

Independent variable Fixed effects Random effects Panel Corrected Standard Errors Feasible Generalized Least Squares
Balanced panel 1971-2019
x -0.024 -0.026 -0.035 -0.054
(0.042) (0.042) (0.040) (0.034)
i/k 0.800*** 0.754*** 0.759*** 0.790***
(0.096) (0.090) (0.119) (0.106)
Constant -3.079*** -2.720*** -2.692** -3.396***
(0.469) (0.941) (1.116) (0.976)
Observations 196 196 196 196
Groups 4 4 4 4
F Statistic 292.1 6***
Wald Chi2 Statistic 434.6*** 40.51*** 56.32***
R2 0.1735 0.1736 0.1558
Unbalaced panel 1961-2019
x -0.052 -0.053 -0.058*
(0.037) (0.036) (0.034)
I/K 0.652*** 0.559*** 0.569***
(0.108) (0.107) (0.087)
Constant -1.665* -0.939 -0.982
(0.717) (1.084) (0.908)
Observations 226 226 226
Groups 4 4 4
F Statistic 22.05**
Wald Chi2 Static 34.07*** 42.57***
R2 0.1510 0.1515 0.1319

Note: the results obtained are maintained when estimating for the subperiods of 1961-1980 and 1981-2019 (see Table A2).

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. * **: *** imply statistical significance at 10 %, 5% and 1%, respectively. Standard errors are shown in parentheses and are robust in all estimates.

Finally, in relation to the above, considering the fact that the trade balance as a proportion of GDP is the main constraint to economies’ growth, we pose the next equation for estimation in order to analyze the effect ofxandI/Konxmy:

xmy=Θ8+Θ9id-IK+Θ10x-IK+uxmy (18)
12

whereΘ i are the parameters to be estimated andu xmy is an error term. Thus, Table 5 presents the unit root tests of the series used in equation (18)’s estimation, by which we conclude that the series are stationary.

Table 5 Unit root for the xmy, id-I/K and x-I/K series. 

Balanced panel
Test Levin-Lin-Chu Harris-Tzavalis Breitung Im-Pesaran-Shin Fisher-type ADF
xmy -8.83*** 0.14*** -7.43*** -8.87*** 105.67***
id-I/K -9.54*** 0.21*** -5.01*** -9.01*** 103.43***
x- I/K -7.40*** 0.28*** -5.19*** -7.66*** 106.89***
Unbalanced panel
xmy -7.84*** 127.89***
id- I/K -8.80*** 106.65***
x- I/K -9.27*** 141.54***

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. * **: *** imply statistical significance at 10 %, 5% and 1%, respectively. The null hypothesis of each test may vary, however, they all refer to the stationarity of the respective series.

Table 6 shows the results of the estimate and one can see thatxpositively affectsxmy; however, the estimated values of the effect ofxonxmytend toward zero, while on the other hand, we obtained negative coefficients corresponding toid. This implies thatxdoes not significantly improvexmy, while the effect ofI/Konxmy, obtained by adding the estimated coefficients ofid-I/Kandx-I/K13 is positive and greater in absolute value than the effect ofx.

Table 6 Estimation of the equation for the annual variation in trade balance as a proportion of GDP (xmy) 

Independent variable Fixed effects Random effects Panel Conected Standard Errors Feasible Generalized Least Squares
Balanced panel 1971-2019
id- I/K -0.308*** -0.288*** -0.302*** -0.256***
(0.034) (0.030) (0.023) (0.019)
x- I/K 0.084** 0.087*** 0.080*** 0.082***
(0.021) (0.021) (0.011) (0.009)
Constant -1.319*** -1.224*** -1.286*** -1.219***
(0.154) (0.225) (0.186) (0.168)
Observations 196 196 196 196
Groups 4 4 4 4
F Statistic 39.83***
Wald Chi2 Statistic 90.01*** 255.04*** 278.47***
R2 0.5798 0.5807 0.5892
Unbalanced panel 1961-2019
id- I/K -0.265*** -0.236*** -0.257***
(0.017) (0.018) (0.020)
x- I/K 0.067** 0.071*** 0.062***
(0.019) (0.019) (0.010)
Constant -1.116*** -0.976*** -1.077***
(0.073) (0.162) (0.179)
Observations 226 226 226
Groups 4 4 4
F Statistic 127.94***
Wald Chi2 Statistic 180.44*** 229.45***
R2 0.4935 0.4961 0.5140

Note: the results obtained are maintained when estimating for the subperiods of 1961-1980 and 1981-2019 (see Table A2).

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. *; **; *** imply statistical significance at 10%, 5% and 1%, respectively. Standard errors are shown in parentheses and are robust in all estimates.

4. FINAL THOUGHTS

According to the empirical evidence presented, in the context of the need to reconfigure growth strategies when faced by both old and new needs which have arisen in economies due to the current pandemic, economic policy needs to promote an accelerated rate of capital accumulation.

This may seem paradoxical given that, as has been mentioned, it is assumed that the study’s sample economies are constrained by the trade balance’s dynamic equilibrium, or rather, by the need to keep the trade balance constant as a proportion of GDP. This would make it seem that the natural candidate for leading growth is the export growth rate as it positively affects both growth as well as the trade balance. However, we must emphasize that imports depend on income which in turn depends on exports and if, as is typical, economies exhibit an income elasticity of import demand greater than one,14 the response of imports to exports may be more than proportional or quasi-proportional. In addition to that demonstrated in the previous section, this means that exports would not be as relevant in the behavior ofxmy, or at least they would not have the importance which they are normally attributed.

In contrast, capital accumulation has two effects on import demand. One positive effect is related to the import of capital goods and another negative, related to the production of goods and services which can lead to substituting imports, thereby improving the trade balance. In other words, capital accumulation increases income and relaxes external constraints on growth, allowing for greater long-term growth as seen in the empirical analysis for the panel composed with data from the four largest Latin American economies.

REFERENCES

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1According to Kristjanpoller and Olson (2014), the Latin American region was distinguished by an export-led economic growth strategy from 1970 to 2010. Likewise, Bolivia, Brazil, Colombia, the Dominican Republic, Ecuador, El Salvador, Mexico and Peru each experienced export growth whilst Argentina, Chile, Costa Rica, Guatemala, Honduras and Peru experienced import-led growth. Nevertheless, it is impossible for an economy to import without exporting.

2It is worth mentioning that according to Ocampo and Ros (2011), the growth trend exhibited by labor productivity in Latin America during the export-led growth period (1990-2008) is less than that seen in the State-led industrialization period (1950-1980). Meanwhile, at the individual level of each of the 18 sampled countries, only Chile and the Dominican Republic saw an improvement.

3For example, Bresser-Pereira (2011) argue that the ability of Latin American economies to achieve a high growth rate depends on their ability to export high value-added manufactured or primary goods. Something which they have been unable to achieve so far.

4In fact, it was Harrod (1933) who preceded the idea of external constraints on growth by proposing a static international trade multiplier (cf. Thirlwall, 2011).

5According to Pérez Caldentey (2015), the average variation rate for the actual real exchange rate is 0.29% for a sample of 93 countries in different parts of the world, based on a quarterly series starting with the first quarter of 1980 and ending with the first quarter of 2015.

6TL note: the ECLAC’s statistical databases and publications.

7The calculation of the growth rate which keeps the trade balance constant as a proportion of the GDP is based on the Ordinary Least Square regressions shown in Figure 1.

8See, among others, López and Cruz (2000), Bértolaet al. (2002), Jayme Jr. (2003), Moreno-Brid (1998 and 1999) and Guerrero de Lizardi (2006) for estimates on equilibrium growth rates consistent with the external constraint on growth in Latin American economies.

9The estimation of equation 13 was carried out using the ordinary least squares methodology.

10The estimated series of net capital stock are available from the authors upon request.

11Domestic demand for domestic products is the difference between GDP and exports. The idea of substitutinggwithidis to obtain the effect that exports and capital accumulation have on the non-export sector’s product growth. The aim of this is to clarify the degree of linkage with the rest of the economy on the one hand and, on the other, the multiplier effect had by capital accumulation on the non-export sector.

12Equation (18) makes it possible to disaggregate the effects of external and domestic demand for domestic goods inxmy. Likewise, we subtract from bothidandxcapital accumulation due to the idea presented in equation (10), according to which, the effect in both import demands and thuslyxmycan be calculated more efficiently if we consider their values in relation to the country’s productive capacity.

13If one considers that g=αid+(1−α)x, where α and 1−α are the share of the GDP held by domestic demand for domestic goods and exports respectively, the above specification can be restated as follows:xmy=θ 11 +θ 12 id+(1−α)xI/k)+uxmy 2 orxmy=θ 11 +θ 12 (α(diI/k))+θ 12 ((1−α)(diI/k))+uxmy 2 , whereupon the sum ofθ 12 α andθ 12 (1−α) is the effect ofI/konxmy.

14See, among others, the references laid out in footnote 7.

APPENDIX

This section reports the estimates made for two subperiods (1961-1980 and 1981-2019) in order to verify that the results are robust. This is because these sub-periods were marked by the implementation of different growth strategies and therefore different evolutions of capital accumulation and exports.

Table Al Estimation of the GDP grouth rate (g) equation 

Independent variable Subperiods
Fixed effects Random effects Panel Conected Standard Errors Feasible Generalized Least Squares
Unbalanced panel 1961-1980
x 0.008 0.009 0.018
(0.047) (0.046) (0.047)
I/K 0.303 0.336*** 0.324***
(0.299) (0.121) (0.123)
Constant 2.193 1.957** 1.925
(2.794) (0.938) (1.737)
Observations 70 70 70
Groups 4 4 4
F Statistic 0.61
Wald Chi2 Statistic 11.39*** 7.73**
R2 0.1563 0.1563 0.1387
Balanced panel 1981-2019
x 0.089 0.086 0.053 0.025
(0.066) (0.066) (0.046) (0.039)
I/K 0.592* 0.571*** 0.7l3*** 0.630***
(0.207) (0.162) (0.197) (0.171)
Constant -1.901 -1.734* -2.567* -2.142
(1.220) (1.015) (1.562) (1.351)
Observations 156 156 156 156
Groups 4 4 4 4
F Statistic 12.83**
Wald Chi2 Statistic 24.78*** 14.56*** 14.05***
R2 0.0849 0.0849 0.0861

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. *; **; *** imply statistical significance at 10%, 5% and 1%, respectively. Standard errors are shown in parentheses and are robust in all estimates.

Table A2 Estimation of the equation for the growth rate of domestic demand for domestic goods (id) 

Independent variable Subperiods
Fixed effects Random effects Panel Conected Standard Errors Feasible Generalized Least Squares
Unbalanced panel 1961-1980
x -0.059 -0.058 -0.046
(0.040) (0.039) (0.050)
I/K 0.365 0.385*** 0.371***
(0.318) (0.120) (0.136)
Constant 1.900 1.784** 1.747
(2.940) (0.884) (1.931)
Observations 70 70 70
Groups 4 4 4
F Statistic 1.27
Wald Chi2 Statistic 10.24*** 7.63**
R2 0.1722 0.1723 0.1450
Balanced panel 1981-2019
x -0.051 -0.063 -0.089* -0.133***
(0.040) (0.040) (0.051) (0.042)
I/K 0.821** 0.726*** 0.904*** 0.860***
(0.217) (0.097) (0.222) (0.194)
Constant -3.159* -2.438*** -3.548** -3.517**
(1.361) (0.524) (1.743) (1.519)
Observations 156 156 156 156
Groups 4 4 4 4
F Statistic 8.22*
Wald Chi2 Statistic 56.94*** 19.31*** 29.13***
R2 0.1024 0.1031 0.1201

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. *; **; *** imply statistical significance at 10%, 5% and 1%, respectively. Standard errors are shown in parentheses and are robust in all estimates.

Table A3 Estimation of the equation for annual variation in the trade balance as a proportion of GDP (xmy) 

Independent variable Subperiods
Fixed effects Random effects Panel Conected Standard Errors Feasible Generalized Least Squares
Unbalanced panel 1961-1980
id- I/K -0.180** -0.122*** -0.143***
(0.050) (0.037) (0.026)
x- I/K 0.039* 0.050*** 0.039***
(0.013) (0.017) (0.010)
Constant -0.719** -0.437*** -0.582**
(0.227) (0.056) (0.248)
Observations 70 70 70
Groups 4 4 4
F Statistic 6.64*
Wald Chi2 Statistic 172.34*** 51.03***
R2 0.3992 0.4376 0.4573
Balanced panel 1981-2019
id- I/K -0.339*** -0.321*** -0.336*** -0.288***
(0.040) (0.038) (0.027) (0.023)
x- I/K 0.092** 0.092*** 0.083*** 0.092***
(0.029) (0.027) (0.015) (0.013)
Constant -1.439*** -1.357*** -1.440*** -1.364***
(0.165) (0.215) (0.213) (0.193)
Observations 156 156 156 156
Groups 4 4 4 4
F Statistic 40.77***
Wald Chi2 Statistic 82.47*** 235.22*** 263.08***
R2 0.5993 0.5995 0.6101

Source: created by the authors using data from the databases from CEPALSTAT, the World Bank World Development Indicators and World Penn Table, version 10.0. *; **; *** imply statistical significance at 10%, 5% and 1%, respectively. Standard errors are shown in parentheses and are robust in all estimates.

Received: August 14, 2021; Accepted: December 06, 2021

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