DP2017-08

Peripherality, Income Inequality,
and Economic Developmentin Latin American Countries
Yoshimichi MURAKAMI
Nobuaki HAMAGUCHI
Revised September 3, 2020

Revised on September 3, 2020

Peripherality, income inequality, and economic development in Latin
American countries
Yoshimichi Murakami a* and Nobuaki Hamaguchi aaResearch Institute for Economics and Business Administration, Kobe University, 2-1,

Rokkodai, Nada-ku, Kobe 657-8501, Japan.
*corresponding author. Email: y-murakami@rieb.kobe-u.ac.jp

1Peripherality, income inequality, and economic development in Latin
American countries

Following a neo-structuralist perspective, this study presents a development
puzzle concept for Latin American countries (LACs) as a triangular relation
amongst peripherality (increased terms-of-trade volatility and technologicalbackwardness), income inequality, and per-capita income. We employ asimultaneous equation model using three-stage least squares (3SLS) to analyse
this triangular relation. We find that a decrease in income inequality and anincrease in per-capita income were mutually reinforcing in 14 LACs during
1995–2014. Although technological progress increases per-capita income, it
partly mitigates this increase by increasing income inequality. Additionally, theincreasing effects of foreign sources of technology, including foreign direct
investment (FDI), on income inequality are mitigated in countries with higher
technological capabilities. While an improvement in commodity terms-of-trade
expectedly increases per-capita income and decreases income inequality in SouthAmerican countries, their volatility is mostly insignificant.

Keywords: commodity terms-of-trade; volatility; technological progress; foreign
direct investment (FDI); neo-structuralism

1. Introduction

A prominent economic feature of Latin American countries (LACs) is vulnerability toexternal shocks and higher volatility (Bértola & Ocampo, 2012; Hausmann & Gavin,
1996; Titelman & Pérez-Caldentey, 2016). The growing instability in global economy
2has caused a substantial change in this region’s economic growth perspectives, poverty,
and income inequality. This high vulnerability is more puzzling considering the region’s
desirable features for economic development: resource-richness, proximity to North
American markets, upper-middle-income status, and a relatively developed
manufacturing base.
To address this puzzling vulnerability, we evaluate the region’s peculiar
characteristics. In revisiting the literature, we find that Latin American structuralism(henceforth structuralism), which has a principal objective of analysing a system (i.e.
interactive relations between its elements as a whole) rather than individual elements in
isolation (Palma, 1987), provides some clues. Thus, the objective of this study is topresent a simultaneous equation model that depicts the relations between key economic
variables based on structuralist ideas, and to test whether these relations held in LACs
from 1995 to 2014, during which they experienced the upheaval in commodity prices
and the Great Recession.
As discussed in Section 2, structuralists specifically address two phenomena:
income inequality and economic peripherality (i.e. factors that restrict an economy tothe underdeveloped peripheral position as opposed to highly developed central
economies). A salient feature of peripherality is the deterioration of terms-of-trade incommodity-dependent LACs (Prebisch, 1950). Accompanying increased intra-industry
3and intra-firm trade and financial globalisation, other new distinctive attributes of
peripherality emerged as technological backwardness (Economic Commission for Latin
America and the Caribbean [ECLAC], 1990) and vulnerability to external shocks
caused by volatile commodity prices and short-term international capital inflows(Ocampo, 1998). In this regard, since around 1990, the structuralist perspective has been
amplified and transformed to a neo-structuralist one, which focuses more on
macroeconomic and financial issues, and emphasises technological backwardness as
fundamental difficulties (Bielschowsky, 2009; Di Filippo, 1998).1
We argue that the distinctive feature of the system that neo-structuralists present
is that there is an interactive relation between income inequality and economic
development (measured by per-capita income), and that the aforementioned peripheralfeatures affect both income inequality and per-capita income. Therefore, a novel
contribution of this study is to model the triangular relation between peripherality,
income inequality, and per-capita income as simultaneous equations, and empiricallydemonstrate this relationship in LACs since the mid-1990s. Moreover, this model can
not only reveal the complex relations between these three variables, but also explain theoccurrence of significant economic fluctuations in LACs, thereby revealing the reasons
behind the region’s puzzling vulnerability. Although some empirical studies have partly
analysed these relations in other developing countries or LACs in previous periods,
4studies focusing on LACs in the chosen period are rare. To the best of our knowledge,
this study is the first to model the neo-structuralist ideas using simultaneous equations
and analyse these triangular relations holistically.
This paper is organised as follows. Section 2 explains the triangular relation
amongst peripherality, income inequality, and per-capita income, following the neostructuralist perspective, and briefly reviews the theoretical and empirical literature.
Section 3 presents the simultaneous equations and describes the data used to empiricallyevaluate the triangular relations in LACs during 1995–2014. Section 4 presents theestimation results. The final section concludes the discussion.

2. Triangular relation amongst peripherality, inequality, and per-capita
income in LACs

Following the neo-structuralist concept of peripherality, this section explains thetriangular relation amongst peripherality, income inequality, and per-capita income, as
shown in Figure 1, and briefly reviews whether any formal economic models andempirical findings have supported these arguments.

[Figure 1 near here]

2.1. The relation between income inequality and per-capita income

Traditional structuralists argued that inequality in LACs, primarily because of structural
5

heterogeneity (i.e. significantly diverse productivity across different sectors) combinedwith historical factors such as land tenure systems, was the driving force behind chronic
inflation, thereby harming economic growth and depressing per-capita income(Prebisch, 1950). Numerous empirical studies based on cross-country or panel data
analysis, including LACs, have supported the argument that income inequality
decreases per-capita income growth (e.g. Alesina & Rodrik, 1994; Bengoa & SanchezRobles, 2005; Woo, 2011). Moreover, Al-Marhubi (2000) showed that the initial level ofincome inequality was positively associated with subsequent inflation rates in developed
and developing countries including LACs. Bengoa and Sanchez-Robles (2003) foundinflation rates to be significantly associated with low per-capita income growth in
LACs.
In contrast, neo-structuralists consider the relation between income inequality
and per-capita income to be bidirectional rather than unidirectional and emphasise that
decreasing inequality and increasing per-capita income are mutually reinforcing(ECLAC, 1990; Fajnzylber, 1990). On the one hand, they argue that less income
inequality can contribute to higher per-capita income directly by promoting humancapital and physical capital investment, and indirectly through technological progress byhuman capital accumulation (ECLAC, 1990; Fajnzylber, 1990). On the other hand, neostructuralists argue that higher per-capita labour income can contribute to decreasing
6

income inequality, as it is more likely to increase than other sources of income if astructural change is accompanied by an increase in labour productivity (ECLAC, 1990;
Fajnzylber, 1990). Galor and Zeira (1993) theoretically supported this argument. Their
model predicts that countries with a more equal income distribution will have higher
per-capita income because of the larger share of individuals who can afford humancapital investment. Meanwhile, countries with higher per-capita income will have moreequal income distribution because wages of unskilled workers will increase such thateven they can afford to invest in human capital and thereby become skilled workers.
Therefore, it predicts the mutual reinforcement of less income inequality and higher percapita income, thereby supporting the neo-structuralist argument.
However, empirical studies on the determinants of income inequality in LACs,
including Székely and Mendoza (2017), ignore the effects of per-capita income. Studies
concerning the latter, including Bengoa and Sanchez-Robles (2005), Cornia (2010), andMorley (2000), do not regard income inequality and per-capita income as determined
simultaneously. As such, we test whether our empirical analysis supports the neostructuralist argument in LACs since the mid-1990s.

2.2. The relation between peripheral features and per-capita income

Traditional structuralists believed that deterioration of terms-of-trade would harm percapita income growth and lower per-capita income in LACs. They considered that the7downward pressure on the export price occurs through specialisation in primarycommodities with low income elasticity and wage reduction due to the low productivity
arising from the unproductive labour surplus in the primary sector (Prebisch, 1959).
Subsequently, Thirlwall (1979) extended this idea and formally modelled it as thebalance-of-payments constraint model. It predicts that long-term economic growth ofcommodity-dependent countries is constrained by stagnant export growth and highincome elasticity of demand for imports, as evidenced by Cimoli et al. (2010) for LACs.
However, the findings of empirical studies based on cross-country or panel data areinconclusive. Although some studies (e.g. Cavalcanti et al., 2015; Deaton & Miller,
1996; Mendoza, 1997) found a positive association between terms-of-trade and percapita income level or growth in developing or commodity-exporting countries, others(e.g. Blattman et al., 2007) found no significant association.
In contrast, according to the neo-structuralist perspective, terms-of-trade
volatility is the relevant factor explaining the region’s excess economic volatility; itadversely affects per-capita income growth and level by discouraging productive
investment and distorting macroeconomic prices (Titelman & Pérez-Caldentey, 2016).
Moreover, neo-structuralists have repeatedly argued that this volatility related to termsof-trade has been increasingly exacerbated by volatility of international capital flows in
LACs under their pro-cyclical fiscal and macroeconomic policies (Bértola & Ocampo,
8

2012; Ocampo, 1998, 2001, 2011; Titelman & Pérez-Caldentey, 2016). Indeed,
Hausmann and Gavin (1996) empirically found that both terms-of-trade volatility andcapital flow volatility are critical determinants of real income volatility in LACs.
Numerous empirical studies have found that higher terms-of-trade volatility is
associated with lower per-capita income growth in developing countries (e.g. Mendoza,1997) and commodity-exporting countries (e.g. Blattman et al., 2007; Cavalcanti et al.,2015). However, they do not analyse LACs since the mid-1990s.
Additionally, neo-structuralists argue that low productivity stemming from low
levels of technological progress, rather than primary commodity dependence, is afundamental feature of peripherality in LACs (Di Filippo, 1998; Hounie et al., 1999).
Since LACs have a limited source of endogenous technological progress, as evidenced
by their low research and development expenditure, foreign direct investment (FDI),
which facilitates access to advanced technology, managerial knowledge, and foreign
markets, is considered a major channel for their technological progress (Di Filippo,
1998; ECLAC, 1990). Similarly, intra-industry trade, especially trade in capital goods
that embody advanced technology, is also considered an important channel oftechnology acquisition, thereby contributing to productivity improvements (Fajnzylber,1990). Nevertheless, structuralists had traditionally argued that FDI would be
detrimental to per-capita income in LACs because it generated substantial reverse
9

capital flows in the form of profit and interest remittances to developed countries,
thereby causing negative effects on balance of payments (Pinto & Kñakal, 1973;
Prebisch, 1969).
Empirical literature also provides mixed evidence. Bengoa and Sanchez-Robles(2003) and Prüfer and Tondl (2008) found that FDI inflows indeed enhanced per-capita
income and productivity growth in LACs, respectively. On the other hand, Alencar et al.
(2019) found that income derived from FDI indeed adversely affected economic growthby its negative impact on the balance of payments in several LACs. Interestingly, somestudies showed that the positive effects of FDI on per-capita income do not occur
automatically. Borensztein et al. (1998) found that the contribution of FDI inflows to adeveloping country’s per-capita income growth was positive only when the country had
more than a threshold level of human capital. Similarly, Prüfer and Tondl (2008) foundthat the positive impact of FDI on productivity growth crucially depended on the
country’s absorptive capacity (e.g. institutional quality) in LACs. Hence, we test
whether the expected positive association between FDI and per-capita income has held
in LACs since the mid-1990s.

2.3. The relation between peripheral features and inequality

We consider the peripherality effects on income inequality. First, structuralists generally
regard the highly concentrated ownership of natural resources as a fundamental reason
10

for uneven income distribution and elite concentration in LACs (Fajnzylber, 1990).
Thus, they consider that rising commodity terms-of-trade will increase income
inequality by increasing rents from natural resources (Sánchez-Ancochea, 2019).
Moreover, rising terms-of-trade will worsen income inequality by preventing efforts fortechnological progress and structural changes (Cimoli & Rovira, 2008). However,
empirical studies including Cornia (2010), Gasparini et al. (2011), and Székely andMendoza (2017) found that an improvement in terms-of-trade was associated withdecreasing inequality in recent LACs. Sánchez-Ancochea (2019) argued that the
governments’ improving ability to redistribute the windfall profits from the commodity
boom through public social spending and taxes can explain the unexpected association.
In contrast, neo-structuralists emphasise the regressive distributional effects ofeconomic volatility because of its asymmetric effects across different income groups(Ffrench-Davis, 2016; Ocampo, 1998). Theoretical and empirical studies also support
the adverse effects of economic volatility on income inequality. Theoretically, extending
the seminal work of Galor and Zeira (1993), Checchi and García-Peñalosa (2004)
showed that since a more volatile economy requires higher non-labour income to invest
in human capital, it induces more unequal human capital distribution, thereby increasing
income inequality. Some empirical studies based on cross-country or panel data analysis
find that higher economic volatility, including commodity price volatility, is indeed11

associated with increasing income inequality (Woo, 2011) or worsening social welfare
indicators (Makhlouf, et al., 2017) in developing countries. Although Hausmann and
Gavin (1996) found the regressive distributional effects in LACs from 1975 to 1989,
they do not analyse LACs since the mid-1990s.
Second, according to the neo-structuralist perspective, structural changes from
specialisation based on natural resources or cheap labour to a diversified, knowledgeintensive production structure based on technological progress can reduce inequality byincreasing labour productivity and workers’ wages, and distributing rents more
equitably (Cimoli and Rovira 2008; ECLAC, 1990). However, they admit thattechnological progress is likely to increase inequality, at least temporarily, because it
takes some time for technological progress to encompass low-productivity sectors in
LACs, which have significant structural heterogeneity between high and low
productivity sectors and scarce linkages between them (ECLAC, 1990; Ocampo 2001).
The recent evolution of the relation between technological progress and inequality is inline with this prediction. Although skill-biased technological changes (SBTCs) were
widely observed in LACs in the 1980s and 1990s, the SBTC effects faded in the 2000s;
the observed income inequality decrease during this period is mostly attributable todecreased skill premiums (Gasparini et al., 2011). These findings support the argumentthat technological progress increases (decreases) short-term (long-term) income12inequality.
Additionally, according to the neo-structuralist perspective including Di Filippo(1998), the development of production linkages with multinational enterprises (MNEs)
can be expected to contribute to the mentioned reduction in inequality in LACs.
Especially, such linkages are expected to provide local firms, including small andmedium enterprises, with unprecedented opportunities to access new technologies andforeign markets, thereby leading to sustained increases in productivity and wages(ECLAC, 2014). Nevertheless, traditional structuralists, especially dependency
theorists, argued that since MNEs were involved in capital intensive activities inperipheral countries, FDI would increase inequality between the limited number ofhighly paid skilled workers and the large number of marginalised unskilled workers(Bornschier & Chase-Dunn, 1985). This argument is well developed by the model ofFeenstra and Hanson (1997), which predicts that since MNEs tend to locate in relatively
skill-intensive sectors in LACs, their activities tend to cause a relative expansion of
those sectors, thereby increasing inequality. Moreover, if the productivity improvements
through spillover effects from foreign to local firms, which usually occur throughdemonstration, labour turnover, and vertical linkages, are associated with theintroduction of skill-biased technologies, they are likely to further increase inequality(Berman & Machin, 2000). Indeed, recent empirical studies based on cross-country
13panel data analyses (e.g. Herzer et al., 2014; Suanes, 2016) found that FDI was
positively associated with Gini coefficients in LACs. Interestingly, Wu and Hsu (2012)
found that FDI was associated with increasing inequality, especially in countries withless absorptive capacities. These findings indicate that the expected distributional effects
are not automatically guaranteed. Hence, we test the distributional effects of FDI inrecent LACs, considering the role of absorptive capacity.

2.4. Summary

According to the triangular relation, a decrease (increase) in peripheral features,
including increased terms-of-trade volatility and technological backwardness, is
expected to increase (decrease) per-capita income both directly and indirectly by
reducing (increasing) income inequality (see Figure 1). Since adverse external shocks
are associated with increasing peripheral features, LACs exhibit a stronger negative
effect on per-capita income in bust periods. Consequently, this increase in the effect ofexternal shocks on per-capita income within the triangular relation might reveal the
puzzling vulnerability of LACs.
3. An empirical analysis of the triangular relation in LACs
3.1. Empirical specification

This section describes our empirical assessment of the triangular relation amongst
14peripherality, inequality, and per-capita income, using panel data of 14 LACs from 1995
to 2014, for which data on both sectoral FDI and number of patent applications isavailable. The 14 countries are Argentina, Brazil, Chile, Colombia, Ecuador, Guatemala,Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, and Venezuela. This
period is chosen not only because it covers the recent transition from bust to boom but
also because data obtained before 1995 are not necessarily reliable because of
hyperinflation and currency fluctuations.2 As discussed in Section 2, the model assumes
that per-capita income and income inequality are determined simultaneously, and are
affected by the peripheral features.
In equation (1), following the specification for estimating the per-capita
production function, log per-capita real GDP (y) is determined by log per-capita realcapital stock (k), variables indicating the peripheral features, and controls. Next, inequation (2), income inequality measured by the Gini coefficient of household percapita income (Gini) is determined by a linear and a quadratic term of per-capita
income, variables indicating the peripheral features, and controls.
Regarding the controls, we include inflation rate (annual change of consumer
price index, inflation) and trade openness (share of sum of exports and imports of goods
to GDP, trade) in equation (1), and additionally include share of social expenditure (sumof education, health, social security, and housing expenditure, social) in equation (2), as15

we assume that it does not directly affect the dependent variable in equation (1).3
We use the following variables as measures of the peripheral features. First, we
assess the effects of primary commodity dependence by log commodity terms-of-trade(CTOT) and their volatility (CTOTV). Following Spatafora and Tytell (2009), we define
the terms-of-trade as the ratio of commodity export prices to commodity import prices,with each price weighted by the share of the relevant commodity in the country’s total
trade. Following Blattman et al. (2007), we measure the volatility by the 12-monthstandard deviation of departures from a Hodrick-Prescott filter trend of monthly CTOT.4
Second, we assess the domestic technological capabilities by the number of patentapplications per thousand population including residents and non-residents (Patent),
while we assess the foreign sources of technology by the share of net FDI inflows in
GDP (FDI), share of intermediate goods (sum of parts and components, i.e. Broad
Economic Categories codes 42 and 53) exports in all exports (Intermediate_X), andshare of intermediate goods imports in all imports (intermediate_M). Alternatively, we
use the share of FDI net inflows in manufacturing sectors (FDI_m) instead of theaggregated total FDI because the knowledge spillover from FDI usually occurs inmanufacturing sectors based on the literature. Additionally, we include interaction terms
between the number of patent applications and each of the measures of foreign sourcesof technology in equations (1) and (2).5 The interaction terms in equation (1) are16

expected to enhance the positive effects of foreign sources of technology on per-capita
income because the absorptive capacity including the host country’s technologicalcapabilities would enhance the positive spillovers (Borensztein et al., 1998; Prüfer &Tondl, 2008). The interaction terms in equation (2) are also expected to enhance thedecreasing effects on income inequality because the positive productivity effects are
more likely to overflow to other firms or sectors if they have higher absorptive capacity(Wu & Hsu, 2012). Therefore, the simultaneous equations are as follows:

yit = β 0 + β1kit + β 2 Giniit + β 3CTOTit + β 4 CTOTVit + β 5 Patentit + β 6 FDI it+ β 7 Intermediate _ X it + β 8 Intermediate _ M it + β 9 Patentit ∗ FDI it+ β10 Patentit ∗ Intermediate _ X it + X′it β11 + α 1i + e1it

(1)

Giniit = γ 0 + γ 1 y it + γ 2 y it2 + γ 3 CTOTit + γ 4 CTOTVit + γ 5 Patent it + γ 6 FDI it+ γ 7 Intermediate _ X it + γ 8 Intermediate _ M it + γ 9 Patent it * FDI it+ γ 10 Patent it ∗ Intermediate _ X it + X′it γ 11 + Z ′it γ 12 + α 2i + e2it(2)where i and t respectively denote country and time; X is a vector of control variables
that would affect both per-capita income and income inequality (comprising inflationand trade); Z is a vector of control variables that would affect only income inequality(comprising social and dummies indicating sample characteristics explained insubsection 3-2); α represents the unobservable time-invariant country characteristics;
and e is the error term.
17

Since the per-capita income and Gini coefficient are simultaneously determinedin equations (1) and (2), estimation methods that assume that the error terms are
orthogonal to each other lead to biased estimates. This simultaneity bias can becorrected by applying equation-by-equation two-stage least squares (2SLS). However,
the error terms of these equations are likely to be contemporaneously correlated because
unobservable factors that affect the error term in one equation likely also affect that in
the other. Given this possibility, we obtain inefficient estimates if we ignore thiscorrelation and estimate the simultaneous equations by 2SLS. Thus, we use the threestage least squares (3SLS), which estimates all equations estimated by 2SLS
simultaneously with generalised least squares and yields more efficient estimates than
2SLS (Zellner & Theil, 1962). Since equation (2) includes a nonlinear endogenous
2

variable (the quadratic term of log per-capita GDP, y ), following Wooldridge (2002),
2

we use a quadratic term of log per-capita capital stock k as the additional instrument
2for y .
Based on the neo-structuralist perspective, Table 2 presents the coefficients ofthe main explanatory variables. As discussed in Section 2, we expect that increasing
income inequality, deteriorating commodity terms-of-trade, and their higher volatility
decrease per-capita GDP, while technological progress increases it. We also expect thatper-capita GDP decreases the Gini coefficient up to a threshold level. Improving
18

commodity terms-of-trade and their higher volatility increase the Gini coefficient, while
technological progress decreases it.
[Insert Tables 1 and 2]

3.2. Data and descriptive statistics

Table 1 reports the data sources. Since no single data source provides complete balanced
panel data for the Gini coefficient of household per-capita income and sectoral FDI
inflows in LACs (Blanco et al., 2019; Suanes, 2016), we source the two variables fromseveral different sources to construct a panel dataset with minimum missing
observations (see Tables A1-A2 in Supplemental File). Moreover, to control potential
time-invariant effects arising from different sample characteristics, we include dummy
variables indicating the geographical coverage (urban areas only versus whole country)
and the data source of Gini coefficients in equation (2).
Following Blanco et al. (2019), the remaining missing observations of the Gini
coefficient, share of FDI inflows in manufacturing sectors, and number of patentapplications are filled in by linear interpolation; this accounts for 15% of allobservations, which is acceptable. Table 3 reports the descriptive statistics. To test therobustness of the results, we estimate the simultaneous equations by 1) only using
observations that are not interpolated; 2) including interpolated observations of the Gini
coefficient and number of patent applications; and 3) including interpolated
19

observations of the three variables and using the share of FDI inflows in manufacturingsectors instead of total FDI.
[Insert Table 3]
4. Results

4.1. Estimation results

Table 4 presents the estimation results of the simultaneous equations. Although weestimated them by pooled, random effects, and fixed effects 3SLS, the equation-byequation F tests and the Hausman specification test strongly justify the use of fixed
effects model in all specifications estimated in this section. Thus, in Tables 4–6, we
present the estimation results by fixed effects (FE) 3SLS only. Note that regarding thenegative value of the Hausman test statistic, following Schreiber (2008), we use its
absolute value to reject the random effects model.
Regarding equation (1), expectedly, the Gini coefficient has a significant
negative effect, while the number of patent applications, share of intermediate goods
exports, and share of FDI have significant positive effects, with the latter’s effect being
larger in manufacturing sectors, thereby supporting the arguments in Section 2.
However, the commodity terms-of-trade are expectedly positive but weakly significant.
Unexpectedly, their volatility has positive effects, and the interaction terms havenegative effects, though they are mostly insignificant. The log per-capita capital stock is20

positively significant, as expected. The control variables (i.e. inflation ratio and trade
openness) are mostly insignificant.
Regarding equation (2), the linear term of the log per-capita GDP is significant
and negative, whereas its quadratic term is positive, showing a U-shaped relation
between log per-capita GDP and Gini coefficient, thereby contradicting earlier studies(e.g. Bengoa & Sanchez-Robles, 2005; Morley, 2000). Moreover, the Gini coefficient isa decreasing function of per-capita GDP over our analysis period. Therefore, a decrease
in inequality and an increase in per-capita income are interactive and reinforcing in
LACs, as argued in subsection 2-1. Furthermore, the effect is economically important;according to the estimates from the first specification in Table 4, while the Gini
coefficient explains 5.3% of the total variance in the log per-capita GDP, the log percapita GDP and its quadratic term explain 52.2% of the total variance in the Gini
coefficient, which makes the largest contribution.6 The commodity terms-of-trade havesignificantly decreasing effects, supporting the recent empirical findings mentioned insubsection 2-3, whereas their volatility is insignificant. The technological progressmeasures, especially FDI in manufacturing sectors, have significantly increasing effects,
contradicting some neo-structuralist arguments, but supporting theoretical and empirical
FDI studies mentioned in subsection 2-3. However, expectedly, the interaction terms
have decreasing effects on the Gini coefficient, supporting Wu and Hsu (2012) and21

indicating the importance of absorptive capacity in receiving positive spillovers fromforeign technology. Nevertheless, the estimates from the first specification in Table 4
indicate that the number of patent applications in LACs is much smaller than thethreshold level necessary to surpass the observed increasing effect of FDI (0.192). Noneof the average values of the 14 LACs exceeds this threshold. Expectedly, the share ofsocial expenditure is negatively significant. Together with the finding on the effects ofcommodity terms-of-trade, progressive social policies financed by revenues from
commodity exports contributed to decreasing inequality, supporting Sánchez-Ancochea(2019). The control variables are insignificant; the insignificance of trade openness
indicates that commodity terms-of-trade rather than trade openness is the primary
determinant of the change in labour demand resulting from the region’s integration into
the global economy.
The mentioned results are robust to the inclusion of interpolated observations
and the use of different FDI measures. Therefore, the findings indicate that althoughtechnological progress increases per-capita income directly, it partly mitigates theincrease indirectly by increasing income inequality. Using the average values, theestimates in Table 4 demonstrate that the direct positive effects dominate the indirect
negative effects on per-capita income except for the share of intermediate goods exports
in the first specification.7 However, the indirect effects are economically important;22

according to the estimates from the first specification, although a 1% increase in theshare of FDI leads to a 1.48% increase in the log GDP capita directly, it leads to 0.47%
decrease indirectly.
[Insert Table 4]

4.2. Robustness checks

To test the robustness of our results, we perform two additional exercises. First, weestimate the equations using different country groups, considering economic andpolitical heterogeneities across the region. The insignificance of commodity-related
variables may be due to the inclusion of Mexico and some Central American countries,
which are integrated into manufacturing value chains and have low levels of commodity
dependence. Thus, following Székely and Mendoza (2017), we concentrate our analysis
on South American countries excluding Guatemala, Honduras, Mexico, Nicaragua, andPanama. Additionally, we exclude Nicaragua and Venezuela from the original 14
countries, considering recent differences in their political regimes.
Table 5 reports the estimation results for the two different groups. Note thatthese estimations include the interpolated observations and use the share of FDI inflows
in manufacturing sectors. We find that the baseline results remain unchanged:
decreasing inequality and increasing per-capita income are mutually reinforcing;technological progress increases per-capita income and income inequality; and higher
23technological capabilities mitigate the increasing effects on inequality derived fromforeign sources of technology. As expected, in South American countries, animprovement in commodity terms-of-trade not only decreases income inequality, butalso increases per-capita income with substantially larger effects than in other countries.
Thus, they receive stronger positive effects on per-capita income not only directly, by
improved commodity terms-of-trade, but also indirectly, by reduced income inequality
in the boom period and vice versa in the bust period, thereby explaining the puzzling
vulnerability. In contrast, their volatility remains insignificant.
Second, following Suanes (2016), we estimate the simultaneous equations using
triennial data (i.e. 36-month standard deviation of the departures from the trend ofmonthly CTOT and three-year average including interpolated observations for othervariables) for the period from 1995 to 2012. The use of triennial data allows us toanalyse longer-term relations between the variables, eliminating the impacts of shortterm fluctuations. Table 6 reports the estimation results. Again, the baseline results
remain unchanged; we confirm the mutually reinforcing relation between lower income
inequality and higher per-capita income, the increasing effects of technological progresson per-capita income and inequality, and the insignificance of commodity terms-of-trade
volatility.
[Insert Tables 5–6]
24

5. Conclusion

Although LACs present some desirable features for economic development, they arehighly vulnerable to global economic changes. To explore their puzzling vulnerability,
we revisited the neo-structuralist idea and presented the region’s development puzzle
concept as a triangular relation amongst peripherality, income inequality, and per-capita
income, testing it on 14 LACs during 1995–2014. The empirical findings are
summarised below.
First, decreasing income inequality and increasing per-capita income were foundto be mutually reinforcing in LACs. Therefore, the reduction of income inequality
promoted an increase in per-capita income, which in turn contributed to decreasing
income inequality, thereby supporting the neo-structuralist arguments. This finding
provides new evidence related to the growth and inequality in LACs and forms thisstudy’s novel contribution to the literature.
Second, technological progress increased per-capita income and income
inequality. These findings indicate that technological progress partly mitigated theincrease in per-capita income indirectly by increasing income inequality. Moreover, theincreasing effects of foreign sources of technology including FDI on income inequality
were mitigated in countries with higher technological capabilities, thereby requiring
policies that promote both of them simultaneously.
25

Third, expectedly, improved commodity terms-of-trade increased per-capita
income and decreased income inequality in South America countries. Therefore, theirper-capita incomes benefited from the stronger positive effects directly throughimproved commodity terms-of-trade and indirectly through reduced income inequality
in the boom period and vice versa in the bust period, thereby revealing the puzzling
vulnerability through this triangular relation. In contrast, the effect of commodity termsof-trade volatility was insignificant; this may indicate the fact that the vigorous countercyclical policies implemented by most of the region’s governments would mitigate oroffset the negative impacts of economic volatility, especially after the 2008 financial
crisis. The fiscal consolidation efforts in prior periods prepared the space for thecounter-cyclical actions (ECLAC, 2012). Flexible exchange rate regimes adopted inprior periods may also mitigate adverse external shocks. The findings are robust to theinclusion of interpolated observations and the use of different measures of FDI, different
country groups, and triennial data.
The observed positive association between the foreign sources of technology andincome inequality, which causes unexpected negative effects on per-capita income byincreasing inequality, demands further analysis. One possible explanation is thatinternational production linkages might not have generated sufficient unskilled labourdemand because of lack of scale attributable to bottlenecks, such as insufficient26

infrastructure and market segmentation. If so, it would be crucial to adopt a policy forthe additional promotion of regional integration within LACs. Although this issue isbeyond the scope of this study, it represents an intriguing subject for future research.

27

Acknowledgements
The authors are deeply grateful to Taeko Hoshino, Mikio Kuwayama, Takahiro Sato,
Patricio Solís Gutiérrez, and Hiroyuki Ukeda for insightful and constructive
comments/suggestions, Seiji Horii and Masahiro Ikeda for assisting the research, andPamela Díaz-Valdés Liberona, Mauricio Escobar, Daniela Gebhard, and Francisca
Guerrero Jelvez for assisting the access to the data from CEPALSTAT. They are also
grateful to João Carlos Ferraz, Atsushi Fukumi, Tatsuji Hayakawa, Katsushi Imai, Jorge
Mario Martínez Piva, Tomoo Marukawa, Enrique Mu, Sachiyo Murase, Enrique
Peruzzotti, Tatsuya Shimizu, Terukazu Suruga, Yuriko Takahashi, and Tsuyoshi
Yasuhara for their insightful comments/suggestions in the early stages of this study. Anyremaining errors are the authors’ own.

Funding details
This work was supported by the JSPS KAKENHI Grant Numbers16H03313,
17K17877, and 20K13482.

Disclosure statement
There are no potential conflicts of interest to declare.

28

Notes
1. More specifically, traditional structuralists defined the “peripheral status” as specialization inprimary commodities, while neo-structuralists associate it with the lack of genuine
competitiveness stemming from technological progress, human capital skill development,
and social equity (Bielschowsky, 2009). Thus, for neo-structuralists, the distinction
between primary commodities and industrial products has lost economic significance (DiFilippo, 1998; Hounie et al., 1999). Furthermore, neo-structuralists consider that the lackof genuine competitiveness leads to external vulnerability through balance-of-payments
predominance (Ocampo, 2011).
2. None of the 14 LACs reported the three-digit inflation rate during 1995–2014.
3. The reason is as follows. Since there are several channels through which government
expenditure affects economic growth, both positively (e.g. by enhancing human capital)and negatively (e.g. through distortionary taxes), the overall effect can be insignificant, as
indicated by Churchill et al. (2017) who found that the effect was indeed insignificant indeveloping countries. Regarding LACs in recent times, although there have been extensive
studies showing that cash transfer programs, the mainstay of social policy in this period,
improved education outcomes and reduced inequality and extreme poverty (e.g. Soares etal., 2010), to the best of our knowledge, there is no convincing empirical research on theeffects on economic growth. This may be due to the time-lag that exists before the impacts
are realised and the fact that the demand expansion of cash transfer is localised andirrelevant at the national level, given the geographical concentration of poor households
eligible for such programs. Moreover, since our dependent variable is the level rather thangrowth of per-capita income, it is more likely that the share of social expenditure, whichindicates a degree of income transfers among different income groups, will not affect the
average per-capita income level in a given country. Indeed, the share of social expenditure29

is insignificant with respect to per-capita income in all specifications estimated in Section
4 (see Supplemental File).
4. Alternatively, we measure it by the 12-month standard deviation of monthly CTOT. However,
the measure generates remarkably similar results (see Supplemental file).
5. The interaction with the share of intermediate goods imports is not included because thevariable is mostly insignificant in both equations.
6. The sum of the product of each variable’s standardised coefficient and its correlation with thedependent variable is equal to the overall R-squared. For more details, see Pratt (1987).
7. Indirect effects can be calculated by multiplying the Gini coefficient from equation (1) withthe coefficient of the variable in question from equation (2).

30

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38

Table 1. Variable definitions and data sources
Variable

Data Source
Statistical Yearbook for Latin America and theyCaribbean of Economic Commission for Latin
America and the Caribbean (ECLAC)k

Penn World Table ver. 9.0

Gini

See Table A-1 in Supplemental File

CTOT

International Monetary Fund (IMF) data

CTOTV

IMF data

Patent
FDI

World Intellectual Property Organization (WIPO)
Statistics Database
Statistical Yearbook for Latin America and the
Caribbean of ECLAC

FDI_m

See Table A-2 in Supplemental File

Intermediate_X

United Nations (UN) Comtrade Database

Intermediate_M UN Comtrade Database
Inflation
Trade
SocialStatistical Yearbook for Latin America and the
Caribbean of ECLAC
Statistical Yearbook for Latin America and the
Caribbean of ECLAC
CEPALSTAT of ECLAC

39

Table 2. Expected signs of explanatory variables
Equation (1) Equation (2)
k

+

Gini

–

y
y

–
2

+

CTOT

+

+

CTOTV

–

+

Patent

+

–

FDI

+

–

Intermediate_X+–

Intermediate_M+–

Note: + (–) denotes that the explanatory variable has a positive (negative) effect on thedependent variable.

40

Table 3. Descriptive statistics
Variable

Observations

Mean

y

280

8.543

k

280

Gini

Standard

Min

Max

0.660

7.018

9.578

10.364

0.580

9.350

11.477

deviation

232

0.509

0.049

0.378

0.595

a

280

0.511

0.047

0.378

0.595

CTOT

280

4.512

0.329

3.134

5.260

CTOTV

280

0.042

0.033

0.009

0.294

Patent

230

0.084

0.060

0.001

0.237

280

0.078

0.058

0.001

0.237

FDI

279

0.030

0.023

-0.011

0.141

FDI_m

241

0.008

0.007

-0.0090.035

FDI_ma

280

0.007

0.009

-0.021

0.037

Intermediate_X

276

0.023

0.034

0.000

0.139

Intermediate_M

277

0.079

0.046

0.029

0.226

Inflation

280

0.102

0.127

-0.012

0.999

Trade

279

0.536

0.285

0.120

1.334

Social

277

0.110

0.047

0.025

0.263

Gini

PatentaNote: a indicates that the observations are linear interpolated.

41

1

Table 4. Estimation results of equations (1) and (2)Estimation technique

k
Gini
y
y2
CTOT
CTOTV
PatentFDI

FE 3SLS
(1)
(2)
y
Gini
0.567 ***
(0.071)-1.795 ***(0.573)-0.279 ***(0.080)0.008 *
(0.005)0.026-0.021 ***(0.031)(0.007)0.138-0.002(0.140)(0.036)
1.471 ***
0.513 ***
(0.389)(0.081)2.100 ***
0.496 ***
(0.584)(0.155)

FE 3SLS
(1)
(2)
y
Gini
0.732 ***
(0.035)-0.501 **(0.201)-0.318 ***(0.082)0.011 **
(0.005)0.029 *
-0.013 **(0.017)(0.006)0.141-0.053(0.116)(0.038)0.626 **
0.379 ***
(0.265)(0.087)1.193 ***
0.345 ***
(0.362)(0.124)

FDI_m
Intermediate_X
Intermediate_M
Patent * FDI

2.086 **
(1.004)-0.457(0.382)-6.858(4.297)1.086 ***
0.849
(0.195)(0.610)-0.099-0.626 **(0.099)(0.301)-2.578 **-1.611(1.133)(3.304)

Patent * FDI_m
Patent * Intermediate_X
InflationTrade-14.97 **(6.730)0.034(0.041)
0.006(0.058)SocialCons

2

Linear interpolation
Number of obs.
Number of countries
R -squared
F testHausman test

3.560 ***
(1.042)
No
190
14
0.992
157.0 ***
-283.5-5.486 ***(1.574)0.007(0.011)0.013(0.015)-0.178 **(0.071)2.369 ***
(0.347)
190
14
0.913
96.91 ***

-5.234(4.553)0.029(0.035)0.072 *
(0.039)1.270 ***
(0.441)

FE 3SLS
(1)
(2)
y
Gini
0.750 ***
(0.034)-0.479 **(0.191)-0.239 ***(0.083)0.007(0.005)0.030 *
-0.012 **(0.017)(0.006)
0.198 *
-0.039(0.116)(0.039)0.710 ***
0.367 ***
(0.217)(0.070)2.217 ***
(0.765)0.793 ***
1.082 *
(0.197)(0.603)0.029-0.588 *(0.102)(0.302)-1.679(1.152)-1.264(9.594)-3.575 **-5.673(1.502)(4.541)-0.001
0.031(0.013)(0.035)-0.0100.063(0.013)(0.040)-0.383 ***(0.074)2.494 ***
1.050 **
(0.366)(0.424)

Yes
274
274
14
14
0.993
0.866
280.3 ***
86.29 ***
330.6 ***

0.924 ***
(0.251)0.764 ***
(0.197)0.084(0.101)-6.406 **(3.248)-3.282 **(1.504)0.001(0.013)-0.021(0.014)-0.384 ***(0.074)2.105 ***
(0.368)
Yes

274
14
0.993
256.8 ***

274
14
0.868
103.2 ***
-1124

3

Note: Numbers in parentheses represent standard errors. ***, **, and * denote4

significance at the 1%, 5%, and 10% levels, respectively. Dummy variables indicating

5

sample characteristics of the Gini coefficients are included in all specifications of

6equation (2).

42

1Table 5. Estimation results of equations (1) and (2) using different country groups
Country groups
Estimation technique

k
Gini
y
y2
CTOT
CTOTV
Patent
FDI_m
Intermediate X
Intermediate M
Patent * FDI_m
Patent * Intermediate X
Inflation
Trade
SocialCons

2

Linear interpolation
Number of obs.
Number of countries
R -squared
F testHausman test

South America
FE 3SLS
(1)
(2)
y
Gini
0.854 ***
(0.034)-0.115 ***(0.044)-0.204(0.217)0.005(0.012)
0.095 ***
-0.021 ***(0.025)(0.008)0.138-0.015(0.142)(0.038)0.278
0.340 ***
(0.270)(0.074)0.961
0.987 ***
(0.967)(0.275)-1.570 *0.651 **
(0.922)(0.286)-0.432-0.105(0.400)(0.112)
19.61-10.12 ***(12.15)(3.49)
6.977-1.929(7.974)(2.166)0.040
0.012(0.039)(0.012)-0.1220.014(0.076)(0.022)-0.370 ***(0.080)-0.4212.074 ***
(0.376)(0.961)
Yes
176
176
9
9
0.986
0.918
98.40 ***
266.6 ***
7261 ***

12 LACsFE 3SLS
(1)
(2)
y
Gini
0.765 ***
(0.037)-0.409 *(0.227)-0.355 ***(0.070)0.014 ***
(0.004)0.013-0.029 ***(0.021)(0.006)0.165
0.010(0.148)(0.045)0.678 ***
0.347 ***
(0.221)(0.064)1.704 **
1.095 ***
(0.807)(0.234)1.201 *
0.754 ***
(0.611)(0.181)-0.418-0.027(0.308)(0.094)9.971-7.682 **(10.036)(3.015)-5.785-2.613 *(4.599)(1.382)0.031
0.019(0.042)(0.013)-0.0090.026 *
(0.046)(0.014)-0.327 ***(0.077)0.916 *
2.669 ***
(0.489)(0.304)
Yes
236
236
12
12
0.992
0.875
145.6 ***
81.50 ***
454.3 ***

3

Note: Numbers in parentheses represent standard errors. ***, **, and * denote4

significance at the 1%, 5%, and 10% levels, respectively. Dummy variables indicating

5

sample characteristics of the Gini coefficients are included in equation (2).

43

1Table 6. Estimation results of equations (1) and (2) using triennial data
Estimation technique

FE 3SLS
y

k
Gini

Gini

0.691 ***
(0.057)-0.702 **(0.307)y

0.357(0.267)-0.026 *(0.015)-0.008(0.011)-0.053(0.066)
0.469 ***
(0.166)
1.378 ***
(0.506)0.470(0.409)0.266(0.197)-17.52 **(7.834)-3.795(3.000)0.006(0.028)-0.077 ***(0.026)-0.448 ***(0.152)-0.597(1.184)

y2
CTOT
CTOTV
Patent
FDI_m
Intermediate_X
Intermediate_M
Patent * FDI_m
Patent * Intermediate_X
Inflation
Trade

0.069 **
(0.027)0.262(0.174)0.834 *
(0.454)2.918 **
(1.354)2.181 **
(1.005)-0.767(0.503)12.04(19.06)-4.840(7.90)
0.108 *
(0.063)0.056(0.069)SocialCons

2

Linear interpolation
Number of obs.
Number of countries
R -squared
F testHausman test

1.550 **
(0.692)
Yes
84
14
0.996
89.56 ***

84
14
0.896
33.99 ***
912.4 ***

3

Note: Numbers in parentheses represent standard errors. ***, **, and * denote4

significance at the 1%, 5%, and 10% levels, respectively. Dummy variables indicating

5

sample characteristics of the Gini coefficients are included in equation (2).

44

1
2

Figure 1.

45

1Figure Captions

2

Figure 1. Triangular relations amongst peripherality, income inequality, and per-capita

3

income.

4

Source: Authors’ elaboration.

5

Note: Arrows indicate that the variable in question affects the other variable in the6particular direction indicated. + (–) denotes that the variable has a positive (negative)

7

effect.

46