Discussion Papers In Economics
And Business

Globalization and Economic Inequality in the Short and Long Run:
The Case of South Korea 1975–1995

Sumie Sato

Mototsugu Fukushige

Discussion Paper 07-43

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANGlobalization and Economic Inequality in the Short and Long Run:
The Case of South Korea 1975–1995

Sumie Sato

Mototsugu Fukushige

Discussion Paper 07-43
November 2007

この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANGlobalization and Economic Inequality in the Short and Long Run:
The Case of South Korea 1975–1995
Sumie SATO
Graduate School of Economics, Kobe University
1-2, Rokkodai-cho, Nada-ku, Kobe, 657-8501, JAPAN

Mototsugu FUKUSHIGE∗

Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka, 560-0043, JAPAN

Abstract
We have analyzed the determinants of the Gini coefficient for income and expenditure.
In both cases, we do not find support for the Kuznets inverted-U hypothesis. From economic
globalization viewpoint, the opening of goods markets reduces income inequality both in the shortrun (the Gini coefficient for income) and in the long run (the Gini coefficient for expenditure). On
the other hand, the opening of capital markets increases income inequality both in the short andlong run, but the latter is not statistically significant. These results suggest that the effect of
economic globalization has two routes and two different speeds to affect income inequality.

Running Title: Globalization and Economic Inequality
Keywords: Globalization, Economic inequality, Gini coefficient, South Korea
JEL classification: .R2, D30, F43∗

Correspond to:Mototsugu FUKUSHIGE
Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka, 560-0043, JAPAN
Phone. & fax: +81-6-6850-5248
E-mail: mfuku@econ.osaka-u.ac.jp (M. Fukushige)

11. Introduction
Barro (1991) conducted an empirical investigation into stable economic growth and its
determinants, estimating a so-called “growth regression” by pooling data from many countries
in the world.

His empirical findings suggest that political stability is necessary for stableeconomic growth. Excess inequality in income distribution is considered to be a major threat topolitical instability. Regarding another aspect of the relationship between economic growth andincome inequality, the inverted-U hypothesis proposed by Kuznets (1955) is a famous hypothesis,
and most empirical researchers have attempted to find empirical evidence supporting its existence.
This hypothesis tells us that at early stages of economic growth income inequality is large,
however during later stages income distribution becomes more equitable. Although there aremany empirical studies, for example by Papanek and Kyn (1986), Deininger and Squire (1998),
Dawson (1997), Vanhoudt (2000), Gelan and Price (2003), and Lee (2006), the results are mixed,with both advantages and disadvantages being identified. Furthermore, when we investigate therelationship between economic growth and income inequality in developing countries, we cannot
neglect the effects of globalization of the economy. Of course, globalization of the economy
includes the liberalization of trade and capital mobility. There are several empirical studies on theeffects of trade liberalization on income distribution and inequality, for example by Beyer, Rojas,
and Vergara (1999), Feenstra and Hanson (1997), and Yao (2006) and Choi (2006). Most of them
have pointed out the increases in income differences caused by economic globalization.
In this study we conduct regression analysis on the determinants of income andexpenditure inequality. Because household expenditure is a proxy variable for life cycle income,
Fukushige (1989) and Deaton and Paxson (1994) proposed measuring life cycle income inequality
based on household expenditure. Empirical studies on the determinants of income or expenditure
inequality are conducted by Blejer and Guerrero (1990), Fukushige (1996), Park (1998), andHeshmati (2006).
In this paper, we investigate the determinants of income and expenditure inequality forSouth Korea because it not only achieved remarkable rates of economic development recently but
also promoted trade liberalization and foreign direct investment.

Of course, studies on thedeterminants of inequality in South Korea already exist, e.g., Mah (2003) and Fields and Yoo2

(2000). In particular, Mah (2003) investigated the effect of globalization and tested for the
Kuznets inverted-U hypothesis, using the same dependent variables as in our study, which wereobtained from Park and Kim (1998). Although Mah investigated the determinants of expenditure
inequality, he does not consider expenditure inequality as a proxy for life cycle income inequality.
In this paper, we focus on this relationship and regard the income inequality in each year asincome inequality in the short run and the expenditure inequality as income inequality in the longrun. We can investigate the difference in the effects of globalization in the short and long run tocompare the determinants of income and expenditure inequalities.
This paper is organized as follows. In Section 2, we describe the data used in this study.
In Section 3, we conduct our empirical analysis on the determinants of income and expenditure
inequality. In Section 4, we summarize the empirical results.

2. Data
In general it is difficult to obtain time series data about income inequality or income
distribution in developing countries, but for South Korea there is a suitable study by Park and Kim(1998). In this paper, we use Park and Kim’s (1998) data set containing Gini coefficients forincome and expenditure. They reported Gini coefficients between 1975 and 1995 (21 years). To
investigate the determinants of the Gini coefficients in regression analysis, we use the following
independent variables: the growth rate of GDP denominated in South Korean won as a variable
representing business conditions in the South Korean economy, per capita GDP in US dollars andits squared value as variables for testing the Kuznets inverted-U hypothesis, thewon/US$ exchange rate and its squared value as variables for adjusting the nomination of theeconomic growth rate (on a won basis) or per capita GDP (on a US dollars basis) and reflecting
the international evaluation of the Korean economy.

Furthermore, we add the following

independent variables in the regression analyses with respect to the globalization index: the totalof exports and imports divided by GDP is a measure of openness of goods markets, and net direct
investment inflows and its accumulated value, both of them denominated in US dollars, asmeasures of the openness of capital markets. To estimate the accumulated values of foreign direct
investment on a US dollar basis, we take the following four steps. Firstly, we convert annual

3foreign direct investment inflows denominated in US dollars into Korean won starting in 1962
using the exchange rate between US dollars and Korean won. Secondly, we deflate annual direct
investment inflows by the consumer price index using 2000 prices as the base year. This process
constructs a real time series of foreign direct investment in South Korea (FDI-FL). Thirdly, we
sum up the real foreign direct investment flows from 1962 with depreciation rate (δ) fixed sevenpercent in each year:(FDI − AC t ) = (1 − δ) × (FDI − AC t −1 ) + (FDI − FL t )Following this, we estimate the accumulated amounts of foreign direct investment denominated in
won in each year. Finally, we convert the total value of foreign direct investment on a US dollar
basis to won, using the exchange rate between US dollars and Korean won in each year. Before
discussing the regression analyses, we examine trends in the data. In Figure 1, we graph the Gini
coefficients for income and expenditure by Park and Kim (1998). The graphs show that income
inequality is always larger than expenditure inequality, and that the fluctuations in both
inequalities seem to be connected over the period 1975 to 1995. To investigate the Kuznets
inverted-U hypothesis visually, we construct a scatter diagram of the Gini coefficient for income
and per capita income measured in Korean won or US dollars in Figure 2. Both graphs suggestthat we could apply the Kuznets inverted-U hypothesis to the data only until around 1992, but it isdifficult to determine if the hypothesis holds after 1992.

3. Empirical results
In this section, we investigate the determinants of the Gini coefficients for income andexpenditure in South Korea by Park and Kim (1998). The dependent variable and the candidates
for the independent variables in the regression analysis and their abbreviations are listed in Table 1.
Table 2 shows the regression results of the Gini coefficient for income, and Table 3 shows theresults of the Gini coefficient for expenditure. Furthermore, Table 4 shows the results of the Gini
coefficient for expenditure that included the Gini coefficient for income as an explanatory variable.
This regression removes the short-run effects on the income inequality of each variable from thelife cycle inequality and makes the differences of the short- and long-run effects clear (seeFukushige (1996)). In each table, we report two sets of results: one model that includes all4

explanatory variables, and another model that includes only those variables that maximize theadjusted r-squared value. In view of the exchange rate regimes, the exchange rate was fixed to the
US dollar in the first five years, so we also report the results from 1980 to 1995 in each table.
From the result for the Gini coefficient for income, first, we find no support for the
Kuznets inverted-U hypothesis because the coefficient for squared per capita GDP is positive. In
Figure 3, including the abovementioned result for the Gini coefficient for expenditure, we graphthe marginal effects of per capita GDP on the Gini coefficient for income (an increase of one US
dollar) from the result for 1980 to 1995. When per capita GDP is in the range 800 to 10 000 US
dollars, the marginal effect gradually approaches zero, but it always has a negative effect. Thismeans that an increase in per capita income reduces income inequality. With regard to theexchange rate, the coefficient of the exchange rate is positive and the coefficient of its squared
variable is negative. Figure 3 shows the marginal effects of the exchange rate when the won
depreciates by one won to the US dollar, using the result from 1980 to 1995. It indicates that theexchange rate has a positive effect when the US dollar is worth more than 850 won, and it has anegative effect when it is lower than this. As for the effect of globalization, the greater the
openness of goods markets, the lower the income inequality.

However, both of the direct

investment inflows and their accumulated values indicate that the degree of openness of capital
markets causes higher income inequality. This result might indicate that the opening of goods
markets increases the chance of wealth for everybody, however the opening of capital markets
provides benefits only to specific industries or corporations.
The Gini coefficient results for expenditure indicate that using data commencing in 1975
or 1980 has little effect. Considering the effect of exchange rate regimes, we investigate the resultwith data commencing in 1980 in this section. Furthermore, we do not obtain results supporting
the Kuznets inverted-U hypothesis in this case (see the marginal effect in Figure 3). As for theexchange rate, its squared value is not significant, so its marginal effect is always positive. This
result is different from that of the Gini coefficient for income. With regards to globalization, theopening of capital markets raises expenditure inequality and the opening of goods markets reduces
expenditure inequality. These are the same results as for income inequality.
From the result in Table 4 where we included the Gini coefficient for income as an5

independent variable, a rise in the openness of goods markets reduces the inequality in the data
commencing in 1975 and in 1980. It also shows that a rise in the openness of capital markets has
the effect of raising inequality, although select variables are slightly different according to the data
period. Considering that this case includes the Gini coefficient for income as an independent
variable, the results suggest that each selected variable has an additional effect on long-run income
inequality.
In particular, the fact that the coefficient on the variable for the openness of capital
markets is not significant and that for the goods markets is significant and negative is important.
This result indicates that the opening of capital markets affects short-run income inequality
however its effects do not continue, while the opening of goods markets affects not only short-run
income inequality but also long-run income inequality.

Furthermore, the former increases

inequality and the latter decreases inequality, so that the effect of economic globalization has tworoutes and two different speeds in affecting income inequality.
As for the marginal effects of per capita GDP, Figure 3 shows that the direct effect of percapita GDP on expenditure inequality from Table 4 is negative and its absolute value is smaller
than the estimated effect from Table 3, however the absolute value of the total effect, which
includes the indirect effect through the Gini coefficient for income calculated from Tables 3 and 4,
is larger than the estimated effects from Table 3. This result suggests that the increase in percapita GDP makes income inequality lower both in the short and long run.

4. Conclusion
In this study, we have econometrically analyzed the determinants of the Gini coefficientfor income and expenditure. In both cases, we do not find support for the Kuznets inverted-U
hypothesis. This is a different result to Mah (2003). As for economic globalization, the openingof goods markets reduces income inequality both in the short run (the Gini coefficient for income)and in the long run (the Gini coefficient for expenditure). On the other hand, the opening of
capital markets increases income inequality both in the short and long run, but the latter is notstatistically significant. These results suggest that the effect of economic globalization has tworoutes and two different speeds to affect income inequality.

6Finally, we have to mention a remaining problem with this research. The time period weused in this study is less than 20 years: from 1975 or 1980 to 1995. We are therefore unable toconsider the robustness of the empirical results. Hopefully, in the near future, sufficient data will
be accumulated to allow a historical data analysis similar to Heshmati (2006).

ACKOWLEDGMENTS
This research was partly supported by the Murata Science Foundation, which we thank forfinancial assistance.

References
Barro, R.J.

(1991) Economic growth in a cross section of countries, Quarterly Journal of

Economics, 106, 407–443.
Beyer, H., Rojas, P. and Vergara, R. (1999) Trade liberalization and wage inequality, Journal of
Development Economics, 59, 103–123.
Blejer, M.I. and Guerrero, I.

(1990) The impact of macroeconomic policies on income

distribution: an empirical study of the Philippines, Review of Economics and Statistics, 72,
414–423.
Choi, C. (2006) Does foreign direct investment affect domestic income inequality? Applied
Economics Letters, 13, 811–814.
Dawson, P.J. (1997) On testing Kuznets’ economic growth hypothesis, Applied Economics Letters,
4, 409–410.
Deaton, A. and Paxson, C. (1994) Intertemporal choice and inequality, Journal of Political
Economy, 102, 437–467.
Deininger, K. and Squire, L. (1998) New ways of looking at old issues: inequality and growth,
Journal of Development Economics, 57, 259–287.
Feenstra, R.C. and Hanson, G.H. (1997) Foreign direct investment and relative wages: evidence
from Mexico’s maquiladoras, Journal of International Economics, 42, 371–393.
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patterns and underlying causes, Review of Income and Wealth, 46, 139–159.
Fukushige, M. (1989) A new approach to the economic inequality based upon the Permanent
Income Hypothesis, Economics Letters, 29, 183–187.
Fukushige, M. (1996) On the determinants of households' consumption inequality: an empiricalanalysis for Japanese workers' households, Applied Economics Letters, 3, 541–544.
Gelan, A. and Price, G.N. (2003) African economies and the Kuznets curve: an exploratory
investigation, Applied Economics Letters, 10, 747–751.
Heshmati, A. (2006) Conditional and unconditional inequality and growth relationships, Applied

7

Economics Letters, 13, 925–931.
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28.
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Letters, 13, 851–855.
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8

Table 1. The Data

Variables

Definition

GINI-IN

×1000

GINI-EX×1000

GROWTH

Growth rate of GDP on won basis

GDP

Per capita GDP on US$ basis (in thousand 1000US$)

EXRATE

US$/won exchange rate

OPENNESS

×100 ( Sum of export and import / GDP )FDI-FL

Foreign direct investment inflows on a US$ basis ( in billion US$)FDI-AC

Accumulated amounts of foreign direct investment flows (in billion US$)9

Table 2. Results for GINI-IN
Period1975–1995

1980–1995
Maximum R

Const.

2–2.46726–417.811–480.672(–0.039)(–0.793)(–1.714)1.22027

0.187126（2.265）（0.145）–79.9319–100.958–102.717（–6.327）（–4.563）（–5.926）

3.84534

4.578394.62554（5.082）（4.498）（5.119）1.729032.547232.68373（5.644）（2.118）（3.830）–0.00104636–0.00150287–0.00158336(–5.040)(–2.065)(–3.589)–48.71810–43.8579–43.2526（–6.024）（–4.301）（–4.963）

27.981024.441124.6278(3.710)(2.466)(2.675)1.14166

1.83738

1.89829（5.171）（2.919）（4.323）Adjusted R-squared

0.87590.81270.8356

Log Likelihood–61.0468–46.0008–46.0248

SBIC

74.7471

58.4775

57.1152

RESET

0.55110.03540.0468LM hetero

2.61950.91520.7666Jarque–Bera

1.06250.47280.5185GROWTH

GNP

GNP２

EXRATEEXRATE２

OPENNESSFDI-FL

FDI-AC

2.400
2.248
2.218
Durbin–Watson
RESET, LM hetero, Jarque–Bera and Durbin–Watson are RESET test, LM test for heteroskedasticity, Jarque–Beratest for normality and Durbin–Watson Ratio for serial correlation, and t-value for each coefficient is inparenthesis..

10Table 3. Results for GINI-EX: Without GINI-IN
Period1975–1995

1980–1995

Maximum R

Const.

GROWTH

GNP

GNP２

EXRATEEXRATE２

FDI-FL

FDI-AC

Maximum R

239.480

264.701111.149

365.884(4.003)(1.651)(0.334)(5.606)0.214297–0.313597(0.421)(–0.384)–25.0880–16.3207–47.6714–40.0920(–2.100)(–2.273)(–3.408)(–4.143)

1.16801

0.697530

2.034211.72864(1.633)(1.375)(3.16112)(3.371)0.675864

0.617638

0.729614

0.100668(2.333)(2.307)(0.960)(2.652)–0.000422711–0.000397446–0.000387780(–2.154)

OPENNESS2(–2.192)2(–0.843)–29.9869–28.5016–25.0296–24.7966（–3.922）（–3.937）（–3.883）（–4.657）

19.1527

20.7925

10.4918

9.90349（2.686）（3.153）（1.675）（1.707）0.189748

0.960533

0.772477（0.909）（2.414）（2.967)

Adjusted R-squared

0.59730.62540.81750.8404

Log Likelihood–59.8698–60.7307–38.6636–39.6000

SBIC

73.5701

71.3865

51.140349.3041

RESET

0.59550.54480.0232

0.0935

LM hetero

2.10545.104*

0.03650.1970Jarque–Bera

1.53601.47420.14200.1097

2.094
2.117
2.380
2.131
Durbin–Watson
RESET, LM hetero, Jarque–Bera and Durbin–Watson are RESET test, LM test for heteroskedasticity, Jarque–Beratest for normality and Durbin–Watson Ratio for serial correlation. * means statistically significant at 5%, and tvalue for each coefficient is in parenthesis.

11

Table 4. Results for GINI-EX: Including GINI-IN
Period1975–1995

1980–1995

Maximum R

Const.

GINI-INGROWTH

GNP

GNP２

EXRATEEXRATE２

OPENNESS

2

Maximum R

240.505241.655

180.277226.559(4.284)(8.371)(0.497)(4.093)0.415585

0.362266

0.165453

0.222264(1.623)(5.478)(0.664)(1.880)–0.292829–0.344557(–0.513)(–0.404)–8.13044–30.9677–25.2049(–0.348)(–1.065)(–1.893)–0.430055

1.27670

1.03435(–0.361)(0.965)(1.633)–0.042693

0.308168

0.076902(–0.082)(0.304)(1.754)0.0000121413–0.000139124(0.037)(–0.229)–9.74041–11.5205–17.7732–14.2616（–0.677）（–4.678）（–1.386）（–2.883）

7.524217.47000

6.44794（0.768）（1.509）（0.772）–0.284706–0.121700

0.656533

0.596010–0.809（–2.232）（1.063）（1.754）

Adjusted R-squared

0.64560.73720.80170.8484

Log Likelihood–57.6156–58.4101–38.0960–39.1932

SBIC

72.8383

66.021451.9589

48.8973

RESET

0.29880.04450.0326

0.3611LM hetero

2.3196

0.66880.08830.0911Jarque–Bera

1.28420.77940.87440.3611

FDI-FL

FDI-AC

2

2.370
2.223
2.503
2.367
Durbin–Watson
RESET, LM hetero, Jarque–Bera and Durbin–Watson are RESET test, LM test for heteroskedasticity, Jarque–Beratest for normality and Durbin–Watson Ratio for serial correlation and t-value for each coefficient is in parenthesis..

12Figure 1. Gini coefficients for income and expenditure
0.5

0.4

0.3

0.2

0.1

0
1975

1977

1979

1981

1983

1985

Gini coefficient for income

1987

1989

1991

1993

Gini coefficient for expenditure

13

1995

Figure 2. Per Capita Income and Gini coefficient for Income(Income on a Won basis and Gini coefficient)
0.41

0.4

0.39

0.38

0.37

0.36

0.35

0.34

0.33
0

2000

4000

6000

8000

10000

12000

Income(Won)(Income on a US$ basis and Gini coefficient)
0.41

0.4

0.39

0.38

0.37

0.36

0.35

0.34

0.33
0

1000

2000

3000

4000

5000

Income(US$)

14

6000

7000

8000

9000

Figure 3 Marginal effects of per capita GDP (1US$)
800

1800

2800

3800

4800

5800

6800

7800

8800

0
-0.001-0.002-0.003-0.004-0.005-0.006-0.007-0.008-0.009-0.01

On Gini-In
On Gini-Ex with Gini-In (direct)

15

On Gini-Ex without Gini-In
On Gini-Ex with Gini-In (total)

9800