Does Financial Development Promote Income Equality?

International Journal of Policy StudiesVol.2, No.2, 2011

Does Financial Development Promote Income Equality?

Dongsook HanUniversity of California, Korea

Kwangbin BaeSeoul National University, Korea

Hosung SohnUniversity of California, USA

AbstractThis study empirically tests if financial development promotes income equality. Using panel data for OECD

countries from 1970 to 2007, a regression analysis using a pooled OLS and a fixed effect model was conducted.

The results of the analysis suggest that (contrary to the findings of previous empirical research examining the

relationship between financial development and income inequality) financial development is positively associated

with income inequality.

Key Words: financial development, income inequality, fixed effect, access to finance

INTRODUCTION

Current financial industries bear tremendous effects on individual economic participants as well as on national economies. Demands on financial services are increasing due to the rapid aging of the general population and the concomitant dependence on savings and passive income among retired people. It is impossible to imagine the operation of firms or engaging in investments without the funding functions of financial institutions. Financial development is important because it is the vehicle to realize sustainable real economic growth (see Figure 1). The data in Figure 1 ranges from 1970 through 2005 and pertains to 30 Organization for Economic Cooperation and Development (OECD) countries. OECD countries are used because most of their cross-country data are available, and the validity of the data is quite strong, given that it was compiled by a single organization. As can be seen from the figure, there is a strong and positive relationship between GDP per capita and value added in

the financial industry; the actual correlation coefficient is 0.7842 that suggests a high positive association between the two variables. Many studies have analyzed the relationship between financial development and economic growth with a majority that shows that national financial development positively affects economic growth (Levine, 1997; Han, 2000; Chakraborty et al., 2006; Deidda et al., 2006).

Many researchers have concluded that economic growth can be brought about by financial development; however, concerns have been voiced about undertaking those financial developments largely because financial development can deepen income inequality. Even if financial development were to exacerbate income inequality, the goal to develop the financial sectors cannot be abandoned because it is a core industry to raise the competitiveness of a country and quickly reform an existing financial system. This point of view assumes that a financial-sector overhaul implies a ‘weeding

4 International Journal of Policy Studies

out’ of certain financial industries as well as a degeneration into a backward nation.

That granted, does financial development aggravate income inequality among people? Income inequality has been a center of debate among many scholars regardless of whether developments in the financial industry increase or decrease. First, when financial sectors develop, there tends to be a lowering of information and transaction costs among corporations and households; subsequently, the access to finances by low-income groups will be enhanced. Accordingly, their incomes will increase and contribute to higher income equality (Burgess et al., 2005). Other researchers argue that since financial development fosters economic growth, it has positive effects on income redistribution (Aghion et al., 1997). In this respect, we can assume that financial development is effective in ameliorating income distribution.

Figure 1. Two-way Graph between GDP per Capita and Value Added in Financial Industry

Data Source: OECD Statistical Database

Some argue that inequality worsens as finance develops to benefit from financial development and ascribe to the high-income bracket (Greenwood et al., 1990). While people who earn large incomes actively pool the risk in using their finances (with many opportunities to raise their income); however, low- income earners are often spurned by firms that offer financial services or encounter obstacles to enjoying the advantages of financial development. In this respect, financial developments have negative effects on income redistribution. Therefore, this study empirically analyzes the relationship between two variables and discusses a way to increase financial developments in terms of income inequality.

Before proceeding, it is essential to define the term ‘financial development’. While doing so is not an easy

task, it might be easier to define it with respect to financial liberalization that has an inseparable relationship to financial development. Financial liberalization increases the fluidity of a fund by transferring monies from economic participants who are relatively affluent to those who are in a liquidity crunch that diversifies asset types; accordingly, parties to the financial transaction can reduce their risk and raise their profits. This in turn broadens the scope and stability of the business. In these manners, financial liberalization affects microeconomic participants as well as the macroeconomy and it is inextricably linked to financial development. Hence, financial development is defined as the generation of financial innovation that acts in a manner that contradicts existing financial regulations and systems; subsequently, it may (as a result) abolish or mitigate existing regulations to secure stability and promote efficiency (Sakura Integrated Research Center, 1993).

In the meantime (although it does operate on an autonomous basis) the financial industry cannot free itself absolutely from governmental control. The government plays an important role in supervising the loans and funding of financial institutions because the failures in firm investments lead to lender insolvency or bankruptcy that can negatively affect the overall economy. Furthermore, the government has a role in protecting financial service consumers as they are at a relative disadvantage in terms of obtaining information. Since the problem of income redistribution falls within the realm of government policy, it is evident that income inequality must be treated as a national policy agenda.

In this study, the relationship between financial development and income inequality has been analyzed using data from 30 OECD countries. Even if financial development is negatively associated with income inequality, we cannot postpone our goal of promoting its development. Therefore, we will inquire into determinants of income inequality other than financial development as well as empirically analyze the relationship.

PREVIOUS RESEARCH

As to what effect financial development has on income inequality, most previous research concludes that income inequality ameliorates as financing develops. First, there

Does Financial Development Promote Income Equality? 5

Author Contents Result*

Theoretical Analysis

Aghion andBolton (1997)

Increased capital accumulation generates trickle-down effects and accelerates the resource distribution

NegativeRelationship

Greenwood andJovanovic (1990)

Benefits from financial development revert to the high-income bracket during the initial stage of development

PositiveRelationship

Empirical Analysis

Levine (1997)Financial developments boosts economic growth and this in turn increases the income of the poor


Burgess andPande (2005)

Financial Development solves the credit constraints of the poor and increase the access to finance


Clarke (2006)Financial intermediary development reduces GINI coefficients


Beck, Demirguc-kuntand Levin (2007)

40% of income growth of the poor is directly generated by development, while 60% is created by the economic growth initiated by the development


Kim (2007)Regional income inequality increases as finance develops

PositiveRelationship

*Relationship in the Result category implies the relationship between financial development and income inequality

Table 1. Summary of Previous Resea

are two competing theoretical hypotheses vis-à-vis the relationship between these two variables: the inequality- narrowing hypothesis and the inequality-expanding hypothesis. Aghion et al. (1997) assume imperfect capital markets and contend that as more capital accumulates in the capital market, the wealth of the rich transfers to the poor; if the capital accumulation rate is sufficiently high, trickle-down effects will occur, stimulate the economy, and accelerate resource distribution. Consequently, it will reduce income inequality. In contrast, Greenwood et al. (1990) raise a slightly different point of view. They discuss the problem of income inequality, assuming that the financial system and economic growth are inseparable. As the economy grows, the funds that are required to develop the financial structure simultaneously increase further; due to financial development, the number of financial intermediaries involved increases. As a result, investments can be made more efficiently that can lead to profits and greater economic growth. However, they contend that in its initial stages, the benefits of financial development revert to the high-income group and (accordingly) income inequality worsens.

Regarding these two theoretical studies, few researchers have conducted empirical analyses of the relationship between financial development and income inequality; the majority of them have derived a negative relationship between these two variables. Financial development reduces income inequality in two ways. First, financial development directly raises the income of the poor. Second, financial development increases the economic growth rate and this abates income inequality.

Research into the direct impacts of financial development has formed the following conclusions. Those whose credit scores are low or who cannot afford a mortgage are usually influenced by the constraints of capital markets with respect to information and transaction costs; however, such credit constraints can be resolved as financial developments occur and lead to a greater increase in income among the poor compared to high-income earners. Burgess et al. (2005) determined that financial developments could resolve issues regarding the credit constraints of the poor. They examined the effects of the Indian Social Banking Program on the borrowing behavior of low-income individuals and found that the rural branch network expands and enhances opportunities for the poor to gain

greater access to financial institutions as financial developments unfold. Accordingly, it contributed to reductions in rural poverty. Using cross-country data from 1960 to 1995, Clarke et al. (2006) explored the link between financial intermediary development and GINI coefficients. Their results conclude that there is a significant negative relationship between these two variables. Beck et al. (2007) provided a more concrete conclusion on the matter and concluded that (among the effects of financial development) 40% of income growth among the poor has been wrought by reductions in income inequality and (due to financial developments) the remaining 60% has been the result of financial developments on economic growth. Furthermore, they also found that the proportion of the population living on less than one dollar a day decreases as finance develops.

Levine (1997) provides proof of the indirect effects of financial development. Pointing to the positive relationship between economic growth and financial development, he states that financial development opens the possibility for the efficient distribution of resources to


the whole of society and boosts the rate of economic growth that increases the income of the poor; however, few empirical studies verify the inequality-expanding hypothesis. Kim (2007), for example, analyzes South Korea’s regional income inequality problem and asserts that financial income deepens the total income inequality with variations by region and year.

The results of the existing research are provided in Table 1.

DATA AND MODEL

Data

The data used in this study is from 1970 to 2007 and pertains to 30 OECD countries. The data are in the form of panel data that are both cross-sectional and time-series in nature (sampling has been repeatedly conducted for a specific sample). Analysis using panel data has several advantages over cross-sectional or time-series analyses. For example, panel data can correct the omitted variable bias (Hausman et al., 1981) that create more variations within the data by combining variation across cross- sectional units with variation over time-series data and alleviate multicollinearity problems (Kennedy, 2008).

Independent Variables

The ratio of value added from the financial industry to the total value added appropriately accounted for financial development. Previous research used the ‘private credit’ variable to estimate financial development. Private credit focuses on individual aspects and it is unlikely to estimate the financial development of an entire country. Accordingly, value added from the financial industry reflected the structural aspects of financial development. Here, the total value added is defined as output minus intermediate consumption and equals the sum of employee compensation, gross operating surplus of government and corporations, gross mixed income of unincorporated enterprises, and taxes less subsidies on production and imports (except for net taxes on products).1 Value added from the financial sectors corresponds to the value added in banks, insurance, real estate, and other business services.2 Real

estate is included in value added because the real estate business has dominated the financial sector in the past. Data on the variable were obtained from the OECD Factbook (2009); however, some of the data on the value added (especially during the 1970s and 1980s) are estimated values to increase the sample size and are used because it was believed that it would not pervert the true relationship.

After careful investigation of existing research, five variables (GDP per capita, trade openness, school attainment, employment rate, and a ratio of social expenditure to GDP) are included as control variables and are expected to influence income inequality. First, we included GDP per capita and the logarithmic transformation of the variable is used in the analysis.3 Stack (1980), Croix et al. (2003), and Beck et al. (2007) point out that GDP per capita is related to income inequality. Data on GDP per capita were collected from the World Bank statistical database from 1970 to 2007.

The second control variable in this study is ‘trade openness’. Stack et al. (1982) argue that as the share of the amount of total exports to GDP increases, the economy grows and it reduces income inequality. The current study, however, follows the lead of Beck et al. (2007), who use the ratio of the average of exports and imports (for goods and services)4 at current prices to GDP to account for trade openness. We concluded that controlling both exports and imports is more conducive to trade openness than just controlling the amount of exports. Again, the data on imports and exports have been acquired from the OECD Factbook (2009).

Next, we examined employment rates and follow the methodology of previous studies that investigated the determinants of income inequality conclude that the employment /unemployment rate affects income inequality. Employment data were obtained from the OECD statistical database.

Fourth, the current study controlled for schooling (level of educational attainment). Beck et al. (2007) used the average years of schooling as a control variable and as a human-capital indicator that can affect individual income level. In this study, we utilized the expected years of schooling, furnished by World Bank data. In the analysis, we used average estimates for females and males. Whenever data were available for only males or only females, we substituted the average value with one


Name ID Source Scale

Dependent Variable

GINI coefficient (GINI) GINIGapminder Foundation

%

Independent Variable

Ratio of value added from financial sectors to total value added (FIN)

FIN

OECD

%

Natural logarithm of GDP per capita

LOGGDPC World Bank Unit

Ratio of export and import to GDP

TRADE OECD %

Employment rate EMPLOY OECD %

Average of expected years of schooling

SCHOOL World Bank Year

Ratio of public social expenditure to GDP

SOCIAL OECD %

Table 2. Summary of Variablesof them to increase the sample size.Finally, the ratio of public social expenditure to GDP

has been controlled. Lee (2005) used a ratio of public expenditure to GDP5 and he asserts that (as the ratio increases) it boosts the overall economy and ultimately has a positive impact on income distribution. Public expenditure, however, includes expenditures (other than social expenditures) that are more closely related to income inequality. Therefore, public social expenditure was used as a variable (rather than public expenditure) in the current study. Public expenditure comprises cash benefits, the direct ‘in-kind’ provision of goods and services, and tax breaks with underlying social purposes.6 The expenditure used in this analysis refers to public social benefits and excludes similar benefits provided by private charities. The data are from the OECD Factbook (2009).

Dependent Variable

Income inequality is the dependent variable and the GINI coefficient7 has been used to account for the degree of income inequality in the analysis. The deciles distribution ratio is sometimes used to account for income inequality; however, the GINI coefficient is more widely used in such analyses since it is difficult to obtain cross-country data on this variable. Regarding the GINI coefficient, there are missing values for some countries. To increase the sample size, we could have substituted missing data with estimates from other organizations8; however, the value of a GINI coefficient is quite different among organizations because each organization uses differently defined incomes in estimating the coefficient. Thus, in the analysis, we use only the estimates of a single organization, the Gapminder Foundation. The Gapminder Foundation releases GINI coefficient values calculated according to household disposable income.9 One must note that the Gapminder Foundation database does not contain data for South of Korea; therefore, we acquired data from Korea’s National Statistical Office and calculated South Korea’s GINI coefficient using disposable income as the basis.

The variables used in the analysis are summarized in Table 2.

Model

A panel regression was employed due to the nature of the datasets used in this study. Among the various panel regression methods available, we used a fixed effects model to estimate the parameters. Another strong method called the random effects model is attractive when the unobserved effect is uncorrelated with all independent variables; however, such an assumption is not a probable in our case (Wooldridge, 2006). If the number of time variables is large and the number of cross-section variables is small, there are few differences between the fixed effects and random effects models, with respect to the values of the estimated parameters (Gujarati, 2003).

In the analysis, we adopted a stepwise method. First, we conducted the simple panel regression while considering only the single variable of financial development. After that, we systematically included other variables; in addition, (other than the fixed effect model) we included the regression results from the pooled ordinary least squares (OLS) to compare whether the estimation yields different estimates.


Figure 2. Two-way Graph between Value Added in Financial Sectors and GINI Coefficient

EMPIRICAL ANALYSIS

Descriptive Statistics

Table 3 summarizes the descriptive statistics of our samples. According to Table 3, income inequality in terms of GINI coefficients shows that the value in the 1970s and 1980s was 0.298, but increased to 0.309 in the 1990s and 2000s. This implies that income inequality was aggravated; however, a comparison of the estimate between the 1970s and 1980s, and the 1990s and 2000s shows that the value added from the financial sectors increased by 36.6%. The GINI coefficient increased about 3.7%. Simple descriptive statistics suggest that compared to the tremendous change in financial development, changes to income inequality were small.

A two-way plot between GINI coefficients and value added from financial sectors is shown in Figure 2. The figure does not display a clear relationship between the two variables and the Pearson correlation coefficient was calculated as 0.0997 that implies quite a weak correlation between the two variables. However, further analyses using other methods should be conducted because the correlation does not imply causality between income inequality and financial development; therefore, we cannot conclude that these two variables are wholly unrelated.

Variable No. Obs. Mean Std. Dev. Min Max

Total Sample

GINI 580 0.306 0.065 0.197 0.518

FIN 1,018 19.822 5.722 6.491 48.528

LOGGDPC 1,027 9.398 0.762 6.675 11.266

TRADE 1,056 34.469 21.333 4.021 164.137

EMPLOY 829 64.520 8.221 45.528 85.743

SCHOOL 420 14.591 2.560 6.344 20.755

SOCIAL 681 19.194 6.403 1.800 35.800

70, 80s Sample

GINI 188 0.298 0.066 0.197 0.485

FIN 496 16.707 3.992 6.492 36.581

90, 00s Sample

GINI 392 0.309 0.065 0.207 0.518

FIN 522 22.782 5.546 9.509 48.528

Table 3. Descriptive Statistics

Regression Analysis

Pooled OLS

Table 4 represents results from a pooled OLS analysis with a stepwise method. There are six models. In the first model, a simple regression was conducted and a negative coefficient was produced for the financial development variable in that the development of finance promotes income equality. The estimate was statistically significant at the 5% level. Undoubtedly, this does not necessarily suggest a causality relationship between income inequality and financial development, for there are many aspects of bias in our estimates of simple regression.

The direction of the effect of financial development on income inequality changed immediately upon the inclusion of other explanatory variables in the model; however, that direction remained the same across all the models (other than the first model). Therefore, we can conclude that the negative coefficient is not an appropriate direction from which we can draw conclusions. Rather, as the result indicates, financial development is positively related to income inequality. Its statistical significance shows a p-value of less than 1% across all models that provides compelling evidence of increased income inequality.

In the pooled OLS regression, all explanatory variables showed a strong relationship with respect to the GINI coefficient (except for the schooling variable). From Model (2) to (5), the variable log GDP per capita, trade openness, employment rates, and public social expenditures all exhibited a consistent negative coefficient as related to the dependent variable. Furthermore, they were all significant


at less than the 1% level. The effects of these variables coincide with previous research findings that show a decrease in the income inequality level.

In our last model, we included the “expected average years of schooling” variable, assuming that education level affects income inequality. Since data on schooling are not accumulated annually,10 our number of observations dropped drastically, from 496 to 240, when this variable was included. Nevertheless, it still satisfies the central limit theorem. In Model (6), the impact of independent variables on income inequality was the same as the previous models. One slight change from Model (5) is that the significance level of the log GDP per capita dropped from the 1% level to the 5% level; however, schooling was insignificant with regard to income inequality (though its coefficient was negative) and it was not statistically significant.

Variable Model

(1) (2) (3) (4) (5) (6)

FIN 0.0012**(0.0005)

0.0062***(0.0006)

0.0048***(0.0006)

0.0057***(0.0006)

0.0043***(0.0005)

0.0040***(0.0007)

LOGGDPC

--

0.1007***(0.0063)

0.0737***(0.0074)

0.0557***(0.0069)

0.0327***(0.0064)

0.0226**(0.0106)

TRADE

--

--

0.0021***(0.0003)

0.0011***(0.0001)

0.0009***(0.0001)

0.0009***(0.0001)

EMPLOY

--

--

--

0.0029***(0.0003)

0.0024***(0.0002)

0.0022***(0.0004)

SOCIAL

--

--

--

--

0.0048***(0.0003)

0.0041***(0.0004)

SCHOOL

--

--

--

--

--

0.0024(0.0016)

Intercept

0.3333***(0.0116)

1.1492***(0.0516)

1.0504***(0.0570)

0.9541***(0.0525)

0.8133***(0.0487)

0.7345***(0.0744)

R2 0.0100 0.5510 0.3822 0.4384 0.6400 0.5975

No. Obs. 578 577 577 533 496 240

1. The numbers in the parenthesis are standard errors.2. *** denotes significance at 1% level; ** at 5% level; * at 10%

level.

Table 4. Pooled OLS (Dependent Variable: GINI Coefficient)

All the models, except Model (1), suggest that financial development does not promote income equality. Even though many other independent variables had been controlled, the positive association between financial development and income inequality remained constant. We now turn to the fixed effect model, to determine whether there were differences in our estimated

coefficients using pooled OLS.

Fixed Effect Model

Contrary to the pooled OLS model, the positive coefficient appeared in Model (1) where financial development is positively related to income inequality. However, the direction of the impacts of financial development was positive throughout all the models and was also the case with the pooled OLS model. Except in Model (6), the statistical significance is less than the 1% level. An interesting part of the regression results that use the fixed effect model was that the effect of the log GDP per capita was positive with respect to the dependent variable; however, the effect of log GDP per capita was negative in the pooled OLS. The coefficients yielded positive values in Model (3) to Model (6) of the fixed effect models and indicate that countries with higher levels of GDP per capita experience higher levels of income inequality. This situation is plausible because developed countries normally experience deepening polarizations of income distribution. For example, in our panel datasets, the average GINI coefficient was 0.3062. However, the average GINI coefficients of the United

Table 5. Fixed Effect Model (Dependent Variable: GINI Coefficient)

Variable Model

(1) (2) (3) (4) (5) (6)

FIN

0.0023***(0.0002)

0.0025***(0.0004)

0.0026***(0.0004)

0.0011***(0.0003)

0.0013***(0.0003)

0.0013**(0.0005)

LOGGDPC

--

0.0024(0.0032)

0.0013(0.0033)

0.0122***(0.0033)

0.0251***(0.0039)

0.0237***(0.0068)

TRADE

--

--

0.0002(0.0001)

0.0001(0.0001)

0.0003**(0.0001)

0.0002(0.0002)

EMPLOY

--

--

--

0.0006***(0.0002)

0.0017***(0.0002)

0.0021***(0.0003)

SOCIAL

--

--

--

--

0.0017***(0.0003)

0.0017***(0.0005)

SCHOOL

--

--

--

--

--

0.0003(0.0009)

Intercept

0.2572***(0.0042)

0.2753***(0.0243)

0.2692***(0.0247)

0.2026***(0.0240)

0.1833***(0.0259)

0.2141***(0.0428)

R2 0.0100 0.0037 0.0002 0.0002 0.2635 0.2565

No. Obs. 578 577 577 533 496 240

1. The numbers in the parenthesis are standard errors.2. *** denotes significance at 1% level; ** at 5% level; * at 10%

level.


States and the United Kingdom are 0.3481 and 0.3376, respectively, which is about 10% higher than the average value.

Unlike the pooled OLS model, the variable trade openness was not significant with respect to income inequality and was significant only in Model (5). In addition, variables such as employment rate and public social expenditures were negatively associated with income inequality, as was the case in the pooled OLS model. In Model (6), the expected average years of schooling was not statistically significant. Both of these methods showed that financial development (in terms of a ratio of value added in financial sectors to total value added) is positively related to income inequality. The only significant difference on the influence of financial development in either method was that the magnitude of the effect of financial development was larger in the pooled OLS model than in the fixed effect model. Using Model (6), the pooled OLS model indicated a 0.4% increase in the GINI coefficient when the ratio of value added in the financial sectors to total value added increased 1%; however, a 1% change in the financial development was associated with a 0.13% increase in the GINI coefficient.

The analysis suggested that it is desirable to implement policies to decrease the unemployment rate and promote active government intervention in order to narrow the income equality gap since the employment rate and public social expenditures variables were negatively associated with income inequality.

The analysis of this article suggests that (contrary to the findings of previous research) financial development is positively associated with income inequality. How can we understand these results? Several reasons can be raised that may account for the positive relationship between income inequality and financial development. First, as noted in Greenwood and Jovanovic (1990), benefits from financial development revert to the high-income bracket during the initial stage of development. It is plausible to assume in this study that most countries are at the initial stage of development compared to most developed countries since we use data only from OECD countries. Accordingly, financial development is positively associated with income inequality.

Second, a significant body of the literature investigates the relationship between ‘access’ to finance and income

inequality (Demirguc-Kunt and Levin, 2008; Beck, Levin, and Lekov, 2007a, 2007b). This specific body of literature argues that the degree of access to finance that poor people possess and not development itself is salient to promote income equality. However, it is not necessarily true that financial development automatically increases access to finance for all people. If access to finance does not benefit people in low-income brackets, it is less likely that financial development decreases income inequality. As to this study, it may be true that poor people in some countries have less access to finance and do not benefit from financial development.

Finally, the results of empirical analysis often vary depending on the context of the variables as well as the sample types utilized in the analysis. Although this article uses most of the independent variables from previous research, it also uses the ratio of value added from the financial industry to the total value added for a financial development variable. Other research used the ‘private credit’ variable to estimate financial development. As indicated in the data section, private credit focuses on individual aspects and it is unlikely to estimate the financial development of an entire country; in addition, this study only uses OECD countries because of the rich set of variables in these countries. These factors might have brought different conclusions.

CONCLUSION

This study empirically tested whether financial development promotes income equality. Using panel data for OECD countries from 1970 to 2007, a regression analysis using a pooled OLS and a fixed effect model was conducted. The results of the analysis suggest that (contrary to the findings of previous empirical research examining the relationship between financial development and income inequality) financial development is positively associated with income inequality. The positive direction was consistent throughout the six models and two methods used. Unlike previous studies, the current study used a different indicator (value added in financial sectors) to account for financial development. As a result, we came to a different conclusion regarding the effect of financial development on income distribution. Hence, our empirical analysis implies that with regard to the relationship


between financial development and income inequality, one should be cautious in making conclusions in relation to the impact of financial development on income distribution. One reason for the positive coefficient might be that (even as the financial sector develops) access to finances is not being guaranteed for people in low-income brackets. Therefore, government interventions in promoting access to finances should be promoted.

The question remains: Should each country restrain itself in increasing value added in its financial sectors? Obviously, it should not. As noted earlier, financial development is key to national economic success and financial sector development should not be mitigated. Therefore, the government should facilitate its development and simultaneously introduce a government policy that promotes income equality. As the analysis suggests, a policy targeted at increasing the employment rate and raising public social expenditures can help accomplish this goal.

Despite its contribution to the existing literature, our analysis possesses some limitations. Since our dataset comprises only OECD countries, further analysis using data from other countries is desirable. Moreover, securing and promoting financial access might be more closely linked to income inequality than was originally understood and we suggest future empirical research into the effect of access to finances on income distribution.

NOTES

[1] Total value added is lower than GDP, because it excludes value-added tax (VAT) and other product taxes.

[2] Real estate covers rents for dwellings and includes the imputed rents of owner-occupiers.

[3] Using a natural logarithm narrows the range of the variable, allowing the estimates to be less sensitive to the outliers on the explained variable.

[4] Goods consist of merchandise imports and exports. Services cover transport, travel, communications, construction, IT, financial, and other businesses; personal and government services; and royalties and license fees.

[5] The ratio of public expenditure to GDP includes transfer

payments as well as consumption expenses.[6] To be considered ‘social’, benefits must address one or

more social goals. Benefits may be targeted at low-income households, but they may also be specifically intended for elderly, disabled, sick, unemployed, or young people. Programs regulating the provision of social benefits must involve: a) a redistribution of resources across households, or b) compulsory participation. Social benefits are regarded as public when the general government (central, state, and local governments, including social security funds) controls relevant financial flows.

[7] The GINI coefficient has a value of 0 to 1; the higher the value is, the higher the level of inequality.

[8] The World Bank also has a GINI coefficient database.[9] Income consists of earnings, self-employment and capital

income, and public cash transfers; income taxes and social security contributions paid by households are deducted.

[10] In the 1990s and 2000s, data were typically collected on an annual basis. However, data availability was dramatically sparser in the 1970s and 1980s.

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Dongsook Han is a master student of the Luskin School of Public Affairs at the University of California, Los Angeles. She was a PhD candidate of the Graduate School of Public Administration at Seoul National University. Her research interest lies in social inequality and policy analysis.

Kwangbin Bae is a PhD student of the Graduate School of Public Administration at Seoul National University. His research interests include public service motivation, nonprofit organization, performance management systems, and social policy.

Hosung Sohn is a PhD candidate of the Goldman School of Public Policy at the University of California, Berkeley. He is primarily interested in program evaluations. His research topics include Labor Economics, Public Economics, Behavioral Economics, and their applications to the field of public policy.

Received: October 4, 2011

Accepted with one revision: November 6, 2011

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Does Financial Development Promote Income Equality?

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