ECONOMIC RESEARCH CENTERDISCUSSION PAPER

No.161

Determinants of Income Inequality among Korean
Farm HouseholdsbyYuko Arayama, Jong Moo Kim and Ayal Kimhi

November 2006

ECONOMIC RESEARCH CENTERSCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Determinants of Income Inequality among Korean Farm HouseholdsbyYuko Arayama
Nagoya University, Japan
Jong Moo Kim
Sung Kyun Kwan University, Korea
Ayal Kimhi*
The Hebrew University, Israel

Abstract
We extend the existing regression-based inequality decomposition methods to accountfor different income sources and different income regimes, and adequately correct for
selectivity into the different income regimes. We apply these extensions to data on
Korean farm households, and find that they lead to different and more informative
conclusions. We also find that the correction for selectivity is essential. In particular,
our results show that much of the inequality in farm household income comes throughvariations in family size and composition and in land ownership. However, family
size and land ownership contribute to income inequality mostly through farm income,while family composition contributes mostly through non-farm labor income. We also
found that education contributes to income inequality mainly through its effect on
non-farm labor income, but this result was obtained only after differentiating thedecomposition results by income regimes. Overall, we found that non-farm labor
income is an equalizing source of income while farm income is disequalizing. Our
results imply that a continued increase in the variability of landholding distribution
could worsen income inequality among farm households in Korea.
___________________________________________________________________* Corresponding author (kimhi@agri.huji.ac.il). The order of authors is alphabetic
and does not imply seniority. This research was initiated while the third author was
visiting at the School of Economics at Sung Kyun Kwan University, and wascompleted while he served as a Visiting Professor at the Economic Research Center,
School of Economics, Nagoya University. The Financial support of these institutions
is gratefully appreciated.

Introduction
The purpose of this paper is to examine income inequality and its determinants
among farm households in Korea. The issue of inequality received much attention in
the economic literature in the last two decades, motivated by the recognition that
inequality is not only an outcome of growth but also a determinant of growth.
Recently, much concern has been expressed with regard to increased inequality in
fast-growing economies such as China, India and Vietnam. The increased availabilityof suitable data has led to numerous empirical studies of inequality based on crosscountry data, labor force surveys, household surveys, and population censuses (Kimhi
2004). Much of this effort has been devoted to low and middle income countries.
However, the methodologies used were in many cases adopted from more developed
countries. For example, analysis of demand and supply factors in the evolution of
wage inequality is perhaps suitable for an advanced economy in which the vast
majority of the population is engaged in full-time wage employment, but not for adeveloping country with considerable self employment, informal employment andmultiple jobholding.
In this paper we adopt and extend regression-based inequality decomposition
methods for the case of multiple income sources, and demonstrate their usefulness
using data on Korean farm households. As can be seen in figure 1, income inequality
is more pronounced among Korean farm households than in the Korean economy as awhole. Using a micro data set collected in 2003, we examine the contribution ofvarious income sources, and their determinants, to overall income inequality of farmhouseholds. Heshmati (2004) reports that inequality can be “decomposed by subgroups, income sources, causal factors and by other socio-demographic
characteristics” (page 1). Decomposition by population groups is perhaps the mostpopular of these, and will not be dealt with in this paper. Regarding decomposition by
income sources, Shorrocks (1983) has shown that the “natural” decomposition rule of
the Gini index of inequality is G(y)= Σk{2Σi[i-(n+1)/2]yik/n2/μ}, where yk is income
derived from source k, y is total income, G is the Gini index, μ is mean income, n is
the number of households, and i is the rank of the household in the total income
distribution. Therefore, the term inside the curled brackets, denoted Sk, is thecontribution of yk to G(y), and the proportional contribution of yk, or the share ofincome from source k in total inequality, is sk = Sk /G(y). Further, Lerman and2

Yitzhaki (1985) have shown that the change in G(y) resulting from a percentagechange in yk is (sk –μk/μ)G(y), where μk is the mean of yk.
Table 1 shows the income shares and the proportional and marginalcontributions to the Gini index of income inequality of several income sources of
Korean farm households. One can see that farm business income, the main single
source of income of these households, contributes more than half of the total income
inequality, proportionately more than its income share. Moreover, a uniform onepercent increase in farm business income would increase total income inequality by
six percentage points. On the other hand, non-farm labor income contributes toinequality less than its income share, and a uniform one-percent increase in non-farmlabor income would decrease total income inequality by three percentage points. Thisimplies that non-farm labor is an equalizing source of income. Non-farm businessincome and capital income contribute to inequality more or less proportionally to theirincome shares, and their marginal effects on inequality are negligible. Transfer
income and irregular income also reduce inequality, but not as much as non-farmlabor income. A similar conclusion is obtained by looking at Gini coefficients fordifferent groups of households defined by income regime. As can be seen in table 2,farm households that derive income from non-farm labor (regimes 2 and 3) havelower per-capita income Gini coefficients than other farm households.
Off-farm income was found as an equalizing income source in other countries
as well, including the U.S. (See El-Osta et al., 1995, and references therein), China(Zhu and Luo, 2006), the Republic of Georgia (Kalakashvili, 2005), Egypt (Adams,
2001), Taiwan (Chinn, 1979), and the Philippines (Leones and Feldman, 1998).
Gallup (2002), on the other hand, found that income other than farming contributed
positively to inequality in Vietnam, and similar results were obtained by Elbers andLanjouw (2001) for Ecuador. de Janvri and Sadoulet (2001) found that in Mexico,
non-farm income as a whole reduced household income inequality, but notagricultural wages in particular increased inequality. On the contrary, Canagarajah etal. (2001) found that in Ghana and Uganda, non-farm self-employment income wasmuch more disequalizing than non-farm wages. Estudillo et al. (2001) found that
nonfarm income changed from an equalizing to a disequalizing source as it became amajor income source in Philippine rice villages.
Morduch and Sicular (2002) proposed a general approach to regression-based
inequality decomposition. This approach brings together inequality decomposition by3

income source (Shorrocks 1982) and decomposition by population sub-groups(Shorrocks 1984). Adams (2001) extended the regression-based decomposition
method of Morduch and Sicular (2002) to the case in which the composition ofincome by the different sources (e.g., labor, capital, transfers) is observed. As
explanatory variables may have different effects on the different sources of income,he computed the income-source-specific contribution to inequality of eachexplanatory variable. The income from each source was estimated by a Tobit model,
since not all households in his sample had positive income from all sources. Bardhamand Boucher (1998) treated the selectivity problem differently. In particular, they
were interested in the earnings equation of non-migrants in order to derive thecounter-factual earnings of migrants. They estimated a Bivariate Probit selection
model for non-migration and for labor force participation, and then corrected theearnings equation for selectivity using the method introduced by Tunali (1986).
In this paper, we carry the regression-based inequality decomposition method
a step forward in two directions. First, we propose a decomposition method thatallows the source-specific contributions to inequality of Adams (2001) to beaggregated and comparable to the Morduch and Sicular (2002) aggregate
contributions. Second, we refine the selectivity-correction scheme, and correct thesource-specific income-generating equations using the semiparametric methodsuggested by Dahl (2002), which offers a more general specification of the selectivity
mechanism than Bardham and Boucher (1998). In particular, the method uses a series
expansion of nonparametric choice probabilities to correct the income-generating
equations for selectivity. As a result, it is straightforward to apply it to more than two
choices.
We demonstrate our method with data for farm households in Korea whichwere collected in 2003. This choice of data is particularly suitable for our purpose
since, as in many other countries, non-farm income is an important source of income
for Korean farm households (Suh, 2004). Thus, many farm households derive income
from the farm as well as from non-farm businesses and/or non-farm labor activities,
and each of these income sources is likely to have a unique income-generating
equation. We proceed by describing the methodology in the next section. After that
we present the data. Next, we move to the empirical application. We present theestimated income-generating equations and the regression-based inequality
decomposition results, first by income source, then by income regime, and finally by4both source and regime. We further demonstrate the importance of differentiating thedecomposition by income sources and income regimes by examining the marginal
effect of landholdings in inequality. The last section summarizes the paper, proposes
several policy implications and portrays avenues for future research.
Methodology
We start with the regression-based decomposition method suggested by
Morduch and Sicular (2002), which is relevant for inequality indices that can be
written as a weighted sum of household incomes:(1)

I(y)=Σiai(y)yi,

where ai are the weights. Income is expressed as a linear regression:(2)

y=Xβ+ε,where X is a matrix of explanatory variables, β is a vector of coefficients, and ε is avector of residuals. Given a vector of consistent estimated coefficients b, income canbe expressed as a sum of predicted income and a prediction error according to:(3)

y = Xb+e.

Substituting (3) into (1) and dividing through by I(y), we obtain that the share ofinequality attributed to explanatory variable m is:(4)

sm = bmΣiai(y)xim/I(y). 1
The partial derivatives of the Gini index of inequality with respect to an

overall change in each explanatory variable can be derived by adapting the Lerman
1

Wan (2004) extended this method to account for the contribution of the intercept of the income
regression to inequality. Wan and Zhou (2005) presented an alternative method. It should be noted that
Morduch and Sicular (2002) suggested a simple procedure to compute standard errors of sm, but theprocedure turns out to be incorrect. At least for the Gini index of inequality, it is not straightforward tocompute standard error of the index itself (See Modarres and Gastwirth 2006 and references therein),so it is reasonable to expect that computing standard errors of components of that index would not be
straightforward either.

5and Yitzhaki (1985) result described above to the formulation of (3) and (4). In
particular, the partial derivative corresponding to xm is (sm –μm/μ)G(y), where μm is the
sample mean of bmxim.
Moving to inequality decomposition differentiated by income sources, we
specify the kth source-specific income-generating function as:(5)yk = Xβk+εk,

where βk could include zero elements corresponding to explanatory variables that do
not affect the k’th source of income. Since y = Σkyk = XΣkβk + Σkεk, using consistentestimates bk of βk and substituting into (1), the fraction of the inequality contributionof explanatory variable m in overall inequality is:(6)sm = (Σkbkm)Σiai(y)xim/I(y).

This can be broken down to source-specific contributions of each explanatory variable
to overall inequality, denoted smk, which is implicitly defined by:(7)

sm = Σk[bkmΣiai(y)xim/I(y)] = Σksmk.

It is easy to see that (4), the decomposition proposed by Morduch and Sicular (2002),
is a special case of (6), in the case of identical income-generating equations for all
income sources. However, this only holds when all households derive income from all
sources. Otherwise, (5) has to be estimated using selectivity-correction methods, andtherefore bkm measures the effect of xim on yik*, which is the latent income ofhousehold i from source k. In this case, the equalities in (6) and (7) do not hold, if xim
affects not only income from source k but also the tendency of household i to haveincome from source k. The intuitive reason is that the contribution of xim to overall
income inequality is also affected by the effects of xim on getting in and out of thedifferent corner solutions. Deriving these effects is beyond the scope of this paper.
However, the source-specific inequality shares smk are still informative for thechannels through which xim contributes to overall income inequality, hence we deriveand present them in the empirical analysis below.

6

As mentioned above, it is reasonable that not all households derive income
from all potential income sources, and this brings up the issue of selectivity correction.
While each source-specific income-generating equation can be estimated by Tobit
maximum likelihood, for example, the potential multiplicity of corner solutions may
require a specification of regime-specific income-generating equations, where eachregime is defined by a specific combination of corner and internal solutions. Thisessentially leads to a switching regression model. Specifically, we define eachregime-specific income-generating function as:(8)

yr=Xrβr+εr,

where yr = Dry, Xr = DrX, εr = Drε, and Dr is a selection matrix with ones on theprincipal diagonal corresponding to observations that belong to regime r and zeros
elsewhere. By construction, ΣrDr = I where I is the identity matrix, and hence Σrεr = εand Σryr = y. Note that yr, Xr and εr have the same number of rows as y, X and ε, buthave zeros corresponding to observations that are not part of income regime r.
Applying the same reasoning as in the case of decomposition by income
source, we note that y = Σryr = ΣrXrβr + Σrεr, and hence the fraction of explanatory
variable m in overall income inequality is:(9)sm = ΣrbrmΣiai(y)xirm/I(y) = ΣrbrmΣiai(y)dirxim/I(y),where dir is the ith element on the diagonal of Dr. The difference between (9) and (6) isthat here we have dir that is indexed by both i and r, and hence we cannot separate thesummation over i and over r. Still, deriving the regime-specific share of inequality ofeach explanatory variable as:(10)smr = brmΣiai(y)dirxim/I(y)is useful for assessing the differential importance of each explanatory variable in each
income regime. Of course, as in the earlier case, the smr will not in general sum up tosm, if xim affects the choice of income regime.
Finally, if the contribution of each explanatory variable to overall income
inequality varies by income source and by income regime, it makes sense to estimate7each source-specific income generating equation separately in each income regime, i.e.
as a switching regression. This leads to regression coefficients brk that are indexed byboth source and regime, and allows a decomposition of overall inequality in bothdimensions. Specifically, write yk = Σryrk where yrk is a vector of income from source
k in regime r. Total income can therefore be written as:(11)

y =Σkyk = ΣkΣryrk = ΣkΣrXrβrk + ΣkΣrεrk,

which leads to the following inequality decomposition:(12)

sm = ΣkΣrbrkmΣiai(y)xirm/I(y) = Σr(Σkbrkm)Σiai(y)dirxim/I(y).

The source- and regime-specific share of explanatory variable m in total income
inequality can be evaluated as:(13)

smkr = brkmΣiai(y)dirxim/I(y)Naturally, the same qualifications apply here, namely that this decomposition ignores
the effect of explanatory variables on the choice of income regime and hence thesummation in (12) will not hold in general.
Note that for each case in which inequality shares are defined, marginal
effects of explanatory variables on inequality can be derived using an appropriate
modification of the Lerman and Yitzhaki (1985) formula described above.
Data
We use data from the 2003 nationally-representative farm book-keeping
survey that included 3,200 farm households. A farm household is defined as ahousehold engaged in farming for the purpose of making a living, in which the farmoperator manages at least 300 pyeong (about 0.1 ha) of cultivated land and generates
annual sales of at least 500,000 Won (roughly $420). Excluded are single-personhouseholds, foreigners, and those employing more than five full-time employees. The
survey provides information about household income from various farm and non-farmsources, as well as assets, expenditures, and demographics.

8

The variables we use to explain per-capita income are listed in table 3. We
include age of the head of household and its squared value, to account for life-cycle
effects. We also include a dummy indicator for the household head being a female.
Next, we use a set of binary indicators of the educational level of the head ofhousehold. Household size and composition are represented by three variables: family
size, the fraction of working-age males in the family, and the fraction of working-age
females in the family. The working age was determined to be from 19 to 64. The
economic resources of the household are represented by per-capita land owned and by
a dummy indicator for landless households. We have experimented with a set of
regional dummies, and eventually decided to include dummy indicators for centereast regions and for south-west regions.
Inequality decomposition by income source
The first column of table 4 shows the coefficients of the per-capita income
generating function (2) for our sample. Most coefficients are statistically significant
and have the expected sign. Age has a nonlinear effect, first positive and subsequently
negative, education has a positive effect, and female-headed households have lower
income per-capita than male-headed households, but the difference is not significantly
different from zero. Per-capita income decreases with family size, but increases withthe fraction of working-age adults (males or females). Income per-capita is lower for
landless households, and increases with the amount of land owned per-capita.
Households located in the southern and western regions have lower per-capita income
than in the rest of the country.
Next, we estimated separate income-generating functions (5) for each sourceof income, except for irregular income which we consider as a residual source ofincome. Except for the case of farm income, which was reported for all households,
each function was estimated by the Tobit maximum likelihood model in order toaccount for censoring from below. We have used the same set of explanatory
variables in all the equations, because these equations are essentially reduced-form
equations (encompassing elements of labor allocation, asset ownership, and returns tolabor and assets), and hence exclusion restrictions do not follow naturally. The results
are shown in the remaining columns of table 4, and it is quite clear that the effects of
explanatory variables on the different sources of income are substantially different.
Examining the three major sources of income, namely farm income, non-farm9

business income and non-farm labor income, we find statistically significant effects in
opposite directions of female headship, family size and landholdings. Education does
not have a statistically significant effect on any of these income sources. Regional
differences are statistically significant for non-farm income but not for farm income.
In summary, the importance of several determinants of income varies considerablyacross income sources.
We now turn to the decomposition of inequality by determinants of income.
The first column in table 5 shows the decomposition of the Gini index of total income
inequality using (4). 2 We find that only 18% of income inequality is explained by theset of explanatory variables as a whole. This is not too bad, given that only 13% of thevariance in income is explained by these explanatory variables (table 4). We find thatmajor contributions to inequality are assigned to family size and composition and toland ownership. The remaining columns in table 5 show the source-specificcontributions of income determinants, computed according to (7). We find that landownership and family size contribute the most to income inequality through farmincome. In fact, land ownership has a dominant role in explaining income inequality
through farm income, but not through any other source of income. This is of course
not surprising. Family size also has a positive contribution to income inequality
through non-farm labor income. Again, this is not surprising. Family composition,and specifically the fraction of working-age family members, has a relatively large
contribution to income inequality through non-farm labor income, but a much smaller
contribution through other sources of income. Contributions to income inequality
through other sources of income, including non-farm business income, capital income,and transfer income, are relatively minor.
The decomposition results in table 5 have shown that different explanatory
variables have different contributions to income inequality from different sources ofincome. For example, education has a positive contribution to overall income
inequality but its contribution through transfer income is negative. This can provide
useful information about potential inequality implications of various policy measures.

2

Computing standard errors of the decomposition results is not an easy task (see previous footnote),
and is left for future research.

10

Inequality decomposition by income regime
The estimation results in table 4 ignore possible cross-equation dependencies
that are implied by the fact that most farm households derive income from more than
one source. In fact, the literature on labor allocations in farm households (e.g.,
Fafchamps and Quisumbing 1999) implies that the farm income-generating function
could be different for farm households who also derive income from non-farm sources.
In order to examine whether the way in which the income generating equations are
estimated is important for inequality decompositions, we estimate a separate percapita income generating equation (8) for each of the income regimes described intable 2. Note that this does not include all possible income regimes. This would beoutside the scope of this paper. We focused on regimes determined mostly by labor
allocation decisions.
The results are in table 6. As in the case of the sources of income, we findqualitatively different income-generating equations in the different income regimes.
The effect of age, for example, is statistically significant only among farm households
who have both non-farm business income and non-farm labor income. The fraction of
adult males is more important among farm households who have non-farm labor
income, while the fraction of adult females is more important among farm households
who do not have non-farm labor income. Land owned (but not landlessness) is notimportant for farm households who do not have non-farm business or labor income.
Regional location is only important for households who have non-farm labor income.
This implies that the contributions of the different income determinants to income
inequality are likely to vary across the income regimes.
Table 7 shows the regime-specific contributions of explanatory variables to
the Gini index of inequality. The first column shows the aggregate decomposition by(4) from table 5, while the other columns present the contributions implied by (10),using the coefficients in table 6. We find that the contribution of age to income
inequality is predominantly among households who have only farm income, while thecontribution of education is predominantly among households who have non-farmbusiness income but no non-farm labor income. The contributions of the household
size and composition variables vary in a similar way to what was observed in table 6.
The combined contribution of landlessness and amount of land owned is lowest forhouseholds who have only farm income.

11Selectivity correction
The shortcoming of the preceding analysis is that the choice of income regime
is implicitly assumed to be exogenous. Since farm households make their labor
allocation decisions based upon expected income, and these labor allocations
determine the income regime of the household, this analysis suffers from potential
selectivity bias. To correct this bias, we need to correct for selectivity when we
estimate the regime-specific income generating equations. Dahl (2002) offers an
attractive method for our purpose. The method is semiparametric in that it uses a
series expansion of choice probabilities to approximate the selectivity-correction
terms in the income equations, and hence does not assume a certain joint distributionof the selection rule and the outcome equations. It also uses nonparametric estimatesof the choice probabilities in order to avoid making distributional and functional form
assumptions in the choice model.
The choice probabilities are derived as cell fractions of the different choices,
after the sample is divided into cells defined by explanatory variables. We define cells
in our sample by age (up to 50, 51-60, 61-66, and 67+), education (up to elementary,
middle school and above), existence and number of children (none, only children up
to 18 years of age, at least one child older than 18), land owned (up to ½ ha, between½ and 1 ha, more than 1 ha), and transfer income (up to 1,000 Won, more than 1,000Won). Adjacent cells with relatively few observations were merged, so that no cell
includes less than 11 observations. A total of 73 cells were generated in this way. The
distributions of the derived choice probabilities are shown in figure 2. The spread ofeach choice probability in the sample illustrates the usefulness of the cell-generation
method. A degenerate distribution would imply that there are no significant
differences between the observations in the different cells. We observe that the spreadof the probabilities of the more frequent choices (see table 2) is larger.
We estimated the models in table 6 with the correction to selectivity, using
both first- and second-degree polynomials of the choice probabilities. F-tests revealed
that the second-degree polynomial did not add explanatory power, and hence we used
the model with the first-degree polynomial. An F-test of the corrected model versus
the uncorrected model showed that only in regimes 1 and 3, those with positive nonfarm business income, the correction is statistically significant. We conducted theregression-based inequality decomposition for these two regimes using thecoefficients of the corrected model, and the results were not much different from12

those in table 5. These regression results and the related decomposition results areavailable from the authors upon request.
We now want to combine the two levels of differentiation of the incomegenerating equations, by income source and by income regime. In particular, weestimate source-specific and regime-specific income generating equations as impliedby (11), including a correction for selectivity as described above. We tested thesemodels in three levels. First, a test of the aggregated results in table 4 versus
disaggregated results. Second, a test of the disaggregated results versus the samemodel corrected for selectivity. Finally, we tested a first-degree polynomial of choiceprobabilities in selectivity correction versus a second-degree polynomial. For all threemajor income sources (farm income, non-farm business income and non-farm labor
income) the disaggregated results were significantly different from the aggregatedresults (p values close to zero). For disaggregated farm income, the results withselectivity correction were significantly different from the results without selectivity
correction, with the exception of households that do not have non-farm business orlabor income. In general, the results corrected using a second-degree polynomial of
choice probabilities were significantly different from those corrected with a firstdegree polynomial. For disaggregated non-farm business income and non-farm labor
income, the corrected results were significantly different from the uncorrected results,
but those corrected with a second-degree polynomial were not significantly different
from those corrected with a first-degree polynomial. Therefore, we present in tables 8
and 9 the corrected disaggregated results, with farm income corrected with a seconddegree polynomial (table 8) and non-farm income corrected with a first-degree
polynomial (table 9). 3
The regression results in table 8 show several notable differences in the farmincome generating equation across income regimes. In particular, the negative effectof female headship exists only among households who have non-farm businessincome (regimes 1 and 3). Also, the quadratic age effect, which was statisticallysignificant in the aggregate results, is not significant in any of the income regimes.
The negative effect of family size also becomes insignificant. The positive effect of
the fraction of adults in the household is statistically significant only among
3

Note that using the Dahl (2002) selectivity correction requires a correction of the standard errors of
the estimated coefficients, which we did not apply here. Hence, the t-values reported in tables 8 and 9
may be biased upwards. We plan to apply this correction in subsequent versions of this paper.

13

households who have non-farm labor income. The negative effect of landlessness isstatistically significant only among households who do not have non-farm business orlabor income. The positive effect of land is not significant among households who
have both non-farm business income and non-farm labor income. Note that thecoefficient of middle school is peculiarly large in regime 1. This is likely to be due tomulticollinearity in this relatively small sub-sample, hence we will not assign too
much importance to this effect.
Considerable differences are also observed in table 9. For non-farm businessincome there is one notable difference, namely that the positive effect of land exists
only for households who have non-farm labor income as well. For non-farm labor
income the differences are more pronounced. As in the case of farm income, thequadratic age effect, which was statistically significant in the aggregate results, is not
significant in any of the income regimes. The positive effect of education, which was
not significant in the aggregate results, becomes statistically significant when the
results are allowed to differ by income regime (although it is quite similar across theregimes). The positive effects of female headship and of family size become
insignificant in the regime-specific results. The positive effect of the fraction of adultfemales and the negative effect of land are significant only among households who
have non-farm business income as well.
Some of the differences in the coefficients of the income-generating equations
across income regimes are also reflected in the contributions to inequality of thecorresponding explanatory variables. These are reported in tables 10 and 11, for farmincome and non-farm income, respectively. In the following discussion we will focus
on explanatory variables that were mentioned earlier as having notably different
coefficients in the source- and regime-specific income generating equations (tables 8
and 9). Beginning with the contributions to inequality through farm income (table 10),
we find that the positive contribution of age (including age squared) to income
inequality is mostly among households that do not have non-farm labor income. The
positive contribution of family size to inequality is mostly among households thathave both non-farm business and non-farm labor income. However, this contribution
is based on a non-significant coefficient in the relevant income-generating equation.
The positive contribution of the family composition variables to inequality also varies
across the income regimes. The positive contribution of landlessness to inequality ismost pronounced among household who do not have non-farm business or labor14

income, while the positive contribution of land owned is least pronounced among
these same households.
Turning to the contributions to inequality through non-farm business income(table 11), the only notable difference is that of land owned, which has a relatively
large contribution among households who also have non-farm labor income and aslight negative contribution among households who do not. The case of non-farmlabor income is more interesting in this respect. The contribution of female headshipto income inequality, which was positive in the aggregate sample, becomes negativein the regime-specific case. The contribution of education to inequality, which wasvery small or even negative in the aggregate sample, becomes positive, regardless of
having non-farm business income or not. Finally, the contribution of land owned,
which was positive in the aggregate sample, becomes negative in the regime-specificcase.
Marginal effects
In order to further illustrate the usefulness and importance of differentiating
the inequality decomposition by income sources and regimes, we compare the partial
derivatives of the Gini index of inequality with respect to an overall change in land
owned per-capita in each of the decomposition routines. The results are in table 12.
When a single income-generating equation for total household income is estimated,
we find that a one-percent increase in landholdings would reduce income inequality
by two percentage points. However, when income is differentiated by source, themarginal effect of landholdings on inequality is only half of that. Moreover, we foundthat an increase in landholdings reduces inequality mostly through farm income, while
it increases inequality through non-farm labor income.
When we differentiate by income regime, we find a positive effect oflandholdings on inequality, mostly contributed by farms that have non-farm businessincome in addition to farm income. When we differentiate farm income by regime, wefind that the marginal effect of landholdings operating through farm income is in fact
positive, and, in this case as well, contributed mostly by farms that have non-farmbusiness income in addition to farm income. We conclude that within income regimes
landholdings increases inequality, so that the overall negative marginal effect isprobably caused by regime switching behavior cause by the increase in landholdings.

15

This supports our earlier conclusion that adequately controlling for income regimes isimportant in the analysis of inequality determinants.
Summary and conclusions
In this paper, we have proposed an extension of the regression-based
inequality decomposition method suggested by Morduch and Sicular (2002), which
differentiates between contributions to inequality of determinants of income operating
through different sources of income, and in different income regimes. We found that
this differentiation does lead to different conclusions. We also found that adequate
correction for selectivity when estimating regime-specific income-generating
equations is essential.
In the case of Korean farm households, we found that non-farm labor income
is an inequality-decreasing source of income, relative to farm business income.
Decomposing aggregate income inequality into components attributable to thedifferent determinants of income, we found that not a large fraction of total income
inequality could be explained by the explanatory variables. This is related to the factthat the explanatory power of these explanatory variables in the income-generating
equations is not large. As a fraction of the explained inequality, family size (23%),family composition (33%) and land ownership (23%) are the major contributors.
However, when looking at specific sources of income, we find that family size andland ownership are mostly contributing to income inequality through farm income,while family composition is mostly contributing to income inequality through nonfarm labor income.
By breaking the contributions to inequality further by income regime, andappropriately correcting for selectivity, we find that the contribution of family size toincome inequality through farm income is most pronounced among households thathave both non-farm business and non-farm labor income. This makes sense because
deriving income from multiple sources is facilitated by using the labor contributionsof relatively many household members. However, the family size coefficient in therelevant income-generating equation is not statistically significant, and therefore thisresult is subject to doubt. In the case of land ownership, we found that its contribution
to income is more widespread across the income regimes, but is more pronounced
among households who have non-farm business income. Perhaps land serves as an
asset that can be relied upon in non-farm business activities, i.e. as collateral for credit.
16

As for family composition, we found that its contribution to inequality does notchange much across the income regimes.
Other determinants of sources of income become significant, bothquantitatively and statistically, when differentiating by income source and income
regime. For example, through non-farm labor income, we find a positive contributionof high school and higher education to income inequality, a negative contribution ofland owned among households who also have non-farm business income, and a larger
contribution of the fraction of adult males than the contribution of the fraction of adultfemales, to income inequality.
Despite the fact that we are not able to explain a large fraction of inequality,
our results can be used for policy analysis, because the parts that we are able toexplain are related to important policy variables such as education and landholdings.
In other words, we explain the part of inequality that is related to inequality inresources and opportunities, and this is most relevant for policy makers. The
unexplained part of inequality could be due to unobserved preference variability thatis less interesting. In this case, our results have several policy implications. First, wefound that land ownership is one of the major contributors to income inequality,
mostly through farm income, and in particular for households who also have non-farmbusiness income. It seems like farming and non-farm family businesses are related
activities. As the size distribution of rice farms in Korea has changed very little in thelast few decades, income inequality could have increased through the expansion of
non-farm business activity. However, income inequality could further increase if
inequality of landholdings starts increasing in the future, as it did in many othercountries.
Second, family composition contributes to income inequality mostly throughnon-farm labor income inequality. Over the years, the extent of off-farm work on
Korean farm households has increased remarkably. While there doesn’t seem to bemuch impact of policy on family composition, it should be noted that to the extent
that farm households are multi-generational, the tendency of farmers’ offspring to join
their parents on the family farm depends largely on their income opportunities. We
can expect to find more adult offspring, and as a result higher fractions of adult family
members, on more profitable farms, and this process could lead to increased income
inequality in the long run. To counteract this effect, authorities should design policies

17to make farming more attractive to younger generations, especially when the objective
prospects of farming are less favorable.
Finally, the role of education in contributing to inequality through non-farmlabor income should not be overlooked. Given that our results imply that non-farmlabor income is an equalizing source of income, the increased tendency by farmhousehold members to work off the farm could reduce income inequality. One of the
key policy tools for achieving this is rural education. However, if rural education is
not expanded in an equitable way, this could lead to an increase rather than a decreasein farm household income inequality.
Methodologically, this research can be completed and expanded in three
dimensions. First, there is a need to compute standard errors of the regression-based
decomposition results (see footnotes 2 and 3). Second, the regression-based
decomposition by income regime will be more informative if it also computes theeffects of explanatory variables on regime choices. Third, our results call for an
extension of this analysis in the time dimension. In particular, it would be very useful
to examine the trends of income inequality and its determinants over time, along thelines of Bourguignon, Fournier, and Gurgand (2001). In this way it might be possible
to endogenize the trends in some of the income determinants. Empirically, moredetailed information on non-farm labor supply could enable one to differentiate
between the effect of labor supply, which could be endogenous, and the effect of the
returns to labor, which are largely exogenous but may be affected by public policy.
Finally, the framework used in this research and its extensions could be applied toother countries in Eastern Asia, and other parts of the world.

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20

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.1

0.2

0.3

0.4

0.5

All Households

Figure 1. Lorenz curves

21

0.6

0.7

0.8

Farm Households

0.9

1Figure 2. Distributions of nonparametric choice probabilities

22

Table 1. Sources of Farm household Income and their Contribution to Inequality_________________________________________________________________
Proportional
Marginal
IncomeContribution Contribution
Share
Income Component
to Ginito Gini___________________________________________________________
Farm business income
0.42470.581+0.0608
Nonfarm business income
0.07780.083+0.0022
Nonfarm labor income
0.19870.118-0.0335
Capital income
0.03000.023-0.0029
Transfer income
0.08460.045-0.0164
Irregular income
0.18430.150-0.0142_________________________________________________________________

Table 2. Per-Capita Income and Inequality by Income Regime___________________________________________________________
Mean percapita
Gini
Number ofGroup of households
incomecoefficient
observations__________________________________________________________All cases
9.06
0.4147
3,042
Regime=08.960.5050423
Regime=1
9.76
0.4564350
Regime=2
8.29
0.3911
1,016
Regime=39.52
0.3863
1,253___________________________________________________________
Notes:
Income is measured in millions of Won. All farm households have farm income bydefinition.
Regime=0: household with no income from non-farm business or labor
Regime=1: household with income from non-farm business only
Regime=2: household with income from non-farm labor only
Regime=3: household with income from both non-farm business and non-farm labor23

Table 3. Explanatory Variablesa________________________________________________________________
Variable
Mean
Std. Dev.
Min
Max___________________________________________________________
Female head

0.036

0.188

0

1

Age

58.8

10.7

27

89

Elementary school

0.410

0.492

0

1

Middle school

0.195

0.396

0

1

High school

0.233

0.423

0

1

Higher education

0.039

0.194

0

1

Family size

3.21

1.401

2

10

Fraction adult males

0.288

0.226

0

1

Fraction adult females

0.303

0.201

0

1

Landless

0.043

0.204

0

1

Land owned per capitab

0.476

0.742

0

21.5

Center-east

0.337

0.473

0

1

South-west
0.441
0.497
0
1
_________________________________________________________________
a. 3,042 households
b. Land is measured in hectares.
.

24

Table 4. Source-Specific Per-Capita Income Generating Equations_________________________________________________________________________
NonNonfarm
farm
Total
Farm
businesslabor
Capital Transfer
Variable
incomeincomeincomeincomeincomeincome_________________________________________________________________________
Female head
-1.146 -2.532** -0.201
0.941**-0.0020.182(-1.50)(-4.05)(-0.42)(2.83)(-0.02)(1.24)
Age
0.504** 0.250*-0.0230.259**
0.026
0.029(3.27)(1.99)(-0.24)(3.68)(0.98)(0.98)
Age squared/100-0.449** -0.275*-0.023 -0.224** -0.004
0.005(-3.28)(-2.46)(-0.27)(-3.55)(-0.18)(0.18)
Elementary school
0.628
0.333
0.070
-0.3670.178*
0.313**(1.31)(0.85)(0.23)(-1.71)(2.21)(3.45)
Middle school
1.183*
0.267
0.356-0.2520.235*
0.529**(2.11)(0.58)(1.02)(-1.01)(2.50)(4.96)High school
1.488**-0.2440.099
0.102
0.472** 0.644**(2.61)(-0.52)(0.28)(0.40)(4.95)(5.95)
Higher education
4.188**
0.498-0.1780.747
0.959** 1.288**(4.80)(0.70)(-0.33)(1.92)(6.67)(7.69)
Family size
-0.988** -0.660**
0.043
0.306** -0.040* -0.132**(-8.10)(-6.62)(0.58)(5.73)(-1.96)(-5.68)Fraction adult males
4.219** 1.467*
1.098*
3.024**
0.339* -0.477**(5.30)(2.26)(2.23)(8.61)(2.55)(-3.15)Fraction adult females 4.151** 1.965**
0.799
1.737**
0.036-0.135(4.89)(2.84)(1.52)(4.60)(0.25)(-0.84)Landless-3.156** -1.583** -0.753-0.308 -0.727** -0.369**(-4.43)(-2.72)(-1.68)(-0.98)(-5.35)(-2.64)
Land owned per capita 1.532** 1.598** 0.551** -0.546**
0.018
0.005
(7.72)(9.85)(4.72)(-6.04)(0.56)(0.12)
Center-east-0.3170.625-0.573* -0.637** 0.163*
0.107(-0.81)(1.96)(-2.40)(-3.69)(2.47)(1.43)South-west-1.767** -0.296 -0.765** -0.773**
0.023
0.029
(-4.71)(-0.97)(-3.34)(-4.67)(0.35)(0.41)
Intercept-4.487-0.7520.539-7.518** -1.716*-1.096(-1.09)(-0.22)(0.21)(-4.00)(-2.45)(-1.37)2
R
12.87%
9.50%
1.02%
2.95%
1.81%
3.05%
Number of positive
3,042
3,042
1,603
2,269
1,995
2,724
cases
(100%) (100%) (52.7%) (74.6%) (65.6%) (89.5%)_________________________________________________________________________
Notes: OLS estimates for total income and farm income, Tobit estimates for other income
sources. All farm households have farm income by definition. R2 in Tobit results is
Pseudo R2. * coefficient significant at 5%. ** coefficient significant at 1%.

25

Table 5. Regression-Based Source-Specific Contributions to Inequality______________________________________________________________________
NonNonfarm
farm
Total
Farm business labor Capital Transfer
Variable
income income income income income income____________________________________________________________________
Female head

0.094

0.819

0.013

0.166

0.000

-0.004Age-2.834

0.204

0.584

-8.220

0.480

2.061

Age squared/100

5.007

0.627

0.712

8.982

-0.0870.397

Elementary school

0.023

0.143

-0.0050.144-0.0150.277

Middle school

0.111

0.037

0.077

-0.069-0.003-0.116

High school

0.371

0.056

0.023

0.036

0.069

-0.616Higher education

0.613-0.042-0.010-0.0060.167-0.142

Family size

4.214

2.628

0.052

1.341

0.098

0.899

Fraction adult males

3.187

0.416

0.528

2.812

0.028

0.410

Fraction adult females

2.926

0.738

0.229

1.015

0.002

0.056

Landless

1.245

0.595

0.139

-0.027

0.357

0.092

Land owned per capita

2.933

4.315

0.277

0.848

0.018

0.007

Center-east-0.0550.206-0.016

0.112

0.086

0.046

South-west

0.445-0.036

0.183

0.325

-0.001

0.008

Intercept

0.000

0.000

0.000

0.000

0.000

0.000

a
a
a
a
a
Residual
81.721______________________________________________________________________

a. Source-specific contributions are computed as percentages of overall inequality, hence
they do not sum up to total source-specific inequality.

26

Table 6. Regime-Specific Per-Capita Income Generating Equations________________________________________________________________________
Variable
All cases Regime=0 Regime=1 Regime=2 Regime=3_______________________________________________________________________
Female head
-1.146-2.045-2.611-1.1460.180(-1.50)(-0.57)(-0.78)(-1.18)(0.16)
Age
0.504**
0.296
0.162
0.384
0.711**(3.27)(0.61)(0.32)(1.51)(3.01)
Age squared/100-0.449**-0.359-0.193-0.305-0.612**(-3.28)(-0.87)(-0.43)(-1.35)(-2.87)
Elementary school
0.628-0.121
1.595
0.903
0.787
(1.31)(-0.08)(0.86)(1.31)(1.09)
Middle school
1.183*
0.567
6.175**
0.665
1.053(2.11)(0.28)(2.91)(0.83)(1.27)High school
1.488**
0.563
3.776
0.918
2.336**(2.61)(0.30)(1.72)(1.11)(2.73)
Higher education
4.188**
3.906
3.280
3.791**
5.454**(4.80)(1.54)(1.10)(2.83)(4.03)
Family size
-0.988**-1.231*-1.133*-0.734**-1.134**(-8.10)(-2.12)(-2.44)(-4.11)(-6.69)Fraction adult males
4.219**
1.931
2.294
3.679**
5.025**(5.30)(0.63)(0.68)(3.28)(4.38)
Fraction adult females
4.151**
6.341*
8.839**
3.374**
2.778*(4.89)(2.09)(2.82)(2.79)(2.20)
Landless-3.156**-6.858**-1.910-3.022**-2.269*(-4.43)(-2.74)(-0.56)(-3.17)(-2.18)Land owned per capita
1.532**
0.697
1.605*
0.905**
2.582**(7.72)(0.89)(2.57)(3.59)(7.45)
Center-east-0.3171.984
1.013-0.196-1.432*(-0.81)(1.36)(0.73)(-0.35)(-2.54)South-west-1.767**0.525-1.130-1.441**-2.841**(-4.71)(0.38)(-0.81)(-2.66)(-5.25)Intercept-4.487
4.973
3.859-3.524-9.707(-1.09)(0.35)(0.28)(-0.51)(-1.57)R2

12.87%

11.86%

14.40%

10.99%

19.64%

Number of cases
3,042
423
350
1,016
1,253
________________________________________________________________________
Notes: Dependent variable: total household income per capita. All farm households havefarm income by definition. * coefficient significant at 5%. ** coefficient significant at
1%.
Regime=0: household with no income from non-farm business or labor.
Regime=1: household with income from non-farm business only.
Regime=2: household with income from non-farm labor only.
Regime=3: household with income from both non-farm business and non-farm labor.
27

Table 7. Regression-Based Regime-Specific Contributions to Inequalitya________________________________________________________________________
Variable
All cases Regime=0 Regime=1 Regime=2 Regime=3_______________________________________________________________________
Female head

0.094

0.080

0.217

0.036

-0.023Age-2.834-9.429-2.432

2.297

3.254

Age squared/100

5.007

15.3154.131-0.837-0.353Elementary school

0.023

0.017

-0.5820.389-0.004

Middle school

0.111

0.067

3.807

-0.032-0.049High school

0.371

0.270

1.489

0.101

0.477

Higher education

0.613

1.713

-0.315

0.557

0.801

Family size

4.214

1.282

1.859

4.074

7.347

Fraction adult males

3.187

1.485

1.009

2.689

4.255

Fraction adult females

2.926

6.100

6.531

2.107

1.816

Landless

1.245

2.382

0.436

1.888

0.801

Land owned per capita

2.933

0.689

3.457

1.860

5.858

Center-east-0.0550.962-0.156-0.046-0.229South-west

0.445

0.038

-0.468

0.349

1.767

Intercept

0.000

0.000

0.000

0.000

0.000

b
b
b
b
Residual
81.721________________________________________________________________________

a. Dependent variable: total household income per capita. All farm households have farmincome by definition.
b. Regime-specific contributions are computed as percentages of overall inequality, hence
they do not sum up to total Regime -specific inequality.
Regime=0: household with no income from non-farm business or labor.
Regime=1: household with income from non-farm business only.
Regime=2: household with income from non-farm labor only.
Regime=3: household with income from both non-farm business and non-farm labor.

28

Table 8. Regime- and Source-Specific Per-Capita Farm Income Generating Equationsa________________________________________________________________________
Variable
All casesb Regime=0 Regime=1 Regime=2 Regime=3_______________________________________________________________________
Female head
-2.532**-0.189-6.097*-1.585*-1.711(-4.05)(-0.06)(-2.27)(-2.10)(-1.90)Age
0.250*
0.052
0.197
0.198
0.382*(1.99)(0.11)(0.50)(1.00)(2.06)
Age squared/100-0.275*-0.209-0.267-0.186-0.321(-2.46)(-0.54)(-0.75)(-1.05)(-1.91)Elementary school
0.333-0.782
1.417
0.380
0.521
(0.85)(-0.55)(0.97)(0.72)(0.93)
Middle school
0.267-0.7505.057**-0.4020.803(0.58)(-0.40)(2.96)(-0.64)(1.23)High school-0.244-0.2701.090-0.8910.708(-0.52)(-0.15)(0.62)(-1.39)(1.05)
Higher education
0.498
0.298
1.359
-0.3711.097(0.70)(0.13)(0.57)(-0.36)(1.04)
Family size
-0.660**-0.911-0.487-0.238-0.611**(-6.62)(-1.62)(-1.19)(-1.49)(-3.98)Fraction adult males
1.467*
3.407-0.2122.183*
3.103**(2.26)(1.13)(-0.07)(2.28)(3.04)
Fraction adult females
1.965**
4.568
4.052
2.692**
0.877(2.84)(1.64)(1.64)(2.84)(0.88)
Landless-1.583**-5.542*-0.523-1.219-0.475(-2.72)(-2.39)(-0.19)(-1.65)(-0.59)Land owned per capita
1.598**
1.620*
2.116**
1.046**
1.387**(9.85)(2.06)(4.13)(5.11)(4.95)
Center-east
0.625
0.981
1.775
0.302
-0.164(1.96)(0.74)(1.59)(0.70)(-0.37)
South-west-0.296-0.505-0.100-0.197-0.967*(-0.97)(-0.40)(-0.09)(-0.48)(-2.32)
Intercept-0.752cccc(-0.22)R2

9.50%

15.86%

20.23%

11.92%

15.94%

Number of cases
3,042
423
350
1,016
1,253
________________________________________________________________________
a. * coefficient significant at 5%. ** coefficient significant at 1%.
b. from table 4.
c. the intercept is not separable from the set of selectivity-correction coefficients, hence it is
not comparable to the first column and is not reported. Selectivity corrected using Dahl
(2002) semiparametric procedure with second-degree polynomial of nonparametric choiceprobabilities. Regimes: (0) no income from non-farm business or labor; (1) income fromnon-farm business; (2) income from non-farm labor only; (3) income from both non-farmbusiness and non-farm labor.
29

Table 9. Regime- and Source-Specific Per-Capita Non-Farm Income Generating Equationsa_________________________________________________________________________
Non-Farm Business Income
Non-Farm Labor Income_________________________ _________________________
Variable
AllbReg. 1
Reg. 3Allb
Reg. 2Reg. 3________________________________________________________________________
Female head
-0.2010.838
0.468
0.941**
0.289
0.416(-0.42)(0.51)(0.85)(2.83)(0.64)(0.94)
Age-0.023-0.024-1.1110.259**-0.031
0.150(-0.24)(-0.10)(-0.98)(3.68)(-0.26)(1.65)
Age squared/100-0.023-0.021
0.072-0.224**0.030-0.120(-0.27)(-0.10)(0.70)(-3.55)(0.29)(-1.46)
Elementary school
0.070-0.128-0.087-0.367-0.0760.085(0.23)(-0.14)(-0.25)(-1.71)(-0.24)(0.31)
Middle school
0.356
0.559
0.489
-0.2520.190
0.204(1.02)(0.54)
1.23)(-1.01)(0.51)(0.64)High school
0.099
1.119
0.412
0.102
0.981** 0.892**(0.28)(1.05)(1.00)(0.40)(2.59)(2.71)
Higher education-0.1781.427-0.2630.747
2.707** 2.179**(-0.33)(0.99)(-0.41)(1.92)(4.41)(4.22)
Family size
0.043
-0.135-0.0370.306**
0.055-0.083(0.58)(-0.55)(-0.40)(5.73)(0.59)(-1.13)Fraction adult males
1.098*
2.553
0.901
3.024** 2.447** 1.759**(2.23)(1.42)(1.43)(8.61)(4.28)(3.50)
Fraction adult females
0.799
2.918
0.988
1.737**
0.930
1.350**(1.52)(1.92)(1.60)(4.60)(1.63)(2.73)
Landless-0.753-1.946-0.260-0.308-0.762-0.654(-1.68)(-1.17)(-0.52)(-0.98)(-1.73)(-1.64)
Land owned per capita 0.551**-0.274
1.286** -0.546** -0.214 -0.522**(4.72)(-0.88)(7.53)(-6.04)(-1.79)(-3.82)Center-east-0.573* -1.602* -0.598* -0.637** -0.710** -0.417(-2.40)(-2.37)(-2.22)(-3.69)(-2.73)(-1.93)
South-west-0.765** -1.414* -0.642* -0.773** -1.105** -0.608**(-3.34)(-2.10)(-2.48)(-4.67)(-4.44)(-2.94)
Intercept
0.539-7.518**cccc(0.21)(-4.00)R2

1.02%

14.38%

8.35%

2.95%

16.52%

12.76%

Number of cases
1,603
350
1,253
2,269
1,016
1,253
________________________________________________________________________
Notes: see table 8.

30

Table 10. Regime- and Source-Specific Contributions to Inequality via Farm Incomea________________________________________________________________________
Variable
All casesb Regime=0 Regime=1 Regime=2 Regime=3_______________________________________________________________________
Female head

0.819

0.007

0.506

0.050

0.220

Age

0.204

-1.649-2.965

1.185

1.750

Age squared/100

0.627

8.899

5.700

-0.511-0.186

Elementary school

0.143

0.109

-0.5170.164-0.003

Middle school

0.037-0.089

3.118

0.020

-0.037

High school

0.056-0.1300.430-0.0980.144

Higher education-0.0420.131-0.130-0.0540.161

Family size

2.628

0.948

0.799

1.325

3.961

Fraction adult males

0.416

2.621

-0.093

1.596

2.628

Fraction adult females

0.738

4.394

2.994

1.682

0.573

Landless

0.595

1.925

0.119

0.762

0.168

Land owned per capita

4.315

1.603

4.557

2.150

3.148

Center-east

0.206

0.475

-0.2740.071-0.026South-west-0.036-0.037-0.041

0.048

0.602

Intercept
0.000
0.000
0.000
0.000
0.000
________________________________________________________________________
a. Regimes: (0) no income from non-farm business or labor; (1) income from non-farmbusiness; (2) income from non-farm labor only; (3) income from both non-farm businessand non-farm labor.
b. from table 5.

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Table 11. Regime- and Source-Specific Contributions to Inequality via Non-Farm Incomea_________________________________________________________________________
Non-Farm Business Income_________________________

Non-Farm Labor Income_________________________

Variable
AllbReg. 1
Reg. 3Alla
Reg. 2Reg. 3_______________________________________________________________________
Female head

0.013

-0.032-0.0630.166-0.008-0.049

Age

0.584

0.649

-0.526-8.220-0.0260.735

Age squared/100

0.712

0.391

0.042

8.982

0.008

-0.083Elementary school-0.005

0.078

0.001

0.144

-0.0260.000

Middle school

0.077

0.222

-0.020-0.069-0.011-0.006

High school

0.023

0.386

0.073

0.036

0.113

0.163

Higher education-0.010-0.124-0.046-0.006

0.407

0.314

Family size

0.052

0.154

0.126

1.341

-0.192

0.644

Fraction adult males

0.528

1.105

0.760

2.812

1.767

1.470

Fraction adult females

0.229

2.018

0.624

1.015

0.614

0.921

Landless

0.139

0.354

0.094

-0.027

0.451

0.227

Land owned per capita

0.277-0.497

2.979

0.848

-0.481-1.091

Center-east-0.0160.248-0.0960.112-0.161-0.070South-west

0.183-0.529

0.399

0.325

0.267

0.382

Intercept
0.000
0.000
0.000
0.000
0.000
0.000
________________________________________________________________________
a. Regimes: (1) income from non-farm business; (2) income from non-farm labor only; (3)income from both non-farm business and non-farm labor.
b. from table 5.

32

Table 12. Marginal Effect on Land Owned Per Capita on Total Gini__________________________________________________________________
Income by
Total
Income by Income by Source andRegimeIncomeSourceRegimea
Variable_________________________________________________________________
Aggregate effectFarm business-0.021-0.010+0.013-0.017+0.016

Regime 0+0.001

Regime 1+0.012

Regime 2+0.002

Regime 3+0.002

Non-farm business-0.005-0.001

Regime 1-0.002

Regime 3+0.001

Non-farm labor+0.012+0.000Regime 2+0.000Regime 3+0.000Capital+0.000Transfers+0.000Other+0.000Regime 0+0.000Regime 1+0.009Regime 2+0.002

Regime 3+0.002___________________________________________________________________
a. Regimes: (1) income from non-farm business; (2) income from non-farm labor only; (3)income from both non-farm business and non-farm labor.

33