ÐÏࡱá>þÿ  ;=þÿÿÿ6789:hägò€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿì¥Á#` ø¿3Pbjbj\.\. .Z>D>Dwùr%ÿÿÿÿÿÿ¤ÄÄÄL 8H H H \ HÿHÿHÿ8€ÿlì¤ \ úʆœ ,"N:ˆˆcccÉÉÉÉÉÉÉ$€ÌhèÎR¥É™H åBccåBåB¥ÉH H ˆˆÛ>ÊPm[m[m[åBš H ˆH ˆÉm[åBÉm[m[ne~H H a€ˆ  ðóÅߥ ÉHÿO m ;’D7ŽÊlúÊÔ:ÏŸTà:Ï@a€a€:ÏH s„È c.‘!ü m[.d ñ8ô ccc¥É¥ÉZîcccúÊåBåBåBåB\ \ õ`þdÿäÿ\ \ `þdÿ\ \ \ H H H H H H ÿÿÿÿ  This version: 28 January 2009 Inequality Comparisons: The Role of Government and Demographic Structure Kathryn Carty Central Statistics Office Ireland Victoria Roshal Israeli Central Bureau of Statistics Shlomo Yitzhaki Israeli Central Bureau of Statistics and Hebrew University The aim of this paper is to compare inequality in incomes between Ireland and Israel. We first decompose the Gini coefficient in a way that resembles the decomposition of the coefficient of variation. This enables us to estimate the Gini correlation for different types of income sources and to evaluate how the transition from one income definition to another affects the measured inequality. Secondly, by decomposing the Gini coefficient by demographic sub-groups (based on household size) we evaluate the contribution of each subgroup to the inequality, thus determining the extent to which the demographic structure of each country affects its Gini. We find that, although there is similar level of inequality in Ireland and Israel if measured by economic (non-governmental) income, moving to net disposable income still yields similar inequality between households, but it is the introduction of inequality between (equalised) individuals that produces the most striking difference of nearly 15% in the inequality between the two countries. This is due to the more coordination in government programs in Ireland, reflected in higher correlation coefficients between the government transfers and the disposable income. Analysing the demographic effect on inequality, we find that no household size group forms a distinctive stratum. Large households are poor in both countries, but are much poorer in Israel relatively to the whole population. The most dispersed group is the singles, who present in both ends of the income distribution. Singles and couples also have the largest inequality in both countries. Key Words: Inequality, Ireland, Israel Corresponding Author: Victoria Roshal,  HYPERLINK "mailto:vikar@cbs.gov.il" vikar@cbs.gov.il We would like to thank Donald Garvey and Gerry O’Hanlon for initiating the project and for their support. We are also grateful to participants of seminars held at CSO, Ireland, Carlton and Ottawa universities, and CBS, Israel, for their helpful comments. Introduction When measuring inequality, the Gini coefficient is the one usually used. It is easily calculated, unitless and it provides a unique coefficient that can be used for comparisons across countries, populations and time periods. But as with any single number (such as the mean, median etc.), it ignores the differences in the structure of the distribution between the countries and yields no information about the influence of the household characteristics on inequality. In addition, the simple comparison provides no details regarding the factors that affect the final result, namely – the choice of the income definition and weighting schemes. Technical issues, like the period over which the income is measured are also important to take into account. The fact that inequality declines, when the period over which income is measured increases, is, by now, well known and well documented. Wodon and Yitzhaki (2003) supply formal proof of the occurrence of this fact. Finkel, Artsev and Yitzhaki (2006) find that the Gini index calculated from a three-month accounting period was by nearly 4% higher than the index based on a twelve-month period. The differences in the demographic structure are important because it is well-known that income levels correlate with certain demographic characteristics such as age, educational level, marital status, family size, the fraction of rural population, the number of immigrants and their level of education and so on. Now, the composition of demographic groups differs in every country, due to different historical, cultural and religious characteristics. Can these demographic differences then explain differences in the inequality as measured by the Gini coefficient? It may happen, for example, that in a certain demographic group the inequality is consistently higher than in the rest of the population. In the group of single people, for example, one finds students at the very beginning of their professional life, people who are at the height of their career, as well as divorcees and widowers. If a country exhibits a higher ratio of such a group, could this contribute to a higher inequality measure? Or consider that there might be a high inequality between groups, for example, if there are special government benefits for large families, who therefore enjoy higher incomes, and these can then increase the income gap between these families and the others. This leads to slightly different questions – since inequality is usually measured (and compared) for net disposable income, what is the implication of the transition from non-governmental (or “economic”) income to the net disposable income and whether there is a possibility to analyse the correlation between different types of incomes? How can correlations between the income sources help in evaluating the progressivity of governmental programs? Since disposable (net) income can be presented as sum of random variables (non-governmental income, government transfers, direct taxes), in the present paper we, firstly, decompose the Gini of net income in a way that resembles the decomposition of the coefficient of variation, plus an additional term, which reflects the deviation of the underlying distributions from "exchangeability up to a linear transformation". This enables us to estimate the Gini correlation for different types of income sources and how those correlations affect transition from one income definition to another. In dealing with the transition from before to after tax income, most of the literature perform a before and after comparisons. For example, Burkhauser, Frick and Schwarze (1997) compare economic well-being and inequality between the United States and Germany, using Theil and Gini indices of inequality. They perform the comparison for before- and after-tax incomes, however, without decomposing the inequality into the contribution of each of the sources. Wolff (1996) compares wealth inequality over time, between eight industrialized countries. Wolff and Zacharias (2007) analyze the changes in the inequality before and after the addition of fiscal components like taxes and transfers. They, however, perform a different decomposition and concentrate on wealth, rather than income inequality. Aaberge et al.(2002) compare income inequality and income mobility between Scandinavian countries and the United States. Our approach allows us to differentiate between the effect of taxes from the effect of allowances, which enables to find out the level of co-ordination between separate organs of the government. Although we do not deal here with mobility, the technique presented here can be also formulated also in terms of mobility (see, for example, Wodon and Yitzhaki 2004, Beenstock 2004). Secondly, our aim is to evaluate the effect of demographic structure on inequality. The methodology we use is the decomposition of the Gini index by population sub-groups. Beblo and Knaus (2001) apply the decomposition of the Theil index of inequality to evaluate the country’s contribution to the overall inequality in the eleven countries of the EMU. We perform the decomposition (based in the present case, on demographic groups) of the Gini index, which allows us to evaluate the contribution of each subgroup to the inequality, and thus to determine the extent to which the demographic structure of each country affects its Gini coefficient. In our analysis we compare the demographical structure of inequality and income composition between two countries: Ireland and Israel. Our findings are the following: The transition from market to disposable income reduces the inequality in Ireland by 36%, but only by 27% in Israel, the share of government transfers in disposable income is higher in Ireland, where the correlation between the transfers, taxes and non-governmental income is much stronger. This means that transfers and taxes are better coordinated in Ireland than in Israel. By decomposing the Gini index of inequality by household size, we find that (1) for market income, the level of inequality is similar for both countries, however, small households (1 to 3 persons) have higher inequality in Ireland, than in Israel and larger households have lower inequality in Israel; (2) for net disposable income, the inequality in Israel is by 15% higher than in Ireland, and in each household group the inequality is also higher in Israel than in Ireland, which means that governmental taxes and benefits programs are more progressive in Ireland; (3) correcting for the household composition differences, we find that for net disposable income, the inequality in Ireland increases by only 0.5%, but in Israel it reduces by 2%. This means that our conclusions with respect to progressivity cannot be explained by the difference in demographic structure. The paper’s structure is as follows: in Section II we briefly describe the methodology of Gini decomposition by income sources and population groups. In Section III we describe the two countries in terms of income distribution, inequality, and demographic differences. The decomposition analysis follows in Section IV. Section V contains the conclusions. Methodology Decomposition of Gini by Income Sources This section presents the decomposition of the Gini index for the sum of income sources, into the Gini measured separately for each one of the sources. The decomposition is similar to the decomposition of the coefficient of variation of a linear combination of random variables and has similar properties. The methodology we rely on is presented in Wodon and Yitzhaki (2003). Yitzhaki (2003) relates the methodology to other properties of the Gini. The statistical tests we are using are developed in Schechtman, Yitzhaki and Artsev (2008). Following is a brief summary of the methodology. The (relative) Gini coefficient can be expressed as (Lerman and Yitzhaki (1984):  EMBED Equation.3  (1) That is, twice the covariance between the variable, Y, and its cumulative distribution, F(Y), divided by the mean income. Let Y1,Y2,…,YK be the income distributions of K income sources. The distribution defined over the combined income is  EMBED Equation.3 . The key parameters for decomposing the variance of the sum of random variables into its components are the covariances between these variables and the Pearson correlation coefficient, one for each pair of variables. When decomposing Gini, two correlation coefficients are formed, which are not symmetric and are not necessarily equal to each other. The Gini correlation between incomes for sources i and j (i, j = 0, 1, 2, …, K), or between income from one source and overall income are defined as: EMBED Equation.3 (2) As discussed in Schechtman and Yitzhaki (1987, 1999), the properties of the Gini correlations are a mixture of Pearson’s and Spearman’s correlation coefficients. The Gini-correlation coefficient is bounded by minus one and one. If Yi and Yj are independent, then  EMBED Equation.3   EMBED Equation.3  is not sensitive to a monotonic transformation of Yj (similar to Spearman’s correlation coefficient).  EMBED Equation.3  is not sensitive to a linear monotonic transformation of Yi (similar to Pearson’s correlation coefficient). The equality,  EMBED Equation.3  holds when Yi and Yj are exchangeable up to a linear transformation (the joint distributions are symmetric). Define Di0 = (i0 - (0i, for i=1,…,K (here, the Gini correlations are taken between the income of each component and the overall income), and ai = (i/(0, where (i > 0 is the expected income of component i, while ai is a share of the income from component i in the overall income. Let EMBED Equation.3, ai = ¼i / ¼0, then EMBED Equation.3 (3) The implication of the additional term (in comparison to the decomposition of variance),  EMBED Equation.3 , is that the distribution of the sum of variables is not similar to the distribution of each of the components. If (ij = (ji for j,i =1, …, K , then: EMBED Equation.3 (4) Equation (4) is identical in its structure to the decomposition of the coefficient of variation, except that every term that is defined in the context of the variance (coefficient of variation, Pearson’s correlation coefficient, variance) is substituted by the appropriate Gini defined term. For it to hold, the Gini correlations between each pair of variables Y0 ,…, YK must be equal. Schechtman and Yitzhaki (1987) show that a sufficient (but not a necessary) condition for (ij = (ji is that the variables are exchangeable up to a linear transformation. Examples of such distributions are the multinormal and the multivariate lognormal, provided that Ãi = Ãj, where à is the logarithmic standard deviation. If the Gini correlations between pairs of variables are not equal, we need to use equation (3), where each  violation of the equality of the Gini correlations is captured by an additional term in the decomposition (hence, we can treat each violation separately and evaluate its effect on the decomposition; in particular we can see whether the violation tends to increase or decrease overall inequality). Yitzhaki (2003) (proposition 3, 303) shows that exchangeability up to a linear transformation is a sufficient condition for (4) to hold but it is not a necessary one. Decomposition of Gini by Population Groups In this section we briefly present the methodology of the Gini decomposition into the economic well-being of population subgroups, described in detail in Frick et. al., 2006, and referred to as ANOGI – ANalysis Of GIni. The analysis is similar to ANOVA (Analysis of Variance), but benefits from the addition of the so-called “overlapping coefficient”, which enables one to evaluate the quality of the classification by sub-groups (Heller and Yitzhaki 2006). Let the population income distribution Yu be composed of the income distributions Yi, i=1,…,n, of the n subpopulations. The Gini of the entire population, denoted by Gu, can be decomposed into three components: a "within" component (intra), a "between" component (inter) and a component that is a function of the amount of overlapping among the subpopulations. Note that  EMBED Equation.3  (5) That is, the cumulative distribution (or ranks) of the overall population is the weighted average of the cumulative distributions of the sub-populations, weighted by the relative sizes of the populations. The Gini of the entire population, Gu, can be decomposed as:  EMBED Equation.3  (6) where Oi is the overlapping index of sub-population i with the entire population (explained below), and Gb is between group inequality. Note that while in ANOVA, the decomposition of the total variability is partitioned into inter and intra variances, in ANOGI we have inter and intra Gini’s, but, in addition, there is an extra parameter, which is the overlapping index. Overlapping should be interpreted as the inverse of stratification. "Social stratification suggests a model in which the mass of society is constructed of layer upon layer of congealed population qualities" (Lasswell, 1965). According to Lasswell, perfect stratification occurs when the observations of each sub-population are confined to a specific range, and the ranges of sub-populations do not overlap. One can rarely find a perfect stratification, and an index describing the degree of stratification is called for. The index of overlapping is actually an index describing the extent to which the different populations are stratified. Formally, overlapping of the overall population by sub-population i is defined as:  EMBED Equation.3  (7) where, for convenience, the index u is omitted and covi means that the covariance is according to distribution i, i.e.  EMBED Equation.3  (8) where  EMBED Equation.3 is the expected rank of population i in the union (all observations of population i are assigned their union’s rank and  EMBED Equation.3 represents the expected value). The overlapping index (7) can be further decomposed to identify the overlapping of subpopulation i with all other subpopulations that comprise the union. In other words, total overlapping of subpopulation i, Oi, is composed of overlapping of i with all sub populations, including group i itself. This further decomposition of Oi is:  EMBED Equation.3  (9) where  EMBED Equation.3  is the overlapping of group j by group i. Following is the summary of major properties of the overlapping index Oji. Full discussion of the properties of the overlapping index can be found in Frick et al. (2006). Oji ( 0. The index is equal to zero if no member of the j distribution lies in the range of distribution i. (i.e., group i is a perfect stratum). If the distribution of group j is identical to the distribution of group i, then Oji=1. Note that by definition Oii=1. This result explains the second equality in (6). Using (6), it is easy to see that Oi ( pi is a result to be borne in mind when comparing different overlapping indices of groups with different sizes. Oji ( 2. That is, Oji is bounded from above by 2. This maximum value will be reached if all observations belonging to distribution j that are located in the range of i, are concentrated at the mean of distribution i. In general, the higher the overlapping index Oji the lower will Oij be. That is, the more group j is included in the range of distribution i, the less distribution i is expected to be included in the range of j. We now move to describe the between-group component. There are two alternative parameters representing between-group Gini. We start with the one appearing in Equation (6). The between group inequality Gb is defined in Yitzhaki and Lerman (1991) as:  EMBED Equation.3 . (10) Gb is twice the covariance between the mean income of sub populations and the sub populations’ mean ranks in the overall population, divided by overall expected income. The term Gb equals zero if either the mean incomes or the mean ranks are equal for all sub-populations. In extreme cases, Gb can be negative, which occurs when the mean income is negatively correlated with mean rank. One may argue that Gb is not really a Gini coefficient because it can be negative. In this case, one may prefer the between-groups Gini, as defined by Pyatt (1976). In this definition, the between-groups are based on the covariance between mean income in each sub population and its rank among the mean incomes of sub-populations. The difference between the two definitions is in the rank that is used to represent the group: under Pyatt’s approach it is the rank of the mean income of the sub population, while under Yitzhaki-Lerman it is the mean rank of all members. Denote the Pyatt between-groups Gini as Gbp . Then, it can be shown that: Gb ( Gbp (11) The upper limit is reached and (11) holds as equality, if the ranges of incomes that groups occupy do not overlap (i.e. perfect stratification). We can now explain the role of overlapping index. Other things being equal, the higher the overlapping among sub-population, the larger the share in the overall Gini of the intra-group component, and the smaller the share of the inter-group component. Data: Main indicators and main differences Data sources The analysis is performed on two countries: Ireland and Israel. For Ireland, the data source is the EU-SILC, the voluntary Survey on Income and Living Conditions of different types of households. All persons aged 16 and above are required to participate. Information is collected continuously throughout the year, with up to 130 households surveyed each week to give a total sample of 5,000 to 6,000 households in each year. In 2006, the achieved sample size was 5,836 households and 14,634 individuals. The income reference period for EU-SILC is the 12 months prior to date of interview. The data source for Israel is the Household Expenditure Survey (HES). The survey aims to obtain data on the components of household budgets, as well as additional data, that characterize various aspects of the living standard of households. Information is obtained throughout the year on the three-months income of the household members aged 15 and above. In 2006 the sample size of the HES was 6,259 households and 20,743 individuals. It is important to note, that HES is part of a broader Income Survey, which consists of nearly 14,000 households and forms a basis for the national inequality and poverty measurements. In the present paper we use the much smaller HES because of its more detailed information on persons in each household, without which most of the compilations could not be performed. Since the accounting period in Israel is shorter than the Irish one, and following Finkel et. Al. (2006), we may conclude the the Israeli figures of inequality are biased upword by about 4 percent relative to the Irish one. We did not correct the figures accordingly, to avoid data manipulation. We analyse two types of income definitions: non-governmental or so-called economic income, which includes only direct income, received by employees, income from capital etc., and net (disposable) income, defined as economic income, plus government transfers minus taxes. Differences and similarities Both countries have exhibited a high economic growth in the past two decades. In Ireland this was due to low corporate taxation, to the EU membership which enlarged the external market and brought in EU aid which increased investments in education, increased participation in the labour market and a policy of restraint in government spending. Israel, on the other hand, has benefited from large waves of Jewish immigration, many of whom are highly educated, from the introduction of market-oriented structural reforms and from the increase in foreign investment due to the Peace Process started in the 1990s. The total GDP (PPP, by the World Bank) for both countries is very close – 171,862 billion dollars for Ireland (ranked 51) and 169,847 for Israel (ranked 52). However, Ireland has one of the highest GDP per capita (nearly $43,000 PPP) and is ranked 9 by the IMF (2006). Ireland has experienced unprecedented rapid economic growth (of nearly 142% in 16 years), which started in the 1990s, during which it was transformed from a mainly agrarian and manufacturing-based country to one of the wealthiest in Europe, on the basis of hi-tech and international trade. Israel is ranked 33 in the GDP per capita list (IMF, 2006), with a GDP per capita that is almost half that of Ireland’s (nearly $26,000 PPP). The Israeli GDP has grown by 100% from 1990 to 2006. Like Ireland’s, Israel’s economy has moved from being mainly agrarian and traditional manufacturing-based into being based mostly on hi-tech, pharmaceutical and chemical industries, with a high percentage of the GDP being spent on research and development in these areas. Ireland has a lower unemployment rate (4.3% to 8.4% in Israel), the percentage of the public debt to the GDP is lower (declined from 94% in 1990 to 25% in 2006 for Ireland, compared to 88% in Israel). Table 1: Gini Coefficient of Inequality: Equalized by Ireland’s scale IrelandIsrael Share (out of net income)GiniChangeShare (out of net income)GiniChangeNon Governmental (Economic) Income107%0.5092107%0.5117 Economic Income – Taxes-26%0.4827-5%-23%0.4760-7%Gross (Economic + Government Transfers)20%0.3782-26%16%0.4224-17%Net (Economic + Transfers - Taxes)100%0.3256-36%100%0.3752-27%* In Ireland the accounting period is 12 month, in Israel – 3 months. As noted earlier, this adds nearly 4% to the Gin in Israel. In 2006, the Gini coefficient officially published by national institutions, (based on the total equalised disposable income) was 0.324 for Ireland and 0.390 for Israel, nearly 20% higher. These coefficients were calculated using equalised disposable income, and based upon national equivalence scales. Equivalence scales reflect the weight by which each member of the household is measured. It is well-known that different equivalence scales result in different measures of inequality and poverty (Cowell, 1984). Disposable income includes non-governmental income (from work, business etc.), government transfers, minus taxes (shares of each component are indicated in the Table). Therefore, the tax and benefits schemes of each country might affect the inequality even further. Table 1 demonstrates the difference between Ireland and Israel in the Gini Coefficients, calculated by using the same (Ireland’s) equivalence scale, by Economic (or non-governmental) income, Gross Income defined as Economic Income plus Government Transfers; Economic Income excluding transfers and excluding taxes; and finally, the disposable income. The table shows that when measured by non-governmental, before-tax income only, equalised by the same scale, both countries have similar level of inequality. The Irish tax and benefit system collect higher taxes and distributes more benefits than the Israeli tax and benefit system, while the net collection to the government coffer are equal. Although it collects less than the Irish one, the tax system in Israel reduces the inequality slightly more than in Ireland, but government benefits programs are much more effective in reducing inequality in Ireland. Overall, the difference between the two countries in Gini coefficient when calculated using the same equivalence scale is less dramatic that the original: 13% compared to 16.5% using national scales. Figure 1 shows the distribution of households by equalised non-governmental personal income for both countries. Mean Income of each country is set to 1 and all other incomes are in reference to the mean. The median for both countries is around 70% of the mean. However, there is a significant proportion of households with zero income, very high - nearly 23% in Ireland, and 16% in Israel. In Israel, on the other hand, there are more households with lower-than-average incomes than in Ireland, but also more households with very high incomes, twice the country's average. Figure 2, presenting the disposable income distributions, shows how governmental benefits and taxes manage to "correct" the initial distributions. The median of each distribution has moved to be located slightly over 80% of the mean, and in Ireland, most of the households are located around it. Israel (23%), where there is a larger portion of households with an income lower than 40% of the mean income. The gap in the two distributions lies in the low incomes region. Figure 1: The Distribution of Non-Governmental (equalised) Income between Households, Ireland and Israel, 2006.  *Mean Income of each country = 1. Ireland’s equivalence scale is used for both countries. Figure 2: The Distribution of Disposable (equalised) Income between Households, Ireland and Israel, 2006.  *Mean Income of each country = 1. Ireland’s equivalence scale is used for both countries. Demographic structure The demographic structure of the two countries is barely comparable. Table 2 presents the basic comparison of household composition between Ireland and Israel. The average household size in Israel (3.3) is larger than in Ireland (2.8), indicating that there are many more persons living in large households in Israel: nearly 45% of persons in Israel live in households consisting of 5 persons and more, compared to only 30% in Ireland. Large households (of 6 persons or more) comprise only 5% of the households in Ireland, but 12% in Israel . On the other hand, nearly half of the households in Ireland comprise one or two persons (50% of households and 27% of persons) whereas in Israel such households make up 42% of the total (20% of persons). Table 2: Distribution of households and persons living in households by household size  IrelandIsraelSample size (Households) 5,836   6,259  Sample size (Persons) 14,634   20,743  Households in population (thousands) 1,494   2,027  Persons in population (thousands) 4,253   6,733  Children (under 15)20.4%28.3%Children (under 18)26.6%33.0%Elderly (65 and above)11.0%9.6%Average household size2.8 3.3 Average Size of Large Households (6+)6.5 7.0 Maximum Household Size12 25 Population Distribution by Household size% of households% of persons% of households% of persons121.9%7.7%18.4%5.5%227.9%19.6%23.4%14.1%317.2%18.1%16.2%14.6%417.7%24.8%16.9%20.4%510.3%18.1%12.8%19.3%6+5.1%11.8%12.3%26.1% 100.0%100.0%100.0%100.0% Although in 2006, the percentage of elderly (aged 65+) in both countries was similar (11% in Ireland and 9.6% in Israel), the percentage of children aged less than 15 was much lower in Ireland – 20.4% in comparison to 28.3% in Israel. Table 3: Distribution of Households with and without children (aged less than 18).  Ireland Israel % of households% of persons% of households% of persons1 adult, no children21.9%7.7%18.4%5.5%2 adults, no children25.0%17.5%22.0%13.2%3+ adults, no children12.9%16.6%13.4%14.5%Adults only59.8%41.8%53.8%33.3%1 adults with children6.2%6.2%3.0%2.6%2 adults with 1-3 children21.9%29.3%24.4%29.1%2 adults with 4+ children1.5%3.4%6.1%12.5%others with children10.5%19.3%12.8%22.5%Households with children40.2%58.2%46.2%66.7%Total100.0%100.0%100.0%100.0% This basic comparison suggests that one of the major differences in the demographic structure of the two countries is that the Israeli household includes more children, which implies lower percentage of working-age persons (Table 3). Households with children comprise 40.2% of Ireland's households (58.2% persons), and 46% of Israel's (66.7% persons). As a consequence, we should expect that inequality comparisons between the two countries will be sensitive to the scale of adult-equivalent income used (Cowell 1984; Coulter, Cowell and Jenkins, 1992). The decomposition of the Gini Coefficient according to contribution of each household's size to overall inequality, enables us to assess the inequality in each group and to differentiate it from the contribution of different distributions of household's size. Impact of Equivalence Scales The determinants of economic well-being include income, and household size. Returns to scale in consumption, and different needs of the household members suggest the use of an adult-equivalent scale that enables the comparison of the economic-well being of households with different needs and structure. In order to calculate equalised income, one divides the household income by the number of equivalent adults (Friedman 1951; OECD, 2006; Klass and Zaidi 1997). The households are then weighted by the number of equivalent adults. Thus, if we compare two households with the same income, the larger one has the lower equalised income, but higher weight in the population, because it represents a larger number of persons. Higher equivalence scale lowers large households' income per equivalent adult while attributing a larger weight in the population to large households. Ireland’s national equivalence scale attributes a weight of 1 to the household head (first adult), 0.66 to each subsequent adult (aged 14+), and 0.33 to each child aged less than 14. The equalised household size is the sum of these weights. Table 4: Equivalence Scales of Ireland and Israel Household compositionPersons in household# of adults# of childrenNormalised Israeli Scale*CSO - IrelandSquare Root of Household Size1 adult1101.001.001.001 adult + 1 child2111.601.331.412 adults2201.601.661.411 adult + 2 children3122.121.661.732 adults + 1 child3212.121.991.733 adults3302.122.321.731 adult + 3 children4132.561.992.002 adults + 2 children4222.562.322.004 adults4402.562.982.002 adults + 3 children5233.002.652.243 adults + 2 children5323.002.982.245 adults5503.003.642.242 adults + 4 children6243.402.982.456 adults6603.404.302.452 adults + 5 children7253.803.312.652 adults + 6 children8264.163.642.83* Original Israeli scale divided by 1.25. Israel’s national equivalence scale does not make a distinction between adults or children, and considers only the total number of persons in the family. Thus, in Israel, given the same household size, the equalised size of households with or without children is the same, whereas in Ireland the higher weight is attached to the household containing a higher proportion of adults. Hence, the Israel's scale gives much more weight than the Ireland's one to families with children. Accordingly, considering that adults are likely to be those who bring money to the family, higher-income households are more likely to be attributed a higher weight by the Irish scale. Table 4 compares the equivalence scales of Ireland, Israel and a scale calculated as the square root of the household size (used by Nolan and Smeeding, 2005). In order to compare the Ireland’s and Israel’s scale, in Table 4 we normalise Israel’s scale to 1 by dividing it by 1.25. In most cases, especially for large households, this scale is the lowest of the three. The Ireland's scale is the highest, especially when considering households without children. Table 5: Gini Coefficient, Using Different Equivalence Scales Income Ireland Israel DifferenceEconomic, Non GovernmentalTotal household income0.56430.5324-5.65%Equalised by Ireland's Scale0.50920.51170.49%Equalised by Israel's Scale0.51660.5145-0.41%Equalised by Square Root0.52180.5105-2.18%Net Disposable IncomeTotal household income0.39620.3934-0.71%Equalised by Ireland's Scale0.3256*0.375215.23%Equalised by Israel's Scale0.33390.3796**13.69%Equalised by Square Root0.33910.37049.21%Difference Between Own-Scale Gini for Equalised Net Income16.58%*Officially Published Inequality Index for Ireland is 0.324. This difference is due to a slightly different calculation procedure. ** Here should stand the officially published inequality index for Israel, which is 0.390. However, the official Gini is calculated using the different (Income) survey instead of HES. We use HES throughout the paper, as it provides more household characteristic indicators. The question we would like to address now is - what is the estimated impact of the different equivalence scale on the Gini coefficient? Table 5 presents the various Gini coefficients calculated by the three definitions of equivalence scales, for Ireland and Israel. When the whole household's income is taken into account, there is higher inequality in economic incomes in Ireland than in Israel. Inequalities between households in the two countries are similar for net disposable incomes. However, the move to equalised income yields a striking change. The differences between the two countries in inequality for net incomes range from 9.2% to 15.2%. The impact of the equivalence scales on the two countries is completely different: whereas in Ireland the Ireland's scale produces the lower inequality and the Square Root – the highest, in Israel the effect is the opposite. For the purpose of comparability, in the rest of the paper we will use only Ireland's national scale in our calculations. Results Decomposition of Gini by Income Sources Tables 6 and 7 present the components of the decomposition for Ireland and Israel, as described in Part II.1. Net disposable income is presented as the sum of three components: economic non-governmental income, government transfers and taxes (mostly negative values). The first line presents the Gini for each one of the variables. The Gini calculated on the net disposable income is 0.3256 for Ireland and 0.3752 for Israel, the lowest of the components. The second line is the Jackknife standard errors. Table 6: The Components of Gini of Equalised Disposable (Net) Income: Economic / Non-Governmental Income, Government Transfers, Taxes. (Ireland, households weighted by household weight*equivalent adult: 2006)  Gini indices of inequality (Gi) and income shares (ai) EconomicTransfersTaxesSum=Net Income Gini Index0.50920.54120.64040.3256 St. Error0.00850.00650.00830.0096 Income share1.0660.196-0.2621.000 Proportion of non-zero0.8720.8530.7821.000 Gini of non-zero0.43720.46250.53880.3255  Gini Correlations matrix (“ij)i/jEconomicTransfersTaxesSum=Net Income Economic-0.6368-0.90700.9489 Transfers-0.71610.6998-0.4221 Taxes-0.94270.6385-0.8632 Sum=Net Income0.9484-0.4402-0.7814  Jackknife Standard Errors for the Difference between “ij and “jii/jEconomicTransfersTaxesSum=Net Income Economic0.01640.00540.0021 Transfers0.02160.0212 Taxes0.0148  GOGO2GO£aiDiOGiai2Gi2££aiajGiGj“ijNet0.32560.1060-0.00370.3340-0.2224 The third line presents the share of each of the components in the disposable income. The share of economic income is larger than 1, as it is usually higher than the disposable income. The share of taxes, however, is negative, balancing the difference. Government transfers comprise nearly 20% of the net income in Ireland, and only 16% in Israel. Fourth line is the proportion of population which receives any (even negative) part of the given component. For example, in Israel, almost 100% of the population pay taxes (or receive returns), compared to only 78% of such population in Ireland. Government transfers arrive to almost the same proportion of the population with Ireland's figure being slightly higher, and non-governmental income is received by higher proportion of the population in Israel (90%) than in Ireland (87%). Table 7: The Components of Gini of Equalised Disposable (Net) Income: Economic / Non-Governmental Income, Government Transfers, Taxes. (Israel, households weighted by household weight*equivalent adult: 2006)  Gini indices of inequality (Gi) and income shares (ai) EconomicTransfersTaxesSum=Net Income Gini Index0.51170.62140.67600.3752 St. Error0.00570.00530.00530.0052 Income share1.0680.158-0.2261.000 Proportion of non-zero0.8980.8361.0001.000 Gini of non-zero0.45600.54720.67590.3752  Gini Correlations matrix (“ij)i/jEconomicTransfersTaxesSum=Net Income Economic -0.4410-0.93230.9528 Transfers-0.4569 0.4209-0.0655 Taxes-0.96510.4821 -0.9095 Sum=Net Income0.9435-0.2181-0.8404   Jackknife Standard Errors for the Difference between “ij and “jii/jEconomicTransfersTaxesSum=Net Income Economic0.01460.00440.0020 Transfers0.02050.0166 Taxes0.0075        GOGO2GO£aiDiOGiai2Gi2££aiajGiGj“ijNet Income0.37520.14080.00360.3316-0.1930 Fifth line is the Gini coefficient calculated over those who have the given component in their income. The third part of the Tables presents the decomposition of the Gini according to Equation (3). The term that distinguishes the decomposition of Gini from the decomposition of the coefficient of variation,  EMBED Equation.3 , subtracts 0.0037 of the Gini in Ireland and adds 0.0036 to the Gini in Israel, however, the results are not statistically significant. The second part of each Table presents the Gini Correlation Coefficients between the components, followed by the statistical tests for the differences between each two coefficients. The coefficients have expected signs – for example, the higher the economic income, the higher are the governmental transfers, but here the progressivity of Ireland's governmental transfers is seen - in Ireland the correlations are much stronger than in Israel, indicating that the transfers are given to a really poorer population, and taxes are paid by richer ones. Another result derived from this part is that there are differences between each two correlation coefficients, meaning that the marginal distributions are not exchangeable. This implies that marginal distributions of the net income components change in a non-linear way, which is, again, not surprising given the progressive type of taxation. However, in Ireland, the differences between the correlation coefficients of the net income with economic income and government transfers are not significant. In Israel the insignificant difference is only between economic income and government transfers. Table 8: Person and Spearman Correlation Coefficients, Net Income, Ireland (Pearson in the lower left triangle, Spearman in the upper right triangle ) i/jEconomicTransfersTaxesSum=Net IncomeEconomic -0.7180-0.88340.8906Transfers-0.3890 0.6403-0.4301Taxes-0.79600.3395 -0.7590Sum=Net Income0.9445-0.1698-0.5947  Table 9: Person and Spearman Correlation Coefficients, Net Income, Israel (Pearson in the lower left triangle, Spearman in the upper right triangle ) i/jEconomicTransfersTaxesSum=Net IncomeEconomic1-0.5077-0.91650.9296Transfers-0.235610.4527-0.2423Taxes-0.87770.18831-0.8374Sum=Net Income0.9477-0.0072-0.72651 Schechtman and Yitzhaki (1999) have pointed out that the properties of the Gini correlations are a mixture of Spearman’s and Pearson’s correlation coefficients. Tables 8 and 9 present the Spearman and Pearson correlation coefficients. As can be seen, the Gini correlations are almost identical in magnitude to the appropriate Spearman correlation coefficients, but may sharply differ from the Pearson correlation coefficients. For example, while in Ireland the Spearman correlation coefficient between net income and government transfers is around -0.4, Pearson coefficient is -0.17. The difference between Spearman and Pearson Coefficients reaches 0.33 in Ireland and 0.26 in Israel (between Non-governmental Income and Government Transfers, in both countries). Decomposition of Gini by Population Groups Following the methodology presented in the previous section, we now move to the decomposition of the inequality in the two countries by population sub-groups. First, we divided the population into groups based on household size, households of 6 persons or more being grouped together. Table 10 summarises the results of the decomposition by household size for Ireland and Israel, using non-governmental equalised income. Column (II) presents the number of observations in each group, column (III) is the proportion of the specific category in the whole country's population of equivalent adults, column (IV) – is the mean income of the group (in national currency), column (V) is the same mean, but calculated in relation to the mean income of 2-persons household. This is the mean income that can be compared between the two countries. Column (VI) requires more explanation. It presents the mean rank of the group members in the overall income. Each individual is given a rank in the income distribution, from 0 to 1, so that the average rank of the population in the whole population is always 0.5. If we considered only the poor, their average rank would be lower than 0.5. Therefore, if for any group the mean rank of its members is lower than 0.5 we conclude that they are relatively poor. Column (VII) is the group’s income share in the total income, the total income being set to 1. The group-specific Gini is listed in column VIII, with the overall Gini indicated in the last line, and the overlapping coefficient with the whole population is to be found in column IX. The contribution of each group to the overall Gini is the product of columns VII, VIII and IX and is listed in column (X). As expected when using only direct, non-governmental incomes, the inequality is very high (nearly 0.51), and this is true for the two countries. There are, however, populations that are more equal than others. Table 10: Decomposition of Gini by Household size, Non-Governmental Income A. IRELAND – Non-Governmental IncomeHH size (I)Obs (II)Pi (III)Mean Income (IV)Relative Mean (V)F0i: Mean Rank (VI)Si: Income share (VII)Gi: Gini of the group (VIII)Oi: Overlap with the whole population (IX)S*G*O (X)1 person18160.11 16,415 0.710.350.080.7171.270.0702 persons16950.23 23,259 1.000.460.230.6151.120.1573 persons8670.18 25,299 1.090.550.200.4530.950.0854 persons7860.23 26,079 1.120.580.260.3970.860.0905 persons4500.16 22,640 0.970.540.160.3910.850.0536+2220.10 18,278 0.790.430.080.5070.920.036Within-group Gini0.491(96%)Between-Group Gini0.018( 4%)(GB Pyatt)(0.073)TOTAL    0.509 B. ISRAEL – Non-Governmental Income IncomeHH size (I)Obs (II)Pi (III)Mean Income (IV)Relative Mean (V)F0i: Mean Rank (VI)Si: Income share (VII)Gi: Gini of the group (VIII)Oi: Overlap with the whole population (IX)S*G*O (X)1 person11120.09 4,537 0.750.420.080.6321.170.0562 persons15450.17 6,047 1.000.510.200.5641.050.1203 persons10120.16 6,244 1.030.580.190.4470.900.0764 persons10450.19 6,049 1.000.570.230.4370.900.0895 persons7960.17 5,523 0.910.550.180.4360.900.0726+7490.21 3,037 0.500.360.120.5260.950.062Within-group Gini0.474(93%)Between-Group Gini0.038( 7%)(GB Pyatt)0.118TOTAL0.512 Looking at column VIII, in both countries, the lowest inequality is between medium-sized (3-5) households. Their mean incomes are the highest. Singles is the least equal group. They also possess the lowest income in terms of mean and distributional rank (column VI). When comparing between the countries, in Ireland, larger households are more equal than in Israel. Also, in Israel large families are, in average, much poorer than in Ireland: their mean income (per equalized person) is half of the income in 2-persons households and their rank is also the lowest. This is not surprising taking into account that these large households are actually much larger in Israel, implying there are less earners per household. In Ireland the poorest group is the singles. The overlapping index in column IX tells us whether one of the groups forms a distinctive stratum. If this index equals the proportion of the group (column III), than this groups is a perfect stratum. The lower the index, the less the groups distribution overlaps that of the whole population. There are no distinctive groups in either of the countries, however, medium-sized households have their distinctive place in the overall distribution. The distribution of the 2 and 3-persons households mimic the distribution function of the whole country population. Singles in Ireland, being the least equal group, have the highest overlapping index, meaning they break into two separate strata and present in both, the highest and the lowest ends of the population distribution. This result is less significant for Israel. The between-group inequality is very low and contributes very little to the overall inequality – 4% in Ireland and 7% in Israel. The relatively high Pyatt's between-group component shows that the overlapping has decreased the between-group component. Pyatt (1976) calculates inequality between groups as if each group member had the same income. According to the last column, the highest contribution (23% in Israel, 30% in Ireland) to the overall inequality in both countries is by the 2-person's households, this is due to the high income share of this group, high inequality and high overlapping component altogether. Table 11 presents the average ranking of members of one group in terms of the other. The diagonal presents each group in its own ranking which is 0.5 by definition. The ranking, unlike mean income, is not sensitive to extreme values. An average ranking above 0.5 means that on average, households in a given group, have higher ranks in the other group's distribution, than in their own, implying they are richer. For example, 4-person's household which is relatively poor in his own group, will be rich in terms of singles. Again, one can see that large households are the poorest in terms of other household groups in both countries (although they are richer than singles in Ireland), but they are much poorer in Israel. Table 11: Ranking of one distribution in terms of another, Non-Governmental Income IrelandHousehold size123456+10.50.3890.3140.2860.3090.36820.6110.50.4140.3840.4160.50230.6860.5860.50.4740.5170.62840.7140.6160.5260.50.5470.66950.6910.5840.4830.4530.50.6296+0.6320.4980.3720.3310.3710.5IsraelHousehold size123456+10.50.4110.3530.3560.3750.52120.5890.50.4420.4450.4690.63630.6470.5580.50.5030.5320.72340.6440.5550.4970.50.5290.71950.6250.5310.4680.4710.50.7006+0.4790.3640.2770.2810.3000.5 Table 12 presents the overlapping matrix between household groups. The rows represent the group whose distribution is used as the base distribution. For example, when 4-person's households are used as base, singles in both countries form a stratum. The interpretation of the two overlapping indices is that there are relatively more large households in the distribution of singles (i.e. poor) than there are singles in the distribution of large households. Table 12: Overlapping between continents, Non-Governmental Income IrelandHousehold size123456+11.01.1671.2911.3481.3801.38720.8061.01.1701.2391.2481.18030.6360.8151.01.0711.0800.99140.5380.7180.9251.01.0050.88650.5350.7090.9020.9761.00.9286+0.6010.8000.9651.0341.0501.0IsraelHousehold size123456+11.01.1051.2091.2141.2371.16820.8681.01.1261.1311.1360.95130.7230.8601.01.0051.0140.75640.7210.8550.9961.01.0140.77850.7180.8460.9770.9811.00.7956+0.7680.8650.9700.9681.0041.0 We now turn to the analysis of the net disposable income (Table 13). Comparing the two countries, we see that in each group the inequality is lower in Ireland than in Israel, although the between-group component contributes significantly in Israel as well. Most of the results based on the non-governmental income still hold: the singles have the highest inequality, although in Israel they improved their situation significantly. Two-person's households are now the richest group in terms of mean income. It is interesting to note their position relatively to the 4-person's group: in terms of non-governmental income, the 4-person's households were the richest group in Ireland be mean income and by mean rank, and the second richest group in Israel. In Ireland, they still have the highest mean rank, but they are "cut off" their extreme values. This cut-off is even more significant in Israel. In Ireland, there is also a change in relative positions of singles versus large households: whilst the relative position of singles has improved, it has worsened for the large households. No significant improvement has occurred in the relative position of large households in Israel. In Israel, 3-person's households have the highest mean rank, the lowest Gini – and the lowest overlapping index, making them the most distinctive stratum. In Ireland, these are the 4-person's households which stand out of the whole distribution. Table 13: Decomposition of Gini by Household size, Disposable Income A. IRELAND – Disposable Income Per Equivalent AdultHH size (I)Obs (II)Pi (III)Mean Income (IV)Relative Mean (V)F0i: Mean Rank (VI)Si: Income share (VII)Gi: Gini of the group (VIII)Oi: Overlap with the whole population (IX)S*G*O (X)1 person18160.11 19,225 0.820.410.100.3591.150.0402 persons16950.23 23,460 1.000.510.250.3721.020.0933 persons8670.18 22,881 0.980.540.190.3080.950.0564 persons7860.23 22,281 0.950.550.240.2850.950.0655 persons4500.16 19,905 0.850.500.150.2580.910.0356+2220.10 18,040 0.770.380.080.3401.060.029Within-group Gini0.318(98%)Between-Group Gini0.008( 2%)(GB Pyatt)0.047TOTAL0.326B. ISRAEL – Disposable Income Per Equivalent AdultHH size (I)Obs (II)Pi (III)Mean Income (IV)Relative Mean (V)F0i: Mean Rank (VI)Si: Income share (VII)Gi: Gini of the group (VIII)Oi: Overlap with the whole population (IX)S*G*O (X)1 person11120.09 5,124 0.850.510.090.3951.020.0372 persons15450.17 6,059 1.000.570.220.3930.950.0803 persons10120.16 5,624 0.930.590.180.3220.880.0514 persons10450.19 5,264 0.870.560.210.3260.920.0635 persons7960.17 4,763 0.790.510.170.3330.940.0536+7490.21 3,026 0.500.310.130.3730.970.048Within-group Gini0.332(89%)Between-Group Gini0.043(11%)(GB Pyatt)(0.114)TOTAL0.375 Table 14 allows us to see the improvement in the position (mean rank) of households when passing from non-governmental to net disposable income. This is not surprising, although one would not expect the change in the relative rank between several groups of households (those who had the higher rank are expected to preserve their relative position. However, the position of singles in Ireland has improved much more significantly than expected. In relation to large (6+) households, by non-governmental income, they had a mean rank of 0.368. Now, they are even richer than the large households, with the rank of 0.517. Couples have also improved their position "on the account" of larger families, with mean rank of 0.625 (compared to 0.502) by non-governmental income). Lerman and Yitzhaki (1995) showed how the inequality impacts of policies are not only in narrowing gaps between people at different income levels, but in fact, these policies also affect the positions of people, moving some initially at low incomes past those at higher incomes. Table 14: Ranking of one distribution in terms of another, Disposable Income IrelandHousehold size123456+10.50.3980.3740.3750.3960.51720.6020.50.4630.4610.4990.62530.6260.5370.50.4950.5470.67140.6250.5390.5050.50.5520.67550.6040.5010.4530.4480.50.6406+0.4830.3750.3290.3250.3600.5IsraelHousehold size123456+10.50.4330.4210.4490.4940.69420.5670.50.4930.5200.5670.75530.5790.5070.50.5280.5790.77640.5510.4800.4720.50.5500.74850.5060.4330.4210.4500.50.7106+0.3060.2450.2240.2520.2900.5 In Israel the change in relative ranking has occurred when comparing couples with households of 4 and 5. In comparison to these households, couples are now richer than 4 and 5-person's households, whilst in terms of non-governmental income they were relatively poorer. Analysis of the overlapping indices (Table 15) shows how closer household's distribution functions are now to the whole population – all the number are closer to 1 than in Table 12. Table 15: Overlapping between household sizes, Disposable Income IrelandHousehold size123456+11.01.1371.1811.1791.2351.07920.8331.01.0811.0841.0940.89230.7940.9211.01.0081.0180.85440.8050.9210.9941.01.0270.87950.7590.8730.9480.9481.00.8866+0.9481.0501.0801.0781.1121.0IsraelHousehold size123456+11.01.0471.1291.0991.0840.79820.9301.01.0761.0411.0030.67730.8500.9131.00.9680.9410.63440.8960.9391.0271.00.9890.72250.9090.9381.0210.9981.00.7726+0.9730.9300.9640.9651.0121.0 Conclusions and Further Research There is similar level of inequality in Ireland and Israel if measured by economic (non-governmental) income. Moving to measurement by net disposable income still yields similar inequality between households, but it is the introduction of inequality between (equalised) individuals that produces the most striking difference of nearly 15% in the inequality between the two countries. In Israel, the taxes are potentially more progressive, since they manage to reduce the inequality by a higher extend than in Ireland, but are less effective because of the low correlation coefficient with the government transfers and the disposable income. More efficient government programs in Ireland, allowing for the greater reduction in inequality. This can be seen from the magnitude of the reduction in inequality as well as from the correlation coefficients between different income sources, which are much stronger in Ireland. Analysing the demographic effect on inequality we find that no household size group forms a distinctive stratum, although medium-sized households differ slightly from the rest of the population. Large households are poor in both countries, but are much poorer in Israel relatively to the whole population. The most dispersed group is the singles, who present in both ends of the income distribution. Singles and couples also have the largest inequality in both countries. Government transfers also cause a change in rankings of households, in both countries, especially in Ireland, where in terms of non-governmental income, singles are on average poorer than large households, but when by net disposable income they are richer Considering the different impacts of the taxes and benefits systems in both countries, it is important to further analyse the distributional implications of different government benefits on inequality. A method for calculating the effect of small changes in taxes and benefits on inequality is developed by Lerman and Yitzhaki (1994) is based on the source decomposition of the Gini coefficient (Lerman and Yitzhaki, 1985) and yields direct estimates of the inequality change following a one dollar change in the income source. Another direction for the further research is the analysis of the effect of the accounting period on the inequality. 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The reader interested in proofs and/or additional properties is referred to the above-mentioned paper.  Actually, pi is not a true population parameter. It is added here for generality and for handling samples of different sizes.  117% from 1995 to 2006, adding it up to 25% from 1990 to 1995, according to Tables 4 at  HYPERLINK "http://www.cso.ie/releasespublications/documents/economy/2006/nie2006tables1995-2006excel.xls" http://www.cso.ie/releasespublications/documents/economy/2006/nie2006tables1995-2006excel.xls and  HYPERLINK "http://www.cso.ie/releasespublications/documents/economy/HistoricalNIETables1970-1995excludingFISIM1.xls" http://www.cso.ie/releasespublications/documents/economy/HistoricalNIETables1970-1995excludingFISIM1.xls  Ireland, Central Statistics Office, EU Survey on Income and Living Conditions (EU-SILC 2006), Table 1. (http://www.cso.ie/releasespublications/documents/eu_silc/current/eusilc.pdf)  Israel, Central Bureau of Statistics, Press Release 13 August 2007. ( HYPERLINK "http://www.cbs.gov.il/hodaot2007n/15_07_150e.pdf" http://www.cbs.gov.il/hodaot2007n/15_07_150e.pdf). As mentioned above, this figure is based on the Income Survey, which we do not use here. The same calculation based on the Household Expenditure Survey would yield the Gini Index = 0.380.  We discuss equalized incomes later.  Source: QNHS (Ireland) and HES (Israel)  Figures in the table are calculated using the data from the QNHS (Ireland) and HES (Israel) and may differ slightly from the demographic figures published in the Statistical Yearbooks.  Source: Statistical Yearbook of each country.  Some researchers tend to use equalised income, but to weight un-equalised persons. This causes internal contradiction leading to ambiguous results. (See Ebert 1997, 1999) Throughout this paper, we weight households by their number of equalised persons only.     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