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Towards a better measure of incomeinequality in IndonesiaKunta Nugraha a & Phil Lewis aa University of CanberraPublished online: 21 Mar 2013.

To cite this article: Kunta Nugraha & Phil Lewis (2013) Towards a better measure of incomeinequality in Indonesia, Bulletin of Indonesian Economic Studies, 49:1, 103-112, DOI:10.1080/00074918.2013.772941

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Bulletin of Indonesian Economic Studies, Vol. 49, No. 1, 2013: 103–12

ISSN 0007-4918 print/ISSN 1472-7234 online/13/010103-10 © 2013 Indonesia Project ANUhttp://dx.doi.org/10.1080/00074918.2013.772941

* The authors are grateful to Tesfaye Gebremedhin, Muni Perumal, Yogi Vidyattama, Ri-yana Miranti and Ross McLeod for their valuable and helpful comments. We also received many comments from participants at the 40th Australian Conference of Economists, in 2011. Responsibility for the final version is that of the authors.

TOWARDS A BETTER MEASURE OF INCOME INEQUALITY IN INDONESIA

Kunta Nugraha* Phil Lewis*

University of Canberra

Indonesia has experienced significant economic growth in recent years (on aver-age, 5% in 2000–08), but many people are still living in poverty. Income inequality, as measured by the official Gini coefficient, has also increased. This paper evalu-ates household income and income inequality in Indonesia, assessing both market and non-market income to reach a more accurate measure of how actual income affects living standards. We find that if household income considers non-market in-come, income distribution is significantly more balanced, the coefficient of income inequality falls from 0.41 to 0.21 and the income share of the population’s poorest deciles increases more than fivefold. The results suggest that market income alone is a misleading measure of income distribution in Indonesia.

Keywords: net income, actual income, income distribution, income in kind, consumption of own production

INTRODUCTIONAccording to Badan Pusat Statistik (BPS), Indonesia’s Central Statistics Agency, 15.4% of Indonesia’s population, or around 35 million people, were poor in 2008, a percentage that has since decreased (BPS 2010). At the time, BPS set the poverty line, or the basic-needs approach for food and non-food, at Rp 182,636 per capita per month, or $33.80 using purchasing-power parity (PPP). Indonesia’s official Gini coefficient, with which BPS measures inequality by capturing household consumption per capita, had been stable at around 0.30 in 1998–2001 but rose in 2002. Leigh and Van der Eng (2010) found that the Gini coefficient based on household consumption is lower than that based on earned household income, owing to the smoothing effects of saving and borrowing.

BPS publishes the Gini coefficient only periodically, but Cameron (2002) lists the Gini coefficient from both Asra (2000) and Booth (2000) during 1964–99, in which it ranges from 0.32 to 0.38. Figure 1 shows that the increase in Indone-sia’s official Gini coefficient after 2001 was not accompanied by an increase in the number of poor. According to data from both BPS and the World Bank, whose

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FIGURE 1 Indonesia’s Poverty Rate and the Gini Coefficient, 1998–2011a

a Includes predicted figures for 2009–11 for the Gini coefficient and the poverty rate based on the World Bank definitions. See the text for the BPS and World Bank definitions of poverty.

Source: Ministry of Finance (2012).

average poverty line for developing countries sits at $2 per capita per day, or $60 per month (World Bank 2009), the number of poor in Indonesia has decreased since 1999 (figure 1). The data suggest that the increase in income at the bottom of the distribution is less than the increase at the top of the distribution, indicating a wider dispersion. However, annual increases of around 5% in average income during 1998–2008 reduced the number of poor in Indonesia by around 8% each year.1

This paper evaluates different measurements of household income distribution in Indonesia. Previous studies (Cameron 2002; Lanjouw et al. 2002; Chung 2004)2 have used household consumption to measure income inequality, because consumption data are generally more reliable than income data and are a better indicator of a household’s permanent income (Deaton 1997). Here, we evaluate income inequality by using household income (comprising both market and non-market income) to strengthen the literature on income inequality in Indonesia.

In a country such as Indonesia, market income may not capture all of the components of ‘actual’ income, which includes, for example, consumption of own

1 Economic growth’s role in reducing poverty has been demonstrated elsewhere. Cameron (2002) found that Indonesia’s rapid rate of average real economic growth (7.1% per annum) during 1968–97 did not change inequality levels markedly. Fields et al. (2003) demonstrated that in Indonesia, South Africa, Spain and Venezuela, those with the lowest average household incomes enjoyed the most favourable income changes during the 1990s.2 Leigh and Van der Eng (2010) compare expenditure- and income-based Gini ratios for 1982–2004 and then use the household income data to analyse trends in top income.

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Towards a better measure of income inequality in Indonesia 105

production (a household’s consumption of the goods it produces itself, such as vegetables) and income in kind (income received in such forms as gifts, money transfers, company cars, meals and barter trade). Kusnic and Davanco (1986) maintain that in developed countries, households once produced many goods and services now supplied by the market. In developing countries, traditional measures that exclude household production under-estimate the ‘actual’ income of the poor. Similarly, expenditure data that include only market purchases will also significantly under-estimate ‘actual’ income and consumption.

This paper uses the household characteristics (module) and individual charac-teristics (core) of the 2009 National Socio-Economic Survey (Susenas) to calculate income distribution and adjusted income per capita for market income and actual income. The term ‘actual income’ refers to all market and non-market income that affects household living standards. Actual income comprises household market income, consumption of own production and income in kind. Evers (1981) con-cluded that, at the time, subsistence production was the third most important sector in the Indonesian economy, behind the formal and informal sectors. Today, subsistence-oriented monoculture production in agriculture, for example, is as important as ever, yet consumption of own production and income in kind have not contributed to analyses of income distribution in Indonesia.

The Susenas core and module data allow us to incorporate personal information, such as the age of each household member, in our analysis. We have taken household market income, that is, income after income tax, from the 2009 Susenas. Non-market income also includes consumption of own production, income in kind, and other income calculated using the household expenditure module and imputing non-market income to households.

The first and second sections of this paper explain, respectively, our methodology and use of data. The third section discusses our findings, and the fourth section concludes.

METHODOLOGYThis paper calculates household market income using the household income reported in the 2009 Susenas. BPS (2009) defines market income as:

• wages and salaries, including money, goods and services; • business income, including food agriculture, other agriculture (such as non-

food agriculture, farming, fisheries, forestry and hunting) and non-agriculture (such as industry, trade, transportation, service, construction and mining); and

• non-business income, including income from rent and other assets (such as interest, dividends, royalties and land rents).

Our analysis of Susenas data from block 5 (household income and expendi-ture) revealed that survey participants rarely include goods and services (that is, their non-market income) in their responses to questions about their income. For example, the data contains gaps between market income plus financial income and household consumption minus own production, and between total house-hold income and total household expenditure. Respondents rarely include their non-market income in Susenas, but they often include all of their consumption expenditure; non-market income should be added to the income they receive

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106 Kunta Nugraha and Phil Lewis

from wages and salaries in kind, gifts, or other unidentified income. Susenas also captures data for household consumption from the week of the survey, and asks, for example, ‘how many eggs did you eat in the last week?’. Participants tend to report all they eat, even though they may not have purchased all of it (it may have been provided by employers, for example, or obtained through bartering).

The formula for income in kind is as follows:

Yki = Exi −Yni −Coi − Fi( )i=i

n∑

(1)

where Yki is the income in kind of household i; Exi is the total expenditure of household i, including financial expenditure (such as savings, debt payments, insurance premiums and loans); Yni is the net income of household i; Coi is the consumption of own production of household i; and Fi is the financial income of household i (such as withdrawals, credit payments, insurance claims and borrow-ings). We introduce financial income and financial expenditure to capture saving and borrowing’s role in financing expenditure. Here, income in kind is the part of household total expenditure that is not financed by, for example, market income, financial income or consumption of own production.

We add consumption of own production to market income, because this consumption can significantly increase a household’s standard of living. We combine household income data and household expenditure data from Susenas, to analyse the quantity and value of each household’s consumption of own production. BPS (2009) calculates the value of consumption of own production based on the relevant region’s market price and adds it to consumption expenditure.

Previous studies (Cameron 2000; Leigh and Van der Eng 2010) have calculated Indonesia’s Gini coefficient using food and non-food consumption (Susenas block 4.3), which is similar to BPS’s method. These studies equate household consumption to household expenditure, but, as our analysis of Susenas block 5 reveals, household consumption differs from household expenditure. We use total household expenditure, which captures income in kind and unreported income (the difference between total expenditure and total income).

Before calculating the effect of total household expenditure’s on income distribution, we divided both categories of income, that is, market income and actual income, by equivalence scales, to account for household size and to determine adjusted per capita income. Equivalence scales compare the income levels of households of different size and composition – a larger household needs to have a higher level of income to achieve the same standard of living as a smaller household (ABS 2007). Such scales recognise that the economic needs of additional adults and children in households are not equal to the economic needs of the first adult and child. Many elements determine the economic need of each household member. Working adults, for example, incur transportation costs, and older children cost more to raise than young children. The most common equivalence scale is that modified by the OECD (Hagenaars, de Vos and Zaidi 1994). However, Ree, Alessie and Pradhan (2013) argue that this scale is not appropriate in Indonesia, in which households spend, on average, a larger fraction of their total budget on food than do households in OECD countries.

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Towards a better measure of income inequality in Indonesia 107

To simplify the measurement of adjusted per capita income, we use an equivalence scale that assigns different weights to each household member: the first adult is assigned 1 point, each additional person above 15 years is assigned 0.5 points, the first child under 15 years is assigned 0.5 points and each additional child under 15 years is assigned 0.35 points (Ree, Alessie and Pradhan 2013). Following the approach of Kim et al. (2006), we define children as those under 15 years and adults as those over 15 years. The formula for adjusted per capita income is as follows:

Yeq =Yi

ai1∗1( )+ ni − ai1( )∗0.5+ 0.5∗aic( )+ nic − aic( )∗0.35

(2)

where Yeq is adjusted per capita income; Yi is household income; ai1 is a dummy variable that takes the value of 1 or 0, indicating whether there is at least one adult in the household, i; ni is the number of adults in the household; aic is a dummy variable indicating whether there is at least one child in the household; and nic is the number of children in the household.

We used four ways of calculating the effects of all income categories on income distribution:

• Nominal and share terms: In nominal terms, we used US dollar values, to enable international comparisons and to use PPP to account for price differences. The average PPP exchange rate in 2008 was Rp 5,410 = $1. Price differences across regions are important, especially in Indonesia, but they are hard to measure, since only inflation rates (and not price indexes) are available for each region. In share terms, we divided the income of each group by the aggregate income of the population.

• Income groups: We ranked the household samples from the lowest to the highest, on the basis of their adjusted income, and then divided them into deciles. Each decile contained 5,754,867 weighted households. Comparing the share of these income deciles gives the dispersion of household income.

• Gini coefficient and percentile ratios: The Gini coefficient is a well-known indicator of income inequality. The formula is as follows:

where Yi and Yj are individual incomes with a mean of Ŷ, and where n is the total number of observations (Rosen and Gayer 2008). A higher figure indicates a higher level of income inequality.

• Decile earnings compared with median earnings: Comparing the earnings of the lowest and highest deciles relative to median earnings gives the dispersion of earning (Lewis et al. 2010). The formula is:

where D is the dispersion of earning, P10 is the lowest percentile of earnings, P50 is median earnings and P90 is the highest percentile of earnings.

Gini = i=1n

j=1n Yi −Yj∑∑

2n n−1( )Y (3)

(4)D = P10P50

and D = P90P50

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108 Kunta Nugraha and Phil Lewis

DATASusenas collects data annually from a sample of households that are individu-ally weighted to represent the Indonesian population. The Susenas core collects individual and household characteristics, such as age, employment status, health, education level and housing type, whereas the Susenas module collects informa-tion on specific topics, such as household consumption and expenditure, in three-year cycles. At the time of our research, the most recent Susenas core data were for 2008 (2009 publication) and were collected from 1,142,675 individuals and 282,387 households; the latest module data on consumption and expenditure were also for 2008, and were collected from the same number of households. To capture a complete set of information on households and their individuals’ characteris-tics, consumption patterns, per capita expenditure and income distribution, we merged Susenas core and module data – the first such merging of individual and household data from Susenas.3

One problem of using Susenas data is the potential under-representation of the very wealthy and the very poor (Cameron 2002); the former tend to refuse to respond to the questions of the BPS officers, and it is hard to collect data from the latter. As in the household surveys of some other countries, the highest earners – especially the top 10% – receive only limited statistical coverage (Deaton 1997). This gap in Susenas data is borne out by the substantial discrepancy between total household expenditure estimated from Susenas and the total private consumption component of GDP (Leigh and Van der Eng 2010). With this in mind, the results of this paper should be considered as representing all but the very poorest and richest households in Indonesia.

FINDINGS AND DISCUSSION We calculated three types of adjusted income for comparison: per capita income; adjusted per capita income, based on the equivalence scale of Ree, Alessie and Pradhan (2013); and adjusted per capita income based on the OECD equivalence scale. Our results differ slightly for certain household types by income quintile, but they were not significantly altered by our choice of method. To capture the impact of household size, then, we present only those results based on the equivalence scale above.

Table 1 shows the distribution of adjusted per capita market, non-market and actual income for each decile. It shows that the dispersion of actual income is greatly reduced after adding income in kind and consumption of own production to all market income, which comprises net income and financial income. The proportion of income by household in the highest decile is 32.0% for market income and 22.2% for actual income; in the lowest decile, these proportions are 2.2% and 8.1% respectively. Actual income’s share in the lowest decile, by market income, is higher than that in the second to fourth deciles. The poorest benefit more from non-market income than those in the second to fourth deciles. The dispersion of income in kind and consumption of own production for each household group is fairly systematic in reducing the level of inequality between income groups.

3 Leigh and Van der Eng (2010) summed individual data to household levels for 1999 and 2002.

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Towards a better measure of income inequality in Indonesia 109

Income in kind and consumption of own production are important for all income groups, including the richest households.

Actual income is significantly higher than all market income in all deciles. For example, in the lowest decile, actual income, $1,590, is more than six times all market income, $254. For households in the lowest decile, non-market income plays a big role in increasing the standard of living. Even for the highest decile, actual income is around 20% higher than all market income. There is far less inequality in actual income than in market income. This finding is in line with those of Evers (1981) and Ravallion and Dearden (1988). Evers (1981) finds that subsistence production plays a major role in the household economy in Indonesia, particularly for the poor. Ravallion and Dearden (1988) mention the significant role of ‘moral economy’ in Java, since there is no social-security system in Indonesia. Moral economy is the transfer payment of money or goods from rich to poor households.

TABLE 1 Household Per Capita Income in 2008, by Decile

Market Income All Market Income

Non-market Income Actual Income

Net Income

Financial Income

Consump-tion of Own Production

Income in Kind

2008 PPP$a

Lowest 143 111 254 215 1,121 1,590 Second 339 134 473 215 654 1,342 Third 443 127 571 215 608 1,394 Fourth 545 133 678 221 599 1,497 Fifth 652 147 800 209 602 1,611 Sixth 770 152 922 197 564 1,683 Seventh 920 171 1,091 194 539 1,824 Eighth 1,100 191 1,291 177 551 2,019 Ninth 1,433 214 1,647 149 599 2,395 Highest 3,225 417 3,641 118 623 4,383

Total

%Lowest 1.5 6.2 2.2 11.3 17.4 8.1 Second 3.5 7.5 4.2 11.2 10.1 6.8 Third 4.6 7.1 5.0 11.3 9.4 7.1 Fourth 5.7 7.4 6.0 11.6 9.3 7.6 Fifth 6.8 8.2 7.0 11.0 9.3 8.2 Sixth 8.1 8.4 8.1 10.3 8.7 8.5 Seventh 9.6 9.5 9.6 10.2 8.3 9.2 Eighth 11.5 10.6 11.4 9.3 8.5 10.2 Ninth 15.0 11.9 14.5 7.8 9.3 12.1 Highest 33.7 23.2 32.0 6.2 9.6 22.2

a PPP$ = purchasing-power-parity dollars.

Source: Authors’ calculations based on data from the 2009 Susenas module.

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110 Kunta Nugraha and Phil Lewis

Table 2 shows the sources of per capita income in each household group. In the lowest and second deciles, the largest source of income is income in kind and consumption of own production. These findings have implications for taxation. For households in these deciles, most of their income is non-market income; low-income households pay less income tax than other deciles. In the third to sixth deciles, most income comes from income in kind and then from business income. Taxes are paid on business income but not on income in kind. In the seventh to highest deciles, most income comes from business income and then from income in kind. The amount of tax paid is higher than that paid by households in other deciles, because most of the income is from business income. Even those households in the highest deciles have some consumption of own production.

The share of financial income in adjusted per capita household income is simi-lar for all deciles. We can speculate that most financial income in the lowest deciles comes from borrowing and in the highest deciles from saving.

Income inequalityTwo of the most common ways of measuring income inequality are the Gini coefficient and relative percentiles. Table 3 shows that the Gini coefficient for adjusted per capita net income is 0.41. Within a range of 0 to 1, a value of 0 means perfectly equal and 1 means perfectly unequal (Lewis et al. 2010). Adding financial income to net income reduces the Gini coefficient to 0.40, indicating a lower level of inequality. When income in kind and consumption of own production are also included, the Gini coefficient reduces further, to 0.21 (table 3). Non-market income improves the standard of living of the lowest income group most, which also reduces income inequality. If we compare this Gini coefficient with that of BPS,

TABLE 2 Sources of Adjusted Household Actual Per Capita Income in 2008 (%)

Decile

Market Income Non-market Income

Wages and Salaries

Business Income

Non- business Income

Financial Income

Consump-tion of Own Production

Income in Kind

Lowest 2.9 2.7 3.4 7.0 13.5 70.5 Second 5.2 12.1 8.0 10.0 16.0 48.8 Third 4.8 18.0 8.9 9.1 15.4 43.6 Fourth 4.6 22.2 9.5 8.9 14.7 40.0 Fifth 4.3 26.2 10.0 9.2 13.0 37.4 Sixth 3.9 31.2 10.7 9.0 11.7 33.5 Seventh 3.5 35.7 11.2 9.4 10.6 29.6 Eighth 3.3 39.6 11.5 9.5 8.8 27.3 Ninth 2.9 45.2 11.8 8.9 6.2 25.0 Highest 2.0 58.2 13.4 9.5 2.7 14.2

All households 3.4 34.5 10.5 9.1 9.7 32.7

Source: Authors’ calculations based on data from the 2009 Susenas module.

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Towards a better measure of income inequality in Indonesia 111

the level of income inequality in Indonesia is much lower than the official level: using expenditure data for individual households, we calculate a Gini coefficient of 0.31, which, based on grouped data, is comparable with BPS’s estimate of 0.37.

This alternative calculation of the Gini coefficient points to a lower level of income dispersion than that measured by BPS and the World Bank, as measured by relative percentiles. The ratio of the lowest 10% to the median of income changes from 0.34 to 0.83 – that is, the income of the lowest decile increases from 34% of the median income to 83%. The ratio of the highest 10% of incomes to the median of income changes from 3.27 to 2.06. This means that the market income of the highest-earning households is around 3.3 times the median income, but this falls to around 2.1 times when actual income is used.

CONCLUSION Indonesia’s poverty rate has fallen rapidly since 2000 but is still higher than that of most of its neighbours. In 2008, the proportion of the population living on less than $2 a day (at 2005 international prices) in Indonesia was 54.4%, compared with 53.3% in Cambodia and 43.4% in Vietnam (World Bank 2012). To reduce poverty and improve income distribution, it is necessary to start with an appropriate measure of income inequality. In this paper, we use a broad definition of income for this purpose, and include both market and non-market income in our calculations of total household income. All income groups in Indonesia earn non-market income, but households in the lowest income deciles have a larger proportion of non-market income than the highest income groups.

Our results suggest that measuring income inequality in Indonesia without assessing non-market income gives misleading results, and that non-market income contributes significantly to lower levels of income inequality. The dispersion of actual income is more balanced after adding income in kind and consumption of own production to income calculations, because both components have the potential to increase the income of the lowest- and middle-income groups. Calculating the Gini coefficient and dispersion of income using market income alone – the method used in most developed countries – is not suitable for Indonesia.

TABLE 3 Inequality Measures of Adjusted Per Capita Income in 2008

Net Income All Market Income Actual Income

Gini coefficient 0.41 0.40 0.21

Percentile ratiosP90/P10 9.67 7.28 2.48 P90/P50 3.27 3.07 2.06 P10/P50 0.34 0.42 0.83 P75/P25 2.48 2.26 1.35

Source: Authors’ calculations based on data from the 2009 Susenas module.

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