Household Expenditure In Indonesia Economics Essay

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Indonesia is an archipelago nation located in South East Asia between the Indian and Pacific Oceans. It has more than 17,000 islands with 6,000 of those permanently inhabited. The total land area is about 1.9 million square kilometres and in 2008, it had a population of about 240 million. Administratively, the Republic of Indonesia is divided into 33 provinces. The World Bank (2009) has classified it as a lower middle income country with per capita GDP of PPP amounting to US$3,979 in 2008. According to the United Nation Development Programme (UNDP, 2009), the Human Development Index (HDI) for Indonesia in 2007 is at 0.734 and its ranking is 111. The official figures of some indicators related to poverty and inequality measures from 1996 to 2008 are shown in Table 1. The trend of inequality is generally increasing while the trend of the poverty index is decreasing, except for the period affected by the crisis in 1999, where inequality decreases and the headcount index increases.

Table 1 Trend in Poverty and Inequality Related Indicators 1996-2008

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Indicator

Region

1996

1999

2002

2005

2008

Nominal Per Capita Mean Expenditure (Rp'000)

Urban

100.64

180.50

273.29

350.20

458.93

Rural

52.71

109.52

152.78

195.51

254.81

Gini Coefficient

Urban

0.36

0.32

0.33

0.32

0.36

Rural

0.27

0.24

0.25

0.27

0.29

Headcount Ratio

Urban

13.39

19.41

14.46

11.68

11.65

Rural

19.78

26.03

21.10

19.98

18.93

Source: BPS Statistics Indonesia (1996, 2003, 2007, 2008)

Susenas Expenditure Data

In the present study, we approach the income distribution using the household expenditure data obtained from the National Socio-Economic Survey (Susenas). Susenas is a cross-sectional household survey for Indonesia which provides national coverage and is available over an extensive time period. A part of Susenas is conducted annually collecting information on the characteristics of over 200,000 households and over 800,000 individuals. This part of Susenas is known as the core Susenas. Another part is conducted every 3 years, collecting information on very detailed consumption expenditures on food and non-food items from approximately 65,000 households. This part is popularly known as the consumption module Susenas.

The dataset is created by merging the core and the module for 1996, 1999, 2002, 2005 and 2008. The created dataset has a combination of information on household consumption from the consumption module Susenas and household characteristics from the core Susenas. The analysed variable is the monthly household expenditure of food and non-food consumption. Naturally, the expenditure level may vary according to relative prices, demographic factors and preferences. In fact, the characteristics of urban and rural areas are very different in terms of these aspects, so this environment will actually determine the wellbeing interpretation in Indonesia. For this reason, we analyse urban and rural areas separately.

The total sample size is around 60,000 each year. The variation in different survey years is from various limitations of the data. For example, in 2002 due to some political instability, the survey did not cover 4 provinces, reducing the sample size at the national level. The treatment of some missing and extreme values in the merging of the Susenas core and module datasets has also reduced the amount of data being processed.

Price Adjustment

In order to make the expenditure data set comparable across different survey years, the data were corrected for inflation using the consumer price index (CPI). The CPI index for food and non-food groups reported by Badan Pusat Statistik (BPS) Statistics Indonesia was constructed for urban prices collected from 27 cities in 1996, 44 cities in 1999 and 45 cities in 2005 and 2008. Due to a limitation in data availability, we used urban price indices as proxies for the changes in prices for the rural areas in each province. For urban and rural areas that were not covered in the CPI series, we approximate them by using the CPI values of the neighbouring cities. In such localities, we expect to have quite similar characteristics in terms of the price index. In this paper, household expenditures were adjusted to real expenditure at 2002 prices.

Equivalence Scale

Household expenditure also has to be adjusted for the demographic differences to incorporate adult-child variation in household composition as well as positive economies of scale as household size increases. Thus, instead of using per capita, we deflated the household expenditure by using an equivalence scale which accommodates an adult equivalent scale and economies of scale. Equivalence scale practice has been researched extensively in the literature. As setting the equivalence scale was not our main research question, we have opted to use the formulation used by Banks and Johnson (1994) and Jenkins and Cowell (1994), which is recommended by Deaton and Zaidi (2002) for the case of developing countries such as Indonesia. It is given by

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(3.1)

where mi is the number of adult equivalents in household i while na,i and nc,i correspondingly denote the number of adults and children in household i. The parameter  is the cost of a child relative to that of an adult while the parameter  represents economies of scale in the cost of equivalent adults. In this study, adults were defined as household members aged 15 years and over because 15 is regarded as the age for beginning work. Further disaggregation of age groups and gender were not considered as demographic factors.

For the case of poor economies, Deaton and Zaidi (2002) suggest setting  as low as 0.3 and  close to 1. The recommended figures are motivated by the fact that child costs in poor countries are relatively inexpensive, and households in poor countries devote a larger share of their expenditure to food. So, there would not be much space for economies of scale. However, these figures may not be applicable for the case of Indonesia as the expenditure shares on food have declined over the years, from about 0.65 in 1996 to around 0.50 in 2008. The shift in expenditure shares is also likely to affect the magnitude of child costs.

When searching for the appropriate values for the economies of scale, , and the size of children relative to adults, , we started by fixing the bounds for the equivalence scales based on a recent study by Lancaster and Ray (2002). The values of  and  were then verified using a simple generalization of the Engel methodology developed by Valenzuela (1996). In summary, we have arrived at the conclusion that  = 0.85 and  = 0.8 are realistic values of the child cost and economies of scale for Indonesia. The characteristics of the size adjusted expenditures can be seen in Table 2 while the associated histograms are reported in Figures 3.1-3.5.

Table 2 Summary Statistics of Per Adult Equivalent Expenditure (Rp'000)

1996-2008

Region

Statistics

1996

1999

2002

2005

2008

Urban

Mean

370.9706

332.6298

432.3625

477.0349

454.0787

Median

292.0631

270.4114

337.8042

357.9471

355.2715

Minimum

34.0048

44.1204

57.6498

38.3182

59.8378

Maximum

9,388.0860

5,973.9160

24,902.6700

30,216.5100

13,181.9900

Std. Deviation

321.9729

249.3224

477.2953

511.1888

401.2329

Observation

23,875

25,175

29,279

24,687

26,648

Rural

Mean

204.3220

199.2879

220.7423

236.2176

251.8224

Median

173.9769

175.1857

191.8688

198.9731

210.0325

Minimum

40.7979

38.2171

37.7086

24.6667

38.4507

Maximum

5,123.6520

6,286.8730

3,595.8790

4,165.3190

23,635.2300

Std. Deviation

139.4953

111.9059

126.0481

153.6882

223.5371

 

Observation

35,977

35,426

35,143

35,320

40,076

The mean, median and standard deviation in 1999 decreased to some extent compared to the 1996 figures due to the impact of the Asian monetary crisis. Those statistics in general increased gradually after 1999, except for the urban areas in 2008 where the figures decreased slightly again. The urban areas were also found to have mean, median and standard deviation almost twice as high as those in the rural areas. The histograms generally show a typical uni-modal and right-skewness pattern for income distributions, with urban areas apparently having a more skewed and dispersed distribution than the rural areas.

Figure 1 Histogram of Per Adult Equivalent Expenditure for Urban and Rural Areas in 1996

Figure 2 Histogram of Per Adult Equivalent Expenditure for Urban and Rural Areas in 1999

Figure 3 Histogram of Per Adult Equivalent Expenditure for Urban and Rural Areas in 2002

Figure 4 Histogram of Per Adult Equivalent Expenditure for Urban and Rural Areas in 2005

Figure 5 Histogram of Per Adult Equivalent Expenditure for Urban and Rural Areas in 2008