Pertumbuhan ekonomi versus kesenjangan income

ADBI Working Paper February 2017

By Eko Wicaksono, Hidayat Amir, and Anda Nugroho


Growing inequality is one important problem for a developing country, and Indonesia is no exception. Narrowing the gap between those at the top and the bottom of income distribution has become one of the government’s main concerns. To achieve this goal, the sources of income inequality must be identified appropriately. Given the availability of household level data in Indonesia (Indonesia Family Life Survey), we are motivated to investigate the source of income inequality in Indonesia. The approach employed in this study is regression-based inequality decomposition using the Shapley value decomposition framework. The results show that education, wealth, as well as the employment sector are significant contributors to income inequality in Indonesia. These findings suggest that any policy aimed at reducing unequal access to education and finance is important to improve income inequality in the future


Given abundant reserves of natural resources as well as a large labor force, Indonesia has successfully achieved decent economic growth in the recent decade. In 2008, Indonesia became a member of G20, making it one of the world’s major economies. It has also been predicted that by 2030 it would be one of the top seven countries in terms of economy size, if it can maintain its rapid growth (Mc Kinsey Global Institute 2012). Furthermore, its decent economic condition has enabled the country to address the high poverty rate typical for a developing country. Nevertheless, another problem arises as the country grows: inequality has increased sharply in the last decade.

Figure 1 shows how economic indicators have evolved overtime. In 1978, almost one-third of Indonesia’s population lived below the poverty line. Twenty years later, as gross domestic product (GDP) per capita grew moderately, the poverty rate decreased significantly to around 15% right before the currency crisis hit Southeast Asia.

Indonesia has managed to recover quickly from the crisis as shown by the higher growth in GDP per capita after 2000. Poverty rate, which was adversely impacted by the crisis, eventually improved over time. On the other hand, higher growth seems to have negative consequence on income distribution as shown by the Gini index, which increased sharply during the last decade. The income gap between those at the bottom decile and those at the top widened as shown by the Gini index, which reached 0.41 in 2014. The 10 percentage point increase in the Gini index over 10 years was considered high among other developing countries. It is also the highest increase for a country in South Asia.

At some point, inequality is necessary to give a sort of incentive for the economy to continually grow faster. However, a persistent gap in income distribution will also affect economic performance later (Stiglitz 2016). Therefore, the widening gap in income distribution has been one of the concerns for the government. In medium-term development, one of the government’s targets is to reduce the Gini index by 2019. In order to address the growing inequality in Indonesia, one must be aware of the sources of income inequality. Once the sources of inequality are identified, the best policies can then be formulated to close the gap in income distribution. The rich literature on income inequality can help elaborate more on the root of the inequality problem in Indonesia. A most interesting study on the sources of income inequality has been conducted using household level data in many countries. The availability of household level data makes it possible to conduct the same study in Indonesia. By employing micro data, we can actually look for the characteristics that determine what is contributing to the higher inequality measure. Moreover, household level data allow us to decompose the inequality measures into some important contributing factors.

Income inequality decomposition can be conducted by using several methods. The most popular method is by employing either population subgroup or factor components decomposition (Shorrocks 1980, 1982, 1984; Bourguignon 1979). The example of population subgroup decomposition includes those that employ gender, age, and race differences in decomposition analysis. Despite its popularity, this method cannot control the contribution of other factors, thus undermining the contribution of other factors such as education and experience (Shorrocks and Wan 2004). In a factor-component decomposition, we can attribute income inequality by the source of income such as wage income, investment income, and other income. Nevertheless, this method cannot explain the fundamental factors that contribute to the difference in income such as education, wealth, and other personal or family characteristics. Fortunately, the other analytical framework—regression-based decomposition—makes it possible to overcome the limitation of the former. This framework was initiated by Oaxaca (1973) and Blinder (1973), and was then developed by Juhn et al. (1998) and Wan and Zhou (2004). By employing this method we can control the contribution of several factors simultaneously as well as identify the contribution of fundamental factors in explaining inequality.


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