Indonesia’s Poverty Profile. Exploration of Indonesia County Level... | by Redi Sunarta | Towards Data Science

The issue of poverty has always been one of the main topics of discussion in various economic and social forums. Poverty is considered a serious problem and must be resolved because it has a high social cost. Poverty itself is usually defined as the inability of an individual to meet basic needs in the form of goods, such as food and clean water, as well as services, such as access to education and health.
Formally, the ability to meet these basic needs is measured in terms of expenditure or individual income. In Indonesia, Central Bureau of Statistics (Badan Pusat Statistik) uses the average expenditure variable as a benchmark which divided into basic food and non-food needs. An individual is categorized as poor if he has expenditure below the poverty line.
Indonesia’s poverty line is calculated from the sum of the value of expenditure of minimum food needs equal to 2100 kilocalories and minimum needs for housing, clothing, education, and health per capita per day. In March 2019, Indonesia’s poverty line was recorded at IDR425,250 per capita/month (approximately $1 per capita/day) with the composition of the Food is 73.66%.
Based on that nominal poverty line, the percentage of poor people in Indonesia is 9.41% or around 25.14 million people and the concentration of origin of the poor population is still dominated by rural residents, even with a poverty rate benchmark, 12.85% compared to 6.89% for urban. In aggregate, these results showed a decrease of 0.41 percentage point compared to the previous year.
The trend of decreasing the number of poor people and poverty rates indeed also occurs in other parts of the earth. This phenomenon began since the end of the second world war and the practice of colonization. This period of peace and independence provides opportunities for ex-colonial countries that are pockets of world poverty to make changes in their countries. Then it results in more inclusive economic growth and finally reduces poverty.
In the Indonesian context, it is noted that each government always has a main program specifically for poverty reduction, even though it has a different name like Takesra, Kukesra, PKH, BPNT, BLSM, Raskin, and many other. However, it is generally given in the form of subsidies or cash assistance. The recipient of the subsidy or cash transfer was selected based on data from the local government to determine the eligibility of the recipient. Even so, there are still many reports about the inaccuracy of program objectives provided. Therefore, the process of disaggregation of poverty data is quite important because it does not only look at the development of poverty rates that are seen to continue to decline.
I will disaggregate to the county level because only up to that level is data available. More specific level data is available only in a few provinces. Even then, it usually has a publication lag from Indonesia’s Central Bureau of Statistics for up to one year. The main picture above is an initial illustration of the distribution of changes (two-point annual data) in the number of poor people in all counties from 2013 compared to 2018, in March. It is clear that almost all counties in Java have succeeded in reducing the number of poor people they have. This can happen because Java has the highest number of poor people, so it is reasonable to be the top priority in every poverty alleviation program. In addition, Java is also the center of Indonesia’s economy and politics
Meanwhile, if I rank by county with the best decrease in the number of poor people in the period, Buton Regency is the best county in that category (out of 494 counties). The local government of Buton Regency should deserve an award for their hard work. Cheers.
But if I look in more detail by comparing these changes with population growth. Buton Regency’s achievement was not as bright as before because as a large decrease in the number of poor peoples followed by a large population decline as well, -65% compared to -62%. This is also evidenced by the change in Headcount Index which only changed 1.58 percent, meaning that if the population of Buton Regency remained constant for the whole year, the poor population would only decrease by 4,113 people. So basically, the number of poor people in Buton Regency has decreased by only 10% over the past five years. Thus, if I only see a reduction in the number of poor people, I don’t think that’s right enough.
What if the population increase as additional requirements for a reward, I assume that poor people are significant effecting growth county population. As a result, 333 counties met these additional requirements and sadly only three of the top fifteen in the graph above fall into this additional category. As a result, the top three counties with different ratio or delta on that two variable, respectively Tolikara Regency, Tanatidung Regency, and Gunung Sitoli City. Because, although the population continues to increase every year, they managed to reduce the number of poor people as well and that really needs hard work.
Ok now, let's talk more about the poverty line. I told you before that Indonesia has a poverty line approximately $1 per capita/day nominally. Then, for your information, Indonesia has positive skewness of expenditure distribution. Thus, if I increase the poverty line to 1.5x, the poverty rate of Indonesia will be increased up to 40%. In other words, many Indonesian middle-income class is in poor prone conditions. In reality, during this five-year period, there were 143 counties that had increased the number of poor people, along with the increasing poverty line from year to year. Although on average the county per capita income has increased. Thus, that county economic growth is not pro-poor.
seaborn.boxplot(showfliers=False, ...)
The headcount index only measures the proportion of the poor to the population. If you want to focus only on consumption distribution of poor group, then P1 and P2 are commonly used. P1 is used to calculate the average distance of consumption of poor people with the poverty line. Meanwhile, P2 calculates the variance in consumption among the poor population. The higher the value of those two indices, the more bad indication.
The chart above shows the condition of the three indexes in all observation counties with “x” as the arithmetic mean. The graph shows that the poverty rate has declined, but P1 and P2 are relatively stagnant. Intuitively, this can explain that poverty programs are difficult to help poor people who are in the lowest percentile. On the other hand, if the poverty program is proven to push poor people who are near the poverty line out of the poverty zone, then the poverty program so far can be said to touch almost all poor people because on average the expenditure of the poor has increased. However, I do not have data whether the increase in expenditure is only in nominal terms, in other words only as compensation for an increase in inflation. I couldn't find a convincing answer yet.
P1 and P2 index in 2015 and 2016 caught my attention. It is clear when the proportion of poor people decreases, the P1 and P2 indexes actually increase. That year was the start of Joko Widodo’s Cabinet government and made major changes in the current state budget structure. Joko Widodo’s government cut a lot of subsidies that the previous government used to divert to infrastructure projects. The cuts in subsidies have really hit the poor. Although then the budget is adjusted, especially nearing the election year in 2019.
The achievement of the decline in the number of poor people during this decade, however, should be appreciated. This at least shows the seriousness of the government in efforts to overcome this poverty problem. However, the problem of distribution inequality — both within the community economy class and between counties — still a big job where almost half of Indonesia’s population is very vulnerable to becoming poor.
Why I always mention the government in this poverty problem. Because the government is the only party that has the ability to share the cake of economic growth. Where the poor do not have enough capital and power to grow in society. Thank you for reading my story so far. I hope you all get to know more about poverty conditions in Indonesia. In this story, I only explored the poverty dataset, not touching too much the relationship of poverty with other related variables. Even I didn’t use a fancy model at all. Maybe next time, I’ll try to write and discuss. Best Regard.