Chapter 3. Effectiveness of social protection

A selection of the programmes identified in Chapter 2 have emerged as central to Indonesia’s strategies for reducing poverty and inequality and promoting inclusive growth. This chapter examines the effectiveness of these key programmes: Rastra (formerly Raskin; Rice for the Poor), Programme Indonesia Pintar (PIP; Assistance for Poor Students), Penerima Bayaran Iuran (PBI; Social Health Insurance for the Poor and Near Poor) and Program Keluarga Harapan (PKH; Family of Hope Programme). It analyses their impact across four dimensions: coverage, adequacy, equity and efficiency. This analysis is intended to inform the evolution of these programmes and support appropriate allocation of resources across the social protection system. The chapter concludes with a gender-based analysis of the pension system.

    

The Government of Indonesia (GoI) has placed social protection at the centre of its inclusive growth strategy. Social assistance represents the most direct means by which the GoI can address persistent levels of poverty and high inequality. However, the extent to which it fulfils this potential depends on the extent to which programmes reach the intended beneficiaries, especially in the constrained financing environment discussed in Chapter 4.

In recent years, it has scaled up four key non-contributory programmes outlined in Chapter 2: Rastra, PKH, PBI and PIP. This chapter provides in-depth analysis of these programmes’ coverage, adequacy, equity and efficiency, shedding light on their potential to alleviate poverty, reduce inequality and protect vulnerable populations. A final section focuses on social insurance and vulnerability in old age through a gender lens.

Rice for the Poor: Rastra

Rastra (formerly Raskin) emerged as a response to national food emergency linked to the 1997 Asian Financial Crisis. The subsidy to purchase rice, Indonesia’s food staple, was subsequently expanded and integrated into the national social protection system. In its non-crisis function, the subsidy aims to reduce low-income household food spending and provide poor and near-poor households with access to rice (CMoHDC, 2017[1]), strengthen food resilience and alleviate poverty (Rahayu, 2014[2]).

In 2017, rice had a market price of IDR 9 220 (Indonesian rupiah) per kg. By contrast, Rastra households paid a fixed rate of IDR 1 600 per kg, nearly 80% below market price. The GoI allocated approximately IDR 19 trillion to cover the difference between marked and subsidised price for fiscal year 2017/18. Between 2011 and 2016, Rastra expenditure amounted to 37.3% of government spending on its main social assistance programmes (World Bank, 2017[3]). Although substantial, this marks a notable decrease, compared with preceding years, as the programme is being integrated into the Bantuan Pangan Non Tunai programme (BPNT; Non-cash Food Assistance) (see Chapter 2 overview.)

Coverage of Rastra

According to the 2016 Survei Sosial Ekonomi Nasional (SUSENAS; National Socio-Economic Survey), an estimated 44.6% of the population reported being covered by Rastra, making it the largest social assistance programme in Indonesia by far (Statistics Indonesia, 2016[4]). Covering nearly half the population is a remarkable feat for any social programme but notably so for a country with a population of 261 million and large geographic disparity. As such, Rastra delivery procedures are complex and contend with significant targeting challenges.

According to 2016 SUSENAS data, Rastra covers a larger proportion of the informally employed (54.3%) and unemployed (49.5%), compared with the formally employed (31.2%). It also reaches 41.1% of those excluded from Indonesia’s labour force (Figure 3.1).

Figure 3.1. A majority of informal and about half of unemployed individuals benefits from Rastra
Rastra coverage by labour force status (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Targeting performance

Rastra aims to cover all households in the bottom 40% of households included in the Unified Database (UDB (CMoHDC, 2017[1]). According to Tim Nasional Percepatan Penanggulangan Kemiskinan (TNP2K; National Team for the Acceleration of Poverty Reduction), 15.5 million households were eligible for Rastra in 2013. By 2017, eligible households had declined to 14.2 million, largely due to budget reallocations to a successor programme, BPNT (Figure 3.2).

Figure 3.2. Rastra coverage is declining
Rastra beneficiaries (2008-17)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Adequacy of Rastra

Analysis of the adequacy of the Rastra benefit compares the subsidy’s generosity against a number of socio-economic indicators, such as food adequacy, the extreme (or food) poverty line (EPL), the national overall poverty line (OPL), the average household consumption threshold and the poverty gap.

On average, per capita monthly rice consumption stands at 9.5 kg (38 kg for a four-member household). By distributing 15 kg of rice to each household, Rastra contributed 39.5% to households’ total rice consumption, equivalent to IDR 108 975 per month or IDR 1 307 700 per year (2016 prices) per household. Table 3.1 summarises benefit values between 2014 and 2016 (Rahayu, 2014[2]).

Table 3.1. Rastra subsidies represent an increasing burden on government
Subsidy amount, GoI purchase price and household purchase price for rice (2014-16)

 

2014

2015

2016

Subsidy (IDR/kg)

6 447

6 725

7 265

Government purchase price (IDR/kg)

8 047

8 325

8 865

Household purchase price (IDR/kg)

1 600

1 600

1 600

Source: CMoHDC (2016).

Between 2014 and 2016, the benefit value decreased across multiple reference indicators, pointing to a lower level of sufficiency. In 2016, the benefit accounted for 47.3% of average rice consumption expenditure per capita but substantially lower shares of the various subsistence levels: 9.6% of the EPL, 7.7% of the OPL and 2.9% of the average household consumption per capita (Table 3.2).

Table 3.2. Rastra benefits represent a decreasing share of living standards
Rastra benefits as a share of selected living standards indicators (2014-16)

Year

Rastra benefits per capita relative to average rice consumption per capita (monthly) (%)

Rastra benefits per capita relative to EPL (%)

Rastra benefits per capita relative to OPL (%)

Rastra benefits per capita relative to average household consumption per capita (%)

2014

50.1

11.2

9.0

3.5

2015

45.4

10.3

8.2

3.1

2016

47.3

9.6

7.7

2.9

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Rastra suffers from severe implementation shortcomings. There are, for instance, substantial disparities between the value of the benefit reported by beneficiaries (reported in the SUSENAS) and the total amount purchased by the government for distribution (World Bank, 2017[3]). Survey respondents reported that, on average, the subsidised rate for rice in 2016 was IDR 2 054/kg, in lieu of the promised IDR 1 600/kg. A single household ought to have received rice to a total value of IDR 1 307 700 in 2016. Survey data show that the average household received IDR 435 900 (World Bank, 2017[3]).1

Inclusion error is also a concern that has been attributed to community-level decisions on programme implementation. Using SUSENAS data, TNP2K-Mahkota estimates that in 2017, for example, Rastra benefited almost double the number of households on its registration rolls (15.6 million registered households versus 28.6 million beneficiary households) (TNP2K, 2018[5]). Discrepancies in the number of targeted versus actual beneficiaries and rice rates are reportedly due to community leaders reduce perceived inequality in distribution directives (TNP2K, 2018[5]; TNP2K, 2015[6]; Mawardi et al., 2007[7]).

Despite widely reported issues with inclusion error, evaluations of programme impact on nutrient consumption of poor households are largely positive. In 2012, it was estimated that the savings associated with Rastra enrolment helped beneficiary households to increase expenditure on food with higher nutritive value and health services (Pangaribowo, 2012[8]). Kustianingrum and Terawaki (2018[9]) similarly find that Rastra improves nutritious intake for poor households, by an average of 5.3 kcal per IDR 100 of subsidy.

Equity of Rastra

Beneficiary incidence and distribution

Figure 3.3 shows the beneficiary incidence with the percentage of households covered in each consumption decile. In rural areas, the programme covers over 60% of the bottom five deciles. The programme demonstrates higher coverage of rural households than urban and of poorer deciles than wealthier. Coverage is wide, but inclusion errors are quite important under Rastra, particularly in rural areas. Coverage of wealthier deciles is significantly higher in rural than in urban areas: 27% of the wealthiest decile in rural areas benefits, compared with 2.4% in urban areas (Figure 3.3).

Figure 3.3. Rastra coverage is wide but inclusion errors are prominent in urban areas
Beneficiary incidence shown as share of each decile covered by Rastra (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Figure 3.4 depicts the distribution of beneficiaries across expenditure deciles. While a higher share of beneficiaries belong to poorer deciles than wealthier deciles, a substantial share of beneficiaries belong to the top seven deciles. The share of poor beneficiaries is higher in urban areas, whereas the distribution is more uniform across deciles in rural areas. In rural areas, households in the lowest decile are 2.6 times more likely to receive the grant than those in the richest decile, compared with 32.1 times more likely in urban areas. The lowest three deciles make up 45.7% of all beneficiaries.

Figure 3.4. Rastra beneficiary distribution displays important differences across rural and urban areas
Share of total beneficiaries in each decile (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Benefit incidence and distribution

Figure 3.5 depicts the percentage of total benefits received by expenditure decile. While the tenth decile receives 2% of benefits, many deciles receive significant payouts. The bottom two deciles receive about a third of all benefits disbursed; the poorest decile receives 16% of total Rastra benefits.

Figure 3.5. About one third of Rastra benefits are received by poorest quintile
Rastra benefits shown as share of total benefits in each decile (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Efficiency of Rastra

Impact studies of Rastra and its predecessor, Raskin, on food consumption show several positive results. A 2005 study found that the subsidy increased household consumption by 4.4%, while reducing the likelihood of falling below the overall poverty line by 3.8% (Sumarto, Suryahadi and Widyanti, 2004[10]). Another study showed that the programme helps beneficiaries smooth their food consumption and build resilience against economic and environmental shocks (Pangaribowo, 2012[11]).

Rastra cost the GoI nearly IDR 16.9 trillion, or 0.014% of gross domestic product (GDP), in 2016 and generated a 7.7% poverty headcount reduction, an 8.2% extreme poverty headcount reduction, a 11.9% poverty gap reduction and a 21.8% extreme poverty gap reduction (Table 3.3).

Table 3.3. Rastra significantly reduces poverty
Rastra cost and poverty impacts (2016)

Disbursed amount (IDR trillion and % of GDP)

Poverty headcount reduction

Extreme poverty headcount reduction

Poverty gap reduction (IDN million)

Extreme poverty gap reduction (IDN million)

17

2 340 996

745 422

2 829 571

980 854

0.014%

7.71%

8.15%

11.89%

21.76%

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

The programme’s efficiency is calculated as the ratio of the poverty gap reduction to the programme cost, presented in percentages. The analysis depicts the change in the poverty gap for every IDR 100 spent. With a poverty-reducing efficiency of approximately 16.8% (i.e. for every IDR 100 spent, the poverty gap reduces by IDR 16.76) and an extreme poverty-reducing efficiency of 5.8%, Rastra demonstrates the lowest poverty reduction cost efficiency across the four programmes analysed in this study.

Assistance for Poor Students: Programme Indonesia Pintar (PIP)

The GoI’s original Bantuan Siswa Miskin (BSM; Cash Assistance to Poor Students) programme was developed in 2008 to complement the Bantuan Operasional Sekolah (School Operational Assistance) programme, a school fee waiver for poor children. BSM covers additional costs, such as books, uniforms, shoes or transport cost (TNP2K, 2014[12]). The combined programmes address supply-side financial constraints and demand-side financial barriers to education to raise attendance (Larasati and Howell, 2014[13]). Recently, BSM was redesigned and implemented as PIP to include children attending informal institutions.

The BSM has undergone many reforms since 2008, largely due to its moderate performance. Based on a 2012 TNP2K study, targeting accuracy was low and suffered from severe inclusion and exclusion errors: many non-poor households received BSM, while some children from poor households did not. The study also confirmed timing problems, especially disbursement delays (Larasati and Howell, 2014[13]).

Coverage of PIP

PIP coverage has significantly increased since 2013 for two main reasons. First, in 2012, the GoI began using the UDB to target beneficiaries. The following years (2013-16) saw a significant increase in beneficiaries, from an annual average of 6.6 million in 2008-12 to 16.9 million in 2013-16 (World Bank, 2017[3]). With this change and a general expansion of the programme, PIP significantly boosted coverage and improved performance. Second, the rise in recipients also reflects a 2015 redesign to include informal education facilities, with a corresponding peak in coverage of 20 million (Figure 3.6). Since 2015, beneficiary numbers have stagnated.

Figure 3.6. PIP coverage has risen strongly but is now steady
Number of PIP, million (2008-17)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Adequacy of PIP

Analysis of the adequacy of PIP benefits compares their generosity against multiple living standards indicators, such as education costs, the EPL, the OPL, the average household consumption threshold and the poverty gap. Table 3.4 presents benefits by education level before and after 2013.

Table 3.4. All PIP benefits increased after 2013
PIP benefits by education level (before and after 2013)

Category

Before 2013

After 2013

Elementary school

IDR 380 000

IDR 450 000

Junior high school

IDR 550 000

IDR 750 000

Senior high school

IDR 750 000

IDR 1 000 000

Source: World Bank (2017[3]).

Overall, benefit levels increased after 2013 due to the dissolution of Bahan Bakar Minyak (fuel subsidies). Senior high school students received the largest transfer, followed by junior high school and elementary school students, corresponding to increasing costs associated with higher levels of education (Larasati and Howell, 2014[13]). Table 3.5 shows the value of benefits students receive relative to various subsistence standards.

Table 3.5. PIP benefits are relatively large
PIP benefit by education level as a share of selected living standards indicators (2016)

Category

PIP benefits per capita relative to EPL (%)

PIP benefits per capita relative to OPL (%)

PIP benefits per capita relative to average household consumption per capita (%)

Average

26.2

21.0

7.9

Elementary school

17.2

13.7

5.1

Junior high school

26.2

20.9

7.9

Senior high school

35.5

28.4

10.6

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

In 2016, the average PIP benefit was equivalent to 26.2% of the per capita EPL, 21.0% of the per capita OPL and 7.9% of the average household consumption per capita. Benefits provided to senior high school students were the most generous of the three categories, representing 35.5% of the per capita EPL and 28.4% of the per capita OPL.

Equity of PIP

Beneficiary incidence and distribution

Like Rastra, PIP is programmed to benefit all households in the bottom two quintiles listed in the UDB with school-age children. PIP covers almost one-third of the bottom 40% of households with school-age children (Figure 3.7). However, coverage among non-targeted households is substantial. Similar to Rastra, rural households with children are more likely to be covered, indicating that social assistance programmes focus strongly on rural populations, which represent 63% of the total population. PIP covers 31% of the poorest decile.

Figure 3.7. PIP beneficiary incidence is pro-poor and much larger in rural areas
Share of each decile covered by PIP (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Figure 3.8 shows the total number of beneficiaries distributed by expenditure decile. This indicator reveals better targeting of urban than rural poor students: over 28% of urban PIP recipients are in the poorest expenditure decile, compared with 17% in rural areas.

Figure 3.8. PIP beneficiaries are better targeted in urban areas
Share of total beneficiaries in each decile (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Figure 3.9 illustrates coverage by decile for each education level. Coverage of senior high school student benefits was somewhat lower than that of other categories among the lower half of the distribution, reflecting lower enrolment rates.

Figure 3.9. PIP incidence by type of benefit
Share of each decile covered by PIP by education level (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Benefit distribution

Figure 3.10 shows benefit distribution by expenditure decile. Households in the poorest three deciles receive approximately half of all PIP benefits.

Figure 3.10. The PIP benefit distribution is pro-poor
Share of total benefits in each decile (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Figure 3.11 shows that the senior high school benefits are more skewed towards the richer quintiles than the elementary and junior high school benefits.

Figure 3.11. PIP benefit distribution varies across education levels
Share of total beneficiaries in each decile by education level (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Efficiency of PIP

PIP cost the GoI 0.006% of GDP in 2016 and generated a 4.6% poverty headcount reduction, 1.0% extreme poverty headcount reduction, 6.6% poverty gap reduction and a 13.7% extreme poverty gap reduction (Table 3.6).

Table 3.6. PIP is a cost-efficient tool to alleviate poverty
PIP cost and poverty impacts (2016)

Disbursed amount (IDR trillion and % of GDP)

Poverty headcount reduction

Extreme poverty headcount reduction

Poverty gap reduction (IDR million)

Extreme poverty gap reduction (IDR million)

7

1 354 955

85 981

1 489 081

559 194

0.01%

4.61%

1.01%

6.63%

13.68%

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

PIP’s poverty-reducing efficiency, measured by the change in the poverty gap for every IDR 100 spent on the programme, is 20%; its extreme poverty-reducing efficiency is 7.5%. PIP is thus more cost efficient than Rastra for poverty alleviation purposes.

Social Health Insurance for the Poor and Near Poor: Penerima Bayaran Iuran (PBI)

PBI was introduced in 2014 as a mandatory contributory scheme replacing Jaskemas, a tax-funded health care fee-waiver programme. It is designed to respond to the high level of out-of-pocket (OOP) expenditure and its impact on access to health services by the poor (WHO, 2017[14]). It is a scheme for poor and near-poor members of the Jaminan Kesehatan Nasional (JKN; the national health insurance programme) to protect them from health care-related financial risks and commitments. As the GoI pays the premium for the poor and near poor, PBI can be considered a social assistance programme for those households.

Coverage of PBI

In 2014, PBI covered 86.4 million people, of which 21.8 million were poor (Ernada, 2015[15]). Beneficiary numbers began to rise steadily in 2016 as a result of GoI efforts to achieve universal health coverage, reaching 92.4 million in 2017(Figure 3.12).

PBI is commonly criticised for poor targeting. PBI mostly consists of Jamkesmas members; however, Jamkesmas applied broader eligibility criteria than PBI (World Bank, 2017[3]). A fortiori, while PBI targeting uses the poorest 40% of households listed in the UDB, Ministry of Health (MoH) staff determined eligibility for its predecessor scheme locally, resulting in vast geographical differences, as well as serious inclusion and exclusion errors (World Bank, 2017[3]). Additionally, only 55% of those covered by PBI access the health services they need, raising concerns regarding awareness and insurance literacy in the target population.

Figure 3.12. PBI coverage is rising
Number of PBI beneficiaries (million) (2008-18)
picture

Source: Agustina, R et al. (2019[16]), “Universal health coverage in Indonesia: concept, progress, and challenges”, The Lancet.

The 2016 SUSENAS data indicate that PBI covers approximately 25.4% of the formally employed, 23.9% of the unemployed and 20.6% of those excluded from Indonesia’s labour force (Figure 3.13). It reaches a comparatively lower share of the formally employed (14.3%).

Figure 3.13. PBI covers around one-quarter of informal or unemployed individuals
PBI coverage by labour force status (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Adequacy of PBI

For this analysis, the benefit value of PBI is equal to the premium the GoI pays for eligible households. Analysis of the adequacy of PBI benefits compares their generosity against selected living standards indicators, such as food adequacy, the EPL, the OPL, the average health utilisation and the poverty gap.

Generosity of benefits

Presidential Decree No. 19 of 2016 capped the monthly PBI premium per person at IDR 19 225 (January 2014 to March 2016). In April 2016, it was raised to IDR 23 000. Under the non-contributory PBI scheme, beneficiaries can access primary health care at a third-class ward of a partnering public or private hospital. Table 3.7 presents the value of PBI benefits relative to various subsistence levels.

Table 3.7. PBI premiums are low
PBI premium as share of selected living standards indicators (2014-16)

Year

PBI benefits per capita relative to EPL (%)

PBI benefits per capita relative to OPL (%)

PBI benefits per capita relative to average household consumption per capita (%)

2014

7.9

6.4

2.5

2015

7.3

5.8

2.2

2016

8.1

6.5

2.4

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

The benefit value represents a very small share of the per capita OPL (8.1%), the EPL (6.5%) and the average per capita household consumption (2.4%) in 2016. However, it is important to note that, while the premium is very low for the majority of individuals living in Java, it is far from affordable for residents of poor provinces, for instance the eastern provinces (Zen and Dita, 2018[17]).

Although this analysis is based on the IDR 23 000 premium, Dewan Jaminan Sosial Nasional (DJSN; National Social Security Board) and various health financing experts have estimated a higher effective monthly cost: up to IDR 36 000 per person (Hidayat, 2015[18]).

Equity of PBI

This section draws on 2016 SUSENAS data to analyse the beneficiary incidence and benefit distribution by decile.

Beneficiary incidence and distribution

Nearly half (44%) of those in the poorest decile and 35% in the second decile received the PBI fee-waiver (Figure 3.14). Although the beneficiary incidence steadily declines for richer deciles, almost 22% of those in the fifth decile claimed benefits.

Figure 3.14. PBI beneficiary incidence is pro-poor
Share of each decile covered by PBI (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Urban PBI targeting is more pro-poor than rural targeting (Figure 3.15). The highest-to-lowest decile coverage ratio is 15.3 in urban areas and 4.6 in rural areas. This means that, in urban areas, the poorest households are 15 times more often covered than the richest, expressing a high degree of pro-poor coverage.

Figure 3.15. PBI beneficiary distribution is more pro-poor in urban areas
Share of total beneficiaries in each decile (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Benefit distribution

Figure 3.16 shows the benefits distribution by decile. Households in the bottom two deciles receive 36% of all benefits, while those in the richest receive 2% (Figure 3.16).

Figure 3.16. PBI benefit distribution
Share of total benefits in each decile (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Efficiency of PBI

PBI cost IDR 12.8 trillion, or approximately 0.01% of GDP, in 2016 and generated a 7.1% poverty reduction, 6.9% extreme poverty headcount reduction, 11.1% poverty gap reduction and 21.1% extreme poverty gap reduction. For a marginally lower cost, the programme generates nearly as much poverty and extreme poverty reduction as Rastra.

Despite the increase in number of beneficiaries, their outpatient and inpatient utilisation rates have only grown three and two percentage points, respectively (World Bank, 2017[3]). Barriers to accessing hospitals, such as travelling long distances, largely explain the low growth in utilisation. Considering this, the GoI cannot effectively increase utilisation rates by providing benefits alone but must also simultaneous ease supply-side barriers (WHO, 2017[14]).

There are two fundamental constraints to accessing full benefits: geographically inaccessible or distant health care centres (Box 3.1); and poorly staffed, equipped and prepared health care facilities. Zen and Dita (2018[17]) illustrate the infrastructure gap by comparing the 2014 supply side and MoH goals for 2019. They show 233 districts had the minimum of one accredited public general hospital, compared with the 477 target; 350 sub-districts had at least one accredited Puskemas (Community Health Centre), compared with the 5 600 target; and less than 70% of these centres were deemed in good condition and had access to tap water (Zen and Dita, 2018[17]).

Box 3.1. Adverse selection and barriers to JKN enrolment

The Indonesia Family Life Survey Wave 5 (IFLS-5)1 can be used to estimate a model of the relationship between supply-side issues (in particular, travel time to the nearest hospital) and adverse selection issues (e.g. individuals enrolling when they are sick). The first three columns of Table 3.8 show results using only travel time to the nearest public hospital as an explanatory variable. Columns 4-6 show results, controlling for per capita expenditure, sex, education, food share of household expenditure, self-reported health and disability/chronic disease status. Columns 1 and 4 show results for the full sample of informal workers. Columns 2 and 5 show results for the sub-sample of the poorest 40% of the sample. Columns 3 and 6 show results for the richest 60% of the sample.

Table 3.8. Supply side and adverse selection are factors in JKN enrolment for informal workers
Odds ratio of JKN enrolment of informal workers by travel time to nearest public hospital and health status

 

 

(1)

(2)

(3)

(4)

(5)

(6)

 

 

Individual has JKN

Nearest public hospital

15-30 mins

0.92**

0.97

0.87***

0.94*

0.97

0.91*

(0.03)

(0.05)

(0.04)

(0.03)

(0.06)

(0.04)

30-60 mins

0.80***

0.79***

0.79***

0.81***

0.76***

0.85***

(0.03)

(0.05)

(0.04)

(0.03)

(0.05)

(0.05)

More than 60 mins

0.61***

0.58***

0.62***

0.65***

0.60***

0.70***

(0.03)

(0.05)

(0.04)

(0.04)

(0.05)

(0.05)

Food share of household expenditure

 

1.00

1.00*

1.00*

 

(0.00)

(0.00)

(0.00)

Male

 

1.06***

1.07***

1.06***

 

(0.02)

(0.03)

(0.02)

Per capita expenditure

 

1.00***

1.00***

1.00

 

(0.00)

(0.00)

(0.00)

Education

 

0.95**

0.89***

0.97

 

(0.02)

(0.03)

(0.03)

Self-reported health

Somewhat healthy

 

1.10**

0.97

1.21***

 

(0.04)

(0.06)

(0.07)

Somewhat unhealthy

 

1.21***

1.11

1.31***

 

(0.06)

(0.08)

(0.09)

Unhealthy

 

1.40**

1.63**

1.23

 

(0.21)

(0.37)

(0.25)

Some disability diagnosed

 

1.23***

1.20**

1.24***

 

(0.07)

(0.11)

(0.08)

 

Mixed formal/informal household

 

 

 

1.93***

1.18**

2.60***

 

 

 

 

 

(0.08)

(0.08)

(0.14)

Labour force status

Informal

Informal

Informal

Informal

Informal

Informal

Poorest 40%

Yes

Yes

No

Yes

Yes

No

 

Richest 60%

Yes

No

Yes

Yes

No

Yes

Notes: Standard errors in parentheses: *** = p<0.01; ** = p<0.05; * p = <0.1. The reference group for travel time was those living fewer than 15 minutes to the nearest public hospital. The reference group for self-reported health was those reporting being healthy.

Source: Authors’ calculations based on RAND Institute (2015[19]), Indonesia Family Life Survey – Wave 5.

Individuals living fewer than 15 minutes from the nearest public hospital represent the reference group. Thus, individuals living further away have lower odds of having health insurance, confirming supply-side constraints to enrolment. The reference group for the variable self-reported health is the healthiest group. Hence, those feeling less than healthy are more likely to get health insurance, confirming the hypothesis of adverse selection.

Informal workers with a diagnosed disability are 1.20 times (bottom 40%) and 1.24 times (top 60%) more likely to have insurance than those not diagnosed with a disability/chronic condition. Informal workers living in mixed households with formal workers are almost twice as likely as those in non-mixed households to have insurance.

1. The IFLS is an ongoing longitudinal survey representative of about 83% of the population in 1993 containing over 30 000 individuals in 13 of the 27 provinces.

Multiple studies confirm JKN’s positive impact on the accessibility of health care (Hidayat, 2015[20]); (Agustina et al., 2019[16]). Nevertheless, continuously high OOP payments curb this impact. A 2015 DJSN study found that 18% of patients incurred some OOP expense under JKN. The survey also revealed that the average OOP cost stood at IDR 235 945 for outpatients and IDR 1 244 786 for in-patients. The main reason for continued OOP payments is the high cost of medicine (Hidayat, 2015[20]).

Conditional cash transfer: Program Keluarga Harapan (PKH)

The PKH CCT programme, launched in 2007, targets the poorest 8% of UDB households to improve their access to health, education and social welfare services (World Bank, 2017[3]). It seeks to reduce household expenditure on health and education while investing in future generations through improved health and education. Key objectives include improving the nutritional status of children and pregnant and post-partum women, reducing the poverty gap across income groups and improving the education levels of children in poor households (Hadna, Dyah and Tong, 2017[21]). PKH benefits are conditional on specified health or education requirements (Table 3.9).

Table 3.9. PKH currently targets children and mothers
PKH conditionalities and benefits

PKH beneficiaries

Core conditionalities to receive benefits

Yearly benefit value (IDR)

(2013-15)

Yearly benefit value (IDR)

(2016)

Pregnant or lactating/post-partum women

Complete 4 antenatal care visits in each trimester of pregnancy and take iron tablets during pregnancy. Newborns should be delivered in a health facility, assisted by a trained health professional. Lactating/post-partum women must complete 2 neonatal care visits before newborns are one month old.

1 000 000

1 200 000

Children aged 0-6

Complete childhood immunisation and monthly growth monitoring check-ups, especially for weight and height. Ensure children take vitamin A capsules twice per year.

1 000 000

1 200 000

Children aged 6-21

Enrol children in the relevant education level. Ensure attendance reaches at least 85% of school days.

500 000 (children in elementary school);

1 000 000 (children in junior high school).

450 000 (children in elementary school);

750 000 (children in junior high school);

1 000 000 (children in senior high school).

Elderly people age 70 or older not covered by other social assistance programmes;

Complete health check-ups at health facilities or at the household via home care and attend day care or social activities, if available.

People suffering heavy disabilities not covered by other social assistance programmes

Complete health check-ups, as needed, at health facilities or via home care, and follow day care or social activities, if available.

Source: MoSA (2016[22]).

Coverage of PKH

PKH eligibility criteria are twofold, taking into account 1) household composition (e.g. presence of a pregnant/lactating woman, one or more children below age 5, children aged 6-15 attending school or children aged 16-18 yet to complete basic education); and 2) household consumption (threshold set at the bottom 14% of households in the UDB.

The PKH was piloted in 2007 in seven provinces, 48 districts and 337 sub-districts, reaching 382 000 households (TNP2K, 2014[23]). The programme grew to reach 1.5 million poor households in all provinces in 2012, 3.5 million in 2015 and 6 million in 2016 (Figure 3.17). It aims to reach 15.6 million families by 2020. This rapid expansion has involved broader geographic coverage, including the poorest districts of Papua and West Papua, and previously not covered areas (World Bank, 2017[24]).

Figure 3.17. PKH coverage has grown significantly since 2014
Number of households covered (million) (2006-20)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Adequacy of PKH

Analysis of the adequacy of PKH benefits compares their generosity against multiple living standards indicators, such as the EPL, the OPL and the average household consumption per capita.

In 2007-12, annual benefits ranged from IDR 600 000 to a maximum of IDR 2 200 000 per household (TNP2K, 2014[23]). In 2013-15, benefits ranged from IDR 800 000 to IDR 2 800 000. In 2016, the maximum increased to IDR 3 700 000, with beneficiary households receiving IDR 500 000 regardless of meeting PKH conditionalities. In 2017, the PKH was reformed to offer a single benefit of IDR 1 890 000 per household per year, including those with members above age 70 or with heavy disabilities. On average, this new benefit accounts for 13% of average household expenditure. The benefit modalities are currently being revised by the Government with a view to increasing benefit values and re-establishing variable benefit levels prior to a further expansion of coverage. As of 2016, various state-owned banks disburse benefits electronically, whereas they were previously cash-based and disbursed through the post (World Bank, 2017[3]).

Table 3.10. PKH benefits have not significantly increased in recent years
PKH benefits for target groups (2014-16)

Household composition conditionalities to receive benefits

Year

PKH benefits per capita as % of EPL

PKH benefits per capita as % of OPL

PKH benefits per capita as % of average household consumption per capita

Pregnant or lactating/post-partum women and/or children aged 0-6

2014

34.4

27.5

10.7

2015

31.5

25.2

9.6

2016

35.3

28.2

10.6

Children aged 6-21 attending elementary school

2014

17.2

13.8

5.4

2015

15.7

12.6

4.8

2016

13.2

10.6

4.0

Children aged 6-21 attending junior high school

2014

34.4

27.5

10.7

2015

31.5

25.2

9.6

2016

22

17.6

6.6

Children aged 6-21 attending senior high school

2014

-

-

-

2015

-

-

-

2016

29.4

23.5

8.8

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

In 2016, the PKH benefit for households with pregnant or lactating/post-partum women and/or children aged 0-6 was 35% of the EPL, 28% of the OPL and 11% of the average household consumption per capita. The benefit level increased from 2014 compared with poverty line indicators but remained stable compared with the average household consumption per capita.

The benefit for households with children aged 6-21 attending elementary school was 13% of the EPL, 11% of the OPL and 4% of the average per capita household consumption. The benefit levels for this group have steadily declined in the last two years, compared with living standard indicators.

In 2016, the benefit for households with children aged 6-21 attending junior high school was 22% of the EPL, 18% of the OPL and 7% of the average consumption per capita, showing benefit levels were lower in 2016 than in 2014. The relative benefit received by households with children aged 6-21 attending senior high school was greater than for those with children in junior high or elementary school: 29% of the EPL, 23% of the OPL and 9% of the average consumption per capita.

Overall, PKH benefit levels are relatively low, compared with other CCT programmes around the world. CCTs in Mexico, Brazil and the Philippines have benefit levels of about 20% of consumption (World Bank, 2017[24]).

Nonetheless, several studies indicate that PKH benefits have positive effects on health and education indicators. A 2015 TNP2K evaluation study found that PKH transfers significantly increased monthly household expenditure and increased the number of visits to health facilities at the posyandus (sub-district) level by 3%. Child growth-monitoring checks also rose by five percentage points. More modest improvements in school attendance and immunisation were also attributed to the receipt of the grant (TNP2K, 2014[23]).

In 2017, a World Bank mid-line evaluation showed that PKH increased average monthly household expenditure by 10%, with most going towards protein food consumption and health care costs. It also showed a 22 percentage point increase in child growth-monitoring checks, a 7 percentage point increase in the share of households receiving immunisation and a 7.1 percentage point increase in the number of neonatal visits. The share of women conducting at least four antenatal care visits increased by nine percentage points, and the share of births delivered at health facilities or by skilled health personnel increased by five percentage points. Other impacts of the grant include reductions in severe stunting and increases in elementary and junior high school participation rates (TNP2K, 2014[23]).

Equity of PKH

This section draws on 2014 SUSENAS data to analyse the incidence of beneficiaries and benefits by decile.

Beneficiary incidence and distribution

Approximately 26.2% of those in the poorest decile and 11.4% of those in the second poorest are PKH recipients (Figure 3.18). Beneficiary incidence sharply declines for richer deciles, with less than 3% of each of the top five deciles receiving benefits.

Figure 3.18. PKH beneficiary incidence is pro-poor
Share of each decile covered by the PKH (2016)
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Figure 3.19 presents the total number of beneficiaries by expenditure decile. Distribution clearly skews towards the poorest deciles: about 75% of beneficiaries are in the first three consumption deciles, while only 1.4% are in the top two. Urban targeting is more pro-poor than rural, as larger numbers of rural households in top deciles receive the grant.

Figure 3.19. PKH beneficiary distribution is more pro-poor in urban areas
Share of total beneficiaries in each decile
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Benefit distribution

Households in the first decile receive the largest share of PKH benefits (46.2%) (Figure 3.20). The bottom three deciles receive 76.1%, while the top three receive 2.5%. PKH targeting thus appears to be the most accurate, compared with other social assistance programmes in the country (World Bank, 2017[3]).

Figure 3.20. PKH benefit distribution
Share of total benefits in each decile
picture

Source: Authors’ calculations, based on Statistics Indonesia (2016[4]), SUSENAS, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

Efficiency of PKH

Based on 2014 SUSENAS data, analysis finds a 5.7% reduction in poverty headcount and 25.9% reduction in extreme poverty headcount, with a cost equivalent to 0.05% of GDP (Table 3.11).

Table 3.11. PKH is the most efficient poverty alleviation programme
PKH cost and poverty impacts (2014)

Disbursed amount (IDR trillion and % of GDP)

Poverty headcount reduction

Extreme poverty headcount reduction

Poverty gap reduction (IDR million)

Extreme poverty gap reduction (IDR million)

5.3

1 806 063.0

2 069 845.0

2 362 689.7

979 580.9

0.05%

5.71%

25.91%

11.92%

30.94%

Note: The analysis of PKH equity, coverage and efficiency is conducted using the 2014 SUSENAS as more recent versions of the survey do not capture the receipt of the grant. The 2014 wave however under-reports coverage of the grant (1.2 million households instead of the reported 2.8 million). For this purpose, a probit regression is run using receipt of the grant as dependent variable and a series of grant receipt determinants as predictors. The latter include household characteristics, receipt of other grants, demographic variables and economic ones. The determinants are selected to maximise the regressions explanatory power and goodness-of-fit. A probability threshold above which households are assumed to be PKH beneficiaries is then selected. This threshold is calibrated to reach the government-reported total beneficiary number. For additional robustness, the poverty rate (both regular and food poverty) among actual receiving households and those determined based on the probit are compared. The findings show that these vary by 3.6 percentage points in the case of the OPL, and 1.8 percentage points for the EPL.

Source: Authors’ calculations, based on Statistics Indonesia, SUSENAS 2014, https://microdata.bps.go.id/mikrodata/index.php/catalog/769.

PKH’s poverty-reducing efficiency, as measured by the change in the poverty gap for every IDR 100 spent on the programme, is 44.2%; its extreme poverty-reducing efficiency is 18.31%. PKH is thus the best-performing programme on both counts.

Beyond poverty levels, a recent evaluation conducted by TNP2K (Cahyadi et al., 2018[25]) found important impacts in terms of primary and secondary school attainment and in the level of deliveries in a facility by trained birth attendants. Importantly, this evaluation conducted six years after the programme’s introduction found that the continued investment in children over time resulted in cumulative outcomes, in particular in terms of reduced stunting.

Pensions in Indonesia: a gender perspective

This section outlines the risks women reaching old age face, followed by results of an analysis based on the IFLS. The IFLS is a very rich survey, with information on respondents and their families, households, communities, and health and education facilities (Strauss, Witoelar and Sikoki, 2016[26]). The wide range of modules in the questionnaire, as well as the panel data nature of the survey, allow in-depth analysis of the labour force histories of women and men.

Women are vulnerable in old age

Indonesia has one of the world’s largest populations, and it is expected to increase sharply, from 258 million in 2015 to 321 million in 2050. Average life expectancy is projected to rise, from 68.6 years in 2015 to 73.9 years in 2050 (UN DESA, 2015[27]). Indonesia has the fifth largest elderly population in the world (HelpAge, 2012[28]), and while the share of men and women is about equal, 57% of the elderly above age 70 are women (Surbakti and Devasahayam, 2015[29]).

Large segments of the global population rely on contributory pension systems for old-age consumption. However, by design, these systems can exclude many potential beneficiaries or provide inadequate benefits, due to contribution density issues.2 When the population age 60 or over is the fastest growing globally (UN DESA, 2015[27]), this exclusion and inadequacy can affect the well-being of older individuals, who must otherwise rely on work income or family and network support.

Women are particularly vulnerable. They live longer than men and are more likely to be widowed. They also tend to be less educated: about one-third of Indonesian women age 70 and over are literate, compared to 65% of men (Priebe and Howell, 2014[30]).

Women’s labour force histories also diverge significantly from men’s. Care-giving responsibilities mean women are more likely to interrupt work or never enter the labour market. They may also be more likely to work informally, including in more precarious jobs, granting them flexibility for child care.

Pension systems may also treat women and men differently, including earlier mandatory retirement for women (up to five years earlier), which exposes them to vulnerability if benefits are computed as a function of years of contribution (Arza, 2015[31]).

Employment histories

IFLS surveys can generate employment histories by combining retrospective histories and labour force status at the time of the surveys. Figure 3.21 displays the proportion of each lifecycle decade that men and women spend in employment, the share of men and women in constant employment throughout the decade and the percentage of working years spent in informal employment. On average, women work less than men in every decade and are much less likely to be in constant employment, but they are more likely to work informally and to be informally employed the older they are.

Figure 3.21. Women work less and are more likely to work informally than men
Labour force history indicators for men and women by lifecycle decade (1997-2014)
picture

Source: Authors’ calculations based on RAND Institute (1993[32]; 1997[33]; 2000[34]; 2007[35]; 2015[19]), Indonesia Family Life Surveys, www.rand.org/labor/FLS/IFLS.html.

Reported type of employment also allows for inferring the share of work life spent in various types of employment, namely as government workers, family workers, private sector workers and self-employed workers. Figure 3.22 shows women are much more likely to be family workers, increasingly so as they age; men are more likely to be self-employed, increasingly so as they age.

Figure 3.22. Women are more likely to be family workers than men
Share of worked years spent in various types of employment by lifecycle decade (1997-2014)
picture

Source: Authors’ calculations based on RAND Institute (1993[32]; 1997[33]; 2000[34]; 2007[35]; 2015[19]), Indonesia Family Life Surveys, www.rand.org/labor/FLS/IFLS.html.

Burden of care and labour force participation

This section focuses on the relationship between household composition and the potential burden of care of children and elderly relying on working-age individuals.

Work density per decade of the lifecycle was computed for each woman and man observed throughout a complete decade (20s, 30s, 40s and 50s), along with the share of those work years spent in informal work (informality density). Regression analysis was then used to identify the relationship, with variables capturing household composition, such as the child dependency ratio (number of children per number of working-age adults) and elderly dependency ratio (number of elderly per number of working-age adults). Poverty and self-reported health were controlled for. Table 3.12 displays work density results by decade and sex. Table 3.13 displays informality density results.

Table 3.12 displays coefficients reflecting the relationship between household composition and work density for women and men in each decade. Overall, women in their 20s and 30s have lower work density than men when their households have a higher child dependency ratio, while men have higher work density the higher the child dependency ratio. The elderly dependency ratio does not have a statistically significant relationship with the work density.

Table 3.12. In households with higher child dependency ratios, women work less than men
Work density by decade, sex and household composition (averaged over lifecycle decade)

 

Women

Men

20s

30s

40s

50s

20s

30s

40s

50s

Child dependency ratio

-0.04**

-0.02**

0.01

-0.01

0.06***

0.01***

0.01**

0.03**

(0.02)

(0.01)

(0.01)

(0.02)

(0.01)

(0.00)

(0.00)

(0.01)

Elderly dependency ratio

0.03

0.02

0.02

0.01

-0.01

-0.02

-0.02*

0.03

(0.04)

(0.03)

(0.02)

(0.02)

(0.02)

(0.01)

(0.01)

(0.02)

Poor

-0.03**

-0.01

-0.02*

-0.02

-0.01

-0.01***

-0.01

0.01

(0.02)

(0.01)

(0.01)

(0.02)

(0.01)

(0.01)

(0.01)

(0.01)

Health

-0.03

-0.07***

-0.02

-0.05**

0.01

-0.01

-0.01

-0.03*

(0.02)

(0.02)

(0.02)

(0.03)

(0.01)

(0.01)

(0.01)

(0.01)

 

 

 

 

 

 

 

 

 

Observations

1 461

1 596

1 455

828

1 736

1 786

1 745

1 106

R-squared

0.01

0.01

0.00

0.01

0.02

0.01

0.01

0.01

Notes: For dependency ratios, working age = aged 15-65, child = under age 10, elderly = age 50 and over. Work density = number of years worked in each decade. *** = p<0.01; ** = p<0.05; * = p<0.1.

Source: Authors’ calculations, based on IFLS-1 through IFLS-5.

While women in their 20s and 30s are less likely to work the higher the child dependency ratio in their households, they are more likely to work informally if they do. Men are both more likely to work and more likely to work informally.

Table 3.13. In households with higher child dependency ratios, women and men are more likely to work informally
Informal work density by decade, sex and burden of care (averaged over lifecycle decade)

 

Women

Men

 

20s

30s

40s

50s

20s

30s

40s

50s

Child dependency ratio

0.09***

0.05***

-0.00

0.06**

0.07***

0.07***

-0.04**

0.05**

(0.02)

(0.01)

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

(0.03)

Elderly dependency ratio

-0.02

0.02

0.02

0.02

0.12***

0.12***

-0.11**

0.11**

(0.05)

(0.03)

(0.03)

(0.02)

(0.04)

(0.04)

(0.04)

(0.04)

Poor

0.05**

0.04**

0.05***

0.02

0.03**

0.03**

0.11***

0.10***

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

(0.02)

Health

-0.01

-0.02

-0.01

-0.01

0.01

0.05*

0.07***

0.03

(0.03)

(0.03)

(0.02)

(0.03)

(0.02)

(0.03)

(0.03)

(0.03)

Observations

1 461

1 596

1 455

828

1 736

1 786

1 745

1 106

R-squared

0.02

0.01

0.01

0.01

0.02

0.02

0.03

0.03

Notes: For dependency ratios, working age = aged 15-65, child = under age 10, elderly = age 50 and over. Work density = number of years worked in each decade. *** = p<0.01; ** = p<0.05; * = p<0.1.

Beyond the relationship between household composition and overall work and informality density, the effect of changes in household composition on the likelihood of working and informal or formal work status was also examined. To this end, a fixed effects model was estimated, which allowed for controlling for time-invariant characteristics that may affect work status (e.g. education, ethnicity or religion) and measuring the relationship between changes in household composition (e.g. having a baby) and work status the following year(s).

Table 3.14 displays the odds ratio by sex of working in a given time period (t), given a child born in the household that year (t) or in the previous years (t-1 or t-2).

Table 3.14. Women with formal jobs are more likely to stay in the labour force when they have children than women in informal jobs
Fixed effects model of likelihood of not working (averaged over the lifecycle)

 

Women

Men

 

All

Informal t-1

Formal t-1

All

Informal t-1

Formal t-1

Child born in t

0.901***

1.810***

0.468***

0.812***

0.833***

0.173***

 

(0.0221)

(0.0616)

(0.0194)

(0.0318)

(0.0361)

(0.0123)

Child born in t-1

0.838***

1.258***

0.918**

0.829***

0.842***

0.920

 

(0.0217)

(0.0513)

(0.0343)

(0.0327)

(0.0369)

(0.0472)

Child born in t-2

0.604***

0.799***

0.679***

0.756***

0.760***

0.679***

 

(0.0168)

(0.0351)

(0.0270)

(0.0300)

(0.0336)

(0.0352)

Married

1.088***

0.806***

0.916**

0.693***

0.699***

0.377***

 

(0.0261)

(0.0385)

(0.0357)

(0.0312)

(0.0356)

(0.0181)

 

 

 

 

 

 

 

Observations

206 162

83 566

81 366

117 321

92 544

54 892

Number of individuals

13 324

8 110

6 984

8 690

7 169

4 510

Notes: Fixed effects model. Coefficients shown as odds ratio. *** p<0.01; ** p<0.05; * p<0.1.

Source: Authors’ calculations, based on IFLS-1 through IFLS-5.

Table 3.14 displays the odds ratio for women (columns 1, 2 and 3) and men (columns 4, 5 and 6) of working in a given time period t given that a child was born in the household in that year t, or in the previous years t-1 or t-2. While women are overall more likely to work when they have had a child, there are significant discrepancies between formally and informally employed women. Previously informally employed mothers are almost twice as likely not to work as informally employed non-mothers. Previously formally employed mothers are more likely to work if they have a child. Previously formally employed fathers are much more likely to work than formally employed non-fathers, while odds are somewhat similar for previously informally employed fathers and informally employed non-fathers.

Women’s support strategies in old age

Work

Women are less likely than men to work when they are age 50 or older. About 87% of men continue to be active in the labour market, compared with 74% of women. When asked whether they planned to stop working, male and female respondents over age 50 answered similarly: about 54% planned not to stop; 25% planned to work until their health failed; about 10% had no plans; about 5% planned to change jobs; and 5% planned to stop.

Family support

Women rely on family, especially children, more than men. Among those age 50 and older, 70% of women reported receiving financial support from children vs. 45% of men (Figure 3.23). More women than men anticipate needing or do receive help from children (e.g. financial, co-habitation). Reported own savings are extremely low: 5.1% among men and 4.5% among women over age 50.

Figure 3.23. Women are more likely than men to rely on children for old-age support
Proportion of population age 50 and over (2014-15)
picture

Source: Authors’ calculations RAND Institute (2015[19]), Indonesia Family Life Surveys, www.rand.org/labor/FLS/IFLS.html.

Women over age 60 are more likely than men to be widowed: 20% vs. less than 5%. This is attributable to women’s longer life expectancy, as well as a trend for men to remarry (Surbakti and Devasahayam, 2015[29]).

Social protection

The great majority of old-age pensions are contributory programmes targeting formal workers, in particular civil servants and members of the armed forces and police. Pension coverage for retirees is low at about one-quarter of the population. Women are much less likely than men to receive a pension: only 12% are beneficiaries (Figure 3.24).

Figure 3.24. Women are much less likely than men to receive a pension in old age
Share of retired individuals reporting to receive old-age pensions
picture

Source: Authors’ calculations RAND Institute (2015[19]), Indonesia Family Life Surveys, www.rand.org/labor/FLS/IFLS.html.

Implications for the pension system

Pension systems vary across dimensions, such as eligibility, vesting periods, contributory or non-contributory features and benefit types. The analysis above identifies a number of factors that vary by sex in terms of labour force participation and type of employment: 1) women work less than men over their lifetimes, and they are more likely to work informally; 2) women in households with higher child dependency ratios work less than men, but both sexes in those households are more likely to work informally, compared with women and men in households with lower ratios; and 3) previously informally employed mothers are twice as likely to stop working as informally employed non-mothers.

A simulation exercise to identify a simplified profile of an average woman’s labour history in comparison to an average man’s demonstrates how the pension system might affect women’s old-age vulnerability. Compiling the decade information (the 40-year period individuals might contribute to a pension system to improve income security in retirement), on average, women spend 32.3 years working, of which 88.7%, or 28.6 years, is in informal work. On average, men spend 37.3 years working, of which 85.2%, or 31.8 years, is in informal work.

Indonesia’s old-age pension system consists of contributory schemes, including the Jaminan Pensiun (JP, established in 2014), Jaminan Hari Tua (Old-Age Savings) and the PT ASABRI and PT TASPEN, which target civil servants and armed forces personnel, respectively. Minimum vesting periods are 15 years (180 months), and the retirement age for both men and women is age 58. Indonesia is planning a significant expansion of pension coverage through the JP, which is run on a defined benefit basis and accessible to formal and informal workers.

The system also includes non-contributory pensions for abandoned or neglected people over age 70 without regular income and unable to perform daily activities. The benefit entitlement is IDR 200 000 per month, but coverage is limited, reaching about 30 000 beneficiaries in 2017.

Given the very few years spent, on average, by women in formal employment, it will be crucial for the contributory pension system to integrate informal workers and take into account fragmented labour force histories due to child care interruptions. At the same time, an expansion of social assistance for the elderly is an important mechanism for ensuring the well-being of women excluded from social insurance. Establishing earlier eligibility ages for social assistance for women than men can partly offset the disadvantages women face earlier in life, although such an approach can codify inequality in a manner that violates constitutional requirements.

References

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Notes

← 1. The annual value of rice to be distributed to households is IDR 1 307 700 (IDR 7 265 x 15 kg x 12 months); the value actually received is IDR 435 900 (IDR 7 265 x 15 kg [average per household] x 12 months).

← 2. Contribution density = the number of periods a worker contributed to a pension system as a percentage of working years.

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