Promoting Active Ageing in Southeast Asia

This first chapter sets the scene by examining the key background demographic, economic and employment characteristics in ASEAN Member States that are relevant for the promotion of active ageing. It first documents the specific features behind fast ageing prospects among ASEAN countries. The chapter then turns to the overall economy by highlighting the fast pace of economic growth in low-income ASEAN countries with a focus on broad macroeconomic issues related to public finance and current account balances. The third section discusses formal and informal employment, emphasising the challenges of a high degree of informality in most ASEAN countries. The last section discusses the interactions between population ageing and productivity growth.

The key findings are the following.

Demographic changes

• All ASEAN countries have seen considerable growth in their working-age population over the last 40 years. While the size of the working-age population is projected to continue increasing in Cambodia, Lao PDR and the Philippines by 25% or more over the next 40 years, it is projected to fall by about 30% in both Singapore and Thailand.

• Fertility rates have fallen sharply from high levels in ASEAN countries. The total fertility rate averaged 6.0 across ASEAN countries in the early 1960s, 3.3 in the early 1990s and now averages 1.8, below the population replacement rate of about 2.1.

• Life‑expectancy gains are not projected to slow on average. Between 1984 and 2024 remaining life expectancy at age 65 increased by 3.1 years on average across the ASEAN countries to 16.3 years. It is projected to further increase by another 3.1 years over the next 40 years.

• Given falling fertility rates and continued gains in life expectancy gains, ageing is set to accelerate. Over the next 30 years the ASEAN old‑age to working‑age ratio is projected to increase by 19 percentage points (p.p.) compared to an increase of less than 6 percentage points for the previous 30 years.

• ASEAN populations will be ageing twice as fast as in the OECD on average. It will take only 36 years for ASEAN countries on average to go from 15 to 40 people aged 65+ per 100 people aged 20‑64 compared to 74 years for the OECD.

• Household sizes have been shrinking over recent decades, which will increase women’s vulnerability risks in old age. Household sizes have fallen by around 0.8 people on average across ASEAN countries over the last 20 years, with Lao PDR recording the largest fall from 6.0 people on average in 2000 to 4.7 in 2017.

• Demographics have been favourable for the growth of GDP-per-capita in ASEAN countries until now, but this positive mechanical effect is expected to disappear in the near future, except in Cambodia, Lao PDR and the Philippines. The drag will be large at about 0.5 percentage points annually in Brunei Darussalam, Singapore and Thailand.

Formal and informal employment

• On average across ASEAN countries the employment rate among those aged 15‑64 is similar to the OECD average, at about 70% in 2022. While the total employment rate has been stable ion average n ASEAN countries over the last decade, employment has shifted from agriculture towards services.

• Informal employment is large albeit shrinking in most ASEAN countries. On average, two‑thirds of workers work informally in ASEAN countries compared to one in nine in OECD countries on average. In Cambodia and Lao PDR, informal workers make around 90% of total employment, around 80% in Indonesia, Myanmar and Philippines and slightly less than 70% in Viet Nam and Thailand. In Brunei Darussalam, Malaysia and Singapore most workers are formal.

• Informal employment is much more widespread in ASEAN countries than what could be expected based on its association with the level of economic development proxied by GDP-per-capita. This suggests that the reasons behind such a large informality are deeply engrained in societies, and that, contrary to the view that prevailed decades ago, economic development alone will not suffice to seriously tackle the issue.

• Informality in ASEAN countries is driven by multiple factors: high share of agriculture; exemptions provided by labour codes, social-security regulations and tax laws granted to some firms or workers; poor law enforcement; tedious and inefficient administrative business procedures; substantial costs of formalisation; and, unclear benefits brought by formal employment as perceived by workers.

• Large informality generates huge social challenges as the vast majority of informal workers suffer from very limited protection against the risks of income losses related to illness, disability and old age. These issues are becoming bigger as populations age. Informality also limits public financial resources and distorts competition.

Macroeconomics

• Population ageing will put heavy strain on public finance and, in particular, on financing pensions, health and long-term care. Public debt as a share of GDP is currently large in Lao PDR and Singapore. Indonesia has an effective fiscal framework based on the parameters of the Maastricht treaty and Malaysia is also taking important steps to strengthen fiscal sustainability.

• Large current account deficits generate risks of macroeconomic imbalances in Cambodia and Lao PDR.

Population structures are changing fast among ASEAN countries, with rapid decline in fertility rates and continued improvements in life expectancy at older ages. This section first highlights the large cross-country variations in projections of the growth of the working-age populations. It then focuses on the recent declining fertility rates and increases in old-age life expectancy, resulting in an acceleration of population ageing prospects. Finally, it shows that household sizes are shrinking but that multigenerational families are still commonplace in many ASEAN countries.

There are huge differences across countries in the projected change in the size of the working-age population (aged 20‑64). Projections based on UN data show it increasing by about 25% or more in Cambodia, Lao PDR and the Philippines and by about 20% in Malaysia by 2064, meaning that these countries still experience a positive demographic dividend (Figure 1.1). By contrast, the size of the working-age population would fall by about 30% in both Singapore and Thailand and remain about stable in the other ASEAN countries. A fall of 30% over 40 years, for example, means that the working-age population would decline by 0.9% annually on average, lowering potential GDP proportionally in the absence of offsetting measures. By comparison, the projected working-age population is projected to decrease by 13% in the OECD on average by 2064, i.e. by 0.3% per year. It would fall by nearly 50% in Korea in total and also by more than 30% in Estonia, Greece, Italy, Japan, Latvia, Lithuania, Poland, the Slovak Republic and Spain as well as in non-OECD China. Only Australia, Israel and Mexico, as well as India would record an increase of over 10%.

All ASEAN countries have seen considerable growth in their working‑age population over the last 40 years with an average annual increase of 2.1% (Figure 1.2), from a low of 1.6% in Thailand and a high of 3.5% in Cambodia. However, over the next 40 years the annual projected growth is under 0.2% across the ASEAN region. In all countries the growth rate will be much lower than in the past. Large declines in the growth of the working‑age population are projected in Brunei Darussalam, Cambodia, Malaysia and Thailand, and especially in Singapore from an annual increase of 2.2% on average between 1984 and 2024 to a shrinkage of 0.9% between 2024 and 2064. The working-age population is also projected to shrink in the next 40 years in Thailand (‑1.0% annually on average), Brunei Darussalam (‑0.2%) and Viet Nam (‑0.1%). Only Cambodia (0.8%), Lao PDR (0.8%) and the Philippines (0.5%) are projected to record an average annual growth of the working-age population above 0.5% between 2024 and 2064.

Fertility rates fell sharply from high levels in ASEAN countries and have kept decreasing in recent years. The total fertility rate (TFR) averaged 6.0 across ASEAN countries in the early 1960s, but this average fell to 2.4 around the start of the millennium and is now at 1.8, below the population replacement rate of 2.1 (Table 1.1). This downward trend is projected to continue, though at a much slower rate, with the TFR across ASEAN countries projected to be 1.7 in 40 years compared to 1.5 in the OECD on average.

Some ASEAN countries still have TFRs above replacement level. In the early 1960s the TFR was above 5.0 in nine of the ten countries. By 2004, the TFR had virtually halved in all countries, and in Singapore and Thailand to even lower at 1.1 and 1.6, respectively. By contrast, Cambodia, Lao PDR and the Philippines still had a rate above 3.2. Currently only Cambodia and Lao PDR are above the replacement level, with Indonesia and Myanmar at 2.1. All the others are below 2.0 with a recent very large fall in the Philippines – from 3.5 to 1.9 over the last two decades – and Singapore at a level that is one of the lowest in the world at 1.0. Declining fertility is a global phenomenon, but the speed of the decline is more profound in ASEAN countries than in the OECD, although from a much higher level.

Changes in future fertility rates may have significant implications for future economic growth and pension finances in particular. For example, pay-as-you-go pensions are financed by current contributions, which means shrinking working-age populations will result in a shortfall in pension revenues unless contribution rates are increased or pension benefits are cut. However, there is large uncertainty about future fertility rates.

Indeed, projecting fertility is notoriously difficult and past estimates of fertility levels for today have proved to be wide of the mark. For example, the 2002 UN populations prospects data projected that the TFR in Thailand in 2020‑25 would be 1.85, much higher than 1.20 currently. Similarly, the estimates for Cambodia, Lao PDR, Malaysia and Singapore were all at least 0.4 higher than the levels reached currently. Likewise, over the last two decades, UN projections of fertility levels in 2040‑45 have been considerably revised downwards. The average fertility rate across all ASEAN countries was projected to be 1.91 in 2045 based on the 2002 revision of the World Population Prospects dataset. This average for the year 2045 declined to 1.81 based on the 2012 revision and to 1.68 for the 2024 revision (Figure 1.3). The sharpest downward revisions, by around 0.6‑0.7 overall between the 2002 and 2024 revisions, were found in Singapore and Thailand. The fall of 0.5 percentage points in the Philippines between the 2012 and 2024 revisions reflects the huge recent fall in current levels as discussed above.

Life‑expectancy gains are not projected to slow on average. Over the last 40 years remaining life expectancy at age 65 has increased from 13.2 years on average across the ASEAN countries to 16.3 years in 2024, an increase of 3.1 years.1 It is projected to further increase by 3.1 years over the next 40 years reaching 19.4 years in 2064 (Figure 1.4). The pace of improvements is projected to slow in Singapore and Thailand, but these two countries recorded the fastest increases in old-age life‑expectancy over the last 40 years at 7.1 and 5.3 years, respectively. In Cambodia, life expectancy at age 65 is also projected to increase more slowly, by 2.3 years over the next 40 years compared to 3.3 years over the last 40 years. All the other ASEAN countries show a projected acceleration. By contrast, across the OECD on average there was an increase of 4.6 years over the last 40 years, which would slow to 3.8 years over the next 40 years.

With continued trends of lower fertility rates and longer lives, the old-age to working-age ratio will increase sharply placing additional burdens on the working-age population to finance pay-as-you-go pensions and healthcare for older people. Indeed, ASEAN populations will be ageing twice as faster as in the OECD on average. Today, ASEAN countries are still relatively young and ageing takes place at a later stage than in OECD countries. However, based on current projections, it will take only 36 years for ASEAN countries on average to go from 15 to 40 people aged 65+ per 100 people aged 20‑64 compared to 74 years for the OECD (Figure 1.5). Ageing on this measure will be the fastest in Thailand and Brunei Darussalam at only 23 and 25 years, respectively, comparable to the fastest ageing country in the OECD, Korea, which is projected to take 23 years, and quicker than Japan where it actually took 30 years between 1980 and 2010; Cambodia, Indonesia and Myanmar are projected to take between 50 years and 62 years. Looking at OECD countries, Finland, France, Greece, Italy and Portugal more recently reached this 40‑to‑100 ratio. By contrast, the pace of ageing is particularly slow in Australia, New Zealand and the United States, all of which are projected to take about 90 years. This is due to relatively high fertility rates in New Zealand and the United States and high levels of immigration of younger workers, particularly in Australia and the United States. This much faster pace of ageing for ASEAN countries highlights the challenges they face to establish both adequate social security systems for older people and an effective institutional setting for long-term care facilities (Chapter 3).

ASEAN countries are currently much younger than OECD countries. There are currently 13.5 individuals aged 65 and over for every 100 persons of working age (20 to 64) across all ASEAN countries compared to 32.6 on average across the OECD (Table 1.2). All ASEAN countries are within a narrow range between 8.4 and 12.4 except for Singapore (19.8), Thailand (24.0) and Viet Nam (15.0). Chile, Colombia, Costa Rica, Mexico and Türkiye are the only OECD countries below the level of Thailand with Japan being highest at 54.9.

The pace of ageing is projected to be much faster in ASEAN countries compared to within the OECD over the next decades. Over the next 30 years the ASEAN old‑age to working‑age ratio is projected to increase by 19 percentage points (p.p.) compared to an increase of less than 6 percentage points for the previous 30 years. The old‑age to working‑age ratio is projected to at least double in all ASEAN countries over the next 30 years with Brunei Darussalam increasing to over 3 times the current level. The acceleration of ageing will also take place in the OECD but a bit more slowly. In the OECD the increase in the old‑age to working‑age ratio is projected to be 23 percentage points compared to 12 percentage points over the previous 30 years.

Although ageing trends are largely common across countries, the range of old-age to working-age ratios among ASEAN countries is projected to widen rapidly during the first half of the 21^st century. During the late 20^th century, all ASEAN countries followed a similar pattern with an old-age to working-age between 5 and 15 people aged 65+ for every 100 aged 20 to 64. The old‑age to working‑age ratio started to increase in ASEAN countries around the mid‑2010s as the falls in fertility Levels 20‑30 years earlier started to have an impact (Figure 1.6 Panel A). Moreover, by 2060, the range is projected to widen to a low of 24 in the Philippines and a high of 66 in Singapore and 61 in Thailand, indicating that the economic pressure from population ageing will differ considerably between countries.2 The range in the OECD is projected to be even larger with an upper rate of 95 in Korea in 2060 (Figure 1.6 Panel B). Korea has gone from the youngest country in the OECD in 1950 to the oldest from 2050 onwards based on this metric.

There has been a gradual shift in the nature of ageing from previous decades. To show the full change it is useful to compare the age distribution of the ASEAN countries today to those of the past (1994) and the future (2054) (Figure 1.7). Between 1994 and 2024 the most significant increases across age groups was concentrated amongst those of middle‑ages (35‑65). However, based on current projections over the next 30 years, there will be fewer individuals of any age until 35 years, as well as a much smaller increase among those aged 35‑65. Ageing over the next 30 years will mean a significant increase amongst the 60+ age group. These trends explain the acceleration in the old‑age to working‑age ratios shown above.

Migration has helped alleviate the ageing pressure in many ASEAN countries. The projections of the old-age to working-age ratios presented above are based on a medium variant migration projection scenario. An alternative zero migration projection is also provided within the UN 2024 data. The importance of migration flows can be highlighted by comparing these two scenarios. Based on this comparison, the impact of migration on the extent of ageing is large in Malaysia, Thailand and particularly Singapore, and in Brunei Darussalam to a lesser extent, but the impact is limited in the other ASEAN countries (Figure 1.8). By 2054, with zero net migration, the old-age to working-age ratio would be between 2 and 5 percentage points higher in these four highlighted countries. By 2084, the impact is much larger, 7 percentage points higher in Brunei Darussalam, 13 percentage points in Malaysia, 16 percentage points in Thailand and 29 percentage points in Singapore, which already has a large migrant labour force.

Household sizes have been shrinking over recent decades in all ASEAN countries. Household sizes have fallen by around 0.8 people over the last 20 years within ASEAN countries, with Lao PDR recording the largest fall from 6.0 people on average in 2000 to 4.7 in 2017 (Figure 1.9). Although there is no data available for China and Malaysia after 2000 both countries had seen falling sizes in the preceding 20 years, by 1.0 and 0.6, respectively. As populations age there will therefore be fewer immediate family members to provide the traditional familial support that has been common in ASEAN countries. Of all the countries shown in the below figure, Australia, New Zealand and the United States have had almost stable sizes although from initially low levels.

Multigenerational households are still common in ASEAN countries. In 2020, 39% of all households contained at least two generations of related members aged 20 or over, whereas in most OECD countries for which data are available the share is below 20% (United Nations, 2022[1]). Overall, three‑generation households – three or more generations of related members, irrespective of age – account for 22% of all ASEAN households.

The proportion of women within older age groups has been fairly steady over the last 40 years, accounting for more than 50% of the population in all older age groups (Figure 1.10). The share of women has increased by 2 percentage points on average for the 75‑89 age group between 1984 and 2024, increasing from 58% to 60% and by 1 percentage point for the 90+ age group, while remaining fairly constant at 53% for the age group 60‑74. Cambodia, Lao PDR, Malaysia and Singapore have all had a declining share of women across all age groups between 1984 and 2024 while the share has been growing in Indonesia, Myanmar and Viet Nam among all three age groups.

Female heads of households are becoming more common. Between 2000 and 2020 the proportion of households with a female head increased from 19.4% to 24.3% across ASEAN countries, compared to an increase from 33.8% to 39.0% across the OECD (United Nations, 2022[1]). Whilst these women are often single mothers many will be elderly single as a result of having outlived their spouses. These elderly single will be more vulnerable to poverty having been less likely to have their own pension entitlements and will be reliant on both state and family support.

ASEAN countries represent a very heterogeneous group economically. Measured by GDP-per-capita, even in Purchasing Power Parity (PPP) terms, economic development is 26 times greater in Singapore than in Myanmar (Figure 1.11).

In terms of GDP-per-capita, Brunei Darussalam and Singapore compare with the richest OECD countries, while for all the other ASEAN countries except Malaysia and Thailand, GDP-per-capita is lower than in Colombia, which has the lowest level among OECD countries (Figure 1.12). Thailand’s standard of living (measured by GDP-per-capita in PPP terms) compares with that in Latin American OECD countries while Malaysia’s is close to that in Greece, Latvia, the Slovak Republic or Türkiye.

Such differences are due to huge variation in economic performance and to a much smaller extent in demographics. Indeed, while Brunei Darussalam and Singapore have the highest share of the working population (aged 20‑64) in total population, the range across countries is relatively small: from 55‑56% in Cambodia, Lao PDR and the Philippines to 64% in Brunei Darussalam and 68% in Singapore. Adjusting for the share of the working-age population reduces cross-country differences to some extent: there is still a very large 1‑to‑21 range in terms of GDP per working-age population between Singapore and Myanmar compared with the aforementioned 1‑to‑26 range in GDP-per-capita.

Economic catch-up over the past 25 years has, however, substantially reduced these overall income gaps. For example, in 1998, GDP-per-capita in Brunei Darussalam and Singapore were 87 and 50 times that of Myanmar compared to 14 and 26, respectively, in 2023. Average annual growth in real GDP-per-capita over that period was 7.4% or higher in Cambodia, Lao PDR, Myanmar and Viet Nam (Figure 1.13) – it was 9.5% in China. Among ASEAN and selected OECD countries, there has been a clear negative relationship between initial (1998) real-GDP levels and growth over the last 25 years, consistent with the notion of economic convergence. The strong correlation between economic growth and initial income levels translates into an annual speed of convergence of 1.4%, which implies, if maintained over the long term, that half of the initial gap in GDP-per-capita between two countries is eliminated after 41 years.3 Since 2010, however, the catch-up process has stalled in Indonesia and the Philippines (OECD, 2024[2]).

Public spending is low in ASEAN countries, which limits the scope of social protection. Public expenditure was equal to 21.5% of GDP on average across ASEAN Member States in 2023, about half the ratio in the OECD on average (42.0%) (Figure 1.14). Differences in economic development may explain part of this pattern, but even the richest ASEAN countries spend a small share of GDP.

While lags in economic development limit the capacity to raise tax revenues, there is no correlation across countries between the levels of GDP-per-capita and public expenditure as a share of GDP, neither among ASEAN countries alone nor among OECD countries alone. For example, Singapore is both the richest ASEAN country and the one with the lowest public expenditure ratio. Cambodia spends 27.1% of GDP, the second highest after Brunei Darussalam, while it is the second poorest based on GDP-per-capita. In the OECD, the four countries with the lowest level of GDP-per-capita (Chile, Colombia, Costa Rica and Mexico) do tend to have lower spending, at 35% of GDP or less, but among the other countries there is no negative correlation between the levels of economic development and public expenditure.

Population ageing will put heavy strain on public finance and, in particular, on financing pensions, health and long-term care. Public spending as a share of GDP is projected to significantly increase in most countries, driven by the acceleration in old-age to working-age ratios which raises spending levels (numerator) and may put downward pressure on GDP in some countries (denominator). Under unchanged pension policies, ageing directly increases the GDP share of spending from defined benefit PAYG pensions while it reduces monthly benefits in defined contribution schemes. However, most studies find only a limited effect of demographics on the past growth of health expenditure compared to non-demographic effects (Rouzet et al., 2019[3]), as what matters most for healthcare expenditures are the death-related costs, and therefore the share of a country’s population being close to death (Marino et al., 2017[4]). Changes in incomes and the associated demand for higher quality services have been the main reason behind increases in health spending in the past decades.

The share of pension and healthcare expenditure in GDP will raise sharply in some ASEAN countries. The projected increase will exceed 10 percentage points between 2022 and 2060 in Malaysia, Thailand and Viet Nam, as well as in China and Korea (Figure 1.15). It would be much smaller, although still significant, in Indonesia, the Philippines and Singapore, as well as in Japan, reflecting differences in the pace of ageing, the scope of healthcare and pension systems and measures already taken to deal with ageing challenges. Under an unchanged policy scenario, the tax burden would have to rise sharply to keep debt-to-GDP ratios constant in many countries (Guillemette and Turner, 2018[5]).

Public debt levels paint a contrasted picture across ASEAN countries. Brunei Darussalam is an outlier with basically no debt. The two most indebted countries are Lao PDR and Singapore, with strong increases in the size of debt as a share of GDP over the last two decades (Figure 1.16) despite low spending as shown above. Given its low development level, the high debt level in Lao PDR (larger than 120% of GDP) raises some concerns in terms of its fiscal space to expand social protection. The size of public debt in Indonesia was strongly reduced from 87% of GDP in 2000 in the wake of the Asian crisis of the late 1990s to below 60% in 2003 and 39% in 2023. Indonesia applies the parameters from the Maastricht Treaty (3% of GDP for the fiscal deficit and 60% of GDP for the total government debt). Those rules – relaxed during COVID‑19 – have been very effective in maintaining general government debt at sustainable levels and pushing down government bond yields (Pulugan and Listiyanto, 2021[6]). Malaysia is also taking important steps to strengthen fiscal sustainability. The new fiscal framework established by the Public Finance and Fiscal Responsibility Act may be instrumental to face fiscal challenges driven by population ageing (OECD, 2024[7]).

Large current account deficits generate risks of macroeconomic imbalances in Cambodia and Lao PDR. Over the past decade, the current account deficit has exceeded 10% of GDP in both countries (Figure 1.17). In the case of Lao PDR, this adds to the large mounting public debt highlighted above. In addition, over the last decade on average, the annual general government net lending was close to a deficit of 4% of GDP in Lao PDR, pointing at twin-deficit weaknesses.4 By contrast, Malaysia and Thailand have recorded persistently large current-account surpluses, and even significantly more for Brunei Darussalam and Singapore where the average annual surplus over the last decade has exceeded 13% of GDP.

On average across ASEAN countries the share of people working, both formally and informally, among those aged 15‑64 was 70% in 2022, similar to the OECD average at 69%. However, while total employment rates increased substantially in the OECD from 64% in 2010, they were broadly stable among ASEAN countries on average (Figure 1.18).5 In 2022, employment rates stood at 70% or more in Cambodia, Singapore, Thailand and Viet Nam while they were 65% or less in Brunei Darussalam, Myanmar and the Philippines.

Many workers at all ages work in agriculture in most ASEAN countries, but overall employment is shifting strongly towards services. In all ASEAN countries except Brunei Darussalam, Malaysia and Singapore more than 20% of workers still work in agriculture, with as much as 70% in Lao PDR and 46% Myanmar in 2022 (Figure 1.19). On average, agriculture employs 28% of workers in ASEAN countries compared to less than 5% in the OECD, 43% in India and 23% in China. In other parts of the world, Eastern and Southern Africa show even a higher share of agriculture at 56%, followed by Western and Central Africa at 45%. With similar average shares of industry in ASEAN and OECD countries – at 21% and 22%, respectively – the higher share of agriculture in ASEAN countries means a lower share of services. The employment share of agriculture in ASEAN countries has been declining fast. In 2022, at 28% it was substantially lower than its 2010 level of 35% on average (World Bank, 2024[8]). As an example, there has been a striking shift in Malaysia in recent decades, from agriculture to manufacturing and services. In the 1960s, agriculture contributed to more than 30% of GDP. The 1980s saw the beginning of a massive industrialisation process, driven by substantial foreign direct investment in manufacturing, mainly from Japan and the United States. In the following decades the services sector expanded rapidly, and it accounted for 58% of GDP in 2022, compared to 24% for manufacturing.

The definition and measurement of informal work is not straightforward. The informal sector includes all enterprises and self-entrepreneurs that produce legal good and services but are not compliant with labour, fiscal and administrative laws and regulations (OECD/ERIA, 2018[9]). There are different degrees of informality, from unregistered enterprises and self-entrepreneurs with no relations with the public administration (total informality) to enterprises that are registered and acknowledged by the public administration but that are not fully compliant (partial informality). Measuring informal economic activity is inherently difficult because informality, by definition, cannot be tracked by official registers, and often takes place in diffused small businesses that might evade formalisation. Small-scale operations often remain below employment or turnover thresholds required for registration, paying taxes or social security contributions, and many businesses can legally be informal. Moreover, some businesses choose to remain unregistered to avoid paying taxes and social security contributions.

Both the ILO and the OECD similarly apply different criteria to classify employees and non-employees as informal (OECD/ILO, 2019[10]). Employees are informal when they do not benefit from paid annual leave and paid sick leave and when their employer does not contribute to a pension scheme. Hence, informal employees may work also in formal companies – that is, formally registered e.g. with tax authorities –, especially when they do not benefit from relevant insurance schemes. The self-employed are informal when they do not belong to the formal sector (that is, their economic unit is not registered with the competent authorities). People who assist another household member to operate a family business or a farm, or to perform a job as employees or dependent contractors, the so-called “contributing family workers”, are always considered informal (Kolev, La and Manfredi, 2023[11]; Frosch and Gardner, 2023[12]).

Informal employment is larger in ASEAN countries than what would be expected based on its simple association with economic development level measured by GDP-per-capita, with the exception of Malaysia (Figure 1.20, Panel A). Indeed, the observed relation between informality and GDP-per-capita is strong across countries, even though causality may run both ways, with GDP growth reducing informal employment and vice versa (Duarte, 2016[13]). In principle, national statistical offices account for the informal activities when measuring GDP, but countries differ in how they adjust GDP and some informal activities elude measurement (Andrews, Caldera Sánchez and Johansson, 2011[14]). As informality is larger in low-income countries, the failure to correct for informality in estimating GDP may mechanically contribute to the correlation between the share of informal employment and GDP-per-capita. Based on cross-country estimates, the levels of GDP-per-capita in Indonesia and Thailand would imply a share of informal in total employment of 51% and 40%, respectively, while it is much higher at 80% and 64%.

On average, two‑thirds of workers work informally in ASEAN countries compared to one in nine in OECD countries on average (Figure 1.20, Panel B).6 In Cambodia and Lao PDR, informal workers make around 90% of total employment, while they make around 80% in Indonesia, Myanmar and Philippines and slightly less than 70% in Viet Nam and Thailand. By contrast, in Brunei Darussalam and Malaysia, only one‑third and one‑quarter of workers are informal, respectively and also in Singapore most workers are formal (Sciortino, 2021[15]). In Brunei Darussalam and Singapore, informal employment concerns mainly migrant workers, who account for about one‑third of all workers in each country and are not mandatorily covered by social security (Ministry of Finance and Economy, 2024[16]; Ministry of Manpower, 2024[17]). Since 2015, the situation has improved substantially in Indonesia, Malaysia, Thailand and Viet Nam, where the share of informal employment declined by 4, 8, 10 and 7 percentage points, respectively. In Thailand, the National Statistical Office (2023[18]) confirms a substantial decline of informal employment from 63% to 51% of total employment between 2012 and 2022, even though the absolute levels suggest a lower incidence of informal employment, than based on ILO data at 65% in 2018.

The share of the informal economy in total output, of 25% on average across ASEAN countries, is much smaller than its share in employment (Figure 1.20, Panel C). This is because informal employment often takes place in formal enterprises and informal work is often less productive than formal work. However, this remains higher than the OECD average at 18%. Among OECD countries in Asia-Pacific, the share of the informal economy in total output is at 12% or less in Australia, Japan and New Zealand, while it is 23% in Korea, which is higher than in Indonesia, Lao PDR Myanmar, Singapore and Viet Nam. Between 1990 and 2020, the share of the informal economy has fallen substantially in all ASEAN countries, from 40% to 25% of output on average. The largest decreases are recorded in Cambodia, Lao PDR and Myanmar – by 19, 21 and 49 percentage points, respectively. The declining trend of the shadow economy has taken place more generally around the world (Quiros-Romero, Alexander and Ribarsky, 2021[19]).

Informality differs by sector, urbanisation level, age and education (Nguyen and Cunha, 2019[20]). First, more than 84% of informal workers are employed by informal firms, 10% by formal enterprises and 6% by households. Second, informal work is common in all sectors, while being more prevalent still in agriculture: across ASEAN countries, 96% of workers in agriculture are informal against 73% and 71% in industry and services, respectively. In total, 44% of informal workers work in agriculture in ASEAN countries, against 19% in industry and 37% in services. Third, informal employment is more prevalent in rural areas than in non-rural areas: the share of informal workers in rural areas is higher by 28 percentage points than in non-rural areas in Viet Nam and between 10 and 20 points in Cambodia, Indonesia, Lao PDR, Myanmar, Philippines and Thailand. Fourth, on average across ASEAN countries, the incidence of informal work is similar among men and women.7 Fifth, informal employment is slightly more common among people younger than 25 and older than 55.8 Sixth, in ASEAN countries, 89% of workers with primary education work informal, while this is the case for 43% of workers with tertiary education. Additionally, Quiros-Romero, Alexander and Ribarsky (2021[19]) point out that most studies show that wages in the informal sector are lower than in the formal sector, but this is largely related to lower education levels of informal workers. Arnold et al. (2024[24]) show that informal workers rarely belong to households including formal workers resulting in limited social protection for whole households of informal workers.

The very large scope of informality complicates the measurement of unemployment. For example, many job-seekers in low-income countries often undertake some informal work for few hours a week, such as selling vegetables from their own garden, and they thereby are counted as workers (Dewan and Pee, 2007[25]). This can partly explain why unemployment rates are low in ASEAN countries. In 2022, it stood at below 3% on average across countries, which was twice lower than the OECD average. The low unemployment rate varied from less than 1% in Cambodia and Thailand to 5.2% in Brunei Darussalam. Indeed, unemployment rates do not capture underemployment, which is likely to be an important driver of poverty. When in 2021 Nigeria stopped including within unemployment both people working fewer than 20 hours a week and agriculture workers producing goods only for their own consumption, the unemployment rate declined from 33% to 5% (Lain and Pape, 2023[26]).

Labour codes, social-security and tax laws do not mandatorily cover all workers in ASEAN countries. Exceptions are provided, in particular to those working in small companies, the self‑employed, migrants as well as part-time, temporary or seasonal workers. Nguyen and Cunha (2019[20]) provide a few examples of such exemptions. The mandatory social insurance scheme in Viet Nam covers only employees with at least a one‑month contract. In Myanmar, in the private sector, social security only covers mandatorily the companies with more than five employees. In Cambodia, social protection coverage was, until recently, only applied to enterprises with more than eight employees. Domestic workers are typically not protected by national labour legislation and do not work under the same conditions as other workers in terms of employment conditions and wages, which affects their access to social security.

Even when registration is mandatory for firms or workers, the enforcement of the rules is often weak. Nguyen and Cunha (2019[20]) assess that the labour and social protection inspection mechanisms in some ASEAN Member States are relatively weak, particularly so in Lao PDR; moreover, some countries do not ensure regular labour or tax inspections and do not apply regular penalties on companies for employing workers informally. Labour inspectors often lack sufficient resources in ASEAN countries and do not have the right to control enterprises for which the registration obligation does not apply, even if they employ workers informally. Many firms are small and short-lived, which makes it difficult to track them, at least in Indonesia (UN, 2022[27]).

Tedious administrative processes of registration and reporting create a barrier for the formalisation of work. For example, in Myanmar, the system for business registration is considered complicated and fragmented by employers, which leads to inefficiencies and disincentives for firms to enter the formal economy (Nguyen and Cunha, 2019[20]). Also in Myanmar, the difficulties in registering an activity in the business register, in turn, often blocks the registration of workers in social security. On top, rigid formal rules might be difficult to be respected by the self-employed, seasonal workers and agriculture workers whose income fluctuates substantially during the year.

When the system is perceived as corrupt, inefficient or ineffective, workers and companies are less inclined to formalise. For example, many workers did not join the social insurance scheme in Viet Nam because they perceived it as financially depleted (Nguyen and Cunha, 2019[20]). More generally, informal businesses are often not well informed about the benefits of formalisation (OECD, 2020[28]).

Informal enterprises often favour the advantages brought by informality while workers might underestimate the benefits of formalisation and have limited opportunities for formal employment. Workers and employers easily see the costs of formalising while the benefits are often diluted, in particular when social services are under-developed and social security is perceived as not providing value for money. Additionally, low-productive firms may not break even if they were to pay full social contributions and taxes and obey minimum-wage regulations (Arnold et al., 2024[24]). Similarly, substantial taxes and social contributions can be difficult to pay by low-income individuals working informally. This is particularly the case in the agriculture, where both low pay and informality are widespread. If the costs of formalisation push workers’ disposable income to very low levels, then the benefits of formalisation are unlikely to be accepted. In Lao PDR and Myanmar, the lack of employment opportunities in the manufacturing and services sectors keep workers in agriculture, in small farms in particular (Nguyen and Cunha, 2019[20]).

Benefits of formalisation are even less compelling to workers as formal employment rarely provides any protection against unemployment or access to active labour market policies in ASEAN countries. Unemployment insurance is not well developed: it does not exist in Brunei Darussalam, Cambodia, Indonesia, Myanmar, Philippines and Singapore, and covers only a few percent of workers in Lao PDR and Malaysia.9 By contrast, around two‑thirds of workers are covered in Thailand and Viet Nam where unemployment insurance was introduced before 2010 (ILO, 2024[29]). Cambodia, Indonesia, Malaysia and Philippines introduced unemployment insurance over the last six years, while Singapore has only announced plans, in 2024, to introduce it. Only about half of countries worldwide offer unemployment insurance (Obinger and Schmitt, 2021[30]). By contrast, OECD countries spend 0.6% of GDP on unemployment benefits on average. Moreover, active labour market policies – which provide support to jobseekers to increase employment opportunities and improve matching them to available jobs – are also underdeveloped among ASEAN countries. The average expenditure on active labour market policies stood at 0.05% of the GDP in 2019, more than 10 times less than among OECD countries. Proper instruments to help workers find new jobs and improve their employability are becoming even more instrumental to prolong working lives as technological progress is rapidly changing the skill requirements of jobs.

Platform work, which has expanded recently around the world, reinforces informality, although platforms by themselves are registered and precisely record work. In ASEAN countries, platform workers are not considered employees and, therefore, they largely work informally, without being covered by social protection schemes (ASEAN, 2022[31]). However, internet platforms may facilitate insurance coverage. For examples, Indonesia introduced a digital mechanism to improve access to accident insurance for taxi rides. When using the application, a small part of the tariff includes fees for accident insurance (ILO and OECD, 2018[32]).

The vast majority of informal workers suffers from very limited protection against the risks of income losses related to illness, disability and old age. This is because most of them are not covered by contribution-based social protection while safety nets are often underdeveloped. For example, the share of active contributors to the pension system as a percentage of the labour force varies from around 60% in Brunei Darussalam, Malaysia, Singapore and Thailand to less than 20% in Indonesia and Myanmar (Chapter 3). The low social protection coverage of informal workers became even more severe during the COVID‑19 pandemic.10 This is a vicious circle because, at the aggregate level, informality limits the financial capacity to provide social protection by narrowing the tax base. Informality also limits access to both training and skill development and protection by labour regulations including the minimum wage. Informal workers report being frequently exposed to long hours and hazardous working conditions (Fleischer et al., 2018[33]). The challenges posed by informal employment are becoming even more pressing in the population-ageing context as discussed in Chapter 4.

Informality distorts competition. Informal enterprises or those outsourcing part of the production to informal entities have lower operating costs. This competitive advantage tends to hinder the expansion of formal enterprises. Lower labour costs and limited access to external financing of informal enterprises skew production towards more labour-intensive processes and have a negative impact on capital accumulation, innovation and technological progress (OECD/ERIA, 2018[9]). By accentuating competitive advantages in labour-intensive products, informality hinders moving up the product ladder.

Ageing is expected to substantially lower the growth rates of GDP-per-capita. The direct channel through which long-term income prospects are negatively affected by population ageing is through the lower share of the working-age population in total population. This is the flip side in countries who benefited from large demographic dividends when that share was increasing. While pension systems are strained by ageing through the increase in the old-age to working-age ratios, the demographic indicator that matters more to capture the impact of demographic shifts on ageing is the young-age and old-age to working-age ratio – this is often referred to as the “total (demographic) dependency ratio”, which is the complement of the share of the working-age in total population. The old-age to working-age ratio has gradually increased over the past decades and is now accelerating. However, the young-age to working-age ratio has decreased sharply and the fall is now slowing down substantially as fertility rates are already “relatively” low. The total effect for ASEAN countries on average has been a decline of the young-age and old-age to working-age ratio (“growth demographic dividend”) to reach a trough in the mid‑2030s, from which it will gradually increase (Figure 1.21).

This means that the mechanical effect of demographics on the growth of GDP-per-capita has been positive, but this positive effect is expected to disappear in the near future, except in Cambodia, Lao PDR and the Philippines. More precisely, the mechanical effect has contributed to the annual growth in real GDP-per-capita of 0.6 percentage point among ASEAN countries on average over the last three decades and will be a drag of 0.1 percentage points over the next three decades based on current demographic projections. The drag will be large at about 0.5 percentage points in Brunei Darussalam, Singapore and Thailand.

The increase in the “total dependency ratio” until 2070 is smaller than that in the old-age to working-age ratio as the continued decline in the young-age ratio is projected to offset half of the latter in ASEAN countries on average (Table 1.3). The slowing down in the young-age ratio is less abrupt in Lao PDR and the Philippines, and Cambodia to a lesser extent. The fall in the “total dependency ratio” was similar across countries since 1980, between 52 and 72 points, except in Singapore where it was 30 points. The increase until 2070 will be very strong in Singapore and Thailand, and relatively large in Brunei Darussalam, Malaysia and Viet Nam (Table 1.3).

This direct negative effect of ageing on GDP-per-capita can be offset or magnified depending on the evolution of the total employment rate, average hours worked and hourly labour productivity. Indeed, GDP-per-capita (GDP pc) is the product of hourly labour productivity (LP), the aggregate employment rate (ER), average hours worked per worker (H) and the share of the working-age in total population ($s_{WA})$:

$GDP\mathrm{ }pc=\frac{GDP}{POP}=\frac{GDP}{L.H}\times \mathrm{ }\frac{L}{WA}\mathrm{ }\times H\mathrm{ }\times \frac{WA}{POP}=LP\times \mathrm{ }ER\mathrm{ }\times H\mathrm{ }\times s_{WA}$

where POP, L and WA denote, respectively, total population, total employment and the working-age population.

One key policy response to longevity trends is to boost total employment, and in particular at older ages where there remains large potential in many countries. In some, there is also some wide margins to raise female or youth employment. Beyond more employment, the impact of ageing on GDP-per-capita will depend on how labour productivity is affected. If ageing lowers labour-productivity growth, this will add to the negative effect from the lower share of the working-age population. By contrast, if ageing were to raise productivity growth, this would at least partially offset the direct demographic effect.

Measuring labour productivity at the individual level is notoriously difficult. The standard view is that productivity increases with age until the early 50s and then decreases at older ages. This is the result of better experience with age and, at some points in the second part of the career, deteriorating health, the obsolescence of skills and a lower capacity to innovate and adapt to innovations. Hence, shifts in the age structure of the working-age population are likely to affect aggregate labour productivity. However, even based on this standard age profile of productivity, the total impact is not straightforward as that profile is non-monotonous, combined with unprecise levels e.g. beyond age 60.

Moreover, labour productivity has two main components: one is total factor productivity (TFP) and the other is captured by the capital-labour ratio and influenced by the substitution between capital and labour. Even if ageing were to lower TFP growth, it may be associated with labour shortages and lower interest rates, which would raise the capital-labour ratio through labour-saving investments, with the total effect being undetermined. The substitution of capital to labour may be especially relevant for automatable tasks or jobs.

The evidence on the overall effect of the shift in the age structure of the working-age population on aggregate productivity is mixed. Aiyar, Ebeke and Shao (2016[34]) find that the ageing of the workforce, measured by the increase in the share of workers aged 55‑64 in the total workforce, has significantly reduced labour productivity growth in the European Union since the mid‑1990s through its effect on TFP growth. Gagnon, Johannsen and Lopez-Salido (2021[35]) find that the impact of demographic factors on GDP growth in the United States between 1960 and 2015 was positive in the 1960s and 1970s, negative from the 1980s, and basically accounts for the total slowdown in the GDP-growth trend since the 1980s. Their estimated large impact comes directly, almost one‑to‑one, from the lower growth in the size of the working-age populations, with very limited effect from lower TFP growth and capital-labour substitution.

By contrast, Acemoglu and Rastrepo (2017[36]) show, across both OECD and non-OECD countries, that ageing is not associated with lower growth in GDP-per-capita. This implies that the declining share of the working-age population has been offset by higher labour productivity and/or higher employment rates. These authors highlight that countries where ageing has been faster are characterised by a higher rate of technology adoption, which can therefore be considered the market response to increasing labour shortages and upward pressure on wages. There is evidence that countries (e.g. Germany, Korea) undergoing a more rapid ageing of their workforces have experienced a faster development and adoption of automation technologies since the 1990s (Rouzet et al., 2019[3]). Acemoglu and Rastrepo (2022[37]) show that rapidly ageing countries have invested significantly more in new robotic and automation technologies, and provide evidence suggesting that this is due to the implied scarcity of middle‑aged workers and that industrial automation is indeed most substitutable with middle‑aged workers. Among OECD countries, Japan and Korea stand out as examples of rapidly ageing societies with a significant reliance on robotics (André, Gal and Schief, 2024[38]). Alongside Singapore, they are the top three adopters of robots in manufacturing, and Japan alone accounts for 47% of global robot production. Börsch-Supan, Hunkler and Weiss (2021[39]) find no decline in average productivity in the age range 20‑60.

However, some inefficiencies may lead to ageing still exerting downward pressure on GDP. The adoption of labour-saving technologies (capital deepening) driven by ageing has operated through lower interest rates, resulting from a possible combination of labour shortages and excess saving. This mechanism breaks down when the adjustment of interest rates is constrained by a lower bound on nominal rates, limiting investment and the possibility to absorb excess savings. Eggertsson, Lancastre and Summers (2019[40]) find that this happened during the decade of the Great Financial Crisis, consistent with the secular stagnation hypothesis. So, if ageing pushes interest rates down structurally such that the zero lower bound becomes effective, lower growth of output-per-capita would result. Additionally, wealth may become more concentrated among older people, who tend to have larger savings. As older people are more risk averse and more likely to invest in real estate or government bonds instead of financial investments that are more productive, less risk-taking overall may slow down growth and innovation (André, Gal and Schief, 2024[38]). Moreover, in some Asian countries in particular, seniority-based wage settings, which remains the norm e.g. in Japan and Korea, leads to the decoupling of wage and productivity at older ages. As ageing increases the share of older workers, this inefficient channel is poised to play a larger role, impeding GDP growth.

Another channel through which ageing may affect aggregate productivity is through the demand side, i.e. shifts in the aggregate consumption basket (André, Gal and Schief, 2024[38]). In particular, it is expected that ageing increases the shares of some services with low-productivity growth in total demand, such as housing and long-term care, which would tend to lower aggregate productivity.

Ultimately, the impact of ageing on income growth will depend on the relative magnitudes of: declining employment-to-population ratios; rising capital per worker; and, productivity growth, which in turn depends on the pace of innovation, technology adoption and human capital investments induced by ageing (Rouzet et al., 2019[3]). As ageing has started to accelerate in many OECD countries over the last decade and will continue at a fast pace in the forthcoming decades, the respective weights of these three factors will change. Hence, evidence about the aggregate effect of ageing in the past may provide little guidance about the aggregate effect in the future. Also, ASEAN countries are following their own ageing patterns, with very fast ageing in some of them.

Overall, ASEAN and OECD countries that have aged faster over the last two decades have not faced lower productivity growth. There is indeed no significant relationship between the pace of ageing and labour productivity growth since 2005 (Figure 1.22). This is despite the fact that Singapore and Thailand, as well as Japan within the OECD, have been ageing fast and recorded low productivity growth.

Ageing fast and at an early stage of economic development means that ASEAN countries have less time and resources to adopt the technological innovations that could raise productivity in an ageing society. Overall, ASEAN countries are much less technology-ready than OECD countries. The World Economic Forum’s (WEF) indices on technological readiness and innovation are part of the WEF’s database on key determinants of productivity and competitiveness. These indices give a score between 1 (worst) and 7 (best) on technological readiness and innovation based on a range of variables. While OECD countries score 5.7 on technological readiness and 4.5 on innovation on average, ASEAN countries only score 4.0 and 3.6, respectively, on average (Figure 1.23). Singapore is above the OECD average while Cambodia, Lao PDR and Myanmar have the lowest scores. Lagging behind is partially due to lower levels of economic development. Fast ageing may make it harder for ASEAN countries to catch up on technology and innovation.

Moreover, some ASEAN countries seem to be unprepared to innovate fast enough to sustain productivity growth given ageing prospects in the forthcoming decades. Based on Chomik and Piggott’s (2021[41]) comparison of countries’ speed of ageing by 2050 and their current score on the WEF’s Innovation Index, OECD countries are generally above world average in both ageing speed and innovation, as are Malaysia and Singapore. However, Thailand and Viet Nam are significantly above world average on ageing speed but not on innovation. Most other ASEAN countries are still below the world average for both dimensions.

[37] Acemoglu, D. and P. Rastrepo (2022), Demographics and Automation.

[36] Acemoglu, D. and P. Rastrepo (2017), Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation.

[34] Aiyar, S., C. Ebeke and X. Shao (2016), The Impact of Workforce Aging on European Productivity.

[38] André, C., P. Gal and M. Schief (2024), Enhancing Productivity and Growth in an Ageing Society: Key Mechanisms and Policy Options.

[14] Andrews, D., A. Caldera Sánchez and Å. Johansson (2011), “Towards a Better Understanding of the Informal Economy”, OECD Economics Department Working Papers, No. 873, OECD Publishing, Paris, https://doi.org/10.1787/5kgb1mf88x28-en.

[24] Arnold, J. et al. (2024), “Towards better social protection for more workers in Latin America: Challenges and policy considerations”, OECD Economics Department Working Papers, No. 1804, OECD Publishing, Paris, https://doi.org/10.1787/76a04c6f-en.

[31] ASEAN (2022), The Asean. Promoting Decent Work and Protecting Informal Workers., https://asean.org/wp-content/uploads/2022/05/The-ASEAN-Magazine-Issue-21-2022-Informal-Economy.pdf.

[39] Börsch-Supan, A., C. Hunkler and M. Weiss (2021), Big data at work: Age and labor productivity in the service sector.

[41] Chomik, R. and J. Piggott (2021), “Population Ageing, Productivity and Technological Change in Asia”, China: An International Journal, Vol. 19/3, pp. 33-52, https://doi.org/10.1353/chn.2021.0027.

[25] Dewan, S. and P. Pee (2007), Beyond the Employment/Unemployment Dichotomy: Measuring the Quality of Employment in Low Income Countries, https://www.ilo.org/wcmsp5/groups/public/@dgreports/@integration/documents/publication/wcms_091732.pdf.

[13] Duarte, P. (2016), “The relationship between GDP and the size of the informal economy: empirical evidence for Spain”, Empirical Economics, Vol. 52/4, pp. 1409-1421, https://doi.org/10.1007/s00181-016-1109-1.

[40] Eggertsson, G., L. Manuel and L. Summers (2019), Aging, Output Per Capita, and Secular Stagnation.

[23] Elgin, C. et al. (2021), “Understanding Informality”, SSRN Electronic Journal, https://doi.org/10.2139/ssrn.3914265.

[33] Fleischer, L. et al. (2018), “Making growth more inclusive in Thailand”, OECD Economics Department Working Papers, No. 1469, OECD Publishing, Paris, https://doi.org/10.1787/263a78df-en.

[12] Frosch, M. and J. Gardner (2023), Defining informality for contributing family workers, ILO Department of Statistics, https://www.ilo.org/media/258026/download.

[35] Gagnon, E., B. Johannsen and D. Lopez-Salido (2021), Undersantding the NEw Normal: The Role of Demographics.

[43] General Statistics Office (2024), Overall situation of workers in informal employment in Viet Nam, https://www.gso.gov.vn/wp-content/uploads/2023/06/LAO-DONG-CO-VIEC-LAM-PHI-CHINH-THUC-O-VIET-NAM-Eg-final.pdf.

[5] Guillemette, Y. and D. Turner (2018), “The Long View: Scenarios for the World Economy to 2060”, OECD Economic Policy Papers, No. 22, OECD Publishing, Paris, https://doi.org/10.1787/b4f4e03e-en.

[42] Harapan, B. et al. (2023), “From Archipelago to Pandemic Battleground: Unveiling Indonesia’s COVID-19 Crisis”, Journal of Epidemiology and Global Health, Vol. 13/4, pp. 591-603, https://doi.org/10.1007/s44197-023-00148-7.

[22] ILO (2024), Statistics on the informal economy, International Labour Organisation, https://ilostat.ilo.org/topics/informality/.

[29] ILO (2024), World Social Protection Data Dashboard, https://www.social-protection.org/gimi/ShowMainPage.action.

[32] ILO and OECD (2018), Promoting adequate social protection and social security coverage for all workers, including those in non-standard forms of employment, https://www.ilo.org/publications/promoting-adequate-social-protection-and-social-security-coverage-all-0.

[11] Kolev, A., J. La and T. Manfredi (2023), “Extending social protection to informal economy workers: Lessons from the Key Indicators of Informality based on Individuals and their Household (KIIbIH)”, OECD Development Centre Working Papers, No. 350, OECD Publishing, Paris, https://doi.org/10.1787/ca19539d-en.

[26] Lain, J. and U. Pape (2023), Nigeria’s dichotomy: low unemployment, high poverty rates, World Bank, https://blogs.worldbank.org/en/opendata/nigerias-dichotomy-low-unemployment-high-poverty-rates.

[4] Marino, A. et al. (2017), “Future trends in health care expenditure: A modelling framework for cross-country forecasts”, OECD Health Working Papers, No. 95, OECD Publishing, Paris, https://doi.org/10.1787/247995bb-en.

[16] Ministry of Finance and Economy (2024), Labour Force Survey 203, Final Report, Department of Statistics, Department of Economic Planning and Statistics, https://deps.mofe.gov.bn/DEPD%20Documents%20Library/DOS/Labour%20force%20survey_KTK/2023/RPT.pdf.

[17] Ministry of Manpower (2024), Summary Table: Employment, https://stats.mom.gov.sg/Pages/employment-Summary-Table.aspx.

[20] Nguyen, Q. and N. Cunha (2019), Extension of social protection to workers in informal employment in the ASEAN region, International Labour Organization, https://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/documents/publication/wcms_735512.pdf.

[18] NSO (2023), The Informal Employment Survey ,, National Statistical Office, Ministry of Digital Economy and Society, http://statbbi.nso.go.th/staticreport/page/sector/en/02.aspx.

[30] Obinger, H. and C. Schmitt (2021), “Unemployment insurance in the Global South since 1950: Drivers of policy adoption”, Global Social Policy, Vol. 22/1, pp. 67-83, https://doi.org/10.1177/14680181211049654.

[2] OECD (2024), OECD Economic Surveys: Indonesia 2024, OECD Publishing, Paris, https://doi.org/10.1787/de87555a-en.

[7] OECD (2024), OECD Economic Surveys: Malaysia 2024, OECD Publishing, Paris, https://doi.org/10.1787/e45ca31a-en.

[28] OECD (2020), Formalisation of Micro Enterprises in ASEAN, https://asean.org/wp-content/uploads/2012/05/Formalisation-of-Micro-Enterprises-in-ASEAN-POLICY-INSIGHT-2020-final-3.pdf.

[9] OECD/ERIA (2018), SME Policy Index: ASEAN 2018: Boosting Competitiveness and Inclusive Growth, SME Policy Index, OECD Publishing, Paris/Economic Research Institute for ASEAN and East Asia, Jakarta, https://doi.org/10.1787/9789264305328-en.

[10] OECD/ILO (2019), Tackling Vulnerability in the Informal Economy, Development Centre Studies, OECD Publishing, Paris, https://doi.org/10.1787/939b7bcd-en.

[6] Pulugan, A. and E. Listiyanto (2021), Debt sustainability and debt management in Indomesia - lessons from China, United Nations.

[19] Quiros-Romero, G., T. Alexander and J. Ribarsky (2021), “Measuring the Informal Economy”, IMF Policy Papers, https://www.imf.org/en/Publications/Policy-Papers/Issues/2021/02/02/Measuring-the-Informal-Economy-50057.

[3] Rouzet, D. et al. (2019), Fiscal challenges and inclusive growth in ageing societies.

[15] Sciortino, R. (2021), Informal Workers in Southeast Asia: Resourceless, yet Resourceful, https://www.wiego.org/blog/informal-workers-southeast-asia-resourceless-yet-resourceful.

[27] UN (2022), Informal Economy in Indonesia, UN Economic and Social Council, https://unece.org/sites/default/files/2022-03/8_Informal%20economy%20in%20Indonesia.pdf.

[1] United Nations, D. (2022), Household Size & Composition, 2019, https://population.un.org/household/#/countries/.

[21] World Bank (2024), Informal employment in Malaysia, World Bank, https://documents1.worldbank.org/curated/en/099022124104015011/pdf/P1810931e836170db184871b5fe7b37157c.pdf.

[8] World Bank (2024), World Development Indicators, https://databank.worldbank.org/source/world-development-indicators.