metadata: authors: 0: author: Ablaza,Christine Marie Jimenez
1: author: Alladi,Vinayak
2: author: Pape,Utz Johann
count: Indonesia
docna: 0: docna: Indonesia's Informal Economy:
            Measurement, Evidence, and a Research Agenda
docty: Policy Research Working Paper
theme: Macroeconomic & Structural Policy Modelling,Job Creation,Gender,Human Development and Gender,Data Development and Capacity Building,ICT,Economic Policy,ICT Solutions,Economic Growth and Planning,Fiscal Policy,Private Sector Development,Public Sector Management,Jobs,Data production, accessibility and use,Tax policy
abstracts: cdata!: Indonesia has made remarkable economic
            progress since the Asian Financial Crisis. To sustain its
            growth and achieve high-income status by 2045, it needs to
            address the long-standing challenge of informality. Doing so
            will require a coordinated policy approach informed by
            robust empirical evidence on the underlying causes and
            consequences of informality. This paper contributes to this
            agenda by reviewing the state of knowledge on the informal
            economy in Indonesia. The study focuses on three key areas
            of relevance to future policies on informality, namely: (1)
            key definitions and measures, (2) existing data sources, and
            (3) findings from previous research. The paper identifies
            remaining gaps in the existing data and empirical literature
            and uses this to construct an agenda for future work on the subject.
display_title: Indonesia's Informal Economy:
            Measurement, Evidence, and a Research Agenda
listing_relative_url: /research/2023/11/34196967/indonesias-informal-economy-measurement-evidence-research-agenda
topicv3: Informal Employment m1503994 2692,Job Quality and Labor Market Performance
            m1504048 2745,Labor Diagnostics m1583605 4430
publishtoextweb_dt: 2023-11-15T00:00:00Z
dois: 10.1596/1813-9450-10608
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1184041
1: author_key: 998624
2: author_key: 1154467
model_extractions: Policy Research Working Paper 10608 Indonesia’s Informal Economy Measurement, Evidence, and a Research Agenda _Christine Ablaza Vinayak Alladi Utz Pape_ Poverty and Equity Global Practice November 2023
Policy Research Working Paper 10608

# **Abstract**

Indonesia has made remarkable economic progress since the Asian Financial Crisis. To sustain its growth and achieve high-income status by 2045, it needs to address the long-standing challenge of informality. Doing so will require a coordinated policy approach informed by robust empirical evidence on the underlying causes and consequences of informality. This paper contributes to this agenda by reviewing the state of knowledge on the informal economy in Indonesia. The study focuses on three key areas of relevance to future policies on informality, namely: (1) key definitions and measures, (2) existing data sources, and (3) findings from previous research. The paper identifies remaining gaps in the existing data and empirical literature and uses this to construct an agenda for future work on the subject.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at upape@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Indonesia's Informal Economy: Measurement, Evidence, and a Research Agenda**

# **Christine Ablaza, Vinayak Alladi and Utz Pape**<sup>**1**</sup>

**Keywords** : informal economy, informal sector, informal employment, Indonesia, literature review **JEL** : E26, J46, J48, O17

1 Authors listed alphabetically. CA: Centre for Policy Futures, The University of Queensland Australia, c.ablaza@uq.edu.au; VA: Poverty and Equity Group, East Asia Pacific, The World Bank, valladi@worldbank.org ; UP: The World Bank, upape@worldbank.org. The authors thank Imam Setiawan, William Seitz, and Rinku Murgai for helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. Financial support for this work was provided by the Government of Australia’s Department of Foreign Affairs and Trade through the Australia-World Bank Indonesia Partnership (ABIP).
# **INTRODUCTION**

Indonesia, the fourth most populous country and tenth largest economy globally (World Bank, 2022), has made remarkable economic progress since the Asian Financial Crisis. <mark>Between 2000 and 2019, GDP grew by a little over 5 percent per year, propelling the country to upper middleincome status. S</mark> ustained economic growth has lifted millions of Indonesians out of poverty, with an estimated 10 percent of individuals living below the national poverty line in 2019 (World Bank, 2022). While the COVID-19 pandemic reversed some of the country’s economic gains, Indonesia remains committed to achieving high-income status by 2045.

To accomplish its objective, Indonesia needs to address its long-standing development challenges. This includes the informal economy, defined as ” _all economic activities by workers and economic units that are – in law or in practice – not covered or insufficiently covered by formal arrangements_ ” (International Labour Conference, 2002, p. 53). Based on estimates from SAKERNAS, Indonesia’s informal economy accounted for an average of 36 percent of GDP between 2011 and 2019 (Hapsari, Yu, Pape, & Mansour, 2022) and nearly 75 percent of total employment in 2019 (Wihardja & Cunningham, 2021).<sup>2</sup> The latter figure is considerably higher than the share of informal employment in neighboring Southeast Asian countries and other countries at a similar level of economic development.

The key to addressing the challenge of informality in Indonesia and elsewhere is understanding its drivers. As Chen (2005) argued, the underlying causes of informality determine the kind of policies needed – if indeed they are needed at all. Dualism, for example, implies that the informal economy would naturally disappear with economic development (Kanbur, 2017), while other theories call for greater government intervention such as stricter enforcement of taxes and labor laws. Policies may also contradict rather than complement each other. As a case in point,

> 2 Wihardja and Cunningham's (2021) estimate is based on the ICLS-17 definition of informal employment, which accounts for employment status, type of accounts kept, social benefits, and type of institution where the worker is employed. This definition is more comprehensive than other measures, such as self-employment, which yield lower estimates (approximately 50 percent) of informal employment.

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the structuralist view of informality (Castells & Portes, 1989) may justify tighter government regulation, whereas the legalist view (de Soto, 1989, 2000) may require deregulation to reduce the costs of doing business.

High-quality data and robust empirical evidence are therefore critical to developing policies to address informality. This study contributes to the policy discourse by reviewing and consolidating the evidence on the informal economy in Indonesia. We focus our attention on three main areas of relevance to future policies, namely: (i) definitions and measures of informality; (ii) existing data sources and their corresponding strengths and weaknesses; and (iii) findings from previous research on informal workers and enterprises in Indonesia. Our review identifies remaining gaps in the data and empirical evidence, which together form the basis of our proposed agenda for the future.

# **DEFINITIONS AND MEASUREMENT OF INFORMALITY**

# **Key definitions**

Informality, in the broadest sense of the word, refers to all economic activities conducted by workers and economic units that fall outside the scope of regulations. It is often viewed from an enterprise or worker perspective (Kanbur, 2017). The enterprise-based approach focuses on the informal sector, which is comprised of household enterprises or “ _units engaged in the production of goods or services which are not constituted as separate legal entities independently of the households or household members that own them, and for which no complete sets of accounts are available..._ ” (ILO, 1993, p.3) Household enterprises can either be: (i) _informal own-account enterprises_ , which are owned and operated by own-account workers either alone or in partnership with members of the same or other households; or (ii) _enterprises of informal employers_ , which are owned and operated by employers who employ one or more employees continuously (ILO, 1993).

The second way to view informality is through the lens of informal employment, which focuses on workers and their jobs. Informal employment refers to “ _the total number of informal jobs, whether carried out in formal enterprises, informal sector enterprises, or households during a_ 3
_given reference period_ .” (ILO, 2003, p. 13) Informal employment is therefore a broader concept encompassing employment in the informal sector (excluding those in formal jobs) and informal jobs in the formal sector. The latter includes employees in formal enterprises who are not covered by social protection and are not entitled to employment benefits (ILO, 2003). Here, we consider both approaches to studying informality and structure the remaining discussion around the informal sector and informal employment.

Countries vary in how they operationalize definitions of the informal sector and informal employment (Kanbur, 2017). In Indonesia, there is no single way of measuring the informal sector. Consistent with the ILO definition, most studies on Indonesia have primarily relied on registration status, tax payments, and/or bookkeeping practices as indicators of informality at the enterprise level (e.g., Rothenburg et al., 2016). Legally, all enterprises in Indonesia, regardless of their size (employment or revenue) must be registered as per Government Regulation 7/2021 art. 37 clause 1.<sup>3</sup> This implies that an unregistered firm operates outside the law and is therefore automatically considered informal. This differs from countries like India, where manufacturing firms with fewer than 10 employees are not required to register (Bussolo & Sharma, 2022). Regarding taxation, Indonesia enforces revenue thresholds below which enterprises may be exempt from paying different taxes. For value-added tax (VAT), firms earning revenue below 4.8 billion IDR per year are considered exempt, while for final income tax, firms with revenue less than 500 million IDR per year are exempt. Perhaps most applicable to smaller household enterprises are local taxes for businesses in the food and entertainment industry, where the threshold is 3 million IDR per year as per Government Regulation 7/2021 art. 124. Accordingly, enterprises earning revenues above these thresholds but not paying taxes are considered informal.

Measures of informal employment, on the other hand, have evolved significantly. Up until 2016, Statistics Indonesia (BPS) used two definitions known as Proxy 1 and Proxy 2 (Figure 1). The

> 3 See <u>https://jdih.setkab.go.id/PUUdoc/176384/PP_Nomor_7_Tahun_2021.pdf for more details.</u>

4
Proxy 1 definition uses employment status to identify informal workers. Under this definition, selfemployed workers with temporary employees, own-account workers, casual workers, and unpaid family workers are considered informal. In contrast, the Proxy 2 definition combines information on employment status and occupation. Accordingly, self-employed workers with temporary employees, own account workers, and casual workers in highly skilled occupations (i.e., professionals and managers) are not considered informal. Because workers in highly skilled occupations may have access to some forms of social protection and/or employment benefits, the Proxy 2 definition may provide more refined estimates of informal employment than Proxy 1.

< Figure 1 > More recently, Statistics Indonesia introduced a new operational definition of informal employment known as Proxy 3 (Pratomo & Manning, 2020). Compared to earlier definitions, Proxy 3 uses an entirely different set of criteria to identify informal workers. In particular, it takes the following factors into account: (i) the type of enterprise employing the worker; (ii) the bookkeeping practices of the enterprise; and (iii) the provision of social security and other benefits to the worker.

# **Data sources**

# _Data on informal enterprises_

Statistics Indonesia maintains several datasets used to study Indonesia's informal sector. These are: (i) the Economic Census; (ii) the Survey of Micro and Small Enterprises ( _Survei Industri Mikro Kecil_ or IMK); and (iii) the Survey of Medium and Large Manufacturing Firms ( _Survei Tahunan Perusahaan Industri Pengolahan Besar dan Sedang_ or Manufacturing Survey). Complementing these main data sources are the World Bank Enterprise Surveys (WBES) and a smaller Informal Sector Survey (ISS) conducted with the assistance of the Asian Development Bank in 2009. This section provides a brief overview of these different data sources.

5
The Economic Census is the primary data source on all non-agricultural enterprises in Indonesia – from micro and small enterprises to medium and large firms.<sup>4</sup> It collects detailed information on various aspects of an enterprise’s operations such as workforce characteristics, assets, cashflows, and income. These two factors – universal coverage of non-agricultural firms and the wide range of data collected – are key to identifying and mapping the distribution of economic activities across the country. For this reason, the Census also functions as the sampling frame for other enterprise surveys in Indonesia (Lestari & Wikaningsih, 2011). But as the Census is only conducted decennially, it is neither designed to monitor business activities regularly nor to study enterprise dynamics.

To fill the gaps left by the Economic Census, Statistics Indonesia conducts other surveys during intercensal years. As mentioned earlier, there are two main ones – the IMK Survey, and the Manufacturing Survey. The IMK Survey, which began in 2009, is conducted annually and covers 1 percent of all micro and small enterprises in Indonesia.<sup>5</sup> It includes a range of questions on production and output, capital, labor, and technology use. The Manufacturing Survey is also conducted every year, but unlike the IMK Survey, covers the entire population of medium and large manufacturing firms.<sup>6</sup> This unique feature enables researchers to construct a panel dataset of all manufacturing firms with 20 or more workers. However, the data also comes with several caveats. As Marquez-Ramos (2020) explained, information on some of the variables in the questionnaire (e.g., capital stock, R&D) may be limited due to non-response. Aside from missing data, there are also issues with sectors needing to be surveyed in certain years, typographical errors and outliers, as well as consistency in variable definitions (Marquez-Ramos, 2020). More

> 4 In Indonesia, firm size is determined in two ways. The first approach, used by Statistics Indonesia, is based on the number of employees. Under this definition, microenterprises are those with 1 to 4 employees; small enterprises with 5 to 19 employees; medium enterprises with 20 to 99 employees; and large enterprises with 100 or more employees. The second definition is based on the National Law 20/2008 (Art. 6), and distinguishes between micro, small, and medium firms based on their net assets or annual revenues.

> 5 The seminal 2009 IMK Survey did not use a nationally representative sample of enterprises. The first nationally representative IMK survey was conducted the following year in 2010 (Rothenburg et al., 2016).

> 6 For this reason, the Manufacturing Survey functions as a census, although the term is not used to avoid confusion with the Economic Census (Marquez-Ramos, 2020).

6
broadly, the Manufacturing Survey does not cover informal enterprises operating in other sectors of the economy such as services.

One limitation common to these data sources concerns the type of information collected. In particular, only a few questions are asked regarding the business environment, or the challenges faced by enterprises (Rothenberg et al., 2016). In this regard, the World Bank Enterprise Surveys offer a valuable alternative. Briefly, the WBES is a survey of non-agricultural enterprises encompassing small, medium, and large firms (World Bank, 2011). The Indonesian version of the survey has been conducted twice – first in 2009 and then in 2015. Like BPS data, the WBES also asks firms about employment, output, and income. What sets it apart from BPS data are specific questions about obstacles to doing business (e.g., access to finance, business licensing, and permits, inadequately educated workforce). Additionally, the two waves of the WBES can be combined to form a panel dataset, which significantly expands analytical pathways. As a limitation, the WBES does not include micro enterprises in its sample which might bias analysis as a vast majority of informal enterprises fall under this category.

With the exception of the Economic Census, no single data source captures the entire population of non-agricultural informal enterprises in Indonesia. This gap was partly ameliorated by the Informal Sector Survey, conducted in 2009 by Statistics Indonesia through technical assistance from the Asian Development Bank (ADB & BPS, 2011). The ISS was conducted in two phases: first, by including a separate module on informality in the August 2009 round of SAKERNAS; and second, by surveying household enterprises operating in Yogyakarta and Banten that were identified through the first phase of the project. Although the mixed survey was a costeffective strategy to measure the size of the informal sector, it has yet to be replicated on a national scale.

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_Data on informal workers_ _Labour Force Survey (SAKERNAS)_ SAKERNAS is a nationally representative household survey designed to collect information on the labor market activities of working-age individuals in selected households. Since its inception in 1976, the survey has undergone several changes to its frequency (Vaccaro et al., 2022). Since 2015, however, SAKERNAS has been conducted every February and August of each year. The February round providing provincial-level estimates now covers approximately 75,000 households, while the August round providing district-level estimates now covers 300,000 households (Vaccaro et al., 2022). Since 2011, SAKERNAS has used a rotating panel design whereby 75 percent of households are retained for two consecutive survey rounds. Households in the sampling frame can be interviewed a maximum of 4 times (Vaccaro et al., 2022).

The survey collects a range of information from respondents, including their educational qualifications, labor market activities, and the characteristics of the respondents’ main and secondary job, if any. Over the years, Statistics Indonesia has added new questions to capture emerging topics relevant to the labor market. In 2018, for example, questions related to the Internet and the digital economy were added and questions to identify “dependent contractors” (e.g., gig workers).<sup>7</sup> Likewise, the August 2020 and February 2021 waves of the SAKERNAS asked respondents about the impact of COVID-19 on their employment status, working hours, and earnings (Vaccaro et al., 2022).

Most relevant to informality are the specific questions used to identify informal workers. Consistent with the Proxy 1 and Proxy 2 definitions of informal employment, SAKERNAS regularly collects information on workers’ occupation and employment status. With the introduction of the

> 7 BPS defines dependent contractors as “someone who is bound to an economic unit (whether a business or company, institution, government, or other unit) to produce goods or services that can provide benefits to the economic unit, but is not a laborer or employee of the economic unit. A dependent contractor is also defined as a person who tries to be self-employed or tries to be assisted by a nonpermanent worker, a family worker, or unpaid labor, but does not have full control over their business in areas such as raw materials, capital goods used, price or tariffs for goods, services produced, or market or customer access, these being governed by the binding economic unit.” (Wihardja & Cunningham, 2021, p. 50)

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Proxy 3 definition of informal employment in 2016, new questions were also added to the questionnaire. In particular, book-keeping practices, types of benefits received from their employer, whether they have an employment contract, and the type of contract they have were all asked about. Additionally, the 2021 questionnaire asked respondents about whether, and when, the company or business in which they worked had been registered. Altogether, these changes imply that estimates of informal employment based on the SAKERNAS are not comparable over time, particularly for the pre-2016 and post-2016 periods. It is also unclear how the new question on registration status will affect estimates' comparability before and after 2021.

Given its comprehensive coverage of the population and the addition of new questions from 2016 onwards, SAKERNAS provides the most accurate estimates of informal employment among existing data sources. However, it also suffers from limitations. For example, information about main and secondary jobs is quite limited (Dong, 2016). This issue has been partly addressed by adding questions related to social insurance, employment benefits, and contract type, although these are only collected from employees. Consequently, data on employers, own-account workers, casual workers, and unpaid family workers remain unavailable, despite accounting for two-thirds of jobs in Indonesia (Wihardja & Cunningham, 2021). In addition, there is a wide range of jobrelated characteristics (e.g., work environment, nature of tasks, job satisfaction) that SAKERNAS does not capture. This hinders a comprehensive analysis of job quality in Indonesia. Finally, SAKERNAS was not designed to be a longitudinal survey. While a panel of households can theoretically be constructed from the rotating panel design, the period covered would be relatively short and not representative of the Indonesian population. Thus, surveys such as the Indonesian Family Life Survey, are needed to complement SAKERNAS.

_Indonesian Family Life Survey_ The Indonesian Family Life Survey (IFLS) is a multi-topic, longitudinal survey that was first conducted in 1993 by RAND Corporation alongside a number of Indonesian organizations. The pilot wave was conducted in 13 provinces, specifically: North Sumatra, West Sumatra, South 9
Sumatra, Lampung, Bali, West Nusa Tenggara, South Kalimantan, South Sulawesi, and all provinces of Java. The baseline sample, comprising more than 7,000 households and over 30,000 individuals, accounted for 83 percent of the Indonesian population (Peterson, 1995).<sup>8</sup> Since 1993, four full follow-up waves in 1997, 2000, 2007, and 2014 have been conducted. In addition, a smaller survey comprising 25 percent of the baseline sample was conducted in 1998 to better understand the impact of the Asian Financial Crisis (Thomas, Frankenberg, & Smith, 2001). The IFLS remains as one of the longest-running high-quality longitudinal surveys among low- and middle-income countries.

Relative to SAKERNAS, the IFLS is a much more comprehensive survey comprised of various modules on different topics, such as household income, expenditure, wealth, health and wellbeing, migration, and employment. The employment module gives the IFLS a distinct advantage over SAKERNAS due to the rich set of information it collects from respondents. In addition to basic labor market information also collected in SAKERNAS, the 2007 and 2014 waves of the IFLS also ask respondents about their overall job satisfaction and other characteristics of their job (e.g., physical effort, job stress). The longitudinal nature of the IFLS, combined with its extensive time coverage spanning 21 years, makes it a key resource for analyzing household and individual-level changes as well as long-term trajectories. This feature is especially important in the context of informality, where labor market transitions and longer-term employment outcomes remain an active area of research. Importantly, the longitudinal dimension of the survey allows panel methods to be deployed to address – or at the very least, minimize – the endogeneity of informal employment.

While the IFLS has clear advantages over SAKERNAS, it also has several limitations. First, it contains limited measures of informal employment that do not fully capture the ICLS-17 definition. Unlike SAKERNAS, which integrated more direct questions to capture informality in 2016, the IFLS

> 8 IFLS provinces were selected to maximize representativeness and capture the country's cultural and socioeconomic diversity while also keeping costs manageable given Indonesia's size and terrain (Frankenberg et al., 1995).

10
only relies on employment status and occupation. Moreover, the sample does not represent the entire country as there is inadequate coverage of the Eastern provinces of Indonesia. Yet, these areas are important from a developmental perspective given historically low GDP per capita levels and high poverty rates. It is also worth highlighting the 7-year gap between the last three waves of the IFLS. While this feature is useful for the study of longer-term trends, it makes it difficult – if not impossible – to study short-term phenomena. Lastly, because the original IFLS sample has not been refreshed or boosted since the pilot wave in 1993, the IFLS may not represent the contemporary labor force, including informal workers. The aging of the sample also means that future waves of the IFLS – if any – will likely suffer from higher levels of attrition, which has remained minimal (though not ignorable) thus far.

As a final point, both SAKERNAS and IFLS researchers need to be aware of changes in key definitions. In 2001, Statistics Indonesia disaggregated employees into two groups – i.e., regular employees and casual workers in the agricultural and non-agricultural sectors. This change has ramifications for the identification and subsequent measurement of informal employment. Because casual workers (informal) were previously lumped together with employees (formal), estimates of informal employment prior to 2001 are likely to be under-estimated.

# **THEORETICAL PERSPECTIVES ON INFORMALITY**

Having reviewed the sources of data on informality in Indonesia, we now turn our attention to the empirical literature. To ground our review, we first briefly summarize the overarching theories of informality.

Two broad schools of thought have dominated the literature on informality. The theory of _segmented labor markets_ views informality as a form of exclusion or employer of last resort (Harris and Todaro, 1962; Tokman, 1992) due to factors such as dualism (Lewis, 1954) as well as high entry barriers into the formal sector (e.g., de Soto, 1989; 2000). In contrast, the theory of _rational exit_ views informality as the result of a private cost-benefit calculation (Magnac, 1991; Maloney, 1999).

In this view, enterprises and workers choose to be informal when the perceived costs of formality 11
outweigh its benefits. Fields (1990; 2005) reconciled these two contrasting views by proposing a _heterogeneous informal sector_ where elements of both segmentation and rational exit co-exist in the informal economy.

The theories of segmentation and rational exit apply to both enterprises and workers. Building on Fields’ (1990; 2005) heterogeneous model of the informal sector, scholars have recently proposed a typology of informal enterprises comprised of outsiders, avoiders, and evaders (Kanbur, 2017; Ulyssea, 2018, 2020). Briefly, outsiders are small, low-productivity enterprises that have little to gain from formalization. Avoiders, while naturally larger than outsiders, choose to stay below the formalization threshold due to high entry costs and to avoid the burden of regulation. Finally, evaders are formal firms that engage in informal activities such as hiring informal workers and/or under-reporting income to avoid taxes.

From a worker’s perspective, segmentation applies to those undertaking informal work for survival but would prefer a formal job. Their prospect of moving to the formal sector is influenced by both supply-side (i.e., worker) and demand-side (i.e., firm) factors, the latter of which is affected by taxes and other government regulations. On the other hand, workers may also choose to be informally employed. There are two scenarios where this can occur. First, workers can sort into jobs where they hold a comparative advantage in the labor market (Roy, 1951). For example, individuals who have entrepreneurial aspirations and skills may select into self-employment rather than taking a job as a salaried employee in the formal sector. Another important consideration relates to the existence of compensating wage differentials (Rosen, 1986), which argues that the non-pecuniary characteristics of informal jobs such as autonomy and flexibility may offset lower wages and other disamenities.

# **FINDINGS FROM THE EMPIRICAL LITERATURE**

These different theories have underpinned a rich and ever-growing empirical literature on informality – including in Indonesia. The overarching objective of these studies is to understand 12
the nature of informality better and to provide empirical support for the theories that best depict the reality on the ground, with an eye for what policies might be most appropriate in a setting of heterogenous firms and workers. The remainder of this section reviews previous studies on the informal sector and informal employment in Indonesia and ends with recent developments relating to informality.

# **The Informal Sector in Indonesia**

As in other countries, measuring the size of the informal sector in Indonesia is a challenging task. One of the key estimates of the size of the informal sector comes from Rothenberg et. al (2016). Using the 2013 IMK Survey, they found that 98 percent of firms with fewer than 10 employees are unregistered. Using Hsieh and Olken’s (2014) previous finding that 95 percent of firms in Indonesia have fewer than 10 employees, Rothenberg and co-authors (2016) projected that 93 percent of Indonesian firms are informal.

Informal enterprises in Indonesia share a number of characteristics that align with previously documented patterns in other countries. For instance, more than 90 percent of informal enterprises in the country are either micro or small firms – suggesting a substantial overlap between MSEs and the informal sector. This contrasts with the situation in Malaysia and Vietnam, both of which have a thriving MSE sector characterized by much lower rates of informality at 9 percent and 27 percent, respectively (Nugroho, 2021). Notably, an estimated 64.5 percent of MSMEs in Indonesia are owned by women, highlighting the important role of women in this space (Susanti et al., 2023).

In addition to firm size, informal enterprises in Indonesia are also characterized by low levels of productivity. For example, estimates from the 2009 ISS showed that labor productivity in the informal sector ranged from 9 percent (in the city of Yogyakarta) to 15 percent (in the province of Banten) of productivity in the formal sector (ADB & BPS, 2011). This reflects the fact that most informal enterprises are primarily labor intensive, rely on outdated technologies, and have little or no access to public goods and services such as electricity (Babbit et al., 2015). Human capital 13
constraints are likewise hampered productivity, as illustrated by the fact that managers in these enterprises have low educational attainment (Rothenberg et al., 2016). As a result of low productivity levels, informal enterprises typically pay low wages and are constrained to serving local markets (Rothenberg et al., 2016).

The empirical evidence has also examined factors influencing informal enterprises’ decision to formalize. One recurring theme based on qualitative interviews is obtaining access to credit. As obtaining credit presents a greater challenge in the informal sector (particularly for women), firms may find it more worthwhile to register their businesses only when they have plans to expand (Babbit et al., 2015; Rothenberg et al., 2016). In addition to accessing credit, workers’ skills may also determine whether they formalize or not. For example, the 2009 ISS revealed that the majority of workers in Banten entered the informal sector because it was the only work they knew (BPS & ADB, 2011). This could reflect a lack of information on, or access to, formal jobs. It could also reflect psychological or behavioral factors, such as preferring a known over an unknown option. These reasons may partly explain why interventions to encourage formalization have largely been unsuccessful. For example, Rothenberg and co-authors (2016) evaluated the effect of ‘one-stop shops’ aimed at streamlining applications for permits and other business licenses and found that the program did not have a significant effect on firms’ registration rates.

Despite advances in our understanding of the informal sector in Indonesia, important gaps remain. First, there is limited knowledge of the barriers or challenges faced by informal enterprises. With some exceptions, previous studies have typically relied on non-representative samples, which can limit the generalizability of findings. Second, empirical studies of Indonesia’s informal sector have adopted a strict dichotomy between formal and informal enterprises. The reality is much more complex as some enterprises start the registration process and fail to complete it, whereas others do not attempt to formalize at all (Rothenberg et al., 2016). This phenomenon of ‘incomplete formalization’ has not been adequately captured or documented in the empirical literature, largely due to the lack of appropriate measures. As Rothenberg et al.

14
(2016, p. 101) suggested, “better data collection, with an intent to collect richer measures of informality along multiple dimensions, may be important for further work”.

Finally, the narrow focus on informal enterprises overlooks broader links with the formal sector despite evidence suggesting these linkages exist. For instance, a small survey of enterprises in Indonesia by the ILO (2015) found that formal enterprises outsource parts of their production process to home-based, informal workers. However, the extent and features of these subcontracting arrangements remain poorly understood. At the same time, formal enterprises may also directly employ workers on an informal (e.g., unpaid) basis in addition to hiring formal workers (Marquez-Ramos, 2020).<sup>9</sup> Why formal enterprises engage in this arrangement is still unclear, highlighting the need for more research.

# **Informal employment in Indonesia**

Indonesia’s thriving informal economy has been a key source of livelihoods for a sizable share of the population. Recent estimates based on the 2019 SAKERNAS suggest that informal employment accounts for 75 percent of total employment in the country (Wihardja & Cunningham, 2021).<sup>10</sup> Even in relative terms, Indonesia’s informality rate emerges as one of the highest in Southeast Asia, exceeding the likes of Thailand, Vietnam, and even Myanmar (ILO, 2018).

Informal workers constitute a highly diverse group, ranging from owners of household enterprises, casual workers, unpaid family workers, and even employees. Of these, nearly half (48 percent) are owners of household enterprises; 26 percent are casual workers or wage employees without contracts working in those enterprises; and 15 percent are unpaid family workers (Wihardja & Cunningham, 2021). All in all, these three groups account for nearly 90 percent of informal employment.<sup>11</sup>

> 9 Both the IMK Survey and the Manufacturing Survey collect data on the number of paid and unpaid workers in an enterprise. The latter – i.e., unpaid workers – are likely informal workers (Marquez-Ramos, 2020). 10 BPS estimates of informal employment are much lower, ranging from roughly 48 to 56 percent of total employment, due to differences in definitions (Wihardja & Cunningham, 2021).

> 11 The remaining 10 percent are comprised of casual workers, wage employees, and unpaid family workers who are not in household enterprises (Wihardja & Cunningham, 2021).

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Earlier studies also showed disparities in the geographic distribution of informal employment. For example, Manning (2018) observed that informal employment was highest in the outer islands of Kalimantan, Sulawesi, Sumatra, and other Eastern regions such as Nusa Tenggara, Maluku, and Papua. In contrast, informal employment was lowest in the densely populated islands of Java and Bali. These geographic patterns partly reflect the highly rural nature of informal employment, where many work as own-account workers in farming, fishing, and livestock (World Bank, 2010).<sup>12</sup> Nevertheless, there was also a significant number of informal workers in urban areas, who are primarily employed in retail trade, construction, and transportation (Manning, 2018; World Bank, 2010). As noted by the World Bank (2010), informal workers in urban areas had higher levels of education and income and were less likely to be poor than informal workers in rural areas. Overall, previous evidence suggests that low levels of human capital characterize informal employment in Indonesia and tend to attract more women than men (Manning, 2018; World Bank, 2010).

Much has been said about the differences in the labor market outcomes of formal and informal workers in Indonesia. For example, the World Bank (2010) estimated that informal workers earn 30 percent less on average than formal workers even after controlling for demographics, educational qualifications, and geographic location. Aside from this sizable earnings penalty, most informal workers do not have social insurance (e.g., health insurance, oldage pension) or employment benefits such as paid leave. While the Indonesian government has taken significant steps to expand the social safety net in recent years by allowing voluntary contributions by informal workers, coverage rates are far from universal. In 2017, for example, only 43 percent of informal workers had ever been enrolled in the national health insurance system (Muttaqien et al., 2021). Furthermore, more than 60 percent of enrolled informal workers

> 12 While agricultural workers are not always informal workers, there is a large overlap between the two groups. For example, the OECD (2019) estimates that nearly 94 percent of agricultural workers globally are informal workers.

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pay the cheapest premium, which only provides limited entitlements through the “basics benefit package” (Dartanto et al., 2020; Muttaqien et al., 2021).

While these outcomes may be taken as evidence of segmentation between formal and informal workers, other factors need to be considered. For example, many employees with “good” jobs in the formal sector have low wages, work without employment contracts, and receive little or no employment benefits despite qualifying for such benefits (World Bank, 2010). More importantly, the intrinsic aspects of informal jobs (e.g., autonomy, flexibility) have yet to be thoroughly examined. This constitutes a critical gap in knowledge as these characteristics are believed to encourage informality, particularly among certain groups. These include women, particularly those with caring responsibilities; young workers, who use informal jobs to acquire skills and work experience; and entrepreneurs, who aspire to start their own businesses. Whether these advantages fully offset the disadvantages of informal employment is a question that remains unanswered.

Likewise, the issue of heterogeneity in informal jobs has not yet been fully unpacked in the context of Indonesia. However, there is evidence to suggest that informal jobs are not always a dead end. Using the 2000 and 2007 waves of the IFLS, for example, the World Bank (2010) showed that 14 percent of formal workers transitioning to an informal job experienced wage growth. Among the “winners” in the informal sector are young workers who eventually found formal jobs, as well as older workers who chose to become informal entrepreneurs (World Bank, 2010). More recent evidence suggests that heterogeneity also manifests in terms of diversity in incomes. Applying quantile regressions on the last three waves of the IFLS, Ablaza and co-authors (2021) showed that the size of the formal-informal earnings differential varies substantially across the income distribution. Women, workers with low levels of education, and employment in the agriculture sector were all associated with larger differentials.

As mentioned in the previous section, informal workers may also be employed by formal enterprises although the extent of this arrangement remains unclear. In other words, how much 17
of informal employment takes place within the formal sector as opposed to the informal sector? Furthermore, there is a lack of knowledge regarding how the characteristics and outcomes of informal workers in formal enterprises differ from those of informal workers in informal enterprises. For instance, do informal workers in formal enterprises earn higher incomes and experience better working conditions than informal workers in informal enterprises?

# **Emerging trends and recent developments**

As in many other countries, the COVID-19 pandemic has caused significant disruption to the Indonesian economy. To contain the spread of the virus, the Indonesian government imposed large-scale social restrictions _(Pembatasan Sosial Berskala Besar)_ at the end of March 2020, which limited people’s movements and activities (Temenggung et al., 2021) _._ These measures contributed to a GDP contraction of 2.1 percent in 2020 – the country’s first since 1998 (OECD, 2021). The associated job losses, particularly in the formal segment of the economy, pushed many workers towards informality (Octavia, 2020; Temenggung et al., 2021). Consequently, informal employment increased between 2019 and 2020.<sup>13</sup> The months that followed the first case of COVID-19 and subsequent declaration of a public health emergency in March 2020 were challenging for all Indonesians, but most especially for those in the informal economy. Many informal enterprises faced a steep drop in domestic demand, which in turn, resulted in a sudden and substantial loss of income (Shinozaki, 2022). Informal enterprises – particularly MSMEs – were forced to lay off workers, reduce hours, and suspend wages to survive (Shinozaki, 2022). Working capital quickly emerged as the top concern of MSMEs at the time.<sup>14</sup> Their informal nature constrained them from accessing credit from

> 13 Using self-employment as a proxy for informality, the OECD (2021) estimates that informal employment increased by 4.6 percentage points from 55 percent in 2019 to 59.6 percent in 2020.

> 14 A 2015 survey of micro and small manufacturing firms by BPS revealed that the median cash buffer of MSMEs in Indonesia could only cover about 11 days of operational costs. The survey also showed that about 80 percent of firms would not be able to survive for more than 30 days without income from operations (Temenggung, et al., 2021).

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financial institutions, leaving them no option but to rely on their own funds or borrow from informal networks (Shinozaki, 2022).

Similarly, many informal workers were adversely impacted by the pandemic. Workers whose jobs require face-to-face interactions – such as street vendors, domestic helpers, and drivers – experienced large reductions in working hours and incomes (Octavia, 2020). Reports from motorcycle drivers, for example, revealed that their incomes had fallen by as much as 70 to 80 percent since the start of the pandemic (Octavia, 2021). The need to travel to earn a daily wage also put informal workers at a greater risk of contracting the virus, especially women who are more likely to use public transportation than men (Setyonaluri & Samudra, 2020). Although the government’s social assistance program for the poorest Indonesians benefited some informal workers, many did not qualify for the in-kind and cash transfers because they either earned incomes above the eligibility threshold or had assets to their name (Octavia, 2020). Notably, there was no specific program designed to assist informal workers partly due to the lack of a universal database that could be used to identify and target recipients (Octavia, 2020; Pitoyo et al., 2021).

Nevertheless, not all informal enterprises and workers suffered because of the pandemic. A series of small MSME surveys conducted between March 2020 and May 2021 by the ADB revealed two distinct groups: those who were disrupted by the pandemic on one hand, and those who benefited from the pandemic on the other hand (Shinozaki, 2022). Several factors influenced outcomes. First, informal enterprises and workers in sectors that were considered ‘essential services’ – such as ICT, finance, and health and social services – fared much better than enterprises and workers in other sectors (Shinozaki, 2022). Second, informal enterprises and workers who were able to leverage the digital economy were shielded from the worst effects of the pandemic (Shinozaki, 2022). Informal enterprises that were primarily engaged in e-commerce, particularly the online buying and selling of goods, fared relatively well (Pitoyo et al., 2021). Likewise, informal workers who had access to social media (e.g., Twitter) and messaging platforms (e.g., WhatsApp) 19
were able to attract and retain customers and earn enough income to keep them afloat (Octavia, 2021).

As Indonesia recovers from the pandemic, the digital economy will play an increasingly important role for both informal enterprises and workers. While it has already benefited some groups, the key challenge now lies in ensuring that these benefits are shared more widely. Accomplishing this will require concerted effort on several fronts, including increasing internet penetration in rural areas where internet access is only half of that in urban areas (OECD, 2021). Moreover, Indonesians in the top 10 percent of the income distribution are 5 times more likely to be connected to the internet than those in the lowest 10 percent (Tiwari et. al 2021). There is also a large urban-rural divide in mobile phone usage for work: 33.6 percent in rural areas relative to 57.7 percent in urban areas (SAKERNAS, 2022).<sup>15</sup> In addition, many Indonesians lack the skills needed to navigate the digital economy effectively (OECD, 2021). Some informal workers, for example, may not know how to use certain platforms such as social media to promote their work (Octavia, 2021). Policy therefore has a critical role to play in bridging these digital inequalities.

Evidently, the pandemic not only exacerbated pre-existing inequalities between the formal and informal segments of the economy, but also within the informal economy itself. In other words, the pandemic contributed to the dualistic nature of informality in Indonesia – increasing both ‘bad’ informal jobs as well as ‘good’ informal jobs. Nonetheless, the pandemic has also reinforced some of the benefits that could be gained from formalization. For example, it is easier for the government to provide assistance to formal enterprises and workers who are registered in national databases. In contrast, individual-level data on informal workers is largely absent in Indonesia, which makes it extremely difficult for the government to assist informal workers in times of crisis (Wihardja & Cunningham, 2021).

> 15 SAKERNAS does not distinguish between mobile phones (texting and calling only) and smart phones, so we report the combined gap here.

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# **AN AGENDA FOR THE FUTURE**

The previous sections have provided an overview of informality in Indonesia, both in terms of existing data sources and empirical evidence. This final section summarizes the remaining gaps and outlines an agenda for the future.

# **Data gaps**

# _Informal sector_

We have identified a number of existing data sources that can be used to study informality in Indonesia. In terms of the informal sector, the IMK Survey is the most viable source of data for a number of reasons. First, its coverage of both micro and small enterprises allows it to capture more than 90 percent of all firms in Indonesia – most of which are informal in nature. Second, the sampling methodology ensures that the survey is representative of the broader MSE sector, which is critical for the generalizability of findings. Nevertheless, the survey has its own limitations. By virtue of its coverage, it excludes medium and large firms, which could also operate informally. This means that relying solely on the IMK survey will underestimate the true size of the informal sector. Moreover, medium or large informal firms may represent a different segment of the informal sector with very different motivations (e.g., rational exit) and characteristics. Finally, the survey contains a limited set of questions that can be used to understand the reasons behind firm informality, or the barriers they face to formalization. Consequently, the survey needs to be complemented with other data sources to provide a deeper and more comprehensive analysis of the informal sector.

Given the limitations of existing firm-level datasets in Indonesia, there is a case to be made for new data to be collected. Ideally, the new survey should include questions of a more qualitative nature, such as the benefits gained from operating informally, linkages with the formal sector, perceived costs or barriers to formalization, and the use of government services. Existing enterprise surveys can be used to inform the exact framing of these questions. For example, the World Bank Enterprise Survey asks firms to identify their challenges to doing business based on a 21
comprehensive list that includes access to finance, business licensing and permits, tax rates and tax administration, and an inadequately educated workforce. Another survey that can be used as a reference for the design of the new survey is the RAND + AKATIGA (R+A) SME Survey. What differentiates the R+A Survey from other existing enterprise surveys are the explicit open-ended questions such as why respondents chose (not) to register their business, the effects of licensing on their growth prospects, and specific steps taken to expand their business (Burger et al., 2015). _Informal employment_ Of the various data sources available, SAKERNAS remains the best option to study informal employment in Indonesia. As discussed earlier, the addition of new questions in 2016 gives SAKERNAS a distinct edge over other existing surveys as it enables informal employment to be measured in line with ICLS definitions. In addition, SAKERNAS is nationally representative, and is conducted regularly by BPS. These features allow us to accurately measure informal employment (particularly from 2016 onwards), and to document its overall growth or decline on a national scale.

As highlighted previously, the main disadvantage of SAKERNAS is the limited set of questions about the non-pecuniary characteristics of informal employment. For instance, the survey does not contain any questions capturing the benefits that have frequently been associated with informality, such as autonomy and flexibility over working arrangements. This limitation has important implications for the kinds of hypotheses that can be tested using SAKERNAS. Without data on these benefits or an overall measure of worker utility (e.g., job satisfaction), it is difficult to determine whether the benefits of being informal outweigh its costs. Moreover, the fact that SAKERNAS was not designed to be a panel survey means it is not an ideal data source to study the dynamics of informality at the individual level. Altogether, these limitations prevent us from making definitive statements about the nature of informal employment – a point that we return to in the next and final section.

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How can we then address the limitations of SAKERNAS? One approach commonly adopted by previous studies is to supplement SAKERNAS with the IFLS (see e.g., World Bank, 2010). However, this approach is no longer feasible as the IFLS has not been conducted since 2014, and the questionnaire contains outdated measures of informality status (see earlier discussion). A more promising approach would be to design a new survey that will allow more targeted questions to be asked of workers.

In principle, the new survey should include a more extensive set of job characteristics than SAKERNAS as well as measures of worker satisfaction. Well-established surveys such as Australia’s Household, Income, and Labour Dynamics Survey, the British Household Panel Survey, and the German Socioeconomic Panel Survey provide a useful starting point for this exercise. The HILDA Survey, for example, asks respondents to rate their overall job satisfaction as well as their satisfaction with specific dimensions such as job security, work hours, and flexibility. In terms of employment benefits, it is worth asking respondents whether they voluntarily participate in social protection schemes and the reasons for not doing so. Finally, the new survey could also include retrospective questions capturing respondents’ labor market histories, which could provide a more dynamic perspective on informality. For example, this can be used to understand whether informality is an absorbing state or a temporary arrangement until workers find a formal job (Jayachandran, 2021).

# **Analytic gaps**

Shortcomings in the existing data inadvertently create gaps in our understanding of informality in Indonesia. For instance, the lack of a dedicated survey makes it difficult to approximate the size and composition of Indonesia’s informal sector. Without this basic building block, estimating the size of different segments that may co-exist within the broader sector becomes more difficult. This latter issue also applies to informal employment, albeit to a lesser degree. Although overall measures of informal employment have improved substantially since 2016, approximating the relative share of workers for whom the forces of exit and exclusion apply 23
presents a challenge due to the limited set of information collected in SAKERNAS. Yet this is an important task as exit and exclusion can lead to very different policy responses.

A number of studies have also alluded to the heterogeneous nature of informality in Indonesia. In other words, elements of exit and exclusion may apply to firms and workers to varying degrees. Empirically, very little is known regarding the nature of this heterogeneity. This may be partly due to difficulties in operationalizing this concept. While singular measures such as income or output provide a useful starting point, future studies will have to think more deeply about how this concept can be captured more effectively and holistically. Once heterogeneity has been established, the next empirical task would be to differentiate between firms and workers for whom informality is a voluntary choice versus those for whom it is a last resort. This would require us to go beyond comparisons based on workers’ employment status (i.e., self-employed informal workers vs. salaried informal workers) – an area that has already received considerable attention in the literature. Instead, more work will be needed to understand to what degree exit and exclusion apply to different socio-demographic groups (e.g., gender, age, skills or educational attainment); by industry or sector; by geographic location (e.g., urban vs. rural); or even by the level of economic development or poverty within a region.

More broadly, previous studies have relied heavily on binary categorizations of informality wherein firms and workers are either considered formal or informal. However, some scholars have suggested that informality is better represented as a continuum. This theme was also touched upon by Rothenburg and co-authors (2016), when they observed incomplete formalization among micro and small enterprises. Future work can thus examine whether this characterization of informality is accurate and identify where firms fall short in the process of formalization. In doing so, policies can be designed to address the specific barriers that prevent firms from completing the registration process.

Indeed, understanding what types of policy interventions are needed to address informality remains an ongoing task. From a firm perspective, Rothenburg et al.’s (2016) study was 24
one of the few – if not only – studies that explored how the streamlining of registration processes through one-stop shops affected firms’ propensity to formalize. Meanwhile, other types of policy interventions, such as the easing of labor market regulations, have yet to be analyzed. This is a timely opportunity given the introduction of the Omnibus Law of Job Creation in November 2020. The new Law, which replaced the 2003 Manpower Law, is aimed at spurring formal job creation through a comprehensive policy reform package. Among the key elements of this new Law was the deregulation of the labor market through the loosening of policies related to outsourcing and fixed term contracts, severance payments, and overtime hours. Future work will be needed to explore whether the Law has achieved its objective of increasing formality rates and creating better jobs in the economy.

On a separate note, there is also scope to investigate linkages between the formal and informal segments of the economy. In particular, are there existing value chains involving both informal and formal firms? If so, what are the characteristics of these informal firms and what types of inputs do they provide to formal firms? How does value-added tax affect these value chains? Similarly, more work is needed to understand whether and why formal enterprises employ informal workers. Chiefly, what are the underlying reasons (e.g., seasonality, costs) for firms to employ both formal and informal workers? Do informal workers in formal enterprises fare better than other types of informal workers?

Finally, the discussion on COVID-19 and informality has raised important questions that may hold valuable lessons in the context of future economic shocks. We also need to ask whether and how the pandemic has altered the landscape for both informal enterprises and workers. For instance, has the pandemic increased the propensity for enterprises to register their businesses and for workers to participate in social insurance schemes? Did the pandemic change the types of constraints faced by both groups, and if so, how? Moving forward, what types of support do informal enterprises and workers need from the government?

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The subject of digitalization also deserves greater attention given its growing role in the informal economy. Research on the links between the digital economy and the informal economy appears to be at its nascent stages, leaving many questions unanswered. For example, what are the distinguishing characteristics of informal workers and enterprises that use digital technologies? What kinds of digital technologies do they use, and how are they used? Moreover, how does the use of digital technologies shape the outcomes and prospects of both informal enterprises and workers? Finally, what are some of the emerging issues or challenges faced by informal workers and enterprises in the digital economy?

In conclusion, numerous questions remain for future research. Some of these questions may be resolved with the help of existing datasets such as the IMK Survey and SAKERNAS. Where this is not possible, new data will have to be collected through other means to properly address the challenge of informality in Indonesia.

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