Informality
Vol 6, Issue 2
24 April, 2025
Photo Credit: Marcel Crozet / ILO
Senior Editor:
Gabriel Ulyssea
Co-Editors:
Matteo Bobba
Lucie Gadenne
Mariaflavia Harari
VoxDevLits are wiki-inspired literature reviews that aim to summarise the evidence base on narrowly defined topics related
to development economics. Each Lit is written by a community of scholars working on the specific topic addressed in
the review. They are intended for both policymakers and researchers. We aim to describe what we have learned from
research and to highlight the important questions for which evidence is lacking. The Lits are living documents that will
be updated approximately once per year. All published versions will be available on the VoxDev website so that scholars
can cite the reviews with confidence that the version cited will be accessible in the future
Please contact us at engage@voxdev.org if you have any questions or feedback.
Informality
Abstract
Most low- and middle-income countries are characterised by a large informal sector, which implies
that a substantial fraction of economic activity and livelihoods in these countries goes on completely
unregulated. This has important implications for the behaviour of firms, workers, families, and consumers,
which combine to have important effects on aggregate outcomes such as productivity, output, growth,
and overall economic wellbeing. This VoxDevLit summarises a wide range of studies from well-identified
empirical analyses to macro and structural equilibrium models of informality. The final goal is to provide
Photo
Credit: Marcel
Crozet / ILO
an accessible, integrated understanding of the causes and consequences of
informality
for economic
development.
Citation: Gabriel Ulyssea, Matteo Bobba, Lucie Gadenne and Mariaflavia Harari, “Informality” VoxDevLit,
6.2, April 2025
Contents
Summary..........................................................................................................................3
1
Introduction...............................................................................................................
5
2
Facts about the Informal Sector: Firms, Workers and Housing.............................
6
3
4
5
2.1
Informal Firms....................................................................................................
6
2.2
Informal Workers...............................................................................................
7
2.3
Informal Housing...............................................................................................
8
Determinants of Informality.....................................................................................
9
3.1
Firms: Costs and Benefists of (In)Formality....................................................
3.2
How Taxes, Trade and Minimum Wages Interact with the Informal Sector... 12
3.3
Why are Workers in the Informal Sector?......................................................... 14
3.4
Migration and the Informal Sector.................................................................... 16
3.5
Determinants of Slum Formation and Growth................................................. 17
9
Consequences of (In)Formality................................................................................ 18
4.1
The Consequences for Firms............................................................................ 18
4.2
How Taxation and Redistribution are Impacted by the Informal Sector........ 18
4.3
Aggregate Consequences of the Informal Sector........................................... 20
4.4
Slums and Social Mobility................................................................................. 24
4.5
Public Polices towards Slums........................................................................... 25
Final Remarks and Key Unanswered Questions on the Informal Sector............... 27
References........................................................................................................................ 28
2
Informality
Summary
Low- and middle-income countries are typically characterised by high levels of informality,
which has significant implications for firms, workers, families, and consumers that, in turn, can
result in potentially large aggregate effects. This VoxDevLit summarises a wide range of studies
to understand what we have learned about the causes of informality and its consequences for
economic development.
Credit: Marcel
Crozet
This review is structured around three key dimensions: firms, workers, Photo
and housing.
We start
by / ILO
summarising the main informality facts established in the literature. There are two key margins
of informality regarding firms: (i) the extensive margin, whether firms register and pay entry fees
to achieve a formal status; and (ii) the intensive margin, whether firms that are formal in the first
sense hire workers without a formal contract – which is large and represents, for example, 56% of
informal employment in Mexico and 40% in Brazil. Informal firms are typically smaller, pay lower
wages, and earn lower profits than formal firms. Nevertheless, there is no empirical support for
the “dual view” of informality, as formal and informal firms operate in the same industries, produce
similar products, and there is a substantial overlap in formal and informal firms’ productivity
distributions, even within industries.
Informal employment exhibits a U-shaped relationship with age, is higher among women and
decreases with education. Informal employment expands during economic downturns, potentially
providing a buffer against unemployment. However, flows in and out of informal jobs are typically
associated with poor labour market outcomes and with a slippery job ladder. In housing markets,
informality is widespread, with approximately 1.1 billion people (25% of urban dwellers globally)
living in informal settlements or “slums”, characterised by a lack of formal property rights,
inadequate infrastructure, and distinctive built environments.
The survey then moves on to discuss the main determinants of informality. We start with empirical
studies that use both experimental and non-experimental designs to investigate the causal effects
of different determinants of firms’ decisions to formalise. Research shows that simply reducing
registration costs has limited effects on firm formalisation. The most effective approaches to
increase formalisation include reducing ongoing costs of being formal (such as the tax burden),
increasing benefits of formality (like access to credit), and increasing enforcement through
inspections.
On the determinants of workers’ informality, we emphasise the role of public policies – such
as conditional cash transfer programmes – and the role of human capital. The evidence
suggests that social protection programmes sometimes create incentives for workers to
remain informal. However, the evidence at the individual level is mixed, while at the local labour
market level there is evidence that conditional cash transfer programmes can increase overall
formal employment. Fertility plays a key role in women’s prevalence in the informal sector, as
they show a much higher probability of being informally employed after the birth of their first
child, while men are unaffected. Human capital levels significantly impact informality rates,
with education being a key determinant of formalisation. Finally, the empirical evidence on the
effects of minimum wages on informality is mixed, but evidence from quantitative equilibrium
models indicates that the overall effect is negative: high minimum wages slow down the
expansion of the formal sector or increase informality. Slum formation is influenced by rural-tourban migration, land market institutions, strategic behaviour of local governments, and rapid
immigration.
3
Informality
In the final part of the review, we summarise research that investigates the consequences of
informality. For firms, the literature shows that formalisation alone has no significant impacts
on profitability or growth for most small businesses. Even when firms do formalise due to policy
incentives, they rarely change behaviour in meaningful ways such as opening business bank
accounts or accessing formal credit. However, the broader macroeconomic impacts of informality
are significant. Higher enforcement against informal firms may increase aggregate productivity
through several channels – by eliminating low-productivity informal businesses, reallocating
resources to more productive formal firms, improving job matching, and encouraging human
capital investment. Enforcement policies can also have adverse effects, potentially increasing
unemployment and hurting vulnerable populations.
Photo Credit: Marcel Crozet / ILO
In the housing sector, informal settlements create potentially important equity-efficiency
trade-offs. While slums provide affordable housing for low-income residents and may offer
better access to labour market opportunities than peripheral formal housing, they create land
misallocation issues that reduce overall urban productivity. Research shows mixed results
regarding social mobility among slum residents, depending on the context. Some studies suggest
slums provide pathways for advancement while others indicate persistent intergenerational
poverty.
The timing and transition costs of formalisation policies remain important yet understudied issues.
While the long-term equilibrium might show productivity gains, the transition period could involve
substantial hardship for those directly affected. For instance, recent research on rural-urban
migration shows stark differences between short-term negative impacts on formal employment
(increasing informality) versus long-term positive effects that actually reduce informality and
increase formal sector jobs.
Various policy approaches have been implemented to address informality. For firms and workers,
enforcement policies can increase formalisation but may have adverse employment effects.
Reducing tax burdens and entry costs has limited but positive effects. Education and skill
development can significantly reduce informality rates. For housing, titling programmes have
positive but not transformative impacts. Relocation schemes often fail due to reduced labour
market access in new locations. Slum upgrading can provide immediate relief but may prolong
slum persistence.
The informal economy represents a complex challenge for developing countries, with important
trade-offs for policymakers. Successful approaches must account for the heterogeneity of
informal actors and the multiple dimensions of informality. Future research should focus on the
dynamics of informality over time, the interconnections between different forms of informality,
and the transition costs of formalisation policies.
4
Informality
1
Introduction
Most low- and middle-income countries are characterised by a large informal sector, which accounts for
30-70% of GDP, 20-80% of the labour force and an equally large share of firms (Ulyssea 2020). This means
that a substantial fraction of economic activity in these countries is completely unregulated, taking place
at the margin of tax, labour and other relevant regulatory frameworks.
An equally large, but often overlooked, dimension of informality refers to housing. Informality in land and
housing markets is widespread in low- and middle-income countries. A striking example is the prevalence
of informal urban settlements, often referred to as “slums”. These areas are estimated to house 1.1 billion
Photo Credit:
Marcel2Crozet
people corresponding to around 25% of urban dwellers globally. Projections indicate
an additional
billion / ILO
residents over the next three decades (UN, The Sustainable Development Goals Report 2023).1
These high informality levels have deep implications for individual behaviour – not only firms and workers,
but also families and consumers – which build all the way up to aggregate outcomes, generating important
effects on outcomes such as productivity, output, human capital, inequality, welfare and growth. Thus, a
complete understanding of the causes and consequences of informality requires integrating both its micro
and macro dimensions. This VoxDevLit does this by covering a wide range of studies that go from wellidentified empirical analyses to macro and structural equilibrium models of informality across different
dimensions.
We start by defining what informality is. We define informal firms and workers as those who do not comply
with the relevant laws and regulations. For firms, this can correspond to, for example, not being registered
with the tax authorities, while informal workers are those who do not have a formal labour contract. This
is the definition used throughout this review. While this criterion might be straightforward for workers,
it is less so for firms, as their compliance decision is unlikely to be a binary choice. In both developed
and developing countries, many formally registered firms underreport revenue to evade taxes and are
therefore in partial compliance with tax regulations. Similarly, many formal firms hire informal workers
to evade labour regulation costs. We thus use the same definitions from Ulyssea (2018) and distinguish
between the following margins of informality: (i) extensive margin, whether firms register and pay entry
fees to achieve a formal status; and (ii) intensive margin, whether firms that are formal in the first sense
hire workers without a formal contract.2
Regarding informal housing, there is no universal consensus on what defines a slum. The UN’s definition
includes tenure insecurity as one of five sufficient but not necessary conditions, alongside lack of durable
housing, insufficient living area, and lack of access to water and sanitation (UN-HABITAT 2006/7). In this
review article we use the term “informal settlements” or slums interchangeably to refer to areas that are
lacking formal property rights and that exhibit slum-like conditions.
Using these definitions as a starting point, in Section 2 we summarise the main informality facts
established in the literature regarding firms, workers, and housing. These facts set the scene for the
discussion that follows and highlight many of the key dimensions of informality. Section 3 discusses the
main determinants of informality investigated in the existing literature. We start in Section 3.1 by covering
the main empirical studies that use both experimental and non-experimental designs to investigate the
causal effects of different determinants of firms’ decisions to formalise. These determinants can be
classified into two broad groups: (i) those associated to the costs of entering the formal sector, such as
the costs of formally registering a business; and (ii) those related to the costs of remaining formal, such as
1
2
The majority of those living in slum-like conditions are concentrated in three key regions: Latin America and the
Caribbean (110 million), Sub-Saharan Africa (228 million), and East and South-eastern Asia (589 million) (UN
Habitat 2024).
We restrict the definition of the intensive margin to compliance or not with labour regulations because of data
limitations, as information on tax evasion at the firm level – another important intensive margin of informality –
is typically unavailable.
5
Informality
tax payments and administrative costs associated to being formal (e.g. tax compliance). In Section 3.2 we
focus on the evidence on the roles of tax structure (and not only the tax burden), trade, and minimum wages
in determining the levels of informality. Section 3.3 discusses the determinants of workers’ informality,
while Section 3.4 reviews the latest evidence on the nexus between rural-urban migration and informality.
Section 3.5 covers the determinants of slum formation and growth.
In Section 4 we review the literature that investigates the consequences of the different dimensions
of informality for firms, taxation and redistribution, human capital accumulation, labour market power,
social mobility, productivity, output and growth. On the latter, most of the literature covered in this
Section combines extensive use of rich micro data, with partial or general equilibrium models that aim at
rationalising a broad set of empirical regularities. In that sense, most of them build from micro behaviour
Photo
Credit:on
Marcel
Crozet
to aggregate outcomes to assess the potential effects of different formalisation
policies
the levels
of / ILO
informality and other key aggregate outcomes of interest.
This is the second release of the VoxDevLit on informality. The first edition largely built on a previous
and quite comprehensive review by Ulyssea (2020), while this second edition expands in several exciting
directions, most notably by incorporating the broad topic of housing informality. As this is a dynamic
literature review, this document will be updated regularly to include new and exciting studies as this body
of academic work continues to evolve. We look forward to readers’ feedback on the review, and to ongoing
discussions on this fascinating topic.
2
Facts about the Informal Sector: Firms, Workers and
Housing
2.1 Informal Firms
The existing literature has systematically shown that informal firms are on average smaller (both in terms
of employees and revenues), pay lower wages, are run by less educated individuals, hire less educated
workers and earn lower profits than formal firms (Ulyssea 2020). This is true for different countries and
different data sets used. These differences have been often interpreted as evidence in favour of a dualistic
view of informality, in which formal and informal firms are not integrated at all and operate in completely
separate economic spaces, using different technologies and producing distinct goods. However, the data
does not seem to support this view. Not only do they coexist within the same industries and produce similar
products (e.g. Ulyssea 2018), but there is a substantial overlap in formal and informal firms’ productivity
distributions, even within industries (Meghir et al. 2015, Ulyssea 2018, Allen et al. 2018).
Another important empirical regularity is that the share of informal firms (i.e. the extensive margin of
informality) rapidly declines as firms grow larger (e.g. Perry et al. 2007, De Paula and Scheinkman 2011),
as shown in Figure 1, Panel A. This fact indicates that the costs of operating in the informal sector are
increasing in firm size (or the benefits decreasing). This is intuitive, as one would expect that larger informal
firms have a harder time remaining undetected by the government. More broadly, the opportunity costs
of operating in the informal sector are likely to be increasing in firm size. For example, larger firms might
have greater need of accessing formal credit lines or issuing invoices to buyers, which is not possible if
they remain informal.
Turning to the intensive margin of informality, it represents a substantial fraction of informal employment
in developing countries: 56% in Mexico, at least 40% in Brazil and 32% in Peru (Samaniego de la Parra
and Bujanda 2024, Ulyssea 2018 and Cisneros-Acevedo 2022). The intensive margin also declines as
firms grow larger, as the average share of informal employees within formal firms declines with firm size
(see Figure 1, Panel B). This fact can also be rationalised by the fact that larger firms are more visible
and therefore more likely to be inspected. Conversely, it is costly to inspect small firms and therefore
government’s inspectors tend to focus on larger firms (e.g. Almeida and Carneiro 2012).
6
Informality
Figure 1:
Informality margins and firm size
Panel B. Intensive margin
Share of informal workers
Share of informal firms
Panel A. Extensive margin
1
0.8
0.6
0.4
0.2
0
1
2
3
4
5
6
Firm size
(number of employees)
7
0.5
0.4
0.3
Photo Credit: Marcel Crozet / ILO
0.2
0.1
0
2
3
4
5
6
7
Firm size
(number of employees)
Notes: Figure from Ulyssea (2018); panel A shows the share of informal firms among firms with size n = 1, ..., 7 (where
size is measured as number of employees); panel B shows the average share of informal workers within formal
firms, among firms with size n = 2,...,7.
Finally, the literature has long emphasised that firms in developing economies grow less over their life
cycle, and the “up or out” dynamics found in countries such as the US seem to be much weaker or absent
(Hsieh and Klenow 2014, Eslava et al. 2022). However, we have very limited evidence on the behaviour
of the extensive and intensive margins of informality over firms’ life cycle, and how they can shape firm
dynamics in developing countries (or lack thereof). A key limitation to this analysis is data availability, as
very few data sets contain longitudinal information on informal firms. Mexico is an important exception,
as the country’s economic census has collected data on firms of all sizes, formality status, and sectors for
over two decades (Fentanes and Levy 2024).
Perhaps as expected, Mexican informal firms show substantially higher exit rates than formal ones over
the 5-year time windows available in the economic census: 25% and 30% higher in 2003-08 and 201318, respectively (our own calculation using the information in Fentanes and Levy 2024). Similarly, entry
rates are much higher in the informal sector with a lot of variation between census waves. Consistent
with the evidence on firm size distributions, informal firms grow much less than formal firms, with some
estimates suggesting that the former are completely stagnant (Fentanes and Levy 2024, Sarıkaya et al.
2024). Nevertheless, some informal firms do formalise, and these are the ones that show the highest
growth rates, even when compared to always formal firms.
2.2 Informal Workers
The literature has extensively shown that informality among workers displays a U-shape pattern with
respect to age (larger among younger and older workers), it is higher among women and decreases
with schooling (e.g. Perry et al. 2007, Gasparini and Tornarolli 2009). Importantly, the life cycle profile of
informality seems to conflate two different trends: informal wage employment is highest among young
workers and declines monotonically with age, while self-employment displays the opposite pattern
(Finamor 2024). Transitions in and out of informality follow a similar pattern: the young, women and lowskill workers have a higher probability of transiting from unemployment and formal jobs into informal
employment (see, for example, Bosch and Maloney 2010).
7
Informality
A second set of facts refer to the ins and outs of informality over the business cycle. Informal employment
(like unemployment) has been shown to be strongly counter-cyclical, expanding during recessions
and decreasing during economic booms (as a fraction of employment). This can be explained by the
combination of three important facts (see Perry et al. 2007, Bosch and Esteban-Pretel 2012). First, the
job finding rate in the formal sector is strongly pro-cyclical, but it is stable in the informal sector. Second,
informal to formal transitions are pro-cyclical. Third, separation rates are countercyclical in both sectors
but more volatile in the informal sector.
More recently, Donovan et al. (2023) have harmonised rotating panels from 49 countries to investigate
overall labour market flows in developing countries. They show that labour market flows are higher in
developing countries due to flows into and out of informal sector jobs (self-employment and informal
Photo
Credit: Marcel
Crozet
wage jobs). However, contrary to developed economies, these higher flows
are associated
with
poor / ILO
labour market outcomes and with a slippery job ladder. This evidence stands in stark contrast with that
from employment dynamics in the formal sector. Brockmeyer et al. (2025) harmonise large, administrative
matched employer-employee datasets that cover the universe of formal workers and firms in ten countries
across three continents: Brazil, Chile, Colombia, Ecuador, Ethiopia, Kenya, Rwanda, Thailand, South Africa
and Uruguay. The authors show that formal labour market dynamics in developing countries display
similar patterns to that observed in developed ones: as countries and regions develop (higher GDP p.c.)
workers hold a higher number of formal jobs, spend less time in each formal job, but also less time
between formal jobs. That is, labour market fluidity in the formal sector increases with development, and
that is associated with substantially higher life cycle wage gains (by a factor of 3). This is particularly
important for younger workers, as the positive association between fluidity and development is much
stronger for them.
A third well-established fact is the existence of a substantial formal-informal wage gap, which persists
even after controlling for several observable characteristics (see Ulyssea 2020). However, Ulyssea (2018)
uses matched employer-employee data on both formal and informal firms in Brazil to estimate the same
log-wage regression estimated in the literature, but adding firm fixed effects. The estimated withinfirm wage gap is statistically and economically zero, which suggests that self-selection is one of the
main drivers of the wage gap between observably equivalent workers. Moreover, it also indicates that,
conditional on workers’ skill, formal and informal workers perform the same tasks within the firm.
2.3 Informal Housing
Measuring housing informality in a systematic way is notoriously challenging. Slums are a multidimensional
phenomenon characterised by significant variation across different contexts. Furthermore, slums are
often underreported in official administrative data, leading to non-classical measurement errors.
One of the defining aspects associated with slums is the legal status of land ownership. While all exhibit
some degree of informality and tenure insecurity, land institutions and tenure arrangements differ widely
across settings. In some cases, informal residents are squatting on government or privately owned land
(e.g. Brazil, as documented in Feler and Henderson 2011). Alternatively, slum dwellers may be tenants
paying rent to absentee landlords (e.g. Henderson et al. 2021 on Nairobi) or providing protection payments
to local “slum lords”.
Rather than a binary distinction between formal rights and no rights, property rights often exist on a
continuum where informal or customary rights coexist with formal systems (yet another form of the
intensive margin of informality). Informal residents sometimes are owner-occupiers holding land under
customary contracts, as documented in Jakarta (Harari and Wong 2024). Harari and Wong (2024)
document the duality in land rights in Jakarta stemming from colonial land rights practices, echoing
similar accounts of multiple property rights regimes coexisting in Kampala, Uganda (Bird and Venables
2019). Customary rights are often locally enforceable and tradeable (Lanjouw and Levy 2002, Collin 2020).
8
Informality
Another defining feature of slums is their distinctive built environment. Physical characteristics commonly
associated with informal neighborhoods include low building heights and extensive horizontal coverage
(Henderson et al. 2021), impermanent building materials such as tin roofs (Marx et al. 2013), narrow and
unpaved roads (Harari and Wong 2024), and irregular building layouts (Michaels et al. 2021). These features
are closely tied to low levels of capital investment, the absence of centralised urban planning, and the
incremental, self-built construction practices that are typical of slum development.
In the absence of parcel- or household-level data on informality, the measurement of slums often relies on
observable characteristics captured through imagery (see Kuffer et al. 2016 for a review of slum detection
in the remote sensing literature). For instance, Henderson et al. (2021) utilise LIDAR data to track changes in
built-up volume associated with the conversion of slums into formal neighbourhoods. Michaels et al. (2021)
Photo Credit:
Marcel
Crozet
and Marx et al. (2019) employ high-resolution imagery to identify building footprints
and roof
materials
as / ILO
proxies for investment levels. Harari and Wong (2024) combine ground imagery from Google Street View with
field photos to ensure representative coverage and use human observation to rank neighbourhoods by quality.3
3
Determinants of Informality
3.1 Firms: Costs and Benefists of (In)Formality
This section discusses the determinants of firms’ decisions regarding the extensive and intensive margins of
informality. Broadly speaking, the costs of informality can be categorised into two large groups: (i) the costs of
entering the formal sector, such as the costs of formally registering a business; and (ii) the costs of remaining
formal, such as tax payments and other ongoing administrative costs associated to being formal (costs
associated to tax compliance beyond direct tax payments, for example). As discussed in Ulyssea (2020), if
policy makers want to increase formalisation rates among firms, they can do so by reducing these costs of
formality. A second approach would be to increase the benefits of formality, via greater access to capital,
for example. Finally, it is possible to increase the costs of informality, which can be achieved by increasing
enforcement of the existing laws and regulations (increasing the number of inspections, for example).
The policies or interventions analysed in papers that empirically investigate the potential determinants of
firms’ formalisation decision can naturally be grouped into these three broad categories. The policies and
interventions analysed in the literature have been highly concentrated in the first group – reducing the costs
of formality – and in particular reducing the costs of entering the formal sector (Bruhn and McKenzie 2014).
However, Figure 2 shows that the available results in the literature are not very encouraging. It summarises
the results from both experimental and quasi-experimental studies that have a credible research design to
identify the causal effect of a given policy.4
Figure 2 shows that providing information about the process of registration and its potential benefits (De
Giorgi and Rahman 2013), information and reimbursing all registration costs (De Mel et al. 2013, de Andrade
et al. 2016), or creating national formalisation programmes that substantially reduce registration costs
(Bruhn 2011, Kaplan et al. 2011, Piza 2018, Rocha et al. 2018) have very limited effects on firm formalisation.
The exception to the rule is Benhassine et al. (2018), who find a positive and significant effect of providing
information and assistance in registering in Benin. However, it seems that this positive result comes from
the high-quality staff used in the experiment (who were responsible for providing the information), rather
than from the informational content itself. Indeed, in a follow-up intervention, the authors provided the same
information without the qualified staff used in the first intervention and found no effects.
3
4
One limitation of using imagery is that it often depends on ground or survey observations for training and rarely
provides consistent measurement over time.
We focus on authors’ preferred specifications and standardise the reported point estimates so that all results
reflect effects on formalisation rates. For the experimental papers, we only report ITT estimates. See Ulyssea
(2020) for more details.
9
Informality
Formalisation effects on firms – experimental and quasi-experimental results
0
-.1
.2
.3
Effect on Formalisation Rates
.4
Figure 2:
Piza (2018)
Registration+Taxes
Rocha et al. (2018)
Registration+Taxes
Rocha et al. (2018)
Registration
Bruhn (2011)
Registration
Kaplam et al. (2011)
Registration
Benhassine et al. (2018)
Info+Assist.Reg.+Business+Tax
Benhassine et al. (2018)
Info+Assist.Reg.+Business
Benhassine et al. (2018)
Info+Assist.Reg.
de Mel et al. (2013)
Info+Regist.+$$$
de Mel et al. (2013)
Info+Regist.+$$
de Mel et al. (2013)
Info+Regist.+$
de Mel et al. (2013)
Info+Regist.
Andrade et al. (2016)
Enforcement
Andrade et al. (2016)
Info+Regist.
De Georgi & Rahman (2013)
Information
-.4
-.3
-.2
-.1
Photo Credit: Marcel Crozet / ILO
Notes: Figure from Ulyssea (2020). Green circles indicate results from experimental studies and grey diamonds the
results from the non-experimental literature. We only report ITT estimates from the experimental papers.
Figure 2 shows that the largest formalisation effects come from interventions that reduce the costs of
staying in the formal sector or that increase the benefits of formality. The results from De Mel et al. (2013)
are particularly illustrative: the authors find no effects from the treatment arm that essentially eliminated
registration costs; however, when firms are offered a substantial compensation for formalising (the
equivalent of two months’ profits for the median firm), in addition to removing registration costs, there
is a substantial formalisation effect of 47%. Rocha et al. (2018) find similar results in the context of a
national formalisation policy in Brazil targeted at entrepreneurs with at most one employee. In its first
phase, this formalisation programme eliminated entry costs for eligible entrepreneurs and in the second
phase it also substantially reduced the tax burden faced by firms. The first phase had a null effect on
formalisation rates, but the second one generated an increase of around 11%. This result is driven by the
formalisation of existing informal firms, not by the creation of new formal businesses, nor greater survival
among formal incumbents.
The third group of policies – which seek to increase the costs of informality – has received far less
attention by policy makers and empirical studies. The first exception is the work of Andrade et al. (2014),
who randomly assign municipal inspectors to firms to assess whether higher enforcement could induce
firms to formalise in the state of Minas Gerais, Brazil. Using an IV approach, the authors show that the
impact of receiving an additional inspector visit is quite high, with an effect of 21-27 percentage points in
firms’ registration. They find no spillovers to neighbouring firms.
A second important exception is the work of Samaniego de la Parra and Bujanda (2024). The authors
use nearly half a million random work-site inspections by the Ministry of Labour in Mexico to analyse
the effects of increasing the cost of the intensive margin of informality, that is, the cost for formal firms
of hiring informal workers. At the firm level, the authors find a large negative effect on total formal
employment: one year after the establishment’s first inspection, formal employment is 11% lower at
10
Informality
inspected firms than in control firms, and this gap stabilises at a negative 15% differential and continues
for 18 months after the inspection. These estimates constitute the net effect of different forces within
the firms. Inspections increase both within-firm formalisation (by 6 percentage points) and separations:
exit to unemployment increases for firms’ informal workers, while job-to-job transitions increase for
formal employees.5
Interpreting the Evidence
The results discussed above show that reducing the formal sector’s entry costs has very limited effects
on formalisation. Reducing the ongoing costs of formality (or increasing its benefits) is more effective,
but the effects are not large enough to make these policies cost effective, as they typically lead to
Photo Credit:
Marcel
Crozet
substantial forgone tax revenues (Ulyssea 2020). Overall, the largest formalisation
effects
come
from / ILO
greater enforcement.
A useful way to organise these results is through the lenses of the three leading views about informality in
the literature (La Porta and Shleifer 2014). The first view – the “De Soto view” – argues that the informal
sector is composed of potentially productive entrepreneurs who are kept out of formality by high regulatory
costs, in particular entry regulation. This view dates back to De Soto (1989) and has motivated numerous
efforts to reduce fixed registration costs around the world. The second – termed the “Parasite view” –
argues that informal firms are “parasite firms”: they could survive in the formal sector but choose to remain
informal to earn higher profits by not complying with the relevant taxes and regulations. The third – the
“Survival view” – sees informality as a survival strategy for low-skill individuals, who are too unproductive
to ever become formal. The policy implications – and their expected results – are clear. According to
the first view, reducing entry costs would lead to higher formality, productivity and growth, as it would
“unleash” constrained informal entrepreneurs. The second view implies that the government should
increase enforcement, which would allow the reallocation of resources from less productive informal
firms to more productive formal ones. Finally, the third view implies that reducing entry costs would have
very limited effects, while increasing enforcement would not lead to formalisation of informal firms, as
they are not able to survive in the formal sector. Moreover, higher enforcement could have negative social
consequences by eliminating the livelihoods of the most vulnerable individuals.
As shown in Ulyssea (2018), these views are not competing but complementary frameworks for
understanding informality, as they simply reflect the underlying heterogeneity in the informal sector. Thus,
the main task becomes determining their relative importance in the data. Ulyssea (2018) proposes a
simple taxonomy of informal firms based on these views and uses a structural model to infer their relative
importance in the Brazilian context. The results are reproduced in Figure 3.
Figure 3 shows that the “De Soto view” accounts for a small fraction of informal firms, 9.3%. The “Parasite
view” corresponds to 41.9%, while the remaining 48.8% correspond to the “Survival view”. To the extent
that these results from Brazil are informative for other contexts, Figure 3 provides a rationale for the
results found in the empirical literature summarised in Figure 2. It is only a small fraction of informal
firms that are constrained by registration costs and therefore reducing these costs does not lead to
substantial formalisation. Increasing enforcement can have a substantial impact, since the parasite view
encompasses a large fraction of informal firms. However, the survival view accounts for nearly half of
all informal firms. Since enforcement policies cannot really distinguish between the two types of firms,
they could lead to potentially large adverse effects by displacing a large number of very low productivity
informal entrepreneurs and their employees. The extent to which these adverse effects would be observed
in equilibrium crucially depends on how much reallocation of labour away from informal firms to more
productive formal firms actually happens. Even if the reallocation does occur in the longer run, the
transition to the new steady state can be costly to displaced individuals.
5
This firm-level evidence is related to work by McKenzie and Sakho (2010) in Bolivia, Ronconi (2010) in Argentina,
Almeida and Carneiro (2005, 2012) and Ponczek and Ulyssea (2022) in Brazil, which exploit geographical
variation in enforcement capacity (via labour inspections) to estimate the impact of enforcement on firm
outcomes, formalisation rates and overall labour market outcomes.
11
Informality
Figure 3:
Taxonomy of informal firms in Brazil
Firm‘s Value Function Net of Entry Costs
Baseline Expected Value of Entry Net of
Entry Costs: Informal Sector
Baseline Expected Value of Entry Net of
Entry Costs: Formal Sector
Counterfactual Expected Value of Entry
Net of Entry Costs: Formal Sector
Photo Credit: Marcel Crozet / ILO
0
Parasite
View
Survival
View
=41.9%
=48.8%
v1
De Soto‘s
View
=9.3%
v2
v3
Firm‘s pre-entry productivity signal
Source: Ulyssea (2018).
3.2 How Taxes, Trade and Minimum Wages Interact with the Informal
Sector
Taxes
The tax structure (and not only the tax burden) that firms face is a key determinant of their decisions
to formalise, but one that has received much less attention in the literature. An early exception is the
work of de Paula and Scheinkman (2010), who analyse the role of value-added tax (VAT) in transmitting
informality via its credit scheme. In this type of scheme, establishments receive a credit for taxes paid
upstream in the production chain, which is used against future tax liabilities. By definition, purchases
from informal suppliers do not generate tax credits and informal firms cannot generate tax credits
from their own suppliers, even if those are formal. Thus, formality/informality could be transmitted
throughout the production chain, as more (in)formal supply chains produce greater incentives for
firms to be (in)formal. The empirical analysis using micro data on formal and informal firms in Brazil
confirms the predictions of their model. Formality of a firm’s suppliers and buyers is correlated with its
own formal status, while greater enforcement upstream or downstream implies a higher probability of
being formal. In related work, Naritomi (2018) uses a unique administrative data from Brazil to show
that introducing incentives similar to the VAT for final sales leads to substantial increase in firms’
reported revenue.
This topic intertwines with the broader discussion related to taxation in developing countries, which goes
beyond the scope of this review. The interested reader is referred to the excellent VoxDevLit on Taxation
and Development by Senior Editor Anders Jensen and Co-Editors Anne Brockmeyer and Lucie Gadenne
(Jensen et al. 2024).
12
Informality
Trade
Using different research designs, Paz (2014), Dix-Carneiro and Kovak (2019), Cruces et al. (2018), and
Ponczek and Ulyssea (2022) find substantial effects of trade opening (tariff reduction) on informal
employment. Moreover, Ponczek and Ulyssea (2022) show that these informality effects are concentrated
on low-skill workers in Brazil. These results thus confirm a long-standing concern that trade reforms could
lead to a reallocation of firms and workers from the formal to the informal sector due to the greater
competitive pressure faced by domestic firms (Goldberg and Pavcnik 2003).
The results in Cisneros-Acevedo (2022) provide an interesting nuance to the results above and illustrate
the importance of distinguishing between the two margins of informality. The author analyses the impact
Photo
Credit:effects.
Marcel Crozet
of import tariff reduction in Peru to show that greater trade opening had two
opposing
On the / ILO
one hand, tariff reduction leads the least productive (informal) firms to exit, which causes informality on
the extensive margin to fall. On the other hand, the same competitive pressure induces formal firms to
cut costs by hiring informal workers, causing an increase in the intensive margin of informality. The latter
effect dominates, and overall informality increases as a result of tariff reduction.
Importantly, the results in Dix-Carneiro and Kovak (2019) and Ponczek and Ulyssea (2022) indicate that the
increase in informality per se is not clearly a negative result, as informality can help reduce employment
losses after a negative shock. In particular, Ponczek and Ulyssea (2022) show that regions with weaker
enforcement observed higher informality effects but no unemployment effects. Not only that, in these
regions formal plants had a larger probability of surviving, most likely due to the intensive margin (which
is consistent with the results of Cisneros-Acevedo 2022).
The flipside of these more negative results can be found in the work of McCaig and Pavcnik (2018). They
show that a positive export shock in Vietnam (from the US-Vietnam Bilateral Trade Agreement) led to a
substantial decline of informality due to the reallocation of workers away from informal firms to more
productive formal firms.
Despite their importance, these empirical studies are not able to account for general equilibrium effects,
which can be quite important in the case of trade opening. Dix-Carneiro et al. (2024) develop an equilibrium
trade model with firm dynamics and firm heterogeneity, formal and informal sectors, labour market
frictions and a rich institutional setting, which is estimated using several data sources from Brazil. Their
results are broadly consistent with those from the empirical literature discussed thus far, whereby the
decline in informality within the tradable sector is a consequence of trade liberalisation. However, the
authors show that even though informality can be an “unemployment buffer” – as shown by Ponczek and
Ulyssea (2022) – it is not a “welfare buffer”, as welfare is higher when enforcement is stronger, even if it
comes at the cost of greater unemployment.
Minimum Wage
The analysis of labour market effects of the minimum wage is one of the oldest and most contentious
topics in labour economics. A full review of this literature is outside the scope of this article and the reader
is referred to the many excellent reviews available in the literature (for the most recent one, see Dube and
Lindner 2024). Instead, we focus on recent work that analyses the effects of minimum wage increases on
informality, which mostly focus on Brazil, a high informality country that increased its national minimum
wage by 130% in real terms between 1996 and 2018.
The results from this recent body of literature present an inconclusive picture. Jales (2018) uses a density
discontinuity design to show that informality would be much lower in Brazil in the absence of the minimum
wage. Most studies, however, leverage the substantial increase in the Brazilian national minimum
wage and cross-regional variation in wage levels (i.e. how binding the national minimum wage is in the
baseline period) to identify its effects. Despite the methodological similarities, they reach quite different
conclusions. Parente (2024) finds an increase in informal employment relative to formal employment,
while Derenoncourt et al. (2021) and Engbom and Moser (2022) estimate zero effects on informality.
13
Informality
Haanwinckel (2024) argues that the reason for such discrepancy across studies is the fact that these
estimators are prone to biases from correlated measurement errors and functional form misspecification.
He shows that these estimators can be plagued by economically significant biases when used in contexts
with a national minimum wage (as in Brazil).
Consistent with the more pessimistic results about minimum wage effects, Haanwinckel and Soares (2021)
use an equilibrium structural model to show that the observed decline in both informality and unemployment
in the 2000’s would have been considerably larger - 2.3 and 2.9 percentage points, respectively - if the
minimum wage had not increased during this period. This is consistent with the counterfactual results in
Parente (2024), which show that minimum wage increases lead to higher informality, an increase in wage
inequality within the informal sector and an increase in overall inequality, despite the inequality-reducing
Photo Credit: Marcel Crozet / ILO
effect within the formal sector.
3.3 Why are Workers in the Informal Sector?
3.3.1
The Role of Public Policies
One important concern in developing countries with large informal sectors is the potential effect of
welfare policies shifting labour supply from the formal to the informal sector. This is clearly a concern in
cash transfer programmes that are means-tested, which could incentivise individuals to work informally to
remain eligible for the benefit. More broadly, universal programmes that increase the benefits of informality
(or reduce its costs) could produce similar effects, as extensively discussed in Levy (2010).
Bosch and Campos-Vazquez (2014) analyse one such programme, the Seguro Popular in Mexico. It was
created in 2002 and introduced universal health coverage, including all informal and previously uninsured
informal workers. Before that, health coverage was tied to payroll contributions, which represented a
large cost of being informally employed. Hence, the programme substantially decreased the costs of
informality. The authors show that Seguro Popular had a negative effect on the formality trend (measured
by social security contribution) in small and medium firms. In the absence of the programme, around 4.6
and 4% additional employers and employees would have formally registered, respectively.6 Camacho et
al. (2013) analyse a similar programme in Colombia (the Subsidised Health Regime) and find that the
programme leads to an increase in informal employment of around 4 percentage points. The reported
effects are not very large, which could indicate that the value of these programmes to workers is not very
high. Indeed, Conti et al. (2018) develop and estimate a household search model with formal and informal
sectors and show that the utility value of Seguro Popular represents 4% and 9% of the mean household
income for high and low education households, respectively.
As discussed above, the availability of cash transfer programmes could also be an important determinant
of informal labour supply and could discourage work more broadly (Banerjee et al. 2017). Evidence from the
Bolsa Família in Brazil (De Brauw et al. 2015), Plan de Atención Nacional a la Emergencia Social in Uruguay
(Bergolo and Cruces 2021), and the Universal Child Allowance in Argentina (Garganta and Gasparini 2015)
suggest that both effects – higher informality and non-employment – are present in the data. For example,
Bergolo and Cruces (2021) find a reduction of formal employment in eligible households of around 8
percentage points, which are equally distributed between informal employment and non-employment.
More recently, however, Leite Mariante (2025) shows that an exogenous increase in Brazil’s Bolsa Família
had no effect on men, and increased women’s formal employment by 7.4% over two years. This effect is
driven by mothers, for whom the transfer relaxes childcare constraints.
6
Azuara and Marinescu (2013) also study the Seguro Popular programme and find a very small effect of SP on
informality among unskilled workers (0.9 percentage points for a baseline informality rate of 60%), and no effect
on the overall sample. The main difference between the two studies is that Bosch and Campos-Vazquez (2014)
use administrative data, which substantially reduces measurement error.
14
Informality
These results refer to the transfer’s direct effects on individuals’ labour supply decisions. However, some
of these programmes are quite large in scale and could have sizable general equilibrium effects in local
economies. Indeed, Gerard et al. (2024) show in recent work that Bolsa Família (PBF) led to an increase in
local formal employment in Brazil. They exploit a large increase in the number of PBF beneficiaries in 2009,
as well as a change in the methodology used to allocate slots across municipalities. Their main result
indicates that municipalities more positively affected by the increase in total PBF payments observed an
increase in the number of formal jobs of up to 2% by 2011 (two years after the reform). The effects are
concentrated in low-skill, private sector jobs with no effects on public sector jobs. Their results suggest
that larger PBF benefits had an important local multiplier effect, as they also find a similar increase in
formal employment for workers who were never part of the programme.
Photo
Credit:and
Marcel
Crozet / ILO
The discussion thus far has focused on the static relationship between public
policies
individuals’
informality decisions. However, there can be important dynamic implications as well, especially when
one considers the effects of social security systems that have some form of non-contributory safety
net that provides a minimum benefit to the elderly. These non-contributory benefits can represent a tax
on individuals who contribute, as they are typically decreasing in one’s contributions, and could hence
discourage formal work, especially for low skill workers. Joubert (2015) investigates the importance
of these forces in a life-cycle, discrete choice model that captures household’s labour supply choice
between formal and informal employment, and saving decisions under the rules of Chile’s pension system.
The results from counterfactual analysis using the estimated model show increasing the mandatory
contribution rate by 5 percentage points increases informality by 12.5% and 9.3% for men and women,
respectively.7
Finamor (2024) extends the existing literature by developing a life-cycle model of labour supply, formal/
informal employment, and savings with search frictions. An important implication of his analysis is that
workers have a high willingness to pay (WTP) for a formal job: on average, an informal employee would
forgo 18.7% of their earnings to have their job “formalised”. His decomposition exercise further shows
that 62% of this value is due to higher stability and better job search prospects, while 38% is due to the
insurance package offered by formal jobs. This estimated value of a formal job is of similar magnitude of
that obtained by Samaniego de la Parra and Sharma (2025): they estimate this value to between 14% and
20% of the median monthly wage in Mexico.
3.3.2
The Role of Human Capital
As discussed in Section 3, a well-established fact about informality is that it is (sometimes sharply)
decreasing with individuals’ schooling levels. This fact could thus suggest that changes in the composition
of the labour force toward a higher share of more-educated workers could be an important force to reduce
informality rates. This seems consistent with broad trends observed in many Latin American countries,
which have recently shown sustained reductions in informality among employees, without experiencing
major changes in labour regulations, minimum wages, or enforcement.
A recent study by Haanwinckel and Soares (2021) investigates to what extent the changes in composition
of the labour force could explain the observed strong reduction in informality levels observed in Brazil in
the 2000s. For this, the authors develop a model with two types of workers—skilled and unskilled—and
a large number of firms, which differ in productivity but are not intrinsically connected to any sector.
Decisions related to formality status are the result of a labour market equilibrium where each agent is
choosing optimally. The model incorporates many features of Brazilian labour regulation: payroll taxes,
mandated benefits, and the minimum wage. Finally, it also includes an informality penalty that increases
with firm size to account for the risk of being caught by labour inspectors and for the eventual punishment.
The model is able to reproduce several patterns in the Brazilian labour market, especially those related to
labour informality, even though it does not impose structural differences across sectors.
7
Finamor (2024) also finds that higher contribution rates or higher minimum pension value lead to higher
informality.
15
Informality
Their key result is that increased schooling is the most important factor in explaining the decline in informality
observed in Brazil between 2003 and 2012. If the schooling composition of the labour force had remained
the same as in 2003, but all other factors had changed according to what was observed during the period,
there would have been an increase in the informality rate instead of a large decrease. In the model, increased
schooling alone can generate a large decline in informality rates, suggesting that education is a key determinant
of formalisation. Two main channels explain this result. First, an expansion in schooling levels leads to higher
low-skill wages due to scarcity and increased productivity of these workers, which results in higher wages in the
informal sector. Second, it stimulates increases in firm size. As a result of the increased incentive to grow, formal
firms hire more workers and, simultaneously, firms operating at the margin of informality find it profitable to
move into the formal sector (since it is difficult to hide in the informal sector if a firm becomes too large).
3.3.3
Photo Credit: Marcel Crozet / ILO
Fertility and Informality
As discussed in Section 2, women are over-represented in informal jobs. Even though formal contracts
in developing countries are more stable and on average offer higher wages, they are typically very rigid in
terms of working hours and work arrangements – e.g. there are limited part-time opportunities. Given that
women are disproportionally burdened with household and childcare responsibilities, informal jobs may be an
attractive option because of the higher flexibility they offer (Berniell et al. 2021). Moreover, informal jobs are
also less spatially concentrated than formal ones and often allow low-skill workers to work from home, thus
being associated to substantially lower commuting times (Zarate 2024).
Berniell et al. (2021) use Chilean data to show that motherhood explains a large part of the gender gap in
informality rates observed in Chile. Similarly to the child penalty literature that focuses on developed economies
(e.g. Kleven et al. 2019), the authors show that the birth of the first child has substantial and long-lasting
effects on labour market outcomes of mothers, while fathers remain unaffected. In particular, they show that
the fall in female employment is explained by a reduction in formal employment, with a 38% increase in the
informality rate among women and no effects on men. These results are corroborated by Leyva (2025), who
analyses the impacts of childbirth and the loss of childcare support on women’s employment outcomes in
Mexico. In both events, mothers are more likely to transition to informal employment and to reduce hours
worked within the informal sector.
Given these results, there remains the question of whether having access to informal jobs makes women better
or worse off in the short- and long-run relative to a counterfactual scenario with, say, stronger enforcement and
lower informality. On the one hand, informal jobs could make it less costly for women to remain attached to the
labour market after having children, thus improving employment outcomes in the short run. On the other hand, if
long spells in the informal sector make it harder to transit back to the formal sector, and if the latter offers higher
life-cycle wage gains, then taking up informal jobs would lead to long-run losses for women. Thus, women might
face an intertemporal trade-off associated to taking up informal jobs after having children.
3.4 Migration and the Informal Sector
The analysis of the relationship between migration – in particular rural-urban migration – and informality
dates back to the seminal works of Harris and Todaro (1970) and Fields (1975). The “Harris-Todaro-Fields”
framework predicts that immigration has negative labour market effects in urban destinations, as migrants
join the pool of unemployed or informal workers. Albeit highly stylised this framework remains very influential,
no less because its predictions have been confirmed by empirical evidence on the short-run effects of ruralurban migration. This literature estimates year-on-year effects of inflows of rural migrants on urban labour
markets, showing that greater immigration leads to employment losses in the formal sector with limited
effects on wages, while negative effects on informal sector wages are large but with lower employment losses
(El Badaoui et al. 2017, Kleemans and Magruder 2017, Corbi et al. 2024). The interpretation of these results
is best summarised by Kleemans and Magruder (2017): “…we propose that this result may be understood as
another consequence of the two-sector labour market with a wage floor in the formal sector where the lessskilled group faces chances of disemployment or employment in the informal sector”.
16
Informality
These predictions are also confirmed by a recent literature that analyses the short-run economic effects of
refugees on labour market outcomes of receiving developing countries. Altındağ et al. (2020), for example,
show that Turkish firms located in regions that receive Syrian refugees benefit from these inflows, but these
positive effects are concentrated in the informal economy. More recently, Delgado-Prieto (2024) shows
that the arrival of Venezuelan refugees in Colombia leads to a strong decline in wages in the informal
sector but with limited decline in employment, while in the formal sector there are no effects on wages but
stronger negative effects on employment. Thus, these results are very much in line with the rural-urban
migration literature, in particular with the results in Kleemans and Magruder (2017) and Corbi et al. (2024).8
More recently, Imbert and Ulyssea (2025) investigate the long-run economic effects of rural-urban migration
on local urban economies in Brazil. In sharp contrast with the previous literature, they show that droughtPhoto
Credit: the
Marcel
Crozet
induced immigration reduces informality, has no effect on unemployment, and
increases
number
of / ILO
formal firms and jobs over a decade. Although these results are seemingly at odds with predictions from
the “Harris-Todaro-Fields” framework, the authors show that these effects are weaker in municipalities
where formal sector wages exhibited greater downward nominal wage rigidity (DNWR) at baseline. In the
short run, when wage rigidity is strongest, they obtain the same results as in the previous literature – i.e.
cities that receive more rural migrants experience an increase in informality. These results highlight the
contrast between the short- and long-run labour market effects of migration. Moreover, their reducedform evidence, combined with the model-based counterfactuals, consistently shows that sluggish wage
adjustment in the formal sector can explain these differences between short- and long-run effects.
3.5 Determinants of Slum Formation and Growth
The earliest literature on slums emphasised the role of rapid rural-to-urban migration (Harris and Todaro
1970), characterising slums as a transitory phenomenon typical of economies undergoing structural
transformation (Frankenhoff 1967). Monge Naranjo et al. (2024) provide a more recent take on slum
formation and structural transformation through the lens of a dynamic macro model.
Subsequent research on the role of slums in urban development has highlighted the duality of housing
markets, formal and informal. One strand of literature embeds residents’ choices of whether to squat
(subject to eviction risk) or rent in the formal market into the classic monocentric city model (Alonso 1964,
Mills 1967, Muth 1969). For example, in Brueckner and Selod (2009), squatters and formal households
compete for land and there is no eviction in equilibrium. Squatting emerges as an equilibrium outcome
when regulation (e.g. minimum lot sizes) forces the poor out of the formal market (Jimenez 1984) or when
formal property titles are costly to obtain (Selod and Tobin 2018).
More recently, Alves (2021) embeds the choice of formal versus informal land tenure in a system-of-cities
model and estimates separate housing elasticities for the formal and informal sectors in Brazil, showing
that faster rent growth in the formal sector forces low-income migrants into slums. In Cavalcanti et al.’s
(2019)’s single-city model, slum residents trade off foregone labour income (as they need to be at home
to protect it from eviction) and the cost of complying with regulation in the formal market.
Henderson et al. (2020) focus on supply-side dynamics. They theoretically and empirically analyse
the dynamics of land use in Nairobi, Kenya, documenting transitions of slums to high-rise formal
neighbourhoods. The model characterises the timing of these transitions as a function of the evolution
of formal land values. The authors highlight the role of spatially heterogeneous formalisation costs in
delaying the redevelopment of slums, resulting in potential land misallocation.
Beyond these contributions, several empirical papers have examined other determinants of slum formation in
cities, including historical land market institutions and legal origin (Harari and Wong 2024b, Fredriksson et al.
2023), strategic behaviour of mayors in withholding public goods provision (Feler and Henderson 2011), and
rapid immigration (Gonzalez Navarro and Undurraga 2023).
8
Corbi et al. (2024) add non-wage benefits in the formal sector as an additional adjustment margin, which was previously
unexplored in the literature and can be an important one, especially in the presence of binding minimum wages.
17
Informality
4
Consequences of (In)Formality
4.1 The Consequences for Firms
The papers that seek to identify the effect of formality on firms’ performance typically estimate a regression
of the outcome of choice (profits, for example) onto a dummy for whether the firm is formal or not, and
additional covariates that capture firms’ (and owners’) observable characteristics. The identification problem
then comes from the self-section of firms into formality, which is expected to be positive – i.e. better firms/
entrepreneurs self-select into being formal. This decision is likely to be influenced by elements that are
Photo
Credit:
Marcel
Crozet / ILO
unobservable to the econometrician, such as firm-level demand or productivity
shocks
and
unobserved,
time-varying entrepreneurial quality. To overcome this identification problem, the literature has largely
resorted to experimental or quasi-experimental variation in access to policies and interventions that change
the costs and benefits of formalisation, such as those discussed in Section 4.1, to construct instruments for
the endogenous regressor of interest, i.e. the formalisation dummy.
The results in the literature generally indicate that formalisation has no statistically significant effects on
different measures of firm performance, such as sales, profits and number of employees (e.g. Rocha et al.
2018, Benhassine et al. 2018). Even when the study does find a positive average effect of formalisation on
profits, as in De Mel et al. (2013), this seems to be driven by few firms experiencing substantial growth. This
lack of effect is consistent with the argument that the perceived benefits of formalisation are very low for
most small-scale entrepreneurs (Bruhn and McKenzie 2014). It might be the case that the positive effects
of formality take a long time to appear, while most of these studies follow these firms for up to three years.
Even if this is the case, these are not very encouraging results, as the costs of formality kick in immediately
upon formalisation (such as tax payments), while the benefits would take much longer, if at all.
Perhaps more revealing, even when firms do formalise as a result of the incentives provided, they do not
seem to change any meaningful behaviour. De Mel et al. (2013) show that formalisation does not increase
tax payments, the likelihood of holding a business bank account nor of applying for a business or personal
loan. The only dimension of such intermediary outcomes affected by formalisation is the probability of
keeping a receipt book. These results therefore challenge the idea that formalisation per se could have an
important causal effect on firms’ performance.
4.2 How Taxation and Redistribution are Impacted by the Informal
Sector
The existence of large informal sectors in low- and middle-income countries could potentially change the
redistributive properties of taxation. This is particularly important for consumption taxes, on which these countries
rely to raise a large share of their revenues. Consumption purchased from informal retailers is, by definition, untaxed.
If the budget share that households spend in the informal sector varies systematically with income, the presence of
informal sectors will change the incidence of consumption taxes. Informal consumption patterns could therefore
also affect the desirability of different consumption tax policies. In particular, if much of consumption (especially
of poorer households) is outside the formal sector, then this could, for example, substantially reduce the motivation
for taxing necessities (food products in particular) at a reduced rate compared to other products.
Bachas et al. (2024) investigate the patterns of informal consumption and their implications for tax policy in 32
developing countries of varying levels of economic development (from Burundi to Chile). Informal sector purchases
are by definition hard to observe and link to consumers’ incomes. To overcome this challenge, they use the places
of purchase reported by households in expenditure surveys to proxy for the share of consumption in the informal
sector. Building on evidence from retail censuses and existing literature, they assign each place of purchase to
the informal or formal sector, based on the idea that large modern retailers are much more likely to remit taxes
than smaller traditional ones (Lagakos 2016). They assume, for example, that consumption from home production,
markets and street stalls is informal, whilst consumption from supermarkets is formal.
18
Informality
The key descriptive result of their paper is the existence of a downward-sloping Informality Engel Curve
(IEC): they find that, in all countries, the informal budget share declines steeply with household income.
Figure 4 shows this in Rwanda and Mexico as an example. In Rwanda, the informal budget share falls from
90% for the poorest decile of households to 70% for the richest decile. In Mexico, it falls from 55% to 25%.
The shape of the informality Engel curves implies that informal consumption patterns make consumption
taxes progressive. Simply setting a uniform tax rate on all goods would lead to the richest quintile paying
twice as much in taxes as the poorest quintile in the average (Figure 5, red line). As a comparison, Figure
5 plots the taxed budget share obtained under the ‘naïve’ assumption that governments can tax all
expenditures but choose to exempt food from taxation (green line). Comparing the green and red lines, one
can see that the de facto exemption of the informal sector from taxation is clearly more progressive than
Photo Credit: dividend’
Marcel Crozet
this naïve scenario. Results country-by-country in the paper show that this ‘progressivity
from / ILO
exempting the informal sector is largest in the poorest countries. The small size of the formal sector in
these countries, together with the downward-sloping informality Engel curves, makes formal transactions
a particularly good ‘tag’ for household income. Finally, once the informal sector is taken into account, we
see that exempting food items from taxation only slightly increases progressivity (orange line). This is
because the formal food Engel curve actually has a small but positive slope in the poorest countries.
Figure 4:
Informality Engel curves in Rwanda and Mexico
80
60
0
20
40
Informal Budget Share
60
40
0
20
Informal Budget Share
80
100
(b) Mexico
100
(a) Rwanda
6
7
8
7
Log Expenditure per Person, Contant 2010 USD
8
9
Log Expenditure per Person, Contant 2010 USD
Notes: Figures reproduced from Bachas et al (2021). These panels show the local polynomial fit of the informality
Engel curves in Rwanda (Panel A) and Mexico (Panel B). Per person total expenditure on the horizontal axis
is measured in log. Informal budget share is on the vertical axis. The shaded area around the polynomial fit
corresponds to the 95% confidence interval. The solid grey line corresponds to the median of each country’s
expenditure distribution, while the dotted lines correspond to the 5th and 95th percentiles.
The authors then use a simple model to study optimal consumption tax policy in the presence of an informal sector, and
calibrate it using their data. The existence of the informal sector affects both the equity characteristics of consumption
taxes (via the shape of the Informality Engel Curves) and their efficiency: informality increases the efficiency cost of
taxing consumption because households can switch to informal varieties of products when taxes on formal ones
increase. They find that, in some of the poorest countries, subsidising food relative to non-food is simply not optimal
once the informal sector is taken into account. Since, in these countries, poor households consume most of their food
from the informal sector, the subsidy ends up either redistributing very little or benefiting mostly richer households.
Overall, the evidence indicates that the presence of large informal sectors in developing countries make consumption
taxes progressive. These results caution that any benefits from reducing the informal sector’s size should be weighed
against potential equity costs. These findings more generally suggest that informality may affect the distributional
consequences of tax policy in developing countries in subtle ways, an important avenue for future research.
19
Informality
Taxed budget shares, average across all countries
.16
Figure 5:
Uniform rate, only formal taxed
.1
.12
Food exempt, only formal taxed
.08
Photo Credit: Marcel Crozet / ILO
.04
.06
Taxed Budget Share
.14
Food exempt, formal & informal taxed
1
2
3
4
5
6
7
8
9
10
Decile of Expenditure Distribution
4.3 Aggregate Consequences of the Informal Sector
Informality is an endogenous outcome as much as the unemployment rate or wages observed in the
economy. As such, the aggregate effects of having lower or higher levels of informality are ultimately
determined by the means used to achieve a lower level of informality. Both the (more extensive) macro
literature and the recent structural literature have approached this question using calibrated/estimated
models and relying on counterfactual exercises relative to specific policy experiments. In what follows
we discuss the main results of the literature by different types of aggregate outcomes in counterfactual
exercises that emphasise specific policy experiments.
4.3.1
Human Capital
Two recent papers (Bobba et al. 2021, Bobba et al. 2022) investigate the negative relationship between
informality and the stock of human capital in the economy, considering both investments prior to labour
market entry and on-the-job human capital accumulation. The broad idea behind both papers is that
informality can work as a “tax” on human capital in developing countries. The authors consider a class of
equilibrium search and matching models of the labour market aimed at capturing two empirical regularities
that are hard to explain in models where the market is competitive or where there is segmentation between
the formal and the informal sector: (i) individual workers transition frequently between formal and informal
jobs; and (ii) conditional on workers’ schooling level, formal and informal earnings distributions overlap.
The framework in Bobba et al. (2022) allows for workers to decide on their schooling level prior to entering
the labour market. The education decision depends on the present discounted value of participating in a
schooling-specific labour market with returns resulting from all the factors discussed above. A key finding
is that informality depresses the returns to schooling. Since workers anticipate this, they will invest less
in their education, and therefore the proportion of workers acquiring a given level of schooling will fall.
The mechanism is as follows: the institutions that generate informality tax high-productivity formal jobs
and increase the relative profitability of informal self-employment and of low-productivity informal salaried
jobs. These taxes and subsidies differ across schooling levels, hurting relatively more those workers with
more schooling. Precisely because those workers are more productive, it is harder for firms to offer them
informal jobs (since expected penalties are higher). In addition, because some benefits are pooled, when
workers with more schooling are formally employed, they subsidise those with less schooling.
20
Informality
The parameters of the model are estimated using data from Mexico, a country where more than half of the labour
force is informally employed.9 Two sets of counterfactual experiments based on the estimated model quantify the
effects of informality on schooling. First, eliminating informal jobs enhances labour market returns to schooling
and increases schooling investments by 10% but at the cost of decreasing welfare for both workers (5%) and firms
(28%). This trade-off originates from the fact that taxes and subsidies operate through the labour market and
are associated with the formality status of the job. Second, the proportion of individuals who acquire the higher
schooling level decreases monotonically with the payroll tax rate – as shown in panel a of Figure 6. Interestingly,
these changes in schooling are paired with an almost constant informality rate (panel b). The phenomenon is
explained by major composition effects resulting from the progressive features of the contributory social security
benefits: as the tax rate increases, a proportionally larger benefit is available to lower earnings individuals.
The effect of changes in the payroll tax rate on schooling and informality
.2
.25
.3
.3
.35
.4
.4
.45
.5
Figure 6:
0
.2
t
Low Ability
.4
0
.6
.2
Low Schooling
High Ability
t
.4
.6
High Schooling
Overall
Note: Outcomes from policy experiments that change the social security contribution rate from 0 to 0.66. The baseline
contribution rate is represented by the vertical lines in the figure. Informality is the proportion of informal
employees and self-employed in the population. Schooling is the proportion of individuals who complete
secondary education by ability type. Source: Bobba, Flabbi and Levy (2022).
Bobba et al. (2021) consider a similar framework where workers are homogenous before entering the labour
market and are allowed to accumulate human capital while on-the-job. The human capital evolution while
participating in the labour market captures the additional productivity that may be acquired on the job. The
authors allow this dynamic to depend on the formality status of a job. While off-the-job and searching (either
as unemployed or as self-employed), individuals may even lose previously accumulated knowledge, leading to
a depreciation of human capital. Estimation results show that human capital accumulation on the job occurs
more rapidly when workers are formally employed. For first entrants in the labour market, it takes on average 1.4
years to start upgrading their human capital if they work formally but it takes 40% longer to do so if they work
informally. Human capital upgrading is harder the higher the stock of skills already acquired on the job but, at any
human capital level, the probability of upgrading remains higher if working formally. Policy experiments reveal
that on-the-job human capital accumulation magnifies the negative impact on productivity of labour market
institutions that give raise to informality. For example, the increase in Seguro Popular benefits (see Section 4.3)
over the course of 10 years is associated with a drop in aggregate human capital of about 5 percentage points.
4.3.2
Labour Market Power
The frictions to firm growth in low- and middle-income countries – such as high entry costs, a shortage of skilled
labour, and inadequate infrastructure – can result in the concentration of employment among a few firms. Relatively
large firms may thus internalise their impact on local labour market conditions, thereby strategically reducing
wages to increase profits. In these contexts, informal jobs may represent a valuable outside option for workers.
Workers can easily switch between informal wage work, or self-employment, and formal wage work within a local
labour market, and thus they can opt for informal jobs when posted wages in the formal sector are too low.
9
The authors focus on the decision to acquire three more years of education (9 versus 12) for male private-sector
workers aged 25 to 55 who reside in urban areas.
21
Informality
A recent working paper by Amodio et al. (2024) formalises this idea using Peruvian data and an equilibrium
model that features firms’ oligopsony power that varies across local labour markets as well as heterogenous
workers’ sorting across wage work and self-employment based on earnings. In the model, informal selfemployment plays a dual role in the presence of labour market power. It can shield workers from the wagesetting power of firms by providing a livelihood when wage opportunities are scarce. However, when formal
employment becomes more attractive, lower self-employment rates decrease the labour supply elasticity
in the formal sector possibly having anti-competitive effects in the labour market. This second channel
would make it more difficult for policies that seek to boost formal employment and wages to succeed.
Another key source of labour market power is different preferences of workers over job amenities. If jobs
are differentiated even small firms face upward sloping labour supply curves, which they internalise by
Credit:
Marcel
reducing wages relative to the competitive benchmark. Although not related Photo
to informal
jobs,
two Crozet
recent / ILO
contributions provide direct empirical evidence of this phenomenon in the context of labour markets in
developing countries. Sharma (2023) shows that in Brazil job amenities that are particularly valued by
women also confer their employers with higher monopsony power. This channel explains a non-negligible
fraction (18 percentage points) of the gender wage gap in the textile and clothing manufacturing industry.
Again, in Brazil, Felix (2022) documents that trade liberalisation increased local labour market concentration
by 7%, an effect driven by firm exit and labour reallocation towards exporters that raised wage markdowns.
4.3.3
Productivity, Output and Growth
Reducing Informality via Higher Enforcement
A common feature of most informality models is the presence of a cost of informality that is increasing
in firm size, which is typically measured as number of employees, capital or revenues (e.g. Fortin et al.
1997, De Paula and Scheinkman 2011, Ordonez 2014, Meghir et al. 2015, Ulyssea 2018). Thus, a common
counterfactual experiment found in the literature is to simulate higher enforcement on informal firms by
making this cost function steeper. The basic intuition is that higher enforcement – via intensified inspections,
say – increases the cost of operating as an informal firm (due to a higher probability of detection, for
example), which would lead to a substantial reduction of informality.
The results from different studies indicate that reducing the size of the informal sector by increasing enforcement
could lead to substantial gains in aggregate productivity. As summarised in Ulyssea (2020), different
mechanisms contribute to generating these positive effects. First, there are positive composition effects, as
greater enforcement eliminates many low-productivity (informal) firms, which then frees up resources that are
reallocated to more productive formal firms (e.g. Ulyssea 2010, Charlot et al. 2015, Bosch and Esteban-Pretel
2012, Meghir et al. 2015, Ulyssea 2018). Second, reducing the availability of low-quality informal jobs can make it
easier for workers to find higher quality formal jobs, especially if there are substantial labour market frictions (e.g.
Meghir et al. 2015). Third, because informal firms face higher financial frictions and are more credit constrained,
formalisation can lead to greater capital accumulation (e.g. D’Erasmo and Boedo 2012, Ordonez 2014). Fourth, it
affects occupational choices by discouraging low-skill individuals to self-select into informal entrepreneurship,
therefore increasing labour supply in the formal sector (e.g. Ordonez 2014, Lopez 2017). Fifth, as discussed in
the previous section, there can be higher investments in formal schooling (before entering the labour market)
and on-the-job human capital accumulation (Bobba et al. 2021, 2022).
Despite these positive aggregate effects, higher enforcement can have adverse effects on those directly
affected and potentially on aggregate outcomes as well. In an earlier paper, Boeri and Garibaldi (2005)
argued that large informal sectors are “tolerated” by governments because increasing enforcement
could lead to substantial increases in unemployment. The results in Ulyssea (2010) and Charlot et al.
(2015) are consistent with this conjecture. Using equilibrium matching models calibrated to the Brazilian
economy, they show that greater enforcement substantially reduces informality, but at the cost of
increasing unemployment. More recently, Meghir et al. (2015) and Haanwinckel and Soares (2021) find no
unemployment effects from higher enforcement. Dix-Carneiro et al. (2024) show non-monotonic effects:
stricter enforcement barely changes unemployment, but completely eradicating the informal sector
causes the unemployment rate to increase substantially.
22
Informality
One of the key dimensions to determine the extent of the positive effects on productivity and the
negative effects on unemployment is how much employment reallocation there can be from lowproductivity informal firms to more productive formal firms. A second important, and completely
overlooked, question is how lengthy the transition between steady states is, and therefore how long
this reallocation process can take. This is key for both the political economy of implementing these
measures, but also to determine the welfare costs in the short- and medium-run for those negatively
affected by these policies.
Even though most of the literature focuses on enforcement policies on the extensive margin of informality
(i.e. increasing the costs of informal firms), Ulyssea (2018) shows that increasing enforcement on the
intensive margin can generate very different results. In particular, even though it reduces informality amongst
Photohigher
Credit:enforcement
Marcel Crozet
workers, it can in fact increase the share of informal firms. This occurs because
in / ILO
the intensive margin effectively increases the costs of operating in the formal sector for small formal
firms. Hence, many of these firms choose to enter the informal rather than the formal sector in the new
equilibrium. As a consequence, this policy generates losses for low-productivity formal firms, while highproductivity firms benefit from it in terms of higher life-time profits. The effects on aggregate productivity
are small – an increase of 1.7% – and output decreases by 1.6%, as the reduction in the number of firms
operating in the economy more than compensates the small gains in aggregate productivity.
Finally, Almeida and Carneiro (2009) use micro data to estimate average aggregate effects of enforcement
across municipalities in Brazil. For that, they exploit the fact that enforcement of labour regulation (the
intensive margin of informality) is implemented in a decentralised way and displays a lot of variation
across local economies. They use an IV estimator to show that an increase in the number of inspections
per hundred formal firms leads to small reductions in output and firm size. In a follow-up paper Almeida
and Carneiro (2012) show that more inspections also lead to small negative effects on the share of formal
workers and self-employed and an increase in non-employment.
The results by Ponczek and Ulyssea (2022) mentioned in previous sections directly speak to this discussion
as well. They examine the effects of local economic shocks generated by the unilateral trade liberalisation
in Brazil, and how they varied across regions with weaker and stronger enforcement levels. This unilateral
trade opening essentially represented a negative demand shock to the affected industries in Brazil, which
generated heterogeneous effects across regions where employment was more and less concentrated in
these industries. As mentioned in Section 4.2, the authors show that regions with stricter enforcement
experienced lower informality effects, but greater losses in overall employment and greater reductions in
the number of formal plants. Regions with weaker enforcement had opposite effects and all the effects
are concentrated among low-skill workers. Thus, similarly to Bujanda and de la Parra (2020), these
results indicate that greater enforcement can lead to greater formalisation but with potentially adverse
employment effects. Conversely, the greater flexibility introduced by informality might allow formal firms
and low-skill workers to cope better with adverse labour market shocks (Ponczek and Ulyssea 2022).
Reducing the costs of formality
As discussed in Section 4.1, the literature that uses experimental and non-experimental empirical
designs to estimate the effects of reducing the costs of formality on firms’ decisions to formalise
shows essentially zero or very limited effects. Despite the importance of these results, it can still be
the case that these policies could have important aggregate effects. Indeed, one of the regularities
that emerges from counterfactual exercises in different papers is that reducing fixed entry costs into
the formal sector can produce positive and sizeable aggregate effects (Ulyssea 2010, D’Erasmo and
Boedo 2012, Charlot et al. 2015, Ulyssea 2018). For example, Ulyssea (2018) shows that reducing entry
costs into the formal sector leads to higher competition, aggregate production in the formal sector
and high-skill wages. However, because it is mostly low-productivity firms that formalise, there is
a negative composition effect that leads to a decrease in aggregate productivity. Total output still
increases because there is a substantial increase in the number of firms in the economy led by an
increase in the number of formal firms (Ulyssea (2010) and Charlot et al. (2015) find similar positive
aggregate effects, including lower unemployment).
23
Informality
If, however, there are important frictions in the formal sector as well – such as financial frictions – then these
positive effects can be limited. That is the case in Lopez-Martin (2018), who finds limited aggregate effects from
reducing entry costs in Mexico and Egypt, of at most 0.5 and 0.7 percentage points in aggregate productivity
and output per capita, respectively. It is only when financial frictions in the formal sector (modelled as the ability
of firms to collateralise their assets) are relaxed that the economies observe substantial gains in aggregate
productivity, output and welfare (D’Erasmo 2016 finds similar results).
As also discussed in Section 4.1, the empirical evidence suggests that reducing the tax burden can induce some
formalisation of informal firms, but even in this case the effects are not very large. The counterfactual results
from macro and structural models corroborate that: reductions in payroll tax seem to generate some positive
but limited formalisation effects (e.g. D’Erasmo and Boedo 2012, Haanwinckel and Soares 2021). Ulyssea
(2018) shows that these effects are stronger on labour informality (via the intensive margin) and weaker on firm
informality. The overall effects on aggregate productivity and output are also positive but quite limited (Ulyssea
2010, D’Erasmo and Boedo 2012, Haanwinckel and Soares 2021, Ulyssea 2018).
4.3.4
Housing Formalisation
A recent line of research analyses housing informality using rich equilibrium models of cities (à la Ahlfeldt et al.
2015), which emphasise the linkages across neighbourhoods via individuals’ decisions about where to live and
work, and externalities such as congestion and agglomeration forces. These models typically feature formalisation
costs to convert land from informal to formal, which reflect the cost of regulation in the formal market but also land
assembly and relocation costs associated with clearing slums. One of the core themes analysed using this class
of models is the equity-efficiency tradeoff involved in preserving slums at the expense of formal development.
Formal neighborhoods yield higher land values, taller buildings, and enhanced agglomeration benefits. On the
other hand, low-height informal housing provides shelter for many residents, enabling them to live in locations
with high access to labour markets.
In Gechter and Tsivanidis (2023), the demand side features heterogeneous households, low- and high-skill, choosing
where to live, where to work, and whether to consume formal or informal housing, subject to eviction risk. On the
supply side, developers optimally choose whether to provide formal housing, subject to formalisation costs, or
informal housing, subject to a technological height constraint. They employ this framework to characterise the local
and spillover effects of building high-rises in previously unbuildable areas of Mumbai, India. Their counterfactual
analysis shows that, in aggregate, building high-rises benefits formal residents and firms, but induces gentrification
of nearby informal settlements inflicting large costs on forcibly displaced slum dwellers. They also consider alternate
compensation schemes that preserve most of the aggregate gain but improve equity. Harari and Wong (2024) employ
a similar model to shed light on these tradeoffs leveraging policy variation from a slum upgrading scheme (see below).
Henderson et al. (2020) calculate that converting Nairobi’s largest slum into a formal area would yield large gains in
an amount equivalent to thirty times the typical annual slum rent payments for each displaced slum household. They
point to slum landlords benefiting from the status quo as a key obstacle to reallocating land toward formal use.
4.4 Slums and Social Mobility
One of the open questions is whether slum residents are “stuck” in a poverty trap - characterised by low human
capital accumulation and limited mobility (Marx et al. 2013) – or whether slums provide access to urban
economic opportunities that would otherwise be out of reach due to high formal housing costs (Glaeser 2011).
We lack the individual-level data required to shed light on these two competing narratives in a definitive way, but the
answer is likely to be context-specific. Several studies suggest that low-quality housing in slums adversely affects
health and well-being (Cattaneo et al. 2009, Galiani et al. 2017). However, the evidence on mobility is mixed: Wong
(2019) describes patterns suggestive of educational and mobility gains across generations of slum dwellers in
Jakarta, Indonesia. In a survey of slum dwellers in Delhi, India, Banerjee et al. (2012) find that a significant portion
are recent migrants, suggesting considerable churn, which contrasts with the view of slums as static “traps”.
However, Krishna (2013) finds that a majority of slum dwellers in Bangalore, India, have lived in slums for many
generations and exhibit little mobility.
24
Informality
Evidence on the link between housing informality and labour market outcomes is also mixed. On one hand,
tenure insecurity has been found to reduce labour supply: Field (2007) finds that a titling programme in Lima,
Peru, led to increases in labour supply, as residents no longer had to stay home to guard against evictions or
land grabs. This is in line with findings by Franklin (2020) for Cape Town, South Africa. At the same time, the
literature on relocations (see below, e.g. Barnhardt et al. 2017) suggests, by revealed preferences, that slums
may provide favourable access to jobs. Notably, these findings mainly come from studies where slum residents
were originally in relatively central locations. While many slums are in accessible locations (Harari 2024), others
are in peripheral areas (Galiani et al. 2017), so the degree of labour market access likely varies considerably.
4.5 Public Polices towards Slums
Many developing country governments have a stated objective to become “slum free,” in line with Sustainable
Development Goal 11.1. There are several common policy approaches.
Titling programmes have been introduced as a market-based solution to secure property rights. Inspired by the
influential work of De Soto (2000), titling aims to incentivise private housing investments and facilitate access to
credit by enabling property to be used as collateral.
The household-level impacts of titling programmes in the literature are generally positive, yet not transformative.
Leveraging the phased implementation of a large-scale land titling programme in Lima, Peru, Field (2005) finds
that titling increases private investment and labour supply, but there is limited evidence of credit effects (Field
and Torero 2006). Galiani and Schargrodsky (2010) similarly find improvements in investments and education in
Buenos Aires, Argentina.
At the city level, implementing titling at scale is challenging because land registration is often complicated by
fees, land disputes, moral hazard, and backlogged courts. Successful titling requires establishing a bundle of
institutions, including mapping and a registration system. Recent pathways to enhance property rights institutions
include technological advances in land mapping (e.g. LIDAR surveying) and leveraging the role of local leaders
(Manara and Regan 2022).
Another common government intervention involves the relocation of slum residents. While evictions without
compensation were common in the 1950s, most subsequent government-sponsored slum clearance schemes
involve some form of compensation, sometimes in the form of subsidised formal housing. Relocations remain
politically charged, as compensation is often thought to be below market value. Due to budget constraints, public
housing is often located in peripheral neighbourhoods with low market access, where land is inexpensive. A priori,
the effects on residents are ambiguous: beneficiaries experience improvements in tenure security, housing quality,
and wealth effects from becoming homeowners, but relocating to a different neighbourhood can be disruptive,
leading to a loss of networks and reduced access to jobs and amenities.
A growing literature documents the effects of relocation schemes. A common lesson is that displacement can be
very harmful for relocated residents, and labour market access in destination locations is crucial to determining
the net effects.
Barnhardt et al. (2017) track beneficiaries of a housing lottery in Ahmedabad, India, offering slum dwellers from
relatively central neighbourhoods subsidised formal housing in the periphery. Fourteen years later, they find low
take-up and significant programme exit among lottery winners, as well as losses from network disruption.
The importance of job access in the destination locations is exemplified by the mixed evidence on a large
public housing scheme implemented in South Africa over the past two decades. Picarelli (2019) studies the
nationwide programme, exploiting a cutoff in the selection rule, and shows that rehousing to areas with low
labour market access negatively affects household outcomes. Franklin (2020) focuses on beneficiaries who
were rehoused nearby in Cape Town. Leveraging quasi-experimental variation in programme selection, he
finds positive labour supply effects associated with housing improvements. Similarly, Kumar (2021) examines
a lottery providing housing on-site in Mumbai, India, and finds positive impacts on housing quality, income,
education, and employment rates.
25
Informality
In contrast with some of the previous studies, Franklin (2024) and Agness and Getahun (2024) find positive
effects from a large-scale subsidised housing lottery in Addis Ababa, Ethiopia, providing improved housing
in the outskirts of the city. Franklin (2024) finds that nearly half of lottery winners chose to leave central
slums for condo housing in the periphery. While amenities and social were initially worse, the destination
neighbourhoods improve over time and see business formation, with no negative impacts on labour
market outcomes, education, or consumption. Agness and Getahun (2024) focus on children’s human
capital outcomes and find large gains in school attendance and completion rates, as well as positive
effects on formal employment among adults in the medium run.
Belchior et al. (2024) analyse the effects of the Minha Casa Minha Vida housing lottery programme in Brazil
and document similar take up rates as those in Franklin (2024) among lottery winners who are allocated
to houses in the outskirts of Rio de Janeiro. Consistent with most of the previous literature, they show that
the programme has no significant effects on formal employment, wages, or job quality within the six years
following the lottery for the overall population of beneficiaries. For low socioeconomic status beneficiaries,
however, receiving a house results in increased formal employment and a substantial reduction in welfare
dependency. These positive impacts on formal employment are concentrated among individuals randomly
assigned to the neighbourhood with the highest access to formal jobs, despite it having worse social
indicators. Their bounding exercise indicate that differences in labour market access across neighborhoods
explain 83-94% of the variation in neighbourhood-specific outcomes for disadvantaged beneficiaries.
Rojas-Ampuero and Carrera (2023) innovate on the prior literature by constructing a unique householdlevel dataset with which they track programme beneficiaries and their children. They provide evidence on
intergenerational effects of a large-scale slum clearance and relocation programme in Santiago, Chile. Using a
propensity score approach, they compare residents of slums slated for clearance and relocation to peripheral
areas versus those chosen for formalisation on-site. They find marked negative effects on displaced children in
terms of earnings and health, mediated by lower education and worse labour market access in the destinations.
Slum upgrading and “sites and services” are other common policy approaches that were historically popularised
as alternatives to slum clearance. Slum upgrading consists of providing basic public goods (such as road paving
or drainage) on-site in slums, without relocating residents. To encourage private investments, basic upgrades
are often bundled with informal guarantees that residents will not be evicted or occupancy certificates.
Proponents of slum upgrading emphasise that these programmes can provide immediate relief to the living
conditions of residents and alleviate negative externalities at a fraction of the cost of public housing and without
inducing displacement. However, a concern is that the improvements may make slums more crowded (Fox 2014)
and ultimately prolong their persistence, leading to potential land misallocation once the city starts formalising.
Harari and Wong (2024) study the long-term effects of the world’s largest slum upgrading programme,
implemented in Jakarta, Indonesia, in the late 1960s. Leveraging localised comparisons between historical slums
that received upgrades and nearby ones that did not, they find that, decades later, upgraded neighbourhoods are
more likely to have remained slums, with lower land values and building heights. These impacts are concentrated
in central areas where formal land values are high. Using a spatial equilibrium model, they highlight equityefficiency trade-offs: in central areas, slum upgrading and the ensuing persistence of slums is associated with
aggregate losses, while in other parts of the city, the benefits of upgrading outweigh the costs.
Leveraging a unique panel of slums across cities in Chile, Gertler et al. (2024) find that slum upgrading
is associated with better housing and neighbourhood quality and higher socio-economic status among
residents compared to slums subject to relocation programmes.
In contrast with the above approaches, “sites and services” target areas that are not yet settled.
These programmes involve creating planned neighborhoods with regular, spaced-out plots and basic
infrastructure (e.g. roads and pipes) before residents move in. Low-income households receive serviced
land rather than fully built homes and construct their own housing. This approach aims to make housing
more affordable and address some coordination failures associated with unplanned settlements, offering
a cost-effective alternative to public housing.
26
Informality
Michaels et al. (2021) evaluate a large-scale sites and services programme across seven cities in Tanzania using
a spatial discontinuity design. Decades later, they find that planned neighbourhoods are more orderly and feature
better-quality housing compared to neighbourhoods that underwent slum upgrading or no intervention. However,
the effectiveness of de novo planning in alleviating slum conditions may be limited, as these neighbourhoods
were ultimately settled by middle-income, rather than low-income households. This was partly due to plot sizes
being too large to cater to the low-income market (Henderson et al. 2024). More generally, this policy approach
appears viable for cities at earlier stages of development that still have land availability in peripheral areas.
5
Final Remarks and Key Unanswered Questions on the
Informal Sector
This VoxDevLit review attempts to provide a comprehensive coverage of the extensive economic literature that
analyses the causes and consequences of informality in developing countries. This literature is not only extensive
but also quite broad in terms of methodologies used, ranging from well-identified empirical studies (both
experimental and non-experimental) to equilibrium macro models and structural empirical models. This seems
not only natural but necessary for a more complete understanding of this important economic phenomenon.
We believe that this review piece shows that the literature has made substantial progress in understanding
the main determinants of firms’ choices regarding informality, both theoretically and empirically. Indeed, the
literature has put a lot more emphasis on the analysis of firms’ than workers’ behaviour. However, even on
firms’ side there remain many important unanswered questions. In particular, the literature has only started
to explore more the dynamics of firms’ decisions regarding the different margins of informality and how they
potentially interact with different frictions that firms face. For example, does informality work as a steppingstone for entrepreneurs with high-growth potential but who might be constrained by, say, credit constraints?
On the workers’ side, the gaps in knowledge are arguably wider. We need a deeper understanding of the
determinants of workers’ choice/allocation between formal and informal jobs, what determines their
permanence and evolution in either, as well as the main tradeoffs they face. As in the case of firms, we do
not know how much informal jobs represent a stepping-stone for younger workers versus the extent to which
there is an “informality trap” that makes future transitions into formal employment very unlikely. Related to this
point, there are still very few studies that investigate the life-cycle dimensions of informality.
Regarding housing informality, there are several avenues for future research. Key open questions remain
about upward and intergenerational mobility, human capital accumulation, and labour market dynamics for
slum residents. Addressing these aspects would also have direct policy implications to help design personbased interventions to complement the prevailing place-based ones.
Advancements in big data sources, such as cell phone records or imagery, offer the potential to generate
richer datasets, which will be useful to capture higher-frequency dynamics of slum evolution and to link people
to places.
An understudied but crucial aspect is the role of the formal housing supply and the “missing market” for lowincome formal housing. The lack of housing finance systems that are accessible to the low-income sector
should also be brought to the attention of policymakers.
Finally, it should be recognised that housing informality is connected with informality in other key markets for
urban residents, including labour, transportation, and credit. Housing, employment, and commuting decisions
are taken jointly. Integrating research on slums with research on urban labour markets, transportation, and
firm location choices will be important to understand the tradeoffs that slum residents face, advance our
understanding of slum economies, and ultimately inform policy.
Finally, on the analysis of the aggregate implications of informality, a very important unexplored dimension is
the transition dynamics between equilibria. This is arguably very important to understand both the political
economy of informality and formalisation policies, but also to provide a more accurate assessment of the
welfare implications of different formalisation policies.
27
Informality
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