Informality: Issue 2

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).

Figure 1: Informality margins and firm size

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 2013-18, 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.

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).

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 wage jobs). However, contrary to developed economies, these higher flows are associated with poor 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.

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).

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) and Marx et al. (2019) employ high-resolution imagery to identify building footprints and roof materials as 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.[1]1. One limitation of using imagery is that it often depends on ground or survey observations for training and rarely provides consistent measurement over time.

References

Allen, J, S Nataraj and T C Schipper (2018), “Strict duality and overlapping productivity distributions between formal and informal firms”, Journal of Development Economics, 135:534–554

Almeida, R and P Carneiro (2012), “Enforcement of labor regulation and informality”, American Economic Journal: Applied Economics, 4: 64-89

Bird, J, and A J Venables (2019), “The efficiency of land-use in a developing city: Traditional vs modern tenure systems in Kampala, Uganda,” CEPR Discussion Paper No. DP13563, Available at SSRN: https://ssrn.com/abstract=3346354.

Bosch, M and J Esteban-Pretel (2012), “Job creation and job destruction in the presence of informal markets”, Journal of Development Economics, 98:270–286

Bosch, M and W F Maloney (2010) “Comparative analysis of labor market dynamics using Markov processes: An application to informality?” Labour Economics, 17:621–631

Brockmeyer, A, F Gerard, G Ulyssea, Linda Wu et al. (2025), “Formal Labor Market Dynamics in Developing Countries”, Working Paper.

Cisneros-Acevedo, C (2022), “Unfolding trade effect in two margins of informality: The Peruvian case”, The World Bank Economic Review, 36: 141–170.

Collin, M (2020), “Tribe or title? The effect of coethnic neighbors on the demand for formal property rights in a Tanzanian slum,” Economic Development and Cultural Change 68(3): 899–938. doi:10.1086/707686.

De Paula, A and J A Scheinkman (2011), “The informal sector: An equilibrium model and some empirical evidence”, Review of Income and Wealth, 57:8–26

Donovan, K., Lu, W. J., & Schoellman, T. (2023). Labor market dynamics and development. The Quarterly Journal of Economics, 138(4), 2287-2325.

Eslava, M., Haltiwanger, J. and Pinzón, Á. (2022), Job Creation in Colombia Versus the USA: ‘Up-or-out
Dynamics’ Meet ‘The Life Cycle of Plants’. Economica, 89: 511-539.

Feler, L., and Henderson, J V. (2011). “Exclusionary policies in urban development: Under-servicing migrant households in Brazilian cities.” Journal of Urban Economics, 69(3): 253–272. doi:10.1016/j.jue.2010.09.006.

Finamor, L. (2024). Labor market informality, risk, and insurance. Mimeo.

Gasparini, L and L Tornarolli (2009), “Labor informality in Latin America and the Caribbean: Patterns and trends from household survey microdata”, Revista Desarrollo y Sociedad, 63:13–80.

Harari, M, and M Wong (2024), “Slum upgrading and long-run urban development: Evidence from Indonesia,” Review of Economic Studies, forthcoming.

Henderson, J V, D Nigmatulina, and S Kriticos (2021), “Measuring urban economic density,” Journal of Urban Economics 125, article 103188. doi:10.1016/j.jue.2019.103188.

Kuffer, M, K Pfeffer, and R Sliuzas (2016), “Slums from space—15 years of slum mapping using remote sensing,” Remote Sensing 8(6), article 455. doi:10.3390/rs8060455.

Lanjouw, J O, and P I Levy (2002), “Untitled: A study of formal and informal property rights in urban Ecuador,” The Economic Journal 112(482): 986–1019. doi:10.1111/1468-0297.00079.

Marx, B, T Stoker, and T Suri (2013), “The economics of slums in the developing world,” Journal of Economic Perspectives 27(4): 187–210. doi:10.1257/jep.27.4.187.

Marx, B, T M Stoker, and T Suri (2019), “There is no free house: Ethnic patronage in a Kenyan slum,” American Economic Journal: Applied Economics 11(4): 36–70. doi:10.1257/app.20160484.

Meghir C, R Narita and J M Robin (2015), “Wages and informality in developing countries”, American Economic Review, 105:1509–1546

Michaels, G, et al. (2021), “Planning ahead for better neighborhoods: Long-run evidence from Tanzania,” Journal of Political Economy 129(7): 2112–2156. doi:10.1086/715940.

Perry, G, W Maloney, O Arias, P Fajnzylber, A Mason and J Saavedra-Chanduvi (2007), “Informality: Exit or exclusion”, The World Bank: Washington, D.C.

Samaniego de la Parra, B., and Fernández Bujanda, L. (2024). Increasing the cost of informal employment: Evidence from Mexico. American Economic Journal: Applied Economics, 16(1), 377-411.

Sarıkaya, Furkan, M. Nazım Tamkoç, and Jesica Torres (2024). “Informality and the Life Cycle of Plants.” Mimeo.

Ulyssea, G (2018), “Firms, informality, and development: Theory and evidence from Brazil”, American Economic Review, 108:2015–47

Ulyssea, G (2020), “Informality: Causes and consequences for development”, Annual Review of Economics, 12:525-546.