metadata: authors: 0: author: Loayza,Norman V.
count: World
docna: 0: docna: Informality in the process of development
            and growth
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Labor Markets,International Trade and Trade Rules,Economic Growth,Employment and Unemployment
abstracts: cdata!: "Informality" is a term used
            to describe the collection of firms, workers, and activities
            that operate outside the legal and regulatory systems. It is
            widespread in the majority of developing countries--in a
            typical developing economy, the informal sector produces
            about 35 percent of gross domestic product and employs 70
            percent of the labor force. This paper studies informality
            in the context of economic development by presenting a model
            and projections that link informality, regulations,
            migration, and economic growth. This analytical framework
            highlights the trade-offs between formality and informality,
            the relationship between the different types of informality,
            and the connection between them and the forces of labor,
            capital, and productivity growth. The paper models the
            behavior of the informal sector based on the following
            fundamental asymmetry: formal firms confront higher labor
            costs while informal firms face higher capital costs and
            lower productivity. Using mandated minimum wages as the
            policy-induced distortion, the model first studies the
            static allocation of formal and informal capital and labor
            in a modern economy. Second, it opens the possibility of
            labor migration from a rudimentary economy with an ample
            supply of labor (rural areas or less advanced neighboring
            countries). Third, the model analyzes the dynamic behavior
            of the formal and informal sectors, considering how they
            affect and are affected by economic growth and labor
            migration. Then, the paper presents projections for the size
            of labor informality, in the modern and rudimentary
            economies, in the next two decades for a large group of
            countries representing all regions of the world. The
            projections are based on the calibration and simulation of
            the model and serve to discuss its usefulness and limitations.
repnme: repnme: Informality in the process of development
            and growth
display_title: Informality in the process of
            development and growth
listing_relative_url: /research/2016/10/26858497/informality-process-development-growth
topicv3: Economic Growth m1326241 471,Informal Sector m1326258 1168,Trade m1326924 698,Informal Employment m1503994 2692,Labor Productivity m1504060 2756
publishtoextweb_dt: 2016-10-12T00:00:00Z
no_of_pages: 87
author_keys: 0: author_key: 97109
model_extractions: WPS7858 Policy Research Working Paper 7858 _Norman V. Loayza_ Development Research Group Macroeconomics and Growth Team October 2016
Policy Research Working Paper 7858

# **Abstract**

“Informality” is a term used to describe the collection of firms, workers, and activities that operate outside the legal and regulatory systems. It is widespread in the majority of developing countries—in a typical developing economy, the informal sector produces about 35 percent of gross domestic product and employs 70 percent of the labor force. This paper studies informality in the context of economic development by presenting a model and projections that link informality, regulations, migration, and economic growth. This analytical framework highlights the trade-offs between formality and informality, the relationship between the different types of informality, and the connection between them and the forces of labor, capital, and productivity growth. The paper models the behavior of the informal sector based on the following fundamental asymmetry: formal firms confront higher labor costs while informal firms face higher capital costs and lower productivity. Using mandated minimum wages as the policy-induced distortion, the model first studies the static allocation of formal and informal capital and labor in a modern economy. Second, it opens the possibility of labor migration from a rudimentary economy with an ample supply of labor (rural areas or less advanced neighboring countries). Third, the model analyzes the dynamic behavior of the formal and informal sectors, considering how they affect and are affected by economic growth and labor migration. Then, the paper presents projections for the size of labor informality, in the modern and rudimentary economies, in the next two decades for a large group of countries representing all regions of the world. The projections are based on the calibration and simulation of the model and serve to discuss its usefulness and limitations.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at nloayza@worldbank.org.

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

Norman V. Loayza<sup></sup>

**JEL:** E26, E24, J46, O17, O11, O15, O40, O47

**Keywords:** Informality, Minimum Wage, Labor Costs, Economic Growth, Migration, Labor Market, Financial Constraints, Productivity

>  This paper was first delivered as _The World Economy Annual Asia Lecture_ , University of Nottingham, Kuala Lumpur, 18<sup>th</sup> of February, 2016. I am grateful to Claudia Meza‐Cuadra for excellent research assistance in all areas of the project, from economic modelling and literature review to data collection and analysis. For insightful comments and suggestions, I thank Ximena Del Carpio, Asli Demirguc‐Kunt, David Greenaway, Mary Hallward‐Driemeier, Carlos Felipe Jaramillo, Mattia Makovec, Chris Milner, Truman Packard, Vivi Alatas, Jamele Rigolini, Luis Servén, and seminar participants at the University of Nottingham and the World Bank. This paper has been partly funded by the World Bank’s Research Support Budget (RSB), under the project “Informality, Migration, and Growth.”  Corresponding author ́s email: nloayza@worldbank.org.
# **1. Introduction**

This paper aims at understanding informal labor, placing it in the process of development, allowing for its heterogeneity, and linking it to migration, modernization, and economic growth. We propose to do this through a theoretical growth model and an international data application. The latter consists of using the model and its calibration to project the behavior of informal labor in the next two decades for a large number of countries around the world.

“Informality” is a term used to describe the collection of firms, workers, and activities that operates outside the legal and regulatory frameworks or outside the modern economy. While informality offers the benefits of avoiding the burden of regulation and taxation, its participants suffer the costs of not having the protection and services that the law and the State can provide. Informality is sometimes the result of agents “exiting” the formal sector as a consequence of cost‐benefit considerations; other times, it is the outcome of agents being “excluded” from formality as this becomes restrictive and the economy segmented.

Informality is a fundamental characteristic of underdevelopment. It is best understood as a complex, multifaceted phenomenon. It is determined by both the inherent characteristics of developing economies (such as low physical and human capital) and by the relationship that the State establishes with private agents (through regulation, monitoring, and the provision of public services). The received literature finds evidence that the relative size of the informal sector declines with overall development, rises with the burden of regulation, and decreases with the strength of enforcement (see Schneider and Enste, 2000; Friedman et al., 2000; and Loayza, Oviedo, and Servén, 2005).

Informality is not only a reflection of underdevelopment; it may also be the source of further economic retardation. It implies misallocation of resources and entails losing the advantages of legality, such as police and judicial protection, access to formal credit institutions, and participation in international markets. Informality can then lead to slow capital accumulation, low economic growth, and sluggish migration to more productive areas. The evidence shows that informal firms tend to be smaller and have lower productivity, and that differences in the size of the informal sector can account for a 2
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its causes and consequences. For example, if labor informality is measured only in connection with firms (or multi‐person enterprises), its largest segment composed of the self‐ and family‐employed will be missing. In turn, confusion on definitions and causes can lead to misguided advice about confronting informality. For instance, if it is perceived as solely the result of weak enforcement, the advice may be to strengthen monitoring and harden penalties against informal firms, which could result in worse problems – unemployment, self‐employment, and further reductions in the size of firms. Likewise, if informality is perceived as purely the result of State regulations, the recommendation may be to lift them, only to realize that the consequent reduction in informality is limited and small.

Informality is a well‐researched topic with contributions from different perspectives. However, there is no encompassing study that endogenizes informal labor alongside worker migration and capital accumulation, considering how they affect and are affected by each other. Moreover, there is no study that allows comparing the expected trajectories of the basic types of informal labor across developing countries. This paper contributes to fill in these gaps. First, it seeks to clarify the definition, causes, and consequences of informality, studying it in the process of development. Allowing for heterogeneity of labor informality – that is, whether linked to firm‐ or self‐employment—is critical for analysis and measurement. To accomplish this objective, we develop a tractable theoretical model that connects informality, government regulations, economic growth, and labor migration. Second, the paper looks to obtain sensible estimates for the evolution of the informal sector in developing countries over the next few decades, linking this evolution to other underlying forces in the economy under various scenarios. For this purpose, we build a secondary database on recent estimates of the informal sector and related variables, and then use it to calibrate and simulate the model for a large selection of countries around the world. We provide a spreadsheet‐based toolkit that contains these simulations and allows researchers to modify parameters, initial conditions, and assumptions to construct their own scenarios.

The rest of the paper proceeds as follows. Section 2 presents a literature review. Section 3 develops the theoretical model. Section 4 describes the data and calibration exercise, and presents and discusses the informality projections. Section 5 offers some concluding remarks.

4
# **2. Literature Review**

The literature on the economics of informality is deep and diverse. It gained momentum in the late 1980s with the publication of _The Other Path_ , where Hernando de Soto and coauthors present the informal sector as the private sector ́s response to an overly regulated economy and an inefficient State. This approach departed from the then prevailing one in which informality was regarded as merely a symptom of underdevelopment rather than the result of misguided policies. This tension between development and policies as determinants and constraints of informality is present, in one way or another, in all modern studies of the subject.

At the risk of oversimplification, the informality literature can be divided in two basic strands, according to the trade‐offs that generate the informal sector. One takes a public finance perspective, emphasizing the trade‐off between taxes and public services: informal firms avoid taxes at the cost of reduced access to public services and being subject to penalties.<sup>1</sup> Another strand takes a labor perspective, focusing on the trade‐off between labor and capital costs: informal firms avoid mandated labor costs (such as minimum wages, benefits, and firing constraints) at the cost of higher capital costs (which may result from informal agents' inability to enter into contractual agreements). The two trade‐offs are indeed related, but choosing one of them provides tractability and emphasis: if the interest is in understanding tax evasion, the first trade‐off is the obvious choice; if informal labor is the primary interest, the second approach is more suitable.

In this paper, we focus on the labor market perspective and, therefore, devote the rest of the review to this strand of the literature. This strand focuses on informal employment, studying its determinants and often placing particular attention on wage differences across formal and informal sectors. Much of this literature has intellectual roots in Harris and Todaro’s (1970) proposed model of migration from an agricultural sector to an urban area, in which high fixed wages can result in open unemployment. Fields (1975) extended this model to include informality as an alternative for rural migrants unable to find formal jobs in urban areas.

> 1 See Loayza (1996), Johnson et al. (1998), Ihrig and Moe (2004), Prado (2011), and D’Erasmo and Moscoso Boedo (2012).

5
Building further on these papers, Rauch (1991) develops a model in which informal firms are created as a result of a minimum wage set above the market wage but only imposed on firms larger than a certain size. Firms thus faced a trade‐off between operating in the formal sector and paying a higher mandated cost of labor, and operating in the informal sector and being unable to hire the profit‐maximizing amount of labor. Allowing for differences in managerial talent, the paper finds that, in equilibrium, the size distribution of firms would reflect the underlying distribution of managerial talent.

A trade‐off between labor costs and capital costs is introduced in Chaudhuri (1989). Chaudhuri presents a three‐sector static model (agricultural sector, and formal and informal urban sectors), with a wage distortion in the form of a unionized wage which is enforced only in the formal sector. Citing evidence from the high interest rates paid by the informal tailoring and tannery industry in Calcutta, Chaudhuri introduces higher capital costs for informal firms into the model. This framework is also used in Gupta (1993) to explore the effects of government subsidies on the size of the informal sector and social welfare. Finally, Kar and Marjit (2001) equalizes the wages in the informal sector and rural sector to reflect the existence of significant mobility between the two.

More recently, Saracoğlu (2008) studies the evolution of informal sector output and employment in the process of long‐term economic growth using an urban‐rural model with migration. However, a different trade‐off is used to generate the informal sector. In the model, firms in each sector produce different goods and have different production functions. As households get wealthier, their demand for urban informal and agricultural goods decreases, which results in a trade‐off with the higher labor costs faced by formal firms due to employment taxes. Over time, this leads to an increase in the share of labor employed in the formal sector.

Finally, a number of authors have used matching labor market models, based on Mortensen and Pissarides (1994), to study informal employment (e.g. Fugazza and Jacques, 2004; Bosch and Maloney, 2010; Albrecht et al., 2009; Gunther and Launov, 2012). Unlike in the majority of literature based on the Harris and Todaro model, in matching‐based models, the wage in the urban formal sector is endogenous. However, these analyses typically focus on worker heterogeneity and the process of job creation and search, rather than on the existence of opportunity costs for formal and informal employment. For instance, 6
Bencivenga and Smith (1997) and Yuki (2007) conduct dynamic analyses of rural‐urban migration, long‐term development, and the size of the informal labor market using this approach. In both models, however, the informal sector is residual, consisting of workers who are unskilled or unable to find jobs in the urban formal sector after migrating to an urban area.

# **3. The Model**

Before delving into the technical aspects of the model, we now present a sketch of its main components and derivation. There are three agents in the economy: Workers provide labor and have similar basic skills; capitalists save and provide capital that may include both physical and human capital; and government sets a minimum wage in theory to benefit workers. There are two coexisting economies: A _modern_ economy that is organized in firms using a superior technology and employing both capital and labor; and a _rudimentary_ economy that represents the self‐employed using an inferior technology that employs only labor. In turn, the economy can be separated into formal and informal sectors: The _formal_ sector belongs to the modern economy and conforms to high government‐mandated labor costs; the _modern informal_ sector also belongs to the modern economy but, contravening labor regulations, pays low labor costs and confronts high capital costs and lower total factor productivity; and the _rudimentary informal_ sector includes the self‐employed, outside the modern economy as result of subsistence and reservation strategies. Considering two types of informal sectors opens the possibility for a more nuanced analysis into the roles of regulations, capital accumulation, and technology in driving informality.

The model then approaches the study of informality based on the following fundamental asymmetry: The formal sector confronts high labor costs while the informal sectors face higher capital costs and lower productivity. The solution of the model proceeds in the following steps. First, we derive the static allocation of capital and labor across formal and informal sectors, holding constant the total endowments of capital, labor, and total factor productivity. Second, we derive the dynamic solution for the paths of the formal and informal sectors as well as for economic growth. The dynamic evolution is driven by capital accumulation through optimal endogenous savings, labor growth according to exogenous 7
population expansion and participation rates, and TFP growth also determined exogenously. The dynamic solution builds on the static one; and in both cases, we first develop the case where the whole economy is modern and then add the rudimentary economy and its interaction with the modern one.

# **3.1 The Modern Economy**

The modern economy is competitive and decentralized, with firms hiring production factors from their owners. All firms produce one good ( _Y_ ), which can be equivalently used for consumption or investment. Firms share a basic production technology, which has the neoclassical characteristics (namely, constant returns to scale, decreasing returns to each factor, and the Inada conditions) and the property that production factors are complementary to each other. For simplicity we assume that the production function is Cobb‐Douglas, with production factors capital ( _K_ ) and labor ( _L_ ) and total factor productivity ( _A_ ). Output production is then given by, We assume that labor‐market regulations are represented by a minimum legal wage rate. Firms that obey the minimum wage belong to the formal sector, and firms that ignore it correspond to the informal sector. Firms cannot belong to both sectors at the same time.

Firms in the informal sector have lower productivity than firms in the formal sector. First, their total factor productivity (TFP) is a fraction of that in the formal sector. Second, the illegal status of informal firms makes contractual agreements more difficult and costly, especially in capital and financial markets. Because of additional monitoring and transaction costs, informal firms face a higher cost of capital than their formal counterparts. We model these (per‐unit) additional capital costs as proportional to the market‐determined rental rate of capital. Capital owners receive the same rental rate (net of monitoring and transaction costs) from either sector and, thus, are indifferent between the two.

# **Static equilibrium**

The static equilibrium solution consists of the market allocation of given aggregate labor and capital across the formal and informal sectors.

Profits for formal firm _i_ are given by, 8
Il, ; = aAK#L75% — W, Li; — ORK; K a MPL; = A(1—a)( ~~=)KF~~ F a =w ~(1-a) MPK; = Aa( ~~=)~~ F =R MPL, = aA(1-a)( ~~“)Ky~~ I a = Ww, MPK, ~~Ky~~ —(1-a@) = aAa ~~(=)~~ I = CR
The minimum legal wage rate plays a pivotal role in the determination of the net capital rental rate and the informal wage rate because it fixes the capital‐labor ratio in the formal sector. From (5), Equation (9) shows that _KF/LF_ depends positively on the minimum wage ( _W_ ) and negatively on the productivity parameter ( _A_ ). In fact, as will be seen often throughout the paper, what matters is the minimum wage relative to productivity. Note that the capital‐ labor ratio in the formal sector is larger than in the overall modern economy, _KF/LF > K/L_ , and that this distortion gets reduced if productivity increases.

The net capital rental rate is a negative function of the minimum wage; substituting (9) into (6), Given that the cost of capital and total factor productivity in the informal sector are equal to fixed proportions of those in the formal sector, the ratio _KI/LI_ is a fraction of the ratio _KF/LF_ . Dividing (8) by (6), which implies that the informal sector is less capital intensive than the formal sector. Under full employment ( _K = KF + KI_ and _L = LF + LI_ ), it follows from the equilibrium conditions that, Since _KI/L_ I is determined by _KF/LF_ , we can express the informal wage rate as a function of the minimum wage rate. Substituting (11) and (9) into (7), The informal wage is lower than the legal minimum, with the difference being a positive function of the relative formal‐to‐informal capital intensity. Note that the informal wage increases with the legal minimum wage, but less than proportionally.<sup>4</sup>

> 4 A strand of the literature labels as “lighthouse effect” the dependence of the informal wage on the minimum wage. Note that this is derived here as an equilibrium condition, unrelated to signaling, efficiency wages, or any other nonstandard effect.

10
<!-- Start of picture text --> 1<br>Lr == 7,4iyp lt 1/(_"”) 1-y (5) + K<br>1<br>l= yh1 oy 1 Gaew) Ww a *K<br>i<br>Kp =- tyk r 1(“\1-y (Ga) L<br>1<br>Ky —~7 %Kiy *4 + 7(_“\1-y (G5)< L<br><!-- End of picture text --> depend on the ratio of formal labor cost (W) to productivity (A).

W' =A(1-a) ~~(>)~~ K a <u>T-a</u> Ky\*% n <u>Z</u> ~~oe~~
- A rise of the informal wage, but also a wider gap between formal and informal wages.

- A fall in the net capital rental rate.

- An expansion of the informal sector and corresponding contraction of the formal sector.

ii. An increase in the fraction of total factor productivity available for informal firms or a reduction in the capital‐market distortion facing informal firms (because, for instance, the informal sector develops enforcement systems that cut down their transaction and monitoring costs) generates,

- A rise of the informal wage, and a narrower gap between formal and informal wages.

- A fall in the informal capital rental rate (only when the capital market distortion decreases), approaching the net capital rental rate (which remains unchanged).

- An expansion of the informal sector and corresponding contraction of the formal sector.

iii. A decrease in the aggregate capital to labor ratio (because, for instance, there is labor immigration) leads to,

- A rise in the relative size of the informal sector.<sup>6</sup>

- However, the wage and capital rental rates remain unchanged, not reflecting the relative scarcities of the factors of production.

# **3.2 The modern economy in the presence of a rudimentary economy**

# **A rudimentary economy and migration equilibrium**

Assume that the modern economy described above is accompanied by a rudimentary economy, active in rural and marginal areas (and possibly in neighboring countries). This primitive economy represents, for example, the subsistence strategies of the self‐employed, for whom labor legislation does not apply. As a simplification, the production technology of the rudimentary economy does not use capital and is linear in labor (Lewis 1954), �� ����� (18) where the labor productivity parameter, _b_ , in the rudimentary economy is a (constant) fraction, 0 ���1 _,_ of total factor productivity in the formal sector ( _A_ ).

> 6 If only one aggregate factor changes, the model can predict the change in the absolute size of each sector. For instance, when labor immigration occurs and the capital stock remains constant, the absolute size of the informal sector rises whereas the absolute size of the formal sector shrinks.

12
We = UWeR economy, and yu represents the wage adjustment between the two economies. The wage modern economy, then yu > 1.

W, = ~~=~~ w+ ~~=w~~ , L L
<!-- Start of picture text --> Lr _ MbA _qimagind<br>L 1-a i-a ¢ i=a<br>L r ara<br>L 1-a l-agi~a<br><!-- End of picture text --> informal firms (¢) and the adjusted wage in the rudimentary economy (ubA). The effects of ¢, and wbA on the absolute size of the formal and informal sectors go in the W'=ubA a ~~cl~~ <u>Ji-a</u> w"=al-aG (ubA) pLbA
<!-- Start of picture text --> 2 K — (_~_) Ww * a _ — (2 4 <a )t -%)f—— 1 ERAw _<br>L (saa) E (: (<) (=)<br>L= (1-arare\(AG2)* 1 -a 1<br>a a nS k= g(A W ,¢,a,a,b, u)K<br><!-- End of picture text --> <!-- Start of picture text --> degree of inefficiency in capital allocation to informal firms (¢), and decreasing in the ratio<br><!-- End of picture text --> rudimentary economy (ubA).

enter symmetrically into the g(.) function, so that proportional changes in
i. A decrease in the adjusted wage of the rudimentary economy (because of, for instance, worsening rural‐urban terms of trade or improving urban public infrastructure and services) leads to,

- A rise in the labor force of the modern economy.

- An expansion in the relative and absolute size of informal labor.

ii. A decrease in aggregate capital (because, for instance, there is international capital flight or capital destruction) generates,

- A decline in the size of the labor force in the modern economy.

- A proportional decrease in both formal and informal labor, so that the relative shares of formal and informal employment remain constant.

# **3.3 Dynamics: Capital accumulation, labor migration, and economic growth**

The aggregate labor force and the capital stock in the economy change through time, thus affecting the size of the formal and informal sectors. The labor force in the modern economy expands by migration from the rudimentary economy and by natural population increase, and the capital stock grows according to a program of saving and investment.

# **Capital Accumulation**

We assume that there are two different groups of individuals: Workers and capitalists. Workers supply labor inelastically and do not save or borrow.<sup>11</sup> Capitalists do not work but rent out their capital to firms, and they save according to an optimal intertemporal program. These assumptions amount to a version of Kaldor's model of distribution (Kaldor (1956) and Alesina and Rodrik (1991)).

Normalize the size of the group of capital owners to 1. Capitalists solve the following dynamic program:

> 11 This assumption means that workers' desired level of current consumption is equal or higher than their wage. Because of financial market imperfections, they are unable to obtain loans against their future higher income and are constrained to consume only their entire current wage.

16
represents instantaneous consumption by capitalists, p is the subjective rate of time preference, and 6 is the capital depreciation rate. The assumption that the instantaneous

# <u>C(t)</u>

se _ limK (t) e- SRW)-d)av _ 9 <!-- Start of picture text --> C(t) = pK(t)<br>The propensity to consume out of capital is equal to p, a constant.<br>KO K® _ COce _ ROO_§- ~P<br>K(t) _ _ )i-@ 7 @) \s poe _ og<br>x = AG a)-* a*)aw d-—p<br>L __ ding(A W,ay,a,b,p) K _ K<br>L dt + E = PO+ ,<br><!-- End of picture text -->
total factor productivity in the formal sector change proportionally (constant A W function g(.) increases over time parameter 1) will make the urban labor force grow faster than the capital stock.

L K m= ~~"—~~ Mm = P(t) + ~~2~~ -— Mn Y _YrY YrY Y Y YF Y Y7 Y A L. K ~~~=-4~~ +(1-a ~~)*~~ + ~~a+~~ YF A LF KF Y A L K ~~+t=-~~ 4+(1-a ~~)+~~ + ~~a—~~ Y] A Ly] Ky]
value of the function g(.) remains the same (see equation (31)). This is the case when the technological parameters in g(.) are constant and changes in the minimum wage ( informal, and total capital are the same (Kz Kp = K, K,; =K K) and likewise for labor Ly Lp =L,; L; =L L). It then follows that when migration is active, the growth rate of <u>y_A</u> 1 ~~1 -G-a) ya~~ AGgt- ah<sup>“a%</sup><sup>~~)a~~w</sup> <u>«a</u> —65—p where, A/A is exogenous and K population growth rate in the modern economy, n,, Wywris the unregulated wage, that is, the market wage in the absence ofamandated minimum: Wyyp = A(1 — a) ~~(*)K~~ a
~~y~~ _ ~~A 1~~ lea a ~~ye yy~~ 5 1+] ~~<u>QF:</u> \w~~ ~~<u>“4-1</u>~~ _ | ~~(~~ ~~<u>(ae</u> Ju~~

misallocation worsens as the mandated minimum wage, W, deviates from the market wage (in the absence of wage regulation), Wyyr = A(1 — a) ~~(=)~~ a
decreasing the urban cost of living. In the model, this implies a declining parameter yp, so that g(t) > 0. This encourages rural-urban migration and an urban labor force that grows faster urban population is not too large (ny, << R—6d—p

> <u>Ky“ (+</u> ~~<u>UGrbA</u>~~ <u>a-1 - ar")a (</u> ~~<u>(qq—wWw.</u>~~ <u>ia</u> °

Lu HbA
rental rate approaches the subjective rate of time preference (p) plus the depreciation rate (6).

> (1 — ay*) ~~(|Ga—>yWwW~~ <u>\a</u>

1 -ay*
Table 1 provides specific information regarding how the values of these key variables and parameters are identified, including brief explanations and data sources.

# **The calibration procedure**

The calibration of the model consists of fitting its basic solution equations to derive unknown variables from known or assumed data and parameters. The procedure can be summarized as follows,

- From the equations that determine the relative size of the informal and formal sectors in the modern economy, derive the implied ratio of the wage in the informal rudimentary sector to the formal wage.

- From the equation that determines the capital‐labor ratio in the modern economy (resulting from migration equilibrium between the modern and rudimentary economy), derive the implied formal wage.

- From the equation that determines the rate of capital growth, obtain the implicit depreciation rate and the subjective rate of time preference. This also helps anchor the projected capital growth rate to its historical level (see Table 1).

Tables 2 and 3 provide values for the initial conditions of key variables and parameters, either directly from the data or resulting from the calibration procedure, for the set of countries under consideration. Table 2 shows the value of initial conditions related to the informal sector. Table 3 presents values of other key economic variables.

# **The projection algorithm for informality scenario analysis**

The objective here is to obtain the projected paths for the shares of formal workers, informal workers in the modern economy, and informal workers in the rudimentary economy in relation to the total labor force; and to do this for a large sample of developed and developing countries over the next two decades. Rather than formal predictions, the goal is to obtain projections under different scenarios that can illustrate the workings of the model. The projection algorithm uses the basic equations from the model to obtain the informality shares, and it can be summarized as follows,

- The main changes driving the projections are, first, the productivity growth rate; second, the labor force growth rate; and third, the rate of change of the minimum wage. The first two are exogenous (see Table 1), and the latter is given according to

23
the “baseline,” “reformist,” and “populist” scenarios. In addition to this basic set of projections, we consider an alternative set where the productivity in the rudimentary economy, the cost of living adjustment in the modern vs. rudimentary economies, and the excess cost of capital in the modern informal sector also change.

- The projected capital growth rate is obtained from the solution of the respective equation of the model. This updates the level of the capital stock. In turn, this determines total labor in the modern economy (through migration to and from the rudimentary economy). Residually, this also determines labor in the rudimentary economy.

- When labor in the rudimentary economy reaches a minimum threshold, then labor in the modern economy is no longer driven by migration but only by the exogenous increase in the total labor force.

- The share of formal and informal workers in the modern economy is driven by the equilibrium condition across sectors at every point in time. This condition leads to different equations depending on whether there is excess supply of labor in the rudimentary economy or not. In the former case, the shares would not depend on the capital labor ratio (which, given migration, is endogenous); while in the latter case, the shares do depend directly on the capital‐labor ratio.

- Under these conditions, we then obtain the shares of formal workers, informal workers in the modern economy, and informal workers in the rudimentary economy, year by year, country by country, under the “baseline,” “reformist,” and “populist” scenarios.

# **Projection results**

Tables 4 through 9 report the projections, for the years 2010, 2020, and 2030. Results under the “baseline” and populist scenarios are presented for all countries, while only developing countries are included when using the “reformist” scenario. In Tables 4‐6, we present the scenarios where the exogenous driving forces are labor force growth, TFP growth, and changes in mandated labor costs. In Tables 7‐9, we present the scenarios where, in addition, the productivity rates in the informal sectors (both rudimentary and modern), the cost of living in the modern economy, and the excess cost of capital in the modern 24
|to increase (annual growth(“) = 1%), the excess capital costforinformal firms to decrease<br>(annual growht(@) = —0.25%),the ratiobetween TFP productivity inthemoderninformal<br>and formal sector to increase (annual growth(a) = 0.25%), and the ratio between TFP<br>productivityintherudimentaryandformaleconomytoincrease(annualgrowth(b)=|
|---|
in the modern and rudimentary economies remain more persistent over time than under the basic scenarios. This is the case under the baseline scenario and, to a lower extent, under the reformist one.

# **5. Concluding Remarks**

To understand how informality changes in size and type, it is necessary to relate it to the long‐run phenomena of labor migration and economic growth. It is also necessary to realize how informality derives from both lack of development and biased policies. The model, simulation, and projection exercises presented in this paper are geared towards understanding informality as both a symptom and a consequence in the process of economic development.

The paper can also help appreciate the possibilities and limits of different types of policies to address informality. For instance, improving financial and contractual participation for informal firms will increase informal wages but will also cause an expansion of the informal sector. Streamlining labor regulations will expand the formal sector in the modern economy but will not eliminate informal labor in the rudimentary economy in the short run. Sustained improvements in labor productivity in the modern economy, through capital accumulation and TFP growth, will lead to a reduction in informality across all areas but only in the long run.

Taking into account that informality can also manifest in the rudimentary economy (as self‐ or sub‐employment) should make policy makers realize the futility of formalization plans based on penalties to firms. It should help them understand the advantages of programs that make formality more attractive to both workers and firms.

26
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28
**Table 1. Description of Variables and Data Sources**

|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|Capital<br>share<br>(�)|In the production function of the<br>modern economy, the capital<br>share measures the share of<br>national income that accrues to<br>owners of capital.|We use a constant estimate of 0.5. This corresponds<br>roughly to the median value of country‐specific<br>estimates we derived from labor shares presented<br>in the Penn World Tables (averaged from 1990‐<br>2012). These country‐specific estimates are used<br>directly in the appendix where, if a country is<br>missing data, we use ��0.5. All other variables<br>used in the appendix are derived using this<br>alternative country‐specific capital share.|
|Subjective rate<br>of<br>time<br>preference (�)|The marginal rate of substitution<br>between<br>present<br>and<br>future<br>consumption.|Using the equation that determines the rate of<br>capital growth in the modern economy and the<br>average growth rate of capital from 2000‐2012, we<br>|
|Capital<br>depreciation<br>rate (�)|The rate at which the economic<br>value<br>of<br>the<br>capital<br>stock<br>decreases over time.|obtain the implicit depreciation rate and the<br>subjective rate of time preference. We set a<br>minimum value of 1 percent.|
|Capital<br>stock<br>(K)|Goods<br>or<br>assets<br>used<br>for<br>production<br>in<br>the<br>modern<br>economy.|We develop historical estimates using Gross Capital<br>Formation in 2005 US dollars from WDI (1960‐<br>present), extrapolating using Investment from<br>Penn World Tables (1950‐2011) for past years in<br>which it is not available. For the initial conditions,<br>we use an average from 2008‐2012 and update<br>future years using the growth rate derived from<br>solutions of the model.|
|Total<br>factor<br>productivity of<br>the<br>formal<br>sector (A)|The total factor productivity of<br>firms operating in the formal<br>sector, using capital and labor.|We develop country‐specific estimates using the<br>production function of the modern economy and<br>our estimates of capital and labor in the modern<br>economy. For the initial conditions, we use an<br>average from 2008‐2012.|
|Growth<br>of<br>Total<br>factor<br>productivity of<br>the<br>formal<br>sector (A)|Growth of total factor productivity<br>of firms operating in the formal<br>sector, using capital and labor.|We calculate an annual log growth rate of the<br>historical estimates of A and take an average from<br>2000‐2012. We set a minimum value of 0.|
|Growth of the<br>Labor force<br>(growth of LF+<br>LI+ LR)|Growth of the total labor force (i.e.<br>in the modern and rudimentary<br>economy) by country.|We use estimates and projections of the size of the<br>labor force by country from the ILO (available until<br>2030) to find an annual growth rate. For the initial<br>year, we use an average of estimates of the size of|

29
|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|||the labor force from 2008‐2012 from WDI and<br>update this using the projected growth rate.|
|Excess capital<br>costs<br>for<br>informal firms<br>(ζ)|Informal firms pay a capital rental<br>rate which is a multiple, R , of the<br>market determined net capital<br>rental rate paid by formal firms.|Based on an estimate by Scheinkman and de Paula<br>of the additional cost of capital for informal firms<br>in Brazil, we assume a constant value of 1.3 for all<br>countries. For the persistent informality<br>parameter options, we set ζ to decrease 0.25%<br>percent annually.|
|Cost of Living<br>Adjustment (μ)|The wage adjustment between the<br>rudimentary<br>and<br>modern<br>economies, which accounts for<br>differences in the cost of living or<br>in the cost of labor participation<br>between the two economies.|We find the ratio between the average monthly<br>wage of all employees and of employees in<br>elementary occupations, available for a subset of<br>countries from the ILO. We use the mean, after<br>excluding outliers, 1.81. For the persistent<br>informality parameter options, we set the growth<br>rate of μ to be 1 percent annually.|
|Ratio of formal<br>to<br>modern<br>informal<br>productivity<br>(a)|Total factor productivity in the<br>informal sector is a fraction,_a_, of<br>productivity in the formal sector<br>(A).|We use an estimate from Perry (2007) that,<br>controlling for firm characteristics, informal firms<br>are 71% as productive as formal firms. For the<br>persistent informality parameter options, we set<br>the growth rate of_a_to be 0.25% percent annually.|
|Ratio of formal<br>to rudimentary<br>informal<br>productivity<br>(b)|Labor<br>productivity<br>in<br>the<br>rudimentary<br>economy<br>is<br>a<br>fraction,<br>_b_,<br>of<br>total<br>factor<br>productivity in the formal sector.|Country‐specific estimates are obtained through<br>calibration of the model. For the persistent<br>informality parameter options, we set the growth<br>rate of_b_to be 0.25% percent annually.|
|Formal Labor<br>(LF)|The total number of workers<br>employed in formal firms, which<br>are subject to labor legislation.|We use the latest available estimates of the rate of<br>contribution to mandatory pensions systems<br>around the world (World Bank; ILO), combined<br>with average employment and labor force<br>estimates from 2008‐2012 from WDI. We assume<br>that unemployed and informal workers contribute<br>to pensions at a fraction,_d,_set to 2%, of the rate<br>among formal employees, and adjust our estimates<br>accordingly.|
|Informal<br>Rudimentary<br>Labor (LR)|The total number of workers in<br>the rudimentary economy, using<br>primary production technologies<br>with<br>little<br>capital<br>and<br>low<br>productivity|We use ILO estimates of the share of ‘vulnerable<br>employment’, defined as the sum of un‐paid family<br>workers and own‐account workers, i.e. self‐<br>employed workers without employees, combined<br>with labor force and employment data from WDI.|

30
|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|Informal<br>Modern Labor<br>(LI)|The total number of workers<br>employed<br>in<br>informal<br>firms<br>operating in the modern economy.|We estimate informal employment in the modern<br>economy as the residual share of the labor force<br>(from WDI) that is not part of the formal sector or<br>the informal rudimentary economy.|
|Legally<br>mandated<br>labor cost, i.e.<br>minimum<br>wage (W)|The legally mandated cost of labor<br>paid by firms in the formal sector.|We use the equation for the aggregate capital‐labor<br>ratio in the modern economy to derive an estimate.|

31
# **Table 2. Initial conditions related to informality by country in 2010**

|**Country**|**Formal**<br>**(% of**<br>**the**<br>**labor**<br>**force)**|<br>**Informal**<br>**Modern**<br>**(% of the**<br>**labor**<br>**force)**|**Informal**<br>**Rud.**<br>**(% of the**<br>**labor**<br>**force)**|**Modern**<br>**Informal**<br>**Wage /**<br>**Expected**<br>**Wage in the**<br>**Modern**<br>**Economy**|**Modern**<br>**Informal**<br>**Wage/**<br>**Min.**<br>**Wage**|**Informal**<br>**Rud. Wage /**<br>**Min. Wage**|
|---|---|---|---|---|---|---|
|Albania|39%|10%|52%|0.44|0.39|0.49|
|Argentina|46%|36%|18%|0.53|0.39|0.41|
|Australia|87%|5%|8%|0.40|0.39|0.54|
|Austria|90%|2%|8%|0.39|0.39|0.55|
|Burundi|7%|7%|86%|0.57|0.39|0.38|
|Belgium|87%|4%|9%|0.40|0.39|0.54|
|Benin|6%|7%|87%|0.59|0.39|0.36|
|Bangladesh|5%|38%|57%|0.85|0.39|0.25|
|Bulgaria|75%|18%|8%|0.44|0.39|0.49|
|Bolivia|13%|33%|54%|0.69|0.39|0.31|
|Brazil|58%|20%|23%|0.46|0.39|0.47|
|Botswana|17%|73%|10%|0.77|0.39|0.28|
|Central African<br>Republic|5%|8%|87%|0.64|0.39|0.34|
|<br>Canada|83%|4%|13%|0.40|0.39|0.54|
|Switzerland|92%|‐1%|9%|0.39|0.39|0.56|
|Chile|56%|21%|23%|0.47|0.39|0.46|
|China|34%|19%|48%|0.50|0.39|0.43|
|Côte d'Ivoire|14%|10%|76%|0.52|0.39|0.41|
|Cameroon|17%|12%|71%|0.52|0.39|0.41|
|Congo, Rep.|12%|17%|71%|0.61|0.39|0.35|
|Colombia|30%|28%|43%|0.55|0.39|0.39|
|Costa Rica|57%|24%|19%|0.48|0.39|0.45|
|Germany|83%|11%|6%|0.42|0.39|0.51|
|Denmark|88%|7%|5%|0.40|0.39|0.53|
|Dominican<br>Republic|30%|34%|37%|0.57|0.39|0.37|
|<br>Ecuador|27%|31%|42%|0.58|0.39|0.37|
|Egypt, Arab<br>Rep.|54%|26%|21%|0.48|0.39|0.44|
|<br>Spain|65%|26%|9%|0.47|0.39|0.46|
|<br>Finland|85%|6%|9%|0.41|0.39|0.53|
|France|82%|11%|6%|0.42|0.39|0.51|
|United<br>Kingdom|88%|1%|10%|0.39|0.39|0.55|
|Ghana|10%|16%|74%|0.62|0.39|0.35|
|Guinea|13%|‐1%|88%|0.37|0.39|0.57|

32
|**Country**|**Formal**<br>**(% of**<br>**the**<br>**labor**<br>**force)**|<br>**Informal**<br>**Modern**<br>**(% of the**<br>**labor**<br>**force)**|**Informal**<br>**Rud.**<br>**(% of the**<br>**labor**<br>**force)**|**Modern**<br>**Informal**<br>**Wage /**<br>**Expected**<br>**Wage in the**<br>**Modern**<br>**Economy**|**Modern**<br>**Informal**<br>**Wage/**<br>**Min.**<br>**Wage**|**Informal**<br>**Rud. Wage /**<br>**Min. Wage**|
|---|---|---|---|---|---|---|
|Greece|79%|‐3%|24%|0.38|0.39|0.57|
|Guatemala|21%|38%|41%|0.64|0.39|0.33|
|Hong Kong<br>SAR, China|76%|17%|7%|0.44|0.39|0.49|
|Honduras|18%|32%|50%|0.64|0.39|0.34|
|Hungary|86%|8%|6%|0.41|0.39|0.53|
|Indonesia|14%|31%|55%|0.68|0.39|0.32|
|India|12%|11%|78%|0.55|0.39|0.39|
|Ireland|83%|7%|10%|0.41|0.39|0.53|
|Iran, Islamic<br>Rep.|41%|23%|36%|0.50|0.39|0.43|
|<br>Iraq|43%|35%|21%|0.53|0.39|0.40|
|<br>Israel|85%|8%|7%|0.41|0.39|0.52|
|Italy|85%|‐2%|17%|0.38|0.39|0.56|
|Jamaica|21%|47%|32%|0.67|0.39|0.32|
|Jordan|52%|41%|7%|0.53|0.39|0.40|
|Japan|92%|‐1%|10%|0.38|0.39|0.56|
|Kenya|19%|28%|53%|0.61|0.39|0.35|
|Cambodia|1%|28%|71%|0.97|0.39|0.22|
|Korea, Rep.|77%|‐1%|24%|0.38|0.39|0.56|
|Lao PDR|2%|15%|83%|0.84|0.39|0.25|
|Lebanon|11%|57%|32%|0.80|0.39|0.27|
|Sri Lanka|25%|36%|39%|0.61|0.39|0.35|
|Lesotho|17%|70%|13%|0.76|0.39|0.28|
|Morocco|30%|23%|46%|0.53|0.39|0.41|
|Madagascar|7%|9%|84%|0.60|0.39|0.36|
|Mexico|28%|44%|28%|0.62|0.39|0.35|
|Mali|11%|7%|82%|0.51|0.39|0.42|
|Mongolia|46%|2%|52%|0.40|0.39|0.54|
|<br>Mozambique|13%|20%|67%|0.62|0.39|0.34|
|Mauritania|24%|30%|45%|0.59|0.39|0.37|
|Malaysia|56%|23%|21%|0.47|0.39|0.45|
|<br>Namibia|20%|60%|20%|0.72|0.39|0.30|
|Niger|4%|7%|89%|0.62|0.39|0.35|
|<br>Nigeria|12%|12%|76%|0.56|0.39|0.39|
|Netherlands|87%|2%|10%|0.39|0.39|0.54|
|Norway|90%|5%|5%|0.40|0.39|0.54|
|Nepal|4%|27%|69%|0.83|0.39|0.26|
|Pakistan|8%|33%|60%|0.77|0.39|0.28|

33
|**Country**|**Formal**<br>**(% of**<br>**the**<br>**labor**<br>**force)**|<br>**Informal**<br>**Modern**<br>**(% of the**<br>**labor**<br>**force)**|**Informal**<br>**Rud.**<br>**(% of the**<br>**labor**<br>**force)**|**Modern**<br>**Informal**<br>**Wage /**<br>**Expected**<br>**Wage in the**<br>**Modern**<br>**Economy**|**Modern**<br>**Informal**<br>**Wage/**<br>**Min.**<br>**Wage**|**Informal**<br>**Rud. Wage /**<br>**Min. Wage**|
|---|---|---|---|---|---|---|
|Panama|62%|10%|28%|0.42|0.39|0.51|
|Peru|22%|32%|46%|0.61|0.39|0.35|
|Philippines|27%|34%|39%|0.59|0.39|0.37|
|Poland|77%|6%|17%|0.41|0.39|0.53|
|Portugal|86%|‐1%|16%|0.38|0.39|0.56|
|Paraguay|14%|44%|41%|0.72|0.39|0.30|
|Romania|65%|5%|30%|0.40|0.39|0.53|
|Rwanda|5%|18%|77%|0.75|0.39|0.29|
|Sudan|12%|37%|51%|0.72|0.39|0.30|
|Senegal|10%|37%|52%|0.74|0.39|0.29|
|Singapore|60%|30%|9%|0.49|0.39|0.44|
|Sierra Leone|7%|5%|88%|0.53|0.39|0.40|
|El Salvador|24%|39%|37%|0.62|0.39|0.34|
|Sweden|84%|10%|6%|0.41|0.39|0.52|
|Chad|6%|11%|83%|0.64|0.39|0.34|
|Togo|7%|12%|81%|0.64|0.39|0.34|
|Thailand|22%|25%|53%|0.57|0.39|0.37|
|Tunisia|62%|14%|24%|0.44|0.39|0.49|
|Turkey|57%|14%|30%|0.44|0.39|0.49|
|Tanzania|5%|12%|83%|0.70|0.39|0.31|
|Uganda|12%|10%|79%|0.54|0.39|0.40|
|Uruguay|75%|4%|21%|0.40|0.39|0.54|
|United States|87%|7%|6%|0.41|0.39|0.53|
|Vietnam|21%|18%|61%|0.54|0.39|0.40|
|South Africa|17%|75%|8%|0.77|0.39|0.28|
|Zimbabwe|22%|12%|66%|0.50|0.39|0.43|

_Note_ : All initial conditions correspond to averages from 2008‐2012, when available. The ratio of the modern informal wage to the minimum wage is constant as it is a function of parameters _a_ , ζ, and �, which are assumed to be constant across countries in the initial period. For sources and descriptions, see Table 1.

34
**Table 3.  Initial conditions related to the overall economy by country in 2010**

|**Country**|**Capital/**<br>**Labor**<br>**Force**|**Capital/**<br>**GDP**|**GDP/**<br>**Labor**<br>**Force**|**Minimum**<br>**Wage**<br>**(W)**|**TFP of**<br>**Formal**<br>**Sector**<br>**(A)**|**Growth**<br>**of A**|
|---|---|---|---|---|---|---|
|Albania|36,002|4.4|8,225|6,304|43|3.0%|
|Argentina|45,046|2.9|15,687|10,264|73|1.6%|
|Australia|236,448|3.4|68,682|36,094|140|0.0%|
|Austria|310,881|4.0|78,499|41,073|140|0.1%|
|Burundi|795|2.5|321|532|11|0.0%|
|Belgium|317,076|3.7|85,805|45,517|152|0.0%|
|Benin|4,390|2.9|1,495|2,608|22|0.4%|
|Bangladesh|3,143|2.4|1,291|1,582|23|0.5%|
|Bulgaria|23,633|2.3|10,265|5,674|66|0.0%|
|Bolivia|5,452|2.1|2,582|2,657|35|1.1%|
|Brazil|27,202|2.6|10,625|6,594|64|0.8%|
|Botswana|36,101|3.0|12,063|9,571|62|0.0%|
|Central|||||||
|African<br>Republic|1,974|2.6|758|1,393|17|1.4%|
|<br>Canada|205,177|3.1|66,079|35,705|145|0.0%|
|Switzerland|409,142|4.1|100,778|52,423|157|0.4%|
|Chile|47,945|2.5|18,846|11,724|85|0.0%|
|China|15,034|3.0|4,935|3,820|39|3.9%|
|Côte d'Ivoire|28,719|11.7|2,463|2,945|14|0.8%|
|Cameroon|7,171|3.0|2,376|2,573|28|0.7%|
|Congo, Rep.|15,878|3.4|4,632|5,497|36|2.0%|
|Colombia|22,391|2.7|8,273|6,611|54|1.0%|
|Costa Rica|29,860|2.5|11,785|7,254|67|0.0%|
|Germany|276,781|3.8|73,267|39,382|139|0.4%|
|Denmark|324,384|3.6|90,895|47,645|159|0.0%|
|Dominican<br>Republic|21,625|2.1|10,275|8,071|69|1.1%|
|<br>Ecuador|21,606|3.0|7,171|5,863|48|1.2%|
|Egypt, Arab<br>Rep.|10,510|2.4|4,388|2,771|42|0.9%|
|<br>Spain|209,021|4.0|51,875|30,396|113|0.0%|
|Finland|303,345|3.8|79,424|42,410|144|0.4%|
|France|284,646|3.7|77,528|41,660|145|0.0%|
|United<br>Kingdom|229,524|2.9|77,954|41,207|162|0.0%|
|<br>Ghana|5,101|3.4|1,500|1,850|20|2.4%|
|Guinea|1,673|2.3|742|1,052|18|0.0%|

35
|**Country**|**Capital/**<br>**Labor**<br>**Force**|**Capital/**<br>**GDP**|**GDP/**<br>**Labor**<br>**Force**|**Minimum**<br>**Wage**<br>**(W)**|**TFP of**<br>**Formal**<br>**Sector**<br>**(A)**|**Growth**<br>**of A**|
|---|---|---|---|---|---|---|
|Greece|189,593|4.0|47,215|26,688|109|0.0%|
|Guatemala|13,774|2.5|5,441|4,770|46|0.0%|
|Hong Kong<br>SAR, China|175,208|2.9|59,610|32,720|141|1.9%|
|<br>Honduras|13,589|3.5|3,914|3,682|33|1.0%|
|Hungary|101,373|3.9|26,178|13,889|82|0.3%|
|Indonesia|9,807|3.0|3,257|3,354|32|2.6%|
|India|7,497|2.9|2,592|3,290|29|1.8%|
|Ireland|332,438|3.3|101,594|54,911|175|0.0%|
|Iran, Islamic<br>Rep.|53,915|5.0|10,837|7,776|46|1.1%|
|<br>Iraq|15,229|1.7|9,055|5,921|70|0.0%|
|Israel|151,898|2.8|54,797|29,028|139|0.3%|
|Italy|273,049|3.7|73,880|40,096|141|0.0%|
|<br>Jamaica|45,877|5.2|8,865|7,540|42|0.0%|
|Jordan|34,573|3.3|10,540|6,523|56|2.1%|
|Japan|299,196|4.3|69,894|36,549|128|0.4%|
|Kenya|3,946|2.5|1,548|1,447|24|0.3%|
|Cambodia|1,814|1.6|1,125|1,838|26|0.5%|
|Korea, Rep.|144,394|3.3|43,568|24,517|113|0.6%|
|Lao PDR|2,697|2.0|1,322|2,508|25|0.8%|
|Lebanon|56,686|2.9|19,498|18,137|81|1.2%|
|Sri Lanka|11,696|2.9|3,986|3,195|35|2.7%|
|Lesotho|8,844|4.2|2,122|1,705|22|2.7%|
|Morocco|22,697|3.5|6,518|5,252|43|1.2%|
|Madagascar|1,827|3.3|558|884|13|0.0%|
|<br>Mexico|61,873|3.2|19,125|14,728|76|0.0%|
|Mali|3,407|2.6|1,327|1,793|22|0.0%|
|Mongolia|14,648|4.8|3,071|2,185|25|3.9%|
|Mozambique|1,141|1.1|998|1,101|28|0.0%|
|Mauritania|7,734|3.1|2,502|2,104|28|0.0%|
|Malaysia|42,992|2.9|14,884|9,245|71|1.6%|
|Namibia|39,181|3.6|10,966|8,783|54|0.7%|
|Niger|2,611|3.1|833|1,608|16|0.8%|
|<br>Nigeria|4,110|1.3|3,155|3,944|48|5.2%|
|Netherlands|286,352|3.5|81,319|43,151|151|0.0%|
|Norway|456,283|3.7|124,576|64,722|183|0.0%|
|Nepal|2,252|3.2|711|1,002|15|0.0%|

36
|**Country**|**Capital/**<br>**Labor**<br>**Force**|**Capital/**<br>**GDP**|**GDP/**<br>**Labor**<br>**Force**|**Minimum**<br>**Wage**<br>**(W)**|**TFP of**<br>**Formal**<br>**Sector**<br>**(A)**|**Growth**<br>**of A**|
|---|---|---|---|---|---|---|
|Pakistan|6,008|2.7|2,233|2,670|29|0.7%|
|Panama|30,157|2.2|13,822|8,390|78|2.6%|
|Peru|16,535|2.5|6,683|5,794|51|1.8%|
|Philippines|9,044|2.8|3,288|2,657|34|2.1%|
|Poland|55,609|2.6|21,378|11,889|90|1.2%|
|Portugal|137,004|3.8|36,096|19,511|97|0.0%|
|Paraguay|9,920|2.7|3,688|3,489|37|0.0%|
|Romania|42,793|3.5|12,347|7,493|59|2.4%|
|Rwanda|1,307|1.7|763|1,166|21|2.4%|
|Sudan|6,953|2.1|3,251|3,376|39|0.0%|
|Senegal|5,251|2.8|1,848|1,975|25|0.2%|
|Singapore|173,308|2.8|61,251|36,062|144|1.7%|
|Sierra Leone|1,566|1.6|994|1,656|24|1.6%|
|El Salvador|17,153|2.4|7,180|5,961|55|0.0%|
|Sweden|314,810|3.7|84,211|44,892|149|0.5%|
|Chad|4,849|2.6|1,854|2,989|26|3.5%|
|Togo|2,315|2.7|847|1,290|17|0.0%|
|Thailand|21,192|3.7|5,761|5,232|39|2.4%|
|Tunisia|34,349|3.3|10,388|6,312|55|1.0%|
|Turkey|50,556|2.2|22,490|14,232|99|1.1%|
|Tanzania|2,909|2.8|1,025|1,747|19|2.1%|
|Uganda|2,216|2.2|1,024|1,310|21|1.9%|
|Uruguay|41,111|3.1|13,445|7,629|66|1.7%|
|United<br>States|279,700|3.2|86,586|45,655|163|0.0%|
|Vietnam|4,606|3.0|1,543|1,471|22|0.0%|
|South Africa|41,674|2.5|16,476|12,979|80|0.9%|
|Zimbabwe|793|1.1|751|726|26|0.0%|

_Note_ : All initial conditions correspond to averages from 2008‐2012, when available. _Sources_ : For methods and sources for variables in columns 4‐6, see Table 1. For columns 1‐3:

- World Development Indicators (WDI), 1960‐2012

- Penn World Tables  (1950‐2011)

- International Labor Organization (1990‐2012)

37
**Table 4. Projections for all countries from 2010 to 2030 under baseline scenario**

|**Country**|**For**|**mal Lab**|**or**|**Mod**|**ern Infor**<br>**Labor**|**mal**|**Rudim**|**entary I**<br>**Labor**|**nformal**|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|<br>**2020**|**2030**|**2010**|<br>**2020**|**2030**|
|Albania|39%|57%|76%|10%|21%|19%|52%|22%|5%|
|Argentina|46%|53%|54%|36%|42%|41%|18%|5%|5%|
|Australia|87%|90%|90%|5%|5%|5%|8%|5%|5%|
|Austria|87%|90%|90%|5%|5%|5%|8%|5%|5%|
|Burundi|7%|8%|9%|7%|9%|11%|86%|83%|80%|
|Belgium|86%|90%|90%|5%|5%|5%|9%|5%|5%|
|<br>Benin|6%|6%|7%|7%|8%|9%|87%|86%|85%|
|Bangladesh|5%|5%|10%|38%|63%|85%|57%|31%|5%|
|<br>Bulgaria|75%|78%|78%|18%|17%|17%|8%|5%|5%|
|Bolivia|13%|14%|18%|33%|37%|50%|54%|48%|32%|
|Brazil|58%|72%|73%|20%|23%|22%|23%|5%|5%|
|Botswana|17%|18%|18%|73%|77%|77%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|4%|3%|8%|6%|5%|87%|90%|92%|
|<br>Canada|82%|90%|90%|5%|5%|5%|13%|5%|5%|
|Switzerland|86%|90%|90%|5%|5%|5%|9%|5%|5%|
|Chile|56%|70%|71%|21%|25%|24%|23%|5%|5%|
|China|34%|60%|61%|19%|35%|34%|48%|5%|5%|
|Côte d'Ivoire|14%|9%|3%|10%|5%|5%|76%|86%|92%|
|Cameroon|17%|17%|17%|12%|12%|12%|71%|71%|70%|
|Congo, Rep.|12%|12%|13%|17%|17%|19%|71%|70%|68%|
|Colombia|30%|37%|51%|28%|37%|44%|43%|26%|5%|
|Costa Rica|57%|68%|69%|24%|27%|26%|19%|5%|5%|
|Germany|83%|85%|85%|11%|10%|10%|6%|5%|5%|
|Denmark|88%|88%|88%|7%|7%|7%|5%|5%|5%|
|Dominican||||||||||
|Republic|30%|38%|46%|34%|49%|49%|37%|13%|5%|
|Ecuador|27%|32%|44%|31%|39%|51%|42%|30%|5%|
|Egypt, Arab||||||||||
|<br>Rep.|54%|65%|65%|26%|30%|30%|21%|5%|5%|
|Spain|65%|70%|70%|26%|25%|25%|9%|5%|5%|
|Finland|85%|89%|89%|6%|6%|6%|9%|5%|5%|
|France|82%|84%|84%|11%|11%|11%|6%|5%|5%|
|United||||||||||
|Kingdom|85%|90%|90%|5%|5%|5%|10%|5%|5%|
|Ghana|10%|11%|16%|16%|20%|30%|74%|69%|54%|
|Guinea|7%|10%|16%|5%|5%|5%|88%|85%|79%|
|Greece|71%|90%|90%|5%|5%|5%|24%|5%|5%|
|Guatemala|21%|22%|24%|38%|40%|43%|41%|38%|33%|

38
|**Country**|**For**|**mal Lab**|**or**|**Mod**|**ern Infor**<br>**Labor**|**mal**|**Rudim**|**entary I**<br>**Labor**|**nformal**|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Hong Kong||||||||||
|<br>SAR, China|76%|79%|80%|17%|16%|15%|7%|5%|5%|
|Honduras|18%|20%|27%|32%|38%|52%|50%|42%|22%|
|Hungary|86%|88%|88%|8%|7%|7%|6%|5%|5%|
|Indonesia|14%|16%|28%|31%|42%|67%|55%|42%|5%|
|India|12%|14%|24%|11%|18%|31%|78%|68%|45%|
|Ireland|83%|89%|89%|7%|6%|6%|10%|5%|5%|
|Iran, Islamic||||||||||
|Rep.|41%|49%|63%|23%|29%|32%|36%|22%|5%|
|Iraq|43%|48%|53%|35%|42%|42%|21%|9%|5%|
|Israel|85%|89%|89%|8%|6%|6%|7%|5%|5%|
|Italy|78%|90%|90%|5%|5%|5%|17%|5%|5%|
|<br>Jamaica|21%|20%|22%|47%|45%|49%|32%|35%|30%|
|Jordan|52%|54%|53%|41%|41%|42%|7%|5%|5%|
|Japan|85%|90%|90%|5%|5%|5%|10%|5%|5%|
|Kenya|19%|21%|25%|28%|33%|40%|53%|46%|35%|
|Cambodia|1%|0%|1%|28%|53%|94%|71%|47%|5%|
|Korea, Rep.|71%|90%|90%|5%|5%|5%|24%|5%|5%|
|<br>Lao PDR|2%|2%|2%|15%|20%|29%|83%|78%|69%|
|Lebanon|11%|11%|18%|57%|59%|77%|32%|30%|5%|
|Sri Lanka|25%|39%|39%|36%|56%|56%|39%|5%|5%|
|Lesotho|17%|18%|18%|70%|77%|77%|13%|5%|5%|
|Morocco|30%|41%|54%|23%|37%|41%|46%|22%|5%|
|Madagascar|7%|7%|8%|9%|10%|11%|84%|83%|81%|
|Mexico|28%|32%|38%|44%|51%|57%|28%|17%|5%|
|Mali|11%|14%|16%|7%|9%|10%|82%|78%|74%|
|Mongolia|43%|59%|90%|5%|5%|5%|52%|36%|5%|
|Mozambique|13%|7%|14%|20%|65%|81%|67%|28%|5%|
|Mauritania|24%|33%|41%|30%|52%|54%|45%|15%|5%|
|Malaysia|56%|67%|69%|23%|28%|26%|21%|5%|5%|
|Namibia|20%|23%|23%|60%|72%|72%|20%|5%|5%|
|Niger|4%|4%|3%|7%|6%|5%|89%|90%|91%|
|Nigeria|12%|14%|41%|12%|17%|54%|76%|69%|5%|
|<br>Netherlands|85%|90%|90%|5%|5%|5%|10%|5%|5%|
|Norway|90%|90%|90%|5%|5%|5%|5%|5%|5%|
|Nepal|4%|4%|5%|27%|33%|43%|69%|63%|52%|
|Pakistan|8%|8%|10%|33%|36%|43%|60%|55%|47%|
|Panama|62%|83%|84%|10%|12%|11%|28%|5%|5%|
|Peru|22%|29%|40%|32%|46%|55%|46%|25%|5%|
|Philippines|27%|31%|43%|34%|42%|52%|39%|27%|5%|

39
|**Country**|**For**|**mal Lab**|**or**|**Mode**|**rn Infor**<br>**Labor**|**mal**|**Rudim**|**entary In**<br>**Labor**|**formal**|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Poland|77%|90%|89%|6%|5%|6%|17%|5%|5%|
|Portugal|79%|90%|90%|5%|5%|5%|16%|5%|5%|
|Paraguay|14%|15%|17%|44%|47%|53%|41%|39%|31%|
|Romania|65%|88%|88%|5%|7%|7%|30%|5%|5%|
|Rwanda|5%|6%|12%|18%|30%|64%|77%|63%|23%|
|Sudan|12%|17%|22%|37%|72%|73%|51%|11%|5%|
|Senegal|10%|11%|12%|37%|40%|44%|52%|49%|45%|
|Singapore|60%|66%|66%|30%|29%|29%|9%|5%|5%|
|Sierra Leone|7%|9%|13%|5%|7%|12%|88%|84%|75%|
|El Salvador|24%|27%|33%|39%|46%|57%|37%|27%|9%|
|Sweden|84%|86%|86%|10%|9%|9%|6%|5%|5%|
|Chad|6%|6%|9%|11%|13%|21%|83%|80%|70%|
|Togo|7%|6%|6%|12%|11%|10%|81%|83%|84%|
|Thailand|22%|28%|46%|25%|35%|49%|53%|37%|5%|
|Tunisia|62%|79%|79%|14%|16%|16%|24%|5%|5%|
|Turkey|57%|71%|77%|14%|23%|18%|30%|6%|5%|
|Tanzania|5%|5%|6%|12%|15%|19%|83%|80%|75%|
|Uganda|12%|14%|23%|10%|14%|25%|79%|71%|52%|
|Uruguay|74%|84%|90%|5%|7%|5%|21%|8%|5%|
|United States|87%|88%|88%|7%|7%|7%|6%|5%|5%|
|Vietnam|21%|30%|49%|18%|35%|46%|61%|35%|5%|
|South Africa|17%|18%|18%|75%|77%|77%|8%|5%|5%|
|Zimbabwe|22%|35%|61%|12%|26%|34%|66%|39%|5%|

40
**Table 5. Projections for all countries from 2010 to 2030 under populist scenario**

|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**|**rmal**|**Ru**|**diment**|**ary**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Labor **||**Info**|**rmal L**|**abor **|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|36%|38%|10%|30%|57%|52%|34%|5%|
|Argentina|46%|31%|19%|36%|48%|59%|18%|21%|22%|
|Australia|87%|68%|48%|5%|27%|47%|8%|5%|5%|
|Austria|87%|68%|45%|5%|23%|37%|8%|9%|18%|
|Burundi|7%|5%|3%|7%|10%|11%|86%|86%|86%|
|Belgium|86%|66%|43%|5%|26%|39%|9%|8%|17%|
|Benin|6%|4%|2%|7%|9%|9%|87%|88%|89%|
|Bangladesh|5%|0%|0%|38%|61%|91%|57%|39%|9%|
|Bulgaria|75%|56%|39%|18%|39%|56%|8%|5%|5%|
|<br>Bolivia|13%|7%|2%|33%|37%|40%|54%|56%|57%|
|Brazil|58%|44%|30%|20%|37%|51%|23%|19%|18%|
|Botswana<br>|17%|7%|0%|73%|88%|95%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|2%|1%|8%|7%|5%|87%|91%|94%|
|Canada|82%|67%|44%|5%|26%|40%|13%|7%|16%|
|Switzerland|86%|64%|41%|5%|19%|32%|9%|17%|27%|
|Chile|56%|48%|34%|21%|45%|61%|23%|7%|5%|
|China|34%|42%|27%|19%|53%|68%|48%|5%|5%|
|Côte d'Ivoire|14%|6%|1%|10%|7%|5%|76%|88%|94%|
|Cameroon|17%|10%|5%|12%|14%|14%|71%|75%|80%|
|Congo, Rep.|12%|7%|3%|17%|19%|19%|71%|74%|78%|
|Colombia|30%|22%|14%|28%|40%|54%|43%|38%|32%|
|Costa Rica|57%|47%|33%|24%|48%|62%|19%|5%|5%|
|Germany|83%|58%|38%|11%|30%|42%|6%|12%|21%|
|Denmark|88%|65%|41%|7%|30%|41%|5%|5%|18%|
|Dominican Republic|30%|22%|14%|34%|51%|69%|37%|28%|16%|
|Ecuador|27%|18%|11%|31%|41%|50%|42%|40%|39%|
|Egypt, Arab Rep.|54%|40%|25%|26%|42%|52%|21%|18%|22%|
|Spain|65%|50%|34%|26%|45%|61%|9%|5%|5%|
|Finland|85%|66%|44%|6%|29%|45%|9%|5%|11%|
|France|82%|62%|38%|11%|33%|43%|6%|5%|19%|
|United Kingdom|85%|65%|39%|5%|22%|32%|10%|13%|29%|
|Ghana|10%|6%|3%|16%|21%|29%|74%|73%|68%|
|Guinea|7%|8%|8%|5%|5%|6%|88%|87%|86%|
|Greece|71%|66%|45%|5%|18%|33%|24%|16%|23%|
|Guatemala|21%|12%|5%|38%|41%|38%|41%|47%|57%|
|Hong Kong SAR,||||||||||
|<br>China|76%|58%|40%|17%|37%|55%|7%|5%|5%|
|Honduras|18%|11%|5%|32%|39%|48%|50%|50%|47%|
|Hungary|86%|65%|46%|8%|30%|49%|6%|5%|5%|
|Indonesia|14%|8%|3%|31%|42%|62%|55%|49%|34%|
|India|12%|8%|6%|11%|19%|31%|78%|72%|63%|

41
|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**|**rmal**|**Ru**|**diment**|**ary**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Labor **||**Info**|**rmal L**|**abor **|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Ireland|83%|66%|46%|7%|29%|49%|10%|5%|5%|
|Iran, Islamic Rep.|41%|30%|22%|23%|36%|50%|36%|34%|28%|
|Iraq|43%|28%|14%|35%|45%|42%|21%|27%|44%|
|Israel|85%|64%|38%|8%|28%|37%|7%|8%|25%|
|Italy|78%|65%|43%|5%|19%|32%|17%|16%|25%|
|Jamaica|21%|10%|4%|47%|46%|47%|32%|43%|49%|
|Jordan|52%|36%|22%|41%|59%|73%|7%|5%|5%|
|Japan|85%|67%|43%|5%|21%|34%|10%|12%|23%|
|<br>Kenya|19%|12%|6%|28%|34%|36%|53%|54%|58%|
|Cambodia|1%|0%|0%|28%|50%|80%|71%|50%|20%|
|Korea, Rep.|71%|72%|53%|5%|23%|42%|24%|5%|5%|
|Lao PDR|2%|0%|0%|15%|20%|25%|83%|80%|75%|
|Lebanon|11%|4%|0%|57%|57%|86%|32%|39%|14%|
|Sri Lanka|25%|20%|13%|36%|67%|82%|39%|12%|5%|
|Lesotho|17%|6%|0%|70%|80%|95%|13%|14%|5%|
|Morocco|30%|24%|19%|23%|42%|65%|46%|34%|16%|
|Madagascar|7%|4%|2%|9%|11%|12%|84%|85%|86%|
|Mexico|28%|18%|9%|44%|53%|57%|28%|29%|34%|
|Mali|11%|8%|5%|7%|11%|12%|82%|81%|83%|
|Mongolia|43%|38%|37%|5%|16%|41%|52%|46%|22%|
|Mozambique|13%|1%|0%|20%|62%|95%|67%|37%|5%|
|Mauritania|24%|19%|13%|30%|54%|80%|45%|27%|8%|
|Malaysia|56%|40%|32%|23%|41%|63%|21%|19%|5%|
|Namibia|20%|10%|2%|60%|79%|93%|20%|11%|5%|
|Niger|4%|2%|1%|7%|6%|5%|89%|91%|94%|
|Nigeria|12%|8%|7%|12%|17%|42%|76%|75%|51%|
|Netherlands|85%|66%|42%|5%|24%|36%|10%|10%|22%|
|Norway|90%|64%|39%|5%|25%|36%|5%|11%|25%|
|Nepal|4%|0%|0%|27%|33%|39%|69%|67%|61%|
|Pakistan|8%|3%|0%|33%|36%|38%|60%|61%|62%|
|Panama|62%|52%|43%|10%|33%|52%|28%|14%|5%|
|Peru|22%|16%|10%|32%|47%|69%|46%|37%|21%|
|Philippines|27%|18%|11%|34%|43%|57%|39%|39%|33%|
|Poland|77%|66%|47%|6%|29%|48%|17%|5%|5%|
|Portugal|79%|73%|52%|5%|22%|41%|16%|5%|7%|
|<br>Paraguay|14%|7%|2%|44%|46%|45%|41%|47%|53%|
|<br>Romania|65%|61%|46%|5%|34%|49%|30%|5%|5%|
|Rwanda|5%|2%|0%|18%|30%|51%|77%|68%|49%|
|Sudan|12%|7%|1%|37%|71%|94%|51%|23%|5%|
|Senegal|10%|5%|0%|37%|40%|38%|52%|56%|61%|
|<br>Singapore|60%|46%|31%|30%|49%|64%|9%|5%|5%|
|Sierra Leone|7%|5%|4%|5%|8%|12%|88%|86%|85%|
|El Salvador|24%|15%|7%|39%|47%|49%|37%|38%|44%|

42
|**Country**|**For**|**mal lab**|**or**|**Mode**|**rn Info**<br>**Labor **|**rmal**|**Ru**<br>**Info**|**diment**<br>**rmal La**|**ary**<br>**bor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Sweden|84%|61%|39%|10%|30%|42%|6%|9%|19%|
|Chad|6%|3%|2%|11%|14%|19%|83%|83%|80%|
|Togo|7%|4%|1%|12%|11%|10%|81%|85%|89%|
|Thailand|22%|16%|12%|25%|38%|59%|53%|46%|29%|
|Tunisia|62%|48%|36%|14%|33%|51%|24%|19%|13%|
|Turkey|57%|43%|34%|14%|32%|51%|30%|24%|15%|
|Tanzania|5%|2%|1%|12%|15%|18%|83%|82%|82%|
|Uganda|12%|9%|6%|10%|16%|24%|79%|76%|70%|
|Uruguay|74%|53%|36%|5%|21%|35%|21%|26%|29%|
|United States|87%|65%|42%|7%|30%|41%|6%|5%|17%|
|Vietnam|21%|18%|15%|18%|38%|66%|61%|44%|19%|
|South Africa|17%|7%|0%|75%|88%|95%|8%|5%|5%|
|Zimbabwe|22%|21%|15%|12%|28%|37%|66%|51%|48%|

43
**Table 6. Projections for developing countries from 2010 to 2030 under reformist scenario**

|**Country**|**Fo**|**rmal la**|**bor**|**Mod**|**ern Info**<br>**Labor**|**rmal**|**Ru**<br>**Info**|**diment**<br>**rmal L**|**ary**<br>**abor**|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|89%|90%|10%|5%|5%|52%|6%|5%|
|Argentina|46%|75%|90%|36%|20%|5%|18%|5%|5%|
|Burundi|7%|12%|26%|7%|7%|5%|86%|81%|69%|
|Benin|6%|10%|18%|7%|6%|5%|87%|84%|77%|
|Bangladesh|5%|14%|35%|38%|64%|60%|57%|22%|5%|
|Bulgaria|75%|90%|90%|18%|5%|5%|8%|5%|5%|
|Bolivia|13%|25%|61%|33%|36%|34%|54%|39%|5%|
|Brazil|58%|90%|90%|20%|5%|5%|23%|5%|5%|
|Botswana|17%|31%|47%|73%|64%|48%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|6%|9%|8%|5%|5%|87%|88%|86%|
|Chile|56%|90%|90%|21%|5%|5%|23%|5%|5%|
|China|34%|83%|90%|19%|12%|5%|48%|5%|5%|
|Côte d'Ivoire|14%|11%|7%|10%|5%|5%|76%|84%|88%|
|Cameroon|17%|27%|45%|12%|7%|5%|71%|66%|50%|
|Congo, Rep.|12%|20%|39%|17%|15%|13%|71%|65%|48%|
|Colombia|30%|60%|90%|28%|29%|5%|43%|11%|5%|
|Costa Rica|57%|90%|90%|24%|5%|5%|19%|5%|5%|
|Dominican Republic|30%|64%|88%|34%|31%|7%|37%|5%|5%|
|Ecuador|27%|52%|87%|31%|32%|8%|42%|16%|5%|
|Egypt, Arab Rep.|54%|89%|90%|26%|6%|5%|21%|5%|5%|
|Ghana|10%|19%|49%|16%|18%|23%|74%|64%|28%|
|Guinea|7%|12%|26%|5%|5%|5%|88%|83%|69%|
|Guatemala<br>Hong Kong SAR,|21%|37%|71%|38%|37%|24%|41%|26%|5%|
|<br>China|76%|90%|90%|17%|5%|5%|7%|5%|5%|
|Honduras|18%|33%|72%|32%|34%|23%|50%|33%|5%|
|Hungary|86%|90%|90%|8%|5%|5%|6%|5%|5%|
|<br>Indonesia|14%|28%|64%|31%|40%|31%|55%|32%|5%|
|India|12%|23%|72%|11%|14%|16%|78%|62%|12%|
|Iran, Islamic Rep.|41%|77%|90%|23%|15%|5%|36%|8%|5%|
|<br>Iraq|43%|73%|90%|35%|22%|5%|21%|5%|5%|
|Israel|85%|90%|90%|8%|5%|5%|7%|5%|5%|
|Jamaica|21%|34%|64%|47%|41%|31%|32%|25%|5%|
|Jordan|52%|74%|90%|41%|21%|5%|7%|5%|5%|
|<br>Kenya|19%|35%|77%|28%|29%|18%|53%|36%|5%|
|Cambodia|1%|4%|22%|28%|55%|73%|71%|41%|5%|
|Korea, Rep.|71%|90%|90%|5%|5%|5%|24%|5%|5%|

44
|**Country**|**Fo**|**rmal la**|**bor**|**Mod**|**ern Info**|**rmal**|**Ru**|**diment**|**ary**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Labor**||**Info**|**rmal L**|**abor**|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Lao PDR|2%|4%|13%|15%|20%|31%|83%|75%|55%|
|Lebanon|11%|22%|46%|57%|59%|49%|32%|19%|5%|
|Sri Lanka|25%|56%|78%|36%|39%|17%|39%|5%|5%|
|Lesotho|17%|31%|47%|70%|64%|48%|13%|5%|5%|
|Morocco|30%|65%|90%|23%|27%|5%|46%|7%|5%|
|Madagascar|7%|12%|22%|9%|8%|6%|84%|80%|72%|
|Mexico|28%|54%|77%|44%|41%|18%|28%|5%|5%|
|Mali|11%|21%|37%|7%|5%|5%|82%|74%|58%|
|Mongolia|43%|70%|90%|5%|5%|5%|52%|25%|5%|
|Mozambique|13%|17%|41%|20%|67%|54%|67%|16%|5%|
|<br>Mauritania|24%|58%|81%|30%|37%|14%|45%|5%|5%|
|Malaysia|56%|90%|90%|23%|5%|5%|21%|5%|5%|
|Namibia|20%|37%|55%|60%|58%|40%|20%|5%|5%|
|Niger|4%|6%|8%|7%|5%|5%|89%|89%|87%|
|Nigeria|12%|23%|90%|12%|16%|5%|76%|61%|5%|
|Nepal|4%|9%|23%|27%|33%|44%|69%|58%|32%|
|<br>Pakistan|8%|16%|37%|33%|36%|46%|60%|49%|17%|
|Panama|62%|90%|90%|10%|5%|5%|28%|5%|5%|
|Peru|22%|48%|80%|32%|42%|15%|46%|10%|5%|
|Philippines|27%|51%|84%|34%|36%|11%|39%|12%|5%|
|Poland|77%|90%|90%|6%|5%|5%|17%|5%|5%|
|Paraguay|14%|27%|56%|44%|45%|39%|41%|28%|5%|
|Romania|65%|90%|90%|5%|5%|5%|30%|5%|5%|
|Rwanda|5%|12%|50%|18%|30%|45%|77%|57%|5%|
|Sudan|12%|35%|52%|37%|60%|43%|51%|5%|5%|
|Senegal|10%|20%|42%|37%|39%|44%|52%|41%|14%|
|Singapore|60%|89%|90%|30%|6%|5%|9%|5%|5%|
|Sierra Leone|7%|14%|39%|5%|5%|5%|88%|81%|56%|
|El Salvador|24%|45%|75%|39%|42%|20%|37%|12%|5%|
|Chad|6%|11%|30%|11%|12%|19%|83%|77%|52%|
|Togo|7%|11%|17%|12%|9%|7%|81%|80%|76%|
|Thailand|22%|46%|89%|25%|29%|6%|53%|25%|5%|
|Tunisia|62%|90%|90%|14%|5%|5%|24%|5%|5%|
|Turkey|57%|90%|90%|14%|5%|5%|30%|5%|5%|
|<br>Tanzania|5%|9%|20%|12%|14%|18%|83%|77%|62%|
|Uganda|12%|23%|72%|10%|11%|11%|79%|66%|16%|
|<br>Uruguay|74%|90%|90%|5%|5%|5%|21%|5%|5%|
|Vietnam|21%|48%|90%|18%|28%|5%|61%|23%|5%|
|South Africa|17%|30%|46%|75%|65%|49%|8%|5%|5%|
|Zimbabwe|22%|59%|90%|12%|19%|5%|66%|22%|5%|

45
**Table 7. Projections for all countries from 2010 to 2030 under baseline scenario and persistent informality parameter options, i.e. annual growth rates of μ = 1%,** **_b_ = 0.25%,** **_a_ = 0.25%, and** � **= ‐ 0.25%.**

|**Country**|**Fo**|**rmal la**|**bor**|**Mod**|**ern Info**<br>**Labor**|**rmal**|**Ru**<br>**Inf**|**diment**<br>**ormal L**|**ary**<br>**abor**|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|46%|64%|10%|15%|25%|52%|40%|12%|
|Argentina|46%|41%|36%|36%|32%|32%|18%|27%|32%|
|Australia|87%|90%|86%|5%|5%|5%|8%|5%|9%|
|Austria|87%|80%|69%|5%|5%|5%|8%|15%|26%|
|Burundi|7%|6%|5%|7%|7%|6%|86%|87%|89%|
|Belgium|86%|82%|71%|5%|5%|5%|9%|13%|24%|
|Benin|6%|5%|4%|7%|6%|5%|87%|89%|91%|
|Bangladesh|5%|3%|0%|38%|50%|67%|57%|47%|32%|
|Bulgaria|75%|77%|76%|18%|18%|19%|8%|5%|5%|
|Bolivia|13%|10%|8%|33%|29%|27%|54%|61%|66%|
|Brazil|58%|57%|55%|20%|19%|19%|23%|24%|27%|
|Botswana|17%|14%|10%|73%|81%|85%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|3%|0%|8%|5%|5%|87%|92%|95%|
|Canada|82%|84%|74%|5%|5%|5%|13%|11%|21%|
|Switzerland|86%|71%|59%|5%|5%|5%|9%|24%|36%|
|Chile|56%|62%|66%|21%|24%|26%|23%|14%|8%|
|China|34%|54%|57%|19%|41%|38%|48%|5%|5%|
|Côte d'Ivoire|14%|6%|0%|10%|5%|5%|76%|89%|95%|
|Cameroon|17%|14%|10%|12%|9%|7%|71%|78%|84%|
|Congo, Rep.|12%|9%|7%|17%|14%|11%|71%|77%|82%|
|Colombia|30%|29%|28%|28%|28%|30%|43%|44%|42%|
|Costa Rica|57%|61%|63%|24%|27%|28%|19%|12%|9%|
|Germany|83%|74%|64%|11%|8%|6%|6%|18%|30%|
|Denmark|88%|85%|71%|7%|5%|5%|5%|10%|24%|
|Dominican Republic|30%|29%|30%|34%|37%|42%|37%|34%|28%|
|Ecuador|27%|25%|22%|31%|30%|29%|42%|46%|49%|
|Egypt, Arab Rep.|54%|52%|46%|26%|25%|23%|21%|24%|30%|
|Spain|65%|69%|68%|26%|26%|27%|9%|5%|5%|
|Finland|85%|85%|76%|6%|5%|5%|9%|10%|19%|
|France|82%|79%|66%|11%|9%|7%|6%|12%|28%|
|United Kingdom|85%|77%|61%|5%|5%|5%|10%|18%|34%|
|<br>Ghana|10%|9%|8%|16%|16%|18%|74%|76%|74%|
|Guinea|7%|6%|6%|5%|5%|5%|88%|89%|89%|
|Greece|71%|72%|62%|5%|5%|5%|24%|23%|33%|
|Guatemala|21%|17%|11%|38%|31%|23%|41%|52%|65%|

46
|**Country**|**Fo**|**rmal la**|**bor**|**Mod**|**ern Info**|**rmal**|**Ru**|**diment**|**ary**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Labor **||**Inf**|**ormal L**|**abor **|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Hong Kong SAR,||||||||||
|China|76%|79%|78%|17%|16%|17%|7%|5%|5%|
|Honduras|18%|15%|13%|32%|29%|30%|50%|55%|58%|
|Hungary|86%|84%|80%|8%|7%|5%|6%|9%|15%|
|Indonesia|14%|12%|11%|31%|33%|41%|55%|55%|48%|
|India|12%|11%|13%|11%|14%|17%|78%|75%|70%|
|Ireland|83%|89%|88%|7%|6%|7%|10%|5%|5%|
|Iran, Islamic Rep.|41%|39%|38%|23%|21%|22%|36%|40%|40%|
|<br>Iraq|43%|37%|28%|35%|30%|23%|21%|32%|49%|
|Israel|85%|82%|67%|8%|5%|5%|7%|13%|28%|
|Italy|78%|73%|61%|5%|5%|5%|17%|22%|34%|
|Jamaica|21%|15%|10%|47%|36%|29%|32%|49%|60%|
|Jordan|52%|49%|49%|41%|43%|46%|7%|8%|5%|
|Japan|85%|77%|63%|5%|5%|5%|10%|18%|32%|
|Kenya|19%|16%|12%|28%|25%|22%|53%|59%|66%|
|Cambodia|1%|0%|0%|28%|42%|58%|71%|58%|42%|
|Korea, Rep.|71%|90%|90%|5%|5%|5%|24%|5%|5%|
|Lao PDR|2%|1%|0%|15%|16%|18%|83%|83%|82%|
|Lebanon|11%|8%|6%|57%|46%|60%|32%|46%|34%|
|Sri Lanka|25%|28%|33%|36%|51%|62%|39%|21%|5%|
|Lesotho|17%|12%|8%|70%|64%|68%|13%|24%|24%|
|Morocco|30%|32%|36%|23%|28%|35%|46%|40%|30%|
|Madagascar|7%|6%|4%|9%|8%|7%|84%|86%|89%|
|Mexico|28%|24%|19%|44%|40%|35%|28%|36%|46%|
|Mali|11%|11%|9%|7%|6%|5%|82%|83%|86%|
|Mongolia|43%|44%|61%|5%|5%|6%|52%|51%|33%|
|Mozambique|13%|4%|3%|20%|51%|86%|67%|45%|11%|
|Mauritania|24%|26%|27%|30%|40%|48%|45%|34%|25%|
|Malaysia|56%|52%|57%|23%|23%|28%|21%|25%|15%|
|<br>Namibia|20%|17%|13%|60%|62%|62%|20%|21%|25%|
|Niger|4%|3%|1%|7%|5%|5%|89%|92%|94%|
|Nigeria|12%|10%|15%|12%|13%|29%|76%|77%|56%|
|Netherlands|85%|80%|66%|5%|5%|5%|10%|15%|29%|
|Norway|90%|80%|65%|5%|5%|5%|5%|15%|30%|
|<br>Nepal|4%|2%|1%|27%|27%|27%|69%|71%|72%|
|Pakistan|8%|6%|4%|33%|29%|25%|60%|65%|71%|
|Panama|62%|68%|82%|10%|13%|13%|28%|19%|5%|
|Peru|22%|22%|23%|32%|35%|43%|46%|43%|35%|
|Philippines|27%|24%|22%|34%|32%|35%|39%|44%|43%|
|Poland|77%|89%|89%|6%|6%|6%|17%|5%|5%|

47
|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Info**|**diment**<br>**rmal La**|**ary**<br>**bor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Portugal|79%|86%|78%|5%|5%|5%|16%|9%|17%|
|Paraguay|14%|11%|7%|44%|36%|30%|41%|53%|63%|
|Romania|65%|78%|87%|5%|11%|8%|30%|11%|5%|
|Rwanda|5%|4%|4%|18%|24%|35%|77%|72%|61%|
|Sudan|12%|12%|12%|37%|57%|78%|51%|32%|11%|
|Senegal|10%|8%|5%|37%|32%|25%|52%|61%|70%|
|Singapore|60%|64%|63%|30%|31%|32%|9%|5%|5%|
|Sierra Leone|7%|7%|7%|5%|6%|6%|88%|88%|87%|
|El Salvador|24%|21%|16%|39%|35%|30%|37%|44%|54%|
|Sweden|84%|78%|67%|10%|7%|5%|6%|15%|28%|
|Chad|6%|5%|4%|11%|10%|12%|83%|85%|84%|
|Togo|7%|5%|3%|12%|9%|6%|81%|87%|91%|
|Thailand|22%|22%|24%|25%|27%|34%|53%|51%|42%|
|Tunisia|62%|62%|62%|14%|14%|15%|24%|25%|23%|
|Turkey|57%|56%|61%|14%|15%|17%|30%|29%|22%|
|Tanzania|5%|4%|3%|12%|12%|11%|83%|84%|86%|
|Uganda|12%|11%|12%|10%|11%|13%|79%|78%|75%|
|Uruguay|74%|65%|61%|5%|5%|5%|21%|30%|34%|
|United States|87%|85%|72%|7%|5%|5%|6%|10%|23%|
|Vietnam|21%|23%|29%|18%|27%|37%|61%|50%|34%|
|South Africa|17%|13%|8%|75%|71%|71%|8%|16%|20%|
|Zimbabwe|22%|27%|29%|12%|18%|19%|66%|55%|53%|

48
**Table 8. Projections for all countries from 2010 to 2030 under populist scenario and persistent informality parameter options, i.e. annual growth rates of μ = 1%,** **_b_ = 0.25%,** **_a_ = 0.25%, and** � **= ‐ 0.25%.**

|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Info**|**diment**<br>**rmal L**|**ary**<br>**abor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|28%|18%|10%|23%|38%|52%|49%|44%|
|Argentina|46%|24%|8%|36%|37%|32%|18%|39%|60%|
|Australia|87%|57%|26%|5%|21%|25%|8%|22%|48%|
|Austria|87%|55%|24%|5%|16%|20%|8%|30%|56%|
|Burundi|7%|3%|1%|7%|8%|7%|86%|89%|92%|
|Belgium|86%|53%|23%|5%|18%|21%|9%|29%|56%|
|Benin|6%|3%|1%|7%|7%|6%|87%|90%|94%|
|Bangladesh|5%|0%|0%|38%|49%|55%|57%|51%|45%|
|Bulgaria|75%|55%|33%|18%|40%|62%|8%|5%|5%|
|Bolivia|13%|5%|0%|33%|29%|23%|54%|66%|77%|
|Brazil|58%|34%|14%|20%|27%|26%|23%|38%|59%|
|Botswana|17%|3%|0%|73%|91%|88%|10%|6%|12%|
|Central African||||||||||
|Republic|5%|2%|0%|8%|5%|5%|87%|93%|95%|
|Canada|82%|53%|23%|5%|18%|21%|13%|29%|57%|
|Switzerland|86%|51%|22%|5%|13%|17%|9%|36%|61%|
|Chile|56%|37%|17%|21%|34%|35%|23%|29%|47%|
|China|34%|30%|20%|19%|53%|75%|48%|17%|5%|
|Côte d'Ivoire|14%|4%|0%|10%|5%|5%|76%|90%|95%|
|Cameroon|17%|8%|2%|12%|11%|8%|71%|81%|89%|
|Congo, Rep.|12%|5%|1%|17%|15%|11%|71%|80%|88%|
|Colombia|30%|16%|6%|28%|31%|30%|43%|53%|64%|
|Costa Rica|57%|37%|16%|24%|36%|36%|19%|27%|48%|
|Germany|83%|46%|19%|11%|21%|22%|6%|33%|58%|
|Denmark|88%|53%|22%|7%|21%|22%|5%|26%|56%|
|Dominican Republic|30%|16%|5%|34%|39%|37%|37%|45%|58%|
|Ecuador|27%|14%|4%|31%|32%|29%|42%|54%|67%|
|Egypt, Arab Rep.|54%|31%|11%|26%|31%|27%|21%|38%|62%|
|Spain|65%|46%|22%|26%|42%|47%|9%|11%|31%|
|Finland|85%|53%|23%|6%|21%|24%|9%|26%|53%|
|France|82%|49%|20%|11%|24%|23%|6%|27%|57%|
|United Kingdom|85%|52%|20%|5%|15%|16%|10%|33%|64%|
|<br>Ghana|10%|4%|1%|16%|17%|18%|74%|79%|82%|
|Guinea|7%|5%|3%|5%|5%|5%|88%|90%|92%|
|Greece|71%|53%|24%|5%|12%|18%|24%|35%|58%|
|Guatemala|21%|9%|1%|38%|32%|22%|41%|59%|77%|

49
|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**|**rmal**|**Ru**|**diment**|**ary**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Labor **||**Info**|**rmal L**|**abor **|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Hong Kong SAR,||||||||||
|China|76%|50%|28%|17%|34%|47%|7%|16%|25%|
|Honduras|18%|8%|1%|32%|31%|29%|50%|61%|70%|
|Hungary|86%|53%|24%|8%|22%|26%|6%|25%|49%|
|Indonesia|14%|6%|0%|31%|34%|36%|55%|61%|64%|
|India|12%|6%|2%|11%|15%|19%|78%|78%|79%|
|Ireland|83%|63%|31%|7%|27%|33%|10%|9%|36%|
|Iran, Islamic Rep.|41%|24%|10%|23%|28%|30%|36%|49%|60%|
|<br>Iraq|43%|21%|6%|35%|33%|21%|21%|45%|73%|
|Israel|85%|51%|19%|8%|19%|18%|7%|30%|63%|
|Italy|78%|52%|23%|5%|13%|17%|17%|35%|60%|
|Jamaica|21%|7%|0%|47%|37%|30%|32%|56%|70%|
|Jordan|52%|29%|11%|41%|48%|51%|7%|23%|38%|
|Japan|85%|54%|23%|5%|14%|18%|10%|32%|59%|
|Kenya|19%|9%|2%|28%|27%|21%|53%|64%|77%|
|Cambodia|1%|0%|0%|28%|40%|48%|71%|60%|52%|
|Korea, Rep.|71%|62%|41%|5%|21%|38%|24%|18%|21%|
|<br>Lao PDR|2%|0%|0%|15%|16%|16%|83%|84%|84%|
|Lebanon|11%|2%|0%|57%|46%|49%|32%|53%|51%|
|Sri Lanka|25%|15%|3%|36%|53%|71%|39%|33%|26%|
|Lesotho|17%|3%|0%|70%|64%|56%|13%|33%|44%|
|Morocco|30%|19%|8%|23%|33%|38%|46%|49%|54%|
|Madagascar|7%|3%|1%|9%|9%|7%|84%|88%|92%|
|Mexico|28%|13%|2%|44%|42%|34%|28%|45%|64%|
|Mali|11%|7%|2%|7%|8%|7%|82%|85%|90%|
|Mongolia|43%|30%|19%|5%|12%|22%|52%|58%|59%|
|<br>Mozambique|13%|0%|0%|20%|48%|64%|67%|52%|36%|
|Mauritania|24%|14%|4%|30%|43%|48%|45%|43%|49%|
|Malaysia|56%|31%|14%|23%|30%|34%|21%|38%|52%|
|<br>Namibia|20%|6%|0%|60%|63%|55%|20%|31%|45%|
|Niger|4%|2%|0%|7%|5%|5%|89%|93%|95%|
|Nigeria|12%|6%|2%|12%|13%|20%|76%|81%|79%|
|Netherlands|85%|53%|22%|5%|16%|19%|10%|30%|59%|
|Norway|90%|51%|20%|5%|18%|19%|5%|31%|61%|
|<br>Nepal|4%|0%|0%|27%|27%|25%|69%|73%|75%|
|Pakistan|8%|2%|0%|33%|29%|23%|60%|70%|77%|
|Panama|62%|41%|23%|10%|24%|35%|28%|36%|42%|
|Peru|22%|12%|3%|32%|37%|38%|46%|51%|59%|
|Philippines|27%|13%|3%|34%|34%|31%|39%|53%|65%|
|Poland|77%|55%|30%|6%|25%|34%|17%|20%|36%|

50
|**Country**|**For**|**mal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Info**|**diment**<br>**rmal La**|**ary**<br>**bor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Portugal|79%|60%|28%|5%|16%|22%|16%|24%|50%|
|Paraguay|14%|5%|0%|44%|37%|27%|41%|59%|73%|
|Romania|65%|48%|32%|5%|25%|44%|30%|27%|24%|
|Rwanda|5%|1%|0%|18%|23%|29%|77%|75%|71%|
|Sudan|12%|4%|0%|37%|56%|64%|51%|40%|36%|
|Senegal|10%|3%|0%|37%|32%|23%|52%|66%|77%|
|Singapore|60%|38%|19%|30%|42%|52%|9%|20%|28%|
|Sierra Leone|7%|4%|2%|5%|7%|7%|88%|89%|92%|
|El Salvador|24%|11%|2%|39%|37%|28%|37%|53%|70%|
|Sweden|84%|48%|20%|10%|21%|22%|6%|30%|58%|
|Chad|6%|2%|0%|11%|11%|11%|83%|87%|89%|
|Togo|7%|3%|0%|12%|9%|6%|81%|88%|94%|
|Thailand|22%|12%|4%|25%|30%|35%|53%|58%|61%|
|Tunisia|62%|38%|18%|14%|24%|27%|24%|38%|55%|
|Turkey|57%|34%|16%|14%|23%|26%|30%|43%|59%|
|Tanzania|5%|2%|0%|12%|12%|11%|83%|86%|89%|
|Uganda|12%|7%|2%|10%|12%|14%|79%|81%|84%|
|Uruguay|74%|42%|18%|5%|15%|18%|21%|43%|65%|
|United States|87%|53%|21%|7%|21%|22%|6%|26%|57%|
|Vietnam|21%|13%|6%|18%|30%|40%|61%|57%|55%|
|South Africa|17%|3%|0%|75%|69%|55%|8%|27%|45%|
|Zimbabwe|22%|16%|6%|12%|20%|18%|66%|64%|76%|

51
**Table 9. Projections for developing countries from 2010 to 2030 under reformist scenario and persistent informality parameter options, i.e. annual growth rates of μ = 1%,** **_b_ = 0.25%,** **_a_ = 0.25%, and** � **= ‐ 0.25%.**

|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Inf**|**diment**<br>**ormal L**|**ary**<br>**abor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Albania|39%|67%|90%|10%|5%|5%|52%|28%|5%|
|Argentina|46%|67%|90%|36%|22%|5%|18%|11%|5%|
|Burundi|7%|10%|13%|7%|5%|5%|86%|85%|82%|
|Benin|6%|8%|8%|7%|5%|5%|87%|87%|87%|
|Bangladesh|5%|10%|27%|38%|51%|68%|57%|40%|5%|
|Bulgaria|75%|90%|90%|18%|5%|5%|8%|5%|5%|
|<br>Bolivia|13%|19%|33%|33%|28%|25%|54%|53%|42%|
|Brazil|58%|89%|90%|20%|5%|5%|23%|6%|5%|
|Botswana|17%|28%|42%|73%|67%|53%|10%|5%|5%|
|Central African||||||||||
|Republic|5%|4%|3%|8%|5%|5%|87%|91%|92%|
|Chile|56%|90%|90%|21%|5%|5%|23%|5%|5%|
|China|34%|82%|90%|19%|13%|5%|48%|5%|5%|
|Côte d'Ivoire|14%|8%|2%|10%|5%|5%|76%|87%|93%|
|Cameroon|17%|22%|22%|12%|5%|5%|71%|73%|73%|
|Congo, Rep.|12%|16%|22%|17%|11%|6%|71%|73%|72%|
|Colombia|30%|47%|90%|28%|20%|5%|43%|32%|5%|
|Costa Rica|57%|90%|90%|24%|5%|5%|19%|5%|5%|
|Dominican||||||||||
|Republic|30%|50%|87%|34%|31%|8%|37%|19%|5%|
|Ecuador|27%|41%|74%|31%|24%|13%|42%|36%|14%|
|Egypt, Arab Rep.|54%|84%|90%|26%|10%|5%|21%|5%|5%|
|Ghana|10%|15%|27%|16%|14%|12%|74%|72%|61%|
|Guinea|7%|8%|12%|5%|5%|5%|88%|87%|83%|
|Guatemala<br>Hong Kong SAR,|21%|29%|42%|38%|28%|17%|41%|43%|42%|
|<br>China|76%|90%|90%|17%|5%|5%|7%|5%|5%|
|Honduras|18%|26%|46%|32%|26%|21%|50%|48%|34%|
|Hungary|86%|90%|90%|8%|5%|5%|6%|5%|5%|
|Indonesia|14%|22%|48%|31%|31%|37%|55%|47%|15%|
|India|12%|19%|40%|11%|11%|7%|78%|71%|53%|
|Iran, Islamic Rep.|41%|62%|90%|23%|9%|5%|36%|29%|5%|
|Iraq|43%|63%|90%|35%|22%|5%|21%|15%|5%|
|<br>Israel|85%|90%|90%|8%|5%|5%|7%|5%|5%|
|Jamaica|21%|26%|37%|47%|32%|21%|32%|42%|42%|
|Jordan|52%|73%|90%|41%|22%|5%|7%|5%|5%|
|Kenya|19%|27%|43%|28%|22%|13%|53%|51%|44%|

52
|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**|**rmal**|**Ru**|**diment**|**ary**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Labor **||**Inf**|**ormal L**|**abor **|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|Cambodia|1%|2%|9%|28%|44%|70%|71%|54%|21%|
|Korea, Rep.|71%|90%|90%|5%|5%|5%|24%|5%|5%|
|Lao PDR|2%|3%|6%|15%|16%|18%|83%|81%|76%|
|Lebanon|11%|16%|41%|57%|46%|54%|32%|38%|5%|
|Sri Lanka|25%|48%|76%|36%|45%|19%|39%|6%|5%|
|Lesotho|17%|25%|41%|70%|63%|54%|13%|13%|5%|
|Morocco|30%|52%|90%|23%|19%|5%|46%|29%|5%|
|Madagascar|7%|9%|11%|9%|6%|5%|84%|84%|84%|
|Mexico|28%|41%|67%|44%|34%|21%|28%|24%|13%|
|Mali|11%|16%|18%|7%|5%|5%|82%|79%|77%|
|Mongolia|43%|53%|90%|5%|5%|5%|52%|42%|5%|
|Mozambique|13%|12%|36%|20%|52%|59%|67%|36%|5%|
|Mauritania|24%|44%|80%|30%|34%|15%|45%|22%|5%|
|Malaysia|56%|84%|90%|23%|8%|5%|21%|8%|5%|
|Namibia|20%|32%|50%|60%|60%|45%|20%|9%|5%|
|Niger|4%|4%|3%|7%|5%|5%|89%|91%|92%|
|<br>Nigeria|12%|18%|75%|12%|11%|20%|76%|71%|5%|
|Nepal|4%|6%|12%|27%|26%|26%|69%|67%|62%|
|Pakistan|8%|12%|19%|33%|28%|25%|60%|60%|56%|
|Panama|62%|90%|90%|10%|5%|5%|28%|5%|5%|
|Peru|22%|38%|78%|32%|31%|17%|46%|31%|5%|
|Philippines|27%|40%|83%|34%|26%|12%|39%|33%|5%|
|Poland|77%|90%|90%|6%|5%|5%|17%|5%|5%|
|Paraguay|14%|20%|31%|44%|35%|27%|41%|45%|42%|
|Romania|65%|90%|90%|5%|5%|5%|30%|5%|5%|
|Rwanda|5%|9%|25%|18%|24%|39%|77%|67%|36%|
|Sudan|12%|24%|48%|37%|56%|47%|51%|20%|5%|
|Senegal|10%|15%|22%|37%|30%|24%|52%|55%|54%|
|Singapore|60%|89%|90%|30%|6%|5%|9%|5%|5%|
|Sierra Leone|7%|11%|20%|5%|5%|5%|88%|84%|75%|
|El Salvador|24%|36%|59%|39%|31%|21%|37%|33%|20%|
|Chad|6%|8%|16%|11%|9%|9%|83%|82%|75%|
|Togo|7%|8%|8%|12%|7%|5%|81%|84%|87%|
|<br>Thailand|22%|36%|80%|25%|21%|15%|53%|42%|5%|
|Tunisia|62%|87%|90%|14%|5%|5%|24%|8%|5%|
|Turkey|57%|84%|90%|14%|5%|5%|30%|11%|5%|
|<br>Tanzania|5%|7%|11%|12%|11%|10%|83%|82%|79%|
|Uganda|12%|18%|37%|10%|8%|5%|79%|74%|58%|
|Uruguay|74%|77%|90%|5%|5%|5%|21%|18%|5%|
|Vietnam|21%|39%|90%|18%|21%|5%|61%|41%|5%|

53
|**Country**|**Fo**|**rmal lab**|**or**|**Mod**|**ern Info**<br>**Labor **|**rmal**|**Ru**<br>**Inf**|**diment**<br>**ormal La**|**ary**<br>**bor **|
|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|**2010**|**2020**|**2030**|
|South Africa|17%|27%|41%|75%|68%|54%|8%|5%|5%|
|Zimbabwe|22%|46%|90%|12%|12%|5%|66%|42%|5%|

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<!-- Start of picture text --> 100%<br>90%<br>g 80%<br>iD oa mTM InformalityRudimentary<br>: | TM Modern Informality<br>2 50%<br>oO<br><!-- End of picture text -->

<!-- Start of picture text --> = 40%<br>S gens ® Formality<br>x& 20%<br>10%<br>0% _<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text --> <!-- Start of picture text --> 100% |<br>90% |<br>BBs | ®@ Rudimentary<br>2 70% | Informality<br>~ 60% |<br><!-- End of picture text -->

<!-- Start of picture text --> = 40% Informality<br>O 30% | ® Formality<br>S 20%10% |<br>0% | :<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100%<br>90%<br>o<br>=<br>3 50%60% # RudimentaryInformalityInformality<br><!-- End of picture text --> 100% 90% o = 3 60%50%60% # RudimentaryInformalityInformality & 40% TM Modern Ses:SS 30% Informality.. S 20% ® Formality 10% 0% 2010 2020 2030 2010 2020 2030 2010 2020 2030 100% 90% o = S 50%60% # InformalityRudimentaryRudimentary & 40% TM Modern Sie . © 30% Informality S 20% TM Formality 10% 0% 2010 2020 2030 2010 2020 2030 2010 2020 2030 <!-- Start of picture text --> & 40% TM Modern<br>Ses:SS 30% Informality..<br>S 20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>o<br>=<br>S 60%50%60% # InformalityRudimentaryRudimentary<br><!-- End of picture text --> <!-- Start of picture text --> & 40% TM Modern<br>Sie .<br>© 30% Informality<br>S 20% TM Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100% |<br>90% |<br>e 60% | # Rudimentary<br>S 50% | Informality<br><!-- End of picture text -->

<!-- Start of picture text --> & 40% TM Modern<br>© 30% | Informality<br>S 20% | TMFormality<br>10% |<br>0% |<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->

<!-- Start of picture text --> 100%<br>90%<br>=oO 60% | # Rudimentary*<br>S 50% | informality<br><!-- End of picture text -->

<!-- Start of picture text --> 2 40% TM Modern<br>2 30% | informality<br>S 20% | TMFormality<br>10%<br>0% |<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100%<br>90%<br>@ 80%<br>S 70%<br>—oO 60% TM Rudimentary*<br>S so% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 2 40% TM Modern<br>4O 30% Informality*<br>S 20% TMFormality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>Box<br>£ 60% @ Rudimentary<br>S sox Informality<br><!-- End of picture text --> <!-- Start of picture text --> a 40% TM Modern<br>O 30% Informality<br>XS 20% © Formality<br>10%<br>0% :<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100% |<br>90% |<br>~jo)jo) 60% | = Rudimentary++<br>S som | Informality<br><!-- End of picture text -->

<!-- Start of picture text --> 100% |<br>90% |<br>~jo)jo) 60% | = Rudimentary++<br>S som | Informality<br>2 40% | TMModern<br>—Oo 30% | Informality.<br>S 20% | = Formality<br>10% |<br>0% ©<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100% |<br>90% |<br>roxo|<br>=<br>S 56 0% || m InformalityRudimentary<br>& aoy | TM Modern<br>—So 30% | Informality.<br>S 20% TM Formality<br>10% |<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100%<br>90%<br>B57om57omom<br>=ie)ie) 60% TM Rudimentary..<br>S so% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>B57om57omom<br>=ie)ie) 60% TM Rudimentary..<br>S so% Informality<br>2 40% ® Modern<br>—2 30% Informality.<br>XS 20% © Formality<br>10% |<br>0% L _<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100%<br>90%<br>Oo .<br>= 60% # Rudimentary<br>S so% Informality<br>a 40% TM Modern<br>2 30% Informality<br>S 20% ® Formality<br>10% |<br>0% L — Laas _ _ _ _ _<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100%<br>90%<br>@ 80%<br>5 70% .<br>2 60% @ Rudimentary<br>S 50% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>@ 80%<br>5 70% .<br>2 60% @ Rudimentary<br>S 50% Informality<br>2 40% TM Modern<br>S 30% Informality<br>XS 20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100%<br>90%<br>@ 80%<br>5 70% .<br>Sox @ Rudimentary<br>S so% Informality<br>B 40% ® Modern<br>—<br>S230% Informality<br>20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
<!-- Start of picture text --> 100%<br>90%<br>=oOoO 60% TM Rudimentary++<br>S so% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>=oOoO 60% TM Rudimentary++<br>S so% Informality<br>= 40% ® Modern<br>O 30% Informality<br>S 20% TMFormality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100%<br>90%<br>@ 80%<br>5 70% .<br>= 60% TM Rudimentary<br>S 50% Informality<br>2 40% TM Modern<br>S 30% informality<br>S 20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->