The Long Shadow of Informality -- Challenges and Policies

Overcoming the challenges of informal economic activity requires a combination of policies

tailored to economy-specific circumstances. In countries where informality is predominantly a

reflection of poor governance, an appropriate policy package could streamline regulatory and

tax frameworks while improving the efficiency of public revenue collection and regulatory

enforcement as well as strengthening public service delivery to bolster tax morale. In countries

where informality is predominantly a reflection of underdevelopment, an appropriate policy

package could include measures to expand access to finance, markets, and inputs to foster firm

productivity and growth; better education to facilitate formal sector employment; and

enhanced safety nets to cushion household risks. Several such policy improvements have been

associated with sustained declines in informality.

Introduction

Widespread informality is a common policy challenge in emerging market and

developing economies (EMDEs). Theoretical models present two major reasons for the

emergence of informal economic activity: lack of development (Harris and Todaro 1970;

Loayza 2016) and poor governance (de Soto 1989). These two reasons suggest different

policy approaches to address informality. The former refers to factors such as an inability

of an urban, modern, formal sector to absorb rural migrants; limited financial

development to provide finance for formal sectors; and insufficient human capital that

prevents workers from finding jobs in the formal sector.

The latter refers to factors such

as excessively burdensome tax and regulatory frameworks that encourage firms to remain

informal, excessive labor regulations that increase the cost of formal employment, and

poor governance and regulatory quality that discourage formal participation (chapter 2).

The former reason emphasizes the inability to benefit from participating in the formal

sector, whereas the latter emphasizes the costs associated with formal-sector

participation.

Many EMDE governments have implemented a wide range of policy reforms in the past

few decades that may have helped to reduce informality (figure 6.1; Jessen and Kluve

2021).

These reforms have often been implemented to either increase the benefits of

CHAPTER 6

ackling Informality: Policy Options

Note: This chapter was prepared by Franziska Ohnsorge and Shu Yu. Research assistance was provided by

Hrisyana Doytchinova and Lorez Qehaja.

See, for example, Amaral and Quintin (2006); Fields (1975); Harris and Todaro (1970); and Loayza (2016).

See Loayza (2018); Oviedo, Thomas, and Karakurum-Özdemir (2009); and chapter 2 for a review of costs and

benefits associated with formal (informal) sector participation and how optimizing participants may choose formality

(or informality).

Some of these reforms had their roots in the “Washington Consensus” (Birdsall, de La Torre, and Caicedo

2010; Naim 1999; Williamson 2000).

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H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE 6.1 Policies to address challenges of informality in EMDEs

Governments have implemented a wide range of reforms that could affect informality.

B. Reforms across EMDE regions A. Reforms in advanced economies and EMDEs

Sources: International Country Risk Guide (ICRG); World Bank (Doing Business).

Note: See World Bank Doing Business database for reform details. EAP = East Asia and Pacific; ECA = Europe and Central Asia;

EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa;

SAR = South Asia; SSA = Sub-Saharan Africa.

A.B. For an average economy, the number of policy reforms that have been implemented after year 2008 and are regarded as

“improvement” in the ease of doing business or “neutral” (which applies only to “labor market regulation”) by Doing Business 2008-18.

C.D. For an average economy, the average number of policy reforms per year that have been implemented during 2008-10 in

comparison to the annual average number of reforms conducted during 2016-18 (shown in bars).

E.F. Bars show the shares of economies with improved control of corruption (in E; the ease of doing business in F) between 2010 and

2018.

D. Reforms by EMDE region, 2008-18 (continued) C. Reforms by EMDE region, 2008-18

F. Economies with improvement in the ease of

doing business

E. Economies with improvement in control of

corruption

EAP

ECA

LAC

MNA

SAR

SSA

Labor market regulation

Starting a business

Paying taxes

Getting credit

Other reforms

Number of reforms

2008-10

2016-18

2008-10

2016-18

2008-10

2016-18

EAP

ECA

LAC

Lab mkt regulation Starting a business

Paying taxes Getting credit

Other reforms

Number per year

2008-10

2016-18

2008-10

2016-18

2008-10

2016-18

MNA

SAR

SSA

Lab mkt regulation Starting a business

Paying taxes Getting credit

Other reforms

Number per year

Advanced economies

EMDEs

Percent of economies

Advanced economies EMDEs

Percent of economies

Advanced economies

EMDEs

Labor market regulation

Starting a business

Paying taxes

Getting credit

Other reforms

Number of reforms

C H A P T E R 6 2

59 T H E L O N G S H A D O W O F I N F O R M A L I T Y

formal-sector participation or reduce the costs of formal activity. For instance, both

corporate and personal income tax rates in EMDEs have been reduced, from 37-39

percent on average in the early 1990s to about 24 percent in 2019 (Végh and Vuletin

2015). Time spent on paying taxes was cut by about one-third in EMDEs between 2006

and 2020. Value added taxes, which can lower tax burdens through a refund on input

taxes, had been adopted in 71 EMDEs by 2020 (World Bank 2020a). Access to

financial services has broadened, with access to automatic teller machines (ATMs) per

100,000 adults and the share of the population with an account at a financial institution

both increasing by more than 50 percent between 2010 and 2018. Over the same

period, one-third to two-thirds of EMDEs improved their governance and institutional

quality.

A review of past policy reforms indicates that some reforms had unintended

consequences for informality. Policy reforms often had more benign effects on

informality when they were implemented in a supportive institutional and

macroeconomic environment. For instance, trade liberalization programs were often

associated with greater informality in the short term—unless they were accompanied by

greater labor market flexibility and an upgrading of skills in the labor force (Goldberg

and Pavcnik 2003; McCaig and Pavcnik 2015; World Bank 2019b).

The current pandemic has provided a reminder of the developmental challenges posed

by the informal sector. Informal participants have suffered more adverse economic and

health consequences from COVID-19 (coronavirus) than their formal counterparts (box

2.1). The untapped potential of informal sectors, if harnessed to boost income growth

and resilience, can help EMDEs build back better from the severe global recession of

2020. This is especially important against the backdrop of a steady decline in potential

growth, the growth an economy can sustain at full employment and capacity, over the

past decade as all fundamental drivers of growth weakened (World Bank 2018a, 2020b).

Specifically, this chapter addresses the following questions:

• Which fiscal measures can help reduce informality?

• Which other policies can help reduce informality?

• What should be the elements of a comprehensive policy package to tackle

informality?

Contributions. The chapter makes the following contributions to the literature. First, it

offers a systematic review of policies that could affect informality, ranging from fiscal

policies to labor market regulations and policies to encourage financial development. It

covers both policies that are intentionally designed to encourage formalization and ones

that could incidentally affect the informal sector.

Second, the chapter describes novel empirical estimates of the cumulative changes in

informality following various policy changes, obtained using a local projection model.

Policy-related variables examined include tax rates, access to credit by the private sector,

60 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Financial development reduces the costs of accessing external financing and thus

incentivizes firms and households to invest, including in higher-productivity projects.

It also incentivizes participants of the informal sector to join the formal sector. In

emerging market and developing economies (EMDEs) with above-median

informality, a significantly larger share of firms relies on internal finance and

identifies access to finance as a major business obstacle than in EMDEs with below-

median informality. Also, in EMDEs with more prevalent informality, a significantly

smaller share of households has access to commercial bank branches, automated teller

machines (ATMs), and credit. Over the past three decades, growing access to financial

services and credit has coincided with a falling share of the informal economy.

Introduction

In recent decades, much research has been devoted to understanding the

determinants of informal economic activity, including the role of financial

development (Loayza 2018; Ulyssea 2020). Financial development can influence

firms’ and individuals’ choices to engage in informal activity and may also,

conversely, be affected by the level of informality (for instance, Capasso and

Jappelli 2013; Elgin and Uras 2013; Straub 2005). Easier access to non-cash-

based payments—whether via mobile phones, cards, or online—can improve the

government’s ability to reach and support informal participants during a

recession like COVID-19 (World Bank 2019c).

Firms in the informal sector are typically characterized by small scale, low capital-

to-labor ratios, lack of investment, a low propensity to implement new and even

high-return technologies, and unskilled managers (Capasso and Jappelli 2013;

Dabla-Norris, Gradstein, and Inchauste 2008; Quintin 2008). By influencing

firms’ behavior, financial development can encourage capital accumulation and

productivity improvements, and thus enhance long-run economic growth,

particularly in the presence of informality (Antunes and Cavalcanti 2007).

Against this background, this box addresses the following questions:

• What links between informality and financial development have been

identified by the literature?

• How does financial development differ between EMDEs with high and low

informality?

• How has financial development in EMDEs evolved?

BOX 6.1 Financial development and the informal economy

Note: This box was prepared by Salvatore Capasso, Franziska Ohnsorge, and Shu Yu.

a. Also see Fang, Kennedy, and Resnick (2020) for detailed examples.

C H A P T E R 6 2

61 T H E L O N G S H A D O W O F I N F O R M A L I T Y

BOX 6.1 Financial development and the informal economy

(continued)

The box examines the nexus between financial development and informality both

theoretically and empirically. It first provides a short literature review on the

channels through which limited financial development can encourage

informality, followed by a summary of existing empirical evidence. It then uses

both descriptive statistics and regression analysis to show that greater informality

is associated with less financial development, and that better access to finance is

associated with lower informality. The conclusion offers policy recom-

mendations.

Lessons from the literature

Theoretical models suggest that financial development reduces informality,

whereas the existence of informality could also hinder financial development.

Such theoretical findings are supported by empirical studies.

Theoretical models. As informal participants hide all or part of their income and

wealth from the authorities, they face high costs of providing collateral or

signaling their profitability to lenders and are often credit-rationed (Blackburn,

Bose, and Capasso 2012; Capasso and Jappelli 2013). The choice of operating

formally or informally thus involves a trade-off between higher financial costs, as

well as restricted access to public goods, and the benefits of lower tax and

regulatory burdens (Franjo, Pouokam, and Turino 2020; Straub 2005). This

trade-off can be faced at the level of the firm or household (extensive margin) or

at the level of individual transactions within a firm (intensive margin).

Theory predicts that, as financial markets develop, the size of the informal sector

will decrease. Financial development, which involves innovations ranging from

the emergence of new and more efficient monitoring and screening technologies

to more intermediated funds, typically reduces the average costs of accessing

financial resources and incentivizes firms and entrepreneurs to operate formally.

Several mechanisms have been explored.

• Lower collateral requirements. By improving screening and monitoring

technologies, financial development will tend to reduce the minimum

collateral required for borrowing, which will tend to attract entrepreneurs

into the formal sector (Straub 2005).

• Stronger legal enforcement. By strengthening financial contract enforceability,

financial development can lower credit costs, which will also tend to attract

entrepreneurs into the formal sector (Amaral and Quintin 2006; Antunes

and Cavalcanti 2007; Quintin 2008).

62 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

• Expanding pool of formal finance. By expanding the pool of formal-sector

funding, financial development can lower the relative cost of formal finance

and attract entrepreneurs into the formal sector (Blackburn, Bose, and

Capasso 2012; Capasso and Jappelli 2013).

• More efficient tax auditing. Financial development can facilitate the

enforcement of tax compliance, which is likely to discourage informal

activity (Guo and Hung 2020).

Conversely, some studies point to informality as holding back financial

development, through several channels.

• Tax evasion. Tax evasion, which is often at the core of informality, erodes

government revenue bases. Countries with pervasive tax evasion have often

used financial transaction taxes to boost revenues. These taxes increase

financial intermediation costs and may slow financial development (Elgin

and Uras 2013; Roubini and Sala-i-Martin 1992, 1995).

• Higher bank monitoring costs. Where informality is prevalent, the lack of

formal, declared incomes and assets may force banks to incur higher

screening and monitoring expenses. This raises borrowing costs (Capasso,

Monferrà, and Sampagnaro 2015).

Empirical evidence. Several measures of financial development have been found

to be statistically significantly associated with smaller informal activity

(Bittencourt, Gupta, and Stander 2014; Bose, Capasso, and Wurm 2012; Gatti

and Honorati 2008). The empirical association has been robust to different

model specifications and estimation methodologies (See table 6B.1 for a detailed

summary).

• Firm-level evidence. Firms that rate financing as a major obstacle to their

businesses have, on average, a 16 percent probability of hiding at least 50

percent of their sales, whereas this probability drops below 6 percent for

firms that consider financing to be a minor obstacle (Dabla-Norris,

Gradstain, and Inchauste 2008). More tax-compliant firms have reported

significantly easier access to credit, and this relationship was stronger in more

formalized economies (Gatti and Honorati 2008).

• Household-level evidence. Italian households reported greater informal activi-

ty, especially in the construction sector, in regions with weaker financial

development (Capasso and Jappelli 2013).

• Cross-economy evidence. Among 137 economies during 1995-2007, both

greater efficiency and depth of the banking sector were associated with

BOX 6.1 Financial development and the informal economy

(continued)

C H A P T E R 6 2

63 T H E L O N G S H A D O W O F I N F O R M A L I T Y

BOX 6.1 Financial development and the informal economy

(continued)

significantly lower informality (Bose, Capasso, and Wurm 2012). Among

150 economies during 1980-2009, faster broad money growth and a smaller

differential between lending and deposit interest rates were associated with

statistically significantly smaller informal economies, even when controlling

for institutional quality and central bank independence (Bittencourt, Gupta,

and Stander 2014).

Stylized facts

Firms and workers in the informal sector have less access to credit and financial

services in EMDEs with above-median informality than in EMDEs with below-

median informality.

Methodology and data. A sample of 122 EMDEs for 1990-2018 (or the latest

available year) is split into those with above-median and below-median shares of

informality by output (as proxied by the dynamic general equilibrium model-

based share of informal output in official gross domestic product [GDP]) and

employment (proxied by the share of self-employment in total employment).

Financial development is proxied, first, by firms’ reported access to bank credit

and capital markets, their difficulty in accessing credit, and the share of internal

finance used in investment. Second, at the household level, financial

development is proxied by the number of commercial bank branches, ATMs, and

bank credit as well as account ownership and reported use of mobile payment

services. Data are available from World Bank Enterprise Surveys, the World

Bank’s Global Financial Development Database, and the World Development

Indicators. In addition, the International Monetary Fund’s Financial

Development Index and its subcomponents are used as proxies for overall

financial development and for development in “financial institutions” and

“financial markets.”

Simple averages of the financial development indicators for

EMDEs with above-median informality and those with below-median

b. Several studies have found nonlinear relationships between informality and financial development.

The impact of financial development on informality is greater in more financially developed economies or

when GDP exceeds a certain level (Canh and Thanh 2020; Gharleghi and Jahanshahi 2020) or may even

be inverse-U-shaped (Elgin and Uras 2013).

c. The results from output informality and employment informality are largely consistent. This box

mainly relies on results from output informality.

d. The “financial markets” development index captures access to, and depth and efficiency of, an

economy’s stock and debt markets, which is less relevant for informal participants in EMDEs. The

“financial institutions” development index measures how developed financial institutions are in terms of

their depth (size and liquidity), access (ability of individuals and companies to access financial services),

and efficiency (ability of institutions to provide financial services at low cost and with sustainable

revenues).

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H E L O N G S H A D O W O F I N F O R M A L I T Y

informality (grouped above) are tested for statistically significant differences.

There is no presumption of causality going either from financial development to

informality or vice versa.

Firms. Firms in the informal sector have reported more restricted access to credit

from the banking sector and capital markets, which limits their ability to invest,

including in productivity-enhancing new technologies (Capasso and Jappelli

2013; D’Erasmo 2016; Ferreira-Tiryaki 2008). In EMDEs with above-median

output informality, on average, 36 percent of firms identified access to finance as

a major constraint—about 9 percentage points more than in other EMDEs

(figure B6.1.1). Firms also rely more on internal finance for operating, starting,

or expanding firms in EMDEs with more pervasive informality (Farazi 2014).

On average in EMDEs with above-median informality, 75 percent of firms

depend on internal finance to invest and 19 percent of firms can use bank funds

to fulfill their investment needs, compared with 68 percent and 29 percent of

firms, respectively, on average in EMDEs with below-median informality.

Households. Households in EMDEs with below-median informality have access

to significantly more commercial bank branches, ATMs, and credit than those in

EMDEs with above-median informality (figure B6.1.1). About 50 percent of the

population in EMDEs with below-median informality owns an account at a

financial institution or recently used a mobile money service—about 17

percentage points more than in EMDEs with above-median informality.

Evolution of financial development and its implications

EMDE financial systems have deepened and financial access has broadened over

the past three decades. This has coincided with a steady decline in the shares of

informal output and employment.

Methodology. A local projection model is used to estimate the cumulative

changes in the share of informal output or informal employment over one to five

years following a shift in financial development (annex 6A). Two dimensions of

financial development that are particularly relevant for informal participants are

examined. The first is the ability of individuals and companies to access financial

services, which is proxied by the number of commercial bank branches per

100,000 adults. The second dimension, financial system depth, is proxied by

domestic credit to the private sector in percent of GDP (Svirydzenka 2016;

World Bank 2020c). The estimation controls for per capita GDP. The sample

covers 125 EMDEs over 1990-2018.

Financial development in EMDEs. Measures to improve access to credit have

been a common policy reform in East Asia and the Pacific, the Middle East and

BOX 6.1 Financial development and the informal economy

(continued)

C H A P T E R 6 2

65 T H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE B6.1.1 F

inancial development and informality in EMDEs

Firms and workers in EMDEs with more pervasive informality are more likely to be

financially constrained, less likely to obtain bank finance, and more likely to have

limited access to other financial services than those in EMDEs with less pervasive

informality. As a result, firms in EMDEs with more pervasive informality rely more on

internal financing.

B. Financing options facing firms A. Financial constraint facing firms and

informality

Sources: International Monetary Fund (Financial Development Index Database); World Bank (Enterprise Surveys,

Global Financial Development Database, World Development Indicators).

Note: Data are from EMDEs and the period 1990-2018. Output informality is measured by DGE-based estimates on

informal output (in percent of official GDP). Employment informality is proxied by self-employment in percent of total

employment. In A-D, *** denotes that the group differences are not zero at 10 percent significance level. “High

informality” (“Low informality”) are EMDEs with above-median (below-median) DGE-based informal output measure

(or employment informality proxied by self-employment shares) over the period 2000-18 (2010-2018 in C-D). ATM =

automated teller machine; DGE = dynamic general equilibrium model; EMDEs = emerging market and developing

economies; IMF = International Monetary Fund; RHS = right-hand side.

A.B. Bars are simple group means using data from latest year available for EMDEs with “high informality” and those

with “low informality.” “Finance constraint” measures the percent of firms identifying access to finance as a major

constraint in an economy. “Bank finance” measures the percent of firms using banks to finance investment in an

economy. “Internal finance” measures the average share of investment financed internally using personal savings.

C.D. Bars are unweighted averages of various financial development indicators for EMDEs with “high informality”

and those with “low informality” over the period 2010-18. Output informality is used in C and employment informality

is used in D. “Bank branches” measures the number of commercial bank branches per 100,000 adults. “ATMs”

measures the number of ATMs per 100,000 adults. “Private credit” measures domestic credit to private sector in

percent of GDP. “FD index” is the financial development index from the International Monetary Fund, which measu-

res the overall level of financial development. “Account ownership” is the percentage of survey respondents (aged

15 or above) who report having an account (by themselves or together with someone else) at a bank or another

type of financial institution or report personally using a mobile money service in the past 12 months. “Internal finan-

cing” is captured by the percentage of respondents (aged 15 or above) who report saving or setting aside any

money in the past 12 months to start, operate, or expand a farm or business.

D. Access to finance and employment

informality (households)

C. Access to finance and output

informality (households)

BOX 6.1 Financial development and the informal economy

(continued)

Bank

finance

Internal

finance

Bank

finance

Internal

finance

Output informality Employment informality

High informality Low informality

Percent of firms/investment

***

0.1

0.2

0.3

0.4

0.5

Bank

branches

ATMs

Private

credit

Account

ownership

Internal

financing

FD index

(RHS)

High informality Low informality

Various units

***

Index

***

0.1

0.2

0.3

0.4

0.5

Bank

branches

ATMs

Private

credit

Account

ownership

Internal

financing

FD index

(RHS)

High informality Low informality

Various units

***

Index

***

High Low High Low

Output informality Employment informality

Percent of firms

***

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H E L O N G S H A D O W O F I N F O R M A L I T Y

BOX 6.1 Financial development and the informal economy

(continued)

North Africa, South Asia, and, more recently, Sub-Saharan Africa (chapter 5).

Measures to expand access to finance have included better personal property

registration to facilitate borrowing by informal firms (for example, Czech

Republic; World Bank 2012) and digital payment systems to encourage a shift

away from informal finance (World Bank 2017).

Overall, access to finance and and the size of financial institutions increased in

more than three-fifths of EMDEs over 2010-18 (figure B6.1.2). The number of

ATMs per 100,000 adults rose from 26 to 40, and the share of population with

an account at a financial institution increased from 33 to 51 percent (Svirydzenka

2016). Domestic credit to the private sector in EMDEs increased by more than 4

percentage points of GDP, on average, over the same period.

Changes in informality following financial development. Financial development

was associated with significant contractions in both output and employment

informality (figure B6.1.3; annex 6A). First, 10 more bank branches per 10,000

adults—about the difference between the averages for EMDEs with above-

median and below-median informality—were associated with a 0.1- to 0.3-

percentage-point decline in the share of informal output in the following one to

five years. The share of informal employment also declined statistically

significantly. Second, a 10-percentage-point-of-GDP increase in domestic credit

to the private sector was associated with a significant contraction in the shares of

output and employment informality in subsequent years.

Conclusion

Both theory and empirical evidence indicate that more advanced financial

development is associated with a smaller informal economy, although the

direction of causality remains a matter of debate and may run both ways.

Financial development is considerably weaker in countries with more pervasive

informality. Financial systems have deepened, and access to financial services has

broadened, in EMDEs over the past three decades.

Policy measures to reduce informality, however, need to go beyond improving

the financial system and facilitating access to credit. Evidence suggests that the

impact of financial development on informality depends on the quality of the

legal and regulatory systems, the level of economic development, and financial

e. The results remain broadly unchanged when levels of informal output and employment are used as

robustness checks (see figure 6A.1 for results using levels of informal output). The robust results suggest

that any movement in the informal share of output or employment is determined by changes in informal

activity (the numerator), not by only changes in formal activity (the denominator).

C H A P T E R 6 2

67 T H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE B6.1.2 E

volution of financial development in EMDEs

In EMDEs, access to financial institutions and the depth of their activities improved

between 1990 and 2018.

B. EMDEs with improved financial

development

A. Financial development, 1990-2018

Sources: International Monetary Fund (Financial Development Index Database); World Bank (Global Financial

Development Database, World Development Indicators).

Note: Data are from EMDEs and the period 1990-2018. Output informality is measured by DGE-based estimates on

informal output (in percent of official GDP). Employment informality is proxied by self-employment in percent of total

employment. ATMs = automated teller machines; DGE = dynamic general equilibrium model; EMDEs = emerging

market and developing economies; IMF = International Monetary Fund; RHS = right-hand side.

A.B. Bars (A) and lines (B) show simple EMDE averages for corresponding time periods. “Overall” is the aggregate

financial development index obtained from the IMF. It measures the overall level of financial development and

captures development in both “financial institutions” and “financial markets.” The latter is about the access, depth,

and efficiency of a economy’s stock and debt market, which was less relevant for informal participants in EMDEs.

The “Institutions” index measures how developed financial institutions are in terms of their depth (size and liquidity),

access (ability of individuals and companies to access financial services), and efficiency (ability of institutions to

provide financial services at low cost and with sustainable revenues). Some of the subindicators for “Institutions” are

used in C-D to show the access (C) and depth (D) of financial institutions.

C. Bars show simple EMDE averages in earliest possible year (2004), 2010, and 2018. “Bank branches” measures

the number of commercial bank branches per 100,000 adults. “ATMs” measures the number of ATMs per 100,000

adults. “Private credit” measures domestic credit to private sector in percent of GDP. “Account ownership” is the

percentage of survey respondents (aged 15 or above) who report having an account (by themselves or together

with someone else) at a bank or another type of financial institution or report personally using a mobile money

service in the past 12 months. In the case of “account ownership,” data from closest years are used.

D. Bars show simple EMDE averages in corresponding years. The indicators captures domestic credit to private

sector as a share of GDP.

D. Domestic credit to private sector C. Access to finance

BOX 6.1 Financial development and the informal economy

(continued)

100

120

1990

1994

1998

2002

2006

2010

2014

2018

Overall Institutions

Number of EMDEs

Bank branches ATMs Account

ownership

(RHS)

Earliest 2010 2018

Per 100,000 adults

Percent of surveyed respondents

1990 2000 2010 2018

Percent of GDP

0.1

0.2

0.3

0.4

Overall Institutions

1990-00 2000-08 2010-18

Index

68 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

development itself. In addition, the effect of measures to promote financial

development may be temporary and differ depending on the structure of

financial markets.

In particular, improvements in the legal system may be a precondition for

achieving broader access to credit that can draw informal firms into the formal

sector. Measures to improve contract enforcement and investor protection may

be particularly effective in EMDEs, which often fall well behind best practices.

Greater competition and access to markets may foster the productivity gains that

are needed for firms to be able to service debt, one aspect of financial

development. The possibility of reverse causality suggests that, in some instances,

measures to reduce informality by streamlining regulations and improving their

enforcement may create a virtuous circle of lowering informality and spurring

financial development.

BOX 6.1 Financial development and the informal economy

(continued)

FIGURE B6.1.3 E

volution of output informality following

financial development in EMDEs

Financial development is found to have been associated with significant subsequent

contractions in output informality. Financial development, especially better access to

financial institutions and increased depth of financial institutions, helps reduce output

informality.

B. Cumulative changes in output

informality following a 10-percentage-

point-of-GDP increase in domestic credit

to the private sector

A. Cumulative changes in output

informality following a 10-unit increase in

the number of bank branches per 100,000

adults

Source: World Bank.

Note: Data are from EMDEs and the period 1990-2018. Output informality is measured by DGE-based estimates on

informal output in percent of official GDP. The results are obtained via a local projection method where informality

measures are detrended using Hodrick-Prescott (HP) filter. See annex 6A for detailed model specifications.DGE =

dynamic general equilibrium model; EMDEs = emerging market and developing economies.

A.B. Bars show the cumulative changes in DGE-based output informality in percent of GDP following a 10-unit

increase in the number of bank branches per 100,000 people (in A) and 10-percentage-point-of-GDP increase in the

share of domestic credit to the private sector in percent of GDP (in B). Whiskers show the upper and lower bounds

of the corresponding 90 percent confidence intervals. “t = n” shows cummulative changes over the n years after the

policy change.

-0.12

-0.09

-0.06

-0.03

t = 2 t = 5

Percentage points of GDP

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

t = 2 t = 5

Percentage points of GDP

C H A P T E R 6 2

69 T H E L O N G S H A D O W O F I N F O R M A L I T Y

labor market efficiency, governance, and regulatory quality. This is the first study to

conduct such empirical analysis for a wide range of policies. It is also the first to examine

the share of informality in both economic output and employment: earlier studies have

tended to focus on either informal output, or informal employment, or informal firms.

Third, the chapter is the first published attempt to comprehensively examine the link

between financial development and informality both theoretically and empirically (box

6.1). It reviews the literature identifying the channels through which limited financial

development can discourage formalization. It uses both descriptive statistics and

regression approaches to show that informality is associated with lack of financial

development, and that improvements in access to finance are associated with declining

informality.

Main findings. First, macroeconomic policies, governance, and business climates have

become more conducive to lowering informality over the past three decades. Over that

period, EMDEs have reduced tax burdens, improved governance and regulatory quality,

and expanded access to finance, education, and public services.

Second, policies that seek to streamline tax regulation, strengthen tax administration,

and improve public service delivery have been associated with declines in informality.

Separately, policies aimed at invigorating private sector activity broadly, such as

measures to increase labor market flexibility, streamline regulatory frameworks for firm

start-up, expand access to finance, and improve governance have also been associated

with declines in informality.

Third, policy measures can have unintended consequences. For instance, trade

liberalization that raised competition in the tradable sector was sometimes associated

with greater informality in the short run, unless accompanied by measures that increase

labor market flexibility. Also, reductions in informality have tended to be greater for

reforms accompanied by business development and training programs, public awareness

campaigns, and stronger enforcement.

Fourth, financial development has been associated with declining informality (box 6.1).

It reduces the average costs of access to external financing and incentivizes firms to

invest in higher-productivity projects and to join the formal sector. Over the past three

decades, increased access to financial services and increased credit availability have been

followed by declining informality.

Fifth, a comprehensive policy package tailored to country circumstances offers the

greatest chance of success in reducing informality. A combination of measures to

strengthen economic development, boost productivity in both formal and informal

sectors, streamline regulations, and ensure effective enforcement can address multiple

See Bosch, Goñi-Pacchioni, and Maloney (2012); Fajnzylber, Maloney, Montes-Rojas (2011); Ihrig and Moe

(2004); and Rocha, Ulyssea, and Rachter (2018).

270 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

sources of informality. The relative priorities will depend on the economy-specific

features of informality.

The rest of the chapter is organized as follows. It first presents a range of fiscal policy

options that may be used to help remove barriers to joining the formal sector. It then

discusses a wide range of policies that can ease the transition from the informal to the

formal sector. The chapter also illustrates the importance of having a comprehensive and

complementary policy package to tackle the challenges posed by informality and how to

implement it successfully. In addition, the chapter describes the implications of digital

technologies for coping with informality. The final section summarizes the conclusions.

Data and methodology

This chapter relies on the database detailed in chapter 2 for measures of output and

employment informality. It applies several statistical tests to quantify the links between a

wide range of policies and informality, without establishing or assuming causality. It

then estimates a series of local projection models to help quantify the cumulative

response of informality to various policy actions over the short and medium terms.

Data. Both output and employment informality are considered here. Output informality

is proxied by estimates based on the dynamic general equilibrium (DGE) model in

percent of official gross domestic product (GDP), and employment informality is

proxied by self-employment in percent of total employment. Both measures are available

for up to 121 EMDEs over the period 1990-2018.

For the local projection estimation,

all data series on informality are detrended using the Hodrick-Prescott filter to mitigate

concerns that the results are driven by the declining trend in informality (chapter 2). A

wide range of policy measures is considered here, ranging from changes in corporate tax

rates to actions to improve the ease of doing business (table 6B.2). Detailed data

descriptions are provided in annex 6A.

Empirical strategy. The chapter applies two empirical approaches to assess the links

between informality and policies.

First, differences between average policies in EMDEs with above-median and below-

median informality are tested for statistical significance. The sample of EMDEs is

grouped into those with an above-median share of informal output and those with a

below-median share of informal output, on average during (up to) 1990-2018.

For each

subsample, simple averages of policy indicators are generated and the difference between

these two group averages is tested for statistical significance. EMDEs with high

In the case of financial development, absolute levels of informal output and informal employment, rather than

their relative share of official GDP or total employment, are used as robustness checks when a local projection

model is estimated (figure 6A.1). Using absolute levels of informal output and informal employment avoids the

possibility that the results are driven by movements in total official GDP or total employment (the denominator)

rather than movements in output or employment in the informal sector (the numerator).

The results are the same when EMDEs are grouped according to employment informality (table 6B.3).

C H A P T E R 6 2

71 T H E L O N G S H A D O W O F I N F O R M A L I T Y

The results are robust to using self-employment as ameasure of employment informality (table 6B.5). As

further robustness checks, both ordinary least squares and quantile regressions are performed using the same set of

policy for both output and employment informality measures. The regression results are largely in line with the

findings from the group comparison approach (tables 6B.6-6B.7).

In contrast, EMDEs with high or low informality often do not differ significantly in their average statutory

rates for social security contributions nor their revenue collections from such taxes, but they do differ significantly in

the amount of social security they provide (see below).

informality refer to EMDEs with above-median informality, and EMDEs with low

nformality refer to EMDEs with below-median informality.

Second, a local projection model as in Jordà (2005), Teulings and Zubanov (2014), and

World Bank (2018a) is estimated to identify the effects of policy changes on informality

over time for a sample of up to 125 EMDEs during 1990-2018. The model estimates

the cumulative changes in informality after policy changes over different time horizons

while controlling for country fixed effects and per capita income levels (table 6B.4).

Policy changes are defined as a unit change in the corresponding policy indicator. For

instance, a 1-percentage-point increase in the personal income tax rate is considered a tax

policy change. Annex 6A details the model specification.

Fiscal measures

High tax rates or payments, complicated tax codes, and administrative burdens have

been commonly cited as reasons for informal activity (Auriol and Warlters 2005; Perry

et al. 2007; Waseem 2018). Lax tax enforcement facilitates poor tax compliance

(Slemrod 2019). Poor government services—often underfunded and inefficiently

delivered—will tend to erode tax morale (Awasthi and Engelschalk 2018). In a sweeping

survey of the literature, measures to address such issues have been identified as having

been particularly effective at encouraging a shift into formal activity (Jessen and Kluve

2021; World Bank 2019b).

Tax rates

Higher tax rates in more informal EMDEs. On average during 2010-2018, average

corporate and personal incomes tax rates were significantly higher, by 3 (corporate) to 4

(personal) percentage points in EMDEs with above-median output informality than in

those with below-median output informality. Value added tax (VAT) rates were also

statistically significantly higher in EMDEs with above-median output informality than

in those with below-median output informality.

Over time, shift away from income taxes. Since 1990, both corporate and personal

income tax rates have been lowered in EMDEs whereas the use of VAT has expanded.

Average corporate and personal income tax rates in EMDEs have fallen by 13 and 15

percentage points, respectively, from close to 40 percent in the beginning of the 1990s to

about 24 percent in 2020 (figure 6.2). About two-thirds of EMDEs lowered their

statutory personal income tax rates and more than three-quarters lowered their statutory

272 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE 6.2 Tax rates and informality in EMDEs

Income tax rates remain higher in EMDEs with more pervasive informality—

even where

governments have cut rates and shifted toward value added taxation. Informality declined after

income tax rate reductions but not after VAT rate reductions.

B. Tax rates, 1990-2020 A. Tax rates and output informality

Sources: Cnossen (1998); KPMG; University of Michigan; Organisation for Economic Co-operation and Development; Végh and

Vuletin (2015); World Bank (Doing Business).

Note: DGE = dynamic general equilibrium model; EMDEs = emerging market and developing economies; RHS = right-hand side;

VAT = value added tax.

A. Bars are group means for EMDEs with above-median DGE-based estimates on informal output (“high informality”) or those with

below-median DGE-bases estimates on informal output (“low informality”) over the period 2010-2018. Data are from about 100 EMDEs

(in the case of individual tax rate, China is dropped as an outlier). Bolivia, Georgia, Panama, and Zimbabwe are dropped as outliers.

*** denotes that the group differences are not zero at 10 percent significance.

B. Lines are simple group averages for EMDEs.

C.D., F. Bars show the cumulative changes in DGE-based output informality in percent of GDP following 10-percentage-point increase

in corporate income tax rate (C), individual income tax rate (D), and VAT rate (F). Whiskers show the upper and lower bounds of the

corresponding 90 percent confidence intervals. “t = n” indicates the cumulative changes in output informality over the n years after a

policy change. Data are for EMDEs over the period 2010-18. See annex 6A for detailed model specifications.

E. Bars show the number of EMDEs and advanced economies that adopted VAT.

D. Cumulative changes in output informality

following a 10-percentage-point increase in

individual income tax rate

C. Cumulative changes in output informality

following a 10-percentage-point increase in

corporate income tax rate

F. Cumulative changes in output informality

following a 10-percentage-point increase in VAT

rate

E. Number of economies with VAT

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

Corporate income tax

Individual income tax

Value added tax (RHS)

Percent

-0.05

0.05

0.10

0.15

0.20

0.25

t = 2 t = 5

Percentage points of GDP

0.05

0.10

0.15

0.20

0.25

t = 2 t = 5

Percentage points of GDP

100

1990 2020 1990 2020

Advanced economies EMDEs

(RHS)

Number of economies

-1.50

-1.00

-0.50

0.50

t = 2 t = 5

Percentage points of GDP

Corporate Individual Value added

High informality Low informality

Percent

***

C H A P T E R 6 2

73 T H E L O N G S H A D O W O F I N F O R M A L I T Y

corporate income tax rates over the sample period.

These efforts often coincided with a

streamlining of tax regulations and a broadening of the tax base (Kopczuk 2005).

Although income tax rates were lowered, often to reduce distortions that discourage

employment, VAT was introduced, which could be less distortionary than income tax

but may lead to a more regressive tax system (Cnossen 1998).

Many economies in

Latin America introduced VAT regimes in the 1970s and 1980s, and their ranks were

joined by a large number of economies in Europe and Central Asia (ECA) during the

1990s. Between 1990 and 2020, the number of EMDEs with VAT systems increased

from 29 to 91 (Végh and Vuletin 2015; World Bank 2020a).

Lower informality after tax rate cuts. Lower corporate or personal income tax rates can

reduce the incentives of firms and households to operate in the informal economy to

lower their costs. In one EMDE, for example, a tax hike in 2010 reduced the number of

formal firms and their sales revenues to such an extent that tax revenues three years after

the hike were lower than they would have been without the tax hike (Waseem 2018). A

sweeping review of past government interventions suggests that tax cuts were particularly

effective in reducing informality (Jessen and Kluve 2021). Similarly, a review of policies

showed that tax simplification and tax cuts were associated with lower informality

(World Bank 2019b).

Meanwhile, the introduction of VAT may strengthen incentives to register in order to

qualify for VAT refunds—or, conversely, may strengthen incentives to operate

informally to offer lower prices excluding VAT. A VAT regime imposes an input tax on

informal firms that do not qualify for refunds but source from formal firms, which in

the right circumstance can motivate them to register, thus raising government revenue

collection (de Paula and Scheinkman 2010; Loayza 2018; World Bank 2018c).

requirement to digitalize sales receipts for accelerated VAT refunds could further

strengthen incentives to register and correctly report sales (Fan et al. 2020). In one case,

electronic invoicing for VAT purposes was rolled out in waves between 2014 and 2018

and resulted in more than 5 percent higher reported firm sales, purchases, and value

added in the first year after adoption (Bellon et al. 2019).

Indeed, since 1990, a 10-percentage-point decrease in the corporate income tax rate has

been associated with a cumulative decline in output informality of about 0.1 percentage

point of GDP, relative to trend, over the following two years (figure 6.2).

A similarly

sized reduction in the personal income tax rate has been associated with a slightly

stronger, and deepening, fall in output informality in the following five years. Despite

finding significant falls in informality following tax cuts, these falls are generally small in

The sample contains up to 53 EMDEs for which data are available in 2020 and 1990.

That said, the presence of an informal economy could lead to incomplete coverage and inefficiencies in the

VAT system (Keen 2008; Piggott and Whalley 2001; Emran and Stigliz 2005). In some case, labor informality was

found to be associated with lower overall VAT collection (Caro and Sacchi 2021).

Poorer households tend to spend a larger share of their budgets in the informal sector than richer households.

As a result, households in the richest quintile can face an effective consumption tax rate that is twice that of the

poorest quintile (Bachas, Gadenne, and Jensen 2020).

274 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

In the case of employment informality, the average annual number of hours spent on complying with VAT

refund requirements amounted to 37 in EMDEs with above-median informality—20 hours per year significantly

more than in EMDEs with below-median informality (table 6B.3).

size, suggesting that cutting tax rates alone is not enough to move all participants from

he formal sector to the informal sector. Other policy measures are needed (Loayza

2018). Meanwhile, increases in VAT have not been associated with any significant

change in output informality. This suggests that some informal firms source their inputs

from informal markets that operate outside the VAT system. The results are robust to

using employment informality, instead of output informality.

Tax compliance

More burdensome tax compliance in more informal EMDEs. Beyond tax rates, tax

compliance can be costly and time-consuming and, thus, discourage formal registration

by firms, especially those with poor profitability (Morales and Medina 2016; Rocha,

Ulyssea, and Rachter 2018; Ulyssea 2018). On average, in EMDEs with above-median

informality during 2010-18, it took the average firm 33 hours longer and required

statistically significantly more payments, estimated at 11 per year, to comply with tax

regulations than in EMDEs with below-median informality (figure 6.3). As a result,

despite higher corporate and personal income tax rates in EMDEs with above-median

informality, revenue collections were lower: on average in EMDE with above-median

output informality, personal and corporate income tax revenues were statistically

significantly lower, by 0.6 and 0.8 percentage point of GDP, than in EMDEs with

below-median informality.

Similar administrative challenges have troubled the administration of VAT regimes in

countries with high informality. During 2016-18, firms spent 29 hours a year, on

average, complying with VAT refund requirements in EMDEs with above-median

informality—7 hours more than in EMDEs with below-median informality, though the

difference is not statistically significant.

It took about 40 weeks for firms in EMDEs

with above-median informality to receive VAT refunds—significantly longer than the

31 weeks in EMDEs with below-median informality.

Over time, less burdensome tax compliance. Firms’ tax compliance costs have declined

in EMDEs in the past few decades. Since 2006, the time spent by firms on paying taxes

has fallen by 68 hours a year, on average, in EMDEs and the average number of tax

payments per year has declined by one-third, to 26 payments per year in 2020 (figure

6.3). In ECA, the introduction of electronic tax filing and payment systems has reduced

the average tax filing time from 473 hours in 2006 to 225 hours in 2020 (World Bank

and PwC 2019).

Efforts to lower tax burdens have been among the most common policy reforms in

EMDEs, especially in East Asia and Pacific (EAP) and Latin America and the Caribbean

(LAC). Measures to make tax compliance less burdensome have ranged widely (Awasthi

C H A P T E R 6 2

75 T H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE 6.3 Firms’ tax compliance burdens and informality in EMDEs

Tax compliance burdens on firms remain higher in EMDEs with more pervasive informality than in

those with less pervasive informality, despite recent declines. Past efforts to lower compliance costs

were not followed by immediate declines in informality.

B. Ease of paying taxes over time A. Ease of paying taxes and output informality

Source: World Bank (Doing Business).

Note: DGE = dynamic general equilibrium model; EMDEs = emerging market and developing economies; RHS = right-hand side; VAT

= value added tax.

A.C. Bars are group means using data from latest year available for EMDEs with “high informality” and those with “low informality.”

“High informality” (“low informality”) are EMDEs with above-median (below-median) DGE model-based informal output measure over

the period 2010-18. Data are from about 100 EMDEs. *** denotes that the group differences are not zero at 10 percent significance.

B. Bars show the average number of tax payments per year by a medium-size company. The line shows the average time spent on

paying taxes per year by a medium-size company. Data are for EMDEs.

D. Bars show the cumulative changes in DGE-based output informality in percent of GDP following a 1-point increase in the score for

ease of paying taxes. Whiskers show the upper and lower bounds of the corresponding 90 percent confidence intervals. “t = n”

indicates the cumulative changes in output informality over the n years after a policy change. Data are for EMDEs over the period

1990-2018. See annex 6A for detailed model specifications.

D. Cumulative changes in output informality

following a 1-point increase in the score for ease

of paying taxes

C. Time to comply with and obtain VAT refund and

output informality

and Engelschalk 2018; Slemrod 2019). Tax bases have been simplified in industries with

a high percentage of undeclared workers (for example, domestic work), and tax

regulations have been harmonized across different types of firms (Oviedo, Thomas, and

Karakurum-Özdemir 2009). At the same time, tax enforcement has been stepped up by

expanding the use of information technology and communication tools, encouraging a

switch from cash-based transactions to bank-based ones, and strengthening the capacity

of tax administrations (for example, Nguimkeu and Okou 2019; Prichard et al. 2019).

160

200

240

280

320

360

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Tax payments

Time spent on tax payments (RHS)

Number per year Hours per year

Comply with VAT refund Obtain VAT refund

Low informality High informality

Hours/Weeks

***

-1.5

-1.0

-0.5

0.5

1.0

t = 2 t = 4 t = 5

Percentage points of GDP (in 100ths)

260

280

300

320

Payments Time

(RHS)

Low informality

High informality

Number per year

***

Hours per year

See Chodorow-Reich et al. (2018); Crouzet, Gupta, and Mezzanotti (2020); and Lahiri (2020) for the impact

of demonetization.

276 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Lower informality after measures to facilitate tax compliance. Measures to reduce the

burden of tax compliance or firm registration can lower the cost for informal firms of

moving into the formal sector (Rocha, Ulyssea, and Rachter 2018). Coordination of

minimum tax thresholds across different types of tax, such as personal income tax, VAT,

and social security contributions, could increase tax compliance and improve welfare

(Kanbur and Keen 2014). Measures to harmonize tax provisions or other regulations

across different types of firms can reduce incentives for firms to evade taxation and

remain small and informal (Dabla-Norris, Gradstein, and Inchauste 2018; Harju,

Matikka and Rouhanen 2019). Measures to strengthen tax administration can increase

the likelihood of detection of informal firms that do not comply with taxes (Carrillo,

Pomeranz, and Singhal 2017; Naritomi 2019).

Measures to facilitate tax compliance have been accompanied by statistically significant

declines in output informality (figure 6.3). The effects have not been immediate, being

insignificant in the first year, but have strengthened over time. Thus four years after

reforms that increased the score for the ease of paying taxes by 1 point, the share of

output informality was 0.1 percentage point of GDP lower—a statistically significant

difference.

Tax morale

Weaker tax morale in more informal economies. Tax morale is weaker in EMDEs with

above-median informality. On average, in EMDEs with above-median output

informality, the average household scores 2.5 points on a scale of 0 to 10, with 10

indicating that underreporting of income for tax purposes is always justifiable—that

score is 0.4 index points, and statistically significantly, higher than in EMDEs with

below-median informality (figure 6.4).

Among the many reasons for weaker tax morale is a lack of trust in the government or

dissatisfaction with the quality of public service delivery.

Indeed, entrepreneurs in

EMDEs with above-median output informality report significantly poorer access to

government support and programs as well as poorer physical and services infrastructure

than entrepreneurs in EMDEs with below-median output informality (figure 6.4;

chapter 4). Similarly, significantly better access to commercial and professional

infrastructure is reported by businesses in EMDEs with above-median tax morale than

those in EMDEs with below-median tax morale. Coverage of unemployment benefits is

significantly lower, by about 3 percentage points of the population, in EMDEs with

above-median informality than in those with below-median informality. On average , in

EMDEs with below-median informality, social insurance programs can cover about 34

percent of the annual income or consumption of the beneficiary household, which is

significantly lower, by 6 percentage points, than in EMDEs with above-median

informality.

See Daude, Gutiérrez, and Mulguizo (2012) for a review of drivers of tax morale. OECD (2019) suggests that

there is a positive association between tax morale and public service provision in Africa, whereas tax morale in Latin

America is more linked with trust in the government.

C H A P T E R 6 2

77 T H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE 6.4 Tax morale and informality in EMDEs

Tax morale is higher in EMDEs with lower informality. Higher tax morale has been associated with

better government services such as social security, infrastructure, education, and health care

systems.

B. Government support and informality A. Tax morale and informality

Sources: Global Entrepreneurship Monitor; Programme for International Student Assessment (PISA) database; World Bank (World

Development Indicators); World Road Statistics (WRS).

Note: Data are from EMDEs and the period 1990-2018. “High (Low) informality” are EMDEs with above (below)-mediaDGE-based

estimates on informal output in percent of GDP. All scores on government support and public infrastructure in B and C are for the period

2000-18 and range from 1 (worst) to 4 (best). DGE = dynamic general equilibrium model; EMDEs = emerging market and developing

economies; RHS = right-hand side. *** denotes that the group differences are not zero at 10 percent significance.

A. Bars show simple group averages between 1990 and 2018. World Value Survey asks whether cheating on taxes is justifiable, with a

higher level suggesting that the economy is more tolerant toward the informal sector.

B.C. Bars show simple group averages. “Governmental support and policies” measures the extent to which policies support

entrepreneurship as a relevant economic issue. “Government programs” captures the presence and quality of programs directly

assisting small and medium enterprises (SMEs) at all levels of government (national, regional, municipal). “Commercial and

professional infrastructure” captures the presence of property rights, commercial, accounting, and other legal and assessment services

and institutions that support or promote SMEs. “Physical and service infrastructure” measures the ease of access to physical resources

at a price that does not discriminate against SMEs.

D. Adequacy of social insurance programs is measured as total transfer amount received by population participating in social insurance

programs in percent of total income or expenditures of beneficiary households.

E. Bars show simple group averages.

F. PISA testing scores are for students aged 15.

D. Adequacy of social security and informality C. Tax morale and access to government services

F. Health and education outcomes and informality E. Access to infrastructure and informality

Governmental

support and

policies

Governmental

programs

Commercial

and

professional

infrastructure

Physical and

services

infrastructure

Low informality High informality

Score (1 = worst, 4 = best)

***

Governmental

support and

policies

Government

programs

Commercial and

professional

infrastructure

Low tax morale High tax morale

Score (1 = worst, 4 = best)

***

Adequacy of social

insurance programs

Coverage of

unemployment benefits

(RHS)

High informality Low informality

Percent of household income Percent of population

***

100

Access to

electricity

Internet

users

Paved

road

Mobile

cellular

subscription

(RHS)

High informality Low informality

Percent of population / road

***

Per 100 people

***

350

370

390

410

430

450

Life expect-

ancy at birth

PISA

(reading, RHS)

High informality Low informality

Year Score

***

0.5

1.0

1.5

2.0

2.5

3.0

High informality Low informality

Average response

1 = never justifiable; 10 = always justifiable

278 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

The indicator for tax morale is taken from World Values Surveys, conducted in various years. The current

round of World Values Surveys will complete its data collection in December 2021.

Transitions from an employment-based social security system to a well-designed model of risk sharing can

further improve the safety net for informal workers and help protect both formal and informal workers during

economic downturns (World Bank 2013, 2018c).

Education and health outcomes are significantly poorer in EMDEs with above-median

nformality, with Programme for International Student Assessment (PISA) test scores for

15-year-old students in EMDEs with above-median informality being lower by about 10

percent than in those with below-median informality, and life expectancy eight years

lower in EMDEs with above-median informality. The poorer outcomes are partly due to

more limited government expenditures on education and health in EMDEs with more

pervasive informality (chapter 4).

Over time, stable tax morale, despite better government services. In contrast to output

and employment informality, tax morale has remained stable over the past three decades.

In the early 1990s, households in EMDEs gave an average score of 2.5 to the

justifiability of cheating on taxes (where a score of 1 means that cheating on taxes is

never justifiable and 10 means that it is always justifiable)—virtually the same as in

2010.

As one of the social capital measures with deep roots in culture, tax morale is

slow-moving by nature (Luttmer and Singhal 2014). In contrast, entrepreneurs in

EMDEs have, on average, perceived statistically significant improvements in government

support or programs for small and medium enterprises (SMEs) and in improving

commercial and professional infrastructure available to SMEs (figure 6.5).

Meanwhile, actual government service delivery has improved by several measures. The

adequacy of social insurance programs has risen in EMDEs from an average of 31

percent of household income in the 2000s to 34 percent a decade later. Infrastructure—

for example, road kilometers, access to reliable power, and access to internet services—

has improved considerably since 2000. Mobile cellular subscriptions rose from 28 to 95

per 100 people between the 2000s and the 2010s. EMDEs’ test scores on PISA

indicators of education outcomes have risen significantly, by 17 points, and life

expectancy has risen by four years, on average, in EMDEs.

A range of measures has been introduced over the past three decades to cultivate better

tax morale, including public appeals to declare activities, campaigns to encourage

a culture of commitment to declaration, and efforts to change perceptions of the tax

system’s fairness (Williams and Schneider 2016). Other measures have included steps to

shift the burden of payments of social security contributions from employers to

employees (for example, in Latvia, Poland, and Slovenia), to reduce employers’ social

security contributions (for example, in Bulgaria), and to link social benefits to personal

contributions (for example, in most of the economies in the European Union; Oviedo,

Thomas, and Karakurum-Özdemir 2009).

Lower informality after improvements in government services. Improvements in the

perception that tax dollars are spent judiciously—that is, for appropriate objectives and

in an efficient way—can encourage greater tax compliance and lessen informality (Sung,

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79 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Awasthi, and Lee 2017). Better education or infrastructure can help raise labor

productivity in both formal and informal activities, thus facilitating a move of previously

insufficiently productive, informal firms into the formal sector.

FIGURE 6.5 Government services in EMDEs, 2000-18

Although infrastructure, social security systems, and health care systems have improved in EMDEs

over the past several decades, entrepreneurs’ perceptions of the adequacy of government services

have remained stable and poor.

B. Social security

A. Government support perceived by

entrepreneurs

Sources: Global Entrepreneurship Monitor; Programme for International Student Assessment (PISA) database; World Bank (World

Development Indicators); World Road Statistics (WRS).

Note: Data are from emerging market and development economies (EMDEs) and the period 2000-2018. All scores on government

support and public infrastructure in A are taken from the National Expert Survey of the Global Entrepreneurship Monitor for the period

2000-18. The scores range from 1 to 4 with a lower score representing poorer entrepreneurial conditions. Bars show simple period

averages for 2000-08 and 2010-2018, correspondingly, with *** indicating that the period differences are not zero at 10 percent

significance level. RHS = right-hand side.

A. “Government support and policies” measures the extent to which policies support entrepreneurship as a relevant economic issue.

“Government programs” captures the presence and quality of programs directly assisting small and medium enterprises (SMEs) at all

levels of government (national, regional, municipal). “Commercial and professional infrastructure” captures the presence of property

rights, commercial, accounting, and other legal and assessment services and institutions that support or promote SMEs. “Physical and

service infrastructure” measures the ease of access to physical resources—communication, utilities, transportation, land, or space—at

a price that does not discriminate against SMEs.

B. Adequacy of social insurance programs are measured in percent of total welfare of beneficiary households.

C-D. PISA scores are for students aged 15. “Paved road” is calculated as 100 minus the share of unpaved road in percent of total road.

“Access to electricity” and “Internet users” are in percent of population. “Mobile cellular subscriptions” and “fixed broadband

subscriptions” are measured as per 100 people.

D. Health and education outcomes C. Public infrastructure

Coverage of

unemployment benefits

Adequacy of social

insurance programs

(RHS)

2000-08 2010-18

Percent of population Percent of household income

100

Access to

electricity

Internet users

Paved road

Mobile cellular

subscriptions

Fixed

broadband

subscriptions

(in tenths)

2000-08 2010-18

Various units

***

380

390

400

410

420

430

440

Life expectancy

at birth

PISA

(reading, RHS)

2000-08 2010-18

Year Score

***

Governmental

support and

policies

Governmental

programs

Commercial

and

professional

infrastructure

Physical and

services

infrastructure

2000-08 2010-18

Score (1 = worst, 4 = best)

***

See, for instance, Benjamin and Mbaye (2012); Kim, Loayza, and Meza-Cuadra (2016); Oviedo, Thomas,

and Karakurum-Özdemir (2009); and World Bank (2018c). Better access to education or infrastructure may boost

productivity growth more in the formal sector than in the informal sector, resulting in a fall in the relative share of

informal output in total economic output.

280 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Empirically, declines in output informality followed improvements in government

services, although the small country sample of data for tax morale blunts the significance

of coefficient estimates (figure 6.6). Measures perceived to improve government support

for SMEs by 1 standard deviation were followed by a 0.1-percentage-point decline in

the share of output informality five years later. A similar, 1-standard-deviation

improvement in SMEs’ access to quality commercial and professional infrastructure was

also followed by a 0.1-percentage-point decline in the informal output share after two

years, although the effect subsequently dissipated.

Other policies

Many reforms designed to invigorate private sector growth can also help lower

informality, such as reducing corruption, improving business climates and governance,

strengthening enforcement of taxes and regulations, and liberalizing labor and product

markets, including through trade liberalization.

Financial development, by lowering

financing costs, can incentivize firms to operate formally, and has often been associated

with a shrinking informal sector (box 6.1). Policy measures that narrow the earnings gap

FIGURE 6.6 Informality after improvements in government services

After improvements in government services, informality has declined.

B. Cumulative changes in output informality

following a 1-point increase in commercial and

professional infrastructure in EMDEs

A. Cumulative changes in output informality

following a 1-point increase in the presence and

quality of government programs in EMDEs

Source: World Bank.

Note: Data are from EMDEs and the period 2000-18. Both scores taken from National Expert Survey of the Global Entrepreneurship

Monitor for the period 2000-18. The scores range from 1 (worst) to 4 (best). DGE = dynamic general equilibrium model; EMDEs =

emerging market and developing economies.

A.B. Bars show the cumulative response of DGE-based output informality in percent of GDP to a 1-point increase in “government

programs” index (A) and “commercial and professional infrastructure” (B). Whiskers show the upper and lower bounds of the

corresponding 90 percent confidence intervals. The results are obtained via a local projection method. Output informality in percent of

GDP is detrended using Hodrick-Prescott filter. “t = n” indicates the cumulative changes over the n years after a policy change. See

annex 6A for detailed model specifications.

-0.2

-0.1

0.1

t = 2 t = 5

Percentage points of GDP

-0.3

-0.2

-0.1

0.1

t = 2 t = 5

Percentage points of GDP

Kuddo (2018) shows that about 60 percent of the reforms implemented between 2007 and 2017 throughout

the world aimed at improving labor market flexibility. Among measures to improve product market flexibility, trade

liberalization has been associated with increased informality unless complementary reforms improved labor market

flexibility (World Bank 2019b).

C H A P T E R 6 2

81 T H E L O N G S H A D O W O F I N F O R M A L I T Y

between informal and formal workers or reduce the labor productivity gap between

informal and formal firms, such as measures that improve access to education or training

programs, can also help reduce informal activity.

Labor market regulations

More restrictive regulations in more informal economies. Although higher minimum

wages may attract informal workers into the formal sector, they are also likely to

discourage firms from hiring workers, resulting in unclear effects on employment of

minimum wages (especially in the presence of imperfect competition).

In a

development context, in which agricultural sectors are large and urbanization is still

under way, a higher minimum wage can slow capital accumulation and push workers

into informal employment (Loayza 2016).

Empirically, labor market regulations in EMDEs with above-median informality are

more restrictive than in EMDEs with below-median informality (figure 6.7). Minimum

wages in EMDEs with above-median output informality average 5.5 percent of per

capita income, which is 1.3 percentage points, and significantly, higher than in EMDEs

with below-median output informality. Flexibility of working hours, often measured by

the inverse of restrictions on night and overtime work, holiday work, and the length of

the work week, is significantly less in EMDEs with above-median output informality

than in EMDEs with below-median output informality.

Over time, increased labor market flexibility. Labor market flexibility and efficiency

have increased in EMDEs over the past three decades (figure 6.7). EMDEs have lowered

their minimum wage by 0.6 percentage points of GDP per capita from its level in the

1990s (Loayza 2016). Between 2010 and 2018 alone, about one-quarter of EMDEs

increased their perceived labor market efficiency, which measures the extent to which

the labor market matches workers with the most suitable jobs for their skillset (WEF

2020). About 40 percent of EMDEs changed regulations to make the hiring and

dismissal of workers more flexibly determined by employers. During the same period, 4

out of 10 EMDEs reduced the costs of advance notice requirements, severance

payments, and the penalties due when dismissing a worker with a 10-year tenure.

These changes reflect several decades of labor market reforms especially in ECA, Sub-

Saharan Africa (SSA), and, more recently, LAC. Regulations with respect to hiring and

dismissal, working hours, and wage rates have been eased in ECA (EBRD 2018).

Incentives have been provided for worker registration—for example, legalization of

undocumented workers—while enforcement of existing labor laws has been tightened

(Anand and Khera 2016; Munkacsi and Saxegaard 2017). In EMDEs, the reduction of

The employment effects of minimum wages have been unclear (Manning 2021). An increase in the real

minimum wage has been associated with a lower probability of being hired in the formal sector, or employment in

general (Gindling and Terrell 2007; Maloney and Nuñez Mendez 2004). However, employment effect was not

found in studies like Hohberg and Lay (2015); Lemos (2009); and Urzua and Saltiel (forthcoming).

282 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

minimum wages encouraged formalization of employment (Betcherman, Meltem

Daysal, and Pagés 2010; Kugler, Kugler, and Herrera-Prada 2017).

Lower informality after labor market reforms. Excessive labor market regulations, such

as excessively high minimum wages, can distort the labor market and provide incentives

for firms to hire workers informally (Kugler 2004; Loayza 2016; Ulyssea 2010).

Increases in labor market flexibility and efficiency have been associated with significant

falls in output informality (figure 6.8). A 1-standard-deviation increase in the Fraser

Institute’s index of hiring and firing regulation, which gauges the extent to which the

FIGURE 6.7 Labor market reforms and informality in EMDEs

Labor market regulations are more restrictive in EMDEs with high informality than in those with low

informality. About one-third of EMDEs have improved their labor market efficiency and eased labor

market regulations over the past several decades.

B. Minimum wage and output informality A. Labor market regulations and output informality

Sources: Fraser Institute; International Labour Organization (ILO); World Economic Forum (WEF).

Note: The labor market regulation index from the Fraser Institute covers issues such as minimum wage, hiring and firing regulations,

centralized collective bargaining, mandated cost of hiring, mandated cost of worker dismissal, regulation of hours, and conscription.

DGE = dynamic general equilibrium model; EMDEs = emerging market and developing economies.

A.-C. Bars are group means using data available between 1990 and 2018 for EMDEs with “high informality” and those with “low

informality.” “High informality” (“Low informality”) are EMDEs with above-median (below-median) DGE-based informal output measure

over the period 1990-2018. In B, data are between 1994 and 2018. Data on labor market regulations are obtained from the Fraser

Institute (in A and C) and are between 1990 and 2018. The WEF index is available between 2007 and 2017.The labor market efficiency

index from WEF measures the extent to which the labor market matches workers with the most suitable jobs for their skillset (1 = worst,

7 = best). The labor market regulation index from the Fraser Institute measures the extent to which these restraints (listed in C) upon

economic freedom are present in the labor market (1 = worst, 10 = best). *** denotes that the group differences are not zero at 10

percent significance.

D. Bars show the share of EMDEs with improved labor market regulations between 2010 and 2018. “Labor market eff. index” is the

labor market efficiency index obtained from WEF.

D. EMDEs with liberalized labor market regulations

between 2010 and 2018

C. Specific labor market regulations and output

informality

High informality Low informality

Percent of GDP per capita

***

Minimum

wage

Hiring and

firing

Working

hours

Dismissal

costs

High informality

Low informality

Score (1 = worst,10 = best)

***

Aggregate

index

Minimum

wage

Hiring-firing

regulations

Working

hours

Dismissal

costs

Labor market

eff. index

Fraser Institute WEF

Percent of EMDEs

Labor market regulation

(Fraser Institute)

Labor market efficiency

(WEF)

High informality

Low informality

Score (1 = worst, 7/10 = best)

***

C H A P T E R 6 2

83 T H E L O N G S H A D O W O F I N F O R M A L I T Y

hiring and dismissal of workers is at the employer’s discretion, was associated with a

significant drop in output informality, by 0.5 percentage point, over the following five

years. A 1-point increase in the World Economic Forum’s labor market efficiency index

was associated with a cumulative drop in output informality by about 0.2 percentage

point of GDP over the following five years.

Firm start-up costs

More difficult firm start-up in more informal economies. Starting a new firm is more

challenging in EMDEs with more pervasive informality (figure 6.9). On average, the

costs of business start-up amount to about 90 percent of per capita gross national income

(GNI) in EMDEs with above-median output informality—three times the level in other

EMDEs. It takes 33 days to start a business in EMDEs with above-median informality—

about 1 day longer than in other EMDEs, although the difference is not statistically

significant.

Over time, easier firm start-up. Business start-up costs have fallen steadily in EMDEs

over the past two decades. Between 2003 and 2018, the costs of business start-up fell

A similarly sized increase in labor market efficiency was associated with a decline in employment informality

by 2 percentage points of employment, cumulatively, over the following two to three years.

FIGURE 6.8 I

nformality after labor market reforms in EMDEs

Efforts to increase labor market flexibility and efficiency have been followed by declines in output

informality.

B. Cumulative response of output informality to a

1-point increase in WEF index of labor market

efficiency in EMDEs

A. Cumulative changes in output informality

following a 1-point increase in Fraser Institute

index of hiring and firing regulations in EMDEs

Source: World Bank.

Note: The labor market efficiency index from WEF measures the extent to which the labor market matches workers with the most

suitable jobs for their skillset (1 = worst, 7 = best). The index on hiring and firing regulations is from the Fraser Institute, which measures

the extent to which hiring and firing regulation are restricting economic freedom in the labor market (1 = worst, 10 = best). Data are for

EMDEs over the period 1990-2018. DGE = dynamic general equilibrium model; EMDEs = emerging market and developing economies;

WEF = World Economic Forum.

A.B. Bars show the cumulative changes in DGE-based output informality in percent of GDP to a 1-point increase in the Fraser

Institute’s index on hiring and firing regulations (1-point increase in WEF labor market efficiency index in B). Whiskers show the upper

and lower bounds of the corresponding 90 percent confidence intervals. The results are obtained via a local projection method. Output

informality in percent of GDP is detrended using Hodrick-Prescott filter. “t = n” indicates the cumulative changes in output informality

over the n years after a policy change. See annex 6A for detailed model specifications.

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

t = 2 t = 5

Percentage points of GDP

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

t = 2 t = 5

Percentage points of GDP

284 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

from above 130 percent of per capita GNI to below 30 percent, and the number of days

required to start a business declined by two-thirds, to 23 days. There has also been a

reduction in the number of procedures needed to start a business (World Bank 2020a).

Various regulations have been used to encourage formal firm start-up. “One-stop-shop”

registrations have been created (for example, in Ukraine) to simplify the firm start-up

process. Similar reforms have been carried out in several other EMDEs (World Bank

2009, 2010, 2011). EMDEs in ECA and SSA have implemented an above-average

number of reforms to reduce the costs of starting a business during the past decade.

FIGURE 6.9 Firm start-up cost and informality in EMDEs

Firm start-ups are more challenging in EMDEs with above-median informality than in those with

below-median informality. Over the past two decades, EMDEs have taken measures to facilitate firm

start-up. Reduced start-up costs have been followed by significant contractions in informal output.

B. Firm start-up costs, 2003-18 A. Firm start-up costs and output informality

Source: World Bank (Doing Business).

Note: Data are from EMDEs and the period 1990-2018. DGE = dynamic general equilibrium model; EMDEs = emerging market and

developing economies; GNI = gross national income; RHS = right-hand side.

A. The bars show unweighted group averages. “High-informality” (“Low-informality”) are EMDEs with above-median (below-median)

DGE-based informal output measures. The data are from 2003-18.*** denotes that the group differences between EMDEs with above

median informality and those with below-median informality are significant at 10 percent level.

B. Lines show unweighted averages for EMDEs for the period 2003-18.

C.D. Bars show the cumulative changes in DGE-based output informality in percent of GDP following a decrease of 10 percentage

points of GNI per capita in the cost of business start-up procedures (C; or following a 1-unit increase in the number of days required to

start a business in D). Whiskers show the upper and lower bounds of the corresponding 90 percent confidence intervals. “t = n”

indicates the cumulative changes in output informality over the n years after a policy change. See annex 6A for detailed model

specifications.

D. Cumulative changes in output informality

following a 1-unit decrease in the number of days

required to start a business

C. Cumulative changes in output informality after

the cost of business start-up procedures falls by

10 percentage points of GNI per capita

100

Cost of

business start-up

procedures

Time required to

start a business

(RHS)

Low informality

High informality

Percent of GNI per capita Days

***

100

120

140

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Cost of business start-up

procedures

Time required to start a

business (RHS)

Percent of GNI per capita Days

-2.0

-1.6

-1.2

-0.8

-0.4

t = 2 t = 5

Percentage points of GDP (in 100ths)

-1.0

-0.8

-0.6

-0.4

-0.2

0.2

t = 2 t = 5

Percentage points of GDP (in 100ths)

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85 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Lower informality after regulatory easing. Easier and less costly firm registration

reduces the costs for firms of entering the formal sector (Haltiwanger, Jarmin, and

Miranda 2013; Loayza 2018; Nguimkeu 2015). Empirically, a reduction in the costs of

business start-up by 10 percentage points of GNI per capita was associated with a

significant reduction in output informality, by 0.1 percentage point of GDP over the

following five years. Similarly, a one-day reduction in the number of days required to

start a business was associated with a significant contraction in output informality, by

0.4 percent point of GDP over the following two years.

Governance

Weaker governance in more informal economies. More corruption, less effective

government, and weaker law and order have been associated with larger informal sectors

in EMDEs (figure 6.10; chapter 4). On average in the past three decades, EMDEs with

above-median output informality scored significantly lower (by about half of a standard

deviation) on government effectiveness, control of corruption, and rule of law than

other EMDEs. Using the International Country Risk Guide (ICRG) indicators,

bureaucracy quality, control of corruption, and law and order in EMDEs with above-

median output informality are significantly lower, by 0.4-0.7 standard deviation, than in

EMDEs with below-median informality.

Over time, improved governance. Governance has generally improved in EMDEs since

1990. Both bureaucracy quality and law and order improved by 0.2-0.6 standard

deviation between 1990 and 2018. Control of corruption, law and order, and

bureaucracy quality strengthened in the early 1990s but weakened again in the second

half of the 1990s with the economic, social, and political disruptions in transition

economies, before stabilizing in the early 2000s.

To improve governance and regulatory quality, countries have increased the frequency

of inspections (for example, in most EU15 economies and Bangladesh), created a

national-level firm or employee registry (Poland), and launched public awareness

campaigns regarding tax compliance (for example, China, Republic of Korea).

Such

measures have been most effective in reducing informality when implemented in

conjunction with steps to improve labor market functioning and when applied even-

handedly to both formal and informal firms (Loayza 2018). In Georgia, during 1996-

2016, the transition to a market economy brought significant improvements in

government effectiveness, control of corruption, and law and order (World Bank

2019b).

With output growth averaging about 6 percent per year, the share of informal

output fell by 9 percentage points of GDP, and the share of informal employment in

total employment fell by a similar magnitude.

See, for instance, Awasthi and Engelschalk (2018); Bruhn and McKenzie (2014); De Giorgi, Ploenzke, and

Rahman (2018); and Oviedo, Thomas, and Karakurum-Özdemir (2009). The EU15 were the members of the

Euroean union before 2004.

From 1996 to 2016, Georgia’s global ranking on regulatory quality improved from 150th to 34th place, and

its ranking on government effectiveness improved from 123th to 55th place.

286 C H A P T E R 6 T

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Lower informality after governance reforms. Stricter enforcement of government

regulations and a better legal framework can increase the costs of remaining in the

informal economy (Dabla-Norris, Gradstein, and Inchauste 2008). Anti-corruption

efforts and stronger law and order may allow fewer opportunities for informal firms to

avoid the obligations of formal firms (Choi and Thum 2005; Dreher and Schneider

2010; Iriyama, Kishore, and Talukdar 2016). Better control of corruption can also

reduce informality via the tax morale channel (DeBacker, Heim, and Tran 2015;

Luttmer and Singhal 2014). Empirically, a 1-standard-deviation improvement in the

FIGURE 6.10 Governance and informality in EMDEs

Governance and regulatory quality tend to be weaker in EMDEs with more pervasive informality but

have improved over the past several decades. Such improvement has often been followed by

declines in informality.

B. Governance in EMDEs, 1990-2018 A. Governance and informality: ICRG indicators

Sources: Transparency International - Corruption Perceptions Index (data set); International Country Risk Guide (ICRG) data set;

World Bank.

Note: Data are from EMDEs and the period 1990-2018. DGE = dynamic general equilibrium model; EMDEs = emerging market and

developing economies; RHS = right-hand side.

A. The bars show the unweighted group averages. “High (Low)-informality” are EMDEs with above-median (below-median) DGE-based

informal output measures. *** denotes that the group differences are significant at 10 percent level.

B. Lines show simple averages for EMDEs using various indicators from ICRG. A higher value indicates better governance.

C. Bars show the unweighted averages of the perceived level of corruption in EMDEs. The measure ranges from 0 (the highest level of

perceived corruption) and 100 (the least level of perceived corruption).

D. Bars show the cumulative changes in DGE-based estimates on output informality to a 1-point increase in control of corruption.

Whiskers show the upper and lower bounds of the corresponding 90 percent confidence intervals. “t = n” indicates the cumulative

changes in output informality over the n years after a policy change. The results are obtained via a local projection method. Output

informality in percent of GDP is detrended using Hodrick-Prescott filter. See annex 6A for detailed model specifications.

D. Cumulative change in output informality

following a 1-point improvement in control of

corruption (ICRG)

C. Perceived level of corruption in EMDEs, 2012-20

1.0

1.5

2.0

2.5

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

Control of corruption

Law and order

Bureaucracy quality (RHS)

Index (0 = worst, 6 = best) Index (0 = worst, 4 = best)

2012-14 2015-17 2018-20

Score (0 = most corruption, 100 = least corruption)

-0.20

-0.15

-0.10

-0.05

0.05

t = 2 t = 5

Percentage points of GDP

Bureaucracy

quality

Control of

corruption

Law and order

High informality Low informality

Index (0 = worst, 4/6 = best)

***

C H A P T E R 6 2

87 T H E L O N G S H A D O W O F I N F O R M A L I T Y

control of corruption was associated with a cumulative decrease in output informality by

about 0.1 percentage point of GDP in the following three to five years.

Education and training programs

Poorer education associated with greater informality. Informal workers tend to be less

skilled, and therefore also less productive, than formal-economy workers (chapter 4;

Loayza 2018; Perry et al. 2007). In fact, wage differentials between formal and informal

workers have primarily reflected differences in educational backgrounds and experience

(box 4.1). Workers in EMDEs with above-median output informality have, on average,

one year less of schooling than those in other EMDEs (figure 6.11). Poorer access to

schooling and qualified teachers has resulted in significantly poorer education outcomes,

measured by PISA test scores, in EMDEs with above-median output informality.

Entrepreneurship training, at all levels of education, including education programs

aimed at equipping entrepreneurs to create and manage SMEs, is significantly less

accessible in EMDEs with above-median output informality.

Over time, improved education and training. Since 1990, education outcomes and skill

levels have improved: thus, on average in EMDEs, average years of schooling have

increased by about two years (figure 6.11). Entrepreneurship training has also become

more accessible in EMDEs, and the improvement has been statistically significantly

more pronounced in EMDEs with above-median informality. In some EMDEs, training

programs have boosted worker income and firm revenue in the informal sectors (Burki

and Abbas 1991; Verner and Verner 2005). These training programs were also

supported by general improvements in access to primary education and literacy rates

(Aziz et al. 2014; Hathaway 2005).

Lower informality after improvements to training and education. To the extent that

workers remain in the informal sector for lack of human capital or skills, better and

more accessible public education may help workers (or their dependents) move into

better paid formal employment (Andrews, Sánchez, and Johansson 2011; Maloney

2004; Perry et al. 2007).

Empirically, additional entrepreneurship training and improved education outcomes

have been accompanied by significant declines in output informality.

Five years after a 2-

standard-deviation increase in access to entrepreneurship training and education at

primary and secondary levels, output informality was statistically significantly lower, by

0.2 percentage point of GDP.

A 10-point increase in the PISA reading score was

associated with a significant decline in output informality, by about 0.1 percentage

point of GDP, over the following two years.

Such an improvement was accompanied by a significant reduction in employment informality, by 1.3

percentage points of employment, over three to four years.

288 C H A P T E R 6 T

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FIGURE 6.11 Education and informality in EMDEs

Workers in EMDEs with more pervasive informality are, on average, less educated and trained than

those in EMDEs with less pervasive informality. Training focused on SMEs has improved over the

past two decades, especially in EMDEs with above-median informality. Better education and

training have coincided with declines in informality.

B. Education, 1990 vs. 2015 A. Education and informality

Sources: Barro and Lee (2013); Global Entrepreneurship Monitor; Program for International Student Assessment (PISA) database;

World Bank (World Development Indicators).

Note: Data are from EMDEs and the period 1990-2018. “High (Low) informality” are EMDEs with above (below)-median output

informality proxied by DGE-based estimates in percent of GDP. All scores regarding entrepreneurship training and education (in C-F)

range from 1 (worst) to 4 (best). “Basic school” measures the extent to which training in creating or managing small SMEs is

incorporated within the education and training system at primary and secondary levels. “Post school” measures the extent to which

training in creating or managing SMEs is incorporated within the education and training system in higher education. DGE = dynamic

general equilibrium model; EMDEs = emerging market and developing economies; RHS = right-hand side; SME = small and medium

enterprise. *** denotes that the group differences are not zero at 10 percent significance.

A.C. Bars show simple group averages. PISA scores are for 15-year-old students.

B.D. Bars show simple group averages for corresponding time periods.

E.F. Bars show the cumulative changes in DGE-based output informality in percent of GDP to a 1-point increase in “Basic school”

score from the Global Entrepreneurship Monitor (10-point increase in PISA reading scores). Whiskers show the upper and lower

bounds of the corresponding 90 percent confidence intervals. “t = n” indicates the cumulative changes in output informality over the n

years after a policy change. See annex 6A for detailed model specifications. PISA scores are for 15-year-old students.

D. Entrepreneurship training, 2000-18 C. Entrepreneurship training and informality

F. Cumulative changes in output informality

following a 10-point increase in national average

PISA score on reading

E. Cumulative changes in output informality

following a 1-point improvement in basic school

entrepreneurial education and training

All EMDEs Low

informality

High

informality

1990 2015

Years of schooling

***

1.0

1.5

2.0

2.5

3.0

High

informality

Low

informality

High

informality

Low

informality

Basic school Post school

***

Score (1 = worst, 4 = best)

1.5

1.8

2.1

2.4

2.7

3.0

All EMDEs

High

informality

Low

informality

All EMDEs

High

informality

Low

informality

Basic school Post school

2000-04 2010-18

Score (1 = worst, 4 = best)

-0.4

-0.3

-0.2

-0.1

0.1

t = 2 t = 5

Percentage points of GDP

-0.15

-0.10

-0.05

0.05

0.10

0.15

t = 2 t = 5

Percentage points of GDP

350

370

390

410

430

450

100

Years of

schooling

(*10)

Literary

PISA (maths,

RHS)

PISA

(reading,

RHS)

PISA

(science,

RHS)

High informality Low informality

Percent of population/years Score

C H A P T E R 6 2

89 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Access to finance

Less access to finance in more informal economies. Firms in the informal sector have

less access to credit from the banking sector and capital markets, which restricts their

ability to invest, including in productivity-enhancing technologies (Capasso and Jappelli

2013; D’Erasmo 2016; Ferreira-Tiryaki 2008; box 6.1; figure 6.12). In EMDEs with

above-median informality, about one-third of firms identified access to finance as a

major constraint—8 percentage points higher than in EMDEs with below-median

informality. Households in EMDEs with below-median informality have access to

significantly more commercial bank branches, ATMs, and credit than those in other

EMDEs. About half of the population in EMDEs with below-median informality owns

an account at a financial institution or used a mobile money service recently—about 17

percentage points higher than in EMDEs with below-median informality.

Over time, expanded access to finance. EMDEs, especially in EAP, Middle East and

North Africa (MNA), South Asia (SAR), and, more recently, SSA, have implemented a

series of reforms to improve access to finance. Such reforms mainly aim to strengthen

credit reporting systems and improve the effectiveness of collateral and bankruptcy laws

(World Bank 2020a). Overall, financial development improved in about 90 out of 142

of EMDEs over the period 2010-18 (figure 6.12). The number of ATMs per 100,000

adults rose by 50 percent between 2010 and 2018, and the share of population with an

account at a financial institution increased from 33 percent to 51 percent. Domestic

credit to the private sector increased by about 4 percentage points of GDP over the same

period. Access to credit has been facilitated for firms in the informal sector by

introducing credit information bureaus and better use of information and

communication technology (Capasso, Monferrà, and Sampagnaro 2018). Personal

property registration has also made loans more accessible for firms operating in the

informal economy (for example, in the Czech Republic; World Bank 2012). Digital

payment systems have provided an entry point into the formal financial system and

encouraged a shift away from informal finance (for example, in Kenya; World Bank

2017).

Lower informality after expanded access to finance. Lower financing costs and easier

access to credit can entice informal firms with promising investment projects that

require external finance to enter the formal economy (box 6.1). Empirically, adding 10

more bank branches per 100,000 adults was followed by a decline of 0.1 to 0.3

percentage point in the share of informal output in the following one to five years. A 10-

percentage-point-of-GDP increase in domestic credit to the private sector was associated

with a significant contraction in output informality, by 0.1 percentage point of GDP

over the subsequent one to five years.

Conclusion

The COVID-19 (coronavirus) pandemic plunged the global economy into an

unprecedented contraction in 2020, and it is likely to leave lasting scars on long-term

290 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

FIGURE 6.12 Access to finance and informality in EMDEs

Firms and workers in EMDEs with more pervasive informality are more likely to be financially

constrained, less likely to obtain bank finance, and more likely to have limited access to other

financial services. Access and depth of financial systems in EMDEs improved between 1990 and

2018. Financial development has been followed by significant declines in output informality.

B. Financial constraints facing firms and output

informality

A. Access to finance and output informality

Sources: International Monetary Fund (IMF); World Bank.

Note: Data are from EMDEs and the period 1990-2018. Output informality is measured by DGE-based estimates on informal output (in

percent of official GDP). See notes below figure B6.1.1 for detailed variable descriptions in A-D. ATM = automated teller machine; DGE

= dynamic general equilibrium model; EMDEs = emerging market and developing economies; RHS = right-hand side. In A-B, ***

denotes that the group differences are not zero at 10 percent significance level.

A.B. Bars are unweighted averages for EMDEs with above-median (“high”) informality and those with below-median (“low”) informality

over the period 2010-18 (in A; and 2000-18 in B). In B, bars are group means using data from latest year available for EMDEs.

C.D. Lines show simple EMDEs averages for corresponding time periods. “Overall” is the aggregate financial development index

obtained from the IMF. The “Institutions” index subcomponent measures how developed financial institutions are.

E.F. Bars show the cumulative changes in DGE-based output informality in percent of GDP to a 1-unit increase in the number of bank

branches per 100,000 people (E) or 10-percentage-point-of-GDP increase in domestic credit to the private sector (F). Whiskers show

upper and lower bounds of 90 percent confidence intervals. “t = n” indicates n years after the rise in bank branches of private credit.

See annex 6A for detailed model specifications.

D. Domestic credit to private sector, 1990-2018 C. EMDEs with improved financial development,

1990-2018

F. Cumulative changes in output informality

following a 10-percentage-point-of-GDP increase

in domestic credit to the private sector

E. Cumulative changes in output informality

following a 10-unit increase in the number of

bank branches per 100,000 adults

Finance

constraint

Bank

finance

Internal finance

(RHS)

High informality

Low informality

Percent of firms

***

Percent of investment

100

120

1990

1994

1998

2002

2006

2010

2014

2018

Overall Institutions

Number of EMDEs

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

EMDE average

Percent of GDP

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

t = 2 t = 5

Percentage points of GDP

-0.12

-0.09

-0.06

-0.03

t = 2 t = 5

Percentage points of GDP

0.1

0.2

0.3

0.4

0.5

Bank

branches

ATMs

Private

credit

Account

ownership

Internal

financing

FD index

(RHS)

High informality Low informality

Various units

***

Index

***

C H A P T E R 6 2

91 T H E L O N G S H A D O W O F I N F O R M A L I T Y

potential output (World Bank 2020b, 2021). Informal sector workers have been among

the hardest hit because they are disproportionately employed in the services sector that

has been particularly disrupted by the pandemic, work in crowded conditions where the

virus can spread easily, and have limited access to savings or government support

programs (box 2.1). The limited access to sanitation facilities and medical resources in

EMDEs with more pervasive informality illustrates further the development challenges

posed by informality. The pandemic has served as a reminder of the long-standing need

for policies to address the challenges associated with, and caused by, informality. To

achieve the Sustainable Development Goals, policy makers need to boost productivity

growth in both formal and informal sectors and reduce the vulnerabilities of firms and

workers in the informal economy.

Policies are more likely to succeed in addressing the challenges of informality if they are

comprehensive and tailored to country circumstances. Past failures of reforms to lower

informality and boost productivity have in part been attributed to reform design that

was not tailored to country specifics and not sufficiently embedded in a supportive

institutional and business environment, in addition to not being consistently

implemented (Birdsall, de La Torre, and Caicedo 2010; Loayza 2018).

A comprehensive strategy: The right policy mix. Individual policy interventions in

isolation may have only a limited impact on informality, and have unwelcome

unintended consequences (annex 6A; Oviedo, Thomas, and Karakurum-Özdemir 2009;

Ulyssea 2018). A coherent reform strategy is needed, with reforms that complement

each other and address the complexity of informality (Loayza 2018). Success also

depends on careful monitoring of potential unintended consequences and on a

supportive macroeconomic, political, and institutional environment. The latter should

ensure the political and fiscal viability of reform implementation and reduce the

transition costs for workers moving from the informal sector to the formal sector.

A tailored strategy: Addressing economy-specific priorities. Because the causes and

features of informality differ considerably across countries, policy makers need to

identify economy-specific reform priorities. In countries where informality is

predominantly associated with poor governance, a policy package could streamline

regulatory and tax frameworks while improving the efficiency of public revenue

collection and regulatory enforcement as well as strengthening public service delivery to

bolster tax morale. In countries where informality is predominantly a reflection of

underdevelopment, a policy package could include expanded access to finance, markets,

and inputs to foster firm productivity and growth; better education to facilitate formal

sector employment; and enhanced safety nets to cushion household risks. In SSA, SAR,

and MNA economies that are not members of the Gulf Cooperation Council, for

example, general education and training programs to raise human capital could be

prioritized (World Bank 2019b; chapter 5). In LAC, reducing high tax and regulatory

costs faced by businesses could incentivize firms to join the formal sector. In ECA,

improving government effectiveness and reducing corruption could be policy priorities.

292 C H A P T E R 6 T

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New policy challenges. The emerging “gig” economy poses opportunities and policy

challenges with its higher accessibility, more fluid labor arrangements, and greater

reliance on digital technology than more traditional forms of informality. Because “gig”

workers do not fully participate in the social security system, they are, by some

definitions, informal workers (Loayza, Servén, and Sugawara 2010). Regulatory changes,

especially in the context of social security systems, can help ensure that “gig” workers’

economic risks are manageable and that they do not permanently lose access to the

formal economy (World Bank 2014, 2016, 2018c). These workers may take on many

different assignments over the course of their careers, making the ability to learn and

adapt essential. Policies can support this adaptability with more provision of education

and (re)training programs (Card, Kluve, and Weber 2018; World Bank 2019a).

Increased emphasis on the development of cognitive skills in primary and secondary

education can also help (Almeida, Behrman, and Robalino 2012; World Bank 2018a,

2018c).

New policy opportunities. New technologies offer governments opportunities both to

reduce the incentives for and increase the cost of operating informally while also

providing boosts to productivity that can propel firms into the formal economy. New

technologies can help strengthen tax administration and improve access to finance,

including by making it easier to broaden the tax net and assess credit worthiness.

Digitalization can lower regulatory burdens. For example, Costa Rica reduced the time

required to register a business by digitizing tax registration records and company books

in 2009 (World Bank 2009). This was followed by a drop in informal employment by 4

percentage points of total employment and a fall in informal output by about 2

percentage points of official GDP during 2009-16 (World Bank 2019b). Similar reforms

have been carried out in Guyana (2010) and Kenya (2011; World Bank 2010, 2011).

Safeguarding informal workers during severe shocks. COVID-19 has taken an

especially heavy humanitarian and economic toll on EMDEs with large informal sectors

(World Bank 2020b). The vulnerabilities of the informal sector, associated with low

incomes and limited access to government benefits and public services, have amplified

the economic shock from COVID-19 and the related threat to livelihoods (OECD

2020). In many countries, the pandemic has revealed severe shortcomings in social

security systems and governments’ ability to support vulnerable groups (Busso et al.

2020; Loayza and Pennings 2020).

Despite their high costs, untargeted programs may

be warranted during such a crisis to maximize the reach to informal-economy

participants; their long-term fiscal burden can be minimized by prioritizing temporary

and reversable measures. To prevent hysteresis in formal-sector job losses, policies can

aim to preserve formal-sector employment opportunities while protecting the poor and

informal workers through food aid and cash transfers (Alfaro, Becerra, and Eslava 2020).

See Awasthi and Engelschalk (2018); Capasso, Monferrà, and Sampagnaro (2018); Gupta et al. (2017); and

Junquera-Varela et al. (2017).

See Fang, Kennedy, and Resnick (2020) for a review of social protection policies implemented under

COVID-19.

C H A P T E R 6 2

93 T H E L O N G S H A D O W O F I N F O R M A L I T Y

ANNEX 6A Policies and informality

The link between policies and informality is analyzed via the local projection model. It

focuses on showing the cumulative change in informality following policy changes.

Definitions

Both output informality and employment are considered in the regression analyses here.

Output informality is proxied by estimates based on the dynamic general equilibrium

(DGE) model in percent of official GDP, and employment informality is proxied by

self-employment in percent of total employment. Both measures cover up to 125

EMDEs over the period 1990-2018. For the estimation of the local projection model,

all data series are detrended using the Hodrick-Prescott (HP) filter.

Policy indicators

The following policy measures were considered, covering up to 121 EMDEs for 1990-

2018 (table 6B.2).

Tax rates. Corporate, individual, and VAT rates from Végh and Vuletin (2015; updated

to 2019) using data from the Organisation for Economic Co-operation and

Development, University of Michigan, and KPMG.

Although the data for some indicators are available for more than 121 EMDEs, the regressions cover up to

121 EMDEs.

Future research.

Some policy areas remain underexplored in the literature. First,

digitalization is a recent development in EMDEs that holds great potential for informal-

economy participants and policy makers. Yet little is known about the impact of

digitalization of government services or private economic activity on the informal

economy, including relative to the formal economy. The possibility that digitalization

will disproportionately benefit formal firms, and thus shrink the relative size of the

informal sector, deserves examination. Second, past studies have focused on the impact

of policies on formalization without looking into their effects on the resilience of the

informal economy. Future studies could examine policies that can improve the resilience

of the informal economy and prevent informal participants from being tipped into

poverty by negative shocks such as COVID-19. Last, the chapter has not touched upon

some emerging ideas regarding how governments can better engage with informal

businesses, such as providing a simplified, intermediate, and temporary legal status to

informal businesses that could be aligned with both business needs and government

goals (Marusic et al. 2020).

294 C H A P T E R 6 T

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Cost of tax compliance. Ease of paying taxes score from the World Bank’s Doing

Business database (World Bank 2020a; 0 = worst, and 100 = best);

Access to finance. Domestic credit to the private sector in percent of GDP, a common

measure for depth of financial institutions, is provided by the World Development

Indicators (WDI; World Bank 2020c); the number of commercial bank branches per

100,000 adults, a common measure for the access to financial institutions, is obtained

from Global Financial Development Database (World Bank 2019c);

Labor market regulation. Labor market efficiency index from World Economic Forum’s

Global Competitiveness Report (ranging from 1 to 7 with a higher score indicating a

more efficient labor market) and the index for hiring and firing regulation from Fraser

Institute (Fraser Institute 2020; 1 = firing and hiring are most determined by regulations

but not by the employer, and 10 = firing and hiring are mostly determined by the

employer but not by regulations).

Governance. Bureaucracy quality, control of corruption, and law and order are from the

International Country Risk Guide (ICRG; 1 = worst governance, and 4/6 = best

governance).

Government services. Survey responses on the presence and quality of programs directly

assisting SMEs at all levels of government (national, regional, municipal; “government

programs”) and on the presence of property rights, commercial, accounting, and other

legal and assessment services and institutions that support or promote SMEs

(“commercial and professional infrastructure”), taken from the National Expert Surveys

of the Global Entrepreneurship Monitor for the period 2000-19 (ranging from 1 to 4

with a lower score representing poorer entrepreneurial conditions). Survey responses on

the extent to which training in creating or managing SMEs is incorporated within the

education and training system at primary and secondary levels (“basic school”) are also

taken from the National Expert Surveys of the Global Entrepreneurship Monitor.

Mobile phone subscriptions per 100 people are taken from World Development

Indicators.

Firm start-up costs. Cost of business start-up procedures in percent of GNI per capita

and time required to start a business in days are from Doing Business (World Bank

2020a).

Education and health outcomes. Life expectancy at birth and PISA test scores for

reading (students aged 15) are from WDI (World Bank 2020c).

Model specification: The local projection model

A local projection model as in Jordà (2005), Teulings and Zubanov (2014), and World

Bank (2018c) is used to identify the effects of policy changes on informality over time.

In impulse responses, the model estimates the effect of policy changes on cumulative

changes in the cyclical component of (DGE-based) informal output in percent of official

C H A P T E R 6 2

95 T H E L O N G S H A D O W O F I N F O R M A L I T Y

, , 1 , 1 , ,

+ − −

− = α + β + θ + + ∈

h h h h

i t h i t i t i t i t

y y d policy X fixed effects

The results are robust to using levels of DGE-based informal output (figure 6A.1).

In the case of corporate and individual tax rates, the levels of both tax rates at year t in country i are also

included as control variables. The results do not change when these two control variables are dropped. Similarly,

when labor tax and contributions is the variable of interest, the level of labor tax and contributions at year t in

country i is included as one of the control variables.

FIGURE 6A.1 R

obustness checks: Evolution of informal output levels

following financial development in EMDEs

Similar to the results on ratios of informality, improved access and depth of financial institutions are

linked with significant contractions in levels of informal output in the following years.

B. Cumulative changes in informal output levels

following a 10-percentage-point-of-GDP increase

in domestic credit to the private sector

A. Cumulative changes in informal output levels

following a 10-unit increase in the number of bank

branches per 100,000 people

Source: World Bank.

Note: Data for the period 1990-2018 and EMDEs. Here informal output level is measured by DGE-based estimates on informal output

(in constant 2011 U.S. Dollars). The results are obtained via a local projection method where informality measures are detrended using

Hodrick-Prescott filter. See annex 6A for detailed model specifications. DGE = dynamic general equilibrium model; EMDEs = emerging

market and developing economies.

A-B. Bars show the cumulative changes in DGE-based output informality in percent of GDP following a 10-unit increase in the

number of bank branches per 100,000 people (A) and 10-percentage-point-of-GDP increase in the share of domestic credit to the

private sector in percent of GDP (B). Whiskers show the upper and lower bounds of the corresponding 90 percent confidence

intervals. “t = n” indicates the cumulative changes in output informality over the n years after a policy change.

-0.02

-0.01

0.01

t = 2 t = 5

Percent

-0.12

-0.09

-0.06

-0.03

t = 2 t = 5

Percent

GDP (or self-employment in percent of total employment) over a time horizon h while

controlling for country fixed effects and per capita income levels:

where y

i,t

is the cyclical component of informality in country i and year t. The variable

d.policy

i,t ˗1

is the variable of interest, which measures the change in policy indicators in

country i and year t

˗1. The policy change variable is lagged here to deal with potential

endogeity issues. Real GDP per capita (constant 2010 U.S. dollars, obtained from the

WDI) is included as the control variable (X

i,t

Results are shown in tables 6B.4-6B.5.

296 C H A P T E R 6 T

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Paper

Measure of financial development

(FD).

Measure of the

informal economy (IE)

Estimators Main database Results

Gatti and

Honorati (2008)

Access to credit, indicating whether

the firm has a credit or overdraft line.

Percentage of firms’

sales reported to tax

authority.

OLS (ordinary

least squares)

and FE (fixed

effects)

estimation.

World Bank investment

climate surveys.

Less informality is robustly and

significantly related with more access

to credit. Moreover, the relationship

between credit and formality is

stronger in high-formality countries.

Dabla-Norris,

Gradstein, and

Inchauste (2008)

Private credit over deposit money at

banks and other financial institutions

as a share of GDP.

Percent of sales not

reported to tax

authorities.

OLS with

clustered

standard errors.

The 2005 Business

Environment and Enterprise

Performance Survey of the

World Bank and the

European Bank for

Reconstruction and

Development.

Firms that rate financing as major

obstacles to their business have, on

average, a 16 percent probability of

hiding 50 percent of their sales.

Bose, Capasso,

and Wurm (2012)

Liquid liabilities and total domestic

credit provided by depository banks,

bank overhead costs, net interest

margin, lending-deposit rate spread,

and level of bank concentration. All

as a percentage of GDP.

Percent of sales not

reported to tax

authorities, dynamic

MIMIC (DYMIMIC)

method (Schneider

2007) and WEF (World

Economic Forum)

measure.

FE and GMM

(generalized

method of

moments).

Beck, Demirgüç-Kunt, and

Levine (2000) database and

the World Bank World

Development Indicators

(WDI).

Improvement in the depth and

efficiency of the banking sector leads

to a smaller informal economy.

Elgin and Uras

(2013)

Money and quasi money, domestic

credit provided by financial

corporations to the private sector,

domestic credit from the financial

sector, and net credit to central

government.

DYMIMIC approach

(Schneider 2007).

GMM World Bank (WDI).

A non-linear inverse-U relationship

between financial development and

shadow economy size.

Capasso and

Jappelli (2013)

Probability of being credit-rationed

(Guiso, Sapienza, and Zingales

2004).

Irregular job rate

Share of income paid in

cash.

OLS and

instrumental

variables

estimation.

Bank of Italy’s Survey of

Households Income and

Wealth (SHIW).

Negative and significant effect of

financial development on the measure

of informal economy. The impact is

particularly strong in construction but

also in the retail and tourism sectors.

TABLE 6B.1 A summary of empirical studies on financial development and informality

ANNEX 6B Tables

C H A P T E R 6 2

97 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Source: World Bank.

Paper

Measure of financial development

(FD).

Measure of the

informal economy (IE)

Estimators Main database Results

Bittencourt,

Gupta, and

Stander (2014)

Domestic credit over GDP; interest rate

differential between loans and deposits;

liquid liabilities as a percentage of real

GDP; market capitalisation of all listed

companies as a percentage of real GDP.

DYMIMIC approach

(Schneider 2010) and

Dynamic general

equilibrium (DGE) model

(Elgin and Öztunali

2012).

FE and GMM

Schneider et al. (2010),

Elgin and Öztunali (2012),

World Bank WDI, Global

Development Finance, and

World Governance

Indicators (WGI).

Lower (higher) levels of financial

development and higher (lower)

levels of inflation lead to a higher

(lower) size of the shadow economy.

Berdiev and

Saunoris (2016)

Money and quasi money (M2) as

percentage of GDP; domestic credit

provided by financial corporations to the

private sector (private credit) as a

percentage of GDP; and domestic credit

from the financial sector to various

sectors and net credit to the central

government (financial credit) as

percentage of GDP

Dynamic general

equilibrium (DGE) model

(Elgin and Öztunali

2012).

GMM and panel

vector

autoregression

(VAR) analysis

World Bank WDI and Elgin

and Öztunali (2012).

A shock to M2 reduces the size of

the shadow economy, and this effect

becomes insignificant after eight

years. A shock to the shadow

economy shrinks financial

development.

Bayar and

Ozturk (2016)

Domestic credit to private sector.

DYMIMIC approach

(Schneider, Raczkowski,

and Mróz 2015).

Cointegration

analysis

Schneider, Raczkowski, and

Mróz (2015), World Bank

and Heritage Foundation.

Financial development and

improvements in institutional quality

reduce the size of the shadow

economy, in the long run.

Canh and Thanh

(2020)

Overall financial development, overall

financial institutions, overall financial

markets, financial institutions’ depth,

financial institutions’ access, financial

institutions’ efficiency, financial markets’

depth, financial markets’ access, and

financial markets’ efficiency.

DYMIMIC approach

(Medina and Schneider

2018).

Dynamic fixed

effects—

autoregressive

distributed lag

Medina and Schneider

(2018), Svirydzenka (2016),

World Bank WDI and WGI;

Heritage Foundation

Non-linear negative relationship

between financial development and

shadow economy for eight out of the

nine financial indicators.

Gharleghi and

Jahanshahi

(2020)

Liquid liabilities, private credit and stock

market capitalisation.

MIMIC and PMM

(predictive mean

matching) method

(Medina and Schneider

2018).

Threshold FE.

World Bank WDI and global

financial development.

Financial development has a

negative and significant effect on the

shadow economy, but only for

countries that have a per capita

GDP of US$33,600 and higher.

TABLE 6B.1 A summary of empirical studies on financial development and informality (continued)

298 C H A P T E R 6 T

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Source: World Bank.

Note: GEM = Global Entrepreneurship Monitor; GNI = gross national income; ICRG = International Country Risk Guide; PISA =

Programme for Internatonal Student Assessment; VAT = value added tax; WDI = World Development Indicators; WEF = World

Economic Forum.

Variable Source No. of EMDEs Years

Corporate income tax rate

Végh and Vuletin (2015; updated

o 2019)

118 1990-2018

ndividual income tax rate

Végh and Vuletin (2015; updated

to 2019)

111 1990-2019

VAT rate

Végh and Vuletin (2015; updated

to 2019)

95 1990-2020

Ease of paying taxes Doing Business (2020) 152 2006-18

Presence and quality of government

programs

GEM (2020) 75 2000-18

Commercial and professional

infrastructure

GEM (2020) 75 2000-18

Life expectancy at birth WDI (2020) 153 1990-2018

Mobile cellular subscriptions WDI (2020) 154 1990-2018

Labor market efficiency WEF (2020) 115 2007-17

Hiring and firing regulation Fraser institute (2020) 121 1990-2018

Cost of business start-up procedures

(percent of GNI per capita)

Doing Business (2020) 152 2003-18

Number of days required to start a

business

Doing Business (2020) 152 2003-18

Control of corruption ICRG (2020) 102 1990-2018

Basic school entrepreneurial

education

GEM (2020) 75 2000-18

PISA score on reading WDI (2020) 47 2000-18

Domestic credit to the private sector

(percent of GDP)

WDI (2020) 148 2000-18

Bank branches (per 100,000 adults) World Bank (2019c) 143 2001-17

TABLE 6B.2 D

ata sources of variables used in annex 6A

C H A P T E R 6 2

99 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Policy

High

nformality

Low

nformality

P-value

or t-test

Corporate income tax rate 26.4 21.4 0.00

ndividual income tax rate 28.3 20.0 0.00

VAT rate 14.0 13.8 0.84

umber of tax payments per year (2010-18) 34.6 24.3 0.00

Number of hours spent on paying taxes per year (2010-18) 311.6 280.4 0.54

Cost of complying with VAT refund (hours) 36.7 15.4 0.00

Number of days needed to obtain VAT refund 41.0 26.3 0.01

Cost of business start-up procedures (percent of GNI pc) 77.6 21.5 0.00

Number of days required to start a business 35.1 27.9 0.04

Labor market regulation index (Fraser Institute) 6.0 6.6 0.01

Labor market efficiency (WEF) 4.1 4.2 0.60

Minimum wage (percent of GDP pc) 6.4 3.3 0.00

Minimum wage (1 = worst, 10 = best) 5.9 6.9 0.02

Hiring and firing (1= worst, 10 = best) 4.8 4.6 0.35

Working hours (1= worst, 10 = best) 8.0 7.6 0.27

Dismissal costs (1= worst, 10 = best) 5.3 6.2 0.11

Tax morale (1 = highest, 10 = lowest) 2.2 2.4 0.22

Governmental support and policies (1 = worst, 4 = best) 2.5 2.6 0.38

Governmental programs (1= worst, 4 = best) 2.4 2.5 0.08

Commercial and professional infrastructure (1 = worst,

4 = best)

2.9 2.9 0.27

Physical and service infrastructure (1= worst, 4= best) 3.5 3.6 0.14

Adequacy of social insurance programs (percent of

household income)

27.4 34.1 0.04

Coverage of unemployment benefits (percent of population) 5.0 5.2 0.91

Access to electricity (percent of population) 56 92.3 0.00

Internet users (percent of population) 10.5 26 0.00

Paved road (percent of road) 31.5 61.0 0.00

Mobile cellular subscriptions (per 100 people) 33.8 55.8 0.00

Nurses and midwives (per 1,000 people) 1.4 3.7 0.00

Physicians (per 1,000 people) 0.6 1.5 0.00

Life expectancy (years) 61.9 71.0 0.00

Bureaucracy quality (ICRG) 1.5 2.0 0.00

Control of corruption (ICRG) 2.3 2.6 0.10

Law and order (ICRG) 3.0 3.6 0.00

Years of schooling 8.2 5.3 0.00

Literacy 69.2 91.1 0.00

PISA score (math) 392.8 432.9 0.00

PISA score (reading) 388.1 434.7 0.00

PISA score (science) 398.2 440.8 0.00

Basic school entrep. edu and training (1= worst, 4 = best) 1.9 2.0 0.39

Post school entrep. edu and training (1= worst, 4 = best) 2.8 2.8 0.72

Identify access to finance as a major constraint (percent of

firms)

32.3 28.9 0.24

TABLE 6B.3 P

olicy indicators and employment informality

300 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

(1) (2) (3) (4) (5)

Dep. var. = DGE-based output informality

(percent of official GDP)

t = 1 t = 2 t = 3 t = 4 t = 5

Corporate income tax rate

0.006* 0.011** 0.012** 0.011** 0.010

(0.003) (0.005) (0.006) (0.006) (0.007)

Observations 1,289 1,210 1,131 1,053 976

R-squared 0.012 0.014 0.014 0.015 0.019

Number of economies 81 80 79 78 76

Individual income tax rate

0.005* 0.009** 0.014*** 0.018*** 0.014**

(0.003) (0.003) (0.005) (0.006) (0.006)

Observations 1,286 1,206 1,127 1,048 971

R-squared 0.012 0.015 0.023 0.030 0.026

Number of economies 82 80 80 78 72

VAT rate

-0.024 -0.043 -0.041 -0.049 -0.038

(0.026) (0.046) (0.044) (0.039) (0.040)

Observations 1,234 1,154 1,075 998 923

R-squared 0.015 0.020 0.018 0.021 0.022

Number of economies 82 80 79 77 73

Ease of paying taxes

-0.002 0.000 -0.002 -0.006* -0.002

(0.002) (0.004) (0.003) (0.003) (0.004)

Observations 684 603 524 445 375

R-squared 0.013 0.008 0.010 0.013 0.017

Number of economies 83 80 80 71 70

Presence and quality of government programs

0.029 0.001 -0.062 -0.055 -0.142**

(0.037) (0.045) (0.064) (0.072) (0.065)

Observations 484 450 407 363 319

R-squared 0.002 0.001 0.003 0.002 0.016

Number of economies 61 60 56 51 47

TABLE 6B.4 Regression results from local-projection models:

DGE-based informal output in percent of GDP

Sources: Global Entrepreneurship Monitor; International Country Risk Guide (ICRG); KPMG; University of Michigan; Organisation for

Economic Co-operation and Development; Végh and Vuletin (2015); World Bank (Doing Business, World Development Indicators);

World Economic Forum (WEF); World Value Surveys.

Note: Data are from emerging market and development economies (EMDEs) over the period 1990-2018. The group differences

between EMDEs with “high informality” and those with “low informality” are tested. “High informality” (“Low informality”) are EMDEs with

above-median (below-median) employment informality (proxied by self-employment shares in percent of total employment) averaged

over the period 1990-2018 (or otherwise specified). “Paved road” is calculated as 100 minus the share of unpaved road in percent of

total road. Outliers are dropped in the case of individual tax rates, tax morale, nurses, and dismissal coasts. ATM = automated teller

machine; GNI = gross national income; IMF = International Monetary Fund; pc = per capita; PISA = Programme for International

Student Assessment; VAT = value added tax. Please see details in the notes to figures 6.2-6.12.

Policy

High

nformality

Low

nformality

P-value for t-

est

Percent of firms using banks to finance investments 20.7 30.9 0.00

roportion of investment financed internally 73.6 66.3 0.00

Commercial bank branches (per 100,000 adults) 10.9 18.5 0.00

TMs (per 100,000 adults) 21.2 52.2 0.00

Domestic credit to private sector (percent of GDP) 35.0 55.1 0.00

Account ownership (percent of age 15+) 37.2 54.0 0.00

Internal financing (percent of age 15+) 16.6 11.1 0.00

IMF financial development index 0.2 0.3 0.00

TABLE 6B.3 P

olicy indicators and employment informality (continued)

C H A P T E R 6 3

01 T H E L O N G S H A D O W O F I N F O R M A L I T Y

(1) (2) (3) (4) (5)

Dep. var. = DGE-based output informality

(

percent of official GDP)

t = 1 t = 2 t = 3 t = 4 t = 5

Commercial and prof. infrastructure

-0.016 -0.091** -0.090 -0.020 -0.051

(0.026) (0.044) (0.057) (0.036) (0.057)

Observations

484 450 407 363 319

R-squared

0.001 0.009 0.007 0.000 0.009

Number of economies

61 60 56 51 47

Life expectancy at birth

0.012 -0.015 -0.060** -0.108*** -0.151***

(0.012) (0.019) (0.024) (0.033) (0.040)

Observations

3,191 3,072 2,952 2,832 2,712

R-squared

0.000 0.000 0.003 0.011 0.022

Number of economies

121 121 121 121 121

Mobile cellular subscriptions (per 100 people)

-0.002*** -0.002** -0.003* -0.005* -0.010**

(0.000) (0.001) (0.002) (0.003) (0.004)

Observations

3,144 3,026 2,907 2,788 2,669

R-squared

0.001 0.001 0.002 0.010 0.022

Number of economies

120 120 120 120 120

Labor market efficiency (WEF)

-0.031 -0.071 -0.127 -0.203 -0.239*

(0.056) (0.088) (0.138) (0.153) (0.131)

Observations

909 816 721 626 527

R-squared

0.003 0.003 0.003 0.005 0.010

Number of economies

106 105 105 103 98

Hiring and firing regulation (Fraser)

0.005 -0.002 -0.017 -0.028 -0.046*

(0.008) (0.012) (0.017) (0.024) (0.027)

Observations

1384 1283 1180 1079 980

R-squared

0.006 0.012 0.014 0.015 0.015

Number of economies

105 104 102 100 100

Cost of business start-up procedures

(in percent of GNI per capita)

-0.000 0.001* 0.001* 0.001 0.001**

(0.000) (0.000) (0.001) (0.000) (0.000)

Observations

1,496 1,377 1,257 1,137 1,017

R-squared

0.002 0.010 0.008 0.005 0.008

Number of economies

121 121 121 121 112

No. of days required to start a business

0.001 0.004** 0.005** 0.006** 0.003

(0.001) (0.002) (0.002) (0.002) (0.003)

Observations

1,478 1,359 1,241 1,124 1,006

R-squared

0.004 0.008 0.006 0.007 0.004

Number of economies

121 121 121 121 112

Control of corruption (ICRG)

0.004 -0.015 -0.057** -0.105*** -0.114***

(0.014) (0.020) (0.026) (0.036) (0.039)

Observations

2,435 2,343 2,250 2,157 2,064

R-squared

0.001 0.001 0.003 0.006 0.006

Number of economies

94 94 94 94 94

Basic school entrep. edu and training

-0.001 -0.062 -0.041 -0.107 -0.173**

(0.034) (0.049) (0.059) (0.066) (0.076)

Observations

484 450 407 363 319

R-squared

0.000 0.004 0.002 0.006 0.020

Number of economies

61 60 56 51 47

TABLE 6B.4 R

egression results from local-projection models:

DGE-based informal output in percent of GDP (continued)

302 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

(1) (2) (3) (4) (5)

Dep. var. = DGE-based output informality

(

percent of official GDP)

t = 1 t = 2 t = 3 t = 4 t = 5

PISA score on reading -0.003** -0.005** -0.004 -0.002 -0.000

(0.001) (0.002) (0.004) (0.005) (0.007)

Observations 405 374 344 313 282

R-squared 0.016 0.024 0.021 0.008 0.001

Number of economies 35 34 33 33 33

Domestic credit to the private sector

(percent of GDP)

-0.005*** -0.005** -0.005*** -0.005** -0.006**

(0.002) (0.002) (0.002) (0.002) (0.003)

Observations 2,515 2,402 2,290 2,178 2,067

R-squared 0.006 0.004 0.009 0.021 0.038

Number of economies 121 118 116 116 116

Bank branches (per 100,000 adults) -0.013*** -0.021*** -0.024*** -0.029** -0.031**

(0.004) (0.008) (0.009) (0.012) (0.012)

Observations 1,435 1,331 1,224 1,111 997

R-squared 0.005 0.006 0.005 0.007 0.012

Number of economies 118 118 118 118 118

TABLE 6B.4 R

egression results from local-projection models:

DGE-based informal output in percent of GDP (continued)

Sources: Global Entrepreneurship Monitor; International Country Risk Guide (ICRG); KPMG; University of Michigan; Organisation for

Economic Co-operation and Development; Végh and Vuletin (2015); World Bank (Doing Business, World Development Indicators);

World Economic Forum (WEF).

Note: Data for the period 1990-2018 and EMDEs. See annex 6A for details. DGE = dynamic general equilibrium model; EMDEs =

emerging market and developing economies; GNI = gross national income; PISA = Programme for International Student Assessment;

VAT = value added tax.* p < 0.10, ** p < 0.05, *** p < 0.01.

(1) (2) (3) (4) (5)

Dep. var. = DGE-based output informality

(percent of official GDP)

t = 1 t = 2 t = 3 t = 4 t = 5

Corporate income tax rate 0.005 -0.037 -0.053 -0.074 -0.043

(0.036) (0.055) (0.066) (0.064) (0.045)

Observations 1,050 975 901 834 769

R-squared 0.002 0.002 0.002 0.005 0.003

Number of economies 76 75 68 66 65

Individual income tax rate 0.011 0.009 0.004 -0.013 -0.017

(0.027) (0.020) (0.028) (0.036) (0.037)

Observations 1,044 968 896 830 765

R-squared 0.002 0.001 0.001 0.003 0.003

Number of economies 77 73 67 66 62

VAT rate 0.013 -0.004 -0.146 0.025 0.102

(0.119) (0.116) (0.129) (0.142) (0.111)

Observations 1,008 934 862 795 730

R-squared 0.002 0.000 0.002 0.003 0.003

Number of economies 75 73 68 66 64

Ease of paying taxes 0.029 0.027 -0.009 -0.006 -0.096

(0.037) (0.029) (0.040) (0.030) (0.076)

Observations 537 461 389 321 264

R-squared 0.011 0.010 0.014 0.044 0.031

Number of economies 77 73 69 58 55

TABLE 6B.5 Regression results from local-projection models:

Self-employment in percent of total employment

C H A P T E R 6 3

03 T H E L O N G S H A D O W O F I N F O R M A L I T Y

(1) (2) (3) (4) (5)

Dep. var. = DGE-based output informality

(

percent of official GDP)

t = 1 t = 2 t = 3 t = 4 t = 5

Presence and quality of government programs -0.172 -1.219 -0.388 -1.517 -0.912

(0.364) (0.745) (0.963) (1.159) (0.884)

Observations 430 390 346 304 266

R-squared 0.001 0.013 0.009 0.018 0.012

Number of economies 53 52 48 43 39

Commercial and professional infrastructure 0.134 -0.506 -0.907 -0.570 0.286

(0.477) (0.744) (0.985) (0.969) (0.541)

Observations 430 390 346 304 266

R-squared 0.001 0.006 0.013 0.010 0.009

Number of economies 53 52 48 43 39

Life expectancy at birth 0.005 -0.009 -0.211 -0.229 -0.273

(0.095) (0.183) (0.357) (0.492) (0.636)

Observations 2,144 2,030 1,916 1,803 1,693

R-squared 0.001 0.000 0.000 0.000 0.000

Number of economies 115 115 114 111 111

Mobile cellular subscriptions -0.003 -0.010 -0.012 -0.003 0.004

(per 100 people) (0.005) (0.010) (0.010) (0.012) (0.013)

Observations 2,136 2,020 1,905 1,791 1,680

R-squared 0.001 0.001 0.001 0.000 0.000

Number of economies 117 117 116 113 112

Labor market efficiency -0.511 -1.965* -1.727* 1.492 0.309

(WEF) (0.672) (1.114) (1.026) (1.449) (1.431)

Observations 667 580 494 415 337

R-squared 0.002 0.004 0.005 0.003 0.001

Number of economies 92 91 83 80 73

Hiring and firing regulation -0.159 0.000 -0.133 -0.185 0.272

(Fraser) (0.301) (0.149) (0.207) (0.195) (0.270)

Observations 1,086 995 903 815 731

R-squared 0.001 0.000 0.000 0.001 0.002

Number of economies 93 93 89 85 79

Cost of business start-up procedures -0.021 -0.007 -0.007 0.011 -0.021**

(percent of GNI per capita) (0.020) (0.007) (0.011) (0.007) (0.010)

Observations 1,039 931 825 722 624

R-squared 0.021 0.004 0.008 0.016 0.029

Number of economies 109 107 104 99 91

-0.021 -0.004 -0.040** -0.025 -0.036*

(0.025) (0.036) (0.020) (0.028) (0.018)

Observations 1,031 924 819 718 620

R-squared 0.002 0.002 0.009 0.012 0.013

Number of economies 109 107 104 99 91

Control of Corruption -0.178 -0.029 -0.100 0.114 0.311

(ICRG) (0.221) (0.318) (0.306) (0.409) (0.479)

Observations 1,708 1,623 1,538 1,454 1,373

R-squared 0.001 0.000 0.000 0.000 0.000

Number of economies 86 86 85 82 82

Basic school entrep. edu and training -0.280 -0.681 -1.326* -1.389* -1.099

(0.411) (0.835) (0.783) (0.758) (0.840)

Observations 430 390 346 304 266

R-squared 0.001 0.007 0.017 0.016 0.014

Number of economies 53 52 48 43 39

No. of days required to start a business

TABLE 6B.5 R

egression results from local-projection models:

Self-employment in percent of total employment (continued)

304 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

(1) (2) (3) (4) (5)

Dep. var. = DGE-based output informality

(

percent of official GDP)

t = 1 t = 2 t = 3 t = 4 t = 5

PISA score on reading 0.01 0.014 0.014 0.005 0.003

(0.008) (0.009) (0.012) (0.014) (0.019)

Observations 395 346 304 273 245

R-squared 0.015 0.016 0.036 0.08 0.043

Number of economies 37 36 35 34 33

Domestic credit to the private sector -0.015* -0.008 0.012 0.010 -0.006

(percent of GDP) (0.008) (0.013) (0.020) (0.013) (0.014)

Observations 1,711 1,602 1,495 1,389 1,285

R-squared 0.001 0.000 0.001 0.000 0.000

Number of economies 113 111 109 106 105

Bank branches 0.010 0.028 0.032 0.064 0.071

(per 100,000 adults) (0.048) (0.066) (0.056) (0.072) (0.074)

Observations 991 891 792 693 598

R-squared 0.002 0.004 0.006 0.007 0.009

Number of economies 104 102 100 96 96

Sources: Global Entrepreneurship Monitor; International Country Risk Guide (ICRG); KPMG; University of Michigan; Organisation for

Economic Co-operation and Development; Végh and Vuletin (2015); World Bank (Doing Business, World Development Indicators);

World Economic Forum (WEF).

Note: Data for the period 1990-2018 and EMDEs. See annex 6A for details. DGE = dynamic general equilibrium model; EMDEs =

emerging market and developing economies; GNI = gross national income; PISA = Programme for International Student Assessment;

VAT = value added tax. * p < 0.10, ** p < 0.05, *** p < 0.01.

TABLE 6B.5 Regression results from local-projection models:

Self-employment in percent of total employment (continued)

Dep. var. = DGE-based output informality

(percent of official GDP)

Quantile regression

Explanatory var= OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Corporate income tax rate 0.226* 0.361* 0.176 0.221

(0.130) (0.216) (0.162) (0.182)

Observations 102 102 102 102

(Pseudo) R-squared 0.030 0.026 0.022 0.025

Individual income tax rate 0.218** 0.300*** 0.159 0.214

(0.097) (0.108) (0.144) (0.140)

Observations 92 92 92 92

(Pseudo) R-squared 0.056 0.072 0.021 0.023

VAT rate 0.480** 0.532** 0.589** 0.592

(0.189) (0.216) (0.271) (0.377)

Observations 85 85 85 85

(Pseudo) R-squared 0.065 0.097 0.051 0.020

Tax payments number per year 0.254*** 0.290*** 0.250*** 0.265***

(0.057) (0.071) (0.071) (0.071)

Observations 122 122 122 122

(Pseudo) R-squared 0.153 0.114 0.099 0.094

Times spent on tax payment (hours per year) 0.005 0.004 0.001 0.001

(0.004) (0.006) (0.005) (0.005)

TABLE 6B.6 Robustness checks: OLS and quantile regressions

between policy measures and DGE-based output informality

C H A P T E R 6 3

05 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var. = DGE-based output informality

(

percent of official GDP)

Quantile regression

Explanatory var = OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Time to comply with VAT refund 0.067 0.087 0.030 0.039

(0.052) (0.086) (0.084) (0.101)

Observations 58 58 58 58

(Pseudo) R-squared 0.023 0.045 0.014 0.003

Time to obtain VAT refund (weeks) 0.037 0.093 0.057 0.076

(0.064) (0.097) (0.091) (0.113)

Observations 58 58 58 58

(Pseudo) R-squared 0.006 0.036 0.023 0.009

Cheating on taxes 2.688 3.431 3.801 2.107

(1.627) (3.225) (2.382) (3.628)

Observations 60 60 60 60

(Pseudo) R-squared 0.027 0.023 0.042 0.020

Coverage of unemployment benefits and ALMP -0.509* -0.283 -0.731** -0.662

(0.261) (0.453) (0.347) (0.424)

Observations 59 59 59 59

(Pseudo) R-squared 0.061 0.028 0.032 0.064

Adequacy of social insurance programs -0.107* -0.102 -0.152** -0.040

(0.060) (0.087) (0.074) (0.086)

Observations 93 93 93 93

(Pseudo) R-squared 0.030 0.022 0.029 0.003

Access to electricity -0.112*** -0.155*** -0.124*** -0.029

(0.022) (0.044) (0.029) (0.039)

Observations 122 122 122 122

(Pseudo) R-squared 0.123 0.114 0.101 0.011

Mobile cellular subscriptions -0.181*** -0.265*** -0.217*** -0.055

(0.046) (0.064) (0.054) (0.073)

Observations 121 121 121 121

(Pseudo) R-squared 0.096 0.119 0.096 0.007

Fixed broadband subscriptions -0.554*** -0.472 -0.855*** -0.338

(0.181) (0.297) (0.222) (0.277)

Observations 120 120 120 120

(Pseudo) R-squared 0.060 0.051 0.071 0.010

Individuals using the internet -0.373*** -0.462*** -0.384*** -0.292***

(0.059) (0.106) (0.071) (0.099)

Observations 121 121 121 121

(Pseudo) R-squared 0.167 0.124 0.154 0.057

Paved road -0.099*** -0.112** -0.114*** -0.082**

(0.031) (0.052) (0.037) (0.037)

Observations 113 113 113 113

(Pseudo) R-squared 0.087 0.037 0.077 0.049

Observations 122 122 122 122

(Pseudo) R-squared 0.017 0.022 0.003 0.000

TABLE 6B.6 R

obustness checks: OLS and quantile regressions

between policy measures and DGE-based output informality (continued)

306 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var.=DGE-based output informality

(

percent of official GDP)

Quantile regression

Explanatory var= OLS Tau=0.25 Tau=0.50 Tau=0.75

bservations 122 122 122 122

(Pseudo) R-squared 0.140 0.124 0.127 0.014

Governmental support and policies -4.395 -3.581 -2.064 -9.491**

(2.750) (4.150) (3.287) (4.184)

Observations 67 67 67 67

(Pseudo) R-squared 0.040 0.025 0.010 0.048

Governmental programs -2.449 -2.754 -2.494 -9.192

(3.429) (5.181) (3.963) (6.068)

Observations 67 67 67 67

(Pseudo) R-squared 0.008 0.008 0.009 0.018

Commercial and professional infrastructure -0.447 11.103 0.231 -7.960

(5.081) (8.123) (6.682) (10.032)

Observations 67 67 67 67

(Pseudo) R-squared 0.000 0.017 0.000 0.018

Physical and services infrastructure -4.913* -10.333*** -7.245** -3.501

(2.916) (3.597) (3.147) (5.325)

Observations 67 67 67 67

(Pseudo) R-squared 0.041 0.085 0.036 0.014

Labor market regulations -1.318** -0.801 -1.108 -0.667

(0.665) (1.136) (0.882) (0.871)

Observations 117 117 117 117

(Pseudo) R-squared 0.029 0.008 0.021 0.005

Labor market efficiency -1.163 -0.919 1.961 0.799

(WEF) (2.627) (4.055) (2.986) (3.013)

Observations 108 108 108 108

(Pseudo) R-squared 0.002 0.003 0.005 0.002

Minimum wage -0.007 0.043 -0.058 -0.171

(percent of GDP per capita) (0.093) (0.323) (0.169) (0.145)

Observations 90 90 90 90

(Pseudo) R-squared 0.000 0.002 0.003 0.006

Hiring regulations and minimum wage -1.163*** -1.376** -1.232** -0.459

(0.379) (0.557) (0.490) (0.529)

Observations 117 117 117 117

(Pseudo) R-squared 0.079 0.062 0.041 0.009

Hiring and firing regulations 0.427 0.797 2.083* 2.681**

(1.287) (2.146) (1.200) (1.240)

Observations 113 113 113 113

(Pseudo) R-squared 0.001 0.003 0.016 0.013

Hours regulations -0.874 -0.117 -1.175 -0.732

(0.582) (1.129) (0.779) (0.725)

Observations 117 117 117 117

(Pseudo) R-squared 0.017 0.000 0.024 0.008

Life expectancy -0.440*** -0.650*** -0.446*** -0.208

(0.088) (0.167) (0.096) (0.168)

TABLE 6B.6 R

obustness checks: OLS and quantile regressions

between policy measures and DGE-based output informality (continued)

C H A P T E R 6 3

07 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var.=DGE-based output informality

(

percent of official GDP)

Quantile regression

Explanatory var= OLS Tau=0.25 Tau=0.50 Tau=0.75

bservations 117 117 117 117

(Pseudo) R-squared 0.000 0.000 0.000 0.001

Cost of business start-up procedures 0.043*** 0.046** 0.047*** 0.028*

(in percent of GNI per capita) (0.011) (0.019) (0.012) (0.015)

Observations 122 122 122 122

(Pseudo) R-squared 0.125 0.097 0.095 0.021

Time required to start a business 0.006 0.016 -0.000 0.009

(days) (0.012) (0.037) (0.028) (0.025)

Observations 122 122 122 122

(Pseudo) R-squared 0.001 0.004 0.000 0.001

Bureaucracy quality -6.672*** -6.480** -6.358*** -6.831***

(ICRG) (0.935) (2.862) (1.419) (1.991)

Observations 95 95 95 95

(Pseudo) R-squared 0.193 0.122 0.167 0.091

Control of corruption -7.268*** -6.200* -8.457*** -7.383***

(ICRG) (1.491) (3.534) (1.944) (2.445)

Observations 95 95 95 95

(Pseudo) R-squared 0.149 0.077 0.105 0.051

Law and order -4.951*** -6.801*** -4.842*** -3.981***

(ICRG) (1.022) (1.537) (1.576) (1.511)

Observations 95 95 95 95

(Pseudo) R-squared 0.185 0.171 0.101 0.043

Years of schooling -0.780** -1.383* -1.022** -0.539

(interpolated over 5-yr period) (0.319) (0.755) (0.501) (0.544)

Observations 99 99 99 99

(Pseudo) R-squared 0.034 0.046 0.046 0.005

Literacy rate, adult total -0.115*** -0.211*** -0.136** -0.039

(percent of people ages 15 and above) (0.034) (0.076) (0.053) (0.059)

Observations 119 119 119 119

(Pseudo) R-squared 0.054 0.083 0.062 0.003

PISA: Mean performance on the

mathematics scale

-0.050 -0.074 -0.057 0.019

(0.032) (0.049) (0.042) (0.065)

Observations 44 44 44 44

(Pseudo) R-squared 0.048 0.056 0.054 0.004

PISA: Mean performance on the reading

scale

-0.075** -0.094* -0.053 -0.016

(0.029) (0.047) (0.042) (0.072)

Observations 44 44 44 44

(Pseudo) R-squared 0.103 0.112 0.062 0.004

Mandated cost of worker dismissal 0.019 -0.015 0.208 0.081

(0.360) (0.524) (0.441) (0.387)

TABLE 6B.6 R

obustness checks: OLS and quantile regressions

between policy measures and DGE-based output informality (continued)

308 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var.=DGE-based output informality

(

percent of official GDP)

Quantile regression

Explanatory var= OLS Tau=0.25 Tau=0.50 Tau=0.75

-0.079** -0.080 -0.058 -0.016

(0.030) (0.049) (0.040) (0.066)

Observations 44 44 44 44

(Pseudo) R-squared 0.114 0.101 0.072 0.005

Basic school entrepreneurial education -1.376 2.037 -1.656 -1.289

and training (3.224) (5.681) (4.398) (6.114)

Observations 68 68 68 68

(Pseudo) R-squared 0.002 0.001 0.006 0.003

Post school entrepreneurial education 1.631 5.478 -0.494 -0.964

and training (3.096) (5.836) (4.124) (6.222)

Observations 68 68 68 68

(Pseudo) R-squared 0.003 0.011 0.001 0.001

Commercial bank branches -0.116 -0.189* -0.205* -0.185

(per 100,000 adults) (0.081) (0.111) (0.109) (0.123)

Observations 121 121 121 121

(Pseudo) R-squared 0.017 0.025 0.036 0.007

ATMs -0.055* -0.087* -0.103** -0.034

(per 100,000 adults) (0.032) (0.045) (0.042) (0.045)

Observations 120 120 120 120

(Pseudo) R-squared 0.026 0.036 0.033 0.003

Domestic credit to private sector -0.120*** -0.163*** -0.155*** -0.137***

(percent of GDP) (0.034) (0.044) (0.039) (0.043)

Observations 120 120 120 120

(Pseudo) R-squared 0.124 0.126 0.086 0.046

Account ownership -0.184*** -0.234*** -0.211*** -0.203***

(percent of age 15+) (0.038) (0.059) (0.051) (0.060)

Observations 110 110 110 110

(Pseudo) R-squared 0.146 0.133 0.129 0.058

Internal financing

(percent of age 15+)

0.263* 0.222 0.351** 0.428**

(0.133) (0.196) (0.166) (0.195)

Observations 105 105 105 105

(Pseudo) R-squared 0.034 0.007 0.040 0.023

IMF financial development index -28.857*** -37.263*** -35.054*** -25.386***

(6.643) (8.042) (6.545) (8.872)

Observations 119 119 119 119

(Pseudo) R-squared 0.187 0.165 0.158 0.073

PISA: Mean performance on the science scale

Identify access to finance as a major 0.138** 0.219*** 0.164** 0.010

constraint (percent of firms) (0.060) (0.079) (0.070) (0.077)

Observations 109 109 109 109

(Pseudo) R-squared 0.050 0.067 0.060 0.001

Percent of firms using banks to finance -0.197*** -0.163 -0.270*** -0.245***

investment (0.064) (0.115) (0.072) (0.087)

Observations 109 109 109 109

(Pseudo) R-squared 0.070 0.037 0.102 0.036

TABLE 6B.6 R

obustness checks: OLS and quantile regressions

between policy measures and DGE-based output informality (continued)

C H A P T E R 6 3

09 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var. = DGE-based output informality

(

percent of official GDP)

Quantile regression

Explanatory var = OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Proportion of investment financed internally 0.115 0.127 0.239*** 0.113

(0.073) (0.118) (0.088) (0.101)

Observations 109 109 109 109

(Pseudo) R-squared 0.019 0.015 0.058 0.016

TABLE 6B.6 R

obustness checks: OLS and quantile regressions

between policy measures and DGE-based output informality (continued)

Sources: Global Entrepreneurship Monitor; International Country Risk Guide (ICRG); KPMG; University of Michigan; Organisation for

Economic Co-operation and Development; Végh and Vuletin (2015); World Bank (Doing Business, World Development Indicators);

World Economic Forum (WEF); World Value Surveys.

Note: Data are from EMDEs averaged over the period 1990-2018 (or otherwise specified). All regressions here use the same sample of

data as in figures 6.2-6.12; please see details above. The cells show the coefficients of regressing various policy measures (listed in the

first column on the left) against the share of informal output (dynamic general equilibrium estimates) in percent of official GDP, with

standard errors shown in parentheses. ALMP = active labor market programs; ATM = automated teller machine; DGE = dynamic gen-

eral equilibrium model; EMDEs = emerging market and developing economies; GNI = gross national income; IMF = International Mone-

tary Fund; OLS = ordinary least squares; PISA = Programme for International Student Assessment; VAT = value added tax. * p < 0.10,

** p < 0.05, *** p < 0.01.

Dep. var = Self-employment

(percent of total employment)

Quantile regression

Explanatory var = OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Corporate income tax rate 1.005** 0.872** 1.042*** 1.671***

(0.383) (0.339) (0.352) (0.431)

Observations 96 96 96 96

(Pseudo) R-squared 0.131 0.084 0.090 0.091

Individual income tax rate 0.869*** 0.840*** 0.958*** 1.217***

(0.167) (0.211) (0.222) (0.327)

Observations 87 87 87 87

(Pseudo) R-squared 0.235 0.176 0.152 0.104

VAT rate 0.610 0.949* 0.094 1.271**

(0.408) (0.505) (0.582) (0.634)

Observations 84 84 84 84

(Pseudo) R-squared 0.031 0.027 0.001 0.017

Payments number per year 0.576*** 0.470*** 0.598*** 0.615**

(0.126) (0.159) (0.224) (0.242)

Observations 107 107 107 107

(Pseudo) R-squared 0.149 0.093 0.072 0.075

Time hours per year 0.010 0.027** 0.019 0.006

(0.011) (0.011) (0.013) (0.017)

Observations 107 107 107 107

(Pseudo) R-squared 0.011 0.017 0.006 0.003

Time to comply with VAT refund 0.367*** 0.471*** 0.440*** 0.210

(0.089) (0.116) (0.140) (0.203)

Observations 57 57 57 57

(Pseudo) R-squared 0.198 0.180 0.190 0.063

TABLE 6B.7 Robustness checks: OLS and quantile regressions

between policy measures and employment informality

310 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var=Self-employment

(

percent of total employment)

Quantile regression

Explanatory var= OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Cheating on taxes -2.961 -5.067 -7.284 -9.456

(5.663) (6.863) (6.395) (9.700)

Observations 60 60 60 60

(Pseudo) R-squared 0.006 0.010 0.037 0.008

Coverage of unemployment benefits and

ALMP

-0.158 -0.130 -0.740 -0.516

(0.484) (0.749) (0.824) (1.069)

Observations 61 61 61 61

(Pseudo) R-squared 0.001 0.006 0.004 0.005

Adequacy of social insurance programs -0.409** -0.549*** -0.407* -0.617**

(0.176) (0.173) (0.218) (0.246)

Observations 100 100 100 100

(Pseudo) R-squared 0.083 0.066 0.033 0.055

Access to electricity -0.646*** -0.777*** -0.681*** -0.600***

(0.044) (0.067) (0.059) (0.054)

Observations 135 135 135 135

(Pseudo) R-squared 0.604 0.347 0.396 0.449

Mobile cellular subscriptions -0.736*** -0.699*** -0.906*** -1.035***

(0.127) (0.097) (0.108) (0.116)

Observations 135 135 135 135

(Pseudo) R-squared 0.392 0.221 0.238 0.302

Fixed broadband subscriptions -3.270*** -2.765*** -3.454*** -3.802***

(0.370) (0.403) (0.522) (0.579)

Observations 133 133 133 133

(Pseudo) R-squared 0.409 0.221 0.237 0.291

Individuals using the internet -1.377*** -1.330*** -1.655*** -1.719***

(0.252) (0.168) (0.159) (0.166)

Observations 135 135 135 135

(Pseudo) R-squared 0.506 0.297 0.340 0.378

Paved road -0.481*** -0.380*** -0.558*** -0.585***

(0.058) (0.086) (0.093) (0.080)

Observations 123 123 123 123

(Pseudo) R-squared 0.331 0.135 0.164 0.250

Life expectancy -2.212*** -2.253*** -2.532*** -2.241***

(0.195) (0.368) (0.240) (0.203)

Observations 134 134 134 134

(Pseudo) R-squared 0.513 0.218 0.325 0.412

Governmental support and policies 0.703 3.155 -2.222 9.974

(6.145) (8.093) (7.662) (12.051)

Observations 67 67 67 67

(Pseudo) R-squared 0.000 0.000 0.004 0.020

Observations 57 57 57 57

(Pseudo) R-squared 0.158 0.094 0.135 0.064

Time to obtain VAT refund (weeks) 0.401*** 0.304* 0.538*** 0.243

(0.106) (0.158) (0.195) (0.247)

TABLE 6B.7 R

obustness checks: OLS and quantile regressions

between policy measures and employment informality (continued)

C H A P T E R 6 3

11 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. Var = Self-employment

(

percent of total employment)

Quantile regression

Explanatory var= OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Governmental programs -6.528 7.571 -9.682 -1.630

(7.281) (9.356) (11.291) (13.709)

Observations 67 67 67 67

(Pseudo) R-squared 0.013 0.003 0.028 0.002

Commercial and professional

infrastructure

-1.596 -6.546 -11.359 9.077

(9.923) (14.381) (15.818) (22.277)

Observations 67 67 67 67

(Pseudo) R-squared 0.000 0.001 0.015 0.011

Physical and services infrastructure -14.349** -17.995** -11.027 -20.035*

(5.998) (7.641) (8.756) (11.638)

Observations 67 67 67 67

(Pseudo) R-squared 0.083 0.052 0.036 0.028

Labor market regulations -5.493*** -7.961*** -6.992*** -2.988

(1.807) (1.792) (2.566) (3.500)

Observations 118 118 118 118

(Pseudo) R-squared 0.076 0.113 0.033 0.010

Labor market efficiency -5.669 -22.362*** -1.195 7.654

(WEF) (5.586) (6.306) (9.499) (10.068)

Observations 111 111 111 111

(Pseudo) R-squared 0.009 0.060 0.000 0.013

Minimum wage 2.678*** 3.739*** 3.729*** 2.989**

(percent of GDP per capita) (0.825) (0.800) (1.065) (1.220)

Observations 88 88 88 88

(Pseudo) R-squared 0.163 0.125 0.108 0.083

Hiring regulations and minimum wage -2.897*** -3.965*** -2.796* -1.898

(0.915) (1.118) (1.633) (1.922)

Observations 118 118 118 118

(Pseudo) R-squared 0.078 0.082 0.044 0.016

Hiring and firing regulations 1.639 -0.599 3.501 6.065

(2.441) (3.891) (3.476) (4.971)

Observations 114 114 114 114

(Pseudo) R-squared 0.004 0.001 0.007 0.005

Hours regulations -1.470 -1.495 -0.930 -1.479

(1.480) (2.096) (2.350) (2.919)

Observations 118 118 118 118

(Pseudo) R-squared 0.008 0.008 0.004 0.006

Mandated cost of worker dismissal -1.360* -3.211*** -1.334 0.528

(0.767) (0.967) (1.236) (1.684)

Observations 118 118 118 118

(Pseudo) R-squared 0.023 0.063 0.017 0.001

Cost of business start-up procedures 0.212*** 0.200*** 0.214*** 0.293***

(in percent of GNI per capita) (0.036) (0.042) (0.038) (0.049)

Observations 128 128 128 128

(Pseudo) R-squared 0.311 0.173 0.185 0.181

TABLE 6B.7 R

obustness checks: OLS and quantile regressions between

policy measures and employment informality (continued)

312 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

Dep. var=Self-employment

(

percent of total employment)

Quantile regression

Explanatory var= OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Time required to start a business 0.002 -0.037 0.086 0.096

(days) (0.064) (0.066) (0.081) (0.098)

Observations 128 128 128 128

(Pseudo) R-squared 0.000 0.004 0.004 0.005

Bureaucracy quality -18.151*** -17.404*** -21.895*** -21.096***

(ICRG) (2.818) (5.502) (3.827) (5.172)

Observations 96 96 96 96

(Pseudo) R-squared 0.261 0.136 0.187 0.209

Corruption -13.561*** -11.653* -18.131*** -19.699**

(ICRG) (3.836) (6.162) (6.052) (8.577)

Observations 96 96 96 96

(Pseudo) R-squared 0.087 0.075 0.059 0.049

Law and order -12.164*** -12.517*** -13.164*** -15.214***

(ICRG) (2.471) (2.503) (4.010) (4.700)

Observations 96 96 96 96

(Pseudo) R-squared 0.192 0.177 0.085 0.085

Years of schooling -7.167*** -7.460*** -7.437*** -7.749***

(interpolated over 5-yr period) (0.600) (1.149) (0.884) (1.030)

Observations 99 99 99 99

(Pseudo) R-squared 0.492 0.234 0.308 0.372

Literacy rate, adult total -0.901*** -1.002*** -0.868*** -0.795***

(percent of age 15 +) (0.063) (0.116) (0.109) (0.142)

Observations 126 126 126 126

(Pseudo) R-squared 0.514 0.287 0.311 0.325

PISA: Mean performance on the

mathematics scale

-0.102* -0.199** -0.134** -0.154

(0.052) (0.080) (0.056) (0.106)

Observations 47 47 47 47

(Pseudo) R-squared 0.077 0.141 0.106 0.046

PISA: Mean performance on the reading

scale

-0.136** -0.172** -0.161*** -0.126

(0.056) (0.081) (0.056) (0.105)

Observations 47 47 47 47

(Pseudo) R-squared 0.130 0.136 0.129 0.082

PISA: Mean performance on the science

scale

-0.125* -0.228*** -0.137** -0.192*

(0.068) (0.083) (0.054) (0.112)

Observations 47 47 47 47

(Pseudo) R-squared 0.108 0.150 0.136 0.077

Basic school entrepreneurial education

and training

-10.050 -8.825 -7.669 -9.750

(8.002) (9.540) (10.866) (14.816)

Observations 73 73 73 73

(Pseudo) R-squared 0.025 0.012 0.010 0.010

-6.402 -1.901 -4.408 -17.024

(8.904) (9.332) (10.969) (15.196)

Observations 73 73 73 73

(Pseudo) R-squared 0.010 0.001 0.001 0.021

Post school entrepreneurial education

and training

TABLE 6B.7 R

obustness checks: OLS and quantile regressions

between policy measures and employment informality (continued)

C H A P T E R 6 3

13 T H E L O N G S H A D O W O F I N F O R M A L I T Y

Sources: Global Entrepreneurship Monitor; International Country Risk Guide (ICRG); KPMG; University of Michigan; Organisation for

Economic Co-operation and Development; Végh and Vuletin (2015); World Bank (Doing Business, World Development Indicators);

World Economic Forum (WEF); World Value Surveys.

Note: Data are from EMDEs averaged over the period 1990-2018 (or otherwise specified). All regressions here use the same sample of

data as in figures 6.2-6.12; please see details above. The cells show the coefficients of regressing various policy measures (listed in the

first column on the left) against the share of informal output (dynamic general equilibrium estimates) in percent of official GDP, with

standard errors shown in parentheses. ALMP = active labor market programs; ATM = automated teller machine; EMDEs = emerging

market and developing economies; GNI = gross national income; IMF = International Monetary Fund; OLS = ordinary least squares;

PISA = Programme for International Student Assessment; VAT = value added tax. * p < 0.10, ** p < 0.05, *** p < 0.01.

Dep. var = Self-employment

(

percent of total employment)

Quantile regression

Explanatory var = OLS Tau = 0.25 Tau = 0.50 Tau = 0.75

Commercial bank branches -0.746*** -0.391 -0.841*** -1.131***

(per 100,000 adults) (0.207) (0.241) (0.249) (0.276)

Observations 108 108 108 108

(Pseudo) R-squared 0.147 0.051 0.091 0.135

ATMs -0.512*** -0.482*** -0.539*** -0.563***

(per 100,000 adults) (0.071) (0.095) (0.092) (0.100)

Observations 107 107 107 107

(Pseudo) R-squared 0.405 0.185 0.237 0.257

Domestic credit to private sector -0.345*** -0.243*** -0.258** -0.455***

(percent of GDP) (0.078) (0.078) (0.113) (0.116)

Observations 106 106 106 106

(Pseudo) R-squared 0.181 0.080 0.083 0.139

Account ownership -0.655*** -0.681*** -0.646*** -0.882***

(percent of age 15+) (0.098) (0.102) (0.131) (0.189)

Observations 99 99 99 99

(Pseudo) R-squared 0.336 0.222 0.194 0.166

Internal financing 1.280*** 1.210*** 1.760*** 1.062**

(percent of age 15+) (0.252) (0.390) (0.378) (0.469)

Observations 96 96 96 96

(Pseudo) R-squared 0.170 0.087 0.136 0.114

IMF financial development index -96.053*** -90.486*** -115.068*** -97.609***

(15.714) (15.812) (19.794) (21.793)

Observations 104 104 104 104

(Pseudo) R-squared 0.341 0.207 0.197 0.210

Identify access to finance as a major

constraint (percent of firms)

0.459*** 0.214 0.656*** 0.585***

(0.147) (0.183) (0.247) (0.213)

Observations 114 114 114 114

(Pseudo) R-squared 0.084 0.011 0.027 0.109

Percent of firms using banks to finance

investments

-0.825*** -0.539*** -0.897*** -1.115***

(0.137) (0.186) (0.258) (0.233)

Observations 114 114 114 114

(Pseudo) R-squared 0.214 0.100 0.129 0.176

Proportion of investment financed

internally

0.731*** 0.551** 0.737*** 1.066***

(0.157) (0.253) (0.271) (0.236)

Observations 114 114 114 114

(Pseudo) R-squared 0.149 0.051 0.088 0.153

TABLE 6B.7 R

obustness checks: OLS and quantile regressions between

policy measures and employment informality (continued)

314 C H A P T E R 6 T

H E L O N G S H A D O W O F I N F O R M A L I T Y

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