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International Journal of Economics and Finance; Vol. 8, No. 9; 2016

ISSN 1916-971X E-ISSN 1916-9728

Published by Canadian Center of Science and Education

140

An Estimation of the Informal Economy in Morocco

Bourhaba Othmane1 & Hamimida Mama1

1 Faculty of Law, Economics and Social Sciences of Mohammedia, Hassan II University, Casablanca, Morocco

Correspondence: Bourhaba Othmane, Faculty of Law, Economics and Social Sciences of Mohammedia, Hassan

II University, BP 145, Mohammedia, Morocco. Tel: 212-643-376-972. E-mail: bourhabaothmane@gmail.com

Received: June 13, 2016 Accepted: July 11, 2016 Online Published: August 25, 2016

doi:10.5539/ijef.v8n9p140 URL: http://dx.doi.org/10.5539/ijef.v8n9p140

Abstract

The paper attempts to both measure the size of the informal economy in Morocco and knows its tendency

through the MIMIC approach. We calculated the size of the informal economy during the period 1999-2015. Our

estimates show that this hidden part of economy constitutes 42.9% of the official GDP in 2015, and also show

that there is a growth and a positive tendency of the informal economy in Morocco. The rise of corruption, the

growth of the rate of urbanization and the tax burden play a determinant role in the magnification of the informal

sector in Morocco.

Keywords: informal economy, MIMIC approach, Morocco, shadow economy

1. Introduction

Economic theory devotes more and more attention to the analysis of the informal economy. Indeed, questions

about the informal economy have fed, until a very recent time, a vast literature of both theoretical and empirical

around the world. Economic development models have constantly explained the role and the weight of the

informal sector in the economies of the countries, gradually shifting their interest from the traditional sector

theory (Lewis, 1954), to the models that evoke the informal sector as an important component in developing

economies (Hart, 1973).

In Morocco, as in other developing countries, the informal sector represents a very important and a growing

share of output and employment. Measuring the informal economy is a difficult task because individuals who

work and produce in this sector try to hide their profit. The definition of the informal economy is not unified

among specialists, but basically, every unregistered activity that generate a value added is considered informal,

i.e. it includes all economic activities that escape taxes, do not include requirements or currency regulations, and

avoid statistical reports.

If it has emerged in recent years a vast literature on the measure of the size of the informal sector, studies dealing

with the informal sector in Morocco are not the case. There were, among others, three papers that treat the

question of the size of shadow economy in Morocco. We will try to summarize the three works. First, very cited

in the literature, the study of Schneider et al. (2010), examines the size of shadow economy in 162 countries

around the world over the period 1999 to 2007. The study uses a MIMIC approach and estimates the size of the

informal sector at 37% as a percentage of GDP. We have a second study which deals with the informal economy

in Morocco, conducted by Alaoui Moustain (2004) using a MIMIC approach with data over the period between

1982 and 2000. Alaoui Moustain (2004) finds that the shadow economy in Morocco represents 38% of GDP in

1998. Third, Elgin and Oztunali (2012) conducted a study using a dynamic general equilibrium model to

calculate the size of informal economy in 161 countries around the world over the period 1950 and 2009. They

found that the size of informal sector in Morocco represent 36% of GDP in 1999. The common point between

the 3 studies is that they provide, more or less, old estimates. There is no estimation of the informal sector in

Morocco for the past decade. Our study provides a logical continuity of the literature by offering new and robust

estimation.

The following text presents a measurement of the size of the informal economy in Morocco. Indeed, the

measurement and the evaluation of the informal economy are an area of research that is always difficult and

surrounded by debates and ambiguities. The non-measurability of the informal economy will lead us to national

skewed statistics, such as the statistics on growth, unemployment, poverty, consumption and welfare. This

implies a risk in the economic diagnosis of the country that induces erroneous economic decisions.

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

141

Moreover, there are very few studies that evaluate the size of the informal sector in Morocco. Hence, our interest

is to estimate the weight of this hiding part of the economy, following an indirect macro-modeling approach,

called multiple indicators multiple causes (MIMIC).

The rest of the paper is organized as follows: In the second section of the paper we explain the outlines of the

MIMIC approach and we build an empirical model which will be utilized to measure the size of the informal

economy. Then we describe the data used in our study. Next, in the fourth section of the paper, we present the

estimation and we discuss the econometric strategy. Finally, we provide conclusions.

2. Methodology

2.1 MIMIC Modeling

The method of the latent variable of Frey and Weck (1983), which relies on a set of explanatory variables, unlike

other methods of indirect measurement which emphasis that the shadow economy can be modeled according to a

few specific variables. The size of the shadow economy is estimated according to the evolution of variables

which on the one hand, affect the magnitude and the growth of the shadow production, and on the other, for the

variables that indicate the hidden activities in the economy. According to Schneider (2002) the MIMIC method is

based on the statistical theory of unobserved variables, which considers that the causes and the indicators which

are measured in a phenomenon are multiple. The unobserved variable, in this case, is the informal economy, and

the model assumes that it is influenced by a number of different factors, as stated in Giles and Tedds (2002), this

type of modeling has several advantages. The first is that it uses several data sources to collect as many

components as possible of the informal economy; it’s an important asset when we try to measure an “elusive”

phenomenon like the informal economy. The second advantage is that the model can determine at once the size

and the development of the informal economic activity over time. The third aspect of MIMIC is its potential to

be deployed at the local level. So far, the model was used to measure the informal economy on the national level.

The MIMIC modeling studies are typically used as causes of the development of the informal sector: The tax

burden, the level of regulation, public spending, and tax morality, and as indicators we often have: the money in

circulation, GDP and the rate of participation of the active population.

2.2 An Application of the MIMIC Model: Case of Morocco

The informal sector is presented in our study as a latent variable that we seek to estimate through the MIMIC

method. This method is a special case of the structural equation modeling (SEM). According to Hoyle (1995),

SEM is defined as a global statistical approach which allows testing the assumptions that deal with the

relationship between the observed variables and the latent variables.

We have created a structural equation model inspired by the studies (Note 1) that are concerned with the

measurement of the size of the informal sector and the relationship between this sector and different

macroeconomic variables. The variables constituting our model are the most significant variables in the

estimations; in fact, we have proceeded to test the significance of several models to finally arrive at the best

model.

The SEM can explain the relationship between, first: the latent variable (μ) and the determinants.Then

between the latent variable and the indicators ().

The equation between the latent variable (informal economy) and its determinants (urbanization and tax burden

(), the rate of urbanization () and corruption ()) is as follows:

(1)

The equations that show the relationship between the informal economy (μ) and the indicators (GDP per Capita,

() and between the informal and the money in circulation ()), are written as follows:

= + + (2)

= + + (3)

To simplify, the following figure shows the general structure of the MIMIC model:

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

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Figure 1. Structure of the MIMIC model used in our study

3. Data Description

To create our database, we used the database of the World Bank, the website of Transparency International (Note

2) (http://www.transparency.org), the website of Bank Al-Maghrib (www.bkam.ma) and also by our own

calculations for some missing variables. Our analysis focuses on Morocco and the data used are for time

intervals between 1999 and 2015.

The MIMIC method requires two types of variables. Some explain the causes and others are indicator variables

of the latent variable. The majority of indicators and causes, used in the case of this study are derived from the

literature which deals with the subject.

3.1 The Causes of the Informal Sector

The tax burden (). The assumption is that each time that the tax burden increases, the informal sector grows.

This variable is, therefore, among the major causes that motivate individuals to go into the informal sector, this is

true, at least for economists who seek to measure the size of the informal economy, since it is the most variable

used in the literature which deals with this subject. The tax burden is measured by tax revenue as a percentage of

GDP.

The urbanization rate () represents the share of people living in an urban area in the country's total population.

We assume that there is a causal relationship between urbanization and the size of the informal economy.

According to Elgin and Oyvat (2013), the relationship between the informal sector and urbanization takes the

form of a Kuznets curve, that is to say, the size of the informal sector increases in the early stages of urbanization

then at some point, it will tend to fall in an advanced stage of urbanization.

Corruption Perceptions Index (). This index measures the degree of corruption in the public sector in each

country. It is calculated annually by Transparency International. Our hypothesis here tries to argue that

corruption has a positive effect on the size of the informal sector, in other words, every time the level of

corruption increases, the size of the informal sector increases. The Corruption Perceptions Index gives the

perceived level of corruption on a scale from 0 (highly corrupt) to 100 (very clean). So, the increase in the value

of this index means the decrease of corruption. This index measures the “cleanliness” of public sector corruption.

Therefore, the expected sign of the coefficient associated with () in the regression would be a negative sign.

3.2 Informal Sector Indicators

We used two variables as indicators of the latent variable (informal economy). First, per capita GDP (in constant

local currency units). Second, the ratio M0 (currency in circulation) M2 (Sight deposits).

The choice of GDP per capita () as an indicator of informal activity is motivated by the fact that there is a

relationship between GDP per capita and the informal economy. Although the direction of the relationship is not

clear in the literature (Note 3). An increase in the per capita GDP official can induce an increase in demand for

goods and services in the informal economy. It is assumed that the sector size is influenced by the level of

development. GDP per capita is considered as a proxy of economic development level.

Following Dell'Anno (2007), we used GDP per capita as a reference variable; otherwise, the associated

coefficient will be set at a non-zero value, since the latent variable is not measurable. Hence, we are in the need

Corruption

Urbanizatio

n

Tax burden

Informal

economy

GDP per

Capita

M0/M2

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

143

to select its unit of measure (Note 4). The data on GDP per capita were collected from the World Bank database.

The second indicator of the informal sector is the ratio M0/M2 (). M0/M2 is calculated as the ratio of currency

in circulation and sight deposits. We used this ratio to get an index that provides information on the share of cash

in the economy. It serves as an indicator of informality because transactions in the informal sector are generally

made in cash. The increase of currency in circulation, which cannot be explained by “natural” factors, is

attributed to the increase of the informal economy. We assume, then, that increasing M0/M2 would be an

indication of the increase in the informal economy.

All variables used in this estimation are continuous. We summarize data in the following table:

Table 1. List of variables

Labels of the used variables

Signification

GDP per Capita

The money supply M0 divided by M2

Income taxes by GDP

Urbanisation rate

Corruption Perceptions Index

4. Results

4.1 Estimation Results

Table 2 shows the results of the MIMIC model. This last is estimated by log through the maximum likelihood

method. All variables are significant at p-value <0.05, the variables have the expected signs and adjustment

indicators show that the quality of the model is acceptable. Amos software is used for the calculations.

Table 2. Estimation of the model coefficients

Model

Corruption

(p-value)

Urbanization

(p-value)

Tax burden

(p-value)

MIMIC

- 0,209

(0.000)

3,711

(0.000)

0,114

(0.010)

Source: the authors’ calculations.

Table 3. Model fit

Model

Chi-square

Degree of freedom

GFI

CFI

RMR

MIMIC

22,71

2

0,76

0.883

0,001

Source: the authors’ calculations.

4.2 Benchmarking

The Benchmarking is an essential step in every estimate of the informal sector through the MIMIC method. It

aims to convert the coefficients estimated by the SEM to absolute values that quantify the size of the informal

economy as a percentage of GDP. There are several benchmark procedures (Note 5) used in the literature that

focus on the estimated size of the informal sector by the MIMIC method. We have chosen Dell'Anno’s (2007)

benchmark, which is widely used in the empirical studies that deal with the size of the informal economy.

The base year is 2000 because in this year there exists an estimate of the informal sector in Morocco of

Schneider, Buehn, and Montenegro (2010), and it's the article the most cited in the literature that focuses on the

measurement of the informal sector through the MIMIC method. This article assesses the informal economy in

36.40% of GDP in 2000. Some similar results were obtained by Alaoui Moustain (2004) and Elgin and Oztunali

(2012), who estimated the informal sector in Morocco in 2000 by, respectively, 37.28% and 35.64% of GDP.

We substitute and μ in equation (2) by the index of the changes of the GDP in 2000 and the index of changes

in the informal sector GDP in 2000.we obtain the following measurement equation:

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

144

(4)

The coefficients of the structural model are used to obtain the index of changes in GDP, with the following

equation:

SEM:

= 0.11+ 3.71 - (5)

Dell'Anno (2007) uses the following formula to estimate the size of the informal economy as a percentage

of GDP:

=

(6)

Where:

: Index calculated with Equation (5).

: The benchmark estimation (exogenous) for the informal economy. It’s equal to 36.4%.

: Index calculated from equation (5) for the base year (2000).

: This index converts the index of the informal economy as changes respect to the base year in the

informal economy respect to current GDP.

: Estimating the size of the informal sector as percentage of GDP.

It should be noted that there is not, so far, a theoretical framework of a benchmark for the MIMIC approach.

Each benchmark procedure for converting the index estimates the informal economy by MIMIC model in

absolute values, led to a different result as Breusch (2005b) highlights.

4.3 Research Findings

Table 4 presents the estimates of the informal economy in Morocco between the period 1999 and 2015.

Table 4. estimation of the informal sector

Years

1999-2000

2001-2002

2003-2004

2005-2006

2007-2008

2009-2010

2011-2012

2013-2013

2015

Informal economy

37.41%

38.09%

38.83%

39.53%

40.16%

40.98%

41.64%

42.28%

42.91%

Source: the authors’ calculation.

According to our results the informal sector has grown in Morocco from 37% in 1999 to 43% in 2015. The size

of the informal sector has a low positive trend with a slope equal to 0.003.

Figure 2 presents a comparison between our results and results of Schneider et al. (2010) and Alaoui Moustain

(2004).

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

145

Figure 2. Size of informal sector in Morocco as percentage of official GDP

Source: the authors’ calculation.

The estimate size of the informal economy shows that there are differences in results due to differences in

methods. Although, all the estimations turn around 35% - 40% as a percentage of GDP.

When we think about the absolute size of the informal economy, we should realize that all the estimates are

approximate because of the complex nature of the informal sector and the difficulties one meets at its extent.

5. Conclusion

In order to estimate the size of the informal economy in Morocco, we have used the MIMIC approach. We find

that the size of the informal economy in Morocco is equal to 43% of GDP in 2015. The main cause of the

informal economy in Morocco is urbanization. In addition, the tax burden also played a critical role in increasing

the size of the informal economy. An increase of the tax burden by 1% leads to an increase in the informal

economy size by 0.11%. Another important reason that has an impact on the size of the informal economy in

Morocco is corruption.

To provide a brief economic explanation of our results, we can say, first, that urbanization is a consequence of

economic development, when a country starts to develop its economy, by industrialization and services, cities

become more attractive for rural population. An increase of the rural flight leads to the overpopulation of the

cities. The formal sector has not got the capacity to absorb all the labor force in the labor market. Hence, a part

of this labor force goes to the informal sector.

Second, we assume that corruption plays a determinate role in the magnification of the informal economy.

Although the theoretical link is not clear in the literature, empirical studies show that there is a positive

relationship between the informal economy and corruption. The assumption is that when economic agents found

a corrupt administration they prefer to choose the informal sector to avoid regulation and maximize their profits

because this corrupt administration will have an attitude of letting things happen and will not impose regulation

on individuals who plays in the dark economy. Hence, an improvement of the quality of institutions will lead to

reducing the size of the informal economy.

Finally, the tax burden is one of the most popular determinant causes of the magnification of the informal

economy. Simply, because individuals try to hide their output to avoid taxation. High taxation contributes to the

amplification of the informal economy and then the reduction of government revenue. In accordance with the

Laffer curve, high taxation leads to less revenue for the state because individuals will lie about their real revenue

and production.

Although the critics, the MIMIC approach still the best method to estimate the size of informal sector, but this

method requires extensive data in order to give reliable results. One of the limitations of our study is that we

don’t have a large amount of data. The problem of availability of data is a common problem for developing

countries. Future works with extensive data or a new approach of measure are welcomed in this area of research.

These results are important to have a real view of the size of the economy, because traditional indicators, such as

2015, 43%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1980 1990 2000 2010 2020

informal sector/GDP

Years

Bourhaba &

Hamimida(2016)

Schneider and

al(2010)

Alaoui

Moustain(2004)

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

146

GDP do not always give the true picture of the economy. In order to establish a good economic policy,

government must first know the real size of the economy. Now, with an informal sector that constitutes 43% of

GDP in 2015, we believe that it is important to take this sector into account in policy making.

References

Alaoui, M. F. Z. (2004). Market distortions and the informal economy: The case of Morocco. Economics

Working Paper Series. The Department of Economics, Lancaster University.

Breusch, T. (2005). Estimating the underground economy using MIMIC models. Working paper, Canberra,

Australia.

Del´Anno, R., & Schneider, F. G. (2006). Estimating the Underground Economy by Using MIMIC Models: A

Response to T. Breusch´s Critique. Economics working papers. Department of Economics, Johannes Kepler

University Linz, Austria.

Del’Anno, R. (2003). Estimating the shadow economy in Italy: A structural equation approach. Discussion

Paper, Department of Economics and Statistics, University of Salerno.

Dell’Anno, R. (2007). The Shadow Economy in Portugal: An Analysis with the MIMIC Approach. Journal of

Applied Economics, 10(2), 253-277.

Dell’Anno, R., Gómez, A. M., & Alañon, P. A. (2007). The shadow economy in three Mediterranean countries:

France, Spain and Greece. A MIMIC approach. Empirical Economics, 33(1), 197-197.

http://dx.doi.org/10.1007/s00181-007-0138-1

Elgin, C., & Oyvat, C. (2013). Lurking in the cities: Urbanization and the informal economy. Structural Change

and Economic Dynamics, Elsevier, 27(C), 36-47. http://dx.doi.org/10.1016/j.strueco.2013.06.003

Elgin, C., & Oztunali, O. (2012). Shadow Economies around the World: Model Based Estimates. Working Papers

2012/05, Bogazici University.

Frey, B. S., & Weck, H. (1983). Bureaucracy and the Shadow Economy: A Macro-Approach. In Anatomy of

Government Deficiencies. Horst Hanusch, Springer, pp. 89-109. http://dx.doi.org/10.1007/978-3-662-

21610-1_6

Giles, D. E. A. (1999). Measuring the hidden economy: Implications for econometric modeling. The Economic

Journal, 109(456), 370-380. http://dx.doi.org/10.1111/1468-0297.00440

Giles, D. E. A., & Tedds, L. M. (2002). Taxes and the Canadian Underground Economy. Canadian Tax

Foundation, Toronto.

Hart, K. (1973). Informal Income Opportunities and Urban Employment in Ghana. The Journal of Modern

African Studies, 11(1), 61-89. http://dx.doi.org/10.1017/S0022278X00008089

Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In

Structural equation modeling: Concepts, issues, and applications. In R. H. Hoyle (Ed.), Thousand Oaks (pp.

1-15). CA: Sage Publications, Inc.

Klarić, V. (2011). Estimating the size of non-observed economy in Croatia using the MIMIC approach. Financial

Theory & Practice, 35(1), 59-90.

Lewis, W. A. (1954). Economic Development with Unlimited Supplies of Labour. The Manchester School, 22(2),

139-191. http://dx.doi.org/10.1111/j.1467-9957.1954.tb00021.x

Loayza, N. A. (1997). The economics of the informal sector: A simple model and some empirical evidence from

Latin America. Policy Research Working Paper Series 1727, The World.Bank.

Loayza, N., & Rigolini, J. (2006). Informality Trends and Cycles. Research Working Paper No. 4078. World

Bank Policy. http://dx.doi.org/10.1596/1813-9450-4078

Schneider, F. (2002). The Size and Development of the Shadow Economies of 22 Transition and 21 OECD

Countries. IZA Discussion Paper No. 514, University of Linz and IZA Bonn.

Schneider, F., & Enste, D. (2002). The Shadow Economy: Theoretical Approaches, Empirical Studies, and

Political Implications. Cambridge (UK): Cambridge University Press.

Schneider, F., Buehn, A., & Montenegro, C. E. (2010). Shadow economies all over the world: New

estimates for 162 countries from 1999 to 2007. Policy Research Working Paper Series 5356, the

World Bank.

ijef.ccsenet.org International Journal of Economics and Finance Vol. 8, No. 9; 2016

147

Notes

Note 1. Dell'Anno (2007) is the author who influenced the most the construction of our model.

Note 2. NGO founded in 1993 is today present in more than 100 countries, it is primarily intended to fight

corruption.

Note 3. See Loayza (1997), Loayza and Rigolini (2006).

Note 4. For more details, see Dell'anno, Gomez and Alanon (2007).

Note 5. Dell'anno and Schneider (2006) present a state of art on benchmarking procedures.

Appendix

Appendix 1. Annual estimates of the informal economy in Morocco

Years

Informal economy

1999

37,41%

2000

37,40%

2001

37,88%

2002

38,29%

2003

38,69%

2004

38,97%

2005

39,34%

2006

39,71%

2007

39,97%

2008

40,35%

2009

40,81%

2010

41,14%

2011

41,51%

2012

41,76%

2013

42,13%

2014

42,42%

2015

42,91%

Source: the authors’ calculations.

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