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An Estimation of the Informal Economy in Morocco


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p>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.</p
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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
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:
Received: June 13, 2016 Accepted: July 11, 2016 Online Published: August 25, 2016
doi:10.5539/ijef.v8n9p140 URL:
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
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. International Journal of Economics and Finance Vol. 8, No. 9; 2016
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
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: International Journal of Economics and Finance Vol. 8, No. 9; 2016
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) (, the website of Bank Al-Maghrib ( 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
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
Tax burden
GDP per
M0/M2 International Journal of Economics and Finance Vol. 8, No. 9; 2016
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
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
Tax burden
- 0,209
Source: the authors’ calculations.
Table 3. Model fit
Degree of freedom
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: International Journal of Economics and Finance Vol. 8, No. 9; 2016
 
 (4)
The coefficients of the structural model are used to obtain the index of changes in GDP, with the following
 = 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)
: 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
Informal economy
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). International Journal of Economics and Finance Vol. 8, No. 9; 2016
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%
1980 1990 2000 2010 2020
informal sector/GDP
Bourhaba &
Schneider and
Moustain(2004) International Journal of Economics and Finance Vol. 8, No. 9; 2016
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.
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,
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
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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
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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.
Elgin, C., & Oyvat, C. (2013). Lurking in the cities: Urbanization and the informal economy. Structural Change
and Economic Dynamics, Elsevier, 27(C), 36-47.
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.
Giles, D. E. A. (1999). Measuring the hidden economy: Implications for econometric modeling. The Economic
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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.
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),
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.
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. International Journal of Economics and Finance Vol. 8, No. 9; 2016
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
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 1. Annual estimates of the informal economy in Morocco
Informal economy
Source: the authors’ calculations.
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... In Morocco, people find taxation extremely burdensome and conceal their earnings to avoid tax payments. The same study also discusses that corruption is another major determinant of why individuals go for informal economic activities (Bourhaba and Hamimida 2016). Hence, corruption and other deficiencies of governance mechanisms not only harm people's loyalty to the system but they also lower the ability of particular groups in the society to have access to public services. ...
In the recent years, Poland has emerged as an attractive migration destination and has witnessed a substantial growth of the migrant population, especially coming from Asian countries. This has been especially visible in the urban and suburban areas around big cities. The chapter discusses the visible shift to diversity in the character of a suburban neighbourhood of Warsaw, and tries to uncover what it means for the different migrant groups in terms of access to the labour market, the formal and informal practices they engage in, and the role it plays in the migrant imaginary of post-socialist Poland. Thus, we take a closer look at migrant networks that are the basis of migrant life in Poland and allow them to legalize their stay, find employment, and build a safe environment for themselves. Not being part of the Polish informal networks leaves migrants unable to use the local strategy of “załatwianie” (getting things done”), and thus not integrated into the official labour market. We argue that using informal migrant networks in order to cope with everyday life in a foreign country is a substitute to the local practice of “getting things done”. Thus, we analyse how migrants, excluded linguistically and socially from the Polish labour market, are also being pushed into ethnic niches. These businesses are concentrated in the food and beauty sector. The last strategy we describe is entering the grey zone economics through undeclared or half-legal work.
Through an economic and geographical cross-section, this article on the Tangier-Med complex and its array of new activities is positioned in a reflexive perspective. It focuses on the difficulties of linking an economic structure that is almost imposed by the state, foreign direct investment, global value chains, and the local political economy, which is in a sense required to accommodate what neo-liberal discourses promote as opportunities. The hypothesis that structures the reflection can be formulated as follows: the strong connectivity that Tangier-Med allows and organises undermines the very foundations of the “proximity synergies” on which the consolidation and productive unification of the territory rests. This accounts for the weakness of the state in the face of local economic and social development issues. In the Moroccan context of a middle-income country, as a result, we question, through a multi-scale analysis, the relationship that exists between economic infrastructures and territories, between networks (logistic networks and production networks resulting from the international division of labour) and territories.
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Despite the progress made by the countries of the Middle East and North Africa (MENA) region in terms of gender equality and social and, above all, educational emancipation, today there are still disparities in terms of the participation of women in the labour market. This paper proposes a descriptive and qualitative analysis of the situation of women in two MENA countries, which are Algeria and Morocco, and of the socioeconomic factors that hinder their autonomy in the field of work. As a result, the role of the family and the social environment on women is emphasised so that they could be active members of society and consequently become an additional labour force trained for the growth of the region, is emphasised, but it is condemned for the achievement of this triple challenge: education, professional integration, and economic empowerment.
On the basis of a fieldwork carried out between Casablanca and Rabat (Morocco) the article sheds light on the technical savoir-faire of waste pickers. The daily gestures used in recovery, collection and recycling are inscribed within flexible and heterogeneous relationships between social actors (waste pickers, institutions, NGOs), work tools (cart, tracks, waste processing machines), materials (discarded objects), workplaces (the street, waste storage centers), and systems of representation. Specifically, I explore the modalities through which institutional behaviours and policies attempt to redefine and value Moroccan waste pickers’ recovery and recycling activities by transforming their savoir-faire into a devoir-faire.
Informality is growingly accepted to be an encompassing concept touching on all aspects of societies and how they are governed, which goes well beyond the contours of economic transactions.
In recent years, the growth of public bureaucracy was one of the topics receiving most attention within political economy. Many theories have been developed dealing with the relationship of public bureaucracy with the political sector, in particular the models of Niskanen (1971, 1975) and Migué, Bélanger (1974), studying the behavior of individual bureaus vis à vis parliament.
This study investigates the empirical relationship between the level of urbanization and size of the informal economy using cross-country datasets proxying GDP and employment shares of urban informal sector. Our estimation results indicate that there is an inverted-U relationship between informality and the level of urbanization. That is, the share of the informal sector grows in the early phases of urbanization due to several pull and push factors; however, it tends to fall in the latter phases. We also show that factors like level of taxes, trade openness, and institutional quality tend to affect the size of the informal economy.
structural equation modeling (SEM) is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables / outline the basic elements of the SEM approach / provide researchers and students trained in basic inferential statistics a nontechnical introduction to SEM approach / refers to concepts from standard statistical approaches in the social and behavioral sciences such as correlation, multiple regression, and analysis of variance (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This article originated in the study of one Northern Ghanaian group, the Frafras, as migrants to the urban areas of Southern Ghana. It describes the economic activities of the low-income section of the labour force in Accra, the urban sub-proletariat into which the unskilled and illiterate majority of Frafra migrants are drawn. Price inflation, inadequate wages, and an increasing surplus to the requirements of the urban labour market have led to a high degree of informality in the income-generating activities of the sub-proletariat. Consequently income and expenditure patterns are more complex than is normally allowed for in the economic analysis of poor countries. Government planning and the effective application of economic theory in this sphere has been impeded by the unthinking transfer of western categories to the economic and social structures of African cities. The question to be answered is this: Does the ‘reserve army of urban unemployed and underemployed’ really constitute a passive, exploited majority in cities like Accra, or do their informal economic activities possess some autonomous capacity for generating growth in the incomes of the urban (and rural) poor?
The paper estimates the Portuguese Shadow Economy (SE) from 1977 to 2004 and tests the statistical relationships between the SE and other economic variables. In order to carry out the econometric analysis, a multiple indicators multiple causes (MIMIC) model with means and intercepts is applied. The main causes of the Portuguese SE are analyzed and economic policies to reduce it are suggested. An appraisal on the reliability of estimates and an alternative benchmark strategy for the MIMIC approach are proposed.
In this volume we report the results of an extensive empirical study into the size of the Canadian underground economy, its development from the mid-1970's to the mid-1990's, and some of the linkages between taxation policy and underground activity in this country. First, we estimate that the Canadian Underground Economy grew from about 3.5% of measured GDP in 1976, to almost 16% in 1995. The latter figure accords well with recent evidence for Canada obtained by Schneider by totally different means - he estimates that it averaged 14.8% in 1994/95 and 16.2% in 1997/98. Second, when the implications of an underground economy of this size are explored in terms of the amount of tax revenue that is lost, we find that the size of this "tax-gap" varied from approximately $2 billion in 1976 to almost $44 billion in 1995, in current-dollar terms. Third, we establish a clear and positive empirical relationship between the aggregate effective tax rate and the (relative) size of the underground economy. We have shown that there is significant statistical evidence of two-way Granger causality, both from the effective tax rate, to the underground economy; and also from the underground economy to the effective tax rate.
This paper gives a quick overview of the approaches that have been used in the research of shadow economy, starting with the defi nitions of the terms “shadow economy” and “non-observed economy”, with the accent on the ISTAT/Eurostat framework. Several methods for estimating the size of the shadow economy and the non-observed economy are then presented. The emphasis is placed on the MIMIC approach, one of the methods used to estimate the size of the nonobserved economy. After a glance at the theory behind it, the MIMIC model is then applied to the Croatian economy. Considering the described characteristics of different methods, a previous estimate of the size of the non-observed economy in Croatia is chosen to provide benchmark values for the MIMIC model. Using those, the estimates of the size of non-observed economy in Croatia during the period 1998-2009 are obtained.